Groundwater Inventory, Monitoring, and Assessment Technical Guide
Contents
2.0
Foundations of Groundwater Inventory, Monitoring, and Assessment .......................................2
2.1
Important Groundwater Concepts .........................................................................................2
2.2
Planning and Design ..............................................................................................................5
2.2.1
Some Basic Recommendations for Contracting ....................................................................... 5
2.3
Inventory or Monitoring Program Objectives .........................................................................7
2.4
Hydrogeologic Conceptual Model ..........................................................................................8
2.4.1
Geologic Framework ................................................................................................................ 9
2.4.2
Hydraulic Properties ................................................................................................................. 9
2.4.3
Flow System............................................................................................................................ 10
2.4.4
Water Budgets ........................................................................................................................ 10
2.5
Use of Remote Sensing Techniques in Groundwater Inventory and Monitoring .................... 11
2.5.1
Overview of Available Remote Sensing Datasets ................................................................... 12
2.5.2
Application of Remotely Sensed Data in Groundwater Inventory and Monitoring ............... 13
2.5.3
Remotely Sensed Data for Groundwater Model Development ............................................. 19
2.5.4
Conclusion .............................................................................................................................. 21
2.6
Hydrogeologic Mapping ...................................................................................................... 22
2.7
Well and Borehole Information ........................................................................................... 26
2.8
Groundwater – Surface Water Interactions .......................................................................... 26
2.8.1
Importance of the Groundwater - Surface Water Interface .................................................. 27
2.8.2
Management Implications of the Hyporheic and Hypolentic Zones ...................................... 27
2.9
Environmental and Water Quality Indicators ....................................................................... 28
2.9.1
Environmental Indicators ....................................................................................................... 29
References ................................................................................................................................... 33
Appendix 2-A – Technical Discussion: Remote Sensing for Groundwater Inventory and Monitoring ... 37
Groundwater Inventory, Monitoring, and Assessment Technical Guide - DRAFT
2.0
Foundations of Groundwater Inventory, Monitoring, and Assessment
Groundwater inventory, monitoring, and assessment programs will necessarily involve various levels of
detail, focus, and spatial extent, depending on the geographic location and the specific resource issues.
There is, however, a common set of basic concepts employed in groundwater inventory, monitoring,
and assessment. The following paragraphs summarize the basic concepts.
The need for groundwater assessments of various kinds is found in many National Forest System (NFS)
programs and activities, such as pesticide management plans, special use authorizations, mining
operations and associated National Environmental Policy Act (NEPA), oil and gas development and
associated NEPA, and land management planning.
Assessments necessarily include groundwater inventory data and their interpretation, which land
management agencies use to define the overall value of aquifers and GDEs, or their susceptibility to
contamination or depletion.
Groundwater monitoring is a critical part of water resource management, making it a key component of
environmental protection for grazing, minerals development, or other land management activities.
Monitoring programs for all water resources should be closely linked because groundwater and surface
waters are hydrologically connected. By acknowledging this close hydrologic connection, groundwater
monitoring can provide critical support to surface and groundwater development and management
programs.
Groundwater quality and quantity data is used to obtain and evaluate information on the physical,
chemical, and biological characteristics of groundwater in relation to human health, aquifer conditions,
and designated groundwater and surface-water uses, including groundwater-dependent ecosystems.
With accurate information the current state of groundwater resources on NFS lands can be better
assessed, water-resource protection programs run more effectively, long-term trends in groundwater
quality and quantity evaluated, and the success of land and water management programs determined.
2.1
Important Groundwater Concepts
This section presents general concepts relating to the origin, occurrence, movement, quantity, and
quality of groundwater. The concepts will be useful in providing the nontechnical reader with a basic
understanding of groundwater. These topics are discussed in more detail throughout the rest of the
technical guide. A glossary of terms is included at the end of the guide. For additional reading on basic
groundwater concepts see Basic Ground-Water Hydrology (Heath 1983).
Aquifer Extent and Yield
Aquifers in the United States range from large regional systems that are nationally significant, to local
aquifers important to smaller communities and ecosystems (http://nationalatlas.gov/mld/aquifrp.html;
Maxwell et al. 1995). Analysis of geologic information is used to delineate the lithostratigraphic and
lateral extent of aquifers and confining units. The vertical extent, degree of confinement, hydraulic
characteristics of each unit, and water yielding characteristics for typical wells are determined through
hydrogeologic assessment of groundwater well data, geologic maps, geophysical logs, etc. The
definition of an aquifer is relative depending on the needs of the user. A viable source of groundwater
could be an isolated fracture in what is otherwise an aquitard.
Groundwater Flow Systems
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Groundwater flow systems describe the ways in which groundwater moves within and among aquifers.
These sustain the baseflow of rivers and supply water to wells, springs, and wetlands. The groundwater
flow regime for a given location is conceptualized and described by analyzing flow paths and barriers,
sources of recharge and discharge, water budget, flow rates, water quality parameters, and age of water
at different points in the system.
Groundwater Quality
The geochemical conditions in aquifers determine the ambient or baseline groundwater quality. Water
quality characteristics for each aquifer include existing condition, range of variation, and established
long-term trends. Understanding baseline or background groundwater quality and geochemistry of
aquifers allows resource managers to identify potential groundwater problems, compare site
concentrations of water quality parameters with background concentrations, and develop information
on regional groundwater quality (Franke 1997). Water quality data are used to establish the historical
effects on, and predict future changes in, groundwater quality as a result of human activity. These
studies focus on identifying the primary natural or human activities which affect groundwater,
quantifying risk to human and ecological receptors, and determining the effectiveness of management
practices.
Groundwater-Dependent Ecosystems
Groundwater-dependent ecosystems include springs and seeps; many caves, sinkholes, and karst areas;
baseflow-dominated streams; some wetlands, lakes, estuaries, and offshore marine environments; and
some terrestrial vegetation such as phreatophytes. These are ecosystems that can be affected by
groundwater depletion or water quality changes in aquifers. These unique ecosystems can be identified
and mapped, and the dependent biological communities assessed and monitored by using standard
methods.
Groundwater Uses
A groundwater-uses inventory involves identifying past, current, and potential future types of
groundwater usage (e.g., spring developments, domestic wells, stock watering, municipal wells,
industrial wells, etc.). For groundwater wells, the past, current, and potential withdrawals for volume,
location, depth, and source are important for analyzing impacts to groundwater-dependent resources
and determining the sustainability of groundwater withdrawals. The habitat integrity of springs and
other GDEs can be altered by uses (e.g., livestock , wildlife, mining, domestic water use). Management
of springs and other GDEs requires ecological data and water quantity/quality data to make proper
management decisions on usage.
Aquifer Vulnerability
Once aquifers and other potential groundwater sources are identified and the relative value and
importance of the groundwater resource for human use or ecosystem health is assessed, then
determinations are made about the aquifer vulnerability and degree of protection or management
required. Groundwater vulnerability assessments are commonly performed by using an index method
wherein numerical scores or ratings are assigned to various physical attributes to develop a range of
vulnerability categories. The relative degrees of groundwater vulnerability are usually delineated as low,
medium, and high. This method can be modified to fit the unique stresses and circumstances
surrounding a particular aquifer or management area. The following aquifers or groundwater source
types can be especially sensitive to human activity:
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Surficial unconsolidated deposits with shallow water tables (including alluvial and glacial-drift
deposits)
Karst bedrock
Fractured bedrock, especially at or near the land surface
Deep aquifers that are extensively pumped, thus causing subsidence, mobilizing lower quality
water, or inducing flow of groundwater from more sensitive aquifers
Groundwater-Surface Water Interaction
Groundwater and surface water interact in different ways, principally within GDEs. For example, in many
parts of the United States direct groundwater discharge to streams (baseflow) is a major component of
the total surface water flow in a watershed. In other places or at different times of the year streams
may be an important source of recharge to groundwater. Knowing the magnitude, duration and timing,
and quality of the flows of groundwater to surface waters, and vice versa, is essential to complete
understanding of (a) health and vulnerabilities of water resources of any watershed, (b) the ecological
sustainability of natural resources that are dependent on those water resources, and (c) the response of
a watershed to climate and land-use change. Groundwater interactions in stream reaches, lakes, and
wetlands can be assessed by various standard trend-analysis methods.
Hydrogeologic Unit
Hydrogeologic units are regions within the subsurface that have mappable, relatively uniform hydraulic
characteristics and can be differentiated from adjacent units. Units are delineated by using geologic
maps and subsurface information. Subsurface information can come from geologic and hydrogeologic
scientific reports, well logs, geologic logs, and project-derived geophysical logs.
Hydrogeologic Framework
The hydrogeologic framework for a groundwater system describes the underlying foundation for that
system: how different combinations of geologic, topographic, and hydrologic conditions control the
occurrence, abundance, chemistry, and movement of groundwater beneath different geologic terrains.
Seemingly subtle differences in the geology can cause groundwater characteristics to vary enormously
from place to place, even within the confines of a relatively small area (Anthony H. Fleming and Robin F.
Rupp, Indiana Geological Survey, http://igs.indiana.edu/MarionCounty/Hydrogeologic.cfm, accessed 25
November 2012).
Hydrogeologic Conceptual Model
A hydrogeologic conceptual model is a generalized description of the groundwater system, often
presented as a pictoral representation in the form of a block diagram or as a narrative description.
Qualitative interpretation of available data and information for a site or area are included in the
conceptual model, along with key simplifying assumptions. In building a conceptual model, there should
be an emphasis on the hydrogeologic framework, including the geologic materials (stratigraphy) and
processes, groundwater flow and transport mechanisms, water sources and sinks, aquifer parameters
and boundaries, and a water budget. Ideally, it should also be accompanied by descriptions of important
limitations or gaps in information that could significantly affect the accuracy of the model, as well as
descriptions of the limitations to how the model may be used.
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2.2
Planning and Design
Groundwater inventories, monitoring and assessments produce maps, data, descriptions and
management interpretations. They provide basic groundwater resource information necessary for
ecological assessments, project planning, watershed analysis, land management plan revisions, and
implementation and monitoring of land management plans. They are essential in predicting the effects
of a given activity on groundwater resource health or condition, and will necessarily involve various
levels of detail, focus, and spatial extent depending on the geographic location of a national forest or
grassland and the specific resource issues the unit is dealing with.
Based on specific objectives and resources available, inventory, monitoring and assessment can be
designed to include individual wells, GDEs, or entire groundwater flow systems, and target contaminants
or groundwater depletion. The effectiveness of an inventory, monitoring program, or assessment will be
linked to the degree to which it identifies and accounts for important physical/chemical processes for
each situation, addresses uncertainty, and meets management objectives.
2.2.1
Some Basic Recommendations for Contracting
Except for the more basic tasks described in this technical guide, a forest will likely obtain assistance
from the regional office, centralized national minerals and geology office (CNO), or a contractor in
performing hydrogeologic activities to achieve the desired objectives. This is because Forest Service
personnel may lack the time, knowledge or experience needed to perform the work. Where the output
of the work may be subject to regulatory or legal review and challenge, or may be part of a controversial
issue, hiring a contractor may be the necessary or preferred route.
The first step in contracting is to develop clear objectives. What are the needs of the project and what
type of output will be necessary to address those needs? The objectives and how they are described
should be clear to someone that has little if any knowledge of the situation and overall needs. This is
particularly critical if the objectives are unusual or unique to Forest Service work.
Once the objectives are defined, a scope of work can be developed. While a detailed scope of work can
make it easier to compare proposals and bids from different contractors, it may also lock you into a less
efficient or more costly approach. It will also increase the potential for disputes over who is at fault if
something does not work as planned. Depending on the nature of the work, it is recommended that you
get the assistance of an agency hydrogeologist or environmental services person in developing a scope
of work for contracting involving groundwater.
The types of work described in this technical guide and in the Forest Service Technical Notes (Appendix
A) should already be familiar to a qualified contractor. It will likely be better to use the methods
described in these resources to assess a contractor’s proposal then to develop a detailed, prescribed
scope of work. The reasons for differences between a contractor’s proposal and the suggested methods
in these guides should be resolved before the work is awarded.
There are some general elements that any contractor’s proposal should have and that should be
specified in the request for quote (RFQ) or request for proposal (RFP).

Identification of any key people that will work on the project, their general qualifications, and
any required specific qualifications necessary for performing the work (i.e., licensure,
certification, and training).
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Identification of any subcontractors they intend to use along with information on their
qualifications. For example, does the proposed laboratory have the necessary certifications for
the analyses they will be performing.
Depending on the size and complexity of the project a contractor’s proposal will either contain a
description of how the work will be performed or a project plan for developing a work plan and
associated documents. The described work or work plan should include descriptions of how the
following will be addressed:
o Health and safety – Every contractor should develop their own site-specific health and
safety plan for their workers. Depending on the location of the work the RFQ or RFP
may have to specifically require that the health and safety plan address wildfire risk,
prevention, and response as this is not normally addressed in site health and safety
plans for these types of work.
o Sampling and analysis – A Sampling and Analysis Plan (SAP) describes the procedural
and analytical requirements for data collection. It documents data quality objectives,
and provides details on how those objectives will be met. It includes items such as field
sampling procedures, analytical methods, and quality assurance/quality control (QA/QC)
procedures.
o Communications – Different contractors have different communications styles. The
contractor should define how they will keep the Forest Service representative informed
and involved on project matters and decisions. Frequent communications between the
Forest Service representative and the contractor can help avoid costly mistakes and
misunderstandings. It is important that the contractor talk to and receive directions
from the appropriate people. It may be advisable to specify communication schedules
and protocols in the RFQ or RFP.
o Project risk planning and contingencies – Project risk planning and contingencies are
about what the contractor might do if it is not feasible to complete the work as
originally planned. Given that many Forest Service work sites are located in remote or
difficult to access places, having plans for alternative approaches that can be performed
without leaving and returning to the site can save significant amounts of time and
money.
Description of how the information will be reported. This may include a description of the
format of the report. Reporting should include providing copies of detailed maps to scale,
borehole and other field logs, photos, laboratory reports, and QA/QC reports. For projects
subject to litigation it may also be advisable to require copies of all hand-written field
documents including log books and annotated photographs be provided with the report or as a
separate deliverable. Data should be submitted electronically if possible.
To ensure that thorough proposals are received, it is important to provide the bidders with sufficient
information in the RFQ or RFP to understand all of the requirements of the project. Most contractors
performing this type of work are not familiar with the many requirements that apply to Forest Service
work or the types of environments it may be performed in. Information provided in the RFQ or RFP
should include:



Any available information on site geology, hydrogeology, and other subjects that otherwise
might affect what data is collected and how it is collected. You do not want pay for collecting
information you already possess.
Any specific Forest Service requirements that may dictate how the work has to be performed.
Detailed information on site access and conditions. The best equipment for performing the
work may not be able to reach the site.
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2.3
Special procedures or requirements that may be necessary to protect other resources at or
near the site, or to mitigate impacts to other people in the area.
Acceptable timeframes for performing the work such as time of year or time of day.
The availability of water and possibly other supplies. This is particularly important for remote
or hard to access sites.
What types of emergency assistance might be available and how can they be obtained. This is
especially important if calling 911 or using a cell phone will not be an option.
Risks in the area of the site that might affect or threaten site work or workers such as, physical
and biological risks, unusual weather risks (i.e., high elevation, extreme weather, hazard trees,
dry lightning, flash flooding), or risks from other human activities (i.e., hunting season).
Acceptable handling and disposal of waste materials. How should they store and dispose of
waste materials such as drill cuttings, fluids, and other waste materials. For example, a
common disposal practice for uncontaminated drill cuttings and fluids at groundwater or
environmental drill sites is to thin-spread or dump them on the ground near the drill site.
Contaminated waste materials that require off-site disposal typically have to be containerized
and left on site or at least on the property until they have been properly characterized and
accepted for off-site disposal. It may be illegal to move them until then.
Inventory or Monitoring Program Objectives
The first step in designing a groundwater inventory or monitoring program is to establish the
objective(s) of the program. For example, a monitoring program to track short-term temporal variations
such as aquifer response to rainfall or evapotranspiration or changes in streamflow would be designed
differently than a monitoring program designed to track long-term temporal variations such as aquifer
response to El Nino/La Nina cycles, or Pacific Decadal Oscillations. Practically, objectives must match
budgetary and staffing constraints, determining, for instance, whether you can drill new wells, and how
many and what types of samples you can take. It may even be better not to collect any new data if time,
money, or resources do not allow you to obtain enough data for sufficient analysis. Assuming sufficient
usable data can be obtained, develop a work plan and identify available human and physical resources
to fit within the budget.
Typical objectives of groundwater inventory or monitoring programs are to:
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Locate and characterize groundwater or groundwater-dependent resources;
Assess background groundwater quantity and quality conditions;
Comply with statutory and regulatory mandates;
Determine changes (or lack of change) in groundwater conditions over time to quantify existing
and emerging problems, to guide monitoring and management priorities, and evaluate
effectiveness of land and water-management practices and programs intended to improve
groundwater conditions;
Improve understanding of the natural and anthropogenic factors (for example, land-use
activities or facilities) affecting groundwater; and
Assess surface-water/groundwater connections.
Several types of groundwater monitoring are conducted by Federal, State, local, and private
organizations to accomplish one or more of these objectives:


Background monitoring (up gradient of the area of interest or in an unaffected “control” area);
Baseline monitoring (in the area of interest in advance of project or activity initiation);
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Monitoring for environmental and natural change (e.g., climate change);
Monitoring for effects of specific land-use alterations for environmental compliance of
permitted facilities; and
Compliance monitoring is conducted in response to specific regulatory requirements or permit
conditions for facilities regulated under various programs such as the Resource Conservation
and Recovery Act (RCRA) or in support of remedial activities [for example, the Comprehensive
Environmental Response, Compensation and Liability Act (CERCLA) site monitoring]. An
important consideration of monitoring is the required level of data reliability or defensibility.
Monitoring for regulatory compliance, litigation, or water supply purposes may require more
stringent planning, protocols and data quality than monitoring for internal or ecosystem support
purposes.
Hydrogeologic Conceptual Model
Development of a conceptual model of the groundwater system is a part of any groundwater inventory,
monitoring, or assessment activity, and is prerequisite to more advanced quantitative analysis such as
numerical simulation of groundwater flow and contaminant transport. The process of developing a
conceptual model is analogous to assembling pieces of a puzzle to create a finished picture. The pieces
of the puzzle include:
1) The hydrogeologic framework of the groundwater system (the thickness, extent, and boundaries
of the hydrogeologic units),
2) Hydraulic characteristics of hydrogeologic units (hydraulic conductivity and storage),
3) Flow system delineation (recharge/discharge areas, direction and rate of flow, and changes in
system with time),
4) Water budgets for the ground- and surface-water systems (recharge and discharge), and
5) The nature of and variations in natural water quality and the things that may control it.
Many of the pieces of the conceptual model will fit smoothly into the picture. Unlike a picture puzzle,
however, some of the water-budget pieces may not be well defined during early phases of the
investigation. Therefore, the conceptual model may change as the understanding of how the system
operates is improved by integrating new information.
Development of a conceptual model differs with scale or focus. For example, understanding the flow
system, aquifer, or groundwater resources of an area will be different than trying to understand the fate
and transport of a contaminant. To create a conceptual model for a large landscape (forest or district)
focus is on big-picture characteristics without giving much attention to small-scale features (e.g., minor
faulting) unless there are a large number of them in the area that, in aggregate, play a major role in the
groundwater system. A conceptual model for a project area, by contrast, often requires capturing the
role of small-scale features in the groundwater system, as those features may be playing a major role in
the system and may be most affected by the project. And if contaminants are involved, these additional
steps are generally needed:
1.
2.
3.
4.
5.
Identify sources of the contamination,
Determine the nature and extent of the contamination,
Identify the dominant fate and transport characteristics of the site,
Specify potential exposure pathways, and
Identify potential receptors that may be impacted by the contamination.
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The first step in developing any conceptual model is to identify, review and collect information already
compiled by other sources. Depending on the purpose of the conceptual model this may be the only
data gathering required. Possible sources include previously developed Forest Service documents, other
State and Federal agencies (i.e., U.S. Geological Survey [USGS], U.S. Environmental Protection Agency
[EPA]), published scientific papers and books, unpublished research work including theses and
dissertations, unpublished reports prepared by consultants and mining companies, and consultation
with people knowledgeable about the area or subject of interest. A lot of the information can be
obtained through the internet. The Forest Service library available at http://fsweb.wo.fs.fed.us/library/
can assist by performing literature searches and obtaining copies of materials not otherwise available.
This information will save time in developing the conceptual model, and help focus the collection of
additional data.
2.4.1
Geologic Framework
A key element of a conceptual model is the geologic framework and how it affects the distribution and
movement of groundwater and the interactions between groundwater and surface water. The
thickness, extent, and boundaries of aquifers and confining units within the planning or analysis area are
the components of the hydrogeologic framework. Use new and existing data to interpret the regional
geologic framework, including: review of previous studies, interviews with other scientists familiar with
the area, lithologic descriptions on water well reports, aerial photographs and other remote sensing
data, drill cuttings or cores, borehole geophysical logs, airborne or surface geophysical surveys, field
reconnaissance, and many others. Determine the specific methods you use in any situation by the
objectives, scale, and resources and available information associated with the inventory, monitoring, or
assessment. Present the interpretations in a descriptive narrative and/or on a composite geologic map,
geologic cross sections, and possibly contour maps of hydrogeologic units. Some of the work elements
of this task that may be performed are listed below:
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2.4.2
Delineate stratigraphy and map any significant hydrogeologic units;
Construct regional litho-facies maps;
Determine location of major structures and assess effects of faults and other structures on
groundwater movement;
Assess change in permeability with change in geologic control (i.e., depth, litho-facies,
metamorphic grade, etc.); and
Hydraulic Properties
Estimate the hydraulic properties of the hydrogeologic units in the area of interest. Key hydraulic
properties include transmissivity or hydraulic conductivity, storage coefficients, porosity, and boundary
conditions. The properties are needed to assess:
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the ability of different hydrogeologic units to transmit and store water;
the existence of confined versus unconfined conditions;
the presence of interconnections between different hydrogeologic units;
the velocity of groundwater flow;
the presence of recharge and discharge boundaries indicating
precipitation/groundwater/surface-water interactions; and
the presence of no-flow boundaries which may relate to geologic structures or changing
lithology.
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For conceptual models of large areas, approximate numbers can be used based upon literature values
for similar types of hydrogeologic units. For conceptual models of local project areas, values specific to
the hydrogeologic units of interest – and potentially specific to the physical area of interest – will be
necessary. If detailed work has been completed on the hydrogeology of the area of interest, available
information can at least be used as a starting point. If that information is not available or the issue at
hand requires detailed knowledge, it may be necessary to collect specific aquifer test data representing
the different hydrogeologic units in the area.
2.4.3
Flow System
Compiling existing long-term groundwater-level monitoring data should be one of the first tasks of a
new assessment. State water management agencies and Federal agencies like the USGS often have
long-term monitoring networks that can provide insights on the status and trends in groundwater
storage in the system. These data and data from National Forest System project networks can be used
to determine the response of the groundwater system to stresses such as climatic variation, surfacewater stage variation, and seasonal groundwater pumping.
Geochemistry can be used in conjunction with water level data to help delineate groundwater recharge
and discharge areas and determine flow paths and residence times for groundwater. This information is
valuable in developing the conceptual model of the system and, ultimately, in calibrating a numerical
groundwater model.
2.4.4
Water Budgets
A water budget is a quantification of all of the inputs and outputs of water to a defined system, for
example a wetland or aquifer. Groundwater and surface-water budgets should be estimated for specific
time periods to quantify how changes in land and water use and climate affect the hydrologic system.
Components of the water budget such as recharge and groundwater pumping are inputs to a
groundwater flow model. Other budget components, such as groundwater discharge to streams, can be
simulated by the groundwater flow models.
Surface-water Budget
Surface-water budgets are developed for selected reaches of streams and rivers. Irrigation districts,
flood control districts, and other agencies can be contacted to obtain hydrologic measurements that
might help define water budgets for the major rivers. The primary purpose of developing water budgets
for these surface-water features is to estimate independently the exchange of water between them and
the groundwater system. Gain/loss, or seepage studies of selected stream reaches can also be
conducted as described below under "groundwater discharge" and as described in the section on
“Methods for Evaluating Groundwater-Surface Water Interactions” (see section 5.3.1).
Groundwater Budget
The groundwater budget consists of two components – recharge and discharge.
Recharge: The most common sources of recharge to groundwater systems are precipitation,
irrigation return-flow (on-farm losses), effluent from wastewater treatment, canal leakage, stream
leakage, and possibly underflow from adjacent basins. The potential contribution of each of these
sources is assessed in the groundwater budget analysis.
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Watershed modeling can be used to estimate the distribution of recharge from precipitation and
irrigation return flow. Many types of spatial and temporal data are required to simulate daily water
balances within watershed models. Spatial data include land use and cover, soils, geology,
elevation, slope, and aspect. Temporal data consist of daily records of precipitation, and
temperature from local weather stations, and weekly or biweekly estimates of irrigation application
rates. Published information on recharge characteristics for specific geographic areas or different
types of land surfaces is a good starting place.
Leakage to and from streams is evaluated using existing stream gage information and project data at
additional sites. Measurements should occur during low-flow periods to better define
groundwater/surface-water interaction. Existing shallow wells near perennial streams are used to
determine elevation differences between groundwater and the streams, to assess groundwater
response to stream elevation.
Discharge: Groundwater systems discharge naturally to springs, streams, lakes, and via
evapotranspiration. Development or changes in land use can create new pathways for discharge,
including pumping or flowing artesian wells. Depending on the type of water use, groundwater
withdrawals might be estimated by using remote sensing techniques for large irrigated areas, or by
power consumption data for smaller areas. Well discharge for municipal and industrial uses is
estimated by using data from water reporting systems, water-rights information files, and similar
sources. Springs are most often field-located and measured or sampled.
Bare surface evaporation and transpiration from plants are able to move water from liquid state at
or near the ground surface to vapor state in the atmosphere, effectively removing water from the
hydrologic system (evapotranspiration or ET). Phreatophytes (plants that draw water directly from
the water table) that grow in wetlands and along streams and lakes are particularly effective at
moving large amounts of groundwater to the atmosphere. Areas with phreatophyte vegetation can
be mapped from aerial photographs and combined with climatic and physiologic data to estimate
amounts of groundwater ET.
2.5
Use of Remote Sensing Techniques in Groundwater Inventory and Monitoring
The application of remotely sensed data in conjunction with in situ data greatly enhances the Forest
Service’s ability to meet the demands of field staff, partners, and the public for groundwater
information. Using remotely sensed data to inventory and monitor groundwater resources directly
supports the agency’s obligations: 1) to conserve, protect, and restore watersheds, water, listed biota,
and safeguard biodiversity; 2) to assess and disclose the impacts of current and future management
actions; and 3) to support decision making through the use of best available science and data (USDA
Forest Service 2010). Remotely sensed data enhances the agency’s ability to support adaptive
management process through the use of high quality, repeated measures of groundwater condition and
changes at multiple spatial and temporal scales.
Although most remotely sensed data have a limited ability to penetrate the Earth’s surface and directly
detect the storage or movement of groundwater (Hoffman 2005, Meijerink 1996), when combined with
in situ data they can provide meaningful information regarding groundwater resources by detecting
indicators of groundwater recharge and discharge, hydrologic boundary conditions, geologic controls,
and potential locations of groundwater-dependent ecosystems.
The use of data developed by remote sensing for groundwater inventory and monitoring is even more
important because the collection of broad areas of in situ data is often cost prohibitive (Becker 2006)
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and often very difficult or resource prohibitive, particularly in remote areas such as wilderness.
Remotely sensed data can be collected from a number of different platforms, but most commonly
involve measurements of emitted or reflected electromagnetic radiation collected via satellite or
airborne platforms (Waters et al. 1990).
This subsection includes the following topics:

An overview of the technology and the types of available of remote sensing datasets,

Applications of remotely sensed data to meet Forest Service management needs,

Using remote sensing data in the development of groundwater models, and

A summary of the use and application of remote sensing techniques and data to support Forest
Service groundwater inventory and monitoring.
Appendix 2-A provides a discussion on the ability to use remote sensing techniques to inventory and
monitor groundwater at multiple spatial and temporal scales in support of the conceptualization,
development, and calibration of groundwater modeling. It includes:

An overview of different types of imagery and techniques that are most common and that
researchers suggested have the greatest potential to improve the inventory and monitoring of
groundwater;

Reviews selected groundwater applications in more detail, including the use of remotely sensed
data to monitor groundwater-dependent ecosystems, land-atmosphere interactions, and
groundwater storage and relative distribution; and

The potential for future sensor technology to support development of groundwater inventory
and monitoring data and information.
2.5.1
Overview of Available Remote Sensing Datasets
A number of different types of remotely sensed datasets are available, each suitable for certain
applications pertaining to groundwater, with none being applicable in all situations. Data from the
visible and near-infrared have most commonly been used to inventory and monitor groundwater in the
past (Meijerink et al. 2007), but other types of data (e.g., LiDAR and radar) are rapidly being accepted as
significantly enhancing the quality of hydrogeologic information derived from remotely sensed imagery.
Each data type has distinct advantages and disadvantages in terms of their sensitivity to unique
landscape components and processes. These components (e.g., soil moisture, inundation,
evapotranspiration, elevation, vegetation biomass and type, soil type, and more) can then be
synthesized to provide enhanced information on groundwater distribution, abundance, and even
quality. For example, because radar and optical data are sensitive to very different landscape
characteristics, this combination can significantly improve the mapping of groundwater-dependent
ecosystems (Silva et al. 2008, Töyrä et al. 2002) and other groundwater-associated features (Meijerink
et al. 2007). Radar data provide information on inundation, soil moisture, plant structure and biomass,
while multispectral and hyperspectral data are better suited for identification of plant communities and
other land cover types with similar structures and moisture contents. Similarly, LiDAR data can be used
to provide information on topography, vegetation height and biomass, and the actual and potential
distribution of water, often more accurately than optical data.
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A detailed discussion of available remote sensing datasets is included in Appendix 2-A. This appendix
provides an evaluation of the advantages and disadvantages of using passive and active sensors to
collect data and information needed to address groundwater management issues. Passive sensors
include aerial photography, multispectral and hyperspectral optical, gravitational, and passive
microwave. Active sensors include radar, LiDAR, and laser and radar altimetry for groundwater
detection and characterization. Active sensors produce and detect their own energy (e.g., lasers) while
passive sensors generally detect energy produced by the sun and other sources of energy external to the
sensor.
It is important not only to differentiate between different types of data provided by these techniques
but also between the platforms used to collect those data. For example, the advantages of using
satellite imagery include spatial coverage, timeliness, repeatability, and often lower costs (Dobson et al.
1995, Federal Geographic Data Committee 1992, Papa et al. 2006). Satellite data, however, come with
certain limitations. These limitations include greater weather and atmospheric interference. In the
past, the use of satellite data has meant relatively coarse spatial resolutions, but satellite-based sensors
are now able to collect data at the sub-meter scale (e.g., GeoEye-1 collects data with a 0.41-meter
spatial resolution). Both spaceborne and airborne sensors are also discussed in Appendix 2-A.
2.5.2
Application of Remotely Sensed Data in Groundwater Inventory and Monitoring
The following section provides examples of how remotely sensed data have been and can be used to
support the inventorying and monitoring of groundwater. There are numerous applications with some
being operational and others still be developed but that the contribution of remotely sensed data to the
detection and monitoring of groundwater has been and continues to be significant. A group of resource
managers and technology experts must determine how remotely sensed data is applied to inventorying
and monitoring of groundwater.
Identifying Groundwater-Dependent Ecosystems
Groundwater-dependent ecosystems (GDEs) include a diverse range of both surface and subsurface
ecosystems whose extent and function are dependent, at least in part, on groundwater (see Section 3
and Appendix 3-A of this technical guide for a detailed discussion of GDEs). GDEs include springs/seeps,
aquifers, perennial streams, and some wetlands, caves, and riparian areas.
This section focuses on the utility of remotely sensed data for the inventory and monitoring of springs
and other groundwater-dependent ecosystems (e.g., fens) associated with wetlands. The utility of this
approach varies across the United States. In the East, nearly all perennial springs are co-located with
wetlands, as a result both systems can be identified by using remote sensing as wetland-spring
complexes. This relationship does not exist in the West because of discontinuous or perched aquifers
and relatively small sites that cannot be detected with the coarse resolution of most remote sensing
datasets.
The inventorying of wetland-spring complexes using remotely sensed data and other ancillary data
involves two main steps: 1) determining whether an area is wet enough to indicate the presence of a
wetland and/or a spring; and 2) determining the degree to which water levels are controlled by
groundwater. The first step has received much more attention from remote sensing researchers and
wetland mappers and so is the focus of this section.
Ideally, wetland-spring complex maps should be based on the direct detection of inundation or soil
moisture at key time intervals; and this is possible using different remotely sensed datasets under a
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defined set of circumstances. Because this is not always possible, due to the often intermittent nature
of inundation and soil moisture, the type of remotely sensed data being applied, or the nature of the
ecosystem itself (e.g., dense evergreen canopy precluding viewing of the ground surface), other
parameters have been commonly used to map wetlands. For example, wetland soils (hydric soils) and
vegetation (hydrophytic vegetation) can often be distinguished from upland soils and vegetation on
remotely sensed images. In addition, certain landscape positions (e.g., topographic depressions or
floodplains) that are identifiable on remotely sensed images are also associated with wetlands.
Remotely sensed data have been used to infer or directly determine the degree of wetness for decades.
Typically this has been done by using aerial photography (e.g., U.S. Fish and Wildlife Service [USFWS]
National Wetlands Inventory [NWI]). More recent advances in remotely sensed data, analysis
techniques, and hardware have made the use of other datasets more common in wetland mapping. As
noted above, since most springs in the West are not associated with wetlands, field inventory is
generally required to accurately locate most springs. The advantages and disadvantages of these
various remotely sensed datasets for wetland-spring complex mapping are outlined below. For a more
in-depth discussion of these datasets and other considerations involved in wetland mapping refer to
Lang et al. (2008).
Aerial photography, when collected at select times, can provide valuable information regarding the
location of wetland-spring complexes. Although it cannot be used quantitatively to measure soil
moisture although it’s relatively fine spatial resolution and extensive historical record provide significant
advantages. However, the detection of inundation in vegetated ecosystems can be compromised by the
presence of a vegetative canopy or the misinterpretation of small water bodies as shadows or
identification of turbid water as being soil. Aerial photography is most accurate when used to identify
wetland-spring complexes dominated by herbaceous vegetation or those without a vegetative canopy.
Errors can be significant when using these images to map forested ecosystems or ecosystems with an
evergreen canopy (Lang et al. 2008).
Multispectral imagery is also used to map wetlands and associated habitats (e.g., NOAA Coastal Change
Analysis Program [C-CAP]). Generally, moderate resolution multispectral data are less capable than
aerial photography for mapping wetland-spring complexes due to the relatively small size of many of
these ecosystems. For this reason, wetland maps derived from aerial photos are commonly used to
assist in the multispectral mapping process. Currently, moderate spatial resolution multispectral data
are primarily being used for wetland mapping, but finer spatial resolution data are being investigated in
limited areas (Nate Herold, NOAA C-CAP, 2012, personal communication). The analysis of time series
multispectral data (e.g., Landsat time series) collected at key time periods also has the potential to
significantly improve wetland-spring complex mapping because subtle temporal trends in spectral
response can help to identify wet areas that may have otherwise been overlooked. The thermal data
provided as part of multispectral satellite datasets has not been shown to improve the identification of
wetland-spring complexes, but airborne thermal data may provide a useful approach under certain
conditions.
Hyperspectral imagery may be used to illuminate the health and vigor of plant species, which can be
indicative of available water. In some instances, specific plant species are also indicative of the
abundance of water. Hyperspectral data are not currently used to operationally map wetlands or
springs. However, these data have been used to improve the characterization of wetlands. Their use for
wetland-spring complex inventory and monitoring could expand as low or no cost hyperspectral data
become more available (Govender et al. 2007, Phinn et al. 1999) and data analysis techniques are
refined. The launch of the Hyperspectral Infrared Imager (HyspIRI) could significantly increase the
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availability of hyperspectral data (see “Potential of New Sensors for Groundwater Inventory and
Monitoring” section in Appendix 2-A). Current wetland-spring complex related hyperspectral
applications include the identification of plant species (e.g., invasive plants, Hirano et al. 2003, Klemas
2011, Schmidt and Skidmore 2003), plant moisture stress (Anderson and Perry 1996), plant biochemical
properties (e.g., nutrient and chlorophyll content, Judd et al. 2007, Schmidt and Skidmore 2003), and
water quality (Klemas 2011). Hyperspectral data are particularly useful for the mapping of submerged
aquatic vegetation (SAV) because the water column reduces reflectance from the vegetation itself often
requiring a detailed examination of spectral characteristics necessary to identify SAV (Klemas 2011, Silva
et al. 2008). Hyperspectral data could be helpful for specialized Forest Service applications when
enhanced spectral resolution is needed to identify specific species and materials. The potential of
multispectral and hyperspectral imagery for the identification and mapping of wetland vegetation is
reviewed in Adam et al. (2010).
The use of synthetic aperture radar (SAR) data provides multiple benefits for wetland mapping and
monitoring. For example, the inclusion of SAR data can improve the ability to monitor wetland-spring
complexes during wet periods, when they are most evident on the landscape but are often obscured by
clouds. The inclusion of SAR data into the wetland mapping process is currently being considered by the
USFWS (John Swords, USFWS NWI, 2011, personal communication), and Canada is now using SAR data
to map wetlands (Li and Chen 2005, Milton et al. 2003). SARs can collect data under almost any
condition and at multiple view angles. In addition, SAR is very sensitive to presence/absence of water
and multiple over-flights should be able to detect changes in surface/near surface groundwater through
time. Radar should be able to measure subtle differences in elevation due to the swelling of soils from
added moisture. Radar has been shown to detect very subtle movements of structures on the order of a
few millimeters.
These capabilities substantially increase the effective temporal resolution of SAR data, allowing not only
for the detection of wetland-spring complexes but also the monitoring of these ecosystems and
important functional drivers through time. The ability of SARs to monitor a wetland-spring complex
hydroperiod is due to the strong influence of hydroperiod on biota (e.g., provision of habitat to rare or
endangered species), biogeochemical processes (e.g., denitrification), and other ecosystem functions.
These hydroperiod maps can be used to infer wetland-spring complex function (e.g., denitrification), and
to update wetland-spring complex boundaries as they shift in response to climate and land-use or land
cover change. SAR data can be used to quantify plant structure and biomass in wetlands, as well as
water height, when the images are collected as interferometric pairs. While optical data provide
superior information concerning the identity of vegetation communities, and because SAR and optical
data are sensitive to very different landscape characteristics, the combination of these datasets can
significantly improve wetland-spring complex inventory and monitoring (Rignot et al. 1997, Ramsey et
al. 1998).
LiDAR data provide unique information that can be used to supplement the wetland-spring complex
inventory and monitoring process. Techniques range from the simple use of LiDAR-derived Digital
Elevation Models (DEMs) to support hand interpretation of wetland boundaries to the use of these
DEMs to create topographic metrics which are then automatically incorporated into the wetland
mapping process. In landscapes whose hydrology has not been severely altered, LiDAR-derived
topographic metrics can provide independent confirmation of wetland-spring complex location.
LiDAR data provide information not only on elevation but also on vegetation characteristics and the
identity of materials via the intensity of the LiDAR return. Although optical data can be used to detect
and characterize vegetation, LiDAR data can be used to enhance this characterization through increased
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information on vegetation height, biomass, and structure (Vierling et al. 2008). LiDAR-derived
topographic information can also be used to map surface hydrologic flow pathways, which regulate the
ability of wetland-spring complexes to provide ecosystem services (e.g., mitigation of nutrients and
sediments). Identifying these hydrologic connections could be key to the preservation of many wetlandspring complexes. The potential of these data to support Forest Service wetland-spring complex
inventory and monitoring projects is already strong and will increase as data become available and the
methods to exploit and incorporate these data into the inventory and monitoring process evolve.
Although the potential of LiDAR data to support these projects is strong, they are best used in
combination with other types of remotely sensed data.
Regardless of the type of remotely sensed data used to inventory or monitor wetland-spring complexes,
there are considerations inherent to the nature of the ecosystems that affect the relative difficulty of
the process. These include temporal and spatial considerations as well as those involving the type of
ecosystem being mapped by using remote sensing techniques as well as other groundwater-related
inventory and monitoring methods. Temporal considerations include the timing and duration of
inundation and soil saturation, especially in ecosystems without perennial inundation/saturation or
distinct vegetation phenology. Wetland-spring complexes are usually easiest to detect when they are
wettest and when live vegetation biomass is at its lowest point, but variations in the remotely sensed
signal can provide valuable clues regarding their location and function of wetland-spring complexes.
Certain wetland-spring ecosystems are normally easier to differentiate from surrounding upland
ecosystems than others. For example, ecosystems with forested or evergreen vegetation are often
more difficult to distinguish than ecosystems with herbaceous vegetation. Smaller, drier ecosystems are
also more difficult to detect than larger, wetter ecosystems. For a more detailed discussion of temporal,
spatial, and ecosystem type considerations see Lang et al. (2008).
Indicators of groundwater dependence
There are a number of indicators that can be used to infer whether a particular wetland-spring complex
is likely to be groundwater dependent:

Elevation of the ecosystem relative to known expressions of the groundwater table - water
bodies, like lakes and larger streams, and their elevations are often identified on DEMs or DLGs.
The elevation of less prominent water bodies can be obtained by overlaying maps of the water
body with a DEM. The same elevation data could then be used to ascertain the position of the
ecosystem in question relative to the elevation at which groundwater is expressed. Murphy et
al. (2008) illustrate how relative depth to groundwater can be obtained.

Hydroperiod, especially relative to weather patterns - the mapping and monitoring of
hydroperiod can generally be accomplished by using multi-temporal aerial photography,
multispectral data, or SAR (see Appendix 2-A, section 2-A.1.4). Aerial photography and
multispectral data are most effective in areas with an open vegetative canopy during the period
of surface hydrologic expression, and SAR data are more advantageous in areas with a closed
vegetative canopy during the period of surface hydrologic expression.

Soil permeability can be assessed by using NRCS SSURGO data that was originally generated by
using a combination of aerial photographs and in situ data or other indicators of permeability,
such as hydrologic response to weather patterns.

Ditches and other landscape features are often evident on aerial photographs and fineresolution multispectral data but can also be mapped by using LiDAR data where available.
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All of these indicators can be ascertained either from remotely sensed imagery or from commonly
available ancillary data. However, some of the indicators listed above are only regionally applicable. For
example, the ability to judge proximity of the groundwater table to the wetland-spring complex based
on elevation of the ecosystem relative to a known expression of the groundwater table is likely limited
to wetter regions of the United States. Surface water is considerably more likely to be an expression of
groundwater in those regions of the United States where the majority of stream flow is attributable to
baseflow (Becker 2006). A map of these regions can be found in Becker (2006).
The use of ancillary data (Li and Chen 2005, Sader et al. 1995), geographic information systems (GIS)
and hydrologic models can greatly benefit the GDE inventory and monitoring process. Ancillary datasets
that are beneficial to this effort include, but are not limited to, information on soils, water bodies,
topography, current and prior land use, tides, and weather. Decision support systems (e.g., decision
trees, GIS, and hydrologic models) can then be used to extract valuable information from these ancillary
datasets in combination with remotely sensed data. The development of hydrologic models is discussed
briefly below and is discussed in detail in section 2.4.
Land-Atmosphere Interaction
Understanding evapotranspiration (ET) in the area under investigation can be beneficial to groundwater
inventory and monitoring. ET is comprised of water that is evaporated directly from the forest canopy
(Ei), soil surface (Es), and water infiltrated into the soil that is taken up by the rooting system of plants
and transpired (T). In relatively flat terrain, groundwater recharge potential over relatively long time
intervals is the difference between precipitation and ET (Brunner et al. 2006). In addition, shallow
groundwater can enhance local ET rates through phreatophytic uptake (increased T) or supplementing
evaporation from the soil through wicking from the capillary fringe into the unsaturated zone (increased
E). Remote sensing of ET can provide valuable, spatially distributed information on rate and areas of
shallow groundwater presence and uptake.
Groundwater recharge is treated as a tuning parameter in most groundwater modeling efforts. A
climatological estimate of ET is initially assumed, based on simple empirical functions of latitude or air
temperature (e.g., Thornthwaite equation). Then recharge is varied within polygonal patches to obtain
optimal match between modeled water table and well measurements. However, as discussed in the
previous section, most areas of GDE ET in the Western United States are very small and not currently
detectable with remote sensing.
With the advent of thermal imaging systems it has become possible to map ET with reasonable accuracy
over a range of scales, from fields to landscapes to continents (Anderson et al. 2012). ET has a distinct
thermal signature, serving to modulate the radiometric temperature of soil and vegetation components
of the imaging pixel through evaporative cooling. Given estimates of net radiation load and green
vegetation cover fraction, surface energy balance models can estimate the ET required to maintain the
surface at the temperature observed with the thermal sensor. The resolution of ET information can
range spatially from 30 meters (Landsat) to 10 kilometers (geostationary weather satellites), and
temporally from several minutes (geostationary) to several weeks (Landsat). Techniques are being
developed to fuse ET information from multiple satellite platforms to facilitate mapping of daily ET at
small spatial scales (less than 100 meters) (Cammalleri et al. 2012). The use of these techniques in semiarid, steeply dissected terrains typical of forested lands in the Western United States has limitations and
should be used with caution.
ET time-series data are critical for overall groundwater model performance. Remotely sensed maps of
ET have recently been integrated into hydrologic modeling systems as an improvement over prior
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empirical estimates (Crow et al. 2008, Kamble and Irmak 2008, Schuurmans et al. 2003). ET can
currently be estimated on an annual basis by using empirical data and can result in adequate water
budgets, but daily variations will not be captured by the models and thus model performance can be
impeded. Processes within some models, including soil moisture and vegetation biomass, depend on
correct daily ET input, ultimately affecting modeled groundwater recharge estimates. Therefore, model
performance can be significantly improved through the use of spatially varied daily ET data provided
using current remotely sensed data products.
Time variability in remotely sensed ET can provide a valuable clue to identifying regions where
evaporation rates are being supplemented by groundwater — such as in the groundwater-dependent
ecosystems discussed in the previous section. Given sufficient data, we can map temporal variability in
ET at different spatial scales. For instance, studies have shown that relatively low variability in ET as a
function of time is observed in areas with a shallow water table and in agricultural areas receiving
routine irrigation. In these areas, ET is more stable with time than in surrounding areas where
evaporative losses are more strongly correlated with precipitation. Even in densely vegetated areas,
transpiration rates maintained near potential through dry spells indicate the influence of resilience.
Resilience and strength of the signal will depend on vegetation type, with deeper rooting types able to
extract water from deeper water tables.
Maps of ET variability derived from thermal remote sensing can help in identifying ecosystems
dependent on supplemental moisture from the water table, complementing the radar and LiDAR
mapping techniques described above. Such applications used at small scales will benefit from an
improvement in image collection frequency with higher-resolution thermal imaging platforms, as in the
proposed HyspIRI mission with 5-day revisit (see “Potential of New Sensors Used for Groundwater
Inventory and Monitoring” section in Appendix 2-A).
Groundwater Flow Storage and Relative Distribution
Groundwater can be inventoried and monitored at multiple spatial scales; however, the inventory and
monitoring of groundwater has unique restraints when conducted at varying spatial scales. At fine
spatial scales, the mapping of groundwater distribution with remotely sensed data involves the
determination of surface boundary conditions but not total storage. Some information regarding total
storage can be obtained developed by integrating these measurements into a groundwater model.
Total groundwater volume at all depths can be estimated from remotely sensed data but are only
meaningful at relatively coarse spatial scales. Discussions of inventory and monitoring groundwater,
flow, storage and relative distribution at multiple spatial scales and relevant depths are provided within
this section.
Inventorying Groundwater Flow
The rate and behavior of groundwater flow is controlled by the geology, which can be partially
determined through remotely sensed observations of the ground’s surface (e.g., geologic structures and
lithology; Becker 2006). The strength of these measurements can be greatly increased through
modeling. Remotely sensed data contribute to the inventory and monitoring of groundwater by
enabling extrapolation of in situ measurements to broader geographic areas and by detecting signatures
and patterns of groundwater influence that are difficult to discern from ground-based observations
(Engman 1996, Moore 1982).
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Monitoring the Relative Distribution of Groundwater
Various types of remotely sensed data can be used to inventory and monitor the relative distribution of
groundwater as indicted by signatures on the Earth’s surface. This can be accomplished in multiple
ways, including the direct detection of the groundwater surface itself (i.e, boundary conditions) or
through the detection of groundwater distribution indicators, such as vegetation type or condition and
topography. This can be accomplished at a very fine spatial resolution (e.g., less than 1 meter), if
necessary, given currently available data (see above). The utility of these approaches will vary
depending on landscape and climate conditions, and should not be construed as working equally well
under all conditions. For example, surface water is more likely to represent groundwater boundary
conditions in relatively wet regions (e.g., portion of the United States east of the Mississippi).
The surface expression of groundwater is often treated as a boundary condition for subsurface flow, and
these boundary conditions are considered to be one of if not the most important data when developing
groundwater models (Becker 2006, Hoffman 2005, Meijerink et al. 2007). Markers of boundary
conditions can take the form of wetlands, streams, lakes, seeps, springs, recharge zones, and even areas
of concentrated evapotranspiration (Becker 2006, Brunner et al. 2006, Meijerink et al. 2007). Even soil
surface elevations can be designated as a boundary, thus improving the modeling process (Hoffman
2005). Surface water elevations can be used to map hydraulic head, which constrains not only the
location of the groundwater table but also the movement of groundwater assuming there is not an
abrupt change in hydraulic conductivity beneath the surface water body (Becker 2006, Meijerink et al.
2007). Remotely sensed data can be used to identify and quantify the elevation of these areas. The
methods for doing so are described in more detail above and include the use of interferometric SAR,
radar and laser altimetry, and LiDAR to identify height and often rely on optical data (e.g., aerial
photography and multispectral images) to map extent. In certain landscapes, soil moisture indicates
near-surface groundwater and can be inventoried and monitored by using a variety of remotely sensed
instruments, including passive and active microwave (SAR) sensors, most accurately, and optical and
thermal data to a lesser degree (see above for further details).
Although the inventory and monitoring of boundary conditions using remotely sensed data provides
information on the relative distribution of groundwater and is key to the improved conceptualization
and parameterization of groundwater models, it does not directly provide information on groundwater
volume. However, the satellite-based sensor GRACE has been used to develop this information.
Appendix 2-A provides a discussion of this technique and case studies where is has been used.
2.5.3
Remotely Sensed Data for Groundwater Model Development
Hydrogeologic models provide a powerful tool for estimating continuous spatial and temporal
information from limited, discrete data sources. Their strengths and limitations are discussed in detail in
section 2.4. Prior measurements of vegetation, soil, landform, and hydrological processes can be used
to conceptualize, parameterize, and calibrate/validate models designed to estimate water budgets,
flood or drought potential, erosion rates, contaminant transport, and water quantity and quality. Once
the models are properly established, scenarios can then be developed to forecast how to conserve or
improve the current state of a watershed by watching how modeled perturbations change the natural
state.
Models can also help inform the design of restoration projects by identifying which proposed alteration
to the system results in the desired condition (e.g., time required to reduce contamination to an
acceptable level with different treatments). Whether conserving current systems or restoring damaged
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areas, groundwater hydrologic models aid decision making through the use of best available science and
data.
The modeling process involves assembling all available data, including maps based on remotely sensed
images and in situ data. This is particularly true for groundwater modeling, where it is not cost effective
to install dense monitoring wells to capture gradients, but it is possible to create a spatially distributed
model, partially through the incorporation of spatially explicit data, such as remotely sensed images.
Geospatial data needed for model conceptualization and development are found at many different
sites, including the U.S. Department of Interior National Atlas (http://www.nationalatlas.gov ), USGS
National Map (http://nationalmap.gov/viewer.html ), USGS National Hydrography Dataset (NHD)
(http://nhd.usgs.gov/index.html ), and Natural Resources Conservation Service Geospatial Data Gateway
(http://datagateway.nrcs.usda.gov ).
Topographic data are critical for groundwater modeling and are used to supply elevation to nodes and
delineate watershed and drainage networks used for channel routing. DEMs can be obtained from
several remotely sensed data sources including LiDAR, aerial photography (e.g., USGS topographic
quads), and interferometric SAR (e.g., Shuttle Radar Topography Mission). Of the aforementioned
sources of DEMs, only LiDAR is considered by Brunner et al. (2006) to appropriately represent depth to
groundwater. Care should be taken when selecting between these datasets since each have specific
strengths and weaknesses and may be more or less suitable for a given application. Various types of
DEMs can be obtained from the USGS National Elevation Dataset website (http://ned.usgs.gov).
Representation of streams is available from the National Hydrologic Dataset (http://nhd.usgs.gov) in
high to low resolution based primarily on aerial photograph interpretation; coarser DEMs should be
altered to respect these line coverage although they are known to contain significant errors in some
environments and can be often be improved through the incorporation of LiDAR data (Lang et al. 2012).
Finally, geologic data, whether obtained from geospatial (e.g., maps) or in situ data, are important to the
groundwater modeling process. For groundwater flow, geologic maps at various scales can be found at
the National Geologic Map Database Project (NGMDB) (http://ngmdb.usgs.gov/Info) and typically
include cross sections illustrating structure critical for the conceptualization of groundwater models.
Well drilling logs, available from most State engineers, water agencies or geological surveys, are also
helpful in determining domain depth and often include a static groundwater level useful for calibration.
Even with the data mentioned above, there are still significant gaps of information necessary for the
conceptualization and parameterization of groundwater models. It is often possible to fill these gaps
with literature values, but remotely sensed data can offer a better, more spatially distributed
alternative. However, these techniques are not well suited for discontinuous, perched aquifers that are
the norm in most of the Western United States and are difficult to monitor synoptically using any
technique.
The various airborne and spaceborne platforms mentioned herein present a variety of additional data
(e.g., soil moisture, evapotranspiration, inundation, elevation, vegetation biomass and type, and soil
type) useful in the creation and parameterization of groundwater models. Aerial photographs have long
been used to map bedrock structures and now more advanced technologies can be used to map
groundwater-dependent ecosystems, boundary conditions, groundwater storage, recharge/discharge,
and land-atmosphere interactions.
All of this information can be useful for the improved representation of groundwater conditions and
dynamics through modeling. Adding these missing attributes to the modeling process, which is often
based on limited geospatial data, helps conceptualize and model a groundwater system with higher
confidence.
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The type of model used for an application is best selected with prior knowledge of the groundwater
system being modeled. Depending on the study site and the goals of the application, empirical, lumped
conceptual, or distributed physically-based models may be appropriate. In a system where groundwater
and surface water are highly connected, a model incorporating mass balance and Darcy’s equation (e.g.,
MODFLOW [McDonald and Harbaugh, 1988] with the unsaturated or variably saturated flow package) is
appropriate.
The in situ inventory and monitoring of groundwater is likely to be insufficient for modeling needs, but
remote sensing can help bridge knowledge gaps and modeling can provide continuous estimates in time
and space. The increased availability of multi-temporal imagery considerably enhances the modeling
process. Rather than using single-moment-in-time maps to initially parameterize the model, multitemporal remotely sensed data can be used to calibrate models at multiple points in time. This is
particularly useful in observing highly dynamic parameters, like soil moisture, inundation, and
vegetation biomass. Undoubtedly, future sensors will provide finer spatial and temporal resolution
data, hence increasing the data available for model conceptualization, parameterization, and
calibration/validation at finer spatial and temporal resolutions. Section 2-A.1 of Appendix 2-A includes a
discussion of remote sensing resources that may be available in the near future to develop data and
information that can be used to address groundwater management issues.
2.5.4
Conclusion
Remotely sensed data have been used to map and characterize groundwater resources for nearly a
century (Lattmann 1958, Meijerink et al. 2007). Initial efforts utilized aerial photography and were
aimed at groundwater exploration, while later efforts involved a diversity of remotely sensed data types,
each with unique capabilities and restrictions. More recent remote sensing tools, such as LiDAR and
satellite-based gravity sensors (i.e., Gravity Recovery and Climate Experiment [GRACE]) are poised to
improve the capacity to detect and predict groundwater distribution and dynamics into the future,
especially when combined with advanced modeling capabilities. The value of information derived from
remote sensing depends on the experience of the interpreter (Meijerink 1996, Rango 1994, Waters et al.
1990) and the quality of available in situ data (Brunner et al. 2006), and this may be particularly true for
groundwater because the level of inference necessary with groundwater applications is often greater
than that with other applications (e.g., land cover mapping, Waters et al. 1990). For example, increased
vegetation cover in an arid region could indicate the increased availability of groundwater within the
root zone or it might indicate greater amounts of precipitation with increased elevation. Remotely
sensed data not only enable extrapolation of in situ measurements to broader geographic areas, but can
also detect signatures and patterns that are difficult to discern from ground-based observations.
Remote sensing and modeling demonstrate the considerable promise these tools have to support
groundwater applications. Remote sensing technology and modeling are now answering important
hydrogeological questions that more conventional ground-based techniques (Hoffman 2005) could not
address. These data are currently incorporated into operational protocols (Meijerink 1996). The Forest
Service groundwater inventory and monitoring can be significantly enhanced through the synergistic use
of remotely sensed and in situ data. Using remotely sensed data in conjunction with in situ data and
models, the best available science, and technology can assist the Forest Service in answering some of
the most challenging groundwater-related natural resource management questions, while balancing
social and economic needs.
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2.6
Hydrogeologic Mapping
Hydrogeologic mapping can assist in the characterization and evaluation of groundwater systems and
can help to assess:





the ability of different hydrogeologic units to transmit water;
the existence of confined versus unconfined conditions;
the presence of interconnections between different hydrogeologic units;
the presence of recharge boundaries indicating groundwater/surface-water interaction; and
the presence of no-flow boundaries which may relate to geologic structures or changing
lithology.
Geological mapping for hydrogeologic purposes focuses on the ability of the various rock units to store
and transmit water. Hydrogeologic maps can be produced from geologic base maps by assigning
hydrogeologic properties to geologic units and then grouping them into aquifer units and confining
units. Hydrogeologic information can come from previously prepared documents (i.e., professional
reports and papers), water well reports, other well reports, available geologic logs, and available or
project-derived geophysical logs. Additional data include aquifer hydraulic properties (transmissivity or
hydraulic conductivity, and storage coefficients), and boundary conditions.
Hydrogeologic mapping requires the systematic and integrated appraisal of soils, geomorphology,
vegetation, geology, hydrology (including meteorological aspects), geochemistry, and water chemistry
as they affect the occurrence, flow, and quality of groundwater. It is also important to understand the
hydrogeologic setting as a whole, including surface water hydrology, and data on meteorological and
other water budget elements within the watershed.
Geological maps are the basis for making interpretations about the movement of water through the
groundwater flow system. Distinctions between unconsolidated and consolidated, permeable and
impermeable rock units are made on a qualitative basis, using rock type, structure and knowledge about
the geologic setting. Interpretations of hydraulic conductivity can be made by using such information,
and estimates of potential groundwater movement through the rock unit. For hydrogeologic purposes,
geological maps showing rock type and genesis are more useful than stratigraphic-age maps. In addition
to bedrock maps, those showing surficial geology are also very useful. In fractured-rock hydrogeologic
settings, geologic maps that show structure such as faults, folds, joint orientation, and strike and dip of
beds, including cross-sections, are useful for depicting distribution and orientation of geologic
discontinuities, which frequently serve as preferential groundwater flow paths.
Maps and remote sensing information, such as aerial photographs, showing variations in vegetation
abundance and type or topography can provide clues about the nature of the underlying geology and
the presence and movement of groundwater in the subsurface. LiDAR data now provide unprecedented
topographic detail which can significantly help interpret lineaments, sinkholes, or other such
geomorphic features.
Another source of potentially useful hydrogeologic information of growing importance in the Forest
Service is Terrestrial Ecological Unit Inventories (TEUIs). TEUIs classify and map areas of common
ecological types based on geology, geomorphology, soils, and vegetation (Winthers et al. 2005; available
online at http://www.fs.fed.us/emc/rig/includes/TEUI_guide.pdf). The smallest and most detailed TEU
is the landtype phase, which integrates information on primary and secondary lithology, soil series and
phases of series, landforms including element landforms and morphometry, and plant associations and
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plant association phases. It may be possible to correlate this information with general or even specific
hydrogeologic conditions.
The character and distribution of soils and landforms are major considerations in hydrogeologic
mapping in humid areas where unconfined aquifers develop in unconsolidated materials and lie at the
land surface. In this setting, the water table generally follows the land surface, although with more
subdued relief. Recharge areas are generally located in upland areas, and groundwater divides tend to
coincide with surface watershed boundaries. Valley bottoms and floodplains with perennial streams
represent discharge areas. For all areas, soils and topography are the primary features that determine
how much precipitation infiltrates into the ground to recharge groundwater, and how much runs off to
surface streams. Highly permeable soils and flat topography favor infiltration; less permeable soils and
steep slopes promote surface runoff.
Although the focus of hydrogeologic mapping is groundwater, the occurrence and flow of groundwater
must be understood in the context of the larger hydrologic cycle, which includes atmospheric water,
water in the vadose (unsaturated) zone and surface water. This is especially true of unconfined aquifers,
which are intimately connected to the hydrologic cycle. Complete characterization of unconfined
aquifers requires considering infiltration of precipitation, the effects of evapotranspiration, soil water
storage, and the relationship between the groundwater and surface water systems.
Figures 2-1 and 2-2 depict example products from a hydrogeologic mapping exercise for the Fishlake
National Forest. Figure 2-1 is a record of the information sources and assumptions concerning the
elements of the hydrogeologic framework. Figure 2-2 shows a map of the hydrogeologic units across
the Forest. These products can then be used to for inventory, monitoring, and assessment activities and
can be leveraged as baseline information for forest and project planning.
The Forest Service GIS Data Dictionary contains information about Forest Service Data Standards for
geology map unit feature classes at national, broad, mid, and base levels, related hydrogeology tables
and domains of valid values. It also contains sample geodatabase designs with standard metadata
templates. The structure also contains standard feature-level metadata fields to document changes of
individual or groups of features consistent with this technical guide. The Forest Service GIS data
dictionary is available on the Forest Service FSWeb at http://fsweb.datamgt.fs.fed.us/index.shtml.
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Hydrogeologic map unit
Symbol
Aquifer media
Hydrogeologic properties
Quaternary Alluvium
Qa
Quaternary Eolian Deposits
Qe
Shallow water table. Generally unconfined. A local aquifer where saturated.
Supports riparian habitats along streams. May be hydrologically connected
to streams.
A local aquifer where saturated. Generally not thick enough to contain
significant ground water.
Quaternary Landslide Deposits
Qls
Quaternary Ground Moraine
Qg
Quaternary-Tertiary Alluvium
Qao, QT
Tertiary Intrusives
Ti
Youngest alluvium in the channels, floodplains, and adjacent low terraces of
rivers and streams; consists of sand, silt, and clay with lenses of gravel;
mostly 0-6 m thick, but may be thicker locally.
Windblown sand in sheets, low irregular mounds, shrub-coppice dunes, and
narrow northeast-trending ridges that are largely stabilized by vegetation;
mostly silty, well-sorted, fine-grained quartz sand; 0-3 m thick.
Mass movements- Masses of soil, sand, rock, and boulders that have
moved downslope under the influence of gravity; includes soil creep,
slopewash, talus, and fan alluvium, and locally slides and slumps; 0-30 m
thick.
Unsorted mixtures of clay, silt, sand, and angular pebbles, cobbles, and
boulders in Pahvant Range; probably late Wisconsin (Pinedale) in age; 0-60
m thick.
Poorly sorted silt, sand, and pebble, cobble, and boulder gravel deposited
by streams, sheetwash, debris flows, and flash floods on alluvial fans, and
in canyons and mountain valleys; generally 0-18 m thick but can be up to 60
m.
Quartz monzonite and granite
Tertiary Extrusives Lava
Tmv, Tpb,
Tmb, Tpr
Tov
Basalt and andesite lava flows, pyroclastic rocks mainly on the Sevier
Plateau and southeastern part of the Pahvant Range.
Andesite breccias of the Bullion Canyon volcanics: massive mudflow
breccias, pebble to boulder size andesite, rhyodacite, and quartz latite lava
flows, includes layers of fluvial sandstone and conglomerate; mostly on the
Sevier Plateau and southeast part
Tertiary Sedimentary Aquifers
T5, T4, T3, Sevier River Fm (T5): poorly to moderately consolidated fluviatile and
T2, T1
lacustrine conglomerate, sandstone and siltstone with layers of tuff and
basaltic lava flows, 180-300 m. Oak City Formation (T4): sandy, bouldery
gravel; poorly to well cemented;
form
Cretaceous Sedimentary Aquifers
TK, K3
North Horn Fm (TK): sandstone with interbeds of siltstone, mudstone,
(Mesaverde Aquifer)
conglomerate, and limestone; proportion of conglomerate decreases
eastward; maximum thickness is in northern Canyon Range, where it is
more than 1,200 m thick. Mesaverde Gp (K3): cong
Cretaceous Sedimentary
K2
Indianola Group/Canyon Range Conglomerate in the Canyon Range and
Aquitards/Aquifers (Mancos Confining
Mancos Shale on eastern edge of forest: massive, well cemented
Unit)
conglomerate; 2500-4500 m characterize the Indianola Gp. In the west
grading to fine-grained sandstone and interbedded mudston
Cretaceous Sedimentary Aquifer
K1
Present as Dakota Sandstone on east edge of the Forest: Sandstone,
(Dakota Aquifer)
carbonaceous shale, and some coal; sandstone is thick bedded, fine- to
coarse-grained becoming more conglomeratic westward. Included in the
lower Canyon Range Conglomerate in the Canyon Mo
Upper Jurassic Sedimentary Aquitard J2
Morrison Fm: Clay-rich mudstone and thin interbedded course-grained
(Morrison Confining Unit)
sandstone, 0 to 140 m thick.
Middle Jurassic Sedimentary
J1
Exposed on southeast edge of forest. Curtis Fm: silty sandstone and
Aquitard/Aquifer
mudstone; Entrada Sandstone: fine-grained sandstone; the middle Jurassic
stratigraphic succession is partially preserved under Canyon Mountains and
Pahvant Range at depths greater than 3
Lower Jurassic Sedimentary Aquifer
Jg
In Pavant Range - Navaho Sandstone: fine-grained cross-bedded
(Glen Canyon Aquifer)
sandstone, 600 m thick. On southeast side of Forest - Navajo SandstoneKayenta Fm-Wingate Sandstone: Navajo and Wingate are massive, finegrained sandstones; Kayenta Fm is shale and siltstone
Moenkopi-Chinle Fm Aquitard
Tr1, Tr2
Siltstone, sandstone, shale, and minor limestone; 300-600 m thick.
Paleozoic Sedimentary Aquifer
P2, P1, PP, Kaibab Limestone (P2), Pakoon Dolomite (P1), Calville Limestone (PP),
(Coconino-DeChelly Aquifer)
M1, D, S, O Redwall Limestome (M1), Devonian carbonates (D), Silurian dolomite (S),
and Ordovician carbonates (O). Exposed in the Canyon Mountains and
Pahvant Range along the Pavant thrust and Re
Cambrian Limestone Aquifer
C2, C3
Limestone and dolomite: C3 includes Ajax Dolomite and Opex Fm. C2
includes Cole Canyon and Bluebird Dolomites, Herkimer, Dagmar and
Teutonic Fms, and Ophir Fm.
Cambrian and Precambrian
C1, PCs
Prospect Mountain and Tintic Quartzite (C1): fine-grained to pebbly,
Metasedimentary Aquifer
vitreous quartzite. Tintic Quartzite is exposed in the Canyon Mountains and
Pahvant Range and in the subsurface to the east at depths approaching
6,000 m; thins eastward; Precambrian st
Tertiary Extrusives Breccias
Unstable deposits. Springs are common at toe of these features.
A local aquifer where saturated. Generally unconfined.
Deep water table. A local aquifer where saturated. Generally unconfined but
could be locally perched or confined.
The hydrogeologic characteristics of these rocks vary with the degree of
fracturing. Generally impermeable and make poor aquifers.
Highly varible hydrogeologic characteristics. Basalt flows can form highly
productive aquifers. Tuffs tend to be less permeable.
Highly permeable and can form productive aquifers where saturated.
Where present on forest, permeability from fractures and primary porosity.
Permeable units interfingered and interbedded with confining units.
Contains locally productive aquifers where saturated.
Mesaverde Aquifer, western edge of a major regional aquifer. Permeability
from fractures and primary porosity. Permeable units are interfingered and
interbedded with confining units. Dissolved solids range from 1000 to 4000
mg/L. This formation are ve
Comprised of several formations that are aquifers and confining units.
These formation are very coarse-grained (conglomerate) in wetern part of
the forest (Canyon Mountains-Pahvant Range) and finer -grained with
mudstone and coal in eastern part of fores
The conglomeratic lithofacies in the west part of the forest (Canyon
Mountains-Pahvant Range) will likely have low dissolved solids even at
depths greater than 600 m; the eastern fine-grained units will likely have
greater disolved solids at depth; also c
Aquitard, only outcrops on eastern edge of forest.
Confining units interlayered with potential local aquifers in sandstone and
limestone units; where present the Entrada sandstone is an aquifer.
These formations comprise the western edge of the major regional Glen
Canyon aquifer. Likely has low disolved solids in the Pahvant Range and
higher dissolved solids toward the east. It is a major oil reservoir in the
region.
Confining unit between the Glen Canyon and Coconino-DeChelly Aquifers.
Western part of the regional Coconino-DeChelly Aquifer of the Colorado
Plateau. Permeability from fractures and primary porosity in sandstones
and fractures and solution channels in limestones. Generally will have high
dissolved solids and are considere
Permeability from fractures; may be locally karstic; contains locally
productive aquifers where saturated. Generally will have high dissolved
solids and are considered oil and gas reservoirs.
Permeability from fractures; contains locally productive aquifers where
saturated; potential aquifer in the Canyon Mountains and Pahvant Range.
Figure 2-1—Worksheet showing how to transpose geologic map units from a published geologic map
of the Fishlake National Forest into hydrogeologic map units based on assigned hydrogeologic
properties. Some of the aquifers and confining units were already established in the literature.
Section 2 – Foundations of Groundwater Inventory, Monitoring, and Assessment (v4.2) 12/19/13 DRAFT 24
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Figure 2-2—Hydrogeologic map of the Fishlake National Forest. This map was developed from a
1:100,000 scale geologic base map.
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2.7
Well and Borehole Information
The most extensive existing data available on subsurface conditions in an area of interest are often
contained in reports filed by drillers for privately owned water wells and wells on NFS lands. Well
reports contain a variety of information, but the most common information includes the driller’s
descriptions of lithologic units penetrated by drilling as well as useful information on well construction,
performance testing, and water leveIs. Well reports have the potential to greatly enhance the
understanding of subsurface conditions; however, they have many limitations. Some drillers provide
excellent descriptions of lithology and carefully conduct and record results from performance testing.
Other reports are incomplete, perhaps even inaccurate. Nonetheless, one of the first steps in an
inventory should be to obtain well reports.
In many states, these well reports are now available in searchable online databases (e.g., Colorado
Division of Water Resources, Oregon Water Resources Department, Washington Department of Ecology,
Idaho Water Management Department (Links will be added). Reports generally include specific-capacity
test data that can be used to infer relative transmissivity of the formation (Bradbury and Rothschild
1985). Oil and gas well logs and mining borehole logs, if available, can also provide information on
subsurface conditions. All reports and logs should be carefully screened to identify data that are
incomplete or show obvious inconsistencies with other reports and logs in the same area.
2.8
Groundwater – Surface Water Interactions
Improved understanding of the connection between surface water and groundwater is becoming a
prerequisite to managing these resources effectively (Sophocleous 2002). Thomas Winter, renowned for
his work on groundwater-surface water (GW/SW) interactions, noted a paradigm shift in how these
interactions are viewed. “Traditionally, management of water resources has focused on surface water
and groundwater as if they were separate entities. As development of land and water resources
increases, it is apparent that development of either of these resources affects the quantity and quality
of the other.” (Winter et al. 1998)
Groundwater interaction with surface water is not limited to rivers and streams, but also includes
interactions with lakes, springs, wetlands, and coastal water bodies. A variety of methods to study
GW/SW interactions have been developed including stream-flow measurements, hydrographic
separation, well networks, seepage meters, minipiezometers, dyes and isotopic tracers, infrared
cameras or other temperature based methods, and numerical modeling (Rosenberry and LaBaugh
2008).
Water-resource managers have many reasons to quantify the flow between surface water and
groundwater (Rosenberry and LaBaugh 2008) including:






calculating hydrological and chemical budgets of surface water bodies,
assessing dissolved and solid phases of contaminant sources,
collecting calibration data for watershed or groundwater models,
locating contaminant plumes,
locating areas of surface-water discharge to groundwater, and
determining the relation of water exchange between surface water and groundwater to aquatic
habitat.
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For many investigations, it is sufficient to make a qualitative determination on groundwater flow into or
out of a surface water body. For other investigations, however, the amount of flow between the ground
and surface waters may need to be quantified.
2.8.1
Importance of the Groundwater - Surface Water Interface
The GW/SW interface is the transitional zone between the subterranean and surface aquatic
environments, and provides a number of ecological goods and services, (UK Environment Agency 2009)
including:








Control of the flux and location of water exchange between the surface and subsurface domain;
Habitat for benthic and interstitial organisms;
Spawning ground and refuge for certain species of fish;
Rooting zone for aquatic plants;
Important zone for the cycling of carbon, energy and nutrients;
Natural attenuation zone for certain pollutants by biodegradation, sorption and mixing;
Moderation of surface water temperature; and
Sink/source of sediment within a surface water body.
The holistic assessment and management of watersheds requires a better understanding of the
interfaces between traditional environmental compartments (UK Environment Agency 2009). These
interfaces tended to be the boundaries of environmental management units, but are now recognized as
important areas for cycling of energy, nutrients and organic compounds (McClain et al. 2003), and exert
significant control over watershed-wide pollutant transfer (Smith et al. 2009) and ecological health
(Brunke and Gonser 1997).
Assessing the processes occurring at the GW/SW interface is critical when estimating and quantifying
water and contaminant fluxes throughout a catchment, and when assessing and protecting aquatic
ecosystems. The full range of ecological services needs be considered when assessing an individual
surface water body, although certain ecosystem services are likely to be more important than others in
different types of systems (UK Environment Agency 2009).
2.8.2
Management Implications of the Hyporheic and Hypolentic Zones
Conceptual models of the GW/SW interface, the hyporheic zone, and the processes occurring within
them, have been developed in a number of different fields. The different backgrounds of the
researchers means there is variation in the terms and definitions (Bencala 2000). The hyporheic zone
comprises fluvial sediments within which there is an exchange of water between a stream and the
subsurface beneath it (Bencala 2005) (fig. 2-3). It is often characterized by chemical and temperature
gradients that exert control on the behavior of chemicals and organisms both at the interface and in the
adjacent aquifer and stream environments (Brunke and Gonser 1997, Hancock et al. 2005). The
hypolentic zone is the equivalent region underlying lakes, ponds, and wetlands with standing water.
While there is a considerable body of knowledge about processes occurring within both rivers and
aquifers, less is known about the processes that occur at the interface of these environmental
compartments (UK Environment Agency 2009).
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Figure 2-3–Surface-water exchange with groundwater in the hyporheic zone is associated
with abrupt changes in streambed slope (A) and with stream meanders (B) (from Winter et
al. 1998).
Hyporheic and hypolentic zones are temporally and spatially dynamic, often exhibiting continuous
changes in chemical, physical, and biological conditions. Human actions can severely alter sediment
structure, or the hydrological, chemical or biological conditions within them. For example, the following
activities have consequences that should be evaluated within management decisions:





Weirs, dams, and diversions: artificial barriers alter the movement of water around the
structure, leading to deposition of fine sediments above the impoundment and starving or
eroding sediments downstream;
Land-management: many activities cause soil erosion, a major source of fine sediments and
nutrients in surface waters which can cause clogging of the top layer of coarser channel
sediments;
Flood-defense: construction of flood barriers (e.g., concrete river walls, levees) leads to reduced
floodplain connectivity and degrades the ecological integrity of both riparian and hyporheic/
hypolentic environments;
Channel modification: straightening or lining of channels to expedite through flow or stabilize
channels can reduce or eliminate groundwater/surface water interactions;
Surface water restoration: seeks to enhance and improve a degraded reach or section of
shoreline, but may only achieve a limited range of benefits (perhaps simply aesthetic
improvement such as putting bends back into a channelized watercourse).
Designers of river restoration schemes should consider how to improve the full range of ecological
services, including hyporheic habitat, GW/SW exchange, and vertical connectivity. Restoration schemes
that fail to think in three dimensions (and beyond the immediate extent of the river channel) are
unlikely to achieve benefits that could be realized by thoughtful design.
An additional set of information that may need to be obtained and considered, especially where the
Forest Service does not control all the water rights, is how surface water and nearby groundwater is
used and how that use is controlled. The best technical solution to a restoration problem may be legally
precluded and the best available solution may require a different approach to information collection and
analyses.
2.9
Environmental and Water Quality Indicators
Hydrogeologic processes are integral to forest management and planning. When measures of natural
change are omitted from monitoring and planning, the assumption that natural systems are stable,
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fixed, and in equilibrium is perpetuated. This may create the illusion that a particular activity is
sustainable until the underlying natural system is pushed to a tipping or failure point (i.e., aquifer
depletion). Natural systems are dynamic, and some may be chaotic; change is the rule, not the
exception. Using environmental and water quality parameters in decision-making processes shifts
management actions to a more proactive mode which is often more successful and less costly than crisis
management.
Selection of indicators for environmental and water quality must be linked to the inventory, monitoring,
or assessment objectives defined for the project. Potential indicators and guidance on their use in
groundwater monitoring are outlined below.
2.9.1
Environmental Indicators
Environmental indictors are physical or chemical parameters that can be measured in the hydrologic
environment and be used to characterize conditions and monitor change. Monitoring these parameters
allows us to detect and evaluate changes in the state of natural environments and ecosystems and
assess their causes (Berger and Iams 1996). Environmental indicators represent a metric that can be
used by the Forest Service to monitor natural as well as human induced changes in groundwater
systems and in the ecosystems they sustain.
The most effective use of environmental indicators is in their application to environmental monitoring
programs. They are designed for use in environmental and ecological monitoring, and general
assessments of environmental sustainability on a local and national scale. They help to answer four
basic questions.




What is happening in the environment? (conditions and trends)
Why is it happening? (causes, links between human influences and natural processes)
Why is it significant? (ecological, social, economic and health effects)
What are we doing about it? (implications for planning and policy)
There are several specific challenges in the use of environmental indicators. One involves defining
natural ranges of variation in the indicator and the other involves identifying thresholds or critical levels.
For each indicator, targets, trends, or threshold values will need to be set to indicate when some type
action would be required. Eight potential indicators are presented in Table 2-1. Of these, the most
commonly used in evaluations of groundwater are groundwater level and groundwater quality, which
are discussed in detail in
. Appropriate indicators can be selected from this list depending on the
terrain and the environmental issues under consideration.
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Table 2-1–Environmental indicators that can be monitored to assess environmental responses – and
their significance – to changes in groundwater systems on National Forest System lands (after Berger
and Iams 1996).
Types of monitoring sites
Method of measurement
Frequency of measurement
Thresholds
Groundwater Quality - The chemical composition of groundwater is a measure of its suitability as a source of water for human
and animal consumption, irrigation, and for industrial and other purposes. It also influences ecosystem health and function.
Therefore, it is important to detect change and early warnings of change both in natural systems and resulting from pollution.
Wells, springs, wetlands, adit Standard sampling and
Seasonal or annual scale;
Water quality standards for
discharges, lakes, stream
laboratory techniques and
sampling frequency of four
applicable beneficial uses
baseflow; water supply
equipment; can be measured times a year is typically used to (human, aquatic); established
aquifers; downstream of
remotely by sensors placed in detect changes in shallow
trend; statistically significant
potential problem areas (e.g., wells or at points of discharge; groundwater sources, but
change in concentration.
mines, urban areas, intensively statistical analysis of data.
annual measurements are
managed agricultural lands,
generally sufficient for deeper
waste disposal sites), wherever
sources. Monthly or more
possible monitoring should be
frequent sampling can be
integrated with other national,
necessary to detect change
State or local water quality
and identify causes in some
networks.
circumstances.
Groundwater Level - Regular measurement of water levels (head) in wells and boreholes or of spring discharge provides the
simplest indicator of changes in groundwater resources. Groundwater levels are used to track aquifer depletion or determine
groundwater flow direction and magnitude.
Boreholes, wells, or springs
Monitoring of the depth to the Minimum monthly intervals to Declining water levels in wells,
representative of the
water table is carried out using reflect seasonal as well as
reduced flow in springs, shrinking
particular aquifer.
tapes or pressure transducers annual changes. The state of wetland or phreatophyte area,
via manual measurements or aquifers should be assessed at etc.
automatic water-level
about 5-year intervals. Water
recorders. Standard
levels can be measured both
hydrogeological methods are seasonally and annually over
used to calculate a water
decades to determine overall
balance, flow, direction, etc. trends. Daily or hourly
measurements using
automatic water-level
recorders can be used to
identify aquifer responses to
rain events and infer dynamic
aquifer properties.
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Vadose zone- Changes in recharge rates have a direct relationship to water resource availability. The unsaturated zone may
store and transmit pollutants, the release of which may have a sudden adverse impact on groundwater quality.
Unconsolidated sediments or Lysimeters can be used to
Lysimeters generally sampled Field capacity not exceeded and
consolidated porous media
sample pore water chemistry in a similar range of frequency no recharge.
(sand, till, sandstone, chalk,
or quantify infiltration/
as wells. Subsurface samples
calcarenite, volcanic ash) on evapotranspiration. In
generally collected 5-10 year
relatively level terrain
addition, subsurface samples intervals to confirm movement
(negligible surface runoff). The can be collected by using
of solutes towards the water
best records are obtained
vibracore, hollow stem auger, table.
where the unsaturated zone is sampling from dug wells,
10-30 meters thick, and where percussion or air-flush rotary
sediments and flow are
drilling. Pore water can be
relatively homogeneous.
extracted from sediments by
high-speed centrifuge
(drainage or immiscible liquid
displacement) or, for nonreactive components such as
Cl and NO3, by elution with deionized water. For isotopic
samples (3H, δ18O, δ2H),
vacuum distillation may be
used.
Types of monitoring sites
Method of measurement
Frequency of measurement
Thresholds
Springs and Stream Baseflow - Groundwater discharge to stream and spring systems plays a key role in the regulation and
maintenance of aquatic health and biodiversity. Human-induced depletion of baseflow has major implications for the health of
riparian ecosystems.
Stream channels and springs Standard techniques for
Continuous to periodic
Established critical level for
measuring streamflow,
sustaining healthy aquatic
hydrograph separation,
ecosystems, environmental flow
isotope analysis, geochemical
needs exceeded.
analysis, synoptic sampling –
tracer injection studies,
streambank and in stream
piezometers.
Surface water quality - The quality of surface water in rivers and streams, lakes, and wetlands is influenced by interactions with
groundwater. The bulk of the solutes in surface waters are derived from groundwater discharge influence by water-rock
interactions.
Stream channels and springs Standard sampling and
4-6 times yearly (minimum
Water quality standards, trend
laboratory techniques and
seasonally), twice yearly for
analysis
equipment; statistical analysis radionuclides and organic
of data.
chemicals. Continuous, realtime monitoring systems
provide the most complete
information.
Lake Levels - Lakes are dynamic systems that are sensitive to local climate and to land-use changes in the surrounding
landscape. Lakes can also be valuable indicators of near-surface groundwater conditions. Where not directly affected by
human actions, lake level fluctuations are excellent indicators of drought conditions. Lake level fluctuations vary with the water
balance of the lake and its catchment, and may reflect changes in shallow groundwater resources. Monitoring may be required
to determine if a lake is connected with groundwater.
Lakes with known or potential Shoreline gages, areal extent Lake level and lake water
Human and aquatic health
groundwater exchange
from successive air photos and composition monthly to
criteria, critical inflow-outflowsatellite images; bio-indicators annual. Areal extent every 5 storage water balance
for inferring past lake water years.
chemistry
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Wetlands are areas of high biological productivity and diversity. They provide important sites for wildlife habitat and human
recreation. Wetlands can affect local hydrology by acting as a filter, sequestering and storing heavy metals and other pollutants,
and serving as flood buffers.
Groundwater supported
Areal extent and distribution, Every 5-10 years for
Change in areal extent or
wetlands.
Permanent transects and plots distribution, extent, and
vegetation community type
can be set up for ease of data structure; For water budget
comparison and establishing and hydrochemistry, initial
temporal trends in vegetation measurements should be
distribution, surface
weekly to monthly (more
morphology,soil accumulation frequently in times of rapid
rates, hydroperiods, soil
change such as spring thaw)
moisture within the top 30
until important times and
centimeters, water budgets, parameters have been
and hydrochemistry,
identified, then less frequently.
piezometers, wells, and weirs;
variations in the chemistry of
water inflows and outflows,
changes in water levels and in
seasonality of flow patterns.
Types of monitoring sites
Method of measurement
Frequency of measurement
Thresholds
Karst - It is estimated that karst landscapes occupy up to 10 percent of the Earth's land surface, and that as much as a quarter
of the world's population is supplied by karst water. Karst systems are sensitive to many environmental factors. Radon levels
in karst groundwater tend to be high in some regions. In addition to natural karst, manmade subsurface features, such as
mines, can function in the same manner and may have to be similarly characterized to properly understand the hydrogeology
of an area.
In certain environments, caves Hydrological and geochemical Continuous measurements are The slow, gradual movement of
can provide unique, productive measurements of springs,
often needed to interpret the soil tends to fill depressions in
and extensive field sites,
sinking streams, drip waters karst system. Surface features the karst bedrock surface,
because they allow direct
into caves, sinkholes, and cave and groundwater chemistry
threshold between dissolution
observation and mapping of streams provide records of
and contamination in karst
and precipitation of calcite.
underground features and
short-term changes in water terrains are notoriously
their relation to the surface
quality and chemical
unstable and can change
and to groundwater flow.
processes. Pumping tests on rapidly.
Wells, borings and quarries are wells, dye tracing. In built-up
also useful as monitoring sites, areas, locate buried cavities
but they provide only
and monitor their potential for
discontinuous points of
collapse, using a combination
information.
of geophysical surveys,
exploratory drilling and
repeated leveling.
These environmental indicators have been developed from standard approaches used in geology,
geochemistry, geophysics, geomorphology, hydrology and other earth sciences. For the most part, the
expertise and technology are available to monitor and analyze the resulting data; however, there is a
wide range in the complexity and cost involved in collecting various indicators.
Environmental indicators help answer Forest Service resource management questions about what is
happening to the hydrologic environment, why it is happening, and whether it is significant. When
quantified, they establish baseline conditions and trends, so that changes can be identified. Applying
this approach will provide science-based information to support resource management decisions and
planning.
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Appendix 2-A – Technical Discussion: Remote Sensing for Groundwater Inventory and
Monitoring
A number of different types of remotely sensed datasets are available, each suitable for certain
applications pertaining to groundwater, with none being applicable in all situations. Data from the
visible and near-infrared have most commonly been used to inventory and monitor groundwater in the
past (Meijerink 2007), but other types of data are rapidly being accepted as significantly enhancing the
quality of hydrogeologic information derived from remotely sensed imagery. Each data type has distinct
advantages and disadvantages, with each being sensitive to unique landscape components and
processes. These components (e.g., soil moisture, inundation, evapotranspiration, elevation, vegetation
biomass and type, soil type, and more) can then be synthesized to provide enhanced information on
groundwater distribution, abundance, and even quality. For example, because radar and multispectral
data are sensitive to very different landscape characteristics, the combination of radar and optical data
can significantly improve the mapping of groundwater-dependent ecosystems (Silva et al. 2008, Töyrä et
al. 2002), and other groundwater associated features (Meijerink 2007). Radar data provide information
on inundation, soil moisture, plant structure and biomass while multispectral and hyperspectral data are
better suited for identification of plant communities and other land cover types with similar structures
and moisture contents based mainly on the molecular and cellular structure of the materials but also
from image texture. Similarly, LiDAR can be used to provide information on topography, vegetation
height and biomass, and the actual and potential distribution of water, often more accurately than
optical data, such as hyperspectral and multispectral images.
2-A.1
Remote Sensing Datasets Available for Groundwater Inventory and Monitoring
This section investigates the advantages and disadvantages of using both passive sensors, including
aerial photography and multispectral, hyperspectral, gravitational, and passive microwave, and active
sensors, including radar, LiDAR, and laser and radar altimetry for groundwater detection and
characterization. Active sensors (e.g., lasers) produce and detect their own energy while passive sensors
generally detect energy produced by the sun and other sources of energy external to the sensor. This is
not an exhaustive review of all types of remotely sensed imagery. Instead it represents the types of
imagery that are most common and that researchers suggested have the greatest potential to improve
the inventorying and monitoring of groundwater.
2-A.1.1 Aerial Photography
Aerial photography is utilized for a wide variety of operational mapping tasks, including the mapping
streams (USGS National Hydrography Dataset [NHD]) and wetlands (e.g., USFWS National Wetland
Inventory [NWI]). The first aerial photograph was taken in 1858 by Gaspard Felix Tournachon aboard a
hot air balloon (Jensen 2000). Since then, aerial photography has matured through advancements in
both platform and sensor. A wide variety of aircraft are now used to collect aerial photographs, and
black and white film has largely been replaced by color film and digital images. Although aerial
photography has no ability to penetrate vegetative canopies, let alone the ground surface, it provides a
consistent long-term historical record of surface conditions and changes at a relatively fine spatial scale.
It is not uncommon to have a historical record of aerial photographs dating back to the 1950s or earlier
(Morgan et al. 2010). Some of the most common sources of aerial photography in the United States are
the U.S. National Aerial Photography Program (NAPP; 1987–2007), the National High Altitude Aerial
Photography (NHAP; 1980–87) program, and the National Agriculture Imagery Program (NAIP; 2001–
Present). Aerial photographs are also commonly collected by states and other regional/local entities.
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Although aerial photographs can be analyzed by using automated techniques, an approach that has
become more popular since digital aerial photography was made available in the 1990s (Morgan et al.
2010), aerial photographs are still commonly analyzed by hand using photo interpretation techniques. A
photo interpreter makes qualitative decisions based on an image’s tone, color, spatial patterns, texture,
height/topography, associations, and other characteristics. When aerial photographs are viewed
stereoscopically, additional information can be gained regarding topography and the height of features
within the image. Digital elevation models (DEMs), originally produced by using stereo-photo
interpretation, are commonly available throughout the United States. The majority of aerial
photographs collected today, however, are not collected as part of stereo-pairs. A review of aerial
photography history, fundamentals, and interpretation is presented in Morgan et al. (2010) and Jensen
(2000).
Landscape Pattern Interpretation
Many fundamental landscape patterns that can be connected with the surface or near-surface
expression of groundwater are evident on aerial photographs. Such patterns include:





Greater amounts of green vegetation cover than expected based on local climate and
anthropogenic influence (El-Baz 2008, Moore 1982, Waters et al. 1990),
Darker soils due either to relatively high soil moisture (Moore 1982) or to higher soil organic
carbon levels caused by anoxic soil conditions,
Some surface waters (e.g., perennial streams and certain lakes and wetlands),
Spatial variations in snow cover unexplained by original snow distribution or subsequent snow
movement caused by wind and other forces (Moore 1982), and
Variations in plant community type or phenology that cannot be explained by anthropogenic
alteration or landscape position unrelated to water abundance (Moore 1982).
Additional inferences can be based on assumed associations such as:


The presence of drainage ditches may indicate groundwater is near the surface long enough to
necessitate installation of these drainage structures.
Buildings or livestock management structures may indicate the presence and locations of water
supply wells not included or accurately located in other data sources.
Surface features may also be utilized to infer groundwater at depth. Aerial photography has been used
for groundwater exploration since its initial widespread collection in the early to mid-1900s (Lattmann
1958, Meijerink 2007). This application primarily focuses on two basic approaches: 1) the creation of
hydrogeologic maps based on a combination of remotely sensed and in situ data (Meijerink 1996,
Waters et al. 1990), and 2) the mapping of lineaments to infer the occurrence of groundwater.
Hydrogeologic maps (see section 2.6) can be produced by inferring aquifer characteristics from remotely
sensed data. For example, literature suggests that drainage density can be used to infer permeability,
with areas of low permeability characterized by higher drainage density and areas of high permeability
characterized by lower drainage density. This is often the case when other drivers of drainage density
(e.g., resistance of substrate to erosion, climate, relief, and time since formation) are controlled for
(Meijerink 2007).
The value of lineament studies relies upon linear surface features expressing a subsurface feature whose
hydrologic role is understood (e.g., fractures and faults, Waters et al. 1990). Lineament studies are most
relevant in areas underlain by consolidated rock of low primary permeability where secondary
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permeability largely controls groundwater flow (Waters et al. 1990). In areas of carbonate rock, for
example, lineaments may provide insight into where sinkholes will form (Waters et al. 1990). The
predictive strength of lineament maps that have not been field checked for their accuracy is limited
(Meijerink 1996; Moore 1982).
The application of remotely sensed data for groundwater exploration (i.e., the search for groundwater
for extraction) reached its maturity in the 1960s and is now primarily being used in areas without
adequate geologic maps (Meijerink 1996). For the above stated reasons, the use of remotely sensed
data for groundwater exploration is not described in detail within this section.
Change Detection
The lengthy historical record of land cover and land-use change that aerial photography, and to a lesser
degree multispectral imagery, provide can be used to infer changes in the hydrologic cycle that are
related to groundwater recharge. Although land cover patterns may not directly indicate the surface
expression of groundwater or even the presence of groundwater at depth, they may provide the only
record of historical changes that can significantly influence groundwater sustainability. For example, the
conversion of native vegetation to cropland and especially urban surfaces usually increases surface
water runoff, therefore decreasing water available for groundwater recharge (Meijerink 2007).
Conversely, forests use more water than grasslands via transpiration, so conversion from forest to
grassland may increase groundwater levels (Meijerink 2007).
Other Considerations
Interpretation and utility of remotely sensed data, including aerial photographs, will vary greatly among
landscapes. For example, in arid regions it is likely that vegetation distribution will be closely tied with
groundwater availability. On the Coastal Plain of the Eastern United States, however, human alteration
will have a stronger control on vegetation distribution, with the potential exception of areas of high
water accumulation (e.g., wetlands).
One readily available source of aerial photography that can be easily accessed and used at no additional
charge is that provided by Google™ Earth. This can be done as the first step for analyzing an area, and
may be sufficient for the purposes of the project. The Google Earth software application has to be first
downloaded from its website (http://www.google.com/earth/index.html) and installed on a local
computer. Once installed it requires an active internet connection as it downloads data on the fly for
the area being viewed. The available data includes aerial photographs taken over multiple years and at
different times of the year for most parts of the United States that can be compared to identify some of
the variations discussed above. In addition, Google Earth’s implementation of DEM data allows the
images to be viewed as overlays to a three-dimensional projection of land surface topography. The
images can be rotated and tilted to enhance viewing. This can facilitate finding patterns in the
photographs that correlate with changes in topography that may have implications for groundwater. It
is also possible to import other map layers or data that have been converted into .kmz or .kml files into
Google Earth to assist with analyses. It should be noted however that there are restrictions on the use
of images generated in Google Earth. Consult “Google Maps/Earth Terms of Service” within the
software application for further information.
The land surface features and groundwater expressions mentioned above may also be evident on other
types of remotely sensed data (e.g., multispectral data) but sometimes the finer spatial resolution and
longer historical record of aerial photographs provide a significant advantage over those other datasets
(Moore 1982).
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2-A.1.2 Multispectral Imaging
Multispectral images are collected by passive sensors that gather data in the visible and near-infrared
portions of the electromagnetic spectrum. Multispectral sensors also collect information on the
reflectance or emittance of longer-wavelength energy, including mid-infrared and thermal, and often
sample several discrete portions of the electromagnetic spectrum. Multispectral sensors are available
on a number of existing satellite platforms and can be included on aerial missions. Satellites also repeat
their surveys much more often than typical aerial photo surveys, so the repeat time of satellite
multispectral sensors is much improved over aerial photography and their digital format and
standardized specifications allow for automated, repeatable detection of groundwater and
groundwater-related phenomena. This repeat coverage is additionally supported by the lower cost of
moderate resolution multispectral data relative to aerial photography (Mumby et al. 1999). The
enhanced temporal resolution provided by multispectral sensors is vital when detecting the dynamic
signatures associated with groundwater is much easier.
In the past there has been a more pronounced tradeoff between spectral and spatial resolution, with
finer spatial resolution sensors having fewer bands (e.g., IKONOS and Quickbird) and sensors with more
bands having coarser spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer
[MODIS]). However, this tradeoff has lessened in recent years with the development of sensors like
WorldView-2 which provides nine data bands with a spatial resolution between 0.5 and 1.8 meters.
Multispectral sensors are usually carried aboard satellite platforms, which provide certain advantages
and disadvantages (see above). Although they are generally limited as to when they can collect data
over certain areas due to specified satellite orbits, the ability of some sensors (e.g., Système Pour
l'Observation de la Terre [SPOT] and Quickbird) to collect data at multiple view angles has the potential
to reduce repeat time between satellite overpasses and therefore increase the likelihood that images
can be collected during times when conditions on the ground are optimal. As with aerial photography,
collection of multispectral images is limited by cloud cover and atmospheric conditions (e.g., haze).
Multispectral data have been broadly available since 1972, when the Earth Resource and Technology
Satellite (ERTS) was launched. The name of the ERTS program was subsequently changed to Landsat.
Several follow-on missions were launched including Landsat 2 (Multispectral Scanner) through 7
(Enhanced Thematic Mapper +; presently operating). The Landsat Data Continuity Mission (LDCM) is
presently scheduled to launch.
The Moderate Resolution Imaging Spectroradiometer (MODIS), collects imagery from 0.41 to 14.39 µm
wavelength with a repeat time of one to two days and has a variable spatial resolution (250 meters,
Bands 1 through 2; 500 meters, Bands 3 through 7; 1000 meters, Bands 8 through 36;
http://modis.gsfc.nasa.gov) that includes spectral bands designed to monitor a number of biophysical
parameters, including vegetation greenness, terrestrial surface temperatures, and atmospheric
conditions. Visible Infrared Imaging Radiometer Suite (VIIRS) was designed to improve and extend the
MODIS and Advanced Very High Resolution Radiometer (AVHRR) historical data record, collecting data in
22 spectral bands spanning the visible as well as the near-, middle-, and thermal infrared wavelength
regions with a spatial resolution of 750 meters (http://npp.gsfc.nasa.gov/viirs.html). The Advanced
Spaceborne Thermal Emission and Reflection Radiometer (ASTER) collects data in 15 bands spanning the
visible, near-infrared, short-wave infrared, and thermal portions of the electromagnetic spectrum with
spatial resolutions between 15 and 90 meters. ASTER initiated data collection in 1999 and is currently
collecting data, with the exclusion of Bands 5 through 9 (1.6 through 2.37 µm; Abrams et al.). Other
commonly used multispectral datasets include the Linear Imaging Self Scanner (LISS), SPOT, Quickbird,
IKONOS, OrbView, GeoEye, and WorldView (see table 2-A-1).
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Table 2-A-1–Common multispectral sensors used to inventory and monitor groundwater.
Table 2-A-1 includes commonly used, relatively recent sensors that have been utilized to inventory and
monitor groundwater resources or have the potential to do so, with the exception of MODIS, VIIRS and
ASTER that are detailed above. The table identifies the sensor name (Sensor), spatial resolution in
meters (Resolution), the portion of the electromagnetic spectrum sampled by each band (Bands), swath
width in kilometers (Width), temporal resolution or number of days between overpasses (Repeat), years
the sensor has operated (Life-Span; Present abbreviated as Pres.), and websites where more
information about the sensors can be found. Bands are named to indicate the portion of the
electromagnetic spectrum that they sample (P = panchromatic, B = blue, G = green, R = red, NIR = nearinfrared, SWIR = short-wave infrared, MIR = mid-wave infrared, TIR = thermal infrared, and FIR = far
infrared; see http://database.eohandbook.com for wavelength ranges). Multiple bands in one portion
of the electromagnetic spectrum are denoted with an “x” and then the number of bands (e.g., x2).
Multispectral data can detect spatial and spectral patterns that indicate the surface and subsurface
expression of groundwater (see Aerial Photography section), if the spatial resolution of the multispectral
dataset is fine enough. Certain multispectral sensors (e.g., GeoEye and WorldView) provide data at a
spatial resolution that is comparable with aerial photographs. Conversely, certain groundwater-related
signatures may be easier to detect by using remotely sensed data with a coarser spatial resolution (i.e.,
larger pixel size) because small-scale features (e.g., cultural features) are less distracting to the
interpreter at this spatial resolution and it is more likely that the entire regional feature is present within
the image (Moore 1982). In addition, land cover changes that appear gradual on the ground often
appear to have sharper boundaries on coarser-scale multispectral images (Moore 1982), which can
assist in the mapping process.
Although the spatial resolution of some multispectral sensors (e.g., AVHRR and even Landsat) may limit
their utility for detecting relatively small groundwater-associated features (e.g., lower order perennial
streams and other small groundwater-dependent ecosystems), the improved spectral resolution of
multispectral images can significantly improve the mapping of other groundwater-related phenomena.
For example, studies have found that the increased spectral resolution of multispectral data enhances
wetland mapping (Federal Geographic Data Committee 1992, Harvey and Hill 2001, Phinn et al. 1999,
Töyrä et al. 2002) and, sometimes helps compensate for reduced spatial resolution (Harvey and Hill
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2001). Multitemporal mapping of certain wetlands, lakes and streams, can provide information on
dynamic groundwater boundary conditions (e.g., Dirichlet), recharge and discharge, and is valuable for
the parameterization of groundwater models (Brunner et al. 2006). The enhanced spectral resolution
provided by multispectral data also enables the detection of salt crusts, which often indicate areas with
high groundwater tables and phreatic evaporation (Brunner et al. 2006).
Data collected in the thermal emission bands between 3.5 to 5.0 and 8.0 to 14.0 µm (Meijerink 2007)
are also useful for the detection of surface moisture and groundwater expression. Evaporation serves to
dissipate heat, leading to cooler surface temperatures. In addition, the strong thermal inertia of water
causes water bodies and moist surfaces to heat and cool more slowly than surrounding materials (El-Baz
2008). Furthermore, the detection of groundwater is often enhanced in the thermal region by the
relatively unique temperature of groundwater. In temperate climates, non-hydrothermal groundwater
is often cooler than surface water during the summer and warmer than surface water during the winter.
Hydrothermal discharges are commonly found to be warmer than surface water during a greater portion
of the year.
Thermal data have traditionally been used with reasonable success to detect the seepage of
groundwater into marine waters (Meijerink 2007) when the amount of groundwater being discharged,
surface water mixing, and salinity levels lead to a significant difference in the thermal signature at the
top of mixed waters (Meijerink 2007). A similar approach has been used in freshwater ecosystems, but
application of thermal data in these systems is somewhat more complicated because the two sources of
freshwater will be vertically distributed according to temperature (i.e., colder water below warmer
water), as opposed to marine waters where non-saline groundwater typically rises to the surface. For
this reason in temperate regions groundwater discharge to freshwater bodies is often studied by using
thermal imagery during the winter when groundwater is often warmer than surface water and will
therefore rise to the surface (Meijerink 2007).
Detecting geothermal springs has also been successful (Waters et al. 1990) if the area affected and
temperature gradient is large enough to be detected given the radiometric and spatial resolution of the
sensor being used. Relatively new applications of thermal data include the inventory and monitoring of
evapotranspiration (ET) and surface moisture status. These applications are introduced briefly below
and are discussed in detail later in this appendix.
Multispectral data in multiple wavebands can be combined to provide improved inventory and
monitoring of groundwater recharge and discharge. Such data help constrain recharge over areas of
relatively flat terrain, where groundwater recharge potential is the difference between precipitation and
evapotranspiration (Brunner et al. 2006). Precipitation can be operationally mapped by using remotely
sensed data in combination with ground data for calibration (Yilmaz et al. 2005). Remote sensing-based
energy balance models can be used to map evapotranspiration, with primary inputs of land surface
temperature retrieved in the thermal infrared band and vegetation cover fractions derived from the
visible and near-infrared. A number of vegetation indices and transformations have been developed for
mapping evapotranspiration, including the normalized difference vegetation index (NDVI; Rouse et al.
1974), the Soil Adjusted Vegetation Index (SAVI; Huete 1988), and tasseled cap (Kauth and Thomas
1976).
2-A.1.3 Hyperspectral Imaging
Hyperspectral data are characterized by numerous, narrow spectral bands collected in the visible, nearinfrared, mid-infrared, and sometimes thermal portions of the electromagnetic spectrum. These bands
are narrower and have higher relative resolution than those used in multispectral imagery. For
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example, NASA’s satellite-borne Hyperion sensor provides imagery with 220 spectral bands nanometers
at a spatial resolution of 30 meters and swath width of 7.5 kilometers (Pearlman et al. 2001). NASA’s
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been flown on a variety of different aircraft
and has 224 narrow, contiguous spectral bands from 0.4 to 2.5 µm (10-nanometers wide bands) and a
pixel size of 20 meters (Vane et al. 1993). These numerous, finely segregated spectral bands allow
analysts to identify different materials based on their “spectral signature” or diagnostic patterns in
absorption and reflection, usually associated with the molecular and/or cellular properties of the
material (Kokaly et al. 2003, Schmidt and Skidmore 2003).
The complete spectral signatures conveyed by hyperspectral data have demonstrated great promise for
detailed mapping surface features relevant to groundwater processes. Applications include the
identification of plant species to characterize groundwater-dependent ecosystems (Hirano et al. 2003,
Klemas 2011, Schmidt and Skidmore 2003), identification of plant stress (Anderson and Perry 1996),
discrimination of different lithologies (Meijerink 2007), and identification of tree species that are
associated with the presence of groundwater (Meijerink 2007).
2-A.1.4 Synthetic Aperture Radar
Synthetic aperture radar (SAR) data are collected by active sensors that sample the electromagnetic
spectrum at much longer wavelengths than optical sensors (e.g., aerial photography, multispectral, and
hyperspectral). For this reason SARs provide information that is fundamentally different than sensors
that operate in the visible and infrared portions of the electromagnetic spectrum. While optical sensors
respond to variations at the cellular and molecular level, SARs are sensitive to differences in water
content, size/roughness, and relatively broad scale structural differences (e.g., tree branching structure
or buildings). Water content determines the dielectric property of most natural materials. Typically, the
higher the water content, the higher the dielectric constant of the material (a measure of the aptitude
of a substance to conduct electrical energy) and therefore the greater the amount of incident SAR
(microwave) energy returned from the material. The capabilities of SARs are also unique. Unlike optical
sensors, radar sensors can collect data regardless of solar illumination, cloud cover, and most rain
events.
In the past, much of the utility of radar sensors for hydrogeologic studies pertained to their use in the
identification of geologic structures. The unique nature of SARs allows the detection of geologic
structures that other types of sensors cannot detect. For example, the ability of radar to transmit
energy through some vegetative canopies and collect data at varying incidence angles allows
identification of some subtle geomorphic features in forested terrain (Waters et al. 1990).
Determining depth of the substrate using SAR depends on a number of system and environmental
parameters including SAR wavelength, polarization and incidence angle, as well as soil moisture, texture,
and iron and salt content (Meijerink 2007). Under ideal circumstances, such as no vegetation, low
moisture, and relatively coarse soils (sand), the SAR signal has been found to transmit between a few
(Berlin et al. 1986, Meijerink 1996) and several (Waters et al. 1990) meters, and locate buried paleodrainage channels that may act to store shallow groundwater (McCauley et al. 1982). These conditions,
however, apply only to limited areas in the United States. Additional information on the use of radar
images for geologic interpretation can be found in Ford et al. (1998).
Interferometric techniques can be used to create extremely accurate digital elevation models by using
SAR data. Differences (i.e., phase shifts) between two or more SAR images collected from slightly
different vantage points make the determination of subtle differences in elevation possible. After
calibration with elevation from in situ measurements or ancillary data, interferometric SAR or InSAR, can
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be used to create digital elevation models over large areas relatively inexpensively or can be used to
monitor surface deformation related to a number of processes, including groundwater extraction
(Meijerink 2007). InSAR can also be used to monitor water levels of relatively large groundwaterdependent ecosystems (e.g., lakes) and the directional flow of those waters (5 centimeters accuracy and
1 to 2 centimeters precision; Wdowinski et al. 2008). Applications of interferometric SAR data have
expanded over the past decade due to increased availability, improvement in data and processing
methods, and the unique benefits provided by the data.
Table 2-A-2–Common satellite based SAR sensors used to inventory and monitor groundwater.
Table 2-A-2 includes relatively recent, commonly used sensors that have been utilized to inventory and
monitor groundwater or have the potential to do so. Note that specifications are approximate and not
all specifications are available at the same time. The table identifies the sensor name (Sensor), platform
type (Plat.), spatial resolution in meters (Resolution), wavelength band (Bands), incidence angle
(Incidence), polarization, swath width in kilometers (Width), the years the sensor has operated (LifeSpan; Present abbreviated as Pres.), and websites where more information about the sensors can be
found.
More recent SAR applications include the identification of groundwater-dependent ecosystems,
hydrologic boundary conditions (e.g., Dirichlet, or defined head from standing water), and areas of
recharge and discharge, due to the inherent sensitivity of radar to water complemented by its ability to
transmit energy through some vegetative canopies. When mapping and monitoring aquatic ecosystems,
imaging radars have many advantages over optical sensors. Microwave energy is sensitive to variations
in soil moisture and inundation, and is only partially attenuated by vegetation canopies, especially in
areas of lower biomass (Lang and Kasischke 2008; Townsend 2001, 2002; Townsend and Walsh 1998) or
when using data collected at longer wavelengths (Hess et al. 1990, Hess et al. 1995, Martinez and Le
Toan 2007). Radar not only has potential to detect different types and the extent of wetlands, it can
also be used to study the condition and function of these valuable areas, through the estimation of key
functional drivers (i.e., hydroperiod) and plant structure (Martinez and Le Toan 2007, Mougin et al.
1999). SAR data can even be used to detect freeze/thaw events (Bartsh et al. 2007) because the
dielectric constant of ice is much lower than that of water.
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2-A.1.5 Passive Microwave
Passive microwave radiometers operate in the same spectral range as active radar sensors, such as
SARs, but they sense microwave energy that is naturally emitted from objects. The emission of this
energy is dependent on numerous factors, including surface roughness, temperature, soil and
vegetation water content, bulk density, and soil texture. Passive sensors can be used to measure soil
moisture and biomass (Jackson et al. 1999, Shutko et al. 1997). Depth to shallow water table may be
inferred if the soil profile is continuous; surface drying has been shown to decouple surface and
subsurface soil moisture (Jackson 2002). Sensitivity of passive microwave systems to soil moisture is
well established (Du et al. 2000, Shi et al. 2006). Soil moisture can be measured to an approximate
depth of 5 to 10 centimeters with a relative accuracy of 10 to 15 percent (Rango 1994).
Passive microwave sensors usually detect energy in wavelengths between 0.5 and 30 centimeters.
Below wavelengths of 0.5 centimeter, passive microwave sensors are sensitive to clouds and rain, and
above 30 centimeters radar, television, and other forms of radiation will interfere with microwave
reception. Passive microwave sensors are most receptive to soil moisture between 0.5 and 30
centimeters.
The broad spatial resolution of most passive microwave sensors, such as the Earth Observation System
(EOS) Advanced Microwave Scanning Radiometer – EOS (AMSR-E), Scanning Multichannel Microwave
Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Electronically Scanned
Thinned Array Radiometer (ESTAR), limits their use to large geographic regions (Martinez and Le Toan
2007). Despite using synthetic aperture technology, spaceborne passive microwave sensors can only
achieve a spatial resolution of between 10 and 30 kilometers (Jackson et al. 1999). This resolution can
be partially remedied by the use of spectral mixture models, which can detect subpixel endmembers.
Even with spectral unmixing, spaceborne microwave radiometers are still limited by their coarse spatial
resolution (Becker 2006, Smith 1997). However, they do provide a relatively fine temporal resolution
with a repeat time of only a few days. An overview of the sensors used for soil moisture measurement
and the use of these data to support groundwater recharge modeling can be found in Jackson (2002).
2-A.1.6 Airborne LiDAR
Light detection and ranging (LiDAR) is a relatively new, but very useful and rapidly developing remote
sensing technology. to The most commonly available form of LiDAR is discrete point return imaging
LiDAR. LiDAR data can be used to calculate precise x,y,z locations by recording the amount of time it
takes for an emitted pulse, or a portion of that pulse, to return to the sensor (Vierling et al. 2008). LiDAR
x,y,z points can be used to make very accurate digital elevation models (DEMs). DEMs created by using
other types of remotely sensed data (i.e., stereo-interpretation of aerial photography or interferometric
SAR) are also available, but airborne LiDAR-based DEMs have a finer spatial resolution and greater
vertical accuracy. In general, non-LiDAR derived DEMs have much coarser vertical accuracies (1–10
meters) than those derived from LiDAR (approximately 10–15 centimeters; Coren and Sterzai 2006).
LiDAR derived DEMs also have relatively fine horizontal resolution (approximately 100–200 centimeters;
Coren and Sterzai 2006). More information on LiDAR sensors can be found in Flood (2001), Goodwin et
al. (2006), and Wehr and Lohr (1999).
LiDAR-derived DEMs can be used to derive primary topographic metrics, such as slope and aspect, and
secondary metrics, which are based on the relationship between multiple primary metrics. One
example of a secondary topographic metric is the topographic wetness index, which has been used to
determine where surface water is likely to accumulate based on slope and upslope contributing area
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(Beven and Kirkby 1979). In some landscapes, other topographic indices may be better suited for
mapping the surface expression of groundwater (Lang et al. 2012, Murphy et al. 2008)
LiDAR return-time provides information on elevation. LiDAR intensity (the strength of the returned
LiDAR signal relative to the amount of energy transmitted [Chust et al. 2008]) provides information
regarding the identity of materials off which the LiDAR signal reflects. LiDAR intensity data are
particularly well suited for detecting inundation because water absorbs the most commonly used LiDAR
laser energy (near-infrared). A considerable strength of LiDAR intensity data over other sensors is the
ability to filter LiDAR returns to display the earth’s surface rather than the tops of the vegetative canopy.
LiDAR intensity was found to improve the accuracy of inundation mapping within a deciduous forest by
approximately 30 percent relative to aerial photography (Lang and McCarty 2009). Note that LiDAR
intensity data are not currently standardized and cannot be compared among different projects that use
varying specifications (Lang and McCarty 2009, Newcomb and Lang 2012).
Although airborne LiDAR data are currently only available for about a third of the conterminous United
States, the majority of airborne LiDAR data have been collected for the Eastern United States,
particularly in coastal areas (Snyder and Lang 2012). These datasets are available and are being used
extensively in southeast Alaska. Information on publicly available LiDAR in the United States can be
obtained from the USGS. This spatial distribution of available LiDAR data is advantageous for the
inventory and monitoring of groundwater-dependent ecosystems and other groundwater parameters
because the majority of wetland-spring complexes are found in the Eastern United States due to a
relatively higher average precipitation to evapotranspiration ratio as compared to the Western United
States. The USGS recently conducted the National Enhanced Elevation Survey (NEEA) to: 1) determine
national requirements for enhanced elevation data; 2) estimate the costs and benefits of meeting the
documented requirements; and 3) assess different national elevation program implementation
scenarios (Snyder and Lang 2012). Because of the NEEA, the USGS endorsed collection of
interferometric SAR data in Alaska and LiDAR data with a horizontal point spacing of 0.7 meters and a
vertical accuracy of 9.25 centimeters throughout the rest of the United States (Snyder and Lang 2012).
The USGS is currently working with other Federal agencies, including the Forest Service, to develop a
funding strategy and governance model to best assure the collection of the endorsed dataset (Snyder
and Lang 2012).
Documents now exist to guide the collection and processing of LiDAR data to appropriate standards
(e.g., http://pubs.usgs.gov/tm/11b4/).
2-A.1.7 Laser and Radar Altimetry
Although most laser and radar altimeters were originally designed to monitor oceanic water levels, they
can also be used to measure water levels in large terrestrial water bodies, such as some lakes and rivers
(Milzow et al. 2011). These measurements allow assessment of hydrologic boundary conditions and the
water balance of groundwater-dependent ecosystems, assuming that the water body is the surface
expression of groundwater. The application of these data is limited by their spatial resolution and is
therefore restricted to monitoring large water bodies (e.g., TOPEX/Poseidon, > 1-kilometer wide river;
Becker 2006 and ENVISAT, 0.15-kilometers wide river; Milzow et al. 2011), although vertical accuracy is
often more than sufficient to monitor water level trends (e.g., 11 to 60 centimeters [Birkett et al. 2002]
and 3 to 4 centimeters root mean square error for very large lakes [Birkett 1995, Shum et al. 2003]). See
Calmant et al. (2008) for a comprehensive review of the history and utility of satellite altimeters for the
monitoring of continental surface waters.
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2-A.1.8 Gravity Sensors
Airborne gravity sensors are used to estimate changes in groundwater storage (Pool and Eychaner
1995), but the collection of such data may be resource prohibitive for the vast majority of Forest Service
applications. Therefore, this discussion focuses on the Gravity Recovery and Climate Experiment
(GRACE) satellite mission, a freely available dataset which has the potential to significantly advance the
study of groundwater resources and dynamics over large areas (Hoffman 2005). GRACE was launched
by NASA and the German Aerospace Center (DLR) in 2002 and measures temporal variations in the
gravity field. Gravity is monitored by measuring small changes in the distance between two satellites
with great precision. GRACE measurements are used to infer variations in terrestrial water storage
(TWS), but cannot measure groundwater potential gradients because aquifers are not continuous at the
resolution of GRACE (Becker 2006). Measurements must be corrected for moisture in the atmosphere,
vegetation and vadose zone, as well as surface water. Results for very large basins (900,000 square
kilometers) are highly accurate, perhaps more so than ground based measurements, but accuracy
decreases for smaller basins (approximately 450,000 square kilometers ; Rodell et al. 2007; Rodell and
Famiglietti 2002).
Despite its low spatial (no better than approximately 160,000 square kilometers) and temporal (ten day
to monthly) resolution compared with those of other Earth-observing satellites, GRACE presents a
unique opportunity to observe terrestrial water storage at large spatial scales, as it can sense water
stored at any depth under any condition. Therefore, GRACE has the potential to address a critical
observational gap, that of monitoring groundwater storage changes. Groundwater represents a much
larger fraction (approximately 30 percent) of global fresh water resources than rivers (approximately
0.006 percent; Dingman 2002). While monitoring networks exist for precipitation and rivers, monitoring
of subsurface water storage, including soil moisture and groundwater, is very limited. Due to data
scarcity, it is difficult to regionalize point-based measurement to assess changes in groundwater storage.
Currently, GRACE data can only be used to directly measure water storage in a small percent of United
States watersheds (Becker 2006). However, the proposed GRACE Follow-on Mission and model-enabled
downscaling have the potential to increase the relevance of GRACE throughout the United States (see
below).
2-A.2
Potential of New Sensors for Groundwater Inventory and Monitoring
The deployment of additional, often enhanced, satellite and airborne sensors by the U.S. Government,
international governments, and commercial entities will increase groundwater inventory and monitoring
capabilities. The deployment of a range of sensor types is being planned, including new multispectral,
hyperspectral, SAR, passive microwave, and gravity sensors. The spatial and temporal resolution of
these new sensors is generally becoming finer while other technical capabilities are improving or are
remaining unchanged (see tables 2-A-1 and 2-A-2).
Irrespective of other sensor improvements, enhancement of spatial and temporal resolution will
significantly influence groundwater inventory and monitoring capacities. The simultaneous
advancement of spatial and temporal resolution is a laudable achievement. In the past, the spatial or
temporal resolution of many sensors improved independently. It is only recently that both have become
possible through advanced technologies, such as the deployment of multiple microsatellites, multiple
view angle technology, or simply the deployment of more fine-resolution sensors. These advancements
are key to the improved inventory and monitoring of groundwater due to the dynamic nature of
groundwater and groundwater drivers and the fine spatial scale over which groundwater and its drivers
are often expressed. Becker (1996) stated that the restriction of spatial resolution may have more
serious implications for the inventory and monitoring of groundwater than the restrictions posed by the
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inability of remotely sensed data to penetrate the Earth’s surface. This is a bold statement, but Becker
(1996) pointed out that shallower groundwater systems, the systems that are often most evident near
the surface and therefore detectable via remotely sensed data, are more likely to be expressed over
smaller areas. Therefore, to detect groundwater interactions near the surface where remotely sensed
data are most useful, one must be able to resolve relatively fine spatial features (on the order of meters
instead of tens or hundreds of meters). We argue that these features are likely to be, on average, more
temporally dynamic as well.
This viewpoint underlines the importance of fine spatial resolution datasets for the inventory and
monitoring of groundwater. Although aerial photography is currently available, unless an extensive
historical record is necessary to meet project goals, fine spatial scale multispectral, SAR, and LiDAR data
provide significantly enhanced capabilities and should therefore be utilized for projects requiring fine
spatial resolution data. As shown in Table 2-A-1 various fine-resolution multispectral satellites have
recently been deployed. Additional enhanced, fine-resolution multispectral sensors are currently being
developed. For example, GeoEye’s GeoEye-2 should be operational in 2013 and will provide 0.34 to 1.36
meters resolution multispectral data (http://launch.geoeye.com/LaunchSite/about). DigitalGlobe is
developing WorldView-3 that will collect 29-band data with spatial resolutions between 0.31 and 30
meters and a one day repeat time (http://www.digitalglobe.com/downloads/WorldView3-DS-WV3Web.pdf). Various SAR sensors are currently planned including Radarsat Constellation which will collect
3 to 100 meters resolution C-band data with variable polarizations and a repeat cycle of 12 days
(http://www.asc-csa.gc.ca/eng/satellites/radarsat/description.asp). Although LiDAR data are currently
only available for about a third of the contiguous United States, LiDAR data are being collected fairly
rapidly in areas without coverage and there is currently a coordinated U.S. Government effort to collect
LiDAR data for the entire United States (Snyder and Lang 2012). Moderate and coarse spatial resolution
sensors are also planned for deployment, with many of these new additions poised to improve
groundwater inventory and monitoring.
The Landsat Data Continuity Mission (LDCM; Landsat-8), a joint USGS and NASA mission, is designed to
extend the Landsat historical record into the future. The Landsat satellite series has collected global
earth observations in the visible and near-infrared bands since 1972 (15 to 60 meters spatial resolution),
and the thermal band since 1982 (60 to 120 meters spatial resolution). The resulting dataset is the only
long-term civilian archive of satellite imagery at scales of human influence, resolving individual farm
fields, deforestation patterns, urban expansion, and other types of land-use and land cover change.
LDCM launched in February 2013, and supports two sensors, the Operational Land Imager (OLI) and the
Thermal Infrared Sensor (TIRS). The OLI collects data in nine visible, near-infrared, and short wave
infrared bands at a spatial resolution of 30 meters for most bands and 15 meters for a panchromatic
band. The TIRS collects data in two thermal bands with a spatial resolution of 100 meters. LDCM will
allow continued moderate resolution mapping of groundwater-dependent ecosystems, indicators of
groundwater distribution, and global mapping of land-atmosphere interactions.
The Hyperspectral Infrared Imager (HyspIRI) has been recommended for development (National
Research Council 2007) and is currently proposed for launch in the 2020s. The HyspIRI mission includes
two sensors: a visible shortwave infrared (VSWIR) imaging spectrometer operating between 0.38 and
2.5 µm in 10 nanometers contiguous bands with a swath width of 145 kilometers and a thermal infrared
(TIR) multispectral scanner operating between 4 and 12 µm with a swath width of 600 kilometers. Both
sensors have a spatial resolution of 60 meters. The larger spatial extent of the TIR instrument will allow
for a revisit time of 5 days, while the smaller spatial extent of the VSWIR instrument will provide a revisit
time of 19 days. Among other applications, HyspIRI will be used to identify plant communities and plant
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stress, both of which can indicate groundwater influence and changes in groundwater recharge and
discharge.
The advanced technology gravity mission (GRACE II) would replace the microwave interferometer
presently in GRACE with a laser and fly at a lower altitude with a drag-free system to achieve improved
ranging precision. This should provide hydrological measurements down to scales approaching 100
kilometers or better (National Research Council 2010; Sheard et al. 2012). However, even this scale of
resolution may be too coarse for small, discontinuous aquifers found in most of the Western United
States that are the source for the vast majority of water supporting groundwater-dependent
ecosystems.
The improvement in spatial resolution would make GRACE II directly applicable to a wider range of
water resource characterization and management activities globally (National Research Council 2007).
GRACE II has been recommended for launch after 2020, but this could cause a significant temporal gap
between the original GRACE and GRACE II datasets. To lessen the risk of a lengthy data gap, NASA has
given preliminary approval to a GRACE Follow-On (GRACE FO) mission targeted for launch in
approximately 2017. GRACE FO would reduce the expected data gap after the terminus of GRACE and
provide time for the technology developments required for GRACE II. The configuration of GRACE FO
would be similar to GRACE, with incremental technological improvements that should afford some level
of error reduction/increased spatial resolution.
NASA is working towards the launch of multiple SAR satellites, including the Deformation, Ecosystem
Structure, and Dynamics of Ice (DESDynI), the Soil Moisture Active-Passive (SMAP), and the Surface
Water and Ocean Topography (SWOT) sensors. The SMAP sensor will contain an L-band quad-pol (four
polarizations) SAR and 1.41 GHz microwave radiometer and is designed to monitor soil moisture and
freeze-thaw events. Doing so would allow an improved understanding of the water cycle through
enhanced detection of drought, flood prediction, and other variables. Although the coarse resolution of
the sensor (planned greater than 1 kilometer) will limit its ability to map many groundwater features at
a regional scale, its fine temporal resolution (planned 2 to 3 day) would provide valuable information
concerning groundwater dynamics. SMAP is currently scheduled for launch in 2014
(http://smap.jpl.nasa.gov/). SWOT is a collaborative effort between NASA and the French Space Agency
(CNES). SWOT will, in part, be designed to inventory freshwater storage in water bodies, many of which
are likely to be groundwater dependent, and will provide refined information on groundwater boundary
conditions. The launch of SWOT is planned for 2019 and it is expected to contain a ka-band radar
interferometer and a nadir-looking altimeter (http://www.jpl.nasa.gov/missions/details.php?id=5998).
The DESDynI sensor will contain a multiple polarization, interferometric L-band SAR capable of collecting
approximately 10-meter resolution data with a 12 to 16 day repeat time. DESDynI is designed to map
and monitor fault geometries, land-surface deformation, ice sheets, and ecosystem characteristics (e.g.,
biomass and tree height) particularly in forests, and is scheduled for launch in 2021
(http://desdyni.jpl.nasa.gov/mission/). Detailed information about the sensors discussed above can be
found in the U.S. National Research Council (2007) publication entitled Earth Science and Applications
from Space: National Imperatives for the Next Decade and Beyond.
2-A.3
Integrating Remote Sensing Techniques in Groundwater Modeling
As discussed in section 2.5.3, data derived from remote sensing techniques can be used to refine
groundwater models. In most cases, remote sensing has application for improving modeling of large,
regional aquifers. As technology and sensors improve, techniques similar to those discussed below may
become available for improving finer-scale groundwater models.
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2-A.3.1 Monitoring Change in Groundwater Storage with GRACE
GRACE measurements can be used to infer variations in total terrestrial water storage (ΔTWS). Despite
its low spatial (no better than approximately 160,000 square kilometers) and temporal (ten day to
monthly) resolution compared with other Earth-observing satellites, GRACE presents a unique
opportunity to observe terrestrial water storage at large spatial scales, as it can sense water stored at
any depth under any conditions (Rodell et al. 2009). This includes surface water, snow and ice, soil
moisture, and groundwater. Due to the scarcity of in situ measurements, particularly in remote areas, it
is currently difficult to assess groundwater storage changes at the regional scale. Therefore, GRACE has
the potential to address the observational gap of monitoring water storage changes, particularly
groundwater resources.
The GRACE mission provides approximately monthly changes in terrestrial water storage (Δ TWS) on the
basis of measurements of the Earth’s global gravity field (Tapley et al. 2004b). This mission consists of
two identical satellites orbiting at approximately 500 kilometers altitude, separated by approximately
220 kilometers. It uses satellite-to-satellite tracking and measurements from onboard global positioning
system (GPS) receivers and accelerometers to obtain global, monthly gravitation solutions at a spatial
resolution of approximately 400 kilometers. These solutions can be used to map monthly changes in the
distribution of mass at Earth’s surface (National Research Council 2010). The GRACE satellites use a kband microwave ranging system (KBR) to track changes in their relative distance with a precision of
approximately 0.2 x 10-6 m/s (Tapley et al. 2004a). This inter-distance varies as the satellite passes
through gravity highs and lows. The observed orbit perturbations represent gravitational changes
caused by all kinds of mass redistribution, mostly on the Earth’s surface (Wahr 2007). These timevarying gravity signals are analyzed with respect to a priori satellite orbits computed on the basis of
various geophysical models (Han et al. 2010). Temporal mass variations such as tides, non-tidal ocean
mass, atmospheric mass, planetary bodies’ attraction, and the steady-state gravity field are typically
included to compute the reference satellite orbits based on the respective models. The nongravitational forces (such as air drag) are corrected by using onboard accelerometer measurements.
Therefore, the observed range-rate, with respect to the computed range-rate, allows one to infer
changes in total terrestrial water storage (Δ TWS) as well as anomalous mass signals that are not
completely removed with applied geophysical models (Han et al. 2010, Wahr 2007).
The GRACE Project provides two end products: (1) Level 1B (L1B) products which include the raw data
needed to construct gravity field solutions, and (2) Level 2 (L2) gravity products (e.g., harmonic
solutions). L2 products are the most commonly used and available from several project related research
groups (e.g., GeoForschungsZentrum in Potsdam Germany; the Center for Space Research at the
University of Texas and the Jet Propulsion Laboratory, United States). These research centers employ
different processing techniques to relate gravity variations to TWS as mean water height. As L2
products provide a “spatially filtered image” of actual TWS, further post-processing that corrects bias
and leakage problems is required to obtain TWS changes over specific target areas of interest (Scanlon
et al. 2012). Klees et al. (2008) compared various regional and global GRACE models for accuracy
analysis, and concluded that 2 centimeters water-equivalent height is currently a reasonable estimate of
GRACE accuracy for monthly mean water storage variation at the regional scale of approximately 106
kilometers. As indicated by Bettadpur et al. (2012) the new release of GRACE L2 data improves current
accuracy by a factor of 2 to 3. GRACE uncertainty and accuracy is inversely related to the size of a target
area and the length of the measurement averaging period (i.e., the time period during which GRACE
contributes to a single global gravity field) (Rodell and Faminglietti 2002).
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As GRACE provides the integrated sum of TWS through the entire water column, groundwater storage
variations can be isolated if auxiliary information on non-groundwater components of TWS is available
from in situ observation or land surface models. Using the water balance equation, GRACE estimates of
TWS variations (ΔTWS) can be partitioned into changes in groundwater storage (ΔGWS), soil moisture
(ΔSM), snow water equivalent (ΔSWE), and surface water storage (ΔSWS), that is, ΔTWS = ΔGWS + ΔSM
+ ΔSWE + ΔSWS. By rearranging, ΔGWS is a residual from the disaggregated equation. Once ΔGWS is
isolated from the GRACE products, it can be compared with ground-based observations of soil moisture
and water table information. GRACE provides an accuracy measure of expected uncertainty in derived
ΔTWS, and this measure can be used to assess the sensitivity of the hydrological signals obtained from
GRACE against ground-based observations or model outputs from land surface models.
2-A.3.2 Regional Monitoring Case Studies of Groundwater Storage Change Using GRACE
There has been increasing interest in using GRACE to remotely monitor groundwater in the United
States at the regional scale. Studies have been conducted at multiple sites across the United States at
various spatial scales. Case study sites include the Mississippi River basin and its four subbasins (greater
than approximately 500,000 square kilometers), the United States High Plains (450,000 square
kilometers), Oklahoma (280,000 square kilometers), Illinois (approximately 200,000 square kilometers ),
and the California Central Valley (52,000 square kilometers) (Famiglietti et al. 2011, Rodell and
Famiglietti 2002, Rodell et al. 2007, Scalon et al. 2012, Swenson et al. 2006, Swenson et al. 2008, Yeh et
al. 2006). At these study sites, groundwater is the primary source for drinking water and irrigation, and
extensively monitored in situ. Given the brief historical record of the GRACE Project, most studies
mainly investigated the ability of GRACE to detect the interannual or seasonal variations in groundwater
storage. As longer-term GRACE records become available, its hydrological application should be
extended to address long-term trends in groundwater storage (Longuevergne et al. 2010; Scanlon et al.
2012) and groundwater depletion due to drought (Houborg et al. 2012). The following case studies
illustrate applications of GRACE to detect change in groundwater storage at the regional scale.
The High Plains Aquifer
The High Plain aquifer underlies 450,000 square kilometers in the Central United States. This includes
parts of Nebraska, Texas, Kansas, Colorado, Wyoming, Oklahoma, New Mexico, and South Dakota. The
climate of the region is mostly semiarid. This is one of the largest areas of agricultural concentration
within the United States with much of that cropland (approximately 175,000 square kilometers) heavily
dependent on irrigation from groundwater. Rodell and Famiglietti (2002) investigated the potential for
GRACE to detect annual changes in groundwater storage. This study focused on assessing the expected
uncertainty in GRACE derived ΔGWS, and comparing it with the magnitude of annual groundwater
storage change observed in situ. The expected total uncertainty in GRACE-derived ΔGWS was estimated
as the sum of uncertainty due to GRACE instrumental errors for averaging time periods, atmospheric
correction errors, and annual variations in soil moisture. Strassberg et al. (2009) conducted in-depth
analysis of using GRACE to monitor groundwater storage change over the period of 2003 –2006. This
study demonstrated GRACE-derived basin-averaged ΔGWS was in a good agreement with those from in
situ groundwater level measurement (R = 0.72to 0.73). GRACE-based estimates of ΔGWS were
partitioned as, ΔGWS =ΔTWS– ΔSM. Soil moisture information was obtained from in situ soil monitoring
data (an average of 983 data points per season) and outputs from the Noah land surface model.
The Mississippi River Basin
Rodell et al. (2007) has computed groundwater storage variations averaged over the Mississippi River
basin and its four major subbasins over the period of 2002–2005. In this study, changes in surface water
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storage (ΔSWS) and the impact of biomass were assumed to be negligible, and soil moisture and snow
water equivalent (SWE) were estimated from the Global Land Data Assimilation System (GLDAS). Then,
GRACE-GLDAS estimates of groundwater variations were calculated as, ΔGWS =ΔTWS – (ΔSWE + ΔSM)
and were compared against water level records from 58 wells set in an unconfined aquifer. Results
were favorable for the full Mississippi River basin and the two larger subbasins that have areas greater
than 900,000 square kilometers (Missouri and Ohio), but poor for the two subbasins that have areas of
approximately 500,000 square kilometers (Upper Mississippi and Lower Mississippi/Red/Arkansas).
The California Central Valley
Although the area of the Central Valley (52,000 square kilometers) is below the limit of the GRACE
footprint, extensive groundwater depletion caused by irrigation allows GRACE to detect changes in
groundwater storage. Groundwater storage changes were estimated for the Sacramento and San
Joaquin River Basin (154,000 square kilometers), which includes the Central Valley. Famiglietti et al.
(2011) used 78 months of GRACE products over the period of October 2003 through March 2010, to
examine water storage variations in this region. It showed that the basins were losing water at a rate of
31.0 + 2.7 mm/yr and water loss due to groundwater depletion was 20.1 + 3.1mm/yr. ΔGWS was
isolated from ΔTWS using the combined results of in situ and simulated results of snow water equivalent
obtained from the National Operational Hydrologic Remote Sensing Center, in situ measurement of
surface water storage obtained from the California Department of Water Resources, and soil moisture
storage obtained from land surface models. Scanlon et al. (2012) developed a new post-processing
method to incorporate the spatial variation in hydrological components when resolving GRACE data.
This study showed that the GRACE-based estimate of groundwater depletion during the drought was
similar to those derived from ground measurement. GWS declined by 31.0 + 3 km3 based on maximum
depletion from October 2006 through March 2010. The annual decline rate was estimated as 8.3
km3/yr, which was consistent with typical decline rates from previous droughts.
Illinois
The Illinois region (approximately 200,000 square kilometers) has a comprehensive hydrological
observational network that provides systematic monitoring of all water storage components over the
last several decades. Previously, Rodell and Famiglietti (2001) estimated the uncertainty of GRACE
products using various in situ observations to assess if water storage changes could be detectable with
GRACE given the size of the study area. Swenson et al. (2006) demonstrated that GRACE-derived TWS
were closely matched with in situ observations of soil moisture and groundwater measurement. They
employed a new data filtering technique to improve the spatial resolution of GRACE products (Swenson
and Wahr 2006). Then, Yeh et al. (2006) studied regional groundwater storage changes on a monthly
basis in Illinois for the period 2002–2005. Since the previous studies and historical records indicated the
largest components of monthly terrestrial water storage variations in Illinois were changes in
groundwater and soil moisture, these two components were only considered in partitioning the GRACEderived TWS variation (ΔTWS). Sixteen soil moisture and well locations with the most complete records
from 2002 to 2005 were used. This study demonstrated that seasonal cycles (i.e, the pattern and
amplitude) of GRACE were well matched with in situ observations of groundwater and soil moisture,
although GRACE could not estimate the month-to-month changes very accurately.
Oklahoma
Swenson et al. (2008) estimated the regional variations in groundwater changes in Oklahoma (280,000
square kilometers). As in Yeh et al. (2006), this study assumed that the primary contributors to changes
in Oklahoma regional water variations are soil moisture and groundwater. Variation in groundwater
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storage (ΔGWS) was isolated from GRACE by subtracting soil moisture observed in situ, and this was
compared with water table records. Soil moisture information was obtained from two different
automated observing network systems: the Oklahoma Mesonet (OM) that provides soil moisture
measurements every 30 minutes at multiple depths of 5 to 75 centimeters (approximately 100 sites),
and the Soil Water and Temperature System (SWATS) of the Department of Energy’s Southern Great
Plains Atmospheric Radiation Measurement (DOE ARM) network that provides hourly profile of soil
moisture at eight depths from 0.05 to 1.75 meters (approximately 10 sites). As the spatial coverage of
DOE ARM dataset is limited (approximately 10 sites), soil moisture information from these sources were
used to understand and empirically model the changes in soil moisture by depth in the saturated zone
above the water table. In situ measurements of groundwater storage were estimated from
approximately 40 USGS wells in the study region. The results showed a close match between the GRACE
and in situ groundwater time series. Both data show mainly a seasonal cycle, and the phases of both
time series agree well, with a correlation coefficients of 0.81 and an RMS difference of 9.1 millimeters.
2-A.3.3 Limitations and Future Opportunities
In general, discrepancy between GRACE estimations and in situ observations are attributed to GRACE
instrumental errors, limitation of the data processing methods to eliminate “other noises” (e.g.,
atmospheric effects), the sparse temporal sampling of the ground measurements, and difference in
spatial scales represented by GRACE and in situ data. Limited spatial resolution and noise contamination
can cause estimation bias and spatial leakage, and they are exacerbated as the region of interest
approaches the GRACE spatial resolution limit of a few hundred kilometers. Continuing efforts have
been made to improve the data processing methods to correct leakage and bias problems
(Longuevergne et al. 2010, Swenson and Wahr 2006) and to better handle the spatial representation of
ground measurements or land surface model outputs for the regional assessment (Scanlon et al. 2012).
Data processing methods based on the L1B products instead of L2 provided sub-monthly retrievals of
hydrological signals with better error characteristics (Han et al. 2005, Han et al. 2009, Rowlands et al.
2005). In addition, it is expected that GRACE instrumental errors would be significantly reduced with the
GRACE Follow-On mission (see section 2-A.2 Potential of New Sensors for Groundwater Inventory and
Monitoring).
Previous studies demonstrate that GRACE is most useful for groundwater monitoring at large scales and
for the analysis of general trends and spatial patterns in groundwater storage change. See the following
article for a more detailed discussion http://earthobservatory.nasa.gov/Features/GRACEGroundwater/.
However, further downscaling is necessary for GRACE to be useful for groundwater monitoring and
decision making at the local scale at which most management takes place. Despite its coarse spatial
resolution, GRACE can help to provide groundwater change information at the local scale when it is
formally incorporated into localized groundwater models. These models may rely on GRACE data to
provide better estimates of groundwater storage variation, while considering localized groundwater
characteristics (Moore and Fisher 2012). In such efforts, GRACE-derived water storage can be used as
independent validation data or to force or constrain localized hydrological models (Han et al. 2010, Lo et
al. 2010, Niu et al. 2007, Sun et al. 2012). GRACE data can be merged with a land surface model via data
assimilation, and this can provide downscaling and quality control of GRACE-derived information
(Zaitchik et al. 2008). Sun et al. (2012) demonstrated that the seasonal variation observed from GRACE
can be used to constrain and calibrate localized groundwater models (with the spatial resolution of 1.6
square kilometers by 1.6 square kilometers) for the Edwards-Trinity Plateau and Pecos Valley aquifers
(115,000 square kilometers). Zaitchik et al. (2008) improved the accuracy of water storage variation by
assimilating GRACE into the Catchment land surface model using an ensemble Kalman smoother in the
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Mississippi River basin. This study showed that model performance was improved even for small subwatersheds, whose scale was less than the scale of GRACE observation. These results indicate that a
GRACE data assimilation system (DAS) could contribute to large-scale drought monitoring (Houborg et
al. 2012). Drought monitoring at the national or continental scales is limited due to a paucity of data on
subsurface water storage, particularly deep soil moisture and groundwater. GRACE DAS results can
provide information on both soil and groundwater storage variations, by facilitating spatial and temporal
downscaling and vertical decomposition of GRACE into various hydrological components in near real
time. Houborg et al. (2012) have applied GRACE DAS to North America and developed supplemental
drought indicators informed by GRACE. The results pointed out modest but statistically significant
improvements in hydrological modeling across the United States, and highlighted the potential value of
drought indicators based on GRACE DAS for identifying drought conditions more comprehensively and
objectively.
In summary, GRACE provides an unprecedented opportunity for groundwater monitoring and
inventorying at a large spatial scale. So far, it has been the only means to assess changes in
groundwater storage at all depths. Despite its limitations, recent studies demonstrate the feasibility of
incorporating GRACE into localized groundwater models, thus improving local modeling capabilities.
These local groundwater models are critical for water resource management and decision making.
GRACE, integrated with in situ observations and groundwater models, could provide valuable
information to meet the needs of a wide range of Forest Service groundwater monitoring and evaluation
efforts.
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