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3 Assessment of managed aquifer recharge sites using GIS and numerical modeling
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5 Tess A. Russo*
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, Andrew T. Fisher
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, Brian S. Lockwood
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, Randall T. Hanson
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(?)
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1 Department of Earth and Planetary Sciences, University of California, Santa Cruz, CA
2
Columbia Water Center, Earth Institute, Columbia University, NY
3
Pajaro Valley Water Management Agency, Watsonville, CA
4 United States Geological Survey, San Diego, CA
*tr2378@columbia.edu, (347) 913-6835
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Abstract
We completed a geographic information system (GIS) analysis to assess suitability for managed aquifer recharge (MAR) using the Pajaro Valley Groundwater Basin, central coastal California
(PVGB), as a case study. With results from the GIS study, we used a groundwater model to assess the hydrologic impact of potential MAR operating scenarios, illustrating how a comprehensive analysis of MAR suitability can help with regional water supply planning. The
GIS analyses used topographic, land use, surficial geology, soil infiltration capacity, aquifer and associated confining layer locations, properties, thicknesses, and historical changes in water levels. A map of MAR site suitability and comparison with an existing project suggests that about 7% (15 km
2
) of the basin may be highly suitable for MAR. Model results show simulated
MAR projects in locations identified as “highly suitable” for MAR reduce seawater intrusion more than projects simulated in “unsuitable” locations, supporting the GIS analysis results.
Results from the model also illustrate the variability in seawater intrusion reduction and head level changes throughout the basin and over time. Projects distributed throughout the PVGB were more effective at reducing seawater intrusion than projects along the coast, at the time scale of several decades. Collectively, these studies introduce a novel data integration method and help to evaluate management options for improving long-term groundwater conditions throughout the
PVGB.
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32 1. Introduction
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Groundwater overdraft can occur when the sum of extraction and other aquifer outputs exceeds the sum of inputs over the long term. Groundwater overdraft can lead to numerous undesirable consequences, including increased pumping costs associated with lowering of aquifer water levels; drying of streams, lakes and wetlands; land subsidence and an associated loss of storage capacity; and seawater intrusion. As water demand rises, unsustainable groundwater use is expected to continue to increase around the world, especially in developing nations, making groundwater management increasingly important (Foster and Chilton 2003; Giordano 2009;
Konikow and Kendy 2005; Rosegrant and Cai 2009).
Strategies for mitigating groundwater overdraft include limiting extraction (water conservation, pumping moratoriums), and enhanced aquifer inputs. The latter can be
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54 accomplished with injection wells, aquifer storage and recovery (ASR, with injection and extraction through the same wells), and managed aquifer recharge (MAR, using surface infiltration systems). Enhanced recharge has become a common and frequently effective method for water resource management (Dillon et al. 2009; Ma and Spalding 1997; Maliva and Missimer
2012). ASR can help to reduce overdraft, (Shammas 2007), but must be planned carefully, as it can have high energy requirements and requires creation and maintenance of conveyance and pumping systems (Bouwer 2002). In contrast, MAR can be part of a more passive operational system, potentially involving less engineering and lower operating costs. Water is diverted to a natural depression or constructed retention area, where it infiltrates into the subsurface over time.
MAR projects have also demonstrated improvements in water quality through denitrification during the infiltration process (Fryar et al. 2000; Ma and Spalding 1997; Missimer et al. 2011;
Rauch-Williams et al. 2010; Schmidt et al. 2011). These improvements can be particularly important for sites lacking reliable access to pristine surplus surface water supplies, such as basins in which there is extensive agricultural development or widespread use of septic systems, resulting in elevated nutrient levels. The primary disadvantages of MAR include relatively large land area requirements, and the challenge in identifying locations with amenable surface and subsurface conditions for infiltration to an unconfined aquifer.
Identifying areas suitable for MAR projects and estimating the influence of these projects on groundwater levels and fluxes are challenging problems with numerous solutions. The first step is to locate regions where surface water can infiltrate and flow to available space within an aquifer. These assessments are often made on a regional basis, within which there may be limited data on complex surface and subsurface geology. In addition, there is a need to determine how the benefits of managed recharge could vary with project location, size, and operating conditions.
Some of these questions can be resolved through field testing, but computational tools can play an important role in evaluating project scenarios and screening potential MAR sites, on the basis of their broader hydrologic impact.
Studies of the spatial distribution of recharge have been developed and applied to assess groundwater vulnerability to contamination. Aller et al. (1987) developed a method for evaluating the potential for groundwater degradation, DRASTIC, which uses a relative ranking system. The method combines multiple datasets related to groundwater infiltration, including net recharge, aquifer and soil properties, and impact of infiltrating water through the vadose zone on
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85 water quality. The parameters for each dataset are classified (generally to values 1 to 10), then multiplied by a dataset weight (1 to 5). The product of value and weight for each area (or subarea) are then summed for all the datasets, resulting in a relative vulnerability index for each area in the study region. This method provides the basis for identifying recharge areas using geographic information system (GIS) based integration. We follow the general structure of this method, but use a different ensemble of datasets, different classification and integration techniques, and different parameter weights.
Many hydrologic applications, including identification of locations for potential MAR projects, are well suited for GIS analysis (Jha et al. 2007). Several studies have used GIS-based integration of spatial coverages pertinent to groundwater recharge, with various data values being classified and weighted before combining (Adham et al. 2010; Chenini et al. 2010;
Chitsazan and Akhtari 2009; Jasrotia et al. 2007; Murray and Mcdaniel 2003; Piscopo 2001;
Saraf and Choudhury 1998; Shankar and Mohan 1998; Yeh et al. 2009). Methods used for classification and weighting generally differ from study to study, due to variations in data availability, local geology, and perceived level of dataset importance to groundwater recharge.
Chowdhury et al. (2010) polled a group of geologists and hydrogeologists to determine a weighting system for their GIS-based recharge location assessment, and found that half the group thought equal weighting was appropriate while the other half agreed on a variable weighting method. As a practical matter, all classification schemes of this kind are somewhat arbitrary, but initial approaches and values can be refined over time as new data becomes available and individual recharge projects are tested and implemented. Some studies have used GIS with a multi-criteria decision analysis that accounts for local preferences, and attempt to reduce the arbitrary nature of weight assignment by using an analytical hierarchy process (Chowdhury et al.
2010; Rahman et al. 2012). Though more rigorous, this decision analysis method still requires
(largely heuristic) estimation of the relative importance of each parameter.
Numerical modeling can also help to identify sites amenable for MAR, and be used for estimating the potential benefits of MAR projects on regional hydrologic conditions during a range of future climatic, water use, and management scenarios(Munevar and Marino 1999).
Groundwater models may be combined with an optimization algorithm to test water management strategies, including artificial recharge (Abarca et al. 2006). These models tend to use simple governing equations and highly generalized aquifer properties. Another option for reducing
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116 computation time is to employ an ensemble of analytical models based on simplified lumped parameters (Smith and Pollock 2012). Combination of the GIS-based integration methods with numerical modeling allows a more detailed and quantitative assessment of MAR opportunities and impacts, and takes advantage of overlapping data requirements for GIS and numerical modeling studies (for example, aquifer geometry and depth and soil properties) (Chenini and
Mammou 2010). The use of numerical modeling as a follow-up to a GIS-based study also provides an opportunity to assess the MAR location suitability analysis developed using GIS, and on time requirements to see specific improvements to resource conditions.
True assessment of MAR location suitability requires field testing to determine how project placement influences local and regional hydrologic conditions. Ultimately this requires field-scale implementation of MAR projects, but budgetary and time constraints generally limit opportunities for large-scale installations purely for testing purposes. Thus, numerical modeling has an important role to play in pre-implementation evaluation of project options, based on a
MAR suitability analysis, helping to reduce the number of choices made in selecting appropriate management strategies. Similarly, evaluation of actual hydrologic responses to implementation of MAR projects can be used to "validate" individual and ensemble groups of groundwater models, contributing to a better understanding of system function and improved basin-wide management of scarce resources.
The present study combines GIS and numerical analyses to address the following questions, as applied to the Pajaro Valley Groundwater Basin (PVGB), central coastal California
( Figure 1 ): 1) How should surface and subsurface information datasets be combined to assess
MAR site suitability? 2) How does MAR suitability vary within the basin? 3) How might hypothetical MAR operating scenarios influence groundwater conditions in the basin over the 34 year model simulation? This project limits analysis to MAR options for the PVGB rather than exploring a more comprehensive assessment of basin management options and anticipated changes to water usage. An extensive technical and public process is currently underway in the
PVGB to evaluate a wide range of supply and conservation options, and develop a new basin management plan, in an effort to improve groundwater conditions in the Pajaro Valley in coming decades. We limit analyses in this study to assessing the spatial distribution of MAR suitability and potential hydrologic impacts of several MAR options.
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2. Study area
The PVGB ( Figure 1 ) underlies a 322 km
2
area managed by the Pajaro Valley Water
Management Agency (PVWMA). The region relies almost entirely on groundwater to satisfy agricultural and municipal/domestic needs (83% and 17%, respectively). Precipitation in the area has averaged ~560 mm/yr in recent decades, ranging from 480 mm/yr in the south to 1270 mm/yr in the northern high elevation area, with considerable year-to-year variability.
Precipitation is highly seasonal, with a majority falling between December and April, resulting in distinct dry and wet seasons. Historic changes in precipitation intensity and air temperature in central coastal California (Dettinger 2005; Russo et al. 2013), suggest that natural recharge rates and crop water requirements are not stable and may contribute to groundwater stress (Gleeson et al. 2010; Taylor et al. 2013).
The PVGB is a coastal basin, bounded to the west by Monterey Bay and to the east by the
San Andreas Fault. Northern and southern boundaries are political rather than (hydro)geologic, with key aquifer units extending beyond the area managed by the PVWMA. Much of the PVGB corresponds to the lower drainage basin of the Pajaro River, which flows into the valley from the east at an average discharge of 1.3 x 10
8
m
3
/yr (USGS Gage #11159000), after draining an upstream area of 3.1 x 10
3
km
2
. Corralitos Creek, a tributary of the Pajaro River having a drainage area contained entirely within the PVGB, contributes an additional 1.4 x 10
7
m
3
/yr of discharge (USGS Gage #11159200), and the Watsonville Sloughs also drain into the lower
Pajaro River before it discharges into Monterey Bay ( Figure 1 ).
The PVGB has six hydrogeologic units that comprise aquifer and confining layers: alluvium, alluvial clay, the Upper Aromas Aquifer, the Aromas confining unit, the Lower
Aromas Aquifer, and the Purisima Aquifer (Dupre 1990; Hanson 2003; Muir 1972). These layers are herein referred to as A1, C1, A2, C2, A3 and A4, respectively, where A signifies an aquifer and C signifies a confining unit ( Table 1 ). The six layers are underlain by hydrogeologic basement rocks consisting of granite and Oligocene-aged deposits.
Groundwater extraction from the basin currently averages 6.2 x 10
7
m
3
/yr, with the majority of water pumped from Layers A1 and A2 (Hanson et al. 2013; PVWMA 2013). Over time, total groundwater outflows (including extraction) have exceeded recharge and inflow rates; the current estimated overdraft in the PVGB is approximately 1.5 x 10
7
m
3
/yr (Hanson et al.
2013). This annual overdraft is approximately equivalent to 24% of annual pumpage and 10% of
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173 precipitation falling on the PVGB. Long-term monitoring in recent decades indicates that water levels within more than half of the PVGB are below sea level, particularly during the dry part of the water year, with the greatest depression of water levels below the City of Watsonville
(PVWMA 2013). Due to chronic over-extraction, a zone of seawater intrusion extends up to 5 km inland and is advancing at ~80 m/yr along much of the coastal edge of the basin (Hanson et al. 2008; Hanson 2003; Wallace and Lockwood 2009) ( Figure 1 ).
Table 1.
Model layer IDs and geologic information
Layer ID
A1
C1
A2
C2
A3
A4
Layer Name
Alluvial aquifer
Alluvial clay
Upper Aromas aquifer
Aromas clay
Lower Aromas aquifer
Purisima aquifer
Thickness
1
(m) Aquifer lithology
2
0 to 116 Unconsolidated, moderately sorted silt, sand, and gravel with discontinuous lenses of clay and
0 to 16
0 to 153
0 to 35
0 to 319 silty clay
--
Sequence of eolian and fluvial sand, silt, clay and gravel
--
Semiconsolidated, fine-grained, oxidized sand and silt
0 to 500 Thick bedded tuffaceous and diatomaceous siltstone with interbeds of finegrained sandstone
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1
Layer thickness obtained from the Pajaro Valley Hydrologic Model (Hanson et al. 2013)
175 2 Aquifer lithology summarized from USGS geologic maps (Brabb et al. 1997; Clark et al. 1997)
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In 199X, the PVWMA constructed the Harkins Slough MAR system which is permitted to divert up to 2.5 x 10
6
m
3
/yr (~2,000 ac-ft/yr) from near the confluence of Harkins and
Watsonville sloughs when flows and water quality are sufficiently high. Diverted water passes through a sand pack filter and is pumped through a 1.5 km pipeline to a 7 acre infiltration pond.
Some of this percolated water is subsequently recovered and blended with other water supplies,
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185 then delivered using a coastal delivery pipeline to local farms and ranches. In addition, the
PVWMA and the City of Watsonville jointly developed and operate a water recycling plant that contributes water to the coastal delivery pipeline, as do inland groundwater wells, allowing project water to be blended to achieve quality and supply goals.
The PVWMA is currently working with regional stakeholders to update their basin management plan in an effort to bring the basin back into hydrologic balance (PVWMA 2013).
Several projects have been implemented, including the MAR system and recycled water plant, and additional projects are planned, including enhanced water conservation, surface storage, and
MAR efforts. The overall goal is to develop about 1.5 x 10
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m
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/yr of demand reduction and additional supply. Local stakeholders are exploring options for development of distributed
(smaller-scale) MAR projects that could benefit from capture of stormwater flows during the rainy seasons. Collectively these options raise questions about how best to identify MAR project locations, and operate and maintain these projects for benefit of basin as a whole.
3. Methods
3.1. GIS analysis
We used GIS for data management, manipulation, and analysis of eleven surface and subsurface data sets to generate a basin-wide map of "MAR suitability". As defined for this study, high
MAR suitability indicates that, if a water supply of sufficient quantity and quality is available, surface and subsurface conditions could be favorable to developing one or more MAR projects.
Surface and subsurface property data sets were initially analyzed separately, and then were combined to produce a final map. For surface analyses, primary data included: (1) surficial geology, (2) soil infiltration capacity, (3) land use, (4) elevation (topographic slope), and (5) verified (measured) infiltration and recharge rates from observational studies. For subsurface analyses, primary data included: (6) aquifer thickness, (7) aquifer hydraulic conductivity, (8) confining layer thickness, (9) aquifer storativity, (10) vadose zone thickness, and (11) historical changes in water table height.
Surficial geology data were obtained from 1:62,500-scale geologic maps of Santa Cruz and Monterey Counties (Brabb et al. 1997; Clark et al. 1997). Lithologic descriptions were used to classify individual geologic units in terms of whether or not they corresponded to PVGB aquifers, or if fine-grained sediment (clay and silt) would be likely to reduce direct connection to
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224 underlying aquifers. Higher MAR suitability is associated with outcropping aquifers units. Soil infiltration capacity data were obtained from the Natural Resources Conservation Service
SSURGO database (NRCS 2010a; NRCS 2010b). Infiltration capacity of basin soils was mapped in irregular polygons having values ranging from 0.2 to 12 m/d. Land use classifications were developed by the PVWMA and the USGS in collaboration with regional stakeholders, based on field visits and parcel-specific reports of crops grown, as part of a broader effort to develop a regional hydrogeologic framework and groundwater model (Hanson 2003; Hydrometrics 2012;
Hanson et al. 2013). Land use classifications include native vegetation, urban, and agricultural areas designated by crop type or presence of a nursery. Land surface slope values were calculated from the 10-m resolution USGS National Elevation Dataset (ned.usgs.gov). Locations of measured seepage rates along losing sections of the Pajaro River were reported in earlier studies based on differential gauging and streambed geothermometry (Hatch et al. 2010; Ruehl et al. 2006).
Subsurface data sets were prepared initially during development of a regional hydrogeologic model (Hanson et al. 2013; Hanson 2003), and modified as needed for use in our
GIS-based analysis. Aquifer properties, including layer thicknesses, hydraulic conductivity, and storativity, were assembled using data from >300 well logs distributed throughout the basin, and compiled on a grid having horizontal resolution of 250 x 250 m and variable cell thickness. The present unsaturated zone thickness was calculated by subtracting the interpolated water table elevations, using data collected in 2010, from the ground elevation. Water levels in the basin were compared using 1998 and 2010 data sets, to quantify decadal changes in groundwater levels. There were sparse water level data from earlier times, in many cases indicating greater absolute changes in water levels, but 1998 was the earliest year for which data covered a majority of the study area. Nevertheless, this data set had the smallest spatial coverage of all data sets used in this study, and thus defined the spatial extent of the final MAR suitability map.
The common approach for combining GIS data sets such as these requires reclassifying relevant datasets to a common scale (e.g., relative values of 1 to 5) and then assigning a weight to each dataset in proportion to its perceived importance for the condition or process being evaluated. For each grid cell in the analysis, an index is calculated by summing the products of value and weight for each dataset:
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𝐼𝑛𝑑𝑒𝑥(𝑥, 𝑦) = ∑ 𝑛 𝑖=1 𝑣 𝑖
(𝑥, 𝑦)𝑤 𝑖
(1) where n is the total number of data sets, v i
is the classified value for data set i at location ( x , y ), and w i
is the weight assigned to data set i . We defined a weighting scheme for use in the present study based on (a) a review of published recharge mapping studies that used a similar GIS-based approach, (b) consideration of available data sets for the PVGB, and (c) inferences as to how groundwater recharge in this region might be influenced by coexisting factors ( Figure 2 ).
Our approach differs in several respects from methods applied in earlier GIS-based studies of natural recharge and potential for development of MAR projects. Most significantly, we evaluated surface data (sets 1 to 4) and subsurface data (sets 6 to 11) independently, with the former indicating the ease of surface water infiltration, and the latter indicating the ease of subsurface transport and extent of available storage. In addition, rather than simply combining all available datasets as independent indicators through a process of weighted summation (as with equation 1), we used some data sets as modifiers for other data sets before combining individual coverages to derive a final assessment of MAR suitability (described below). Finally, locations for which there were direct measurements of recharge rates (set 5) were subsequently assigned
MAR suitability values based entirely on field observations, which are considered to be the most reliable of available data types.
3.1.1. Data classification
We standardized several of the datasets by classifying values or properties on a scale of 1 to 5, where 1 represents an unfavorable attribute for MAR suitability, and 5 represents a favorable attribute. Both numerical and non-numerical datasets (e.g., soil infiltration capacity and surficial geology, respectively) were used in this study, requiring different methods for classification before data could be combined. We used three approaches for classifying numerical datasets: (1) classify values based on knowledge of field properties and past MAR operations, (2) classify values using the Jenks optimization method based on the distribution of property values (Jenks
1967), and (3) operate on raw data. The first method was applied to soil infiltration capacity and locations with stream seepage rates measured in the field ( Table 2 ). The second method uses the
ArcMap modification of the Fisher-Jenks algorithm, and was applied to specific yield, unsaturated zone thickness, and historical changes in water table height. The third method was
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286 applied to surface slope values. Non-numerical datasets, including surficial geology and land use, were classified based on interpretation of associated properties that could influence MAR.
For surficial geology, we assigned each lithologic unit a value based on whether the mapped lithology and texture (Brabb et al. 1997; Clark et al. 1997) corresponded to a known aquifer or would likely be connected to a known aquifer. This reduced 54 and 85 (for Santa Cruz and
Monterey Counties, respectively) lithologic categories to three possible connection classes
(Table 2). For land use, we classified descriptions based on associated roughness coefficient values (Chow 1959) ( Table 2 ), where roughness coefficients range from 14 to 100, for nursery/pavement to forested/native vegetation. Roughness coefficients were assigned to agricultural areas according to crops grown in rows, fields, or pasture.
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287 Table 2.
Classification of data based on physical properties
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2
Soil infiltration capacity
Rate
[m/d]
>3
Value
5
Stream seepage
Rate
[m/d]
>1
Value
80
1
1.2
0.6
0.2
0
4
3
2
1
0.2 to 1 60
Aquifer storage
𝑆 𝑦
𝑇 𝑢
[m]
64.01-131
40.01-64
22.01-40
8.01-22
0-8
Value
5
4
3
2
1
Surficial geology
Connection to aquifer
Good
Moderate
Poor
Land use
Value Description Roughness coefficient 2
5 100
3
Forest/
Nat. veg.
Pasture 40
1 Field crop
Row crop
Fallow
Turf
Pavement
1
Stream seepage rates were determined from direct observations and assigned values that
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14 represent highly suitable locations for MAR. For locations where L is measured, the MAR suitability index = L (equation 7)
Roughness coefficients modified from Chow [1959] are used in equation 2
3.1.2. Data integration
Earlier studies of recharge potential have treated infiltration capacity, slope, and/or land use as independent variables (e.g., Jasrotia et al. 2007; Yeh et al. 2009). We reasoned that the primary influence of slope and land use should be to modify soil infiltration capacity ( I
C
), and developed an equation to facilitate this approach. The equation incorporates a dependence on land slope ( s ) and roughness ( n ), similar to those used with the Manning equation for calculating mean runoff velocity in open channels, to generate an effective infiltration capacity ( I
E
):
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𝐼
𝐸
= 𝐼
𝐶
+ 𝑙𝑛 [
(𝑛 √𝑠 ) 𝑚𝑎𝑥
] (2) where I
C
is infiltration capacity (based only on soil type), n is a surface roughness coefficient
(with values ranging from 14 to 100, based on land use classificaiton), and s is slope in radians
(extracted from the regional digital elevation model). The second term in Eqn. 2 is intended to be proportional to the quantity of water that will not infiltrate. Because the product of squareroot-slope and surface roughness is normalized by the maximum (optimal) conditions for the region, the second term is ≤ 0. Calculated effective infiltration values are thus dependent on the
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337 soil infiltration capacity, but modified by the relative likeliness of water running off the site rather than infiltrating, based on surface slope and roughness ( Figure 3 ). For example, if the soil infiltration capacity is low, the influence of low-slope and native vegetation (high roughness coefficient), which might help to maintain good infiltration conditions through a permeable soil, becomes negligible. Conversely, a soil having a high infiltration capacity located in a region with high slope and a land use of turf grass (low roughness coefficient), will have an I
E
value that is lower than the I
C
value associated with the land use alone. I
E
will equal I
C
for optimal surface and slope conditions (the second term goes to 0 as the term in brackets goes to 1), but otherwise the I
C
value will be reduced by the second term, so that I
E
≤ I
C
.
Transmissivity ( T ) is in important subsurface parameter and can be difficult to estimate across a large spatial area. For operating a MAR system, high transmissivity is necessary for avoiding excessive mounding (which could lead to waterlogging of the root zone of crops or surface flooding), and for allowing infiltrated water to flow to nearby recovery wells. The primary constraints on transmissivity with respect to MAR are aquifer hydraulic conductivity ( K ) and thickness ( b ) and the presence or absence of confining layers between the ground surface and the underlying aquifer (three separate subsurface data coverages). To account for spatially variable K and b and the presence of confining layers in the subsurface, we use the following equation to calculate an effective transmissivity ( T
E
) as it applies to MAR suitability:
𝑇
𝐸
= 𝐾
𝐴1 𝑏
𝐴1
+ 𝐾
𝐶1 𝑏
𝐶1
+ 𝐹
1
[𝐾
𝐴2 𝑏
𝐴2
+ 𝐾
𝐶2 𝑏
𝐶2
+ 𝐹
2
(𝐾
𝐴3 𝑏
𝐴3
+ 𝐾
𝐴4 𝑏
𝐴4
)] (3)
𝐹
1
= 1 − 𝑏
𝐶1
9
−1
for 1 ≤ 𝑏
𝐶1
≤ 10 (4)
𝐹
2
= 1 − 𝑏
𝐶2
9
−1
for 1 ≤ 𝑏
𝐶2
≤ 10 (5) where F
1
and F
2
are confining unit factors that affect the influence of underlying aquifer units. F
1 and F
2
scale linearly between 1 and 0 for confining unit thicknesses ranging between 1 and 10 m, respectively. Thus, the transmissivities of multiple aquifer layers can be combined (at least in part), using an arithmetic mean rule, if confining layers between separate aquifer layers are <10 m in thickness. The vertical integration accounts for noncontinuity of thin confining layers that were readily apparent in hundreds of well logs and drilling records from across the basin.
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Calculated T
E
values were subsequently classified on a scale of 1 to 5 per method 2, described in
§
3.1.1
.
Available storage space ( V ) was assessed by multiplying aquifer specific yield ( S y
) by the unsaturated (vadose zone) thickness ( T u
) of each cell: 𝑉 = 𝑆 𝑦
𝑇 𝑢
. Specific storage is likely to be negligible in comparison to S y
, especially with respect to MAR surface infiltration, which was the focus of this study. MAR suitability was additionally enhanced in areas where there has been a large drop in water levels during the period of 1998 to 2010.
Following calculations and classifications, each dataset was assigned a weight based on the perceived importance of individual properties and conditions to positioning of potential MAR projects. The normalized weights used in this study are comparable to those obtained from a review of similar peer-reviewed studies ( Figure 2 ), although there is considerable variability between studies depending on the number and type of available datasets and local hydrogeology for each study. We note that in all of the earlier studies shown for comparison, individual parameters were added as independent variables on a cell by cell basis. As described earlier, we used land use and topographic slope data to modify the MAR suitability implied by soil property data sets, rather than applying land use and slope data independently. Values shown for these parameters in Figure 2 are the means of weights applied in the present study when calculating effective infiltration (Equation 2). In general, the relative weight of each data set is lower in the present study than other studies, but this is mainly because we used more data sets than most other studies, distributing the assessment of MAR suitability across more coverages.
A final map of MAR suitability was created by summing the weighted, classified values
(all varying from 1 to 5, from least to most suitable for MAR) for every 10-by-10 m grid cell in the basin for which all data sets existed:
𝑀𝐴𝑅 𝑠𝑢𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑖𝑛𝑑𝑒𝑥 = 5𝐼
𝐸
+ 4𝐺 + 5𝑉 + 4𝑇
𝐸
+ 2𝐷 (6)
If L exists, MAR suitability index = L (7) where: G is surficial geology, D is an historic change in water table height, and L is the index for a losing stream reach within which recharge rates have been measured and indicate high MAR suitability. The weights applied to the various indices are somewhat arbitrary, but generally align with the perceived importance of each data set from other studies ( Figure 2 ). We reasoned that
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399 effective infiltration properties and the volume of storage space should be weighted most strongly at 5, with formation transmissivity and outcropping of primary aquifers weight at 4. The historic change in water level was given the least weight (2) because of the uncertainty associated with interpolating from only a few measurements. The full process was constructed using ArcGIS ModelBuilder, which can be modified as additional datasets become available, field data are collected to test GIS predictions, or weighting methods are changed based on availability of new information.
3.2. Numerical modeling of MAR scenarios
To model the relative hydrologic impact of hypothetical MAR projects, and the importance of project placement and operational parameters, we modified a hydrogeologic model developed to assess a range of conditions and management options for the Pajaro Valley. The details of developing a hydrogeologic framework for the Pajaro Valley, and of creating and applying a complex model for assessing historical groundwater extraction and conditions, are presented elsewhere (Hanson, 2003; Hydrometrics, 2012, PVWMA 2013, and Hanson et al., 2013), and summarized briefly herein. Surface and subsurface hydrologic processes were simulated using
MODFLOW-2005 (Harbaugh 2005). The model domain extends from the back of the basin
(bounded by the San Andreas Fault) to >10 km offshore ( Figure 4A ), with grid resolution of 250 x 250 m. The six model layers vary in thickness across the basin, corresponding to aquifer and confining layer thicknesses ( Figure 4B ). The model includes nearly 1000 active production
(agricultural, municipal, domestic) groundwater wells, and uses the Farm Process (Hanson et al.
2010; Schmid and Hanson 2009) which modifies agricultural groundwater pumping rates during the simulation based on changes in land-use, climate, and groundwater availability. The simulations used in the present study represent 34 years (nominally conditions from 1976 to
2009) divided into 408 (monthly) stress periods, each having two time steps.
We worked with a Basecase simulation developed to represent a 34 year time period beginning nominally in 2009 (Hydrometrics, 2012). Climate conditions for the Basecase simulation were constructed to be a "mirror image" of climate during the preceding 34 years, and land use in the simulation was fixed to be that in 2009. This approach allowed us to assess the influence of MAR operating scenarios in the context of a historically realistic climatological scenario. After this simulation was completed, we ran 31 additional simulations, each with a
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404 different combination of hypothetical MAR projects adding water in different locations and at different rates around the basin ( Table 3 ). Differences in simulated water levels and the extent of seawater intrusion, as compared to results from the Basecase model, are considered to be simulated MAR "benefit."
Table 3.
Description of MAR scenario model simulations
Back-basin
Back-basin
Back-basin
Back-basin
Back-basin
Good
Good
Good
Good
Good
Good
Good
Good
Good
Location
Coastal
Coastal
Coastal
Coastal
Coastal
Coastal
Coastal
Coastal
Coastal
Coastal
Good
Poor
Poor
Poor
Poor
Poor
Poor
50
150
200
800
800
400
800
800
50
50
800
800
800
50
Inf. Rate
(ac-ft/yr) Quantity
50 5
50
50
800
10
10
5
800
150
200
400
800
1200
10
10
10
10
10
10
1200
50
50
800
800
800
800
10
5
10
5
5
10
10
10
10
10
5
5
10
10
10
5
10
5
10
10
5
505
505
674
8085
2695
1347
8085
2695
505
505
8085
8085
2695
505
Inf. Rate
(m 3 /d)
505
505
169
8085
8085
505
674
1347
2695
4043
4043
505
505
8088
2695
8085
2695
4
12
12
4
12
12
4
12
4
4
4
4
12
4
12
4
4
4
12
4
12
4
12
12
12
12
12
Active
(mo/yr)
4
4
12
4
Total
MAR
Water
(m 3 /yr)
3.1
x 10 5
6.2
x 10 5
6.2
x 10 5
4.9
x 10 6
9.9
x 10 6
Total
MAR
Water
(ac-ft/yr)
250
500
500
4000
8000
1.9
x 10 6
2.5
x 10 6
4.9
x 10 6
1500
2000
3.1
x 10 5
4000
9.9
x 10 6 8000
1.5
x 10 7 12000
250
6.2
x 10 5
4.9
x 10 6
9.9
x 10 6
9.9
x 10 6
3.1
x 10 5
6.2
x 10 5
500
4000
8000
8000
250
500
1.8
x 10 6
2.5
x 10 6
4.9
x 10 6
1500
2000
4000
4.9
x 10 6
4.9
x 10 6
4000
4000
9.9
x 10 6
9.9
x 10 6
8000
8000
1.5
x 10 7 12000
3.1
x 10 5
6.2
x 10 5
4.9
x 10 6
4.9
x 10 6
9.9
x 10 6
9.9
x 10 6
250
500
4000
4000
8000
8000
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MAR projects were simulated by adding water to the surface aquifer layer using a headindependent boundary condition. It was assumed that each MAR project existed within a single model cell (6.3 hectares,15.6 acres). Adding water directly to the subsurface did not allow evaluation of how surface properties (slope, land use, and soil infiltration capacity) influenced recharge dynamics, but subsurface storativity, transmissivity and the presence of confining units did govern flow after infiltration.
MAR project scenarios had four variables: (1) project locations, (2) number of projects,
(3) quantity of applied water per project (and in total), and (4) duration of activity during each year. We evaluated the influence of locating MAR projects in four general regions: coastal area
(“Coastal”), back (eastern side) of the basin (“Back-basin”), areas identified as being particularly suitable for MAR (“Good”) and areas identified as being considerably less suitable for MAR
(“Poor”). We expected that the MAR project distance from the coast would have a significant influence on seawater intrusion, so locations for good and poor simulations were selected in pairs such that the sites in each pair were equidistant from the coast. MAR sites in each location group recharge to different layers, depending on which aquifer is exposed at the surface in each location. For example, sites used for MAR in the back of the basin (simulation group Backbasin) recharge directly to aquifer layer A4 (Purisima Formation), whereas sites used for MAR projects based on the most suitable conditions (simulation group Good) are located over a mix of aquifer layers A1, A2 and A4.
Each modeling scenario had either 5 or 10 MAR projects. The rate of MAR-associated recharge applied at individual project sites ranged from 6.2 x 10
4
m
3
/yr (50 ac-ft/yr) to 1.5 x 10
6 m 3 /yr (1200 ac-ft/yr), comparable to the amount of water that might be applied based on stormwater capture of runoff (near the lower end) or based on diversion from major aquatic systems (near the higher end). Water was applied evenly during periods of either 4 or 12 months/yr. The 4-month MAR operation was intended to represent projects that run only during the wet season, when runoff or diversion from other surface water supplies is most likely to be available. MAR projects that use water supplied by a water recycling plant, or water conveyed from higher in the river basin using the Pajaro River, might theoretically operate throughout the water year. This set of model scenarios was not intended to be exhaustive or representative of actual basin management plan scenarios under consideration by local stakeholders (PVWMA,
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2012). Rather, the goal was to assess from a purely hydrologic perspective how MAR project number, placement, and operations could influence groundwater conditions over several decades.
To analyze MAR scenario results, we compared model output of head levels and flows from the ocean into the aquifer below the Pajaro Valley. Changes in head levels were quantified in two ways: 1) for a given time over the entire basin, and 2) at a given location over the duration of the model simulation. The first method was applied to compare MAR scenario groundwater head levels from layer A2 (the volumetrically most significant aquifer layer in the region) during the final time-step to the respective head levels in the Basecase simulation. Using the second method, we selected a single grid cell and extracted the head values in each aquifer layer during six stress periods. Flux of water from the offshore zone to coastal zones was classified as seawater intrusion. With seawater intrusion as an active concern in the study region, it was natural to use coastal flux as a metric for comparing MAR scenarios to the Basecase model.
Model coastal flux values were calculated for each stress period, and then summed to provide flux per year over the entire duration of the model run. Flux values are given for the six model layers combined either as seawater intrusion (flow inland from the ocean) or flow to the offshore zone (loss of water to the ocean).
4. Results
4.1. Distribution of Classified Properties and MAR Suitability
Results from classification of six of the surface and subsurface properties are shown in Figure 5 .
The majority of the surficial geology in the PVGB indicates moderate to good connectivity to shallow local aquifers, except on the floodplain of the Pajaro River system, where there are significant shallow silt and clay layers ( Figure 5A ). Effective infiltration ( I
E
) shows similar breadth of suitable areas, except in urban areas or nurseries, and on the floodplain of the Pajaro
River ( Figure 5B ). As discussed earlier, I
E
was calculated from soil infiltration capacity ( I
C
), land use (roughness) and elevation (slope) ( Figure 3 ). The roughness coefficient, which is based on land use, varies throughout the basin, with urban and turf areas concentrated in and around
Watsonville, CA, near the center of the basin. Urban and turf areas account for 21% of the total area, whereas agricultural fields and pastures account for 41%. The remaining 38% is native vegetation and unfarmed land, predominately located in the higher sloped northern and northwestern edges of the basin. Classified values of effective transmissivity ( T e
) are highly variable
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478 across the basin, illustrating considerable heterogeneity in aquifer properties at a fine scale
( Figure 5C ). The lower values generally correspond to locations with thicker alluvial clay layers. T e
values represent three datasets: aquifer layer thickness, aquifer hydraulic conductivity, and confining layer thickness. Available storage ( V ) is low for much of the lower valley and coastal region, with higher values in the northwest and southeast higher elevation areas ( Figure
5D ). Groundwater levels are generally lower near the coast and along the most northern and western parts of the basin, relative to water levels in 1998, but there is a band of higher groundwater levels that runs north-south through the center and to the southwestern side of the basin ( Figure 5E ). Classification of measured infiltration rates are shown for two reaches on the
Pajaro River and Corralitos Creek ( Figure 5F ).
These and other coverages were combined to generate a map of MAR suitability across the PVGB ( Figure 6 ). This map has a nominal resolution of 10 x 10 m, although resolution of the individual datasets varies considerably ( Figure 5 ). The full spatial extent of the MAR suitability map is limited by the intersection of the extents of all data sets used in the analyses
(228 km
2
). The normalized weights used to integrate the classified datasets are generally low compared to weights for similar data sets used in other peer-reviewed studies ( Figure 2 ). The low weights in the present study result mainly from use of more datasets than were used in the other studies, which reduces the relative influence of any individual dataset.
Calculated MAR suitability index values from across the PVGB range from 6 to 97 (low to high suitability) and appear to follow a roughly normal distribution, with a mean of 22 and a standard deviation of 52 ( Figure 7 ). The upper quartile of this range, comprising land areas being the most suitable for MAR, accounts for 7% of the analyzed land area in the PVGB (15 km 2 ). These areas are located throughout the basin, but are particularly concentrated along the coast north and south of the Pajaro River, inland south of the Pajaro River, and along the eastern side (back) of the basin ( Figure 6 ). The site of the Harkins Slough MAR project ( Figure 6 ), which is permitted to recharge up to 2.5 x 10
6
m
3
/yr diverted water to a perched aquifer, has a
MAR suitability index of 78.
4.2. Modeling the Influence of Distributed MAR Projects Options on Resource Conditions
Thirty one MAR scenarios ( Table 3 ) illustrate how these projects could help to raise aquifer water levels and reduce (or reverse) seawater intrusion, relative to the 34 year Basecase model.
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Unsurprisingly, groundwater levels increased the most in locations closest to simulated MAR projects (e.g. Figure 9 ). The Good location scenario ( Figure 9A ) shows the greatest increase in the northwest part of the PVGB, and produces >1 m head level increase in over 80% of the onshore area. The Coastal location scenario ( Figure 9B ) raises the head levels mostly along the coast, on the western side of the PVGB, and produces a >1 m head level increase across ~60% of the onshore area. There are significantly greater head level increases offshore with MAR projects located in Coastal positions, compared to Good positions.
Simulated benefits to water levels within the aquifer layers vary based on MAR location because water is applied to the exposed surface layer, which differs in properties and geometry
(thickness, extent of connection to deeper aquifer layers) throughout the basin. In general, and at the mid-basin location ( Figure 8 ), head levels increase by similar amounts in the shallowest aquifer layers, A1 and A2 ( Figure 10A and 10B ), with coastal and MAR-good model scenarios showing the most long-term improvement. Increases in head within the deepest aquifer layer,
A4, are more similar for the four sets of MAR project locations ( Figure 10C ), and net benefit is generally lower than seen in shallower aquifers.
For all tested scenarios, simulated MAR projects reduced seawater intrusion compared to the Basecase, with the benefit increasing overall with time ( Figure 11 ). There is a period of abrupt reduction in the extent of seawater intrusion, between simulation years 21 and 27, which coincides with a period of increased precipitation and reduced groundwater pumping. The location of simulated MAR projects has a notable effect on the magnitude of reductions in seawater intrusion. The greatest benefit is achieved by simulating MAR projects in the MARgood locations, followed by Back-basin, Poor, and Coastal, in order of decreasing benefit
( Figure 11A ). Though the rate of change of benefit varies with time, the benefit in the Good scenarios increases approximately twice as quickly as that for the Coastal scenario over the 34 year simulation. MAR project location also affects the quantity of flow from the aquifer to the ocean, and the rate of change over time ( Figure 11B ). The increase in flow to the ocean is greatest early in the model runs and subsequently decreases over time, as head levels rise and/or more water is extracted from the basin by pumping. The Coastal scenario results in the greatest increase in flow to the ocean compared to the Basecase, followed in order by Good, Poor and
Back-basin scenarios.
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The reduction of seawater intrusion varies with the amount of water applied in simulated
MAR projects, and as a function of project location and the passage of time. As expected, increasing water applied to the system through simulated MAR projects results in larger reductions of seawater intrusion along the coast ( Figure 12 ). But increasing the amount of MAR water applied at Coastal locations ( Figure 12A ) produces a smaller reduction in seawater intrusion than does increasing the amount of MAR water applied at Good locations ( Figure
12B ). The difference in benefit between the two MAR project locations groups is minimal for the lowest applied water rate (1.5
x 10 6 m 3 /yr), and increases to ~50% greater benefit at the Good
MAR locations for the highest applied water rate (1.5
x 10
7
m
3
/yr).
We divide reduction in seawater intrusion by the total applied water to measure MAR efficiency ( Figures 12C and 12D ). For a given MAR location group, the efficiency is approximately equal for the full range of applied water quantities at time = 1 yr. In other words, the initial benefit is linearly related to the amount of MAR water applied to the surface. For example, with MAR projects in Good locations, the seawater intrusion reduction efficiency is approximately 1.5% of the total water applied for any given amount of water ( Figure 12D ). Over time, the efficiency increases at a rate dependent on the amount of applied water, where scenarios with less applied water show the greatest increase in efficiency.
Changing the number of MAR projects from 5 to 10 appears to have an influence similar to that of doubling the total applied water, although locations of the additional 5 MAR projects are likely to influence specific results. If the additional projects have a different average proximity to the coast and/or MAR suitability index, then their influence on seawater intrusion will be different than simply doubling the total applied water.
The intra-annual duration of MAR operations has minimal effect on the reduction of seawater intrusion over the full (34 year) simulations. The scenarios active for 4 months and 12 months per year have nearly identical influence on seawater intrusion for the first 20 years of the model simulation, then the projects active year-round tend to have an impact ~5 to 8% greater than do the projects operating only 4 months per year (assuming the same amount of total MAR water is applied).
5. Discussion
5.1. Classification and merging of GIS data sets
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There are no "standard" practices for classifying indices used to assess suitability for managed recharge. Most peer-reviewed, GIS-based studies completed to assess recharge properties and processes have emphasized natural or incidental recharge, rather than MAR ( Figure 2 ). Each of these studies used a different weighting system for combining disparate datasets, and few earlier studies attempted to test the results of GIS-based analyses for accuracy or applicability. We attempted to address this latter issue, in part, by linking the GIS analysis to deterministic modeling, discussed below, though this approach cannot confirm the "correctness" of regional interpretations.
One approach for development of a suitable weighting system for applying GIS data is to generate a suitability map that follows a desired distribution (e.g., normal, log-normal). If the fundamental goal is to distinguish between the relative suitability of candidate field sites within a basin, this approach could be useful, allowing clear delineation of land areas having characteristics of a desired percentile of analyses (top 10%, best 100 hectares, etc). On the other hand, application of different data sets and methods for combining them could lead to challenges in comparing results from multiple basins, particularly if there are fundamental differences in subsurface geology, weather patterns, land use, and other factors. In either case, using the GIS analyses can guide or inform (rather than dictate) MAR placement as a component of critical water resource decisions. The wide distribution of areas amenable for MAR projects may encourage more landowners to participate in distributed recharge enhancement efforts, not just those who are experiencing the consequences of aquifer overdraft.
Our data integration approach differed from those taken in earlier studies, in that we combined several data sets to generate interim interpretations of effective properties. Effective infiltration capacity encompasses the relationship between traditional soil infiltration capacity, ground slope, and surface roughness. We reasoned that a greater slope and smoother land surface would serve mainly to reduce the relative rate of infiltration, given intrinsic soil properties. Slope and roughness should have less influence for soils that have a low infiltration capacity, but these factors could result in a larger reduction in infiltration through highly permeable soils. Similarly, we calculated effective transmissivity values for a series of aquifer layers, by summing all (or part of) the values of individual layers from the surface downwards until a significant confining unit was encountered. A more traditional approach for calculating transmissivity could either under-represent effective values of areas where there are multiple (partly confined) aquifer units,
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620 but only the shallowest is assessed, or over-estimate transmissivity if the presence of shallow confining layers were ignored. Our calculations suggest the highest transmissivity values, with respect to potential MAR projects, are located around the margins of the PVGB, but there are also areas of moderately high transmissivity near the center of the basin, north of the Pajaro
River ( Figure 5C ). Different aquifer layers are responsible for these high transmissivity values: the shallow alluvial and Aromas aquifers (A1 to A3) near the western and central parts of the basin, and deeper Purisima (A4) aquifer to the west and north ( Figures 4 and 5 ).
5.2 Integration of GIS analyses and numerical modeling
This study was designed from the start to link GIS-based assessment of MAR suitability to results from a regional numerical model. Several of the surface and subsurface datasets used for the GIS analysis were created originally as part of regional model development. This was fortunate both in terms of the effort that would have been required to gather and classify each dataset independently, and to co-register spatial data and modeling domains, but also for making sure that data and assumptions applied for the GIS analyses were not violated when moving to the modeling stage of the study. Resource managers and stakeholders in many groundwater basins have access to similar datasets, although their resolution, accuracy, and completeness is highly variable. The availability of a detailed and up-to-date regional groundwater model that can be run on the basis of a GIS-based analysis of MAR is more unusual.
Modification of the PVGB model to include MAR projects facilitated evaluation of the relative influence of major MAR characteristics, including project location, number of projects, amount of water applied, and duration of operation through the year. The original model included the Harkins Slough MAR project, and was calibrated to PVGB conditions under past and existing climatological and water use conditions. Results presented in the current study, evaluating hypothetical MAR projects in comparison to a baseline model, are thus most useful in assessing relative impacts.
5.3. Implications for MAR in the Pajaro Valley
Our GIS-based analysis of MAR suitability shows considerable variability throughout the
PVGB, on the basis of eleven physical characteristics ( Figure 6 ). The most prominent feature in the final MAR suitability map is the Pajaro River floodplain, which has relatively low MAR
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632 suitability primarily due to soil infiltration and surficial geology classifications. This is not surprising given that floodplain lithology tends to comprise silt and clay lithologies which limit surface infiltration. The GIS analysis would result in assigning similar properties to the bed of the Pajaro River, but recent field studies documented streambed losses on the order of 1 m/d along part of the river near the back of the basin (Ruehl et al. 2006; Hatch et al. 2010). This discrepancy illustrates another limitation of the GIS-based analysis, within which the assessment of soil properties is based on surveys near, rather than in, the active channel. In this case, there is heterogeneity in lithology and in hydrologic behavior that is not captured by regional soil property maps.
We can assess GIS results with respect to an active MAR project in the PVGB operated by the PVWMA which recharges approximately 10
6
m
3
/yr (Racz et al. 2011; Schmidt et al.
2011). We define this as a successful MAR project, and therefore characterize the location as highly suitable. The projected MAR suitability index based on the GIS analysis for this site is 78
( Figure 6 ), an index value met or exceeded by 4% of the basin (8.7 km
2
). These highly suitable areas are distributed throughout the basin. There would need to be ~15 similarly performing
MAR projects to offset annual overdraft in the PVGB (PVWMA, 2012), but this would equate to only ~4.8% of the land area that was classified as equal to or more suitable for MAR than the
Harkins Slough recharge project, or 0.19% of the total area analyzed. Of course, this assessment does not account for surface water availability to supply MAR projects, and that may be the most important limiting factor in applying this approach. The new Basin Management Plan emphasizes conservation and water recycling, in additional to MAR, and development of a portfolio of approaches is most likely to be successful in this setting. Nevertheless, knowing about the potential for development of MAR projects, based on an assessment of surface and subsurface conditions, can assist in efforts to bring the basin back into hydrologic balance.
Model results showed that MAR project location, amount of applied water, and years of operation affect groundwater conditions in different ways. Projects located close to the coast provide the greatest immediate benefit through reduction of seawater intrusion, but after ~3 years, seawater intrusion reduction is greatest for scenarios that place MAR projects throughout the PVGB or in the back (East side) of the basin. Also, as the amount of water recharged increases over time, the project efficiency (defined by reduction in seawater intrusion per unit of
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662 water recharged) decreases due to flows to the ocean. These offshore flows may help to improve groundwater quality, a parameter not assessed in this study.
Results show that the benefit from MAR projects varies depending on which evaluation metric is used (groundwater rise or seawater intrusion reduction), and for the former, where the metric is applied in the basin. MAR projects located at coastal sites result in the largest groundwater head increase along the coast ( Figure 9 ), but also the lowest long-term seawater intrusion reduction ( Figure 11A ). MAR projects located where there is the highest suitability based on the GIS analysis are most effective at reducing seawater intrusion, and on average are farther from the coast than Coastal MAR projects. This illustrates the importance of assessing both surface and subsurface properties and conditions when comparing locations for MAR projects.
Comparing results from the Good and Poor scenarios provides confidence in the applicability of the MAR suitability map. There was negligible difference in aquifer head levels for the two simulated location groups of MAR projects. However, on average, there was a 25% greater reduction of seawater intrusion for MAR projects located in areas identified as highly suitable in the GIS analysis ( Figure 11 ). The inefficiency of Poor MAR projects located in unsuitable locations might be partially because they tended to recharge mainly layer A1. With seawater intrusion occurring in all aquifer layers in the PVGB, it is beneficial to distribute MAR projects among the regions where each aquifer is unconfined, in addition to selection by suitability index. For example, if all MAR projects recharge to layer A1, where the presence of underlying confining units restrict downward flow, layers A2 and A4 might only have limited reduction of seawater intrusion. Note that Poor sites were not intentionally selected in areas where layer A1 is unconfined; rather, sites with poor suitability often correspond to locations where the alluvial layer contains low permeability flood plain deposits.
Modeled groundwater levels indicate that confining units do not restrict all flow between aquifer layers, though flow between geologic layers is limited in some parts of the basin. While groundwater head levels increase the most in the layer being recharged, modeled head increases also occur in under- and over-lying aquifers. For example, the Back-basin MAR projects recharge to A2, A3 and A4, but the head levels also increase by ~0.3 m in A1 near the City of
Watsonville over the 34 year model simulation ( Figure 10A ). With the majority of groundwater extraction occurring in layer A2 (Hanson et al. 2013), having discontinuous confining units to
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5.4. Study limitations and next steps
Several critical factors are not accounted for in the GIS and modeling analyses, including water availability, solute sources and transport, unsaturated zone transport, site access, land use and climate change, sea level rise, and proximity to areas that are already intruded by seawater intrusion. These factors should be considered as evaluations are done to identify locations for field and pilot testing. The GIS analyses were not intended to be the primary basis for making placement and operational decisions for MAR project sites.
This study does not formally evaluate water availability for MAR projects. The analysis shows many areas in the PVGB that are highly suitable for MAR, especially near the coast, which coincides with the blending and distribution system developed for use of water from the recycling plant. Locations in the Back-basin could use runoff from adjacent hills as a recharge water source.
Numerical model results suggest that placement of MAR projects according to the GIS suitability index provides the greatest reduction of seawater intrusion along the coast. The quantity of water applied using MAR is proportional to the long-term benefit. However, in this water-stressed area, it will be necessary to optimize the quantity of water applied with respect to desired reduction in seawater intrusion. Larger applied quantities of water will provide a greater benefit, though at a lower efficiency than smaller applied quantities of water. Water availability will likely govern the quantity of applied water on an annual basis.
The current model does not include solute sources or advection, and therefore cannot estimate the influence of recharge on salinity of the seawater intruded areas or overall water quality elsewhere in the basin. Future studies could add solute transport capabilities to the MAR suitability assessment. For example, placing MAR projects within the seawater intruded area might be a feasible option for reducing the rate of future intrusion, but might not have a strong enough influence on water quality benefit to allow extraction from areas that are already intruded. Conversely, recharging onto a local perched aquifer above the seawater intruded area can provide an alternate source for users, allowing coastal farming to continue and reducing
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723 demand on overdrafted aquifers below (Racz et al. 2011; Schmidt et al. 2011). Recharging to a perched aquifer was not considered an advantage, as in this particular case, in the GIS analysis.
The model does not include unsaturated flow, which is important for understanding aquifer recharge. Within the model, water applied to the surface moves immediately down to the uppermost saturated cell. Results from studies of the Harkins Slough MAR project suggest that infiltrating water may recharge the shallow aquifer in just a few days (Schmidt et al. 2011). At least in this location, the assumption of instantaneous transport through the vadoze zone may be reasonable when using month-long stress periods. However, this approximation could lead to errors in assessing MAR projects located where infiltration rates are slower.
The groundwater model uses the Farm Process (Schmid and Hanson 2009), which varies the amount of water pumped based on land use, climate and water availability. As MAR water recharges the aquifer, groundwater availability increases, and this allows an increase in pumping.
The rate at which pumping increases is modest relative to the rate of recharge. However, if higher head levels due to MAR cause pumping in the model to increase beyond realistic projections, then estimations of seawater intrusion reduction would be conservative. One could disable the Farm Process and fix factors such as natural recharge and pumping, but this could result in model scenarios that are less realistic; land use and climate are expected to vary year by year, and these changes influence the locations and rates of groundwater extraction. For example, there is an eight-year increase in seawater intrusion reduction starting in model year 21 for all
MAR scenarios ( Figures 11 and 12 ), largely in response to a modeled wet climate period and associated reductions in water use. This model response also shows how climate and changes to pumping can compound the benefit provided by simulated MAR projects. But for these reasons, we consider model results mainly in the context of relative benefits from MAR scenarios, rather than quantitative predictions. Indeed, the scenarios tested in this study deviate considerably from those explored in the most recent Basin Management Plan (PVWMA, 2013). The latter focused on realistic project alternatives based on physical, chemical, economic, and social factors, many of which were not explored in this paper. Narrowly focused scenarios presented in this paper should not be viewed as alternatives to those proposed in the Basin Management Plan.
The next step in determining where to implement MAR projects is field testing soil infiltration properties at locations that have been identified as suitable for MAR by the GIS and numerical modeling analyses. Assessing sites for MAR is a complicated problem, as evidenced
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773 by the number of studies and variety of methods. We produced a MAR suitability index map, and show relative impacts on seawater intrusion and groundwater levels, though this must be an iterative process. Field testing and MAR implementation are required both to reduce groundwater overdraft and to help calibrate the suitability mapping method. With more field data, and comparisons to similar results from other study areas, the GIS-based integration method will become more robust for use in this region and in other groundwater basins.
6. Conclusions
This paper proposed a physically based, GIS integration method for identifying locations that may be suitable for MAR projects, and quantified the relative benefit of such projects using a numerical model. We developed a method that allows data to be combined using traditional approaches (overlying coverages and adding indices) and by allowing some properties to operate on other properties before coverages are combined. We propose that this method can provide a more accurate understanding of relationships between geology, hydrology, and managed groundwater recharge. Results suggest that ~15 km
2
of the PVGB may be highly suitable for
MAR projects, as delineated by having a suitability index in the upper quartile of the quantitative range. Using a numerical model to simulate MAR projects, we show that project sites on high
MAR suitability areas could reduce seawater intrusion to a greater extent than if MAR projects were located on low suitability areas. Modeling suggests that reducing seawater intrusion is achieved most efficiently with MAR projects distributed throughout the PVGB in highly suitable locations, rather than focusing only along the coast.
Groundwater development is expected to continue increasing around the world, providing significant economic benefits and maintaining food security for the growing population.
Unfortunately, the financial returns from increased agriculture and industrial development are rarely used towards water management, resulting in declining water tables, reduction in groundwater storage, and water quality issues. MAR projects may contribute towards sustainable groundwater use as a low-cost, low-maintenance, and potentially distributed method. This paper illustrates a nuanced approach for identifying suitable locations for MAR projects, and for determining the relative impacts of various recharge project scenarios using numerical modeling.
Careful field studies and assessment of implemented projects will be required to test and refine these conclusions.
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Acknowledgements
We thank Mike Cloud, Michael Cahn, and Marc Los Huertos for their thoughtful advice on land use and Pajaro Valley geology and hydrogeology. This work was supported by the National
Science Foundation Graduate Research Fellowship Program (ID# 2009083666), the National
Institute for Water Resources (Grant 08HQGR0054), and The Recharge Initiative
(rechargeinitiative.org).
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Figure 1 Location of the Pajaro Valley, CA, with extent of seawater intrusion measured in
2001(Hanson 2003), elevation and major streams. Area shown is the local water management’s
(Pajaro Valley Water Management Agency) boundary of operation. The Harkins Slough MAR project is indicated with a square, and the mid-basin measurement point used in the modeling section is indicated with a white circle. Add slough shapefile.
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Figure 2.
Comparison of dataset weights used in other studies to map groundwater recharge with a GIS. The normalized weights used in this study are shown in orange. Values shown for land use and slope are calculated means of values used, because these data sets were used as modifiers for other data sets, as discussed in the text.
Replace with Andy’s new B&W version.
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Figure 3 Example calculated effective infiltration ( I
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Figure 4 Pajaro Valley Hydrologic Model (PVHM), (A) map view of model domain showing grid cells, (B) cross section showing model layers along transect A-A’. Modified from (Hanson et al. 2013).
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Figure 5 Classified surface and subsurface properties used to determine relative MAR suitability. (A) surficial geology, (B) effective infiltration, (C) effective transmissivity, (D) storage availability, (E) change in groundwater elevation (2010-1998), (F) measured streambed infiltration.
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Figure 6 Map of relative MAR suitability determined by GIS-based integration. The location of the existing Harkin Slough MAR project is indicated with a circle.
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Figure 7 Histogram of the MAR suitability index values for the PVGB. The suitability index value of the Harkin Slough project site is 78, which represents field tested managed recharge of approximately 10 6 m 3 /yr. Four percent (8.7 km 2 , 2160 ac) of the PVGB has similar or higher suitability index values.
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Figure 8 MAR scenario location groups shown on the MAR suitability index map. Figure 4 shows the spatial extent of the model domain. Ten site locations are shown for each of the four groups: Coastal, Back-basin, Good, and Poor. Head levels were compared to the Basecase simulation at a location in Watsonville ( Figure 10 ), indicated with a filled black circle. Add midbasin monitoring location.
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Figure 9 Increase in head levels in Layer A2 at model yr-34 relative to the Basecase due to MAR projects simulated in Good Run-22 (A) and Coastal Run-8 (B) locations. Both scenarios have 10
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Figure 10 Increase in head levels near the center of the PVGB (location shown in Figure 1 ) relative to the Basecase due to MAR projects simulated in four regions of the basin, (A) in Layer
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Figure 11 Benefits relative to the Basecase due to MAR projects simulated in four regions of the basin, respectively, for (A) Reduction of seawater intrusion shown versus time, and (B) Increase in flow to offshore zone shown versus time. Each scenario has 5 MAR projects, applying 9.8
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Figure 12 seawater intrusion reduction relative to the Basecase due to simulated MAR projects with varying rates of total applied water. Each scenario uses 10 MAR projects operating 12mo/yr, located at (A) Coastal sites, and (B) Good sites. The efficiency, with respect to seawater intrusion, is shown for MAR projects located at (C) Coastal sites, and (D) Good sites.
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