Research Plan
Project Summary
This project aims to improve our understanding of the ecological and hydrological
functioning of cloud forests in Puerto Rico’s National Caribbean Forest. These unique
ecosystems are tightly coupled to regular cycles of cloud inundation, and thus are likely
to be quite sensitive to global changes like tropical ocean warming and land use/cover
changes. Water isotope tracers can provide a powerful constraint on cloud inputs to these
forests and local watersheds, since the isotopic signature of cloud water is typically
distinct from that of rainfall. Samples collected from cloud water, rain water, soil water,
and stream water, as well as tree and epiphyte water, will be collected and analyzed for
their hydrogen and oxygen isotopic composition. The goal of this research is to answer
the following questions:
1) How much of the cloud forest water budget comes directly from cloud
inundation on a seasonal and interannual basis, and how does this vary by
forest component (canopy epiphytes, trees, understory vegetation)?
2) How much does cloud water contribute to the water budget of local
watersheds and communities, and Puerto Rico as a whole?
3) Can isotopic tracers provide quantitative evidence for changes in cloud
inundation cycles and cloud forest hydrology associated with either ocean
warming or lowland deforestation?
4) How will climate change and land use/cover change affect the cycles of
cloud inundation, and how does this ramify through the forest and human
communities?
Figure 1. Location of the Caribbean National Forest within Puerto Rico.
Isotopic sampling of cloud water, precipitation and stream flow will be compared
extensively with remote sensing data provided by the suite of instruments on NASA’s
earth observing system (EOS) satellites (Terra, Aqua, and TRMM). Several of these
sensors (MODIS, CERES, and MISR) provide useful cloud products, including cloud
amount, altitude, and thickness. The MISR cloud products will be especially useful, as
the multi-angle imaging provides unprecedented information on cloud structure that can
be related to cloud water inputs and precipitation over Puerto Rico. Vertical profiles of
temperature and humidity from the AIRS/AMSU/HSB family of sensors will be
compared with orographic cloud formation patterns at El Yunque. Ocean surface
temperature, water vapor, and cloud liquid water content over the Caribbean basin
derived from AMSR-E will be compared with cloud water variations (amounts and
isotopic ratio) at El Yunque to better understand synoptic conditions affecting the cloud
forest.
The modeling component of the study will focus on questions number 2 and 4
above. To investigate the hydrology of local watersheds and Puerto Rico as a whole, we
propose to combine the Regional Atmospheric Model (RAMS) with models dealing with
other aspects of the climate system. Our unique modeling approach is to couple the
RAMS/LEAF-2/TOPMODEL system (Walko et al., 2000) to MODFLOW (McDonald
and Harbaugh, 1984), a fully three-dimensional, multi-layer groundwater flow model. In
our approach, we will explicitly simulate the interchange of water between multiple
aquifer systems where significant aquifer systems exist. Where significant deep aquifer
systems do not exist, we will utilize TOPMODEL to simulate shallow hill slope
groundwater flow. Another innovative aspect of this research is that we will simulate the
complete hydrologic cycle for the entire island of Puerto Rico, which has never been
attempted. The coupled modeling system will allow us to evaluate the effect of the
Luquillo Experimental Forest (LEF) on the hydrology of Puerto Rico (PR). The
modeling system is already under development.
The education component of this proposal will execute a three-year program of
undergraduate and graduate education at UCSB and UPRM, along with aggressive
community outreach through UPRM and the US Forest Service El Portal Tropical Forest
Center. An integral aspect of the education will be developing a short course at UCSB on
cloud forest ecology, climate change, and isotope hydrology. Ideally this will include
development of a Spanish language curriculum on global change and cloud forest
ecosystems with specific focus on the Caribbean and Central America, for eventual use at
UPRM. Another emphasis will be to work with the US Forest Service El Portal Tropical
Forest Center to design an exhibit on cloud forests, cloud water, and climate change for
visitors to El Yunque.
1. Introduction and Background
Tropical montane cloud forests (TMCFs) are unique among terrestrial ecosystems
in their tight coupling to the atmospheric hydrologic cycle. This coupling is accomplished
partly through regular cycles of inundation by orographic cloudbanks at the forest
elevation, and the moisture inputs from such cloud inundations are a significant fraction
of annual rainfall in many cloud forests (Bruijnzeel and Proctor 1995; Clark et al. 2000).
The unique environment produced by these cloudbanks has contributed to the high
species diversity and endemism found in these forests, which are critically endangered
throughout the tropics (Stadtmuller 1987; Hamilton et al. 1995; Haber 2000). Because
orographic cloud formation is determined by such processes as ocean evaporation and
vertical atmospheric profiles of temperature and humidity, it is strongly sensitive to
climate change (Still et al. 1999; Pounds et al. 1999). Acceleration of the tropical
hydrological cycle via enhanced ocean temperatures is expected to change these profiles,
with concomitant impacts on lapse rates and freezing surfaces (Diaz and Graham 1996).
Indeed, enhanced atmospheric warming with height (decreasing lapse rate) has been
observed over the tropics (Gutzler 1992), and another analysis suggests an enhancement
of the tropical hydrological cycle in recent decades (Flohn and Kapala 1989). Gaffen et
al. (2000) showed decreasing trends in the lapse rate over 1960-1997, as derived from
radiosonde observations, and consistent with enhancements to the tropical hydrological
cycle, although the reverse trend occurs from 1979-1997. Finally, climate model
simulations driven by recent tropical sea surface temperatures (SSTs) reproduce observed
tropospheric warming via an enhancement in the tropical hydrological cycle and
increased latent heat releases (Graham 1995).
Climate model simulations of doubled CO2 conditions also suggest an
enhancement in the tropical ocean evaporation, with impacts on vertical profiles of
temperature and humidity. Taking the grid-box average relative humidity surface at
current cloud forest elevations as a proxy for cloud formation height, Still et al. (1999)
showed that the elevation of this surface increases hundreds of meters under doubled CO2
conditions in the winter season for four regions containing well-known cloud forests. If
this proxy is reasonable, the height of cloud formation would also rise, and thus adversely
affect cloud forests, in particular those already located on mountaintops or ridgetops.
Indeed there is already evidence at the well-studied cloud forest in Monteverde, Costa
Rica, of a lift in cloud base height during the dry season. This has driven a drying trend,
which has been linked to anuran extinctions, and is strongly correlated with tropical sea
surface temperature variations (Pounds et al. 1999). In addition to climatic effects
accompanying tropical ocean warming, lowland deforestation and consequent changes in
the surface energy balance and evapotranspiration may also contribute to the cloud base
rise and drying trend observed at Monteverde (Lawton et al. 2001). These authors show
an effect on both convective and orographic cloud formation resulting from deforestation,
such that these clouds have lower cloud water mixing ratios and higher cloud bases. They
also present satellite data (Landsat and GOES) showing reduced cumulus cloud
development over upwind, lowland deforested areas in Costa Rica (Lawton et al. 2001).
Despite the significant water inputs from cloud inundation and the potential
changes in cloud formation regimes from tropical ocean warming and deforestation, the
reliance by forest vegetation on cloud water is poorly understood. To date, the vast
majority of studies in cloud forests examining cloud water inputs have constructed water
budgets of the inputs and outputs (Bruijnzeel and Proctor 1995). The inputs from cloud
water are typically given as a fraction of the precipitation amount, either on an annual or
seasonal basis. The cloud inputs are most important in the dry season, when convective
rainfall is suppressed. However, very few studies have demonstrated or attempted to
quantify how cloud water inputs contribute directly to the water status of forest
vegetation, on either a seasonal or interannual basis. The study of Field and Dawson
(1998) in the Monteverde cloud forest is a notable exception. This work illustrated the
power of isotopic tracers in delineating the reliance on fog water by different forest
components. This is possible because of the different isotopic composition of fog and
mist (so-called ‘horizontal precipitation’) versus rainfall. They showed that early life
stages of a forest pioneer hemiepiphytic species rely almost exclusively on fog water,
while later stages rely on soil water.
Another well-studied cloud forests exists in El Yunque Caribbean National Forest
of PR, which contains the LEF, an NSF Long-term Ecological Research (LTER) site. The
cloud water contribution to forest hydrology is significant, accounting for 10% of mean
annual rainfall, which is 4.9 m/year at the highest elevations (Scatena LTER site). The
cloud water contribution is undoubtedly much greater during the dry season. The
contribution of cloud water to downstream watersheds and PR as a whole is unknown.
However, El Yunque is critical to the water supply of PR. At present, 20 % of the island’s
population depends on water from the forest (USFS web site), and this is likely to
increase as development and population pressures increase. Data on cloud water inputs to
forests and streams in this system will be used to better understand regional hydrological
cycles and to predict the impacts of climate change that affect cloud formation over El
Yunque’s forests.
2. Project Objectives and Strategy
The objective of this research is to use water isotope tracers to enhance our
understanding of the ecological and hydrological function of the cloud forest at El
Yunque. This includes understanding the contribution of cloud water to forest
components (epiphytes, trees, understory vegetation) and to downstream watersheds used
by human communities. Critical to this effort is obtaining samples over several years to
understand how seasonal and interannual dynamics in cloud processes related to synoptic
atmospheric and oceanic conditions influence cloud water inputs. This understanding will
be employed to predict changes to this ecosystem resulting from the suite of global
changes including climate change and land cover conversions.
This project will focus on the cloud forest within the LEF. Isotopic data will be
collected from the various inputs (cloud water and precipitation), stocks (vegetation water
and soil water), and outputs (stream flow). Mixing models will be used to partition
cloudwater usage by vegetation components (epiphytes, hemiepiphytes, understory
plants, and trees) on seasonal and interannual bases. This project will take advantage of
the numerous ancillary data collected within LEF, including information on forest
ecology and biogeochemistry, as well as meteorological and stream flow data collected in
LEF and in adjacent watersheds by the LTER researchers and by the USGS Water
Energy and Biogeochemical Budgets program.
3. Field Sampling
Isotopic tracers in the hydrological cycle will play a central role in this research.
Horizontal (wind-blown mist, fog and cloud water) and vertical precipitation, vegetation
water, stream water, and soil water will be collected to assess water inputs, outputs and
stocks. Clouds and mist will be sampled on a weekly basis with collectors built in the
laboratory (e.g., Dawson 1998; Scholl et al. 2002). Precipitation will be sampled on a biweekly basis using a bottle and attached funnel, filter, and venting tube designed to
minimize evaporative enrichment (Dawson 1998; Riley et al. 2003). Vegetation water
(epiphyte tanks, tree stem water, and understory stem water) will be collected during
intensive field campaigns throughout the year. During these campaigns, soil water will be
collected from soil cores or soil pits dug in the forest floor. Local streams and springs
will be sampled with lower frequency (2-3 times/year). All water samples will be
analyzed for their hydrogen and oxygen isotopic composition at the UPRM Geology
department stable isotope facility.
Although stream water and soil water will be sampled for isotopic analysis and
compared with cloud water inputs to look at their contribution to downstream watersheds,
this will not be a primary focus of the isotopic work. However, stream water isotopic data
offer potential constraints on the modeling, which will simulate the hydrological cycle on
the entire island, including groundwater flow. This approach was demonstrated by Scholl
et al. (1996, 2002), who used isotopic tracers in interpreting regional hydrology and
quantifying cloud water inputs on the islands of Hawaii and Maui.
4. Remote Sensing Data
This project will benefit from NASA’s intense focus on remote sensing of cloud
properties and dynamics for reducing uncertainty attending the role of clouds in the
climate system. The array of sensors onboard NASA EOS satellites, primarily Terra,
Aqua, and TRMM, will provide a large-scale, top-down context for understanding cloud
inputs to El Yunque. As the higher-level products become available, we will incorporate
them into our analyses. We anticipate that several MODIS cloud products will be
especially useful, including cloud amount, altitude, and thickness. The MISR cloud
products will be especially useful, as the multi-angle imaging provides unprecedented
information on cloud structure that can be related to cloud water inputs and precipitation
over PR.
Dry season cloud formation in El Yunque is driven by evaporation over the ocean
and subsequent orographic uplift as air is advected over the island. The lifting
condensation level (LCL) of these orographic clouds is determined by the water content
of this air, along with vertical atmospheric profiles of temperature and humidity. All of
these variables are obtainable from the sensors on Aqua. Vertical profiles of temperature
and humidity will be taken from the AIRS/AMSU/HSB family of sensors. Ocean surface
temperature (including regions under cloud cover), water vapor content over the ocean,
and cloud liquid water content will be taken from AMSR-E products. These quantities
will be used to better understand synoptic conditions that contribute to cloud water
variations (amounts and isotopic ratio) sampled in El Yunque.
5. Modeling of Puerto Rico Hydrology
The objectives of the hydrologic modeling component of the study include:
1. Develop a meso-scale atmospheric/land/groundwater model that could simulate
the hydrological balance of the island.
2. Calibrate the models for PR conditions.
3. Validate the models.
4. Conduct simulation studies of the hydrological cycle on PR, specifically,
 How much does cloud water contribute to the water budget of local
watersheds and communities, and PR as a whole?
 How will climate change and land use/cover change affect the cycles of
cloud inundation, and how does this ramify through the forest and human
communities?
The approach will consist in integrating all possible positive and negative
contributors including synoptic and orographic cloud formations, soil storage, surface
runoff, local anthropogenic and vegetative consumption, evapotranspiration, and
groundwater flow into a common system at a regional level. The investigation will
include a statistical analysis of the monthly and annual water balance in the island based
on reported observations by the USGS, the National Weather Service, and local water
distribution companies. A second major task will be to develop a meso-scale
atmosphere/land/groundwater model that simulates the hydrological balance of the island.
This modeling effort will be validated with results from the analysis and will be used to
predict future events in which local and large-scale events will be present, with special
emphasis on the PR cloud forest. An interdisciplinary team that includes climatologists,
hydrologists, remote sensing, and computational fluid dynamics experts has been
assembled to address this challenging but yet interesting problem.
Specific models that will be coupled include:
o RAMS – Atmospheric processes
o LEAF-2/TOPMODEL – Near surface processes (i.e., soil moisture, runoff,
evapotranspiration, subsurface hill slope moisture transport )
o MODFLOW – Groundwater flow
Figure 2 shows how the various models will work together.
Water that
discharges to
surface water
bodies
RAMS
Water and
energy fluxes
Water and
energy fluxes
LEAF-2
Water that
discharges to
surface water
bodies
Grid cell is located within
Interior Mountain area
TOPMODEL
Grid cell is
located withing
coastal or karstic
limestone area
Aquifer
Recharge
Calculation
MODFLOW
Figure 2. Flowchart of numerical models used to simulate Puerto Rico water
balance.
RAMS will be the simulation tool for the atmospheric component. It is a highly
versatile numerical code developed for simulating and forecasting meteorological
phenomena. It consists of three major components, 1) a data analysis component, 2) an
atmospheric model, and 3) a post-processing component. The data analysis component
prepares the data for model initialization and nudging from observed meteorological data.
The atmospheric model is built around the full set of non-hydrostatic, dynamical
equations that governs atmospheric dynamics and thermodynamics, plus conservation
equations for scalar quantities like mass, water vapor, liquid and ice hydrometeor mixing
ratios. These equations are complemented by a large selection of parameterizations
available in the model. Pielke et al. (1992) describes the data analysis technique
available in RAMS. The data analysis for the initial and boundary conditions is as
follows. An isentropic analysis interpolates the pressure data in the vertical direction to
specified isentropic levels, and horizontally interpolates this data to the higher resolution
grid to be used in the simulation. Then the vertical isentropic data set is interpolated to
the model, to obtain a full set of prognostic fields for model integration. The transient
data introduced in the model is the pressure level data provided by the National Center of
Environment Prediction (NCEP) at 2.5 degree of resolution. In this study the model will
include three nodes of 20, 5 and 1 km to avoid possibly instabilities.
We plan to make full use of remotely sensed (RS) data, which will be obtained
from NOAA and NASA via the Internet, and from the Direct Broadcast (DB) station at
UPRM. RS products will be used for both model calibration and validation in situations
where there are either sparse or no appropriate in-situ data. The local DB station is called
the Space Information Laboratory (SIL) and is a component of the NASA-funded
Tropical Center for Earth and Space Studies (TCESS). One of SIL’s main functions is to
support projects at UPRM that require RS data over the Caribbean region. The SIL
collects AVHRR, SeaWiFS, MODIS, Radarsat, and Landsat 7 data. SIL is able to
process AVHRR data to level 3, and SeaWiFS and MODIS to level 2. At present SIL
cannot process Landsat and Radarsat data beyond level 0. This situation could change by
2003 if licensing negotiations are successful.
The major tasks associated with configuring LEAF-2 and TOPMODEL model
include:
o Delineation of Land Cover. This work will utilize a variety of sources of
data including remotely-sensed data for PR. Remotely-sensed data may be
obtained from the Moderate Resolution Imaging Spectroradiometer
(MODIS), the Landsat Multispectral Scanner (MSS), the Landsat
Thematic Mapper (TM), and/or the Calibrated Airborne Multispectral
Scanner (CAMS). Although air photos are generally too detailed for the
scale of this modeling project, they will be used in some cases where
remote sensing techniques are inadequate.
o Delineation of Soil Type. A soil GIS has been previously developed for
PR and will be used in this project. The GIS database will provide soil
hydraulic properties (e.g., layer texture, permeability, bulk densities, water
holding capacity, layer thicknesses, etc.)
o Topography. Development of slope directions will be obtained from the
USGS DEM (Digital Elevation Model) for PR. Slope directions will be
determined within ARC/INFO/ArcView.
o Delineation of Watershed Boundaries and Stream Network.
Watershed boundaries and the stream network will be delineated using
USGS Digital Line Graph (DLG) files.
Data from hydrologic models previously developed in PR (e.g., Cruise and Miller,
1993 and 1994; Miller and Cruise, 1995; Mashriqui and Cruise, 1997; Boyington, 1998;
PRWRERI, 2002; Vélez-Rodrigüez, 2002; Pérez-Alegría, 2002) will be used to assist in
configuring LEAF-2 and TOPMODEL.
The TOPOG modeling effort by Schellekens
(2000) within the LEF will be an especially important source of information.
Modifications will be made to the version of TOPMODEL currently being used in
the RAMS/LEAF-2/TOPMODEL modeling system. In those areas of the island where
deep vertical seepage occurs (e.g., coastal alluvial and karstic limestone aquifers), aquifer
recharge will be estimated and passed to the MODFLOW groundwater flow model.
MODFLOW will only simulate groundwater flow in selected areas of the island. In those
areas of the island where significant aquifer systems are not present (e.g., the interior
mountain area/volcanic rock), TOPMODEL will be used as is to simulate lateral
downslope transport of water within saturated regions of the soil. Modifications to the
computer codes will be made using the Lahey FORTRAN 95 and/or Digital Visual
Fortran compilers, depending upon which compiler was used for developing the original
code.
The groundwater flow model will be configured using data from numerous USGS
groundwater resources studies conducted in PR (e.g., Puig and Rodríguez-Martínez,
1993; Rodríguez-Martínez, 1996; Pérez-Blair and Carrasquillo-Nieves, 1994; PérezBlair, 1997; Graves, 1991; Rodríguez-Martínez and Richards, 2000; Rodríguez-Martínez,
2001; Ramos-Ginés, 1994). Data will also be obtained from previously developed
groundwater flow models in PR (e.g., Torres-González, 1985; Quiñones-Aponte, 1986;
Graves, 1989; Tucci and Martínez, 1995; Quiñones-Aponte et al., 1996; Sepúlveda, 1999;
and Kipp, 1987). The locations of these studies are summaries in Figure 3. The GISbased user interface GMS (Groundwater Modeling System; Brigham Young University,
1997) will be used to manipulate input and output databases for the groundwater flow
model. GMS was developed under the direction of the U.S. Army Corps of Engineers
and involved support from the Department of Defense, the Department of Energy, and
the Environmental Protection Agency. Tools are provided for site characterization,
model conceptualization, finite-difference grid generation, geostatistics, telescopic model
refinement, and output post-processing.
Figure 3. Locations of Groundwater Resource and Modeling Studies in Puerto Rico.
Calibration of RAMS/ LEAF-2/TOPMODEL
RAMS/LEAF-2/TOPMODEL will be calibrated by adjusting model parameters until
simulated calibration variables correspond reasonably close to actual measured data.
Calibration variables will include rainfall, near surface temperature and relative humidity,
reference evapotranspiration, stream base flow and storm discharge. The calibration will
be conducted during a one-month period during the dry season (e.g., February) and a onemonth period during the wet season (e.g., November). Surface water data is collected at
forty stream flow stations throughout the island. Stream flow data is available from nine
USGS gaging stations in the vicinity of the LEF. Weather data (rainfall and air
temperature) is collected at over seventy stations throughout PR. Daily information will
be used to perform a transient calibration. We will perform the RAMS/LEAF2/TOPMODEL calibration with the assistance of a commercially available nonlinear
optimization program such as PEST (Doherty, 1994). PEST is able to "take control" of a
complex, multi-dimensional, transient model, running it as many times as it needs to
while adjusting its parameters until the discrepancies between selected model outputs and
a complementary set of field measurements is reduced to a minimum in the weighted
least squares sense. PEST implements a particularly robust variant of the GaussMarquardt-Levenberg method of nonlinear parameter estimation. The program can run
within a Windows or UNIX operating environment.
MODFLOW
Where sufficient data exist, steady-state and transient model calibrations will be
performed. Many of the areas being modeled will be based on data from previously
calibrated groundwater flow models. However, for various reasons, it may be necessary
to configure the larger regional-scale model differently from the smaller local-scale
models (e.g., because different grid spacing may be used), and these differences may
have an effect on the simulated groundwater levels.
The groundwater flow model will initially be calibrated for long-term average steadystate conditions. Calibration will be achieved by adjusting aquifer properties within
reasonable limits in order to match observed average groundwater levels and discharge
rates. Discharges will include base flow to rivers and discharges to the ocean. These
data will be obtained from published reports. We will perform the MODFLOW
calibration with the assistance of a commercially available nonlinear optimization
program such as PEST (Doherty, 1994). In addition, a one-year transient model
calibration will be performed in aquifers where synoptic groundwater level and discharge
data exist.
Model Validation
We propose validating the model in two ways.
1. Compare model estimates with ground-based historical data; and
2. Compare the model-estimated island-wide water balance with a water
balance obtained from ground-based and remotely-sensed data.
Validation Step 1
The data used for Validation Step 1 will be of the same form as was used in the
model calibrations (i.e., from data collection stations), except that the data will be
selected from different years. For example, if the transient calibration data for the
groundwater flow model were from 1994, the validation data set would be from some
other year, preferably a year with significantly different conditions (e.g., more wet or
more dry).
Validation Step 2
The monthly water balance for the island, over a period of one year, will be
calculated using the following simple equation:
DP = P + ET + RO + BF - S
(1)
where DP is deep percolation or aquifer recharge, P is precipitation, ET is
evapotranspiration, RO is surface runoff, BF is river base flow, and S is change in
moisture storage. Each component of equation 1 is a function of space and time. On
average S is negligible for long periods (e.g., one year), however, it will be important
for shorter periods (e.g., one month).
The components on the right-hand-side of equation 1 will be estimated using
ground-based and remotely-sensed data. Soil moisture will be estimated using the
coupled hydrologic/radiobrightness model (Laymon et al., 2002) with data from the
Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Daily soil moisture
content will also be estimated using a simplified water budget approach. The GIS-based
water budget procedure is as follows:
1. Infiltration will be estimated by subtracting surface runoff from rainfall. Runoff
will be estimated using the curve number (CN) approach. Soils data (e.g., CN and
soil moisture holding capacity) currently exist in GIS form for PR.
2. If water initially within the soil profile plus the infiltrating water does not exceed
the soil water holding capacity, then soil moisture content is equal to the initial
volume plus infiltration.
3. If water initially within the soil profile plus the infiltrating water exceeds the soil
water holding capacity, then the excess water will be considered percolation and
the soil moisture content will be adjusted to the value of the soil moisture holding
capacity.
Remotely-sensed evapotranspiration will be obtained from the Aqua/MODIS
system. Evapotranspiration will also be determined (within a GIS) using simplified
procedures for estimating average monthly climate data for PR described by Harmsen et
al., 2002.
Microwave, Surface and Precipitation Products (MSPPS) suite of products, which
includes rain rate, land surface temperature, and land-surface emissivity, will be obtained
from NOAA/NESDIS. These are proven hydrological data products produced from the
NOAA polar orbiters and are updated globally every four hours. These products have
coarse spatial resolution (16-48 km) and will be downscaled. The most useful NASA
products will be those generated from MODIS data. MODIS data can now be obtained
from both the Terra (AM) and Aqua (PM) satellites. They have good temporal (1-2 days)
and spatial (1-km) resolution. Products include land-surface temperature, land-cover
type, vegetation indices, and leaf area index.
Simulation Studies
A series of short-term simulations on the order of days and months will be
conducted to determine the sources and sinks of the precipitation across the island and
parameters that could influence the hydrological balance. The hydrological sources and
sinks will be stratified into evapotranspiration, runoff, soil storage, aquifer recharge,
precipitation from convective clouds, frontal systems, and easterly waves, the LEF cloud
forest, etc. These simulations will be configured for the following scenarios:
o Doubling CO2. Doubling the atmospheric carbon dioxide concentration
represents a realistic condition that may exist in the future if
concentrations continue to increase at present rates. This scenario was
considered, for example, by Bouraoui et al. (1999) in a study that
evaluated the impact of climate change on water storage and groundwater
recharge at the watershed scale. Doubling the CO2 is expected to increase
the mean air temperatures by 1 to 5 oC.
o Land use Change in the LEF. This scenario assumes that deforestation
occurs within the Experiment Forest. The purpose of the simulation is to
evaluate the change that will occur in the hydrology “down stream” from
the Forest.
o ROBIN – PLEASE ADD A COUPLE MORE.
Specific questions that we will attempt to answer, relative to the above scenarios include:
o How will river flows be affected?
o How might reservoir levels drop or rise due to changing surface water
evaporation rates?
o How might groundwater levels drop or rise due to changing aquifer
recharge rates?
o If groundwater levels drop, owing to a reduction in aquifer recharge
rates, how might saltwater intrusion increase in the coastal areas?
o What will be the water requirements by agriculture, and how might
competition between water users (agriculture, urban and industrial)
increase?
6. Relevance of Research to Earth Science Enterprise Research Questions
This proposed research is directly responsive to at least two of the questions
advanced in the NRA:
 How are global ecosystems changing?
 How are variations in local weather, precipitation and water resources
related to global climate variation?
Cloud forests are especially vulnerable to global change, as SST increases and
regional land use both may impact the quantity and quality of cloud formation over these
ecosystems (Pounds et al. 1999; Still et al. 1999; Lawton et al. 2001). Diagnosing these
impacts is especially promising using remote sensing, since clouds are a major focus of
numerous earth-observing sensors. This suite of sensors promises to provide an
incredibly rich data trove for exploring variations in cloud amounts, locations, and
properties. This research will enhance our understanding of cloud forests in the earth
system and in particular their role in tropical hydrology. It will help us to forecast how
land use and climatic changes will affect these unique ecosystems and their relationship
to local communities. With this information, we will better understand the complex
nature of interactions determining the response of cloud forests to the suite of ongoing
global changes.
Education and Outreach Plan
The objectives of the education component in this proposal are:
1) To integrate local Puerto Rican students and scientists into the research
and with their collaboration develop a Spanish curriculum on global
change and Caribbean cloud forests.
2) To create an innovative short course for upper-level undergraduate and
beginning graduate students at UCSB and UPRM in collaboration with R.
Williams, J. Gonzalez, and D. Erickson that directly involves students in
the knowledge-discovery process. Ideally, this course will introduce
students to multidisciplinary and complex systems thinking by
incorporating atmospheric science, hydrology, isotope biogeochemistry,
and forest ecology.
The educational component of this project includes a PR element and a UCSB
element. As part of this component, I will develop new courses at UCSB that integrate
the research directly into course curricula. These courses will be at the undergraduate and
graduate levels. At the undergraduate level, I plan to offer a freshman seminar on climate
change and tropical islands. At the upper-division undergraduate and graduate level, I
will develop a short course on cloud forest ecology and climate change.
Beyond the university environment, I strongly believe in community outreach,
since the public funds almost all of my work. For this outreach, I will take advantage of
the numerous opportunities afforded by the US Forest Service El Portal Tropical Forest
Center, including the creation of an exhibit on cloud forests, cloud water, and climate
change El Yunque’s visitors.
-
Outline of work plan
Year 1 (Research). Build fog water and precipitation collectors for amount
and isotope ratio measurements; install collectors in El Yunque cloud forest;
sample water isotopes along a transect from the Caribbean coast of PR to El
Yunque; collect first vegetation, soil, and stream water samples; instrument El
Yunque canopy with portable sensors and dataloggers to monitor variations in
temperature, relative humidity, and light associated with cloud inundation cycles;
analyze water samples for deuterium and oxygen-18 content at UPRM-Geology
facility; obtain and analyze processed cloud data fields from Terra, Aqua, and
TRMM. Model related activities will include: literature review and data
compilation, and configuration of the numerical models.
Year 1 (Education). Develop short course on cloud forests and climate
change at UCSB
Year 2 (Research). Continue collection of fog and precipitation samples;
conduct intensive field campaigns in dry and wet seasons to sample vegetation
and soil water; sample water isotopes along a transect from the Caribbean coast of
PR to El Yunque; sample stream water (1-2 times); analyze water samples for
deuterium and oxygen-18 content at UPRM-Geology facility; obtain and analyze
processed cloud data fields from Terra, Aqua, and TRMM. Model related activies
will include: model calibration and validation.
Year 2 (Education). Meet with UPRM collaborators to adapt short course
for UPRM, including development of a Spanish language version
Year 3 (Research). Continue collection of fog and precipitation samples;
conduct fewer intensive field campaigns in dry and wet seasons to sample
vegetation and soil water; sample stream water (1-2 times); analyze water samples
for deuterium and oxygen-18 content at UPRM-Geology facility; obtain and
analyze processed cloud data fields from Terra, Aqua, and TRMM. Model
activities will include: Coupling of the numerical models and simulation studies.
Year 3 (Education). Work with UPRM collaborators and El Portal
Tropical Science Center to develop a small display on cloud forests and climate
change in the Caribbean basin
The project schedule is summarized in the following table:
1st
Semester
2003
2nd
Semester
2003
Summer
Semester
2003
1st
Semester
2004
2nd
Semester
2005
Summer
Semester
2005
1st
Semester
2005
2nd
Semester
2006
Summer
Semester
2006
Build Fog Water and
Precipitation Collectors.
Sample and perform
isotopic analysis.
Develop short course on
cloud forests and climate
change at UCSB
Literature Review and
Data Compilation for
Models
Model Configuration
RAMS
LEAF-2
TOPMODEL
MODFLOW
Continue fog water
sampling and analysis
Development of UPR
Short Course
Model Calibration
RAMS/LEAF2/TOPMODEL
MODFLOW
Model Validation
Ground-based data
validation
Island-wide water balance
validation
Coupling of MODFLOW
and LEAF-2/TOPMODEL
Model Simulations
Continue fog water
sampling and analysis
Develop a small display
on cloud forests and
climate change in the
Caribbean basin
Presentation/Publication
of Results
References
Boyington, T. M. 1998. A Runoff-Erosion Model for the Añasco Watershed, Puerto Rico,
Utilizing a Remotely Sensed Database, Geographic information System and SoilLumped Computational Zones. Master’s Thesis. Tulane University.
Brigham Young University, 1997. Groundwater Modeling System (GMS), User's
Manual, Version 2.1. Engineering Computer Graphics Laboratory.
Cruise, J. G. and R. L. Miller, 1993. Hydrologic Modeling with Remotely Sensed
Databases. Water Resources Bulletin 29(6):997-1001.
Cruise, J. F. and R. L. Miller, 1994. Hydrologic Modeling of Land Processes in Puerto
Rico Using Remotely Sensed Data. Water Resources Bulletin 30(3):419-428.
Doherty, J. 1994. PEST Model Independent Parameter Estimation. Watermark
Company.
Graves. R. P. 1989. Water Resources of the Humacao-Naguabo Area, Eastern Puerto
Rico. U.S. Geological Survey Water-Resources Investigation Report 87-4088. San
Juan, Puerto Rico. pp 69.
Graves, R. P., 1991. Ground-water resources in Lajas Valley, Puerto Rico. U.S.
Geological Survey. Water-Resources Investigations Report 89-4182.
Kipp, K. L., 1987. HST3D: A computer code for simulation of heat and solute transport
in three-dimensional ground-water flow systems: U.S. Geological Survey WaterResources Investigations Report 86-4095, 512 p.
Mashriqui, H. S. and J. F. Cruise, 1997. Sediment Yield Modeling by Grouped Response
Units. Journal of Water Resources Planning and Management. ASCE 123(2): 95104.
McDonald, M. G. and A. W. Harbaugh. 1984. A Modular Three-Dimensional Finite
Difference Ground-Water Flow Model. U.S. Geological Survey.
Miller, R. L. and J. F. Cruise, 1995. Effects of Suspended Sediments on Coral Growth:
Evidence from Remote Sensing and Hydrologic Modeling. Journal of
Environmental Remote Sensing. 53:177-187.
Pérez-Alegría, L. R. 2002. Total Maximum Daily Load (TMDL) Environmental
Regulations: Proceedings of the March 11-13, 2002 Conference, Fort. Worth, TX,
USA, Pp. 163.
Pérez-Blair, F., 1997. Ground-water resources of alluvial valleys in northeastern Puerto
Rico: Río Espíritu Santo to Río Demajagua area. U.S. Geological Survey. WaterResources Investigations Report 96-4201.
Perez-Blair, F. and Carrasquillo-Nieves, R. A., 1994. Potentiometric surface of the
principal alluvial aquifers in the Rio Espiritu Santo to Rio Demajagua area, Puerto
Rico, U.S. Geological Survey. Water-Resources Investigations Report 94-4152.
Pielke, R. A., W. R. Cotton, R. L. Walko, C.J. Tremback, W. A. Lyons, L.D. Grasso,
M.E. Nicholls, M. D. Moran, D. A. Wesley, T. J. Lee, and J. H. Copeland, 1992: A
comprehensive meteorological modeling system – RAMS. Meteor.Atmos.Phys.,
49, 69-91.
Puerto Rico Water Resources and Environmental Research Institute, 2002. Assessing
Cumulative and Secondary Point and Non-Point Pollution Sources at Jobos Bay
Watershed. Website: http://www.ece.uprm.edu/rumhp/prwrri/
Puig, J. C. and Rodríguez-Martínez, J. 1993. Ground-water resources of the CaguasJuncos Valley, Puerto Rico.
U.S. Geological Survey.
Water-Resources
Investigations Report 91-4079.
Quiñones-Aponte, V., 1986. Simulation of ground-water flow in the Río Yauco alluvial
valley, Puerto Rico. U.S. Geological Survey Water-Resources Investigations
Report 85-4179. San Juan, Puerto Rico. 32 p.
Quiñones-Aponte, V., F. Gómez-Gómez, and R. A. Renken, 1996. Geohydrology and
simulation of ground-water flow in the Salinas to Patillas Area, Puerto Rico. U.S.
Geological Survey Water-Resources Investigations Report 95-4063. San Juan,
Puerto Rico. 37 p.
Ramos-Ginés, O. 1994. Effects of changing irrigation practices on ground-water
hydrology of the Santa Isabel-Juana Díaz area, South Central Puerto Rico. U.S.
Geological Survey Water-Resources Investigations Report 91-4183. San Juan,
Puerto Rico. 22 p.
Rodríguez-Martínez, J., 1996. Hydrogeology and ground-water/surface-water relations in
the Bajura area of the Municipio of Cabo Rojo, southwestern Puerto Rico. U.S.
Geological Survey. Water-Resources Investigations Report 95-4159.
Rodríguez-Martínez and Richards, R. T., 2000. Detection of conduit-controlled groundwater flow in northwestern Puerto Rico using aerial photographs interpretation and
geophysical methods. U.S. Geological Survey. Water-Resources Investigations
Report 00-4147.
Schellekens, J. 2000. Hydrologic Processes in Humid Tropical Rain Forest: A Combined
Experimental and Modeling Approach. Doctoral thesis. Vrije University,
Amsterdam.
Sepúlveda, N., 1999. Groundwater flow, solute transport, and simulation of remedial
alternatives for the water-table aquifer in Vega Alta, Puerto Rico. U.S. Geological
Survey Water-Resources Investigations Report 97-4170. San Juan, Puerto Rico. 96
p.
Torres-González, A. 1985. Simulation of ground-water flow in the water table aquifer
near Barceloneta, Puerto Rico. U.S. Geological Survey Water-Resources
Investigations Report 84-4113. San Juan, Puerto Rico. 39 p.
Tucci, P. and M. I. Martínez, 1995. Hydrology and simulation of groundwater flow in the
Aguadilla to Río Camuy area, Puerto Rico. U.S. Geological Survey WaterResources Investigations Report 95-4028. San Juan, Puerto Rico. 39 p.
.
Vélez-Rodrigüez, L. L. 2002. Monitoring Land Use and Land Cover in the Jobos Bay
National Estuary Watershed. Puerto Rico Water Resources and Environmental
Research Institute. Website: http://www.ece.uprm.edu/rumhp/prwrri/
Walko, R. L, L. E. Band, J. Baron, T. G. F. Kittel, R. Lammers, T. J. Lee, D. Ojima, R.
A. Pielke Dr., C. Taylor, C. Tague, C. J. Tremback, and P. L. Vidale. 2000.
Coupled Atmosphere-Biophysics-Hydrology Models for Environmental Modeling.
Journal of Applied Meteorology. June 2000: 931-944.