Grantee Research Project Results
Final Report: Integrated Ecological Economic Modeling and Valuation of Watersheds
EPA Grant Number: R824766Title: Integrated Ecological Economic Modeling and Valuation of Watersheds
Investigators: Costanza, Robert , Voinov, Alexey , Villa, Ferdinando , Voinov, Helena , Wainger, Lisa , Boumans, Roelof , Maxwell, Thomas
Institution: University of Maryland Center for Environmental Science
EPA Project Officer: Packard, Benjamin H
Project Period: November 1, 1995 through November 1, 1998
Project Amount: $1,000,000
RFA: Water and Watersheds (1995) RFA Text | Recipients Lists
Research Category: Watersheds , Water
Objective:
There has been a major movement over the last decade toward place-based, ecosystem-based, and watershed-based management. To support this effort to effectively manage the complex interactions between human and natural systems at the watershed scale, integrated (across scales and across disciplines) scientific and technical knowledge and models are needed. We have developed an integrated modeling framework aimed at addressing these goals. The approach evolved from work in coastal Louisiana (Costanza et al., 1990) and in the Everglades (Fitz, et al., 1993). Current work is focused on the Patuxent river watershed in Maryland, one of the best studied tributaries of the Chesapeake Bay, and one that has often been used as a model of the entire Bay system. In particular, the modeling framework is aimed at addressing the following general questions:
- What are the quantitative, spatially explicit and dynamic linkages between land use and terrestrial and aquatic ecosystem productivity and health-
- What are the quantitative effects of various combinations of natural and anthropogenic stressors on ecosystems and how do these effects change with scale?
- What are useful ways to measure changes in the total value of the landscape including both marketed and non-marketed (natural system) components and how effective are alternative mitigation approaches, management strategies, and policy options toward increasing this value?
The Patuxent Landscape Model was designed to serve as a tool in a systematic analysis of the interactions among physical and biological dynamics of the watershed, conditioned on socioeconomic behavior in the region. A companion socioeconomic model of the region's land use dynamics was developed to link with the PLM and provide a means of capturing the feedbacks between ecological and economic systems. Because of the complex feedbacks and nonlinear dynamics of this watershed, a "systems" approach was necessary. A key part of this process was the development of an integrated, dynamic, spatially explicit simulation model.
In the ecological component of the model, the important processes that affect plant communities are simulated within the varying habitats distributed over the landscape. The principal dynamics modeled are: (1) plant growth in response to available sunlight, temperature, nutrients, and water; (2) flow of water plus dissolved nutrients in three dimensions; and (3) decomposition of dead organic material and formation of soil organic matter. Using this approach to incorporating process-based data at a reasonably high spatial, temporal and complexity resolution within the entire watershed, the changing spatial patterns and processes can be analyzed within the context of altered management strategies, such as the use of agricultural Best Management Practices (BMPs) (e.g., reduced tillage).
Summary/Accomplishments (Outputs/Outcomes):
The Model. The modeled landscape is partitioned into a spatial grid of nearly 2,500 square unit cells. The model is hierarchical in structure, incorporating an ecosystem-level "unit" model that is replicated in each of the unit cells representing the landscape. The General Ecosystem Model (GEM) which was developed for the Everglades Landscape Model (ELM) (Fitz et al., 1996), was modified for use within the framework of the PLM. The model was reformulated on a modular basis, with modules representing functional components of the system and capable of being run and calibrated independently (Voinov, et al., 1999).
The same unit model structure runs in each cell. Individual modules are parameterized according to habitat type and georeferenced information for a particular cell. The habitat-dependent information is stored in a parameter database which includes initial conditions, rate parameters, stoichiometric ratios, etc. The habitat type and other location-dependent characteristics are referenced through links to geographic information system (GIS) files. Thus, when run within the spatial framework of the PLM, the landscape response to hydrology and water quality is effectively simulated as material flows between adjacent cells. The independent modules and the full unit model have also been run in the spatial implementation and rigorously tested at the full watershed scale. Sensitivity analysis was used to gain insight about the model dynamics, showing the varying response of plant production to different nutrient requirements, with subsequent changes in the soil water nutrient concentrations and total water head. Changes in the plant canopy structure resulted in differences in transpiration and, consequently, water levels and plant production.
The spatial model combines the dynamics of the unit model which are calculated at each time step for each cell in the landscape, and adds the spatial fluxes which control the movement of water and materials between cells. Each cell generates stock and flow values which provide input to or accept output from the spatial flux equations.
After the water head in each raster cell is modified by the vertical fluxes
controlled in the GEM unit model, the surface water and its dissolved or
suspended components move between cells based on one of the two algorithms used.
In the first algorithm a certain portion of water is taken out of a cell and
added to a cell downstream defined by the link map. This may not be the adjacent
cell, but a cell several links down the path of the flow. The length of the flow
path is defined by the amount of water fluxed and is calibrated so that the
water flow rates match gage data. The other algorithm checks that water movement
stops when the water heads in two adjacent cells equilibrate. While the first
algorithm works well for the piedmont area with significant elevation gradients,
the second one is more appropriate for the coastal plain region where there are
significant areas of low relief and tidal forces which permit counterflows.
The ecological model is linked to a companion economic model that predicts
the probability of land use change within the seven counties of the Patuxent
watershed (Bockstael, 1996). The economic model allows human decisions to be
modeled as a function of both economic and ecological spatial variables. Based
on empirically estimated parameters, spatially heterogeneous probabilities of
land conversion are predicted as functions of predicted land values in
residential and alternative uses, and costs of conversion. Land value
predictions themselves are modeled as functions of local and regional
characteristics. The predictive model of land use conversion generates the
relative likelihood of conversion of cells, and thus the spatial pattern of
greatest development pressure. To predict the absolute amount of new residential
development, the probabilistic land use conversion model must be combined with
models of regional growth pressure. Linking the ecological and economic models
allows the effects of both direct land use change through human actions and
indirect effects through ecological change to be evaluated, as well as the
feedbacks between the two.
A variety of spatially and temporally disaggregated data is required to develop and calibrate the PLM model. The database we have assembled includes time series, spatial coverages (maps) and parameters. The model data base contains the data which drive the model forcing functions, parameterize equations, and provide calibration and verification data for adjusting model parameters and comparing model output to the real system. The data base was developed from extensive data sets collected for the Patuxent watershed by various governmental agencies, academic institutions and research programs. Existing data for the local region were supplemented with broader regional data bases.
To adequately test model behavior and to reduce computational time, we
performed the calibration and testing at several time and space scales, and for
the unit model independently of the full spatial model. We developed a Model
Performance Index (MPI: Villa, 1997) to study the model's response to parameter
changes. The MPI framework allows one to develop an error function which can
handle the full range of variables and data quality that usually confront
complex models. It employs a multi-criteria approach, which allows user
weighting of the model variables to reflect their degree of importance and also
weighting of the data to reflect its quality. It can deal with both quantitative
and semi-quantitative information about the expected behavior of the state
variables (like the pattern of temporal autocorrelation, boundaries, steady
state, etc.).
Calibrating and running a spatial model of this level of
complexity and resolution requires a multi stage approach. We first identified
two spatial scales at which to run the model-a 200 m and 1 km cell resolution.
We then identified a hierarchy of subwatersheds. The whole watershed has been
divided into a set of nested subwatersheds to perform analysis at three scales.
The inclusion of plant and nutrient dynamics improved the model's hydrologic
performance in comparison to the output generated with no account for these
modules. The spatially explicit representation of plant and nutrient dynamics
modifies the evapotranspiration and interception fluxes in the model, making the
model performance more realistic. It was also essential for scenario runs that
take into account land use and cover changes, in which these changes modify the
hydrologic fluxes in the watershed.
Scenarios. The goal of the linked ecological economic model development was to test alternative scenarios of land use management. A wide range of future and historical scenarios may be explored using the calibrated model. We have developed scenarios based on the concerns of county, state and federal government agencies, local stakeholders and researchers. The following set of initial scenarios was considered:
A group of historical scenarios based on the U.S. Geological Survey (USGS) reconstruction (Buchanan, et al. 1998) of land use in the Patuxent watershed:
- 1700-pre-development era. Most of the area forested, zero emissions.
- 1850-agro-development. Almost all the area under agricultural use, traditional fertilizers (marl, river mud, manure, etc.), low emissions.
- 3. 1950-decline of agriculture, start of reforestation and fast urbanization.
- 1972-maximal reforestation, intensive agriculture, high emissions.
- Baseline scenario. We use 1990 as a baseline to compare the modeling results. The 1990-1991 climatic patterns and nutrient loadings were used.
- 1997 land use pattern. This data set has just recently been released and we used it with the 1990-1991 forcings to estimate the effect of landuse change alone
- Buildout scenario. With the existing zoning regulations, we assumed that all the possible development in the area occurred. This may be considered as the worst case scenario in terms of urbanization and it's associated loadings.
- Best Management Practices (BMP)-1997 land use with lowered fertilizer application and crop rotation. These management practices were also assumed in the remaining scenarios.
A group of scenarios of change in land use over the 5 years following 1997 (i.e., for 2003) developed based on the Economic Land Use Conversion (ELUC) Model by N. Bockstael:
- Development as usual
- Development with all projected sewer systems in place
- Development with no new sewers but contiguous patches of forest 500 acres and more protected
- Development with all sewers in place and contiguous forest protected
Another group of hypothetical scenarios to study more dramatic change in land use patterns using the 1997 land use as the starting point:
- Conversion of all agricultural land into residential
- Conversion of all agricultural land into forested
- Conversion of all residential land into forested
- Conversion of all forested land into residential
- Residential clustering-conversion of all low density residential land use into urban around 3 major centers
- Residential sprawl-conversion of all high density urban into residential randomly spread across the watershed.
The scenarios were driven by changes in the Landuse map, the Sewers map, patterns of fertilizer application, amounts of atmospheric deposition, and location and number of dwelling units. We compare the model output in the different scenarios looking at nitrogen concentration in the Patuxent River as an indicator of water quality, changes in the hydrologic flow and changes in the net primary productivity of the landscape.
Comparing the effect of various land use change scenarios on the water
quality in the river shows that there is no obvious connection between the
nutrient loading to the watershed and the nutrient concentration in the river.
However, some conclusions can be drawn. The effects of loadings which are
distributed more evenly over the year are much less pronounced than those which
occur sporadically. For example, fertilizer applications that occur once or
twice a year increase the average nutrient content and especially the maximum
nutrient concentration quite significantly, whereas the effect of, say,
atmospheric deposition is much more obscure. The difference in atmospheric
loading between Scenarios (1) and (3) is almost 2 orders of magnitude, yet the
nutrient response is only 5-6 times higher, even though loadings from other
sources also increase. Similarly the effect of septic loadings that are
occurring continuously is not so large.
The average N concentration is well
correlated (corr=0.77) with the total amount of nutrients loaded. The effect of
fertilizers is also high (corr=0.74), while the effect of other sources is much
less (septic corr=-0.0075; decomposition corr=-0.2267; atmosphere corr=0.49).
The fertilizer application defines the maximum nutrient concentrations
(corr=0.71), with the total load also playing an important role (corr=0.60),
whereas the contributions of individual sources is less pronounced (septic
corr=0.0878; decomposition corr=-0.271; atmosphere corr=0.28). Even the
groundwater concentrations of nutrients are closely related to the fertilizer
applications
(corr=0.89), however in this case the septic loadings play a
larger role (corr=0.59), even a more important one than the total N loading
(corr=0.44).
The hydrologic response is quite strongly driven by the land use patterns. The peak flow (max 10% of flow) is almost entirely determined by urbanization (corr=0.94). The baseflow (min 50% of flow) is somewhat related with the number of forested cells (corr=0.54), but obviously many other factors also influence it.
Different land use patterns result in quite significant variations in the net primary productivity (NPP) of the watershed, both in the temporal and in the spatial domains. The predevelopment 1700 conditions produce the largest NPP, while under Build Out conditions NPP is the lowest. In the latter case the dynamics of NPP are more representative of the agricultural landuse with higher NPP values attained later in the year as crops mature. Interestingly under the BMP scenario with lower fertilizer applications we still get a higher NPP than under reference conditions of 1997, because of the crop rotation and growth of winter wheat that matures earlier in the season than corn.
The major result of the analysis performed thus far is that the model behaves well and produces plausible output under significant variations in forcing functions and land use patterns. It can therefore be instrumental for analysis and comparisons of very diverse environmental conditions that can be formulated as scenarios of change and further studied and refined as additional data and information are obtained. The real power of the model comes from its ability to link hydrology, nutrients, plant dynamics and economic behavior via land use change. The model allows fairly site specific effects to be examined as well as regional impacts so that both local water quality and Chesapeake Bay inputs can be considered. The linked ecological economic model is a potentially important tool for addressing issues of land use change. The model integrates our current understanding of certain ecological and economic processes to give best available estimates of effects of land use or land management change. The model also highlights areas where knowledge is lacking and where further research could be targeted for the most impact.
Conclusions:
We are finalizing the project and reformulating the results and methods developed to transfer to the next phase of our work under the auspice of the Water and Watersheds program. The Patuxent Landscape Model is to become a crucial part of our future work on the project: "Whole watershed health and restoration: applying the Patuxent and Gwynns Falls landscape models to designing a sustainable balance between humans and the rest of nature."
Journal Articles on this Report : 15 Displayed | Download in RIS Format
Other project views: | All 79 publications | 28 publications in selected types | All 16 journal articles |
---|
Type | Citation | ||
---|---|---|---|
|
Bell KP, Bockstael NE. Applying the generalized-moments estimation approach to spatial problems involving microlevel data. Review of Economics and Statistics 2000;82(1):72-82. |
R824766 (1998) R824766 (Final) |
Exit |
|
Costanza R, Voinov A, Boumans R, Maxwell T, Villa F, Wainger L, Voinov H. Integrated ecological economic modeling of the Patuxent River watershed, Maryland. Ecological Monographs 2002;72(2):203-231. |
R824766 (1998) R824766 (Final) R825792 (1999) R825792 (2000) R825792 (Final) R827169 (Final) |
Exit |
|
Costanza R. Ecological economics: reintegrating the study of humans and nature. Ecological Applications 1996;6(4):978-990. |
R824766 (1998) R824766 (Final) |
Exit |
|
Costanza R, Ruth M. Using dynamic modeling to scope environmental problems and build consensus. Environmental Management 1998;22(2):183-195. |
R824766 (1998) R824766 (Final) |
Exit |
|
Geoghegan J, Wainger LA, Bockstael NE. Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS. Ecological Economics 1997;23(3):251-264. |
R824766 (1998) R824766 (Final) R825309 (1997) R825309 (Final) |
Exit Exit Exit |
|
Maxwell T, Costanza R. An open geographic modeling environment. Simulation 1997;68(3):175-185. |
R824766 (1998) R824766 (Final) |
Exit |
|
Maxwell T, Costanza R. A language for modular spatio-temporal simulation. Ecological Modelling 1997;103(2-3):105-113. |
R824766 (1998) R824766 (Final) |
Exit Exit |
|
Villa F, Ceroni M, Mazza A. A GIS-based method for multi-objective evaluation of park vegetation. Landscape and Urban Planning 1996;35(4):203-212. |
R824766 (1998) R824766 (Final) |
Exit Exit |
|
Voinov A, Fitz C, Costanza R. Landscape model provides management tool. GIS World 1997;10(3):48-50. |
R824766 (1998) R824766 (Final) |
not available |
|
Voinov AA. Paradoxes of sustainability. Zhurnal Obshchei Biologii (Journal of General Biology) 1998;59(2):209-218. |
R824766 (1998) R824766 (Final) |
not available |
|
Voinov AA, Fitz HC, Costanza R. Surface water flow in landscape models: I. Everglades case study. Ecological Modelling 1998;108(1-3):131-144. |
R824766 (1998) R824766 (Final) |
Exit Exit Exit |
|
Voinov A, Costanza R. Watershed management and the Web. Journal of Environmental Management 1999;56(4):231-245. |
R824766 (1998) R824766 (Final) R827169 (Final) |
Exit Exit |
|
Voinov AA, Voinov H, Costanza R. Surface water flow in landscape models: 2. Patuxent watershed case study. Ecological Modelling 1999;119(2-3):211-230. |
R824766 (1998) R824766 (Final) R827169 (Final) |
Exit Exit Exit |
|
Voinov A, Costanza R, Wainger L, Boumans R, Villa F, Maxwell T, Voinov H. Patuxent landscape model: integrated ecological economic modeling of a watershed. Environmental Modelling & Software 1999;14(5):473-491. |
R824766 (1998) R824766 (Final) R825792 (1999) R825792 (2000) R825792 (Final) R827169 (Final) |
Exit Exit Exit |
|
Voinov A, Fitz C, Boumans R, Costanza R. Modular ecosystem modeling. Environmental Modelling & Software 2004;19(3):285-304. |
R824766 (1998) R824766 (Final) R827169 (Final) |
Exit Exit Exit |
Supplemental Keywords:
landscape simulation modeling, ecological economics, ecology, economics, Patuxent River watershed, Maryland, MD., RFA, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, Water, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, Water & Watershed, Ecology, Hydrology, Ecosystem/Assessment/Indicators, Ecosystem Protection, exploratory research environmental biology, Chemical Mixtures - Environmental Exposure & Risk, State, Ecological Effects - Environmental Exposure & Risk, Economics, Ecological Effects - Human Health, decision-making, Watersheds, Ecological Indicators, Economics & Decision Making, EPA Region, Social Science, risk assessment, ecosystem valuation, remote sensing, valuation of watersheds, biodiversity option values, human activities, model aggregation methods, watershed, valuation, Region 3, Maryland (MD), aquatic ecosystems, water quality, regional scale model, land useRelevant Websites:
Patuxent Landscape Model (PLM) Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.