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THE PROJECT |
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THE PROJECT |
This project is aimed at developing integrated knowledge and new tools
to enhance predictive understanding of watershed ecosystems (including
processes and mechanisms that govern the interconnected dynamics of water,
nutrients, toxins, and biotic components) and their linkage to human factors
affecting water and watersheds. The goal is effective management at the
watershed scale.
Jointly with state, local, and federal management agencies we are developing an integrated, adaptive, framework for managing watersheds, particularly the impacts of land-use and non-point source pollution (nutrients, sediments, and toxins) on both the terrestrial and aquatic components of the system. We are using the Patuxent subwatershed of the Chesapeake Bay in Maryland as a test site for the project. This allows us to:
Building on our past and ongoing projects, we can combine generic models of ecosystem and economic site-specific processes with remote sensing and GIS data on land use changes and other landscape changes and field monitoring measurements in both aquatic and terrestrial environments in a unique spatial modeling framework for broad applications linking science and policy. This allows us to simulate the detailed spatial dynamics of the Patuxent river watershed, including the interaction of the ecological and economic components.
Origin of the Patuxent ecological economic modeling project
In 1992 the Office of Policy, Planning and Evaluation (OPPE) of the US Environmental Protection Agency sponsored an Ecosystem Valuation Forum ( Bingham et al.1995 ). The Forum was comprised of a group of distinguished ecologists, economists and other social scientists brought together to advance the state of knowledge and develop common understandings of the process of valuing ecosystems. One of the key recommendations of the Forum was to begin case studies that fully integrated ecological and economic analysis. A wetland case study was one of several cases that the Forum recommended.
A small group of ecologists and economists at OPPE who had participated in the forum decided to build a case study on the existing wetland simulation model that had been developed by Dr. Robert Costanza who was also a participant in the Forum. The University of Maryland economist, Dr. Nancy Bockstael was recommended to be the project's lead economist. The initial team of principal investigators also included Dr. Walter Boynton and Dr. Ivar Strand. Additionally, OPPE staff substantively contributed to the initial scoping of this project and provided the policy context for scenarios that the modeling effort should be able to address.
Initial funding for this project was through EPA Cooperative Agreement with the University of Maryland, CR818227010, "Ecosystem Valuation and Restoration and their Impacts on Environmental Accounting and Concepts of Sustainable Development." Continuing funding for this project has been through EPA Cooperative Agreement CR821925, "Ecosystem Valuation and Environmental Regulation."
There are several ongoing efforts at the University of Maryland and in state and federal agencies that contribute to this program, including:
Policy Dialogue Workshops, involving federal and state management agency and academic participants, are designed to both drive the research agenda and communicate results to major stakeholder groups. In this way the project is adaptive and interactive and serves to build capacity in both the university and the management agencies for integrated multiscale assessment and modeling. The workshops provide updates to the model and data base and can result in detailed scenarios for analysis aimed at addressing the range of questions outlined above as well as new questions that will arise in the workshops.
Large drainage basins are composed of multiple smaller catchments. Each of these catchments contain a heterogeneous collection of land uses which vary in composition and spatial pattern (structure) and thus differ in functions such as nutrient retention. Two problems arise from this heterogeneity that present major challenges to both research and management. First, variation in structure and function inevitably prevents true replication in intensive field studies that attempt to relate landscape function to landscape structure. Second, variation among land uses within watersheds makes it difficult to directly extrapolate intensive studies to larger spatial scales. Even though drainage basins can be broken down hierarchically into smaller catchments based on topography, "scaling up" from intensive catchment studies is not a linear additive process because of differences among catchments and interactions between adjacent land uses. Management of water quality over large drainage basins clearly must address both problems with innovative methods synthesizing data from intensive experimental studies on a few watersheds, then extrapolating important generalizations to large drainages using appropriate modeling techniques.
The most comprehensive approach to understanding nutrient flux from heterogeneous watersheds is through process modeling. Process models are based on understanding the ecological processes that either retain or release nutrients in watersheds. A process-based modeling approach to watershed-nutrient export linkages relies on (1) identification of ecological or anthropogenic processes important in making labile nutrients available for export, (2) simulation of watershed hydrologic flow paths as potential "routes" of nutrient export, and (3) linking spatial patterns of labile nutrient concentrations with water flow paths. By focusing on processes, this approach gains generality of application at different spatial scales. Application of process models to any specific watershed depends on spatial representation of the landscape composition and topography, but also promotes identification of specific nutrient export problems and selection of management actions to correct them. General process models require intensive data for development and independent data for testing. Thus these models are best developed from intensive empirical and model calibration studies of contrasting watersheds at several scales and tested by their ability to predict nutrient export from a large variety of watersheds.
We have developed a General Ecosystem Model (GEM) that is designed to simulate a variety of ecosystem types using a fixed model structure (Fitz et al. 1995). Driven largely by hydrologic algorithms for upland, wetland and shallow-water habitats, the model captures the response of macrophyte and algal communities to simulated levels of nutrients, water, and environmental inputs. It explicitly incorporates ecological processes that determine water levels, plant production, nutrient cycling associated with organic matter decomposition, consumer dynamics, and fire.
Figure 1. The process-oriented feedbacks among the biotic and abiotic sectors
of the GEM unit model. The boxes represent simulations modules of different
ecosystem components.
While the model may be used to simulate ecosystem dynamics for a single homogenous habitat, our primary objective is to replicate it as a "unit" model in heterogeneous, grid-based dynamic spatial models using different parameter sets for each habitat. Thus, we constrained the process (i.e., computational) complexity, yet targeted a level of disaggregation that would effectively capture the feedbacks among important ecosystem processes. A basic version was used to simulate the response of sedge and hardwood communities to varying hydrologic regimes and associated water quality. Sensitivity analyses provided examples of the model dynamics, showing the varying response of macrophyte production to different nutrient requirements, with subsequent changes in the sediment water nutrient concentrations and total water head. Changes in the macrophyte canopy structure resulted in differences in transpiration, and thus the total water levels and macrophyte production. The GEM's modular design facilitates understanding the model structure and objectives, inviting variants of the basic version for other research goals. Importantly, we hope that the generic nature of the model will help alleviate the "reinventing-the-wheel" syndrome of model development, and we are implementing it in a variety of systems to help understand their basic dynamics.
Figure 2. The structure of the PLM. Each cell is parameterized according
to its habitat type. The unit model that is replicated in each cell simulates
"vertical" ecosystem dynamics, while dissolved and suspended
materials flux horizontally among cells in the domain of the spatial model.
The PLM is an outgrowth of the coastal ecological landscape spatial simulation (CELSS) model developed by Costanza et al. (1990). This modeling approach has been applied in two previous studies. The model was first implemented in the Atchafalaya Delta Area of coastal Louisiana where it was developed to model spatial ecosystem processes, succession, and land loss problems and used to evaluate the impacts of management strategies and specific projects designed to alleviate coastal erosion problems (Costanza et al. 1990 ). A more sophisticated version of the model is currently being implemented in the Everglades in Florida to examine the implications of management strategies on elements of the ecosystem such as water levels, nutrient dynamics, and plant successional patterns (Fitz et al. 1993).
The PLM uses an integrated spatial simulation modeling approach and was constructed using the Spatial Modeling Workstation (SMW) (Maxwell and Costanza 1994). Central to the PLM is a General Ecosystem Model (GEM; Fitz, et. al. 1996 ) that is replicated in each of the cells that compose the landscape. A study area is divided into a grid of square cells linked to GIS files. The unit model simulates fundamental ecological processes, with hydrology as its core. The hydrologic sector module of GEM, for example, simulates the availability of water and its movements, determining the hydrologic head of surface and ground water within each cell. Primary production, nutrient fluxes, organic/inorganic sediment suspension and deposition, basic "consumer" dynamics, and decomposition are also simulated. The GEM model is simulated for each cell with parameters unique for each ecosystem type. The GEM is also being applied to MEERC multicosms and these will allow both more rigorous testing of the model and calibration of parameters as well as identification of how parameters need to be adjusted to extrapolate to the landscape scale.
The dynamics of various ecological processes are expressed in GEM as the interaction between state variables (stocks) and flows of material, energy, and information. After the vertical or within cell dynamics have been simulated, the results of the unit model are processed by the spatial modeling program. The model calculates the exchange of material between cells (horizontal fluxes) and simulates the resulting temporal changes in water availability, water quality, and habitat/ecosystem type. The landscape mode employs a successional algorithm for determining the habitat type of each cell at all times during the simulation. The successional algorithm redefines the habitat/ecosystem type of cells as conditions change and selects parameter sets as necessary.
The ecosystem functions and the parameters of those functions that are simulated for any given cell in the landscape are dictated by the cell's land use or habitat designation at the beginning of any simulation time step. Then, conditioned on that land use and the stocks of the state variables at that point in time in the cell, the processes and fluxes are calculated. Conceptually, there are two levels at which human behavior could be expected to affect the simulation. One is in the land use designation of a cell; the other is in the nature of ecological processes that occur within a cell conditioned on its land use.
The ecosystem model, run without economic components, imposes this human behavior from the outside, rather than modeling it internally. We are attempting in our current modeling work in the Patuxent to construct truly integrated ecological economic models, with the major components and linkages shown in Figure 3 (Bockstael et al. 1994). Consider the land use designation. The ecological model calculates land use designation through a "habitat switching" model which determines when, through natural succession or weather-driven ecological catastrophe (e.g. flood, forest fire), the habitat shifts from one type to another. Human instigated land use changes must be imposed exogenously and hypothetically. Recognizing that the ecological effects of human activity are driven by the specific uses humans choose to make of the stock of natural capital, one of the major contributions of the economics modeling effort is an understanding of how land use decisions are made by individuals and how they are related to both the ecological and economic features of the landscape. This economics extension of the PLM is aimed at developing the ability to predict future land use of a parcel or unit of land, given information on its history, relevant zoning and other land use restrictions, the general level of regional economic activity, and the variety of other spatially related economic and ecological variables that affect the value of the parcel in different uses.
Human interactions with the environment, conditioned on land use, are similarly imposed from the outside in the current ecological model, which uses "fixed coefficients" to represent impacts (like levels of nutrient runoff). For example, if a cell is designated as being in cropland, then a given set of processes and parameters are assumed to operate, conditioned on ecological features such as slope and soil type. Variation across individuals or responses to external stimuli, like changing prices or the effects through expected profits of changing policies, cannot be captured until the economics components are added to the PLM. The second type of contribution being made by the economics extension of the PLM is in modeling these conditional human interactions. Models of farmer's behavior, both in crop choice and best management practices adoption, as functions of ecological and economic forces are currently underway. Other sectors, such as transportation, will follow.
Data are first collected to calibrate or initialize the model for a given set of ecosystems and a particular landscape. Effort is then devoted to simulating past behavior of the landscape. This allows for calibration of the data and functions of the model against actual historical data. For the Patuxent project, historical land use and geographic data (GIS) have been acquired from the Maryland Department of Natural Resources (DNR), Maryland Department of Environment (MDE), and the US Geological Survey (USGS), and NOAA. Land use maps for 1973, 1981, and 1985 (with 1991 now available), as well as mappings of streams, soils, slopes, contaminants, highways, marinas, railroads, water quality, and wetlands have been acquired, along with detailed historical climate records. The PLM contains about 6,000 spatial cells each containing a GEM with 21 state variables. It is to be calibrated with land use from 1973, 1981, 1985, and 1991, stream flow, water quality and other data . The intent is to run scenarios to the year 2020.
The spatial resolution of the PLM (0.4 km2) allows evaluation of impacts from changes in land use type or practices for particular groups of cells in the watershed. The aim is to be able to estimate selected key indicator variables for the Patuxent River watershed. The model can estimate impacts of specific land use patterns on loads to the Patuxent River estuary. When these changes are programmed, the simulation can estimate impacts on runoff variables including nitrogen, phosphorus, organic matter and suspended inorganic sediments. For example, in the growth process nutrients are removed from soil water, which affects the quantity and quality of vegetative growth (particularly roots), which in turn affects the rates of soil erosion and nutrient retention. Application of fertilizers and mechanical soil manipulation (e.g. tillage) affect nutrient dynamics and erosion and the PLM is designed to provide estimates of these effects for the watershed. The effects of vegetative buffers and retention ponds on urban and agriculture runoff can be simulated as these plants retard erosion and take up nutrients. Evaluations of various mixes and ratios of land use can also be performed.
The State of Maryland has significant experience with watershed- based approaches to water quality protection and restoration. This has come about largely as a part of the multistate federal Chesapeake Bay Program and because the water quality of downstream tidal and estuarine waters has been recognized to be heavily influenced by upstream sources, particularly nonpoint sources. In addition, the nature of the Maryland portion of the Bay watershed lends itself to delineation of discrete tributary watersheds which include tidal rivers. A major focus of the program to restore the water quality of the Bay involves a "tributary strategy" in which the sources of pollutants are estimated for each tributary watershed, fluxes are modeled, loadings are related to ecological conditions and living resources in the receiving subestuary, and goals are set for reduction of contaminants by generating sector (e.g. sewage treatment plants and agriculture) and location in the watershed.
Two prominent and relatively well-studied tributary watersheds in Maryland are the Patuxent river, which drains the rapidly suburbanizing region between Washington and Baltimore, and the Choptank river, which drains largely agricultural land on the Delmarva Peninsula. Because both of these watersheds are almost completely within the state of Maryland, and two of the laboratories of the University of Maryland Center for Environmental and Estuarine Studies are located on the estuaries of these two tributaries, they have been reasonably well studied and there is much scientific information to build upon in order to demonstrate approaches to watershed scale management of water quality. Furthermore, there exists a strong collaborative relationship among state environmental and natural resource management agencies (MDE and DNR), agricultural agencies, and university scientists. We will first focus on the Patuxent watershed and then, if additional funding becomes available, we will extend our research to the Choptank region.
The project can be divided into three major groups of scientific studies structured around the three major categories of questions we are attempting to address;
In addition, there is a key element in the project on applications to management.
What are the quantitative, spatially explicit and dynamic linkages between land use and terrestrial and aquatic ecosystem productivity and health? Some more specific subquestions under this heading include:
To address these questions we will build on our existing modeling and monitoring work, in particular with the extended Patuxent Landscape Model (PLM). As discussed above, our approach can directly address these questions in an integrated way, but it is complex and difficult to assemble all the pieces of the model, along with its requisite calibration data sets and scenario analyses. This project will add several features to the data collection and modeling that will make it much more effective as both a scientific and a management tool.
Specific tasks include:
What are the quantitative effects of various combinations of natural and anthropogenic stressors on ecosystems and how do these effects change with scale? Some more specific subquestions under this heading include:
To address these questions we will build on our ongoing studies in the Multiscale Experimental Ecosystem Research Center (MEERC), which will directly address these questions at scales ranging from microcosms to mesocosms. What is currently missing from the MEERC program is the connection to the larger watershed scale. The current project will provide that connection, and thereby greatly increase the effectiveness of both the MEERC program and the current project.
Specific tasks include:
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? For example, Bingham et al. (1995) identified two broad areas of research in need of improvement:
To address these questions we will build on our ongoing EPA funded Patuxent Ecological Economic Modeling and Valuation Project (see below), which is a preliminary attempt to link ecological and economic landscape models ( Bockstael et al. 1994 ). The current project will allow us to significantly extend this work and directly address the critical valuation and management issues. Some of the work will be mainly conceptual (how can we use this integrated modeling approach to address valuation issues?) and some will be applied (what sort of answers do we get using various valuation assumptions and how can they be used in management?)
Specific tasks include:
Last updated Dec.22, 1997 by Alexey Voinov