The Need for Scale Sensitive Approaches in Spatially Explicit Land Use Change Modeling

This paper introduces some of the issues that are relevant to the spatially explicit modeling of land use systems. A short overview is given of the ways and means in which a number of different land use change models describe the land use system. Specific attention is given to the empirical modeling approach used in the CLUE (Conversion of Land Use and its Effects) modeling framework. This approach is demonstrated for three case studies in China, Ecuador and the Atlantic Zone of Costa Rica. These case-studies illustrate the methodology for multi-scale analysis of land use driving factors and their application in spatially explicit modeling exercises. Model functioning, performance and limitations are discussed. The presented case-studies indicate that empirical modeling results can contribute to a better theoretic imbedding of land use change research in scale sensitive and integrated theories.     

MODELING THE BUILDUP AND WASHOFF OF POLLUTANTS ON URBAN WATERSHEDS1

ABSTRACT: A model for urban stormwater quality was developed in this study. The basis for the model is the process by which pollutants build up on the watershed surface. For the wet climate of the study site, it was assumed that there exists an interval of time over which the pollutant buildup equals the pollutant washoff (no accumulation of pollutant). The buildup model was represented by a linear function of the antecedent dry time. The buildup function was then linked with a pollutant washoff model represented by a power function of the storm runoff volume. Various time intervals for no net accumulation were tested to calibrate the model. The model was calibrated to observed data for two small urban basins in Baton Rouge, Louisiana, and model results were used to analyze the behavior of phosphorus concentrations in storm runoff from these basins over a long period of time.     

EFFECTS OF WATERSHED REPRESENTATION ON RUNOFF AND SEDIMENT YIELD MODELING1

ABSTRACT: This paper evaluates the effects of watershed geometric representation (i.e., plane and channel representation) on runoff and sediment yield simulations in a semiarid rangeland watershed. A process based, spatially distributed runoff erosion model (KINEROS2) was used to explore four spatial representations of a 4.4 ha experimental watershed. The most complex representation included all 96 channel elements identifiable in the field. The least complex representation contained only five channel elements. It was concluded that oversimplified watershed representations greatly influence runoff and sediment yield simulations by inducing excessive infiltration on hillslopes and distorting runoff patterns and sediment fluxes. Runoff and sediment yield decrease systematically with decreasing complexity in watershed representation. However, less complex representations had less impact on runoff and sediment‐yield simulations for small rainfall events. This study concludes that the selection of the appropriate level of watershed representation can have important theoretical and practical implications on runoff and sediment yield modeling in semiarid environments.     

Using the ECLPSS software environment to build a spatially explicit component-based model of ozone effects on forest ecosystems

We have developed a modeling framework to support grid-based simulation of ecosystems at multiple spatial scales, the Ecological Component Library for Parallel Spatial Simulation (ECLPSS). ECLPSS helps ecologists to build robust spatially explicit simulations of ecological processes by providing a growing library of reusable interchangeable components and automating many modeling tasks. To build a model, a user selects components from the library, and then writes new components as needed. Some of these components represent specific ecological processes, such as how environmental factors influence the growth of individual trees. Other components provide simulation support such as reading and writing files in various formats to allow inter-operability with other software. The framework manages components and variables, the order of operations, and spatial interactions. The framework provides only simulation support; it does not include ecological functions or assumptions. This separation allows biologists to build models without becoming computer scientists, while computer scientists can improve the framework without becoming ecologists. The framework is designed to operate on multiple platforms and be used across networks via a World Wide Web-based user interface. ECLPSS is designed for use with both single processor computers for small models, and multiple processors in order to simulate large regions with complex interactions among many individuals or ecological compartments. To test Version 1.0 of ECLPSS, we created a model to evaluate the effect of tropospheric ozone on forest ecosystem dynamics. This model is a reduced-form version of two existing models: , which represents an individual tree, and , which represents forest stand growth and succession. This model demonstrates key features of ECLPSS, such as the ability to examine the effects of cell size and model structure on model predictions.     

VELOCITY EQUATION FOR WATER QUALITY MODELING IN GEORGIA1

ABSTRACT: Tabletop water quality modeling still plays an important role in the water pollution control activities of the Georgia Environmental Protection Division. Tabletop models are those developed with out the aid of extensive field data. One important component of GEORGIA DOSAG, our basic water quality model, is the equation used to predict flow through velocity. However, Georgia is characterized by wide physiographic diversity which reduces the effectiveness of uncalibrated velocity equations. Using 15 years of accumulated time-of-travel studies, a series of empirical velocity equations were developed and calibrated to various physiographic conditions in Georgia. Equations are available for each major soil province and for three stream flow ranges within each province - Q<100 cfs, 100<Q<1000 cfs, and Q>1000 cfs. Now, in the absence of extensive field data, we have data based velocity equations which can be tailored to each site under study.     

Simple Spatial Modeling Tool for Prioritizing Prescribed Burning Activities at the Landscape Scale

Abstract:   Resources for prescribed fire are frequently insufficient to manage public lands for all conservation and resource management objectives, necessitating prioritization of the application of fire across the landscape within any given year. Defining tradeoffs when applying prescribed fire to large landscapes is problematic not only because of the complexity of weighing competing management objectives at the landscape scale, but also because of the difficult nature of independently applying need-to-burn criteria to large areas. We present a case study of a simple modeling process implemented at Eglin Air Force Base in the Florida Panhandle (U.S.A.) to prioritize the application of prescribed fire. In a workshop setting, managers and biologists identified key conservation criteria and landscape management objectives that drive the application of prescribed fire. Remote sensing and other spatial data were developed to directly or indirectly represent all these criteria. Using geographic information system software, managers and biologists weighted each criterion according to its relative contribution to overall burn prioritization, and individual values for the criterion were scored according to how they influence the need to burn. Subsequently, this process has been validated and modified through ecological monitoring. This modeling process has also been applied to the 77,400-ha Blackwater River State Forest, public land adjacent to Eglin Air Force Base, demonstrating its applicability to lands with varying management priorities. The advantages of this model-based approach for prioritizing prescribed fire include the reliance on accessible, inexpensive software, the development of spatially explicit management objectives, the ease of transferability, and clearly stated assumptions about management that may be tested and reviewed through monitoring and public comment.     

BUILDUP,WASHOFF, AND EVENT MEAN CONCENTRATIONS1

ABSTRACT: Most watershed water quality simulation models require the user to specify pollutant buildup and washoff rate parameters for pollutants, by land use. Buildup and washoff rates are difficult to measure directly, and only limited guidance and few observed data are available from the literature. Many studies, however, report storm event mean concentrations (EMCs). These EMCs must arise as a result of the buildup and washoff processes, but typically represent the net contribution from a variety of pervious and impervious surfaces. This paper explores the relationship between EMCs and buildup/washoff parameters. An assumption of the mathematical form of the buildup/washoff relationship gives an algebraic expression for the EMC consistent with model assumptions. This yields techniques to separate observed EMCs into contributions from different land uses and from pervious and impervious surfaces. Given this relationship, numerical optimization may be used to estimate site specific values of buildup and washoff parameters from observed storm EMCs for use in modeling. Use of this approach helps ensure that model parameters are consistent with observed data, providing a rational starting point for final model calibration. Several site examples demonstrate use of the method.     

USE OF A PREDICTIVE MODEL IN EVALUATING WATER CONSUMPTION CONSERVATION1

ABSTRACT: The effectiveness of water conservation policies on reduction of water consumption was evaluated by use of a multiple regression predictive model. Data were obtained from eight Iowa communities, with regard to water consumption and water conservation policies adopted during the drought of 1977. The proposed model, which takes precipitation, temperature, month effects, and uniqueness of communities into account, is considered superior to the use of simple percent changes in evaluating the significance of reduction in water consumption due to conservation efforts. Among the variety of policies reviewed, mandatory policies with per capita restrictions were found to be most effective. However, voluntary conservation policies also resulted in significant reduction in some communities located in close proximity to other communities with severer water shortages. High credibility of local governments as to water shortage information was singled out as the most important factor in successful conservation by water resource policymakers of these communities. Use of the proposed predictive model for water consumption was demonstrated. Recommendations regarding future water conservation were made.     

Spatial modeling and prediction under stationary non-geometric range anisotropy

For modeling spatial processes, we propose a rich parametric class of stationary range anisotropic covariance structures that, when applied in R², greatly increases the scope of variogram contors. Geometric anisotropy, which provides the most common generalization of isotropy within stationarity, is a special case. Our class is built from monotonic isotropic correlation functions and special cases include the Matérn and the general exponential functions. As a result, our range anisotropic correlation specification can be attached to a second order stationary spatial process model, unlike ad hoc approaches to range anisotropy in the literature. We adopt a Bayesian perspective to obtain full inference and demonstrate how to fit the resulting model using sampling-based methods. In the presence of measurement error/microscale effect, we can obtain both the usual predictive as well as the noiseless predictive distribution. We analyze a data set of scallop catches under the general exponential range anisotropic model, withholding ten sites to compare the accuracy and precision of the standard and noiseless predictive distributions.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.