GIS implications for hydrologic modeling: Simulation of nonpoint pollution generated as a consequence of watershed development scenarios

João, Elsa M.; Walsh, Stephen J.

doi:10.1016/0198-9715(92)90052-s

Cited by 32 publications

(11 citation statements)

References 8 publications

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“…Integrated hydrological computer models are increasingly used to facilitate watershed planning and management (Joao and Walsh, 1992;Wilson, 1996;Lohani et al, 2002). One way to build hydrological models capable of modeling complex natural systems is to integrate a number of simpler process-based sub-models.…”

Section: Introductionmentioning

confidence: 99%

Effects of distribution-based parameter aggregation on a spatially distributed agricultural nonpoint source pollution model

Chen

Mackay

2004

Journal of Hydrology

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Section: Introductionmentioning

confidence: 99%

Effects of distribution-based parameter aggregation on a spatially distributed agricultural nonpoint source pollution model

Chen

Mackay

2004

Journal of Hydrology

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“…Geographic information systems (GIS) are being used increasingly to model biophysical processes, landscape characteristics and feature distributions (Walsh et af., 1990;Davis and Dozier, 1990;Moore et af., 1991;Joao and Walsh, 1992). GIS technologies are characterized by data organized within a spatial coordinate system, by hardware and software systems used to encode, store, retrieve, manipulate and display spatial information, and by a set of analytical tools and techniques used to integrate disparate data for model building and spatial analyses (Goodchild, 1992).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Parameters for Assessing Potential Wetness: Comparison of Model Responses Through Gis

Townsend

Walsh

1996

Earth Surf. Process. Landforms

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A geographic information system (GIS) is utilized to model wetness potential for a portion of Uwharrie National Forest, North Carolina. The wetness index is derived from TOPMODEL, a hillslope-scale runoff simulation model. The wetness index is a distributed-parameter model, with the input parameters obtained from a digital elevation model (DEM) and Soil Conservation Service (SCS) soils data. The primary objectives of the research are to: (1) compare methods of estimating soil parameters for input into the wetness potential model; and (2) determine how the model outputs vary spatially as a consequence of different methods of estimating soil parameters. Three methods of estimating soil parameters are used: (a) assuming uniform soil properties; (b) using SCS data presented as ranges; and (c) using alternative literature-based estimates of soil parameters. Results indicate that the wetness model responds similarly regardless of how the soil parameters are estimated, but differences in the spatial variability of the wetness potentials occur as a result of estimating soil parameters through alternative approaches. Correlation, pair-wise regression and analysis of regression residuals are used to compare model responses within a GIS environment.

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“…Poiani and Bedford (1995) recently presented a cursory review of GE-based NPS pollution models emphasizing surface applications. Numerous hydrologic-water quality models of runoff and soil erosion have been used with a GIS to determine surface sources of NPS pollutants from watersheds (Pelletier, 1985;Potter et al, 1986;Oslin et al, 1988;Sivertun et al, 1988;DeRoo et al, 1989DeRoo et al, , 1992Rudra et al, 1991;Bhaskar et al, 1992;Drayton et al, 1992;Joao & Walsh, 1992;Tim et al, 1992;Walker et al, 1992;Wolfe, 1992;He et al, 1993;Heidtke & Auer, 1993;Levine et al, 1993;Mitchell et al, 1993;Warwick & Haness, 1994) agricultural areas (Hopkins & Clausen, 1985;Gilliland & Baxter-Potter, 1987;Hession & Shanholtz, 1988Panuska & Moore, 1991;Hamlett et al, 1992;Lee & White, 1992;Geleta et al, 1994;Tim & Jolly, 1994) and urban areas (Smith & Brilly, 1992;Smith, 1993;Ventura & Kim, 1993). In addition, several groundwater models have been coupled to a GIS to simulate water flow and/or NPS pol-lutants in aquifers (Kernodle & Philip, 1989;Baker & Panciera, 1990;Hinaman, 1993;Roaza et al, 1993;El-Kadi et al, 1994;Darling & Hubbard, 1994).…”

Section: Gis-based Models For Nps Pollution Estimationmentioning

confidence: 99%

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Corwin

2015

SSSA Special Publications

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In recent years, worldwide attention has shifted from point source to non-point source (NPS) pollutants, particularly with regard to the pollution of surface and subsurface sources of drinking water. This is due to the widespread occurrence and potential chronic health effects of NPS pollutants. The ubiquitous nature of NPS pollutants poses a complex technical problem. The area1 extent of their contamination increases the complexity and sheer volume of data required for assessment far beyond that of typical point source pollutants. The spatial nature of the NPS pollution problem necessitates the use of a geographic information system (GIS) to manipulate, retrieve, and display the large volumes of spatial data. This chapter provides an overview of the components (i.e., spatial variability, scale dependency, parameter-data estimation and measurement, uncertainty analysis, and others) required to successfully model NPS pollutants with GIS and a review of recent applications of GIS to the modeling of non-point source pollutants in the vadose zone with deterministic solute transport models. The compatibility, strengths, and weaknesses of coupling a GIS to deterministic one-dimensional transport models are discussed. BACKGROUND IN NON-POINT SOURCE POLLUTANTS

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GIS implications for hydrologic modeling: Simulation of nonpoint pollution generated as a consequence of watershed development scenarios

Cited by 32 publications

References 8 publications

Effects of distribution-based parameter aggregation on a spatially distributed agricultural nonpoint source pollution model

Effects of distribution-based parameter aggregation on a spatially distributed agricultural nonpoint source pollution model

Estimation of Soil Parameters for Assessing Potential Wetness: Comparison of Model Responses Through Gis

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

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