76
N.M. Mattikalli and E.T. Engman
In urban watersheds, the spatial analysis capabilities of a GIS can be used for
hydrological analysis. Watershed attributes such as soils information (infiltration
rates, hydraulic conductivity, and storage capacities), surface characteristics (pervious, impervious, slope, roughness), geometry and dimensions of flow planes,
routing lengths (overland, gutter, and sewer) and geometry and characteristics of
routing segments can be efficiently stored and utilized for urban runoff calculations. Most of earlier studies have used GISs to derive parameters of lumped models. For example, Johnson (1989) used a GIS for generation of input data for digital map-based modeling system that supported lumped parameter models such as
unit hydro graph, time-area, and cascade of reservoirs. Moeller (1991) determined
input parameters for the HEC-l model while Sircar et al. 1991) derived time-area
curves. Djokic and Maidment (1991) used Arc/Info with a rational method to
determine inlet and pipe capacity of an urban storm sewer system. Greene and
Cruise (1996) employed Arcllnfo to derive urban watershed feature attributes
(location coordinates, parameters of runoff generating polygons, gutters and storm
drains) for input into a hydrologic modeling procedure to estimate runoff.
Vieux (1991) developed a procedure for modeling direct surface runoff using a
combination of a [mite element method and Arc/Info Triangular Irregular Network
(TIN) module. An internal integration of this model (viz. r. water jea) allows
seamless simulation of storm water runoff using flow networks (derived from
DEM) and spatially distributed parameters. Schultz (1994) presents three different
examples on hydrological modeling using remote sensing within the framework of
ILWIS and Arc/Info. These examples demonstrate merging of Landsat TM and
Meteosat geostationary image products and ancillary data (viz. DEM and its derived products) for rainfall! runoff modeling and water balance parameter computation at 30 m, 5 km and HSU scales. Ott et al. (1991) employed the HSU concept
in a rainfall-runoff model for flood forecasting and for impact assessment of
changing land-use in the Mosel basin. Fett et al. (1990) simulated rainfall-runoff
process in a vegetated hillslope area in the humid climate of the Volme river basin. This study utilized input parameters derived from remote sensing and elevation data, and employed a distributed hydrologic model on a pixel by pixel basis to
derive 3-day hydrographs. Romanowicz et al. (1993) employed the TOPMODEL
within Water Information System and simulated hourly runoff in the Severn basin,
UK.
4.3.5
Monitoring and Modeling of Water Quality
Applications of GIS and remote sensing to water quality have mainly concentrated
on Non Point Source (NPS) pollution. This is because remotely sensed data products (such as land-use/ land-cover) could be directly utilized in NPS modeling.
Several watershed models have been interfaced with GIS including the export
coefficient model, AGNPS and DRASTIC.
Agricultural Non Point Source pollution (AGNPS) model estimates nitrogen,
phosphorus and chemical oxygen demand concentration in runoff and assesses
agricultural impact on surface water quality based on spatially varying controlling
parameters (e.g., topography, soils, land-use etc.). Srinivasan and Engel (1994)
N.M. Mattikalli and E.T. Engman
In urban watersheds, the spatial analysis capabilities of a GIS can be used for
hydrological analysis. Watershed attributes such as soils information (infiltration
rates, hydraulic conductivity, and storage capacities), surface characteristics (pervious, impervious, slope, roughness), geometry and dimensions of flow planes,
routing lengths (overland, gutter, and sewer) and geometry and characteristics of
routing segments can be efficiently stored and utilized for urban runoff calculations. Most of earlier studies have used GISs to derive parameters of lumped models. For example, Johnson (1989) used a GIS for generation of input data for digital map-based modeling system that supported lumped parameter models such as
unit hydro graph, time-area, and cascade of reservoirs. Moeller (1991) determined
input parameters for the HEC-l model while Sircar et al. 1991) derived time-area
curves. Djokic and Maidment (1991) used Arc/Info with a rational method to
determine inlet and pipe capacity of an urban storm sewer system. Greene and
Cruise (1996) employed Arcllnfo to derive urban watershed feature attributes
(location coordinates, parameters of runoff generating polygons, gutters and storm
drains) for input into a hydrologic modeling procedure to estimate runoff.
Vieux (1991) developed a procedure for modeling direct surface runoff using a
combination of a [mite element method and Arc/Info Triangular Irregular Network
(TIN) module. An internal integration of this model (viz. r. water jea) allows
seamless simulation of storm water runoff using flow networks (derived from
DEM) and spatially distributed parameters. Schultz (1994) presents three different
examples on hydrological modeling using remote sensing within the framework of
ILWIS and Arc/Info. These examples demonstrate merging of Landsat TM and
Meteosat geostationary image products and ancillary data (viz. DEM and its derived products) for rainfall! runoff modeling and water balance parameter computation at 30 m, 5 km and HSU scales. Ott et al. (1991) employed the HSU concept
in a rainfall-runoff model for flood forecasting and for impact assessment of
changing land-use in the Mosel basin. Fett et al. (1990) simulated rainfall-runoff
process in a vegetated hillslope area in the humid climate of the Volme river basin. This study utilized input parameters derived from remote sensing and elevation data, and employed a distributed hydrologic model on a pixel by pixel basis to
derive 3-day hydrographs. Romanowicz et al. (1993) employed the TOPMODEL
within Water Information System and simulated hourly runoff in the Severn basin,
UK.
4.3.5
Monitoring and Modeling of Water Quality
Applications of GIS and remote sensing to water quality have mainly concentrated
on Non Point Source (NPS) pollution. This is because remotely sensed data products (such as land-use/ land-cover) could be directly utilized in NPS modeling.
Several watershed models have been interfaced with GIS including the export
coefficient model, AGNPS and DRASTIC.
Agricultural Non Point Source pollution (AGNPS) model estimates nitrogen,
phosphorus and chemical oxygen demand concentration in runoff and assesses
agricultural impact on surface water quality based on spatially varying controlling
parameters (e.g., topography, soils, land-use etc.). Srinivasan and Engel (1994)