Abstract:Resolution of data is a sensitive issue in environmental modeling. Professionals from diverse background use Geographic Information Systems (GIS) for environmental modeling. Availability of GIS software and publicly available digital data make modeling more accessible to anyone, but without proper understanding of the resampling and resolution of the digital database, model results are of little practical significance. It is imperative that the government agencies and their contractors responsible for using GIS-based modeling to implement regulatory goals must have strong foundational knowledge of resolution of data and their implications. This study utilizes the SWAT (Soil and Water Assessment Tool) model integrated with ArcView to examine how sensitive the SWAT model was to the resolution of Digital Elevation Models (DEMs) while predicting the streamflow. This research uses a case study to demonstrate to the modeling community that resolution of data matters when predicting flow. If there were no effect of resolution on the modeled results then the original 90 m DEM and the original 30 m DEM resampled to 90 m would show similar trends. Initial input layers into SWAT were: DEMs, soils, landuse (LU) and meteorological data. The model-predicted streamflow was validated against USGS (US Geological Survey) stream gauge data. DEMs are available at 30, 90 and 300 m resolution originally; therefore, this study analysed the sensitivity of the predicted streamflow at 30, 90 and 300 m resolution. Results indicated that SWAT is indeed sensitive to the resolution of the DEMs: original 90 and 30 m DEM resampled to 90 m did not show the same trend. Therefore, the effects of resolution cannot be ignored and resampling may not be adequate in modeling stream flows using a distributed watershed model.
As changes in landuse and the demand for water accelerate, regulators and resource managers are increasingly asked to evaluate water allocation against the need for protection of in-stream habitat. In the United States, only a small number of river basins have the longterm hydrograph data needed to make these assessments. This paper presents an example of how to bridge the conceptual and physical divide between GIS-based watershed modelling of basin-discharge and in-stream hydraulic habitat models. Specifically, we used a Soil and Water Assessment Tool (SWAT) model for the Hillsborough River to produce data for use in a Physical HABitat SIMulation (PHABSIM) model of the same river. This coupling of models allowed us to develop long-term discharge data in ungauged river systems based on watershed characteristics and precipitation records. However this approach is not without important limitations. Results confirm that accuracy of the SWAT-predicted hydrograph declines significantly when either the DEM resolution becomes too coarse or if DEM data are resampled to a coarser or finer resolution. This is due to both changes in the size and shape of the river basin with the varying DEMs and subsequent shifts in the proportions of land use, soils and elevation. Results show the use of 30 m DEMs produced hydrographic patterns amenable for using in-stream habitat protocols like PHABSIM model, especially where little or no hydrographic and land use information exists.
Adequate characterization of potential evapotranspiration (PET) plays a critical role in hydrologic budgets, rainfall–runoff models, infiltration calculations, and drought prediction models (to name a few applications). The availability of reliable and continuous meteorological data remains a challenge; therefore, it is common to use modeled (simulated) meteorological data. This research used the Soil and Water Assessment Tool (SWAT) to estimate PET using different meteorological input data (simulated vs. real data) and the three commonly used PET calculation methods (viz. Penman–Monteith, Hargreaves, and Priestley‐Taylor). The overall goal of this research was to determine the accuracy of prediction using simulated and real meteorological data when used with three PET calculation methods. Initial input layers to SWAT were: digital elevation models, soils, and land use. Real meteorological data were obtained from three local meteorological stations, whereas simulated meteorological data were generated by SWAT using one nearby national meteorological site. The model‐predicted PET results were validated using independent PET measurements from Florida Automated Weather Network sites. The results of the study indicate that the difference in predicted PET between simulated (modeled) and real meteorology for a given PET calculation method is not significant; however, it is significant across the methods of PET calculation.
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