Lumped Modeling and GIS in Flood Prediction

Abstract

Hydrology is a geoscience linked to processes occurring at the earth’s surface. Application of geographic information systems (GIS), a computer based methodology of capture, storage, manipulation and display of spatially distributed data, is a predictable step in the evolution of hydrology. The ability of GISs to map spatial attributes such as land use and cover, soil types, rainfall, etc., to any degree of resolution is not matched by the present technology of hydrologic data collection for majority of watersheds. It is suggested that lumped hydrology models, specifically the unit hydrograph method, will continue to play a significant role in prediction of design flood hydrographs. Application of GISs improves estimation of unit hydrographs by new techniques, making the use of whatever spatial information is available for the watershed. Three approaches to unit hydrograph derivation and application using a GIS are discussed; the geomorphoclimatic, distributed, and dimensionless unit hydrographs, respectively. It is shown that the GIS derived unit hydrographs are in fact distributed rainfall-runoff models. The application of a GIS also facilitates the mergence of deterministic and stochastic models into one unified modeling approach. Regional flood analysis are greatly improved through GIS support. Examples of GIS supported flood hydrograph and flood frequency curve prediction are given. It is a widely held view that hydrology as a science has failed to make a significant progress in recent years. It is suggested that the future of hydrology may largely depend on our ability to successfully develop and integrate new generation of hydrologic models with the emerging technologies of GIS and remote sensing.KeywordsGeographic Information SystemExcess RainfallUnit HydrographSoil Conservation ServiceFlood HydrographThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.