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Please use this identifier to cite or link to this item: http://hdl.handle.net/10362/8334

Título: Hydrologic modeling and uncertainty analysis of an ungauged watershed using mapwindow-swat
Autor: Boluwade, Alaba
Orientador: Mahiques, Jorge Mateu
Pebesma, Edzer
Cabral, Pedro
Palavras-chave: GIS Applications
Uncertainty Analysis
Semi-distributed parameter model
Geographical Information Systems
Streamflow modeling
Soil and Water Assessment Tool
Land Use management
Digital Elevation Model
Water Quality
Issue Date: 5-Mar-2012
Relatório da Série N.º: Master of Science in Geospatial Technologies;TGEO0063
Resumo: Modeling of an ungauged watershed with the associated uncertainties of the input data is presented. The MapWindow versions of the Soil and Water Assessment Tool (SWAT) have been applied to a complex and ungauged watershed of about 248,000ha in an area close to the Niger River, Nigeria. The Kwara State Government of Nigeria in collaboration with the newly relocated former Zimbabwean farmers now occupied the largest portion of this watershed for an “Agricultural Estate Initiative ”. The government and these farmers are decision makers who need to take appropriate actions despite little or no data availability. SWAT being a physically based model, allow the use of Geographical Information System (GIS) inputs like the Digital Elevation Model(DEM), landuse and soil maps. The MapWindow-SWAT(MSWAT) involves processes like the Watershed Delineation, Hydrological Response Units (HRUs) Process and the SWAT run. The watershed was delineated into 11 subbasins and 28 HRUs. There were 8 landuse classes and 5 soil types. The model was able to simulate and forecast for several years(1990-2016). The results look 'reasonable' since there is no observed data from the watershed for statistical validation. However, using the Water Balance equation as a validation criteria, the correlation coefficient between the simulated rainfall and runoff was 0.84 for the subbasin 11 (outlet). Thereafter, the uncertainties in the continuous numerical input (i.e. rainfall) was examined using the Data Uncertainty Engine (DUE). One parameter exponential probability model was used for the daily rainfall amount based on the histogram. 700 realizations were generated from this uncertain input. Randomly selected numbers of the realizations were prepared and used as inputs into the MWSWAT model. It was surprising that there were no changes in the results when compared to the initial 'real' value (outflows from outlet) although other parameters of the model were kept constant.
Descrição: Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies
URI: http://hdl.handle.net/10362/8334
Appears in Collections:ISEGI - MSc Dissertations Geospatial Technologies (Erasmus-Mundus)

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