INCREASING THE SPATIAL RESOLUTION AND ACCURACY OF PRECIPITATION DATA WITHIN THE SWAT MODEL THROUGH THE INCORPORATION OF CHIRPS DATA

The Soil and Water Assessment Tool (SWAT) is a widely used hydrologic model that can determine climate change impacts on water balance, streamflow, and sediment and nutrient transport. However, one of the SWAT tool’s largest limitations is that it relies heavily on the availability of reliable and consistent datasets, especially those for precipitation, due to its poor simulation of data when compensating for incomplete datasets. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset from the University of California – Santa Barbara is a 30-year rainfall dataset that combines moderate-high resolution satellite imagery with in situ station data. Unlike precipitation products derived solely from satellite imagery, CHIRPS is able to correct for estimates that often underestimate the intensity of precipitation events by including station data. This study compares the accuracy of the SWAT model estimation of streamflow over a 10-year period while using the CHIRPS dataset, rainfall gauge station data, and the Climate Forecast System Reanalysis dataset respectively in the data-scarce Nzoia Basin in western Kenya.