Regionalization of Low Flow Analysis in Data Scarce Region: The Case of the Lake Abaya-Chamo Sub-basin, Rift Valley Lakes Basin, Ethiopia

Abstract

Prediction of low flows in ungauged catchments is desirable for planning and management of water resources development and for sustaining the environment. The main objective of this study was to regionalize low flow indexes (the baseflow index BFI, Q80, Q90, and Q95) in the Lake Abaya–Chamo sub-basin by using multiple linear regression models. To develop the regional equation, nine baseflow separation methods were compared: two digital graphical methods and seven recursive digital filters were compared and applied in eight gauged catchments. The methods were evaluated through the coefficient of determination (R2) and the root mean square error (RMSE) as performance measures. The flow duration analyses were conducted to compute the flow exceedance quantiles Q80, Q90, and Q95. Regionalizing those indexes required the identification of homogeneous regions, which was accomplished through cluster analysis, based on physiographic and climatic data. Three significantly different homogeneous areas were identified using k-means clustering, and multiple linear regression models were developed for every low flow index in each homogeneous region. The R2 values in the model developed for BFI, Q80, Q90, and Q95 range from 0.75 to 0.98 throughout the region. For checking the performance of the model, verification of regional models was carried out by determining the relative error over four gauged catchments assuming they were ungauged. All regional models performed well by having relative errors <10% in the regions showing high performance. Therefore, the developed regional models could potentially solve the low flow estimation in the vast majority of ungauged catchments in the sub-basin. Consequently, current and future water resources development endeavors may use such estimation methods for planning, designing, and management purposes.