Abstract
Study regionThis research is carried out in the Kabompo Basin, a headwater catchment of the Zambezi Basin which has an area of 67,261 km2. Kabompo River originates in North-Western Province of Zambia between the Zambezi and Congo River Basins. Study focusThis study focuses on error propagation of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center-MORPHing (CMORPH) rainfall product on simulated streamflow. Assessments are based on automated multi-objective calibration of the Representative Elementary Watershed (REW) model (2006–2012). Parameters of the model were optimized using the ɛ-NSGAII algorithm. Assessments on error propagation targeted streamflow modelling for hydrograph shape and volume, specific hydrograph characteristics, and water balance composition. New hydrological insights for the regionBy use of multiple objective functions, this study shows that uncorrected CMORPH results in substantial augmentation of rainfall error to streamflow simulation mismatch whereas bias corrected estimates result in attenuation of error. Analysis for water balance composition has great potential to improve application of satellite precipitation products in water management and decision making in the Zambezi basin. This study advises optimization of model parameters for each respective rainfall input data source so to identify outcomes and effects of respective rainfall data sources on the simulated water balance composition and closure.
Highlights
Propagation effects of rainfall errors in streamflow simulation commonly are assessed by use of in situ-observed rainfall (Fallah et al, 2020; Shin and Kim, 2019)
Observed streamflow hydrographs indicate that the Kabompo Basin is a slow responding system as is evident from lack of rapid changes in streamflow over short periods by high rainfall
Though the model shows small underestimation of highest peaks and small overestimation of baseflow, the simulated streamflow successfully resembles the observed streamflow pattern during both the calibration (Y = 0.77) and validation period (Y = 0.71). These objective function values are appropriate to allow for error propagation analysis in this study
Summary
Propagation effects of rainfall errors in streamflow simulation commonly are assessed by use of in situ-observed rainfall (Fallah et al, 2020; Shin and Kim, 2019). This is the case for the vastly ungauged Zambezi Basin in southern Africa (Winsemius et al, 2006) which supports livelihoods of over 40 million inhabitants (Hughes and Farinosi, 2020). To overcome the constraints by limited rain gauge data, satellite rainfall estimates (SREs) can be considered to supplement, or to replace rain gauge data. Satellite rainfall products provide areal coverages and allow construction of time series since observations are consistently repeated over time.
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