Estimation of Daily Streamflow Using Remote Sensing Data: The Case of Bilate River Sub-Basin, Ethiopia
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Date
2024-06
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Addis Ababa University
Abstract
Most river basins in the world are ungauged. Especially in developing countries, catchments have less coverage of gauging stations. In Ethiopia, most river basins are ungauged. Streamflow data are collected at more than 506 operational gauging stations. Improving data accuracy and reliability of streamflow estimation in poorly gauged stations is mandatory. Different Streamflow estimation methods are developing from time to time. Traditionally, streamflow is directly measured through manual or automated ground based instruments installed within a monitoring station. In fact, there are different methods of Streamflow estimation. This study was conducted to gain better understanding on the use of remote sensing products for streamflow estimation in Bilate River sub-basin, Ethiopia. Four Remote sensed (satellite/reanalysis) precipitation products of different categories, source and resolution, namely GPM_3IMERGM v 07, CHIRPS 4.8 Daily, TRMM 3B42 v7 and ERA5 AG were compared against in situ observations. The main aim was estimating streamflow using bias-corrected remote sensed products and comparing the results with observed flows. Statistical measures such as Bias measurements, Coefficient of Regression, the Root Mean Square Error and the Mean Error were used. Also, Bias Component Hits, Missed and False rainfall and rainfall detection capability analysis and contingency table score such the Critical Index Success, Probability of Detection, Bias Frequency, and False Alarm Ratio were used to compare the satellite precipitation products with the gauge station data. The comparison were made depending on the stations' locations, point-to-pixel and sub basin scale analysis. Eight meteorological stations and five hydrological stations within the catchment were screened as reference data. The result from the comparison of observed daily time series data for 32 years (1991-2022) with satellite precipitation product indicates CHRIPs and IMERG performed better in terms of rainfall depth than TRMM and ERA5. Also, the degree of agreement R2 using the four satellite precipitation products in Bilate river sub basin shows 0.9, 0.87, 0.8 0.77 for IMERG, CHRIPs, TRMM and ERA5 Ag respectively. For model calibration 11 years (2001-2011) and for validation process 4 years (2012-2015) were used to simulate HBV Light model. The model performance was evaluated based on three objective functions namely: Nash Sutcliffe Efficiency Coefficient (NSE), Relative Volumetric Error (RVE), and the Root Mean Square Error (RMSE). Satellite precipitation products IMERG and CHRIPS with ERA5 Ag Satellite (Temperature and potential evapotranspiration) products used as an input for HBV Light model. The calibration results for IMERG R2 =0.7431 and PBIAS=1.08, objective functions NSE= 0.54, RMSE =0.87 and RVE =-1.37 for CHRIPS R2 =0.7474 and PBIAS=4.94, objective functions NSE = 0.51, RMSE =3.59 and RVE = -5.65.
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Keywords
Streamflow, Remote sensing, Bias-correction, HBV Light, Bilate River, Ethiopia