The Combined Power of Double Mass Curves and Bias Correction for the Maximisation of the Accuracy of an Ensemble Satellite-Based Precipitation Estimate Product

Precise estimation of the spatial and temporal characteristics rainfall is essential for producing reliable catchment response needed proper management water resources. However, in most parts world, gauged stations are sparsely distributed fail to properly capture variability rainfall. Furthermore, data can sometimes be short length or require validation. Following this, we present a procedure that enhances trustworthiness accuracy estimations five satellite-based precipitation estimate (SPE) products by validating them using 1779 across Thailand. The SPE considered include CMORPH-BLD; TRMM-3B42; CHIRPS; CHIRPS-PL; TRMM-3B42RT. Prior validation, dataset was verified double mass curve (DMC) analysis eliminate questionable inconsistent readings. This led improvement Nash–Sutcliffe Efficiency (NSE) between station interest its surroundings 13.9% (0.758–0.863), together with an average 11.8% increase products, whilst dropping only 7% dataset. Three different bias correction (BC) procedures were applied correct gauge-based gridded (GGR). Once DMC BC implemented together, performance found significantly. Finally, application ensemble weighted three best-performing bias-corrected (Bias-CMORPH-BLD, Bias-TRMM-3B42, Bias-CHIRPS) further enhanced NSE 0.907 0.880 calibration validation time periods, respectively. proposed DMC-based weighting multiple allows easy means obtaining daily remote locations sufficient accuracy.