Evaluating the Three-Cornered Hat Method for Satellite Precipitation Data Fusion and its Influence on Runoff Forecasting

Runoff forecasting remains a critical challenge in many basins worldwide, particularly those featuring a complex topography, where the scarcity of hydrometeorological data is a prevalent challenge. Data fusion offers a promising alternative to conventional single-source data modelling, which often fails to capture the full spatial and temporal variability of precipitation. By integrating multiple sources, data fusion seeks to generate enhanced satellite precipitation datasets, essential for data-driven runoff forecasting models. This study aims to evaluate the effectiveness of the Three-Cornered Hat (TCH) method for fusing satellite precipitation products (SPPs) and its influence on the performance of a Random Forest-based runoff forecasting model.
Three scenarios were evaluated: (i) a TCH-fused dataset combining three SPPs: Integrated Multi-satellitE Retrievals for GPM – Early Run (IMERG-ER), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) and the Global Satellite Mapping of Precipitation – Near Real Time (GSMaP-NRT); (ii) an individual SPP (IMERG-ER); and (iii) an already fused benchmark product, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). All scenarios performed comparably for lead times of 3, 6, 12, and 24 hours, with MSWEP slightly outperforming across Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Root Mean Square Error metrics. However, TCH demonstrated better bias reduction as reflected by the Percent Bias metric.
A key limitation of the fusion method was identified at hourly scales, where statistical dependence arises during periods with no precipitation over the basin, hindering the effectiveness of TCH. The introduction of a matrix regularization step addressed this issue. This study provides valuable insights for enhancing SPP fusion methods and offers a replicable framework for improving runoff forecasting, particularly in data-scarce regions and other hydrological contexts.