Improving Rainfall Fields in Data-Scarce Basins: Influence of the Kernel Bandwidth Value of Merging on Hydrometeorological Modeling

Abstract

Accurate rainfall fields are important for several hydrological and meteorological applications. In poorly instrumented basins, the lack of rain gauges heavily affects the spatial rainfall estimation, yet neither remote sensed nor climate model estimates are good enough for management applications. To tackle this problem, we investigate the impacts of combining both sources of information by varying the kernel bandwidth value of the double smoothing merging algorithm and analyzing the error of the rainfall fields. We explored the correlation between rain gauge density and bandwidth and compared the results against classical geostatistical interpolation methods based solely on in-situ measurements. Propagation of rainfall error into hydrological modelling is usual, and therefore we evaluated the influence of the bandwidth in streamflow simulations implementing two hydrological models. The hydrological evaluation considered the analysis of hydrological signatures rather than just performance metrics. We found that there is a clear correlation between kernel bandwidth and monitoring network density and that the bandwidth also affects hydrological performance. Simple bilinear downscaling did not produce a significant difference in meteorological or hydrological errors, and rain gauge network configuration also impacts the error of the field. We conclude that merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains. Reliable rainfall fields are important for estimating water resource availability and other related applications. However, using just rain gauges can misrepresent the estimated spatial variability of the field. Around the world, the number of rainfall gauges has been decreasing, making this task more challenging. Satellite and reanalysis data can help to overcome these problems, but their direct use is also insufficient. Merging the two mentioned data sources (in-situ and reanalysis data) is the option the authors implemented in this work. The authors focused on evaluating the advantages and disadvantages of the merging by comparing both rainfall and streamflow observations with estimates simulated by a hydrological model. The authors chose the double smoothing algorithm because it is oriented to enhance rainfall estimates in areas with very sparse rain gauge data, yet we evaluate its behavior in two basins with different monitoring conditions. The authors conclude that the merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains to improve hydrological modelling estimates and use their results in water resource management and planning, in climate classification, and in the study of droughts and floods, among others.