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Low-density precipitation measurements impair the ability of hydrological models to estimate surface water resources accurately. Remote sensing techniques and climate models can help to improve the estimation of the space-time rainfall variability. However, they alone are not good enough to be used in surface models built to support water management. In this research, we test the improvement of rainfall field estimation by using hydrological modelling based on the premise that a higher hydrological performance generally implies that precipitation is more consistent with streamflow observations and evaporation estimates in the basin. The SWAT model was forced with two satellite and rain gauge blending techniques and with the traditional IDW deterministic interpolation method from stations. The three simulated streamflows were compared separately against observed records. We do not only focus the comparison on one hydrological performance metric but also conduct a deeper evaluation using several hydrological signatures and statistics. We included the bias, the temporal correlation, the relation of general variability, and an analysis of the Flow Duration Curves (we found that low and medium segments were estimated correctly, whereas the high segments were underestimated). We conclude that either combination technique has its advantages over the other and that both outperform the performance achieved by the IDW in most of the defined criteria, with an overall 10% improvement and with individual streamflow gauge performance enhancement up to 50%.
Duque-Gardeazábal et al. (Tue,) studied this question.