Performance of satellite rainfall estimates for flood and drought monitoring

In recent years, many researchers indicated that earth-observing satellites perform well in measuring or estimating precipitation rates. However, it has been highlighted that the performance of satellite rainfall estimates (SREs) is affected by many factors. In this study, a meta-data analysis was conducted to assess the performance of different SREs for flood and drought monitoring under diverse settings to test the influence of factors related to climate, topography, watershed size, and length of SREs data record. Koppen climate classification was used to classify the different studies into different climatic zone. Mean elevation was used as an indicator of varying topography. Studies were grouped into three different categories depending upon their available data record length. The impact of various factors on the performance of SREs was assessed with three statistical indices: Pearson correlation coefficient, Root Mean Square Error, and Nash-Sutcliffe Efficiency. Results showed that the performance of SREs for drought and flood monitoring is influenced by the climate, length of the data record, interactions between the applied hydrological model and type of SRE, and topography. Microwave-based SREs performed were found to perform better than infrared-based SREs. Low lying landscapes exhibited higher accuracy of SREs in flood and drought monitoring compared to complex mountainous terrain. In most cases, IMERG and CMORPH outperformed other SREs.  IMERG showed the best drought monitoring performance with Pearson correlation values ranging between 0.96-0.99. It was found that the best SREs that can represent the observed streamflow vary depending on the type of hydrological models. Also, the hydrological model performance for flood prediction significantly increases (p<0.05) when using the SREs for model calibration compared to when the model is manually calibrated with historical gauge data. Bias-adjusted SREs performed better than their counterpart. Overall, SREs offer great potential for flood and drought monitoring, but their performance needs to be enhanced for hydrological applications.