Abstract
Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.
Highlights
Ground rainfall estimates using rain gauges is a traditional way of the most common methods (Brauer et al 2016) that is used to correct the bias of satellite rainfall estimates (Ma et al 2019; Park et al 2019) for water resources application and related studies
Accurate and reliable satellite-based rainfall is necessary to be merged with gauge-based rainfall to catch the diurnal cycle of precipitation in the tropics (Rahmawati 2020)
Before satellite-based rainfall is merged to rain gauge observation, it is important to evaluate the performance of satellite-based rainfall
Summary
Ground rainfall estimates using rain gauges is a traditional way of the most common methods (Brauer et al 2016) that is used to correct the bias of satellite rainfall estimates (Ma et al 2019; Park et al 2019) for water resources application and related studies. It is the input for hydrological modeling in basin scale (Andersen et al 2001) and for modeling base flow (Becker and Braun 1999). Satellite observing system has a complex uncertainty and error because of precipitation estimates from cloud and precipitation parameters (Stephens and Kummerow 2007)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
