Abstract:In Indonesia, rainfall is still significant spatially and temporally. In order to gain optimal results from utilising water resources, we have to ensure that the precipitation data is provided in good quality and quantity. Several spatial rainfall measurement sources have become available in recent years, such as GPM data (Global Precipitation Measure). In this study, the GPM IMERG V.06 product was evaluated using rain gauge measurements in the Lau Simeme watershed in North Sumatra Province, Indonesia. The rel… Show more
“…Accurate and precise evaluations and regional as well as global precipitation estimations are important in scientific inquiries and practical applications. Satellite rainfall estimation has evolved significantly, with various products such as Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [5], Climate Prediction Center morphing method (CMORPH) [6], TRMM Multi-satellite Precipitation Analysis (TMPA) [7] and The Global Precipitation Measurement Mission (GPM) [8] is currently undergoing development to offer essential rainfall data to the academic community, specifically in areas where there is a scarcity of sufficient rain gauges or radar systems [9]. However, satellite-based precipitation estimates have advantages and disadvantages, highlighting the need for further improvement.…”
Rising air temperatures, increased rainy runoff, reduced dry season runoff, and severe weather conditions have intensified floods and droughts, significantly affecting the reservoir water supply. The accuracy of reservoir water balance is crucial for meeting water needs. The study compares satellite data and ground measurements to analyze the water budget of Sutami Reservoir in Indonesia. Satellite data collected included precipitation (Tropical Rainfall Measuring Mission-TRMM) and evaporation (Global Land Data Assimilation System-GLDAS). The water balance approach was utilized to analyze the water budget. The suitability tests used were Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (CC), and Relative Error (RE). The study revealed that the data from TRMM and GLDAS satellites closely resembled ground measurements. The reservoir water balance analysis revealed that satellite data aligns with ground measurements, indicating water shortages in the dry season and excess water in the rainy season. Satellite data is particularly beneficial for watershed management in areas lacking ground measurement equipment, as it can be analyzed for various purposes.
“…Accurate and precise evaluations and regional as well as global precipitation estimations are important in scientific inquiries and practical applications. Satellite rainfall estimation has evolved significantly, with various products such as Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [5], Climate Prediction Center morphing method (CMORPH) [6], TRMM Multi-satellite Precipitation Analysis (TMPA) [7] and The Global Precipitation Measurement Mission (GPM) [8] is currently undergoing development to offer essential rainfall data to the academic community, specifically in areas where there is a scarcity of sufficient rain gauges or radar systems [9]. However, satellite-based precipitation estimates have advantages and disadvantages, highlighting the need for further improvement.…”
Rising air temperatures, increased rainy runoff, reduced dry season runoff, and severe weather conditions have intensified floods and droughts, significantly affecting the reservoir water supply. The accuracy of reservoir water balance is crucial for meeting water needs. The study compares satellite data and ground measurements to analyze the water budget of Sutami Reservoir in Indonesia. Satellite data collected included precipitation (Tropical Rainfall Measuring Mission-TRMM) and evaporation (Global Land Data Assimilation System-GLDAS). The water balance approach was utilized to analyze the water budget. The suitability tests used were Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (CC), and Relative Error (RE). The study revealed that the data from TRMM and GLDAS satellites closely resembled ground measurements. The reservoir water balance analysis revealed that satellite data aligns with ground measurements, indicating water shortages in the dry season and excess water in the rainy season. Satellite data is particularly beneficial for watershed management in areas lacking ground measurement equipment, as it can be analyzed for various purposes.
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