Application of TRMM in the Hydrological Analysis of Upper Bengawan Solo River Basin

Rainfall is a major water resource with a significant role in terms of growth, environment concerns, and sustainability. Several human activities demand adequate water supply for drinking, agriculture, domestic, and commercial consumption. The accuracy of any hydrologic study depends heavily on the availability of good-quality precipitation estimates. Most countries are unable to provide sufficient climatic data, including rainfall and observed discharge statistics. This scarcity is a huge obstacle in conducting thorough hydrologic studies over a certain period. For instance, Indonesia, as an archipelagic country, has long been faced with data availability problems. For this reason, Tropical Rainfall Measuring Mission (TRMM), which was developed by NASA, became an alternative solution to rainfall data limitations. However, to be applied in hydrologic investigations, TRMM data require proper estimation and adjustment. The aim of this study was to evaluate the quality of TRMM rainfall data and its application in determining design flood and water availability. Dividing the data into several groups based on its magnitude and multiplying each unit with a correction coefficient are parts of the modification process. Subsequently, objective functions, including false alarm ratio (FAR), probability of detection (POD), and root mean square error (RMSE) were also applied. Rainfall-runoff modeling and design storm analysis at Delingan dam were used to study the TRMM correction performance. Based on the analysis, corrected TRMM showed considerable findings compared to ground station data.  Model calibration and verification using corrected TRMM data provide satisfactory model parameters compared to ground station derivatives. The results also disclosed a closer fit of the corrected TRMM to catchment response translated from derived rainfall-runoff model parameters to ground station compared to control.  Furthermore, design storm calculated from corrected TRMM reflects an improvement compared to uncorrected TRMM data.Rainfall is a major water resource with a significant role in terms of growth, environment concerns, and sustainability. Several human activities demand adequate water supply for drinking, agriculture, domestic, and commercial consumption. The accuracy of any hydrologic study depends heavily on the availability of good-quality precipitation estimates. Most countries are unable to provide sufficient climatic data, including rainfall and observed discharge statistics. This scarcity is a huge obstacle in conducting thorough hydrologic studies over a certain period. For instance, Indonesia, as an archipelagic country, has long been faced with data availability problems. For this reason, Tropical Rainfall Measuring Mission (TRMM), which was developed by NASA, became an alternative solution to rainfall data limitations. However, to be applied in hydrologic investigations, TRMM data require proper estimation and adjustment. The aim of this study was to evaluate the quality of TRMM rainfall data and its application in determining design flood and water availability. Dividing the data into several groups based on its magnitude and multiplying each unit with a correction coefficient are parts of the modification process. Subsequently, objective functions, including false alarm ratio (FAR), probability of detection (POD), and root mean square error (RMSE) were also applied. Rainfall-runoff modeling and design storm analysis at Delingan dam were used to study the TRMM correction performance. Based on the analysis, corrected TRMM showed considerable findings compared to ground station data.  Model calibration and verification using corrected TRMM data provide satisfactory model parameters compared to ground station derivatives. The results also disclosed a closer fit of the corrected TRMM to catchment response translated from derived rainfall-runoff model parameters to ground station compared to control.  Furthermore, design storm calculated from corrected TRMM reflects an improvement compared to uncorrected TRMM data.