TRMM 3B42 Correction Based on Linear Regression Model

Rainfall has a vital role in sustainable river watershed management, while river watershed management can also be carried out in various physical and non-physical ways. In certain areas, efforts to physically manage river basins are often hampered by the lack of rainfall data that is representative enough according to the characteristics of the river basin in question so that the existence of TRMM precipitation data can be an alternative to solving solutions. This research used five rain observation stations in the Lesti Watershed with an observation period of 21 years. This research aims to determine the relationship between TRMM 3B42 and observation station postal data by carrying out regression analysis as the method. The study results show that the TRMM 3B42 data shows fairly good accuracy over the entire region on daily and annual time scales, and the TRMM 3B42 rainfall data trend is slightly more significant than the data from rain gauge stations. With the help of the SPSS application, it can be seen that the results of the significance test for two variables have a value of <0.05, so it can be said that the correlation is moderately positive. A positive correlation value means that the relationship between the trend of the observation station rain data and the TRMM rain data has the same direction; this shows that the higher the rain data at the observation station, the higher the TRMM rain data, and vice versa. The validation results of corrected TRMM rain data produce Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Relative Error (RE). The correction factor for using TRMM rain data is the linear regression equation Y = 0.8876X so that with the same watershed characteristics, it can be used in other watersheds.