Abstract

Flooding is one of the natural disasters that can occur in various parts of the world and may arise suddenly. However, flood events can be predicted or anticipated through relevant scientific approaches. One such method is by estimating the flood discharge in a given area. Rainfall data is one of the essential inputs required to determine flood discharge. In practice, however, ground-based rainfall measurements often have limitations. To overcome these shortcomings, satellite-based rainfall data can be utilized. There are notable differences between directly measured rainfall data and satellite-derived rainfall data; therefore, satellite data must be calibrated or validated prior to conducting further analysis. One of the most widely used satellite rainfall datasets is the GPM (Global Precipitation Measurement) satellite data, which has a spatial resolution of 0.1° x 0.1°. This study employs a combination of two statistical methods—validation and calibration—to evaluate rainfall data. Prior to evaluation, the RMSE and NSE values did not meet acceptable standards, and the correlation value was low. However, after the evaluation using both methods, improvements were observed: RMSE and NSE values became acceptable, and the correlation increased. These results indicate that the applied methods are effective for evaluating rainfall data. For future research, monthly or annual rainfall data can be utilized to further explore the relationship between different temporal scales of rainfall observations.

Concepts :

Access to Document

Citations by Year