A Comparative Analysis of Gridded Precipitation Data in the Himalayan Region of Bhutan
Kirtan Adhikari, Kiran Chettri, Eshan Basnet, Karma Chozom, Kabita Sharma, Aman Giri and Leki Bumpa,
Civil Engineering and Architecture Department, College of Science and Technology, Royal University of Bhutan,
Corresponding author: adhikari.cst@rub.edu.bt
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Abstract - For hydrological modelling and weather forecasting, precise measurement of precipitation is crucial since rainfall is the main input to most hydrological systems. The distribution of the ground-based precipitation data is not homogeneous due to inconsistent results from the majority of stations situated close to the towns. Nevertheless, it has been demonstrated that these flaws may be corrected by using information gathered remotely. In this study, four gridded products—including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis—are compared for their precipitation data. TMPA 3B42RT, Global Precipitation Measurement (GPM) IMERG V06, Asian Precipitation Highly Resolved Observation Data Integration towards Evaluation (APHRODITE), and Precipitation estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) against 54 reliable ground-based observations recorded by National Centre for Hydrology and Meteorology (NCHM) over the entire region of Bhutan. A double Mass curve was utilized to examine the consistency of the gauge precipitation data and underperforming stations were not considered. To compare the gridded data to ground-based data using several conventional statistical indices and rainfall detection indices, the average of points to grid value was employed. The results show that the gridded products' correlation coefficient with respect to ground-based observations is negligible, with values often averaging between -0.05 and 0.5. Except for APHRODITE, which had a POD of 0.056, all samples had a probability of detection (POD) greater than 0.5. Additionally, it is shown that whereas APHRODITE and PERSIANN underestimate rainfall by 93% and 86% of the total grids, respectively, TRMM and GPM overestimate rainfall by 70% and 58%. Relative Root Mean Square Error (RMSER) exceeded 50% over the entire grid, indicating the non-reliability of the products. Additionally, the results of the investigation indicate that the gridded products need to have their biases corrected because they perform poorly in Bhutan's Himalayas.
Key Words: Precipitation, Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), PERSIANN, APHRODITE.
