Evaluation of the accuracy of seven gridded satellite precipitation products over the Godavari River basin, India

High-resolution reanalysis products could improve the representativeness of rainfall on high Andean basins, but their performance must be locally validated. We addressed the performance, accuracy, and ability of TerraClimate and CHIRPSv.2 to represent 36 years of rainfall (1985–2020) from 23 stations in the Upper Chicamocha River, a Colombian basin of complex terrain and tropical hydrometeorology. Using several statistical metrics at monthly, seasonal and annual scales, we found how both datasets overestimate rainfall as a function of elevation, with better performance and accuracy from CHIRPS (r ∼0.76, R2 ∼0.58, NSE ∼0.56, and low RMSE ∼33.7 mm/month, MAE ∼25.2 mm/month, ME ∼6.4 mm/month, and PBIAS ∼9.3), while TerraClimate overestimates inter-annual variability, especially between June and August. Seasonally, the datasets exhibit different spatial patterns and magnitudes, even after bias correction. The findings highlight the potential use and challenges of high-resolution datasets in basins with similar topography and hydrometeorology in the Andean region. 

Performance of high-resolution precipitation datasets CHIRPS and TerraClimate in a Colombian high Andean Basin

Abstract High-resolution reanalysis products could improve the representativeness of rainfall on high Andean basins, but their performance must be locally validated. We addressed the performance, accuracy, and ability of TerraClimate and CHIRPSv.2 to represent 36 years of rainfall (1985–2020) from 23 stations in the Upper Chicamocha River, a Colombian basin of complex terrain and tropical hydrometeorology. Using several statistical metrics at monthly, seasonal and annual scales, we found how both datasets overestimate rainfall as a function of elevation, with better performance and accuracy from CHIRPS (r ∼0.76, R2 ∼0.58, NSE ∼0.56, and low RMSE ∼33.7 mm/month, MAE ∼25.2 mm/month, ME ∼6.4 mm/month, and PBIAS ∼9.3), while TerraClimate overestimates inter-annual variability, especially between June and August. Seasonally, the datasets exhibit different spatial patterns and magnitudes, even after bias correction. The findings highlight the potential use and challenges of high-resolution datasets in basins with similar topography and hydrometeorology in the Andean region.

Satellite as well as reanalysis-based datasets are widely available and useful in detecting spatial and temporal variability of rainfall at a finer resolution. These products have been widely used in weather forecasting and hydrological and climate studies. However, the accuracy of satellite products varies spatially and across different datasets. In this study, the accuracy of five satellite-based precipitation products with different spatial resolutions, i.e., CHIRPS, ERA5, TRMM, GPM, and TerraClim available on Google Earth Engine (GEE) were compared with India Meteorological Department (IMD) gridded data in six climate zones in India. The statistics such as RMSE, R2, MSE, and PBIAS were computed. It was observed that the performance of each product varies in different climatic zones. The GPM was observed to have high accuracy in arid, semi-arid, and tropical wet zones. TRMM showed a good match in tropical wet and dry, tropical wet, and semi-arid zones. TerraClim and ERA5 showed high accuracy in humid subtropical and montane regions, respectively. It was also observed that CHRIPS was found to be least suitable in all the climate zones across India. The findings from the present studies will serve as a guiding document for the researcher to select appropriate datasets for different applications such as drought monitoring, precipitation anomaly, hydrological models, or other related studies in India. 

Performance Evaluation of Google Earth Engine Based Precipitation Datasets Under Different Climatic Zones over India

Remodeling Rainfall Prediction Using Artificial Neural Network and Machine Learning Algorithms

Climate hazard group infrared precipitation with station data (CHIRPS) is one of the latest high-resolution quasi-global satellite-based rainfall datasets. It is available in daily, pentadal and monthly time scale from the year 1981 to present. In the present study, the performance of the CHIRPS product is evaluated over the Kerala state on a monthly time scale. For the evaluation of this climate hazard group, product rain gauge data from sixty-seven-gauge stations which are distributed all over Kerala was used. Validation statistics such as mean absolute error (MAE), multiplicative bias (Mbias), Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) were used for the evaluation. The results show that the efficiency of this satellite rainfall estimate is very high with an overall NSE value of 0.72. The accuracy of CHIRPS data was very high mainly in the low-lying areas of Kerala, i.e. at the coastal areas and it was found to be decreasing when in approaches towards the Western Ghats. Overall CHIRPS product is good enough for use in water resource applications in Kerala.

P. Divya

Papers

Evaluation of CHIRPS Satellite Rainfall Datasets Over Kerala, India