Evaluation and ranking of different gridded precipitation datasets for Satluj River basin using compromise programming and f-TOPSIS

Accuracy of datasets is the prime challenge to climate-resilient water resources planning. The present study proposes a framework that combines deterministic and fuzzy scenario-based methods of ranking datasets. The framework was applied to rank gridded precipitation datasets for the Himalayan basin of the river Satluj using observed station data as reference. The Compromise Programming and Technique for Order Preference by Similarity to an Ideal Solution in Fuzzy field, f-TOPSIS, were applied to carry out the ranking using selected performance indicators. The analysis revealed that the APHRODITE consistently performed better in all the stations (correlation coefficient (CC), root mean square error (RMSE), and skill score (SS) vary from 0.90 to 0.98, 0.44 to 0.56, and 0.87 to 0.96, respectively), followed by gridded and reanalysis rainfall product of IMD and ERA interim, respectively. It was also observed that both the methods provided similar outcomes (Spearman rank correlation, R >= 92%), which consequently increased the confidence of the ranking results. Furthermore, the results indicate that the performance indicators used within the f-TOPSIS complement the entropy-based deterministic nature of compromise programming. Finally, it was found that APHRODITE was the best dataset for the whole study area using the Group Decision Making methodology.