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.

数据集的准确性是气候适应性水资源规划的主要挑战。本研究提出了一个框架，该框架结合了基于确定性和模糊场景的数据集排名方法。该框架已应用观测站数据作为参考，对萨特卢日河喜马拉雅盆地的网格化降水数据集进行排序。应用与模糊领域中的理想解决方案相似的妥协编程和顺序偏好技术f-TOPSIS，以使用选定的性能指标进行排名。分析显示，APHRODITE在所有测站中始终表现更好（相关系数（CC），均方根误差（RMSE）和技能得分（SS）分别从0.90至0.98、0.44至0.56和0.87至0.96不等），其次分别是IMD和ERA中期的网格化和重新分析降雨产品。还观察到，两种方法都提供相似的结果（Spearman等级相关性，R≥92％），因此提高了等级结果的置信度。此外，结果表明，在f-TOPSIS中使用的性能指标补充了折衷编程的基于熵的确定性。最后，使用小组决策方法，发现APHRODITE是整个研究区域的最佳数据集。结果表明，在f-TOPSIS中使用的性能指标补充了折衷编程的基于熵的确定性。最后，使用小组决策方法，发现APHRODITE是整个研究区域的最佳数据集。结果表明，在f-TOPSIS中使用的性能指标补充了折衷编程的基于熵的确定性。最后，使用小组决策方法，发现APHRODITE是整个研究区域的最佳数据集。