Evaluation and ranking of different gridded precipitation datasets for Satluj River basin using compromise programming a
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ORIGINAL PAPER
Evaluation and ranking of different gridded precipitation datasets for Satluj River basin using compromise programming and f-TOPSIS Bratati Chowdhury 1 & N. K. Goel 1 & M. Arora 2 Received: 29 May 2020 / Accepted: 21 September 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract 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, fTOPSIS, 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. Keywords Satluj . Entropy . Compromise programming . f-TOPSIS . Spearman rank correlation coefficient . Ranking
1 Introduction Precipitation datasets are the starting point of most hydrological analysis, and the accuracy of the datasets has a compound effect on the results of the study. Precipitation data is generally obtained from observed datasets (gauge data, satellite data) and from global or regional gridded precipitation datasets (Xie and Arkin 1997). Rain gauge observations are most widely used to measure precipitation directly at the Earth’s surface (Kidd 2001). However, gauge observations have some disadvantages, such as uneven areal coverage over most marine and sparsely populated areas (Xie and Arkin 1997; Rana et al. 2015; Salio et al. 2015; Kidd et al. 2017). This challenge gets multiplied when physical access to different regions
* Bratati Chowdhury [email protected] 1
Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
2
National Institute of Hydrology, Roorkee, Uttarakhand 247667, India
becomes constrained due to factors like local topography, transport networks, etc. In this context, to overcome the deficiencies of gauge observations, it becomes essential to work with freely available global gridded precipitation datasets (viz. model-based datasets). The gridded precipitation datasets
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