Correction to: Evaluation and ranking of different gridded precipitation datasets for Satluj River basin using compromis
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CORRECTION
Correction to: Evaluation and ranking of different gridded precipitation datasets for Satluj River basin using compromise programming and f-TOPSIS Bratati Chowdhury 1,2 & N. K. Goel 1 & M. Arora 3
# Springer-Verlag GmbH Austria, part of Springer Nature 2020
Correction to: Theoretical and Applied Climatology https://doi.org/10.1007/s00704-020-03405-y The original version of this article unfortunately contained mistakes. The proof corrections in Eq. 9 and Tables 3 and 5 were unfortunately not implemented. The corrected data are given below. In addition, affiliations of the 1st author have been updated. The original article has been corrected.
C¼
DS Minus DS Minus þ DS Plus
ð9Þ
The online version of the original article can be found at https://doi.org/ 10.1007/s00704-020-03405-y * Bratati Chowdhury [email protected] 1
Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
2
Faculty of Technology, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, West Bengal 736 165, India
3
National Institute of Hydrology Roorkee, Roorkee, Uttarakhand 247667, India
B. Chowdhury et al. Table 3 Rank of the different gridded precipitation dataset for each station from Compromise Programming along with its parameter (Lp) and performance indicator payoff matrix (RMSE, CC, and SS) Root Mean Square Error (RMSE) CFSR
MERRA
APHRODITE
ERAInterim
IMD
PFD
Bhakra Berthin Daslehra Kahu Kasol Kuddi
0.81 0.75 0.79 0.80 0.78 0.80
0.78 0.73 0.76 0.77 0.75 0.78
0.56 0.50 0.54 0.55 0.54 0.56
0.79 0.72 0.77 0.77 0.73 0.77
0.59 0.54 0.58 0.62 0.53 0.59
1.00 1.00 1.00 1.00 1.00 1.00
Rampur Suni
0.67 0.61 0.72 0.69 Correlation Coefficient (CC) 0.01 0.07 0.00 0.05 0.01 0.06 0.02 0.04 0.02 0.02 0.02 0.04 0.01 0.01 0.01 0.02 Skill Score (SS) 0.94 0.97 0.94 0.97 0.94 0.98 0.92 0.96 0.94 0.91 0.92 0.91
0.49 0.44
0.57 0.67
0.46 0.49
1.00 1.00
0.99 0.99 0.99 0.99 0.97 0.99 0.98 0.99
0.46 0.44 0.44 0.40 0.46 0.44 0.40 0.38
0.91 0.93 0.92 0.82 0.94 0.91 0.94 0.91
0.14 0.15 0.18 0.14 0.15 0.14 0.09 0.12
0.97 0.98 0.97 0.94 0.97 0.91
0.88 0.88 0.88 0.86 0.87 0.84
0.99 0.98 0.94 0.95 0.98 0.93
0.90 0.90 0.90 0.97 0.97 0.96
0.90
0.85
0.92
0.90
0.00002 0.00001 0.00002 0.00006 0.00884 0.00002 0.00825 0.00011
0.18894 0.19103 0.19228 0.22721 0.18353 0.20180 0.20445 0.21740
0.03201 0.02503 0.02746 0.06788 0.00001 0.03232 0.00007 0.02980
0.30111 0.29246 0.28163 0.32355 0.28736 0.30698 0.30935 0.30646
1 1 1 1 2 1 2
3 3 3 3 3 3 3
2 2 2 2 1 2 1
4 4 4 4 4 4 4
1
3
2
4
Bhakra Berthin Daslehra Kahu Kasol Kuddi Rampur Suni Bhakra Berthin Daslehra Kahu Kasol Rampur Suni
Bhakra Berthin Daslehra Kahu Kasol Kuddi Rampur
0.93 0.97 Lp Matric 0.34626 0.32585 0.34206 0.32785 0.34210 0.32620 0.36889 0.36036 0.33194 0.33289 0.34992 0.34309 0.33652 0.33733 0.34620 0.34051 Rank from Compromise Programming 6 5 6 5 6 5 6 5 5 6 6 5 5 6
Suni
6
Bhakra Berthin Daslehra Kahu Kasol Kuddi Rampur Suni
5
Correction to: Evaluation and ranking of different gridded precipitation dataset
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