Abstract
The comparison of the discrete and finite set of predefined alternatives requires a simple, effective, and reliable Multi-Attribute Decision-Making (MADM) method. This paper shows an application of a simple and effective MADM technique named the Best Holistic Adaptable Ranking of Attributes Technique (BHARAT) to find the best-performing ensemble prediction system (EPS) in issuing flood warnings in the study area. The three well-behaved EPSs, European Centre for Medium-Range Weather Forecast (ECMWF), the National Centre for Medium-Range Weather Forecasting (NCMRWF), and the United Kingdom Meteorological Office (UKMO) for 1-day and 5-day leadtime were considered in the study. The EPSs are considered as the alternatives and the evaluation metrics of the precipitation forecasts and hydrologic forecasts are considered as the factors (attributes) in the BHARAT method. The evaluation is categorized into three factors, (1) performance metrics for the evaluation of precipitation forecasts, (2) evaluation metrics based on the hydrological model performance, and (3) threshold-based evaluation metrics of the hydrologic forecasts. These factors are further divided into eleven sub-factors. The BHARAT method includes assigning numerical weights by the decision maker, which are directly multiplied with the normalized values based on the “best” attribute value of an alternative and summed up to get the best-performing alternative. The results of the BHARAT method showed that the total scores of the alternative NCMRWF are 0.976 and 0.916 for 1-day and 5-day leadtime. Thus, the NCMRWF EPS is found to be the best performing in both the leadtime for issuing the flood warning in the Vishwamitri River basin. At the global scale, BHARAT can be applied in similar studies of decision-making with variable attributes in the fields of hydrology to find the best alternative.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All relevant data are available from an online repository or repositories. ECMWF, UKMO and NCMRWF data can be downloaded from https://apps.ecmwf.int/datasets/data/tigge. Gridded IMD rainfall data can be downloaded from https://imdpune.gov.in/cmpg/Griddata/Rainfall_25_Bin.html.
References
Ahrens B, Jaun S (2007) On evaluation of ensemble precipitation forecasts with observation-based ensembles. Adv Geosci 10:139–144. https://doi.org/10.5194/adgeo-10-139-2007
Alfieri L, Burek P, Dutra E, Krzeminski B, Muraro D, Thielen J, Pappenberger F (2013) GloFAS-global ensemble streamflow forecasting and flood early warning. Hydrol Earth Syst Sci 17(3):1161–1175. https://doi.org/10.5194/hess-17-1161-2013
Aruldoss M, Lakshmi TM, Venkatesan VP (2013) A Survey on Multi Criteria decision making methods and its applications. Am J Inform Syst 1(1):31–43. https://doi.org/10.12691/ajis-1-1-5
Bao H, Zhao L (2012) Development and application of an atmospheric-hydrologic- hydraulic flood forecasting model driven by TIGGE ensemble forecasts. Acta Meteorologica Sinica 26(1):93–102. https://doi.org/10.1007/s13351-012-0109-0
Ceballos B, Lamata MT, Pelta DA (2016) A comparative analysis of multi-criteria decision-making methods. Progress Artif Intell 5(4):315–322. https://doi.org/10.1007/s13748-016-0093-1
Chandniha SK, Kansal ML (2017) Prioritization of sub-watersheds based on morphometric analysis using geospatial technique in Piperiya watershed, India. Appl Water Sci 7(1):329–338. https://doi.org/10.1007/s13201-014-0248-9
Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375(3–4):613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005
Cloke HL, Schaake JC (2018) Handbook of Hydrometeorological Ensemble Forecasting. In Handbook of Hydrometeorological Ensemble Forecasting. https://doi.org/10.1007/978-3-642-40457-3
Debebe Y, Otterpohl R, Islam Z (2023) Remote sensing and multi-criterion analysis for identifying suitable rainwater harvesting areas. Acta Geophys 71(2):855–872. https://doi.org/10.1007/s11600-022-00910-8
Derdour A, Bouanani A, Babahamed K (2018) Modelling rainfall runoff relations using HEC-HMS in a semi-arid region: case study in Ain Sefra watershed, Ksour Mountains (SW Algeria). J Water Land Dev 36(1):45–55. https://doi.org/10.2478/jwld-2018-0005
Duan M, Ma J, Wang P (2012) Preliminary comparison of the CMA, ECMWF, NCEP, and JMA ensemble prediction systems. Acta Meteorologica Sinica 26(1):26–40. https://doi.org/10.1007/s13351-012-0103-6
Dube A, Ashrit R, Singh H, Arora K, Iyengar G, Rajagopal EN (2017) Evaluating the performance of two global ensemble forecasting systems in predicting rainfall over India during the southwest monsoons. Meteorol Appl 24(2):230–238. https://doi.org/10.1002/met.1621
Ghavidelfar S, Alvankar SR, Razmkhah A (2011) Comparison of the lumped and quasi-distributed Clark Runoff models in simulating Flood hydrographs on a semi-arid Watershed. Water Resour Manage 25(6):1775–1790. https://doi.org/10.1007/s11269-011-9774-5
Gohil M, Mehta D, Shaikh M (2024) An integration of geospatial and fuzzy-logic techniques for multi-hazard mapping. Results Eng 21(1):101758. https://doi.org/10.1016/j.rineng.2024.101758
Goodarzi L, E Banihabib M, Roozbahani A (2019) A decision-making model for fl ood warning system based on ensemble forecasts. J Hydrol 573(October 2018):207–219. https://doi.org/10.1016/j.jhydrol.2019.03.040
Hani HM, Din NE, Khalifa MM, A., Elalfy E (2023) Sensitivity analysis for Multi-criteria decision analysis Framework for Site Selection of Aquifer recharge with reclaimed water. Sustain (Switzerland) 15(6). https://doi.org/10.3390/su15065399
Himayoun D, Roshni T (2019) Spatio-temporal variation of drought characteristics, water resource availability and the relation of drought with large scale climate indices: a case study of Jhelum basin, India. Quatern Int 525(July):140–150. https://doi.org/10.1016/j.quaint.2019.07.018
Liu L, Ping Xu Y, Li Pan S, Xu Bai Z (2019) Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China. Hydrol Earth Syst Sci 23(8):3335–3352. https://doi.org/10.5194/hess-23-3335-2019
Louvet S, Sultan B, Janicot S, Kamsu-Tamo PH, Ndiaye O (2016) Evaluation of TIGGE precipitation forecasts over West Africa at intraseasonal timescale. Clim Dyn 47(1–2):31–47. https://doi.org/10.1007/s00382-015-2820-x
Mehta D, Patel P, Sharma N, Eslamian S (2024) Comparative analysis of DRASTIC and GOD model for groundwater vulnerability assessment. Model Earth Syst Environ 10(1):671–694. https://doi.org/10.1007/s40808-023-01795-2
Messner JW (2018) Ensemble postprocessing with R. Statistical postprocessing of Ensemble forecasts. Elsevier Inc. https://doi.org/10.1016/B978-0-12-812372-0.00011-X
Nasiri Khiavi A, Vafakhah M, Sadeghi SH (2023) Flood-based critical sub-watershed mapping: comparative application of multi-criteria decision making methods and hydrological modeling approach. Stoch Env Res Risk Assess 37(7):2757–2775. https://doi.org/10.1007/s00477-023-02417-0
Papaioannou G, Vasiliades L, Loukas A, Aronica GT (2017) Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling. Adv Geosci 44:23–34. https://doi.org/10.5194/adgeo-44-23-2017
Papaioannou G, Efstratiadis A, Vasiliades L, Loukas A, Papalexiou SM, Koukouvinos A, Tsoukalas I, Kossieris P (2018) An operational method for Flood Directive implementation in ungauged urban areas. Hydrology 5(2):1–23. https://doi.org/10.3390/hydrology5020024
Patidar N, Mohseni U, Pathan AI, Agnihotri PG (2022) Groundwater potential zone mapping using an Integrated Approach of GIS-Based AHP-TOPSIS in Ujjain District, Madhya Pradesh, India. Water Conserv Sci Eng 7(3):267–282. https://doi.org/10.1007/s41101-022-00141-4
Paudel M, Nelson EJ, Scharffenberg W (2009) Comparison of lumped and Quasi-distributed Clark Runoff models using the SCS curve number equation. J Hydrol Eng 14(10):1098–1106. https://doi.org/10.1061/(asce)he.1943-5584.0000100
Pokhrel K, Ram K (2021) Rainfall-Runoff Simulation of Tamor River Basin using SCS-CN based HEC-HMS Model. Proc 9th IOE Graduate Conf 9(March):129–139
Prakash S, Srinivasan J (2021) A comprehensive evaluation of near-real-time and research products of imerg precipitation over India for the southwest monsoon period. Remote Sens 13(18). https://doi.org/10.3390/rs13183676
Ramkar P, Yadav SM (2021) Flood risk index in data-scarce river basins using the AHP and GIS approach. Nat Hazards 109(1):1119–1140. https://doi.org/10.1007/s11069-021-04871-x
W, Liu Y, Li T, Shi K, Sivakumar B (2018a) Evaluation of quantitative precipitation predictions by ECMWF, CMA, and UKMO for Flood forecasting: application to two basins in China. Nat Hazards Rev 19(2):05018003. https://doi.org/10.1061/(asce)nh.1527-6996.0000282
Ran Q, Fu W, Liu Y, Li T, Shi K, Sivakumar B (2018b) Evaluation of quantitative precipitation predictions by ECMWF, CMA, and UKMO for Flood forecasting: application to two basins in China. Nat Hazards Rev 19(2):1–13. https://doi.org/10.1061/(asce)nh.1527-6996.0000282
Rana VK, Suryanarayana TMV (2020) GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed. Ann GIS 26(2):149–168. https://doi.org/10.1080/19475683.2020.1733083
Rana VK, Suryanarayana TMV (2021) Estimation of flood influencing characteristics of watershed and their impact on flooding in data-scarce region. Ann GIS 27(4):397–418. https://doi.org/10.1080/19475683.2021.1960603
Rana VK, Maruthi T, Suryanarayana V (2020) GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed. Ann GIS 00(00):1–20. https://doi.org/10.1080/19475683.2020.1733083
Randrianasolo A, Ramos MH, Thirel G, Andréassian V, Martin E (2010) Comparing the scores of hydrological ensemble forecasts issued by two different hydrological models. Atmospheric Sci Lett 11(2):100–107. https://doi.org/10.1002/asl.259
Rane NL, Achari A, Choudhary SP, Mallick SK, Pande CB, Srivastava A, Moharir KN (2023) A decision framework for potential dam site selection using GIS, MIF and TOPSIS in Ulhas river basin, India. J Clean Prod 423(June):138890. https://doi.org/10.1016/j.jclepro.2023.138890
Rao RV (2024a) A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-1: multi-attribute decision-making applications in the industrial environment. Int J Ind Eng Comput 15(1):13–40. https://doi.org/10.5267/j.ijiec.2023.12.003
Rao RV (2024b) A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-2: role in multi- and many-objective optimization problems. Int J Ind Eng Comput 15(1):1–12. https://doi.org/10.5267/j.ijiec.2023.12.004
Rao RV, Lakshmi RJ (2021) Ranking of pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems using R-method. Soft Comput Lett 3:100015. https://doi.org/10.1016/j.socl.2021.100015
Rashidi Shikhteymour S, Borji M, Bagheri-Gavkosh M, Azimi E, Collins TW (2023) A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods. Appl Geogr 158(July):103035. https://doi.org/10.1016/j.apgeog.2023.103035
Roulin E, Vannitsem S (2005) Skill of medium-range hydrological ensemble predictions. J Hydrometeorol 6(5):729–744. https://doi.org/10.1175/JHM436.1
Sahraei R, Kanani-Sadat Y, Homayouni S, Safari A, Oubennaceur K, Chokmani K (2023) A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds. J Flood Risk Manag 16(2). https://doi.org/10.1111/jfr3.12879
Shaikh M, Yadav S, Manekar V (2022) Application of the Compound Factor for Runoff Potential in sub-watersheds Prioritisation based on quantitative morphometric analysis. J Geol Soc India 98(5):687–695. https://doi.org/10.1007/s12594-022-2045-7
Shiru MS, Shahid S, Chung ES, Alias N, Scherer L (2019) A MCDM-based framework for selection of general circulation models and projection of spatio-temporal rainfall changes: a case study of Nigeria. Atmos Res 225(March):1–16. https://doi.org/10.1016/j.atmosres.2019.03.033
Singh PK, Chudasama H (2021) Pathways for climate change adaptations in arid and semi-arid regions. J Clean Prod 284:124744. https://doi.org/10.1016/j.jclepro.2020.124744
Subramanya K (2009) FLOW IN OPEN CHANNELS. Tata McGraw-Hill Publishing
Wu W, Emerton R, Duan Q, Wood AW, Wetterhall F, Robertson DE (2020) Ensemble flood forecasting: current status and future opportunities. WIREs Water 7(3):1–32. https://doi.org/10.1002/wat2.1432
Xiang Y, Peng T, Gao Q, Shen T, Qi H (2022) Evaluation of TIGGE Precipitation Forecast and its applicability in streamflow predictions over a Mountain River Basin, China. Water (Switzerland) 14(15). https://doi.org/10.3390/w14152432
Xuan Y, Cluckie ID, Wang Y (2006) Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction. Hydrol Earth Syst Sci Dis 3(5):3211–3237. https://doi.org/10.5194/hessd-3-3211-2006
Yadav R, Yadav SM (2023) Evaluation of parametric postprocessing of ensemble precipitation forecasts of the NCMRWF for the Vishwamitri River Basin. J Hydroinformatics 00(0):1–20. https://doi.org/10.2166/hydro.2023.113
Yu W, Nakakita E, Yamaguchi K (2013) Assessment of Probabilistic Flood forecasting using ensemble NWP rainfall with 30hr Forecast Time during typhoon events. Adv River Eng 19(January 2013):235–240
Zhang W, Liu X, Yu W, Cui C, Zheng A (2022) Spatial-temporal sensitivity analysis of Flood Control Capability in China based on MADM-GIS model. Entropy 24(6). https://doi.org/10.3390/e24060772
Acknowledgements
The authors are thankful to TIGGE, ECMWF for providing the ensemble precipitation dataset globally. Thankful to Flood Cell, Vadodara Irrigation Circle, Vadodara for providing the necessary data at Citybridge.
Funding
No funding is received for the study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Consent for publication
All authors are aware of this submission.
Conflict of interest
The authors declare there is no conflict.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yadav, R., Yadav, S.M. BHARAT: a MADM approach to prioritizing the best performing EPS in a semi-arid river basin. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06566-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11069-024-06566-5