A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

Abstract

Regional climate models (RCMs) are key in the current context of global warming, and they are increasingly used to support decision-making and to identify adaptation measures in response to climate change. However, considering the wide range of available RCMs, it is important to identify the most suitable ones prior to climate impact studies, especially at small scales like catchments. In this study, a multicriteria decision analysis approach, namely the technique for order preferences by similarity to an ideal solution (TOPSIS) was applied to select the best performing RCMs in the Mono River Basin of Benin and Togo (West Africa). The TOPSIS method was used to systematically rank 15 RCMs accessed from the coordinated regional downscaling experiment (CORDEX) database. Six RCMs were finally selected and averaged into an ensemble to assess the future climate in the Mono River Basin until 2070 compared to the period 1966–2015. Two climate change scenarios, RCP 4.5 and RCP 8.5, were considered. The results show that under both climate change scenarios, the annual temperature has an increasing trend during the period 1966–2070, whereas annual rainfall for the next 50 years presents high variability and no statistically significant trend. Furthermore, seasonal cycles of rainfall are expected to change in the different parts of the catchment with delayed onset of rainfall, longer dry seasons, and rainfall intensification. In response to the projected changes, impact studies and risk assessments need to be carried out to evaluate potential implications for human security in the Mono River Basin and to provide adequate adaptation measures.

A novel approach for evaluation of CMIP6 GCMs in simulating temperature and precipitation extremes of Pakistan

“…This all‐encompassing analysis gives a more solid and well‐rounded appraisal of GCMs. Multiple studies have confirmed the usefulness of selecting GCMs based on some criteria (Ahmadalipour et al, 2015; Hounguè et al, 2022; M. J. U. Khan et al, 2020; McSweeney et al, 2014; Salehie, Hamed et al, 2022; Shiru et al, 2019). The studies indicated that the rigour, transparency, and efficiency of GCM selection for climate change research improves using criteria‐based GCM selection.…”

“…This all-encompassing analysis gives a more solid and well-rounded appraisal of GCMs. Multiple studies have confirmed the usefulness of selecting GCMs based on some criteria (Ahmadalipour et al, 2015;Hounguè et al, 2022; M. J. U. Khan et al, 2020;McSweeney et al, 2014;Shiru et al, 2019).…”

Transboundary Collaborative Modeling: Consensual Identification and Ranking of Flood Adaptation Measures—A Case Study in the Mono River Basin, Benin, and Togo

“…In addition, poverty, the proximity of farms and settlements to the river, and the lack of early warning systems were identified as the main vulnerability drivers in the LMR basin [42,43]. Moreover, recent studies based on climate and land use change scenarios in the catchment reported a potential intensification of rainfall and more frequent extreme flood events by 2050 [44,45]. In that regard, the two countries are planning a second dam, the Adjarala Dam, which is intended, among other goals, to reduce extreme flood impacts downstream.…”

“…Supporting the discussions and engagement of stakeholders, numerous tools and products were used to communicate flood hazards, scenarios of return periods as well as climate and land use change [44,46]. For spatio-temporal products on flood hazard base cases and scenarios, the Telemac-2D and Soil Water and Assessment Tool (SWAT) were used as the hydrodynamic and hydrological models, respectively.…”

Evaluating the skills of the CMIP5 global climate models using multicriteria decision-making analysis in Ethiopia

“…Different studies have attempted to evaluate the ability of the CMIP5 GCMs and CORDEX Africa RCMs in simulating precipitation and temperature at the global scale [ 2 , 9 , 10 , 13 , 14 , 17 , 21 , 22 , 24 , 26 , 28 , 30 ], regional scale [ 23 , 29 , [32] , [33] , [34] , [35] , [36] , [37] , [38] ], and sub-regional (national) scale [ [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ]. So far, the multimodel ensembles of the CMIP5 GCMs for the projection of climate variables have been effectively used [ 47 ], and some authors have confirmed the superiority of these ensembles over individual GCMs [ 48 ].…”