Multicriteria decision analysis framework for hydrological decision support using environmental flow components

• Published in: Ecological indicators Vol. 93; pp. 470 - 480
• Main Authors: Butchart-Kuhlmann, Daniel, Kralisch, Sven, Fleischer, Melanie, Meinhardt, Markus, Brenning, Alexander
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2018
• Subjects: basins; climate; Climate change; Decision support systems; Environmental flow components; environmental indicators; governance; hydrologic models; Hydrology; Indicators of hydrologic alteration; land use change; multi-criteria decision making; Multicriteria decision analysis; rivers; stream flow; water quantity; water stress; Decision support systems; Multicriteria decision analysis; Hydrology; Climate change; Indicators of hydrologic alteration; Environmental flow components
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2018.04.057

[Display omitted] •Decision support using hydrological indicators and multicriteria decision analysis.•Indicators based on climate change scenario driven simulated hydrological datasets.•Indicators used as input for a Promethee multicriteria decision analysis.•Results as expected using simplified, hypothetical weights.•Great potential for spatial analysis, resources management, and RCP/SSP coupling. Assessing the quantity and quality of water available in water stressed environments under various potential climate and land-use changes is necessary for good water and environmental resources management and governance. We present a novel approach with which to assess water quantity in the Luanginga basin, a subbasin of the upper Zambezi River, using existing results of hydrological modelling based on climate change scenarios, hydrological indicators, and multicriteria decision analysis. Flexible input criteria and open-source software are used to the greatest degree possible. Scenarios are represented through a combination of model input data and parameter settings in the hydrological model, and preferences are represented through criteria weighting in the multicriteria decision analysis. The resulting methodology, combining hydrological indicators with MCDA, is thus adaptable, in terms of both application platform and subject matter. The method results were largely as expected. When a decision maker expressed a preference for wetter conditions, the RCP 4.5 scenarios were generally deemed most suitable, and thus ranked highest. When drier conditions where preferred, the RCP 8.5 scenarios, resulting in less streamflow and less frequent flood events, were ranked highest. Validation of the results is not possible, due to the methodologically exploratory nature of the research being undertaken. The promising results do, however, allow for further research that builds upon the work undertaken here, to be envisaged.