期刊
SCIENTIFIC PROGRAMMING
卷 2020, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2020/8847571
关键词
-
资金
- research projects SIGLO-AN from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I Orientada a los Retos de la Sociedad) [RTI2018-101397-B-I00]
- GeoERA Organization - European Union's Horizon 2020 Research and Innovation Program [GeoE.171.008-TACTIC]
Global warming associated with greenhouse emissions will modify the availability of water resources in the future. Methodologies and tools to assess the impacts of climate change are useful for policy making. In this work, a new tool to generate potential future climate scenarios in a water resources system from historical and regional climate models' information has been developed. The GROUNDS tool allows generation of the future series of precipitation, temperature (minimum, mean, and maximum), and potential evapotranspiration. It is a valuable tool for assessing the impacts of climate change in hydrological applications since these variables play a significant role in the water cycle, and it can be applicable to any case study. The tool uses different approaches and statistical correction techniques to generate individual local projections and ensembles of them. The non-equifeasible ensembles are created by combining the individual projections whose control or corrected control simulation has a better fit to the historical series in terms of basic and droughts statistics. In this work, the tool is presented, and the methodology implemented is described. It is also applied to a case study to illustrate how the tool works. The tool was previously tested in different typologies of water resources systems that cover different spatial scales (river basin, aquifer, mountain range, and country), obtaining satisfactory results. The local future scenarios can be propagated through appropriate hydrological models to study the impacts on other variables (e.g., aquifer recharge, chloride concentration in coastal aquifers, streamflow, snow cover area, and snow depth). The tool is also useful in quantifying the uncertainties of the future scenarios by combining them with stochastic weather generators.
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