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Drought risk assessment under climate change is sensitive to methodological choices for the estimation of evaporative demand

Several studies have projected increases in drought severity, extent and duration in many parts of the world under climate change. We examine sources of uncertainty arising from the methodological choices for the assessment of future drought risk in the continental US (CONUS). One such uncertainty i...

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Detalles Bibliográficos
Autores principales: Dewes, Candida F., Rangwala, Imtiaz, Barsugli, Joseph J., Hobbins, Michael T., Kumar, Sanjiv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354442/
https://www.ncbi.nlm.nih.gov/pubmed/28301603
http://dx.doi.org/10.1371/journal.pone.0174045
Descripción
Sumario:Several studies have projected increases in drought severity, extent and duration in many parts of the world under climate change. We examine sources of uncertainty arising from the methodological choices for the assessment of future drought risk in the continental US (CONUS). One such uncertainty is in the climate models’ expression of evaporative demand (E(0)), which is not a direct climate model output but has been traditionally estimated using several different formulations. Here we analyze daily output from two CMIP5 GCMs to evaluate how differences in E(0) formulation, treatment of meteorological driving data, choice of GCM, and standardization of time series influence the estimation of E(0). These methodological choices yield different assessments of spatio-temporal variability in E(0) and different trends in 21(st) century drought risk. First, we estimate E(0) using three widely used E(0) formulations: Penman-Monteith; Hargreaves-Samani; and Priestley-Taylor. Our analysis, which primarily focuses on the May-September warm-season period, shows that E(0) climatology and its spatial pattern differ substantially between these three formulations. Overall, we find higher magnitudes of E(0) and its interannual variability using Penman-Monteith, in particular for regions like the Great Plains and southwestern US where E(0) is strongly influenced by variations in wind and relative humidity. When examining projected changes in E(0) during the 21(st) century, there are also large differences among the three formulations, particularly the Penman-Monteith relative to the other two formulations. The 21(st) century E(0) trends, particularly in percent change and standardized anomalies of E(0), are found to be sensitive to the long-term mean value and the amplitude of interannual variability, i.e. if the magnitude of E(0) and its interannual variability are relatively low for a particular E(0) formulation, then the normalized or standardized 21(st) century trend based on that formulation is amplified relative to other formulations. This is the case for the use of Hargreaves-Samani and Priestley-Taylor, where future E(0) trends are comparatively much larger than for Penman-Monteith. When comparing Penman-Monteith E(0) responses between different choices of input variables related to wind speed, surface roughness, and net radiation, we found differences in E(0) trends, although these choices had a much smaller influence on E(0) trends than did the E(0) formulation choices. These methodological choices and specific climate model selection, also have a large influence on the estimation of trends in standardized drought indices used for drought assessment operationally. We find that standardization tends to amplify divergences between the E(0) trends calculated using different E(0) formulations, because standardization is sensitive to both the climatology and amplitude of interannual variability of E(0). For different methodological choices and GCM output considered in estimating E(0), we examine potential sources of uncertainty in 21(st) century trends in the Standardized Precipitation Evapotranspiration Index (SPEI) and Evaporative Demand Drought Index (EDDI) over selected regions of the CONUS to demonstrate the practical implications of these methodological choices for the quantification of drought risk under climate change.