Summary & Purpose

In semiarid and arid regions with intensively managed water supplies, water scarcity is a product of interactions between complex biophysical processes and human activities. Evaluating water scarcity under climate change necessitates modeling how these coupled processes interact and redistribute waters in the system under alternative climate conditions. A particular challenge on the climate input lies in adequately capturing the plausible range of variability of future climate change along with central tendencies. This study generates a large ensemble of daily climate realizations by combining a stochastic weather generator, historical climate observations, and statistically downscaled General Circulation Model projections. Three climate change scenario groups, reflecting the historical, RCP4.5, and RCP8.5 conditions, are developed. A modeling framework is built using the Envision alternative futures modeling platform to 1) explicitly capture the spatiotemporally varying irrigation activities as constrained by local water rights; and 2) project water scarcity patterns under climate change. The study area is the Treasure Valley, an irrigation-intensive semi-arid human-environment system. Climate projections for the region show future increases in both precipitation and temperature. The projected increase in temperature has a significant influence on the increase of the allocated and unsatisfied irrigation amount. Projected changes in precipitation produce more modest responses. The scenarios identify spatially distinct areas more sensitive to water scarcity, highlight the importance of climate change as a driver of scarcity, and identify potential shortcomings of the current water management. The approach of creating climate ensembles overcomes deficiencies of using a few or mean values of individual GCM realizations.

Author Identifier

Bangshuai Han: https://orcid.org/0000-0003-4510-9993

DOI

https://doi.org/10.18122/B20133

Funding Citation

This publication was made possible by the National Science Foundation (NSF) Idaho Established Program to Stimulate Competitive Research (EPSCoR) under award number IIA-1301792; NSF CAREER Award EAR-1352631; Ball State University new faculty start-up fund under award number 120198.

Single Dataset or Series?

Series

Data Format

csv, shp, WP1 (ASCII file; could be read as text file)

Data Attributes

A total of 210 sets of climate data are stored in folder "climate"; daily climate variables include precip (precipitation), tavg(average temperature); tmax (maximum temperature); tmin (minimum temperature); srad (solar radiation); windspd (wind speed); rh (relative humidity); hspec (specific humidity). Model simulated water use and water scarcity results were summarized in a ESRI shp file. Attributes include average value, 15 percentile and 85 percentile values of each simulated variable. Ensemble climate data variables were summarized in the folder "variables", along with a "readme.text" to help readers understand the variables.

Time Period

2010 - 2100

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We are explicitly compliant with federal and state laws surrounding data privacy including the protection of personal financial information through the Gramm-Leach-Bliley Act, personal medical information through HIPAA, HITECH and other regulations. All human subject data (e.g., surveys) has been collected and managed only by personnel with adequate human subject protection certification.

Use Restrictions

Data will be provided to all who agree to appropriately acknowledge the National Science Foundation (NSF), Idaho EPSCoR and the individual investigators responsible for the data set. By downloading these data and using them to produce further analysis and/or products, users agree to appropriately acknowledge the National Science Foundation (NSF), Idaho EPSCoR and the individual investigators responsible for the data set. Use constraints: Acceptable uses of data provided by Idaho EPSCoR include any academic, research, educational, governmental, recreational, or other not-for-profit activities. Any use of data provided by the Idaho EPSCoR must acknowledge Idaho EPSCoR and the funding source(s) that contributed to the collection of the data. Users are expected to inform the Idaho EPSCoR Office and the PI(s) responsible for the data of any work or publications based on data provided.

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Available for download on Tuesday, September 18, 2018

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