Assessing regional groundwater stress for nations using multiple data sources with the groundwater footprint

• 發表在: Environmental Research Letters
• Main Authors: Tom Gleeson, Yoshihide Wada
• 格式: Article
• 語言: 英语
• 出版: IOP Publishing 2013-01-01
• 主題: groundwater; water stress; agricultural water use; footprint; India; USA
• 在線閱讀: https://doi.org/10.1088/1748-9326/8/4/044010

Groundwater is a critical resource for agricultural production, ecosystems, drinking water and industry, yet groundwater depletion is accelerating, especially in a number of agriculturally important regions. Assessing the stress of groundwater resources is crucial for science-based policy and management, yet water stress assessments have often neglected groundwater and used single data sources, which may underestimate the uncertainty of the assessment. We consistently analyze and interpret groundwater stress across whole nations using multiple data sources for the first time. We focus on two nations with the highest national groundwater abstraction rates in the world, the United States and India, and use the recently developed groundwater footprint and multiple datasets of groundwater recharge and withdrawal derived from hydrologic models and data synthesis. A minority of aquifers, mostly with known groundwater depletion, show groundwater stress regardless of the input dataset. The majority of aquifers are not stressed with any input data while less than a third are stressed for some input data. In both countries groundwater stress affects agriculturally important regions. In the United States, groundwater stress impacts a lower proportion of the national area and population, and is focused in regions with lower population and water well density compared to India. Importantly, the results indicate that the uncertainty is generally greater between datasets than within datasets and that much of the uncertainty is due to recharge estimates. Assessment of groundwater stress consistently across a nation and assessment of uncertainty using multiple datasets are critical for the development of a science-based rationale for policy and management, especially with regard to where and to what extent to focus limited research and management resources.