Assessing the large-scale sensitivity of groundwater resources to climate change

评估地下水资源对气候变化的大范围敏感性

• 批准号: 513270661
• 负责人: Dr. Andreas Wunsch
• 金额: --
• 依托单位: Department of Earth, Ocean and Atmospheric Sciences
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2022
• 资助国家: 德国
• 起止时间: 2021-12-31 至 无数据
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/513270661?language=en
• 关键词: Assessing; large; scale; sensitivity; groundwater

The climate crisis is increasingly altering the spatial and temporal availability of groundwater, which is the major source of global freshwater. Quantitative understanding of the interactions between groundwater and climate, especially at national and continental scales, is crucial for optimal groundwater management. To date, however, knowledge about the large-scale sensitivity of groundwater resources to climate change is limited. The goal of this project is to investigate the effects of climate change and associated environmental changes on the quantitative availability of groundwater resources at the national and continental scale. Established process-based models (PBMs) for hydro(geo)logical modeling at this scale (mostly global hydrological models - GHMs) are subject to distinct limitations and uncertainties due to strong simplifications of the reality. Thus GHMs, unlike other PBMs, exhibit limited physical consistency and interpretability, and their application can therefore lead to misleading conclusions about groundwater availability and safety in the light of climate change. Especially their transferability to data-sparse regions is limited. In recent years, deep learning (DL) models have emerged as an accurate and easily transferable alternative approach in the domain of water resources modeling. For surface water modeling, it was shown that a general DL can even outperform more specialized and locally calibrated process-based models. This project aims to take advantage of the lessons learned therein and build a DL model to investigate the sensitivity of groundwater to climate change on a continental scale. A "big data" approach will be applied for this purpose, using data from >2200 catchments in North America (extension is conceivable). Such a general model can learn to transfer knowledge across different regions and thus gains strongly in generalization capability (e.g. to data-sparse regions) and therefore trustworthiness. Further, the issue of the lack of interpretable and physically consistent models at the national scale will be addressed by incorporating physical knowledge and processes into the DL models. The goal is to build a plausible and interpretable model that is, most importantly, trustworthy and therefore suitable for investigating climate change scenarios. Trustworthiness is particularly critical here, as no validation is possible for future time periods. The designed model is subsequently used to answer the overarching research question, by using climate model data based on RCP and SSP scenarios to explore the large-scale impact of climate change on groundwater resources. Furthermore, specialized analyses (scenarios) on the impact of individual influencing factors (such as a land use) will be carried out.

气候危机正在日益改变地下水的空间和时间供应，而地下水是全球淡水的主要来源。定量了解地下水与气候之间的相互作用，特别是在国家和大陆尺度上，对于优化地下水管理至关重要。然而，迄今为止，关于地下水资源对气候变化的大规模敏感性的知识有限。该项目的目标是调查气候变化和相关环境变化对国家和大陆规模地下水资源定量可用性的影响。建立基于过程的模型（PBM）在这个规模的水文（地质）模拟（主要是全球水文模型-GHM）受到明显的限制和不确定性，由于强烈的简化现实。因此，温室气体模型与其他基于物理的模型不同，其物理一致性和可解释性有限，因此，其应用可能导致对气候变化背景下地下水的可利用性和安全性得出误导性结论。特别是他们的数据稀疏区域的可转移性是有限的。近年来，深度学习（DL）模型已成为水资源建模领域中准确且易于转移的替代方法。对于地表水建模，结果表明，一般DL甚至可以优于更专业和本地校准的基于过程的模型。该项目旨在利用从中吸取的经验教训，并建立一个DL模型，以调查大陆尺度上的地下水对气候变化的敏感性。为此目的，将采用“大数据”方法，使用来自北美超过2 200个集水区的数据（可以考虑扩大）。这样的通用模型可以学习跨不同区域传递知识，从而在泛化能力（例如，到数据稀疏区域）以及因此的可信度方面获得强大的收益。此外，在全国范围内缺乏可解释的和物理上一致的模型的问题将通过将物理知识和过程纳入DL模型来解决。目标是建立一个合理和可解释的模型，最重要的是，值得信赖，因此适合调查气候变化情景。在这里，可信度尤其重要，因为未来的时间段不可能进行验证。设计的模型随后被用来回答首要的研究问题，通过使用基于RCP和SSP情景的气候模型数据来探索气候变化对地下水资源的大规模影响。此外，还将对个别影响因素（如土地使用）的影响进行专门分析（设想）。