Mapping anthropogenic water cycle impacts in a future climate: A global digital twin for scenario-driven exploration

Accurate representation of the hydrological cycle, especially in the context of climate change and human interventions (anthropogenic stressors), is crucial for ensuring robust quantitative assessments of water availability, climate risk, and effective management. The intricacies of non-linear processes inherent in the hydrological cycle are further compounded by the dynamic nature of climate processes and anthropogenic influences, governing the risks associated with water availability and extremes (such as floods and droughts) across diverse landscapes. Integrating climate, land, and anthropogenic-related processes is essential to creating a more realistic depiction of the regional to global hydrological cycle, enabling accurate quantification of water risks in critical hotspot regions. The current suite of physical models is highly deficient in representing such management impacts and is computationally expensive to deploy over large spatio-temporal scales. Consequently, assessment reports from major climate authorities do not adequately include realistic representations of the changes in water use stemming from human interventions. Leveraging remote sensing data of the water cycle through data assimilation methods has been proven to be an effective approach for characterization of human management processes. However, those modeling and data assimilation systems are only effective for characterizing changes and impacts during the historical record. The proposed work will focus on incorporating and extending this knowledge for future scenario development through the use of a suite of machine learning-based water cycle digital twin models at a global scale. We plan to build deep-learning-based Digital Twin (DT) models, leveraging land reanalysis, remote sensing, and climate projection datasets. The land reanalysis is developed based on the most comprehensive inclusion of available land remote sensing datasets using the NASA Land Information System (LIS). Thus, the proposed work will capitalize on the advancements in physical models, data assimilation, machine (deep) learning concepts, parallel computing, and transfer learning, along with the utilization of DT technology, enabling scenario development of water availability risks and predictions of extreme events. Our proposed DT technology-driven platform is designed to deliver a comprehensive and robust quantitative assessment of complex hydrological cycle systems, addressing the key limitation of unobserved anthropogenic impacts in traditional hydrologic model simulations. The proposed work is relevant to the Earth Science Digital Twin (ESDT) theme of the solicitation under the Early-Stage technology category. The deep learning models developed through the proposed work will be able to provide characterization of the historical record as well as projections and what-if scenarios, with a particular focus on extending the inferences on human activities for future predictions. The proposed work is timely, addressing the urgent need for reliable scenarios incorporating anthropogenic impacts. This is crucial for analyzing the exacerbation of climate change on the water cycle and projecting water availability and climate extremes. This is also crucial to realizing NASA's 'Advancing the Climate Strategy (2023)' Climate Action plan. By delivering scenarios of terrestrial water cycle availability that incorporate explicit scenarios of human water use, the proposed work will provide capabilities that enable planning, adaptation, and mitigation strategies in the face of climate change impacts.

Expand current definitions of modeling and leverage state-of-the-art computer and information science for innovating advanced modeling techniques as well as new technologies and frameworks that will be essential in the development of Earth System Digital Twins

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