Terrestrial Environmental Rapid-Replicating Assimilation Hydrometeorology System: A machine-learning coupled water, energy, and vegetation terrestrial Earth System Digital Twin (TERRAHydro)

The Earth's environment is changing rapidly, resulting in more extreme weather and increased risk from weather and climate related phenomena. Land Surface Models (LSMs) are a critical component of climate and weather forecasting models, and are integral tools for regional drought monitoring, agricultural monitoring and prediction, famine early warning systems, and flood forecasting, among other things. A vital part of NASA's Earth Science mission includes supporting terrestrial models (LSMs) that can leverage available Earth observation data (EOD) to provide accurate and timely information about the terrestrial water, energy, and carbon cycles. The increase in adverse weather conditions makes near real-time and short-term capabilities increasingly critical for early response systems and mitigation. In the past 5 years, Machine Learning (ML) has emerged as one of the most powerful ways to extract information from large and diverse sets of terrestrial observation data (see references in the Open Source Software Licensing section). Our group and others in the hydrological community have successfully developed models of most of the key states (streamflow, soil moisture, latent and sensible heat, and vegetation) and fluxes that are significantly more accurate than the traditional process-based models (PBMs) currently deployed by NASA. Importantly, they run several orders of magnitude faster than traditional PBMs. This arises from a simpler numerical structure and from the ability to efficiently use hardware accelerators (GPUs, TPUs). Their rapid-adaptation and increased throughput can provide unprecedented near real-time and short-term forecasting capabilities that far exceed the current PBM approaches in use today. Although ML models do not currently provide a process-based scientific explainability, and there remains skepticism about long-term forecasting in the presence of non-stationarity (changing climate), their accuracy and ability to enhance the current near real-time and short-term capabilities is undeniable. The current NASA LSM software infrastructures (SI), (e.g., the NASA Land Information System; [1]) are not designed in ways that allow them to fully leverage ML technologies due to several differences such as: native programming languages, software stacks, numerical algorithms, methodologies, space-time structures, and High-Performance Computing (HPC) capabilities. The effort to merge these technologies into a unified SI, if feasible, would be significant and likely result in something brittle, not easily-extensible, hard to maintain, and impractical. We propose to develop a terrestrial Earth System Digital Twin (TESDT) that is designed from the ground-up to couple state-of-the-art ML with NASA (and other) EOD. This TESDT will combine the best ML hydrology models with capabilities for uncertainty quantification and data assimilation to provide a comprehensive TESDT. The software infrastructure will be developed in Python and specifically designed to provide a flexible, extensible, modern, and powerful framework that will be a prototype AI/ML based TESDT. It will be able to perform classically expensive tasks like ensemble and probabilistic forecasting, sensitivity analyses, and counterfactual "what if'' experiments that will provide critical hydrometeorological information to aid in decision and policy making. We will build the SI to integrate and couple the land surface components including data management, training, testing, and validation capabilities. Different coupling approaches will be deployed, researched, and tested, as well as, an ML specific data assimilation framework. In the optional year, relevant hydrometeorological events, e.g., the 2006-2010 Syrian drought and current changes to water storage in the Himalayan mountains, will be used to demonstrate and validate the performance of the aforementioned capabilities to real world applications.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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