Development of Value-Added Products in Support of the North American Land Data Assimilation System – Phase 3

To maximize the impact of NASA water missions and enhance their utility for Earth Science to Action, a multi-center initiative is leveraging NASA land surface modeling (LSM) and data assimilation (DA) tools to address evolving scientific and societal challenges of the hydrologic cycle. Built upon the NASA Land Information System (LIS) framework, the North American Land Data Assimilation System-Phase 3 (NLDAS-3) aims to provide spatially-distributed, high-quality, observation-informed estimates of hydrologic variables at 1-km resolution using the Noah with Multiple Parameterizations (Noah-MP) LSM, run on a domain covering North and Central America, initially spanning the Global Precipitation Measurement Mission-era time window. Driven by stakeholder requirements and a focus on optimized data-delivery platforms, NLDAS-3 represents a significant advancement over existing land DA systems, offering improved forcing and multivariate assimilation to deliver retrospective, real-time, and predictive products for agricultural, hydrological, water resources, drought, and wildfire applications.

We are developing and adapting numerous value-added applications and products to use NLDAS-3 data, in close collaboration with stakeholders. A subset of these products is highlighted in this presentation, spanning a wide variety of disciplines. For drought monitoring and prediction, we are adapting tools and indices such as the Evaporative Demand Drought Index, the Drought Fingerprint Plot, and Soil Moisture Volatility Index. For agriculture, we are customizing the Alabama Lawn and Garden Index, Decision Support System for Agrotechnology Transfer crop yield estimates, and metrics for heat and cold stress days. In human health, we are adapting the Wet Bulb Global Temperature Index (used by the military) to gridded NLDAS-3 data, which can also be applied to help minimize livestock mortality during heat waves. For wildfire management, we are ingesting NLDAS-3 outputs to inform fire fuel moisture estimates, and also combining high-resolution antecedent soil moisture with lightning flash data to identify lightning-initiated wildfire risks. In addition, we are developing machine learning tools to efficiently emulate NLDAS-3 soil moisture states. Finally, we are advancing the use of NLDAS-3 for meteorological applications like the Standardized Precipitation Index, Effective Precipitation, and Vapor Pressure Deficit calculations. Each of these tools and indices benefit from the enhanced spatial and temporal resolution of NLDAS-3, enabling more effective application across disciplines.