Marshall et al. (2025) A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-12-13
• Authors: Sebastian R.O. Marshall, Thanh-Nhan-Duc Tran, Venkataraman Lakshmi
• DOI: 10.1016/j.jhydrol.2025.134784

Research Groups

• Department of Civil and Environmental Engineering, University of Virginia, VA 22904, USA

Short Summary

This systematic review synthesizes two decades of advancements in the NASA Land Information System (LIS), demonstrating its impact on Earth system science through improved land surface estimates, enhanced forecast skill via model coupling, and successful transition to operational applications.

Objective

• To systematically review and critically evaluate two decades of scientific advancements enabled by the NASA Land Information System (LIS), quantifying its impact on Earth system science, identifying persistent limitations, and outlining key future research directions.

Study Configuration

• Spatial Scale: Local to global (e.g., local evapotranspiration, continental-scale soil moisture, regional forecasts, global snow analysis).
• Temporal Scale: Two decades (review period), seasonal (e.g., seasonal precipitation regimes).

Methodology and Data

• Models used: NASA Land Information System (LIS), various Land Surface Models (LSMs), Weather Research and Forecasting (WRF) model, Hydrological Modeling and Analysis Platform (HyMAP).
• Data sources: Diverse observational datasets, satellite observations from missions including Soil Moisture Active Passive (SMAP), Gravity Recovery and Climate Experiment (GRACE), and Airborne Snow Observatory (ASO).

Main Results

• LIS-Data Assimilation (DA) consistently generates enhanced estimates of land surface conditions (e.g., soil moisture, snow, total water storage), showing quantifiable error reductions such as over 60% reduction in Root Mean Square Error (RMSE) for snow estimates and improvement in streamflow Kling-Gupta Efficiency (KGE) skill scores from 0.04 to 0.44.
• Coupling LIS with atmospheric models (e.g., WRF) and advanced hydrological routing models (e.g., HyMAP) demonstrably improves the skill of regional weather and flood forecasts by providing physically consistent, observationally constrained initial conditions.
• LIS has successfully transitioned from a research tool to a proven operational asset (R2O), serving as the backbone for critical decision-support systems like the Famine Early Warning Systems Network (FLDAS) and the U.S. Air Force’s global snow analysis.
• A clear methodological trend toward multivariate DA (MVDA) is observed, addressing complex human-natural system interactions such as flash droughts and irrigation impacts.

Contributions

• Provides a systematic synthesis and critical evaluation of two decades of scientific advancements enabled by the NASA Land Information System (LIS).
• Quantifies the impact of LIS on Earth system science, correcting prior mischaracterizations.
• Identifies persistent limitations and outlines key future research directions for LIS.
• Establishes LIS as a cornerstone of modern hydrological modeling.

Funding

• Not specified in the provided text.

Citation

@article{Marshall2025systematic,
  author = {Marshall, Sebastian R.O. and Tran, Thanh-Nhan-Duc and Lakshmi, Venkataraman},
  title = {A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134784},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134784}
}