Enhancing S2S Hydro-Climate Forecasting and Agricultural Water Management in Thailand Through Advanced Noah-MP Land-Surface Modeling

Accurate early warning of droughts and floods, as well as effective agricultural and water-resources planning, depends heavily on reliable representation of land-surface processes, particularly soil moisture and soil temperature dynamics. Soil moisture acts as an important source of land-surface “memory,” influencing evapotranspiration, surface energy exchange, runoff generation, drought development, and land–atmosphere interactions. Improving its representation is therefore important for strengthening sub-seasonal to seasonal (S2S) hydro-climate prediction.

To advance this capability in Thailand, ALICE-LAB is collaborating with the Hydro-Informatics Institute (HII) on a comprehensive land-surface modeling and evaluation initiative. The project focuses on the independent development and implementation of an offline Noah-MP Land Surface Model, providing a controlled modeling environment in which land-surface processes, model physics, parameterizations, and forcing uncertainties can be systematically evaluated without interference from atmospheric model feedbacks.

The research demonstrates ALICE-LAB’s capability in high-resolution land-surface and hydrological modeling, hydro-meteorological data processing, model benchmarking and evaluation. Particular emphasis is placed on improving the simulation of soil moisture and related water and energy fluxes, and on assessing how advances in land-surface representation can contribute to improved drought monitoring, flood and runoff prediction, agricultural water management, and S2S hydro-climate forecasting.

The modeling system is comprehensively benchmarked against meteorological and land-surface reanalysis products, satellite-based soil-moisture observations, and other independent Earth-observation datasets. This multi-source evaluation enables ALICE-LAB to identify model strengths and limitations across different climatic conditions, land-cover types, and hydro-meteorological events, while providing a scientific basis for improving model physics and land-surface parameterization.

Ultimately, the research aims to provide a scientifically validated pathway for integrating an enhanced land-surface modeling capability into HII’s coupled S2S forecasting system. Through this work, ALICE-LAB is developing expertise and infrastructure that bridge process-based land-surface modeling, satellite Earth observation, hydroinformatics, and AI, supporting the next generation of high-resolution hydro-climate prediction systems for Thailand and Southeast Asia.