Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning

TL;DR

This paper introduces NoahMP-AI, a deep learning framework that enhances land surface model predictions of soil moisture during extreme events by combining physics-based models with machine learning corrections, improving accuracy and physical consistency.

Contribution

The study presents a novel hybrid approach integrating Noah-MP with deep learning to correct biases in soil moisture predictions during extreme events, advancing earth system modeling.

Findings

R-squared improved from -0.7 to 0.5 during drought

Maintains physical consistency and spatial coherence

Establishes a benchmark for physics-data integration

Abstract

Accurate soil moisture prediction during extreme events remains a critical challenge for earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface models (LSMs) provide physically-based predictions, they exhibit systematic biases during extreme conditions when their parameterizations operate outside calibrated ranges. Here we present NoahMP-AI, a physics-guided deep learning framework that addresses this challenge by leveraging the complete Noah-MP land surface model as a comprehensive physics-based feature generator while using machine learning to correct structural limitations against satellite observations. We employ a 3D U-Net architecture that processes Noah-MP outputs (soil moisture, latent heat flux, and sensible heat flux) to predict SMAP soil moisture across two contrasting extreme events: a prolonged drought (March-September 2022) and Hurricane Beryl (July 2024) over Texas. When comparing NoahMP-AI with NoahMP, our results demonstrate an increase in R-squared values from -0.7 to 0.5 during drought conditions, while maintaining physical consistency and spatial coherence. The framework's ability to preserve Noah-MP's physical relationships while learning observation-based corrections represents a significant advance in hybrid earth system modeling. This work establishes both a practical tool for operational forecasting and a benchmark for investigating the optimal integration of physics-based understanding with data-driven learning in environmental prediction systems.