Work

Machine learning-enhanced ecohydrological modelling of monthly-scale oasis dynamics driven by ecological water diversion: A case study of the Qingtu Oasis, China

Year: 2026

Type: article

Abstract: Ecohydrological models support oasis restoration projects driven by ecological water diversion. However, standalone machine learning models fail to capture underlying ecohydrological mechanisms and interactions. To address this limitation, we introduce a framework that integrates machine learning with empirical ecohydrological equations to construct a mechanistically grounded modelling approach. Ten candidate machine learning models, including Support Vector Regression, Categorical Boosting, and Deep Deterministic Policy Gradient (DDPG), were evaluated to construct independent modules for lake area, water table depth, oasis area, and fractional vegetation coverage. Empirical ecohydrological equations for oasis evapotranspiration and the groundwater-based carrying capacities of oasis area and fractional vegetation coverage were integrated into these modules, allowing the framework to incorporate ecohydrological mechanisms, which were further validated using SHapley Additive exPlanations (SHAP) analysis. The enumeration method was used to integrate the four modules, enabling the representation of groundwater–vegetation interactions and the simulation of key ecohydrological processes. A recursive strategy was then applied to generate continuous, monthly simulations of lake area, water table depth, oasis area, and fractional vegetation coverage. The framework was tested in the Qingtu Oasis of the Shiyang River Basin, China, as a representative case study. Results demonstrate both the feasibility and methodological validity of the approach, indicating that combining the DDPG model with empirical ecohydrological equations provides a useful reference for ecohydrological simulation. Future research will aim to reduce data uncertainty, incorporate soil salinity into the modelling framework, and explore transferability across different arid endorheic basins.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2026.103808

Authors: Feng Huang, Yiping Li, Carlos G. Ochoa, Xin Li, Jisheng Li

Institutions: Hohai University, Oregon State University

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Climate action, Life on land

Open Access status: gold

Estimated APC paid: $2,770