Closing the research–practice gap in waterlogging modelling through holistic systems thinking and spatially explicit simulation

Soil waterlogging is a major constraint to global crop production yet remains poorly integrated into agricultural monitoring and decision-support frameworks because waterlogging events are episodic and spatially heterogeneous, while existing modelling approaches largely address individual components of the problem in isolation. We argue that, alongside continued improvement in the representation of key processes, a major and under-addressed bottleneck lies in integrating existing modelling capabilities into an operational decision-support pipeline. Here we propose a conceptual, systems-oriented framework, intended for further development and testing rather than as an implemented decision-support system, that links spatially explicit hazard assessment, crop response simulation and multi-objective evaluation to support adaptation planning. We outline a three-stage modelling pipeline that identifies where and when waterlogging is likely to occur, evaluates alternative engineering, agronomic and genetic interventions, and quantifies trade-offs among productivity, profitability and environmental outcomes. We specify the information exchanged between stages, illustrate the pipeline with a literature-based example, and outline how it could be evaluated and how uncertainty could be handled. Finally, we identify key priorities for advancing waterlogging modelling, including improved process representation of drainage, raised beds, irrigation–groundwater interactions and other adaptation strategies, stronger integration across modelling domains, and closer alignment between modelling outputs and stakeholder needs, beginning with joint framing of the decision problem. By providing a structured pathway for integrating current and future modelling advances into decision support, this framework aims to accelerate the translation of waterlogging research into practical adaptation.