Flood damage functions for rice: synthesizing evidence and building data-driven models

Abstract. Floods are a major cause of agricultural losses, yet flood damage models for crops are scarce, often lack validation, uncertainty estimates, and assessments of their performance in new regions. This study evaluates and compares flood damage modelling approaches for rice crops. We compile and review 20 damage models from 12 countries, identifying key gaps and limitations. Using empirical survey data from Thailand and Myanmar, we develop a suite of empirical models, including deterministic and probabilistic stage-damage functions, Bayesian regression, and Random Forest, based on key flood characteristics like water depth, duration, and plant growth stage. We assess predictive performance through cross-validation and test how well models trained in one region perform when applied to another. Our results show that model performance depends on complexity and context: Random Forest achieves the highest accuracy, while simpler models offer ease of use in data-scarce settings. The results also demonstrate the potential errors introduced by transferring models spatially, highlighting the need for diverse training data or local calibration. In summary, we present the most comprehensive review of flood damage models for rice crops to date and provide practical guidance on model selection and expected errors when transferring models across regions.