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Understanding the effects of climate change on agricultural crops is an essential part of flood-risk management and adaptation measures. However, the quantitative impact of flood damage on agricultural crops under climate change is still not clearly understood. It is thus crucial to investigate flood damage to agricultural crops under climate change scenarios to understand future crop damage better. In this study, we first explored the relationship between extreme rainfall and damaged paddy area for a historical period, and then focused on the quantitative analysis of flood impact on rice crops under climate change using MRI-AGCM3.2S climate model outputs for the past (1979–2002) and future (2075–2098) climate periods for the Solo River basin in Indonesia. We developed a quantitative damage assessment method by coupling water and energy budget-based rainfall-runoff-inundation model outputs and a flood loss model. Flood damage to rice crops was defined as a function of flood depth and duration, and depth-duration-damage curves were applied to quantify the damage. The results show that flood damage to rice crops will be more severe in the future than in the past, and the findings can be useful in establishing risk reduction and adaptation measures.

Extreme rainfall or heavy rainfall events (HREs) causes significant socio-economic damages annually affecting local development especially in developing countries. Thus, assessing changes in frequency and magnitude of HREs under climate change using global climate model (GCM) projections became ubiquitous to hydrological impact studies. Here, we present a framework for evaluating GCM's ability in reproducing the seasonal frequency of HREs in Davao River basin and the associated weather patterns that led to HREs. Our results show that HREs in MRI-AGCM 3.2S occurred 81 % in DJF and 4 % in JJA, which showed over(under) estimation bias during DJF(JJA) season compared to ERA5 HREs that show occurrence of 50 % in DJF and 18 % in JJA. Furthermore, we examined the weather pattern and anomalies that led to anomalous conditions of the season specific HREs in Davao River basin, which showed MRI-AGCM3.2S was able to reproduce the general structure of anomalous conditions fairly well on both seasons in comparison with ERA5. However, the slight over(under)estimation of the surface anomalous conditions in DJF(JJA) are directly proportional to the over(under)estimation in rainfall magnitude in the basin.