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Flooding has been detrimental to economies, globally causing severe damage to human lives and property. Developing countries, including Sri Lanka, are highly vulnerable to heavy floods mainly due to the lack of infrastructure and expertise on efficient flood risk management. Gin River basin in Sri Lanka, having a catchment area of 932 km², is one such river basin that has been affected by heavy floods in the past. Therefore, bias-corrected General Circulation Model (GCM) outputs from Coupled Model Inter-comparison Project Phase 5 (CMIP5) were used as inputs for the Rainfall-Runoff Inundation (RRI) model to simulate the impact of climate change on flood peak discharge, maximum inundation depths of the Gin River basin. Future climate projections of five GCMs that performed well in the target area, corresponding to the RCP4.5 emission scenario, were used to simulate the future flood flow using the validated RRI model. GCM averaged projections indicated a 10% increase in southwest monsoon rainfall from May to September compared with the past (1980-2000). Projected changes in the precipitation have declined the mean annual discharge by 34% while aggravating the flow exceeding 5% of the time by 5% on average at the Baddegama river gauging station (downstream) of the basin. Furthermore, the RRI model simulations corresponding to the RCP4.5 emission scenario for 2040-2060 revealed critical information, including expansion of peak inundation extent, which will aid efficient flood risk planning and management in the basin.

Among natural disasters in Pakistan, flooding is the most frequent and devastating. In Punjab, the flood-prone areas suffer heavy damage during the heaving rainfall of the monsoon. The population density of Punjab region is increasing every year. Conversely, the dependence of population on rivers and urbanization on agricultural land is increasing every year. Many researchers tried to investigate flood mechanism and predict flood disasters. Researchers have mainly focused on the upstream of the Indus River due to flash flood disaster risk. At the downstream of Indus river, the high population density area after the confluence of the Indus and Chenab rivers is also important due to the riverine. In this region, severe inundation by flooding occurred in 2015. In this study, Rainfall-Runoff-Inundation Model (RRI) and River model (iRIC) were used to evaluate the inundation. We aimed to comprehensively evaluate the inundation risks (inundation depth, peak inundation discharge, and inundation area) by considering extreme rainfall events over densely populated areas located downstream of the confluence point of the Indus and Chenab Rivers. In addition, sensitivity experiments for land cover were conducted to reveal the impact of land-cover change (urbanization and afforestation) on inundation. The Nash-Sutcliffe model efficiency coefficient for river discharge calculation at the Tounsa barrage and Trimmu headworks (on just before the confluence point of Indus and Chenab) was 0.83 and 0.67 respectively. Furthermore, the flooding at the confluence point of the Indus River was reproduced by iRIC with high accuracy. The results showed that planting and afforestation will mitigate flooding scale, but urbanization increases the risk of flooding especially after the confluence point of two rivers at the high population dense area. Planting between the Indus River and Chenab River could mitigate flood disasters downstream of the confluence point.