Estimating the economic loss of rice production due to climate-change-reinforced floods: An application of GIS and hydrological modelling

Estimating flood damage to crops is important for post-flood relief and recovery planning as well as for long-term adaptation and mitigation actions in the agricultural sector; however, this is a challenging task due to the... [ view full abstract ]

Estimating flood damage to crops is important for post-flood relief and recovery planning as well as for long-term adaptation and mitigation actions in the agricultural sector; however, this is a challenging task due to the complex interaction between flooding processes and crop systems, especially when climate change is considered.

In this study, a GIS/modelling based method was applied to estimate the economic loss of rice production due to climate-change-reinforced floods. The method included: (1) using GEV distribution and GCMs to project climate-change-reinforced extreme precipitation; (2) establishing maps of inundation depth and inundation duration of extreme precipitation-generated floods from the results of flood model simulations; and (3) calculating economic loss of rice production using GIS methods coupled with stage-damage functions.

The highly agricultural Ca River Basin in north-central Vietnam, which is home to more than 4 million people was selected for this case study. The results reveal that under medium-high greenhouse gas concentration scenario RCP6.0, in the future period 2061-2080 the intensity of 10-year extreme precipitation would increase approximately 12% (compared to the baseline period of 1986-2005), leading inundation to rise 0.6 m and last 0.5 days longer. As a result, the inundation of rice cultivation areas is expected to expand by 27% and the economic loss of rice production is expected to increase by 32%.

The method used in this study is a powerful tool for flood damage prediction under climate change impacts. It is expected to help better decision-making regarding the prevention of flood damage to agriculture.