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Abstract: Flood damage estimation is an important element in flood risk assessment; it is useful for mobilizing investment and developing policies for flood loss prevention since most flood risk reduction measures are based on cost benefit analysis. From a global or regional view‐point, it is necessary to understand relative impacts on economies of countries to mobilize international support and develop response strategies. This requires a unified approach to flood damage estimation across countries and over large spatial extents. Spatial resolutions used in the assessment have a direct impact on the estimation of potential flood heights as well as representation of property exposure and, consequently, the flood damage estimates. Using Ichinomiya river basin, Japan, as a case study, the sensitivity of spatial scale in estimating potential flood economic damages is investigated and approaches are delineated to reduce errors arising from coarse spatial resolutions. [Correction added on 5 May 2016, after first online publication: in the first sentence of the abstract the word “is” has been inserted between “it” and “useful”.] [ABSTRACT FROM AUTHOR]

Recent extreme weather events like the August 2016 flood disaster have significantly affected farmland in mid-latitude regions like the Tokachi River (TR) watershed, the most productive farmland in Japan. The August 2016 flood disaster was caused by multiple typhoons that occurred in the span of two weeks and dealt catastrophic damage to agricultural land. This disaster was the focus of our flood model simulations. For the hydrological model input, the rainfall data with 0.04° grid space and an hourly interval were provided by a regional climate model (RCM) during the period of multiple typhoon occurrences. The high-resolution data can take account of the geographic effects, hardly reproduced by ordinary RCMs. The rainfall data drove a conceptual, distributed rainfall–runoff model, embedded in the integrated flood analysis system. The rainfall–runoff model provided discharges along rivers over the TR watershed. The RCM also provided future rainfall data with pseudo-global warming climate, assuming that the August 2016 disaster could reoccur again in the late 21st century. The future rainfall data were used to conduct a future flood simulation. With bias corrections, current and future flood simulations showed the potential inundated areas along riverbanks based on flood risk levels. The crop field-based agricultural losses in both simulations were estimated. The future cost may be two to three times higher as indicated by slightly higher simulated future discharge peaks in tributaries. [ABSTRACT FROM AUTHOR]