Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed, northern Iran

Abstract

Spatial information on flood risk and flood-related crop losses is important in flood mitigation and risk management in agricultural watersheds. In this study, loss of water bound in agricultural products following damage by flooding was calculated using water footprint and agricultural statistics, using the Talar watershed, northern Iran, as a case. The main conditioning factors on flood risk (flow accumulation, slope, land use, rainfall intensity, geology, and elevation) were rated and combined in GIS, and a flood risk map classified into five risk classes (very low to very high) was created. Using average crop yield per hectare, the amount of rice and wheat products under flood risk was calculated for the watershed. Finally, the spatial relationships between agricultural land uses (rice and wheat) and flood risk areas were evaluated using geographically weighted regression (GWR) in terms of local R2 at sub-watershed scale. The results showed that elevation was the most critical factor for flood risk. GWR results indicated that local R2 between rice farms and flood risk decreased gradually from north to south in the watershed, while no pattern was detected for wheat farms. Potential production of rice and wheat in very high flood risk zones was estimated to be 7972 and 18,860 tons, on an area of 822 ha and 7218 ha, respectively. Loss of these crops to flooding meant that approximately 34.04 and 12.10 million m3 water used for production of wheat and rice, respectively, were lost. These findings can help managers, policymakers, and watershed stakeholders achieve better crop management and flood damage reduction.

Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

“…The accuracy of the AHP technique depends on the assigned weights, which vary for each topic. For instance, elevation [44] and slope [45] as the most important factors in the topic of flood prone areas receive the highest weights. These results indicate that flooding occurs most often along riverbanks and only occasionally in areas far from rivers.…”

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

“…There are 58 types of soil with varying physical and chemical properties in the region. In addition, the hydrological soil groups A, B, and C cover 60%, 21%, and 19% of the watershed, respectively (Mohammadi et al 2020).…”

Flood susceptibility mapping using support vector regression and hyper‐parameter optimization

“…Although Iran is a semi‐arid country, it has experienced devastating floods in past centuries, and the frequency of such events in the future is expected to increase as climate changes (Modarres, Sarhadi, & Burn, 2016; Mohammadi, Darabi, Mirchooli, Bakhshaee, & Haghighi, 2021). Damage and loss of life from floods in Iran have been exacerbated by poor management of floodplains, land‐use changes, loss of forest, overgrazing, and construction of inefficient hydraulic structures (Hooshyaripor, Faraji‐Ashkavar, Koohyian, Tang, & Noori, 2020; Tang, 2020).…”