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Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi-collinearity analysis and K-fold cross-validation,

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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.

Exploring spatial and temporal land-use changes is valuable for local governments to address issues of sustainability and planning policy where urbanization and industrialization are taking place. Besides anthropogenic effects, natural driving forces like climate change may also affect sustainability. However, such relationships have not been studied minutely. Hence, this study first investigates the land-use changes and their relationship with land surface temperature (LST) for the Shazand Watershed, Iran, in 1986, 1998, 2008, and 2016 coincided with supplementary industrialization stages. Furthermore, the relations among LST and other biophysical parameters, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI), and Normalized Difference Water Index (NDWI), were analyzed, and corresponding variations were explored. The results indicated that the mean LST of the study watershed has an increasing trend from 1986 to 2008 due to land-use change and drought intensification. Later, LST decreased in 2016. Lower LST was associated with irrigation farming and orchard, and higher LST was related to sparse oak forest areas. There was also a negative correlation between LST and NDVI. As a result, it was inferred that greenery declined LST. Conversely, a positive correlation was found between LST and NDBI resulting from the built-up areas. Since LST could influence biological, physical, chemical processes, it can therefore be supported as an effective index for environmental sustainability assessment.

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