Identification of flooding susceptible areas by soil infiltration spatial correlation patterns

Soil properties at the lands lot scale is a process that effects on infiltration rates, which can lead to an increased stormwater runoff. Sustainable land management for runoff controls requires reliable information on the spatial distribution of soil infiltration rate. The traditional method of soil analysis and interpretation are laborious, time-consuming, hence becoming expensive. Therefore spatial analysis of infiltration rate is necessary for the determination of susceptible areas for surface runoff. The purpose of this study was to propose alternative approaches in searching for infiltration rate and surface runoff hotspots. The study was conducted on an agricultural field at Khodafarin, East Azarbaijan province, Iran. It is with 675500 to 692500 longitudinal coordinates and 4332500 to 4349000 latitude coordinates. To arrive at the goal of this research, 88 sample points were selected with using a geographic information system and considering the Soil distribution pattern and land utilization. Infiltration rate data was collected from double rings method with 3 replicated at each point. Soil samples were obtained from each point and then sent to soil analyzed laboratory to determine physical and chemical properties. Kriging method was used for the interpolation of the study area. Finally, soil properties distribution layer expended in Arc GIS software that analyzing hot spot surface runoff hazard. The significant correlation of infiltration rate with soil parameter including porosity, sand, silt, and organic content. Overall, at wide distances of case study lands organic material and porosity were assessed effectively to water permeability into the soil and other measured parameters due to statistics were without specified spatial distribution. The results showed that Simulation generates equally likely sets of values for a variable, which are consistent with available in-situ measurements. The information on spatial sampling density and hotspot pattern could be useful for long-term monitoring and assessment.