Output from a runoff generation models is compared here with information from field measurements, making use of 11 years data from rainfall and runoff events at the Sierra de Enguera Soil Erosion Experimental Station in Eastern Spain.. The model presented simulates overland storm flow on a sloping rough and unvegetated surface, representing an area of 320x320 m. Green-Ampt infiltration constants are randomly assigned to each cell in a 128x128 grid, and rectangular storms applied at a range of total amounts and intensities to simulate runoff at each transect across the area. A simple algebraic expression is developed to estimate total runoff and storage in terms of storm size and duration, and plot length, with parameters that reflect infiltration behaviour, and this expression is compared with the SCS curve number approach. Output of these simulations is compared with measured storm runoff data on bare runoff plots at the Sierra de Enguera experimental Station in SE Spain and gives further support to the proposed expression for storm runoff.

Soil erosion is threatening land sustainability. Piping erosion is one of the land degradation processes that lead to significant landscape and environmental changes, and request a proper mapping survey. The purpose of this study is to survey piping erosion susceptibility maps in Zarandeieh watershed of Markazi province using Random Forest (RF), Support Vector Machine (SVM), and Bayesian Generalized Linear Models (Bayesian GLM) machine learning methods. For this purpose, due to the influence of different physiographic, environmental and soil conditions on the development and formation of piping, 18 variables were considered for modeling the piping erosion sensitivity in Zarandieh watershed. Based on field surveys and aerial photographs, 152 points of piping erosion were identified in the studied area, 70% of which was used for modeling, and 30% for model validation. The area under curve (AUC) was used to evaluate the performance of the models used. The results of the pipping erosion susceptibility showed that all three RF, SVM and Bayesian GLM models, have a good performance in the validation stage such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, PH and Bulk density are the variables that had the most impact on the pipping erosion sensitivity in the study area. This result shows that topographical and soil chemical factors are responsible for the piping distribution in the Zarandieh watershed.