Search

Among the land degradation processes, gully erosion is the one that poses more environmental and societal challenges in arid regions. Predicting spatiotemporal gully development in a region under changing conditions is important to adopt proper mitigation measures. Here we investigate the Ghapan-Olya watershed in Golestan province in Iran, which is affected by many erosional landforms, including gully phenomena. We apply the pixel-based distributed LANDPLANER model to predict where rainfall induced gullies will occur by exploiting input maps including UAV data, the region soil, and seasonal land use information. We compare our topographic thresholds and an erosion index with field observations through the application of quantitative metrics such as sensitivity, specificity, fallout, precision, and recall. Our study reveals that the spatial density of the gully's location is more frequently predicted in the areas with an altitude of about 200–300 m, steep slope (between 15 and 30 degrees), and low average accumulation value (< 100) in the southeast facing slope. We obtain higher values of erosion index and topographic threshold for the minimum curve number where intense rainfalls are more frequent and where land use and cover conditions are more predisposing for gully occurrence. We obtain the largest values of soil erosion indices in the fall scenario when the daily rainfall is 80 mm (6.27), followed by the summer scenario with 80 mm daily rainfall (4.88), and spring again with 80 mm daily rainfall (2.99). In addition, topographic threshold maps illustrate the largest amount of soil erosion for the curve number scenario (without considering daily rainfall) in autumn. Our approach allows simulating gully erosion under changing conditions. [ABSTRACT FROM AUTHOR]

Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide susceptibility mapping in the study area. Moreover, results obtained showed that the M-AHP model performed slightly better than the MARS, GLM, and GAM models in prediction. These algorithms can be very useful for landslide susceptibility and hazard mapping and land use planning in regional scale. [ABSTRACT FROM AUTHOR]