Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed

Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha(-1) h(-1)y(-1) in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha(-1) y(-1), which will generate 5.52 t ha(-1) y(-1) sediment. The difference between estimated and observed sediment was 1.42 t ha(-1) year(-1) at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.FCT-Foundation for Science and Technology - PTDC/GES-URB/31928/2017; FEDER ALG-01-0247-FEDER-037303info:eu-repo/semantics/publishedVersio

Avaliação de suscetibilidade de erosão gully na bacia hidrográfica de Kondoran usando algoritmos de aprendizagem de máquina e a seleção de recursos boruta

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.PTDC/GES-URB/31928/2017info:eu-repo/semantics/publishedVersio

Comparison of statistical and machine learning approaches in land subsidence modelling

This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8)

Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment

Floods are among the most frequently occurring natural disasters and the costliest in terms of human life and ecosystem disturbance. Identifying areas susceptible to flooding is important for developing appropriate watershed management policies. As such, the goal of the present study was to develop an integrated framework for flood susceptibility assessment in data-scarce regions, using data from the Haraz watershed in Iran. Flood-influencing indices best suited to the identification of areas particularly prone to flooding were selected. The decision-making trial and evaluation laboratory (DEMATEL) approach was used to investigate the interdependence among criteria and to develop a network structure representative of the problem. The relative importance of different flood-influencing factors was determined using the analytical network process (ANP). A flood susceptibility map was produced using weights obtained through the ANP and fuzzy-value function (FVF) and validated using 72 available flood locations where flooding occurred between 2006 and 2018. After validating the results, fuzzy theory served to better delineate the flood susceptibility scores among the region’s sub-watersheds. Incorporating the DEMATEL-ANP approach with FVF yielded an accuracy of 89.1%, as was assessed through the area under the curve (AUC) of a receiver operating characteristics (ROC) curve. The results indicated that the strongest flood-influencing (occurrence/nonoccurrence) factors were elevation, land use, soil texture, and frequency of heavy rainstorms. The fuzzy theory showed sub-watershed C1 to be highly susceptible to flooding, and thus, most in need of flood management

Modelling gully-erosion susceptibility in a semi-arid region, Iran: investigation of applicability of certainty factor and maximum entropy models

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development