Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation due to wind and water erosion in some areas of the world, the combined study of both types of erosion in the same area receives relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran, encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing the artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the area under the curve (AUC) of the receiver operating characteristic (ROC) metric. The water and aeolian soil erosion maps are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for in an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere

Assessment of the impact of dust aerosols on crop and water loss in the Great Salt Desert in Iran

Knowledge of the spatial distribution of dust aerosols and their effects on crops is important for policy formulation and food security. This study aims to investigate the impact of dust source susceptibility areas (DSSA) on the loss of agricultural crop and corresponding water consumption in terms of Water Footprint in the Great Salt Desert, Iran. To this goal, MODIS satellite images during the 2005-2020 period were used to identify dust sources and 135 dust source zones were identified. Machine learning algorithm viz. Random Forest (RF), generalized linear model (GLM), and Artificial neural network (ANN) were tested to reproduce DSSA. The best method was RF and applied to calculate and classify DSSA in five risk levels from very low to very high. The amount of wheat production under high risk of DSSA was estimated using the average crop yield from recent years using agriculture statistics. We calculated the loss of crops and corresponding water consumption for three scenarios, assuming a typical loss of 20, 40, and 60% of the wheat production for better crop loss estimation. Finally, the spatial relationships between wheat farmland and high-risk DSSA were assessed using ordinary least squares regression (OLS) and geographically weighted regression (GWR) at sub-watershed scale. The area of wheat cultivation in high and very high risk of DSSA is 10188.04 km(2), which is 36% of all agricultural land for wheat in the region. Loss of wheat crop to DSSA meant that 1270.58 to 3811 million m(3) water used for the production of wheat were lost, corresponding to 2%, to 7% of lost water compared to the total water consumption for wheat production in the study area

Tracking the origin of trace metals in a watershed by identifying fingerprints of soils, landscape and river sediments

The identification of the spatial distribution of soil trace-elements and the contribution of different sources to the sediment yield is necessary for a better watershed and river water quality management. Until now, less attention has been paid to comprehensive assessments of sediment sources and soil trace-elements with respect to the suspended sediment production. The present study aimed at modelling the spatial distribution of soil trace-elements, quantifying the sediment sources apportionment and relating the landforms to polluted soils. Different techniques and approaches such as the Nemerow pollution index, machine learning algorithms (Random Forest (RF), generalised boosting methods (GBM), generalised linear models (GLM) and sediment fingerprinting were applied to the Kan watershed. A total of 79 soil samples having different Nemerow index values were considered for spatial modelling. Using statistical methods (Range test, Kruskal-Wallis and discrimination function analysis), an optimal set of tracers was selected. An unmixing model was applied to calculate the relative contribution of landforms for eight rainfall events. The results of the soil trace-element mapping showed that RF had the best performance with an accuracy of 83%. The evaluation of polluted soil areas showed that the landforms ‘steep hills’ and ‘valley’ contributed the most with 51% and 27% in the riparian zone, respectively. In addition, these landforms give a high contribution to sediment production in late-winter—spring events (29%) with a GOF (goodness of fit) of 0.65. The landform ‘plain’ had the highest contribution (28%) in sediment yield with a GOF of 0.72 in early-winter events. This means that the valley and steep hill landforms accelerate the transport of trace-elements across the watershed. Interestingly, the contribution of landforms varies during the year. Overall, the new proposed approach enables to better trace the origin of suspended sediments and trace-elements discharge into the river environment

Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed, northern Iran

Abstract 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

How do data-mining models consider arsenic contamination in sediments and variables importance?

Arsenic (As) is one of the most important dangerous elements as more than 100 million of people are exposed to risk, globally. The permissible threshold of As for drinking water is 10 μg/L according to both the WHO’s drinking water guidelines and the Iranian national standard. However, several studies have indicated that As concentrations exceed this threshold value in several regions of Iran. This research evaluates an As-susceptible region, the Tajan River watershed, using the following data-mining models: multivariate adaptive regression splines (MARS), functional data analysis (FDA), support vector machine (SVM), generalized linear model (GLM), multivariate discriminant analysis (MDA), and gradient boosting machine (GBM). This study considers 12 factors for elevated As concentrations: land use, drainage density, profile curvature, plan curvature, slope length, slope degree, topographic wetness index, erosion, village density, distance from villages, precipitation, and lithology. The susceptibility mapping was conducted using training (70%) and validation (30%). The results of As contamination in sediment showed that classifications into 4 levels of concentration are very similar for two models of GLM and FDA. The GBM calculated the areas of highest arsenic contamination risk by MARS and SVM with percentages of 30.0% and 28.7%, respectively. FDA, GLM, MARS, and MDA models calculated the areas of lowest risk to be 3.3%, 23.0%, 72.0%, 25.2%, and 26.1%, respectively. The results of ROC curve reveal that the MARS, SVM, and MDA had the highest accuracies with area under the curve ROC values of 84.6%, 78.9%, and 79.5%, respectively. Land use, lithology, erosion, and elevation were the most important predictors of contamination potential with a value of 0.6, 0.59, 0.57, and 0.56, respectively. These are the most important factors. Finally, these data-mining methods can be used as appropriate, inexpensive, and feasible options to identify As-susceptible areas and can guide managers to reduce contamination in sediment of the environment and the food chain