No abstract

Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha−1 y−1. The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha−1 y−1 and 4.1 t ha−1 y−1, respectively. They were followed by agriculture fields with 1.31 t ha−1 y−1 and 1.33 t ha−1 y−1. The lowest results were located in the residential areas with 0.61 t ha−1 y−1 and 0.63 t ha−1 y−1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by − 0.24 t ha−1 y−1 (2050), and − 0.18 t ha−1 y−1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.

Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha −1 y −1 . The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha −1 y −1 and 4.1 t ha −1 y −1 , respectively. They were followed by agriculture fields with 1.31 t ha −1 y −1 and 1.33 t ha −1 y −1 . The lowest results were located in the residential areas with 0.61 t ha −1 y −1 and 0.63 t ha −1 y −1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by − 0.24 t ha −1 y −1 (2050), and − 0.18 t ha −1 y −1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.Changes in land uses have consistently been described because of rapid population growth and the expansion of human settlement around the world 1-7 . These changes play important roles in shaping the landscape and altering land resources, sometimes with negative impacts 8 . Numerous scholars have concluded that unregulated land-use changes lead to environmental degradation that poses a major threat to the socioeconomic and ecological sustainability of soil as a vital resource [9][10][11] . Increasing pressure on land resources because of unsustainable cultivation, overgrazing, deforestation, climate change and drought, urbanization and poor land management practices are worsening land degradation on a global scale [12][13][14][15] . Among them, soil erosion (SE) is one of the common forms of land degradation that is related to unsustainable environmental management. Soil erosion is particularly severe in arid and semi-arid regions [15][16][17][18][19][20] .SE is a complex process resulting from the impacts of wind, precipitation, human activities and associated runoff processes that are influenced by parent material, soil properties, relief and vegetation cover 21,22 . Although

Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield.

A reliable overview of the spatial distribution of soil properties is a straightforward approach in soil policies and decision-making. Soil organic carbon (SOC) content, SOC stock and bulk density (BD) directly affect soil quality and fertility. Therefore, an accurate assessment of these crucial soil parameters is required. To do this, we used machine learning algorithms (MLAs) including, multiple linear regression (MLR), random forest (RF), artifi cial neural network (ANN), and support vector machine (SVM) with the help of environmental covariates to predict SOC content, BD, and SOC stock. The study was conducted in two different areas, Látókép and Westsik (East Hungary), both experimental research fi elds but different from physio geographic points of view. Thirty topsoils (0-10 cm) samples were collected for each study area using conditioned Latin Hypercube Sampling strategy. Environmental covariates were extracted from a digital elevation model (DEM) and satellite images based on the representation of soil forming factors. We validated the results by randomly splitting the dataset into a train (two-third) and test (one-third) and calculated the root mean square error and R 2 . Our results showed that RF provided the most accurate spatial prediction with R 2 of about 80% for each soil property in both study areas. This study highlighted the importance of terrain attributes (including plan and profi le curvature, elevation and valley depth) and NDVI derived from satellite images in presenting a spatial distribution of selected soil properties in two different areas. We conclude that comparing these methods can help to determine the most accurate maps under diverse geographical conditions and heterogeneities at different scales, which can be used in precision soil quality management.