Significance: Iran is facing a state of water bankruptcy that threatens its socioeconomic development and natural environments. Using an exceptionally rich measured groundwater dataset, we illustrate the extent and severity of Iran’s groundwater depletion and salinization problems during the 2002 to 2015 period, when the number of groundwater extraction points nearly doubled. Iran’s nonrenewable groundwater withdrawal was about 66 million m ³ in 1965, which cumulatively grew to approximately 133 × 10 ³ million m ³ in 2019. This increase is about 3.4 times the capacity of the famous Three Gorges Dam in China. Groundwater decline due to extensive overexploitation of nonrenewable groundwater and rising salinity levels are documented in almost all subbasins, pointing to dire, worsening water security risks across the country.

Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

The influence of seasonally frozen ground (SFG) on water, energy, and solute fluxes is important in cold climate regions. The hydrological role of permafrost is now being actively researched, but the influence of SFG has received less attention. Intuitively, SFG restricts (snowmelt) infiltration, thereby enhancing surface runoff and decreasing soil water replenishment and groundwater recharge. However, the reported hydrological effects of SFG remain contradictory and appear to be highly site- and event-specific. There is a clear knowledge gap concerning under what physiographical and climate conditions SFG is more likely to influence hydrological fluxes. We addressed this knowledge gap by systematically reviewing published work examining the role of SFG in hydrological partitioning. We collected data on environmental variables influencing the SFG regime across different climates, land covers, and measurement scales, along with the main conclusion about the SFG influence on the studied hydrological flux. The compiled dataset allowed us to draw conclusions that extended beyond individual site investigations. Our key findings were: (a) an obvious hydrological influence of SFG at small-scale, but a more variable hydrological response with increasing scale of measurement, and (b) indication that cold climate with deep snow and forest land cover may be related to reduced importance of SFG in hydrological partitioning. It is thus increasingly important to understand the hydrological repercussions of SFG in a warming climate, where permafrost is transitioning to seasonally frozen conditions.