Earth

The mountainous Samian Watershed hosts important rivers recently, significantly triggered by fast and unplanned urbanization, population growth, environmentally hazardous industrialization, and inappropriate dam construction. Nonetheless, this watershed has not yet been evaluated through the lens of river restoration. Therefore, this study aims (1) to apply the River Restoration Index (RRI), (2) to assess the significance of each river restoration criterion and sub-index, and (3) to identify priority hotspots for immediate restoration efforts across 27 sub-watersheds in this case study. First, we built a database containing meteorological, hydrological, land use, physiographic, soil, and economic data. Then, we calculated the general state of the watershed (GSW), connectivity (Con), riverbank conditions (RbC), and hydraulic risk reduction (HRR) sub-indices to develop a multi-domain RRI. Finally, the MEREC-ORESTE hybrid method supported sustainable government planning. The findings reveal significant environmental issues, notably in sanitation conditions, transversal connectivity, and urban encroachment on riverbanks. Sanitation risks were high throughout the watershed, while other eco-environmental risks varied across regions. The weights of 0.36, 0.16, 0.32, and 0.16 were assigned for GSW, Con, RbC, and HRR, respectively, highlighting the importance of GSW and RbC in river restoration activities. Priority management areas (with RRI below 0.50) cover 78% of the watershed. Full article

Mineralization of groundwater for drinking purposes is a complex parameter of groundwater chemical composition. In the Shaim oil- and gas-bearing area, as in the whole West Siberian megabasin, the main target horizon for solving the issues of domestic and technical water supply is the Oligocene aquifer. It has significant groundwater reserves to cover the needs of the population and production requirements. However, it also faces a huge anthropogenic load in the form of water withdrawal and possible contamination from the surface with oil products. In Western Siberia, various deviations in the chemical composition of groundwater of the Oligocene horizon are recorded in connection with significant water withdrawal; for example, a sharp increase in chromaticity or total iron concentration, with changes in mineralization acting as a factor necessarily accompanying these deviations. Based on the data obtained in the course of monitoring for the period from 2013 to 2023, the main factors and trends of changes in the components of mineralization of the Oligocene horizon were determined. The lithological and mineralogical peculiarities of the water-bearing rocks of the horizon, the paleogeographic conditions of its formation and their relation to trends in mineralization change were studied. Water withdrawal data were processed for two cluster water withdrawal sites (50 and 5 wells, respectively). Analysis of the results showed that the increase in water withdrawal leads to an increase in infiltration from the overlying Neogene-Quaternary aquifer, which leads to the dilution of groundwater of the Oligocene horizon and a decrease in its mineralization. Here, we show that, during further monitoring, it is necessary to pay attention to the appearance of sites where significant amounts of chloride ions are fixed in the anion composition, which can potentially lead to a sharp deterioration in the quality of drinking groundwater. Full article

The current study is essential for obtaining an accurate representation of weather conditions in the Ziz and Guir watersheds, characterized by an arid climate. This study combined climate data from the ERA5 model with data from observation stations in order to evaluate the ERA5 model in Morocco’s arid environment and increase the temporal and geographical coverage of climate data. From the data collected, precipitation, minimum and maximum temperatures were predicted under the RCP4.5 and RCP8.5 scenarios by applying the SDSM model in the two watersheds for the 2025 and 2100 periods. These forecasts contribute to the development of adaptation strategies in the face of climate change by giving precise indications of future trends and providing local communities with tools for enhancing their resilience capacity. At all climatic stations, the temperature changes predicted under these scenarios showed a marked positive trend for both minimum and maximum temperatures. By the end of the century, minimum temperatures may increase by 1.84 °C and 2.39 °C under the RCP4.5 and RCP8.5 scenarios, respectively. Similarly, maximum temperatures may increase by 1.78 °C and 2.9 °C under the RCP4.5 and RCP8.5 scenarios, respectively. In addition, the precipitation forecast under the RCP 4.5 scenario showed a significant negative trend at the Ait Haddou station, while under the RCP 8.5 scenario, significant negative trends were predicted for the Sidi Hamza, Ait Haddou, Tit N’Aissa, and Bouanane stations. Full article

Understanding the dynamic relationships between geoenvironmental factors and forest vegetation cover is crucial for effective forest management and planning. This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. The results show that in 2013, altitude and rainfall were the primary factors influencing FVC, with areas of higher altitudes and adequate rainfall exhibiting up to 30% denser forest cover. By 2023, soil moisture and groundwater levels had emerged as the dominant factors, with soil moisture levels accounting for 25% of the variation in FVC. This shift underscores the increasing importance of water management strategies to maintain forest health. The Random Forest model demonstrated high predictive accuracy, achieving an R² value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. This study’s insights are crucial for guiding sustainable forest management practices and assisting decision-makers in formulating strategies for resource management, environmental preservation, and future planning. This study underscores the necessity of adaptive management strategies that consider evolving climatic and hydrological conditions, emphasizing continuous monitoring and advanced technologies to ensure the resilience of forest ecosystems. Full article

To understand the impact of climate change on water resources, this research investigates long-term rainfall trends and anomalies across Northeastern India (NEI), covering Assam and Meghalaya (A&M); Nagaland, Manipur, Mizoram, and Tripura (NMMT); and Sub-Himalayan West Bengal and Sikkim (SHWB&S) using different statistical tests including innovative trend analysis (ITA). The study scrutinizes 146 years of rainfall statistics, trend analyses, variability, and probability distribution changes to comprehend its implications. Furthermore, the change in the assured rainfall probabilities was also worked out to understand the impact on rainfed agriculture of Northeastern India. Comparative analysis between all India (AI) and NEI reveals that NEI receives nearly double the annual rainfall compared to AI (2051 mm and 1086 mm, respectively). Despite resembling broad rainfall patterns, NEI displays intra-regional variations, underscoring the necessity for region-specific adaptation strategies. Statistical characteristics like the coefficient of skewness (CS) and coefficient of kurtosis indicate skewed rainfall distributions, notably during the winter seasons in NMMT (CS~1.6) and SHWB&S (CS~1.5). Trend analyses reveal declining rainfall trends, especially conspicuous in NEI’s winter (−1.88) and monsoon (−2.9) seasons, where the rate of decrease was higher in the last three decades. The return periods of assured rainfall at 50% and 75% probability levels also increased sharply during the winter and monsoon seasons by over 30% during the recent half, posing challenges for rainfed upland hill farming. Furthermore, this study highlights increasing variability and negative anomalies in monsoon rainfall over NEI, exacerbating decreasing rainfall trends and significantly impacting agricultural productivity. These findings underscore the urgency for adaptive measures tailored to evolving rainfall patterns, ensuring sustainable agricultural practices and efficient water resource management. Full article

Resolving the status of soil carbon with land cover is critical for addressing the impacts of climate change arising from land cover conversion in boreal regions. However, many conventional inferential approaches inadequately gauge statistical significance for this issue, due to limited sample sizes or skewness of soil properties. This study aimed to address this drawback by adopting inferential approaches suitable for smaller samples sizes, where normal distributions of soil properties were not assumed. A two-step inference process was proposed. The Kruskal–Wallis (KW) test was first employed to evaluate disparities amongst soil properties. Generalized estimating equations (GEEs) were then wielded for a more thorough analysis. The proposed method was applied to soil samples (n = 431) extracted within the southern transition zone of the boreal forest (49°–50° N, 80°40′–84° W) in northern Ontario, Canada. Sites representative of eight land cover types and seven dominant tree species were sampled, investigating the total carbon (C), carbon-to-nitrogen ratio (C:N), clay percentage, and bulk density (BD). The KW test analysis corroborated significance (p-values < 0.05) for median differences between soil properties across the cover types. GEEs supported refined robust statistical evidence of mean differences in soil C between specific tree species groupings and land covers, particularly for black spruce (Picea mariana) and wetlands. In addition to the proposed method, the results of this study provided application for the selection of appropriate predictors for C with digital soil mapping. Full article

The demand for intensive agriculture to boost food and crop production has increased. High nitrogen (N) fertilizer use is crucial for increasing agricultural productivity but often leads to significant nitrate losses, posing risks to surface and groundwater quality. This study examines the role of biochar as a soil amendment to enhance nutrient retention and mitigate nitrate leaching. By improving nitrogen efficiency, biochar offers a sustainable strategy to reduce the environmental impacts of intensive agriculture while maintaining soil fertility. An incubation study investigated four biochar feedstocks: spruce bark biochar at 550 °C (SB550), hardwood biochar (75% sugar maple) at 500 °C (HW500), sawdust (fir/spruce) biochar at 427 °C (FS427), and softwood biochar at 500 °C (SW500), to identify the most effective nitrate adsorbent. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR) were employed to analyze biochar morphology and surface functional groups. Adsorption isotherms were modeled using the Langmuir and Freundlich equations. The results indicated that surface functional groups, such as aromatic C=C stretching and bending, aromatic C–H bending, and phenolic O–H bending, play crucial roles in enhancing electrostatic attraction and, consequently, the nitrate adsorption capacity of biochar. The equilibrium adsorption data from this study fit well with both the Langmuir and Freundlich isotherm models. Among the four biochar types tested, SB550 exhibited the highest nitrate adsorption capacity, with a maximum of 184 mg/g. The adsorption data showed excellent conformity to the Langmuir and Freundlich models, with correlation coefficients (R²) exceeding 0.987 for all biochar types. These findings highlight the high accuracy of these models in predicting nitrate adsorption capacities. Full article

Assessing community-level resilience and implementing strategies to enhance it are essential for maintaining fundamental community functions, coping with and mitigating risks, effectively reducing hazards, and promoting sustainable regional development. Accordingly, this study aimed to measure hazard exposure and livelihood resilience in the counties of Khuzestan Province. Hazard exposure to earthquakes, flooding, and drought was evaluated using decision-making techniques within a geographic information system (GIS). Additionally, a multi-criteria decision-making approach incorporating eight indicators was employed to calculate the integrated livelihood resilience indicator for the counties. The results indicated that the northern and northeastern counties exhibit the highest potential for flooding and earthquake hazards, whereas the southern and southwestern counties are most vulnerable to flooding and drought. Moreover, Dezful, Shadegan, and Ahvaz counties demonstrated the highest levels of livelihood resilience, while Lali, Haftkel, and Andika counties exhibited the lowest levels. Assessing hazard exposure and livelihood resilience represents critical steps in risk reduction management programs and initiatives. Evaluating community-level livelihood resilience in multi-hazard areas is a vital component in advancing the global objectives of the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. Full article

The objective of this research is to determine the meteorological drought index and the effective rainfall model that exhibit the highest correlation with the yield of rainfed wheat in the Karkheh watershed. Additionally, using spatial statistics analysis, the trend and status of drought in various parts of the watershed will be identified. This will allow for the determination of suitable areas for rainfed wheat cultivation in the near future. In this research, meteorological drought monitoring was conducted using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) drought indices at 34 synoptic stations of the Karkheh watershed in southwestern Iran from 1981 to 2016. Effective precipitation (EPCP) was calculated using four methods, namely the United States bureau of reclamation method (USBR), the simplified version of soil conservation service of the United States department of agriculture method (USDA-SCS simplified), the food and agriculture organization of the United Nations method (FAO), and the CROPWAT version of the USDA-SCS method (USDA-SCS CROPWAT). The correlation between SPI and SPEI and between wheat yield and these indices was performed using Pearson’s correlation coefficient (R) to select the best index for assessing the effects of meteorological droughts on the agricultural sector. The analysis of the drought features showed that the SPEI reported milder and longer droughts in most synoptic stations. The spatial pattern of drought in the Karkheh watershed was determined using spatial statistics methods, and global Moran’s I statistic showed that the drought in the Karkheh watershed had spatial autocorrelation and a clustered pattern with a 99% confidence level. The results of global and local Moran’s I and the Getis-Ord statistic (Gi*) showed that milder droughts prevailed in the northern clusters and more severe droughts in the southern clusters. Also, the correlation between wheat yield and the SPI and SPEI was positive in all stations except for the stations of Kermanshah province and Aqa Jan Bolaghi, which is due to the existence of numerous dams and springs in these areas. Considering the impact of temperature (Tmean) on meteorological drought, it is suggested that in addition to the SPI, the SPEI should also be used to predict droughts in the Karkheh watershed. Full article

The study specifies the changes in radon activity concentration (RAC) in soil gas with depth and emphasizes the significance of the meteorological factor for deeper boreholes. Radon activity concentration was measured in 0.6 m, 1 m and 1.5 m boreholes, and the depth gradient was also calculated. Spatial patterns were estimated using an autocorrelation index. RAC ranged from 9454 ± 439 Bq/m³ for 0.6 m, 16,031 ± 602 Bq/m³ for 1 m and 22,049 ± 937 Bq/m³ for 1.5 m. RAC increased with depth at most of the study sites and behaved quite uniformly in spatial terms. At the same time, no significant differences in the activity of uranium (²³⁸U) series isotopes at different soil depths were detected. Significant spatial variability in radioactivity and the physical properties of soils is noted. The highest gradient was between 0.6 and 1 m. It was found that with increasing depth the connection between the RAC and the meteorological conditions (temperature and humidity) of the surface layer of the atmosphere is lost. It follows that for shallow boreholes it is necessary to consider the influence of meteorological conditions. RAC in 1 m boreholes correlates with 1.5 m and 0.6 m boreholes, but no correlation was found for the 1.5 m and 0.6 m boreholes themselves. Thus, 1 m boreholes are optimal for radon monitoring. A high level of RAC indicates a high potential for indoor radon exposure in this territory, with corresponding epidemiological consequences in the long term. Full article

Aquifer Storage and Recovery (ASR) can involve injecting available surface water into an unconfined aquifer and then extracting it to provide secondary water for irrigation. This study demonstrates a method for evaluating the appropriateness of steady injection versus unsteady injection for an assumed situation. In design, it can be important to affect the transient: the proportion of the injected water that would be subsequently extracted (versus that remaining in the aquifer) and the proportion within the extracted water that would be an injectate (versus ambient groundwater). These proportions can be predicted from the predicted value of an ASR well’s Recovery Effectiveness (REN)—the time-varying proportion of injectate that is extracted subsequently from the same fully penetrating well. Applying the demonstrated procedure with appropriately detailed data and simulation models can predict the REN values resulting from steady versus unsteady injection, followed by steady extraction. For convenience in displaying and computing REN, the injectate was assumed to have a 100 ppm conservative solute concentration. For this demonstration, a homogenous isotropic unconfined one-layer aquifer was assumed. The scenarios involved steady or unsteady injection for 61 days via a fully penetrating ASR well. Then, 91 days of steady pumping led to the extraction of a total volume equal to that injected. For the assumed hydrogeologic data—31 years of Salt Lake City, Utah, rainfall data and estimated captured runoff—the results show that steady injection is more likely to cause a predictable REN but might not cause a higher REN than daily varying injection of the same total volume. Assuming different runoff or hydrogeologic flows would lead to different REN values. Steady injection causes a predictable groundwater mound and can assure a sufficient vadose zone thickness for overlying plants. Augmentation and storage of captured rainwater can help to provide a steady injection rate. For a situation that requires REN management, appropriate simulations can help water managers design ASR systems that will achieve REN goals and increase sustainable groundwater availability. Full article

Species distribution modeling (SDM) is a vital tool for ecological and biogeographical research, allowing precise predictions of species distributions based on environmental variables. This study reviews the evolution of SDM techniques from 1985 to 2023, focusing on model development and applications in conservation, climate change adaptation, and invasive species management. We employed a mixed review with bibliometric and systematic element approaches using the Scopus database, analyzing 982 documents from 275 sources. The MaxEnt model emerged as the most frequently used technique, applied in 85% of the studies due to its adaptability and accuracy. Our findings highlight the increasing trend in international collaboration, particularly between China, the United Kingdom, and Brazil. The study reveals a significant annual growth rate of 11.99%, driven by technological advancements and the urgency to address biodiversity loss. Our analysis also shows that while MaxEnt remains dominant, deep learning and other advanced computational techniques are gaining traction, reflecting a shift toward integrating AI in ecological modeling. The results emphasize the importance of global cooperation and the continued evolution of SDM methodologies, projecting further integration of real-time data sources like UAVs and satellite imagery to enhance model precision and applicability in future conservation efforts. Full article

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected by SAR techniques. Two models, the generalized linear model (GLM) and random forest (RF) model, were used to predict the mass of residues using interferometric variables (phase, amplitude, and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R² of 0.48 for GLM and 0.13 for RF), with an acceptable bias and RMSE. It was concluded that it is possible to derive useful indications about the mass of harvesting residues from SAR data and the findings could lead to the improved monitoring and management of forest residues, contributing to sustainable forestry practices and the enhanced utilization of bioenergy resources. Full article

The Lobo reservoir, designed to supply water to the Daloa city population (central west of Côte d’Ivoire), is facing the phenomenon of eutrophication due to the agricultural plots located upstream of the reservoir inputs. Studies have highlighted the reservoir pollution and sedimentation problems. This study was initiated to test the effectiveness of a transfer model in the fight against the eutrophication problem. The objective of this study was to determine the optimal width of a vegetative filter strip on the banks of the Lobo reservoir, where the water intake installations of the drunk water company SODECI are located, using the VFSMOD model. The methodological approach is structured into four (4) main stages: 1—Evaluation of runoff from the source of pollution (contributing surface) during a rainy episode; 2—Calculation of the incoming runoff volume and the sediment load; 3—Simulation of runoff reduction and incoming sediments within the grassy filter strip; and 4—Determination of the optimal width of the grassy strip. The results obtained showed a contributing source area of 13.5 ha and 2% as the average slope. A runoff volume of 4680 m³ coming from the source of pollution is reduced by 1119 m³ (24%) through a grassy strip that is 3 m wide. The sediment load goes from 132 kg to 33 kg, a reduction of 75% in the quantity of sediment entering. The scenarios tested showed that a grassy strip width equal to 3 m should allow for a reduction of 75% of upstream sediments. This grassy strip width of 3 m should be considered for implementing a vegetative filter strip around the Lobo reservoir. Full article

The Irawan Watershed in Puerto Princesa, Philippines, is an important resource that supports domestic, agricultural, and industrial water needs. This study applies the Water Evaluation and Planning (WEAP) model to project the impacts of climate change on future surface water availability, integrating the findings into an Integrated Water Resource Management (IWRM) framework. Using bias-corrected General Circulation Models (GCMs) under four shared socioeconomic pathways (SSPs), this study examines scenarios from low to high emissions (SSP126, SSP245, SSP370, and SSP585) for the assessment of potential variations in water supply. The results indicate a significant vulnerability to water availability, especially under SSP370 and SSP585, where climate warming is pronounced, leading to significant reductions in streamflow. Conversely, SSP126 suggests relatively stable conditions with less pronounced hydrological changes. The study also explores the socioeconomic drivers that affect water demand, including population growth and land use changes that influence agricultural water needs. The findings underscore the urgency of using adaptive management strategies to conserve water resources in the face of these anticipated challenges. Key recommendations include optimizing water use efficiency in all sectors, establishing protective zones around natural ecosystems, implementing climate-resilient infrastructure, and promoting community engagement in water management. These measures are critical for enhancing water security and promoting sustainable development within the watershed, contributing to the broader goals of Sustainable Development Goal (SDG) 6. This study offers decision-makers and resource managers an evidence-based framework for integrating hydrological modeling into IWRM, providing valuable insights to navigate the complexities of climate change and ensure the long- term sustainability of water resources in the Philippines. Full article

Urban water systems are increasingly vulnerable to climate change. Traditional planning, often based on past conditions, fails to address these new challenges. We suggest policy options for integrating climate scenarios into urban water planning, which will enhance the resilience of drinking water, wastewater, and stormwater systems. The policy options are (1) requiring climate scenario analysis in planning processes, (2) developing climate-resilient infrastructure standards, (3) promoting low-impact development and nature-based solutions, (4) creating regional planning bodies, (5) educating professionals for climate-responsive planning, and (6) securing funding for climate adaptation. We discuss our experience in the state of Utah, USA, and summarize case studies in Copenhagen, New York, and Melbourne. The policy options align with Sustainable Development Goals and offer a roadmap for building adaptable, sustainable urban water systems. Full article

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects. Full article

South America has experienced significant changes in climate patterns over recent decades, particularly in terms of precipitation and temperature extremes. This study analyzes trends in climate extremes from 1979 to 2020 across South America, focusing on their relationships with sea surface temperature (SST) anomalies in the Pacific and Atlantic Oceans. The analysis uses precipitation and temperature indices, such as the number of heavy rainfall days (R10mm, R20mm, R30mm), total annual precipitation (PRCPTOT), hottest day (TXx), and heatwave duration (WSDI), to assess changes over time. The results show a widespread decline in total annual precipitation across the continent, although some regions, particularly in the northeast and southeast, experienced an increase in the intensity and frequency of extreme precipitation events. Extreme temperatures have also risen consistently across South America, with an increase in both the frequency and duration of heat extremes, indicating an ongoing warming trend. The study also highlights the significant role of SST anomalies in both the Pacific and Atlantic Oceans in driving these climate extremes. Strong correlations were found between Pacific SST anomalies (Niño 3.4 region) and extreme precipitation events in the northern and southern regions of South America. Similarly, Atlantic SST anomalies, especially in the Northern Atlantic (TNA), exhibited notable impacts on temperature extremes, particularly heatwaves. These findings underscore the complex interactions between SST anomalies and climate variability in South America, providing crucial insights into the dynamics of climate extremes in the region. Understanding these relationships is essential for developing effective adaptation and mitigation strategies in response to the increasing frequency and intensity of climate extremes. Full article