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AbstractThis research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

Abstract The snowline exhibits significant seasonal shifts upward and downward, reflecting the ever-changing dynamics of the seasons and being influenced by climate variations, which can vary annually. These fluctuations profoundly impact the cryosphere, biota, and ecosystem processes in high mountain regions. Despite the critical role of snowline variations, comprehensive information on how actual climate variability affects snow cover trends in the central mountain range of the western Himalayas is scarce. In the 'Pindari' region of the Uttarakhand district, India, which is part of the Himalayas, these challenges are exacerbated by the unchecked growth of anthropogenic activities and the broader impacts of climate change. This study analyses snowline variations in the Pindari glacial region from 1972 to 2018. The findings revealed that the snowline elevation significantly shifted upward between 1972 and 2018. Notably, this research revealed a decrease in snow-covered areas of approximately 5.01 km2 over the course of 46 years. This decrease is attributed to a direct response to the increasing number of high-temperature events that occurred during this extended period. This study emphasizes the urgent need for conservation measures in the study region and similar high mountains to combat global warming and safeguard the snowline, which serves as a visible proxy indicator to safeguard high-altitude Himalayan glaciers.

Globally, ecosystems and human health are at risk due to declining river water quality. The current study focuses on the River Asan, Uttarakhand, which faces significant water quality challenges due to various environmental, industrial, and domestic factors. This research presents an exhaustive study that intricately blends local community perceptions with scientific data, employing Geographic Information Systems to map water quality across seven critical locations along the river. Participatory Rural Appraisal (PRA) and systematic water quality test methods were applied to find the objective of this study. The research highlights the importance of considering social, cultural, and environmental factors in understanding water quality issues. The detailed, location-specific analysis, enriched by the vast array of community insights, offers a unique lens through which the environmental challenges at each site are examined. Significant findings represent that in Nayagaon, from 2019 to 2023, rising water temperature (1.6 °C increase) and decreasing pH (7.3–6.5) were observed. Reduced dissolved oxygen (9.7–6.1 mg/L) aligns with community concerns about water quality, highlighting the urgent need for interventions to protect the Asan River and its dependent communities. Integrating local perceptions with scientific data provides a nuanced understanding of water quality issues, emphasizing the urgent need for targeted interventions to safeguard the river ecosystem and the well-being of the communities dependent on it, thereby offering valuable insights for sustainable environmental management.

Study region: Prahova river basin located in the central-southern region of Romania. Study focus: This study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the region: The PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin.

This study will comprehensively evaluate the Rispana River's aquatic environment from February to June 2021, integrating physicochemical, biological, and hydrological data. Key objectives include identifying pollution sources, assessing climate change impacts, and evaluating health effects on humans and aquatic life. Analysis of 50 water samples reveals significant pH variations across five sampling sites influenced by natural and human factors. Fluctuating water temperatures (16.41 °C–20.81 °C) impact various parameters, including dissolved oxygen (DO), total dissolved solids (TDS), and biochemical oxygen demand (BOD). Microbiological assessment detects coliforms, indicating contamination with potential health risks. The study highlights the degradation of the Rispana River due to pollution and climate change. Urgent action is needed to mitigate impacts and safeguard human and aquatic health. Sustained monitoring and strategic management approaches are crucial for the river's sustainability. Collaborative efforts are necessary to address pollution sources and restore the river's health, emphasizing the importance of protecting this vital resource for current and future generations.

This study systematically investigates the comparative impacts of organic and inorganic farming on soil quality and crop yield in four distinct agricultural fields located in Dehradun and Jalandhar, India. Organic farming, characterized by the use of environmentally benign fertilizers such as manure, is contrasted with inorganic farming, employing chemical fertilizers. Our findings reveal noteworthy variations in nutrient profiles. Organic farms exhibit elevated concentrations of nitrogen and phosphorus, whereas inorganic farms manifest higher levels of potassium. Additionally, organic farming practices engender increased levels of organic carbon and organic matter, indicative of a positive influence on soil organic content. In terms of crop productivity, inorganic farms generally outperform organic counterparts initially. However, a compelling narrative emerges post-conversion of inorganic farms to organic methodologies. Despite an initial decline in production, the consistent application of organic manure, complemented by effective pest and weed management, precipitates a substantial rebound in productivity. Notably, over a 15-year temporal span, organic farms demonstrate a twofold increase in productivity. Economic valuation and the life cycle cost analysis reveal that the net profit from organic farming is 43.91 % higher than that of inorganic farming. This corroborates the long-term economic and environmental viability of organic farming, emphasizing its inherent capacity to foster nutrient-rich soil and promote sustainable agricultural practices.

Intensive agricultural practices lead to a deterioration in soil quality, causing a decline in farm productivity and quality, and disturbing the ecosystem balance in command areas. To achieve sustainable production and implement effective soil management strategies, understanding the state and spatial variability of soil quality is essential. The study aims to enhance the understanding of soil quality variability and provide actionable insights for sustainable soil management. In this regard, principal component analysis (PCA) and digital soil mapping were used to assess and map the spatial variability of the soil quality index (SQI) in the Cauvery command area, Mandya district, Karnataka, India. A total of 145 georeferenced soil samples were drawn at 0–15 cm depth and analyzed for physico-chemical properties. PCA was used to reduce the dataset into a minimum dataset as eight important soil indicators and to determine relative weightage factors, which were used for assessing SQI with linear and non-linear scoring methods. For spatial assessment of SQI, the random forest algorithm with environmental covariates was used to map eight soil indicators selected in the minimum dataset. The soil property maps were subjected to linear and non-linear scoring, followed by multiplying with corresponding weightage factors and summation to produce SQI maps. Results reveal that values of SQI calculated using linear scoring, range from 0.10 to 0.64, with a mean of 0.39, while non-linear scoring exhibits a wider range of 0.12 to 0.78 and a mean of 0.48. With a slight higher sensitivity index of 6.5, non-linear scoring proved to be the better scoring method compared to linear scoring. Spatial assessment shows that the R2 and LCC between the calculated and predicted SQI were higher for non-linear scoring (0.66 and 0.66) compared to linear scoring (0.60 and 0.65). The SQI maps reveal high spatial variability with more than 40 percent of soils classified as moderate-to-low index. The soils with low SQI were distributed in eastern parts, whereas western parts exhibited high-to-very-high soil quality. To achieve production goals and improve soil quality in the eastern region, sustainable soil and crop management strategies must be developed, and their effects on soil quality should be assessed.

This research elucidates the ecological status and spatial extent of two non-native invasive species, Lantana camara L. and Ageratina adenophorum Sprengel, within dominant forest communities across an altitude gradient in the western Himalayan region. The study, conducted from 2017 to 2019 in the Nainital district of Kumaun Himalaya, focused on four forest communities; Sal, Chir-pine, Banj-oak, and Mixed-oak, spanning an altitudinal range from 300 to 2250 meters. Environmental predictors encompassing topographic, physiographic, and climatic factors, along with species occurrence records, were employed to determine spatial extent. The MaxEnt statistical model, a machine learning algorithm, facilitated the assessment of the spatial distribution of invasives. Results indicate that L. camara has invaded Sal, Chir-pine, and Banj oak forests but has not established in high-elevation mixed-oak stands. Conversely, A. adenophora has invaded Chir-pine, Banj-oak, and Mixed-oak stands but has not been observed in low-altitude Sal forests. No significant variation in species number, diversity, or distribution pattern along the elevation gradient was observed for either species. Spatial assessment using MaxEnt revealed an estimated area of 380 km² for A. adenophora and 285 km² for L. camara. The study highlights the mid-altitude zone as the most vulnerable to species invasion. This scientific inquiry contributes valuable insights for the conservation of non-native shrubs, offering guidance for effective, species-specific conservation and management strategies in the Himalayan region.

AbstractIn this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) and Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) were generated, which had as input data the values of 10 flood predictors and a number of 158 points where historical floods occurred. In the first step, the Certainty Factor values were calculated, which were then used in the Fuzzy-Analytical Hierarchy Process and Multiclass Alternating Decision Tree models. It should be mentioned that the Multiclass Alternating Decision Tree model was optimized with the help of the Iterative Classifier Optimizer. In the case of both ensemble models the slope angle was the most important flood conditioning factor. Moreover, according to Certainty Factor modelling the 8 classes/categories achieved the maximum value of 1. Next, the susceptibility to floods on the surface of the study area was derived. On average, about 20% of the study area has areas with high and medium susceptibility to flash floods. After evaluating the quality of the models through Receiver Operating Characteristics (ROC) Curve, the following results emerged: Success Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.985) and Flood Potential Index (FPI) Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.967); Prediction Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.952) and Flood Potential Index Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.913). At the same time, the accuracies of the models were: Training dataset − 0.943 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.931 (Fuzzy-Analytical Hierarchy Process – Certainty Factor); Validating dataset − 0.935 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.926 (Fuzzy-Analytical Hierarchy Process – Certainty Factor). As main conclusion, it can be mentioned that the 2 ensemble models outperform the previous machine learning models applied on the same study area before.

The Trans-Himalayan region is home to valuable medicinal plants with therapeutic properties. However, overgrazing, excessive harvesting, habitat destruction, and climate change have led to a decline in their wild populations. This study focuses on high-value and threatened medicinal plant species in the Trans-Himalayan region of Nanda Devi Biosphere Reserve, Uttarakhand. It analyzes their population status, trade, price trends, operative threats, and pressure level. The study also develops a habitat suitability map to understand the factors influencing their distribution. The research identified 34 high-value and threatened medicinal plant species in the region. Sinopodophyllum hexandrum had the largest potentially suitable area (11.10 km2), followed by Picrorhiza kurroa (7.65 km2) and Arnebia benthamii (4.39 km2). Saussurea obvallata (2.55 km2) and Dactylorhiza hatagirea (1.09 km2) had the smallest potential suitable areas. The excessive collection of these plants for medicine, local consumption and illegal trade is causing destructive impacts. This uncontrolled exploitation endangers their wild population, regeneration, and survival. To address this issue, the study recommends preparing micro-plans, assessing available growing stock, and implementing sustainable management practices to conserve the dwindling populations. The study highlights the urgent need for conservation efforts in the Trans-Himalayan region.

AbstractLandslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis D2 (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis D2 for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.