Groundwater is an essential freshwater resource in the Western Himalayas, where increasing anthropogenic pressure and environmental variability are raising concerns regarding groundwater quality and water security. However, regionally integrated assessments of groundwater-quality variability across the Western Himalayan states remain limited. This study evaluates groundwater quality across Jammu and Kashmir, Himachal Pradesh, and Uttarakhand using groundwater-monitoring data obtained from the Central Ground Water Board (CGWB). A total of 338 observation wells monitored during 2019-2022 were analyzed using the weighted arithmetic Water Quality Index (WQI) based on Bureau of Indian Standards (BIS) and World Health Organization (WHO) drinking-water guidelines. Spatial and temporal variability were examined through hydrochemical, correlation, and geospatial analyses. The results reveal substantial regional and district-level variability in groundwater quality across the Western Himalayas. Groundwater in Himachal Pradesh and Uttarakhand is predominantly classified as excellent to good, whereas Jammu and Kashmir exhibit greater hydrochemical heterogeneity and localized groundwater deterioration. Elevated WQI values are concentrated within foothill and valley-transition districts, while high-altitude recharge zones generally maintain lower WQI values. Hydrochemical analyses indicate that groundwater-quality variability is primarily associated with mineralization processes, lithological controls, and localized anthropogenic influences. Temporal analysis further indicates moderate groundwater-quality improvement between 2019 and 2022, particularly in parts of Jammu and Kashmir. Overall, the findings demonstrate that groundwater systems across the Western Himalayas remain largely controlled by hydrogeological conditions but are increasingly modified by localized anthropogenic pressures. Strengthened groundwater monitoring, protection of recharge zones, and targeted management of vulnerable foothill and valley-transition environments will be essential for sustaining long-term water security in this climate-sensitive mountain region.

Migration in the Indian Himalayan Region (IHR) is commonly framed as a unidirectional process driven by environmental change and agrarian distress, often leading to rural decline and depopulation. However, such perspectives overlook the complexity and context-specific nature of migration patterns. This study examines the nature, drivers, and development implications of migration in Himachal Pradesh to provide a context-specific understanding of migration processes in the Indian Himalaya. The study adopts a mixed-methods approach based on a complete household survey of 486 households (N = 1,894 individuals) across nine villages representing diverse altitudinal zones, complemented by focus group discussions and key informant interviews. Quantitative analyses, including descriptive statistics and binary logistic regression, were integrated with qualitative approaches to examine migration patterns, socio-economic determinants, and household-level strategies. The findings reveal that migration in Himachal Pradesh is predominantly temporary and circular, with no evidence of permanent out-migration or abandoned settlements. Migration is primarily driven by education and employment aspirations and is shaped by differential access to resources, capabilities, and socio-economic opportunities, indicating the selective and socially differentiated nature of migration. The logistic regression shows that individuals with tertiary education are nearly ten times more likely to migrate than illiterates, while migration is concentrated among working-age adults, highlighting the central role of human capital and capability enhancement in shaping mobility decisions. Migration contributes to rural development through remittances, human capital formation, and strengthened rural-urban linkages, although these benefits remain unevenly distributed across households. Drawing on the New Economics of Labour Migration (NELM) and the Aspirations-Capabilities framework, the study interprets migration as a multi-causal and socially embedded process shaped by the interaction of household strategies, individual capabilities, and opportunity structures. Based on the empirical evidence, the study proposes the concept of a “Balanced Mobility System,” which conceptualizes how temporary and circular migration can coexist with continued rural habitation, sustained livelihood systems, and strong rural-urban linkages without resulting in permanent depopulation. Overall, the findings demonstrate that migration in Himachal Pradesh functions as an opportunity-driven and development-supporting process, underscoring the importance of context-specific interpretations of migration and contributing to a more nuanced understanding of migration diversity across mountain regions.

Abstract Urban stormwater best management practices (BMPs) are a core component of urban-resilience portfolios worldwide, yet per-asset performance monitoring is limited and difficult to enumerate at the global scale. We argue that publicly available satellite Earth observation has reached the capability point at which this accountability gap can be closed at the portfolio scale and that the binding constraint is now community benchmark data rather than satellite data. We identify three operational capabilities available now: human-in-the-loop segmentation efficiently yields survey-grade asset footprints; the BMP type, historically the most difficult attribute to discriminate, is recoverable from learned multiphysical embeddings; and per-asset spectral departures beyond seasonality and regional weather are detectable. All three share the same underlying gap: a lack of community benchmark data that limits operationalization. The 36-site United States pilot supporting these claims is an existence proof of these capabilities rather than a deployment, and the identical workflow generalizes globally through ERA5-Land and globally available imagery. Closing the benchmark gap is a principal research agenda priority capable of converting present-day satellite capability into deployable accountability for infrastructure that today operates with little per-asset verification.

Treated wastewater (TWW) irrigation is increasingly used in arid regions, and its effects on soil salinity are well documented. However, its influence on soil carbon fractions, aggregation, and stable carbon (δ13C) and nitrogen (δ15N) isotopes remains poorly understood. This study evaluated the impacts of TWW irrigation on soil total carbon (TC), soil organic carbon (SOC), active carbon (AC), inorganic carbon (IOC), water-stable aggregates (WSA), δ13C, δ15N, and aggregate-associated carbon and nitrogen isotopes across multiple soil depths. Compared to freshwater (FW), TWW significantly increased AC at 0-15, 15-30, and 30-45 cm by 16.8%, 23.0%, and 32.4%, respectively, and enhanced SOC by 31.7% at 45-60 cm depth. The >2 mm WSA increased by 16.7% and 48.9% at 0-15 and 30-45 cm, respectively, under TWW irrigation. TWW also enhanced aggregate-associated carbon at depths of 0-15 and 15-30 cm and enriched δ15N at depths of 30-45 and 45-60 cm. In contrast, aggregate-associated δ13C decreased at 30-60 cm, and δ13C values across aggregate fractions were consistently lower than bulk soil at depths of 15-60 cm. Variations in SOC and AC were over 30% greater in subsoil than topsoil, whereas δ13C and δ15N showed no comparable depth trend, indicating a decoupling between soil carbon accumulation and organic matter quality. Structural equation modeling further revealed that soil total carbon, δ13C, and δ15N jointly act as dominant drivers of WSA, highlighting that aggregate stabilization under TWW irrigation is governed by integrated carbon quantity and transformation processes. Overall, TWW irrigation enhances subsoil carbon accumulation and aggregate stability while influencing soil isotopic composition, revealing new insights into soil carbon dynamics in arid agroecosystems.

Environmental and indoor air pollution causes respiratory infection related morbidity and mortality. Hence, the study tries to explore the relationship of environmental PM2.5 and indoor air pollution with the prevalence of Lower Respiratory Infection (LRIs) related neonatal (NMR) and under-five child mortality (U5MR) in India. The study extracted NMR, U5MR, PM2.5 and other environmental data from Global Burden of Disease (GBD) database (2021), and collected state level indoor air pollution and socioeconomic information of the child, mothers, and community from the fifth round of the National Family Health Survey (NFHS-5), 2019–21 dataset. The investigation employed join point regression analysis, Ordinary Least Square (OLS) regression models and spatial analysis technique to establish the relationship between PM2.5, indoor pollution and NMR or U5MR caused by LRIs in India. The trend analysis indicates that NMR and U5MR declined significantly by 66

Soil organic carbon (SOC) dynamics in arid regions are influenced by climatic and edaphic factors; however, limited research has assessed how flood irrigation affects carbon fractions and their role in stabilizing soil aggregates in pecan orchards. This study evaluated the impact of three management zones, such as tree root zone (RT), inter-row (IR), and bare land (BL), on soil physical and chemical properties in a pecan orchard in El Paso, Texas. At the 0-15 cm depth, soil organic carbon (OC) and permanganate oxidizable carbon (POXC) were significantly higher in RT by 90.2% and 50.1%, than in IR. These differences were even more impressive with depth: at 15-30 cm, OC and POXC in RT were 124.6% and 97.2% higher than IR. Water-stable aggregates across all size classes were significantly higher under RT and BL compared to IR at all depths (0-75 cm). At 0-15 and 15-30 cm, the RT zone had significantly greater >2 mm by 48.6% and 70.2%, respectively, than the IR zone. At 0-15 cm, the 0.5 and 0.25 mm aggregate fractions contained significantly higher total carbon (TC) than the 1 mm and >2 mm fractions. At 15-30 cm, OC in the RT zone was higher than in the IR by 49.2% and 34.5% in the >2 mm and 1 mm aggregate fractions, respectively. In contrast, IR showed greater soil inorganic carbon (SIC) at 15-30, 30-45, and 60-75 cm. In all management zones, Olsen-P in the 0-15 cm layer was higher than in the 30-70 cm layer. Positive significant correlations between >2 mm aggregates and TC, OC, POXC, clay, silt, CEC, Olsen-P, and exchangeable cations suggest that organic matter inputs from pecan roots and leaf litter enhance soil structural stability. These findings indicate that increased OC and POXC in the RT zone contribute to improved soil resilience under arid conditions.

Dryland agriculture contributes substantially to greenhouse gas (GHG) emissions, primarily due to flood irrigation, excessive nitrogen fertilization, and intensive soil disturbance. However, the influence of climate-smart agriculture (CSA) practices in mitigating GHG emissions under dry conditions remains inconsistent. This meta-analysis was conducted by reviewing 87 peer-reviewed papers to assess the impact of CSA practices, such as no-tillage (NT), drip irrigation (DI), plastic mulching (PSM), straw mulching (STM), and nitrogen fertilization (NFY), on soil organic carbon (SOC) content, global warming potential (GWP), GHG intensity (GHGI), and crop yields in arid and semi-arid agroecosystems. DI was the most effective single practice, reducing CO2, N2O, GWP, and GHGI by 9.8 %, 54.7 %, 9.5 %, and 10.6 %, respectively. Compared to conventional tillage (CT), NT with straw retention (NTS) significantly increased SOC content by 14.8 % and wheat yield by 5.2 %, while long-term (>5 years) NT reduced GWP and GHGI by 14.2 % and 14.1 %, respectively. Conversely, STM and high NFY rate increased GWP by 27.7 % and 41.5 %, respectively. Although the high NFY rate increased overall crop yield by 70.6 %, indicating at a substantial environmental cost. In contrast, a low NFY rate reduced GHGI by 42.6 %, suggesting a viable mitigation pathway. Overall, these findings underscore a fundamental trade-off between yield and emissions, indicating that integrating precise nutrient management, drip irrigation, and no-tillage with optimized residue retention can provide a synergistic strategy to enhance productivity while simultaneously mitigating GHG emissions in dryland agroecosystems.

Climatic variability is increasingly reshaping agricultural systems across the Indian Himalayan Region (IHR), with significant implications for smallholder livelihoods. This study examines how observed climatic trends interact with farmer perceptions, adaptation practices, and livelihood outcomes in the mid-altitude Garhwal Himalaya. The study adopts a mixed-methods approach, combining 128 household surveys, focus group discussions, and key informant interviews across five villages with 20 years of climatic records from the India Meteorological Department. Trend analysis reveals statistically significant warming and increasing rainfall variability, closely aligning with farmers’ reported perceptions. Farmers respond through incremental adaptation strategies, including adjustments in sowing and harvesting calendars, crop diversification, adoption of stress-tolerant varieties, and selective integration of traditional ecological knowledge with modern practices. A gradual shift toward market-oriented horticulture is observed; however, this transition remains uneven across households and is mediated by access to irrigation, markets, and institutional support. While commercialization is associated with rising agricultural incomes, it coincides with declining food self-sufficiency, increased labour burdens on women, and greater market dependence, indicating a reconfiguration rather than uniform strengthening of livelihood resilience. Persistent structural constraints, including limited irrigation, uneven institutional reach, and labour shortages, continue to shape adaptation pathways. Overall, the findings show that adaptation in mid-hill Himalayan systems is incremental and household-contingent rather than structurally transformative, highlighting the need for context-specific and gender-responsive adaptation strategies that account for household heterogeneity and elevation-specific conditions in mountain agriculture.

Streamflow modeling has significantly advanced with the advent of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, which have become the state-of-the-art for rainfall-runoff simulations. Despite their success, challenges remain in forecasting due to uncertainties inherent in watershed systems and hydrological processes across varying flow conditions. In this study, we propose a novel approach to improve streamflow forecasts by training LSTM models using diverse objective (loss) functions to capture those varying flow conditions and combining their outputs through Linear Stacking Ensemble. By leveraging eight different loss functions (Mean squared error, Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, Huber loss, quantile losses at 0.15 and 0.85, and expectile losses at 0.15 and 0.85), we aimed to capture various aspects of the hydrograph and associated uncertainties. We employ the CAMELS dataset via the Caravan framework, utilizing 482 catchments across the United States. Our results showed that the ensemble forecasts obtained through ensemble improves overall predictive performance and also effectively quantifies uncertainty, outperforming individual models across multi-step forecasting horizons (daily up to 30 days). Statistically, the Ensemble achieved a significant improvement in Nash-Sutcliffe Efficiency against the individual models across all horizons. And, training the model with multiple loss functions achieved an uncertainty coverage of 93.41%, 83.32%, and 81.72% for 1, 7, and 30 days forecast, respectively on the test (unseen) dataset. This methodology provides a scalable and operationally feasible solution for enhancing streamflow forecasts, with potential applications in data-scarce regions and real-time operational hydrology.

Extreme heat, in the form of heatwave, is an escalating climate-related threat, impacting ecosystems, human health, and the economy. While heatwaves are often characterized by elevated temperatures, precipitation and humidity also play substantial roles. In this study, we present our perspective on the interaction between heat, humidity, and precipitation based on the historical data from 1980 to 2020 across the contiguous United States (US). Our preliminary analysis explores how precipitation can mitigate heat and increase humidity, thereby affecting heat comfort and stress. Our findings reveal a noticeable increase in heatwave duration in recent times (post-2000), particularly in the Southwest, Gulf Coast, and Eastern US. Humidity levels during heatwaves have also risen, especially in the Southeast and coastal areas. Additionally, precipitation events that terminate heatwaves often lead to heightened humidity, compounding the health risks of extreme heat. For instance, regions like Florida and the Gulf Coast experience heightened humidity following rain, underscoring the complex interplay between heat, moisture, and health impacts. This study highlights the need to understand how precipitation influences heatwaves and the resulting risks. These insights can inform climate adaptation strategies, urban planning, and public health policies to address the growing challenges posed by heat and humidity in a warming world.

Abstract Pollution reduction strategies based on approaches such as total maximum daily loads (TMDLS) are typically formulated as a load reduction quantification, followed by implementation planning, and an adaptive management approach. This formulation could restrict the exploration of many possible watershed management approaches using green infrastructure and best management practices. This is because pollutant load reductions are first fixed based on required water quality criteria, followed by finding solutions that may be able to achieve those reductions. This approach restricts the set of solutions to only those that will have to work at first pass, failing which the entire load-reduction calculation must be reworked. An enhanced approach is presented here that recasts the pollution reduction calculation as an optimization problem that factors in the implementation planning needed to achieve the proposed load reductions. This enhanced approach is shown to be more amenable to a holistic search through possible solutions, as well as with an increased potential for adaptive management or phased pollution reduction than the conventional approach.

This study examines climate change impacts and adaptation strategies among local communities in the Barsu cluster of Rudraprayag district, Uttarakhand, in the Garhwal Himalaya. Long-term climate data (1981-2022) were analyzed using Mann-Kendall trend tests and Sen’s slope estimators, and complemented with household-level survey and qualitative data collected from 208 households across nine villages using Probability Proportionate to Size (PPS) sampling. The results indicate a statistically significant increase in mean annual temperature at a rate of 0.021 °C yr⁻¹ and a decline in annual precipitation of 19.23 mm yr⁻¹, closely aligning with community perceptions of warming (84.3

Mental models guide how people understand and respond to socio-environmental systems, but recovering their causal structure from empirical data remains difficult. Fuzzy Cognitive Maps (FCMs) represent mental models as directed graphs, yet their edge weights are typically assigned subjectively. We introduce causal inference cognitive mapping, a causal inference framework that estimates FCM edge weights directly from time-series data using Double Machine Learning (DML). Each directed edge is treated as a treatment–outcome relationship, and DML estimates causal effects, adjusting for confounding via orthogonalization and cross-fitting. Effects are scaled into signed edge weights, yielding causally interpretable adjacency matrices compatible with standard FCM analysis. We implement the framework in a Python package, causal-mm, and we validate it using synthetic datasets, achieving low bias and high sign accuracy, and then apply it to a real-world socio-environmental system with multidecadal observations, identifying dominant causal pathways across mental models.

Accurately characterizing conterminous soil pH dynamics is essential for assessing soil health risk and land degradation trends. However, the lack of robust modeling of temporal pH change from legacy observations has hindered quantifying pH risk and estimating time-to-critical (TTC), the time remaining until soils reach crop-limiting pH thresholds, thereby delaying identification of hotspots requiring timely intervention. To address this gap, we developed and validated an ensemble modeling framework using legacy soil profile data for projecting time-adjusted historical and current soil pH conditions across the conterminous United States (CONUS). Our framework combines spatial-temporal harmonization with machine learning to predict pH trajectories and estimate the TTC. Results showed that model performance was consistent across time periods, yielding mean absolute errors (MAE) of 0.51-0.55, root mean square errors (RMSE) of 0.66-0.72, and coefficients of determination (R2) of 0.64-0.66 from 1980 to 2025. TTC analysis indicated that approximately 59.0% (62.91 M ha) of U.S. croplands were projected to remain within safe pH thresholds for more than 20 years. However, nearly one-third of croplands (33.8%, 35.99 M ha) were projected to reach critical pH conditions within 0-5 years, indicating an urgent need for region-specific soil management. Acid-sensitive crops such as soybeans and peanuts were most vulnerable in acidic soils, while barley, oats, and canola faced strong alkalinity constraints. Spatially, alkalinity risks were widespread in the western and Midwest and Southwest regions, whereas acidity risks dominated in the Midwest, Southeast, and northern U.S. The study results demonstrated that soil pH risk modeling and assessment of TTC is an effective framework for quantifying temporal risks and identifying near-term hotspots, thereby guiding targeted management, land-use planning, and policy interventions.

Increasing freshwater scarcity and salinity risks in arid regions worldwide necessitate alternative water sources and salt-tolerant crops for sustainable agriculture. However, the effects of such water sources on crop performance and root-zone soil salinity need to be evaluated before advocating the wider use of saline water. This two-year field study used a split plot design to evaluate the effects of saline municipal treated wastewater (TWW) and freshwater (FW, control) (main-plot factor) on the performance of three spring canola (Brassica napus L.) cultivars (CP930RR, CP955RR, and CP9978TF, subplot factor), and root-zone soil salinity (top 0.6 m). Results indicated that canola seed and straw yields did not differ significantly between TWW and FW across three cultivars. Across treatments, seed yield ranged from 2327 kg ha−1 under FW to 2675 kg ha−1 under TWW for CP9978TF, while the straw yield ranged from 5095 kg ha−1 for CP955RR under TWW to 6471 kg ha−1 for CP930RR under TWW. After two growing seasons, average soil salinity (ECe) increased from a baseline level of 2.3 dS m−1 to 3.5 dS m−1 under FW and 4.6 dS m−1 under TWW, while SAR rose from 4.2 to 5.2 and 7.5, respectively. Despite these increases, ECe and SAR remained well below canola thresholds for salinity (9.7 dS m−1) and sodicity (SAR 13). Salinity and sodicity increased with time and depth, particularly under TWW, indicating progressive salt accumulation and redistribution within the soil profile. The findings demonstrated that saline alternative water sources, particularly treated wastewater (TWW), can supplement or partially replace conventional freshwater irrigation and thereby contribute to the sustainability and resilience of irrigated agriculture in water-scarce regions.

This study investigates the application of hyperspectral imaging and machine learning techniques for detecting and classifying salinity stress in canola (Brassica napus L.). After analysis of various methods, we employed a ridge classifier model to categorize six classes of salinity, utilizing various spectral bands and vegetation indices across multiple model iterations. Spectral signature analysis revealed significant changes in reflectance patterns for wavelengths exceeding 740 nm, corresponding to the near-infrared (NIR) region. We developed two novel vegetation indices tailored for salinity stress detection, which, when combined with established indices and selected spectral bands, significantly improved classification accuracy. Our sequential model refinement process demonstrated incremental improvements in accuracy, with the final model achieving 82.61 % accuracy on the test set using only 15 features. This represents a substantial reduction from the initial 331 features while maintaining high accuracy. The most effective features primarily spanned wavelengths corresponding to Sentinel-2A bands, with notable exceptions at 405.04 nm and 983.96 nm. Comparison with Sentinel-2 spectral bands revealed that while some important wavelengths align with the satellite sensor's capabilities, several fall outside its capture range. Notably, our findings suggest that Sentinel-2 bands B1, B5, B6, B7, and B9 may have limited efficacy in identifying salinity stress in canola, highlighting the potential for crop-specific optimization of spectral bands in remote sensing applications. This comprehensive analysis provides insights into the most effective spectral regions and vegetation indices for salinity classification in canola, offering the potential for improved precision agriculture practices. Our findings contribute to the growing body of knowledge on non-invasive crop stress detection and pave the way for future research in hyperspectral imaging applications for sustainable agriculture.

Open-source legacy data available for training soil organic carbon (SOC) models are limited and not uniformly distributed in space or time. While some process-based models predict SOC changes, most of the large-scale data-driven SOC modeling efforts overlook temporal shifts. Accounting for the expected temporal drift allows us to increase the accuracy of dataset available for machine learning models. Here we present an approach for creating proximity-based distance matrices using the legacy data available in contiguous US (CONUS) and generating spatially resolved temporal shift projections that adjust observations to the target date. The approach was evaluated by comparing SOC observations projected to two reference years, SOC1980 and SOC2020 and without temporal adjustment (SOCno−adj). Stocks of SOC projections showed significant differences between SOCno−adj and SOC2020. Baseline estimate of SOC stocks in CONUS croplands (top 1 m) were higher based on SOCno−adj (14.49 Pg C) compared to SOC2020 (13.29 Pg C), for pasture lands 15.49 Pg (SOCno−adj) and 14.22 Pg C (SOC2020), for forest lands at 39.52 Pg C (SOCno−adj) and 40.83 Pg C (SOC2020). The study results confirmed the validity of our methodology, and its capability to enhance SOC stock projections effectively with temporal adjustments. Potential users of this study’s outcomes include many stakeholders involved in carbon incentive programs, including farmers, scientists, policy makers, and industry partners.

Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations.