The spatial variability of soil attributes plays an important role in hydrological processes, soil fertility, and environmental conservation in tropical semiarid regions. Texture, organic matter (OM), and available phosphorus (P) directly influence nutrient dynamics and the risk of phosphorus export to water bodies. This study analyzes the spatial distribution of soil texture, OM, and P in different soil classes of the Potengi River Basin (PRB), assessing their relationship with weathering and phosphorus mobility. A total of 110 soil samples were collected from different pedological classes, following standardized physical and chemical analysis methods. The data were spatially interpolated using the IDW method in ArcMap 10.8, and statistical analyses, including correlation, principal component analysis (PCA), and two-way cluster analysis, were applied to identify distribution patterns. The results revealed a predominance of sandy soils, moderate OM levels, and high phosphorus content. PCA identified two soil groups: Group 1, composed of more developed soils with higher clay and OM content, and Group 2, consisting of less developed soils with a higher risk of phosphorus export. The negative correlation between P and clay content emphasized the influence of texture on nutrient retention and mobility. This study highlights the relevance of spatial analyses for soil quality assessment and provides essential insights for sustainable land management strategies aimed at mitigating diffuse phosphorus pollution in semiarid watersheds.

Identifying priority zones for rainwater harvesting to support sustainable water management in arid and semi-arid regions.

Bacterial biofilms are crucial for microbial survival during drought by modifying soil-water dynamics. Their influence on water vapor transport in dry soils remains unexplored. This study systematically investigates biofilm-mediated changes in soil water vapor sorption (SWVS) dynamics across a wide matric potential range (-400 MPa to -10 MPa). Using soils of varying textures inoculated with wild-type and mutant strains of Pseudomonas putida and Bacillus subtilis, we characterized SWVS isotherms and concomitant changes in soil and biofilm properties. Key findings reveal that active cells and biofilms persist in dry soils (water activity aw < 0.93), altering SWVS behavior through two distinct mechanisms: (1) Enhanced SWVS capacity (10-49% increase across soils) resulting from biofilm hydration through swelling and extensive water retention within extracellular polymeric substance (EPS) matrices; (2) Hysteresis reversal at critical water activity values (aw < 0.6), where biofilm-dominated adsorption exceeds particle surface desorption. Mechanistic analysis demonstrates that EPS components influence SWVS through functional groups (-COOH and -OH) with strong water-binding affinity, EPS swelling, and suppression of cation/surface hydration, which physically obscures localized mineral surfaces. A conceptual framework for vapor dynamics in biofilm-colonized soils is proposed, and such results address a gap in understanding biological controls on soil hydrology under drought.

ABSTRACT We propose a robust and parametric–parsimonious framework to design the agricultural water balance at the planning level. The methodology is based on Melisenda’s climate index, Benfratello’s water balance and Budyko’s curve, and deploys standard crop scenarios. It allows us to estimate the annual irrigation deficit and to assess the hydrological sustainability of the agroecosystems. We tested the method on two Mediterranean semiarid case studies, the Capitanata irrigation district (Southern Italy) and the Mygdonia basin (Northern Greece). Results show how the methodology effectively depicts (i) the crop suitability to the local climatology, (ii) the noteworthy effect of field capacity on the irrigation deficit and (iii) the irrigation water footprint of each crop scenario, thus providing a guideline for stakeholders to address a hydrologically-sustainable agricultural development. The methodology moreover highlights the field capacity as a key hydrological soil–health parameter and allows us to explicitly estimate its effect on the annual water balance.

Understanding soil hydrology is crucial for effective watershed management, particularly in landscapes characterized by pedogenetic discontinuities and gravelly soils. This study investigates soil hydraulic properties within a tobacco-cultivated watershed, focusing on granulometry, bulk density, water retention (from saturation to permanent wilting point), steady-state infiltration rate (Is), and saturated hydraulic conductivity (Ksat and Keff) across different hillslopes, slope positions, and soil layers. Results show a wide range of Ksat (0 to 387mm h-1), with the highest conductivity consistently found in surface layers, attributed to intense tillage from ridge construction in tobacco farming. Variability was also high in Is (4 to 402mm h-1) and Keff (0 to 224mm h-1). Acrisols exhibited some low infiltration values, while midslope positions generally showed higher Is across soil types. A linear relationship between Keff and Is (r² = 0.67) was observed, though the Ksat of surface soil was not related to Is. More permeable soils (higher Ksat) had lower bulk density and reduced water retention under wet or dry conditions. In surface layers, Ksat correlated positively with water retention near field capacity, Keff related directly to Is and gravel content but inversely to retention at permanent wilting point, and Is correlated positively with fine sand and saturation water retention and negatively with bulk density. This spatial heterogeneity reflects the influence of texture, compaction, land use, and microtopography in sloped terrains. Pedogenetic discontinuities shape soil horizon development and hydrological response, especially under saturation, highlighting the importance of incorporating these factors into hydrological models and watershed management to mitigate subsurface erosion risks. Enhanced understanding of these soil layers aids prediction of infiltration, runoff, and chemical transport, improving land and soil management in similar environments.

Temperate forests are key terrestrial carbon sinks, yet their capacity to sequester carbon is often limited by nitrogen (N), a nutrient whose availability is declining in many ecosystems. Among factors contributing to declining N availability, climate-driven changes in soil hydrology could force N loss via the emission of both nitric oxide (NO) and nitrous oxide (N2O); however, forecasting these losses is challenging because they are regulated by soil moisture, a factor that regulates microbial activity and substrate availability but varies significantly across space and time. Here, we ask: how do topography-driven soil moisture gradients and changes in seasonality (e.g., spring-thaw cycles) mediate hillslope-scale N emissions? We measured over 2 years of high-resolution insitu NO and N2O fluxes from 16 automated chambers deployed along a topographic gradient in a temperate forest to show that soil moisture gradients governed spatial and temporal patterns of soil N emissions. These gradients produced tradeoffs in process controls, whereby temperature regulated N emissions in drier upper positions, giving way to soil moisture regulating microbial pathways and emissions in wetter downslope positions. We then used these relationships among soil moisture, temperature, and N availability to develop models for predicting hillslope-scale NO + N2O losses. Annual emissions averaged 0.2 kg NO-N ha-1 (range: 0.1-0.3) and 1.0 kg N2O-N ha-1 (range: 0.7-4.9), with N2O showing a stronger response to moisture-driven changes than NO. The spring freeze-thaw period accounted for 15%-26% of NO and 24%-58% of N2O annual emissions, with the highest emissions measured consistently at lower topographic positions. These findings establish topography-mediated hydrology as a primary control of forest soil N dynamics and gaseous N emissions, reducing uncertainty in forecasts of hillslope-scale N losses under a changing climate.

Artificial (tile) drainage systems are extensively implemented across the U.S. Midwest to enhance crop production in poorly drained soils; however, they also pose environmental challenges by significantly altering nitrogen fluxes within agricultural landscapes. In response, sustainable intensification strategies seek to increase agricultural productivity while reducing environmental impacts, often through improved management practices such as cover cropping and conservation tillage. Effectively evaluating the trade-offs and synergies of agricultural management practices demands advanced modeling tools capable of representing coupled biogeochemical and hydrological processes across diverse spatial and temporal scales. This study presents the first application of an enhanced version of the Soil and Water Assessment Tool (SWAT), integrated with Century/DayCent-based biogeochemical modules, to simulate both nitrate (NO3⁻) loss and nitrous oxide (N2O) fluxes in a tile-drained corn-soybean system. The model was applied to long-term field data (2004–2010) from an Iowa site with two treatments: with and without winter rye cover crops. With careful calibration, the model reproduced tile discharge and crop yields well and captured the direction and magnitude of cover-crop reductions in NO3⁻ losses. However, interannual variability in NO3⁻ export and event-scale N2O peaks remained difficult to reproduce, likely due to limited sampling frequency and structural constraints in soil hydrology, solute transport, and vertical resolution. The model simulated a ∼41 % reduction in NO3⁻ leaching with cover crops, close to the observed ∼50 %. In contrast, effects on average daily N2O flux varied by year and conditions, ranging from −30–67 % (observed: −24–28 %). These results support the model’s use for assessing long-term nitrogen-loss responses to cover crops in tile-drained systems, while highlighting priorities for improving event-scale biogeochemical simulations.

Soil hydrology affected by crushed rock content modulates the alpine ecological restoration: implications from soil reconstruction.

• A 90-meter resolution K s map is generated via the GEE platform. • Remote sensing integration significantly reduces K s estimation errors. • The generated K s dataset outperforms existing datasets in accuracy and spatial resolution. The accurate high-resolution mapping of saturated soil hydraulic conductivity ( K s ) is crucial for the advancement of hydrological modeling and soil–water management practices. Traditional approaches, including pedo-transfer functions (PTFs) and coarse-resolution digital soil mapping, encounter constraints in delineating fine-scale spatial heterogeneity. This is attributed to their dependence on sparse soil properties or low-resolution environmental covariates. This study proposes a novel approach that integrates multi-sensor Sentinel-1 and Sentinel-2 (S1/S2) remote sensing data with environmental covariates (including climate, vegetation, topography, and soil properties). This integration is achieved via a random forest regression model on the Google Earth Engine (GEE) platform, thereby facilitating the generation of 90-meter resolution K s maps in China’s drylands. More than 5,000 lab-based K s samples were employed to assess the contributions of synthetic aperture radar (SAR), optical, and environmental variables. The results demonstrated that high-resolution remote sensing data significantly improve the accuracy of K s prediction at both surface (0–10 cm) and subsurface (10–30 cm) layers. Specifically, the root mean square error (RMSE) of ln( K s /(cm min −1 )) ranged from 1.24 to 1.61, and the coefficient of determination (R 2 ) from 0.61 to 0.79. The generated 90-m K s map outperformed the existing global and regional datasets in terms of both spatial detail and statistical accuracy. Validation using Taylor diagrams and probability density functions confirmed a closer match with the field data distributions, with the standard deviation reduced by 13.61%–22.81% compared to benchmarks. In particular, our findings elucidated the fine-scale heterogeneity influenced by soil texture and topography—such as the high- K s zones in the Loess Plateau and Taklamakan Desert, which were not clearly visible in coarser products, were successfully resolved. This approach bridges the gap between coarse-scale PTFs and the requirement for high-resolution hydrological inputs. Additionally, it provides a scalable solution for arid and semi-arid regions worldwide.

Influence pathways of hydrological processes: Perspectives from the pathway probability in different types of watersheds.

Land use and land cover change (LULCC), predominantly driven by human endeavors such as urbanization and agricultural intensification, has become a significant global problem. The primary aim of this research was to evaluate the effects of LULCC on surface runoff in the Meki watershed. The Google Earth Engine platform’s Random Forest machine learning classifier was used to gather, process, validate, and analyze a variety of satellite photos in order to analyze the rate of LULC changes over four time references, beginning with 1990–2022. Before using the satellite images, preprocessing, classification, and accuracy assessment were performed sequentially. During the four periods from 1990 to 2022, the watershed’s six LULC classes, cultivated land, water, shrub land, grassland, forest, and bare land, were recognized. A significant rate change of LULC was observed in the watershed in each decade. Accordingly, the growth of agricultural land increased from 47.77 to 81.16%, followed by bare land 2.88% to 7.42%. In contrast, over the course of three decades, from 1990 to 2022, the percentages of forest cover, shrub land, and grassland declined sharply from 26.62 to 7.89%, 17.73% to 1.68, and 4.11% to 1.05%, respectively. In order to calculate surface runoff for the Meki watershed, the hydrological Soil and Water Assessment Tool (SWAT) model was set up and parameterized for flow and sediment load. Each LULC scenario’s model calibration and validation are carried out utilizing SWAT-CUP software’s SUFI-2. Model performance statistics, such as R2, NSE, RSR, and PBIAS, as well as model uncertainty metrics, such as p-factor and r-factor, were checked after the model was calibrated and validated. The mean annual surface runoff of the watershed is 117.15, 121.48, 133.93, and 158.84 mm. Accordingly, the Change in LULC from 1990 to 2001, 2001 to 2013, 2013 to 2022, and 1990 to 2022 resulted in an increment of 3.69%, 10.24%, 18.56%, and 35.58% in surface runoff, respectively.

Climate change leads to less water in forest ecosystems and higher evapotranspiration during the growing season, increasing the risk of drought. This study evaluates microclimate and soil hydrology at two different sites in the Dobroč Primeval Forest (National Nature Reserve, NATURA 2000): a near-natural fir–beech buffer zone and a managed Norway spruce monoculture. Measurements cover two hydrological years with very different climatic conditions. The Climatic Water Balance (CWB) was used to assess precipitation deficit, and soil moisture dynamics were simulated with the GLOBAL mathematical model. In 2021, precipitation was 223.7 mm below the long-term average, and the cumulative CWB deficit from March to September was 224 mm. Drought risk peaked in summer 2021. The spruce stand’s A/B horizon was 197 days below the point of decreased availability (PDA), compared to 179 days in the beech buffer zone. Drought moved through the soil profile with a 3–4-day lag between horizons at both sites. Results confirm that Norway spruce monocultures are more drought-vulnerable than near-natural beech stands under identical conditions, supporting active forest conversion in Central European mountain regions.

The assessment and management of surface runoff is important for watershed planning and water resource utilization, especially in areas where agriculture is mostly dependent on rainfall. This study aims to evaluate and compare the runoff potential of 98 delineated watersheds within the Hazaribagh Plateau region using two distinct approaches namely morphometric analysis and the Soil Conservation Service-Curve Number (SCS-CN) methods using Geographic Information System (GIS) and Remote Sensing (RS) techniques. A Compound Factor (CF), which represents the structural runoff potential of each watershed was computed using morphometric parameters under linear, areal, and relief aspects that were obtained from the Digital Elevation Model (DEM) and is categorised into five ranks. Similarly, runoff coefficients were estimated using the SCS-CN approach based on land use/land cover, hydrologic soil groups and antecedent moisture conditions, which are further categorised into five classes. Comparative analysis revealed that 46 watersheds exhibited matching ranks, while 35 showed lower SCS-CN ranking than morphometric parameter ranking and 17 showed higher. These variations highlight the combined influence of terrain characteristics, land use patterns, soil properties, and moisture conditions on runoff generation. The results demonstrate that morphometric analysis provides a preliminary assessment of runoff behaviour, while the SCS-CN method offers a more dynamic and quantitative evaluation, and their integration improves the reliability of runoff assessment for efficient watershed planning.

The present study aims to delineate the Banas River Basin of south-eastern Rajasthan, India, to generate hydrological response units (HRUs) using the Soil and Water Assessment Tool (SWAT) in a geographic information system (GIS) environment. The objective of the research is to develop a reliable spatial framework that represents basin heterogeneity for future hydrological simulation and water resource planning in a semi-arid region. Watershed delineation was carried out using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model, while land use/land cover, soil and slope layers were integrated through the ArcSWAT interface to derive HRUs. Threshold values of 20 % for land use, 10 % for soil and 10 % for slope were applied to balance spatial detail and computational efficiency. The analysis resulted in the delineation of 33 sub-basins and 580 HRUs, reflecting significant spatial variability within the basin. Agricultural land was found to be the dominant land use (approximately 55 %), followed by forest and pasture areas, while silt loam soils and gentle slopes (0–10 %) prevailed across large portions of the basin. These physiographic characteristics indicate moderate runoff potential with localised erosion risk. The outcomes of this study provide a robust spatial foundation for hydrological modelling, soil and water conservation planning and assessment of land-use and climate change impacts in the Banas River Basin.

Irrigation-driven greening is essential for northwest China’s dryland ecosystems, where vegetation growth depends on key hydrological factors, including precipitation (PRE), evapotranspiration (ET), soil moisture (SM), and irrigation water use (IWU), which affect water availability to a certain extent. To assess greening sustainability, a 1 km IWU dataset was created for 2001–2022 by combining remote sensing and ancillary data using machine learning, overcoming limited irrigation records. By linking IWU with the normalized difference vegetation index (NDVI) and analyzing trends in irrigated areas, we implemented a regional zonation approach to identify specific risk areas and evaluated both greening sustainability and vegetation responses using water balance (WB) and various hydrological variables. The results show that NDVI has increased widely over the past two decades, with sustained positive WB and stable irrigation, indicating improved water availability. However, spatial differences exist: 35.98% of irrigated areas have rising NDVI but falling IWU, especially in the east, where higher NDVI, IWU, WB, PRE, and ΔSM (soil moisture difference between growing season end and start) reflect favorable climate and hydrology; attention should also be directed toward potential deep percolation and saline sinks. In contrast, areas with high IWU often displayed elevated NDVI but declining water availability, suggesting unsustainable greening due to excessive irrigation. In addition, the SCDIWU-SCDNDVI class dominates among significant NDVI-IWU trends, indicating potential for sustainable irrigation under certain drought and climate conditions. Overall, the northwestern portion of the study area exhibits the lowest water availability; cities such as Urumqi warrant particular attention. These findings identify at-risk areas and those with better water resilience, supporting targeted water–vegetation management.

Abstract While wetlands represent a small fraction (~5%–10%) of the world's land surface, it is estimated that one‐third of wetlands have been lost due to human activities. Wetland habitat loss decreases ecosystem benefits, including improved water quality and climate change mitigation. These microbially mediated functions are dependent on redox conditions, which are altered by soil hydrology and the presence of plants. We tested the overarching hypothesis that while microbial community composition would be resistant to change due to long‐term hydrologic history, key functions like greenhouse gas production would remain plastic and responsive to short‐term environmental shifts. Using a mesocosm design, we manipulated the duration of hydrologic conditions (i.e., stable dry, stable flooding, and alternating wet/dry) and the presence of plants to induce soil redox changes in wetland soils. We measured soil redox status, used targeted amplicon and shotgun metagenomic sequencing to characterize microbial communities, and measured greenhouse gas production to assess microbial function. The 8‐week hydrologic treatment shifted community composition but did not override the stronger effects of long‐term hydrologic history. Methane and carbon dioxide fluxes were altered by short‐term hydrologic treatment, with methane production favored in the wet treatment and carbon dioxide production favored in the dry treatment. Plant presence versus absence manipulation had little impact on soil microbiome composition or soil greenhouse gas production. The results highlight the resistance of microbial community structure shaped by historical hydrologic regimes, and emphasize that hydrologic conditions exert a stronger influence than plant presence on microbial composition and function. Predicting the outcomes of wetland disturbance and restoration requires an enhanced understanding of community stability and functional plasticity. Our results suggest that wetland hydrologic restoration can establish a stable microbial community that is resistant to environmental shifts, but microbial functions such as greenhouse gas emissions remain responsive to hydrologic disturbances, including flooding and drought.

Spatiotemporal dynamics, source and flux of dissolved black carbon in the monsoon-influenced Yangtze River system.

Runoff, soil erosion, and crop responses to Palash (Butea monosperma) leaf mulching in semi-arid vegetable production

Abstract Water availability governs ecosystem productivity, yet estimates of vegetation sensitivity to water can differ greatly depending on whether the sensitivity is examined spatially or temporally. In particular, the spatial sensitivity is often reported to be much stronger than temporal sensitivities, leading to highly uncertain projections of ecosystem responses to future climate change when using space‐for‐time substitution. The large difference between spatial and temporal sensitivities remains unexplained. Prior research, however, primarily relied on precipitation as the water availability proxy, whereas vegetation responds to soil moisture. Here, we combined satellite estimates of vegetation productivity with soil moisture data across water‐limited ecosystems of the continental United States (CONUS) to identify a convergent sensitivity of productivity to water availability. Using precipitation, we show that temporal sensitivity is 66% lower than spatial sensitivity overall. Our analysis identified the cause of the difference to be primarily driven by the seasonal variability of water availability, rooting depth, and soil properties. When using soil moisture instead of precipitation, we observed widespread convergence in the spatial and temporal sensitivities—that is, the two sensitivities became much more similar in magnitude across all water‐limited ecosystems within CONUS. These results show that overlooking soil hydrology can inflate perceived discrepancies between spatial and temporal vegetation sensitivities, leading to biased projections of ecosystem dynamics under future hydro‐climatic change.

This study investigates how land cover and use have changed over a 30-year time period and its impact on runoff. The researchers used GIS and the SCS CN method to estimate runoff and analyze trends in rainfall. The study divided the time period into ten-year intervals and focused on the catchment Madamsilli dam watershed. ArcGIS was used to process USGS Landsat images from 1991, 2001, 2011, and 2021 in order to classify land cover and land use. The results revealed a notable decline in the amount of water cover by 15.27 sq. km, suggesting possible modifications to the hydrological regime in the region. This reduction accounted for 68.99% of the original water cover. Forest cover experienced a minimal decrease of only 0.87 sq. km, suggesting some deforestation but not at a significant level. Barren land cover increased by 48.53 sq. km, implying changes in land use practices suchas vegetation clearing, mining, or overgrazing. The increase in barren land cover amounted to 31.55%. Building cover moderately increased by 4.87 sq. km, indicating changes in urbanization patterns or population growth leading to new construction or urban expansion. The building cover increased by 4.09%. The researchers computed runoff using maps of land use/cover and soil in the Madamsilli watershed. The hydrologic soil group was created based on the FAO soil map, and the Curve Number (CN) was calculated considering the land cover and soil group. Runoff was then determined using the assigned CN values. The rainfall and runoff analysis revealed the least amount of precipitation and runoff in 2002 (611 mm and 66.24 mm, respectively). The highest rainfall occurred in 2003, while the highest runoff was observed in 1991 (1991.3 mm and 381.95 mm, respectively).