Search Results (352)

Water infiltration into soil is a key process in regulating the hydrological cycle and sustaining ecosystem services in high-Andean environments. However, limited information is available regarding its dynamics in these ecosystems. This study evaluated the influence of three types of vegetation cover and soil properties on water infiltration in a high-Andean environment. A double-ring infiltrometer, the Water Drop Penetration Time (WDPT, s) method, and laboratory physicochemical characterization were employed. Soils under forest cover exhibited significantly higher quasi-steady infiltration rates (i_s, 0.248 ± 0.028 cm·min⁻¹) compared to grazing areas (0.051 ± 0.016 cm·min⁻¹) and agricultural lands (0.032 ± 0.013 cm·min⁻¹). Soil organic matter content was positively correlated with i_s. The modified Kostiakov infiltration model provided the best overall fit, while the Horton model better described infiltration rates approaching i_s. Sand and clay fractions, along with K⁺, Ca²⁺, and Mg²⁺, were particularly significant during the soil’s wet stages. In drier stages, increased Na⁺ concentrations and decreased silt content were associated with higher water repellency. Based on WDPT, agricultural soils exhibited persistent hydrophilic behavior even after drying (median [IQR] from 0.61 [0.38] s to 1.24 [0.46] s), whereas forest (from 2.84 [3.73] s to 3.53 [24.17] s) and grazing soils (from 4.37 [1.95] s to 19.83 [109.33] s) transitioned to weakly or moderately hydrophobic patterns. These findings demonstrate that native Andean forest soils exhibit a higher infiltration capacity than soils under anthropogenic management (agriculture and grazing), highlighting the need to conserve and restore native vegetation cover to strengthen water resilience and mitigate the impacts of land-use change. Full article

Climate change has intensified over recent decades, prompting shifts in forest management strategies, particularly in the Sudetes region of Poland, where native species like Norway spruce (Picea abies), European beech (Fagus sylvatica), and silver fir (Abies alba) have historically dominated. To address these changes, non-native species such as Douglas fir (Pseudotsuga menziesii) have been introduced as potential alternatives. This study, conducted in the Jugów and Świerki forest districts, compared the soil properties and water retention capacities of Douglas fir (Dg) and Norway spruce (Sw) stands (age classes from 8–127 years) in the Fresh Mountain Mixed Forest Site habitat. Field measurements included temperature, humidity, organic matter content, water capacity, and granulometric composition. Results indicate that, in comparison to Sw stands, Dg stands were consistently linked to soils that were naturally finer textured. The observed hydrological changes were mostly supported by these textural differences: In all investigated circumstances, Dg soils demonstrated greater water retention, displaying a water capacity that was around 5% higher. In addition to texture, Dg stands showed reduced soil water repellency and a substantially greater organic matter content (59.74% compared to 27.91% in Sw), which further enhanced soil structure and moisture retention. Conversely, with increasing climatic stress, Sw soils, with coarser textures and less organic matter, showed decreased water retention. The study highlights the importance of species selection in sustainable forest management, especially under climate change. Future research should explore long-term ecological impacts, including effects on microbial communities, nutrient cycling, and biodiversity, to optimize forest resilience and sustainability. Full article

More than half of the world’s rivers are intermittent, and climate change is increasing their intermittency, affecting water resources and ecosystems. In Taiwan, steep topography and uneven rainfall have led to increased intermittency in some areas, reflecting changes in hydrological conditions. Using streamflow data, this study applied intermittency ratio (IR), modified 6-month dry period seasonality (SD6), and trend analysis, as well as watershed properties and climate indices. Results showed that 92% of stations had low flows for less than 20% of the time. The dry season was mainly from November to April, and intermittency was spatially affected mainly by upstream soil moisture, moderately by potential evapotranspiration and infiltration, and less by actual evapotranspiration and catchment area. Intermittency increased in the east and decreased in the west. It was negatively correlated with upstream soil moisture and strongly associated with rainfall frequency, especially the proportion of days with precipitation less than 1 mm. These patterns highlight regional differences in river responses to climate. Full article

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations. Full article

Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models. Full article

Non-Newtonian mud and debris flows include a wide range of physical processes depending on the setting, concentration, and soil properties. Numerical modelers have developed a variety of non-Newtonian algorithms to simulate this range of physical processes. However, the assumptions and limitations in any given model or software package can be difficult to replicate. This diversity in the physical processes and algorithmic approach to non-Newtonian numerical modeling makes a modular computation library approach advantageous. A computational library consolidates the algorithms for each process. This work presents a flexible numerical library framework (DebrisLib) that has a diverse range of software implemented to simulate geophysical flows using steady flow, kinematic wave, diffusion wave, and shallow-water models with finite difference, finite element, and finite volume computational schemes. DebrisLib includes a variety of non-Newtonian closures that predict a range of geophysical flow conditions and modular code designed to operate with any Newtonian parent-code architecture. This paper presents the DebriLib algorithms and framework and laboratory validation simulation. The simulations demonstrate the utility of the algorithms and the value of the library architecture by calling it from different modeling frameworks developed by the US Army Corps of Engineers (USACE). We present results with the one-dimensional (1D) and two-dimensional (2D) Hydrologic Engineering Center River Analysis System (HEC-RAS) and the 2D Adaptive Hydraulics (AdH) numerical models, each calling the same library. Full article

Plant roots and soil properties play crucial roles in regulating soil hydrological processes, particularly in determining soil water infiltration capacity. However, the infiltration patterns and underlying mechanisms across different forest types in subtropical regions remain poorly understood. In this study, we measured the infiltration characteristics of three typical stands (pure Phyllostachys edulis forest, mixed Phyllostachys edulis-Cunninghamia lanceolata forest, and pure Cunninghamia lanceolata forest) using a double-ring infiltrometer. Stepwise multiple regression and structural equation modeling (SEM) were employed to analyze the effects of root traits and soil physicochemical properties on soil infiltration capacity. The results revealed the following: (1) The initial infiltration rate (IIR), stable infiltration rate (SIR), and average infiltration rate (AIR) followed the order pure Phyllostachys edulis stand > mixed stand > pure Cunninghamia lanceolata stand. (2) Compared to the pure Cunninghamia lanceolata stand, the IIR, SIR, and AIR in the pure Phyllostachys edulis stand increased by 6.66%, 35.63%, and 28.51%, respectively, while those in the mixed stand increased by 28.79%, 28.82%, and 33.51%. (3) Fine root biomass, root length density, non-capillary porosity, and soil bulk density were identified as key factors influencing soil infiltration capacity. (4) Root biomass and root length density affected infiltration capacity through both direct pathways and indirect pathways mediated by alterations in non-capillary porosity and soil bulk density. These findings provide theoretical insights into soil responses to forest types and inform sustainable water–soil management practices in Phyllostachys edulis plantations. Full article

Agroforestry systems (AFSs) have been recognized for their ecological potential, yet quantitative assessments of their hydrological functions in semi-arid regions remain limited. This study evaluates soil moisture retention and potential aquifer recharge in two agroforestry systems compared to a traditional rainfed maize system in the semi-desert region of Celaya, Mexico, where aquifer depletion is a growing concern. Field measurements during the 2022 rainy season included precipitation, soil moisture at multiple depths, and soil physical properties across seven vegetation covers. The results show significantly higher moisture content, improved uniformity, and enhanced recharge potential under tree species such as Bursera graveolens and Lysiloma divaricatum. These effects are attributed to vegetation cover, organic matter input, and reduced evaporation. This study provides empirical evidence supporting the integration of AFSs into regional water management strategies, offering a nature-based solution for aquifer recovery and climate adaptation in arid landscapes. Full article

The Atlantic Forest has undergone deforestation and prolonged droughts, affecting ecosystem services. This study assesses the water balance using hydrological observations from representative tree species within a Montane Semideciduous Seasonal Forest (MF) remnant. Gross precipitation (GP), canopy interception (CI), and effective precipitation (EP = Throughfall + Stemflow) were recorded daily, and soil moisture was measured down to 1.80 m every two days during the dry period of the 2023/2024 hydrological year. Additionally, aboveground biomass (AGB), fresh root biomass (BR), and soil hydrological properties in the soil profile were obtained to support the water balance results. The highest EP values were recorded in Miconia willdenowii, while the lowest were in Xylopia brasiliensis. Root zone water storage exhibited a declining trend, with the highest values in Miconia willdenowii. ET remained low, mainly in April, July, and September, with Miconia willdenowii and Copaifera langsdorffii showing the highest values, and AGB correlated with CI and ET. The dynamic of this ecosystem is apparent in the temporal variations (CVt) of soil moisture, influenced by EP and ET. The greatest variability was recorded in the surface layer (0–20 cm), stabilizing with depth, especially below 120 cm. The Temporal Stability Index (TSI) of soil water storage indicated greater stability in Blepharocalyx salicifolius. This study highlights the significance of soil water storage and ET in a tropical forest ecosystem, particularly under drought conditions, suggesting potential species that may be more effective in recovering degraded areas. Full article

Rational and effective utilization of rainfall is crucial to vegetation restoration and ecological reconstruction for engineering slopes. However, plant and vegetated concrete considerably affect soil water distribution and rainfall replenishment, which is rarely accounted for in current studies. To this end, the effects of plant and vegetated concrete on spatiotemporal distribution and soil water recharge were explored. First, four field model slopes were constructed to monitor soil water content. The spatiotemporal variations and distribution characteristics of soil water under different restoration modes were analyzed. The indicators including amount, efficiency, and threshold of soil water recharge in ecological slopes were assessed. At last, the effects of plant and vegetated concrete on the spatiotemporal distribution and recharge characteristics of soil water were discussed. Results showed that ecological restoration alters spatiotemporal distribution characteristics and reduces soil water content of engineering slopes. During rainfall process, ecological restoration extends the lag time but increases amount and efficiency of rainfall replenishment. Comparably, ecological shrub slope gains the highest lag time and rainfall threshold. Cynodon dactylon is superior to Magnolia multiflora in raising rainfall replenishment capacity. Additionally, vegetated concrete enhances rainfall replenishment efficiency by altering soil properties and interacting with plants. This study deepened the understanding of hydrological effects of ecological restoration on slopes and provided a theoretical basis for ensuring sustainable slope management. Full article

This study explores the relationship between abiotic factors, farming practices, and weed growth in winter wheat fields in Eastern Hungary. It examines the order of weed dominance and the influence of soil, environmental, and agricultural variables on weed composition and diversity before herbicide application. The research was conducted across four sub-regions in the Great Hungarian Plain, each with distinct soil, hydrological, and geographical conditions. Between 2018 and 2021, 103 fields were surveyed and weed species cover was recorded using EPPO-based identification and quadrat sampling. Soil properties, environmental conditions, and farming practices were documented through soil analysis, geographical data, and farmer interviews. Statistical analyses were preformed including ANCOVA, redundancy analysis, and Shannon diversity index calculations. The results show that common weed species include Veronica hederifolia, Stellaria media, and Apera spica-venti, with winter annuals dominating. Soil compaction and salinity affected weed diversity, while increased copper and zinc concentrations had minor effects on weed coverage. Farming practices, particularly tillage systems and fertilizer use, had a significant effect on species richness and diversity. Different regional and annual weed distributions were observed, with correlation between certain tillage systems and specific weed species. The results emphasize the need for climate-conscious farming practices, and we recommend prioritising shallow cultivation and deep loosening over ploughing in order to manage weed populations effectively. These insights contribute to sustainable weed management strategies in cereal production. Full article

As a form of green infrastructure, urban forests play a key role in the provision of hydrological ecosystem services (ESs) in cities. Understanding how urban forest structure and soil properties influence water regulation ESs is crucial for managing and planning green infrastructure in cities. We analysed two indicators—the runoff to precipitation (Q/P) and the evapotranspiration to precipitation (ETP/P) ratios—for five different urban forests. We used the hydrological model Brook90 over 16 years to simulate runoff, evapotranspiration, canopy interception, transpiration and soil evaporation. The results showed that mixed forests have the highest water retention capacity, with the lowest Q/P (0.41) and the highest ETP/P (0.59). In contrast, riparian deciduous forests had the lowest water retention capacity, with the highest Q/P (0.75) and the lowest ETP/P (0.25). Both indicators showed similar annual and seasonal results. However, Q/P showed strong inter-annual variation and a strong correlation with precipitation, while ETP/P remained consistent despite precipitation fluctuations in the observed years. In conclusion, the ETP/P ratio is better suited to assess the water regulation ES of urban forests. Full article

In the 21st century, the adoption of solar energy has witnessed significant growth, driven by the increased use of ground-mounted photovoltaic (GPV) systems, recognized as solar farms, which have emerged as major players in this sector. Nevertheless, their extensive land utilization may impact local ecosystem services (ESSs), especially those related to water resources. In the context of the water–energy–food–ecosystem (WEFE) nexus, it becomes vital to investigate how solar park construction will impact water-related ESSs. This paper developed a framework that assesses the effect of constructing a solar park on water-related ecosystem services. We focused on solar farm construction and its interactions with various hydrological cycle components; then, we evaluated the implications for water-related ESSs. This approach encompasses a systematic literature review that identifies the hydrological factors most affected by the construction of solar farms. As a result, thirteen ESSs were selected to be included in an evaluation framework, and a definition of a scoring system of each ESS was defined based on the economic value, a predetermined indicator, or land use and land cover (LULC) properties. The allocation of weighting factors for these scores can be determined based on individual experience and stakeholders. This study presents a DSS-integrated framework to assess the impact of solar park constructions on water-related ecosystem services (ESSs) within the WEFE nexus. The framework was applied to a case study in Darstadt, Bavaria, revealing that, among the water-related ESSs in favor of ground-mounted PV systems (GPVs) compared to traditional agricultural practices, there could be soil erosion and nitrate leaching reduction. The DSS tool enables stakeholders to efficiently evaluate trade-offs between energy production and ecosystem impacts. The findings underscore the potential of integrating renewable energy projects with ecosystem management strategies to promote sustainable land-use practices. Full article

The integrity of earth infrastructure, encompassing slopes, dams, pavements, and embankments, is fundamental to the functioning of transportation networks, energy systems, and urban development. However, these infrastructures are increasingly threatened by a range of natural and anthropogenic factors. Conventional monitoring techniques, including inclinometers and handheld instruments, often exhibit limitations in spatial coverage and operational efficiency, rendering them insufficient for comprehensive evaluation. In response, Uncrewed Aerial Vehicles (UAVs) and Electrical Resistivity Imaging (ERI) have emerged as pivotal technological advancements, offering high-resolution surface characterization and critical subsurface diagnostics, respectively. UAVs facilitate the detection of deformations and geomorphological dynamics, while ERI is instrumental in identifying zones of water saturation and geological structures, detecting groundwater, characterizing vadose zone hydrology, and assessing subsurface soil and rock properties and potential slip surfaces, among others. The integration of these technologies enables multidimensional monitoring capabilities, enhancing the ability to predict and mitigate infrastructure instabilities. This article focuses on recent advancements in the integration of UAVs and ERI through data fusion frameworks, which synthesize surface and subsurface data to support proactive monitoring and predictive analytics. Drawing on a synthesis of contemporary research, this study underscores the potential of these integrative approaches to advance early-warning systems and risk mitigation strategies for critical infrastructure. Furthermore, it identifies existing research gaps and proposes future directions for the development of robust, integrated monitoring methodologies. Full article

Prioritizing agricultural land use is a significant challenge for sustainable development in the rapidly urbanizing, semi-arid riverine basins of South Asia, especially under climate variability and water scarcity. This study introduces a systematic framework combining remote sensing and geospatial data with the Soil and Water Assessment Tool (SWAT) model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India, while reducing the environmental impact, in line with Sustainable Development Goals (SDGs). The SWAT model simulation reveals varied hydrological patterns, with basin water yields from 965.9 to 1012.9 mm and a substantial baseflow (~64% of total flow), emphasizing essential groundwater–surface water interactions for sustainable agriculture. However, the discrepancy between percolation (47% of precipitation) and deep recharge (2% of precipitation) signals potential long-term groundwater challenges. VIKOR analysis offers a robust prioritization framework, ranking SW4 as the most suitable (Qi = 0.003) for balanced hydrological and morphometric features, in agreement with the SWAT outcomes. SW4 and SW5 display optimal agricultural conditions due to stable terrain, effective water retention, and favorable morphometric traits (drainage density 3.0–3.15 km/km²; ruggedness 0.3–0.4). Conversely, SW2, with high drainage density (5.33 km/km²) and ruggedness (2.0), shows low suitability, indicating risks of erosion and poor water retention. This integrated AgLP framework advances sustainable agricultural development and supports SDGs, including SDG 2 (Zero Hunger), SDG 6 (Clean Water), SDG 13 (Climate Action), and SDG 15 (Life on Land). Incorporating hydrological dynamics, land use, soil properties, and climate variables, this approach offers a precise assessment of agricultural suitability to address global sustainability challenges in vulnerable riverine basins of developing nations. Full article