Search Results (464)

The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article

The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various rainfall events during fallow seasons in Mississippi by applying the DRAINMOD model. The analysis revealed that the average rainfall during the fallow season was 760 mm over the past 100 years, accounting for 65% of the annual total. In dry, normal, and wet fallow seasons, the average rainfall was 528, 751, and 1010 mm, respectively, corresponding to runoff of 227, 388, and 602 mm. Runoff frequency increased with wetter weather conditions, rising from 16 events in dry seasons to 23 in normal seasons and 30 in wet seasons. Over the past century, runoff dynamics were predominantly regulated by high-intensity rainfall events during the fallow season. Very heavy rainfall events (mean frequency = 11 events) generated 215 mm of runoff and accounted for 53% of the total runoff, while extreme rainfall events (mean frequency = 2 events) contributed 135 mm of runoff, making up 34% of the total runoff. Water table depth played a critical role in shaping spring runoff dynamics. As the water table decreased from 46 mm in March to 80 mm in May, the soil pore space increased from 5 mm in March to 14 mm in May. This increased soil infiltration and water storage capacity, leading to a steady decline in runoff. The study found that the mean daily runoff frequency dropped from 13.5% in March to 7.6% in May, while monthly runoff decreased from 74 to 38 mm. Increased extreme rainfall (R95p) in April contributed over 45% of the total runoff and resulted in the highest daily mean runoff of 20 mm, compared to 18 mm in March and 16 mm in May. The results from this century-long historical weather data could be used to enhance field-scale water resource management, predict potential runoff risks, and optimize planting windows in the humid east-central Mississippi. Full article

Coastal wetlands play important environmental roles. However, their hydrogeomorphological dynamics remain poorly understood under scenarios of extreme climate events. The aim of this study was to characterize the temporal and spatial variability of hydrogeomorphological attributes (vegetation, water, and soil) in the wetlands of Lagoa do Peixe National Park, Brazil. The methodology involved applying Principal Component Analysis (PCA) in both temporal (T) and spatial (S) modes, decomposing spectral indices for each attribute to identify variability patterns. The results revealed that vegetation and water are strongly correlated with seasonal dynamics influenced by ENSO (El Niño/La Niña) events. Soils reflected their textural characteristics, with a distinct temporal response to the water balance. PCA proved to be a useful tool for synthesizing large volumes of multitemporal data and detecting dominant variability patterns. It highlighted the Lagoon Terraces and the Lagoon Fringe, where low slopes amplified hydrological variations. Temporal variability was more responsive to climate extremes, with implications for ecosystem conservation, while spatial variability was modulated by geomorphology. 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

Machine learning models are increasingly used for streamflow prediction due to their promising performance. However, their data-driven nature makes interpretation challenging. This study explores the interpretability of a Random Forest model trained on high streamflow events from a hydrological perspective, comparing methods for assessing feature influence. The results show that the mean decrease accuracy, mean decrease impurity, Shapley additive explanations, and Tornado methods identify similar key features, though Tornado presents the most notable discrepancies. Despite the model being trained with events of considerable temporal variability, the last observed streamflow is the most relevant feature accounting for over 20% of importance. Moreover, the results suggest that the model identifies a catchment region with a runoff that significantly affects the outlet flow. Accumulated local effects and partial dependence plots may represent first infiltration losses and soil saturation before precipitation sharply impacts streamflow. However, only accumulated local effects depict the influence of the scarce highest accumulated precipitation on the streamflow. Shapley additive explanations are simpler to apply than the local interpretable model-agnostic explanations, which require a tuning process, though both offer similar insights. They show that short-period accumulated precipitation is crucial during the steep rising limb of the hydrograph, reaching 72% of importance on average among the top features. As the peak approaches, previous streamflow values become the most influential feature, continuing into the falling limb. When the hydrograph goes down, the model confers a moderate influence on the accumulated precipitation of several hours back of distant regions, suggesting that the runoff from these areas is arriving. Machine learning models may interpret the catchment system reasonably and provide useful insights about hydrological characteristics. Full article

Green roofs are increasingly recognized as effective Nature-Based Solutions (NBS) for urban stormwater management, contributing to sustainable and climate-resilient cities. The Soil Conservation Service Curve Number (SCS-CN) model is commonly used to simulate their hydrological performance due to its simplicity and low data requirements. However, the standard assumption of a fixed initial abstraction ratio (Ia/S = 0.2), long debated in hydrology, has been largely overlooked in green roof applications. This study investigates the variability of Ia/S and its impact on runoff simulation accuracy for a green roof under a humid subtropical climate. Event-based analysis across multiple storms revealed Ia/S values ranging from 0.01 to 0.62, with a calibrated optimal value of 0.17. This variability is primarily driven by the physical and biological characteristics of the green roof rather than short-term rainfall conditions. Using the fixed ratio introduced consistent biases in runoff estimation, while intermediate ratios (0.17–0.22) provided higher accuracy, with the optimal ratio yielding a median Curve Number (CN) of 89 and high model performance (NSE = 0.95). Additionally, CN values followed a positively skewed Weibull distribution, highlighting the value of probabilistic modeling. Though limited to one green roof design, the findings underscore the importance of site-specific parameter calibration to improve predictive reliability. By enhancing model accuracy, this research supports better design, evaluation, and management of green roofs, reinforcing their contribution to integrated urban water systems and global sustainability goals. Full article

Moso bamboo (Phyllostachys pubescens) is one of the most common species of bamboo in East Asia, and plays a crucial role in regulating hydrological and biogeochemical processes in forest ecosystems. However, throughfall variability and its time stability in Moso bamboo forests remain unclear. Here, we investigated the spatial variability and temporal stability of throughfall in a Moso bamboo forest in China, and the effects of rainfall characteristics and leaf area index (LAI) on the variability of throughfall, and tree locations on the temporal stability of throughfall were systematically evaluated. The results show that throughfall occupied 74.3% of rainfall in the forest. The coefficient of variation of throughfall (throughfall CV) for rainfall events and throughfall collectors were 18.1% and 19.5%, respectively, and the spatial autocorrelation of the throughfall CV was not significant according to the global Moran’s I. Throughfall CV had a significantly negative correlation with rainfall amount and rainfall intensity, whereas it increased with the increase in LAI. The temporal stability plot indicated that the extreme wet and dry persistence were highly stable. We also found that normalized throughfall increased with the increase in distance from the nearest tree trunk. Our findings are expected to assist in the accurate assessment of throughfall and soil water within bamboo forests. Full article

(1) Background: Understanding how forest management practices regulate hydrological cycles is critical for sustainable water resource management and addressing global water crises. However, the effects of light-felling (selective thinning) on hydrological processes in temperate mixed forests remain poorly understood. This study comprehensively evaluated the impacts of light-felling intensity levels on three hydrological layers (canopy, litter, and soil) in mid-rotation Korean pine (Pinus koraiensis) forests managed under the “planting conifer and preserving broadleaved trees” (PCPBT) system on Changbai Mountain, China. (2) Methods: Hydrological processes—including canopy interception, throughfall, stemflow, litter interception, soil water absorption, runoff, and evapotranspiration—were measured across five light-felling intensity levels (control, low, medium, heavy, and clear-cutting) during the growing season. The stand structure and precipitation characteristics were analyzed to elucidate the driving mechanisms. (3) Results: (1) Low and heavy light-felling significantly increased the canopy interception by 18.9%~57.0% (p < 0.05), while medium-intensity light-felling reduced it by 20.6%. The throughfall was significantly decreased 10.7% at low intensity but increased 5.3% at medium intensity. The stemflow rates declined by 15.8%~42.7% across all treatments. (2) The litter interception was reduced by 22.1% under heavy-intensity light-felling (p < 0.05). (3) The soil runoff rates decreased by 56.3%, 16.1%, and 6.5% under the low, heavy, and clear-cutting intensity levels, respectively, although increased by 27.1% under medium-intensity activity (p < 0.05). (4) The monthly hydrological dynamics shifted from bimodal (control) to unimodal patterns under most treatments. (5) The canopy processes were primarily driven by precipitation, while litter interception was influenced by throughfall and tree diversity. The soil processes correlated strongly with throughfall. (4) Conclusions: Low and heavy light-felling led to enhanced canopy interception and reduced soil runoff and mitigated flood risks, whereas medium-intensity light-felling supports water supply during droughts by increasing the throughfall and runoff. These findings provide critical insights for balancing carbon sequestration and hydrological regulation in forest management. Full article

Understanding how spatial drought variability influences streamflow is critical for sustainable water management under changing climate conditions. This study developed a novel Combined Drought Index (CDI) and a method to assess spatial drought impacts on different flow components by integrating remote sensing and hydrological modelling frameworks with generic applicability. The CDI was constructed using Principal Component Analysis to merge multiple standardized indicators: the Standardized Precipitation Evapotranspiration Index, Temperature Condition Index, Vegetation Condition Index, and Soil Moisture Condition Index. The developed framework was applied to the Giriulla sub-basin of the Maha Oya River Basin, Sri Lanka. The CDI strongly correlated with standardized streamflow with a Pearson correlation coefficient of 0.74 and successfully captured major drought and flood events between 2015 and 2023. A semi-distributed hydrological model was used to simulate streamflow variations across sub-catchments under varying drought conditions. Results show upstream sub-catchments were more sensitive to droughts, with sharper declines in specific discharge. Spatial drought variability had different impacts under high- and low-flow conditions: wetter sub-catchments contributed more during high flows, while resilience during low flows varied with catchment characteristics. This integrated approach provides a valuable framework that can be generically applicable for enhanced drought impact assessments. 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

Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and daily actual evapotranspiration (DAE) by integrating climate data and geophysical insights, with a focus on mountainous areas such as the Adige catchment. The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. Model accuracy is assessed by comparing the similarity of Fuzzy C-Means (FCM) clusters before and after fusion, providing a metric for feature reduction. A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). Furthermore, Kernel density estimation is used to ensure the reproducibility of the RFC-GBR process across large-scale applications. A comparative analysis is conducted across multiple SDL architectures, including LSTM, GRU, TCN, and ConvLSTM, over 50 epochs to better evaluate the beneficial effect of the transformed parameters on model performance and accuracy. Results indicate that adjusted parameter calibration improves model performance in all cases, with better alignment to Wflow ground truth during both wet and dry periods. The proposed model increases the accuracy by 20% to 42% when using simpler SDL models like LSTM and GRU, even with fewer epochs. Full article

This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 10⁷ m² study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors of the groundwater environment in the context of soil salinity–alkalinity improvement, as well as the impact of irrigation on the ionic characteristics of groundwater. Furthermore, based on this analysis, a groundwater numerical model and a prediction model for the study area were developed using Visual MODFLOW Flex 6.1 software to forecast the future groundwater levels in the study area and evaluate the effects of varying irrigation scenarios on these levels. The key findings are as follows: (1) The groundwater depth stabilized at 1.63 ± 0.15 m (0.4 m increase) post-improvement measures, maintaining equilibrium under current irrigation but increasing with reductions in water supply. The groundwater salinity increased by 0.59–1.2 g/L across the crop growth period. (2) Spring irrigation raised the groundwater total dissolved solids by 15.6%, as influenced by rock weathering (38.2%), evaporation (31.5%), and cation exchange (30.3%). (3) Maintaining current irrigation systems and planting structures could stabilize groundwater levels at 1.60–1.65 m over the next decade, confirming the sustainable hydrological effects of soil improvement measures. Reducing irrigation to 80% of the current water supply of the Yellow River enables groundwater level stabilization (2.05 ± 0.12 m burial depth) within 5–7 years. This approach decreases river water dependency by 20% while boosting crop water efficiency by 18.7% and reducing root zone salt stress by 32.4%. Full article

Wetlands and meadows are two terrestrial ecosystems that are strikingly distinct in terms of hydrological conditions and biogeochemical characteristics. Wetlands generally feature saturated soils, high accumulation of organic matter, and hypoxic environments. They support unique microbial communities and play crucial roles as carbon sinks and nutrient retainers. In contrast, meadows are characterized by lower water supply, enhanced aeration, and accelerated turnover of organic matter. The transition from wetlands to meadows under global climate change and human activities has triggered severe ecological consequences in the Sanjiangyuan region, yet the mechanisms driving microbial network stability remain unclear. This study integrates microbial sequencing, soil physicochemical analyses, and structural equation modeling (SEM) to reveal systematic changes in microbial communities during wetland degradation. Key findings indicate: (1) critical soil parameter shifts (moisture: 48.5%→19.3%; SOM: −43.6%; salinity: +170%); (2) functional microbial restructuring with drought-tolerant Actinobacteria (+62.8%) and Ascomycota (+48.3%) replacing wetland specialists (Nitrospirota: −43.2%, Basidiomycota: −28.6%); (3) fundamental network reorganization from sparse wetland connections to hypercomplex meadow networks (bacterial nodes +344%, fungal edges +139.2%); (4) SEM identifies moisture (λ = 0.82), organic matter (λ = 0.68), and salinity (λ = −0.53) as primary drivers. Particularly, the collapse of methane-oxidizing archaea (−100%) and emergence of pathogenic fungi (+28.6%) highlight functional thresholds in degradation processes. These findings provide microbial regulation targets for wetland restoration, emphasizing hydrologic management and organic carbon conservation as priority interventions. Future research should assess whether similar microbial and network transitions occur in degraded wetlands across other alpine and temperate regions, to validate the broader applicability of these ecological thresholds. Restoration efforts should prioritize re-saturating soils, reducing salinity, and enhancing organic matter retention to stabilize microbial networks and restore essential ecosystem functions. Full article

Soil temperature and soil moisture are significant interactive parameters that influence many ecological and hydrological processes within forest ecosystems. Furthermore, they are affected by the above canopy characteristics, which determine the amount of sunlight penetration. These canopy characteristics spatially vary within isolated or narrowed forest patches, which include interior parts and edges. On the other hand, forest patches display different effects on the soil temperature and moisture than agricultural meadows within rural landscapes. This study aimed to analyze and evaluate the influences of interior–edge canopies and meadow cover on soil temperature and moisture. Hence, the mutual responses of canopy phenology and physiology, along with the soil temperature and moisture beneath, were analyzed and determined on a temporal basis throughout one year. For this purpose, the air–soil temperature and precipitation data of close meteorological stations were utilized. In addition, soil temperature and moisture parameters were analyzed using an on-site measuring device. Furthermore, canopy parameters—namely LAI, LT, CO, and GF—were determined using a hemispherical photographing procedure and image processing–analysis methodology. Moreover, the LAI of the meadow cover was determined using an on-site analysis device. The maximum LAI, with mean values of 3.69 m² m⁻² and 2.54 m² m⁻², occurred in late May (DOY: 142) within the forest canopies of the interior parts and the patch edge, respectively. On the other hand, the maximum LAI with a mean value of 2.77 m² m⁻² occurred again in late May within the meadow field. On the contrary, during the same period, the lowest percentages were observed for LT and CO, each at 5%, and for GF with 0.5% within the interior parts of the forest patch. However, their lowest percentages were 23% and 16%, respectively, within the forest patch edge. For that late May period, the mean soil temperatures were 17.2, 26.0, and 21.0 °C under the forest canopies of the interior parts, the patch edge, and the meadow field, respectively. Meanwhile, their mean soil moistures were 56.4%, 51.6%, and 32.9% when the mean air temperature was 16.2 °C. Definite correlation did not exist between the canopy–vegetation parameters and the soil temperature–moisture values for all the interior parts, for the edge of the multi-story grove forest patch, and for the meadow field. Based on the overall results of this study, there were apparent differences amongst the interior parts, the edge of the forest patch, and the meadow field in terms of both the canopy–vegetation parameters and the soil temperature–moisture values. The multi-story structure of the interior parts and the edge of the forest patch determined the temporal patterns of their canopy–vegetation parameters. This study elucidated ecology, hydrology, and therefore management of narrow forest patches between agricultural areas within rural landscapes. Full article

Aquifer health assessment is essential for sustainable groundwater management, particularly in semi-arid regions with challenging geological conditions. This study presents a novel methodology for assessing aquifer health in the Barakar River Basin, a hard-rock terrain, by integrating tree-based classification, deep learning, and the Soil and Water Assessment Tool (SWAT) model. Employing Random Forest, Decision Tree, and Convolutional Neural Network (CNN) models, the research examines 20 influential factors, including hydrological, water quality, and socioeconomic variables, to classify aquifer health into four categories: Good, Moderately Good, Semi-Critical, and Critical. The CNN model exhibited the highest predictive accuracy, identifying 33% of the basin as having good aquifer health, while Random Forest assessed 27% as Critical heath. Pearson correlation analysis of CNN-predicted aquifer health indicates that groundwater recharge (r = 0.52), return flow (r = 0.50), and groundwater fluctuation (r = 0.48) are the most influential positive factors. Validation results showed that the CNN model performed strongly, with a precision of 0.957, Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) of 0.95, and F1 score of 0.828, underscoring its reliability and robustness. Geophysical Electrical Resistivity Tomography (ERT) field surveys validated these classifications, particularly in high- and low-aquifer health zones. This study enhances understanding of aquifer dynamics and presents a robust methodology with broader applicability for sustainable groundwater management worldwide. Full article