Search Results (467)

Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. Due to the limited availability of in situ SSM data, the performance of three global SSM datasets—SMAP, MERRA-2, and CFSv2—from 2015 to 2023 was evaluated using agrometeorological stations. SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. The RF model yielded R² values of 0.89, 0.83, 0.70, and 0.75 for winter, spring, summer, and autumn, respectively, with corresponding RMSE values of 0.076, 0.081, 0.098, and 0.061 m³/m³. SHAP analysis revealed that climatic factors primarily drive SSM in winter and autumn, while vegetation and soil characteristics are more influential in spring and summer. The clustering results showed that Iran’s catchments can be grouped into five categories based on the SHAP method coefficients, highlighting regional differences in SSM controls. Full article

Forest harvesting can alter sedimentary processes in catchments by reducing vegetation cover and exposing the soil surface. To mitigate these effects, post-harvest residue management is commonly used, though its effectiveness needs individual evaluation. This study assessed how windrowed harvest residues influence structural sediment connectivity in two forest catchments in south-central Chile with a Mediterranean climate. Using digital terrain models and the Index of Connectivity, scenarios with and without windrows were compared. Despite similar windrow characteristics, effectiveness varied between catchments. In catchment N01 (12.6 ha, average slope 0.28 m m⁻¹), with 13.6% windrow coverage, connectivity remained unchanged, but in contrast, catchment N02 (14 ha, average slope 0.27 m m⁻¹), with 21.9% coverage, showed a significant connectivity reduction. A key factor was windrows’ orientation: 83.9% aligned with contour lines in N02 versus 58.6% in N01. Distance to drainage channels also played a role, with the decreasing effect of connectivity at 50–60 m in N02. Bootstrap analysis confirmed significant differences between catchments. These results suggest that windrow configuration, particularly contour alignment, may be more critical than coverage percentage. For effective connectivity reduction, especially on moderate to steep slopes, forest managers should prioritize contour-aligned windrows. This study enhances our understanding of structural sediment connectivity and offers practical insights for sustainable post-harvest forest management. Full article

Land cover changes have significant implications for ecosystem services, influencing agricultural productivity, soil stability, hydrological processes, and biodiversity. This study assesses the impacts of land use and land cover (LULC) change on soil erosion in the Upper Guder River catchment, Ethiopia, from 1986 to 2020. We analyzed Landsat imagery for three periods (1986, 2002, and 2020), achieving a classification accuracy of 89.21% and a kappa coefficient of 0.839. Using the Revised Universal Soil Loss Equation (RUSLE) model, we quantified spatial and temporal variations in soil erosion. Over the study period, cultivated land expanded from 51.89% to 78.40%, primarily at the expense of shrubland and grassland, which declined to 6.61% and 2.98%, respectively. Forest cover showed a modest decline, from 13.60% to 11.24%, suggesting a partial offset by reforestation efforts. Built-up areas nearly tripled, reflecting increasing anthropogenic pressure. Mean annual soil loss increased markedly from 107.63 to 172.85 t ha⁻¹ yr⁻¹, with cultivated land exhibiting the highest erosion rates (199.5 t ha⁻¹ yr⁻¹ in 2020). Severe erosion (>50 t ha⁻¹ yr⁻¹) was concentrated on steep slopes under intensive cultivation. These findings emphasize the urgent need for integrated land management strategies that stabilize erosion-prone landscapes while improving agricultural productivity and ecological resilience. Full article

The event scale method, employed for assessing changes in nitrogen (N) dynamics pre- and post-rain, provides insights into its transport to surface water systems. However, the relationships between N discharge in catchments dominated by different land uses and water quality remain unclear. This study quantified variations in key N components in ponds across forest, paddy field, and orchard catchments before and after six rainfall events. The results showed that nitrate (NO₃⁻-N) was the main N component in the ponds. Post-rainfall, N concentrations increased, with ammonium (NH₄⁺-N) and particulate nitrogen (PN) exhibiting significant elevations in agricultural ponds. Orchard catchments contributed the highest N load to the ponds, while forest catchments contributed the lowest. Following a heavy rainstorm event, total nitrogen (TN) loads in the ponds within forest, paddy field, and orchard catchments reached 6.68, 20.93, and 34.62 kg/ha, respectively. These loads were approximately three times higher than those observed after heavy rain events. The partial least squares structural equation model (PLS-SEM) identified that rainfall amount and changes in water volume were the dominant factors influencing N dynamics. Furthermore, the greater slopes of forest and orchard catchments promoted more N loss to the ponds post-rain. In paddy field catchments, larger catchment areas were associated with decreased N flux into the ponds, while larger pond surface areas minimized the variability in N concentration after rainfall events. In orchard catchment ponds, pond area was positively correlated with N concentrations and loads. This study elucidates the effects of rainfall characteristics and catchment heterogeneity on N dynamics in surface waters, offering valuable insights for developing pollution management strategies to mitigate rainfall-induced alterations. 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

Increasing forest destruction from land use and land cover change (LULCC) has altered catchment hydrological processes worldwide. This trend is also endemic to the Belize River Watershed (BRW), a significant source of land and water resources for Belize. This study aims to understand LULCC impacts on BRW hydrological responses from 2000 to 2020 by applying the widely used Soil and Water Assessment Tool (SWAT). This study identified historical trends in LULCC in the BRW and explored an alternative 2020 land cover scenario to elucidate the role of protected forests for hydrological response regulation. A SWAT model for the BRW was developed at the monthly timescale and calibrated on in situ streamflow using SWAT Calibrations and Uncertainty Programs (SWAT-CUP). The results showed that the BRW SWAT model performed satisfactorily for streamflow simulation at the Benque Viejo (BV) gauge station but performed variably at the Double Run (DR) gauge station. Overall, the findings revealed watershed-level increases in monthly average sediment yield (34.40%), surface runoff (24.95%), streamflow (16.86%), water yield (16.02%), baseflow (11.58%), and percolation (3.40%), and decreases in monthly average evapotranspiration (ET) (3.52%). In conclusion, the BRW SWAT model is promising for uncovering the hydrological impacts of LULCCs with opportunities for further model improvement. Full article

This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a Probability of Detection (POD) of 0.92 and a Threat Score (TS) of 0.72 for rainfall estimates in the Chi Basin. Rainfall data from the Weather Research and Forecasting (WRF) numerical models were used as inputs for the HEC-HMS model to simulate water inflows into the dam. To refine rainfall estimates, various microphysics schemes were tested, including WSM3, WSM5, WSM6, Thompson, and Thompson Aerosol-Aware. Among these, the Thomson Aerosol-Aware scheme demonstrated the highest accuracy, achieving an average POD of 0.96, indicating highly reliable rainfall predictions for the Lam Pao Dam catchment. The findings underscore the potential benefits of using satellite-derived meteorological data for rainfall estimation, particularly where installing and maintaining ground-based measurement stations is difficult, e.g., forests/mountainous areas. This research contributes to a better understanding of satellite-derived rainfall patterns and their influence on catchment hydrology for enhanced water resource analysis. 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

The quality characteristics of runoff water during selected precipitation events in planted coniferous (C_P) and natural deciduous (D_N) forest stands in Pocheon-si, 27.0 km north of Seoul, were assessed via the mean event concentrations and discharge loads. The relationship between stream water quality and the runoff time differential (dQ/dt) indicated that the characteristics of the latter differed during the rising and falling stages of the two catchments. Pearson’s product moment correlation analysis revealed that chemical oxygen demand was significantly correlated with total organic carbon in the rising and falling limbs of the two catchments. When discharge loads were transported with actual precipitation events, the event load at the two sites increased with increasing discharge load. In particular, the total organic carbon and total nitrogen were higher in the C_P catchment than in the D_N catchment, whereas biological oxygen demand, total suspended solids, total nitrogen, and total phosphorus were higher in the D_N catchment than in the C_P catchment. Sequences of high and intense precipitation elevated discharge loads, with differences in loads related to the vegetation conditions in headwater areas (≤100 ha) with steep slopes (>20°) and narrow valleys. Full article

Short-term observations suggest that environmental changes affect the diversity and composition of soil fungi, significantly influencing forest resilience, plant diversity, and soil processes. However, time-series experiments should be supplemented with geobiological archives to capture the long-term effects of environmental changes on fungi–soil–plant interactions, particularly in undersampled, floristically diverse tropical forests. We recently conducted trnL-P6 amplicon sequencing to generate a sedimentary ancient DNA (sedaDNA) record of the regional catchment vegetation of the tropical waterbody Lake Towuti (Sulawesi, Indonesia), spanning over one million years (Myr) of the lake’s developmental history. In this study, we performed 18SV9 amplicon sequencing to create a parallel paleofungal record to (a) infer the composition, origins, and functional guilds of paleofungal community members and (b) determine the extent to which downcore changes in fungal community composition reflect the late Pleistocene evolution of the Lake Towuti catchment. We identified at least 52 members of Ascomycota (predominantly Dothiodeomycetes, Eurotiomycetes, and Leotiomycetes) and 12 members of Basidiomycota (primarily Agaricales and Polyporales). Spearman correlation analysis of the relative changes in fungal community composition, geochemical parameters, and paleovegetation assemblages revealed that the overwhelming majority consisted of soil organic matter and wood-decaying saprobes, except for a necrotrophic phytopathogenic association between Mycosphaerellaceae (Cadophora) and wetland herbs (Alocasia) in more-than-1-Myr-old silts and peats deposited in a pre-lake landscape, dominated by small rivers, wetlands, and peat swamps. During the lacustrine stage, vegetation that used to grow on ultramafic catchment soils during extended periods of inferred drying showed associations with dark septate endophytes (Ploettnerulaceae and Didymellaceae) that can produce large quantities of siderophores to solubilize mineral-bound ferrous iron, releasing bioavailable ferrous iron needed for several processes in plants, including photosynthesis. Our study showed that sedaDNA metabarcoding paired with the analysis of geochemical parameters yielded plausible insights into fungal-plant-soil interactions, and inferred changes in the paleohydrology and catchment evolution of tropical Lake Towuti, spanning more than one Myr of deposition. Full article

With the intensification of aging, the imbalance between the supply and demand of elderly care services has become increasingly prominent. Taking Changsha as a case study, this research constructs an accessibility evaluation system based on the 15-min life circle theory, utilizing multi-source data. Spatial weighting characteristics of elderly care facility locations were analyzed through machine learning algorithms, and service coverage disparities between urban districts and suburban towns were assessed under 5-, 10-, and 15-min walking thresholds. Street view semantic segmentation technology was employed to extract street environmental elements in central urban areas, and a multiple regression model was established to elucidate the impact mechanisms of the built environment on walking accessibility. Key findings include: (1) Significant urban-rural service disparities exist, with 91.4% of urban core facilities offering seven service categories within 15-min walking catchments compared to 26.86% in township areas, demonstrating suburban infrastructure’s heavy reliance on administrative resource allocation. (2) Street environmental factors exhibit significant correlations with walking accessibility scores. At the 15-min walking threshold, building space ratio and transportation infrastructure coverage positively influenced walking convenience, while sky view ratio showed a negative correlation. (3) A random forest-based location prediction framework identified multiple service gaps in existing facilities. Suburban service deficiencies (e.g., 59.8% medical facility coverage within walkable catchments) emerge as critical equity barriers, prompting recommendations for integrated “micro-clinic + smart pharmacy” networks and prioritized mixed-use zoning in new urban planning. This research advances a data-driven framework for reconciling urbanization-aging conflicts, offering practical insights for developing nations in creating age-friendly urban environments. Full article

Understanding the impact of vegetation on organic matter content in sediments is essential for sustainable reservoir management and water quality protection. This study examined the relationship between land cover, erosion processes, and organic matter accumulation in the sediments of four small water reservoirs in the Republic of Serbia. Organic matter content was quantified and analyzed in relation to basin characteristics, including land-use composition, absolute and mean flow gradients, and sediment grain size distribution. Field sampling was conducted across the catchments of four small water reservoirs—Duboki potok, Resnik, Ljukovo, and Sot—with sediment samples collected from main tributaries and accumulation basins. A multi-method approach was employed, combining remote sensing for vegetation-cover assessment, granulometric analysis, organic matter evaluation via loss-on-ignition at 350 °C, and statistical correlation analysis to assess the influence of land use and hydrological gradients on sediment composition. The results revealed a strong correlation (R = 0.892) between forest cover and sedimentary organic matter content, confirming the significant role of vegetation in stabilizing sediments and promoting organic matter deposition. Reservoirs with higher forest and shrub cover (e.g., Sot and Duboki potok) exhibited greater organic matter accumulation (5.79–5.98%), while the agriculture-dominated Ljukovo catchment (76.85% agricultural land) recorded the lowest organic matter content (3.89%) due to increased sediment displacement and reduced erosion resistance. These findings underscore the critical role of vegetation in regulating sediment dynamics and enhancing organic matter retention in small water reservoirs. To mitigate excessive organic matter deposition and improve water quality, sustainable watershed management strategies—such as vegetation buffer strips, afforestation, and erosion control measures—are recommended. Full article

Land Use and Land Cover Change (LULCCs) shapes catchment dynamics and is a key driver of hydrological risks, affecting hydrological responses as vegetated land is replaced with urban developments and cultivated land. The resultant hydrological risks are likely to become more critical in the future as the climate changes and becomes increasingly variable. Understanding the effects of LULCC is vital for developing land management strategies and reducing adverse effects on the hydrological cycle and the environment. This study examines LULCC dynamics in the Niger Delta Region (NDR) of Nigeria from 1986 to 2024. A supervised maximum likelihood classification was applied to Landsat 5 TM and 8 OLI images from 1986, 2015, and 2024. Five land use classes were classified: Water bodies, Rainforest, Built-up, Agriculture, and Mangrove. The overall accuracy of the land use classification and Kappa coefficients were 93% and 0.90, 91% and 0.87, 84% and 0.79 for 1986, 2015, and 2024, respectively. Between 1986 and 2024, built-up and agriculture areas substantially increased by about 8229 and 6727 km² (561% and 79%), respectively, with a concomitant decrease in mangrove and vegetation areas of about 14,350 and 10,844 km² (−54% and −42%), respectively. The spatial distribution of changes across the NDR states varied, with Delta, Bayelsa, Cross River, and Rivers States experiencing the highest decrease in rainforest, with losses of 64%, 55, 44%, and 44% (5711 km², 3554 km², 2250 km², and 1297 km²), respectively. The NDR’s mangroves are evidently under serious threat. This has important implications, particularly given the important role played by mangrove forests in regulating hydrological hazards. The dramatic decrease in the NDR mangrove and rainforest could exacerbate climate-related impacts. The study provides quantitative information on LULCC dynamics that could be used to support planning on land management practices in the NDR as well as sustainable development. Full article

Small, forested catchments are prototypes of terrestrial ecosystems and have been studied in several disciplines of environmental science over several decades. Time series of water and matter fluxes and nutrient concentrations from these systems exhibit a bewildering diversity of spatiotemporal patterns, indicating the intricate nature of processes acting on a large range of time scales. Nonlinear dynamics is an obvious framework to investigate catchment time series. We analyzed selected long-term data from three headwater catchments in the Bramke valley, Harz mountains, Lower Saxony in Germany at common biweekly resolution for the period 1991 to 2023. For every time series, we performed gap filling, detrending, and removal of the annual cycle using singular system analysis (SSA), and then calculated metrics based on ordinal pattern statistics: the permutation entropy, permutation complexity, and Fisher information, as well as their generalized versions (q-entropy and α-entropy). Further, the position of each variable in Tarnopolski diagrams is displayed and compared to reference stochastic processes, like fractional Brownian motion, fractional Gaussian noise, and β noise. Still another way of distinguishing deterministic chaos and structured noise, and quantifying the latter, is provided by the complexity from ordinal pattern positioned slopes (COPPS). We also constructed horizontal visibility graphs and estimated the exponent of the decay of the degree distribution. Taken together, the analyses create a characterization of the dynamics of these systems which can be scrutinized for universality, either across variables or between the three geographically very close catchments. Full article

Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land surfaces, infrastructure, and inhabited areas. Forest cover and management play a fundamental role in stabilizing soil and reducing landslide susceptibility. This study focuses on landslide forecasting models, which integrate geological, climatic, and topographic data to predict landslide probability and severity. Specifically, we compare the predictive accuracy of the 4SLIDE model with the established SHALSTAB model in a forested mountain catchment within Sila National Park, Southern Italy, using GIS-based analysis. The results demonstrate that both models effectively identify high-risk areas, with ROC analysis confirming the superior predictive capability of the 4SLIDE model. Our findings underscore the critical importance of early landslide identification, supporting timely interventions and the implementation of forest engineering and Civil Protection measures to mitigate the impact of landslides on communities and infrastructure. Full article