Search Results (2,048)

Green roofs are used as nature-based solutions for urban stormwater management and for improving the thermal performance of buildings. Their hydrological performance depends on structural properties and rainfall characteristics, but the relative importance of these factors has not been fully quantified. Therefore, this study aimed to identify the key variables controlling the hydrological effectiveness of a green roof. A conceptual model of a flat roof representing a typical single-family building in south-eastern Poland was developed in the Storm Water Management Model (SWMM), with a modeled roof area of 232 m² and 100% of the roof surface covered by the green roof LID system. A total of 24,576 simulation cases were analyzed, considering different values of soil thickness, berm height, initial saturation, vegetation-related storage, rainfall duration, rainfall probability, and rainfall temporal distribution. The hydrological response was evaluated using peak runoff reduction and cumulative runoff volume ratio determined at selected times after rainfall. Predictive models based on the eXtreme Gradient Boosting (XGBoost) algorithm were developed, and their interpretation was performed using the SHapley Additive exPlanations (SHAP) method. The main novelty of the study is its application-oriented framework combining SWMM simulations, XGBoost modeling, and SHAP explainability to distinguish the factors controlling peak runoff reduction and delayed runoff release from a green roof. The results showed that peak runoff reduction ranged from 10.97% to 100.00%, with a median of 99.91%, indicating a generally high capacity of the analyzed system to attenuate peak flow. In contrast, the cumulative runoff volume ratio increased over time, with median values rising from 0.05% immediately after rainfall to 7.91% after 24 h, confirming the significant retention and detention potential of the green roof. SHAP analysis revealed that peak runoff reduction was governed primarily by berm height, whereas cumulative runoff volume was controlled mainly by initial substrate saturation. The results confirm that different mechanisms control short-term and long-term green roof performance. Full article

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin. Full article

Longnan, located in Gansu Province, China, at the junction of Shaanxi, Gansu, and Sichuan provinces, represents one of the key corridors of the Northern Tea-Horse Ancient Road. This region preserves abundant traditional timber covered bridges with distinct local characteristics. This study employs ArcGIS spatial analysis and documentary research methods to explore the spatial distribution, spatiotemporal evolution, and influencing factors of these bridges. Spatial analyses (nearest neighbor index, kernel density, and standard deviational ellipse) are based on 71 bridges with traceable coordinates, while the temporal evolution analysis incorporates 80 bridges (64 with definite construction periods and 16 with unknown dates; the latter are handled through a sensitivity analysis as described later in this paper The results indicate that the timber covered bridges in Longnan exhibit a significantly clustered distribution, presenting a pattern of “dense in the southwest and sparse in the northeast”, with Wen County and Kang County as the core clustering areas. Temporally, they follow a unimodal evolution pattern: initiation in the Ming Dynasty, peak in the Qing Dynasty, decline in the Republic of China period, and near stagnation in modern times. The location and distribution of the covered bridges show a strong statistical association with natural conditions (e.g., topography, hydrology) and exhibit spatial coincidence with modern vegetation coverage—the latter treated solely as a contemporary context variable rather than a historical driver. Spatial coincidence with the ancient road is quantified (60.56% within a 2000 m buffer), while settlement proximity is only qualitatively noted as background. Socio-economic factors (e.g., population, transportation, and settlements) are examined qualitatively and display spatial coincidence rather than quantitatively measured influence; these factors cannot be directly compared with natural factors. Full article

Data-driven models can be used to understand basin-wide hydrological processes and generate predictions for future conditions, particularly in cases of scarce data availability related to basin characteristics. Although they have long been applied in hydrological modeling, there is still limited information regarding their ability to produce reliable long-term projections under climate change conditions. This study evaluates the long-term predictive performance of data-driven models by employing a hybrid deep learning architecture combining Wavelet Transform (WT) and Deep Neural Network (DNN). The dataset used in this study was obtained from the Yamula Reservoir Basin, a semi-arid agricultural basin in Türkiye. Monthly streamflow was simulated based on climate projection data from the HadGEM2-ES model under the RCP4.5 and RCP8.5 scenarios. Results showed that the WT–DNN framework was successful in learning the system dynamics and reproducing observed streamflow behavior. The model produced continuous projections for the future period; however, these projections should be interpreted with caution due to the increasing uncertainty associated with long-term climate forcing and the sensitivity of data-driven approaches to shifts in climatic and hydrological regimes. Full article

As an important port in the Beibu Gulf of the South China Sea, Tieshangang Bay is potentially at risk of PTE pollution, yet systematic research integrating multi-hydrological period data remains limited. By applying pollution indices (C_f, WQI, I_geo, RI) combined with PCA, and PMF, we investigated PTE distribution characteristics, risk assessment, and source apportionment across different hydrological seasons. The results indicate that average PTE concentrations in surface seawater meet Class II standards of the Sea Water Quality Standard, with Zn and As showing relatively high concentrations compared to other PTEs. High-concentration areas were mainly located in the inner and middle bay. In sediments, concentrations of Zn and Cr were relatively high, with values generally higher inside the bay than outside. Both C_f and WQI values for seawater PTEs were below 1, indicating an overall low pollution risk. However, Cd and Hg in sediments presented a moderate potential ecological risk. Source apportionment revealed that seawater PTEs primarily originated from an industrial–aquaculture composite source (44.60%), while sediment PTEs were mainly attributed to composite terrestrial inputs (53.16%). These findings provide a scientific basis for PTE pollution management and sustainable development in Tieshangang Bay. Full article

Current climate change has led to significant changes in precipitation patterns in the Loess Hilly Region, resulting in frequent extreme rainfall events, which have a significant impact on restoring the soil hydrological function of grasslands in this area. This study focuses on the restoration of grasslands through the conversion of farmland in the Loess Hilly Region. Using natural rainfall as the control, seven precipitation gradient treatments were established with rainout shelters: +20%, +40%, and +60% rainfall increases, and −20%, −40%, and −60% rainfall decreases. The changes in infiltration characteristics were then analyzed. Long-term increased rainfall promoted vegetation restoration and improved soil physicochemical properties. Compared with the natural rainfall control, the +20%, +40%, and +60% rainfall increase treatments enhanced the total porosity of the 0–5 cm soil layer by 0.29%, 4.64%, and 3.18%, respectively, and increased the soil organic carbon content by 28.42%, 62.46%, and 63.16%, respectively. Soil infiltration rate was also enhanced accordingly. Relative to the steady-state infiltration rate of the control (4.76 mm/min), the +20%, +40%, and +60% treatments increased the rate by 1.13%, 16.67%, and 22.54%, respectively, with the +60% treatment achieving the highest steady-state infiltration rate of 5.83 mm/min. The macroaggregate content in the +40% treatment was 47.70%, which was significantly higher than that in the other treatments. The increase in infiltration was related to the increase in total porosity, organic carbon, and the content and stability of large aggregates. Moderate rainfall increases can promote organic carbon accumulation and the formation of large aggregates, enhancing soil infiltration capacity; however, rainfall intensities exceeding 60% can damage the soil structure, and infiltration no longer significantly increases. Full article

Mangrove forests in northern Mozambique were impacted by human and natural pressures, causing channel blockage, permanent flooding, and tree die back. To address the issue, hydrological restoration was carried out in August 2024, excavating 6.88 km of channels, with impact in 38 ha of degraded mangrove. The intervention area was divided into three zones, upper, middle, and lower, based on ecological and environmental characteristics. This study reports on the monitoring carried out 4 and 10 months later. Site salinity approached optimal levels for mangrove growth, dropping by 56% in high-salinity zones, and increasing above 100% in freshwater-invaded zones. The intervention also homogenized the previously distinct upper, middle, and lower zones to more statistically similar groups (Dunn post hoc: p > 0.05). Moreover, seedling density increased from 57.1 ± 44.1 to 4864 ± 1778.6 seedlings/ha; additionally, regenerating species increased in numbers (1 to 3 mangrove species in middle zone; and 0 to 3 mangrove species in lower zone). The study also reports the dieback of competing species, Juncus kraussii and Cyperus articulates. These changes result from the improved tidal flow and general habitat conditions in the restored site. This restoration offers a model for scaling up restoration efforts across the region, where ecological restoration remains underrepresented in many mangrove restoration initiatives. Full article

This study investigates the dynamical behavior of hydrological time series using nonlinear analysis methods, with emphasis on the Visual Boundary Recurrence Plots (VBRPs) approach. Water level data from three monitoring stations along the Nestos River (Greece) are analyzed to uncover underlying system dynamics and variability across different hydrological settings. The VBRP methodology is employed to identify and quantify recurrent and non-recurrent structures in the time series, enabling the detection of temporal persistence, fluctuations, and transitions between system states. The derived VBRP rates reveal significant differences among the examined stations, reflecting variations in basin characteristics such as topography, flow organization, and storage capacity. Specifically, the Temenos (E6) station exhibits higher recurrence-related structure, indicating a more coherent and rapidly responding system, while the Arkoudorema (E8) station shows increased non-recurrent behavior, suggesting stronger fluctuations associated with tributary dynamics. The Papades (E7) station displays intermediate characteristics. The results demonstrate that VBRP provides physically interpretable insights into hydrological dynamics and constitutes a robust tool for the analysis of complex environmental time series, complementing traditional nonlinear approaches. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

Agricultural intensification in Eastern Africa has raised concerns about the transport of pesticides and nutrients from farmland into surface waters, posing risks to ecosystems and human health. This study systematically reviews the peer-reviewed literature published between 2010 and 2024 to assess the extent, patterns, and drivers of agrochemical contamination in rivers, lakes, and reservoirs across the region. Reported pesticide concentrations ranged from <0.01 to 0.55 μg L⁻¹, with several studies indicating exceedances of drinking-water or environmental guideline values, particularly for organophosphate and carbamate compounds. Nutrient enrichment was widespread, with nitrate concentrations of 0.99–5.6 mg L⁻¹ and phosphate levels of 0.16–2.0 mg L⁻¹, frequently linked to eutrophication. Many studies showed strong seasonal variability, with higher concentrations during rainy periods due to increased runoff and erosion. Variability among findings reflected differences in land use, catchment characteristics, sampling design, and analytical approaches. Where evaluated, mitigation measures such as vegetated buffer strips, cover cropping, and improved nutrient management were associated with reductions in agrochemical runoff of approximately 50–80%. Overall, agrochemical contamination is widespread across Eastern African basins and influenced by agricultural practices and hydrological dynamics, highlighting the need for strengthened monitoring, improved stewardship, and broader adoption of mitigation strategies. Full article

Prior to this study, knowledge on the evolutionary lineage of Garra remained inadequate, as previous phylogenetic investigations were primarily based on partial gene sequences. Although several mitogenomes of Garra species have been reported, their structural organization and comprehensive genomic characteristics have not been thoroughly evaluated. In this study, Garra manipurensis, endemic to the Indo-Burma biodiversity hotspot, was identified based on its detailed morphology and meristic counts. The circular mitogenome of G. manipurensis is 16,776 bp in length and contains the canonical set of 37 genes, along with duplicated control regions separated by tRNA-Proline. The comparative assessments across Garra species indicate predominantly conserved GTG start codons, occasional alternative ATA initiation codons, and incomplete stop codons. The selection pressure examinations within Garrini taxa reveal a purifying selection across all protein-coding genes. The control region comprises four conserved sequence blocks and species-specific tandem repeats, reflecting a balance between functional constraint and lineage-dependent evolutionary dynamics. The phylogenetic inference supports the monophyly of Garra and places G. manipurensis in close affinity with Garra flavatra, which is native to the western slope of Rakhine Yoma in Myanmar and Mizoram State in northeastern India. The genetic diversity analyses revealed haplotype differentiation, with shallow intraspecific genetic distances (0.000–0.011) observed samples between two distinct drainage systems in Manipur and Mizoram, northeastern India. The observed pattern of haplotype divergence in G. manipurensis may reflect the historical or seasonal hydrological connectivity among the western-slope drainages of the Chin Hills, with the subsequent geographic isolation potentially contributing to the emergence of distinct genetic lineages. Nevertheless, the extent and evolutionary significance of this differentiation remain uncertain and warrant further investigation through expanded geographic sampling and the incorporation of additional molecular data. Collectively, these findings provide in-depth insights into the mitogenomic architecture, comparative gene arrangements, phylogenetic patterns, and matrilineal evolutionary history of G. manipurensis and other congeners, thereby improving our understanding of the systematics and genetic diversity of this important cyprinid fish lineage. Full article

Large-scale urbanization in China has altered land surface characteristics, affected climate and hydrological cycles, and changed the spatial and temporal distribution of precipitation. The combined effects of global warming and the urban heat island effect have further intensified changes in urban rainfall patterns. Therefore, it is essential to clarify the spatiotemporal evolution of precipitation under China’s rapid urbanization process in order to reduce multiple disaster risks. To achieve this, historical precipitation data and multisource remote sensing imagery were integrated to construct a spatiotemporal coupling model for analyzing the relationship between urbanization patterns and precipitation distribution in China. In addition, combined with the background of global climate change, the spatiotemporal evolution characteristics of annual, monthly, and seasonal precipitation were investigated. The main conclusions are as follows: (1) China still has great potential for urbanization and economic development and is currently in a new stage of rapid growth; (2) During 1992–2020, the national area proportion receiving annual precipitation of (200, 400] mm decreased by approximately 0.12 percentage points per year, whereas the area proportion receiving (400, 800] mm increased by approximately 0.11 percentage points per year, indicating a measurable shift toward wetter precipitation conditions; (3) Heavy rainfall events in China are expected to increase in the future, mainly occurring from June to August, with a maximum monthly precipitation reaching 1137.9 mm; (4) Urbanization may be one of the important factors associated with precipitation changes in China, with 2008 identified as a key turning point, when the urbanization rate approached 50% and began to exhibit a preliminary scale effect. Full article

Under complex and changing environmental conditions, hydrological drought in the upper Hanjiang River (UHR) is becoming increasingly severe, so investigating the variation characteristics and influencing factors of hydrological drought in this basin can provide favorable support for drought prevention and water resources management. In this study, based on monthly runoff data from the Ankang Hydrological Station of the UHR, the mutation change year at the Ankang Station was first identified using the Pettitt mutation test and the B-G segmentation algorithm. Subsequently, the ABCD hydrological model coupled with eight machine learning algorithms was employed to simulate the runoff variation process in the Ankang Station. Finally, we used the Standardized Runoff Index to describe the hydrological drought conditions and quantitatively analyzed the impacts of human activities and climate change on the seasonal hydrological drought in the UHR. The results showed that (1) the coupled machine learning–hydrological model can effectively improve the simulation accuracy of the runoff change process. (2) The coupled ABCD–Random Forest model has the highest accuracy. (3) Hydrological drought exhibits a significant increasing trend in spring and autumn, a significant decreasing trend in winter, and a non-significant increasing trend in summer. (4) Climate change serves as the primary driver of hydrological drought variations across four seasons in the UHR. Full article

Land-use and land-cover changes around reservoirs mediate the interface between watershed land systems and managed surface-water resources. In regulated reservoirs, water-level regulation can rapidly expose or inundate shore-zone land, yet evidence remains limited on where these transitions occur, how landscape configuration changes, and how such information can inform watershed and reservoir-margin management. Using 0.5 m Jilin-1 optical imagery from April and September of 2024 and 2025, this study mapped land-use/land-cover change (LUCC) in the Danjiangkou Reservoir shore zone and integrated transition matrices, class-level landscape metrics, shoreline-distance gradients, reach-level zoning, paired hydrological records, and multiscale geographically weighted regression (MGWR). The classification achieved an overall accuracy of 93.1% and a Kappa coefficient of 0.921. The strongest land-cover shift occurred between September 2024 and April 2025, when the water proportion declined from 78.74% to 60.10% and bare land expanded during the lowest observed reservoir stage (151.02 m). Subsequent refill was accompanied by partial re-inundation and increases in grassland, cropland, and forest. The 0–30 m shoreline belt was the principal response zone, indicating that hydrologically driven land-cover replacement was concentrated in the immediate reservoir margin. MGWR showed spatially varying positive associations between change-patch characteristics, distance to permanent water, and elevation, but the low explanatory power requires these results to be interpreted as spatial diagnostics rather than causal attribution. The study links land-cover monitoring with reservoir water-level regulation, identifies priority shoreline belts, and provides spatial information for field verification and reservoir-margin management. Full article

Small and medium-sized (SMS) lakes in cold–arid regions are highly sensitive to climate change and play critical roles in regional hydrological and ecological processes. However, their long-term dynamic evolution along aridity–humidity gradients remains insufficiently understood. This study aims to reveal the spatiotemporal variations in SMS lakes on the Inner Mongolia Plateau and clarify their climatic driving mechanisms. Based on Landsat imagery and meteorological data (1984–2021) on the Google Earth Engine (GEE) platform, this study quantified the spatiotemporal variations in SMS lakes and adopted an ecological–geographical zoning framework to characterize lake responses across aridity–humidity gradients. Results indicate that, from 1984 to 2021, the total area of SMS lakes showed an insignificant linear trend but a net increase of 117% (396.50–860.33 km²), while the lake number increased by 155%, with 59 new lakes. The dynamics followed four stages: expansion (1984–1993), fluctuation (1994–2002), low-level stability (2003–2011), and recovery (2012–2021). Notably, recovery levels remained below the pre-2003 peak, with 2003 identified as a critical turning point. Lake numbers responded to climatic stress earlier than area changes. Spatially, lake variations in arid regions were primarily controlled by energy-related factors (e.g., temperature and potential evapotranspiration), while lake changes in semi-humid regions were dominated by precipitation-regulated water availability. Semi-arid regions presented transitional characteristics constrained by both energy and water factors. Although extreme weather events did not dominate long-term lake evolution, they significantly exacerbated short-term lake fluctuations. Overall, the controlling mechanism of SMS lakes shifted from energy limitation to water regulation under ongoing climate warming, highlighting pronounced regional differences in climate–lake interactions. Full article