Hydrological Processes最新文献

Understanding how rainfall and snowmelt influence baseflow, the groundwater-fed component of streamflow, is essential for sound water resources management. Current approaches that aim to uncover the spatial couplings between these processes and baseflow are limited. The most commonly used methods include geochemical tracers and hydrologic models. A key limitation of the first is cost, while the second is limited by the need for simplifying assumptions. This study developed a data-driven approach which leverages satellite Earth observation data and ground-based data to assess the degree to which baseflow is influenced by upstream rainfall and snowmelt in California's Sierra Nevada. The procedure involved: (1) Separation of baseflow from streamflow time series using a low-pass filtering technique, (2) application of time series and information theory methods to identify the areas which have the greatest impacts on baseflow through both rainfall and snowmelt, and (3) characterisation of the elevation zones which have a prevailing influence on baseflow. Results suggest that areas which have the strongest impact on baseflow through rainfall and snowmelt are not necessarily the areas which experience the highest annual rates of snowmelt or rainfall; snowmelt occurring in the 3000–3700 m elevation range was found to be the most important driver of baseflow.

Addressing the unclear mechanisms underlying the influence of rock exposure in the surface-subsurface “dual” erosion structure of karst regions, this study employed indoor artificial rainfall simulation techniques to investigate the runoff and sediment yield characteristics and temporal variation patterns across the surface, soil interflow, rock–soil interface (RSI), and fissures under the influence of rock exposure rate, rainfall intensity, and slope. The results indicate that (1) The total surface runoff volume is positively correlated with rainfall intensity and rock exposure rate, whereas the sediment yield exhibits a nonlinear relationship with rock exposure rate, increasing initially and then decreasing (with a critical value of 20%). Runoff and sediment yield from underground fissures and the underlying RSI were significantly controlled by the rock exposure rate, exhibiting an inverse relationship. Surface runoff variation over time increased rapidly during the first 10 min before stabilizing or decreasing, whereas the sediment yield followed a V-shaped pattern. Subsurface runoff began to form and increased after 20 min, with the sediment yield generally showing a gradual or fluctuating increase. (2) No soil interflow was observed on the bare karst slopes with homogeneous soil. The runoff process at the RSI was positively influenced by rainfall intensity and slope, whereas the sediment yield exhibited a single-peak response to slope changes. (3) Importance analysis of influencing factors showed that rainfall intensity significantly dominated surface runoff and sediment yield processes (p < 0.05), rock exposure rate was the primary controlling factor for subsurface erosion processes, and slope primarily influenced sediment yield at the RSI. (4) Interaction analysis indicated that the interaction between rainfall intensity and rock exposure rate significantly affected the surface runoff and sediment yield. The research findings reveal the differentiated response mechanisms of the “dual” erosion structure in karst regions, providing a theoretical basis for regional soil and water conservation efforts.

Reliable river water level forecasting is essential for hydrological risk management and process understanding. This study presents TP-Transformer, a transformer-based model integrating a time perception attention mechanism and Monte Carlo dropout to capture temporal dependencies and quantify predictive uncertainty. Unlike standard sequence models, TP-Transformer adaptively weights historical inputs and provides confidence intervals, enabling both accurate forecasts and interoperability. Applied to over 60 years of daily water level data from Yichang Station on the Yangtze River, the model outperforms state-of-the-art baselines across short-, medium-, and long-term horizons. Uncertainty analysis reveals periods of high predictive variance, offering insights into hydrological dynamics and model reliability. This framework contributes to advancing data-driven hydrological modelling by combining temporal learning with uncertainty quantification and is transferable to other basins and variables.

Deforestation profoundly alters watershed hydrology, degrading infiltration capacity, reducing soil moisture retention, amplifying runoff dynamics, increasing flood risks and decreasing groundwater recharge. This study compared hydrological implications in two climatically similar but ecologically distinct basins in the Western Himalayas: the Kunhar watershed, characterised by stable forest cover, and the Swat Watershed, with progressive forest loss coupled with rapid urban expansion. Using the Digital Watershed Model (DWM) integrated with the Ensemble Kalman Filter (EnKF) for data assimilation, the hydrological behaviours were quantified across these basins. Results identified a critical threshold of 62% forest cover (with an uncertainty range of ±5%), below which hydrological stability is compromised. While applying EnKF data assimilation significantly improved model accuracy, reducing peak flow errors by 52% in Kunhar and 38% in Swat. Monte Carlo simulations ensured statistically robust identification of critical forest cover thresholds that influence watershed stability. This study provides new insights into the hydrological consequences of forest loss in the Himalayas and proposes actionable thresholds for maintaining forest cover to ensure watershed stability.

Analysing the hydrological regime of watersheds and driving factors is important for understanding hydrological processes. This study used hydrological data from 1986 to 2018 from the Luoyugou (LYG) watershed to quantitatively analyse the hydrological regime and contribution rates of driving factors using the range of variability approach (RVA) and the Soil and Water Assessment Tool (SWAT) model, Budyko and two empirical methods. The results show that: (1) The degree of hydrological alteration (DHA) for most ecologically relevant hydrologic indicators (ERHIs) was 68.16% and 69.13%, indicating a high degree of alteration. The hydrological regime and river ecosystem have undergone significant changes. (2) Under the influence of human activities, the flood sediment load decreased by 66.74% and the flood sediment-carrying capacity increased significantly. (3) Large-scale human activities, such as land use/cover change (LUCC) and soil and water conservation measures (SWCMs), are the dominant factors causing changes in the hydrological regime. The SWAT model and double mass curve (DMC) method have higher accuracy of the contribution rate than Budyko and the linear regression model (LRM) method, as the latter two have limitations that may lead to an underestimation of contributions. This study can serve as a reference for assessing hydrological regimes and the impacts of ecological restoration in other regions worldwide.

Urbanisation profoundly disrupts natural hydrological connectivity (HC), yet its spatiotemporal dynamics in urban watersheds remain insufficiently explored. This study introduces a novel tracer-based framework that separates total, natural, surface, and shallow subsurface HC using a process-based distributed model (VELMA). Unlike conventional approaches that simulate integrated flows, our method separates pipe-influenced routing to reveal the ecological structure of natural HC, which is critical for watershed resilience. By assigning unique tracers to each sub-catchment, the approach provides spatially explicit and temporally resolved insights into connectivity patterns that conventional aggregate runoff metrics cannot capture. Application to a highly urbanised watershed in Seattle, USA (0.67 km², 53% impervious) reveals several novel dynamics. Subsurface HC persists during dry periods and can exceed surface HC once antecedent moisture passes a soil filling threshold, demonstrating a clear memory effect, whereas extreme storms suppress subsurface contributions due to rapid surface runoff. Spatially, connectivity emerged not only downstream but also in unexpected upstream areas, while some mid to downstream units remained disconnected. During dry periods, when forest sub-catchments were disconnected, several upstream urban sub-catchments sustained measurable HC, with upstream areas showing stronger connectivity than downstream regions, underscoring the role of hidden subsurface pathways and drainage alignments. These findings demonstrate that drainage infrastructure obscures and reshapes natural connectivity, while the tracer-informed framework reveals threshold behaviours, state-dependent persistence, and spatial anomalies otherwise hidden in urban watersheds. Beyond methodological innovation, the results provide a diagnostic basis for identifying vulnerable or restoration-prone sub-catchments, offering new insights for resilience-oriented urban water management.

Glaciers worldwide, including those in the Himalaya, are retreating under climate change, often leading to the formation and expansion of glacial lakes and an increased risk of Glacial Lake Outburst Floods (GLOFs). This study examines the evolution of the proglacial Bhilangana Lake (~0.37 km²; ~4750 m asl) and the associated glacier changes, including thinning and retreat, between 1968 and 2025 using satellite imagery, field measurements and hydrodynamic modelling. Results show lake expansion from ~0.12 km² in 2001 to ~0.37 km² in 2025, with an estimated volume of ~10.7 × 10⁶ m³, indicating exponential growth over time. A potential GLOF could release peak discharge of ~3645 m³/s, with average flow velocity ~12 m/s, inundating ~6.8 km² and threatening hydropower projects, settlements and infrastructure downstream. Rising mean, maximum and minimum air temperatures at rates of 0.028, 0.052 and 0.05°C/year, respectively, are identified as the primary drivers of lake expansion and accelerated melt, particularly in July–August. The zero-degree isotherm is shifting to higher elevations, and bias-corrected ERA5 data show good agreement at such altitudes, making it reliable for climate change analysis. Several over-deepening sites were also mapped as potential future lakes, with CMIP6 projections (~0.8°C/decade) indicating substantial glacial and hydrological changes, further elevating GLOF risk by the century's end.

This study investigates the spatiotemporal evolution of hydrological connectivity in China's highly urbanised Taihu Lake Basin from 1985 to 2020, integrating remote sensing, graph theory, and landscape metrics to assess structural and functional connectivity dynamics. Results reveal a 7.85% decline in total water area and a 44.9% reduction in river-network density (2005–2020), with structural connectivity initially improving (α: +81%, 1985–2005) before declining by 6%–17% (2015–2020). Functional connectivity exhibited strong spatial heterogeneity, with high-connectivity zones concentrated in the eastern basin (IC > −3.0) and degraded areas in urban centres (IC < −5.0). Basin-wide mean IC correlated positively with mean annual rainfall (r = 0.59), especially in the HX region (r = 0.88). The construction of hydraulic works in the Taihu Basin has focused chiefly on channel rectification, inter-basin diversion, and polder management, with various quantities in different regions. Therefore, human impacts varied spatially: inhibiting connectivity (CC < 0, Trend CC < 0) in the southeast but generally promoting it elsewhere (north, Trend CC > 0), revealing regional disparities in anthropogenic effects. The study advances prior research by providing the first 35-year integrated assessment and offers a policy-relevant template for balancing hydrological integrity with urbanisation pressures in densely populated river–lake systems.