Hydrological Processes最新文献

Stemflow redistributes rainfall entrained on canopy surfaces, creating spatially concentrated chemical and biological fluxes, yet the relative roles of its principal meteorological drivers remain uncertain. We monitored stemflow for 15 mature trees (5 each of Fagus grandifolia, Acer saccharum, Liriodendron tulipifera) across 40 storms (April–November 2024) in a closed-canopy forest (Holden Arboretum, Ohio, USA) and normalised responses by LiDAR-derived stemflow drainage areas obtained with a pruning algorithm (CanoPyHydro). A linear mixed-effects model tested rain amount, species (as a proxy for bark roughness within the context of branch architecture derived from CanoPyHydro), and their interaction; the relationships between residual structure (derived from separate OLS regressions) and other meteorological drivers were investigated using PCA including eight pre−/in-storm meteorological descriptors. Rainfall amount dominated variability and interacted with species: rain (p < 0.001), species (p = 0.045), and rain × species (p < 0.001) were significant. Species-wise OLS fits showed high explanatory power of rain alone (R²: beech = 0.84, maple = 0.79, poplar = 0.75) and a steeper rain-stemflow slope for beech (0.072 mm stemflow per mm rain) than for maple or poplar (0.03 mm mm⁻¹). PCA summarised non-rain structure along a temperature/radiation axis and a pressure-change axis; only beech exhibited a weak residual association with these axes (adj. R² = 0.137, p = 0.025). Occult (non-rain) inputs were detected for all species and were largest for beech. Results indicate that, in closed-canopy temperate forests, event-scale stemflow is primarily set by rain amount and bark class, with fine-scale architectural effects muted; modest, species-specific non-rain influences likely act through bark storage and near-bark vapour processes. Incorporating explicit bark storage and non-rain inputs into interception models should improve predictions of stemflow-mediated water (and solute) delivery to near-stem soils.

Dynamic surface water wetting and drying are characteristic of most headwater stream networks, influencing ecological function and downstream water quality. While topography and subsurface properties have been shown to influence spatial surface water persistence, it is unclear how sensitive non-perennial stream network flow regimes are to persistent drought conditions. To address this unknown, we monitored surface water presence and absence for three consecutive drought years at 31 locations across a 0.25 km² non-perennial headwater stream network in central coastal California. We coupled these observations with landscape characteristics and climatic conditions to examine both physical and climatic drivers of spatiotemporal flow activation and persistence. We observed non-stationarity in flow regimes throughout the stream network; reaches that were characterised by seasonal flow in 1 year became ephemeral as the drought conditions progressed. We observed declining spatial variability in surface water persistence and declining correlations between persistence and landscape attributes with ongoing drought. From year one to year three, as drought conditions intensified, correlation coefficients between surface water persistence and topographic wetness index declined from r = 0.60 to r = 0.17, stream channel slope declined from r = 0.47 to r = 0.21, profile curvature declined from r = −0.51 to r = −0.21, and elevation declined from r = −0.33 to r = −0.06. A principal component analysis suggests that, unlike prior years, flow activation events after several years of drought were more closely associated with precipitation event characteristics than antecedent storage states. This work suggests semi-arid landscapes such as central coastal California may see shifts in the flow regimes of aquatic systems as landscape aridity intensifies with climate change. As predictive indicators of flow may shift from physical to climatic factors, the timing, intensity and frequency of individual storm events may play increasingly larger roles in driving annual-scale flow conditions.

Stemflow redistributes rainfall entrained on canopy surfaces, creating spatially concentrated chemical and biological fluxes, yet the relative roles of its principal meteorological drivers remain uncertain. We monitored stemflow for 15 mature trees (5 each of Fagus grandifolia, Acer saccharum, Liriodendron tulipifera) across 40 storms (April–November 2024) in a closed-canopy forest (Holden Arboretum, Ohio, USA) and normalised responses by LiDAR-derived stemflow drainage areas obtained with a pruning algorithm (CanoPyHydro). A linear mixed-effects model tested rain amount, species (as a proxy for bark roughness within the context of branch architecture derived from CanoPyHydro), and their interaction; the relationships between residual structure (derived from separate OLS regressions) and other meteorological drivers were investigated using PCA including eight pre−/in-storm meteorological descriptors. Rainfall amount dominated variability and interacted with species: rain (p < 0.001), species (p = 0.045), and rain × species (p < 0.001) were significant. Species-wise OLS fits showed high explanatory power of rain alone (R²: beech = 0.84, maple = 0.79, poplar = 0.75) and a steeper rain-stemflow slope for beech (0.072 mm stemflow per mm rain) than for maple or poplar (0.03 mm mm⁻¹). PCA summarised non-rain structure along a temperature/radiation axis and a pressure-change axis; only beech exhibited a weak residual association with these axes (adj. R² = 0.137, p = 0.025). Occult (non-rain) inputs were detected for all species and were largest for beech. Results indicate that, in closed-canopy temperate forests, event-scale stemflow is primarily set by rain amount and bark class, with fine-scale architectural effects muted; modest, species-specific non-rain influences likely act through bark storage and near-bark vapour processes. Incorporating explicit bark storage and non-rain inputs into interception models should improve predictions of stemflow-mediated water (and solute) delivery to near-stem soils.

Stable isotopes of hydrogen and oxygen in precipitation provide insights into water cycle dynamics; yet, characterising the meteorological processes driving isotopic variability remains challenging. We introduce a method to rasterize air mass trajectory data from HYSPLIT for isotopic analysis and assess the impact of trajectory initiation height on HYSPLIT's representation of local precipitation and temperature. Incorporating HYSPLIT rasters into spatial analysis improved deuterium composition isoscape accuracy for precipitation compared with a model relying solely on surface predictors (i.e., temperature, elevation). The resulting isoscape patterns reflected strong, temperature-dependent seasonality, aligning well with previous studies. Trajectory cluster analysis further elucidated the links between meteorological patterns and observed seasonality in isotope compositions. We also analysed daily precipitation at two sites (n = 204, n = 138) over ~2 years using Random Forest and Multiple Linear Regression. Our analysis identified a significant amount effect and evidence for sub-cloud evaporation. However, additional factors—such as cloud-top temperature, reflectivity, and convective versus stratiform fractions—are likely needed to explain residual daily-scale variance. Finally, we determined that a HYSPLIT model initiation height of 1000 m was sufficient for our study domain. Using the local condensation level as the initiation height provided no substantial improvement in correlations between modelled and observed precipitation or temperature.

Hydrological models have become fundamental in groundwater recharge estimation, but they require large amounts of input data that are often lacking in the regions needing these studies the most. Innovative solutions are needed to constrain groundwater flow contribution to streamflow, despite the lack of fundamental hydroclimatic data. In this study, we assess four calibration procedures simulating groundwater contribution to streamflow in the semi-arid, data-deficient Goukou catchment of South Africa. The model realism is tested through a complementary evaluation with stable isotopes. Groundwater contributions were simulated after calibration with the non-dominated sorting genetic algorithms-2 (NSGA2), DiffeRential Evolution Adaptive Metropolis (DREAM), Monte Carlo analysis (MCA), and Latin Hypercube (LHC) calibration procedure in the fully distributed, conceptual rainfall-runoff model J2000. DREAM performed best in finding a parameter set with a good balance between a realistic groundwater flow proportion as published for the Table Mountain Group aquifer (~31%), while maintaining fair Nash Sutcliffe efficiencies (~0.38) for streamflow. Isotopic characterisation showed that the catchment is recharged episodically during the most intense rainfall events, with precipitation to groundwater ratios all having negative values. The isotopes corroborated modelled groundwater contribution to streamflow in the upper part of the catchment, but results suggested over-estimation of groundwater contribution to streamflow by J2000 in the middle section of the catchment. Isotopes proved effective in validating model results and detecting shortcomings in the J2000 model due to sparse hydroclimatic data and the catchment's position in a transition zone between winter and summer rainfall. We also provided evidence that the calibration procedure selection should be carefully considered in data-scarce circumstances, as good model efficiency does not necessarily guarantee a realistic representation of hydrological processes.

Stream temperature plays a fundamental role in the functioning of aquatic ecosystems and water quality, yet it remains sparsely monitored in many regions, including Brazil. Consequently, empirical air-stream temperature models are often applied without adequate evaluation of their suitability to local hydroclimatic conditions. This study evaluates stream temperature dynamics and the performance of empirical air-stream temperature models in three subtropical headwater catchments in southern Brazil. Linear and nonlinear regressions incorporating backward moving averages (BMA) were tested to represent delayed and attenuated stream temperature responses associated with thermal inertia. The results reveal strong seasonal patterns, with daily averages capturing the dominant thermal signals. Hydrological processes characteristic of subtropical headwaters (such as thermal inertia, groundwater buffering and riparian shading) were found to shape the air-stream temperature relationship and limit the transferability of widely used models. Although locally calibrated equations outperformed those from the literature, underscoring the importance of context-specific modelling, the analysis also provides broader insight: empirical models that account for lagged responses (e.g., BMA) tend to be more robust in systems with pronounced thermal damping. These findings help bridge a regional knowledge gap and offer guidance for adapting stream temperature models to other tropical and subtropical regions with limited data availability, supporting improved water resource management and ecological assessments.