Hydrological Processes最新文献

Springs provide critical water resources that are sensitive to changing climate and catchment processes. In many regions, understanding the temporal variability and spatial distribution of spring discharge is therefore crucial for sustainable water management. Knowledge of these discharge characteristics, organised in a coherent framework, is essential for protecting spring water and preventing shortages. To establish such a framework, we conducted a comparative analysis of long-term discharge records from 96 springs across Austria. Based on discharge seasonality and autocorrelation, we derived a broad-scale classification through cluster analysis and explored associations between individual clusters. The identified similarities in discharge patterns were grouped into four distinct spring categories, each demonstrating common behaviour. To determine the main factors influencing discharge across these four groups, we compared their spatial and temporal patterns with regional climate and catchment characteristics. They align with physical drivers of spring discharge, including precipitation frequency and intensity, snow cover duration, and dominant aquifer type. As these factors were not included in the classification procedure, their alignment supports the validity of our statistical approach. We conclude that the quantitative information derived from this analysis provides a valuable complement to traditional spring classification schemes, which are often based on qualitative knowledge. Our proposed strategy refines these classification approaches, enhances objectivity and reproducibility, and promotes conformity across hydrological disciplines.

Increasing interest in solute transport phenomena in agricultural systems on a sub-annual basis necessitates a better understanding of seasonal changes in natural systems and how these changes can be incorporated into modelling. A better understanding of the seasonal timing of nutrient loading in tile drained agricultural systems in particular is essential for efforts trying to replicate or predict the occurrence of harmful algal blooms. Literature exists showing there are seasonal dynamics (freeze–thaw, plant-root processes, land management practices, etc.) that may cause changes in the hydraulic properties of the soil zone including hydraulic conductivity and porosity. To test whether these changes are important in an agricultural system, a MODFLOW-6 model using the unsaturated zone flow package was constructed. The simulation was comprised of separate, seasonal models to be run sequentially with each year being broken into a winter and summer seasons. As part of this architecture, model parameters representing soil hydraulic properties were allowed to vary by season. The model was calibrated against soil moisture observations at multiple depths using a genetic algorithm machine learning technique. The parameters of the sub-models were compared for the winter and summer seasons. Brook-Corey epsilon, saturated vertical conductivity, saturated volumetric water content and residual volumetric water content were found to be consistently different between the modelled summer and winter periods. A more traditional model which did not allow hydraulic properties to vary seasonally was also run and compared to the seasonal architecture and the seasonal architecture was found to improve simulation results. The hydrologic dynamics of the unsaturated zone—particularly in tile drained agricultural systems—control the residence time for water and solutes, which is critical for in-field chemical processes such as denitrification. This work has important implications for seasonal transport phenomena in agricultural systems and improving the simulation and prediction of harmful algal blooms.

At-a-station hydraulic geometry (AASHG) relationships describe the dependence of a river's width, mean depth and mean velocity on discharge at a given location, and are typically modelled as power-law functions. They are often used when modelling stream temperature under unsteady flow conditions. Deriving AASHG relationships is challenging for steep proglacial streams due to the combination of complex morphology and velocity distributions, and rapidly varying flow. The objective of this study was to combine tracer injections with drone-based photogrammetry to derive AASHG relationships for a steep proglacial channel and to quantify whitewater coverage and its relationship with discharge to support process-based stream temperature modelling. Velocity–discharge and width–discharge relationships were reasonably well characterised using power-law functions, but varied amongst sub-reaches. Whitewater coverage as a fraction of total stream surface area generally exceeded 50% for the range of flows sampled, and exhibited a statistically significant positive relationship with discharge, which varied amongst sub-reaches. For the range of flows captured during drone flights, the relationship could be represented by a linear function. However, an asymptotic model would be required to extend the relationship to higher flows. The magnitude of whitewater coverage indicates that the albedo of the stream should be substantially higher than values typically used in stream temperature models, and the relationship with discharge means that ongoing glacier retreat, and the associated reduction in summer discharge, should result in lower albedo and higher downstream warming rates, reinforcing the effects of decreasing velocity and mean depth as flows decline.

This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analysed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees (r/h), in order to classify the gaps as small-size, medium-size, large-size, or open areas at both sites independently. Then Kendall's correlation coefficients between the snow depth, FSCG and topographic variables were computed and a Random Forest (RF) model for each survey was implemented, to determine the influence of these variables in explaining snow depth patterns. The results demonstrate the consistency of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At the northeast exposed Site 1, the larger the r/h observed, the greater was the snow depth obtained. This pattern was not evident at the southwest exposed Site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth and was consistently the highest-ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at Site 1. The findings suggest differences in the main drivers throughout each site and surveys of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains.

The urgency of understanding the intricate input–output relationships of the hydrologic cycle is amplified by the accelerating climate change impacts in mountain environments. This study focuses on optimising water resource management of a dammed valley in the Central Alps (Northern Italy). The research aims to integrate radar data and precipitation interpolation techniques (TIN, Copula, cumulative distribution function; CDF techniques, inverse distance weighting; IDW, thin plate spline; TPS, ordinary kriging; OK and detrended kriging; DK) into a semi-distributed hydrologic model, by utilising hourly precipitation data from 22 rain gauges and a composite weather radar product spanning 2010–2020. Two main objectives were pursued: (i) to develop and evaluate various radar precipitation correction methods against a benchmark dataset and (ii) to calibrate and assess the performance of the GEOFrame hydrologic model forced with corrected precipitation input. Point-based and spatial correction approaches were evaluated against ground measurements through leave-one-out tests. The former derives dependence functions between the biased radar series and those of the closest three rain gauges to the target point applying a triangular irregular network. The latter combines deterministic and geospatial interpolations to the rain gauge/radar residuals to derive a corrected surface by incorporating radar values as trends. Precipitation series exceeding the composite scaled score of the benchmark dataset were used as input for hydrologic modelling. The spatial method combining radar values with ordinary kriging provided the best results for both correction and modelling (hourly KGE > 0.75). The spatial approaches proved easier to apply than the point-based methods. In addition, correcting precipitation significantly improved low-flow simulation from negative hourly lnNSE to values greater than 0.25. As a further step, given the overall good performance of the spatial methods, they could be used operationally as an ensemble to analyse management scenarios.

Saturation development and distribution at the soil–bedrock interface are important for predicting shallow landslide occurrence. Previous studies have indicated that saturation is generated in bedrock depressions and valleys and that bedrock groundwater seepage generates locally saturated areas. However, the effects of soil permeability, which is known to be heterogeneously distributed, on saturation development and distribution are poorly understood. In this study, we performed unprecedented high-resolution (approximately 50 cm grid) soil pore water pressure and soil temperature monitoring using 141 tensiometer–thermocouple sets in a plot measuring approximately 5 × 4 m to investigate the effects of topography and bedrock groundwater seepage on saturation development and distribution. We then measured permeability distribution of two soil profiles, including at the soil–bedrock interface, using the Guelph Permeameter method (GP method) for comparison with saturated zone distribution and saturation duration. The results indicated that a perennial saturated area was formed by bedrock groundwater seepage and was distributed downstream from a certain bedrock surface altitude in the lower region of the study plot. After a peak of rainfall, the perennial saturated area expanded upslope owing to the increased seepage. In areas without the influence of bedrock groundwater, saturation was observed to retreat rapidly at high permeability points and persist over long periods at low permeability points; however, the saturation duration was inconsistent with the bedrock surface topography. Therefore, it is suggested that the bedrock altitude controls the saturation distribution generated by bedrock groundwater, whereas the distribution of saturation that is associated with direct rainwater infiltration may be controlled by the permeability distribution during recession periods. Although the plot size was small, the unprecedented high-resolution observations suggest that the permeability distribution, rather than the bedrock topography, may control the saturated zone distribution following rainfall.