Hydrological Processes最新文献

Low-flow events, characterized by a significant water deficiency in river systems, have profound impacts on various water users and river ecology. Recent low-flow events in Europe have had severe economic and ecological consequences such as disruptions to hydropower production, irrigation bans, constraints on navigation and complete river drying. These events highlight the urgent need for effective low-flow risk management and demand a holistic risk analysis as a basis. The existing approaches to low-flow analysis often focus on hydrological aspects, utilizing indices such as the Standardized Runoff Index (SRI) or Low-flow Index. However, these indices lack information regarding consequences and impacts. Other approaches consider parts of a risk approach but often focus on special aspects, such as the economy; in general, no holistic assessment is made. This study introduces a conceptual approach to a holistic low-flow risk analysis. The approach provides a continuous long-term simulation to capture the special long-term behaviour of low-flow events and therefore avoids the complex definition of scenarios. In this conceptual approach, the low-flow risk is analysed using a combination of various analyses that cover all aspects from occurrence to consequences. Meteorological analysis is used to generate synthetic long-term weather data time series, which are transformed into runoff time series in hydrological analysis. Based on these results, hydrodynamic analysis quantifies the water levels, water temperatures, and flow velocities along the river. The consequences are analysed in terms of socio-economic and ecological consequences. The results represent a long-term series of damage values. Finally, the damage values are summed in the risk analysis and divided by the number of years considered in the analysis. For testing and demonstration purposes, the presented conceptual risk approach is partly applied to a proof-of-concept at the Selke catchment, a small river catchment in Germany. Finally, the results are presented, evaluated, and discussed.

Post-fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post-fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post-fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration-excess overland flow and decrease in saturation-excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post-fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well-performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post-fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post-fire hydrologic hazards after wildfire occurs.

A comprehensive understanding of the hydrochemical evolution and spatial patterns of shallow groundwater systems is essential for water resource management and wetland ecological restoration. The Baiyangdian Wetland is one of the most concerning areas because of the development of the Xiong'an New Area. The spatial characteristics of groundwater hydrochemistry and potential controlling factors associated with hydrochemical evolution remain unclear. In this study, hydrogeochemistry together with the hierarchical cluster analysis were used to elucidate the hydrochemical processes and hydrological zoning patterns of shallow groundwater systems in the Baiyangdian Wetland, North China Plain. The results showed that hydrochemical compositions of shallow groundwater had considerable spatial variations, which was closely related to the inflow rivers hydrochemistry and the dynamics of groundwater–surface water interactions. A significant increase in SO₄²⁻ concentration occurring at the cone of the depression was related to extensive pumping caused by anthropogenic activities. Anthropogenic activities were also a major factor controlling the spatial distribution patterns of shallow groundwater hydrochemistry. Ca²⁺, Mg²⁺, and SO₄²⁻ in the wetland and shallow groundwater were primarily derived from carbonate and gypsum dissolution, while Na⁺ and Cl⁻ originated from halite and silicate dissolution. Rock weathering predominated the geochemical evolution of shallow groundwater in conjunction with carbonate precipitation and cation exchange. The hydrochemistry of the shallow groundwater system presented distinct spatial zonation patterns that were classified into four clusters corresponding to seven subzones. In Zones I–IV, water-rock interaction was the dominant factor controlling shallow groundwater chemistry, which was driven by the positive groundwater–surface water exchange. The coupled effects of anthropogenic activities and river infiltration and mixing caused the high levels of dissolved components in Zones V–VII. This study contributes to have a better understanding of the water cycle and hydraulic connections among different bodies, and will benefit the rational evaluation of hydrochemical evolution and wetland ecological restoration in the Baiyangdian Wetland.

In this study, we investigate the combined effect of different rainfall-runoff event types and antecedent soil moisture (ASM) on runoff processes in the headwater elementary discharge area of a small forested upland catchment. The study focuses on (i) the relationship between soil moisture thresholds and runoff generation; (ii) the combined effect of ASM and tree vicinity and (iii) the relationship between different rainfall-runoff event types and different types of runoff (baseflow and stormflow). The results suggest that ASM has a strong impact on local runoff generation processes. Soil water content (35%–36%) threshold exceedance was related to stormflow runoff generation caused by the activation of quick preferential flow paths in the soil during storm events, especially in the upper and the deepest soil layers. At the same time, unexpected non-linear increases in baseflow runoff ratios were documented during dry, precipitation-free, periods and when the 31%–34% soil moisture threshold was exceeded, presumably due to the hydrological connection of farther slope areas during these conditions. Multiple stormflow periods, which exhibited the lowest runoff coefficient, were the most significant events in terms of water retention and soil water recharge due to increased vertical hydrological connectivity enabling more rapid transport to deeper soil layers. However, this rainfall type occurred least often over the study period. The important role of forest stands (individual trees) in creating spatial patterns of soil moisture and preferential infiltration paths to deeper soil layers was also confirmed. These results contribute towards a better conceptualisation of hydrological behaviour in elementary headwater discharge areas and highlight the potential dangers associated with expected increases in extreme weather events.

Flood hazard mapping is an essential tool for determining the risk and susceptibility of flood-prone locations. This constitutes various criteria and factors that require a methodical and comprehensive decision-making framework. The analytic hierarchy process (AHP), the popular multi-criterion decision-making (MCDM) technique, which deals with the complicated problems, including qualitative and quantitative factor, is utilized in this study for developing flood hazard maps in integration with geographic information system (GIS). Flood hazard maps are developed for the Krishna River basin using AHP-GIS. Digital elevation model, rainfall data, soil data and Landsat images are used to extract the various hazard indicators. Nine hazard indicators are employed in this study to prepare the thematic layers, which includes Topographical Wetness Index, elevation, slope rainfall, LULC, Normalized Difference Wetness Index, distance from river, soil type and drainage density. The thematic layers are combined in Arc GIS using weighted overlay method to prepare the flood hazard zonation maps. The hazard maps are classified in three categories as low, moderate and high hazard zones. For the Krishna River basin, 30 percent area was found in high hazard. The outcomes derived from the AHP-GIS method are validated by comparing them with situational reports pertinent to the area, showcasing a robust agreement with the available dataset.

Climatic forcing and landscape properties control catchments' hydrological responses over both seasonal and mean annual timescales. Controls on annual and seasonal water balances are usually studied separately which limits and fragments understanding of catchment behaviour. Establishing process controls on hydrological responses that act across multiple time scales could better unify hydrological theory. Here, we use streamflow and climate data from 56 catchments in the Ozarks and Appalachian regions (US) to test how climate (aridity index) and soil drainage together shape seasonal and mean annual water balances for these humid, snow-free catchments with little precipitation seasonality. We calibrate a simple conceptual model to observed seasonal streamflow and obtain an effective parameter that summarizes the nonlinearity of drainage of soil moisture to groundwater. Our comparative analysis of catchments in the Ozarks and the Appalachian regions indicates that catchments in the more humid climates have lower streamflow seasonality and higher mean annual flow, irrespective of the nonlinearity of soil drainage. In contrast, in relatively drier climates, more nonlinear soil drainage increases the seasonality of streamflow and reduces mean annual flow. Additional testing across 204 humid catchments with little precipitation seasonality and snowfall in the Southeastern United States further supports our hypothesis that soil drainage nonlinearity significantly modulates seasonal and mean annual water balances. These results reveal how soil drainage nonlinearity provides a process interpretation that connects seasonal and annual water balances and highlights the importance of nonlinearity of soil drainage in hydrological modelling.

Riverine water temperature (WT) is a crucial factor affecting habitat quality and ecological effect of aquatic ecosystems. To accurately quantify and classify WT variation features caused by climate change and reservoir construction and operation, a framework was developed that integrates multivariate vine copula model for accurately reconstructing the WT process and general evaluation indicators for comprehensively characterizing of WT variation. In this framework, month-wise R-vine copula models were employed to depict the multivariate dependence structure between WT and related hydrometeorological factors, and the change of WT process in the fluctuation range and thermal deviation was analogized as the change of simple harmonic wave in amplitude and phase. A testing-oriented application of this framework in Yichang section of the Yangtze River highlighted that climate change and the Three Gorges Reservoir (TGR) dominated or participated in the fluctuation range changing and phase deviation of different monthly WT processes, as the ratios of affected months were 1.08:1 and 1.25:1 during the construction phase, and 1:2 and 1:1.28 during the operation phase. WT process also exhibited diverse monthly variation trends during construction and operation phases of the TGR. Therefore, it is inappropriate to neglect the impact of the TGR construction phase and climate change on WT variation. The proposed framework achieved systematic quantification and attribution analysis of WT variation, thereby providing an enhanced understanding of the variation characteristics of river thermal regimes under the individual and combined effects of climate change and artificial reservoir. Considering the significant influence of WT variation on aquatic organism reproduction, the identification of the sources and categories of monthly WT variation can also serve as a foundation for future targeted thermal and hydrological regime regulation, aiming to protecting aquatic species and preventing biodiversity loss.

Hydrological data-driven models require the time series of several hydrological events with different time resolutions. The interpretation of any time series event is generally difficult without some sort of filtering or converting it to a single index value. The simultaneous analysis of two or more hydrological events over a definite time span may be more informative about the region of interest. For this purpose, a new index, referred to as the successive coincidence deficit index (SCDI), was introduced to identify sinkhole-prone regions using the persistent water deficit concept. In this study, monthly integrated multi-satellite retrievals for GPM based precipitation (P) and gravity recovery and climate experiment-based groundwater storage (GWS) datasets over Konya Closed Basin (KCB) in Türkiye were used to analyse the sinkhole occurrence. The main finding of this study is that SCDI distribution with high index values, concentrated on the southwestern part of KCB, is in line with the sinkholes occurred mainly after 2010. The proposed SCDI could also serve as a kind of drought index, which enables practitioners to quantify the relationship between drought and sinkhole occurrence. Moreover, the event coincidence analysis was utilized to detect deficiency in GWS over the KCB, which was associated with a rate of 0.8 for P deficiency, and this rate reaches up to 0.9 in the sinkhole region analysed in this study. As a conclusion, the proposed methodology can detect sinkhole-prone regions to construct risk maps for stakeholders, policymakers, and end users.

The relationship between catchment storage and discharge is nonlinear. The dynamicity of this relationship is dependent on the distance of the storage measurement from the stream, the depth of the soil moisture (SM) measurement, antecedent SM storage, and precipitation characteristics. Understanding the relative influence of these factors is critical for interpreting runoff generation processes and predicting discharge. In this study, we used a hysteresis index approach and analysed the nonlinear dynamics of catchment storage–discharge relationship across points, hillslope and catchment scales, and their controlling factors. A small headwater forested catchment located in the southern part of the Sierra Nevada region, California, was selected as a case study. In this Mediterranean catchment, the anticlockwise class IV hysteresis loop, indicating an earlier discharge peak than SM, was observed as a prevalent hysteresis class across all the scales, irrespective of seasons (i.e., dry vs. wet) and years (i.e., normal vs. drought). A few clockwise hysteresis loops were observed at the shallow depths (10 and 30 cm) of upslope and lower slope topographic positions. Further, we found a shorter lag time between SM peak to discharge peak at 60 and 90 cm soil depths during the wet season, and during the drought period. The shorter lag at the deeper depth was due to the presence of subsurface flow during high antecedent SM storage conditions and preferential flow through the soil pores during the drought periods. The variability in hysteresis at catchment and hillslope scales was controlled by peak rainfall intensity and antecedent SM storage. However, rainfall characteristics (intensity and depth) were major governing factors for most of the point scale locations. Overall, the current study highlighted the role of SM sensor's location in characterizing storage–discharge behaviour.

Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large-scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS-Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long-term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments.