Hydrological Processes最新文献

Sediment sources and sinks are an effective reflection of the comprehensive management of soil and water conservation in a watershed. However, human interference has made the sediment transport process in watersheds more complex. Research that distinguishes hillslopes and channels to reveal changes in sediment sources and sinks within a watershed and the relationships with key driving factors requires strengthening. In this study, the characteristics of sediment sources and sinks on hillslopes in the Mahuyu watershed, located on the Loess Plateau, were simulated using the Revised Universal Soil Loss Equation model and hillslope sediment delivery ratio model. Furthermore, variations in channel scour and siltation at the event scale were analysed based on the simulated hillslope sediment yield and measured sediment yield at the outlet station. Additionally, the principal hydrological driving factors affecting the sediment yield at the outlet were explored. The results show that 66 sediment yield events occurred in the Mahuyu watershed during the period 2006–2018, and channel sediment yield has emerged as the leading contributor to the watershed sediment yield, accounting for a minimum of 69.8%. There is also a marked decoupling between hillslope sediment yield and watershed sediment yield in the Mahuyu watershed. Furthermore, the maximum daily average streamflow is identified as the critical driving factor responsible for determining the watershed sediment yield, indicated by a coefficient of determination of 0.850. Therefore, we recommend that the future focus of soil and water conservation measures should be shifted from hillslopes to channels.

The essay describes how a combination of scaling theory from percolation, that relates pore scale flow and transport through catchment scales to global scales (bottom-up), as well as water fluxes to soil formation and vegetation growth, can be used to support an accurate ecological optimization that (top-down): solves the central problem of hydrology, that is., “the water balance,” and generates critically important derived quantities, namely streamflow response to climate change, net primary productivity, and plant species richness. Moreover, the essay describes how this particular theoretical approach came to be designed and how it, in retrospect, fits in with the vision of the Committee on Opportunities in the Hydrologic Sciences which met 34 years ago to formulate a research, teaching, and infrastructure guide for the community, and “rebrand our science as a geoscience.” Finally, it demonstrates how the research satisfies the present desires of the community to unite Darwinian and Newtonian scientific methods in the solution of this central problem and how it relates to present research directions in the fields of hydrologic sciences and ecology.

In the western United States, water supplies largely originate as snowmelt from forested land. Forests impact the water balance of these headwater streams, yet most predictive runoff models do not explicitly account for changing snow-vegetation dynamics. Here, we present a case study showing how warmer temperatures and changing forests in the Henrys Fork of the Snake River, a seasonally snow-covered headwater basin in the Greater Yellowstone Ecosystem, have altered the relationship between April 1st snow water equivalent (SWE) and summer streamflow. Since the onset and recovery of severe drought in the early 2000s, predictive models based on pre-drought relationships over-predict summer runoff in all three headwater tributaries of the Henrys Fork, despite minimal changes in precipitation or snow accumulation. Compared with the pre-drought period, late springs and summers (May–September) are warmer and vegetation is greener with denser forests due to recovery from multiple historical disturbances. Shifts in the alignment of snowmelt and energy availability due to warmer temperatures may reduce runoff efficiency by changing the amount of precipitation that goes to evapotranspiration versus runoff and recharge. To quantify the alignment between snowmelt and energy on a timeframe needed for predictive models, we propose a new metric, the Vegetation-Water Alignment Index (VWA), to characterize the synchrony of vegetation greenness and snowmelt and rain inputs. New predictive models show that in addition to April 1st SWE, the previous year's VWA and summer reference evapotranspiration are the most significant predictors of runoff in each watershed and provide more predictive power than traditionally used metrics. These results suggest that the timing of snowmelt relative to the start of the growing season affects not only annual partitioning of streamflow, but can also determine the groundwater storage state that dictates runoff efficiency the following spring.

Floodplains are one of the most threatened ecosystems. Even though the vegetation composition in floodplain forests is expected to reflect the variation in groundwater levels and flood duration and frequency, there is little field data on the inundation dynamics (e.g., the variability in flood duration and flood frequency), especially for the understudied seasonally dry tropics. This limits our understanding of these ecosystems and the mechanisms that cause the flooding. We, therefore, investigated six floodplain forests in the state of Minas Gerais in Brazil for 1.5 years (two wet seasons): Capivari, Jacaré, and Aiuruoca in the Rio Grande basin, and Jequitaí, Verde Grande, and Carinhanha in the São Francisco basin. These locations span a range of climates (humid subtropical to seasonal tropical) and biomes (Atlantic forest to Caatinga). At each location, we continuously measured water levels in five geomorphologically distinct eco-units: marginal levee, lower terrace, higher terrace, lower plain, and higher plain, providing a unique hydrological dataset for these understudied regions. The levees and terraces were flooded for longer periods than the plains. Inundation of the terraces lasted around 40 days per year. The levees in the Rio Grande basin were flooded for shorter durations. In the São Francisco basin, the flooding of the levees lasted longer and the water level regime of the levees was more similar to that of the terraces. In the Rio Grande basin, flooding was most likely caused by rising groundwater levels (i.e., “flow pulse”) and flood pulses that caused overbank flooding. In the São Francisco basin, inundation was most likely caused by overbank flooding (i.e., “flood pulse”). These findings highlight the large variation in inundation dynamics across floodplain forests and are relevant to predict the impacts of changes in the flood regime due to climate change and other anthropogenic changes on floodplain forest functioning.

Water is essential for humans as well as for all living organisms to sustain their lives. Therefore, any climate-driven change in available resources has significant impacts on the environment and life. Global climate models (GCMs) are one of the most practical methods to evaluate climate change. Based on this, this research evaluated the capability of GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical flow of climate prediction centre data for the Konya Closed basin and to project the climate of the basin using the selected GCMs. Global climate models based on the CMIP6 under the scenario of common socioeconomic pathways (SSP245 and SSP 585) were used to analyse the climate change effect on streamflow of the study area by Bias Correction of GCM Models using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), AdaBoost, Gradient Boosting, Regression Tree, and Random Forest methods. The coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) were used to assess the performance of the methods. Findings show that the Random Forest Model consistently outperformed other models in both the testing and training phases. A significant downward in the volume of water flowing through the region's rivers and streams in the next decades. It is critical to enhance climate-resilient water infrastructure financing, establish an early warning system for drought, introduce best management practices, implement integrated water resource management, public awareness, and support water research to alleviate the negative consequences of drought and increase resilience against the effects of climate change on Turkey's water resources.

While green roofs have gained widespread popularity as a measure to detain and retain runoff in urban areas, their performance during extreme events is not well studied. In this study 15 years of runoff and precipitation observations from a small extensive green roof in Norway are analysed. GEV-distributions were fitted to the annual max values for precipitation and runoff in order to develop intensity-duration-frequency (IDF) and runoff-duration-frequency (RDF) data. Using the IDF and RDF data a total of 31 extreme events were identified (containing precipitation or runoff values with return period greater than 2 years for one or more durations). While nearly all extreme runoff events were caused by extreme precipitation, only 69% of the extreme precipitation events resulted in extreme runoff. The assumption of 1:1 equivalency of return periods did not hold true, and deviations were mainly explained by variations in substrate water content prior to the extreme event. Moreover, in 50% of the events, the runoff duration with the greatest return period was shorter than the precipitation duration with the greatest return period. Hence, the results indicate that the use of design storms to predict runoff from green roofs may be inappropriate. The potential of having IDF and RDF data available was demonstrated by the development of simple empirical equations, which ensure conservations of both return period and duration. To generate reliable green roof RDF data, future research should prioritize evaluating various continuous models with the aim of accurately describing extreme events.

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.