Hydrological Processes最新文献

Mountain meadows are ecologically important groundwater dependent ecosystems that retain and store water in upland forested landscapes. They tend to occur in low gradient, broad valleys where water slows and sediment accumulates, making them efficient locations for restoration. Over a century and a half of land use has degraded many meadows in the Sierra Nevada, reducing their hydrological and ecological functionality. Process-based restoration (PBR) is an ecosystem rehabilitation approach that utilises biogeomorphic processes to facilitate functional ecosystem recovery. Low-tech applications of PBR leverage fluvial processes, plant growth and the manipulation of onsite materials to increase structural and hydrological complexity. In meadows, typical goals associated with restoration are to increase groundwater elevations, expand wetted area, encourage sediment capture and create diffuse flow paths leading to improved ecological function over time. This study compares surface and groundwater conditions in a degraded riparian meadow in the Sierra Nevada, California, USA for 1 year before and after process-based restoration to understand initial changes in meadow hydrogeomorphic function. Restoration included the installation of 39 postless beaver dam analog structures in ~1 km of incised meadow channel. Stage-discharge data at the inlet and outlet of the project area were paired with groundwater data collected from 13 wells distributed across the meadow to estimate increased water storage of 3700 m³ due to restoration. After the wet winter of 2023, we estimated that pools upstream of structures filled to over half their volume with fine sediment. We also applied hydrodynamic modelling to evaluate fluvial changes at high flows and found that restoration increased flow complexity and wetted surface area. These short-term responses highlight the potential speed and ability of low-tech, process-based restoration in achieving restoration outcomes.

Flow velocity is a key hydraulic variable in the exploration of rill erosion and is usually estimated by multiplying the surface flow velocity of runoff (measured with the dye tracer method) by the flow correction factor (a). However, there are differences among different experimental conditions, and the selection of the right value of a has become critical for accurately estimating the mean flow velocity. There has been little research on velocity correction factors for hyperconcentrated flows on steep slopes. In this study, gravel-laden sediment (mass fraction of gravel in the sample ranging from 0% to 70%, corresponding to a median diameter of 0.08–2.95 mm) was used as the test material, and different slopes (18%–84%) and unit flow discharges (1.11–4.44 × 10⁻³ m² s⁻¹) were considered to investigate the effects of gravel-laden sediment particle characteristics on runoff a and to elucidate the mechanism of the effects of different hydrodynamic parameters on runoff a. Under the experimental conditions, the value of a ranged from 0.285 to 0.690. a increases with increasing flow discharge and slope, with flow discharge having a greater effect than slope. With increasing gravel content and median diameter (d₅₀), a decreased initially but then stabilised. Additionally, a decreased with increasing sediment content but increased with increasing Reynolds number (Re). Based on the results of this experiment, 0.37, 0.49 and 0.60 are recommended as the correction factors of surface flow velocity for laminar flow (Re ≤ 500), transitional flow (500 < Re ≤ 2000) and turbulent flow (Re > 2000), respectively. Equation (16), which is based on the hydraulic parameters and sediment particle characteristics, has the best accuracy (Nash–Sutcliffe efficiency coefficient [NSE] > 0.9). The research results quantified the impact of sediment particle characteristics on a, contributing to the advancement of hydrodynamic studies on rill flow.

The intricate climate and surface composition of the wind-water erosion crisscross region create a distinctive environment for erosion and sediment production. However, research on the hydrological characteristics and responses to vegetation restoration in this area is limited. This study focuses on a representative catchment (3253 km²) in the northern Loess Plateau of China, examining the streamflow and sediment transport dynamics before (P1: 1977–1988) and after (P2: 2006–2017) vegetation restoration. Our results show that streamflow is relatively evenly distributed throughout the year, while sediment transport is highly concentrated over a few days during the wet season. Flood events account for the majority of sediment yield, contributing over 70% in both periods, with hyperconcentrated flows (SSC_p ≥ 300 kg m⁻³) being particularly significant. Vegetation restoration has resulted in an 85% reduction in annual sediment yield and an 89% decrease in the frequency of hyperconcentrated flood events. Despite these reductions, hyperconcentrated floods remain the dominant sediment transport mechanism, with just 9.7% of events in P2 responsible for nearly half of the sediment transported. Analyses of effective sediment transport discharge and sediment rating curves indicate a higher discharge threshold for hyperconcentrated floods post-vegetation restoration, leading to a greater sediment transport magnitude in P2. Hysteresis analysis shows a predominant counter-clockwise pattern in both periods, driven by abundant sediment sources and the high transport capacity of hyperconcentrated floods. Vegetation restoration has reduced the availability of sediment for transport, resulting in more linear relationships and decreased complexity in hysteresis patterns. Under future scenarios of intensified climate extremes, this region remains at high risk of erosion and sediment yield.

With increasing extreme weather events, ground water crisis and population expansion, crop stress and production failure have emerged as critical challenges. Agricultural drought vulnerability (ADV) at local and regional scales has become a global concern as it is directly related to food security, hunger issues and poverty. The Kangsabati river basin is one of the major drought-prone river basin in the eastern India and frequently affected by the reduction of crop production or crop failure because of fluctuation of monsoonal rainfalls, poor irrigation system and harsh edaphic factors. In this context, this study focuses on assessing agricultural vulnerability in the Kangsabati basin using multi-sensor datasets and geospatial techniques. The ADV has been assessed through multi-source data sets covering meteorological, agricultural, soil and socio-economic aspects using a powerful, systematic, and flexible decision-making fuzzy-based analytic hierarchy process (fuzzy-AHP) technique. The ADV index is a functional product of two composite indices: the sensitivity index (SI) and the adaptivity index. The SI is derived from components like the intensity of agricultural drought index, groundwater stress, soil erosion, percentage of cultivators, marginal workers and agricultural land. Adaptive capacity depends upon human, financial, physical, infrastructural and natural capital. Each index was derived considering various factors using fuzzy-AHP methods for weightage calculation. The composite indices revealed the variation of resource distribution precisely in each geographically distinct zone. The study shows that almost 60% of the highly sensitive zone is situated in the upper basin region characterised by undulating lands. A large part of the entire basin (48%) is moderately drought-sensitive. The result also shows that a significant part (35%) of the upper and middle basin is highly vulnerable to agricultural drought. In contrast, the lower basin exhibits low to very low levels of vulnerability to drought. The results indicate that even though some areas are moderate to less sensitive, the vulnerability of agricultural drought has become high due to their limited adaptive capacity. The comprehensive framework developed for assessing ADV has the potential for region-specific policy implementation and sustainable growth.

The complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation methods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model (WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating the inadequacy of using a constant lapse rate for hydrological modelling in high-elevation regions like the TP. The geostatistical technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation performance based on cross-validation results. However, although KED provides superior results based on cross-validation performance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting the importance of not solely relying on cross-validation results but also considering the practical implications of interpolation methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource management in a data-scarce, high-elevation region like the TP.