Hydrological Processes最新文献

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.

Stable isotopic methods in hydroclimate monitoring are powerful for improving water resources management, but applications are limited, especially in semi-arid regions where such management is needed most. Here, we show that we can address shortcomings related to the lack of a seasonal signal using stable water isotopic signatures measured in precipitation over the East San Francisco Bay area, California, during two contrasting events sampled at more than 20 locations in the winter of 2023. The observed range in δ¹⁸O in the rain samples is similar for both storms. However, the distributions do not overlap—the mean air temperature and δ¹⁸O during Winter Storm Olive (February 2023) were 2°C and − 12‰, respectively, while a warm atmospheric river event (March 2023) had a mean temperature of 9°C and δ¹⁸O of −6‰, close to the long-term average δ¹⁸O measured in local precipitation. The Winter Storm showed expected trends in δ¹⁸O related to geography (i.e., lower with greater distance inland and elevation), while the atmospheric river δ¹⁸O pattern was more spatially uniform. We use hydrometric data from a gaged watershed in the study area and isotopic signatures of rain sampled during the two storm events and apply a solute transport model (StorAge selection) with a travel-time approach to examine predicted watershed responses and potential water tracing applications. In this virtual experiment, we find that event size exerts a strong control on the relative amounts of runoff versus pre-event water in the stream, while uncertainty in stream hydrograph separation is related to the degree of contrast between precipitation/runoff and pre-event water. Key to flood prediction, adaptation, and mitigation, especially in coastal urban areas, is knowledge of the contributing water sources and timing of stream flow. The strong contrast in stable isotopes between these two events, close in time and over the same area, illustrates the potential to use stable isotope signatures to track the transport and mixing of events through natural and engineered watersheds that are threatened by climate whiplash events.

Land use change, as a major driving factor of watershed hydrological process, has a significant influence on watershed hydrological change. In addition, a series of hydrological models, as important tools for simulating hydrological impacts, are widely employed in studying land use change. However, when employing hydrological model to analyse the hydrological impacts of land use changes, most previous studies focused on the evolution of historical land use change and lacked reasonable predictions of future land use. Therefore, it is necessary to extend such studies to future scenarios to cope with possible future hydrological variations in the basin. Given this, this paper making the Wuwei section of Shiyang River Basin as the study area, coupled the SWAT (Soil and Water Assessment Tool) model for hydrological simulation with the CA-Markov (cellular automata-Markov chain) model for future land use prediction to analyse the regional hydrological effects caused by historical and future land use changes. In addition, the general CA-Markov model directly uses a system-generated suitability atlas. In contrast, this study applied logistic regression and Multi-criteria evaluation (MCE) methods to construct the suitability atlas, thereby establishing the Logistic-CA-Markov and MCE-CA-Markov models. Based on the model results, the main results are as follows: (1) The land use in study area is mainly grassland and barren, accounting for more than 80%. Additionally, forest is changing at the highest rate among all land use types. (2) In terms of the percentage of grassland and forest, the future land use predicted by MCE-CA-Markov (Multi-criteria evaluation-cellular automata-Markov chain) has the largest forest and grassland coverage (57.78%), whereas the future land use predicted by Logistic CA-Markov has the lowest (54.69%), indicating that the former pays more attention to the sustainable development of ecological environment. (3) The study area's R² = 0.83, NSE = 0.79, PBIAS = −18.6%, and validation R² = 0.81, NSE = 0.76, PBIAS = −17.8% demonstrate the favourable application of the SWAT model. (4) Based on simulated runoff results under historical and future land use scenarios, the amount of increasing grassland and forest coverage in the study area would eventually rise water yield (WYLD) by increasing lateral runoff (LATQ), increasing subsurface runoff (GWQ), and reducing surface runoff (SURQ). The study contributes to a better understanding of the impact of land use change on regional water resources and water balance, thus guiding regional water resources management and sustainable development.