Journal of Hydrology最新文献

Water quality, aquatic habitat, and groundwater recharge, which have a critical impact on the river ecosystem health, are associated with the vertical hydraulic conductivity of streambed ($K_v$). Macroinvertebrates bioturbation can generate heterogeneity in the benthic and hyporheic zones to influence $K_v$ by particle reworking and burrow construction, which are directly and indirectly related to the physicochemical conditions of sediments. However, understanding how these factors and processes influence $K_v$ and its role is deficient. Here the spatial distribution of benthic macroinvertebrates and its effects on $K_v$ in the Weihe River were investigated. Macroinvertebrate bioturbation had a significant effect on $K_v$ when the biomass was greater than 1 mg/m², and yet when the biomass exceeded 10² mg/m², bioturbation had a significant inhibitory effect on $K_v$ as the result of clogging due to the fine sediment particles and secrete mucus produced by benthic Macroinvertebrates. Macroinvertebrate distribution and biomass are often influenced by temperature which could also decrease the fluid viscosity, thus improving $K_v$. Moreover, the mixed bioturbation mode with the predominated Tubificidae was better than the bioturbation mode of single-type benthic macroinvertebrates in improving sediment permeability. These findings have important implications for revealing the disturbance mechanism of benthic macroinvertebrates on $K_v$ and provide a reliable theoretical basis for the protection and planning of river ecosystems.

The importance of floodplains in carbon (C) evasion from the lotic systems is especially important in continental plains of low runoff such as the organic-rich Western Siberian Lowland (WSL). To quantify the relative importance of the floodplain compared to main stem CO₂ emissions, we monitored a large region of the Ob River’s middle course (permafrost-free zone) over 3 months from spring to summer. We calculated seasonal water coverage using remote sensing, GIS and hydrologically-based approaches and measured CO₂ emissions using floating chambers. There was a strongly pronounced seasonality in the water area’s extent of the floodplain with water covering > 40 % of land during the ∼ 30 days of the most intensive spring flood (May – June) and subsequently declining to ≤ 10 % during summer (July-August). Maximal CO₂ emissions were recorded in most shallow water bodies of the floodplain, notably in temporary flooded fens and birch forests. The CO₂ emissions during the study period ranged from 0.2 ± 0.2 to 0.9 ± 0.2 g Cm⁻² d^-1 for the floodplain and 0.03 ± 0.34 g C m⁻² d^-1 for the Ob’s main channel.

CO₂ emissions from the floodplain were ∼ 163 ± 20 t C per km for the river’s main stem during the 95 day study period. The partial contributions of temporary flooded zones, main stem, and permanent lakes / secondary channels to total emissions (1820 km² area) were 70, 16, and 14 %, respectively. Over spring and summer seasons, contributions from flooded zones ranged from 43 to 99 % of total CO₂ emissions from water surfaces of the Ob River’s middle course. Extrapolation of obtained results to the entire territory of the Ob River floodplain indicates that not accounting for the floodplain emissions may sizably—up to an order of magnitude—underestimate the CO₂ emissions from riverine systems in Western Siberia during open water period. Future work on the Ob River floodplain in the permafrost-bearing zone should be prioritized and would allow adequate upscaling of C emission from this environmentally important territory.

The Penman and Penman–Monteith equations are widely used for estimating surface evapotranspiration (ET) at regional and global scales. These nonlinear equations were derived from the turbulent transport of heat fluxes and, in theory, need to be applied to a temporal scale ranging from half hour to an hour. However, these equations have been frequently applied with hydrometeorological variables averaged at daily, monthly, and even decadal time intervals, resulting in biases due to their nonlinearities. In this study, we used global reanalysis data and Taylor expanded Penman and Penman–Monteith equations to explore their nonlinear components and the biases associated with the timescale mismatches. We found that global average biases for approximating Penman equation range from 0.72 to 1.31 mm day⁻¹ from daily to annual timescales, which mainly stem from the temperature–radiation, temperature–vapor pressure deficit (VPD), and aerodynamic conductance–VPD covariances. For Penman–Monteith equation, the corresponding biases vary from 0.47 to 0.53 mm day⁻¹, which may be associated with the addition of stomatal conductance–VPD covariances. As a reference, the global averages from Penman and Penman–Monteith at hourly timescale over one year are 7.1 and 1.7 mm day⁻¹. Large biases also exist around the world across various climate zones, where one or multiple covariances between meteorological variables makes the first-order approximations of Penman and Penman–Monteith equations less accurate. This analysis serves as a reminder of nonlinearities in Penman and Penman–Monteith equations, hence the requirement of data at high temporal resolution for estimating potential or actual evapotranspiration.

Accurate and reliable incoming flood forecasting is an important prerequisite for flood warning, flood risk analysis and reservoir flood control operation. This paper proposes a hybrid model for real-time flood forecasting that couples process-driven hydrological models (HMs) with data-driven models (DDMs). The generic hybrid model framework adds DDMs as the post-processing procedure for residual correction to the original results of HMs, and considers multiple uncertainties in input data, parameter and model structure simultaneously. The performance of the hybrid model is evaluated comprehensively in terms of deterministic forecast accuracy, interval forecast reliability, and the reliability and sharpness of probabilistic forecast. Taking the multireservoir system at the east Pi River as a study case, the results indicate that: (1) Compared to the benchmark model (ensemble XAJ model), the hybrid model with additional residual analysis show a significant improvement. The average continuous ranked probability score (CRPS) metric values calculated by the Stacking-Hybrid model improved by 71.5 %, 67.0 % and 38.1 % in the three data samples. Furthermore, the adaptability of the Stacking-Hybrid model for residual correction during short-duration intense rainfall events has been validated, with the relative error of the peak discharge improved to within ± 10 %. (2) The Stacking-Hybrid model, which also takes into account structure uncertainty, is able to better exploit the combined advantages and improve the stability of the model performance compared to those that only apply a single DDM. (3) When the number of iterations within the BOA reaches 300, the parameter optimization process is capable to search for the hyperparameters that bring out the best performance of the DDMs. (4) When the ensemble size reaches 200, the uncertainty of HM parameters can be fully defined, and the consumed computational resources can be controlled within an acceptable bound while ensuring stable model performance. Overall, the hybrid model that takes into account multiple sources of uncertainty generates both interval and probabilistic forecast in addition to deterministic forecast, which can provide richer risk information for subsequent flood warning and reservoir operation, making the flood prevention decisions more reliable and scientific.

Water erosion considerably affects the stability and particle size distribution of soil aggregates, but the underlying mechanisms of water erosion remain unclear. To this end, we selected four landscape positions (top-, up-, mid-, and toe-slope) with distinct erosion and deposition characteristics on a typical eroded slope in southern China to conduct experiments- aiming to investigate the main drivers of soil aggregate stability during erosion and deposition processes. Soil samples were collected from 12 sites, and 4 size classes (>2, 2–0.25, 0.25–0.053, and <0.053 mm) of soil aggregates were obtained using the wet sieving method. The composition and stability of the soil aggregates, as well as the contents of organic (organic matter components) and inorganic (iron-aluminum oxides) cementing materials of different particle sizes, were determined. The results indicated that erosion significantly reduced the aggregate stability and the >0.25 mm water-stable aggregate content (WR_0.25) (P < 0.05). The mean weight diameter (MWD) and geometric mean diameter (GMD) values of the soil aggregates at the eroding site decreased, and the fractal dimension (FD) increased. Furthermore, erosion markedly reduced the humic acid (HA) and fulvic acid (FA) contents in the bulk soils and soil aggregates, while the HA content showed no obvious difference between the eroding and depositional sites. In addition to the presence of complexed iron/alumina oxides (Fe_p/Al_p), erosion markedly reduced the contents of amorphous (Fe_o/Al_o) and free-form (Fe_d/Al_d) iron/alumina oxides in the bulk soils and size fractions. Moreover, Fe_d/Al_d, Fe_p and Fe_o/Al_o were present in the microaggregates, while Al_p was found in the macroaggregates. Additionally, boosted regression tree (BRT) analysis indicated that FA (36.70 %), Fe_o (19.00 %), and Al_d (12.71 %) were the crucial predictors of soil aggregate stability. These findings further confirm that the organic and inorganic cementing materials in red soils collectively contribute to aggregate stabilization under the impact of erosion. This study facilitates a deeper understanding of the mechanisms governing soil aggregate stability in eroded landscapes, and provides a theoretical basis for biogeochemical cycling processes.

Flood waves generated by reservoir discharge play a crucial role in the potential for riparian denitrification downstream. The efficiency of nitrogen (N) removal from river water by riparian zones is typically enhanced with greater wave amplitude (A) and duration (T). However, a knowledge gap exists with respect to the impact of flood waves on N removal when considering a fixed-value integral over time of flood waves (IOFW). This study explored N transport and transformation in the riparian zone downstream of the dammed Inbuk River, Korea under various wave conditions with a fixed IOFW. The results revealed a logarithmic enhancement in solute infiltration into the riverbank with an increasing wave amplitude/duration ratio (A/T ratio). The solute residence time within the riparian zone displays an initial increase followed by a decrease with the increment in the A/T ratio. We identified a threshold for the wave A/T ratio that maximizes riverine N removal by the riparian zone, a phenomenon observed across various dammed rivers. Our findings indicated that riparian denitrification is reaction time-limited when the wave A/T ratio exceeds the threshold; contrarily, it becomes transport capacity-limited. Additionally, we observed a significant relationship between the net nitrate removal and the return/infiltration time ratio of water (r² = 0.93, p < 0.05). The research results contribute to a better understanding of the impact of water level fluctuations on the N cycle in riparian zones, and provide valuable insights for developing sustainable reservoir operation strategies.

Numerous field observations show that the channels in one fracture are narrow and the solute penetration depth might be larger than the width. For this case, the diffusion from a channel into the matrix is more realistic to be modeled as radial diffusion than one-dimensional. In present work, the single channel model with radial diffusion is revisited and a simple and robust analytical solution is developed. This solution takes a convolution form of two functions, in which different transport mechanisms are accounted for. The statistical interpretations of the two functions and the analytical solution aid to develop a simple Time Domain Random Walk (TDRW) algorithm and an extension is made to improve its accuracy, efficiency and applicability. To demonstrate the accuracy and efficacy of the extended algorithm, three groups of simulations are performed and it is found that the results of all approaches are identical. The TDRW algorithm, having the same performance as that of inverse Laplace transform solution, is superior to Gaussian quadrature method in computational time. However, due to Monte Carlo nature of the algorithm, the computational burden of TDRW algorithm is dependent on the number of particles applied, which also influences the calculation accuracy. Therefore, a trade-off between computational burden and calculation accuracy should always be made, once the TDRW algorithm is used.

The preferential flow path development is potentially the result of spatial variations in soil properties with soil depth. However, visualizing the evolution of the preferential flow path with soil depth remains a challenge. This paper presents dye tracer and species diversity theory methods for characterizing preferential flow paths. Field dye tracer experiments were performed at three sites (tree, bush, and grass) in the Yellow River Delta wetland and dye distribution diversity indices (Simpson index (D_s), Shannon-Wiener index (H), Margalef index (D_m), and Pielou index (E)) were applied to verify their availability for preferential flow assessment. The results showed that the uniformity of the shallow-infiltrated dye at the tree site, non-uniformity of the shallow-infiltrated dye at the bush site, and deep dye infiltration at the grass site were the three typical infiltration types. The average proportion of dye-stained areas (PDA) gradually decreased with increasing soil depth. The quantitative effects of soil properties on PDA changes were profound, indicating that soil clay content at 0–10 cm depth, soil sand content at 10–20 cm depth, soil drainage capacity at 20–30 cm depth, and soil bulk density at 30–40 cm depth were the most predictive factors controlling PDA changes. Our results also showed that dye-stained patches with extremely high and high dye concentrations were the most distributed; D_s, H, D_m, and E were the highest at the tree site and E was the diversity index with the greatest importance for PDA change. The findings reveal the soil properties controlling the formation of preferential flow paths, which will improve our understanding of water resource management in the vadose zones of coastal wetlands.

The availability of comprehensive stable isotope data in China is limited, which hinders a thorough understanding of interpreting runoff sources and land–atmosphere water fluxes on a national scale. In this study, we have undertaken the task of establishing a dataset of surface water isotopes (δ¹⁸O and δ²H) to create surface water isoscapes, and identify its controlling factors. Our analysis, utilizing a random forest model (RF), indicates that the isotopic patterns observed in precipitation are well-reflected in river water across China. Specifically, the δ¹⁸O isoscape in river demonstrates enrichment in the Western arid zone and depletion in the Tibetan Plateau. These patterns are strongly influenced by hydro-climatic factors such as relative humidity, precipitation, and catchment properties, such as elevation. Notably, elevation is a significant variable in the RF model, governing the isotopic composition (δ¹⁸O and d-excess) of rivers throughout China, primarily due to the rainout effect resulting in isotopically-depleted precipitation from lowlands to elevated mountain regions. In contrast, surface water d-excess isoscape reveals a more complex spatial variability in China, mainly associated with contrasting moisture sources including maritime vapor from tropical oceans and inland recycling vapor. In addition, secondary evaporation processes resulted from cumulative dams and developed irrigation systems also contribute to this variability. Hence, catchment-scale evapotranspiration and instream evaporative processes contribute to the enrichment of downstream river water isotopes. The predictive surface water isoscapes will help understand the impact of changes in the hydrological cycle on a larger scale and provide practical guidance for future monitoring efforts and isotopic simulations.