Journal of Hydrology最新文献

Profile soil water content (SWC) is a vital variable in the atmosphere-vegetation-soil system. Although remote sensing currently can provide reliable surface SWC data (∼5 cm depth), acquiring accurate subsurface SWC data from existing reanalysis products remain challenging. In this study, we evaluated two widely used methods in estimating subsurface SWC, namely the exponential filter (ExpF) and the principle of maximum entropy (POME). The evaluation is carried out in two distinct areas on the Tibetan Plateau using ground observations collected from two monitoring networks: the Maqu network area characterized by cold humid climate and grassland and the Shiquanhe network area characterized by cold arid climate and bare ground. The results indicate that POME generally performs better than ExpF in both areas, particularly in deeper soil layers. Specifically, the accuracy of estimated SWC using the ExpF method decreases with depth, while it increases with depth using the POME method. Additionally, both methods achieve commendable performance at a depth of 10 cm in both areas. The deficiency of ExpF is mainly reflected in underestimations for dry cases, which is amplified with increasing depth. Dry cases account for 51 % in the humid area and 68 % in the arid area throughout the study period. Consequently, the ExpF method yields higher root mean square differences (RMSD) by 30 % and 113 % in the humid area at depths of 20 and 40 cm, respectively, compared to the POME method. Similarly, it results in higher RMSD values by 220 % and 200 % in the arid area. As expected, the superior performance of POME in deeper soil layers is primarily attributed to the incorporation of additional bottom and profile mean SWC observations. However, it also potentially introduces uncertainties when integrated with satellite-based data, which inherently contains errors compared to ground observations. To assess the potential of these two methods in large-scale applications combined with satellite-based datasets, this study conducted further evaluation of both methods with required input data derived from the soil moisture active and passive mission (SMAP). The results demonstrate that the performance of both methods in estimating subsurface SWC is acceptable in both humid and arid areas, although some bias is transferred from the input data. They achieve average RMSD values of 0.034 and 0.055 m³/m⁻³(−|−) in the humid area for the ExpF and POME methods, respectively, and 0.021 and 0.014 m³/m⁻³(−|−) in the arid area.

Riverbed scouring and siltation, affected by variations in river flow and sediment concentration, can cause changes in the sediment thickness, leading to the complex feedback between riverbed permeability and nutrient–reactive transport processes. Currently, the migration and transformation of iron and manganese under changed riverbed sediment thickness have not been taken into account. Based on indoor one-dimensional soil column simulation experiments, along with in-situ monitoring, Rhizon pore water sampling, and 16 s rRNA high-throughput sequencing technologies, this study revealed the migration and transformation patterns of iron and manganese in the river infiltration zone and their contribution rates under different sediment thickness conditions. The results demonstrated that as the riverbed sediment thickness increased, the river infiltration rate and sediment permeability coefficient demonstrated a significant decrease over time. For instance, a 5 cm sediment thickness can decrease the sediment permeability coefficient by 30 % within 32 d of infiltration, expand the disconnection zone to 25 cm, narrow the oxidation–reduction zone, and cause the iron and manganese reduction zone to evolve from a single peak to a double peak pattern. Additionally, as the sediment thickness increased, the contribution of organic matter bound iron-manganese oxidation and iron-manganese oxide reduction to Mn²⁺ and Fe²⁺ concentrations in sediment pore water decreased, while the contribution of adsorption and complexation-precipitation increased. This study holds great significance for ensuring the safety of drinking water supply and promoting the sustainable utilization of groundwater resources.

Sediment connectivity influences sediment flux in the Tarim River Basin (TRB), a region facing severe sedimentation and desertification, which directly threaten the region’s ecological security. To analyze the potential connectivity of sediment from hillslope to catchment outlets, we calculated the index of connectivity (IC) of TRB from 1990 to 2020 using a sediment connectivity model, referencing catchment outlets, and analyzed the impacts of climatic and geomorphic drivers on sediment connectivity. The results showed that the annual average IC ranged from −10.36 to 2.26 during study period. Approximately 30.56 % of the area exhibited a decreasing trend, and 8.57 % showed an increasing trend. The IC was lower in the downstream area (sediment sinks) than in the upstream area (sediment sources). Additionally, the IC increased with elevation and slope. Furthermore, the pattern and rate of land-use transformation had a substantial influence on sediment connectivity. Land restoration (increase in vegetation cover) resulted in a reduction of IC, particularly in the case of anthropogenic land restoration (${\Delta IC}_{\mathrm{mean}}$ = -0.09). Conversely, land degradation led to an increase in IC (${\Delta IC}_{\mathrm{mean}}$ = 0.04). Attribution analysis indicated that climatic factors had a greater influence on IC than geomorphologic factors, with temperature being the primary driver of IC variation (26.99 %). The explanatory power of geomorphologic drivers (elevation, slope) gradually increased over time. Moreover, a non-linear increase in explanatory power of factor interactions on IC was identified, with clear spatial differentiation characteristics in the interactions. These findings improve our our understanding of the spatial heterogeneity of soil erosion processes in the TRB and provide theoretical support for the implementing soil and water conservation strategies.

Comprehending the hydrological conditions in wetlands is a critical aspect of successfully enhancing wetland conservation. The interaction between wetland surface water and groundwater is a complex process, requiring detailed onsite hydrological and soil surveys, laboratory experiments, and modeling to clarify this relationship. However, conventional investigation methods often cause significant disruptions and thus may affect the natural environment and compromise data reliability. In this study, a high-accuracy and low-environmental-disturbance (LED) approach was proposed involving modified falling head permeability and modified seepage meter tests to elucidate the groundwater characteristics in an ecological reserve. A water balance (WB) calculation method was employed to examine the performance of the proposed LED approach. The results revealed that the LED performed better than conventional methods in hydraulic conductivity and seepage velocity exploration, thereby improving the accuracy of quantifying groundwater flow. Moreover, the experimental findings and ecohydrological observations were used to assess the groundwater flow regime, and the data were consistent with the field survey results. The contradiction between conducting research and protecting ecological reserves can pose difficulties in the sustainable and effective management of wetlands. The LED approach can be applied broadly, especially in areas where significant disturbance should be avoided. The water budget model can thus be developed to help deduce the interaction between groundwater and surface water. We suggest that these innovative methods are effective tools and can assist both scientists and authorities in formulating corresponding habitat management strategies.

This study represents the first attempt to define vulnerability indicators for offshore fresh groundwater, extending prior analyses of the key threats to onshore coastal aquifers. The method applies an existing steady-state sharp-interface coastal aquifer model with semi-confined offshore extension to characterise the sensitivities of the tip and toe locations (top and bottom of the freshwater–seawater interface, respectively) and the freshwater and seawater volumes to environmental changes that threaten offshore freshwater resources. Sensitivity analysis applied to seven case studies quantifies their vulnerability to seawater intrusion from sea level rise, recharge change and a regional change in the onshore groundwater heads. The analysis demonstrates that the tip-to-toe distance in offshore aquifers is constant for different fresh groundwater discharge rates (under certain conditions). Otherwise, the interface is usually longer (flatter slope) in offshore aquifers than in onshore aquifers, except where the offshore limit of the aquifer is reached. The offshore extension of the aquifer leads to more seaward toe positions, increases the interface length and reduces the onshore seawater volume. As the freshwater discharge increases, the offshore freshwater volume becomes more vulnerable to impacts from changes in the freshwater discharge until the tip reaches the offshore limit or the toe crosses the coastline. For high values of freshwater discharge, the volume of freshwater stored offshore is more vulnerable to losses from changes in the freshwater discharge rate than is the onshore freshwater storage. For lower values of freshwater discharge, however, offshore freshwater storage is generally less vulnerable than onshore freshwater storage. Contrasts in the behaviour of onshore and offshore freshwater require that both need to be considered in coastal aquifer vulnerability assessments.

Accurate runoff forecasting facilitates effective water resource management, and ensures the sustainable allocation of water for agricultural, industrial, and domestic use. Accurate runoff prediction has become more challenging due to the increased complexity associated with climate change and human activities. This paper proposes a new forecasting model, Deep Convolutional Residual Network with Temporal Attention and Transformer (DTTR), which is innovatively embedded with a temporal attention deep convolutional network to form a multimodal fusion “encoding-decoding” architecture. First, the weight allocation of higher-order hidden features extracted by the Deep Convolutional Residual Network (DCRN) is optimized by introducing the Temporal Attention Mechanism (TAM) to enhance the capture ability of sequence features. Secondly, the model adopts the “encoding–decoding” architecture to extend the feature dimensions and learns the temporal location information to enhance the global feature inter-feeding. Finally, the DTTR model successfully integrates the global information and maps the sequence features from multiple perspectives, which significantly improves the data’s feature abstraction ability and thus realizes the accurate prediction of the monthly runoff sequence. To verify the validity and sophistication of the DTTR model, the Taolai River, Hongshan River, and Fengle River were selected as experimental subjects. The model performance was tested using five evaluation indicators and nine comparison models. The results show that the DTTR model performs better than the benchmark model in different cases. For example, at Fengle River station, the mean absolute error (MAE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency coefficient (NSE), correlation coefficient (R), and Kling-Gupta efficiency (KGE) metrics of the DTTR model are improved by 30.49%, 37.18%, 7.87%, compared to the LSTM model, 3.16% and 10.34%. The R and KGE of each site exceeded 0.9, and the DTTR model also showed significant performance improvement in other cases. The experimental results demonstrate that the DTTR model, as an advanced model for predicting menstrual flow, can help to improve the accuracy of monthly runoff prediction and support the subsequent development of water resource optimization allocation and management plans.

In arid and semi-arid regions, accurate estimates of global primary productivity (GPP) and evapotranspiration (ET) are critical for understanding and managing water and carbon cycling in these fragile ecosystems. In this study, an improved ET-photosynthesis model (PT-JPL-GPP) was used to optimize GPP and ET estimates in these ecosystems by introducing the near infrared reflectance index (NIRv). NIRv, an indicator of the light use efficiency of vegetation, was integrated into the PT-JPL model. Compared to the original PT-JPL and existing remote sensing models, this PT-JPL-GPP model displayed a higher correlation (R² = 0.73) and lower BIAS (−19.57 %) for GPP estimation. ET estimates were also noticeably improved, the R² increased by 0.03(SN-Dhr) to 0.16(US-SRC), and the Root Mean Square Error (RMSE) reduced by 0.57 mm/month (SN-Dhr) to 4.64 mm/month (US-SRC). Particularly at the GRA site, the R² was increased from 0.63 to 0.74, and the RMSE and bias was decreased by 1.25 mm/month and 10.51 %, respectively. The PT-JPL-GPP model was comparable with GLEAM, VPM, MOD17, MOD16, and PML-V2 models. The PT-JPL-GPP model exhibits a lower root mean square error and higher correlation for estimating GPP, compared to the VPM, MOD17, and PML-V2 models. The PT-JPL-GPP model outperformed PT-JPL, MOD16 models for estimating ET, but was slightly poorer than GLEAM and PML-V2 models. Our results highlight the merits of NIRv for improving GPP and ET estimates.

We present an analytic element model for three-dimensional flow in fractured impermeable rock. The flow in each fracture is governed by both Darcy’s law and mass balance. We formulate the problem in terms of complex variables and introduce a complex potential in each fracture, defined inside a circular impermeable boundary. Some of the fractures are connected to stream- or riverbeds and wells may be drilled through the system. The fractures may have intersections that connect some of them; the flow is from one fracture to another via the intersections. The complex potential in each fracture is the sum of analytic elements; some represent sources of water, mostly head-specified, others sinks. Each intersection is modeled with a special analytic element. Conditions along the intersections are that the flow out of one fracture enters the connected one and that the heads in connected fractures match.

In this paper, the results of the characterization of Kamphorst’s rainfall simulator obtained by laboratory experiments carried out at the Department of Agricultural, Food, and Forest Sciences of the University of Palermo, are presented. At first, the rainfall uniformity distribution was positively verified considering several pressure heads (ranging from 1.9 cm to 11.9 cm) and water temperatures (from 24 °C to 27 °C), achieving a uniformity coefficient ranging from 96 to 99 %. Then, using a single nozzle, the simulator has been characterized in terms of kinetic power and momentum by applying both a photographic and a weighing technique. In particular, terminal drop velocity was measured by the displacement of a single raindrop measured between two consecutive frames, while the mean mass of single drops was evaluated by weighing a fixed number of drops. The analysis of the experimental data highlighted that the rainfall intensity, which increases with water temperature and pressure head, is the variable affecting the measurement of the single raindrop mass. Measurements also showed that an increase in rainfall intensity determines a decrease in the mean mass of the raindrops and an increase in the number of raindrops that fall in the unit time and area. This circumstance allowed to justify the increasing trend of the rainfall kinetic power and momentum with rainfall intensity. The measurements allowed to develop empirical relationships relating kinetic power and momentum with the simulated rainfall intensity and falling height of the raindrops. Finally, a theoretical expression suggested in the literature for estimating simulated rainfall intensity was positively tested.