Hydrological Processes最新文献

We investigate how seasonal flow variations and a climatic regime that is dominated by the El Niño–Southern Oscillation (ENSO) influence Sb flux dynamics in an Australian river impacted by mining. Sampling (n = 496) spans a hydrologically complex 7-year period of drought, bushfires and floods from 2016 to 2023, during which 17% of samples exceeded the Sb drinking water guideline concentration (3 μg L⁻¹). Aqueous Sb (Sb_Aq) concentration–discharge (C–Q) relationships are non-continuous/non-linear across the flow range, with chemodynamic behaviour at moderate flows reflecting hydrological connection to the primary Sb-source area combined with variable dilution. In contrast chemostatic behaviour occurred at extreme low and high flows, reflecting hydrological disconnection from the source area and persistent dilution, respectively. Sb_Aq was significantly positively correlated (p < 0.01, Spearman's ρ = 0.58) with a Q index representing the proportional contribution of sub-catchment flow from the mineral-field area, suggesting sufficient localised rainfall in the Sb mining-impacted sub-catchment contributes to downstream peaks in Sb_Aq concentrations. Aqueous and particulate Sb (Sb_P) annual loads (L_a) during the study period spanned 24–5174 and 1.2–2820 kg, respectively and were strongly flow dependant with extreme interannual variability reflecting dry and wet years. We extrapolate daily load-daily discharge (L_d–Q_d) relationships for Sb_Aq and Sb_P to estimate L_d over a 53-year period (1970–2023) of continuous Q data (mean total Sb L_a = 1865 kg ± [SE] 247). Positive correlations between the annual Southern Oscillation Index and both Sb L_a (p < 0.05) and proportional Sb_P L_a over 53 years suggests ENSO fluctuations influence annual Sb transport dynamics. Upstream Sb_P load estimates correspond with downstream estimates of coastal floodplain sedimentary Sb mass, with approximately 10%–45% of the estimated Sb_P exported downstream since approximately 1880 accumulated on the Macleay coastal floodplain. Data suggest at current rates of export, complete flushing-leaching of mine tailings-derived Sb from the upper Macleay catchment may take in the order approximately 600–1000 years.

Wildfires and heatwaves have recently affected the hydrological system in unprecedented ways due to climate change. In cold regions, these extremes cause rapid reductions in snow and ice albedo due to soot deposition and unseasonal melt. Snow and ice albedo dynamics control net shortwave radiation and the available energy for melt and runoff generation. Many albedo algorithms in hydrological models cannot accurately simulate albedo dynamics because they were developed or parameterised based on historical observations. Remotely sensed albedo data assimilation (DA) can potentially improve model performance by updating modelled albedo with observations. This study seeks to diagnose the effects of remotely sensed snow and ice albedo DA on the prediction of streamflow from glacierized basins during wildfires and heatwaves. Sentinel-2 20-m albedo estimates were assimilated into a glacio-hydrological model created using the Cold Regions Hydrological Modelling Platform (CRHM) in two Canadian Rockies glacierized basins, Athabasca Glacier Research Basin (AGRB) and Peyto Glacier Research Basin (PGRB). The study was conducted in 2018 (wildfires), 2019 (soot/algae), 2020 (normal) and 2021 (heatwaves). DA was employed to assimilate albedo into CRHM to simulate streamflow and was compared to a control run (CTRL) using off-the-shelf albedo parameters. Albedo DA benefited streamflow predictions during wildfires for both basins, with a KGE coefficient improvement of 0.18 and 0.20 in AGRB and PGRB, respectively. Four-year DA streamflow predictions were superior to CTRL in PGRB, but DA was slightly better in AGRB. DA was not beneficial to streamflow predictions during heatwaves. DA improved streamflow predictions by decreasing positive bias, showing that albedo DA can reveal unknown albedo and snowpack dynamics in remote glacier zones that are poorly simulated in models. These findings corroborate the power of observational tools to incorporate near real-time information into hydrological models to better inform water managers of the streamflow response to wildfires and heatwaves.

The hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state-of-the-art data-driven models (or DDMs) in quantifying the prediction uncertainty of process-based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian-based data assimilation (DA) technique is used to quantify uncertainty in process-based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process-based hydrological models: HBV and HyMOD. Particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process-aware DDMs: HBV-PF-RF, HBV-PF-SVM, HyMOD-PF-RF and HyMOD-PF-SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash-sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process-based and DDMs being involved, thereby ensuring generalizability. Thus, the framework is expected to assist in effective decision-making, including various environmental management and disaster preparedness.

Accurate simulation of water balance components is crucial for effective water and land management practices. The performance of process-based hydrological models relies on the accurate determination of input variables. The objective of this study is to quantify the magnitude of the effect of soil properties (depth and texture) and biophysical parameters on water balance simulation for a forested catchment using the Soil and Water Assessment Tool (SWAT+). Simulations were carried out for a baseline scenario using the default soil inputs, followed by extending the soil profile depth up to 15 m under three different rainfall scenarios. Sensitivity analysis of model outputs was performed using the SENSitivity ANalysis (SENSAN) programme of the Parameter ESTimation (PEST) suite, coupled with SWAT+. The results showed that increasing soil profile depth to 15 m led to around 50% increase in water yield, and around 20% reduction in percolation with slight variations across the three rainfall scenarios. Evapotranspiration rates were slightly increased in deeper soil profiles. The sensitivity of evapotranspiration, surface runoff, and percolation to LAI-related biophysical parameters was pronounced, highlighting the need to include such parameters in SWAT+ model calibration. The water uptake from deeper soil layers by deep roots, even in rocky substrates, as documented in the literature, is not adequately captured by the SWAT+ model. Our work showed that in general, developing local soil databases with detailed information on deeper layers is needed, to improve the accuracy and reliability of hydrological models in predicting water fluxes, thereby supporting informed water resources management decisions.

Wildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF-Hydro) to a suite of fire-related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire-related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire-related perturbations enhanced post-fire model accuracy across the 11–12 post-fire months evaluated herein. The most comprehensive fire-aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire-enhanced streamflow was predominately attributable to fire-induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early-summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8-days) from the most comprehensive fire-aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire-related perturbations reduced post-fire streamflow anomaly biases across the three study catchments. However, remaining large post-fire streamflow uncertainties in the fire-perturbed simulation underscores the importance of additional observationally constrained fire-disturbance model developments.

Dried soil layer (DSL) is a phenomenon of deep soil desiccation caused by soil water content (SWC) deficiency. Relevant studies in the fragmented terrain of Chinese Loess Plateau (CLP) remain limited. In a typical slope-gully unit near the Liudaogou catchment, SWC was measured using neutron probes on 19 occasions at 15 observational locations. In order to reveal the temporal stability and elimination degree of DSLs, available soil moisture (ASM) and DSL were estimated by representative sites which were determined through the temporal stability method, and the reliability of simulating mean condition of the study area via representative locations was assessed. Results show that: (1) the dynamics of DSL was characterised by complexity and diversity. The ASM within the DSL (DSL-ASM), ASM within the sandwiched DSL (SDSL-ASM) and quantitative index (QI) varied within the range of 2.75%–3.11%, 2.98%–4.22% and 0.254–0.356, respectively. (2) The possibility of development and recovery for DSL and SDSL in deep layers were less than that in shallow layers. The maximum depth of DSL (DSLMD) was significantly and negatively related to the standard deviation of relative difference (SDRD) of DSL-ASM, the maximum depth of SDSL (SDSLMD) was negatively related to the SDRD of SDSL-ASM. (3) The prediction results of ASM above 300 cm depth were more accurate than other layers (R² = 0.89). The DSL-ASM had more accurate ability of prediction than SDSL-ASM and QI. On the analysis of time stability characteristics of ASM and DSLs, the locations of A2 and C3 can better represent the mean conditions of ASM at three and four soil layers, respectively. C2, A1 and A1 can better represent the average levels of DSL-ASM, SDSL-ASM and QI, respectively (R² = 0.43, 0.14 and 0.18). (4) The restoration degrees of DSLs mainly showed no elimination and slight elimination, the DSLs cannot be completely eliminated within a short time. We proposed that scientific regulation of SWC can alleviate the formation and development of DSLs at a certain extent, and provide the possibility for DSLs nonoccurrence.

A surface water body fed by groundwater is normally known as a terminal place of groundwater flow systems originating from precipitation recharge on highlands. The theory of Tóth predicted that these flow systems form a hierarchically nested structure of groundwater circulation in a composite basin. In this study, we will report new flow paths among groundwater flow systems that were unknown in Tóth's theory, identified as special seepage paths linking different surface water bodies. These seepage paths do not start from the groundwater table but can transmit water between lakes or streams that already serve as discharge zones of traditional local flow systems. As indicated in theoretical models and two real-world cases, special seepage paths are developed if some parametric conditions are satisfied, especially when surface water bodies cut deeply below the water table or are large enough. Different surface water bodies or different river reaches can directly exchange water, chemicals and heat through deep seepage paths even when both surface and subsurface water divides exist between them. Special seepage paths may play a role in the regional scale hyporheic flow or contribute to inter-basin groundwater flow. The knowledge of special seepage paths could greatly improve our conventional perception of surface water-groundwater interaction, groundwater age and geochemical and heat transport at the river basin scale.