Hydrological Processes最新文献

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.

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.