Hydrological Processes最新文献

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.

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.