Hydrological Processes最新文献

Parameterisation of fully coupled integrated hydrological models is challenging. The state-of-the-art hydrogeology models rely on solutions of coupled surface and subsurface partial differential equations. Calibration of these models with traditional optimisation methods are not yet viable due to the high computational costs. Prior knowledge of the range of the parameters can be helpful as a starting point, however, due to natural variations, abstractions and conceptualizations used in modelling, a systematic exploration of the variable space is needed. In this study, we utilise the natural clustering of the soils based on their saturated and unsaturated hydraulic behaviour derived from soil texture maps in conjunction with two level Latin hypercube sampling to effectively explore model parameter spaces. Soil texture maps are similar to USDA soil classifications; however, the objective is to classify the soil based on their unsaturated behaviour, rather than soil texture. The method has never been utilised in the modelling and the results show that it can be applied to larger watersheds. The area of study is Hubbard Brook Experimental Forest, a northern hardwood forest in the White Mountains of New Hampshire, USA. An average Nash–Sutcliffe value of 0.80 is achieved for hourly discharge for the eight streams in the catchment. The Nash–Sutcliffe measure shows a 7% improvement with the addition of the snow melt and evapotranspiration parameters in the second stage. Exchange flux patterns vary seasonally in the catchment with largest infiltration occurring in spring.

Climate change and extreme weather events affect hydrology and water resources in catchments worldwide. This study analysed Blue Water (BW) and Green Water (GW) scarcity in the McKenzie Creek watershed in Ontario, Canada, and explored how changes in temperature and precipitation may impact water scarcity dynamics. The McKenzie Creek is the main water source for agricultural activities for the Six Nations of the Grand River reserve (the largest Indigenous community in Canada) and other non-Indigenous communities in the watershed. Data from the water use surveys and streamflow simulations performed using the Coupled Groundwater and Surface-Water Flow Model (GSFLOW) under the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5, representing moderate and high greenhouse gas emissions and climate warming, respectively, were used to calculate BW and GW scarcity. Study results showed that BW scarcity may increase to ‘moderate’ levels if water users extract the maximum permitted water withdrawal allocation. This level of scarcity has the potential to cause ecological degradation and water quality issues in the watershed. GW scarcity will steadily increase throughout the 21st century due to climate warming with the western portion of the McKenzie Creek watershed projected to experience slightly higher levels of GW scarcity. This may cause users to withdraw more water resources, thereby decreasing BW available for downstream communities, including the Six Nations of the Grand River. This study provides water resource managers and regional planners with important information about potential challenges facing the watershed due to increased water use and changing climate conditions.

Drought is caused by an imbalance between precipitation and evapotranspiration. A prolonged lack of precipitation and/or excess evapotranspiration results in insufficient replenishment of runoff and groundwater. Choosing an appropriate drought index is crucial for managing water resources effectively. The Reconnaissance Drought Index (RDI) which considers both precipitation and potential evapotranspiration is recommended for identifying droughts in a changing climate. Standardising the index involves using a probability distribution, and choosing the correct distribution is important for accurate assessments of drought characteristics. Furthermore, identifying the optimal distributions for RDI assessments ensures reliable evaluations of subsequent hydrological processes. Based on a regional study, the index developers suggest using gamma or log-normal probability distributions to compute the index using real observations. Furthermore, there is a lack of research on suitable distributions for RDI calculation using GCMs projections (simulated data) in drought projection studies. This global study aims to address these gaps in research by evaluating the performance of probability distributions in calculating RDI. The study consists of two phases: The first phase involves identifying the appropriate distribution for historical observed data, whilst the second phase does the same for future projections from GCMs. To achieve this, 17 probability distributions are applied. The 0.5° × 0.5° gridded CRU data from 1950 to 2018 and projections of 18 GCMs from 2006 to 2080 are utilised. The analysis identified the log logistic, inverse Gaussian and gamma distributions as the best fits for the historical period. For future projections, the gamma, inverse Gaussian and Nakagami distributions are recommended. Finally, the findings revealed for both periods, Fitting to the Best Distribution of any Grid (FBDG) performs the best for large-scale drought studies using gridded data.

The precipitation–streamflow relationship (PSR) is one of the most crucial aspects of hydrological process studies. Previous studies have analysed the changes of the PSR at specific timescales (e.g., annual or seasonal), overlooking the characteristics of the PSR across multiple timescales and the changes that occur over time. This study presented an integrated framework to address these issues from three perspective: the inconsistencies, the response sensitivity of streamflow to precipitation and the PSR across multiple oscillation periods. This study analysed monthly streamflow and precipitation data from three representative reaches located in the upper and middle sections of the Yellow River Basin from 1961 to 2021. The results indicate that the proposed integrated framework effectively reveals the evolving patterns of the PSR. The evolution patterns of PSR vary across different time scales. Notably, the inconsistencies in PSR variations in the middle and upper reaches of the Yellow River are significant and manifest differently across various timescales. These differences were particularly pronounced in the middle reaches when comparing the periods before and after 2000. The changes in PSR varied among different oscillation periods, and an examination of the resonant period variability revealed a shift from strong-to-weak resonance within the 32–64-month period, followed by a weak-to-strong transition within the 128-month period. This study has significantly enhanced our understanding of the evolution of the PSR and has provided valuable insights for effectively managing hydrological processes in a changing environment.

As climatic conditions change globally, so too will stream thermal regimes, with implications for water quality and habitat suitability for aquatic life. Stream temperature measurements are sparse in many regions, motivating the development of models that are able to extrapolate to past and future climatic conditions to support decision-making for aquatic resource management. This study assesses the performance of air2stream, a hybrid, at-a-site stream temperature model that was developed to simplify the data requirements of process-based models while maintaining their predictive performance. The air2stream model requires only time series of daily mean air temperature and stream discharge as input variables, and was calibrated for 23 streams in British Columbia, Canada, using data recorded at Water Survey of Canada gauging stations for the available periods of record up to 2020. Daily mean air temperature time series were interpolated to each monitoring site from the ERA-5 gridded surface data product. Air2stream was validated with data from the years 2021 and 2022, which included an extreme summer heat wave and autumn drought conditions that fall outside the range of conditions observed during the calibration period. The validation results were compared to those of a set of linear mixed-effects models with the same predictor variables, as well as a simplified version of air2stream that only uses air temperature as an input variable. The air2stream model produced higher errors during the extreme weather conditions compared to the calibration period, though its performance under extreme conditions overall remained superior to that of the statistical models and the simplified air2stream model. The results highlight the importance of representing hydrological and thermal processes and their seasonal variation in models for predicting stream temperature under changing climatic conditions.

The Qinghai-Tibetan Plateau (QTP) experienced noticeable warming and glacial retreat during the past decades. However, it is unclear how these changes affect QTP wetland distribution in the past and future. To this end, this study estimated the potential wetland distribution in the QTP under present and future climate scenarios using five machine learning methods. We further decoupled the sensitivity of wetland area to temperature, precipitation, and glacier changes based on the control experiment, and quantified the environmental niche of QTP wetland distribution. The RusBoost algorithm model has the best performance and shows that the current potential wetland area is about 1.6 × 10⁵ km², accounting for 6.22% of the land surface. By 2100, QTP wetlands are projected to increase by 9.6% and 77.3% relative to the current potential wetland area under the SSP1-2.6 and SSP5-8.5 scenarios, respectively. Climate warming and wetting are positively correlated with the future wetland areas. Each 1°C increase in the warmest season temperature can lead to a 9.0% increase in QTP wetland areas. Glacial retreat to some extent leads to wetland increase, for example, in the southeastern QTP, likely due to glacial meltwater recharge. However, wetlands will decrease due to longer glacial distances in the northeast QTP, because wetlands tend to grow within a suitable distance of 30 km to glaciers. As more current wetlands spread within the recharge range of glacier meltwater, QTP wetlands expect to increase in the near future. This research provides a valuable reference for predicting wetland changes in alpine regions in the context of global warming.

Effective management of water resources in anthropogenically shaped lowlands requires a comprehensive understanding of hydrological processes and balancing various effects in complex settings, especially like lowland hydrology. Unlike mountainous headwater catchments with shallow soils, lowland hydrology is typically dominated by groundwater dynamics, often exhibiting pronounced spatial correlation lengths, though other factors may also contribute. This necessitates consideration of distant anthropogenic impacts in water resources management. This study focuses on the Lusatia region in the northern German, a lowland area heavily altered by mining activities, including extensive groundwater lowering and rebound, impacting the overall water regime. We applied an efficient, data-based approach to unravel various impacts on the landscape water balance over a 30-year period (1993–2022). We integrated over 1800 ground-based time series data on groundwater levels, surface water dynamics and runoff, supplemented by evapotranspiration estimates from multi-temporal Landsat satellite data to account for land use effects. Through principal component analysis, we identified key patterns driving water balance dynamics. The first four components explained 84% of the variance in groundwater and surface water levels, as well as of runoff dynamics. The dominant processes attributed to these components include anthropogenic influences from mining activities, as well as natural hydrogeological effects such as seasonal variability and the damping of the groundwater recharge signal in the unsaturated zone. A separate principal component analysis that included evapotranspiration data explained 87% of the variance, with the first component predominantly reflecting seasonal variations and subsequent components elucidating land use impacts and long-term vegetation changes. By linking both analyses, we generated comprehensive maps detailing the spatial distribution of effects on regional water balance. Our approach provides a quantitative tool to assess the size and influence of natural and anthropogenic effects on water resources, offering a comprehensive tool for assessing spatial and temporal effects on hydrological dynamics in a lowland region affected by human activities.

Precipitation processes in the monsoon border zone (MBZ) of China remain unclear because of the complex hydro-climatic interactions. This study analyzes the temporal variation of δ²H and δ¹⁸O in precipitation and its influencing factors based on samples of precipitation events in Lake Dali during summer from 2018 to 2020. The results showed significant monthly isotopic variability, characterised by lower δ²H and δ¹⁸O values in July with averages of −103.62‰ and −14.87‰, respectively. This clear isotopic variability is largely related to the East Asian summer monsoon (EASM) activity during the prevailing summer monsoon season and hydrometeorological processes. The isotopic composition of precipitation is mainly determined by the amount of precipitation and relative humidity, but not related to temperature. The results of backward trajectory modelling show that precipitation primarily originates from northern China, inland Xinjiang, and the western Pacific with different monthly contributions. The increase in δ²H and δ¹⁸O isotopes from June to August indicates sub-cloud evaporation. The mean sub-cloud evaporation rate was 3.5% during the summer. Sub-cloud evaporation is associated with relative humidity and precipitation. Recycled moisture accounts for 12% of local precipitation and 2.5%–31.6% of total monthly precipitation. After considering the contribution of evaporation, the estimated average recycling ratio increased from 12% to 24%, suggesting the important role of evaporation processes in lakes in the formation of precipitation.

The contemporary era is marked by the faster exploitation of groundwater resources due to the combined effects of burgeoning population and rapid industrialisation. This study tries to delineate the groundwater potential zones (GWPZs) in a fragile and agriculturally dominant watershed of North-East India using the GIS-based multi-criteria decision analysis (MCDA) approach and the Analytical Hierarchy Process (AHP) technique. The study has undertaken 10 influencing factors: geomorphology, geology, land use/land cover (LU/LC), drainage density, rainfall, soil texture, slope, lineament density, topographic wetness index (TWI) and normalised difference water index (NDWI). Suitable weights for the parameters are assigned according to their relative importance and association with groundwater storage based on a pairwise comparison matrix (PCM). Four GWPZs with their respective coverages namely poor (3.39%), moderate (24.98%), good (33.36%) and excellent (38.27%) categories are found. The central and southern parts of the study area covering a portion of Udalguri, Sonitpur and Darrang districts of Assam have porous geological settings and floodplains, indicating high groundwater potentiality. In contrast, the northern part with hard and rugged terrain lacks groundwater storage. Incorporating the socio-economic aspect, particularly the number of villages with or without access to suitable groundwater, significantly enhances the study's utility. The outcome is cross-verified with the well data obtained from the Central Groundwater Board (CGWB) and field data which is validated using the receiver operating characteristics (ROC) curve resulting in an accuracy of 72.9%. Hence, this inquiry has implications for both regional and global significance and will assist stakeholders and authorities in creating a roadmap for sustainable and effective water use.

Consumption of arsenic (As)-contaminated groundwater adversely impacts the health of almost 20 million people in China. Determining the sources of As-affected groundwaters may help to improve our understanding of the controlling processes on As mobilization in groundwater systems. In this study, stable hydrogen and oxygen isotopes of water (δ¹⁸O and δD) were employed to delineate the groundwater recharge sources and the interactions between river/pond and groundwater in Shahu village, a typical high-As groundwater area in Jianghan Plain, central China. Utilizing a two-component mixing model based on δ¹⁸O and δD, we successfully calculated the river water contribution to groundwater including its uncertainty analysis and roughly distinguished the different water bodies within the aquifer system. Cl/Br was used to further identify the recharge contributions for shallow (10 m below the ground surface, low As concentrations), intermediate (25 m below the ground surface, high-As concentrations) and deep (50 m below the ground surface, high-As concentrations) groundwaters. The hydrogen and oxygen stable isotope signatures of high As and high total organic carbon (TOC) groundwaters (intermediate and deep aquifer) generally plotted near the local meteoric water line (LMWL). However, the δ¹⁸O and δD signatures of low As and low TOC groundwaters (shallow aquifer) tended to shift away from the LMWL along evaporation lines. These relationships revealed that the low As groundwater principally derived from surface water (river and pond), while the high-As groundwater mainly recharged from local precipitation through preferential channel as well as the bedrock and/or adjacent aquifer. Our results will enhance the comprehension of the genesis of high-As groundwater in Jianghan Plain.