Journal of Hydrology X最新文献

Apparent groundwater age dating has been proven useful and robust in understanding water origin and mixing processes, particularly when multiple tracers are considered. However, even though now extensively used, the age tracers have not been widely applied in the general practice of flow and transport model calibration. A multi tracer-study was carried out in the Neogene aquifer in Flanders to quantify the apparent age and construct a joint interpretation for the delineation of different groundwater flow systems. This understanding is critical as part of the safety and feasibility studies for the underlying Boom Clay Formation that has been considered as a potential host rock for the geological disposal of radioactive waste. In this study, we combine evidence from tritium/helium-3 (³H/³He), helium-4 (⁴He) and radiocarbon (¹⁴C) dating as well as stable isotopic (δ¹⁸O, δ²H) and hydrochemical signatures in combination with particle tracking-based age distributions from the 3D groundwater flow model. The results of the study indicate that mixing of groundwater with young and old fractions occurs predominantly in the central part of the aquifer which is made evident by the coexistence of ³H (pre and post-bomb pulse Era), ¹⁴C and ⁴He in several groundwater samples. The mixing between water of different origin is also supported by the sampled stable isotopic and hydrochemical composition of groundwater. Particle tracking residence time results show an acceptable agreement with apparent ages derived from age tracers for young (≤100 years) and old (>1000 years) groundwater. Groundwater with ages between 100 and 1000 years is likely a mixture of water with young/old fractions and shows the strongest discrepancies between advective model ages and age tracer based apparent ages. On the basis of our findings, we distinguish between three groundwater flow systems in the Neogene aquifer: i) a shallow/local flow system, with groundwater originating from modern meteoric water; ii) a deep/semi-regional flow system, characterized by old groundwater where the presence of ⁴He_rad is significant; iii) a mixed zone of groundwater flow where the recently infiltrated meteoric water mixes with discharging old groundwater. These results have helped us to refine previously proposed conceptual models for the study area and will in the end reduce uncertainties relevant to the potential future geological disposal of radioactive waste.

Hydrologic models are robust tools for estimating key parameters in the management of water resources, including water inputs, storage, and pathway fluxes. The selection of process-based versus data-driven modeling structure is an important consideration, particularly as advancements in machine learning yield potential for improved model performance but at the cost of lacking physical analogues. Despite recent advancement, there exists an absence of cross-model comparison of the tradeoffs between process-based and data-driven model types in settings with varying hydrologic controls. In this study, we use physically-based (SWAT), conceptually-based (LUMP), and deep-learning (LSTM) models to simulate hydrologic pathway contributions for a fluvial watershed and a karst basin over a twenty-year period. We find that, while all models are satisfactory, the LSTM model outperformed both the SWAT and LUMP models in simulating total discharge and that the improved performance was more evident in the groundwater-dominated karst system than the surface-dominated fluvial stream. Further, the LSTM model was able to achieve this improved performance with only 10–25% of the observed time-series as training data. Regarding pathways, the LSTM model coupled with a recursive digital filter was able to successfully match the magnitude of process-based estimates of quick, intermediate, and slow flow contributions for both basins (ρ ranging from 0.58 to 0.71). However, the process-based models exhibited more realistic time-fractal scaling of hydrologic flow pathways compared to the LSTM model which, depending on project objectives, presents a potential drawback to the use of machine learning models for some hydrologic applications. This study demonstrates the utility and potential extraction of physical-analogues of LSTM modeling, which will be useful as deep learning approaches to hydrologic modeling become more prominent and modelers look for ways to infer physical information from data-driven predictions.

The sensitivity of future groundwater recharge and temperature development was investigated for three alluvial aquifers in the urban agglomeration of the city of Basel, Switzerland. For selected climate projections groundwater recharge and the associated temperature imprinting of aquifers, which are mainly determined by artificial groundwater recharge and infiltrating surface water, were investigated.

3D numerical groundwater flow and heat-transport modeling, allowed quantifying and differentiating between natural and artificial groundwater recharge and thermal impacts. For aquifers where the infiltration of river water is an important component in the groundwater balance, the effects of climate change will be influenced by changes in river flow and thermal regimes and also by artificial groundwater recharge of surface water. Considering all climate scenarios investigated, the net heat input from river water infiltration for the Lange Erlen case study area increases by an average of 42 % by 2055 and 62 % by 2085 compared to the reference year 2000. Together with further heat inputs, particularly by artificial groundwater recharge, the temperatures of the extracted drinking water would increase by 0.4 to 1.3 K by 2055 and 0.7 to 3.1 K by 2085. In the Hardwald case study area, the most significant heat exchange occurs by artificial groundwater recharge. As a result, and considering all climate scenarios investigated, heat loss by groundwater extraction increases by an average of 38 % during the winter months from the year 2000 to the year 2085. The increased heat input, especially in the summer months, results in a temperature increase of the extracted drinking water of 0.2 to 1.0 K by 2055 and 0.6 to 4.0 K by 2085. In the Lower Birs Valley case study area, net heat input from river water infiltration increases by an average of 42 % by 2055 and 62 % by 2085. Correspondingly, the temperatures of the extracted drinking water increase by 0.9 to 3.2 K by 2055 and by 0.3 to 5.4 K by 2085.

The quantitative assessment of climate change impacts on the groundwater resources presented allows to differentiate between hydraulic and thermal impacts of natural and artificial groundwater recharge processes. Accordingly, individual drinking water wells are exposed differently to the various components of groundwater recharge. Seasonal shifts in natural groundwater recharge processes and adaptation strategies related to artificial groundwater recharge could therefore be an important factor affecting groundwater resources in future. Moreover, increased groundwater recharge from artificial groundwater recharge systems in summer months and the interaction with surface waters during high runoff periods, which will occur more often in winter months, are likely to strongly influence groundwater recharge and temperatures.

Ecological values of water have gained increasing attention over the past decades in both (eco)hydrological research and water resources management. Water quality is an important ecological steering variable, and graphical water quality diagrams may aid in rapid interpretation of the hydrochemical status of a site. Traditionally used water quality diagrams for showing multiple variables (e.g. Stiff, Maucha) were developed primarily for hydrogeological purposes, with limited information on ecologically relevant nutrient parameters.

This paper presents adapted classical water quality diagrams that retain the traditional information on ions for hydrogeological characterization, and additionally provide information on nutrients for ecological water quality characterization.

A scaling factor is used for the minor ions to visually get them across more equally compared to the macro-ion ions in the water quality diagram. Scaling of minor ions is presented based on average concentrations, as well as on water quality policy norms. Four different water quality diagrams are presented, all with the same ions included, but with different appearances to suit different preferences of individual users. Regional, national and continental scale data are used to illustrate how the different diagrams show spatial and temporal water quality characteristics.

The adapted diagrams are innovative with respect to adding comprehensive visual information on the four ecohydrologically relevant nutrient species levels (NO₃, NH₄, PO₄, K), advanced insight in redox status from the combination of four redox sensitive parameters (Fe, NO₃, SO₄, NH₄) and the option to scale minor ions relative to average measured concentrations or to water quality policy norms. Using policy norms for scaling has the advantage of providing an ‘alarm function’ of exceedance of norms when concentrations surpass the ring used in the diagram. We discuss possible standardisation of scaling factors to enable comparability between sites.

While nonstationary flood frequency analysis (NSFFA) methods have proliferated, few studies have rigorously compared them for modeling changes in both the central tendency and variability of annual peak-flow series, also known as the annual maximum series (AMS), in hydrologically diverse areas. Through Monte Carlo experiments, we appraise five methods for updating estimates of 10- and 100-year floods at gauged sites using synthetic records based on sample moments and change trajectories of observed AMS in the conterminous United States (CONUS). We compare two methods that consider changes in both central tendency and variability - a Gamma generalized linear model estimated with weighted least squares and the Generalized Additive Model for Location, Scale, Shape (GAMLSS) - with a distribution-free approach (quantile regression), and baseline cases assuming stationarity or only changes in central tendency.

‘Trend-space’ plots identify realistic AMS changes for which modeling trends in both central tendency and variability were warranted based on fractional root mean squared errors (fRMSE). They also reveal statistical properties of AMS under which NSFFA models perform especially well or poorly. For instance, quantile regression performed especially well (poorly) under strong negative (positive) skewness. Although the nonstationary LP3 distribution accommodates most AMS with trends well, the sensitivity of NSFFA model performance to different sample moments and trends suggests the need for more flexibility in prescribing design-flood adjustments in the CONUS. A follow-up comparison of regional NSFFA models pooling at-site AMS would further illuminate NSFFA guidance, especially for AMS with properties less conducive to NSFFA modeling, such as positive skewness and increasing variability.

This review paper briefly summarizes the research results of the majority (∼70%) women team of the Hydrogeology Research Group of Eötvös Loránd University, Hungary, led by Judit Mádl-Szőnyi. The group had originally focused on basin-scale groundwater flow systems and the related processes and phenomena but extended its research activity to other geofluids in answer to global challenges such as the water crisis, climate change, and energy transition. However, the core concept of these studies remained the basin-scale system approach of groundwater flow, as these flow systems interact with the rock framework and all other geofluids resulting in a systematic distribution of the related environmental and geological processes and phenomena. The presented methodological developments and mostly general results have been and can be utilized in the future in any sedimentary basins. These cover the following fields of hydrogeology and geofluid research: carbonate and karst hydrogeology, asymmetric basin and flow pattern, geothermal and petroleum hydrogeology, radioactivity of groundwater, groundwater and surface water interaction, groundwater-dependent ecosystems, effects of climate change on groundwater flow systems, managed aquifer recharge.

Satellite remote sensing data (SAR and Ocean Color), MERRA-2 reanalysis and records at Astrakhan meteorological station were used to investigate interannual variability of ice cover characteristics in the North Caspian Sea for 23 winter seasons (November 1 – April 15) from 1999/2000 to 2021/2022. The maximum annual ice cover area, ice freeze onset and melt dates and ice cover duration were determined from satellite remote sensing data, mostly SAR instruments on board the European Space Agency’s satellites, ranging from ERS-2 to the Sentinel-1A, -1B tandem. We propose a new band combination for Sentinel-2 MSI and Landsat-8 OLI that allows better distinguishing ice cover from clouds or land than the standard RGB composites. In the absence of SAR data, this method was used to estimate the above mentioned parameters with high spatial and temporal resolution. To assess the severity of winters, the criterion on the basis of the sum of freezing degree-days (SFDD) was applied. For this purpose, we used values of daily minimum air temperature over the North Caspian (44.46°–47.14°N, 46.70–52.90°E), daily mean and daily minimum ones over its coldest eastern part (with the western border at 50°E), obtained from the MERRA-2 reanalysis, as well as data from the meteorological station in Astrakhan (46.35°N, 48.07°E). The resulting SFDD sequences show that until the winter of 2011/2012, there was a cooling trend on average (with noticeable interannual variability), whereas after that winter it changed to warming for Astrakhan and virtually disappeared for the North Caspian and its eastern part. A noticeable interannual variability is also shown by the maximum ice area and the duration of the ice period, both parameters with maximums in the winter of 2011/2012. We discuss in detail the correspondence between the SFDD and ice cover characteristics variations, as well as previously published results. In agreement with the other authors, we find that in the 21st century, compared to the 20th century, the number of very severe and severe winters has decreased, while the number of mild winters has increased.

The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPER_PNW) to the upper Missouri River basin (PROSPER_UM), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPER_UM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPER_UM are generally consistent with the PROSPER_PNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPER_UM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.

River water temperature measurement networks suffer from an inadequate spatial coverage and a lack of data. No methods exist for the regional estimation of river water temperature at ungauged sites based on data series from gauged sites. The development of such methods is hence of significant importance. It is proposed in this study to develop a Temperature-Duration-Curve (TDC) based method to estimate river water temperature at ungauged sites on a real-time basis. A Generalised Additive Model (GAM) based method is used to estimate TDCs at ungauged sites. The estimated TDCs are then used in combination with a spatial interpolation method to obtain daily temperature estimates at ungauged sites. Results are compared with a simple method based on the geographical distance weighted average of neighboring stations. The approaches are applied to 126 river thermal stations located on Atlantic salmon rivers in eastern Canada. Leave-one-out cross validation results indicate that the TDC based methods are robust and outperform the geographical distance weighted method.

Water temperature plays a primary role in driving ecological processes in streams due to its direct impact on biogeochemical cycles and the physiological processes of stream fauna, such as growth, development, and the timing of life history events. Streams influenced by snowpack melt are generally cooler in the summer and demonstrate less sensitivity to climate variability in what is commonly referred to as “climate buffering”. Despite the substantial influence of snowpack on stream temperature and expected changes in snowpack accumulation and melt timing with climate change, methods for representing snowpack in statistical models for stream temperature have not been well explored. In this investigation, we quantified the extent of stream temperature buffering in free-flowing streams across a geographically diverse region in the Pacific Northwest USA. We demonstrated that statistical models of daily mean stream temperature can be improved by explicitly accounting for temporal variability in a small number of climate covariates believed to be mechanistically related to stream temperature. Our novel statistical approach included as predictors combinations and interactions between the following variables: (1) air temperature, (2) lagged air temperature (where the lag duration varied according to its relationship with flow on a given day at that site), (3) flow, (4) snowpack in the upstream catchment, and (5) day of year. We found that sites with substantial snow influence were associated with increased air temperature buffering during the warm season and longer air temperature lags (>30 days during spring high flows and ∼ 10 days during late summer low flows) compared to sites where precipitation predominantly fell as rain (<6 days year-round). By accounting for snowpack and temporal variation in lagged heat transfer processes, our models were able to accurately predict seasonal patterns and interannual variability in stream temperature in validation data from years not used in model fits using publicly available data sources (average RMPSE ∼ 0.80).