Journal of Hydrology X最新文献

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.

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.

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.

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).

Data from a 389 km² watershed and one of its 14.5 km² subbasins were used to assess the effects of sampling frequency on the estimation accuracy of the exceedance frequency (EF) for suspended solids and nitrate-nitrogen in streams. Values of EF estimated from 17 subsampling schemes were compared with the actual EF (EF_a) at different threshold concentrations. The coefficient of variation and relative bias were used to measure the estimation accuracy. Results indicated that the EF_a of the larger watershed was much lower than that of the smaller watershed for both suspended solids and nitrate-nitrogen. We also found that EF_a can be modeled as an exponential function of the threshold concentration. For the EF estimations, the coefficient of variation decreased with increasing sampling frequency and increasing EF_a. The relative bias tended to be negative when EF_a was low or the threshold concentration was high, reaching -75% in some cases. This result implies that reported EF values based on low-frequency data could be severely underestimated due to the high possibility of missing large events. However, there were also cases with positive relative bias, implying overestimation of EF due to over representation of large events. These findings can be used to determine adequate sampling frequencies for water-quality parameters, avoiding common observed biases (mostly negative) in the estimation of EF for extreme pollution events.

Laura K. Lautz is a premier mentor, collaborator, and researcher at the intersection of natural hydrologic systems and humans. Her research has shifted the paradigm around measuring and understanding the impacts of surface water and groundwater interactions across spatial and temporal scales. She has done this by testing and refining new methods and by collaborating with, training, supporting, and mentoring diverse scientists. Here, we review her research across five themes, summarizing the prior status of the field, what Lautz contributed, as well as new directions in the field inspired by her work. Lautz’s research expanded our understanding of the impacts of stream restoration on surface water-groundwater interactions, where she tested new field methods and showed that restoration structures increase hyporheic exchange, locally altering biogeochemical function of the streambed. She refined novel methods for measuring surface water-groundwater exchanges and worked to make these methods easily accessible through freely available software. Her research group greatly expanded the use of heat as a quantitative tracer of hydrologic processes via the well-used VFLUX and HFLUX programs. Her research evaluated the impacts of surface water-groundwater interactions in urban streams, showing the substantial fluxes of nutrients and chloride that can move through those exchanges and the potential for groundwater to help buffer contamination. To assess groundwater impacts on streamflow below tropical glaciers, she used a wide range of field methods to reveal the sensitivity of these systems to climate change. Finally, she built tools to quantify natural brine contamination of drinking water wells in areas that may later be subject to high-volume hydraulic fracturing, creating a needed ‘pre-fracking’ dataset. Through this process, she identified multiple sources of salinity that are already reaching wells in these systems. Overall, this research has been done with a focus on mentoring and training the next generation of hydrologists, including work to specifically train for careers beyond academia, and facilitating early career scientists to realize their innate potentials. With former trainees in careers across industry, government, and academia, Dr. Laura K. Lautz is now working to build cross-disciplinary research at even larger scales, across federal research units, guaranteeing that an even larger impact on hydrology is still to come.