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Open accessJournal ArticleDOI: 10.1038/S41467-021-21651-0

Continental-scale analysis of shallow and deep groundwater contributions to streams.

04 Mar 2021-Nature Communications (Nature Publishing Group)-Vol. 12, Iss: 1, pp 1450-1450
Abstract: Groundwater discharge generates streamflow and influences stream thermal regimes. However, the water quality and thermal buffering capacity of groundwater depends on the aquifer source-depth. Here, we pair multi-year air and stream temperature signals to categorize 1729 sites across the continental United States as having major dam influence, shallow or deep groundwater signatures, or lack of pronounced groundwater (atmospheric) signatures. Approximately 40% of non-dam stream sites have substantial groundwater contributions as indicated by characteristic paired air and stream temperature signal metrics. Streams with shallow groundwater signatures account for half of all groundwater signature sites and show reduced baseflow and a higher proportion of warming trends compared to sites with deep groundwater signatures. These findings align with theory that shallow groundwater is more vulnerable to temperature increase and depletion. Streams with atmospheric signatures tend to drain watersheds with low slope and greater human disturbance, indicating reduced stream-groundwater connectivity in populated valley settings.

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Topics: Groundwater discharge (73%), Groundwater (61%), Baseflow (61%) ... read more
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Open accessJournal ArticleDOI: 10.1016/J.EJRH.2021.100930
Hejiang Cai1, Hejiang Cai2, Haiyun Shi1, Suning Liu1  +1 moreInstitutions (2)
Abstract: Study region Central eastern continental United States. Study focus Groundwater level prediction is of great significance for the management of global water resources. Recently, machine learning, which can deal with highly nonlinear interactions among complex hydrological factors, has been widely applied to groundwater level prediction. However, previous studies mainly focused on improving the simulation performance in specific regions using different machine learning methods, while this study focused on the impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning. New hydrological insights for the region A gated recurrent unit (GRU) neural network was built for groundwater level simulation in 78 catchments in the study region, and principal component analysis was used to cluster a variety of catchment hydrological variables and determine the input variables for the GRU model. Detrended fluctuation analysis was applied to analyze the autocorrelation of groundwater level in each catchment. This study further explored the influences of the hydrogeological properties of different catchments and the autocorrelation of groundwater levels on machining learning simulations. The results showed that the GRU model performed better in regions where hydrogeological properties could promote more effective responses of groundwater to external changes. Moreover, a negative correlation between the simulation performance of machine learning and the autocorrelation of the groundwater level was found.

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Topics: Groundwater (52%), Hydrogeology (51%)

3 Citations


Journal ArticleDOI: 10.1029/2020WR029284
Topics: Baseflow (68%), Aridity index (57%)

3 Citations


Open accessJournal ArticleDOI: 10.5194/HESS-25-5355-2021
Abstract: Water resource management (WRM) practices, such as groundwater and surface water abstractions and effluent discharges, may impact baseflow Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on Baseflow Index (BFI) using statistical models of 429 catchments from Great Britain Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates) The LR and RF models show good agreement between explanatory covariates In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography, and aridity are significant or important natural covariates in explaining BFI When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes, and effluent discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model Reservoir storage covariates are not significant or are unimportant in both the LR and RF models for this large-sample analysis Inclusion of WRM terms improves the performance of some models in specific catchments The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR models of catchments with moderate to high effluent discharges However, there is no evidence that the inclusion of the WRM covariates improves the performance of LR models for catchments with high surface water abstraction or that they improve the performance of the RF models These observations are discussed within a conceptual framework for baseflow generation that incorporates WRM practices A wide range of schemes and measures are used to manage water resources in the UK These include conjunctive-use and low-flow alleviation schemes and hands-off flow measures Systematic information on such schemes is currently unavailable in CAMELS-GB, and their specific effects on BFI cannot be constrained by the current study Given the significance or importance of WRM terms in the models, it is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological datasets and in the analysis and prediction of BFI and other measures of baseflow

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Topics: Baseflow (54%)

3 Citations


Open accessJournal ArticleDOI: 10.2166/NH.2021.011
Lijun Tian1, Lijun Tian2, Yongli Gao2, Guang Yang3  +7 moreInstitutions (6)
01 Jun 2021-Hydrology Research
Abstract: The Edwards Aquifer (EA) in Central Texas provides water supply for over two million people and contains springs that are hydrologically and ecologically important to the region. The residence time of groundwater in the EA ranges from a few days to many thousands of years, since water in the aquifer is contained and transported within both matrix porosity and large conduits. In this study, stable isotopes of water from five springs are investigated for tracing the origin of water and hydrological processes in the EA system during 2017–2019. There is a quick response of the isotopic signals measured at these springs to changes in the isotopic compositions of precipitation. By utilizing an isotope mixing model, we have identified sources of water for these springs with a bi-modal distribution of groundwater supply in the EA: water supplied from deep groundwater with a longer residence time (an average of 67%) and supplemental epikarst interflow with a shorter residence time (an average of 33%). The evolution of hydrochemical water types from HCO3–Ca to HCO3·Cl–Ca·Mg along the EA flowpaths indicates that inputs from epikarst interflow are greater in springs within the artesian zone than the springs within the contributing zone.

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Topics: Aquifer (66%)

2 Citations


Open accessJournal ArticleDOI: 10.1002/ECO.2295
07 May 2021-Ecohydrology

1 Citations


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70 results found


Open accessJournal ArticleDOI: 10.1038/S41592-019-0686-2
Pauli Virtanen1, Ralf Gommers, Travis E. Oliphant, Matt Haberland2  +33 moreInstitutions (15)
03 Feb 2020-Nature Methods
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

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6,244 Citations


Journal ArticleDOI: 10.1175/JTECH-D-11-00103.1
Abstract: A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic...

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1,138 Citations



Journal ArticleDOI: 10.1016/J.ENVSOFT.2011.09.008
David C. Carslaw1, Karl Ropkins2Institutions (2)
Abstract: openair is an R package primarily developed for the analysis of air pollution measurement data but which is also of more general use in the atmospheric sciences. The package consists of many tools for importing and manipulating data, and undertaking a wide range of analyses to enhance understanding of air pollution data. In this paper we consider the development of the package with the purpose of showing how air pollution data can be analysed in more insightful ways. Examples are provided of importing data from UK air pollution networks, source identification and characterisation using bivariate polar plots, quantitative trend estimates and the use of functions for model evaluation purposes. We demonstrate how air pollution data can be analysed quickly and efficiently and in an interactive way, freeing time to consider the problem at hand. One of the central themes of openair is the use of conditioning plots and analyses, which greatly enhance inference possibilities. Finally, some consideration is given to future developments.

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Topics: OpenAIR (61%), Air quality index (55%)

980 Citations