Articles | Volume 22, issue 6
https://doi.org/10.5194/hess-22-3351-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-3351-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jun 2018
Research article |
| 18 Jun 2018
| 18 Jun 2018
Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods
Adrian A. S. Barfod
CORRESPONDING AUTHOR
Department of Groundwater and Quaternary Geology Mapping, Geological
Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, 8000
Aarhus C, Denmark
Hydrogeophysics Group, Department of Geoscience, Aarhus University,
C.F. Møllers Allé 4, 8000 Aarhus C, Denmark
Ingelise Møller
Department of Groundwater and Quaternary Geology Mapping, Geological
Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, 8000
Aarhus C, Denmark
Anders V. Christiansen
Hydrogeophysics Group, Department of Geoscience, Aarhus University,
C.F. Møllers Allé 4, 8000 Aarhus C, Denmark
Anne-Sophie Høyer
Department of Groundwater and Quaternary Geology Mapping, Geological
Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, 8000
Aarhus C, Denmark
Júlio Hoffimann
Stanford Center for Reservoir Forecasting, School of Earth, Energy
& Environmental Sciences, Stanford University, Green Earth Sciences, 367
Panama St, Stanford, CA 94305, USA
Julien Straubhaar
Centre d'Hydrogéologie et de Géothermie (CHYN), Université
de Neuchâtel, Switzerland
Jef Caers
Stanford Center for Reservoir Forecasting, School of Earth, Energy
& Environmental Sciences, Stanford University, Green Earth Sciences, 367
Panama St, Stanford, CA 94305, USA
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Arun Ravindranath, Naresh Devineni, Upmanu Lall, and Paulina Concha Larrauri
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We present a framework for forecasting water storage requirements in the agricultural sector and an application of this framework to water risk assessment in India. Our framework involves defining a crop-specific water stress index and applying a particular statistical forecasting model to predict seasonal water stress for the crop of interest. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra.
Paula Rodríguez-Escales, Arnau Canelles, Xavier Sanchez-Vila, Albert Folch, Daniel Kurtzman, Rudy Rossetto, Enrique Fernández-Escalante, João-Paulo Lobo-Ferreira, Manuel Sapiano, Jon San-Sebastián, and Christoph Schüth
Hydrol. Earth Syst. Sci., 22, 3213–3227, https://doi.org/10.5194/hess-22-3213-2018, https://doi.org/10.5194/hess-22-3213-2018, 2018
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Minh Tu Pham, Hilde Vernieuwe, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 22, 1263–1283, https://doi.org/10.5194/hess-22-1263-2018, https://doi.org/10.5194/hess-22-1263-2018, 2018
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Cristina Aguilar, Alberto Montanari, and María-José Polo
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Assuming that floods are driven by both short- (meteorological forcing) and long-term perturbations (higher-than-usual moisture), we propose a technique for updating a season in advance the flood frequency distribution. Its application in the Po and Danube rivers helped to reduce the uncertainty in the estimation of floods and thus constitutes a promising tool for real-time management of flood risk mitigation. This study is the result of the stay of the first author at the University of Bologna.
Veit Blauhut, Kerstin Stahl, James Howard Stagge, Lena M. Tallaksen, Lucia De Stefano, and Jürgen Vogt
Hydrol. Earth Syst. Sci., 20, 2779–2800, https://doi.org/10.5194/hess-20-2779-2016, https://doi.org/10.5194/hess-20-2779-2016, 2016
Claus Davidsen, Suxia Liu, Xingguo Mo, Dan Rosbjerg, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 20, 771–785, https://doi.org/10.5194/hess-20-771-2016, https://doi.org/10.5194/hess-20-771-2016, 2016
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In northern China, rivers run dry and groundwater tables drop, causing economic losses for all water use sectors. We present a groundwater-surface water allocation decision support tool for cost-effective long-term recovery of an overpumped aquifer. The tool is demonstrated for a part of the North China Plain and can support the implementation of the recent China No. 1 Document in a rational and economically efficient way.
H. Macian-Sorribes, M. Pulido-Velazquez, and A. Tilmant
Hydrol. Earth Syst. Sci., 19, 3925–3935, https://doi.org/10.5194/hess-19-3925-2015, https://doi.org/10.5194/hess-19-3925-2015, 2015
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One of the most promising alternatives to improve the efficiency in water usage is the implementation of scarcity-based pricing policies based on the opportunity cost of water at the basin scale. Time series of the marginal value of water at selected locations (reservoirs) are obtained using a stochastic hydro-economic model and then post-processed to define step water pricing policies.
C. Dong, Q. Tan, G.-H. Huang, and Y.-P. Cai
Hydrol. Earth Syst. Sci., 18, 1793–1803, https://doi.org/10.5194/hess-18-1793-2014, https://doi.org/10.5194/hess-18-1793-2014, 2014
F. Lombardo, E. Volpi, D. Koutsoyiannis, and S. M. Papalexiou
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B. M. C. Fischer, M. L. Mul, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 17, 2161–2170, https://doi.org/10.5194/hess-17-2161-2013, https://doi.org/10.5194/hess-17-2161-2013, 2013
J. Lorenzo-Lacruz, E. Morán-Tejeda, S. M. Vicente-Serrano, and J. I. López-Moreno
Hydrol. Earth Syst. Sci., 17, 119–134, https://doi.org/10.5194/hess-17-119-2013, https://doi.org/10.5194/hess-17-119-2013, 2013
E. Baratti, A. Montanari, A. Castellarin, J. L. Salinas, A. Viglione, and A. Bezzi
Hydrol. Earth Syst. Sci., 16, 4651–4660, https://doi.org/10.5194/hess-16-4651-2012, https://doi.org/10.5194/hess-16-4651-2012, 2012
J. E. Bremer and T. Harter
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Short summary
Three-dimensional geological models are important to securing and managing groundwater. Such models describe the geological architecture, which is used for modeling the flow of groundwater. Common geological modeling approaches result in one model, which does not quantify the architectural uncertainty of the geology.
We present a comparison of three different state-of-the-art stochastic multiple-point statistical methods for quantifying the geological uncertainty using real-world datasets.
Three-dimensional geological models are important to securing and managing groundwater. Such...
