Articles | Volume 23, issue 5
https://doi.org/10.5194/hess-23-2279-2019
© Author(s) 2019. 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-23-2279-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 May 2019
Research article |
| 13 May 2019
| 13 May 2019
Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios
Syed M. Touhidul Mustafa
CORRESPONDING AUTHOR
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
M. Moudud Hasan
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Ajoy Kumar Saha
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Rahena Parvin Rannu
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Els Van Uytven
Department of Civil Engineering – Hydraulics Section, KU Leuven, Kasteelpark 40 box 2448, 3001 Leuven, Belgium
Patrick Willems
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Department of Civil Engineering – Hydraulics Section, KU Leuven, Kasteelpark 40 box 2448, 3001 Leuven, Belgium
Marijke Huysmans
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Related authors
No articles found.
Min Lu, Bart Rogiers, Koen Beerten, Matej Gedeon, and Marijke Huysmans
Hydrol. Earth Syst. Sci., 26, 3629–3649, https://doi.org/10.5194/hess-26-3629-2022, https://doi.org/10.5194/hess-26-3629-2022, 2022
Short summary
Short summary
Lowland rivers and shallow aquifers are closely coupled. We study their interactions here using a combination of impulse response modeling and hydrological data analysis. The results show that the lowland catchments are groundwater dominated and that the hydrological system from precipitation impulse to groundwater inflow response is a very fast response regime. This study also provides an alternative method to estimate groundwater inflow to rivers from the perspective of groundwater level.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Karen Gabriels, Patrick Willems, and Jos Van Orshoven
Nat. Hazards Earth Syst. Sci., 22, 395–410, https://doi.org/10.5194/nhess-22-395-2022, https://doi.org/10.5194/nhess-22-395-2022, 2022
Short summary
Short summary
As land use influences hydrological processes (e.g., forests have a high water retention and infiltration capacity), it also impacts floods downstream in the river system. This paper demonstrates an approach quantifying the impact of land use changes on economic flood damages: damages in an initial situation are quantified and compared to damages of simulated floods associated with a land use change scenario. This approach can be used as an explorative tool in sustainable flood risk management.
Hossein Tabari, Santiago Mendoza Paz, Daan Buekenhout, and Patrick Willems
Hydrol. Earth Syst. Sci., 25, 3493–3517, https://doi.org/10.5194/hess-25-3493-2021, https://doi.org/10.5194/hess-25-3493-2021, 2021
Bertold Mariën, Inge Dox, Hans J. De Boeck, Patrick Willems, Sebastien Leys, Dimitri Papadimitriou, and Matteo Campioli
Biogeosciences, 18, 3309–3330, https://doi.org/10.5194/bg-18-3309-2021, https://doi.org/10.5194/bg-18-3309-2021, 2021
Short summary
Short summary
The drivers of the onset of autumn leaf senescence for several deciduous tree species are still unclear. Therefore, we addressed (i) if drought impacts the timing of autumn leaf senescence and (ii) if the relationship between drought and autumn leaf senescence depends on the tree species. Our study suggests that the timing of autumn leaf senescence is conservative across years and species and even independent of drought stress.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
Short summary
Short summary
We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Buruk Kitachew Wossenyeleh, Kaleb Asnake Worku, Boud Verbeiren, and Marijke Huysmans
Nat. Hazards Earth Syst. Sci., 21, 39–51, https://doi.org/10.5194/nhess-21-39-2021, https://doi.org/10.5194/nhess-21-39-2021, 2021
Short summary
Short summary
Droughts are mainly caused by a reduction of precipitation, and they affect both surface and groundwater resources. Drought propagates through the hydrological cycle and may impact vulnerable ecosystems. We investigated drought propagation in the hydrological cycle, focusing on assessing its impact on a groundwater-fed wetland ecosystem in the Doode Bemde wetland in central Belgium. We used a method combining meteorological drought indices, water balance models and groundwater models.
Benjamin Campforts, Veerle Vanacker, Frédéric Herman, Matthias Vanmaercke, Wolfgang Schwanghart, Gustavo E. Tenorio, Patrick Willems, and Gerard Govers
Earth Surf. Dynam., 8, 447–470, https://doi.org/10.5194/esurf-8-447-2020, https://doi.org/10.5194/esurf-8-447-2020, 2020
Short summary
Short summary
In this contribution, we explore the spatial determinants of bedrock river incision in the tropical Andes. The model results illustrate the problem of confounding between climatic and lithological variables, such as rock strength. Incorporating rock strength explicitly into river incision models strongly improves the explanatory power of all tested models and enables us to clarify the role of rainfall variability in controlling river incision rates.
Els Van Uytven, Jan De Niel, and Patrick Willems
Hydrol. Earth Syst. Sci., 24, 2671–2686, https://doi.org/10.5194/hess-24-2671-2020, https://doi.org/10.5194/hess-24-2671-2020, 2020
Short summary
Short summary
In recent years many methods have been developed for the statistical downscaling of climate model outputs. Each statistical downscaling method has strengths and limitations, but those are rarely evaluated. This paper illustrates an approach to evaluating the skill of statistical downscaling methods for the specific purpose of impact analysis in hydrology.
Jan De Niel and Patrick Willems
Hydrol. Earth Syst. Sci., 23, 871–882, https://doi.org/10.5194/hess-23-871-2019, https://doi.org/10.5194/hess-23-871-2019, 2019
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Edouard Goudenhoofdt, Laurent Delobbe, and Patrick Willems
Hydrol. Earth Syst. Sci., 21, 5385–5399, https://doi.org/10.5194/hess-21-5385-2017, https://doi.org/10.5194/hess-21-5385-2017, 2017
Short summary
Short summary
Knowing the characteristics of extreme precipitation is useful for flood management applications like sewer system design. The potential of a 12-year high-quality weather radar precipitation dataset is investigated by comparison with rain gauges. Despite known limitations, a good agreement is found between the radar and the rain gauges. Using the radar data allow us to reduce the uncertainty of the extreme value analysis, especially for short duration extremes related to thunderstorms.
Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Susana Ochoa-Rodriguez, Patrick Willems, Abdellah Ichiba, Li-Pen Wang, Rui Pina, Johan Van Assel, Guendalina Bruni, Damian Murla Tuyls, and Marie-Claire ten Veldhuis
Hydrol. Earth Syst. Sci., 21, 2361–2375, https://doi.org/10.5194/hess-21-2361-2017, https://doi.org/10.5194/hess-21-2361-2017, 2017
Short summary
Short summary
Data from 10 urban or peri-urban catchments located in five EU countries are used to analyze the imperviousness distribution and sewer network geometry. Consistent scale invariant features are retrieved for both (fractal dimensions can be defined), which enables to define a level of urbanization. Imperviousness representation in operational model is also found to exhibit scale-invariant features (even multifractality). The research was carried out as part of the UE INTERREG IV RainGain project.
Tanja de Boer-Euser, Laurène Bouaziz, Jan De Niel, Claudia Brauer, Benjamin Dewals, Gilles Drogue, Fabrizio Fenicia, Benjamin Grelier, Jiri Nossent, Fernando Pereira, Hubert Savenije, Guillaume Thirel, and Patrick Willems
Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, https://doi.org/10.5194/hess-21-423-2017, 2017
Short summary
Short summary
In this study, the rainfall–runoff models of eight international research groups were compared for a set of subcatchments of the Meuse basin to investigate the influence of certain model components on the modelled discharge. Although the models showed similar performances based on general metrics, clear differences could be observed for specific events. The differences during drier conditions could indeed be linked to differences in model structures.
Hossein Tabari, Rozemien De Troch, Olivier Giot, Rafiq Hamdi, Piet Termonia, Sajjad Saeed, Erwan Brisson, Nicole Van Lipzig, and Patrick Willems
Hydrol. Earth Syst. Sci., 20, 3843–3857, https://doi.org/10.5194/hess-20-3843-2016, https://doi.org/10.5194/hess-20-3843-2016, 2016
Vincent Wolfs, Quan Tran Quoc, and Patrick Willems
Proc. IAHS, 373, 1–6, https://doi.org/10.5194/piahs-373-1-2016, https://doi.org/10.5194/piahs-373-1-2016, 2016
Short summary
Short summary
Water management is constantly evolving. Trends, such as population growth, urbanization and climate change, pose new challenges to water management. We developed a new and flexible modelling approach to generate very fast models of catchment hydrology, rivers and sewer systems that can be tailored to numerous applications in water management. To illustrate the developed framework, a case study of integrated hydrological-hydraulic modelling for the Grote Nete catchment in Belgium is elaborated.
C. Onyutha and P. Willems
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-12167-2015, https://doi.org/10.5194/hessd-12-12167-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
To investigate the possible change in catchment behavior, which may interfere with the flow-rainfall relationship, three rainfall-runoff models were applied to the main catchments of the Nile Basin in Africa based on inputs covering the period from 1940 to 2003. There was close agreement between the changes in the observed and simulated overland flow from all the models. Thus, change in catchment behavior due to anthropogenic influence in the Nile basin over the selected time period was minimal.
L.-P. Wang, S. Ochoa-Rodríguez, C. Onof, and P. Willems
Hydrol. Earth Syst. Sci., 19, 4001–4021, https://doi.org/10.5194/hess-19-4001-2015, https://doi.org/10.5194/hess-19-4001-2015, 2015
Short summary
Short summary
A new methodology is proposed in this paper, focusing on improving the applicability of the operational weather radar data to urban hydrology with rain gauge data. The proposed methodology employed a simple yet effective technique to extract additional information (called local singularity structure) from radar data, which was generally ignored in related works. The associated improvement can be particularly seen in capturing storm peak magnitudes, which is critical for urban applications.
C. Onyutha and P. Willems
Hydrol. Earth Syst. Sci., 19, 2227–2246, https://doi.org/10.5194/hess-19-2227-2015, https://doi.org/10.5194/hess-19-2227-2015, 2015
Short summary
Short summary
Variability of rainfall in the Nile Basin was found linked to the large-scale atmosphere-ocean interactions. This finding is vital for a number of water management and planning aspects. To give just one example, it may help in obtaining improved quantiles for flood or drought/water scarcity risk management. This is especially important under conditions of (1) questionable data quality, and (2) data scarcity. These conditions are typical of the Nile Basin and inevitably need to be addressed.
M. A. Sunyer, Y. Hundecha, D. Lawrence, H. Madsen, P. Willems, M. Martinkova, K. Vormoor, G. Bürger, M. Hanel, J. Kriaučiūnienė, A. Loukas, M. Osuch, and I. Yücel
Hydrol. Earth Syst. Sci., 19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, https://doi.org/10.5194/hess-19-1827-2015, 2015
A. Ochoa, L. Pineda, P. Crespo, and P. Willems
Hydrol. Earth Syst. Sci., 18, 3179–3193, https://doi.org/10.5194/hess-18-3179-2014, https://doi.org/10.5194/hess-18-3179-2014, 2014
D. Vrebos, T. Vansteenkiste, J. Staes, P. Willems, and P. Meire
Hydrol. Earth Syst. Sci., 18, 1119–1136, https://doi.org/10.5194/hess-18-1119-2014, https://doi.org/10.5194/hess-18-1119-2014, 2014
D. E. Mora, L. Campozano, F. Cisneros, G. Wyseure, and P. Willems
Hydrol. Earth Syst. Sci., 18, 631–648, https://doi.org/10.5194/hess-18-631-2014, https://doi.org/10.5194/hess-18-631-2014, 2014
M. T. Taye and P. Willems
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-7857-2013, https://doi.org/10.5194/hessd-10-7857-2013, 2013
Revised manuscript not accepted
Related subject area
Subject: Groundwater hydrology | Techniques and Approaches: Uncertainty analysis
Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties
Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed
Interpretation of multi-scale permeability data through an information theory perspective
Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization
Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions
Numerical modeling and sensitivity analysis of seawater intrusion in a dual-permeability coastal karst aquifer with conduit networks
On the efficiency of the hybrid and the exact second-order sampling formulations of the EnKF: a reality-inspired 3-D test case for estimating biodegradation rates of chlorinated hydrocarbons at the port of Rotterdam
Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models
Groundwater flow processes and mixing in active volcanic systems: the case of Guadalajara (Mexico)
Analyses of uncertainties and scaling of groundwater level fluctuations
Analyzing the effects of geological and parameter uncertainty on prediction of groundwater head and travel time
Interpolation of groundwater quality parameters with some values below the detection limit
An approach to identify urban groundwater recharge
Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile
Falk Heße, Sebastian Müller, and Sabine Attinger
Hydrol. Earth Syst. Sci., 28, 357–374, https://doi.org/10.5194/hess-28-357-2024, https://doi.org/10.5194/hess-28-357-2024, 2024
Short summary
Short summary
In this study, we have presented two different advances for the field of subsurface geostatistics. First, we present data of variogram functions from a variety of different locations around the world. Second, we present a series of geostatistical analyses aimed at examining some of the statistical properties of such variogram functions and their relationship to a number of widely used variogram model functions.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Aronne Dell'Oca, Alberto Guadagnini, and Monica Riva
Hydrol. Earth Syst. Sci., 24, 3097–3109, https://doi.org/10.5194/hess-24-3097-2020, https://doi.org/10.5194/hess-24-3097-2020, 2020
Short summary
Short summary
Permeability of natural systems exhibits heterogeneous spatial variations linked with the size of the measurement support scale. As the latter becomes coarser, the system appearance is less heterogeneous. As such, sets of permeability data associated with differing support scales provide diverse amounts of information. In this contribution, we leverage information theory to quantify the information content of gas permeability datasets collected with four diverse measurement support scales.
Robert Reinecke, Laura Foglia, Steffen Mehl, Jonathan D. Herman, Alexander Wachholz, Tim Trautmann, and Petra Döll
Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, https://doi.org/10.5194/hess-23-4561-2019, 2019
Short summary
Short summary
Recently, the first global groundwater models were developed to better understand surface-water–groundwater interactions and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. In this study we present the first global maps of model sensitivity according to their parameterization and build a foundation to improve datasets, model design, and model understanding.
Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger
Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, https://doi.org/10.5194/hess-23-171-2019, 2019
Short summary
Short summary
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
Zexuan Xu, Bill X. Hu, and Ming Ye
Hydrol. Earth Syst. Sci., 22, 221–239, https://doi.org/10.5194/hess-22-221-2018, https://doi.org/10.5194/hess-22-221-2018, 2018
Short summary
Short summary
This study helps hydrologists better understand the parameters in modeling seawater intrusion in a coastal karst aquifer. Local and global sensitivity studies are conducted to evaluate a density-dependent numerical model of seawater intrusion. The sensitivity analysis indicates that karst features are critical for seawater intrusion modeling, and the evaluation of hydraulic conductivity is biased in continuum SEAWAT model. Dispervisity is no longer important in the advection-dominated aquifer.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
Short summary
Short summary
The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Nikolaj Kruse Christensen, Steen Christensen, and Ty Paul A. Ferre
Hydrol. Earth Syst. Sci., 20, 1925–1946, https://doi.org/10.5194/hess-20-1925-2016, https://doi.org/10.5194/hess-20-1925-2016, 2016
Short summary
Short summary
Our primary objective in this study is to provide a virtual environment that allows users to determine the value of geophysical data and, furthermore, to investigate how best to use those data to develop groundwater models and to reduce their prediction errors. When this has been carried through for alternative data sampling, parameterization and inversion approaches, the best alternative can be chosen by comparison of prediction results between the alternatives.
A. Hernández-Antonio, J. Mahlknecht, C. Tamez-Meléndez, J. Ramos-Leal, A. Ramírez-Orozco, R. Parra, N. Ornelas-Soto, and C. J. Eastoe
Hydrol. Earth Syst. Sci., 19, 3937–3950, https://doi.org/10.5194/hess-19-3937-2015, https://doi.org/10.5194/hess-19-3937-2015, 2015
Short summary
Short summary
A conceptual model of groundwater flow processes and mixing was developed using a combination of hydrogeochemistry, isotopes and multivariate analysis. The implementation to the case of Guadalajara showed that groundwater was classified into four groups: cold groundwater, hydrothermal water, polluted groundwater and mixed groundwater. A multivariate mixing model was used to calculate the proportion of different fluids in sampled well water. The result helps authorities in decision making.
X. Y. Liang and Y.-K. Zhang
Hydrol. Earth Syst. Sci., 19, 2971–2979, https://doi.org/10.5194/hess-19-2971-2015, https://doi.org/10.5194/hess-19-2971-2015, 2015
Short summary
Short summary
The error or uncertainty in head, obtained with an analytical or numerical solution, at an early time is mainly caused by the random initial condition. The error reduces with time, later reaching a constant error. The constant error at a later time is mainly due to the effects of the uncertain source/sink. The error caused by the uncertain boundary is limited to a narrow zone. Temporal scaling of head exists in most parts of a low permeable aquifer, mainly caused by recharge fluctuation.
X. He, T. O. Sonnenborg, F. Jørgensen, A.-S. Høyer, R. R. Møller, and K. H. Jensen
Hydrol. Earth Syst. Sci., 17, 3245–3260, https://doi.org/10.5194/hess-17-3245-2013, https://doi.org/10.5194/hess-17-3245-2013, 2013
A. Bárdossy
Hydrol. Earth Syst. Sci., 15, 2763–2775, https://doi.org/10.5194/hess-15-2763-2011, https://doi.org/10.5194/hess-15-2763-2011, 2011
E. Vázquez-Suñé, J. Carrera, I. Tubau, X. Sánchez-Vila, and A. Soler
Hydrol. Earth Syst. Sci., 14, 2085–2097, https://doi.org/10.5194/hess-14-2085-2010, https://doi.org/10.5194/hess-14-2085-2010, 2010
R. Rojas, O. Batelaan, L. Feyen, and A. Dassargues
Hydrol. Earth Syst. Sci., 14, 171–192, https://doi.org/10.5194/hess-14-171-2010, https://doi.org/10.5194/hess-14-171-2010, 2010
Cited articles
Ali, R., McFarlane, D., Varma, S., Dawes, W., Emelyanova, I., Hodgson, G.,
and Charles, S.: Potential climate change impacts on groundwater resources of
south-western Australia, J. Hydrol., 475, 456–472,
https://doi.org/10.1016/j.jhydrol.2012.04.043, 2012
Abdollahi, K., Bashir, I., Verbeiren, B., Harouna, M. R., Van Griensven, A.,
Huysmans, M., and Batelaan, O.: A distributed monthly water balance model:
formulation and application on Black Volta Basin, Environ. Earth Sci., 76,
1–18, https://doi.org/10.1007/s12665-017-6512-1, 2017.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration-Guidelines for computing crop water requirements-FAO
Irrigation and drainage paper 56, FAO Rome, 300, D05109, available at:
http://www.fao.org/3/X0490E/X0490E00.htm (last access: 8 May 2019),
1998.
Asad-uz-Zaman, M. and Rushton, K. R.: Improved yield from aquifers of limited
saturated thickness using inverted wells, J. Hydrol., 326, 311–324,
https://doi.org/10.1016/j.jhydrol.2005.11.001, 2006.
Batelaan, O. and De Smedt, F.: WetSpass: a flexible, GIS based, distributed
recharge methodology for regional groundwater modelling, IAHS-AISH P., 269,
11–18, 2001.
Bonsor, H. C., MacDonald, A. M., Ahmed, K. M., Burgess, W. G., Basharat, M.,
Calow, R. C., Dixit, A., Foster, S. S. D., Gopal, K., Lapworth, D. J.,
Moench, M., Mukherjee, A., Rao, M. S., Shamsudduha, M., Smith, L., Taylor, R.
G., Tucker, J., van Steenbergen, F., Yadav, S. K., and Zahid, A.:
Hydrogeological typologies of the Indo-Gangetic basin alluvial aquifer, South
Asia, Hydrogeol. J., 25, 1377–1406, https://doi.org/10.1007/s10040-017-1550-z, 2017.
Bredehoeft, J.: The conceptualization model problem–surprise, Hydrogeol. J.,
13, 37–46, https://doi.org/10.1007/s10040-004-0430-5, 2005.
Brouyère, S., Carabin, G., and Dassargues, A.: Climate change impacts on
groundwater resources: modelled deficits in a chalky aquifer, Geer basin,
Belgium, Hydrogeol. J., 12, 123–134, https://doi.org/10.1007/s10040-003-0293-1, 2004.
Chakravarti, I. M. and Laha, R. G.: Handbook of methods of applied
statistics, in: Handbook of methods of applied statistics, John Wiley &
Sons, New York, USA, 1967.
Chen, Z., Grasby, S. E., and Osadetz, K. G.: Relation between climate
variability and groundwater levels in the upper carbonate aquifer, southern
Manitoba, Canada, J. Hydrol., 290, 43–62, https://doi.org/10.1016/j.jhydrol.2003.11.029,
2004.
Chiang, W.-H. and Kinzelbach, W.: Processing MODFLOW: a simulation system for
modeling groundwater flow and pollution, Softw. Instr. Book Hambg.-Zurich,
Zurich, Switzerland, 1998.
Crosbie, R. S., McCallum, J. L., Walker, G. R., and Chiew, F. H.: Modelling
climate-change impacts on groundwater recharge in the Murray-Darling Basin,
Australia, Hydrogeol. J., 18, 1639–1656,
https://doi.org/10.1007/s10040-010-0625-x, 2010.
Dams, J., Salvadore, E., Van Daele, T., Ntegeka, V., Willems, P., and
Batelaan, O.: Spatio-temporal impact of climate change on the groundwater
system, Hydrol. Earth Syst. Sci., 16, 1517–1531,
https://doi.org/10.5194/hess-16-1517-2012, 2012.
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: the role of internal variability, Clim. Dynam., 38,
527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012.
Doherty, J.: PEST: a unique computer program for model-independent parameter
optimisation, Water 94 Groundwater Surface Hydrol. Common Interest Pap.
Prepr. Pap., Watermark Computing, Washington DC, USA, 551, 1994.
Döll, P., Hoffmann-Dobrev, H., Portmann, F. T., Siebert, S., Eicker, A.,
Rodell, M., Strassberg, G., and Scanlon, B. R.: Impact of water withdrawals
from groundwater and surface water on continental water storage variations,
J. Geodyn., 59, 143–156, https://doi.org/10.1016/j.jog.2011.05.001, 2012.
Domenico, P. A. and Mifflin, M. D.: Water from low-permeability sediments and
land subsidence, Water Resour. Res., 1, 563–576,
https://doi.org/10.1029/WR001i004p00563, 1965.
Domenico, P. A. and Schwartz, F. W.: Physical and chemical hydrogeology,
Wiley New York, New York, USA, 1998.
Draper, D.: Assessment and propagation of model uncertainty, J. R. Stat. Soc.
B, 57, 45–97, https://doi.org/10.1111/j.2517-6161.1995.tb02015.x, 1995.
Faisal, I. M., Parveen, S., and Kabir, M. R.: Sustainable development through
groundwater management: a case study on the Barind tract, Int. J. Water
Resour. D., 21, 425–435, https://doi.org/10.1080/07900620500160800, 2005.
Fetter, C. W.: Applied Hydrogeology, 4th Edition, Prentice Hall, Upper Saddle
River, 2, 598 pp., 2001.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W.
J., Cox, P., Driouech, F., Emori, S., and Eyring, V.: Evaluation of Climate
Models, in: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, Clim. Change, Cambridge University Press, Cambridge, UK,
5, 741–866, https://doi.org/10.1017/CBO9781107415324.020, 2013.
Gaganis, P. and Smith, L.: Evaluation of the uncertainty of groundwater model
predictions associated with conceptual errors: A per-datum approach to model
calibration, Adv. Water Resour., 29, 503–514,
https://doi.org/10.1016/j.advwatres.2005.06.006, 2006.
Goderniaux, P., Brouyère, S., Fowler, H. J., Blenkinsop, S., Therrien,
R., Orban, P., and Dassargues, A.: Large scale surface–subsurface
hydrological model to assess climate change impacts on groundwater reserves,
J. Hydrol., 373, 122–138, https://doi.org/10.1016/j.jhydrol.2009.04.017, 2009.
Goderniaux, P., Brouyere, S., Blenkinsop, S., Burton, A., Fowler, H. J.,
Orban, P., and Dassargues, A.: Modeling climate change impacts on groundwater
resources using transient stochastic climatic scenarios, Water Resour. Res.,
47, W12516, https://doi.org/10.1029/2010WR010082, 2011.
Goderniaux, P., Brouyère, S., Wildemeersch, S., Therrien, R., and
Dassargues, A.: Uncertainty of climate change impact on groundwater
reserves–application to a chalk aquifer, J. Hydrol., 528, 108–121,
https://doi.org/10.1016/j.jhydrol.2015.06.018, 2015.
Gosling, S. N., Taylor, R. G., Arnell, N. W., and Todd, M. C.: A comparative
analysis of projected impacts of climate change on river runoff from global
and catchment-scale hydrological models, Hydrol. Earth Syst. Sci., 15,
279–294, https://doi.org/10.5194/hess-15-279-2011, 2011.
Hamed, K. H. and Rao, A. R.: A modified Mann-Kendall trend test for
autocorrelated data, J. Hydrol., 204, 182–196,
https://doi.org/10.1016/S0022-1694(97)00125-X, 1998.
Harbaugh, A. W. and McDonald, M. G.: Programmer's documentation for
MODFLOW-96, an update to the US Geological Survey modular finite-difference
ground-water flow model, US Geological Survey, available at:
https://pubs.er.usgs.gov/publication/ofr96486 (last access: 8 May 2019), 1996.
Hassan, A. E.: A methodology for validating numerical ground water models,
Groundwater, 42, 347–362, https://doi.org/10.1111/j.1745-6584.2004.tb02683.x, 2004a.
Hassan, A. E.: Validation of numerical ground water models used to guide
decision making, Groundwater, 42, 277–290,
https://doi.org/10.1111/j.1745-6584.2004.tb02674.x, 2004b.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional
climate predictions, B. Am. Meteorol. Soc., 90, 1095–1107,
https://doi.org/10.1175/2009BAMS2607.1, 2009.
Hawkins, E. and Sutton, R.: Time of emergence of climate signals, Geophys.
Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087, 2012.
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.: Bayesian
model averaging: a tutorial, Stat. Sci., 14, 382–401, 1999.
Højberg, A. L. and Refsgaard, J. C.: Model uncertainty–parameter
uncertainty versus conceptual models, Water Sci. Technol., 52, 177–186,
https://doi.org/10.2166/wst.2005.0166, 2005.
Holman, I. P., Allen, D. M., Cuthbert, M. O., and Goderniaux, P.: Towards best
practice for assessing the impacts of climate change on groundwater,
Hydrogeol. J., 20, 1–4, https://doi.org/10.1007/s10040-011-0805-3, 2012.
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9,
90–95, https://doi.org/10.1109/MCSE.2007.55, 2007.
IPCC: Summary for Policymakers, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK
and New York, NY, USA, 2013.
Jackson, C. R., Meister, R., and Prudhomme, C.: Modelling the effects of
climate change and its uncertainty on UK Chalk groundwater resources from an
ensemble of global climate model projections, J. Hydrol., 399, 12–28,
https://doi.org/10.1016/j.jhydrol.2010.12.028, 2011.
Jahan, C. S., Mazumder, Q. H., Ghose, S. K., and Asaduzzaman, M.: Specific
yield evaluation: Barind area, Bangaladesh, Geol. Soc. India, 44, 283–290,
1994.
Jahani, C. S. and Ahmed, M.: Flow of groundwater in the Barind area,
Bangladesh: implication of structural framework, Geol. Soc. India, 50,
743–752, 1997.
Johnson, A. I.: Specific yield: compilation of specific yields for various
materials, US Government Printing Office, Washington, USA, 1967.
Karim, Z.: Accelerated agricultural growth in Bangladesh, in: Seminar on
agricultural research on development in Bangladesh, BARC, Dhaka, Bangladesh,
1997.
Li, X. and Tsai, F. T.-C.: Bayesian model averaging for groundwater head
prediction and uncertainty analysis using multimodel and multimethod, Water
Resour. Res., 45, W09403, https://doi.org/10.1029/2008WR007488, 2009.
McDonald, M. G. and Harbaugh, A. W.: A modular three-dimensional
finite-difference ground-water flow model, available at:
https://pubs.usgs.gov/twri/twri6a1/
(last access: 8 May 2019), 1988.
McKinney, W.: Data structures for statistical computing in python, in:
Proceedings of the 9th Python in Science Conference, 28 June–3 July 2010, vol. 445, 51–56,
SciPy Austin, TX, 2010.
Mehl, S. and Hill, M. C.: Grid-size dependence of Cauchy boundary conditions
used to simulate stream–aquifer interactions, Adv. Water Resour., 33,
430–442, https://doi.org/10.1016/j.advwatres.2010.01.008, 2010.
Michael, H. A. and Voss, C. I.: Controls on groundwater flow in the Bengal
Basin of India and Bangladesh: regional modeling analysis, Hydrogeol. J., 17,
1561, https://doi.org/10.1007/s10040-008-0429-4, 2009a.
Michael, H. A. and Voss, C. I.: Estimation of regional-scale groundwater flow
properties in the Bengal Basin of India and Bangladesh, Hydrogeol. J., 17,
1329–1346, https://doi.org/10.1007/s10040-009-0443-1, 2009b.
Mondal, M. H.: Challenges and Opportunities of sustainable crop production in
Bangladesh, in: Eighth Biennial Agronomy Convention, Bangladesh Society of
Agronomy, Dhaka, Bangladesh, 2005.
Mondal, M. H.: Crop agriculture of Bangladesh: Challenges and opportunities,
Bangladesh J. Agric. Res., 35, 235–245, https://doi.org/10.3329/bjar.v35i2.5886, 2010.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900,
https://doi.org/10.13031/2013.23153, 2007.
Mustafa, S. M. T., Vanuytrecht, E., and Huysmans, M.: Combined deficit
irrigation and soil fertility management on different soil textures to
improve wheat yield in drought-prone Bangladesh, Agr. Water Manage., 191,
124–137, https://doi.org/10.1016/j.agwat.2017.06.011, 2017a.
Mustafa, S. M. T., Abdollahi, K., Verbeiren, B., and Huysmans, M.:
Identification of the influencing factors on groundwater drought and
depletion in north-western Bangladesh, Hydrogeol. J., 25, 1357–1375,
https://doi.org/10.1007/s10040-017-1547-7, 2017b.
Mustafa, S. M. T., Nossent, J., Ghysels, G., and Huysmans, M.: Estimation and
impact assessment of input and parameter uncertainty in predicting
groundwater flow with a fully distributed model, Water Resour. Res., 54,
6585–6608, https://doi.org/10.1029/2017WR021857, 2018.
Nakicenovic, N., Alcamo, J., Grubler, A., Riahi, K., Roehrl, R. A., Rogner,
H.-H., and Victor, N.: Special Report on Emissions Scenarios (SRES), A
Special Report of Working Group III of the Intergovernmental Panel on Climate
Change, Cambridge University Press, Cambridge, UK, 1–10, available
at: http://pure.iiasa.ac.at/6101 (last access: 8 May 2019), 2000.
Neukum, C. and Azzam, R.: Impact of climate change on groundwater recharge in
a small catchment in the Black Forest, Germany, Hydrogeol. J., 20, 547–560,
https://doi.org/10.1007/s10040-011-0827-x, 2012.
Neuman, S. P.: Maximum likelihood Bayesian averaging of uncertain model
predictions, Stoch. Env. Res. Risk A., 17, 291–305,
https://doi.org/10.1007/s00477-003-0151-7, 2003.
Ntegeka, V., Baguis, P., Roulin, E., and Willems, P.: Developing tailored
climate change scenarios for hydrological impact assessments, J. Hydrol.,
508, 307–321, https://doi.org/10.1016/j.jhydrol.2013.11.001, 2014.
Poeter, E. and Anderson, D.: Multimodel ranking and inference in ground water
modeling, Groundwater, 43, 597–605, https://doi.org/10.1111/j.1745-6584.2005.0061.x,
2005.
Qureshi, A. S., Ahmed, Z., and Krupnik, T. J.: Groundwater management in
Bangladesh: an analysis of problems and opportunities, Cereal Systems
Initiative for South Asia Mechanization and Irrigation (CSISA-MI) Project,
Research Report No. 2., CIMMYT, Dhaka, Bangladesh, 2014.
Rahman, M. M. and Shahid, S.: Modeling groundwater flow for the delineation
of wellhead protection area around a water-well at Nachole of Bangladesh, J.
Spat. Hydrol., 4, 1–10, 2004.
Rahman, M. M., Kamal, A. H. M., Al Mamun, A., and Miah, M. S. U.: Study on
the irrigation water distribution system developed by barind multipurpose
development authority, J. Bangladesh Assoc. Young Res., 1, 63–71,
https://doi.org/10.3329/jbayr.v1i2.10032, 2011.
Rahman, M. R. and Bulbul, S. H.: Adoption of water saving irrigation
techniques for sustainable rice production in Bangladesh, Environ. Ecol.
Res., 3, 1–8, https://doi.org/10.13189/eer.2015.030101, 2015.
Refsgaard, J. C., Van der Sluijs, J. P., Brown, J., and Van der Keur, P.: A
framework for dealing with uncertainty due to model structure error, Adv.
Water Resour., 29, 1586–1597, https://doi.org/10.1016/j.advwatres.2005.11.013, 2006.
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-based estimates
of groundwater depletion in India, Nature, 460, 999–1002,
https://doi.org/10.1038/nature08238, 2009.
Rojas, R., Feyen, L., and Dassargues, A.: Conceptual model uncertainty in
groundwater modeling: Combining generalized likelihood uncertainty estimation
and Bayesian model averaging, Water Resour. Res., 44, W12418, https://doi.org/10.1029/2008WR006908, 2008.
Rojas, R., Kahunde, S., Peeters, L., Batelaan, O., Feyen, L., and Dassargues,
A.: Application of a multimodel approach to account for conceptual model and
scenario uncertainties in groundwater modelling, J. Hydrol., 394, 416–435,
https://doi.org/10.1016/j.jhydrol.2010.09.016, 2010.
Scanlon, B. R., Faunt, C. C., Longuevergne, L., Reedy, R. C., Alley, W. M.,
McGuire, V. L., and McMahon, P. B.: Groundwater depletion and sustainability
of irrigation in the US High Plains and Central Valley, P. Natl. Acad. Sci.
USA, 109, 9320–9325, https://doi.org/10.1073/pnas.1200311109, 2012.
Scibek, J., Allen, D. M., Cannon, A. J., and Whitfield, P. H.:
Groundwater–surface water interaction under scenarios of climate change
using a high-resolution transient groundwater model, J. Hydrol., 333,
165–181, https://doi.org/10.1016/j.jhydrol.2006.08.005, 2007.
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau,
J. Am. Stat. Assoc., 63, 1379–1389, 1968.
Shahid, S.: Spatial assessment of groundwater demand in Northwest Bangladesh,
Int. J. Water, 5, 267–283, https://doi.org/10.1504/IJW.2010.030590, 2009.
Shahid, S.: Impact of climate change on irrigation water demand of dry season
Boro rice in northwest Bangladesh, Climatic Change, 105, 433–453,
https://doi.org/10.1007/s10584-010-9895-5, 2011.
Shahid, S. and Behrawan, H.: Drought risk assessment in the western part of
Bangladesh, Nat. Hazards, 46, 391–413, https://doi.org/10.1007/s11069-007-9191-5, 2008.
Shamsudduha, M., Chandler, R. E., Taylor, R. G., and Ahmed, K. M.: Recent
trends in groundwater levels in a highly seasonal hydrological system: the
Ganges-Brahmaputra-Meghna Delta, Hydrol. Earth Syst. Sci., 13, 2373–2385,
https://doi.org/10.5194/hess-13-2373-2009, 2009.
Shamsudduha, M., Taylor, R. G., Ahmed, K. M., and Zahid, A.: The impact of
intensive groundwater abstraction on recharge to a shallow regional aquifer
system: evidence from Bangladesh, Hydrogeol. J., 19, 901–916,
https://doi.org/10.1007/s10040-011-0723-4, 2011.
Shamsudduha, M., Taylor, R. G., and Chandler, R. E.: A generalized regression
model of arsenic variations in the shallow groundwater of Bangladesh, Water
Resour. Res., 51, 685–703, https://doi.org/10.1002/2013WR014572, 2015.
Stoll, S., Hendricks Franssen, H. J., Butts, M., and Kinzelbach, W.: Analysis
of the impact of climate change on groundwater related hydrological fluxes: a
multi-model approach including different downscaling methods, Hydrol. Earth
Syst. Sci., 15, 21–38, https://doi.org/10.5194/hess-15-21-2011, 2011.
Sulis, M., Paniconi, C., Marrocu, M., Huard, D., and Chaumont, D.: Hydrologic
response to multimodel climate output using a physically based model of
groundwater/surface water interactions, Water Resour. Res., 48, W12510, https://doi.org/10.1029/2012WR012304, 2012.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y.,
Longuevergne, L., Leblanc, M., Famiglietti, J. S., and Edmunds, M.: Ground
water and climate change, Nat. Clim. Change, 3, 322–329,
https://doi.org/10.1038/NCLIMATE1744, 2013.
Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in
probabilistic climate projections, Philos. T. R. Soc. S.-A, 365, 2053–2075,
https://doi.org/10.1098/rsta.2007.2076, 2007.
Teklesadik, A. D., Alemayehu, T., van Griensven, A., Kumar, R., Liersch, S.,
Eisner, S., Tecklenburg, J., Ewunte, S., and Wang, X.: Inter-model comparison
of hydrological impacts of climate change on the Upper Blue Nile basin using
ensemble of hydrological models and global climate models, Climatic Change,
141, 517–532, https://doi.org/10.1007/s10584-017-1913-4, 2017.
Troldborg, L., Refsgaard, J. C., Jensen, K. H., and Engesgaard, P.: The
importance of alternative conceptual models for simulation of concentrations
in a multi-aquifer system, Hydrogeol. J., 15, 843–860,
https://doi.org/10.1007/s10040-007-0192-y, 2007.
Van Roosmalen, L., Sonnenborg, T. O., and Jensen, K. H.: Impact of climate
and land use change on the hydrology of a large-scale agricultural catchment,
Water Resour. Res., 45, W00A15, https://doi.org/10.1029/2007WR006760, 2009.
Van Straten, G. T. and Keesman, K. J.: Uncertainty propagation and
speculation in projective forecasts of environmental change: A
lake-eutrophication example, J. Forecasting, 10, 163–190,
https://doi.org/10.1002/for.3980100110, 1991.
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., and Lamarque, J.-F.: The representative
concentration pathways: an overview, Climatic Change, 109, 5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011.
Vrugt, J. A.: MODELAVG: A MATLAB Toolbox for Postprocessing of Model
Ensembles, Department of Civil and Environmental Engineering, University of
California Irvine, 4130 Engineering Gateway, Irvine, CA, 2016.
Wada, Y., Van Beek, L. P., Wanders, N., and Bierkens, M. F.: Human water
consumption intensifies hydrological drought worldwide, Environ. Res. Lett.,
8, 034036, https://doi.org/10.1088/1748-9326/8/3/034036, 2013.
Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal,
allocation and consumptive use of surface water and groundwater resources,
Earth Syst. Dynam., 5, 15–40, https://doi.org/10.5194/esd-5-15-2014, 2014.
Walt, S. van der, Colbert, S. C., and Varoquaux, G.: The NumPy array: a
structure for efficient numerical computation, Comput. Sci. Eng., 13, 22–30,
https://doi.org/10.1109/MCSE.2011.37, 2011.
Willems, P.: Model uncertainty analysis by variance decomposition, Phys.
Chem. Earth Pt. ABC, 42, 21–30, https://doi.org/10.1016/j.pce.2011.07.003, 2012.
Wisser, D., Frolking, S., Douglas, E. M., Fekete, B. M., Vörösmarty,
C. J., and Schumann, A. H.: Global irrigation water demand: Variability and
uncertainties arising from agricultural and climate data sets, Geophys. Res.
Lett., 35, L24408, https://doi.org/10.1029/2008GL035296, 2008.
Woldeamlak, S. T., Batelaan, O., and De Smedt, F.: Effects of climate change
on the groundwater system in the Grote-Nete catchment, Belgium, Hydrogeol.
J., 15, 891–901, https://doi.org/10.1007/s10040-006-0145-x, 2007.
Wu, J. and Zeng, X.: Review of the uncertainty analysis of groundwater
numerical simulation, Chinese Sci. Bull., 58, 3044–3052,
https://doi.org/10.1007/s11434-013-5950-8, 2013.
Ye, M., Neuman, S. P., and Meyer, P. D.: Maximum likelihood Bayesian
averaging of spatial variability models in unsaturated fractured tuff, Water
Resour. Res., 40, W05113, https://doi.org/10.1029/2003WR002557, 2004.
Zhou, Y. and Herath, H.: Evaluation of alternative conceptual models for
groundwater modelling, Geosci. Front., 8, 437–443,
https://doi.org/10.1016/j.gsf.2016.02.002, 2017.
Short summary
This study evaluates the effect of conceptual hydro(geo)logical model (CHM) structure, climate change and groundwater abstraction on future groundwater-level prediction uncertainty. If the current groundwater abstraction trend continues, groundwater level is predicted to decline quickly. Groundwater abstraction in NW Bangladesh should decrease by 60 % to ensure sustainable use. Abstraction scenarios are the dominant uncertainty source, followed by CHM uncertainty and climate model uncertainty.
This study evaluates the effect of conceptual hydro(geo)logical model (CHM) structure, climate...
