Articles | Volume 16, issue 9
https://doi.org/10.5194/hess-16-3083-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-16-3083-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Sep 2012
Research article |
| 03 Sep 2012
Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data
H. Xie
International Food Policy Research Institute, 2033 K Street NW, Washington D.C. 20006, USA
L. Longuevergne
CNRS – UMR 6118, Géosciences Rennes Université Rennes 1, 35042 Rennes, France
C. Ringler
International Food Policy Research Institute, 2033 K Street NW, Washington D.C. 20006, USA
B. R. Scanlon
Bureau of Economic Geology, Jackson School of Geosciences, University of Texas, Austin, TX 78713-8926, USA
Related subject area
Subject: Global hydrology | Techniques and Approaches: Uncertainty analysis
Leveraging multi-variable observations to reduce and quantify the output uncertainty of a global hydrological model: evaluation of three ensemble-based approaches for the Mississippi River basin
Information content of soil hydrology in a west Amazon watershed as informed by GRACE
Diagnostic evaluation of river discharge into the Arctic Ocean and its impact on oceanic volume transports
The 63-year changes in annual streamflow volumes across Europe with a focus on the Mediterranean basin
Multivariable evaluation of land surface processes in forced and coupled modes reveals new error sources to the simulated water cycle in the IPSL (Institute Pierre Simon Laplace) climate model
Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates
Historical and future changes in global flood magnitude – evidence from a model–observation investigation
A global-scale evaluation of extreme event uncertainty in the eartH2Observe project
Assessment of precipitation error propagation in multi-model global water resource reanalysis
The potential of global reanalysis datasets in identifying flood events in Southern Africa
Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model
Global change in streamflow extremes under climate change over the 21st century
Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?
Sensitivity of future continental United States water deficit projections to general circulation models, the evapotranspiration estimation method, and the greenhouse gas emission scenario
Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use
Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach
Flood and drought hydrologic monitoring: the role of model parameter uncertainty
Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration
Climate change impacts on runoff in West Africa: a review
Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis
Disinformative data in large-scale hydrological modelling
The impact of climate mitigation on projections of future drought
Monitoring and quantifying future climate projections of dryness and wetness extremes: SPI bias
Improving runoff estimates from regional climate models: a performance analysis in Spain
A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models
Error characterisation of global active and passive microwave soil moisture datasets
Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations
Petra Döll, Howlader Mohammad Mehedi Hasan, Kerstin Schulze, Helena Gerdener, Lara Börger, Somayeh Shadkam, Sebastian Ackermann, Seyed-Mohammad Hosseini-Moghari, Hannes Müller Schmied, Andreas Güntner, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
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Currently, global hydrological models do not benefit from observations of model output variables to reduce and quantify model output uncertainty. For the Mississippi River basin, we explored three approaches for using both streamflow and total water storage anomaly observations to adjust the parameter sets in a global hydrological model. We developed a method for considering the observation uncertainties to quantify the uncertainty of model output and provide recommendations.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
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The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
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We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Hiroki Mizuochi, Agnès Ducharne, Frédérique Cheruy, Josefine Ghattas, Amen Al-Yaari, Jean-Pierre Wigneron, Vladislav Bastrikov, Philippe Peylin, Fabienne Maignan, and Nicolas Vuichard
Hydrol. Earth Syst. Sci., 25, 2199–2221, https://doi.org/10.5194/hess-25-2199-2021, https://doi.org/10.5194/hess-25-2199-2021, 2021
Samuel Saxe, William Farmer, Jessica Driscoll, and Terri S. Hogue
Hydrol. Earth Syst. Sci., 25, 1529–1568, https://doi.org/10.5194/hess-25-1529-2021, https://doi.org/10.5194/hess-25-1529-2021, 2021
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We compare simulated values from 47 models estimating surface water over the USA. Results show that model uncertainty is substantial over much of the conterminous USA and especially high in the west. Applying the studied models to a simple water accounting equation shows that model selection can significantly affect research results. This paper concludes that multimodel ensembles help to best represent uncertainty in conclusions and suggest targeted research efforts in arid regions.
Hong Xuan Do, Fang Zhao, Seth Westra, Michael Leonard, Lukas Gudmundsson, Julien Eric Stanislas Boulange, Jinfeng Chang, Philippe Ciais, Dieter Gerten, Simon N. Gosling, Hannes Müller Schmied, Tobias Stacke, Camelia-Eliza Telteu, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 24, 1543–1564, https://doi.org/10.5194/hess-24-1543-2020, https://doi.org/10.5194/hess-24-1543-2020, 2020
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We presented a global comparison between observed and simulated trends in a flood index over the 1971–2005 period using the Global Streamflow Indices and Metadata archive and six global hydrological models available through The Inter-Sectoral Impact Model Intercomparison Project. Streamflow simulations over 2006–2099 period robustly project high flood hazard in several regions. These high-flood-risk areas, however, are under-sampled by the current global streamflow databases.
Toby R. Marthews, Eleanor M. Blyth, Alberto Martínez-de la Torre, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 24, 75–92, https://doi.org/10.5194/hess-24-75-2020, https://doi.org/10.5194/hess-24-75-2020, 2020
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Climate change impact modellers can only act on predictions of the occurrence of an extreme event in the Earth system if they know the uncertainty in that prediction and how uncertainty is attributable to different model components. Using eartH2Observe data, we quantify the balance between different sources of uncertainty in global evapotranspiration and runoff, making a crucial contribution to understanding the spatial distribution of water resources allocation deficiencies.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Jan Polcher, Clément Albergel, Emanuel Dutra, Gabriel Fink, Alberto Martínez-de la Torre, and Simon Munier
Hydrol. Earth Syst. Sci., 23, 1973–1994, https://doi.org/10.5194/hess-23-1973-2019, https://doi.org/10.5194/hess-23-1973-2019, 2019
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This study investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period of 11 years based on six global hydrologic models and five precipitation datasets. Results show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure.
Gaby J. Gründemann, Micha Werner, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 22, 4667–4683, https://doi.org/10.5194/hess-22-4667-2018, https://doi.org/10.5194/hess-22-4667-2018, 2018
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Flooding in vulnerable and data-sparse regions such as the Limpopo basin in Southern Africa is a key concern. Data available to local flood managers are often limited, inconsistent or asymmetrically distributed. We demonstrate that freely available global datasets are well suited to provide essential information. Despite the poor performance of simulated discharges, these datasets hold potential in identifying damaging flood events, particularly for higher-resolution datasets and larger basins.
Emiliano Gelati, Bertrand Decharme, Jean-Christophe Calvet, Marie Minvielle, Jan Polcher, David Fairbairn, and Graham P. Weedon
Hydrol. Earth Syst. Sci., 22, 2091–2115, https://doi.org/10.5194/hess-22-2091-2018, https://doi.org/10.5194/hess-22-2091-2018, 2018
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We compared land surface model simulations forced by several meteorological datasets with observations over the Euro-Mediterranean area, for the 1979–2012 period. Precipitation was the most uncertain forcing variable. The impacts of forcing uncertainty were larger on the mean and standard deviation rather than the timing, shape and inter-annual variability of simulated discharge. Simulated leaf area index and surface soil moisture were relatively insensitive to these uncertainties.
Behzad Asadieh and Nir Y. Krakauer
Hydrol. Earth Syst. Sci., 21, 5863–5874, https://doi.org/10.5194/hess-21-5863-2017, https://doi.org/10.5194/hess-21-5863-2017, 2017
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Multi-model analysis of global streamflow extremes for the 20th and 21st centuries under two warming scenarios is performed. About 37 and 43 % of global land areas show potential for increases in flood and drought events. Nearly 10 % of global land areas, holding around 30 % of world’s population, reflect a potentially worsening hazard of flood and drought. A significant increase in streamflow of the regions near and above the Arctic Circle, and decrease in subtropical arid areas, is projected.
Sebastian Sippel, Jakob Zscheischler, Martin Heimann, Holger Lange, Miguel D. Mahecha, Geert Jan van Oldenborgh, Friederike E. L. Otto, and Markus Reichstein
Hydrol. Earth Syst. Sci., 21, 441–458, https://doi.org/10.5194/hess-21-441-2017, https://doi.org/10.5194/hess-21-441-2017, 2017
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The paper re-investigates the question whether observed precipitation extremes and annual totals have been increasing in the world's dry regions over the last 60 years. Despite recently postulated increasing trends, we demonstrate that large uncertainties prevail due to (1) the choice of dryness definition and (2) statistical data processing. In fact, we find only minor (and only some significant) increases if (1) dryness is based on aridity and (2) statistical artefacts are accounted for.
Seungwoo Chang, Wendy D. Graham, Syewoon Hwang, and Rafael Muñoz-Carpena
Hydrol. Earth Syst. Sci., 20, 3245–3261, https://doi.org/10.5194/hess-20-3245-2016, https://doi.org/10.5194/hess-20-3245-2016, 2016
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Projecting water deficit depends on how researchers combine possible future climate scenarios such as general circulation models (GCMs), evapotranspiration estimation method (ET), and greenhouse gas emission scenarios. Using global sensitivity analysis, we found the relative contribution of each of these factors to projecting future water deficit and the choice of ET estimation method are as important as the choice of GCM, and greenhouse gas emission scenario is less influential than the others.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, https://doi.org/10.5194/hess-20-2877-2016, 2016
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The assessment of water balance components of the global land surface by means of hydrological models is affected by large uncertainties, in particular related to meteorological forcing. We analyze the effect of five state-of-the-art forcings on water balance components at different spatial and temporal scales modeled with WaterGAP. Furthermore, the dominant effect (precipitation/human alteration) for long-term changes in river discharge is assessed.
Dave MacLeod, Hannah Cloke, Florian Pappenberger, and Antje Weisheimer
Hydrol. Earth Syst. Sci., 20, 2737–2743, https://doi.org/10.5194/hess-20-2737-2016, https://doi.org/10.5194/hess-20-2737-2016, 2016
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Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.
N. W. Chaney, J. D. Herman, P. M. Reed, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, https://doi.org/10.5194/hess-19-3239-2015, 2015
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Land surface modeling is playing an increasing role in global monitoring and prediction of extreme hydrologic events. However, uncertainties in parameter identifiability limit the reliability of model predictions. This study makes use of petascale computing to perform a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty.
H. Müller Schmied, S. Eisner, D. Franz, M. Wattenbach, F. T. Portmann, M. Flörke, and P. Döll
Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, https://doi.org/10.5194/hess-18-3511-2014, 2014
P. Roudier, A. Ducharne, and L. Feyen
Hydrol. Earth Syst. Sci., 18, 2789–2801, https://doi.org/10.5194/hess-18-2789-2014, https://doi.org/10.5194/hess-18-2789-2014, 2014
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
A. Kauffeldt, S. Halldin, A. Rodhe, C.-Y. Xu, and I. K. Westerberg
Hydrol. Earth Syst. Sci., 17, 2845–2857, https://doi.org/10.5194/hess-17-2845-2013, https://doi.org/10.5194/hess-17-2845-2013, 2013
I. H. Taylor, E. Burke, L. McColl, P. D. Falloon, G. R. Harris, and D. McNeall
Hydrol. Earth Syst. Sci., 17, 2339–2358, https://doi.org/10.5194/hess-17-2339-2013, https://doi.org/10.5194/hess-17-2339-2013, 2013
F. Sienz, O. Bothe, and K. Fraedrich
Hydrol. Earth Syst. Sci., 16, 2143–2157, https://doi.org/10.5194/hess-16-2143-2012, https://doi.org/10.5194/hess-16-2143-2012, 2012
D. González-Zeas, L. Garrote, A. Iglesias, and A. Sordo-Ward
Hydrol. Earth Syst. Sci., 16, 1709–1723, https://doi.org/10.5194/hess-16-1709-2012, https://doi.org/10.5194/hess-16-1709-2012, 2012
S. N. Gosling, R. G. Taylor, N. W. Arnell, and M. C. Todd
Hydrol. Earth Syst. Sci., 15, 279–294, https://doi.org/10.5194/hess-15-279-2011, https://doi.org/10.5194/hess-15-279-2011, 2011
W. A. Dorigo, K. Scipal, R. M. Parinussa, Y. Y. Liu, W. Wagner, R. A. M. de Jeu, and V. Naeimi
Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, https://doi.org/10.5194/hess-14-2605-2010, 2010
G. Schumann, D. J. Lunt, P. J. Valdes, R. A. M. de Jeu, K. Scipal, and P. D. Bates
Hydrol. Earth Syst. Sci., 13, 1545–1553, https://doi.org/10.5194/hess-13-1545-2009, https://doi.org/10.5194/hess-13-1545-2009, 2009
Cited articles
Alkama, R., Decharme, B., Douville, H., Becker, M., Cazenave, A., Sheffield, J., Voldoire, A., Tyteca, S., and Le Moigne, P.: Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part I: Comparison to GRACE Terrestrial Water Storage Estimates and In Situ River Discharges, J. Hydrometeorol., 11, 583–600, 2010.
Amogu, O., Descroix, L., Yéro, K. S., LeBreton, E., Mamadou, I., Ali, A., Vischel, T., Bader, J. C., Moussa, I. B., Gautier, E., Boubkraoui, S., and Belleudy, P.: Increasing River Flows in the Sahel?, Water, 2, 170–199, 2010.
Arnold, J. G., Srinivasin, R., Muttiah, R. S., and Williams, J. R.: Large Area Hydrologic Modeling and Assessment: Part I. Model Development, Journal of American Water Resources Association, 34, 73–89, 1998.
Becker, M., Llovel, W., Cazenave, A., Güntner, A., and Crétaux, J. F.: Recent hydrological behaviour of the East African Great Lakes region inferred from GRACE, satellite altimetry and rainfall observations, CR Geoscience, 342, 223–233, 2010.
Bettadpur, S.: Level-2 Gravity Field Product User Handbook, GRACE 327–734, The GRACE Project, Center for Space Research, University of Texas at Austin, 2007.
Beven, K. J. and Binley, A. M.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Bruinsma, S., Lemoine, J. M., Biancale, R., and Vales, N.: CNES/GRGS 10-day gravity field models (release 2) and their evaluation, Adv. Space Res., 45, 587–601, 2010.
Chen, J. L., Wilson, C. R., Tapley, B. D., Longuevergne, L., Yang, Z. L., and Scanlon, B. R.: Recent La Plata basin drought conditions observed by satellite gravimetry, J. Geophys. Res., 115, D22108, https://doi.org/10.1029/2010JD014689, 2010.
Crétaux, J.-F., Jelinski, W., Calmant, S., Kouraev, A., Vuglinski, V., Bergé-Nguyen, M., Gennero, M.-C., Nino, F., Abarca Del Rio, R., Cazenave, A., and Maisongrande, P.: SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data, Adv. Space Res., 47, 1497–1507, 2011.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE T. Evolut. Comput., 6, 182–197, 2002.
Dhar, S. and Mazumdar, A.: Hydrological modelling of the Kangsabati river under changed climate scenario: case study in India, Hydrol. Process., 23, 2394–2406, 2009.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.1), FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2009.
FAO: The State of Food Insecurity in the World 2010: Addressing Food Insecurity in Protracted Crises, Rome, 2010.
FAO (Food and Agriculture Organization of the United Nations): Water Resources Development and Management Service, AQUASTAT database, available at: http://www.fao.org/nr/water/aquastat/dbases/index.stm, last access: July, 2011.
Falkenmark, M. and Rockstrom, J.: The New Blue and Green Water Paradigm: Breaking New Ground for Water Resources Planning and Management, J. Water Res. Pl. ASCE, 132, 129–132, 2006.
Famiglietti, J. S., Lo, M., Ho, S. L., Bethune, J., Anderson, K. J., Syed, T. H., Swenson, S. C., de Linage, C. R., and Rodell, M.: Satellites measure recent rates of groundwater depletion in California's Central Valley, Geophys. Res. Lett., 38, L03403, https://doi.org/10.1029/2010GL046442, 2011.
Fenicia, F., McDonnell, J. J., and Savenije, H. H. G.: Learning from model improvement: On the contribution of complementary data to process understanding, Water Resour. Res., 44, W06419, https://doi.org/10.1029/2007WR006386, 2008.
Fiedler, K. and Döll, P.: Global modelling of continental water storage changes – sensitivity to different climate datasets, Adv. Geosci., 11, 63–68, https://doi.org/10.5194/adgeo-11-63-2007, 2007.
Grippa, M., Kergoat, L., Frappart, F., Araud, Q., Boone, A., de Rosnay, P., Lemoine, J.-M., Gascoin, S., Balsamo, G., Ottlé, C., Decharme, B., Saux-Picart, S., and Ramillien, G.: Land water storage variability over West Africa estimated by Gravity Recovery and Climate Experiment (GRACE) and land surface models, Water Resour. Res., 47, W05549, https://doi.org/10.1029/2009WR008856, 2011.
Guntner, A.: Improvement of global hydrological models using GRACE data, Surv. Geophys., 29, 375–397, 2008.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Towards Improved Calibration of Hydrologic Models: Multiple and Non-Commensurable Measures of Information, Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97WR03495, 1998.
Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1, 96–99, 1985.
Huffman, G. J., Adler, R. F., Morrissey, M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations, J. Hydrometeor., 2, 36–50, 2001.
IPCC: Climate Change 2007: Impacts, Adaptation, and Vulnerability, IPCC Working Group II, Fourth Assessment Report, edited by: Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J., and Hanson, C. E., Cambridge University Press, 2007.
Kim, N. W., Chung, I. M., Won, Y. S., and Arnold, J. G.: Development and application of the integrated SWAT-MODFLOW MODEL, J. Hydrol., 356, 1–16, 2008.
Konz, M. and Seibert, J.: On the value of glacier mass balances for hydrological model calibration, J. Hydrol., 386, 238–246, 2010.
Leblanc, M. J., Tregoning, P., Ramillien, G., Tweed, S. O., and Fakes, A.: Basin-scale, integrated observations of the early 21st century multiyear drought in southeast Australia, Water Resour. Res., 45, W04408, https://doi.org/10.1029/2008WR007333, 2009.
Lehner, B. and Döll, P.: Development and validation of a global database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22, 2004.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, Transactions, AGU, 89, 93–94, 2008.
Longuevergne, L., Scanlon, B. R., and Wilson, C. R.: GRACE Hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USA, Water Resour. Res., 46, W11517, https://doi.org/10.1029/2009WR008564, 2010.
Milzow, C., Krogh, P. E., and Bauer-Gottwein, P.: Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment, Hydrol. Earth Syst. Sci., 15, 1729–1743, https://doi.org/10.5194/hess-15-1729-2011, 2011.
Molden, D. (Ed.): Water for food, water for life. A comprehensive assessment of water management in agriculture, London, UK: Earthscan; Columbo, Sri Lanka: International Water Management Institute, 2007.
Monteith, J. L.: Evaporation and the environment, 205–234, in: The state and movement of water in living organisms, 19th Symposia of the Society for Experimental Biology, Cambridge Univ. Press, London, UK, 1965.
Morris, M. D.: Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161–174, 1991.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and Water Assessment Tool Theoretical Documentation, Version 2005, Grassland, Soil and Water Research Laboratory, USDA-ARS, 2005.
Ngo-Duc, T., Laval, K., Ramillien, G., Polcher, J., and Cazenave, A.: Validation of the land water storage simulated by Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) with Gravity Recovery and Climate Experiment (GRACE) data, Water Resour. Res., 43, W04427, https://doi.org/10.1029/2006WR004941, 2007.
Niu, G.-Y. and Yang, Z.-L.: Assessing a land surface model's improvements with GRACE estimates, Geophys. Res. Lett., 33, L07401, https://doi.org/10.1029/2005GL025555, 2006.
Oeurng, C., Sauvage, S., and Sánchez-Pérez, J.-M.: Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model, J. Hydrol., 401, 145–153, 2011.
Parajka, J., Naeimi, V., Blöschl, G., Wagner, W., Merz, R., and Scipal, K.: Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale, Hydrol. Earth Syst. Sci., 10, 353–368, https://doi.org/10.5194/hess-10-353-2006, 2006.
Priestley, C. H. B. and Taylor, R. J.: On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–82, 1972.
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952, 2008.
Rockström, J., Hatibu, N., Oweis, T., Wani, S., Barron, J., Bruggeman, A., Qiang, Z., Farahani, J., and Karlberg, L.: Managing Water in Rainfed Agriculture, in: Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture, edited by: Molden, D., Chapter 9, 315–348, Earthscan, London, 2007.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, 2004.
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite esti-mates of groundwater depletion in India, Nature, 460, 999–1002, 2009.
Rosegrant M. W., Cai, X., and Cline, S. A.: World Water and Food to 2025: Dealing With Scarcity. Washington, International Food Policy Research Institute, Washington D.C., 2002.
Rosegrant, M. W., Fernandez, M., Sinha, A., Alder, J., Ahammad, H., de Fraiture, C., Eickhout, B., Fonseca, J., Huang, J., Koyama, O., Omezzine, A. M., Pingali, P., Ramirez, R., Ringler, C., Robinson, S., Thornton, P., van Vuuren, D., Yana-Shapiro, H., Ebi, K., Kruska, R., Munjal, P., Narrod, C., Ray, S., Sulser, T., Tamagno, C., van Oorschot, M., and Zhu, T.: Looking into the future for agriculture and AKST (Agricultural Knowledge Science and Technology), Chapter 5, in: Agriculture at a Crossroads, edited by: McIntyre, B. D., Herren, H. R., Wakhungu, J., and Watson, R. T., Island Press, Washington DC, 307–376, 2009.
Sangrey, D. A., Harrop-Williams, K. O., and Klaiber, J. A.: Predicting groundwater response to precipitation. J. Geotech. Eng., ASCE, 110, 957–975, 1984.
Saxton, K. E., Rawls, W. L., Rosenberger, J. S., and Papendick, R. I.: Estimating generalized soil-water characteristics from texture, Soil Sci. Soc. Am. J., 50, 1031–1036, 1986.
Scanlon, B. R., Longuevergne, L., and Long, D.: Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, US, Water Resour. Res., 48, W04520, https://doi.org/10.1029/2011WR011121, 2012.
Schaap, M. G., Leij, J. F., and van Genuchten, M. Th.: ROSETTA: a computer program for estimating soilhydraulic parameters with hierarchical pedotransfer functions, J. Hydrol., 251, 163–176, 2001.
Schuol, J., Abbaspour, K. C., Srinivasan, R., and Yang, H.: Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model, J. Hydrol., 352, 30–49, 2008a.
Schuol, J., Abbaspour, K. C., Yang, H., Srinivasan, R., and Zehnder, A. J. B.: Modeling blue and green water availability in Africa, Water Resour. Res., 44, 1–18, 2008b.
Schmidt, R., Flechtner, F., Meyer, U., Neumayer, K. H., Dahle, C., König, R., and Kusche, J.: Hydrologic signals observed by the GRACE satellites, Surv. Geophys., 29, 319–334, https://doi.org/10.1007/s10712-008-9033-3, 2008.
Siebert, S., Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T.: Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880, https://doi.org/10.5194/hess-14-1863-2010, 2010.
Soil Conservation Service: Section 4: Hydrology in National Engineering Handbook, 1972.
Swenson, S., and Wahr, J.: Post-processing removal of correlatederrors in GRACE data, Geophys. Res. Lett., 33, L08402, https://doi.org/10.1029/2005GL025285, 2006.
Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., and Wilson, C. R.: Analysis of terrestrialwater storage changes from GRACE and GLDAS, Water Resour. Res., 44, W02433, https://doi.org/10.1029/2006WR005779, 2008.
Tang, Q., Gao, H., Yeh, P., Oki, T., Su, F., and Lettenmaier, D. P.: Dynamics of terrestrial water storage change from satellite and surface observations and modelling, J. Hydrometeorol., 11, 156–170, https://doi.org/10.1175/2009JHM1152.1, 2010.
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M.: GRACE measurements of mass variability in the Earth system, Science, 305, 593–505, https://doi.org/10.1126/science.1099192, 2004.
Tiwari, V. M., Wahr, J., and Swenson, S.: Dwindling groundwater resources in northern India, from satellite gravity observations, Geophys. Res. Lett., 36, L18401, https://doi.org/10.1029/2009GL039401, 2009.
Venetis, C.: A study of the recession of unconfined aquifers, Bull. Int. Assoc. Sci. Hydrol., 14, 119–125, 1969.
Vrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., and Sorooshian, S.: Effective and efficient algorithm for multiobjective optimisation of hydrologic models, Water Resour. Res., 39, 1214, https://doi.org/10.1029/2002WR001746, 2003.
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the earth's gravity field: Hydrologic and oceanic effects and their possible detection using GRACE, J. Geophys. Res., 103, 30205–30230, 1998.
Werth, S. and Güntner, A.: Calibration analysis for water storage variability of the global hydrological model WGHM, Hydrol. Earth Syst. Sci., 14, 59–78, https://doi.org/10.5194/hess-14-59-2010, 2010.
Werth, S., Güntner, A., Petrovic, S., and Schmidt, R.: Integration of GRACE mass variations into a global hydrological model, Earth Planet. Sci. Lett., 277, 166–173, 2009.
Winchell, M., Srinivasan, R., di Luzio, M., and Arnold, J. G.: ArcSWAT interface for SWAT 2005, User'sGuide, Blackland Research Center, Texas Agricultural Experiment Station, Temple, 2007.
World Bank: available at: http://data.worldbank.org/indicator/SP.POP.GROW (last access: September 2011), 2009.
Xie, H., Eheart, J. W., and An, H.: Hydrologic and economic implications of climate change for typical river basins of the agricultural Midwestern United States, J. Water Res. Pl.-ASCE, 134, 205–213, 2008.
Xie, H., Nkonya, E., and Wielgosz, B.: Assessing the risks of soil erosion and small reservoir siltation in a tropical river basin in Mali using the SWAT model under limited data condition, Appl. Eng. Agr., 27, 895–904, 2011.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., and Tewari, M.: The community Noah land surface model with multiparameterisation options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Yirdaw, S. Z., Snelgrove, K. R., Seglenieks, F. R., Agboma, C. O., and Soulis, E. D.: Assessment of the WATCLASS hydrological model result of the Mackenzie River basin using the GRACE satellite total water storage measurement, Hydrol. Process., 23, 3391–3400, https://doi.org/10.1002/hyp.7450, 2009.
Zaitchik, B. F., Rodell, M., and Reichle, R. H.: Assimilation of GRACE terrestrial water storage data into a Land Surface Model: Results for the Mississippi River basin, J. Hydrometeorol., 9, 535–548, 2008.
