Temporal Variance-Based Sensitivity Analysis of the River-Routing Component of the Large-Scale Hydrological Model ISBA–TRIP: Application on the Amazon Basin

Charlotte M. Emery Laboratoire d'Études en Géophysique et Océanographie Spatiale, Toulouse, France

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Sylvain Biancamaria Laboratoire d'Études en Géophysique et Océanographie Spatiale, Toulouse, France

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Aaron Boone CNRM-GAME, Météo-France, Toulouse, France

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Pierre-André Garambois Institut National des Sciences Appliquées Strasbourg, Strasbourg, France

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Sophie Ricci Climat, Environnement, Couplages et Incertitudes, CERFACS-CNRS, Toulouse, France

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Mélanie C. Rochoux Climat, Environnement, Couplages et Incertitudes, CERFACS-CNRS, Toulouse, France

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Bertrand Decharme CNRM-GAME, Toulouse, France

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Abstract

The continental part of the water cycle is commonly represented with hydrological models. Yet, there are limits in their capacity to accurately estimate water storage and dynamics because of their coarse spatial resolution, simplified physics, and an incomplete knowledge of atmospheric forcing and input parameters. These errors can be diminished using data assimilation techniques. The model’s most sensitive parameters should be identified beforehand. The objective of the present study is to highlight key parameters impacting the river-routing scheme Total Runoff Integrating Pathways (TRIP) while simulating river water height and discharge as a function of time focusing on the annual cycle. Thus, a sensitivity analysis based on the decomposition of model output variance (using a method called ANOVA) is utilized and applied over the Amazon basin. Tested parameters are perturbed with correcting factors. First, parameter-correcting coefficients are considered uniform over the entire basin. The results are specific to the TRIP model and show that geomorphological parameters explain around 95% of the water height variance with purely additive contributions, all year long, with a dominating impact of the river Manning coefficient (40%), the riverbed slope (35%), and the river width (20%). The results also show that discharge is essentially sensitive to the groundwater time constant that makes up more than 90% of the variance. To a lesser extent, in rising/falling flow period, the discharge is also sensitive to geomorphological parameters. Next, the Amazon basin is divided into nine subregions and the sensitivity analysis is carried out for regionalized parameter-correcting coefficients. The results show that local-region parameters impact water height, while upstream-region parameters affect discharge.

Corresponding author address: Charlotte M. Emery, Laboratoire d'Études en Géophysique et Océanographie Spatiale, 16 Av. Édouard Belin, 31400 Toulouse, France. E-mail: emery@legos.obs-mip.fr

Abstract

The continental part of the water cycle is commonly represented with hydrological models. Yet, there are limits in their capacity to accurately estimate water storage and dynamics because of their coarse spatial resolution, simplified physics, and an incomplete knowledge of atmospheric forcing and input parameters. These errors can be diminished using data assimilation techniques. The model’s most sensitive parameters should be identified beforehand. The objective of the present study is to highlight key parameters impacting the river-routing scheme Total Runoff Integrating Pathways (TRIP) while simulating river water height and discharge as a function of time focusing on the annual cycle. Thus, a sensitivity analysis based on the decomposition of model output variance (using a method called ANOVA) is utilized and applied over the Amazon basin. Tested parameters are perturbed with correcting factors. First, parameter-correcting coefficients are considered uniform over the entire basin. The results are specific to the TRIP model and show that geomorphological parameters explain around 95% of the water height variance with purely additive contributions, all year long, with a dominating impact of the river Manning coefficient (40%), the riverbed slope (35%), and the river width (20%). The results also show that discharge is essentially sensitive to the groundwater time constant that makes up more than 90% of the variance. To a lesser extent, in rising/falling flow period, the discharge is also sensitive to geomorphological parameters. Next, the Amazon basin is divided into nine subregions and the sensitivity analysis is carried out for regionalized parameter-correcting coefficients. The results show that local-region parameters impact water height, while upstream-region parameters affect discharge.

Corresponding author address: Charlotte M. Emery, Laboratoire d'Études en Géophysique et Océanographie Spatiale, 16 Av. Édouard Belin, 31400 Toulouse, France. E-mail: emery@legos.obs-mip.fr
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  • Alkama, R., Kageyama M. , Ramstein G. , Marti O. , Ribstein P. , and Swingedouw D. , 2008: Impact of a realistic river routing in coupled ocean–atmosphere simulations of the Last Glacial Maximum climate. Climate Dyn., 30, 855869, doi:10.1007/s00382-007-0330-1.

    • Search Google Scholar
    • Export Citation
  • Alkama, R., and Coauthors, 2010: Global evaluation of the ISBA–TRIP continental hydrological system. Part I: Comparison to GRACE terrestrial water storage estimates and in situ river discharges. J. Hydrometeor., 11, 583600, doi:10.1175/2010JHM1211.1.

    • Search Google Scholar
    • Export Citation
  • Allen, G., and Pavelsky T. , 2015: Patterns of river width and surface area revealed by the satellite-derived North American River Width data set. Geophys. Res. Lett., 42, 395402, doi:10.1002/2014GL062764.

    • Search Google Scholar
    • Export Citation
  • Alsdorf, D., Rodriguez E. , and Lettenmaier D. , 2007: Measuring surface water from space. Rev. Geophys., 45, RG2002, doi:10.1029/2006RG000197.

    • Search Google Scholar
    • Export Citation
  • Apel, H., Thieken A. , Merz B. , and Bloschl G. , 2004: Flood risk assessment and associated uncertainty. Nat. Hazards Earth Syst. Sci., 4, 295308, doi:10.5194/nhess-4-295-2004.

    • Search Google Scholar
    • Export Citation
  • Aronica, G., Hankin B. , and Beven K. , 1998: Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data. Adv. Water Resour., 22, 349365, doi:10.1016/S0309-1708(98)00017-7.

    • Search Google Scholar
    • Export Citation
  • Arora, V., Chiew F. , and Grayson R. , 1999: A river flow routing scheme for general circulation models. J. Geophys. Res., 104, 14 34714 357, doi:10.1029/1999JD900200.

    • Search Google Scholar
    • Export Citation
  • Balsamo, G., Viterbo P. , Beljaars A. , den Hurk B. , Hirchi M. , Betts A. , and Scipal K. , 2009: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the integrated forecast system. J. Hydrometeor., 10, 623643, doi:10.1175/2008JHM1068.1.

    • Search Google Scholar
    • Export Citation
  • Bates, P., Horritt M. , Aronica G. , and Beven K. , 2004: Bayesian updating of flood inundation likelihoods conditioned on flood extent data. Hydrol. Processes, 18, 33473370, doi:10.1002/hyp.1499.

    • Search Google Scholar
    • Export Citation
  • Beven, K., and Binley A. , 1992: The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Processes, 6, 279298, doi:10.1002/hyp.3360060305.

    • Search Google Scholar
    • Export Citation
  • Biancamaria, S., Lettenmaier D. P. , and Pavelsky T. M. , 2015: The SWOT mission and capabilities for land hydrology. Surv. Geophys., 37, 307337, doi:10.1007/s10712-015-9346-y.

    • Search Google Scholar
    • Export Citation
  • Blackadar, A.-F., 1976: Modeling the nocturnal boundary layer. Preprints, Third Symp. on Atmospheric Turbulence, Diffusion, and Air Quality, Raleigh, NC, Amer. Meteor. Soc., 46–49.

  • Blazkova, S., and Beven K. , 2004: Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic. J. Hydrol., 292, 153172, doi:10.1016/j.jhydrol.2003.12.025.

    • Search Google Scholar
    • Export Citation
  • Boone, A., Calvet J.-C. , and Noilhan J. , 1999: Inclusion of a third soil layer in a land surface scheme using the force–restore method. J. Hydrometeor., 38, 16111630, doi:10.1175/1520-0450(1999)038<1611:IOATSL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Coe, M., 1998: A linked global model of terrestrial hydrologic processes: Simulation of modern rivers, lakes, and wetlands. J. Geophys. Res., 103, 88858899, doi:10.1029/98JD00347.

    • Search Google Scholar
    • Export Citation
  • Compo, G., and Coauthors, 2011: The Twentieth Century Reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 128, doi:10.1002/qj.776.

  • Cukier, R., Fortuin C. , and Shuler K. , 1973: Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I. Theory. J. Chem. Phys., 59, 38733878, doi:10.1063/1.1680571.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., and Douville H. , 2006: Introduction of a subgrid hydrology in the ISBA land surface model. Climate Dyn., 26, 6578, doi:10.1007/s00382-005-0059-7.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., Douville H. , Boone A. , Habets F. , and Noilhan J. , 2006: Impact of an exponential profile of saturated hydraulic conductivity within the ISBA LSM: Simulations over the Rhône basin. J. Hydrometeor., 7, 6180, doi:10.1175/JHM469.1.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., Douville H. , Prigent C. , Papa F. , and Aires F. , 2008: A new river flooding scheme for global climate applications: Off-line evaluation over South America. J. Geophys. Res., 113, D11110, doi:10.1029/2007JD009376.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., Alkama R. , Douville H. , Becker M. , and Cazenave A. , 2010: Global evaluation of the ISBA–TRIP continental hydrological system. Part II: Uncertainties in river routing simulation related to flow velocity and groundwater storage. J. Hydrometeor., 11, 601617, doi:10.1175/2010JHM1212.1.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., Alkama R. , Papa F. , Faroux S. , Douville H. , and Prigent C. , 2012: Global off-line evaluation of the ISBA–TRIP flood model. Climate Dyn., 38, 13891412, doi:10.1007/s00382-011-1054-9.

    • Search Google Scholar
    • Export Citation
  • Doksum, K., and Samarov A. , 1995: Nonparametric estimation of global functionals and a measure of the explanatory power of covariates in regression. Ann. Stat., 23, 14431473, doi:10.1214/aos/1176324307.

    • Search Google Scholar
    • Export Citation
  • Ducharne, A., Golaz C. , Leblois E. , Laval K. , Polcher J. , Ledoux E. , and de Marsily G. , 2003: Development of a high resolution runoff routing model, calibration and application to assess runoff from the LMD GCM. J. Hydrol., 280, 207228, doi:10.1016/S0022-1694(03)00230-0.

    • Search Google Scholar
    • Export Citation
  • Efron, B., and Stein C. , 1981: The jackknife estimate of variance. Ann. Stat., 9, 586596, doi:10.1214/aos/1176345462.

  • Fang, S., Gertner G. , Shinkareva S. , Wang G. , and Anderson A. , 2003: Improved generalized Fourier amplitude sensitivity test (FAST) for model assessment. Stat. Comput., 13, 221226, doi:10.1023/A:1024266632666.

    • Search Google Scholar
    • Export Citation
  • Fjortoft, R., and Coauthors, 2014: KaRIn on SWOT: Characteristics of near-nadir Ka-band interferometric SAR imagery. IEEE Trans. Geosci. Remote Sens., 52, 21722185, doi:10.1109/TGRS.2013.2258402.

    • Search Google Scholar
    • Export Citation
  • Francos, A., Elorza F. , Bouraoui F. , Bidoglio G. , and Galbiati L. , 2003: Sensitivity analysis of distributed environmental simulation models: Understanding the model behaviour in hydrological studies at the catchment scale. Reliab. Eng. Syst. Saf., 79, 205218, doi:10.1016/S0951-8320(02)00231-4.

    • Search Google Scholar
    • Export Citation
  • Freer, J., Beven K. , and Ambroise B. , 1996: Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the glue approach. Water Resour. Res., 32, 21612173, doi:10.1029/95WR03723.

    • Search Google Scholar
    • Export Citation
  • Gatelli, D., Kucherenko S. , Ratto M. , and Tarantola S. , 2009: Calculating first-order sensitivity measures: A benchmark of some recent methodologies. Reliab. Eng. Syst. Saf., 94, 12121219, doi:10.1016/j.ress.2008.03.028.

    • Search Google Scholar
    • Export Citation
  • Garambois, P., Roux H. , Larnier K. , Castaings W. , and Dartus D. , 2013: Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments. Hydrol. Earth Syst. Sci., 17, 23052322, doi:10.5194/hess-17-2305-2013.

    • Search Google Scholar
    • Export Citation
  • Gleason, C., and Smith L. , 2014: Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proc. Natl. Acad. Sci. USA, 111, 47884791, doi:10.1073/pnas.1317606111.

    • Search Google Scholar
    • Export Citation
  • Grayson, R., and Blöschl G. , 2001: Spatial Patterns in Catchment Hydrology. Cambridge University Press, 416 pp.

  • Guse, B., Reusser D. , and Fohrer N. , 2014: How to improve the representation of hydrological processes in SWAT for a lowland catchment—Temporal analysis of parameter sensitivity and model performance. Hydrol. Processes, 28, 26512670, doi:10.1002/hyp.9777.

    • Search Google Scholar
    • Export Citation
  • Hagemann, S., and Dümenil L. , 1997: A parameterization of the lateral waterflow for the global scale. Climate Dyn., 14, 1731, doi:10.1007/s003820050205.

    • Search Google Scholar
    • Export Citation
  • Hall, J., Tarantola S. , Bates P. , and Horritt M. , 2005: Distributed sensitivity analysis of flood inundation model calibration. J. Hydraul. Eng., 131, 117126, doi:10.1061/(ASCE)0733-9429(2005)131:2(117).

    • Search Google Scholar
    • Export Citation
  • Hornberger, G., and Spear R. , 1981: An approach to the preliminary analysis of environmental systems. J. Environ. Manage., 12, 718.

  • Jansen, M., 1999: Analysis of variance designs for model output. Comput. Phys. Commun., 117, 3543, doi:10.1016/S0010-4655(98)00154-4.

    • Search Google Scholar
    • Export Citation
  • Kavetski, D., Kuczera G. , and Franks S. , 2006: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resour. Res., 42, W03407, doi:10.1029/2005WR004368.

    • Search Google Scholar
    • Export Citation
  • Lehner, B., and Grill G. , 2013: Global river hydrography and network routing: Baseline data and new approaches to study the world’s large river systems. Hydrol. Processes, 27, 21712186, doi:10.1002/hyp.9740.

    • Search Google Scholar
    • Export Citation
  • Leopold, L., and Maddock T. Jr., 1953: The hydraulic geometry of stream channels and some physiographic implications. USGS Professional Paper 252, 57 pp. [Available online at http://pubs.usgs.gov/pp/0252/report.pdf.]

  • Lucas-Picher, P., Arora V. , Caya D. , and Laprisse R. , 2003: Implementation of a large-scale variable velocity river flow routing algorithm in the Canadian Regional Climate Model (CRCM). Atmos.–Ocean, 41, 139153, doi:10.3137/ao.410203.

    • Search Google Scholar
    • Export Citation
  • Maidment, D. R., 1993: Handbook of Hydrology. McGraw-Hill, 1424 pp.

  • Manning, R., 1891: On the flow of water in open channels and pipes. Inst. Civ. Eng. Irel., 20, 161207.

  • Masson, V., and Coauthors, 2013: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev., 6, 929960, doi:10.5194/gmd-6-929-2013.

    • Search Google Scholar
    • Export Citation
  • Meade, R., Rayol J. , Conceicão S. D. , and Natividade J. , 1991: Backwater effects in the Amazon River basin of Brazil. Environ. Geol. Water Sci., 18, 105114, doi:10.1007/BF01704664.

    • Search Google Scholar
    • Export Citation
  • Molinier, M., Guyot J.-L. , Guimarães V. , and de Oliveira E. , 1993: Hydrologie du bassin de l’amazone. Colloque Grands Bassins Fluviaux Périatlantiques, Paris, France, PEGI-INSA-CNRS-ORSTOM, 335–345.

  • Moody, J., and Troutman B. , 2002: Characterization of the spatial variability of channel morphology. Earth Surf. Processes Landforms, 27, 12511266, doi:10.1002/esp.403.

    • Search Google Scholar
    • Export Citation
  • Moradkhani, H., 2008: Hydrological remote sensing and land surface data assimilation. Sensors, 8, 29863004, doi:10.3390/s8052986.

  • Ngo-Duc, T., Oki T. , and Kanae S. , 2007: A variable streamflow velocity method for global river routing model: Model description and preliminary results. Hydrol. Earth Syst. Sci., 4, 43894414, doi:10.5194/hessd-4-4389-2007.

    • Search Google Scholar
    • Export Citation
  • Noilhan, J., and Planton S. , 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536549, doi:10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Oki, T., and Sud Y. , 1998: Design of Total Runoff Integrating Pathways (TRIP)—A global river channel network. Earth Interact., 2, doi:10.1175/1087-3562(1998)002<0001:DOTRIP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Olivera, F., Famiglietti J. , and Asante K. , 2000: Global-scale flow routing using a source-to-sink algorithm. Water Resour. Res., 36, 21972207, doi:10.1029/2000WR900113.

    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., and Beven K. , 2004: Functional classification and evaluation of hydrographs based on multi-component mapping. Int. J. River Basin Manage., 2, 89100, doi:10.1080/15715124.2004.9635224.

    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., Beven K. , Roo A. D. , Thielen J. , and Gouweleeuw B. , 2004: Uncertainty analysis of the rainfall runoff model LisFlood within the Generalized Likelihood Uncertainty Estimation (GLUE). Int. J. River Basin Manage., 2, 123133, doi:10.1080/15715124.2004.9635227.

    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., Cloke H. , Balsamo G. , Ngo-Duc T. , and Oki T. , 2010: Global runoff routing with the hydrological component of the ECMWF NWP system. Int. J. Climatol., 30, 21552174, doi:10.1002/joc.2028.

    • Search Google Scholar
    • Export Citation
  • Paris, A., and Coauthors, 2016: Stage–discharge rating curves based on satellite altimetry and modeled discharge in the amazon basin. Water Resour. Res., 52, 37873814, doi:10.1002/2014WR016618.

    • Search Google Scholar
    • Export Citation
  • Pedinotti, V., Boone A. , Ricci S. , Biancamaria S. , and Mognard N. , 2014: Assimilation of satellite data to optimize large-scale hydrological model parameters: A case study for the SWOT mission. Hydrol. Earth Syst. Sci., 18, 44854507, doi:10.5194/hess-18-4485-2014.

    • Search Google Scholar
    • Export Citation
  • Rabitz, H., Aliş O. , Shorter J. , and Shim K. , 1999: Efficient input–output model representations. Comput. Phys. Commun., 117, 1120, doi:10.1016/S0010-4655(98)00152-0.

    • Search Google Scholar
    • Export Citation
  • Ratto, M., Pagano A. , and Young P. , 2007: State dependent parameter metamodelling and sensitivity analysis. Comput. Phys. Commun., 177, 863876, doi:10.1016/j.cpc.2007.07.011.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., 2008: Data assimilation methods in earth sciences. Adv. Water Resour., 31, 14111418, doi:10.1016/j.advwatres.2008.01.001.

    • Search Google Scholar
    • Export Citation
  • Reusser, D., and Zehe E. , 2011: Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity. Water Resour. Res., 47, W07550, doi:10.1029/2010WR009946.

    • Search Google Scholar
    • Export Citation
  • Reusser, D., Buytaert W. , and Zehe E. , 2011: Temporal dynamics of model parameter sensitivity for computationally expensive models with the Fourier amplitude sensitivity test. Water Resour. Res., 47, W07551, doi:10.1029/2010WR009947.

    • Search Google Scholar
    • Export Citation
  • Roux, H., Labat D. , Garambois P.-A. , Maubourguet M.-M. , Chorda J. , and Dartus D. , 2011: A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments. Nat. Hazards Earth Syst. Sci., 11, 25672582, doi:10.5194/nhess-11-2567-2011.

    • Search Google Scholar
    • Export Citation
  • Saltelli, A., and Bolado R. , 1998: An alternative way to compute Fourier amplitude sensitivity test (FAST). Comput. Stat. Data Anal., 26, 445460, doi:10.1016/S0167-9473(97)00043-1.

    • Search Google Scholar
    • Export Citation
  • Saltelli, A., Chan K. , and Scott E. M. , 2008: Sensitivity Analysis. Wiley, 494 pp.

  • Saltelli, A., Annoni P. , Azzini I. , Campolongo F. , Ratto M. , and Tarantola S. , 2010: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun., 181, 259270, doi:10.1016/j.cpc.2009.09.018.

    • Search Google Scholar
    • Export Citation
  • Sampson, C., Smith A. , Bates P. , Neal J. , Alfieri L. , and Freer J. , 2015: A high-resolution global flood hazard model. Water Resour. Res., 51, 73587381, doi:10.1002/2015WR016954.

    • Search Google Scholar
    • Export Citation
  • Sieber, A., and Uhlenbrook S. , 2005: Sensitivity analyses of a distributed catchment model to verify the model structure. J. Hydrol., 310, 216235, doi:10.1016/j.jhydrol.2005.01.004.

    • Search Google Scholar
    • Export Citation
  • Sioli, H., 1984: The Amazon and its main affluents: Hydrography, morphology of the river courses, and river types. The Amazon, H. Sioli, Ed., Springer, 127–165, doi:10.1007/978-94-009-6542-3_5.

  • Sobol, I., 1967: On the distribution of points in a cube and the approximate evaluation of integrals. URSS Comput. Math. Math. Phys., 7 (4), 86112, doi:10.1016/0041-5553(67)90144-9.

    • Search Google Scholar
    • Export Citation
  • Sobol, I., 1993: Sensitivity analysis for non-linear mathematical models. Math. Model. Comput. Exp., 2, 407414.

  • Sobol, I., 2001: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul., 55, 271280, doi:10.1016/S0378-4754(00)00270-6.

    • Search Google Scholar
    • Export Citation
  • Stocker, T., and Coauthors, 2013: Technical summary. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 33–115, doi:10.1017/CBO9781107415324.005.

  • Trenberth, K., 2011: Changes in precipitation with climate change. Climate Res., 47, 123138, doi:10.3354/cr00953.

  • van Griensven, A., Meixner T. , Grunwald S. , Bishop T. , Diluzio M. , and Srinivasan R. , 2006: A global sensitivity analysis tool for the parameters of multi-variable catchment models. J. Hydrol., 324, 1023, doi:10.1016/j.jhydrol.2005.09.008.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C., Moore B. , Grace A. , Gildea M. , Melillo J. , Peterson B. , Rastetter E. , and Steudler P. , 1989: Continental scale models of water balance and fluvial transport: An application to South America. Global Biogeochem. Cycles, 3, 241265, doi:10.1029/GB003i003p00241.

    • Search Google Scholar
    • Export Citation
  • Wagener, T., McIntyre N. , Lees M. , Wheater H. , and Gupta H. , 2003: Towards reduced uncertainty in conceptual rainfall–runoff modelling: Dynamic identifiability analysis. Hydrol. Processes, 17, 455476, doi:10.1002/hyp.1135.

    • Search Google Scholar
    • Export Citation
  • Werner, M., Hunter N. , and Bates P. , 2005: Identifiability of distributed floodplain roughness values in flood extent estimation. J. Hydrol., 314, 139157, doi:10.1016/j.jhydrol.2005.03.012.

    • Search Google Scholar
    • Export Citation
  • Winsemius, H., Beek L. V. , Jongman B. , Ward P. , and Bouwman A. , 2013: A framework for global river flood risk assessments. Hydrol. Earth Syst. Sci., 17, 18711892, doi:10.5194/hess-17-1871-2013.

    • Search Google Scholar
    • Export Citation
  • Wisser, D., Feketa B. , Vörösmarty C. J. , and Schumann A. H. , 2010: Reconstructing 20th century global hydrography: A contribution to the Global Terrestrial Network–Hydrology (GTN-H). Hydrol. Earth Syst. Sci., 14, 124, doi:10.5194/hess-14-1-2010.

    • Search Google Scholar
    • Export Citation
  • Yamazaki, D., Kanae S. , Kim H. , and Oki T. , 2011: A physically based description of floodplain inundation dynamics in a global river routing model. Water Resour. Res., 47, W04501, doi:10.1029/2010WR009726.

    • Search Google Scholar
    • Export Citation
  • Yamazaki, D., O’Loughlin F. , Trigg M. , Miller Z. , Pavelsky T. , and Bates P. D. , 2014: Development of the global width database for large rivers. Water Resour. Res., 50, 34673480, doi:10.1002/2013WR014664.

    • Search Google Scholar
    • Export Citation
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