Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-117-2017
© Author(s) 2017. 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-21-117-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
09 Jan 2017
Research article |
| 09 Jan 2017
Assessing the impact of hydrodynamics on large-scale flood wave propagation – a case study for the Amazon Basin
Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, the Netherlands
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Arjen V. Haag
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Arthur van Dam
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Hessel C. Winsemius
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Ludovicus P. H. van Beek
Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, the Netherlands
Marc F. P. Bierkens
Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, the Netherlands
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Related authors
Barry van Jaarsveld, Niko Wanders, Edwin H. Sutanudjaja, Jannis Hoch, Bram Droppers, Joren Janzing, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Earth Syst. Dynam., 16, 29–54, https://doi.org/10.5194/esd-16-29-2025, https://doi.org/10.5194/esd-16-29-2025, 2025
Short summary
Short summary
Policy makers use global hydrological models to develop water management strategies and policies. However, it would be better if these models provided information at higher resolution. We present a first-of-its-kind, truly global hyper-resolution model and show that hyper-resolution brings about better estimates of river discharge, and this is especially true for smaller catchments. Our results also suggest that future hyper-resolution models need to include more detailed land cover information.
Jerom P. M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, and Rolf W. Hut
Hydrol. Earth Syst. Sci., 28, 5011–5030, https://doi.org/10.5194/hess-28-5011-2024, https://doi.org/10.5194/hess-28-5011-2024, 2024
Short summary
Short summary
For users of hydrological models, model suitability often hinges on how well simulated outputs match observed discharge. This study highlights the importance of including discharge observation uncertainty in hydrological model performance assessment. We highlight the need to account for this uncertainty in model comparisons and introduce a practical method suitable for any observational time series with available uncertainty estimates.
Safaa Naffaa, Frances F. E. Dunne, Jannis Hoch, Geert Sterk, Steven S. M. de Jong, and Rens L. P. H. van Beek
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-255, https://doi.org/10.5194/hess-2024-255, 2024
Revised manuscript not accepted
Short summary
Short summary
This paper introduces the RDSM model. Human impacts such as climate change, land cover change and reservoir construction can be explicitly modelled and evaluated. We applied RDSM to the Amazon. We also validated the model and we conclude that RDSM effectively represents the patterns of monthly and annual variations of discharge and sediment transport across the Amazon Basin and to the ocean. Our findings are relevant to the research community working on the Amazon Basin and on similar topics.
Jannis M. Hoch, Edwin H. Sutanudjaja, Niko Wanders, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, https://doi.org/10.5194/hess-27-1383-2023, 2023
Short summary
Short summary
To facilitate locally relevant simulations over large areas, global hydrological models (GHMs) have moved towards ever finer spatial resolutions. After a decade-long quest for hyper-resolution (i.e. equal to or smaller than 1 km), the presented work is a first application of a GHM at 1 km resolution over Europe. This not only shows that hyper-resolution can be achieved but also allows for a thorough evaluation of model results at unprecedented detail and the formulation of future research.
Jennie C. Steyaert, Edwin H. Sutanudjaja, Marc Bierkens, and Niko Wanders
Hydrol. Earth Syst. Sci., 29, 6499–6527, https://doi.org/10.5194/hess-29-6499-2025, https://doi.org/10.5194/hess-29-6499-2025, 2025
Short summary
Short summary
Using machine learning techniques and remotely sensed reservoir data, we develop a workflow to derive reservoir storage bounds. We put these bounds in a global hydrologic model, PCR-GLOBWB 2, and evaluate the difference between generalized operations (the schemes typically in global models) and this data derived method. We find that modelled storage is more accurate in the data derived operations. We also find that generalized operations over estimate storage and can underestimate water gaps.
Sneha Chevuru, Rens L. P. H. van Beek, Michelle T. H. van Vliet, Jerom P. M. Aerts, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 29, 4219–4239, https://doi.org/10.5194/hess-29-4219-2025, https://doi.org/10.5194/hess-29-4219-2025, 2025
Short summary
Short summary
This study combines the global hydrological model
PCRaster Global Water Balancewith the
World Food Studiescrop model to analyze feedbacks between hydrology and crop growth. It quantifies one-way and two-way interactions, revealing patterns in crop yield and irrigation water use. Dynamic interactions enhance understanding of climate variability impacts on food production, highlighting the importance of two-way model coupling for accurate assessments.
Nicole Gyakowah Otoo, Edwin H. Sutanudjaja, Michelle T. H. van Vliet, Aafke M. Schipper, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 29, 2153–2165, https://doi.org/10.5194/hess-29-2153-2025, https://doi.org/10.5194/hess-29-2153-2025, 2025
Short summary
Short summary
The contribution of groundwater to groundwater-dependent ecosystems (GDEs) is declining as a result of an increase in groundwater abstractions and climate change. This may lead to loss of habitat and biodiversity. This proposed framework enables the mapping and understanding of the temporal and spatial dynamics of GDEs on a large scale. The next step is to assess the global impacts of climate change and water use on GDE extent and health.
Barry van Jaarsveld, Niko Wanders, Edwin H. Sutanudjaja, Jannis Hoch, Bram Droppers, Joren Janzing, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Earth Syst. Dynam., 16, 29–54, https://doi.org/10.5194/esd-16-29-2025, https://doi.org/10.5194/esd-16-29-2025, 2025
Short summary
Short summary
Policy makers use global hydrological models to develop water management strategies and policies. However, it would be better if these models provided information at higher resolution. We present a first-of-its-kind, truly global hyper-resolution model and show that hyper-resolution brings about better estimates of river discharge, and this is especially true for smaller catchments. Our results also suggest that future hyper-resolution models need to include more detailed land cover information.
Jerom P. M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, and Rolf W. Hut
Hydrol. Earth Syst. Sci., 28, 5011–5030, https://doi.org/10.5194/hess-28-5011-2024, https://doi.org/10.5194/hess-28-5011-2024, 2024
Short summary
Short summary
For users of hydrological models, model suitability often hinges on how well simulated outputs match observed discharge. This study highlights the importance of including discharge observation uncertainty in hydrological model performance assessment. We highlight the need to account for this uncertainty in model comparisons and introduce a practical method suitable for any observational time series with available uncertainty estimates.
Safaa Naffaa, Frances F. E. Dunne, Jannis Hoch, Geert Sterk, Steven S. M. de Jong, and Rens L. P. H. van Beek
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-255, https://doi.org/10.5194/hess-2024-255, 2024
Revised manuscript not accepted
Short summary
Short summary
This paper introduces the RDSM model. Human impacts such as climate change, land cover change and reservoir construction can be explicitly modelled and evaluated. We applied RDSM to the Amazon. We also validated the model and we conclude that RDSM effectively represents the patterns of monthly and annual variations of discharge and sediment transport across the Amazon Basin and to the ocean. Our findings are relevant to the research community working on the Amazon Basin and on similar topics.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Hubert T. Samboko, Sten Schurer, Hubert H. G. Savenije, Hodson Makurira, Kawawa Banda, and Hessel Winsemius
Geosci. Instrum. Method. Data Syst., 12, 155–169, https://doi.org/10.5194/gi-12-155-2023, https://doi.org/10.5194/gi-12-155-2023, 2023
Short summary
Short summary
The study investigates how low-cost technology can be applied in data-scarce catchments to improve water resource management. More specifically, we investigate how drone technology can be combined with low-cost real-time kinematic positioning (RTK) global navigation satellite system (GNSS) equipment and subsequently applied to a 3D hydraulic model so as to generate more physically based rating curves.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
Short summary
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Dirk Eilander, Anaïs Couasnon, Frederiek C. Sperna Weiland, Willem Ligtvoet, Arno Bouwman, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 2251–2272, https://doi.org/10.5194/nhess-23-2251-2023, https://doi.org/10.5194/nhess-23-2251-2023, 2023
Short summary
Short summary
This study presents a framework for assessing compound flood risk using hydrodynamic, impact, and statistical modeling. A pilot in Mozambique shows the importance of accounting for compound events in risk assessments. We also show how the framework can be used to assess the effectiveness of different risk reduction measures. As the framework is based on global datasets and is largely automated, it can easily be applied in other areas for first-order assessments of compound flood risk.
Jannis M. Hoch, Edwin H. Sutanudjaja, Niko Wanders, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, https://doi.org/10.5194/hess-27-1383-2023, 2023
Short summary
Short summary
To facilitate locally relevant simulations over large areas, global hydrological models (GHMs) have moved towards ever finer spatial resolutions. After a decade-long quest for hyper-resolution (i.e. equal to or smaller than 1 km), the presented work is a first application of a GHM at 1 km resolution over Europe. This not only shows that hyper-resolution can be achieved but also allows for a thorough evaluation of model results at unprecedented detail and the formulation of future research.
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
Short summary
Short summary
In coastal deltas, flooding can occur from interactions between coastal, riverine, and pluvial drivers, so-called compound flooding. Global models however ignore these interactions. We present a framework for automated and reproducible compound flood modeling anywhere globally and validate it for two historical events in Mozambique with good results. The analysis reveals differences in compound flood dynamics between both events related to the magnitude of and time lag between drivers.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Hubert T. Samboko, Sten Schurer, Hubert H. G. Savenije, Hodson Makurira, Kawawa Banda, and Hessel Winsemius
Geosci. Instrum. Method. Data Syst., 11, 1–23, https://doi.org/10.5194/gi-11-1-2022, https://doi.org/10.5194/gi-11-1-2022, 2022
Short summary
Short summary
The study was conducted along the Luangwa River in Zambia. It combines low-cost instruments such as UAVs and GPS kits to collect data for the purposes of water management. A novel technique which seamlessly merges the dry and wet bathymetry before application in a hydraulic model was applied. Successful implementation resulted in water authorities with small budgets being able to monitor flows safely and efficiently without significant compromise on accuracy.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Marc F. P. Bierkens, Edwin H. Sutanudjaja, and Niko Wanders
Hydrol. Earth Syst. Sci., 25, 5859–5878, https://doi.org/10.5194/hess-25-5859-2021, https://doi.org/10.5194/hess-25-5859-2021, 2021
Short summary
Short summary
We introduce a simple analytical framework that allows us to estimate to what extent large-scale groundwater withdrawal affects groundwater levels and streamflow. It also calculates which part of the groundwater withdrawal comes out of groundwater storage and which part from a reduction in streamflow. Global depletion rates obtained with the framework are compared with estimates from satellites, from global- and continental-scale groundwater models, and from in situ datasets.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
Short summary
Short summary
Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Sepehr Eslami, Piet Hoekstra, Herman W. J. Kernkamp, Nam Nguyen Trung, Dung Do Duc, Hung Nguyen Nghia, Tho Tran Quang, Arthur van Dam, Stephen E. Darby, Daniel R. Parsons, Grigorios Vasilopoulos, Lisanne Braat, and Maarten van der Vegt
Earth Surf. Dynam., 9, 953–976, https://doi.org/10.5194/esurf-9-953-2021, https://doi.org/10.5194/esurf-9-953-2021, 2021
Short summary
Short summary
Increased salt intrusion jeopardizes freshwater supply to the Mekong Delta, and the current trends are often inaccurately associated with sea level rise. Using observations and models, we show that salinity is highly sensitive to ocean surge, tides, water demand, and upstream discharge. We show that anthropogenic riverbed incision has significantly amplified salt intrusion, exemplifying the importance of preserving sediment budget and riverbed levels to protect deltas against salt intrusion.
Jan L. Gunnink, Hung Van Pham, Gualbert H. P. Oude Essink, and Marc F. P. Bierkens
Earth Syst. Sci. Data, 13, 3297–3319, https://doi.org/10.5194/essd-13-3297-2021, https://doi.org/10.5194/essd-13-3297-2021, 2021
Short summary
Short summary
In the Mekong Delta (Vietnam) groundwater is important for domestic, agricultural and industrial use. Increased pumping of groundwater has caused land subsidence and increased the risk of salinization, thereby endangering the livelihood of the population in the delta. We made a model of the salinity of the groundwater by integrating different sources of information and determined fresh groundwater volumes. The resulting model can be used by researchers and policymakers.
Edward R. Jones, Michelle T. H. van Vliet, Manzoor Qadir, and Marc F. P. Bierkens
Earth Syst. Sci. Data, 13, 237–254, https://doi.org/10.5194/essd-13-237-2021, https://doi.org/10.5194/essd-13-237-2021, 2021
Short summary
Short summary
Continually improving and affordable wastewater management provides opportunities for both pollution reduction and clean water supply augmentation. This study provides a global outlook on the state of domestic and industrial wastewater production, collection, treatment and reuse. Our results can serve as a baseline in evaluating progress towards policy goals (e.g. Sustainable Development Goals) and as input data in large-scale water resource assessments (e.g. water quality modelling).
Cited articles
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS-global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.
Alfieri, L., Salamon, P., Bianchi, A., Neal, J. C., Bates, P., and Feyen, L.: Advances in pan-European flood hazard mapping, Hydrol. Process., 28, 4067–4077, https://doi.org/10.1002/hyp.9947, 2014.
Andreadis, K. M., Schumann, G. J.-P., and Pavelsky, T. M.: A simple global river bankfull width and depth database, Water Resour. Res., 49, 7164–7168, https://doi.org/10.1002/wrcr.20440, 2013.
Bates, P. D. and de Roo, A.: A simple raster-based model for flood inundation simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X, 2000.
Baugh, C. A., Bates, P. D., Schumann, G. J.-P., and Trigg, M. A.: SRTM vegetation removal and hydrodynamic modeling accuracy, Water Resour. Res., 49, 5276–5289, https://doi.org/10.1002/wrcr.20412, 2013.
Berry, P. A. M., Garlick, J. D., and Smith, R. G.: Near-global validation of the SRTM DEM using satellite radar altimetry, Remote Sens. Environ., 106, 17–27, https://doi.org/10.1016/j.rse.2006.07.011, 2007.
Biancamaria, S., Bates, P. D., Boone, A., and Mognard, N. M.: Large-scale coupled hydrologic and hydraulic modelling of the Ob river in Siberia, J. Hydrol., 379, 136–150, https://doi.org/10.1016/j.jhydrol.2009.09.054, 2009.
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015.
Bierkens, M. F. P., Bell, V. A., Burek, P., Chaney, N., Condon, L. E., David, C. H., de Roo, A., D??ll, P., Drost, N., Famiglietti, J. S., Flörke, M., Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S., Maxwell, R. M., Reager, J. T., Samaniego, L., Sudicky, E., Sutanudjaja, E. H., van de Giesen, N., Winsemius, H. C., and Wood, E. F.: Hyper-resolution global hydrological modelling: What is next?: "Everywhere and locally relevant", Hydrol. Process., 29, 310–320, https://doi.org/10.1002/hyp.10391, 2015.
Butts, M., Drews, M., Larsen, M. A. D., Lerer, S., Rasmussen, S. H., Grooss, J., Overgaard, J., Refsgaard, J. C., Christensen, O. B., and Christensen, J. H.: Embedding complex hydrology in the regional climate system – Dynamic coupling across different modelling domains, Adv. Water Resour., 74, 166–184, https://doi.org/10.1016/j.advwatres.2014.09.004, 2014.
Carabajal, C. C. and Harding, D. J.: SRTM C-band and ICESat laser altimetry elevation comparisons as a function of tree cover and relief, Photogramm. Eng. Remote Sens., 73, 287–298, https://doi.org/10.14358/PERS.72.3.287, 2006.
Castro Gama, M., Popescu, I., Mynett, A., Shengyang, L., and van Dam, A.: Modelling extreme flood hazard events on the middle Yellow River using DFLOW-flexible mesh approach, Nat. Hazards Earth Syst. Sci. Discuss., 1, 6061-6092, https://doi.org/10.5194/nhessd-1-6061-2013, 2013.
Ceola, S., Laio, F., and Montanari, A.: Satellite nighttime lights reveal increasing human exposure to floods worldwide, Geophys. Res. Lett., 41, 7184–7190, https://doi.org/10.1002/2014GL061859, 2014.
Chen, C., Liu, H., and Beardsley, R. C.: An Unstructured Grid, Finite-Volume, Three-Dimensional, Primitive Equations Ocean Model: Application to Coastal Ocean and Estuaries, J. Atmos. Ocean. Tech., 20, 159–186, https://doi.org/10.1175/1520-0426(2003)020<0159:AUGFVT>2.0.CO;2, 2003.
CSDMS: CSDMS Basic Model Interface (version 1.0), available at: https://csdms.colorado.edu/wiki/BMI_Description, last access: 25 August 2016.
de Graaf, I. E. M., Sutanudjaja, E., van Beek, L. P. H., and Bierkens, M. F. P.: A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19, 823–837, https://doi.org/10.5194/hess-19-823-2015, 2015.
Deltares: D-Flow Flexible Mesh Technical Reference Manual (Draft), available at: http://content.oss.deltares.nl/delft3d/manuals/D-Flow_FM_Technical_Reference.pdf (last access: 21 October 2015), 2016.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003.
Dottori, F., Salamon, P., Bianchi, A., Alfieri, L., Hirpa, F., and Feyen, L.: Development and evaluation of a framework for global flood hazard mapping, Adv. Water Resour., 94, 87–102, https://doi.org/10.1016/j.advwatres.2016.05.002, 2016.
Finaud-Guyot, P., Delenne, C., Guinot, V., and Llovel, C.: 1D–2D coupling for river flow modeling, Comptes Rendus Mécanique, 339, 226–234, https://doi.org/10.1016/j.crme.2011.02.001, 2011.
Getirana, A. C. V, Bonnet, M., and Martinez, J.: Evaluating parameter effects in a DEM "burning" process based on land cover data, Hydrol. Process., 23, 2316–2325, https://doi.org/:10.1002/hyp.7303, 2009.
Gupta, H. V, Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria?: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hagen, E. and Lu, X. X.: Let us create flood hazard maps for developing countries, Nat. Hazards, 58, 841–843, https://doi.org/10.1007/s11069-011-9750-7, 2011.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Publ. Gr., 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013.
Huang, C., Chen, Y., and Wu, J.: Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery, Int. J. Appl. Earth Obs. Geoinf., 26, 350–362, https://doi.org/10.1016/j.jag.2013.09.002, 2014.
Jongman, B., Ward, P. J., and Aerts, J. C. J. H.: Global exposure to river and coastal flooding: Long term trends and changes, Global Environ. Change, 22, 823–835, https://doi.org/10.1016/j.gloenvcha.2012.07.004, 2012.
Jongman, B., Hochrainer-Stigler, S., Feyen, L., Aerts, J. C. J. H., Mechler, R., Botzen, W. J. W., Bouwer, L. M., Pflug, G., Rojas, R., and Ward, P. J.: Increasing stress on disaster-risk finance due to large floods, Nat. Clim. Change, 4, 1–5, https://doi.org/10.1038/NCLIMATE2124, 2014.
Kållberg, P., Berrisford, P., Hoskins, B., Simmons, A., Uppala, S., Lamy-Thepaut, S., and Hine, R.: ERA-40 atlas, ERA-40 Proj. Rep. Ser. 19, Eur. Cent. for Medium Range Weather Forecasts, Reading, UK, 2005.
Karssenberg, D., Schmitz, O., Salamon, P., de Jong, K., and Bierkens, M. F. P.: A software framework for construction of process-based stochastic spatio-temporal models and data assimilation, Environ. Model. Softw., 25, 489–502, https://doi.org/10.1016/j.envsoft.2009.10.004, 2010.
Kernkamp, H. W. J., van Dam, A., Stelling, G. S., and de Goede, E. D.: Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf, Ocean Dynam., 61, 1175–1188, https://doi.org/10.1007/s10236-011-0423-6, 2011.
Kinzel, P. J., Legleiter, C. J., and Nelson, J. M.: Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges, J. Am. Water Resour. Assoc., 49, 183–204, https://doi.org/10.1111/jawr.12008, 2013.
Kling, H., Stanzel, P., Fuchs, M., and Nachtnebel, H.-P.: Performance of the COSERO precipitation–runoff model under non-stationary conditions in basins with different climates, Hydrolog. Sci. J., 60, 1374–1393, https://doi.org/10.1080/02626667.2014.959956, 2015.
Legleiter, C. J.: Calibrating remotely sensed river bathymetry in the absence of field measurements: Flow REsistance Equation-Based Imaging of River Depths (FREEBIRD), Water Resour. Res., 51, 2865–2884, https://doi.org/10.1002/2014WR016624, 2015.
Legleiter, C. J.: Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT), Water Resour. Res., 52, 3722–3741, https://doi.org/10.1002/2016WR018730, 2016.
Lehner, B., Verdin, K., and Jarvis, A.: New Global Hydrography Derived From Spaceborne Elevation Data, Eos T. Am. Geophys. Un., 8, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Leopold, L. B. and Maddock, T. J.: The hydraulic geometry of stream channels and some physiographic implications, US Geol. Surv. Prof. Pap. 252, US Geological Survey, 56 pp., 1953.
Li, H., Beldring, S., and Xu, C.-Y.: Stability of model performance and parameter values on two catchments facing changes in climatic conditions, Hydrolog. Sci. J., 60, 1317–1330, https://doi.org/10.1080/02626667.2014.978333, 2015.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Lima, I. B. T., Rosa, R. R., Ramos, F. M., and de Moraes Novo, E. M. L.: Water level dynamics in the Amazon floodplain, Adv. Water Resour., 26, 725–732, https://doi.org/10.1016/S0309-1708(03)00052-6, 2003.
Liu, Q., Qin, Y., Zhang, Y., and Li, Z.: A coupled 1D–2D hydrodynamic model for flood simulation in flood detention basin, Nat. Hazards, 75, 1303–1325, https://doi.org/10.1007/s11069-014-1373-3, 2015.
Meade, R. H., Rayol, J. M., Da Conceição, S. C., and Natividade, J. R. G.: Backwater Effects in the Amazon River of Basin, Environ. Geol. Water Sci., 18, 105–114, https://doi.org/10.1007/BF01704664, 1991.
Molinier, M., Ronchail, J., Guyot, J. L., Cochonneau, G., Guimarães, V., and de Olveira, E.: Hydrological variability in the Amazon drainage basin and African tropical rivers, Hydrol. Process., 23, 3245–3252, https://doi.org/10.1002/hyp.7400, 2009.
Moussa, R. and Bocquillon, C.: Criteria for the choice of flood-routing methods in natural channels, J. Hydrol., 186, 1–30, https://doi.org/10.1016/S0022-1694(96)03045-4, 1996.
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H., and Ward, P. J.: A global reanalysis of storm surges and extreme sea levels, Nat. Commun., 7, 11969, https://doi.org/10.1038/ncomms11969, 2016.
Munich Re: Topics Geo, natural catastrophes 2009: analyses, assessments, positions, Munich Reinsurance Group, Munich, Germany, 2010.
Naz, B. S., Frans, C. D., Clarke, G. K. C., Burns, P., and Lettenmaier, D. P.: Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model, Hydrol. Earth Syst. Sci., 18, 787–802, https://doi.org/10.5194/hess-18-787-2014, 2014.
Neal, J. C., Schumann, G. J.-P., and Bates, P. D.: A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas, Water Resour. Res., 48, 1–16, https://doi.org/10.1029/2012WR012514, 2012.
Paiva, R. C. D., Collischonn, W., and Tucci, C. E. M.: Large scale hydrologic and hydrodynamic modeling using limited data and a GIS based approach, J. Hydrol., 406, 170–181, https://doi.org/10.1016/j.jhydrol.2011.06.007, 2011.
Paiva, R. C. D., Collischonn, W., and Buarque, D. C.: Validation of a full hydrodynamic model for large-scale hydrologic modelling in the Amazon, Hydrol. Process., 27, 333–346, https://doi.org/10.1002/hyp.8425, 2013.
Pappenberger, F., Matgen, P., Beven, K. J., Henry, J. B., Pfister, L., and Fraipont, P.: Influence of uncertain boundary conditions and model structure on flood inundation predictions, Adv. Water Resour., 29, 1430–1449, https://doi.org/10.1016/j.advwatres.2005.11.012, 2006.
Pappenberger, F., Dutra, E., Wetterhall, F., and Cloke, H. L.: Deriving global flood hazard maps of fluvial floods through a physical model cascade, Hydrol. Earth Syst. Sci., 16, 4143–4156, https://doi.org/10.5194/hess-16-4143-2012, 2012.
Peckham, S. D., Hutton, E. W. H., and Norris, B.: A component-based approach to integrated modeling in the geosciences: The design of CSDMS, Comput. Geosci., 53, 3–12, https://doi.org/10.1016/j.cageo.2012.04.002, 2013.
Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., Tomasella, J., and Waterloo, M. J.: HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia, Remote Sens. Environ., 112, 3469–3481, https://doi.org/10.1016/j.rse.2008.03.018, 2008.
Rudorff, C. M., Melack, J. M., and Bates, P. D.: Flooding dynamics on the lower Amazon floodplain: 1. Hydraulic controls on water elevation, inundation extent, and river-floodplain discharge, Water Resour. Res., 50, 619–634, https://doi.org/10.1002/2013WR014091, 2014a.
Rudorff, C. M., Melack, J. M., and Bates, P. D.: Flooding dynamics on the lower Amazon floodplain: 2. Seasonal and interannual hydrological variability, Water Resour. Res., 50, 635–649, https://doi.org/10.1002/2013WR014714, 2014b.
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015.
Sanders, B. F.: Evaluation of on-line DEMs for flood inundation modeling, Adv. Water Resour., 30, 1831–1843, https://doi.org/10.1016/j.advwatres.2007.02.005, 2007.
Savage, J. T. S., Bates, P. D., Freer, J., Neal, J. C., and Aronica, G.: When does spatial resolution become spurious in probabilistic flood inundation predictions?, Hydrol. Process., 30, 2014–2032, https://doi.org/10.1002/hyp.10749, 2016.
Schumann, G. J.-P., Neal, J. C., Voisin, N., Andreadis, K. M., Pappenberger, F., Phanthuwongpakdee, N., Hall, A. C., and Bates, P. D.: A first large-scale flood inundation forecasting model, Water Resour. Res., 49, 6248–6257, https://doi.org/10.1002/wrcr.20521, 2013.
Senatore, A., Mendicino, G., Gochis, D. J., Yu, W., Yates, D. N., and Kunstmann, H.: Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales, J. Adv. Model. Earth Syst., 7, 1693–1715, https://doi.org/10.1002/2015MS000510, 2015.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res., 116, 1–12, https://doi.org/10.1029/2011JG001708, 2011.
Trigg, M. A., Wilson, M. D., Bates, P. D., Horritt, M. S., Alsdorf, D. E., Forsberg, B. R., and Vega, M. C.: Amazon flood wave hydraulics, J. Hydrol., 374, 92–105, https://doi.org/10.1016/j.jhydrol.2009.06.004, 2009.
UNISDR: Global Assessment Report on Disaster Risk Reduction, United Nations International Strategy for Disaster Reduction Secretariat, Geneva, Italy, 2011.
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Van De Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteorol. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
van Beek, L. P. H.: Forcing PCR-GLOWB with CRU data, Department of Physical Geography, Utrecht University, available at: http://vanbeek.geo.uu.nl/suppinfo/vanbeek2008.pdf (last access: 20 December 2016), 2008.
van Beek, L. P. H. and Bierkens, M. F. P.: The Global Hydrological Model PCR-GLOBWB: Conceptualization, Parameterization and Verification, available at: http://vanbeek.geo.uu.nl/suppinfo/vanbeekbierkens2009.pdf (last access: 20 December 2016), 2008.
van Beek, L. P. H., Wada, Y., and Bierkens, M. F. P.: Global monthly water stress: 1. Water balance and water availability, Water Resour. Res., 47, W07517, https://doi.org/10.1029/2010WR009791, 2011.
Visser, H., Bouwman, A., Ligtvoet, W., and Petersen, A. C.: A statistical study of weather-related disasters: past, present and future, PBL Netherlands Environ. Assess. Agency, Hague, Bilthoven, the Netherlands, 2012.
Wagner, S., Fersch, B., Yuan, F., Yu, Z., and Kunstmann, H.: Fully coupled atmospheric-hydrological modeling at regional and long-term scales: Development, application, and analysis of WRF-HMS, Water Resour. Res., 52, 3187–3211, https://doi.org/10.1002/2015WR018185, 2016.
Wanders, N. and Wada, Y.: Human and climate impacts on the 21st century hydrological drought, J. Hydrol., 526, 208–220, https://doi.org/10.1016/j.jhydrol.2014.10.047, 2015.
Ward, P. J., Jongman, B., Salamon, P., Simpson, A., Bates, P. D., De Groeve, T., Muis, S., de Perez, E. C., Rudari, R., Trigg, M. A., and Winsemius, H. C.: Usefulness and limitations of global flood risk models, Nat. Clim. Change, 5, 712–715, https://doi.org/10.1038/nclimate2742, 2015.
Weiland, F. S., van Beek, L. P. H., Kwadijk, J. C. J., and Bierkens, M. F. P.: The ability of a GCM-forced hydrological model to reproduce global discharge variability, Hydrol. Earth Syst. Sci., 14, 1595–1621, https://doi.org/10.5194/hess-14-1595-2010, 2010.
Wilson, M. D., Bates, P. D., Alsdorf, D. E., Forsberg, B., Horritt, M., Melack, J., Frappart, F., and Famiglietti, J.: Modeling large-scale inundation of Amazonian seasonally flooded wetlands, Geophys. Res. Lett., 34, 4–9, https://doi.org/10.1029/2007GL030156, 2007.
Winsemius, H. C., van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013.
Winsemius, H. C., Aerts, J. C. J. H., van Beek, L. P. H., Bierkens, M. F. P., Bouwman, A., Jongman, B., Kwadijk, J. C. J., Ligtvoet, W., Lucas, P. L., van Vuuren, D. P., and Ward, P. J.: Global Drivers of Future River Flood Risk, Nat. Clim. Change, 6, 381–385, https://doi.org/10.1038/NCLIMATE2893, 2016.
Wood, E. F., Lettenmaier, D. P., and Zartarian, V. G.: A land-surface hydrology parameterization with subgrid variability for general circulation models, J. Geophys. Res., 97, 2717, https://doi.org/10.1029/91JD01786, 1992.
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., Van De Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B., Lettenmaier, D. P., Lidard, C. P., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, 1–10, https://doi.org/10.1029/2010WR010090, 2011.
Yamazaki, D., Kanae, S., Kim, H. and Oki, T.: A physically based description of floodplain inundation dynamics in a global river routing model, Water Resour. Res., 47, 1–21, https://doi.org/10.1029/2010WR009726, 2011.
Yamazaki, D., Baugh, C. A., Bates, P. D., Kanae, S., Alsdorf, D. E., and Oki, T.: Adjustment of a spaceborne DEM for use in floodplain hydrodynamic modeling, J. Hydrol., 436–437, 81–91, https://doi.org/10.1016/j.jhydrol.2012.02.045, 2012a.
Yamazaki, D., Lee, H., Alsdorf, D. E., Dutra, E., Kim, H., Kanae, S., and Oki, T.: Analysis of the water level dynamics simulate by a global river model: A case study in the Amazon River, Water Resour. Res., 48, 1–15, https://doi.org/10.1029/2012WR011869, 2012b.
Yamazaki, D., O'Loughlin, F., Trigg, M. A., Miller, Z. F., Pavelsky, T. M., and Bates, P. D.: Development of the Global Width Database for Large Rivers, Water Resour. Res., 50, 2108–2123, https://doi.org/10.1002/2013WR014664, 2014.
Yamazaki, D., Trigg, M. A. and Ikeshima, D.: Development of a global ∼ 90 m water body map using multi-temporal Landsat images, Remote Sens. Environ., 171, 337–351, https://doi.org/10.1016/j.rse.2015.10.014, 2015.
Yoon, Y., Durand, M., Merry, C. J., Clark, E. A., Andreadis, K. M., and Alsdorf, D. E.: Estimating river bathymetry from data assimilation of synthetic SWOT measurements, J. Hydrol., 464, 363–375, https://doi.org/10.1016/j.jhydrol.2012.07.028, 2012.
Yossef, N. C., van Beek, L. P. H., Kwadijk, J. C. J., and Bierkens, M. F. P.: Assessment of the potential forecasting skill of a global hydrological model in reproducing the occurrence of monthly flow extremes, Hydrol. Earth Syst. Sci., 16, 4233–4246, https://doi.org/10.5194/hess-16-4233-2012, 2012.
Zabel, F. and Mauser, W.: 2-way coupling the hydrological land surface model PROMET with the regional climate model MM5, Hydrol. Earth Syst. Sci., 17, 1705–1714, https://doi.org/10.5194/hess-17-1705-2013, 2013.
Zhao, Q., Ye, B., Ding, Y., Zhang, S., Yi, S., Wang, J., Shangguan, D., Zhao, C., and Han, H.: Coupling a glacier melt model to the Variable Infiltration Capacity (VIC) model for hydrological modeling in north-western China, Environ. Earth Sci., 68, 87–101, https://doi.org/10.1007/s12665-012-1718-8, 2013.
Short summary
Modelling inundations is pivotal to assess current and future flood hazard, and to define sound measures and policies. Yet, many models focus on the hydrologic or hydrodynamic aspect of floods only. We combined both by spatially coupling a hydrologic with a hydrodynamic model. This way we are able to balance the weaknesses of each model with the strengths of the other. We found that model coupling can indeed strongly improve discharge simulation, and see big potential in our approach.
Modelling inundations is pivotal to assess current and future flood hazard, and to define sound...
