Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6181-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6181-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 Aug 2022
Development and technical paper |
| 08 Aug 2022
| 08 Aug 2022
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
School of Civil Engineering and Environmental Science, University of
Oklahoma, Norman, OK, 70372, USA
Shang Gao
School of Civil Engineering and Environmental Science, University of
Oklahoma, Norman, OK, 70372, USA
Mengye Chen
School of Civil Engineering and Environmental Science, University of
Oklahoma, Norman, OK, 70372, USA
Jonathan Gourley
NOAA/National Severe Storms Laboratory, Norman, OK, 73072, USA
Naoki Mizukami
National Centers for Atmospheric Research, Boulder, CO, 80307, USA
School of Civil Engineering and Environmental Science, University of
Oklahoma, Norman, OK, 70372, USA
Related authors
No articles found.
Matthias Cuntz, Stephan Thober, Anne Verhoef, Yijian Zeng, Aaron Boone, Agnès Ducharne, Sujan Koirala, David M. Lawrence, Philipp de Vrese, Carsten Montzka, Sonia I. Seneviratne, Harry Vereecken, Dani Or, Salma Tafasca, Naoki Mizukami, Rich Ellis, Patrick C. McGuire, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2026-4223, https://doi.org/10.5194/egusphere-2026-4223, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Land surface models (LSMs) are essential tools to analyze future climate change. Here we pushed state-of-the-art LSMs out of their comfort zone revealing hidden connections between processes encoded into the models. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of different water fluxes. We identified surface runoff and soil evaporation as the two main reasons behind model differences, which should be improved by using more mechanistic descriptions.
Chayan Roychoudhury, Rajesh Kumar, Cenlin He, William Y. Y. Cheng, Kirpa Ram, Naoki Mizukami, and Avelino F. Arellano
Earth Syst. Sci. Data, 18, 5209–5257, https://doi.org/10.5194/essd-18-5209-2026, https://doi.org/10.5194/essd-18-5209-2026, 2026
Short summary
Short summary
Snow and ice in the high mountains of Asia feed rivers supplying water to over a billion people, but airborne soot and dust darken the snow and speed up melting. We built a 17-year record of these conditions by blending satellite observations with a complex model that simulates weather, air pollution, land, and snow as one connected system. The record agrees well with independent measurements and traces soot on the glaciers to its regional sources, helping assess risks to Asia's water supply.
Nicolás A. Vásquez, Pablo A. Mendoza, Wouter Knoben, Martyn Clark, Tricia Stadnyk, and Naoki Mizukami
EGUsphere, https://doi.org/10.5194/egusphere-2026-1363, https://doi.org/10.5194/egusphere-2026-1363, 2026
Short summary
Short summary
Although distributed hydrological models are often calibrated using only streamflow data, this practice may provide unrealistic representations of the water cycle. We show that, while streamflow annual cycles can be reasonably simulated, the seasonality of other key variables - such as evapotranspiration, soil moisture, and snow cover - may be severely misrepresented. Our results highlight the need to assess seasonal patterns of variables beyond streamflow when calibrating hydrological models.
Naoki Mizukami, Samar Minallah, Cenlin He, Chayan Roychoudhury, William Y.Y. Cheng, and Rajesh Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2025-4586, https://doi.org/10.5194/egusphere-2025-4586, 2025
Short summary
Short summary
We examined how air pollution particles that darken snow affect water resources in High Mountain Asia, a region that supplies rivers vital to millions of populations. Using computer models, we found that these particles cause snow to melt two weeks to a month earlier, shifting when water becomes available. This leads to more runoff at first but less later in the year, slightly reducing annual river flow. The findings highlight the need to link air pollution control with water management.
Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, and Naoki Mizukami
Hydrol. Earth Syst. Sci., 29, 4515–4537, https://doi.org/10.5194/hess-29-4515-2025, https://doi.org/10.5194/hess-29-4515-2025, 2025
Short summary
Short summary
We present a new strategy to calibrate large-domain land/hydrology models over diverse regions. Using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models, our approach integrates catchment attributes, parameters, and performance metrics to optimize streamflow simulations. Leveraging advances in machine learning for hydrology, we improve calibration and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci., 29, 1981–2002, https://doi.org/10.5194/hess-29-1981-2025, https://doi.org/10.5194/hess-29-1981-2025, 2025
Short summary
Short summary
Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite the fact that they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what they truly reflect in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and timescales for accurate drought characterization and monitoring.
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024, https://doi.org/10.5194/nhess-24-1163-2024, 2024
Short summary
Short summary
High-resolution convection-permitting climate models (CPMs) are now available to better simulate rainstorm events leading to flash floods. In this study, two hydrological models are compared to simulate floods in a Mediterranean basin, showing a better ability of the CPM to reproduce flood peaks compared to coarser-resolution climate models. Future projections are also different, with a projected increase for the most severe floods and a potential decrease for the most frequent events.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
Short summary
Short summary
This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Hanqing Chen, Debao Wen, Bin Yong, Jonathan J. Gourley, Leyang Wang, and Yang Hong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-42, https://doi.org/10.5194/essd-2023-42, 2023
Manuscript not accepted for further review
Short summary
Short summary
A novel multi-source precipitation data fusion (MPDF) algorithm, which considers the dependency of precipitation errors on seasonality, was proposed to fully take advantage of the complementary strengths from satellite-only IMERG-Late, ERA5 reanalysis, and ground-based precipitation observations for generating a higher-quality global precipitation product. A new global precipitation product, namely MGP, is provided to the public.
Ulises M. Sepúlveda, Pablo A. Mendoza, Naoki Mizukami, and Andrew J. Newman
Hydrol. Earth Syst. Sci., 26, 3419–3445, https://doi.org/10.5194/hess-26-3419-2022, https://doi.org/10.5194/hess-26-3419-2022, 2022
Short summary
Short summary
This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly used macroscale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Cited articles
Allen, R. G., Pereira, L., Raes, D., and Smith, M.: Crop Evapotranspiration,
Food and Agriculture Organization of the United Nations, Rome, Italy, FAO
publication 56, 290 pp., ISBN 92-5-104219-5, 1998.
Anderson, E. A.: Snow accumulation and ablation model – SNOW-17, US National
Weather Service, Silver Spring, MD, 61, 2006.
Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141–153, https://doi.org/10.5194/hess-13-141-2009, 2009.
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36,
https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006.
Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., and Wada, Y.: Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management, Geosci. Model Dev., 13, 3267–3298, https://doi.org/10.5194/gmd-13-3267-2020, 2020.
Chen, M., Li, Z., and Gao, S.: Multisensor Remote Sensing and the
Multidimensional Modeling of Extreme Flood Events, in: Remote Sensing of
Water-Related Hazards, edited by: Zhang, K., Hong, Y., and AghaKouchak, A., https://doi.org/10.1002/9781119159131.ch5, 2022.
Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied Hydrology:
McGraw-Hill Series in Water Resources and Environmental Engineering,
McGraw-Hill, Inc., New York, 1988.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta,
H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural
Errors (FUSE): A modular framework to diagnose differences between
hydrological models, Water Resour. Res., 44, W00B02,
https://doi.org/10.1029/2007WR006735, 2008.
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D.
J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M.,
Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of
hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015a.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski D., Rupp, D. E.,
Woods, R. A., Freeer, J. E., Gutmann, E. D., Wood, A. W., Gochis, D. J.,
Rasmussen, R. M., Tarboton, D. G., Mahat, V., Flerchinger G. N., and Marks,
D. G.: A unified approach for process-based hydrologic modeling: 1. Modeling
concept, Water Resour. Res., 51, 2498–2514,
https://doi.org/10.1002/2015WR017198, 2015b.
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J.
M., Tang, G., Shervan, G., Freer J. E., Whitfield, P. H., Shook, K. R., and
Papalexiou, S. M.: The abuse of popular performance metrics in hydrologic
modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021.
Cloke, E. and Pappenberger, F.: Ensemble flood forecasting: A review, J.
Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. A.: Physiographically-sensitive mapping
of temperature and precipitation across the conterminous United States, Int.
J. Climatol., 28, 2031–2064, 2008.
David, C. H., Maidment, D. R., Niu, G., Yang, Z., Habets, F., and Eijkhout,
V.: River Network Routing on the NHDPlus Dataset, J. Hydrometeorol., 12,
913–934, 2011.
de Almeida, G. A. M. and Bates, P.: Applicability of the local inertial
approximation of the shallow water equations to flood modeling, Water
Resour. Res., 49, 4833–4844, https://doi.org/10.1002/wrcr.20366, 2013.
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.
Fenicia, F., Kavetski, D., and Savenije, H. H. G.: Elements of a flexible
approach for conceptual hydrological modeling: 1. Motivation and theoretical
development, Water Resour. Res., 47, W11510,
https://doi.org/10.1029/2010WR010174, 2011.
Flamig, Z. L., Vergara, H., and Gourley, J. J.: The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study, Geosci. Model Dev., 13, 4943–4958, https://doi.org/10.5194/gmd-13-4943-2020, 2020.
Franz, K. J., Hogue, T. S., and Sorooshian, S.: Operational snow modeling:
Addressing the challenges of an energy balance model for National Weather
Service forecasts, J. Hydrol., 360, 48–66, 2008.
Freeze, R. A.: Role of subsurface flow in generating surface runoff: 2.
Upstream source areas, Water Resour. Res., 8, 1272–1283,
https://doi.org/10.1029/WR008i005p01272, 1972.
Gao, S., Chen, M., Li, Z., Cook, S., Allen, D., Neeson, T., Yang, T., Yami,
T., and Hong, Y.: Mapping dynamic non-perennial stream networks using
high-resolution distributed hydrologic simulation: A case study in the upper
blue river basin, J. Hydrol., 600, 126522, https://doi.org/10.1016/j.jhydrol.2021.126522, 2021.
Gharari, S., Clark, M. P., Mizukami, N., Knoben, W. J. M., Wong, J. S., and Pietroniro, A.: Flexible vector-based spatial configurations in land models, Hydrol. Earth Syst. Sci., 24, 5953–5971, https://doi.org/10.5194/hess-24-5953-2020, 2020.
Gharari, S., Vanderkelen, I., Tefs, A., Mizukami, N., Stadnyk, T. A.,
Lawrence, D., and Clark, M. P.: A Flexible Multi-Scale Framework to Simulate
Lakes and Reservoirs in Earth System Models, Earth and Space Science Open
Archive, https://doi.org/10.1002/essoar.10510902.1, 2022.
Gourley, J. J., Flamig, Z. L., Vergara, H., Kirstetter, P., Clark, R. A.,
III, Argyle, E., Arthur, A., Martinaitis, S., Terti, G., Erlingis, J. M.,
Hong, Y., and Howard, K. W.: The FLASH Project: Improving the Tools for
Flash Flood Monitoring and Prediction across the United States, B. Am.
Meteorol. Soc., 98, 361–372, https://doi.org/10.1175/BAMS-D-15-00247.1, 2017.
Hanasaki, N, Kanae, S., and Oki, T.: A reservoir operation scheme for global
river routing models, J. Hydrol., 327, 22–41, 2006.
Horton, P., Schaefli, B., and Kauzlaric, M.: Why do we have so many
different hydrological models? A review based on the case of Switzerland,
Wiley Interdiscip. Rev., Water, e1574, https://doi.org/10.1002/wat2.1574, 2021.
Knoben, W. J. M., Freer, J. E., Peel, M. C., Fowler, K. J. A., and Woods, R.
A.: A brief analysis of conceptual model structure uncertainty using 36
models and 559 catchments, Water Resour. Res., 56, e2019WR025975, https://doi.org/10.1029/2019WR025975, 2020.
Knoben, W. J. M., Clark, M., Bates, J., Bennet, A., Gharari, S., Marsh, C.,
Nijssen, B., Pietroniro, A., Spiteri, R., Tarboton, D. J., and Wood, A. J.:
Community Workflows to Advance Reproducibility in Hydrologic Modeling:
Separating model-agnostic and model-specific configuration steps in
applications of large-domain hydrologic models, Earth and Space Science Open
Archive, https://doi.org/10.1002/essoar.10509195.1, 2021.
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B.,
Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson,
C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.:
High-resolution mapping of the world's reservoirs and dams for sustainable
river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Li, Z.: CREST-VEC: A framework towards more accurate and realistic flood
simulation across scales (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6305817, 2022.
Li, Z., Chen, M., Gao, S., Hong, Z., Tang, G., Wen, Y., Gourley, J. J., and
Hong, Y.: Cross-examination of similarity, difference and deficiency of
gauge, radar and satellite precipitation measuring uncertainties for extreme
events using conventional metrics and multiplicative triple collocation,
Remote Sens., 12, 1258, https://doi.org/10.3390/rs12081258, 2020.
Li, Z., Chen, M., Gao, S., Luo, X., Gourley, J. J., Kirstetter, P., Yang,
T., Kolar, R., McGovern, A., Wen, Y., Rao, B., Yami, T., and Hong, Y.:
CREST-iMAP v1. 0: A fully coupled hydrologic-hydraulic modeling framework
dedicated to flood inundation mapping and prediction, Environ. Model. Softw.,
141, 105051, https://doi.org/10.1016/j.envsoft.2021.105051, 2021.
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., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994.
Lighthill, M. J. and Whitham, G. B.: On kinematic waves I. Flood movement in
long rivers, Proc. R. Soc. Lond., 229, A229281–316, https://doi.org/10.1098/rspa.1955.0088, 1955.
Lin, P., Rajib, M. A., Yang, Z., Somos-Valenzuela, M., Merwade, V.,
Maidment, D. R., Wang, Y., and Chen, L.: Spatiotemporal Evaluation of
Simulated Evapotranspiration and Streamflo over Texas Using the
WRF-Hydro-RAPID Modeling Framework, J. Am. Water Resour. Assoc., 54,
40–54, https://doi.org/10.1111/1752-1688.12585, 2018.
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David,
C. G., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood,
E. F.: Global reconstruction of naturalized river flows at 2.94 million
reaches, Water Resour. Res., 55, 6499–6516, https://doi.org/10.1029/2019WR025287, 2019.
Lin, P., Pan, M., Wood, E.F. Yamazaki, D., and Allen, G. H.: A new vector-based
global river network dataset accounting for variable drainage density, Sci
Data, 8, 28, https://doi.org/10.1038/s41597-021-00819-9, 2021.
Liston, G. E., Sud, Y. C., and Wood, E. F.: Evaluating GCM land surface
hydrology parameterizations by computing river discharges using a runoff
routing model, J. Appl. Met., 33, 394–405, 1994.
Lohman, D., Nolte-holube, R., and Raschke, E.: A large-scale horizontal
routing model to be coupled to land surface parametrization schemes, Tellus
A, 48, 708–721, https://doi.org/10.1034/j.1600-0870.1996.t01-3-00009.x, 1996.
Martinaitis, S. M., Gourley, J. J., Flamig, Z. L., Argyle, E. M., Clark, R.
A., III, Arthur, A., Smith, B. R., Erlingis, J. M., Perfater, S., and
Albright, B.: The HMT Multi-Radar Multi-Sensor Hydro Experiment, B. Am.
Meterorol. Soc., 98, 347–359, 2017.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.:
Estimating the volume and age of water stored in global lakes using a
geo-statistical approach, Nat. Comm., 13603,
https://doi.org/10.1038/ncomms13603, 2016.
Mizukami, N., Clark, M. P., Sampson, K., Nijssen, B., Mao, Y., McMillan, H., Viger, R. J., Markstrom, S. L., Hay, L. E., Woods, R., Arnold, J. R., and Brekke, L. D.: mizuRoute version 1: a river network routing tool for a continental domain water resources applications, Geosci. Model Dev., 9, 2223–2238, https://doi.org/10.5194/gmd-9-2223-2016, 2016.
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D.,
Nijssen, B., Rakovec, O., and Samaniego, L.: Towards seamless large-domain
parameter estimation for hydrologic models, Water Resour. Res., 53, 8020–8040, https://doi.org/10.1002/2017WR020401, 2017.
Mizukami, N., Clark, M. P., Gharari, S., Kluzek, E., Pan, M., Lin, P., Beck,
H. E., and Yamazaki, D.: A vector-based river routing model for Earth System
Models: Parallelization and global applications, J. Adv. Model. Earth Sy.,
13, e2020MS002434, https://doi.org/10.1029/2020MS002434, 2021.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
NOAA National Centers for Environmental Information (NCEI): U.S.
Billion-Dollar Weather and Climate Disasters,
https://doi.org/10.25921/stkw-7w73, 2022.
Ohmura, A.: Physical basis for the temperature-based melt-index method, J.
Appl. Meteorol., 40, 753–761, 2001.
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.
Ponce, V. M., Li, R.-M., and Simons, D. B.: Applicability of kinematic and
diffusion models, J. Hydraul. Div., 104, 353–360, 1978.
PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu
(last access: 16 September 2020), 2014.
Quinn, P., Beven, K., Chevallier, P., and Planchon, O.: The prediction of
hillslope flow paths for distributed hydrological modelling using digital
terrain models, Hydrol. Process., 5, 59–79, https://doi.org/10.1002/hyp.3360050106, 1991.
Salas, F. R., Somos-Valenzuela, Marcelo A., Dugger, A., Maidment, D. R.,
Gochis, D. J., David, C. H., Yu, W., Ding, D., Clark, E. P., and Noman, N.:
Towards Real-Time Continental Scale Streamflow Simulation in Continuous and
Discrete Space, J. Am. Water Resour. Assoc. 54, 7–27, https://doi.org/10.1111/1752-1688.12586, 2018.
Savenije, H. H. G.: HESS Opinions “The art of hydrology”, Hydrol. Earth Syst. Sci., 13, 157–161, https://doi.org/10.5194/hess-13-157-2009, 2009.
Shaad, K.: Evolution of river-routing schemes in macro-scale models and their
potentials for watershed management, Hydrol. Sci. J., 63, 1062–1077,
https://doi.org/10.1080/02626667.2018.1473871, 2018.
Shen, X., Hong, Y., Zhang, K., and Hao, Z.: Refining a distributed linear
reservoir routing method to improve performance of the CREST model, J.
Hydrol. Eng., 22, 04016061, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001442, 2017.
Solvik, K., Bartuszevige, A. M., Bogaerts, M., and Joseph, M. B.: Predicting
playa inundation using a long short-term memory neural network, Water
Resour. Res., 57, e2020WR029009, https://doi.org/10.1029/2020WR029009, 2021.
Tang, G., Zeng, Z., Long, D., Guo, X., Yong, B., Zhang, W., and Hong, Y.:
Statistical and Hydrological Comparisons between TRMM and GPM Level-3
Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA
3B42V7?, J. Hydrometeorol., 17, 121–137, 2016.
Tarboton, D. G.: A new method for the determination of flow directions and
upslope areas in grid digital elevation models, Water Resour. Res., 33,
309–319, https://doi.org/10.1029/96WR03137, 1997.
Tavakoly, A. A., Gutenson, J. L., Lewis, J. W., Follum, M. L., Rajib, A.,
LaHatte, W. C., and Hamilton, C. O.: Direct integration of numerous dams and
reservoirs outflow in continental scale hydrologic modeling, Water Resour.
Res., 57, e2020WR029544, https://doi.org/10.1029/2020WR029544,
2021.
Tellman, B., Sullivan, J. A., Kuhn, C. Kettner, A. J., Doyle, C. S.,
Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging
reveals increased proportion of population exposed to floods, Nature, 596,
80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Tijerina, D., Condon, L., FitzGerald, K., Dugger, A., O'Neill, M. M.,
Sampson, K., Gochis, D., and Maxwell, R.: Continental hydrologic intercomparison
project, phase 1: A large-scale hydrologic model comparison over the
continental United States, Water Resour. Res., 57, e2020WR028931, https://doi.org/10.1029/2020WR028931, 2021.
Tokuda, D., Kim, H., Yamazaki, D., and Oki, T.: Development of a coupled simulation framework representing the lake and river continuum of mass and energy (TCHOIR v1.0), Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, 2021.
Vanderkelen, I., Gharari, S., Mizukami, N., Clark, M. P., Lawrence, D. M., Swenson, S., Pokhrel, Y., Hanasaki, N., van Griensven, A., and Thiery, W.: Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling, Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, 2022.
Vergara, H., Kirstetter, P., Gourley, J. J., Flamig, Z. L., Hong, Y., Arthur,
A., and Kolar, R.: Estimating a-priori kinematic wave model parameters based
on regionalization for flash flood forecasting in the Conterminous United
States, J. Hydrol., 541, 421–433, https://doi.org/10.1016/j.jhydrol.2016.06.011, 2016.
Vrugt, J. A., ter Braak, C., Diks, C., Robinson, B. A., Hyman, J. M., and
Higdon, D.: Accelerating Markov Chain Monte Carlo Simulation by Differential
Evolution with Self-Adaptive Randomized Subspace Sampling, Int. J. Nonlinear
Sci. Numer. Simul., 10, 273–290,
https://doi.org/10.1515/ijnsns.2009.10.3.273, 2009.
Wang, J., Hong, Y., Li, L., Gourley, J. J., Khan, S. I., Yilmaz, K. K., Adler,
R. F., Policelli, F. S., Habib, S., Irwn, D., Limaye, A. S., Korme, T., and
Okello, L.: The coupled routing and excess storage (CREST) distributed
hydrological model, Hydrol. Sci. J., 56, 84–98, 2011.
Wang, X., White-Hull, C., Dyer, S., and Yang, Y.: GIS-ROUT: A River Model
for Watershed Planning, Environ. Plann. B, 27, 231–246,
https://doi.org/10.1068/b2624, 2000.
Xue, X., Hong, Y., Limaye, A. S., Gourley, J. J., Huffman, G. J., Khan, S.
I., Doriji, C., and Chen, S.: Statistical and hydrological evaluation of
TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of
Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use
in ungauged basins?, J. Hydrol., 499, 91–99,
https://doi.org/10.1016/j.jhydrol.2013.06.042, 2013.
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., Sato, T., Kanae, S., Hirabayashi, Y., and Bates, P. D.:
Regional flood dynamics in a bifurcating mega delta simulated in a global
river model, Geophys. Res. Lett., 41, 3127–3135,
https://doi.org/10.1002/2014GL059744, 2014.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy
map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and
Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based
on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073,
https://doi.org/10.1029/2019WR024873, 2019.
Yang, T., Zhang, L., Kim, T., Hong, Y., Zhang, D., and Peng, Q.: A
large-scale comparison of Artificial Intelligence and Data Mining (AI&DM)
techniques in simulating reservoir releases over the Upper Colorado Region,
J. Hydrol., 602, 126723, https://doi.org/10.1016/j.jhydrol.2021.126723, 2021.
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C.
H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-Level 3-Hourly
River Flood Reanalysis (1980–2019), B. Am. Meteorol. Soc., 102,
E2086–E2105, 2021.
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., and Kitzmiller, D.: Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities, B. Am. Meteorol. Soc., 97, 621–638, https://doi.org/10.1175/BAMS-D-14-00174.1, 2016.
Short summary
Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Operational streamflow prediction at a continental scale is critical for national water...
