Articles | Volume 27, issue 19
https://doi.org/10.5194/hess-27-3505-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3505-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2023
Research article |
| 06 Oct 2023
| 06 Oct 2023
To what extent does river routing matter in hydrological modeling?
Nicolás Cortés-Salazar
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
Nicolás Vásquez
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
Naoki Mizukami
National Center for Atmospheric Research, Boulder, CO, USA
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile
Ximena Vargas
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
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EGUsphere, https://doi.org/10.5194/egusphere-2026-4223, https://doi.org/10.5194/egusphere-2026-4223, 2026
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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
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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.
Cyril Thébault, Wouter J. M. Knoben, Nans Addor, Andrew J. Newman, Diana Spieler, Nicolás A. Vásquez, Yalan Song, Gaby J. Gründemann, Shaun Carney, Mukesh Kumar, Katie van Werkhoven, Chaopeng Shen, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 30, 3945–3977, https://doi.org/10.5194/hess-30-3945-2026, https://doi.org/10.5194/hess-30-3945-2026, 2026
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Matías Moreno, Pablo Mendoza, Eduardo Muñoz-Castro, Mauricio Zambrano-Bigiarini, and Alonso Pizarro
EGUsphere, https://doi.org/10.5194/egusphere-2026-2912, https://doi.org/10.5194/egusphere-2026-2912, 2026
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We developed a new method called SPEAK to better evaluate how well hydrological models reproduce spatial patterns such as evapotranspiration across different regions. We tested it in 99 Chilean catchments with different climates and terrain types. Compared with existing methods, SPEAK more accurately represented spatial patterns and was less affected by map resolution or catchment characteristics. This could help improve water resource modelling and planning across diverse local conditions.
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
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Millie Spencer, Emma Tyrrell, Robert Clasing, Alfonso Fernandez, Enrique Muñoz, Pablo Mendoza, and Noah P. Molotch
EGUsphere, https://doi.org/10.5194/egusphere-2025-5795, https://doi.org/10.5194/egusphere-2025-5795, 2026
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Mountain glaciers are important freshwater reservoirs. Quantifying their loss is essential for forecasting water availability. While glacier retreat is well studied in many regions, glacier loss on the Nevados de Chillán volcanic complex in south-central Chile remains uncertain. Using satellite and drone images, we find all 28 glaciers on Nevados de Chillán lost volume over the past 70 years, with melt rates increasing by 2–3× since 2000.
Eduardo Muñoz-Castro, Bailey J. Anderson, Paul C. Astagneau, Daniel L. Swain, Pablo A. Mendoza, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 825–848, https://doi.org/10.5194/hess-30-825-2026, https://doi.org/10.5194/hess-30-825-2026, 2026
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Flood impacts can be enhanced when they occur after droughts, yet the effectiveness of hydrological models in simulating these events remains unclear. Here, we calibrated four conceptual hydrological models across 63 catchments in Chile and Switzerland to assess their ability to detect streamflow extremes and their transitions. We show that drought-to-flood transitions are generally poorly captured, especially in semi-arid high-mountain catchments than in humid low-elevation ones.
David R. Casson, Guoqiang Tang, Nicolás Vásquez, Andrew W. Wood, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2025-6066, https://doi.org/10.5194/egusphere-2025-6066, 2026
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This study generates meteorological ensembles tailored for mountain snow estimation, accounting for topography, wind-driven undercatch, and large-scale atmospheric patterns. Driving a physics-based snow model with these ensembles allowed uncertainty in weather inputs to carry through to estimates of snow accumulation and melt. Across three mountain basins, this led to realistic and reliable snow estimates useful for data assimilation, water supply and forecasting applications.
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
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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
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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.
Sofía Segovia, Pablo A. Mendoza, Miguel Lagos-Zúñiga, Lucía Scaff, and Andreas Prein
EGUsphere, https://doi.org/10.5194/egusphere-2025-3061, https://doi.org/10.5194/egusphere-2025-3061, 2025
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High-resolution climate simulations can improve our understanding of precipitation and temperature patterns in regions with complex terrain. We evaluate a new climate dataset against in-situ observations, and its potencial for hydrological modeling. Results show that, despite some limitations in dry areas, high-resolution climate models can provide information of a quality comparable to that of observation-based products, supporting their use in water resources planning and decision-making.
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
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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.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
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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.
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
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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
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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.
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Pablo A. Mendoza, Ian McNamara, Hylke E. Beck, Joschka Thurner, Alexandra Nauditt, Lars Ribbe, and Nguyen Xuan Thinh
Hydrol. Earth Syst. Sci., 25, 5805–5837, https://doi.org/10.5194/hess-25-5805-2021, https://doi.org/10.5194/hess-25-5805-2021, 2021
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Most rivers worldwide are ungauged, which hinders the sustainable management of water resources. Regionalisation methods use information from gauged rivers to estimate streamflow over ungauged ones. Through hydrological modelling, we assessed how the selection of precipitation products affects the performance of three regionalisation methods. We found that a precipitation product that provides the best results in hydrological modelling does not necessarily perform the best for regionalisation.
Cited articles
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007.
Allen, G. H., David, C. H., Andreadis, K. M., Hossain, F., and Famiglietti, J. S.: Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, Geophys. Res. Lett., 45, 7551–7560, https://doi.org/10.1029/2018GL077914, 2018.
Andreadis, K. M., Storck, P., and Lettenmaier, D. P.: Modeling snow accumulation and ablation processes in forested environments, Water Resour. Res., 45, W05429, https://doi.org/10.1029/2008WR007042, 2009.
Andréassian, V., Bourgin, F., Oudin, L., Mathevet, T., Perrin, C., Lerat, J., Coron, L., and Berthet, L.: Seeking genericity in the selection of parameter sets: Impact on hydrological model efficiency, Water Resour. Res., 50, 8356–8366, https://doi.org/10.1002/2013WR014761, 2014.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment part I: model development, J. Am. Water Resour. As., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arora, V., Seglenieks, F., Kouwen, N., and Soulis, E.: Scaling aspects of river flow routing, Hydrol. Process., 15, 461–477, https://doi.org/10.1002/hyp.161, 2001.
Arora, V. K. and Boer, G. J.: A variable velocity flow routing algorithm for GCMs, J. Geophys. Res.-Atmos., 104, 30965–30979, https://doi.org/10.1029/1999JD900905, 1999.
Barnes, H. H.: Roughness characteristics of natural channels, U.S. Govt. Print. Off., https://doi.org/10.3133/wsp1849, 1967.
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010.
Beck, H. E., Pan, M., Lin, P., Seibert, J., van Dijk, A. I. J. M., and Wood, E. F.: Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4229 Headwater Catchments, J. Geophys. Res.-Atmos., 125, e2019JD031485, https://doi.org/10.1029/2019JD031485, 2020.
Boisier, J. P., Alvarez-Garretón, C., Cepeda, J., Osses, A., Vásquez, N., and Rondanelli, R.: CR2MET: A high-resolution precipitation and temperature dataset for hydroclimatic research in Chile, in: 20th EGU General Assembly, EGU2018, Proceedings from the conference held 4–13 April, 2018 in Vienna, Austria, p. 19739,
https://ui.adsabs.harvard.edu/abs/2018EGUGA..2019739B/abstract (last access: January 2022),
2018.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith, M.: Toward improved streamflow forecasts: Value of semidistributed modeling, Water Resour. Res., 37, 2749–2759, https://doi.org/10.1029/2000WR000207, 2001.
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.: Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods, Water Resour. Res., 52, 8343–8373, https://doi.org/10.1002/2016WR018850, 2016.
Brooks, R. H. and Corey, A. T.: Hydraulic properties of porous media, Hydrology Paper 1964, no. 3, Civ. Eng. Dep., Color. State Univ., Fort Collins, Color., 27 pp., https://mountainscholar.org/bitstream/handle/10217/61288/HydrologyPapers_n3.pdf
(last access: January 2022), 1964.
Bruneau, P., Gascuel-Odoux, C., Robin, P., Merot, P., and Beven, K.: Sensitivity to space and time resolution of a hydrological model using digital elevation data, Hydrol. Process., 9, 69–81, https://doi.org/10.1002/hyp.3360090107, 1995.
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H.: An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation, J. Hydrol., 298, 242–266, https://doi.org/10.1016/j.jhydrol.2004.03.042, 2004.
Chegwidden, O. S., Nijssen, B., Rupp, D. E., Arnold, J. R., Clark, M. P., Hamman, J. J., Kao, S. C., Mao, Y., Mizukami, N., Mote, P. W., Pan, M., Pytlak, E., and Xiao, M.: How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates, Earths Future, 7, 623–637, https://doi.org/10.1029/2018EF001047, 2019.
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., Gharari, S., 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, 1–16, https://doi.org/10.1029/2020WR029001, 2021.
Cortés-Salazar, N., Vásquez, N., Mizukami, N., Mendoza, P. A., and Vargas, X.: Hydrology and river routing models for the Cautín River basin, Araucanía, Chile, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7838673, 2023.
David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V.: River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1, 2011.
Decharme, B., Alkama, R., Douville, H., Becker, M., and Cazenave, A.: Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part II: Uncertainties in River Routing Simulation Related to Flow Velocity and Groundwater Storage, J. Hydrometeorol., 11, 601–617, https://doi.org/10.1175/2010JHM1212.1, 2010.
Dickinson, R. E.: Modeling Evapotranspiration for Three-Dimensional Global Climate Models. In Climate Processes and Climate Sensitivity, edited by: Hansen, J. E. and Takahashi, T., https://doi.org/10.1029/GM029p0058, 1984.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and Efficient Global Optimization for Conceptual Rainfal-Runoff Models, Water Resour. Res., 28, 1015–1031, 1992.
ElSaadani, M., Krajewski, W. F., Goska, R., and Smith, M. B.: An Investigation of Errors in Distributed Models' Stream Discharge Prediction Due to Channel Routing, J. Am. Water Resour. As., 54, 742–751, https://doi.org/10.1111/1752-1688.12627, 2018.
Emerton, R. E., Stephens, E. M., Pappenberger, F., Pagano, T. C., Weerts, A. H., Wood, A. W., Salamon, P., Brown, J. D., Hjerdt, N., Donnelly, C., Baugh, C. A., and Cloke, H. L.: Continental and global scale flood forecasting systems, WIRes Water, 3, 391–418, https://doi.org/10.1002/wat2.1137, 2016.
Fleischmann, A., Paiva, R., and Collischonn, W.: Can regional to continental river hydrodynamic models be locally relevant? A cross-scale comparison, J. Hydrol. X, 3, 100027, https://doi.org/10.1016/j.hydroa.2019.100027, 2019.
Fleischmann, A. S., Paiva, R. C. D., Collischonn, W., Siqueira, V. A., Paris, A., Moreira, D. M., Papa, F., Bitar, A. A., Parrens, M., Aires, F., and Garambois, P. A.: Trade-Offs Between 1-D and 2-D Regional River Hydrodynamic Models, Water Resour. Res., 56, 1–30, https://doi.org/10.1029/2019WR026812, 2020.
Fowler, K., Peel, M., Western, A., and Zhang, L.: Improved Rainfall-Runoff Calibration for Drying Climate: Choice of Objective Function, Water Resour. Res., 54, 3392–3408, https://doi.org/10.1029/2017WR022466, 2018.
Franchini, M. and Pacciani, M.: Comparative analysis of several conceptual rainfall-runoff models, J. Hydrol., 122, 161–219, https://doi.org/10.1016/0022-1694(91)90178-K, 1991.
Gong, L., Widén-Nilsson, E., Halldin, S., and Xu, C. Y.: Large-scale runoff routing with an aggregated network-response function, J. Hydrol., 368, 237–250, https://doi.org/10.1016/j.jhydrol.2009.02.007, 2009.
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in Snowpack Simulations – Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance, Water Resour. Res., 55, 2779–2800, https://doi.org/10.1029/2018WR023403, 2019.
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.
Guse, B., Pfannerstill, M., Gafurov, A., Kiesel, J., Lehr, C., and Fohrer, N.: Identifying the connective strength between model parameters and performance criteria, Hydrol. Earth Syst. Sci., 21, 5663–5679, https://doi.org/10.5194/hess-21-5663-2017, 2017.
Hanasaki, N., Kanae, S., and Oki, T.: A reservoir operation scheme for global river routing models, J. Hydrol., 327, 22–41, https://doi.org/10.1016/j.jhydrol.2005.11.011, 2006.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The
ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hoch, J. M., Eilander, D., Ikeuchi, H., Baart, F., and Winsemius, H. C.: Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic–hydrodynamic model coupling framework, Nat. Hazards Earth Syst. Sci., 19, 1723–1735, https://doi.org/10.5194/nhess-19-1723-2019, 2019.
Iziomon, M. G., Mayer, H., and Matzarakis, A.: Downward atmospheric longwave irradiance under clear and cloudy skies: Measurement and parameterization, J. Atmos. Sol.-Terr. Phy., 65, 1107–1116, https://doi.org/10.1016/j.jastp.2003.07.007, 2003.
Karki, R., Krienert, J. M., Hong, M., and Steward, D. R.: Evaluating Baseflow Simulation in the National Water Model: A Case Study in the Northern High Plains Region, USA, J. Am. Water Resour. As., 57, 267–280, https://doi.org/10.1111/1752-1688.12911, 2021.
Kavetski, D., Fenicia, F., and Clark, M. P.: Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: Insights from an experimental catchment, Water Resour. Res., 47, W05501, https://doi.org/10.1029/2010WR009525, 2011.
Kazezyılmaz-Alhan, C. M., Medina Jr., M. A., and Richardson, C. J.: A wetland hydrology and water quality model incorporating surface water/groundwater interactions, Water Resour. Res., 43, W04434, https://doi.org/10.1029/2006WR005003, 2007.
Khatami, S., Peel, M. C., Peterson, T. J., and Western, A. W.: Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty, Water Resour. Res., 55, 8922–8941, https://doi.org/10.1029/2018WR023750, 2019.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, 2005.
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.
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.
Lohmann, 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/10.3402/tellusa.v48i5.12200, 1996.
Lohmann, D., Raschke, E., Nijssen, B., and Lettenmaier, D. P.: Regional scale hydrology: II. Application of the VIC-2L model to the Weser River, Germany, Hydrolog. Sci. J., 43, 143–158, https://doi.org/10.1080/02626669809492108, 1998.
Lucas-Picher, P., Arora, V. K., Caya, D., and Laprise, R.: Implementation of a large-scale variable velocity river flow routing algorithm in the Canadian Regional Climate Model (CRCM), Atmos. Ocean, 41, 139–153, https://doi.org/10.3137/ao.410203, 2003.
Mantilla, R.: Physical Basis of Statistical Scaling in Peak Flows and Stream Flow Hydrographs for Topologic and Spatially Embedded Random Self-Similar Channel Networks, University of Colorado, https://www.proquest.com/openview/021b12c989b2af1722839ccf93bf8840/1?pq-origsite=gscholar&cbl=18750 (last access: January 2022), 2007.
McCarthy, G. T.: The Unit Hydrograph and Flood Routing. Unpublished manuscript presented at a conference of the North Atlantic Division, U.S. Army, Corps of Engineers, 24 June 1938 in New London, CT, USA, 608–609, 1938.
Melsen, L., Teuling, A., Torfs, P., Zappa, M., Mizukami, N., Clark, M., and Uijlenhoet, R.: Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin, Hydrol. Earth Syst. Sci., 20, 2207–2226, https://doi.org/10.5194/hess-20-2207-2016, 2016.
Melsen, L., Teuling, A. J., Torfs, P. J. J. F., Zappa, M., Mizukami, N., Mendoza, P. A., Clark, M. P., and Uijlenhoet, R.: Subjective modeling decisions can significantly impact the simulation of flood and drought events, J. Hydrol., 568, 1093–1104, https://doi.org/10.1016/j.jhydrol.2018.11.046, 2019.
Melsen, L. A., Addor, N., Mizukami, N., Newman, A. J., Torfs, P. J. J. F., Clark, M. P., Uijlenhoet, R., and Teuling, A. J.: Mapping (dis)agreement in hydrologic projections, Hydrol. Earth Syst. Sci., 22, 1775–1791, https://doi.org/10.5194/hess-22-1775-2018, 2018.
Mendoza, P. A., McPhee, J., and Vargas, X.: Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment, Water Resour. Res., 48, W09532, https://doi.org/10.1029/2011WR011089, 2012.
Mendoza, P. A., Clark, M. P., Mizukami, N., Gutmann, E. D., Arnold, J. R., Brekke, L. D., and Rajagopalan, B.: How do hydrologic modeling decisions affect the portrayal of climate change impacts?, Hydrol. Process., 30, 1071–1095, https://doi.org/10.1002/hyp.10684, 2016.
Miguez-Macho, G. and Fan, Y.: The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands, J. Geophys. Res.-Atmos., 117, 1–30, https://doi.org/10.1029/2012JD017539, 2012.
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., 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, 1–20, https://doi.org/10.1029/2020MS002434, 2021.
Munier, S. and Decharme, B.: River network and
hydro-geomorphological parameters at 1/12∘ resolution for global hydrological and climate studies, Earth Syst. Sci. Data, 14,
2239–2258, https://doi.org/10.5194/essd-14-2239-2022, 2022.
Muñoz-Castro, E., Mendoza, P. A., Vásquez, N., and Vargas, X.: Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall-runoff models, Hydrolog. Sci. J., 68, 1754–1768, https://doi.org/10.1080/02626667.2023.2231434, 2023.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Murillo, O., Mendoza, P. A., Vásquez, N., Mizukami, N., and Ayala, Á.: Impacts of Subgrid Temperature Distribution Along Elevation Bands in Snowpack Modeling: Insights From a Suite of Andean Catchments, Water Resour. Res., 58, e2022WR032113, https://doi.org/10.1029/2022WR032113, 2022.
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Newman, A. J., Stone, A. G., Saharia, M., Holman, K. D., Addor, N., and Clark, M. P.: Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system, Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, 2021.
Nguyen-Quang, T., Polcher, J., Ducharne, A., Arsouze, T., Zhou, X., Schneider, A., and Fita, L.: ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database, Geosci. Model Dev., 11, 4965–4985, https://doi.org/10.5194/gmd-11-4965-2018, 2018.
Oki, T. and Sud, Y. C.: Design of Total Runoff Integrating Pathways (TRIP) – A Global River Channel Network, Earth Interact., 2, 1–37, https://doi.org/10.1175/1087-3562(1998)002<0001:dotrip>2.3.co;2, 1998.
Olivera, F., Famiglietti, J., and Asante, K.: Global-scale flow routing using a source-to-sink algorithm, Water Resour. Res., 36, 2197–2207, https://doi.org/10.1029/2000WR900113, 2000.
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., Buarque, D. C., Collischonn, W., Bonnet, M.-P., Frappart, F., Calmant, S., and Bulhões Mendes, C. A.: Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin, Water Resour. Res., 49, 1226–1243, https://doi.org/10.1002/wrcr.20067, 2013a.
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, 2013b.
Pereira, F. F., Farinosi, F., Arias, M. E., Lee, E., Briscoe, J., and Moorcroft, P. R.: Technical note: A hydrological routing scheme for the Ecosystem Demography model (ED2+R) tested in the Tapajós River basin in the Brazilian Amazon, Hydrol. Earth Syst. Sci., 21, 4629–4648, https://doi.org/10.5194/hess-21-4629-2017, 2017.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, Soil, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Pokhrel, P., Yilmaz, K. K., and Gupta, H. V.: Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures, J. Hydrol., 418–419, 49–60, https://doi.org/10.1016/j.jhydrol.2008.12.004, 2012.
Qiao, X., Nelson, E. J., Ames, D. P., Li, Z., David, C. H., Williams, G. P., Roberts, W., Sánchez Lozano, J. L., Edwards, C., Souffront, M., and Matin, M. A.: A systems approach to routing global gridded runoff through local high-resolution stream networks for flood early warning systems, Environ. Modell. Softw., 120, 104501, https://doi.org/10.1016/j.envsoft.2019.104501, 2019.
Qiu, H., Qi, J., Lee, S., Moglen, G. E., McCarty, G. W., Chen, M., and Zhang, X.: Effects of temporal resolution of river routing on hydrologic modeling and aquatic ecosystem health assessment with the SWAT model, Environ. Modell. Softw., 146, 105232, https://doi.org/10.1016/j.envsoft.2021.105232, 2021.
Salas, F. R., Somos-Valenzuela, M. 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. As., 54, 7–27, https://doi.org/10.1111/1752-1688.12586, 2018.
Sepúlveda, U. M., Mendoza, P. A., Mizukami, N., and Newman, A. J.: Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient, Hydrol. Earth Syst. Sci., 26, 3419–3445, https://doi.org/10.5194/hess-26-3419-2022, 2022.
Shaad, K.: Evolution of river-routing schemes in macro-scale models and their potential for watershed management, Hydrolog. Sci. J., 63, 1062–1077, https://doi.org/10.1080/02626667.2018.1473871, 2018.
Shafii, M. and Tolson, B. A.: Optimizing hydrological consistency by incorporating hydrological signatures into model calibration objectives, Water Resour. Res., 51, 3796–3814, https://doi.org/10.1002/2014WR016520, 2015.
Sheikholeslami, R., Gharari, S., Papalexiou, S. M., and Clark, M. P.: VISCOUS: A Variance-Based Sensitivity Analysis Using Copulas for Efficient Identification of Dominant Hydrological Processes, Water Resour. Res., 57, e2020WR028435, https://doi.org/10.1029/2020wr028435, 2021.
Siqueira, V. A., Paiva, R. C. D., Fleischmann, A. S., Fan, F. M., Ruhoff, A. L., Pontes, P. R. M., Paris, A., Calmant, S., and Collischonn, W.: Toward continental hydrologic–hydrodynamic modeling in South America, Hydrol. Earth Syst. Sci., 22, 4815–4842, https://doi.org/10.5194/hess-22-4815-2018, 2018.
Sturm, T. W.: Open Channel Hydraulics, 3rd edn., McGraw-Hill Education, New York, ISBN 9781260469707, https://www.accessengineeringlibrary.com/content/book/9781260469707 (last access: July 2022), 2021.
Sulla-Menashe, D. and Friedl, M. A.: User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, http://girps.net/wp-content/uploads/2019/03/MCD12_User_Guide_V6.pdf (last access: January 2022), 2018.
Tang, Y., Reed, P., van Werkhoven, K., and Wagener, T.: Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis, Water Resour. Res., 43, 1–14, https://doi.org/10.1029/2006WR005813, 2007.
Thober, S., Cuntz, M., Kelbling, M., Kumar, R., Mai, J., and Samaniego, L.: The multiscale routing model mRM v1.0: simple river routing at resolutions from 1 to 50 km, Geosci. Model Dev., 12, 2501–2521, https://doi.org/10.5194/gmd-12-2501-2019, 2019.
Tolson, B. A. and Shoemaker, C. A.: Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resour. Res., 43, 1–16, https://doi.org/10.1029/2005WR004723, 2007.
Vano, J. A., Das, T., and Lettenmaier, D. P.: Hydrologic Sensitivities of Colorado River Runoff to Changes in Precipitation and Temperature, J. Hydrometeorol., 13, 932–949, https://doi.org/10.1175/JHM-D-11-069.1, 2012.
Verzano, K., Bärlund, I., Flörke, M., Lehner, B., Kynast, E., Voß, F., and Alcamo, J.: Modeling variable river flow velocity on continental scale: Current situation and climate change impacts in Europe, J. Hydrol., 424–425, 238–251, https://doi.org/10.1016/j.jhydrol.2012.01.005, 2012.
Wang, Y., He, B., and Takase, K.: Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge, Hydrolog. Sci. J., 54, 886–898, https://doi.org/10.1623/hysj.54.5.886, 2009.
Wobus, C., Gutmann, E., Jones, R., Rissing, M., Mizukami, N., Lorie, M., Mahoney, H., Wood, A. W., Mills, D., and Martinich, J.: Climate change impacts on flood risk and asset damages within mapped 100 year floodplains of the contiguous United States, Nat. Hazards Earth Syst. Sci., 17, 2199–2211, https://doi.org/10.5194/nhess-17-2199-2017, 2017.
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.-Atmos., 97, 2717–2728, https://doi.org/10.1029/91JD01786, 1992.
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., De Almeida, G. A. M., and Bates, P. D.: Improving computational efficiency in global river models by implementing the local inertial flow equation and a vector-based river network map, Water Resour. Res., 49, 7221–7235, https://doi.org/10.1002/wrcr.20552, 2013.
Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrologic models, J. Hydrol., 204, 83–97, https://doi.org/10.1016/S0022-1694(97)00107-8, 1998.
Ye, A., Duan, Q., Zhan, C., Liu, Z., and Mao, Y.: Improving kinematic wave routing scheme in Community Land Model, Hydrol. Res., 44, 886–903, https://doi.org/10.2166/nh.2012.145, 2013.
Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007WR006716, 2008.
Zhao, F., Veldkamp, T. I. E., Frieler, K., Schewe, J., Ostberg, S., Willner, S., Schauberger, B., Gosling, S. N., Schmied, H. M., Portmann, F. T., Leng, G., Huang, M., Liu, X., Tang, Q., Hanasaki, N., Biemans, H., Gerten, D., Satoh, Y., Pokhrel, Y., Stacke, T., Ciais, P., Chang, J., Ducharne, A., Guimberteau, M., Wada, Y., Kim, H., and Yamazaki, D.: The critical role of the routing scheme in simulating peak river discharge in global hydrological models, Environ. Res. Lett., 12, 075003, https://doi.org/10.1088/1748-9326/aa7250, 2017.
Zhao, R.-J., Zhang, Y.-L., Fang, L.-R., Liu, X.-R., and Zhang, Q.-S.: The Xinanjiang model, in: Hydrological Forecasting: Proceedings of the Symposium on the Application of Recent Developments in Hydrological Forecasting to the Operation of Water Resource Systems, Oxford, April 1980, International Association of Hydrological Sciences Press, Wallingford, UK, IAHS
Publication No. 129, 351–356, 1980.
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.
This paper shows how important river models can be for water resource applications that involve...
