Articles | Volume 29, issue 5
https://doi.org/10.5194/hess-29-1221-2025
© Author(s) 2025. 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-29-1221-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Mar 2025
Research article |
| 04 Mar 2025
| 04 Mar 2025
Impact of runoff schemes on global flow discharge: a comprehensive analysis using the Noah-MP and CaMa-Flood models
Mohamed Hamitouche
CORRESPONDING AUTHOR
University School for Advanced Studies IUSS, Pavia, Italy
Climate Modeling Laboratory, ENEA – Italian National Agency for New Technologies, Energy and Sustainable Economic Development, CR Casaccia, Viale Anguillarese 301, 00123 Santa Maria di Galeria, Rome, Italy
ICSC Italian Research Center on High-Performance Computing, Big Data and Quantum Computing, Bologna, Italy
Giorgia Fosser
University School for Advanced Studies IUSS, Pavia, Italy
Alessandro Anav
Climate Modeling Laboratory, ENEA – Italian National Agency for New Technologies, Energy and Sustainable Economic Development, CR Casaccia, Viale Anguillarese 301, 00123 Santa Maria di Galeria, Rome, Italy
ICSC Italian Research Center on High-Performance Computing, Big Data and Quantum Computing, Bologna, Italy
Cenlin He
Research Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, Colorado, USA
Tzu-Shun Lin
Research Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, Colorado, USA
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Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
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Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
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Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Alessandro Anav, Adriana Carillo, Massimiliano Palma, Maria Vittoria Struglia, Ufuk Utku Turuncoglu, and Gianmaria Sannino
Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, https://doi.org/10.5194/gmd-14-4159-2021, 2021
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The Mediterranean Basin is a complex region, characterized by the presence of pronounced topography and a complex land–sea distribution including a considerable number of islands and straits; these features generate strong local atmosphere–sea interactions.
Regional Earth system models have been developed and used to study both present and future Mediterranean climate systems. The main aims of this paper are to present and evaluate the newly developed regional Earth system model ENEA-REG.
Jasdeep Singh Anand, Alessandro Anav, Marcello Vitale, Daniele Peano, Nadine Unger, Xu Yue, Robert J. Parker, and Hartmut Boesch
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-125, https://doi.org/10.5194/bg-2021-125, 2021
Publication in BG not foreseen
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Ozone damages plants, which prevents them from absorbing CO2 from the atmosphere. This poses a potential threat to preventing dangerous climate change. In this work, satellite observations of forest cover, ozone, climate, and growing season are combined with an empirical model to estimate the carbon lost due to ozone exposure over Europe. The estimated carbon losses agree well with prior modelled estimates, showing for the first time that satellites can be used to better understand this effect.
Julián Gelman Constantin, Lucas Ruiz, Gustavo Villarosa, Valeria Outes, Facundo N. Bajano, Cenlin He, Hector Bajano, and Laura Dawidowski
The Cryosphere, 14, 4581–4601, https://doi.org/10.5194/tc-14-4581-2020, https://doi.org/10.5194/tc-14-4581-2020, 2020
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We present the results of two field campaigns and modeling activities on the impact of atmospheric particles on Alerce Glacier (Argentinean Andes). We found that volcanic ash remains at different snow layers several years after eruption, increasing light absorption on the glacier surface (with a minor contribution of soot). This leads to 36 % higher annual glacier melting. We find remarkably that volcano eruptions in 2011 and 2015 have a relevant effect on the glacier even in 2016 and 2017.
Cited articles
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, in: Progress in Photosynthesis Research: Volume 4 Proceedings of the VIIth International Congress on Photosynthesis Providence, edited by: Biggins, J., Rhode Island, USA, 10–15 August 1986, Springer Netherlands, Dordrecht, 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009.
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., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017.
Bonan, G. B.: Land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and users guide, Technical note, National Center for Atmospheric Research, Boulder, CO United States, https://doi.org/10.5065/D6DF6P5X, 1996.
Chang, M., Liao, W., Wang, X., Zhang, Q., Chen, W., Wu, Z., and Hu, Z.: An optimal ensemble of the Noah-MP land surface model for simulating surface heat fluxes over a typical subtropical forest in South China, Agr. Forest Meteorol., 281, 107815, https://doi.org/10.1016/j.agrformet.2019.107815, 2020.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H.-L., Koren, V., Duan, Q. Y., Ek, M., and Betts, A.: Modeling of land surface evaporation by four schemes and comparison with FIFE observations, J. Geophys. Res.-Atmos., 101, 7251–7268, https://doi.org/10.1029/95JD02165, 1996.
Chen, F., Janjić, Z., and Mitchell, K.: Impact of Atmospheric Surface-layer Parameterizations in the new Land-surface Scheme of the NCEP Mesoscale Eta Model, Bound. Lay. Meteorol., 85, 391–421, https://doi.org/10.1023/A:1000531001463, 1997.
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, 2015.
David, C. H., Hobbs, J. M., Turmon, M. J., Emery, C. M., Reager, J. T., and Famiglietti, J. S.: Analytical Propagation of Runoff Uncertainty Into Discharge Uncertainty Through a Large River Network, Geophys. Res. Lett., 46, 8102–8113, https://doi.org/10.1029/2019GL083342, 2019.
Decharme, B.: Influence of runoff parameterization on continental hydrology: Comparison between the Noah and the ISBA land surface models, J. Geophys. Res.-Atmos., 112, D19108, https://doi.org/10.1029/2007JD008463, 2007.
Decharme, B. and Colin, J.: Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3091, 2024.
Dickinson, R. E.: Land Surface Processes and Climate–Surface Albedos and Energy Balance, in: Advances in Geophysics, 25, edited by: Saltzman, B., Elsevier, 305–353, https://doi.org/10.1016/S0065-2687(08)60176-4, 1983.
Dickinson, R. E., Shaikh, M., Bryant, R., and Graumlich, L.: Interactive Canopies for a Climate Model, J. Climate, 11, 2823–2836, https://doi.org/10.1175/1520-0442(1998)011<2823:ICFACM>2.0.CO;2, 1998.
Diks, C. G. H. and Vrugt, J. A.: Comparison of point forecast accuracy of model averaging methods in hydrologic applications, Stoch. Env. Res. Risk A., 24, 809–820, https://doi.org/10.1007/s00477-010-0378-z, 2010.
Dümenil, L. and Todini, E.: A rainfall–runoff scheme for use in the Hamburg climate model, in: Advances in Theoretical Hydrology, Chapter 9, edited by: O'Kane, J. P., Elsevier, Amsterdam, 129–157, https://doi.org/10.1016/B978-0-444-89831-9.50016-8, 1992.
Dutta, R. and Markonis, Y.: Does ERA5-land capture the changes in the terrestrial hydrological cycle across the globe?, Environ. Res. Lett., 19, 024054, https://doi.org/10.1088/1748-9326/ad1d3a, 2024.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Fan, Y., Miguez-Macho, G., Weaver, C. P., Walko, R., and Robock, A.: Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations, J. Geophys. Res.-Atmos., 112, D10125, https://doi.org/10.1029/2006JD008111, 2007.
Fang, Y.-H., Zhang, X., Corbari, C., Mancini, M., Niu, G.-Y., and Zeng, W.: Improving the Xin'anjiang hydrological model based on mass–energy balance, Hydrol. Earth Syst. Sci., 21, 3359–3375, https://doi.org/10.5194/hess-21-3359-2017, 2017.
Gan, Y., Liang, X.-Z., Duan, Q., Chen, F., Li, J., and Zhang, Y.: Assessment and Reduction of the Physical Parameterization Uncertainty for Noah-MP Land Surface Model, Water Resour. Res., 55, 5518–5538, https://doi.org/10.1029/2019WR024814, 2019.
Georgakakos, K. P., Seo, D.-J., Gupta, H., Schaake, J., and Butts, M. B.: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles, J. Hydrol., 298, 222–241, https://doi.org/10.1016/j.jhydrol.2004.03.037, 2004.
Gochis, D. J., Barlage, M., Cabell, R., Casali, M., Dugger, A., Fitzgerald, K., Karsten, L., Mcallister, M., McCreight, J., Rafieeinasab, A., Read, L., Sampson, K., Yates, D., and Zhang, Y.: The WRF-Hydro Modeling System Technical Description, (Version 5.1.1), NCAR Technical Note, 108 pp., https://ral.ucar.edu/sites/default/files/docs/water/wrf-hydro-v511-technical-description.pdf (last access: 27 February 2025), 2020.
Global Hydrodynamics Lab: CaMa-Flood v4, GitHub [code], https://github.com/global-hydrodynamics/CaMa-Flood_v4 (last access: 11 December 2023), 2023.
GRDC: Data Portal, https://grdc.bafg.de/data/data_portal/ (last access: 28 April 2024), 2024.
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.
Hagemann, S. and Stacke, T.: Complementing ERA5 and E-OBS with high-resolution river discharge over Europe, Oceanologia, 65, 230–248, https://doi.org/10.1016/j.oceano.2022.07.003, 2023.
Hao, F., Sun, M., Geng, X., Huang, W., and Ouyang, W.: Coupling the Xinanjiang model with geomorphologic instantaneous unit hydrograph for flood forecasting in northeast China, Int. Soil Water Conserv. Research, 3, 66–76, https://doi.org/10.1016/j.iswcr.2015.03.004, 2015.
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability, Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, 2023a.
He, C., Valayamkunnath, P., Barlage, M., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: The Community Noah-MP Land Surface Modeling System Technical Description Version 5.0, NCAR Tech. Note, No. NCAR/TN-575+STR, https://doi.org/10.5065/ew8g-yr95, 2023b.
Heber Green, W. and Ampt, G. A.: Studies on Soil Phyics, J. Agr. Sci., 4, 1–24, https://doi.org/10.1017/S0021859600001441, 1911.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013.
Hou, Y., Guo, H., Yang, Y., and Liu, W.: Global Evaluation of Runoff Simulation From Climate, Hydrological and Land Surface Models, Water Resour. Res., 59, e2021WR031817, https://doi.org/10.1029/2021WR031817, 2023.
Huo, W., Li, Z., Wang, J., Yao, C., Zhang, K., and Huang, Y.: Multiple hydrological models comparison and an improved Bayesian model averaging approach for ensemble prediction over semi-humid regions, Stoch. Env. Res. Risk A., 33, 217–238, https://doi.org/10.1007/s00477-018-1600-7, 2019.
Jayawardena, A. W. and Zhou, M. C.: A modified spatial soil moisture storage capacity distribution curve for the Xinanjiang model, J. Hydrol., 227, 93–113, https://doi.org/10.1016/S0022-1694(99)00173-0, 2000.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Li, J., Chen, F., Lu, X., Gong, W., Zhang, G., and Gan, Y.: Quantifying Contributions of Uncertainties in Physical Parameterization Schemes and Model Parameters to Overall Errors in Noah-MP Dynamic Vegetation Modeling, J. Adv. Model. Earth Sy., 12, e2019MS001914, https://doi.org/10.1029/2019MS001914, 2020.
Li, J., Miao, C., Zhang, G., Fang, Y.-H., Shangguan, W., and Niu, G.-Y.: Global Evaluation of the Noah-MP Land Surface Model and Suggestions for Selecting Parameterization Schemes, J. Geophys. Res.-Atmos., 127, e2021JD035753, https://doi.org/10.1029/2021JD035753, 2022.
Liang, J., Yang, Z., and Lin, P.: Systematic Hydrological Evaluation of the Noah-MP Land Surface Model over China, Adv. Atmos. Sci., 36, 1171–1187, https://doi.org/10.1007/s00376-019-9016-y, 2019.
Liang, X. and Xie, Z.: A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models, Adv. Water Resour., 24, 1173–1193, https://doi.org/10.1016/S0309-1708(01)00032-X, 2001.
Liang, X. and Xie, Z.: Important factors in land–atmosphere interactions: surface runoff generations and interactions between surface and groundwater, Global Planet. Change, 38, 101–114, https://doi.org/10.1016/S0921-8181(03)00012-2, 2003.
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.
Liang, Y.-C., Lo, M.-H., Lan, C.-W., Seo, H., Ummenhofer, C. C., Yeager, S., Wu, R.-J., and Steffen, J. D.: Amplified seasonal cycle in hydroclimate over the Amazon river basin and its plume region, Nat. Commun., 11, 4390, https://doi.org/10.1038/s41467-020-18187-0, 2020.
Liu, L., Yi, Y., Jiang, H., Ran, Y., and Chen, D.: ERA5-Land overestimates runoff coefficient but underestimates runoff recession rate in the central Tibetan permafrost region, J. Hydrol. Reg. Stud., 53, 101792, https://doi.org/10.1016/j.ejrh.2024.101792, 2024.
Miguez-Macho, G., Fan, Y., Weaver, C. P., Walko, R., and Robock, A.: Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation, J. Geophys. Res.-Atmos., 112, D13108, https://doi.org/10.1029/2006JD008112, 2007.
Miller, D. A. and White, R. A.: A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling, Earth Interact., 2, 1–26, https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2, 1998.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the atmosphere near the ground, vol. 24, Tr. Akad. Nauk. SSSR Geofiz. Inst., 163–187, 1954.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
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.
Nguyen, A. D., Le Nguyen, P., Vu, V. H., Pham, Q. V., Nguyen, V. H., Nguyen, M. H., Nguyen, T. H., and Nguyen, K.: Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum analysis-based denoising, Sci. Rep., 12, 19870, https://doi.org/10.1038/s41598-022-22057-8, 2022.
Niu, G. Y. and Yang, Z.: The Community Noah Land Surface Model (LSM) with Multi-Physics Options, User's Guide, Center for Integrated Earth System Science, The University of Texas at Austin, Austin, TX, USA, https://www.jsg.utexas.edu/noah-mp/users-guide/ (last access: 27 February 2025), 2011.
Niu, G.-Y. and Yang, Z.-L.: Effects of Frozen Soil on Snowmelt Runoff and Soil Water Storage at a Continental Scale, J. Hydrometeorol., 7, 937–952, https://doi.org/10.1175/JHM538.1, 2006.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., and Gulden, L. E.: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models, J. Geophys. Res.-Atmos., 110, D21106, https://doi.org/10.1029/2005JD006111, 2005.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data, J. Geophys. Res.-Atmos., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
NSF National Center for Atmospheric Research (NSF NCAR): Noah-MP Land Surface Model, version 5.0.0., GitHub [code], https://github.com/NCAR/noahmp (last access: 5 June 2023), 2023.
Oda, T., Iwasaki, K., Egusa, T., Kubota, T., Iwagami, S., Iida, S., Momiyama, H., and Shimizu, T.: Scale-Dependent Inter-Catchment Groundwater Flow in Forested Catchments: Analysis of Multi-Catchment Water Balance Observations in Japan, Water Resour. Res., 60, e2024WR037161, https://doi.org/10.1029/2024WR037161, 2024.
Philip, J. R.: The infiltration joining problem, Water Resour. Res., 23, 2239–2245, https://doi.org/10.1029/WR023i012p02239, 1987.
Pires, C. and Martins, M. V: Enhancing Water Management: A Comparative Analysis of Time Series Prediction Models for Distributed Water Flow in Supply Networks, Water, 16, 1827, https://doi.org/10.3390/w16131827, 2024.
Reale, M., Giorgi, F., Solidoro, C., Di Biagio, V., Di Sante, F., Mariotti, L., Farneti, R., and Sannino, G.: The Regional Earth System Model RegCM-ES: Evaluation of the Mediterranean Climate and Marine Biogeochemistry, J. Adv. Model. Earth Sy., 12, e2019MS001812, https://doi.org/10.1029/2019MS001812, 2020.
Ren-Jun, Z.: The Xinanjiang model applied in China, J. Hydrol., 135, 371–381, https://doi.org/10.1016/0022-1694(92)90096-E, 1992.
Rummler, T., Wagner, A., Arnault, J., and Kunstmann, H.: Lateral terrestrial water fluxes in the LSM of WRF-Hydro: Benefits of a 2D groundwater representation, Hydrol. Process., 36, e14510, https://doi.org/10.1002/hyp.14510, 2022.
Sakaguchi, K. and Zeng, X.: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5), J. Geophys. Res.-Atmos., 114, D01107, https://doi.org/10.1029/2008JD010834, 2009.
Schaake, J. C., Koren, V. I., Duan, Q.-Y., Mitchell, K., and Chen, F.: Simple water balance model for estimating runoff at different spatial and temporal scales, J. Geophys. Res.-Atmos., 101, 7461–7475, /https://doi.org/10.1029/95JD02892, 1996.
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration, Int. J. Remote Sens., 6, 1335–1372, https://doi.org/10.1080/01431168508948283, 1985.
Sheng, M., Lei, H., Jiao, Y., and Yang, D.: Evaluation of the Runoff and River Routing Schemes in the Community Land Model of the Yellow River Basin, J. Adv. Model. Earth Sy., 9, 2993–3018, https://doi.org/10.1002/2017MS001026, 2017.
Shoaib, M., Shamseldin, A. Y., Khan, S., Khan, M. M., Khan, Z. M., and Melville, B. W.: A wavelet based approach for combining the outputs of different rainfall–runoff models, Stoch. Env. Res. Risk A., 32, 155–168, https://doi.org/10.1007/s00477-016-1364-x, 2018.
Smith, R. E. and Parlange, J.-Y.: A parameter-efficient hydrologic infiltration model, Water Resour. Res., 14, 533–538, https://doi.org/10.1029/WR014i003p00533, 1978.
Sorribas, M. V., de Paiva, R. C. D., Fleischmann, A. S., and Collischonn, W.: Hydrological Tracking Model for Amazon Surface Waters, Water Resour. Res., 56, e2019WR024721, https://doi.org/10.1029/2019WR024721, 2020.
Stephens, G. L., Slingo, J. M., Rignot, E., Reager, J. T., Hakuba, M. Z., Durack, P. J., Worden, J., and Rocca, R.: Earth's water reservoirs in a changing climate, P. Roy. Soc. A, 476, 20190458, https://doi.org/10.1098/rspa.2019.0458, 2020.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Verseghy, D. L.: Class–A Canadian land surface scheme for GCMS, I. Soil model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991.
Wipfler, E. L., Metselaar, K., van Dam, J. C., Feddes, R. A., van Meijgaard, E., van Ulft, L. H., van den Hurk, B., Zwart, S. J., and Bastiaanssen, W. G. M.: Seasonal evaluation of the land surface scheme HTESSEL against remote sensing derived energy fluxes of the Transdanubian region in Hungary, Hydrol. Earth Syst. Sci., 15, 1257–1271, https://doi.org/10.5194/hess-15-1257-2011, 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, W04501, 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.
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.
Yang, W.-Y., Li, D., Sun, T., and Ni, G.-H.: Saturation-excess and infiltration-excess runoff on green roofs, Ecol. Eng., 74, 327–336, https://doi.org/10.1016/j.ecoleng.2014.10.023, 2015.
Yang, Z.-L. and Dickinson, R. E.: Description of the Biosphere-Atmosphere Transfer Scheme (BATS) for the Soil Moisture Workshop and evaluation of its performance, Glob. Planet. Change, 13, 117–134, https://doi.org/10.1016/0921-8181(95)00041-0, 1996.
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Zhang, G., Chen, F., and Gan, Y.: Assessing uncertainties in the Noah-MP ensemble simulations of a cropland site during the Tibet Joint International Cooperation program field campaign, J. Geophys. Res.-Atmos., 121, 9576–9596, https://doi.org/10.1002/2016JD024928, 2016.
Zhang, G., Li, J., Zhou, G., Cai, X., Gao, W., Peng, X., and Chen, Y.: Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah-MP Land Surface Model Over China, J. Geophys. Res.-Atmos., 126, e2021JD034542, https://doi.org/10.1029/2021JD034542, 2021a.
Zhang, Q., Wang, B.-D., He, B., Peng, Y., and Ren, M.-L.: Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting, Water Resour. Manage., 25, 2683–2703, https://doi.org/10.1007/s11269-011-9833-y, 2011.
Zhang, X., Chen, L., Ma, Z., and Gao, Y.: Assessment of surface exchange coefficients in the Noah-MP land surface model for different land-cover types in China, Int. J. Climatol., 41, 2638–2659, https://doi.org/10.1002/joc.6981, 2021b.
Zhang, Z., Chen, F., Barlage, M., Bortolotti, L. E., Famiglietti, J., Li, Z., Ma, X., and Li, Y.: Cooling Effects Revealed by Modeling of Wetlands and Land-Atmosphere Interactions, Water Resour. Res., 58, e2021WR030573, https://doi.org/10.1029/2021WR030573, 2022.
Zheng, D., Van Der Velde, R., Su, Z., Wen, J., and Wang, X.: Assessment of Noah land surface model with various runoff parameterizations over a Tibetan river, J. Geophys. Res.-Atmos., 122, 1488–1504, https://doi.org/10.1002/2016JD025572, 2017.
Zheng, H., Yang, Z.-L., Lin, P., Wei, J., Wu, W.-Y., Li, L., Zhao, L., and Wang, S.: On the Sensitivity of the Precipitation Partitioning Into Evapotranspiration and Runoff in Land Surface Parameterizations, Water Resour. Res., 55, 95–111, https://doi.org/10.1029/2017WR022236, 2019.
Zipper, S. C., Motew, M., Booth, E. G., Chen, X., Qiu, J., Kucharik, C. J., Carpenter, S. R., and Loheide II, S. P.: Continuous separation of land use and climate effects on the past and future water balance, J. Hydrol., 565, 106–122, https://doi.org/10.1016/j.jhydrol.2018.08.022, 2018.
Short summary
This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
This study evaluates how different methods of simulating runoff impact river flow predictions...
