Articles | Volume 27, issue 24
https://doi.org/10.5194/hess-27-4385-2023
© 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-27-4385-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Dec 2023
Research article |
| 14 Dec 2023
| 14 Dec 2023
Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling
Diego Araya
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile
Eduardo Muñoz-Castro
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
James McPhee
Department of Civil Engineering, Universidad de Chile, Santiago, Chile
Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3837, https://doi.org/10.5194/egusphere-2025-3837, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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Rock debris covers many of the world glaciers, modifying the transfer of atmospheric energy to the debris and into the ice. Models of different complexity simulate this process, and we compare 14 models at 9 sites to show that the most complex models at the debris-atmosphere interface have the highest performance. However, we lack debris properties and their derivation from measurements is ambiguous, hindering global modelling and calling for both model development and data collection.
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
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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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.
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1391, https://doi.org/10.5194/egusphere-2025-1391, 2025
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When flood happen during, or shortly after, droughts, the impacts of can be magnified. In hydrological research, defining these events can be challenging. Here we have tried to address some of the challenges defining these events using real-world examples. We show how different methodological approaches differ in their results, make suggestions on when to use which approach, and outline some pitfalls of which researchers should be aware.
Eduardo Muñoz-Castro, Bailey J. Anderson, Paul C. Astagneau, Daniel L. Swain, Pablo A. Mendoza, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-781, https://doi.org/10.5194/egusphere-2025-781, 2025
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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 more difficult to capture in semi-arid high-mountain catchments than in humid low-elevation catchments.
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
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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.
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.
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
Addor, N. and Melsen, L. A.: Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models, Water Resour. Res., 55, 378–390, https://doi.org/10.1029/2018WR022958, 2019.
Alvarez-Garreton, C., Mendoza, P. A., Pablo Boisier, J., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018.
Anderson, E.: National Weather Service River Forecast system – snow accumulation and ablation model, NOAA Tech. Memo. NWS HYDRO-17, NOAA, https://repository.library.noaa.gov/view/noaa/13507 (last access: 23 March 2023), 1973.
Araya, D., Mendoza, P. A., McPhee, J., and Muñoz-Castro, E.: A hydrological modeling dataset for ensemble streamflow forecasting in 22 snow-influenced basins in Central Chile, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7853556, 2023.
Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018.
Ayala, Á., Farías-Barahona, D., Huss, M., Pellicciotti, F., McPhee, J., and Farinotti, D.: Glacier runoff variations since 1955 in the Maipo River basin, in the semiarid Andes of central Chile, The Cryosphere, 14, 2005–2027, https://doi.org/10.5194/tc-14-2005-2020, 2020.
Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Mendoza, P. A., McNamara, I., Beck, H. E., Thurner, J., Nauditt, A., Ribbe, L., and Thinh, N. X.: On the selection of precipitation products for the regionalisation of hydrological model parameters, Hydrol. Earth Syst. Sci., 25, 5805–5837, https://doi.org/10.5194/hess-25-5805-2021, 2021.
Baker, S. A., Rajagopalan, B., and Wood, A. W.: Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi-Model Climate Forecasts, J. Am. Water Resour. Assoc., 57, 906–922, https://doi.org/10.1111/1752-1688.12960, 2021.
Bergström, S.: Development and application of a conceptual runoff model for Scandinavian catchments, Report RHO 7, SMHI, Norrköping, Sweden, http://www.diva-portal.org/smash/record.jsf?pid=diva2:1456191&dswid=-4221 (last access: 8 March 2023), 1976.
Bohn, T. J., Sonessa, M. Y., and Lettenmaier, D. P.: Seasonal hydrologic forecasting: Do multimodel ensemble averages always yield improvements in forecast skill?, J. Hydrometeorol., 11, 1358–1372, https://doi.org/10.1175/2010JHM1267.1, 2010.
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, Center for Climate and Resilience Research [data set], https://www.cr2.cl/datos-productos-grillados/ (last access: 11 March 2023), 2018.
Budyko, M. I.: Climate and Life, Academic Press, London, ISBN 9780080954530, 1974.
Burnash, R., Ferral, R., and McGuire, R.: A generalized streamflow simulation system – Conceptual modeling for digital computers, Sacramento, California, https://searchworks.stanford.edu/view/753303 (last access: 7 March 2023), 1973.
Cook, B. I., Smerdon, J. E., Cook, E. R., Williams, A. P., Anchukaitis, K. J., Mankin, J. S., Allen, K., Andreu-Hayles, L., Ault, T. R., Belmecheri, S., Coats, S., Coulthard, B., Fosu, B., Grierson, P., Griffin, D., Herrera, D. A., Ionita, M., Lehner, F., Leland, C., Marvel, K., Morales, M. S., Mishra, V., Ngoma, J., Nguyen, H. T. T., O'Donnell, A., Palmer, J., Rao, M. P., Rodriguez-Caton, M., Seager, R., Stahle, D. W., Stevenson, S., Thapa, U. K., Varuolo-Clarke, A. M., and Wise, E. K.: Megadroughts in the Common Era and the Anthropocene, Nat. Rev. Earth Environ., 3, 741–757, https://doi.org/10.1038/s43017-022-00329-1, 2022.
Cornwell, E., Molotch, N. P., and McPhee, J.: Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover, Hydrol. Earth Syst. Sci., 20, 411–430, https://doi.org/10.5194/hess-20-411-2016, 2016.
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The suite of lumped GR hydrological models in an R package, Environ. Model. Softw., 94, 166–171, https://doi.org/10.1016/j.envsoft.2017.05.002, 2017.
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
Crochemore, L., Ramos, M. H., Pappenberger, F., and Perrin, C.: Seasonal streamflow forecasting by conditioning climatology with precipitation indices, Hydrol. Earth Syst. Sci., 21, 1573–1591, https://doi.org/10.5194/hess-21-1573-2017, 2017.
Crochemore, L., Ramos, M. H., and Pechlivanidis, I. G.: Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale?, Water Resour. Res., 56, 1–21, https://doi.org/10.1029/2019WR025700, 2020.
Day, G. N.: Extended Streamflow Forecasting Using NWSRFS, J. Water Resour. Pl. Manage., 111, 157–170, https://doi.org/10.1061/(ASCE)0733-9496(1985)111:2(157), 1985.
DeChant, C. M. and Moradkhani, H.: Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination, J. Hydrol., 519, 2967–2977, https://doi.org/10.1016/j.jhydrol.2014.05.045, 2014.
DGA: Actualización del balance hídrico nacional, SIT No. 417, Ministerio de Obras Públicas, Dirección General de Aguas, División de Estudios y Planificación, Santiago, Chile, https://snia.mop.gob.cl/repositoriodga/handle/20.500.13000/6961 (last access: 24 March 2023), 2017.
DGA: Pronóstico de caudales de deshielo periodo septiembre/2022-marzo/2023, SDT No. 448, https://snia.mop.gob.cl/repositoriodga/handle/20.500.13000/125978 (last access: 22 March 2023), 2022.
Donegan, S., Murphy, C., Harrigan, S., Broderick, C., Foran Quinn, D., Golian, S., Knight, J., Matthews, T., Prudhomme, C., Scaife, A. A., Stringer, N., and Wilby, R. L.: Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times, Hydrol. Earth Syst. Sci., 25, 4159–4183, https://doi.org/10.5194/hess-25-4159-2021, 2021.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and Efficient Global Optimization for Conceptual Rainfal-Runoff Models, Water Resour. Res., 28, 1015–1031, 1992.
Duethmann, D., Peters, J., Blume, T., Vorogushyn, S., and Güntner, A.: The value of satellite-derived snow cover images for calibrating a hydrological model in snow-dominated catchments in Central Asia, Water Resour. Res., 50, 2002–2021, https://doi.org/10.1002/2013WR014382, 2014.
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, 2018a.
Fowler, K., Coxon, G., Freer, J., Peel, M., Wagener, T., Western, A., Woods, R., and Zhang, L.: Simulating Runoff Under Changing Climatic Conditions: A Framework for Model Improvement, Water Resour. Res., 54, 9812–9832, https://doi.org/10.1029/2018WR023989, 2018b.
Franz, K. J., Hartmann, H. C., Sorooshian, S., and Bales, R.: Verification of National Weather Service Ensemble Streamflow Predictions for water supply forecasting in the Colorado River Basin, J. Hydrometeorol., 4, 1105–1118, https://doi.org/10.1175/1525-7541(2003)004<1105:VONWSE>2.0.CO;2, 2003.
Garreaud, R., Alvarez-Garreton, C., Barichivich, J., Pablo Boisier, J., Christie, D., Galleguillos, M., LeQuesne, C., McPhee, J., and Zambrano-Bigiarini, M.: The 2010–2015 megadrought in central Chile: Impacts on regional hydroclimate and vegetation, Hydrol. Earth Syst. Sci., 21, 6307–6327, https://doi.org/10.5194/hess-21-6307-2017, 2017.
Garreaud, R. D., Boisier, J. P. P., Rondanelli, R., Montecinos, A., Sepúlveda, H. H. H., and Veloso-Aguila, D.: The Central Chile Mega Drought (2010–2018): A climate dynamics perspective, Int. J. Climatol., 40, 1–19, https://doi.org/10.1002/joc.6219, 2019.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: An approach to identify time consistent model parameters: Sub-period calibration, Hydrol. Earth Syst. Sci., 17, 149–161, https://doi.org/10.5194/hess-17-149-2013, 2013.
Girons Lopez, M., Vis, M. J. P., Jenicek, M., Griessinger, N., and Seibert, J.: Assessing the degree of detail of temperature-based snow routines for runoff modelling in mountainous areas in central Europe, Hydrol. Earth Syst. Sci., 24, 4441–4461, https://doi.org/10.5194/hess-24-4441-2020, 2020.
Girons Lopez, M., Crochemore, L., and G. Pechlivanidis, I.: Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden, Hydrol. Earth Syst. Sci., 25, 1189–1209, https://doi.org/10.5194/hess-25-1189-2021, 2021.
Giuliani, M., Crochemore, L., Pechlivanidis, I., and Castelletti, A.: From skill to value: isolating the influence of end user behavior on seasonal forecast assessment, Hydrol. Earth Syst. Sci., 24, 5891–5902, https://doi.org/10.5194/hess-24-5891-2020, 2020.
Greuell, W., Franssen, W. H. P., and Hutjes, R. W. A.: Seasonal streamflow forecasts for Europe – Part 2: Sources of skill, Hydrol. Earth Syst. Sci., 23, 371–391, https://doi.org/10.5194/hess-23-371-2019, 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.
Hamlet, A. F. and Lettenmaier, D. P.: Effects of climate change on hydrology and water resources in the Columbia River basin, J. Am. Water Resour. Assoc., 35, 1597–1623, 1999.
Harder, P. and Pomeroy, J. W.: Hydrological model uncertainty due to precipitation-phase partitioning methods, Hydrol. Process., 28, 4311–4327, https://doi.org/10.1002/hyp.10214, 2014.
Hargreaves, G. H. and Samani, Z. A.: Reference Crop Evapotranspiration from Temperature, Appl. Eng. Agric., 1, 96–99, https://doi.org/10.13031/2013.26773, 1985.
Harpold, A. A., Kaplan, M. L., Zion Klos, P., Link, T., McNamara, J. P., Rajagopal, S., Schumer, R., and Steele, C. M.: Rain or snow: Hydrologic processes, observations, prediction, and research needs, Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017, 2017.
Harrigan, S., Prudhomme, C., Parry, S., Smith, K., and Tanguy, M.: Benchmarking ensemble streamflow prediction skill in the UK, Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, 2018.
Hernandez, D., Mendoza, P. A., Boisier, J. P., and Ricchetti, F.: Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28∘–41∘ S), Water Resour. Res., 58, e2021WR031860, https://doi.org/10.1029/2021WR031860, 2022.
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000.
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017.
Huang, C., Newman, A. J., Clark, M. P., Wood, A. W., and Zheng, X.: Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States, Hydrol. Earth Syst. Sci., 21, 635–650, https://doi.org/10.5194/hess-21-635-2017, 2017.
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.
Kunnath-Poovakka, A., Ryu, D., Renzullo, L. J., and George, B.: The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction, J. Hydrol., 535, 509–524, https://doi.org/10.1016/j.jhydrol.2016.02.018, 2016.
Ladson, A., Brown, R., Neal, B., and Nathan, R.: A standard approach to baseflow separation using the Lyne and Hollick filter, Aust. J. Water Resour., 17, 25–34, https://doi.org/10.7158/13241583.2013.11465417, 2013.
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007.
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.
Lucatero, D., Madsen, H., Refsgaard, J. C., Kidmose, J., and Jensen, K. H.: Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: The effect of preprocessing and post-processing on skill and statistical consistency, Hydrol. Earth Syst. Sci., 22, 3601–3617, https://doi.org/10.5194/hess-22-3601-2018, 2018.
Martinez, G. F. and Gupta, H. V.: Toward improved identification of hydrological models: A diagnostic evaluation of the “abcd ” monthly water balance model for the conterminous United States, Water Resour. Res., 46, W08507, https://doi.org/10.1029/2009WR008294, 2010.
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.
Mendoza, P. A., Rajagopalan, B., Clark, M. P., Cortés, G., and McPhee, J.: A robust multimodel framework for ensemble seasonal hydroclimatic forecasts, Water Resour. Res., 50, 6030–6052, https://doi.org/10.1002/2014WR015426, 2014.
Mendoza, P. A., Clark, M. P., Mizukami, N., Newman, A., Barlage, M., Gutmann, E., Rasmussen, R., Rajagopalan, B., Brekke, L., and Arnold, J.: Effects of hydrologic model choice and calibration on the portrayal of climate change impacts, J. Hydrometeorol., 16, 762–780, https://doi.org/10.1175/JHM-D-14-0104.1, 2015.
Mendoza, P. A., Wood, A. W., Clark, E., Rothwell, E., Clark, M. P., Nijssen, B., Brekke, L. D., and Arnold, J. R.: An intercomparison of approaches for improving operational seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, 2017.
Mendoza, P. A., Shaw, T. E., McPhee, J., Musselman, K. N., Revuelto, J., and MacDonell, S.: Spatial Distribution and Scaling Properties of Lidar-Derived Snow Depth in the Extratropical Andes, Water Resour. Res., 56, e2021WR031860, https://doi.org/10.1029/2020WR028480, 2020.
Micheletty, P., Perrot, D., Day, G., and Rittger, K.: Assimilation of Ground and Satellite Snow Observations in a Distributed Hydrologic Model for Water Supply Forecasting, J. Am. Water Resour. Assoc., 58, 1030–1048, https://doi.org/10.1111/1752-1688.12975, 2021.
Mizukami, N., Rakovec, O., Newman, A. J., Clark, M. P., Wood, A. W., Gupta, H. V., and Kumar, R.: On the choice of calibration metrics for “high-flow” estimation using hydrologic models, Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, 2019.
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.
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.
Najafi, M. and Moradkhani, H.: Ensemble Combination of Seasonal Streamflow Forecasts, J. Hydrol. Eng., 21, 04015043, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001250, 2015.
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.
Nemri, S. and Kinnard, C.: Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability, J. Hydrol., 582, 124474, https://doi.org/10.1016/j.jhydrol.2019.124474, 2020.
Nijzink, R. C., Almeida, S., Pechlivanidis, I. G., Capell, R., Gustafssons, D., Arheimer, B., Parajka, J., Freer, J., Han, D., Wagener, T., Nooijen, R. R. P., Savenije, H. H. G., and Hrachowitz, M.: Constraining Conceptual Hydrological Models With Multiple Information Sources, Water Resour. Res., 54, 8332–8362, https://doi.org/10.1029/2017WR021895, 2018.
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Le Moine, N.: Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments, Water Resour. Res., 44, 1–15, https://doi.org/10.1029/2007WR006240, 2008.
Parajka, J., Merz, R., and Blöschl, G.: Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrol. Process., 21, 435–446, https://doi.org/10.1002/hyp.6253, 2007.
Pauwels, V. R. N. and De Lannoy, G. J. M.: Ensemble-based assimilation of discharge into rainfall-runoff models: A comparison of approaches to mapping observational information to state space, Water Resour. Res., 45, W08428, https://doi.org/10.1029/2008WR007590, 2009.
Pechlivanidis, I. G., Crochemore, L., Rosberg, J., and Bosshard, T.: What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?, Water Resour. Res., 56, 1–19, https://doi.org/10.1029/2019WR026987, 2020.
Peñuela, A., Hutton, C., and Pianosi, F.: Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK, Hydrol. Earth Syst. Sci., 24, 6059–6073, https://doi.org/10.5194/hess-24-6059-2020, 2020.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
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.
Pool, S., Vis, M. J. P., Knight, R. R., and Seibert, J.: Streamflow characteristics from modeled runoff time series – Importance of calibration criteria selection, Hydrol. Earth Syst. Sci., 21, 5443–5457, https://doi.org/10.5194/hess-21-5443-2017, 2017.
Pool, S., Vis, M., and Seibert, J.: Evaluating model performance: towards a non-parametric variant of the Kling–Gupta efficiency, Hydrolog. Sci. J., 63, 1941–1953, https://doi.org/10.1080/02626667.2018.1552002, 2018.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Saavedra, D., Mendoza, P. A., Addor, N., Llauca, H., and Vargas, X.: A multi-objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments, Hydrol. Process., 36, e14446, https://doi.org/10.1002/hyp.14446, 2022.
Sabzipour, B., Arsenault, R., and Brissette, F.: Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting, J. Hydrol., 595, 125656, https://doi.org/10.1016/j.jhydrol.2020.125656, 2021.
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.
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.
Shi, W., Schaller, N., MacLeod, D., Palmer, T. N. N., and Weisheimer, A.: Impact of hindcast length on estimates of seasonal climate predictability, Geophys. Res. Lett., 42, 1554–1559, https://doi.org/10.1002/2014GL062829, 2015.
Shi, X., Wood, A. W., and Lettenmaier, D. P.: How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting?, J. Hydrometeorol., 9, 1350–1363, https://doi.org/10.1175/2008JHM1001.1, 2008.
Singla, S., Céron, J.-P. P., Martin, E., Regimbeau, F., Déqué, M., Habets, F., and Vidal, J.-P. P.: Predictability of soil moisture and river flows over France for the spring season, Hydrol. Earth Syst. Sci., 16, 201–216, https://doi.org/10.5194/hess-16-201-2012, 2012.
Skøien, J. O., Blöschl, G., Laaha, G., Pebesma, E., Parajka, J., and Viglione, A.: rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks, Comput. Geosci., 67, 180–190, https://doi.org/10.1016/j.cageo.2014.02.009, 2014.
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., Villarini, G., Wilby, R. L., Wood, A., and Zappa, M.: Hybrid forecasting: blending climate predictions with AI models, Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, 2023.
Sleziak, P., Szolgay, J., Hlavčová, K., Danko, M., and Parajka, J.: The effect of the snow weighting on the temporal stability of hydrologic model efficiency and parameters, J. Hydrol., 583, 124639, https://doi.org/10.1016/j.jhydrol.2020.124639, 2020.
Şorman, A. A., Şensoy, A., Tekeli, A. E., Şorman, A. Ü., and Akyürek, Z.: Modelling and forecasting snowmelt runoff process using the HBV model in the eastern part of Turkey, Hydrol. Process., 23, 1031–1040, https://doi.org/10.1002/hyp.7204, 2009.
Tachikawa, T., Hato, M., Kaku, M., and Iwasaki, A.: Characteristics of ASTER GDEM version 2, in: Int. Geosci. Remote Sens. Symp., 24–29 July 2011, Vancouver, BC, Canada, 3657–3660, https://doi.org/10.1109/IGARSS.2011.6050017, 2011.
Taner, M.: sacsmaR: SAC-SMA Hydrology Model, R Package version 0.0.1, https://github.com/tanerumit/sacsmaR (last access: 7 January 2023), 2019.
Tang, G., Clark, M. P., and Papalexiou, S. M.: SC-earth: A station-based serially complete earth dataset from 1950 to 2019, J. Climate, 34, 6493–6511, https://doi.org/10.1175/JCLI-D-21-0067.1, 2021.
Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B., Kubáň, M., Valent, P., Vreugdenhil, M., Wagner, W., and Blöschl, G.: The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model, Hydrol. Earth Syst. Sci., 25, 1389–1410, https://doi.org/10.5194/hess-25-1389-2021, 2021.
Trambauer, P., Werner, M., Winsemius, H. C., Maskey, S., Dutra, E., and Uhlenbrook, S.: Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa, Hydrol. Earth Syst. Sci., 19, 1695–1711, https://doi.org/10.5194/hess-19-1695-2015, 2015.
Tuo, Y., Marcolini, G., Disse, M., and Chiogna, G.: A multi-objective approach to improve SWAT model calibration in alpine catchments, J. Hydrol., 559, 347–360, https://doi.org/10.1016/j.jhydrol.2018.02.055, 2018.
Valéry, A., Andréassian, V., and Perrin, C.: “As simple as possible but not simpler”: What is useful in a temperature-based snow-accounting routine? Part 1 – Comparison of six snow accounting routines on 380 catchments, J. Hydrol., 517, 1166–1175, https://doi.org/10.1016/j.jhydrol.2014.04.059, 2014a.
Valéry, A., Andréassian, V., and Perrin, C.: `As simple as possible but not simpler': What is useful in a temperature-based snow-accounting routine? Part 2 – Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, J. Hydrol., 517, 1176–1187, https://doi.org/10.1016/j.jhydrol.2014.04.058, 2014b.
Vásquez, N., Cepeda, J., Gómez, T., Mendoza, P. A., Lagos, M., Boisier, J. P., Álvarez-Garretón, C., and Vargas, X.: Catchment-Scale Natural Water Balance in Chile, in: Water Resources of Chile, Springer International Publishing, 189–208, https://doi.org/10.1007/978-3-030-56901-3_9, 2021.
Verkade, J. S., Brown, J. D., Reggiani, P., and Weerts, A. H.: Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales, J. Hydrol., 501, 73–91, https://doi.org/10.1016/j.jhydrol.2013.07.039, 2013.
Viglione, A. and Parajka, J.: TUWmodel: Lumped/Semi-Distributed Hydrological Model for Education Purposes, R Package version 1.1-1, https://cran.r-project.org/web/packages/TUWmodel/ (last access: 7 January 2023), 2020.
Wanders, N., Thober, S., Kumar, R., Pan, M., Sheffield, J., Samaniego, L., and Wood, E. F.: Development and evaluation of a pan-European multimodel seasonal hydrological forecasting system, J. Hydrometeorol., 20, 99–115, https://doi.org/10.1175/JHM-D-18-0040.1, 2019.
Werner, K., Brandon, D., Clark, M., and Gangopadhyay, S.: Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts, J. Hydrometeorol., 5, 1076–1090, https://doi.org/10.1175/JHM-381.1, 2004.
Woldemeskel, F., McInerney, D., Lerat, J., Thyer, M., Kavetski, D., Shin, D., Tuteja, N., and Kuczera, G.: Evaluating post-processing approaches for monthly and seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 22, 6257–6278, https://doi.org/10.5194/hess-22-6257-2018, 2018.
Wood, A. W. and Schaake, J. C.: Correcting Errors in Streamflow Forecast Ensemble Mean and Spread, J. Hydrometeorol., 9, 132–148, https://doi.org/10.1175/2007JHM862.1, 2008.
Wood, A. W., Sankarasubramanian, A., and Mendoza, P.: Seasonal Ensemble Forecast Post-processing, in: Handbook of Hydrometeorological Ensemble Forecasting, Springer, Berlin, Heidelberg, 1–27, ISBN 364239924X, ISBN 9783642399244, 2018.
Woods, R. A.: Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks, Adv. Water Resour., 32, 1465–1481, https://doi.org/10.1016/j.advwatres.2009.06.011, 2009.
Yang, L., Tian, F., Sun, Y., Yuan, X., and Hu, H.: Attribution of hydrologic forecast uncertainty within scalable forecast windows, Hydrol. Earth Syst. Sci., 18, 775–786, https://doi.org/10.5194/hess-18-775-2014, 2014.
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.
Yuan, X. and Zhu, E.: A First Look at Decadal Hydrological Predictability by Land Surface Ensemble Simulations, Geophys. Res. Lett., 45, 2362–2369, https://doi.org/10.1002/2018GL077211, 2018.
Yuan, X., Wood, E. F., Roundy, J. K., and Pan, M.: CFSv2-Based seasonal hydroclimatic forecasts over the conterminous United States, J. Climate, 26, 4828–4847, https://doi.org/10.1175/JCLI-D-12-00683.1, 2013.
Yuan, X., Wood, E. F., and Liang, M.: Integrating weather and climate prediction: Toward seamless hydrologic forecasting, Geophys. Res. Lett., 41, 5891–5896, https://doi.org/10.1002/2014GL061076, 2014.
Zhao, Y., Feng, D., Yu, L., Wang, X., Chen, Y., Bai, Y., Hernández, H. J., Galleguillos, M., Estades, C., Biging, G. S., Radke, J. D., and Gong, P.: Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data, Remote Sens. Environ., 183, 170–185, https://doi.org/10.1016/j.rse.2016.05.016, 2016.
Short summary
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.
Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts,...
