Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jul 2022
Research article |
| 07 Jul 2022
| 07 Jul 2022
Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient
Ulises M. Sepúlveda
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
Naoki Mizukami
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Andrew J. Newman
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
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Matthias Cuntz, Stephan Thober, Anne Verhoef, Yijian Zeng, Aaron Boone, Agnès Ducharne, Sujan Koirala, David M. Lawrence, Philipp de Vrese, Carsten Montzka, Sonia I. Seneviratne, Harry Vereecken, Dani Or, Salma Tafasca, Naoki Mizukami, Rich Ellis, Patrick C. McGuire, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2026-4223, https://doi.org/10.5194/egusphere-2026-4223, 2026
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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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Reliable river flow prediction guide water supply planning and flood protection. We tested whether selecting or combining multiple models improves accuracy compared with a single model. 78 models were used and tested in 559 river basins across the United States. A carefully chosen single model nearly matched more complex multi-model approaches, while combining models gave slightly higher accuracy and lower uncertainty. However, no approach worked best everywhere.
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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Although distributed hydrological models are often calibrated using only streamflow data, this practice may provide unrealistic representations of the water cycle. We show that, while streamflow annual cycles can be reasonably simulated, the seasonality of other key variables - such as evapotranspiration, soil moisture, and snow cover - may be severely misrepresented. Our results highlight the need to assess seasonal patterns of variables beyond streamflow when calibrating hydrological models.
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.
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.
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andy Wood
Hydrol. Earth Syst. Sci., 29, 1117–1133, https://doi.org/10.5194/hess-29-1117-2025, https://doi.org/10.5194/hess-29-1117-2025, 2025
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There is a perceived mismatch between the spatial scales on which global climate models can produce data and those needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We assessed the credibility of a set of water management decision metrics in the Community Earth System Model v2 (CESM2). CESM2 shows potentially greater use of its output in long-range water management decisions.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
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.
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.
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.
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.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
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This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
Cited articles
Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H.,
and Kløve, B.: A continental-scale hydrology and water quality model for
Europe: Calibration and uncertainty of a high-resolution large-scale SWAT
model, J. Hydrol., 524, 733–752, https://doi.org/10.1016/j.jhydrol.2015.03.027, 2015.
Abdulla, F. A. and Lettenmaier, D. P.: Development of regional parameter
estimation equations for a macroscale hydrologic model, J. Hydrol., 197, 230–257, https://doi.org/10.1016/S0022-1694(96)03262-3, 1997.
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.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M.
P.: A Ranking of Hydrological Signatures Based on Their Predictability in
Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018.
Al Nakshabandi, G. and Kohnke, H.: Thermal conductivity and diffusivity of
soils as related to moisture tension and other physical properties, Agric.
Meteorol., 2, 271–279, https://doi.org/10.1016/0002-1571(65)90013-0, 1965.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., 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.
Andreadis, K. M. and Lettenmaier, D. P.: Trends in 20th century drought over
the continental United States, Geophys. Res. Lett., 33, 1–4,
https://doi.org/10.1029/2006GL025711, 2006.
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.
Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M.,
Hasan, A., and Pineda, L.: Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation, Hydrol. Earth Syst.
Sci., 24, 535–559, https://doi.org/10.5194/hess-24-535-2020, 2020.
Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J., and Yang,
Z. L.: Sensitivity analysis of a land surface scheme using multicriteria
methods, J. Geophys. Res., 104, 19481–19490, 1999.
Bastidas, L. A., Hogue, T. S., Sorooshian, S., Gupta, H. V., and Shuttleworth, W. J.: Parameter sensitivity analysis for different complexity
land surface models using multicriteria methods, J. Geophys. Res., 111,
D20101, https://doi.org/10.1029/2005JD006377, 2006.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Miralles, D. G., McVicar, T.
R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of
hydrologic model parameters, Water Resour. Res., 52, 3599–3622,
https://doi.org/10.1002/2015WR018247, 2016.
Bennett, K. E., Urrego Blanco, J. R., Jonko, A., Bohn, T. J., Atchley, A.
L., Urban, N. M., and Middleton, R. S.: Global Sensitivity of Simulated Water
Balance Indicators Under Future Climate Change in the Colorado Basin, Water
Resour. Res., 54, 132–149, https://doi.org/10.1002/2017WR020471, 2018.
Berg, P., Donnelly, C., and Gustafsson, D.: Near-real-time adjusted reanalysis forcing data for hydrology, Hydrol. Earth Syst. Sci., 22, 989–1000, https://doi.org/10.5194/hess-22-989-2018, 2018.
Blondin, C.: Parameterization of Land-Surface Processes in Numerical Weather
Prediction, L. Surf. Evaporation, Springer, 31–54,
https://doi.org/10.1007/978-1-4612-3032-8_3, 1991.
Bohn, T. J. and Vivoni, E. R.: Process-based characterization of evapotranspiration sources over the North American monsoon region, Water Resour. Res., 52, 358–384, https://doi.org/10.1002/2015WR017934, 2016.
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, ADS [data set],
https://www.cr2.cl/datos-productos-grillados/ (last access: 13 June 2022), 2018.
Brooks, R. H. and Corey, A. T.: Hydraulic Properties of Porous Media and Their Relation to Drainage Design, T. ASAE, 7, 0026–0028, https://doi.org/10.13031/2013.40684, 1964.
Budyko, M. I.: Climate and Life, Academic Press, ISBN 0121394506, ISBN 13 978-0121394509, 1974.
Casper, M. C., Grigoryan, G., Gronz, O., Gutjahr, O., Heinemann, G., Ley, R.,
and Rock, A.: Analysis of projected hydrological behavior of catchments based on signature indices, Hydrol. Earth Syst. Sci., 16, 409–421,
https://doi.org/10.5194/hess-16-409-2012, 2012.
Cayan, D. R., Das, T., Pierce, D. W., Barnett, T. P., Tyree, M., and Gershunov, A.: Future dryness in the southwest US and the hydrology of the
early 21st century drought, P. Natl. Acad. Sci. USA, 107, 21271–21276, https://doi.org/10.1073/pnas.0912391107, 2010.
Chaney, N. W., Herman, J. D., Reed, P. M., and Wood, E. F.: Flood and drought
hydrologic monitoring: The role of model parameter uncertainty, Hydrol.
Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, 2015.
Chawla, I. and Mujumdar, P. P.: Isolating the impacts of land use and climate change on streamflow, Hydrol. Earth Syst. Sci., 19, 3633–3651,
https://doi.org/10.5194/hess-19-3633-2015, 2015.
Chegwidden, O. S. S., Nijssen, B., Rupp, D. E. E., Arnold, J. R. R., Clark,
M. P. P., Hamman, J. J. J., Kao, S. C. 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, Earth's Future,
7, 623–637, https://doi.org/10.1029/2018EF001047, 2019.
Chen, F., Barlage, M., Tewari, M., Rasmussen, R., Jin, J., Lettenmaier, D.,
Livneh, B., Lin, C., Miguez-Macho, G., Niu, G., Wen, L., and Yang, Z.: Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: A model intercomparison study, J. Geophys. Res.-Atmos., 119, 13795–13819, https://doi.org/10.1002/2014JD022167, 2014.
Cherkauer, K. A., Bowling, L. C., and Lettenmaier, D. P.: Variable
infiltration capacity cold land process model updates, Global Planet. Change,
38, 151–159, https://doi.org/10.1016/S0921-8181(03)00025-0, 2003.
Chipman, H. A., George, E. I., and McCulloch, R. E.: BART: Bayesian additive
regression trees, Ann. Appl. Stat., 4, 266–298, https://doi.org/10.1214/09-AOAS285, 2010.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for
process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res.,
51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015.
Clark, M. P., Bierkens, M. F. P., Samaniego, L., Woods, R. A., Uijlenhoet,
R., Bennett, K. E., Pauwels, V. R. N., Cai, X., Wood, A. W., and Peters-Lidard, C. D.: The evolution of process-based hydrologic models:
historical challenges and the collective quest for physical realism, Hydrol.
Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, 2017.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical
Exploration of the Relationships of Soil Moisture Characteristics to the
Physical Properties of Soils, Water Resour. Res., 20, 682–690,
https://doi.org/10.1029/WR020i006p00682, 1984.
C3S and Copernicus Climate Change Service (C3S): ERA5: Fifth generation of
ECMWF atmospheric reanalyses of the global climate, C3S, https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 20 January 2018), 2017.
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O.,
Attinger, S., and Thober, S.: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, J.
Geophys. Res.-Atmos., 121, 10676–10700, https://doi.org/10.1002/2016JD025097, 2016.
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.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Demaria, E. M., Nijssen, B., and Wagener, T.: Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity
model, J. Geophys. Res., 112, 1–15, https://doi.org/10.1029/2006JD007534, 2007.
DGA: Actualización del Balance Hídrico Nacional, SIT No. 417, https://snia.mop.gob.cl/repositoriodga/handle/20.500.13000/6919 (last access: 13 June 2022), 2017.
DGA: Aplicación de la metodología de actualización del balance
hídrico nacional a las macrozonas Norte y Centro, SIT No. 435, https://snia.mop.gob.cl/repositoriodga/handle/20.500.13000/6718 (last access: 13 June 2022), 2018.
DGA: Aplicación de la metodología de actualización del balance
hídrico nacional en las cuencas de la macrozona Sur y parte de la
Macrozona Austral, SIT No. 441, https://snia.mop.gob.cl/repositoriodga/handle/20.500.13000/7038 (last access: 13 June 2022), 2019a.
DGA: Aplicación de la metodología de actualización del balance
hídrico nacional en las cuencas de la parte sur de la macrozona Austral
e Isla de Pascua, SIT No. 444, https://snia.mop.gob.cl/repositoriodga/handle/20.500.13000/7043 (last access: 13 June 2022), 2019b.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global
Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a
daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Dorman, J. and Sellers, P.: A global climatology of albedo, roughness length
and stomatal resistance for atmospheric general circulation models as represented by the simple biosphere model (SiB), J. Appl. Meteorol., 28,
833–855, 1989.
Ducoudré, N. I., Laval, K., and Perrier, A.: SECHIBA, a New Set of
Parameterizations of the Hydrologic Exchanges at the Land-Atmosphere Interface within the LMD Atmospheric General Circulation Model, J. Climate,
6, 248–273, https://doi.org/10.1175/1520-0442(1993)006<0248:sansop>2.0.co;2, 1993.
Fang, H., Wei, S., Jiang, C., and Scipal, K.: Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method, Remote Sens. Environ., 124, 610–621,
https://doi.org/10.1016/j.rse.2012.06.013, 2012.
Fang, H., Jiang, C., Li, W., Wei, S., Baret, F., Chen, J. M., Garcia-Haro,
J., Liang, S., Liu, R., Myneni, R. B., Pinty, B., Xiao, Z., and Zhu, Z.:
Characterization and intercomparison of global moderate resolution leaf area
index (LAI) products: Analysis of climatologies and theoretical uncertainties, J. Geophys. Res.-Biogeo., 118, 529–548,
https://doi.org/10.1002/jgrg.20051, 2013.
Foglia, L., Hill, M. C., Mehl, S. W., and Burlando, P.: Sensitivity analysis,
calibration, and testing of a distributed hydrological model using
error-based weighting and one objective function, Water Resour. Res., 45,
W06427, https://doi.org/10.1029/2008WR007255, 2009.
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.
Friedman, J. H.: Multivariate Adaptive Regression Splines, Ann. Stat., 19, 590–606, https://doi.org/10.1214/aos/1176347963, 1991.
Gates, D. M. and Evans, L. T.: Environmental Control of Plant Growth, Bull.
Torrey Bot. Club, 91, 235, https://doi.org/10.2307/2483533, 1964.
Gharari, S., Clark, M. P., Mizukami, N., Wong, J. S., Pietroniro, A., and
Wheater, H. S.: Improving the representation of subsurface water movement in
land models, J. Hydrometeorol., 20, 2401–2418, https://doi.org/10.1175/JHM-D-19-0108.1, 2019.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019.
Göhler, M., Mai, J., and Cuntz, M.: Use of eigendecomposition in a parameter sensitivity analysis of the Community Land Model, J. Geophys. Res.-Biogeo., 118, 904–921, https://doi.org/10.1002/jgrg.20072, 2013.
Gou, J., Miao, C., Duan, Q., Tang, Q., Di, Z., Liao, W., Wu, J., and Zhou, R.: Sensitivity Analysis-Based Automatic Parameter Calibration of the VIC
Model for Streamflow Simulations Over China, Water Resour. Res., 56, 1–19, https://doi.org/10.1029/2019WR025968, 2020.
Gou, J., Miao, C., Samaniego, L., Xiao, M., Wu, J., and Guo, X.: CNRD v1.0: A
High-Quality Natural Runoff Dataset for Hydrological and Climate Studies in
China, B. Am. Meteorol. Soc., 102, E929–E947, https://doi.org/10.1175/BAMS-D-20-0094.1, 2021.
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, 2009.
Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y.: The
variable infiltration capacity model version 5 (VIC-5): Infrastructure
improvements for new applications and reproducibility, Geosci. Model Dev.,
11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, 2018.
Hengl, T., De Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda,
M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hogue, T. S., Bastidas, L., Gupta, H., Sorooshian, S., Mitchell, K., and
Emmerich, W.: Evaluation and transferability of the Noah land surface model
in semiarid environments, J. Hydrometeorol., 6, 68–84, 2005.
Hornberger, G. M. and Spear, R. C.: Approach to the preliminary analysis of
environmental systems, J. Environ. Manage., 12, 7–18, 1981.
Hou, Z., Huang, M., Leung, L. R., Lin, G., and Ricciuto, D. M.: Sensitivity
of surface flux simulations to hydrologic parameters based on an uncertainty
quantification framework applied to the Community Land Model, J. Geophys.
Res.-Atmos., 117, 1–18, https://doi.org/10.1029/2012JD017521, 2012.
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J.,
Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret,
U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013.
Huang, M. and Liang, X.: On the assessment of the impact of reducing parameters and identification of parameter uncertainties for a hydrologic
model with applications to ungauged basins, J. Hydrol., 320, 37–61,
https://doi.org/10.1016/j.jhydrol.2005.07.010, 2006.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Syst., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Liang, X. and Guo, J.: Intercomparison of land-surface parameterization
schemes: sensitivity of surface energy and water fluxes to model parameters,
J. Hydrol., 279, 182–209, https://doi.org/10.1016/S0022-1694(03)00168-9, 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., 99, 14415–14428,
https://doi.org/10.1029/94jd00483, 1994.
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture
parameterization of the VIC-2L model: Evaluation and modification, Global
Planet. Change, 13, 195–206, https://doi.org/10.1016/0921-8181(95)00046-1, 1996.
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Modeling ground heat flux in
land surface parameterization schemes, J. Geophys. Res.-Atmos., 104,
9581–9600, https://doi.org/10.1029/98JD02307, 1999.
Lilhare, R., Pokorny, S., Déry, S. J., Stadnyk, T. A., and Koenig, K. A.:
Sensitivity analysis and uncertainty assessment in water budgets simulated by the variable infiltration capacity model for Canadian subarctic watersheds, Hydrol. Process., 34, 2057–2075, https://doi.org/10.1002/hyp.13711, 2020.
Lohmann, D., Raschke, E., Nijssen, B., and Lettenmaier, D. P.: Hydrologie
à l'échelle régionale: II. Application du modèle VIC-2L sur
la rivière Weser, Allemagne, Hydrolog. Sci. J., 43, 143–158,
https://doi.org/10.1080/02626669809492108, 1998.
Marks, D. and Dozier, J.: Climate and Energy Exchange at the Snow Surface in
the Alpine Region of the Sierra Nevada 2. Snow Cover Energy Balance, Water
Resour. Res., 28, 3043–3054, 1992.
Massoud, E. C., Xu, C., Fisher, R. A., Knox, R. G., Walker, A. P., Serbin, S. P., Christoffersen, B. O., Holm, J. A., Kueppers, L. M., Ricciuto, D. M., Wei, L., Johnson, D. J., Chambers, J. Q., Koven, C. D., McDowell, N. G., and
Vrugt, J. A.: Identification of key parameters controlling demographically
structured vegetation dynamics in a land surface model: CLM4.5(FATES),
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, 2019.
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R.,
Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi,
J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology,
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017,
2017.
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. A. and Guse, B.: Climate change impacts model parameter sensitivity-implications for calibration strategy and model diagnostic
evaluation, Hydrol. Earth Syst. Sci., 25, 1307–1332,
https://doi.org/10.5194/hess-25-1307-2021, 2021.
Melsen, L. A., 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., Clark, M. P., Barlage, M., Rajagopalan, B., Samaniego, L.,
Abramowitz, G., and Gupta, H.: Are we unnecessarily constraining the agility
of complex process-based models?, Water Resour. Res., 51, 716–728, https://doi.org/10.1002/2014WR015820, 2015a.
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, 2015b.
Misirli, F., Gupta, H. V, Sorooshian, S., and Thiemann, M.: Bayesian recursive estimation of parameter and output uncertainty for watershed models, in: vol. 6, edited by: Duan, Q., Gupta, H. V., Sorooshian, S.,
Rousseau, A. N., and Turcotte, R., American Geophysical Union, Washington, DC, 113–124, https://doi.org/10.1029/WS006, 2003.
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C.,
Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins, W.,
Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H., Entin,
J. K., Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B. H., and Bailey, A.
A.: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90, https://doi.org/10.1029/2003JD003823, 2004.
Mizukami, N., Clark, M., Slater, A., Brekke, L., Elsner, M., Arnold, J., Gangopadhyay, S., Clark, M. P., Slater, A. G., Brekke, L. D., Elsner, M. M., Arnold, J. R., Gangopadhyay, S., Clark, M., Slater, A., Brekke, L., Elsner, M., and Arnold, J.: Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions, J. Hydrometeorol., 15, 474–488, https://doi.org/10.1175/JHM-D-13-036.1, 2014.
Mizukami, N., Clark, M. P., Gutmann, E. D., Mendoza, P. A., Newman, A. J.,
Nijssen, B., Livneh, B., Hay, L. E., Arnold, J. R., and Brekke, L. D.:
Implications of the Methodological Choices for Hydrologic Portrayals of
Climate Change over the Contiguous United States: Statistically Downscaled
Forcing Data and Hydrologic Models, J. Hydrometeorol., 17, 73–98,
https://doi.org/10.1175/JHM-D-14-0187.1, 2016.
Montgomery, D. C.: Design and analysis of experiments, Wiley, ISBN 9781118146927, 1991.
Morris, M. D.: Factorial sampling plans for preliminary computational
experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991.
Myneni, R. B., Ramakrishna, R., Nemani, R., and Running, S. W.: Estimation of
global leaf area index and absorbed par using radiative transfer models, IEEE T. Geosci. Remote, 35, 1380–1393, https://doi.org/10.1109/36.649788, 1997.
Nash, J. and Sutcliffe, J.: River ?ow 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.
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., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Pi, H. and Peterson, C.: Finding the Embedding Dimension and Variable
Dependencies in Time Series, Neural Comput., 6, 509–520,
https://doi.org/10.1162/neco.1994.6.3.509, 1994.
Pokhrel, P. and Gupta, H. V.: On the use of spatial regularization strategies to improve calibration of distributed watershed models, Water Resour. Res., 46, W01505, https://doi.org/10.1029/2009WR008066, 2010.
Prihodko, L., Denning, A. S. S., Hanan, N. P. P., Baker, I., and Davis, K.:
Sensitivity, uncertainty and time dependence of parameters in a complex land
surface model, Agr. Forest Meteorol., 148, 268–287,
https://doi.org/10.1016/j.agrformet.2007.08.006, 2008.
Rakovec, O., Hill, M. C., Clark, M. P., Weerts, A. H., Teuling, A. J., and
Uijlenhoet, R.: Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 1–18, https://doi.org/10.1002/2013WR014063, 2014.
Rawls, W. J., Ahuja, L. R., Brakensiek, D. L., and Shirmohammadi, A.: Infiltration and soil water movement, in: Handbook of hydrology, edited by:
Maidment, D. R., McGraw-Hill Inc., New York, 5.1–5.51, ISBN 0070397325, 1992.
Razavi, S. and Gupta, H. V.: What do we mean by sensitivity analysis? The
need for comprehensive characterization of “global” sensitivity in Earth
and Environmental systems models, Water Resour. Res., 51, 3070–3092,
https://doi.org/10.1002/2014WR016527, 2015.
Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust, and
efficient global sensitivity analysis: 2. Application, Water Resour. Res., 52, 423–439, https://doi.org/10.1002/2015WR017558, 2016.
Reba, M. L., Marks, D., Link, T. E., Pomeroy, J., and Winstral, A.: Sensitivity of model parameterizations for simulated latent heat flux at the
snow surface for complex mountain sites, Hydrol. Process., 28, 868–881,
https://doi.org/10.1002/hyp.9619, 2014.
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: Estimating soil water-holding capacities by linking the Food and Agriculture Organization
soil map of the world with global pedon databases and continuous pedotransfer functions, Water Resour. Res., 36, 3653–3662, https://doi.org/10.1029/2000WR900130, 2000.
Rosero, E., Yang, Z.-L., Wagener, T., Gulden, L. E., Yatheendradas, S., and
Niu, G.-Y.: Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface
model over transition zones during the warm season, J. Geophys. Res., 115, 1–21, https://doi.org/10.1029/2009JD012035, 2010.
Rosolem, R., Gupta, H. V., Shuttleworth, W. J., Zeng, X., and de Gonçalves, L. G. G.: A fully multiple-criteria implementation of the
Sobol' method for parameter sensitivity analysis, J. Geophys. Res., 117, 1–18, https://doi.org/10.1029/2011JD016355, 2012.
Roudier, P., Andersson, J. C. M., Donnelly, C., Feyen, L., Greuell, W., and
Ludwig, F.: Projections of future floods and hydrological droughts in Europe
under a +2 ∘C global warming, Climatic Change, 135, 341–355,
https://doi.org/10.1007/s10584-015-1570-4, 2016.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli,
D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis The Primer,
https://doi.org/10.1002/9780470725184, 2008.
Schmied, H. M., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T.,
Flörke, M., and Döll, P.: Sensitivity of simulated global-scale
freshwater fluxes and storages to input data, hydrological model structure,
human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, 2014.
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, 1–24, https://doi.org/10.1029/2020wr028435, 2021.
Shi, X., Wood, A. W., and Lettenmaier, D. P.: How essentialis hydrologic model calibration to seasonal stream flow forecasting?, J. Hydrometeorol., 9, 1350–1363, https://doi.org/10.1175/2008JHM1001.1, 2008.
Shukla, S., Sheffield, J., Wood, E. F., and Lettenmaier, D. P.: On the sources of global land surface hydrologic predictability, Hydrol. Earth Syst. Sci., 17, 2781–2796, https://doi.org/10.5194/hess-17-2781-2013, 2013.
Sobol', I. M.: Global sensitivity indices for nonlinear mathematical models
and their Monte Carlo estimates, Math. Comput. Simul., 55, 271–280, 2001.
Sobol', I. M. and Kucherenko, S.: A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices, Comput. Phys. Commun., 181, 1212–1217, https://doi.org/10.1016/j.cpc.2010.03.006, 2010.
Tang, G., Clark, M. P., Papalexiou, S. M., Newman, A. J., Wood, A. W., Brunet, D., and Whitfield, P. H.: Emdna: An ensemble meteorological dataset
for north america, Earth Syst. Sci. Data, 13, 3337–3362,
https://doi.org/10.5194/essd-13-3337-2021, 2021.
Tian, S., Tregoning, P., Renzullo, L. J., van Dijk, A. I. J. M., Walker, J.
P., Pauwels, V. R. N., and Allgeyer, S.: Improved water balance component estimates through joint assimilation of GRACE water storage and SMOS soil
moisture retrievals, Water Resour. Res., 53, 1820–1840,
https://doi.org/10.1002/2016WR019641, 2017.
Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou,
L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Häme, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., and Myneni, R. B.: Multiscale analysis and validation of the MODIS LAI product I. Uncertainty assessment,
Remote Sens. Environ., 83, 414–430, https://doi.org/10.1016/S0034-4257(02)00047-0,
2002.
Todini, E.: The ARNO rainfall-runoff model, J. Hydrol., 175, 339–382, 1996.
Tonkin, M. J. and Doherty, J.: A hybrid regularized inversion methodology
for highly parameterized environmental models, Water Resour. Res., 41, 1–16, https://doi.org/10.1029/2005WR003995, 2005.
UNEP: World atlas of desertification, ed. 2, edited by: Middleton, N. and
Thomas, D., Arnold, Hodder Headline, PLC, https://wedocs.unep.org/20.500.11822/30300 (last access: 13 June 2022), 1997.
USACE: Snow hydrology: Summary report of the snow investigations, North
Pacific Division, Corps of Engineers, US Army, https://usace.contentdm.oclc.org/digital/collection/p266001coll1/id/4172/ (last access: 13 June 2022), 1956.
USGS: EarthExplorer, USGS [data set], https://earthexplorer.usgs.gov/, last access: 13 June 2022.
Vano, J. A. and Lettenmaier, D. P.: A sensitivity-based approach to evaluating future changes in Colorado River discharge, Climatic Change, 122,
621–634, https://doi.org/10.1007/s10584-013-1023-x, 2014.
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, 189–208, https://doi.org/10.1007/978-3-030-56901-3_9, 2021.
Verbist, K., Santibañez, F., Gabriels, D., and Soto, G.: Documento
Técnico No. 25, Atlas de Zonas Áridas de América Latina y el
Caribe, https://unesdoc.unesco.org/ark:/48223/pf0000216333 (last access: 13 June 2022), 2010.
Vicuña, S., Vargas, X., Boisier, J. P., Mendoza, P. A., Gómez, T.,
Vásquez, N., and Cepeda, J.: Impacts of Climate Change on Water Resources
in Chile, in: Water Resources of Chile, vol. 13, Springer, 347–363, https://doi.org/10.1007/978-3-030-56901-3_19, 2021.
Vořechovský, M.: Hierarchical Refinement of Latin Hypercube Samples,
Comput. Civ. Infrastruct. Eng., 30, 394–411, https://doi.org/10.1111/mice.12088, 2015.
Wi, S., Ray, P., Demaria, E. M. C., Steinschneider, S., and Brown, C.: A
user-friendly software package for VIC hydrologic model development, Environ. Model. Softw., 98, 35–53, https://doi.org/10.1016/j.envsoft.2017.09.006, 2017.
Wood, A. W., Kumar, A., and Lettenmaier, D. P.: A retrospective assessment of
National Centers for Environmental prediction climate model-based ensemble
hydrologic forecasting in the western United States, J. Geophys. Res.-Atmos., 110, 1–16, https://doi.org/10.1029/2004JD004508, 2005.
Wood, E. F., Lettenmaier, D. P.., and Zartarian, V. G.: A Land-Surface Hydrology Parameterization With Subgrid Variability for General Circulation Models, J. Geophys. Res., 97, 2717–2728, https://doi.org/10.1029/91JD01786, 1992.
Woodward, J. L.: Estimating the Flammable Mass of a Vapor Cloud, John Wiley & Sons, Inc., Hoboken, NJ, USA, https://doi.org/10.1002/9780470935361, 1999.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo,
L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V.,
Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048, 2012.
Xie, Z., Yuan, F., Duan, Q., Zheng, J., Liang, M., and Chen, F.: Regional
parameter estimation of the VIC land surface model: Methodology and application to river basins in China, J. Hydrometeorol., 8, 447–468,
https://doi.org/10.1175/JHM568.1, 2007.
Yadav, M., Wagener, T., and Gupta, H.: Regionalization of constraints on
expected watershed response behavior for improved predictions in ungauged
basins, Adv. Water Resour., 30, 1756–1774, https://doi.org/10.1016/j.advwatres.2007.01.005, 2007.
Yang, Y., Pan, M., Beck, H. E., Fisher, C. K., Beighley, R. E., Kao, S. C.,
Hong, Y., and Wood, E. F.: In Quest of Calibration Density and Consistency in
Hydrologic Modeling: Distributed Parameter Calibration against Streamflow
Characteristics, Water Resour. Res., 55, 7784–7803, https://doi.org/10.1029/2018WR024178, 2019.
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C.
H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global reach-level 3-hourly
river flood reanalysis (1980–2019), B. Am. Meteorol. Soc., 102, E2086–E2105, https://doi.org/10.1175/BAMS-D-20-0057.1, 2021.
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.,
116, 1–16, https://doi.org/10.1029/2010JD015140, 2011.
Yeste, P., García-Valdecasas Ojeda, M., Gámiz-Fortis, S. R.,
Castro-Díez, Y., and Esteban-Parra, M. J.: Integrated sensitivity analysis of a macroscale hydrologic model in the north of the Iberian Peninsula, J. Hydrol., 590, 125230, https://doi.org/10.1016/j.jhydrol.2020.125230, 2020.
Zegers, G., Mendoza, P. A., Garces, A., and Montserrat, S.: Sensitivity and
identifiability of rheological parameters in debris flow modeling, Nat.
Hazards Earth Syst. Sci., 20, 1919–1930, https://doi.org/10.5194/nhess-20-1919-2020,
2020.
Zhao, Q., Ye, B., Ding, Y., Zhang, S., Yi, S., Wang, J., Shangguan, D., Zhao, C., and Han, H.: Coupling a glacier melt model to the Variable Infiltration Capacity (VIC) model for hydrological modeling in north-western China, Environ. Earth Sci., 68, 87–101, https://doi.org/10.1007/s12665-012-1718-8, 2013.
Zhao, R.-J., Zuang, Y.-L., Fang, L.-R., Liu, X.-R., and Zhang, Q.-S.: Xinanjiang Model, IAHS-AISH Publ., 129, 351–356, 1980.
Zink, M., Kumar, R., Cuntz, M., and Samaniego, L.: A high-resolution dataset
of water fluxes and states for Germany accounting for parametric uncertainty, Hydrol. Earth Syst. Sci., 21, 1769–1790, https://doi.org/10.5194/hess-21-1769-2017, 2017.
Short summary
This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly used macroscale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.
This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly...
