Articles | Volume 24, issue 12
https://doi.org/10.5194/hess-24-5835-2020
© Author(s) 2020. 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-24-5835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2020
Research article |
| 08 Dec 2020
| 08 Dec 2020
Simultaneously determining global sensitivities of model parameters and model structure
Department Civil and Environmental Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
James R. Craig
Department Civil and Environmental Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
Bryan A. Tolson
Department Civil and Environmental Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
Related authors
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 5005–5029, https://doi.org/10.5194/hess-29-5005-2025, https://doi.org/10.5194/hess-29-5005-2025, 2025
Short summary
Short summary
Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Samah Larabi, Juliane Mai, Markus Schnorbus, Bryan A. Tolson, and Francis Zwiers
Hydrol. Earth Syst. Sci., 27, 3241–3263, https://doi.org/10.5194/hess-27-3241-2023, https://doi.org/10.5194/hess-27-3241-2023, 2023
Short summary
Short summary
The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modeling domains. Here, using a large-scale Variable Infiltration Capacity (VIC) deployment, we show that watershed classification helps identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.
Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-69, https://doi.org/10.5194/hess-2023-69, 2023
Revised manuscript not accepted
Short summary
Short summary
A blended model allows multiple hydrologic processes to be represented in a single model, which allows for a model to achieve high performance without the need to modify its structure for different catchments. Here, we improve upon the initial blended version by testing more than 30 blended models in twelve catchments to improve the overall model performance. We validate our proposed, updated blended model version with independent catchments, and make this version available for open use.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
Short summary
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Michelle Viswanathan, Tobias K. D. Weber, Sebastian Gayler, Juliane Mai, and Thilo Streck
Biogeosciences, 19, 2187–2209, https://doi.org/10.5194/bg-19-2187-2022, https://doi.org/10.5194/bg-19-2187-2022, 2022
Short summary
Short summary
We analysed the evolution of model parameter uncertainty and prediction error as we updated parameters of a maize phenology model based on yearly observations, by sequentially applying Bayesian calibration. Although parameter uncertainty was reduced, prediction quality deteriorated when calibration and prediction data were from different maize ripening groups or temperature conditions. The study highlights that Bayesian methods should account for model limitations and inherent data structures.
Nicolas Gasset, Vincent Fortin, Milena Dimitrijevic, Marco Carrera, Bernard Bilodeau, Ryan Muncaster, Étienne Gaborit, Guy Roy, Nedka Pentcheva, Maxim Bulat, Xihong Wang, Radenko Pavlovic, Franck Lespinas, Dikra Khedhaouiria, and Juliane Mai
Hydrol. Earth Syst. Sci., 25, 4917–4945, https://doi.org/10.5194/hess-25-4917-2021, https://doi.org/10.5194/hess-25-4917-2021, 2021
Short summary
Short summary
In this paper, we highlight the importance of including land-data assimilation as well as offline precipitation analysis components in a regional reanalysis system. We also document the performance of the first multidecadal 10 km reanalysis performed with the GEM atmospheric model that can be used for seamless land-surface and hydrological modelling in North America. It is of particular interest for transboundary basins, as existing datasets often show discontinuities at the border.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 5005–5029, https://doi.org/10.5194/hess-29-5005-2025, https://doi.org/10.5194/hess-29-5005-2025, 2025
Short summary
Short summary
Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Robert Chlumsky, James R. Craig, and Bryan A. Tolson
Geosci. Model Dev., 18, 3387–3403, https://doi.org/10.5194/gmd-18-3387-2025, https://doi.org/10.5194/gmd-18-3387-2025, 2025
Short summary
Short summary
We aim to improve mapping of floods and present a new method for hydraulic modelling that uses a combination of novel geospatial analysis and existing hydraulic modelling approaches. This method is wrapped into a modelling software called Blackbird. We compared Blackbird with two other existing options for flood mapping and found that the Blackbird model outperformed both. The Blackbird model has the potential to support real-time and large-scale flood mapping applications in the future.
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
Short summary
Short summary
We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Samah Larabi, Juliane Mai, Markus Schnorbus, Bryan A. Tolson, and Francis Zwiers
Hydrol. Earth Syst. Sci., 27, 3241–3263, https://doi.org/10.5194/hess-27-3241-2023, https://doi.org/10.5194/hess-27-3241-2023, 2023
Short summary
Short summary
The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modeling domains. Here, using a large-scale Variable Infiltration Capacity (VIC) deployment, we show that watershed classification helps identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.
Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-69, https://doi.org/10.5194/hess-2023-69, 2023
Revised manuscript not accepted
Short summary
Short summary
A blended model allows multiple hydrologic processes to be represented in a single model, which allows for a model to achieve high performance without the need to modify its structure for different catchments. Here, we improve upon the initial blended version by testing more than 30 blended models in twelve catchments to improve the overall model performance. We validate our proposed, updated blended model version with independent catchments, and make this version available for open use.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
Short summary
Short summary
We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
Short summary
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Michelle Viswanathan, Tobias K. D. Weber, Sebastian Gayler, Juliane Mai, and Thilo Streck
Biogeosciences, 19, 2187–2209, https://doi.org/10.5194/bg-19-2187-2022, https://doi.org/10.5194/bg-19-2187-2022, 2022
Short summary
Short summary
We analysed the evolution of model parameter uncertainty and prediction error as we updated parameters of a maize phenology model based on yearly observations, by sequentially applying Bayesian calibration. Although parameter uncertainty was reduced, prediction quality deteriorated when calibration and prediction data were from different maize ripening groups or temperature conditions. The study highlights that Bayesian methods should account for model limitations and inherent data structures.
Nicolas Gasset, Vincent Fortin, Milena Dimitrijevic, Marco Carrera, Bernard Bilodeau, Ryan Muncaster, Étienne Gaborit, Guy Roy, Nedka Pentcheva, Maxim Bulat, Xihong Wang, Radenko Pavlovic, Franck Lespinas, Dikra Khedhaouiria, and Juliane Mai
Hydrol. Earth Syst. Sci., 25, 4917–4945, https://doi.org/10.5194/hess-25-4917-2021, https://doi.org/10.5194/hess-25-4917-2021, 2021
Short summary
Short summary
In this paper, we highlight the importance of including land-data assimilation as well as offline precipitation analysis components in a regional reanalysis system. We also document the performance of the first multidecadal 10 km reanalysis performed with the GEM atmospheric model that can be used for seamless land-surface and hydrological modelling in North America. It is of particular interest for transboundary basins, as existing datasets often show discontinuities at the border.
Olivia Carpino, Kristine Haynes, Ryan Connon, James Craig, Élise Devoie, and William Quinton
Hydrol. Earth Syst. Sci., 25, 3301–3317, https://doi.org/10.5194/hess-25-3301-2021, https://doi.org/10.5194/hess-25-3301-2021, 2021
Short summary
Short summary
This study demonstrates how climate warming in peatland-dominated regions of discontinuous permafrost is changing the form and function of the landscape. Key insights into the rates and patterns of such changes in the coming decades are provided through careful identification of land cover transitional stages and characterization of the hydrological and energy balance regimes for each stage.
Cited articles
Abily, M., Bertrand, N., Delestre, O., Gourbesville, P., and Duluc, C.-M.: Spatial Global
Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood
modelling, Environ. Model. Softw., 77, 183–195, 2016. a
Bajracharya, A., Awoye, H., Stadnyk, T., and Asadzadeh, M.: Time Variant Sensitivity Analysis of
Hydrological Model Parameters in a Cold Region Using Flow Signatures, Water, 12, 961,
https://doi.org/10.3390/w12040961, 2020. a
Bergström, S.: The HBV model, in: Computer
Models of Watershed Hydrology, edited by Singh, V., Water Resources Publications, Highlands Ranch, CO, USA, 443–476, 1995. a
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., and Hill, M. C.: Making the most out of a
hydrological model data set: Sensitivity analyses to open the model black-box, Water Resour. Res.,
53, 7933–7950, 2017. a
Campolongo,
F., Saltelli, A., and Cariboni, J.: From screening to quantitative sensitivity analysis. A
unified approach, Comput. Phys. Commun., 182, 978–988, 2011. a
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta,
H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A
modular framework to diagnose differences between hydrological models, Water Resour. Res., 44,
W00B02, https://doi.org/10.1029/2007WR006735, 2008. a, b, c
Clark, M. P., Kavetski, D.,
and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling,
Water Resour. Res., 47, 5468–16, 2011. a
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, 2015. a
Craig, J. R.: Raven: User's and Developer's Manual v3.0,
available at: http://raven.uwaterloo.ca/files/v3.0/RavenManual_v3.0.pdf, last access: 2 December 2020. a
Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, W.,
Jost, G., Lee, K., Mai, J., Serrer, M., Snowdon, A. P., Sgro, N., Shafii, M., and Tolson, B. A.:
Flexible watershed simulation with the Raven hydrological modelling framework,
Environ. Model. Softw., 129, 104728, https://doi.org/10.1016/j.envsoft.2020.104728, 2020. a, b, c, d, e
Cuntz, M., Mai, J., Zink, M., Thober, S., Kumar, R., Schäfer, D.,
Schrön, M., Craven, J., Rakovec, O., Spieler, D., Prykhodko, V., Dalmasso, G., Musuuza, J.,
Langenberg, B., Attinger, s., and Samaniego, L.: Computationally inexpensive identification of
noninformative model parameters by sequential screening, Water Resour. Res., 51, 6417–6441,
2015. a, b, c, d, e, f
Dai, H., Ye, M., Walker, A. P.,
and Chen, X.: A new process sensitivity index to identify important system processes under
process model and parametric uncertainty, Water Resour. Res., 53, 3476–3490, 2017. a
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, 2007. a
Dobler, C. and Pappenberger, F.: Global
sensitivity analyses for a complex hydrological model applied in an Alpine watershed,
Hydrol. Process., 27, 3922–3940, 2012. a
Evin, G., Thyer, M., and
Kavetski, D.: Comparison of joint versus postprocessor approaches for hydrological uncertainty
estimation accounting for error autocorrelation and heteroscedasticity, Water Resour. Res., 50,
1–26, 2014. a
Fenicia, F., Kavetski,
D., and Savenije, H. H. G.: Elements of a flexible approach for conceptual hydrological modeling:
1. Motivation and theoretical development, Water Resour. Res., 47, W11510,
https://doi.org/10.1029/2010wr010174, 2011. a
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst, L., and Wiberg, D.: Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008), IIASA, Laxenburg, Austria and FAO, Rome, Italy, 2008. a
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, 1–18, 2009. a
Freeze, R. A. and Harlan, R. L.: Blueprint for a
physically-based, digitally-simulated hydrologic response model, J. Hydrol., 9, 237–258, 1969. a
Friedl, M., Sulla-Menashe, D., Boston University and MODAPS SIPS, NASA: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid, NASA LP DAAC, https://doi.org/10.5067/MODIS/MCD12Q1.006, 2015. a
Green, W. H. and Ampt, G. A.: Studies on Soil
Physics, J. Agric. Sci., 4, 1–24, https://doi.org/10.1017/S0021859600001441, 1911. a
Gupta, H. V.,
Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards a comprehensive assessment of
model structural adequacy, Water Resour. Res., 48, W08301, https://doi.org/10.1029/2011WR011044, 2012. a
Haghnegahdar, A., Razavi, S., Yassin, F., and Wheater, H.: Multicriteria sensitivity analysis as
a diagnostic tool for understanding model behaviour and characterizing model uncertainty,
Hydrol. Process., 31, 4462–4476, 2017. a
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models, Hydrol. Earth Syst. Sci., 17, 5109–5125, https://doi.org/10.5194/hess-17-5109-2013, 2013. a
Homma, T. and Saltelli, A.: Importance measures
in global sensitivity analysis of nonlinear models, Reliab. Engin. Syst. Safe., 52, 1–17,
1996. a
Mai, J. and Craig, J. R.: julemai/PieShareDistribution: PieShareDistribution v1.0 (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.4300332, 2020. a
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb,
R. M., Payn, R. A., and LaFontaine, J. H.: PRMS-IV, the Precipitation-Runoff Modeling System,
Version 4., in: US Geological Survey Techniques and Methods, U.S. Department of the Interior, U.S. Geological Survey, Reston, Virginia, Book 6, chapt. B7, p. 158, 2015. a
McMillan, H., Freer, J., Pappenberger, F., Krueger, T., and Clark, M.: Impacts of uncertain river
flow data on rainfall-runoff model calibration and discharge predictions, Hydrol. Process.,
47, 1270–1284, 2010. a
McMillan, H.,
Krueger, T., and Freer, J.: Benchmarking observational uncertainties for hydrology: rainfall,
river discharge and water quality, Hydrol. Process., 26, 4078–4111, 2012. a
Morris, M. D.: Factorial sampling plans for preliminary
computational experiments, Technometrics, 33, 161–174, 1991. a
Perrin, C.,
Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow
simulation, J. Hydrol., 279, 275–289, 2003. a
Pfannerstill, M., Guse, B., Reusser, D., and Fohrer, N.: Process verification of a hydrological
model using a temporal parameter sensitivity analysis, Hydrol. Earth Syst. Sci., 19, 4365–4376,
https://doi.org/10.5194/hess-19-4365-2015, 2015. a
Pianosi, F. and Wagener, T.: A simple and
efficient method for global sensitivity analysis based on cumulative distribution functions,
Environ. Model. Softw., 67, 1–11, https://doi.org/10.1016/j.envsoft.2015.01.004, 2015. a, b
Pianosi, F. and Wagener, T.:
Distribution-based sensitivity analysis from a generic input-output sample,
Environ. Model. Softw., 108, 197–207, 2018. a
Quick, M. C. and Pipes, A.: U.B.C. WATERSHED
MODEL/Le modèle du bassin versant U.C.B, Hydrol. Sci. Bull., 22, 153–161,
https://doi.org/10.1080/02626667709491701, 1977. a
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, 409–426, 2014. a
Razavi, S. and Gupta, H. V.: A new
framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application,
Water Resour. Res., 52, 440–455, 2016a. a
Razavi, S. and Gupta, H. V.: A new
framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory,
Water Resour. Res., 52, 423–439, 2016b. a
Schürz, C., Hollosi, B., Matulla, C., Pressl, A., Ertl, T., Schulz,
K., and Mehdi, B.: A comprehensive sensitivity and uncertainty analysis for discharge and
nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions,
Hydrol. Earth Syst. Sci., 23, 1211–1244, https://doi.org/10.5194/hess-23-1211-2019, 2019. a, b, c
Stanfill,
B., Mielenz, H., Clifford, D., and Thorburn, P.: Simple approach to emulating complex computer
models for global sensitivity analysis, Environ. Model. Softw., 74, 140–155, 2015. a
Van Hoey, S., Seuntjens, P., van der Kwast, J., and Nopens, I.: A qualitative model structure
sensitivity analysis method to support model selection, J. Hydrol., 519, 3426–3435, 2014. a
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, 1992. a
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(3468 KB) - Full-text XML
- Corrigendum
-
Supplement
(310 KB) - BibTeX
- EndNote
