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Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4685-2024
https://doi.org/10.5194/hess-28-4685-2024
Research article
 | 
28 Oct 2024
Research article |  | 28 Oct 2024

Simulation-based inference for parameter estimation of complex watershed simulators

Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon

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Using simulation-based inference to determine the parameters of an integrated hydrologic model: a case study from the upper Colorado River basin
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345,https://doi.org/10.5194/hess-2022-345, 2022
Publication in HESS not foreseen
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Cited articles

Alsing, J. and Wandelt, B.: Nuisance Hardened Data Compression for Fast Likelihood-Free Inference, Mon. Not. R. Astron. Soc., 488, 5093–5103, https://doi.org/10.1093/mnras/stz1900, 2019. 
Alsing, J., Charnock, T., Feeney, S., and Wandelt, B.: Fast likelihood-free cosmology with neural density estimators and active learning, Mon. Not. R. Astron. Soc., 488, 4440–458, https://doi.org/10.1093/mnras/stz1960, 2019. 
Bastidas, L., Gupta, H., Sorooshian, S., Shuttleworth, W., and Yang, Z.-L.: Sensitivity analysis of a land surface scheme using multicriteria methods, J. Geophys. Res., 104, 19481–19490, https://doi.org/10.1029/1999JD900155, 1999. 
Beven, K.: Parameter Estimation and Predictive Uncertainty, in: Rainfall‐Runoff Modelling, John Wiley & Sons, Ltd, 231–287, https://doi.org/10.1002/9781119951001.ch7, 2012. 
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992. 
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and...
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