Articles | Volume 15, issue 11
https://doi.org/10.5194/hess-15-3327-2011
https://doi.org/10.5194/hess-15-3327-2011
Research article
 | 
04 Nov 2011
Research article |  | 04 Nov 2011

Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space

D. Brochero, F. Anctil, and C. Gagné

Related subject area

Subject: Global hydrology | Techniques and Approaches: Mathematical applications
Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed
Fadji Z. Maina, Alan Rhoades, Erica R. Siirila-Woodburn, and Peter-James Dennedy-Frank
Hydrol. Earth Syst. Sci., 26, 3589–3609, https://doi.org/10.5194/hess-26-3589-2022,https://doi.org/10.5194/hess-26-3589-2022, 2022
Short summary
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar
Hydrol. Earth Syst. Sci., 26, 3263–3297, https://doi.org/10.5194/hess-26-3263-2022,https://doi.org/10.5194/hess-26-3263-2022, 2022
Short summary
Coherence of global hydroclimate classification systems
Kathryn L. McCurley Pisarello and James W. Jawitz
Hydrol. Earth Syst. Sci., 25, 6173–6183, https://doi.org/10.5194/hess-25-6173-2021,https://doi.org/10.5194/hess-25-6173-2021, 2021
Short summary
Design flood estimation for global river networks based on machine learning models
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021,https://doi.org/10.5194/hess-25-5981-2021, 2021
Short summary
Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021,https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary

Cited articles

Alpaydin, E.: Introduction to Machine Learning. Adaptive Computation and Machine Learning, 2nd Edn., The MIT Press, Cambridge, 2010.
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.
Bishop, C. M.: Pattern Recognition and Machine Learning (Information Science and Statistics), ISBN0387310738, Springer-Verlag New York, Inc., Secaucus, NJ, USA,2006.
Boucher, M.-A., Perreault, L., and Anctil, F.: Tools for the assessment of hydrological ensemble forecasts obtained by neural networks, J. Hydroinform., 11, 297–307, https://doi.org/10.2166/hydro.2009.037, 2009.
Download