Using maximum likelihood to derive various distance-based goodness-of-fit indicators for hydrologic modeling assessment-Reference-Cited by-同舟云学术

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Using maximum likelihood to derive various distance-based goodness-of-fit indicators for hydrologic modeling assessment

Published:2017-12-30 Issue:4 Volume:32 Page:949-966
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Cheng Qin-Bo^ORCID,Chen Xi,Xu Chong-Yu,Zhang Zhi-Cai,Reinhardt-Imjela Christian,Schulte Achim

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

National Key R&D Program of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://link.springer.com/article/10.1007/s00477-017-1507-8/fulltext.html

Reference61 articles.

1. Andréassian V, Le Moine N, Perrin C, Ramos MH, Oudin L, Mathevet T, Lerat J, Berthet L (2012) All that glitters is not gold: the case of calibrating hydrological models. Hydrol Process 26:2206–2210

2. Baratti R, Cannas B, Fanni A, Pintus M, Sechi GM, Toreno N (2003) River flow forecast for reservoir management for neural networks. Neurocomputing 55:421–437

3. Bates BC, Campbell EP (2001) A Markov Chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling. Water Resour Res 37(4):937–947

4. Bennett ND, Croke BFW, Guariso G, Guillaume JHA, Hamilton SH, Jakeman AJ, Marsili-Libelli S, Newham LTH, Norton JP, Perrin C, Pierce SA, Robson B, Seppelt R, Voinov AA, Fath BD, Andreassian V (2013) Characterising performance of environmental models. Environ Model Softw 40:1–20

5. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298

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