Debates-the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science-Reference-Cited by-同舟云学术

Debates-the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science

Published:2014-06 Issue:6 Volume:50 Page:5351-5359
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Gupta Hoshin V.¹,Nearing Grey S.²³

Affiliation:

1. Department of Hydrology & Water Resources; The University of Arizona; Tucson Arizona USA

2. Hydrological Sciences Laboratory; NASA Goddard Space Flight Center; Greenbelt Maryland USA

3. Science Applications International Corporation; McLean Virginia USA

Funder

Andy Pitman and the Australian Research Council through the Centre of Excellence for Climate System Science

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Reference28 articles.

1. The future of distributed models: Model calibration and uncertainty prediction;Beven;Hydrol. Processes,1992

2. On red herrings and real herrings: Disinformation and information in hydrological inference;Beven;Hydrological Processes,2011

3. Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation;Bulygina;Water Resour. Res.,2009

4. How Bayesian data assimilation can be used to estimate the mathematical structure of a model;Bulygina;Stochastic Environ. Res. Risk Assess.,2010

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