The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.
Catchment scale hydrological modelling: A review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications
Open accessAbstract
In catchment hydrology, it is in practice impossible to measure everything we would like to know about the hydrological system, mainly due to high catchment heterogeneity and the limitations of measurement techniques. These limitations and the need to extrapolate information from the available measurements in both space and time initiated the application of hydrological models. However, hydrological models suffer from uncertainty in their predictions, which reduces applicability of and confidence in such models. In this review, we summarise the different classifications of hydrological model types, and discuss relative advantages and disadvantages of each type of model. In addition, we summarise established model calibration processes and discuss the sources of uncertainty that affect model predictions. We summarise different methods to quantify uncertainty in the model predictions that could sit well within a model evaluation framework. And, finally, some recent developments in hydrological modelling are reviewed. KEYWORDS:
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River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.08.003 · Cited by 6,758
The future of distributed models: model calibration and uncertainty prediction
Beven, K., & Binley, A. (1992) · Hydrological Processes · doi:10.1002/hyp.3360060305 · Cited by 4,527
Computer Models of Watershed Hydrology
Singh, V. P. (1995) · Medical Entomology and Zoology · Cited by 3,273
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
Helton, J. C., & Davis, F. J. (2003) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(03)00058-9 · Cited by 2,688
A manifesto for the equifinality thesis
Beven, K. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2005.07.007 · Cited by 2,581
Making best use of model evaluations to compute sensitivity indices
Saltelli, A. (2002) · Computer Physics Communications · doi:10.1016/s0010-4655(02)00280-1 · Cited by 2,228
Beven, K., & Freer, J. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00421-8 · Cited by 2,189
Fowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007) · International Journal of Climatology · doi:10.1002/joc.1556 · Cited by 2,158
Cited by
Chanapathi, T., & Thatikonda, S. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137736 · Cited by 153
Metadata
- Venue
- Global NEST Journal
- Publisher
- University of the Aegean
- Published
- 2013-04-30
- Volume
- 13
- Issue
- 3
- Pages
- 193-214
- ISSN
- 1790-7632 (ISSN-L)
- 2241-777X
- OpenAlex
- W180764761
- Cited by
- 312
- References
- 111
- FWCI
- 19.46
- Type
- journal article
- Language
- en
- Open access
- bronze
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