The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Catchment Classification and Hydrologic Similarity
Abstract
Hydrology does not yet possess a generally agreed upon catchment classification system. Such a classification framework should provide a mapping of landscape form and hydro‐climatic conditions on catchment function (including partition, storage, and release of water), while explicitly accounting for uncertainty and for variability at multiple temporal and spatial scales. This framework would provide an organizing principle, create a common language, guide modeling and measurement efforts, and provide constraints on predictions in ungauged basins, as well as on estimates of environmental change impacts. In this article, we (i) review existing approaches to define hydrologic similarity and to catchment classification; (ii) discuss outstanding components or characteristics that should be included in a classification scheme; and (iii) provide a basic framework for catchment classification as a starting point for further analysis. Possible metrics to describe form, hydro‐climate, and function are suggested and discussed. We close the discussion with a list of requirements for the classification framework and open questions that require addressing in order to fully implement it. Open questions include: How can we best represent characteristics of form and hydro‐climatic conditions? How does this representation change with spatial and temporal scale? What functions (partition, storage, and release) are relevant at what spatial and temporal scale? At what scale do internal structure and heterogeneity become important and need to be considered?
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Cites
BEVEN, K. J., & KIRKBY, M. J. (1979) · Hydrological Sciences Bulletin · doi:10.1080/02626667909491834 · Cited by 6,670
Beven, K., & Freer, J. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00421-8 · Cited by 2,189
Scanlon, T. M., Caylor, K. K., Manfreda, S., Levin, S. A., & Rodriguez-Iturbe, I. (2005) · Advances in Water Resources · doi:10.1016/j.advwatres.2004.10.014 · Cited by 126
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What Role Does Hydrological Science Play in the Age of Machine Learning?
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., & Gupta, H. V. (2021) · Water Resources Research · doi:10.1029/2020wr028091 · Cited by 894
Chien, H., Yeh, P. J.-F., & Knouft, J. H. (2013) · Journal of Hydrology · doi:10.1016/j.jhydrol.2013.03.026 · Cited by 202
Guide for using green infrastructure in urban environments for stormwater management
McFarland, A. R., Larsen, L., Yeshitela, K., Engida, A. N., & Love, N. G. (2019) · Environmental Science: Water Research & Technology · doi:10.1039/c8ew00498f · Cited by 107
Rice, J. S., Emanuel, R. E., Vose, J. M., & Nelson, S. A. C. (2015) · Water Resources Research · doi:10.1002/2014wr016367 · Cited by 95
Fanelli, R., Prestegaard, K., & Palmer, M. (2017) · Hydrological Processes · doi:10.1002/hyp.11266 · Cited by 48
Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records
Clasing, R., Muñoz, E., Arumí, J. L., & Parra, V. (2023) · Water · doi:10.3390/w15213813 · Cited by 16
Metadata
- Authors
- Wagener, T., Sivapalan, M., Troch, P., & Woods, R.
- Venue
- Geography Compass
- Publisher
- Wiley
- Published
- 2007-05-25
- Volume
- 1
- Issue
- 4
- Pages
- 901-931
- ISSN
- 1749-8198 (ISSN-L)
- OpenAlex
- W2126240438
- Cited by
- 749
- References
- 3
- FWCI
- 29.96
- Type
- journal article
- Language
- en
- Open access
- closed
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