The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review
Abstract
Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information. This article is categorized under: Science of Water > Science of Water
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A manifesto for the equifinality thesis
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A decade of Predictions in Ungauged Basins (PUB)—a review
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., et al. (2013) · Hydrological Sciences Journal · doi:10.1080/02626667.2013.803183 · Cited by 1,295
SIVAPALAN, M., TAKEUCHI, K., FRANKS, S. W., GUPTA, V. K., KARAMBIRI, H., LAKSHMI, V., LIANG, X., McDONNELL, J. J., MENDIONDO, E. M., O'CONNELL, P. E., et al. (2003) · Hydrological Sciences Journal · doi:10.1623/hysj.48.6.857.51421 · Cited by 1,284
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S. (2019) · Water Resources Research · doi:10.1029/2019wr026065 · Cited by 990
Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data
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Interpolation of Spatial Data: Some Theory for Kriging
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Catchment Classification and Hydrologic Similarity
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Regionalisation of catchment model parameters
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Cited by
Feng, D., Lawson, K., & Shen, C. (2021) · Geophysical Research Letters · doi:10.1029/2021gl092999 · Cited by 162
Metadata
- Authors
- Guo, Y., Zhang, Y., Zhang, L., & Wang, Z.
- Venue
- WIREs Water
- Publisher
- Wiley
- Published
- 2020-10-19
- Volume
- 8
- Issue
- 1
- ISSN
- 2049-1948 (ISSN-L)
- OpenAlex
- W3094518900
- Cited by
- 336
- References
- 95
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- closed
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