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Prediction in ungauged basins: a grand challenge for theoretical hydrology
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“Many rivers remain ungauged, leaving significant data gaps, while estimating discharge at such locations, known as the regionalization problem, remains a grand challenge in hydrology9–11.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [9]
In the citing paper
“Many rivers remain ungauged, leaving significant data gaps, while estimating discharge at such locations, known as the regionalization problem, remains a grand challenge in hydrology9–11.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [9]
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Research works printed but not linked: 19
- 10.5194/hess-4-203-2000 · DOI · Paper page
- 10.1098/rspa.2002.0986 · DOI · Paper page
- Budyko MI, 1974, Climate and Life
- Capra F, 1996, The Web of Life: A New Scientific Understanding of Living Systems
- 10.1029/WR022i09Sp0046S · DOI · Paper page
- Dunne T, 1998, Hydrologic Sciences: Taking Stock and Looking Ahead
- 10.1017/CBO9780511535680 · doi:10.1017/cbo9780511535680
- 10.1016/S0960-0779(03)00048-1 · doi:10.1016/s0960-0779(03)00048-1
- 10.1017/CBO9780511551864.005 · doi:10.1017/cbo9780511551864.005
- 10.1063/1.1522164 · DOI · Paper page
- 10.1016/0022-1694(83)90208-1 · DOI · Paper page
- Lovejoy S, 1995, New Uncertainty Concepts in Hydrology and Water Resources, 62
- McMahon TA, 1992, Global Runoff — Continental Comparisons of Annual Flows and Peak Discharges
- National Research Council, 1991, Opportunities in the Hydrologic Sciences
- 10.1016/S0309-1708(99)00005-6 · DOI · Paper page
- 10.1029/2000WR900066 · DOI
- 10.1103/PhysRevE.61.2418 · DOI
- 10.1002/hyp.5109 · doi:10.1002/hyp.5109
- 10.1002/hyp.1425 · DOI · Paper page
Cited by
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114
Ensemble predictions of runoff in ungauged catchments
McIntyre, N., Lee, H., Wheater, H., Young, A., & Wagener, T. (2005) · Water Resources Research · doi:10.1029/2005wr004289 · Cited by 371
Hybrid forecasting: blending climate predictions with AI models
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., et al. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-1865-2023 · Cited by 281
Feng, D., Beck, H., Lawson, K., & Shen, C. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-2357-2023 · Cited by 197
Global scale river discharge and mean depth from radar altimetry and model
Paris, A., Garambois, P.-A., Gal, L., Cerbelaud, A., Larnier, K., David, C. H., Jucá Oliveira, R. A., Correa, S. W., Tourian, M. J., & Calmant, S. (2026) · Nature Communications · doi:10.1038/s41467-026-78275-5
Metadata
- Authors
- Sivapalan, M.
- Venue
- Hydrological Processes
- Publisher
- Wiley
- Published
- 2003-10-07
- Volume
- 17
- Issue
- 15
- Pages
- 3163-3170
- ISSN
- 0885-6087 (ISSN-L)
- 1099-1085
- OpenAlex
- W2162719469
- Cited by
- 483
- References
- 0
- FWCI
- 10.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Prediction in ungauged basins: a grand challenge for theoretical hydrology. https://app.cochat.ai/papers/10.1002/hyp.5155
- The paper
- Sivapalan, M. (2003). Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrological Processes. https://doi.org/10.1002/hyp.5155
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
