The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Changes in Continental Freshwater Discharge from 1948 to 2004
Open accessAbstract
A new dataset of historical monthly streamflow at the farthest downstream stations for the world’s 925 largest ocean-reaching rivers has been created for community use. Available new gauge records are added to a network of gauges that covers ∼80 × 106 km2 or ∼80% of global ocean-draining land areas and accounts for about 73% of global total runoff. For most of the large rivers, the record for 1948–2004 is fairly complete. Data gaps in the records are filled through linear regression using streamflow simulated by a land surface model [Community Land Model, version 3 (CLM3)] forced with observed precipitation and other atmospheric forcings that are significantly (and often strongly) correlated with the observed streamflow for most rivers. Compared with previous studies, the new dataset has improved homogeneity and enables more reliable assessments of decadal and long-term changes in continental freshwater discharge into the oceans. The model-simulated runoff ratio over drainage areas with and without gauge records is used to estimate the contribution from the areas not monitored by the gauges in deriving the total discharge into the global oceans. Results reveal large variations in yearly streamflow for most of the world’s large rivers and for continental discharge, but only about one-third of the top 200 rivers (including the Congo, Mississippi, Yenisey, Paraná, Ganges, Columbia, Uruguay, and Niger) show statistically significant trends during 1948–2004, with the rivers having downward trends (45) outnumbering those with upward trends (19). The interannual variations are correlated with the El Niño–Southern Oscillation (ENSO) events for discharge into the Atlantic, Pacific, Indian, and global ocean as a whole. For ocean basins other than the Arctic, and for the global ocean as a whole, the discharge data show small or downward trends, which are statistically significant for the Pacific (−9.4 km3 yr−1). Precipitation is a major driver for the discharge trends and large interannual-to-decadal variations. Comparisons with the CLM3 simulation suggest that direct human influence on annual streamflow is likely small compared with climatic forcing during 1948–2004 for most of the world’s major rivers. For the Arctic drainage areas, upward trends in streamflow are not accompanied by increasing precipitation, especially over Siberia, based on available data, although recent surface warming and associated downward trends in snow cover and soil ice content over the northern high latitudes contribute to increased runoff in these regions. The results are qualitatively consistent with climate model projections but contradict an earlier report of increasing continental runoff during the recent decades based on limited records.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global hydrological cycles and world water resources
Oki, T., & Kanae, S. (2006) · Science · doi:10.1126/science.1128845 · Cited by 4,392
Fragmentation and Flow Regulation of the World's Large River Systems
Nilsson, C., Reidy, C. A., Dynesius, M., & Revenga, C. (2005) · Science · doi:10.1126/science.1107887 · Cited by 3,509
Cited by
Increasing drought under global warming in observations and models
Dai, A. (2013) · Nature Climate Change · doi:10.1038/nclimate1633 · Cited by 4,762
Self-citationDrought under global warming: a review
Dai, A. (2011) · WIREs Climate Change · doi:10.1002/wcc.81 · Cited by 3,523
Self-citationSchneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., & Rudolf, B. (2014) · Theoretical and Applied Climatology · doi:10.1007/s00704-013-0860-x · Cited by 1,385
Ocean salinities reveal strong global water cycle intensification during 1950 to 2000.
Durack, P. J., Wijffels, S. E., & Matear, R. J. (2012) · Science · doi:10.1126/science.1212222 · Cited by 1,070
Nobre, C. A., Sampaio, G., Borma, L. S., Castilla-Rubio, J. C., Silva, J. S., & Cardoso, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1605516113 · Cited by 1,045
Metadata
- Authors
- Dai, A., Qian, T., Trenberth, K. E., & Milliman, J. D.
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2009-05-15
- Volume
- 22
- Issue
- 10
- Pages
- 2773-2792
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W1987988199
- Cited by
- 923
- References
- 2
- FWCI
- 26.13
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.