The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Incorporating uncertainty of groundwater modeling in sea-level rise assessment: a case study in South Florida
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Monte Carlo simulation has been used extensively for the modelling of DEM uncertainty in viewshed analysis (e.g.Fisher, 1991Fisher, , 1992;;Nackaerts et al., 1999), flooding (e.g.Cooper et al., 2015;Gesch, 2018;Gesch et al., 2020;Hawker et al., 2018;Leon et al., 2014) and slope failure prediction (e.g.Davis & Keller, 1997;Holmes et al., 2000;Zhou et al., 2003).”
From Probabilistic modelling for incorporating uncertainty in least cost path results: a postdictive roman road case study (2021) · cites this paper as Cooper et al., 2015
Citations
Cites
Climate change 2007: the physical science basis
Solomon, S., Qin, D., Manning, M., Marquis, M. C., Averyt, K. B., Tignor, M., Miller, H. L., & Chen, Z. (2007) · Cited by 23,383
Climate Change 2007: The Physical Science Basis.
Schiavon, S., & Zecchin, R. (2007) · eScholarship (California Digital Library) · Cited by 16,994
Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998) · Environment and Planning A: Economy and Space · doi:10.1068/a301905 · Cited by 1,152
Church, J. A., & Gregory, J. M. (2001) · Encyclopedia of Ocean Sciences · doi:10.1006/rwos.2001.0268 · Cited by 847
Raster modelling of coastal flooding from sea‐level rise
Poulter, B., & Halpin, P. N. (2008) · International Journal of Geographical Information Science · doi:10.1080/13658810701371858 · Cited by 301
Assessment of groundwater inundation as a consequence of sea-level rise
Rotzoll, K., & Fletcher, C. H. (2013) · Nature Climate Change · doi:10.1038/nclimate1725 · Cited by 281
The Geomorphology of the Florida Peninsula
(1970) · doi:10.35256/b51 · Cited by 260
Gesch, D. B. (2009) · Journal of Coastal Research · doi:10.2112/si53-006.1 · Cited by 244
Sun, Y., Kang, S., Li, F., & Zhang, L. (2009) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2009.03.009 · Cited by 228
On the kriging of water table elevations using collateral information from a digital elevation model
Desbarats, A. J., Logan, C. E., Hinton, M. J., & Sharpe, D. R. (2002) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00504-2 · Cited by 198
Research works printed but not linked: 1
- Qian SS (1997) Estimating the area affected by phosphorous runoff in an Everglades wetland: a comparison of universal kriging and Bayesian kriging. Environ Ecol Stat 4:1–29 · doi:10.1023/a:1018503923695
Cited by
Lewis, J. (2021) · Journal of Archaeological Method and Theory · doi:10.1007/s10816-021-09522-w · Cited by 53
Metadata
- Authors
- Cooper, H. M., Zhang, C., & Selch, D.
- Venue
- Climatic Change
- Published
- 2015-01-30
- Volume
- 129
- Issue
- 1-2
- Pages
- 281-294
- ISSN
- 0165-0009
- 1573-1480
- Cited by
- 25
- References
- 30
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Incorporating uncertainty of groundwater modeling in sea-level rise assessment: a case study in South Florida. https://app.cochat.ai/papers/10.1007/s10584-015-1334-1
- The paper
- Cooper, H. M., Zhang, C., & Selch, D. (2015). Incorporating uncertainty of groundwater modeling in sea-level rise assessment: a case study in South Florida. Climatic Change. https://doi.org/10.1007/s10584-015-1334-1
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.
