Ontology type: schema:ScholarlyArticle
2015-03-11
AUTHORSDaniel B. Wright, Thomas R. Knutson, James A. Smith
ABSTRACTThe eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980–2006 tropical cyclone frequency and rainfall intensity for the months of August–October are compared against results from previous studies and observation-based datasets. The 1980–2006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500 km of the storm center increase by 8–17 % in the future climate projections relative to control. This is at least as much as expected from the Clausius–Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300–500 km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones. More... »
PAGES3365-3379
http://scigraph.springernature.com/pub.10.1007/s00382-015-2544-y
DOIhttp://dx.doi.org/10.1007/s00382-015-2544-y
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1010613514
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Earth Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0401",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Atmospheric Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0406",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Physical Geography and Environmental Geoscience",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Oak Ridge Associated Universities, 37831, Oak Ridge, TN, USA",
"id": "http://www.grid.ac/institutes/grid.410547.3",
"name": [
"NASA Hydrological Sciences, Goddard Space Flight Center, 8800 Greenbelt Rd, 20771, Greenbelt, MD, USA",
"Oak Ridge Associated Universities, 37831, Oak Ridge, TN, USA"
],
"type": "Organization"
},
"familyName": "Wright",
"givenName": "Daniel B.",
"id": "sg:person.011362350654.08",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011362350654.08"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Geophysical Fluid Dynamics Laboratory/NOAA, 08542, Princeton, NJ, USA",
"id": "http://www.grid.ac/institutes/grid.482795.5",
"name": [
"Geophysical Fluid Dynamics Laboratory/NOAA, 08542, Princeton, NJ, USA"
],
"type": "Organization"
},
"familyName": "Knutson",
"givenName": "Thomas R.",
"id": "sg:person.01203466135.07",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01203466135.07"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Civil and Environmental Engineering, Princeton University, 08544, Princeton, NJ, USA",
"id": "http://www.grid.ac/institutes/grid.16750.35",
"name": [
"Department of Civil and Environmental Engineering, Princeton University, 08544, Princeton, NJ, USA"
],
"type": "Organization"
},
"familyName": "Smith",
"givenName": "James A.",
"id": "sg:person.014440036077.10",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014440036077.10"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/ngeo779",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007726005",
"https://doi.org/10.1038/ngeo779"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nclimate1530",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036821809",
"https://doi.org/10.1038/nclimate1530"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ngeo202",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042758208",
"https://doi.org/10.1038/ngeo202"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature09763",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028595909",
"https://doi.org/10.1038/nature09763"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature03906",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003972056",
"https://doi.org/10.1038/nature03906"
],
"type": "CreativeWork"
}
],
"datePublished": "2015-03-11",
"datePublishedReg": "2015-03-11",
"description": "The eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980\u20132006 tropical cyclone frequency and rainfall intensity for the months of August\u2013October are compared against results from previous studies and observation-based datasets. The 1980\u20132006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500\u00a0km of the storm center increase by 8\u201317\u00a0% in the future climate projections relative to control. This is at least as much as expected from the Clausius\u2013Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300\u2013500\u00a0km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones.",
"genre": "article",
"id": "sg:pub.10.1007/s00382-015-2544-y",
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.3000230",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.4049485",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1049631",
"issn": [
"0930-7575",
"1432-0894"
],
"name": "Climate Dynamics",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "11-12",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "45"
}
],
"keywords": [
"regional climate model",
"tropical cyclones",
"eastern United States",
"rain rate",
"high-resolution regional climate model",
"twenty-first century projections",
"regional climate model projections",
"resolution regional climate model",
"Atlantic tropical cyclone activity",
"general circulation model results",
"atmospheric water vapor content",
"area-averaged rain rate",
"tropical cyclone rainfall",
"tropical cyclone frequency",
"tropical cyclone activity",
"climate model projections",
"observation-based datasets",
"future climate projections",
"future flood risk",
"future climate scenarios",
"water vapor content",
"Clausius\u2013Clapeyron relation",
"little net change",
"CMIP3 projections",
"CMIP5 projections",
"cyclone activity",
"dynamical downscaling",
"climate models",
"cyclone frequency",
"century projections",
"climate projections",
"rainfall changes",
"rainfall properties",
"future climate",
"warmer atmosphere",
"model projections",
"climate scenarios",
"vapor content",
"warmer climate",
"control simulation",
"flood risk",
"averaging radius",
"cyclones",
"model results",
"eastern U.S.",
"rainfall",
"recent observational studies",
"frequency of occurrence",
"climate",
"net change",
"land",
"projections",
"CMIP5",
"CMIP3",
"RCP4.5",
"Ocean",
"downscaling",
"A1B",
"storms",
"important implications",
"United States",
"atmosphere",
"previous studies",
"intensity",
"changes",
"occurrence",
"simulations",
"U.S.",
"attribution",
"scenarios",
"dataset",
"fraction",
"center increases",
"frequency",
"implications",
"content",
"results",
"model",
"rate",
"study",
"decrease",
"major challenge",
"increase",
"percent enhancement",
"radius",
"state",
"relation",
"activity",
"months",
"enhancement",
"properties",
"approach",
"paper",
"control",
"challenges",
"risk",
"promising approach",
"observational study",
"findings"
],
"name": "Regional climate model projections of rainfall from U.S. landfalling tropical cyclones",
"pagination": "3365-3379",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1010613514"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00382-015-2544-y"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00382-015-2544-y",
"https://app.dimensions.ai/details/publication/pub.1010613514"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T17:02",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_662.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00382-015-2544-y"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00382-015-2544-y'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00382-015-2544-y'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-015-2544-y'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-015-2544-y'
This table displays all metadata directly associated to this object as RDF triples.
205 TRIPLES
21 PREDICATES
129 URIs
115 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s00382-015-2544-y | schema:about | anzsrc-for:04 |
2 | ″ | ″ | anzsrc-for:0401 |
3 | ″ | ″ | anzsrc-for:0406 |
4 | ″ | schema:author | Nd43106cf606c43f0970f6676100c35ee |
5 | ″ | schema:citation | sg:pub.10.1038/nature03906 |
6 | ″ | ″ | sg:pub.10.1038/nature09763 |
7 | ″ | ″ | sg:pub.10.1038/nclimate1530 |
8 | ″ | ″ | sg:pub.10.1038/ngeo202 |
9 | ″ | ″ | sg:pub.10.1038/ngeo779 |
10 | ″ | schema:datePublished | 2015-03-11 |
11 | ″ | schema:datePublishedReg | 2015-03-11 |
12 | ″ | schema:description | The eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980–2006 tropical cyclone frequency and rainfall intensity for the months of August–October are compared against results from previous studies and observation-based datasets. The 1980–2006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500 km of the storm center increase by 8–17 % in the future climate projections relative to control. This is at least as much as expected from the Clausius–Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300–500 km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones. |
13 | ″ | schema:genre | article |
14 | ″ | schema:isAccessibleForFree | false |
15 | ″ | schema:isPartOf | N76510d5abbfd449c8c73d9413f27fb54 |
16 | ″ | ″ | N83500512616c4f2da06aa94d43ec6665 |
17 | ″ | ″ | sg:journal.1049631 |
18 | ″ | schema:keywords | A1B |
19 | ″ | ″ | Atlantic tropical cyclone activity |
20 | ″ | ″ | CMIP3 |
21 | ″ | ″ | CMIP3 projections |
22 | ″ | ″ | CMIP5 |
23 | ″ | ″ | CMIP5 projections |
24 | ″ | ″ | Clausius–Clapeyron relation |
25 | ″ | ″ | Ocean |
26 | ″ | ″ | RCP4.5 |
27 | ″ | ″ | U.S. |
28 | ″ | ″ | United States |
29 | ″ | ″ | activity |
30 | ″ | ″ | approach |
31 | ″ | ″ | area-averaged rain rate |
32 | ″ | ″ | atmosphere |
33 | ″ | ″ | atmospheric water vapor content |
34 | ″ | ″ | attribution |
35 | ″ | ″ | averaging radius |
36 | ″ | ″ | center increases |
37 | ″ | ″ | century projections |
38 | ″ | ″ | challenges |
39 | ″ | ″ | changes |
40 | ″ | ″ | climate |
41 | ″ | ″ | climate model projections |
42 | ″ | ″ | climate models |
43 | ″ | ″ | climate projections |
44 | ″ | ″ | climate scenarios |
45 | ″ | ″ | content |
46 | ″ | ″ | control |
47 | ″ | ″ | control simulation |
48 | ″ | ″ | cyclone activity |
49 | ″ | ″ | cyclone frequency |
50 | ″ | ″ | cyclones |
51 | ″ | ″ | dataset |
52 | ″ | ″ | decrease |
53 | ″ | ″ | downscaling |
54 | ″ | ″ | dynamical downscaling |
55 | ″ | ″ | eastern U.S. |
56 | ″ | ″ | eastern United States |
57 | ″ | ″ | enhancement |
58 | ″ | ″ | findings |
59 | ″ | ″ | flood risk |
60 | ″ | ″ | fraction |
61 | ″ | ″ | frequency |
62 | ″ | ″ | frequency of occurrence |
63 | ″ | ″ | future climate |
64 | ″ | ″ | future climate projections |
65 | ″ | ″ | future climate scenarios |
66 | ″ | ″ | future flood risk |
67 | ″ | ″ | general circulation model results |
68 | ″ | ″ | high-resolution regional climate model |
69 | ″ | ″ | implications |
70 | ″ | ″ | important implications |
71 | ″ | ″ | increase |
72 | ″ | ″ | intensity |
73 | ″ | ″ | land |
74 | ″ | ″ | little net change |
75 | ″ | ″ | major challenge |
76 | ″ | ″ | model |
77 | ″ | ″ | model projections |
78 | ″ | ″ | model results |
79 | ″ | ″ | months |
80 | ″ | ″ | net change |
81 | ″ | ″ | observation-based datasets |
82 | ″ | ″ | observational study |
83 | ″ | ″ | occurrence |
84 | ″ | ″ | paper |
85 | ″ | ″ | percent enhancement |
86 | ″ | ″ | previous studies |
87 | ″ | ″ | projections |
88 | ″ | ″ | promising approach |
89 | ″ | ″ | properties |
90 | ″ | ″ | radius |
91 | ″ | ″ | rain rate |
92 | ″ | ″ | rainfall |
93 | ″ | ″ | rainfall changes |
94 | ″ | ″ | rainfall properties |
95 | ″ | ″ | rate |
96 | ″ | ″ | recent observational studies |
97 | ″ | ″ | regional climate model |
98 | ″ | ″ | regional climate model projections |
99 | ″ | ″ | relation |
100 | ″ | ″ | resolution regional climate model |
101 | ″ | ″ | results |
102 | ″ | ″ | risk |
103 | ″ | ″ | scenarios |
104 | ″ | ″ | simulations |
105 | ″ | ″ | state |
106 | ″ | ″ | storms |
107 | ″ | ″ | study |
108 | ″ | ″ | tropical cyclone activity |
109 | ″ | ″ | tropical cyclone frequency |
110 | ″ | ″ | tropical cyclone rainfall |
111 | ″ | ″ | tropical cyclones |
112 | ″ | ″ | twenty-first century projections |
113 | ″ | ″ | vapor content |
114 | ″ | ″ | warmer atmosphere |
115 | ″ | ″ | warmer climate |
116 | ″ | ″ | water vapor content |
117 | ″ | schema:name | Regional climate model projections of rainfall from U.S. landfalling tropical cyclones |
118 | ″ | schema:pagination | 3365-3379 |
119 | ″ | schema:productId | N3f0e802492834ea5ad8313258007985a |
120 | ″ | ″ | N774e5aa4626a49fbb8bdb672d261970c |
121 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1010613514 |
122 | ″ | ″ | https://doi.org/10.1007/s00382-015-2544-y |
123 | ″ | schema:sdDatePublished | 2022-08-04T17:02 |
124 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
125 | ″ | schema:sdPublisher | Ne8450804665f4d9f98e501b74b693045 |
126 | ″ | schema:url | https://doi.org/10.1007/s00382-015-2544-y |
127 | ″ | sgo:license | sg:explorer/license/ |
128 | ″ | sgo:sdDataset | articles |
129 | ″ | rdf:type | schema:ScholarlyArticle |
130 | N3f0e802492834ea5ad8313258007985a | schema:name | dimensions_id |
131 | ″ | schema:value | pub.1010613514 |
132 | ″ | rdf:type | schema:PropertyValue |
133 | N4029bfe473b245e6afe5192d3f00daaa | rdf:first | sg:person.014440036077.10 |
134 | ″ | rdf:rest | rdf:nil |
135 | N70f4b88b52bc417b951f0af79c52f6b1 | rdf:first | sg:person.01203466135.07 |
136 | ″ | rdf:rest | N4029bfe473b245e6afe5192d3f00daaa |
137 | N76510d5abbfd449c8c73d9413f27fb54 | schema:issueNumber | 11-12 |
138 | ″ | rdf:type | schema:PublicationIssue |
139 | N774e5aa4626a49fbb8bdb672d261970c | schema:name | doi |
140 | ″ | schema:value | 10.1007/s00382-015-2544-y |
141 | ″ | rdf:type | schema:PropertyValue |
142 | N83500512616c4f2da06aa94d43ec6665 | schema:volumeNumber | 45 |
143 | ″ | rdf:type | schema:PublicationVolume |
144 | Nd43106cf606c43f0970f6676100c35ee | rdf:first | sg:person.011362350654.08 |
145 | ″ | rdf:rest | N70f4b88b52bc417b951f0af79c52f6b1 |
146 | Ne8450804665f4d9f98e501b74b693045 | schema:name | Springer Nature - SN SciGraph project |
147 | ″ | rdf:type | schema:Organization |
148 | anzsrc-for:04 | schema:inDefinedTermSet | anzsrc-for: |
149 | ″ | schema:name | Earth Sciences |
150 | ″ | rdf:type | schema:DefinedTerm |
151 | anzsrc-for:0401 | schema:inDefinedTermSet | anzsrc-for: |
152 | ″ | schema:name | Atmospheric Sciences |
153 | ″ | rdf:type | schema:DefinedTerm |
154 | anzsrc-for:0406 | schema:inDefinedTermSet | anzsrc-for: |
155 | ″ | schema:name | Physical Geography and Environmental Geoscience |
156 | ″ | rdf:type | schema:DefinedTerm |
157 | sg:grant.3000230 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s00382-015-2544-y |
158 | ″ | rdf:type | schema:MonetaryGrant |
159 | sg:grant.4049485 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s00382-015-2544-y |
160 | ″ | rdf:type | schema:MonetaryGrant |
161 | sg:journal.1049631 | schema:issn | 0930-7575 |
162 | ″ | ″ | 1432-0894 |
163 | ″ | schema:name | Climate Dynamics |
164 | ″ | schema:publisher | Springer Nature |
165 | ″ | rdf:type | schema:Periodical |
166 | sg:person.011362350654.08 | schema:affiliation | grid-institutes:grid.410547.3 |
167 | ″ | schema:familyName | Wright |
168 | ″ | schema:givenName | Daniel B. |
169 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011362350654.08 |
170 | ″ | rdf:type | schema:Person |
171 | sg:person.01203466135.07 | schema:affiliation | grid-institutes:grid.482795.5 |
172 | ″ | schema:familyName | Knutson |
173 | ″ | schema:givenName | Thomas R. |
174 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01203466135.07 |
175 | ″ | rdf:type | schema:Person |
176 | sg:person.014440036077.10 | schema:affiliation | grid-institutes:grid.16750.35 |
177 | ″ | schema:familyName | Smith |
178 | ″ | schema:givenName | James A. |
179 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014440036077.10 |
180 | ″ | rdf:type | schema:Person |
181 | sg:pub.10.1038/nature03906 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1003972056 |
182 | ″ | ″ | https://doi.org/10.1038/nature03906 |
183 | ″ | rdf:type | schema:CreativeWork |
184 | sg:pub.10.1038/nature09763 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1028595909 |
185 | ″ | ″ | https://doi.org/10.1038/nature09763 |
186 | ″ | rdf:type | schema:CreativeWork |
187 | sg:pub.10.1038/nclimate1530 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1036821809 |
188 | ″ | ″ | https://doi.org/10.1038/nclimate1530 |
189 | ″ | rdf:type | schema:CreativeWork |
190 | sg:pub.10.1038/ngeo202 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1042758208 |
191 | ″ | ″ | https://doi.org/10.1038/ngeo202 |
192 | ″ | rdf:type | schema:CreativeWork |
193 | sg:pub.10.1038/ngeo779 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1007726005 |
194 | ″ | ″ | https://doi.org/10.1038/ngeo779 |
195 | ″ | rdf:type | schema:CreativeWork |
196 | grid-institutes:grid.16750.35 | schema:alternateName | Department of Civil and Environmental Engineering, Princeton University, 08544, Princeton, NJ, USA |
197 | ″ | schema:name | Department of Civil and Environmental Engineering, Princeton University, 08544, Princeton, NJ, USA |
198 | ″ | rdf:type | schema:Organization |
199 | grid-institutes:grid.410547.3 | schema:alternateName | Oak Ridge Associated Universities, 37831, Oak Ridge, TN, USA |
200 | ″ | schema:name | NASA Hydrological Sciences, Goddard Space Flight Center, 8800 Greenbelt Rd, 20771, Greenbelt, MD, USA |
201 | ″ | ″ | Oak Ridge Associated Universities, 37831, Oak Ridge, TN, USA |
202 | ″ | rdf:type | schema:Organization |
203 | grid-institutes:grid.482795.5 | schema:alternateName | Geophysical Fluid Dynamics Laboratory/NOAA, 08542, Princeton, NJ, USA |
204 | ″ | schema:name | Geophysical Fluid Dynamics Laboratory/NOAA, 08542, Princeton, NJ, USA |
205 | ″ | rdf:type | schema:Organization |