Uncertainty in the future change of extreme precipitation over the Rhine basin: the role of internal climate variability View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-08-31

AUTHORS

S. C. van Pelt, J. J. Beersma, T. A. Buishand, B. J. J. M. van den Hurk, J. Schellekens

ABSTRACT

Future changes in extreme multi-day precipitation will influence the probability of floods in the river Rhine basin. In this paper the spread of the changes projected by climate models at the end of this century (2081–2100) is studied for a 17-member ensemble of a single Global Climate Model (GCM) and results from the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble. All climate models were driven by the IPCC SRES A1B emission scenario. An analysis of variance model is formulated to disentangle the contributions from systematic differences between GCMs and internal climate variability. Both the changes in the mean and characteristics of extremes are considered. To estimate variances due to internal climate variability a bootstrap method was used. The changes from the GCM simulations were linked to the local scale using an advanced non-linear delta change approach. This approach uses climate responses of the GCM to transform the daily precipitation of 134 sub-basins of the river Rhine. The transformed precipitation series was used as input for the hydrological Hydrologiska Byråns Vattenbalansavdelning model to simulate future river discharges. Internal climate variability accounts for about 30 % of the total variance in the projected climate trends of average winter precipitation in the CMIP3 ensemble and explains a larger fraction of the total variance in the projected climate trends of extreme precipitation in the winter half-year. There is a good correspondence between the direction and spread of the changes in the return levels of extreme river discharges and extreme 10-day precipitation over the Rhine basin. This suggests that also for extreme discharges a large fraction of the total variance can be attributed to internal climate variability. More... »

PAGES

1789-1800

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-014-2312-4

DOI

http://dx.doi.org/10.1007/s00382-014-2312-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043439923


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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": "Earth System Science \u2013 Climate Change and Adaptive Land and Water Management, Wageningen UR, Droevendaalsesteeg 3, 6708 PB, Wageningen, The Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.4818.5", 
          "name": [
            "Earth System Science \u2013 Climate Change and Adaptive Land and Water Management, Wageningen UR, Droevendaalsesteeg 3, 6708 PB, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van Pelt", 
        "givenName": "S. C.", 
        "id": "sg:person.010237345623.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010237345623.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.8653.8", 
          "name": [
            "Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Beersma", 
        "givenName": "J. J.", 
        "id": "sg:person.07657525105.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07657525105.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.8653.8", 
          "name": [
            "Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Buishand", 
        "givenName": "T. A.", 
        "id": "sg:person.016677345041.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016677345041.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.8653.8", 
          "name": [
            "Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van den Hurk", 
        "givenName": "B. J. J. M.", 
        "id": "sg:person.010707714545.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010707714545.84"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Deltares, Boussinesqweg 1, 2629 HV, Delft, The Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.6385.8", 
          "name": [
            "Deltares, Boussinesqweg 1, 2629 HV, Delft, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schellekens", 
        "givenName": "J.", 
        "id": "sg:person.011204201337.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011204201337.34"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nature02771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036499414", 
          "https://doi.org/10.1038/nature02771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-008-9471-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016554480", 
          "https://doi.org/10.1007/s10584-008-9471-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-011-1210-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015736062", 
          "https://doi.org/10.1007/s00382-011-1210-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015242699", 
          "https://doi.org/10.1007/s003820050010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-010-0810-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001203373", 
          "https://doi.org/10.1007/s00382-010-0810-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1011142402374", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004749278", 
          "https://doi.org/10.1023/a:1011142402374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-007-4479-0_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010206779", 
          "https://doi.org/10.1007/978-94-007-4479-0_11"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-08-31", 
    "datePublishedReg": "2014-08-31", 
    "description": "Abstract\nFuture changes in extreme multi-day precipitation will influence the probability of floods in the river Rhine basin. In this paper the spread of the changes projected by climate models at the end of this century (2081\u20132100) is studied for a 17-member ensemble of a single Global Climate Model (GCM) and results from the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble. All climate models were driven by the IPCC SRES A1B emission scenario. An analysis of variance model is formulated to disentangle the contributions from systematic differences between GCMs and internal climate variability. Both the changes in the mean and characteristics of extremes are considered. To estimate variances due to internal climate variability a bootstrap method was used. The changes from the GCM simulations were linked to the local scale using an advanced non-linear delta change approach. This approach uses climate responses of the GCM to transform the daily precipitation of 134 sub-basins of the river Rhine. The transformed precipitation series was used as input for the hydrological Hydrologiska Byr\u00e5ns Vattenbalansavdelning model to simulate future river discharges. Internal climate variability accounts for about 30\u00a0% of the total variance in the projected climate trends of average winter precipitation in the CMIP3 ensemble and explains a larger fraction of the total variance in the projected climate trends of extreme precipitation in the winter half-year. There is a good correspondence between the direction and spread of the changes in the return levels of extreme river discharges and extreme 10-day precipitation over the Rhine basin. This suggests that also for extreme discharges a large fraction of the total variance can be attributed to internal climate variability.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00382-014-2312-4", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7-8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "44"
      }
    ], 
    "keywords": [
      "internal climate variability", 
      "global climate models", 
      "climate variability", 
      "climate models", 
      "Rhine basin", 
      "extreme precipitation", 
      "river discharge", 
      "climate trends", 
      "future changes", 
      "IPCC SRES A1B emission scenario", 
      "single global climate model", 
      "SRES A1B emission scenario", 
      "Hydrologiska Byr\u00e5ns Vattenbalansavdelning (HBV) model", 
      "delta change approach", 
      "future river discharge", 
      "A1B emission scenario", 
      "extreme river discharge", 
      "river Rhine basin", 
      "average winter precipitation", 
      "characteristics of extremes", 
      "probability of floods", 
      "total variance", 
      "CMIP3 ensemble", 
      "GCM simulations", 
      "climate response", 
      "daily precipitation", 
      "winter precipitation", 
      "extreme discharges", 
      "emission scenarios", 
      "precipitation series", 
      "large fraction", 
      "return levels", 
      "river Rhine", 
      "precipitation", 
      "basin", 
      "local scale", 
      "variability", 
      "systematic differences", 
      "good correspondence", 
      "ensemble", 
      "discharge", 
      "floods", 
      "change approach", 
      "Rhine", 
      "winter", 
      "trends", 
      "extremes", 
      "changes", 
      "uncertainty", 
      "model", 
      "fraction", 
      "input", 
      "scale", 
      "century", 
      "variance", 
      "scenarios", 
      "contribution", 
      "simulations", 
      "series", 
      "direction", 
      "spread", 
      "correspondence", 
      "characteristics", 
      "bootstrap method", 
      "end", 
      "analysis", 
      "results", 
      "differences", 
      "response", 
      "means", 
      "approach", 
      "probability", 
      "variance model", 
      "levels", 
      "role", 
      "method", 
      "paper"
    ], 
    "name": "Uncertainty in the future change of extreme precipitation over the Rhine basin: the role of internal climate variability", 
    "pagination": "1789-1800", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043439923"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-014-2312-4"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-014-2312-4", 
      "https://app.dimensions.ai/details/publication/pub.1043439923"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:39", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_644.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00382-014-2312-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s00382-014-2312-4'

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/s00382-014-2312-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-014-2312-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-014-2312-4'


 

This table displays all metadata directly associated to this object as RDF triples.

200 TRIPLES      21 PREDICATES      109 URIs      93 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-014-2312-4 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 anzsrc-for:0406
4 schema:author N71fdf0f2bc154fd3b2429ca678be5e57
5 schema:citation sg:pub.10.1007/978-94-007-4479-0_11
6 sg:pub.10.1007/s00382-010-0810-6
7 sg:pub.10.1007/s00382-011-1210-2
8 sg:pub.10.1007/s003820050010
9 sg:pub.10.1007/s10584-008-9471-4
10 sg:pub.10.1023/a:1011142402374
11 sg:pub.10.1038/nature02771
12 schema:datePublished 2014-08-31
13 schema:datePublishedReg 2014-08-31
14 schema:description Abstract Future changes in extreme multi-day precipitation will influence the probability of floods in the river Rhine basin. In this paper the spread of the changes projected by climate models at the end of this century (2081–2100) is studied for a 17-member ensemble of a single Global Climate Model (GCM) and results from the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble. All climate models were driven by the IPCC SRES A1B emission scenario. An analysis of variance model is formulated to disentangle the contributions from systematic differences between GCMs and internal climate variability. Both the changes in the mean and characteristics of extremes are considered. To estimate variances due to internal climate variability a bootstrap method was used. The changes from the GCM simulations were linked to the local scale using an advanced non-linear delta change approach. This approach uses climate responses of the GCM to transform the daily precipitation of 134 sub-basins of the river Rhine. The transformed precipitation series was used as input for the hydrological Hydrologiska Byråns Vattenbalansavdelning model to simulate future river discharges. Internal climate variability accounts for about 30 % of the total variance in the projected climate trends of average winter precipitation in the CMIP3 ensemble and explains a larger fraction of the total variance in the projected climate trends of extreme precipitation in the winter half-year. There is a good correspondence between the direction and spread of the changes in the return levels of extreme river discharges and extreme 10-day precipitation over the Rhine basin. This suggests that also for extreme discharges a large fraction of the total variance can be attributed to internal climate variability.
15 schema:genre article
16 schema:isAccessibleForFree false
17 schema:isPartOf N323fa6ac0e5f447bac82dd70052e5f69
18 Nc8e1953f65f34032b7dd90fc60678d01
19 sg:journal.1049631
20 schema:keywords A1B emission scenario
21 CMIP3 ensemble
22 GCM simulations
23 Hydrologiska Byråns Vattenbalansavdelning (HBV) model
24 IPCC SRES A1B emission scenario
25 Rhine
26 Rhine basin
27 SRES A1B emission scenario
28 analysis
29 approach
30 average winter precipitation
31 basin
32 bootstrap method
33 century
34 change approach
35 changes
36 characteristics
37 characteristics of extremes
38 climate models
39 climate response
40 climate trends
41 climate variability
42 contribution
43 correspondence
44 daily precipitation
45 delta change approach
46 differences
47 direction
48 discharge
49 emission scenarios
50 end
51 ensemble
52 extreme discharges
53 extreme precipitation
54 extreme river discharge
55 extremes
56 floods
57 fraction
58 future changes
59 future river discharge
60 global climate models
61 good correspondence
62 input
63 internal climate variability
64 large fraction
65 levels
66 local scale
67 means
68 method
69 model
70 paper
71 precipitation
72 precipitation series
73 probability
74 probability of floods
75 response
76 results
77 return levels
78 river Rhine
79 river Rhine basin
80 river discharge
81 role
82 scale
83 scenarios
84 series
85 simulations
86 single global climate model
87 spread
88 systematic differences
89 total variance
90 trends
91 uncertainty
92 variability
93 variance
94 variance model
95 winter
96 winter precipitation
97 schema:name Uncertainty in the future change of extreme precipitation over the Rhine basin: the role of internal climate variability
98 schema:pagination 1789-1800
99 schema:productId N88d78b491b75491cb0bc5dca808072ad
100 N9ecbac8bb57541978d0e82a448b738a6
101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043439923
102 https://doi.org/10.1007/s00382-014-2312-4
103 schema:sdDatePublished 2022-10-01T06:39
104 schema:sdLicense https://scigraph.springernature.com/explorer/license/
105 schema:sdPublisher Nd501b64bc02c4e96ac97a8b5a539c0d8
106 schema:url https://doi.org/10.1007/s00382-014-2312-4
107 sgo:license sg:explorer/license/
108 sgo:sdDataset articles
109 rdf:type schema:ScholarlyArticle
110 N323fa6ac0e5f447bac82dd70052e5f69 schema:volumeNumber 44
111 rdf:type schema:PublicationVolume
112 N70765bddfb3f4d77bda99f03e46b46a0 rdf:first sg:person.016677345041.08
113 rdf:rest Nd64b1b2a3a89407c8a28590762335d49
114 N71fdf0f2bc154fd3b2429ca678be5e57 rdf:first sg:person.010237345623.09
115 rdf:rest Na2f5d7088564417d8287285de4e29090
116 N88d78b491b75491cb0bc5dca808072ad schema:name dimensions_id
117 schema:value pub.1043439923
118 rdf:type schema:PropertyValue
119 N9a52b8d5f54b4973960caeec60e3f5d9 rdf:first sg:person.011204201337.34
120 rdf:rest rdf:nil
121 N9ecbac8bb57541978d0e82a448b738a6 schema:name doi
122 schema:value 10.1007/s00382-014-2312-4
123 rdf:type schema:PropertyValue
124 Na2f5d7088564417d8287285de4e29090 rdf:first sg:person.07657525105.40
125 rdf:rest N70765bddfb3f4d77bda99f03e46b46a0
126 Nc8e1953f65f34032b7dd90fc60678d01 schema:issueNumber 7-8
127 rdf:type schema:PublicationIssue
128 Nd501b64bc02c4e96ac97a8b5a539c0d8 schema:name Springer Nature - SN SciGraph project
129 rdf:type schema:Organization
130 Nd64b1b2a3a89407c8a28590762335d49 rdf:first sg:person.010707714545.84
131 rdf:rest N9a52b8d5f54b4973960caeec60e3f5d9
132 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
133 schema:name Earth Sciences
134 rdf:type schema:DefinedTerm
135 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
136 schema:name Atmospheric Sciences
137 rdf:type schema:DefinedTerm
138 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
139 schema:name Physical Geography and Environmental Geoscience
140 rdf:type schema:DefinedTerm
141 sg:journal.1049631 schema:issn 0930-7575
142 1432-0894
143 schema:name Climate Dynamics
144 schema:publisher Springer Nature
145 rdf:type schema:Periodical
146 sg:person.010237345623.09 schema:affiliation grid-institutes:grid.4818.5
147 schema:familyName van Pelt
148 schema:givenName S. C.
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010237345623.09
150 rdf:type schema:Person
151 sg:person.010707714545.84 schema:affiliation grid-institutes:grid.8653.8
152 schema:familyName van den Hurk
153 schema:givenName B. J. J. M.
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010707714545.84
155 rdf:type schema:Person
156 sg:person.011204201337.34 schema:affiliation grid-institutes:grid.6385.8
157 schema:familyName Schellekens
158 schema:givenName J.
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011204201337.34
160 rdf:type schema:Person
161 sg:person.016677345041.08 schema:affiliation grid-institutes:grid.8653.8
162 schema:familyName Buishand
163 schema:givenName T. A.
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016677345041.08
165 rdf:type schema:Person
166 sg:person.07657525105.40 schema:affiliation grid-institutes:grid.8653.8
167 schema:familyName Beersma
168 schema:givenName J. J.
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07657525105.40
170 rdf:type schema:Person
171 sg:pub.10.1007/978-94-007-4479-0_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010206779
172 https://doi.org/10.1007/978-94-007-4479-0_11
173 rdf:type schema:CreativeWork
174 sg:pub.10.1007/s00382-010-0810-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001203373
175 https://doi.org/10.1007/s00382-010-0810-6
176 rdf:type schema:CreativeWork
177 sg:pub.10.1007/s00382-011-1210-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015736062
178 https://doi.org/10.1007/s00382-011-1210-2
179 rdf:type schema:CreativeWork
180 sg:pub.10.1007/s003820050010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015242699
181 https://doi.org/10.1007/s003820050010
182 rdf:type schema:CreativeWork
183 sg:pub.10.1007/s10584-008-9471-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016554480
184 https://doi.org/10.1007/s10584-008-9471-4
185 rdf:type schema:CreativeWork
186 sg:pub.10.1023/a:1011142402374 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004749278
187 https://doi.org/10.1023/a:1011142402374
188 rdf:type schema:CreativeWork
189 sg:pub.10.1038/nature02771 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036499414
190 https://doi.org/10.1038/nature02771
191 rdf:type schema:CreativeWork
192 grid-institutes:grid.4818.5 schema:alternateName Earth System Science – Climate Change and Adaptive Land and Water Management, Wageningen UR, Droevendaalsesteeg 3, 6708 PB, Wageningen, The Netherlands
193 schema:name Earth System Science – Climate Change and Adaptive Land and Water Management, Wageningen UR, Droevendaalsesteeg 3, 6708 PB, Wageningen, The Netherlands
194 rdf:type schema:Organization
195 grid-institutes:grid.6385.8 schema:alternateName Deltares, Boussinesqweg 1, 2629 HV, Delft, The Netherlands
196 schema:name Deltares, Boussinesqweg 1, 2629 HV, Delft, The Netherlands
197 rdf:type schema:Organization
198 grid-institutes:grid.8653.8 schema:alternateName Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands
199 schema:name Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE, De Bilt, The Netherlands
200 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...