Ontology type: schema:ScholarlyArticle Open Access: True
2020-10-03
AUTHORSDanyang Yu, Yuanyuan Zha, Liangsheng Shi, Andrei Bolotov, Chak-Hau Michael Tso
ABSTRACTAccurate characterization and prediction of soil moisture distribution and solute transport in vadose zone require detailed knowledge of the spatial distribution of soil hydraulic properties. Since the direct measurements of these unknown properties are challenging, many studies invert the soil hydraulic parameters by incorporating observation data (e.g., soil moisture and pressure head) at selected point sampling locations into soil moisture flow models. However, a cost-effective sampling strategy for where and when to collect the data, which is vital for saving the costs for monitoring and data interpretation, is relatively rare compared to the direct parameter retrieving efforts. Here, an optimal spatial–temporal sampling strategy was proposed based on cross-correlation analysis between observed state variables and soil hydraulic parameters. Besides, the effects of meteorological condition, observation type, bottom boundary condition, and correlation scale of soil hydraulic parameters are also demonstrated. The proposed sampling strategy was assessed by both synthetic numerical experiments and a real-world case study. Results suggest the retrieval accuracy of heterogeneous soil is acceptable if the spatial/temporal sampling interval is set to be one spatial/temporal correlation length of soil moisture. Besides, surface observation contains the most plentiful information which could be used to derive root-zone soil moisture/parameters, but this ability depends on the correlation scale of soil hydraulic parameters. Besides, the temporal value of soil moisture depends on meteorological condition. It is not necessary to sample repeatedly during dry periods, but more attention should be paid to the observations after rainfall events. More... »
PAGES737-757
http://scigraph.springernature.com/pub.10.1007/s00477-020-01882-1
DOIhttp://dx.doi.org/10.1007/s00477-020-01882-1
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1131417187
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/2008/09",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Engineering",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0907",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Environmental Engineering",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China",
"id": "http://www.grid.ac/institutes/grid.49470.3e",
"name": [
"State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China"
],
"type": "Organization"
},
"familyName": "Yu",
"givenName": "Danyang",
"id": "sg:person.010463715523.68",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010463715523.68"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, 400074, Chongqing, China",
"id": "http://www.grid.ac/institutes/grid.440679.8",
"name": [
"State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China",
"Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, 400074, Chongqing, China"
],
"type": "Organization"
},
"familyName": "Zha",
"givenName": "Yuanyuan",
"id": "sg:person.01041615256.23",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041615256.23"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China",
"id": "http://www.grid.ac/institutes/grid.49470.3e",
"name": [
"State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China"
],
"type": "Organization"
},
"familyName": "Shi",
"givenName": "Liangsheng",
"id": "sg:person.013753244525.37",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013753244525.37"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Meteorology and Climatology, Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, 127550, Moscow, Russia",
"id": "http://www.grid.ac/institutes/None",
"name": [
"Department of Meteorology and Climatology, Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, 127550, Moscow, Russia"
],
"type": "Organization"
},
"familyName": "Bolotov",
"givenName": "Andrei",
"id": "sg:person.010770330265.82",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010770330265.82"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Lancaster Environment Centre, Lancaster University, Lancaster, UK",
"id": "http://www.grid.ac/institutes/grid.9835.7",
"name": [
"UK Centre for Ecology and Hydrology, Lancaster, UK",
"Lancaster Environment Centre, Lancaster University, Lancaster, UK"
],
"type": "Organization"
},
"familyName": "Tso",
"givenName": "Chak-Hau Michael",
"id": "sg:person.011557056561.74",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011557056561.74"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s00477-008-0289-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029417657",
"https://doi.org/10.1007/s00477-008-0289-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-019-01764-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1124590212",
"https://doi.org/10.1007/s00477-019-01764-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10596-013-9351-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000005781",
"https://doi.org/10.1007/s10596-013-9351-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-020-01815-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1127517851",
"https://doi.org/10.1007/s00477-020-01815-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-018-1541-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103191079",
"https://doi.org/10.1007/s00477-018-1541-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-010-0392-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029308599",
"https://doi.org/10.1007/s00477-010-0392-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-030-18383-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1117043784",
"https://doi.org/10.1007/978-3-030-18383-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-012-0613-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008018272",
"https://doi.org/10.1007/s00477-012-0613-x"
],
"type": "CreativeWork"
}
],
"datePublished": "2020-10-03",
"datePublishedReg": "2020-10-03",
"description": "Accurate characterization and prediction of soil moisture distribution and solute transport in vadose zone require detailed knowledge of the spatial distribution of soil hydraulic properties. Since the direct measurements of these unknown properties are challenging, many studies invert the soil hydraulic parameters by incorporating observation data (e.g., soil moisture and pressure head) at selected point sampling locations into soil moisture flow models. However, a cost-effective sampling strategy for where and when to collect the data, which is vital for saving the costs for monitoring and data interpretation, is relatively rare compared to the direct parameter retrieving efforts. Here, an optimal spatial\u2013temporal sampling strategy was proposed based on cross-correlation analysis between observed state variables and soil hydraulic parameters. Besides, the effects of meteorological condition, observation type, bottom boundary condition, and correlation scale of soil hydraulic parameters are also demonstrated. The proposed sampling strategy was assessed by both synthetic numerical experiments and a real-world case study. Results suggest the retrieval accuracy of heterogeneous soil is acceptable if the spatial/temporal sampling interval is set to be one spatial/temporal correlation length of soil moisture. Besides, surface observation contains the most plentiful information which could be used to derive root-zone soil moisture/parameters, but this ability depends on the correlation scale of soil hydraulic parameters. Besides, the temporal value of soil moisture depends on meteorological condition. It is not necessary to sample repeatedly during dry periods, but more attention should be paid to the observations after rainfall events.",
"genre": "article",
"id": "sg:pub.10.1007/s00477-020-01882-1",
"inLanguage": "en",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.9415548",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.8270794",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.8281612",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1039987",
"issn": [
"1436-3240",
"1436-3259"
],
"name": "Stochastic Environmental Research and Risk Assessment",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "35"
}
],
"keywords": [
"soil hydraulic parameters",
"hydraulic parameters",
"soil moisture",
"heterogeneous soils",
"hydraulic properties",
"moisture flow model",
"bottom boundary condition",
"soil moisture distribution",
"cost-effective sampling strategy",
"sampling strategy",
"soil hydraulic properties",
"synthetic numerical experiments",
"moisture distribution",
"meteorological conditions",
"flow model",
"correlation scale",
"temporal sampling interval",
"vadose zone",
"observed state variables",
"boundary conditions",
"solute transport",
"dry period",
"rainfall events",
"spatial distribution",
"surface observations",
"soil",
"state variables",
"temporal correlation length",
"accurate characterization",
"sampling interval",
"observation types",
"moisture",
"observation data",
"direct measurement",
"numerical experiments",
"properties",
"parameters",
"real-world case study",
"unknown properties",
"plentiful information",
"case study",
"detailed knowledge",
"conditions",
"scale",
"temporal values",
"distribution",
"characterization",
"retrieval accuracy",
"data interpretation",
"correlation length",
"more attention",
"zone",
"measurements",
"transport",
"accuracy",
"monitoring",
"cost",
"prediction",
"strategies",
"cross-correlation analysis",
"experiments",
"location",
"efforts",
"model",
"observations",
"variables",
"data",
"results",
"length",
"point",
"values",
"study",
"effect",
"types",
"period",
"knowledge",
"analysis",
"information",
"attention",
"ability",
"events",
"interpretation",
"interval"
],
"name": "Spatiotemporal sampling strategy for characterization of hydraulic properties in heterogeneous soils",
"pagination": "737-757",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1131417187"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00477-020-01882-1"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00477-020-01882-1",
"https://app.dimensions.ai/details/publication/pub.1131417187"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:37",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_836.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00477-020-01882-1"
}
]
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/s00477-020-01882-1'
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/s00477-020-01882-1'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00477-020-01882-1'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00477-020-01882-1'
This table displays all metadata directly associated to this object as RDF triples.
218 TRIPLES
22 PREDICATES
116 URIs
100 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s00477-020-01882-1 | schema:about | anzsrc-for:09 |
2 | ″ | ″ | anzsrc-for:0907 |
3 | ″ | schema:author | N11f17fcaf0ae4af8af32b31139e15964 |
4 | ″ | schema:citation | sg:pub.10.1007/978-3-030-18383-7 |
5 | ″ | ″ | sg:pub.10.1007/s00477-008-0289-4 |
6 | ″ | ″ | sg:pub.10.1007/s00477-010-0392-1 |
7 | ″ | ″ | sg:pub.10.1007/s00477-012-0613-x |
8 | ″ | ″ | sg:pub.10.1007/s00477-018-1541-1 |
9 | ″ | ″ | sg:pub.10.1007/s00477-019-01764-1 |
10 | ″ | ″ | sg:pub.10.1007/s00477-020-01815-y |
11 | ″ | ″ | sg:pub.10.1007/s10596-013-9351-5 |
12 | ″ | schema:datePublished | 2020-10-03 |
13 | ″ | schema:datePublishedReg | 2020-10-03 |
14 | ″ | schema:description | Accurate characterization and prediction of soil moisture distribution and solute transport in vadose zone require detailed knowledge of the spatial distribution of soil hydraulic properties. Since the direct measurements of these unknown properties are challenging, many studies invert the soil hydraulic parameters by incorporating observation data (e.g., soil moisture and pressure head) at selected point sampling locations into soil moisture flow models. However, a cost-effective sampling strategy for where and when to collect the data, which is vital for saving the costs for monitoring and data interpretation, is relatively rare compared to the direct parameter retrieving efforts. Here, an optimal spatial–temporal sampling strategy was proposed based on cross-correlation analysis between observed state variables and soil hydraulic parameters. Besides, the effects of meteorological condition, observation type, bottom boundary condition, and correlation scale of soil hydraulic parameters are also demonstrated. The proposed sampling strategy was assessed by both synthetic numerical experiments and a real-world case study. Results suggest the retrieval accuracy of heterogeneous soil is acceptable if the spatial/temporal sampling interval is set to be one spatial/temporal correlation length of soil moisture. Besides, surface observation contains the most plentiful information which could be used to derive root-zone soil moisture/parameters, but this ability depends on the correlation scale of soil hydraulic parameters. Besides, the temporal value of soil moisture depends on meteorological condition. It is not necessary to sample repeatedly during dry periods, but more attention should be paid to the observations after rainfall events. |
15 | ″ | schema:genre | article |
16 | ″ | schema:inLanguage | en |
17 | ″ | schema:isAccessibleForFree | true |
18 | ″ | schema:isPartOf | N7bfd4ea470974e39b017478f9eb9089b |
19 | ″ | ″ | N81092f8824124308b13d8a3c6d1ef1dd |
20 | ″ | ″ | sg:journal.1039987 |
21 | ″ | schema:keywords | ability |
22 | ″ | ″ | accuracy |
23 | ″ | ″ | accurate characterization |
24 | ″ | ″ | analysis |
25 | ″ | ″ | attention |
26 | ″ | ″ | bottom boundary condition |
27 | ″ | ″ | boundary conditions |
28 | ″ | ″ | case study |
29 | ″ | ″ | characterization |
30 | ″ | ″ | conditions |
31 | ″ | ″ | correlation length |
32 | ″ | ″ | correlation scale |
33 | ″ | ″ | cost |
34 | ″ | ″ | cost-effective sampling strategy |
35 | ″ | ″ | cross-correlation analysis |
36 | ″ | ″ | data |
37 | ″ | ″ | data interpretation |
38 | ″ | ″ | detailed knowledge |
39 | ″ | ″ | direct measurement |
40 | ″ | ″ | distribution |
41 | ″ | ″ | dry period |
42 | ″ | ″ | effect |
43 | ″ | ″ | efforts |
44 | ″ | ″ | events |
45 | ″ | ″ | experiments |
46 | ″ | ″ | flow model |
47 | ″ | ″ | heterogeneous soils |
48 | ″ | ″ | hydraulic parameters |
49 | ″ | ″ | hydraulic properties |
50 | ″ | ″ | information |
51 | ″ | ″ | interpretation |
52 | ″ | ″ | interval |
53 | ″ | ″ | knowledge |
54 | ″ | ″ | length |
55 | ″ | ″ | location |
56 | ″ | ″ | measurements |
57 | ″ | ″ | meteorological conditions |
58 | ″ | ″ | model |
59 | ″ | ″ | moisture |
60 | ″ | ″ | moisture distribution |
61 | ″ | ″ | moisture flow model |
62 | ″ | ″ | monitoring |
63 | ″ | ″ | more attention |
64 | ″ | ″ | numerical experiments |
65 | ″ | ″ | observation data |
66 | ″ | ″ | observation types |
67 | ″ | ″ | observations |
68 | ″ | ″ | observed state variables |
69 | ″ | ″ | parameters |
70 | ″ | ″ | period |
71 | ″ | ″ | plentiful information |
72 | ″ | ″ | point |
73 | ″ | ″ | prediction |
74 | ″ | ″ | properties |
75 | ″ | ″ | rainfall events |
76 | ″ | ″ | real-world case study |
77 | ″ | ″ | results |
78 | ″ | ″ | retrieval accuracy |
79 | ″ | ″ | sampling interval |
80 | ″ | ″ | sampling strategy |
81 | ″ | ″ | scale |
82 | ″ | ″ | soil |
83 | ″ | ″ | soil hydraulic parameters |
84 | ″ | ″ | soil hydraulic properties |
85 | ″ | ″ | soil moisture |
86 | ″ | ″ | soil moisture distribution |
87 | ″ | ″ | solute transport |
88 | ″ | ″ | spatial distribution |
89 | ″ | ″ | state variables |
90 | ″ | ″ | strategies |
91 | ″ | ″ | study |
92 | ″ | ″ | surface observations |
93 | ″ | ″ | synthetic numerical experiments |
94 | ″ | ″ | temporal correlation length |
95 | ″ | ″ | temporal sampling interval |
96 | ″ | ″ | temporal values |
97 | ″ | ″ | transport |
98 | ″ | ″ | types |
99 | ″ | ″ | unknown properties |
100 | ″ | ″ | vadose zone |
101 | ″ | ″ | values |
102 | ″ | ″ | variables |
103 | ″ | ″ | zone |
104 | ″ | schema:name | Spatiotemporal sampling strategy for characterization of hydraulic properties in heterogeneous soils |
105 | ″ | schema:pagination | 737-757 |
106 | ″ | schema:productId | N98cc5c564a1442b882439e1f4a3b6331 |
107 | ″ | ″ | Nd15f6ad848c84b7498687a12ba749ec2 |
108 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1131417187 |
109 | ″ | ″ | https://doi.org/10.1007/s00477-020-01882-1 |
110 | ″ | schema:sdDatePublished | 2022-05-20T07:37 |
111 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
112 | ″ | schema:sdPublisher | Nc920adab3fa4474faf7db5bf542ed589 |
113 | ″ | schema:url | https://doi.org/10.1007/s00477-020-01882-1 |
114 | ″ | sgo:license | sg:explorer/license/ |
115 | ″ | sgo:sdDataset | articles |
116 | ″ | rdf:type | schema:ScholarlyArticle |
117 | N11f17fcaf0ae4af8af32b31139e15964 | rdf:first | sg:person.010463715523.68 |
118 | ″ | rdf:rest | N4be23139b66d4e23b718a7f9a56e9567 |
119 | N1ffc4d70deb04f2d80ee2f6bface1449 | rdf:first | sg:person.011557056561.74 |
120 | ″ | rdf:rest | rdf:nil |
121 | N4be23139b66d4e23b718a7f9a56e9567 | rdf:first | sg:person.01041615256.23 |
122 | ″ | rdf:rest | Nfe9076a05ef94a119e47fc29a8752957 |
123 | N73150d3d18794e78ac0b2115ed38ad13 | rdf:first | sg:person.010770330265.82 |
124 | ″ | rdf:rest | N1ffc4d70deb04f2d80ee2f6bface1449 |
125 | N7bfd4ea470974e39b017478f9eb9089b | schema:volumeNumber | 35 |
126 | ″ | rdf:type | schema:PublicationVolume |
127 | N81092f8824124308b13d8a3c6d1ef1dd | schema:issueNumber | 3 |
128 | ″ | rdf:type | schema:PublicationIssue |
129 | N98cc5c564a1442b882439e1f4a3b6331 | schema:name | dimensions_id |
130 | ″ | schema:value | pub.1131417187 |
131 | ″ | rdf:type | schema:PropertyValue |
132 | Nc920adab3fa4474faf7db5bf542ed589 | schema:name | Springer Nature - SN SciGraph project |
133 | ″ | rdf:type | schema:Organization |
134 | Nd15f6ad848c84b7498687a12ba749ec2 | schema:name | doi |
135 | ″ | schema:value | 10.1007/s00477-020-01882-1 |
136 | ″ | rdf:type | schema:PropertyValue |
137 | Nfe9076a05ef94a119e47fc29a8752957 | rdf:first | sg:person.013753244525.37 |
138 | ″ | rdf:rest | N73150d3d18794e78ac0b2115ed38ad13 |
139 | anzsrc-for:09 | schema:inDefinedTermSet | anzsrc-for: |
140 | ″ | schema:name | Engineering |
141 | ″ | rdf:type | schema:DefinedTerm |
142 | anzsrc-for:0907 | schema:inDefinedTermSet | anzsrc-for: |
143 | ″ | schema:name | Environmental Engineering |
144 | ″ | rdf:type | schema:DefinedTerm |
145 | sg:grant.8270794 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s00477-020-01882-1 |
146 | ″ | rdf:type | schema:MonetaryGrant |
147 | sg:grant.8281612 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s00477-020-01882-1 |
148 | ″ | rdf:type | schema:MonetaryGrant |
149 | sg:grant.9415548 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s00477-020-01882-1 |
150 | ″ | rdf:type | schema:MonetaryGrant |
151 | sg:journal.1039987 | schema:issn | 1436-3240 |
152 | ″ | ″ | 1436-3259 |
153 | ″ | schema:name | Stochastic Environmental Research and Risk Assessment |
154 | ″ | schema:publisher | Springer Nature |
155 | ″ | rdf:type | schema:Periodical |
156 | sg:person.01041615256.23 | schema:affiliation | grid-institutes:grid.440679.8 |
157 | ″ | schema:familyName | Zha |
158 | ″ | schema:givenName | Yuanyuan |
159 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041615256.23 |
160 | ″ | rdf:type | schema:Person |
161 | sg:person.010463715523.68 | schema:affiliation | grid-institutes:grid.49470.3e |
162 | ″ | schema:familyName | Yu |
163 | ″ | schema:givenName | Danyang |
164 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010463715523.68 |
165 | ″ | rdf:type | schema:Person |
166 | sg:person.010770330265.82 | schema:affiliation | grid-institutes:None |
167 | ″ | schema:familyName | Bolotov |
168 | ″ | schema:givenName | Andrei |
169 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010770330265.82 |
170 | ″ | rdf:type | schema:Person |
171 | sg:person.011557056561.74 | schema:affiliation | grid-institutes:grid.9835.7 |
172 | ″ | schema:familyName | Tso |
173 | ″ | schema:givenName | Chak-Hau Michael |
174 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011557056561.74 |
175 | ″ | rdf:type | schema:Person |
176 | sg:person.013753244525.37 | schema:affiliation | grid-institutes:grid.49470.3e |
177 | ″ | schema:familyName | Shi |
178 | ″ | schema:givenName | Liangsheng |
179 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013753244525.37 |
180 | ″ | rdf:type | schema:Person |
181 | sg:pub.10.1007/978-3-030-18383-7 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1117043784 |
182 | ″ | ″ | https://doi.org/10.1007/978-3-030-18383-7 |
183 | ″ | rdf:type | schema:CreativeWork |
184 | sg:pub.10.1007/s00477-008-0289-4 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1029417657 |
185 | ″ | ″ | https://doi.org/10.1007/s00477-008-0289-4 |
186 | ″ | rdf:type | schema:CreativeWork |
187 | sg:pub.10.1007/s00477-010-0392-1 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1029308599 |
188 | ″ | ″ | https://doi.org/10.1007/s00477-010-0392-1 |
189 | ″ | rdf:type | schema:CreativeWork |
190 | sg:pub.10.1007/s00477-012-0613-x | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1008018272 |
191 | ″ | ″ | https://doi.org/10.1007/s00477-012-0613-x |
192 | ″ | rdf:type | schema:CreativeWork |
193 | sg:pub.10.1007/s00477-018-1541-1 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1103191079 |
194 | ″ | ″ | https://doi.org/10.1007/s00477-018-1541-1 |
195 | ″ | rdf:type | schema:CreativeWork |
196 | sg:pub.10.1007/s00477-019-01764-1 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1124590212 |
197 | ″ | ″ | https://doi.org/10.1007/s00477-019-01764-1 |
198 | ″ | rdf:type | schema:CreativeWork |
199 | sg:pub.10.1007/s00477-020-01815-y | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1127517851 |
200 | ″ | ″ | https://doi.org/10.1007/s00477-020-01815-y |
201 | ″ | rdf:type | schema:CreativeWork |
202 | sg:pub.10.1007/s10596-013-9351-5 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1000005781 |
203 | ″ | ″ | https://doi.org/10.1007/s10596-013-9351-5 |
204 | ″ | rdf:type | schema:CreativeWork |
205 | grid-institutes:None | schema:alternateName | Department of Meteorology and Climatology, Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, 127550, Moscow, Russia |
206 | ″ | schema:name | Department of Meteorology and Climatology, Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, 127550, Moscow, Russia |
207 | ″ | rdf:type | schema:Organization |
208 | grid-institutes:grid.440679.8 | schema:alternateName | Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, 400074, Chongqing, China |
209 | ″ | schema:name | Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, 400074, Chongqing, China |
210 | ″ | ″ | State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China |
211 | ″ | rdf:type | schema:Organization |
212 | grid-institutes:grid.49470.3e | schema:alternateName | State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China |
213 | ″ | schema:name | State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, 430072, Wuhan, Hubei, China |
214 | ″ | rdf:type | schema:Organization |
215 | grid-institutes:grid.9835.7 | schema:alternateName | Lancaster Environment Centre, Lancaster University, Lancaster, UK |
216 | ″ | schema:name | Lancaster Environment Centre, Lancaster University, Lancaster, UK |
217 | ″ | ″ | UK Centre for Ecology and Hydrology, Lancaster, UK |
218 | ″ | rdf:type | schema:Organization |