Changes
On April 27, 2021 at 2:16:42 PM UTC, Giulia Mazzotti:
-
Updated description of Forest canopy structure data for radiation and snow modelling (CH/FIN) from
This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankylä, Finland. Data include: * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each) * a Canopy Height Model derived from airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2) These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication: _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. doi: 10.1029/2020WR029064_ This publication must be cited when using the data. ### See also: For additional information on the FSM2 model, see the corresponding GitHub repository: https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. doi: 10.1029/2020WR027572_
toThis dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankylä, Finland. Data include: * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each) * a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2) These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication: _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. doi: 10.1029/2020WR029064_ This publication must be cited when using the data. ### See also: For additional information on the FSM2 model, see the corresponding GitHub repository: https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. doi: 10.1029/2020WR027572_
f | 1 | { | f | 1 | { |
2 | "author": "[{\"affiliation\": \"SLF\", \"affiliation_02\": \"\", | 2 | "author": "[{\"affiliation\": \"SLF\", \"affiliation_02\": \"\", | ||
3 | \"affiliation_03\": \"\", \"data_credit\": [\"collection\", | 3 | \"affiliation_03\": \"\", \"data_credit\": [\"collection\", | ||
4 | \"curation\", \"publication\", \"supervision\"], \"email\": | 4 | \"curation\", \"publication\", \"supervision\"], \"email\": | ||
5 | \"giulia.mazzotti@slf.ch\", \"given_name\": \"Giulia\", | 5 | \"giulia.mazzotti@slf.ch\", \"given_name\": \"Giulia\", | ||
6 | \"identifier\": \"0000-0003-3857-7449\", \"name\": \"Mazzotti\"}, | 6 | \"identifier\": \"0000-0003-3857-7449\", \"name\": \"Mazzotti\"}, | ||
7 | {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", | 7 | {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", | ||
8 | \"affiliation_03\": \"\", \"data_credit\": \"validation\", \"email\": | 8 | \"affiliation_03\": \"\", \"data_credit\": \"validation\", \"email\": | ||
9 | \"clare.webster@wsl.ch\", \"given_name\": \"Clare\", \"identifier\": | 9 | \"clare.webster@wsl.ch\", \"given_name\": \"Clare\", \"identifier\": | ||
10 | \"0000-0002-6386-6392\", \"name\": \"Webster\"}, {\"affiliation\": | 10 | \"0000-0002-6386-6392\", \"name\": \"Webster\"}, {\"affiliation\": | ||
11 | \"SLF\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | 11 | \"SLF\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | ||
12 | \"data_credit\": \"supervision\", \"email\": \"jonas@slf.ch\", | 12 | \"data_credit\": \"supervision\", \"email\": \"jonas@slf.ch\", | ||
13 | \"given_name\": \"Tobias \", \"identifier\": \"0000-0003-0386-8676\", | 13 | \"given_name\": \"Tobias \", \"identifier\": \"0000-0003-0386-8676\", | ||
14 | \"name\": \"Jonas\"}]", | 14 | \"name\": \"Jonas\"}]", | ||
15 | "author_email": null, | 15 | "author_email": null, | ||
16 | "creator_user_id": "e0656282-3e6c-40f0-a967-c78d92e9392a", | 16 | "creator_user_id": "e0656282-3e6c-40f0-a967-c78d92e9392a", | ||
17 | "date": "[{\"date\": \"2019-01-01\", \"date_type\": \"collected\", | 17 | "date": "[{\"date\": \"2019-01-01\", \"date_type\": \"collected\", | ||
18 | \"end_date\": \"2020-01-01\"}]", | 18 | \"end_date\": \"2020-01-01\"}]", | ||
19 | "doi": "10.16904/envidat.220", | 19 | "doi": "10.16904/envidat.220", | ||
20 | "funding": "[{\"grant_number\": \"200021_169213\", \"institution\": | 20 | "funding": "[{\"grant_number\": \"200021_169213\", \"institution\": | ||
21 | \"Swiss National Science Foundation \", \"institution_url\": \"\"}]", | 21 | \"Swiss National Science Foundation \", \"institution_url\": \"\"}]", | ||
22 | "groups": [], | 22 | "groups": [], | ||
23 | "id": "117187b4-59b9-403c-a493-d2a6e1c97631", | 23 | "id": "117187b4-59b9-403c-a493-d2a6e1c97631", | ||
24 | "isopen": false, | 24 | "isopen": false, | ||
25 | "language": "en", | 25 | "language": "en", | ||
26 | "license_id": "wsl-data", | 26 | "license_id": "wsl-data", | ||
27 | "license_title": "WSL Data Policy", | 27 | "license_title": "WSL Data Policy", | ||
28 | "license_url": | 28 | "license_url": | ||
29 | ps://www.wsl.ch/en/about-wsl/programmes-and-initiatives/envidat.html", | 29 | ps://www.wsl.ch/en/about-wsl/programmes-and-initiatives/envidat.html", | ||
30 | "maintainer": "{\"affiliation\": \"SLF\", \"email\": | 30 | "maintainer": "{\"affiliation\": \"SLF\", \"email\": | ||
31 | \"giulia.mazzotti@slf.ch\", \"given_name\": \"Giulia\", | 31 | \"giulia.mazzotti@slf.ch\", \"given_name\": \"Giulia\", | ||
32 | \"identifier\": \"0000-0003-3857-7449\", \"name\": \"Mazzotti\"}", | 32 | \"identifier\": \"0000-0003-3857-7449\", \"name\": \"Mazzotti\"}", | ||
33 | "maintainer_email": null, | 33 | "maintainer_email": null, | ||
34 | "metadata_created": "2020-10-22T19:57:45.319314", | 34 | "metadata_created": "2020-10-22T19:57:45.319314", | ||
n | 35 | "metadata_modified": "2021-04-27T14:16:02.378496", | n | 35 | "metadata_modified": "2021-04-27T14:16:42.314923", |
36 | "name": "chm-hp-4rtm", | 36 | "name": "chm-hp-4rtm", | ||
37 | "notes": "This dataset contains forest canopy structure data | 37 | "notes": "This dataset contains forest canopy structure data | ||
38 | acquired in a spruce forest at Laret, Switzerland, and a pine forest | 38 | acquired in a spruce forest at Laret, Switzerland, and a pine forest | ||
39 | at Sodankyl\u00e4, Finland. Data include: \r\n\r\n* Hemispherical | 39 | at Sodankyl\u00e4, Finland. Data include: \r\n\r\n* Hemispherical | ||
40 | photographs taken at transect intersection points of 13 experimental | 40 | photographs taken at transect intersection points of 13 experimental | ||
t | 41 | plots (40x40m each)\r\n* a Canopy Height Model derived from airborne | t | 41 | plots (40x40m each)\r\n* a Canopy Height Model (tree height map) |
42 | LiDAR data, encompassing the entire simulation domain at Laret | 42 | derived by rasterizing airborne LiDAR data, encompassing the entire | ||
43 | (150'000 m2)\r\n\r\nThese data provide the necessary basis for | 43 | simulation domain at Laret (150'000 m2)\r\n\r\nThese data provide the | ||
44 | creating canopy structure datasets to be used as input to the forest | 44 | necessary basis for creating canopy structure datasets to be used as | ||
45 | snow snow model FSM2. These datasets, the model input derivatives and | 45 | input to the forest snow snow model FSM2. These datasets, the model | ||
46 | the radiation and snow modelling are described in detail in the | 46 | input derivatives and the radiation and snow modelling are described | ||
47 | following publication:\r\n\r\n_Mazzotti, G., Webster, C., Essery, R., | 47 | in detail in the following publication:\r\n\r\n_Mazzotti, G., Webster, | ||
48 | and Jonas, T. (2021) Improving the physical representation of forest | 48 | C., Essery, R., and Jonas, T. (2021) Improving the physical | ||
49 | snow processes in coarse-resolution models: lessons learned from | 49 | representation of forest snow processes in coarse-resolution models: | ||
50 | upscaling hyper-resolution simulations. Water Resources Research 57, | 50 | lessons learned from upscaling hyper-resolution simulations. Water | ||
51 | e2020WR029064. doi: 10.1029/2020WR029064_\r\n\r\nThis publication must | 51 | Resources Research 57, e2020WR029064. doi: | ||
52 | be cited when using the data. \r\n\r\n### See also: \r\nFor additional | 52 | 10.1029/2020WR029064_\r\n\r\nThis publication must be cited when using | ||
53 | information on the FSM2 model, see the corresponding GitHub | 53 | the data. \r\n\r\n### See also: \r\nFor additional information on the | ||
54 | repository: | 54 | FSM2 model, see the corresponding GitHub repository: | ||
55 | //github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy\r\n\r\nThe | 55 | //github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy\r\n\r\nThe | ||
56 | datasets and the model have also been used in _Mazzotti et al. (2020) | 56 | datasets and the model have also been used in _Mazzotti et al. (2020) | ||
57 | Process-level evaluation of a hyper-resolution forest snow model using | 57 | Process-level evaluation of a hyper-resolution forest snow model using | ||
58 | distributed multi-sensor observations. doi: | 58 | distributed multi-sensor observations. doi: | ||
59 | 10.1029/2020WR027572_\r\n\r\n", | 59 | 10.1029/2020WR027572_\r\n\r\n", | ||
60 | "num_resources": 2, | 60 | "num_resources": 2, | ||
61 | "num_tags": 5, | 61 | "num_tags": 5, | ||
62 | "organization": { | 62 | "organization": { | ||
63 | "approval_status": "approved", | 63 | "approval_status": "approved", | ||
64 | "created": "2016-09-02T14:17:02.029939", | 64 | "created": "2016-09-02T14:17:02.029939", | ||
65 | "description": " The Research Unit investigates natural hazard | 65 | "description": " The Research Unit investigates natural hazard | ||
66 | processes in mountainous areas, in particular the triggering and | 66 | processes in mountainous areas, in particular the triggering and | ||
67 | propagation of floods, sediment transport, landslides, debris flows | 67 | propagation of floods, sediment transport, landslides, debris flows | ||
68 | and rock fall. Process studies on the scale of slopes, channels and | 68 | and rock fall. Process studies on the scale of slopes, channels and | ||
69 | catchments form the basis for the development of simulation models and | 69 | catchments form the basis for the development of simulation models and | ||
70 | of hazard assessment procedures and for the design of countermeasures. | 70 | of hazard assessment procedures and for the design of countermeasures. | ||
71 | To this end worldwide unique observation systems are developed, such | 71 | To this end worldwide unique observation systems are developed, such | ||
72 | as a debris flow balance and geophone systems for bedload transport. | 72 | as a debris flow balance and geophone systems for bedload transport. | ||
73 | Damage and damaging processes due to frequent and extreme events are | 73 | Damage and damaging processes due to frequent and extreme events are | ||
74 | assessed as prerequisites for the risk-based and sustainable | 74 | assessed as prerequisites for the risk-based and sustainable | ||
75 | management of natural hazards.\r\n\r\nA second focus of the unit is on | 75 | management of natural hazards.\r\n\r\nA second focus of the unit is on | ||
76 | the estimation and prediction of snow and water resources, e.g. for | 76 | the estimation and prediction of snow and water resources, e.g. for | ||
77 | hydropower production or early recognition of drought. The unit | 77 | hydropower production or early recognition of drought. The unit | ||
78 | operates a snow hydrological service for federal and cantonal agencies | 78 | operates a snow hydrological service for federal and cantonal agencies | ||
79 | and a drought information platform for a broad range of water | 79 | and a drought information platform for a broad range of water | ||
80 | users.\r\n\r\n__Further information__: | 80 | users.\r\n\r\n__Further information__: | ||
81 | .ch/en/about-wsl/organization/research-units/mountain-hydrology.html", | 81 | .ch/en/about-wsl/organization/research-units/mountain-hydrology.html", | ||
82 | "id": "8ca28da8-ad8f-401a-9356-0aa7fdba2604", | 82 | "id": "8ca28da8-ad8f-401a-9356-0aa7fdba2604", | ||
83 | "image_url": "2018-07-10-091052.004438LogoWSL.svg", | 83 | "image_url": "2018-07-10-091052.004438LogoWSL.svg", | ||
84 | "is_organization": true, | 84 | "is_organization": true, | ||
85 | "name": "gebirgshydrologie", | 85 | "name": "gebirgshydrologie", | ||
86 | "state": "active", | 86 | "state": "active", | ||
87 | "title": "GebirgsHydrologie", | 87 | "title": "GebirgsHydrologie", | ||
88 | "type": "organization" | 88 | "type": "organization" | ||
89 | }, | 89 | }, | ||
90 | "owner_org": "8ca28da8-ad8f-401a-9356-0aa7fdba2604", | 90 | "owner_org": "8ca28da8-ad8f-401a-9356-0aa7fdba2604", | ||
91 | "private": false, | 91 | "private": false, | ||
92 | "publication": "{\"publication_year\": \"2020\", \"publisher\": | 92 | "publication": "{\"publication_year\": \"2020\", \"publisher\": | ||
93 | \"EnviDat\"}", | 93 | \"EnviDat\"}", | ||
94 | "publication_state": "pub_pending", | 94 | "publication_state": "pub_pending", | ||
95 | "related_datasets": "", | 95 | "related_datasets": "", | ||
96 | "related_publications": "Mazzotti, G., Webster, C., Essery, R., and | 96 | "related_publications": "Mazzotti, G., Webster, C., Essery, R., and | ||
97 | Jonas, T. (2020) Improved representation of forest snow processes in | 97 | Jonas, T. (2020) Improved representation of forest snow processes in | ||
98 | coarse-resolution models: lessons learnt from upscaling | 98 | coarse-resolution models: lessons learnt from upscaling | ||
99 | hyper-resolution simulations. Submitted to Water Resources Research.", | 99 | hyper-resolution simulations. Submitted to Water Resources Research.", | ||
100 | "relationships_as_object": [], | 100 | "relationships_as_object": [], | ||
101 | "relationships_as_subject": [], | 101 | "relationships_as_subject": [], | ||
102 | "resource_type": "dataset", | 102 | "resource_type": "dataset", | ||
103 | "resource_type_general": "dataset", | 103 | "resource_type_general": "dataset", | ||
104 | "resources": [ | 104 | "resources": [ | ||
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108 | "created": "2020-10-22T20:08:02.820426", | 108 | "created": "2020-10-22T20:08:02.820426", | ||
109 | "description": "Hemispherical images at the intersection points | 109 | "description": "Hemispherical images at the intersection points | ||
110 | of the experimental plots at Laret (CH) and Sodankyl\u00e4 (FIN)", | 110 | of the experimental plots at Laret (CH) and Sodankyl\u00e4 (FIN)", | ||
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136 | "created": "2020-10-22T20:10:44.306886", | 136 | "created": "2020-10-22T20:10:44.306886", | ||
137 | "description": "Canopy height model of the lidar domain at | 137 | "description": "Canopy height model of the lidar domain at | ||
138 | Laret", | 138 | Laret", | ||
139 | "doi": "", | 139 | "doi": "", | ||
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161 | ], | 161 | ], | ||
162 | "spatial": | 162 | "spatial": | ||
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164 | "spatial_info": "Switzerland, Finland", | 164 | "spatial_info": "Switzerland, Finland", | ||
165 | "state": "active", | 165 | "state": "active", | ||
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167 | "tags": [ | 167 | "tags": [ | ||
168 | { | 168 | { | ||
169 | "display_name": "ENERGY BALANCE", | 169 | "display_name": "ENERGY BALANCE", | ||
170 | "id": "b1503a80-568e-4f95-b638-7267bc12cc0a", | 170 | "id": "b1503a80-568e-4f95-b638-7267bc12cc0a", | ||
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173 | "vocabulary_id": null | 173 | "vocabulary_id": null | ||
174 | }, | 174 | }, | ||
175 | { | 175 | { | ||
176 | "display_name": "FOREST CANOPY", | 176 | "display_name": "FOREST CANOPY", | ||
177 | "id": "e09c7bc3-8d21-4a36-8bd0-0c222dd74e7b", | 177 | "id": "e09c7bc3-8d21-4a36-8bd0-0c222dd74e7b", | ||
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179 | "state": "active", | 179 | "state": "active", | ||
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181 | }, | 181 | }, | ||
182 | { | 182 | { | ||
183 | "display_name": "MASS BALANCE", | 183 | "display_name": "MASS BALANCE", | ||
184 | "id": "3a7f131c-08f0-430b-874f-1165ac70bbfa", | 184 | "id": "3a7f131c-08f0-430b-874f-1165ac70bbfa", | ||
185 | "name": "MASS BALANCE", | 185 | "name": "MASS BALANCE", | ||
186 | "state": "active", | 186 | "state": "active", | ||
187 | "vocabulary_id": null | 187 | "vocabulary_id": null | ||
188 | }, | 188 | }, | ||
189 | { | 189 | { | ||
190 | "display_name": "SNOW MODELLING", | 190 | "display_name": "SNOW MODELLING", | ||
191 | "id": "ac0cab5d-dbcb-47da-bf2b-412714fd87ec", | 191 | "id": "ac0cab5d-dbcb-47da-bf2b-412714fd87ec", | ||
192 | "name": "SNOW MODELLING", | 192 | "name": "SNOW MODELLING", | ||
193 | "state": "active", | 193 | "state": "active", | ||
194 | "vocabulary_id": null | 194 | "vocabulary_id": null | ||
195 | }, | 195 | }, | ||
196 | { | 196 | { | ||
197 | "display_name": "UPSCALING", | 197 | "display_name": "UPSCALING", | ||
198 | "id": "fd5090a2-303f-4860-8d2e-de26f8f230de", | 198 | "id": "fd5090a2-303f-4860-8d2e-de26f8f230de", | ||
199 | "name": "UPSCALING", | 199 | "name": "UPSCALING", | ||
200 | "state": "active", | 200 | "state": "active", | ||
201 | "vocabulary_id": null | 201 | "vocabulary_id": null | ||
202 | } | 202 | } | ||
203 | ], | 203 | ], | ||
204 | "title": "Forest canopy structure data for radiation and snow | 204 | "title": "Forest canopy structure data for radiation and snow | ||
205 | modelling (CH/FIN)", | 205 | modelling (CH/FIN)", | ||
206 | "type": "dataset", | 206 | "type": "dataset", | ||
207 | "url": null, | 207 | "url": null, | ||
208 | "version": "1.0" | 208 | "version": "1.0" | ||
209 | } | 209 | } |