ClustSnow: Utilizing temporally persistent forest snow patterns under variable environmental conditions Article Swipe
Joschka Geissler
,
Giulia Mazzotti
,
Lars Rathmann
,
Clare Webster
,
Markus Weiler
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/essoar.172222597.78203131/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/essoar.172222597.78203131/v1
Snow plays a crucial role in regulating water availability in eco-hydrological systems. Its spatial distribution is key for understanding melting dynamics, particularly in forests where snow amounts vary on small spatial scales (<3 m) compared to open areas. Uncrewed Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR) measurements can quantify the snow distribution in forests at sufficiently high spatial resolution. Such datasets showed that snow distribution can be aggregated into spatial patterns featuring similar snow dynamics. However, there is no suitable dataset available to investigate whether these patterns persist throughout different seasons and how they differ between sites. This study introduces a new dataset comprising UAV-based LiDAR surveys, a dense automatic snow depth sensor network, and additional ground measurements, covering three seasons at two forested sites. The identification of snow distribution patterns from LiDAR data in one season is achieved using a clustering workflow, first presented by Geissler et al. (2023a). Identified patterns are subsequently used for spatially extrapolating observed time series of snow depth and snow water equivalent from a few locations. The results show that snow patterns are influenced by site-specific factors such as wind or radiation but are persistent over time. A comparison with physics-based snow model simulations underlines the added value of the presented observation-based snow products allowing a spatiotemporally continuous analysis of discrepancies. This study therefore advances our understanding of using forest snow patterns to create high-resolution, spatiotemporally continuous snow products with reduced measurement or computational effort.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/essoar.172222597.78203131/v1
- https://essopenarchive.org/doi/pdf/10.22541/essoar.172222597.78203131
- OA Status
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- References
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- OpenAlex ID
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https://doi.org/10.22541/essoar.172222597.78203131/v1Digital Object Identifier
- Title
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ClustSnow: Utilizing temporally persistent forest snow patterns under variable environmental conditionsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-29Full publication date if available
- Authors
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Joschka Geissler, Giulia Mazzotti, Lars Rathmann, Clare Webster, Markus WeilerList of authors in order
- Landing page
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https://doi.org/10.22541/essoar.172222597.78203131/v1Publisher landing page
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https://essopenarchive.org/doi/pdf/10.22541/essoar.172222597.78203131Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://essopenarchive.org/doi/pdf/10.22541/essoar.172222597.78203131Direct OA link when available
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Snow, Environmental science, Lidar, Spatial distribution, Remote sensing, Spatial ecology, Physical geography, Meteorology, Geography, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 17, 156 |
| abstract_inverted_index.how | 93 |
| abstract_inverted_index.key | 16 |
| abstract_inverted_index.new | 102 |
| abstract_inverted_index.one | 136 |
| abstract_inverted_index.our | 222 |
| abstract_inverted_index.the | 50, 202, 206 |
| abstract_inverted_index.two | 123 |
| abstract_inverted_index.Snow | 0 |
| abstract_inverted_index.Such | 60 |
| abstract_inverted_index.This | 98, 218 |
| abstract_inverted_index.data | 134 |
| abstract_inverted_index.from | 132, 169 |
| abstract_inverted_index.high | 57 |
| abstract_inverted_index.into | 69 |
| abstract_inverted_index.open | 36 |
| abstract_inverted_index.over | 192 |
| abstract_inverted_index.role | 4 |
| abstract_inverted_index.show | 175 |
| abstract_inverted_index.snow | 25, 51, 64, 74, 111, 129, 163, 166, 177, 198, 209, 227, 234 |
| abstract_inverted_index.such | 184 |
| abstract_inverted_index.that | 63, 176 |
| abstract_inverted_index.they | 94 |
| abstract_inverted_index.time | 160 |
| abstract_inverted_index.used | 155 |
| abstract_inverted_index.vary | 27 |
| abstract_inverted_index.wind | 186 |
| abstract_inverted_index.with | 196, 236 |
| abstract_inverted_index.LiDAR | 106, 133 |
| abstract_inverted_index.Light | 42 |
| abstract_inverted_index.added | 203 |
| abstract_inverted_index.dense | 109 |
| abstract_inverted_index.depth | 112, 164 |
| abstract_inverted_index.first | 144 |
| abstract_inverted_index.model | 199 |
| abstract_inverted_index.plays | 1 |
| abstract_inverted_index.small | 29 |
| abstract_inverted_index.study | 99, 219 |
| abstract_inverted_index.there | 77 |
| abstract_inverted_index.these | 86 |
| abstract_inverted_index.three | 120 |
| abstract_inverted_index.time. | 193 |
| abstract_inverted_index.using | 140, 225 |
| abstract_inverted_index.value | 204 |
| abstract_inverted_index.water | 7, 167 |
| abstract_inverted_index.where | 24 |
| abstract_inverted_index.Aerial | 39 |
| abstract_inverted_index.areas. | 37 |
| abstract_inverted_index.create | 230 |
| abstract_inverted_index.differ | 95 |
| abstract_inverted_index.forest | 226 |
| abstract_inverted_index.ground | 117 |
| abstract_inverted_index.scales | 31 |
| abstract_inverted_index.season | 137 |
| abstract_inverted_index.sensor | 113 |
| abstract_inverted_index.series | 161 |
| abstract_inverted_index.showed | 62 |
| abstract_inverted_index.sites. | 97, 125 |
| abstract_inverted_index.(LiDAR) | 46 |
| abstract_inverted_index.Ranging | 45 |
| abstract_inverted_index.Vehicle | 40 |
| abstract_inverted_index.amounts | 26 |
| abstract_inverted_index.between | 96 |
| abstract_inverted_index.crucial | 3 |
| abstract_inverted_index.dataset | 81, 103 |
| abstract_inverted_index.effort. | 241 |
| abstract_inverted_index.factors | 183 |
| abstract_inverted_index.forests | 23, 54 |
| abstract_inverted_index.melting | 19 |
| abstract_inverted_index.persist | 88 |
| abstract_inverted_index.reduced | 237 |
| abstract_inverted_index.results | 174 |
| abstract_inverted_index.seasons | 91, 121 |
| abstract_inverted_index.similar | 73 |
| abstract_inverted_index.spatial | 13, 30, 58, 70 |
| abstract_inverted_index.whether | 85 |
| abstract_inverted_index.(2023a). | 150 |
| abstract_inverted_index.Geissler | 147 |
| abstract_inverted_index.However, | 76 |
| abstract_inverted_index.Uncrewed | 38 |
| abstract_inverted_index.achieved | 139 |
| abstract_inverted_index.advances | 221 |
| abstract_inverted_index.allowing | 211 |
| abstract_inverted_index.analysis | 215 |
| abstract_inverted_index.compared | 34 |
| abstract_inverted_index.covering | 119 |
| abstract_inverted_index.datasets | 61 |
| abstract_inverted_index.forested | 124 |
| abstract_inverted_index.network, | 114 |
| abstract_inverted_index.observed | 159 |
| abstract_inverted_index.patterns | 71, 87, 131, 152, 178, 228 |
| abstract_inverted_index.products | 210, 235 |
| abstract_inverted_index.quantify | 49 |
| abstract_inverted_index.suitable | 80 |
| abstract_inverted_index.surveys, | 107 |
| abstract_inverted_index.systems. | 11 |
| abstract_inverted_index.Detection | 43 |
| abstract_inverted_index.UAV-based | 105 |
| abstract_inverted_index.automatic | 110 |
| abstract_inverted_index.available | 82 |
| abstract_inverted_index.different | 90 |
| abstract_inverted_index.dynamics, | 20 |
| abstract_inverted_index.dynamics. | 75 |
| abstract_inverted_index.featuring | 72 |
| abstract_inverted_index.presented | 145, 207 |
| abstract_inverted_index.radiation | 188 |
| abstract_inverted_index.spatially | 157 |
| abstract_inverted_index.therefore | 220 |
| abstract_inverted_index.workflow, | 143 |
| abstract_inverted_index.Identified | 151 |
| abstract_inverted_index.additional | 116 |
| abstract_inverted_index.aggregated | 68 |
| abstract_inverted_index.clustering | 142 |
| abstract_inverted_index.comparison | 195 |
| abstract_inverted_index.comprising | 104 |
| abstract_inverted_index.continuous | 214, 233 |
| abstract_inverted_index.equivalent | 168 |
| abstract_inverted_index.influenced | 180 |
| abstract_inverted_index.introduces | 100 |
| abstract_inverted_index.locations. | 172 |
| abstract_inverted_index.persistent | 191 |
| abstract_inverted_index.regulating | 6 |
| abstract_inverted_index.throughout | 89 |
| abstract_inverted_index.underlines | 201 |
| abstract_inverted_index.(UAV)-based | 41 |
| abstract_inverted_index.investigate | 84 |
| abstract_inverted_index.measurement | 238 |
| abstract_inverted_index.resolution. | 59 |
| abstract_inverted_index.simulations | 200 |
| abstract_inverted_index.availability | 8 |
| abstract_inverted_index.distribution | 14, 52, 65, 130 |
| abstract_inverted_index.measurements | 47 |
| abstract_inverted_index.particularly | 21 |
| abstract_inverted_index.subsequently | 154 |
| abstract_inverted_index.sufficiently | 56 |
| abstract_inverted_index.computational | 240 |
| abstract_inverted_index.extrapolating | 158 |
| abstract_inverted_index.measurements, | 118 |
| abstract_inverted_index.physics-based | 197 |
| abstract_inverted_index.site-specific | 182 |
| abstract_inverted_index.understanding | 18, 223 |
| abstract_inverted_index.discrepancies. | 217 |
| abstract_inverted_index.identification | 127 |
| abstract_inverted_index.eco-hydrological | 10 |
| abstract_inverted_index.high-resolution, | 231 |
| abstract_inverted_index.spatiotemporally | 213, 232 |
| abstract_inverted_index.observation-based | 208 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5084830192 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I161046081 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.18579426 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |