A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags Article Swipe
Benjamin H. Letcher
,
Daniel J. Hocking
,
Kyle O’Neill
,
Andrew R. Whiteley
,
Keith H. Nislow
,
Matthew J. O’Donnell
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.7287/peerj.preprints.1578
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.7287/peerj.preprints.1578
Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y -1 ) and a widening of the synchronized period (2.9 d · y -1 ). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.7287/peerj.preprints.1578
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4232546597
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4232546597Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7287/peerj.preprints.1578Digital Object Identifier
- Title
-
A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lagsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-12-10Full publication date if available
- Authors
-
Benjamin H. Letcher, Daniel J. Hocking, Kyle O’Neill, Andrew R. Whiteley, Keith H. Nislow, Matthew J. O’DonnellList of authors in order
- Landing page
-
https://doi.org/10.7287/peerj.preprints.1578Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7287/peerj.preprints.1578Direct OA link when available
- Concepts
-
Autocorrelation, Missing data, Mean squared error, Environmental science, Air temperature, STREAMS, Climate change, Meteorology, Statistics, Computer science, Mathematics, Geography, Geology, Oceanography, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2018: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4232546597 |
|---|---|
| doi | https://doi.org/10.7287/peerj.preprints.1578 |
| ids.doi | https://doi.org/10.7287/peerj.preprints.1578 |
| ids.openalex | https://openalex.org/W4232546597 |
| fwci | 0.3531557 |
| type | preprint |
| title | A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10302 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9987000226974487 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2309 |
| topics[0].subfield.display_name | Nature and Landscape Conservation |
| topics[0].display_name | Fish Ecology and Management Studies |
| topics[1].id | https://openalex.org/T10330 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9973000288009644 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2312 |
| topics[1].subfield.display_name | Water Science and Technology |
| topics[1].display_name | Hydrology and Watershed Management Studies |
| topics[2].id | https://openalex.org/T11490 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9926999807357788 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2305 |
| topics[2].subfield.display_name | Environmental Engineering |
| topics[2].display_name | Hydrological Forecasting Using AI |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C5297727 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6502864360809326 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q786970 |
| concepts[0].display_name | Autocorrelation |
| concepts[1].id | https://openalex.org/C9357733 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5901258587837219 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q6878417 |
| concepts[1].display_name | Missing data |
| concepts[2].id | https://openalex.org/C139945424 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5699160099029541 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1940696 |
| concepts[2].display_name | Mean squared error |
| concepts[3].id | https://openalex.org/C39432304 |
| concepts[3].level | 0 |
| concepts[3].score | 0.5384760499000549 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[3].display_name | Environmental science |
| concepts[4].id | https://openalex.org/C2983363897 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5045515298843384 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q845339 |
| concepts[4].display_name | Air temperature |
| concepts[5].id | https://openalex.org/C42090638 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4658423662185669 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q4048907 |
| concepts[5].display_name | STREAMS |
| concepts[6].id | https://openalex.org/C132651083 |
| concepts[6].level | 2 |
| concepts[6].score | 0.41018468141555786 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7942 |
| concepts[6].display_name | Climate change |
| concepts[7].id | https://openalex.org/C153294291 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3375764489173889 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[7].display_name | Meteorology |
| concepts[8].id | https://openalex.org/C105795698 |
| concepts[8].level | 1 |
| concepts[8].score | 0.33405694365501404 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[8].display_name | Statistics |
| concepts[9].id | https://openalex.org/C41008148 |
| concepts[9].level | 0 |
| concepts[9].score | 0.31622737646102905 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[9].display_name | Computer science |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.19964075088500977 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.1898401975631714 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C127313418 |
| concepts[12].level | 0 |
| concepts[12].score | 0.09408077597618103 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[12].display_name | Geology |
| concepts[13].id | https://openalex.org/C111368507 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q43518 |
| concepts[13].display_name | Oceanography |
| concepts[14].id | https://openalex.org/C31258907 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[14].display_name | Computer network |
| keywords[0].id | https://openalex.org/keywords/autocorrelation |
| keywords[0].score | 0.6502864360809326 |
| keywords[0].display_name | Autocorrelation |
| keywords[1].id | https://openalex.org/keywords/missing-data |
| keywords[1].score | 0.5901258587837219 |
| keywords[1].display_name | Missing data |
| keywords[2].id | https://openalex.org/keywords/mean-squared-error |
| keywords[2].score | 0.5699160099029541 |
| keywords[2].display_name | Mean squared error |
| keywords[3].id | https://openalex.org/keywords/environmental-science |
| keywords[3].score | 0.5384760499000549 |
| keywords[3].display_name | Environmental science |
| keywords[4].id | https://openalex.org/keywords/air-temperature |
| keywords[4].score | 0.5045515298843384 |
| keywords[4].display_name | Air temperature |
| keywords[5].id | https://openalex.org/keywords/streams |
| keywords[5].score | 0.4658423662185669 |
| keywords[5].display_name | STREAMS |
| keywords[6].id | https://openalex.org/keywords/climate-change |
| keywords[6].score | 0.41018468141555786 |
| keywords[6].display_name | Climate change |
| keywords[7].id | https://openalex.org/keywords/meteorology |
| keywords[7].score | 0.3375764489173889 |
| keywords[7].display_name | Meteorology |
| keywords[8].id | https://openalex.org/keywords/statistics |
| keywords[8].score | 0.33405694365501404 |
| keywords[8].display_name | Statistics |
| keywords[9].id | https://openalex.org/keywords/computer-science |
| keywords[9].score | 0.31622737646102905 |
| keywords[9].display_name | Computer science |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.19964075088500977 |
| keywords[10].display_name | Mathematics |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.1898401975631714 |
| keywords[11].display_name | Geography |
| keywords[12].id | https://openalex.org/keywords/geology |
| keywords[12].score | 0.09408077597618103 |
| keywords[12].display_name | Geology |
| language | en |
| locations[0].id | doi:10.7287/peerj.preprints.1578 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.7287/peerj.preprints.1578 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5076551257 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0191-5678 |
| authorships[0].author.display_name | Benjamin H. Letcher |
| authorships[0].affiliations[0].raw_affiliation_string | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Benjamin H Letcher |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[1].author.id | https://openalex.org/A5008626690 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-1889-9184 |
| authorships[1].author.display_name | Daniel J. Hocking |
| authorships[1].affiliations[0].raw_affiliation_string | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Daniel J Hocking |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[2].author.id | https://openalex.org/A5021898226 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Kyle O’Neill |
| authorships[2].affiliations[0].raw_affiliation_string | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kyle O'Neill |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[3].author.id | https://openalex.org/A5046465487 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8159-6381 |
| authorships[3].author.display_name | Andrew R. Whiteley |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I24603500 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Environmental Conservation, University of Massachusetts, Amherst, USA |
| authorships[3].institutions[0].id | https://openalex.org/I24603500 |
| authorships[3].institutions[0].ror | https://ror.org/0072zz521 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I24603500 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Massachusetts Amherst |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Andrew R Whiteley |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Environmental Conservation, University of Massachusetts, Amherst, USA |
| authorships[4].author.id | https://openalex.org/A5068671517 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-8051-5765 |
| authorships[4].author.display_name | Keith H. Nislow |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1313416372, https://openalex.org/I4210107718 |
| authorships[4].affiliations[0].raw_affiliation_string | Northern Research Station, US Forest Service, Amherst, Massachusetts, USA |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I24603500 |
| authorships[4].affiliations[1].raw_affiliation_string | Department of Environmental Conservation, University of Massachusetts, Amherst, USA |
| authorships[4].institutions[0].id | https://openalex.org/I4210107718 |
| authorships[4].institutions[0].ror | https://ror.org/019jdc178 |
| authorships[4].institutions[0].type | government |
| authorships[4].institutions[0].lineage | https://openalex.org/I1313416372, https://openalex.org/I1336096307, https://openalex.org/I4210107718 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Northern Research Station |
| authorships[4].institutions[1].id | https://openalex.org/I1313416372 |
| authorships[4].institutions[1].ror | https://ror.org/03zmjc935 |
| authorships[4].institutions[1].type | government |
| authorships[4].institutions[1].lineage | https://openalex.org/I1313416372, https://openalex.org/I1336096307 |
| authorships[4].institutions[1].country_code | US |
| authorships[4].institutions[1].display_name | US Forest Service |
| authorships[4].institutions[2].id | https://openalex.org/I24603500 |
| authorships[4].institutions[2].ror | https://ror.org/0072zz521 |
| authorships[4].institutions[2].type | education |
| authorships[4].institutions[2].lineage | https://openalex.org/I24603500 |
| authorships[4].institutions[2].country_code | US |
| authorships[4].institutions[2].display_name | University of Massachusetts Amherst |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Keith H Nislow |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Environmental Conservation, University of Massachusetts, Amherst, USA, Northern Research Station, US Forest Service, Amherst, Massachusetts, USA |
| authorships[5].author.id | https://openalex.org/A5041382454 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9089-2377 |
| authorships[5].author.display_name | Matthew J. O’Donnell |
| authorships[5].affiliations[0].raw_affiliation_string | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Matthew J O'Donnell |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Conte Anadromous Fish Research Center, USGS, Turners Falls, USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.7287/peerj.preprints.1578 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10302 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9987000226974487 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2309 |
| primary_topic.subfield.display_name | Nature and Landscape Conservation |
| primary_topic.display_name | Fish Ecology and Management Studies |
| related_works | https://openalex.org/W2010317732, https://openalex.org/W2483328176, https://openalex.org/W4380150146, https://openalex.org/W3024870410, https://openalex.org/W2410652950, https://openalex.org/W2061705145, https://openalex.org/W747394405, https://openalex.org/W4283773154, https://openalex.org/W3139174110, https://openalex.org/W4289597203 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2018 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.7287/peerj.preprints.1578 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.7287/peerj.preprints.1578 |
| primary_location.id | doi:10.7287/peerj.preprints.1578 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.7287/peerj.preprints.1578 |
| publication_date | 2015-12-10 |
| publication_year | 2015 |
| referenced_works_count | 0 |
| abstract_inverted_index.) | 106 |
| abstract_inverted_index.= | 93 |
| abstract_inverted_index.a | 3, 39, 84, 97, 108, 132, 135, 156 |
| abstract_inverted_index.d | 115 |
| abstract_inverted_index.y | 104, 117 |
| abstract_inverted_index.(~ | 140, 160 |
| abstract_inverted_index.). | 119 |
| abstract_inverted_index.-1 | 105, 118 |
| abstract_inverted_index.In | 83 |
| abstract_inverted_index.We | 37, 120 |
| abstract_inverted_index.as | 24 |
| abstract_inverted_index.at | 204 |
| abstract_inverted_index.be | 197 |
| abstract_inverted_index.in | 144, 164, 178 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 6, 14, 19, 110, 183 |
| abstract_inverted_index.on | 138 |
| abstract_inverted_index.to | 196 |
| abstract_inverted_index.· | 103, 116 |
| abstract_inverted_index.0.6 | 161 |
| abstract_inverted_index.10% | 147 |
| abstract_inverted_index.Our | 54 |
| abstract_inverted_index.air | 61 |
| abstract_inverted_index.all | 153 |
| abstract_inverted_index.and | 9, 62, 76, 78, 107, 203 |
| abstract_inverted_index.are | 22, 65 |
| abstract_inverted_index.but | 28, 166 |
| abstract_inverted_index.can | 48, 79 |
| abstract_inverted_index.for | 44, 155 |
| abstract_inverted_index.had | 134 |
| abstract_inverted_index.how | 124 |
| abstract_inverted_index.the | 12, 25, 57, 88, 111, 179 |
| abstract_inverted_index.was | 169 |
| abstract_inverted_index.°C | 102, 162 |
| abstract_inverted_index.(2.9 | 114 |
| abstract_inverted_index.0.59 | 94 |
| abstract_inverted_index.RMSE | 145 |
| abstract_inverted_index.also | 121 |
| abstract_inverted_index.data | 75, 126, 130, 154, 172 |
| abstract_inverted_index.days | 149 |
| abstract_inverted_index.drop | 143 |
| abstract_inverted_index.from | 175 |
| abstract_inverted_index.jump | 163 |
| abstract_inverted_index.land | 187 |
| abstract_inverted_index.make | 49 |
| abstract_inverted_index.many | 45 |
| abstract_inverted_index.that | 42, 47 |
| abstract_inverted_index.this | 167, 193 |
| abstract_inverted_index.time | 70 |
| abstract_inverted_index.well | 91 |
| abstract_inverted_index.were | 173 |
| abstract_inverted_index.when | 60, 171 |
| abstract_inverted_index.with | 73, 146, 150 |
| abstract_inverted_index.year | 133, 157 |
| abstract_inverted_index.(RMSE | 92 |
| abstract_inverted_index.(e.g. | 186 |
| abstract_inverted_index.0.05% | 141 |
| abstract_inverted_index.Water | 0 |
| abstract_inverted_index.allow | 192 |
| abstract_inverted_index.basin | 189 |
| abstract_inverted_index.basis | 13 |
| abstract_inverted_index.clear | 98 |
| abstract_inverted_index.daily | 30 |
| abstract_inverted_index.deals | 72 |
| abstract_inverted_index.fewer | 148 |
| abstract_inverted_index.forms | 11 |
| abstract_inverted_index.lags, | 71 |
| abstract_inverted_index.model | 41, 55, 89 |
| abstract_inverted_index.other | 176 |
| abstract_inverted_index.poses | 33 |
| abstract_inverted_index.sites | 202 |
| abstract_inverted_index.size) | 190 |
| abstract_inverted_index.small | 85, 136 |
| abstract_inverted_index.trend | 100 |
| abstract_inverted_index.water | 63 |
| abstract_inverted_index.°C), | 95 |
| abstract_inverted_index.(0.063 | 101 |
| abstract_inverted_index.RMSE), | 165 |
| abstract_inverted_index.Robust | 17 |
| abstract_inverted_index.across | 200 |
| abstract_inverted_index.cover, | 188 |
| abstract_inverted_index.data). | 151 |
| abstract_inverted_index.driver | 5 |
| abstract_inverted_index.effect | 137 |
| abstract_inverted_index.models | 18 |
| abstract_inverted_index.period | 59, 113 |
| abstract_inverted_index.should | 191 |
| abstract_inverted_index.stream | 7, 15, 20, 31, 50, 86 |
| abstract_inverted_index.within | 131 |
| abstract_inverted_index.yearly | 58 |
| abstract_inverted_index.Missing | 129, 152 |
| abstract_inverted_index.applied | 199 |
| abstract_inverted_index.average | 142 |
| abstract_inverted_index.climate | 26 |
| abstract_inverted_index.drivers | 185 |
| abstract_inverted_index.include | 80 |
| abstract_inverted_index.missing | 74, 125 |
| abstract_inverted_index.primary | 4 |
| abstract_inverted_index.readily | 198 |
| abstract_inverted_index.scales. | 207 |
| abstract_inverted_index.several | 34 |
| abstract_inverted_index.spatial | 206 |
| abstract_inverted_index.streams | 177 |
| abstract_inverted_index.warming | 99 |
| abstract_inverted_index.accounts | 43 |
| abstract_inverted_index.changes, | 27 |
| abstract_inverted_index.commonly | 10 |
| abstract_inverted_index.critical | 23 |
| abstract_inverted_index.decrease | 168 |
| abstract_inverted_index.drivers. | 82 |
| abstract_inverted_index.external | 81, 184 |
| abstract_inverted_index.modeling | 194 |
| abstract_inverted_index.multiple | 201, 205 |
| abstract_inverted_index.network, | 87 |
| abstract_inverted_index.network. | 180 |
| abstract_inverted_index.widening | 109 |
| abstract_inverted_index.available | 174 |
| abstract_inverted_index.carefully | 122 |
| abstract_inverted_index.decreased | 158 |
| abstract_inverted_index.developed | 38 |
| abstract_inverted_index.evaluated | 123 |
| abstract_inverted_index.framework | 195 |
| abstract_inverted_index.important | 35 |
| abstract_inverted_index.moderated | 170 |
| abstract_inverted_index.performed | 90 |
| abstract_inverted_index.challenges | 46 |
| abstract_inverted_index.difficult. | 53 |
| abstract_inverted_index.ecosystems | 8 |
| abstract_inverted_index.estimating | 29 |
| abstract_inverted_index.estimation | 52 |
| abstract_inverted_index.identified | 96 |
| abstract_inverted_index.identifies | 56 |
| abstract_inverted_index.influenced | 127 |
| abstract_inverted_index.challenges. | 36 |
| abstract_inverted_index.hysteresis, | 68 |
| abstract_inverted_index.performance | 139, 159 |
| abstract_inverted_index.statistical | 40 |
| abstract_inverted_index.temperature | 1, 21, 32, 51, 64 |
| abstract_inverted_index.accommodates | 67 |
| abstract_inverted_index.incorporates | 69 |
| abstract_inverted_index.predictions. | 128 |
| abstract_inverted_index.synchronized | 112 |
| abstract_inverted_index.incorporation | 182 |
| abstract_inverted_index.synchronized, | 66 |
| abstract_inverted_index.Straightforward | 181 |
| abstract_inverted_index.autocorrelation | 77 |
| abstract_inverted_index.classifications. | 16 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.77208927 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |