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Record W2034607695 · doi:10.1080/07055900.2011.587238

Evaluation of Land Surface Scheme SABAE-HW in Simulating Snow Depth, Soil Temperature and Soil Moisture within the BOREAS Site, Saskatchewan

2011· article· en· W2034607695 on OpenAlexafffundvenueabout
Alireza Hejazi, Allan D. Woodbury

Bibliographic record

VenueATMOSPHERE-OCEAN · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Manitoba
FundersCanadian Water Network
KeywordsEnvironmental scienceSnowWater contentWater tableBorealSoil scienceSoil waterHydrology (agriculture)PrecipitationMoistureVegetation (pathology)Atmospheric sciencesGroundwaterGeologyMeteorologyGeographyGeomorphologyGeotechnical engineering

Abstract

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The Soil Atmosphere Boundary, Accurate Evaluation of Heat and Water (SABAE-HW) model is a multilayered, one-dimensional, physically based version of the Canadian Land Surface Scheme (CLASS) and uses the same methodologies as CLASS, version 2.6. SABAE provides an improved interface for groundwater modelling to simulate soil moisture, soil temperature, energy fluxes and snow depth for a wide range of soil and vegetation. This paper reports the results of the first field comparison of SABAE-HW using an extensive ten-year dataset from the Boreal Ecosystem Atmosphere Study (BOREAS) and the Boreal Ecosystem Research and Monitoring Sites (BERMS) project, an area in central Saskatchewan, Canada, rich in terms of hydrological and meteorological data. The model is also independently tested and verified with the Simultaneous Heat and Water (SHAW) model, which is an unsaturated-zone transport model. Two boundary conditions are considered at the bottom of the soil profile: a water table boundary condition and a unit gradient boundary condition. There was substantial agreement between the results of the simulations and observations in terms of snow depth and soil temperature. Snow depth and soil temperature were simulated reasonably well by SABAE, with correlation values of 0.96 and 0.98, respectively. However, there were some discrepancies for simulated soil temperature in winter. General agreement was obtained in terms of unfrozen soil moisture results, especially at greater depths, but there were general similarities in observed and simulated soil moisture trends in winter. An average correlation of 0.55 was found for SABAE while the correlation for SHAW was much smaller (less than 0.30), which indicates a better fit between simulated and field data by SABAE. Although a unit gradient boundary condition does not influence soil moisture, it was found that unit gradient boundary runs resulted in increased bias towards overestimation of the soil temperature. Thus, a safer and more accurate approach, we believe, is to adopt a first type boundary (i.e., water table) condition at the bottom of the domain. This has implications for climate and weather modelling in general. The result of this field testing demonstrated the potential and high accuracy of SABAE-HW as a Canadian model capable of simulating snow depth, snow temperature, soil moisture, energy fluxes, and we believe it is now appropriate to include this land surface scheme with its counterparts. R ésumé [Traduit par la rédaction] Le modèle Soil Atmosphere Boundary, Accurate Evaluation of Heat and Water (SABAE-HW) est une version multicouche, à une dimension, basée sur la physique du schéma CLASS (Canadian Land Surface Scheme) qui utilise les mêmes méthodologies que le CLASS version 2.6. Le SABAE offre une interface améliorée pour la modélisation des eaux sous-terraines permettant de simuler l'humidité du sol, la température du sol, les flux d'énergie et l'épaisseur de la neige pour une grande variété de sols et de végétation. Cet article présente les résultats de la première comparaison terrain du SABAE-HW en utilisant une base de données étendue de dix ans de l'Étude de l'atmosphère et des écosystèmes boréaux (BOREAS) et du projet des Sites de recherche et de surveillance des écosystèmes boréaux (BERMS), une région du centre de la Saskatchewan, au Canada, riche en données hydrologiques et météorologiques. Le modèle est aussi indépendamment testé et vérifié à l'aide du Simultaneous Heat and Water (SHAW), un modèle de transport en zone non saturée. Deux conditions aux limites sont supposées au fond du profil du sol : une condition aux limites de nappe phréatique et une condition aux limites de gradient unitaire. On a trouvé une concordance importante entre les résultats des simulations et les observations en ce qui a trait à l'épaisseur de la neige et à la température du sol. L'épaisseur de la neige et la température du sol ont été raisonnablement bien simulées par le modèle SABAE, avec des corrélations de 0,96 et 0,98, respectivement. Cependant, il y avait certaines divergences pour la température simulée du sol en hiver. Pour ce qui est des résultats concernant l'humidité du sol non gelé, ils s'accordaient généralement, surtout pour les plus grandes profondeurs, mais il y avait des similarités générales dans les tendances observées et simulées de l'humidité du sol en hiver. Nous avons trouvé une corrélation moyenne de 0,55 pour le SABAE alors que la corrélation pour le SHAW était beaucoup plus faible (0,30), ce qui indique un meilleur ajustement des données simulées aux données de terrain pour le SABAE. Même si une condition aux limites de gradient unitaire n'influence pas l'humidité du sol, il ressort que des passes faites avec un gradient unitaire aux limites ont produit un biais accru vers la surestimation de la température du sol. Donc, nous croyons qu'une approche plus sûre et plus précise serait d'adopter une condition aux limites de Dirichlet (c.-à-d. une nappe phréatique) au fond du domaine. Ceci a des répercussions sur la modélisation du climat et du temps en général. Le résultat de cet essai sur le terrain a démontré le potentiel et la grande exactitude du SABAE-HW en tant que modèle canadien capable de simuler l'épaisseur de la neige, la température du sol, l'humidité du sol et les flux d'énergie et nous croyons qu'il est maintenant approprié d'inclure ce schéma de surface avec ses contreparties.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.243
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2011
Admission routes4
Has abstractyes

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