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Record W4402275415 · doi:10.1002/eco.2714

Evaluating the Use of the Penman–Monteith and Priestley–Taylor Algorithms for Modelling Peatland Evapotranspiration Using the Cold Regions Hydrological Model

2024· article· en· W4402275415 on OpenAlexafffund
Brandon Van Huizen, Richard M. Petrone, Xing Fang, John W. Pomeroy

Bibliographic record

VenueEcohydrology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsCanmore Museum and Geoscience CentreUniversity of SaskatchewanUniversity of Waterloo
FundersImperial Oil ResourcesNatural Sciences and Engineering Research Council of CanadaGlobal Water FuturesCanada First Research Excellence FundShell CanadaSuncor Energy Incorporated
KeywordsEvapotranspirationPeatHydrology (agriculture)Environmental scienceAlgorithmComputer scienceGeologyGeographyEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Methods used to quantify evapotranspiration (ET) from Sphagnum ‐dominated peatlands often assume that soil moisture is not a limiting factor; actual ET (AET) equals potential ET (PET). However, soil moisture can become limiting as peatlands dry, lowering AET below PET and necessitating the use of a surface resistance term in AET estimations. Quantifying and calculating surface resistance is a challenge for the non‐vascular plant surfaces such as those dominated by Sphagnum moss. This paper explores and quantifies the ecohydrological processes that drive Sphagnum resistance to ET. It is hypothesized that a relationship exists between the Sphagnum moss resistance and the ratio of unsaturated to saturated hydraulic conductivity (K‐ratio) for boreal peatlands, where the K‐ratio is a proxy for the hydrophysical properties of the porous medium. An empirical relationship between Sphagnum moss resistance and the K‐ratio was developed from data collected from a boreal peatland and implemented in the cold regions hydrological model. Empirically modelled resistance values (0–800 s m −1 ) did not match well with estimates from inverting observations and the Penman–Monteith (PM) algorithm (0–5000 s m −1 ). Difficulties in validating resistance values were possibly due to lack of moisture limiting conditions although this is seemingly contradicted by the alpha value being less than 1. Priestley–Taylor (PT) and PM algorithms in CRHM were used to estimate AET and compared with each other and with observations from an onsite eddy covariance (EC) system. The PT algorithm, using a site‐specific alpha value (0.75) performed the best with a mean difference of 9.4% (±12.0%) when compared to EC measurements of AET. The PM algorithm consistently overestimated EC measurements with a mean difference of 68.4% (±50.0%), even with a moss resistance incorporated into its use. The performance of PM algorithm is impeded by the uncertainty in quantifying Sphagnum resistance. Reducing this uncertainty should be a focus of future studies, as it does not require the use of a site‐specific alpha value.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.435

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.205
GPT teacher head0.334
Teacher spread0.130 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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