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Record W4403778868 · doi:10.5194/egusphere-2024-3037

Towards a semi-asynchronous method for hydrological modeling in climate change studies

2024· preprint· en· W4403778868 on OpenAlexaffabout
Frédéric Talbot, Simon Ricard, Jean‐Daniel Sylvain, Guillaume Drolet, Annie Poulin, Jean‐Luc Martel, Richard Arsenault

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMinistère des Ressources naturelles et des Forêts (Québec)Université LavalÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsClimate changeAsynchronous communicationComputer scienceEnvironmental scienceClimatologyHydrology (agriculture)GeologyGeotechnical engineeringOceanographyTelecommunications

Abstract

fetched live from OpenAlex

Abstract. This study assesses the performance of the asynchronous approach used in hydrological modeling, which stands apart from the conventional approach by calibrating streamflow distributions without relying on meteorological observations. The focus is on comparing the two methods within the context of climate change impact studies, particularly in their ability to simulate key hydroclimatic processes across catchments. The analysis, conducted across multiple catchments, including a detailed case study of the Matane catchment in Southern Quebec, explores the potential of the asynchronous method as a viable alternative for future hydrological modeling. By eliminating the dependency on meteorological observations, the asynchronous approach offers potential advantages in regions with limited or unreliable observational data, providing a more flexible tool for climate change impact assessments. The results reveal that while the asynchronous method effectively captures the overall distribution of streamflow and preserves extreme values, it faces significant challenges in accurately representing the timing of hydrological events, particularly those related to snowmelt. This issue stems, in part, from the method’s decision to work directly with the biases present in raw climate model outputs, without adjusting for the timing discrepancies in meteorological inputs. Consequently, the asynchronous approach inherits these biases, leading to timing inconsistencies and increased variability across different climate models, which raises concerns about the method's ability to reliably simulate critical hydroclimatic variables under future climate scenarios. In contrast, the conventional method, which incorporates bias correction, demonstrates greater reliability in capturing the timing and magnitude of streamflow events, making it a more robust tool for most hydrological applications. The study also highlights the concept of equifinality, where different methods achieve similar outcomes through potentially flawed mechanisms, particularly in the case of the asynchronous method. Despite projecting changes in hydroclimatic variables similar to those of the conventional method, the asynchronous approach may do so for reasons that are not hydrologically sound, particularly in snow-dominated catchments. While the asynchronous method shows promise in preserving streamflow extremes, its current implementation requires further refinement to improve its accuracy and reliability, particularly in how it simulates the timing of seasonal dynamics. However, as climate model simulations continue to improve and their biases are progressively reduced, the asynchronous approach is poised to benefit significantly, enhancing its potential for more accurate and reliable future hydrological projections. The conventional method remains the preferred choice for applications requiring hydrological simulations, but future research should focus on developing semi-asynchronous approaches that combine the asynchronous method’s strength in preserving extremes with the conventional method’s ability to handle event-specific timing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.107
GPT teacher head0.351
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2024
Admission routes2
Has abstractyes

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