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Exploring the existence of hydrological tipping points at the catchment-scale

2020· article· en· W3080211089 on OpenAlexaff
Fernando Jaramillo, Stefano Manzoni, Anne‐Sophie Crépin, Juan Rocha, Lan Wang‐Erlandsson, Samuel C. Zipper, Tom Gleeson, Paolo D’Odorico

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEnvironmental scienceClimate changeDrainage basinWater cycleScale (ratio)Tipping point (physics)Hydrology (agriculture)State variableGeographyEcologyGeologyPhysics

Abstract

fetched live from OpenAlex

The identification of tipping points in the water cycle has been recently ranked Nr. 1 in the list of the top 23 unresolved problems in Hydrology by the International Association of Hydrological Sciences (IAHS) and as a priority in the field of hydrology and water resources by several studies. Such daunting task is mainly attributed to the concerns that greenhouse gas emission climate change may tip the water cycle into an unfavorable new state. Up to date, tipping points occurring in complex dynamical systems have been identified across a large set of disciplines. In most proven tipping points, hydrologic variables are always taken as the control variables, as changes in water fluxes and stocks are known to act as stressors of socioecological systems, and the affected aquatic and terrestrial ecosystems as the response variables. The main objective of this study is to explore the existence of tipping points in catchment-scale freshwater availability, that is, the tipping points were the response variable is catchment water storage. We first review the existence of reported tipping points in the field of hydrology and water resources, to establish a coherent framework for the identification of hydrological tipping points. We explore their mathematical existence at the catchment scale by Linear Stability Analysis, illustrating cases with potential functions and bifurcation diagrams. We then explore any possible contribution to the existence of hydrological tipping points by adding complexity to the hydrological dynamic system through the inclusion of sociological feedbacks. We find that even with the inclusion of the moisture feedback of evapotranspiration to precipitation, constant socioecological conditions will most likely not present tipping points of water storage in the catchment. However, the inclusion of socioecological feedbacks does generate tipping points under certain assumptions, even without assuming a moisture feedback between evapotranspiration and precipitation. We hope that this study sheds some light on the existence, conditions, assumptions and characteristics of large-scale hydrological tipping points with long-term implications.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.220
Teacher spread0.166 · 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 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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Citations0
Published2020
Admission routes1
Has abstractyes

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