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Record W3176469409 · doi:10.5194/egusphere-egu21-15579

The spatio-temporal evolution of groundwater dependent precipitation

2021· article· en· W3176469409 on OpenAlexaboutno aff
Cristina Passet, Lan Wang‐Erlandsson, Yoshihide Wada, Agnes Pranindita, Agatha M. de Boer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterPrecipitationEnvironmental scienceIrrigationHydrology (agriculture)GeographyEcologyGeologyMeteorologyBiology

Abstract

fetched live from OpenAlex

A substantial portion of groundwater abstracted from aquifers is used for irrigation and evaporated to the atmosphere, potentially contributing towards downwind precipitation. While the fate of evaporation fluxes from land have been analysed, the atmospheric pathways of evaporation originating from groundwater have not yet been globally quantified. This study analysed the geographical distribution, the seasonality and the magnitude of groundwater-dependent precipitation (Pgw) at a global scale and for a selection of countries and river basins. The Eulerian moisture tracking WAM-2layers model was used to process meteorological and groundwater abstraction input data from 1980 to 2010. Results show considerable contributions of groundwater to precipitation downwind of the most heavily irrigated areas, leading to net groundwater losses over these areas. Globally, 40% of the Pgw precipitates directly in the oceans, and do not contribute to biomass production in terrestrial ecosystems. Some of the countries with the highest rates of groundwater abstraction (India, the USA, Pakistan and Iran), receive low volumes of Pgw and are net losers of groundwater resources. The countries with the highest net gain of groundwater are China, Canada and Russia. At river basin scale, the Indus, Ganges and Mississippi basins are net losers of groundwater to downwind Pgw, while the Yangtze, Tarim and Brahmaputra basins receive more Pgw than their groundwater withdrawals. The share of precipitation that originates from groundwater varies considerably with seasons, and can be especially high when low local precipitation levels occur in combination with high upwind groundwater abstraction. Furthermore, precipitation dependence on groundwater (ρgw), has steadily increased between 1980 to 2010 in all studied areas and globally. Our study suggests that the countries and basins with a high and increasing dependency on ρgw to support their precipitation can be vulnerable to groundwater availability upwind.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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