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Record W2859949867 · doi:10.1088/1748-9326/aad246

Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts

2018· article· en· W2859949867 on OpenAlexaff
Aneseh Alborzi, Ali Mirchi, Hamed Moftakhari, Iman Mallakpour, Sara Alian, Ali Nazemi, Elmira Hassanzadeh, Omid Mazdiyasni, Samaneh Ashraf, Kaveh Madani, H. Norouzi, M. Azarderakhsh, Ali Mehran, Mojtaba Sadegh, Andrea Castelletti, Amir AghaKouchak

Bibliographic record

VenueEnvironmental Research Letters · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsPolytechnique MontréalConcordia University
Fundersnot available
KeywordsEnvironmental scienceClimate changeWater resourcesWater scarcityHydrology (agriculture)Resource (disambiguation)Water resource managementOceanographyEcologyGeology

Abstract

fetched live from OpenAlex

The rapid shrinkage of Lake Urmia, one of the world's largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia's contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin's water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998–2002). The collapse was caused by a marked overshoot of the basin's hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr −1 ) during dry conditions to 5400 mcm yr −1 during wet periods with the average being 4100 mcm yr −1 . Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

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 communication0.0010.001
Open science0.0000.001
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 head0.044
GPT teacher head0.352
Teacher spread0.308 · 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
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

Citations120
Published2018
Admission routes1
Has abstractyes

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