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Record W4388528461 · doi:10.1061/jhyeff.heeng-6014

Review of Climate Change Adaptation Strategies in Water Management

2023· article· en· W4388528461 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Hydrologic Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUnited Nations University Institute for Water, Environment, and HealthMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdaptation (eye)Climate changeClimate change adaptationEnvironmental scienceComputer scienceEnvironmental resource managementHydrology (agriculture)Hydrological modellingWater resource managementClimatologyGeologyOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Climate change (CC) is considered one of the most critical threats to human lives and activities due to dramatic increases in the frequency and severity of droughts and floods under global warming. To alleviate such impacts, many studies on CC adaptation strategies in water management have emerged. This review covers 131 relevant studies published over the past two decades. It aims to robustly synthesize the applied strategies/techniques and identify findings and gaps. In addition, a bibliometric analysis is performed to describe the co-citation network and statistical characteristics of the reviewed papers and identify the related research clusters. A typical procedure for CC adaptation studies is proposed based on previous studies. It is found that systems reoperation was preferred for CC adaptation in water resources management, specifically by updating the reservoir operation curves using optimization algorithms. However, low impact development (LID) measures were favored in storm drainage and flood mitigation systems. As for future relevant research, the main recommendations are integrating environmental, social, and economic aspects in evaluating CC adaptation strategies and incorporating land use and land cover (LULC) and water demand changes in CC adaptation studies. This state-of-the-art review represents essential information for improving CC adaptation strategies in water management.

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.

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)0.000
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 score0.508
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.228
Teacher spread0.203 · 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