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Record W3176062022 · doi:10.5194/egusphere-egu21-5033

Adaptation to climate change induced water stress in major glacierized mountain regions

2021· article· en· W3176062022 on OpenAlexaff
Anubha Aggarwal, Holger Frey, Graham McDowell, Fabián Drenkhan, Marcus Nuesser, Adina Racoviteanu, Martin Hoelzle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWater scarcityEnvironmental scienceClimate changeTourismWater resource managementGlacierAgricultureAdaptive capacityEnvironmental resource managementWater resourcesGeographyHydrology (agriculture)Physical geographyEcologyEngineering

Abstract

fetched live from OpenAlex

Mountains are a critical source of water and home to a large proportion of the world’s population. Cryospheric and hydrological changes combined with increasing water demand are impacting water availability, livelihoods and cultural values, threatening long-term water security of downstream populations. Here, we present a global systematic review in which 83 peer-reviewed articles were critically evaluated to unravel and assess different types of adaptation measures that have been undertaken to manage water stress. We observe that changes in glacier extent and snowfall amount are the main cryospheric changes motivating adaptations. However, changes in precipitation patterns, such as increasing extremes or alterations of the rain-snow line, which lead to both increasing water stress and seasonal flooding or glacier lake outburst floods (GLOFs), and are also observed to be important motivators of adaptive actions. The main sectors affected by hydrological and cryospheric changes are agriculture, tourism, hydropower generation and health and safety. To reduce risks of water scarcity and water-related disasters, and to enhance the resilience of human and natural systems, a broad set of adaptation measures have been implemented in the world’s mountain regions. Such adaptations include crop diversification, new irrigation practices, dams and water storage infrastructure, training programs and the establishment of Early Warning Systems, artificial snow making, shifts to non-snow-based tourism, and changes to cultural practices. We find that globally the most commonly used adaptation practices correspond to the improvement of water storage infrastructure, agricultural and irrigation practices, economic diversification and water governance and laws. However, our systematic review reveals these and other adaptation actions have strong regional variation. For example, adaptation in the agricultural sector is most prevalent in Africa, Asia and South America; while in Europe, Australia and New Zealand responses in the tourism sector are more common. Socio-ecological trade-offs associated with adaptations are often reported. For example, the promotion of snow-making reduces socio-economic vulnerability but adds pressure on water resources and environment. However, successful implementation of adaptation measures are limited by a diverse set of factors. This includes reduced capacities and resources in infrastructure maintenance, mismanagement, conflicts and mistrust in government together with lack of funding and insufficient collaboration between stakeholders as well as delayed implementation of laws and mountain development programs. Moreover, extreme events and climate change impacts together with discontinuities and errors in climate data need to be considered. In order to address or overcome these limitations, it is important to raise awareness of local communities about climate change and to demonstrate the positive effects of adaptation measures and environmental laws; increase funding for mountain programs and motivate combined activities of governments and stakeholders to build their trust on each other.

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)0.003
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.065
GPT teacher head0.250
Teacher spread0.185 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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