The future of extreme climate in Iran View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Saeid Ashraf Vaghefi, Malihe Keykhai, Farshid Jahanbakhshi, Jaleh Sheikholeslami, Azade Ahmadi, Hong Yang, Karim C. Abbaspour

ABSTRACT

Iran is experiencing unprecedented climate-related problems such as drying of lakes and rivers, dust storms, record-breaking temperatures, droughts, and floods. Here, we use the ensemble of five high-resolution climate models to project maximum and minimum temperatures and rainfall distribution, calculate occurrences of extreme temperatures (temperatures above and below the historical 95th and 5th percentiles, respectively), analyze compound of precipitation and temperature extremes, and determine flooding frequencies across the country. We found that compared to the period of 1980-2004, in the period of 2025-2049, Iran is likely to experience more extended periods of extreme maximum temperatures in the southern part of the country, more extended periods of dry (for ≥120 days: precipitation <2 mm, Tmax ≥30 °C) as well as wet (for ≤3 days: total precipitation ≥110 mm) conditions, and higher frequency of floods. Overall, the combination of these results projects a climate of extended dry periods interrupted by intermittent heavy rainfalls, which is a recipe for increasing the chances of floods. Without thoughtful adaptability measures, some parts of the country may face limited habitability in the future. More... »

PAGES

1464

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-38071-8

DOI

http://dx.doi.org/10.1038/s41598-018-38071-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111950502

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30728418


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38071-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38071-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38071-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38071-8'


 

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