2025 : 10 : 14
Marjan Moazamnia

Marjan Moazamnia

Academic rank: Assistant Professor
ORCID: .
Education: PhD.
ScopusId: .
HIndex: 9/00
Faculty: Faculty of Engineering
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Phone: .

Research

Title
Aggregated drought characteristics using particle swarm optimisation incorporating duration, severity, reliability, resilience, and vulnerability characteristics
Type
JournalPaper
Keywords
hard evidence, Lake Urmia, meteorological drought, precipitation, SPI
Year
2025
Journal Journal of Water and Climate Change
DOI
Researchers Sina Sadeghfam ، Salar Teihooie ، Rahman Khatibi ، Marjan Moazamnia

Abstract

A deeper understanding of drought risks is presented for the basin of Lake Urmia, where the lake disappeared catastrophically during the years 1995–2023. Received wisdom attributes the catastrophe to droughts and/or climate change, but the authors identify more than 40 dams in this small basin as the singular most likely cause, which has sprung up since 1995. The paper adds further evidence by studying droughts through a modelling strategy comprising the following: (i) use the standardised precipitation index (SPI) to map SPI on one, three, six and nine-month timescales with 21 years of recorded data; (ii) derive duration and severity (DS) values at 55 observation stations; (iii) derive reliability, resilience, and vulnerability (RRV) indicators using DS; (iv) aggregate the subsequent five maps (DS þ RRV) for the four timescales using a particle swarm optimisation algorithm to compact their inherent information. The aggregated results show: (i) onemonth timescale: wet and dry zones are demarcated by an axis running along northwest–southeast of the basin; (ii) three-month timescale: the aforementioned behaviour prevails, but the axis runs along east–west; (iii) higher timescales: the wet and dry zones flip. The overall results show that droughts are natural features of the basin but not catastrophic.