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. 2022 Mar 9;12(1):4178.
doi: 10.1038/s41598-022-07891-0.

Buffering the impacts of extreme climate variability in the highly engineered Tigris Euphrates river system

Affiliations

Buffering the impacts of extreme climate variability in the highly engineered Tigris Euphrates river system

Karem Abdelmohsen et al. Sci Rep. .

Abstract

More extreme and prolonged floods and droughts, commonly attributed to global warming, are affecting the livelihood of major sectors of the world's population in many basins worldwide. While these events could introduce devastating socioeconomic impacts, highly engineered systems are better prepared for modulating these extreme climatic variabilities. Herein, we provide methodologies to assess the effectiveness of reservoirs in managing extreme floods and droughts and modulating their impacts in data-scarce river basins. Our analysis of multiple satellite missions and global land surface models over the Tigris-Euphrates Watershed (TEW; 30 dams; storage capacity: 250 km3), showed a prolonged (2007-2018) and intense drought (Average Annual Precipitation [AAP]: < 400 km3) with no parallels in the past 100 years (AAP during 1920-2020: 538 km3) followed by 1-in-100-year extensive precipitation event (726 km3) and an impressive recovery (113 ± 11 km3) in 2019 amounting to 50% of losses endured during drought years. Dam reservoirs captured water equivalent to 40% of those losses in that year. Additional studies are required to investigate whether similar highly engineered watersheds with multi-year, high storage capacity can potentially modulate the impact of projected global warming-related increases in the frequency and intensity of extreme rainfall and drought events in the twenty-first century.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Location map of the TEW. Figure shows the spatial variations in elevation in m.a.m.s.l across the TEW and the distribution of stream networks extracted from Shuttle Radar Topography Mission (SRTM) data using ArcGIS 10.8 hydrological tools (https://www.arcgis.com/). Also shown are the distribution of Tigris and Euphrates rivers and the source areas (Taurus and Zagros Mountains), to the north and east, the deserts to the west (Western Desert) and south (Southern Desert), and the central Mesopotamian Plain. Also shown, the groundwater flow directions,, the main reservoirs (blue circle), lakes (red circle). Also shown are time series of surface water level variations (2003–2020) from radar altimetry (Global Reservoir and Lake Monitoring (GRLM) database; available at https://www.pecad.fas.usda.gov/cropexplorer/globalreservoir/) over the TEW lakes (e.g., Hammar 4 in Iraq) and reservoirs (Karkheh in Iran, Mosul and Tharthar in Iraq, Karakaya and Ataturk in Turkey, and Assad in Syria) showing a significant rise in water levels following the extreme precipitation event in 2019.
Figure 2
Figure 2
The areal extent of the Mesopotamian marshes before and after the 2019 extreme precipitation event in years 2017 and 2020, respectively. Comparison between the areal extent of the Mesopotamian marshes (Al-Huwaizah, Central, and Al-Hammar) in southern Iraq using false-color composites generated from 30 m multispectral Landsat 8 data (https://www.usgs.gov/) using ArcGIS 10.8 Spatial analyst tools (https://www.arcgis.com/) before and after the extreme 2019 precipitation event.
Figure 3
Figure 3
Comparisons between the time series of the TWSGRACE, SWSALT, GWSGRACE, (SMS + SWE + CWS)GLDAS, and seasonal precipitation. Time series were derived over the TEW for each of the investigated time periods (Phases I–V). The comparisons are made in units of monthly variations in water mass averaged over the TEW.
Figure 4
Figure 4
Precipitation and variability index (δ) time series derived from GPCC data (1920–2020). (a) Comparison between precipitation during wet seasons (winter and spring: November–April; blue columns) and dry season (summer: May–October; red columns) showing much higher precipitation rates during the wet seasons. (b) Use of variability index (δ) time series to identify the drought (− δ) and wet (+ δ) periods; the figure shows a severe and prolonged drought (2007–2018; highlighted in yellow) and the wettest years (highest index values) in 1969 and 2019.

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