Lake Urmia restoration success story: A natural trend or a planned remedy?

Nikraftar, Zahir; Parizi, Esmaeel; Hosseini, Seiyed Mossa; Ataie-Ashtiani, Behzad

doi:10.1016/j.jglr.2021.03.012

Cited by 28 publications

(8 citation statements)

References 39 publications

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“…From 2015 to 2019, it has had a steady trend and there are not many changes, the main reason being the policies of Lake Urmia restoration. After 2019, there is an increase of about 1.5 meters, which is due to good rainfall this year [1], and after that, a decreasing trend is observed [3]. Due to the shallow depth of the lake, this level drop has led to a significant percentage of dryness of the lake surface and more than 30 billion cubic meters of its water volume has been lost due to evaporation and lack of sufficient water resources.…”

Section: Resultsmentioning

confidence: 99%

“…In total, three factors can be considered as the cause of drying of Lake Urmia, including overwithdrawal of water resources from the lake catchment area, uncontrolled development of the agricultural sector around the lake by exploiting the dams built, and perhaps finally, intensification of climate change and the occurrence of drought is along with two other factors [1][2][3]. Geodesy can monitor changes on Earth with mathematical tools and satellite observations [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Lake Urmia level in 2020 based on satellite altimetry

2021

Preprint

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In this paper, the final output of the satellite altimetry on the surface of Lake Urmia is investigated. From 1995 onwards, the level of Lake Urmia has been declining. From 2015 to 2019, it has had a steady trend and there are not many changes, the main reason being the policies of reviving Lake Urmia. After 2019, there is an increase of about 1.5 meters, which is due to good rainfall this year, and after that, a decreasing trend is observed. In the middle of 2020, with the increase of precipitation, the level of the lake increases by about 20 cm and then reaches the level of 1271.5 meters again. However, after 2015, with the policies of Lake Urmia restoration, the downward trend has been controlled and the level has remained constant. However, these policies do not seem to be sufficient and there is a need to reconsider the water resources management in the region.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lake Urmia level in 2020 based on satellite altimetry

2021

Preprint

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“…The SST anomaly is presented in terms of large-scale indices such as ENSO, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) [77,97,98]). Also referred to as teleconnections, these ocean precursors are often used for seasonal scale drought forecasting at large spatial scales, such as large river basins [99].…”

Section: Statistical Modelsmentioning

confidence: 99%

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

AghaKouchak

Pan

Mazdiyasni

et al. 2022

Phil. Trans. R. Soc. A.

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Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.

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“…In this area, Lake Urmia, once one of the largest salt lakes globally, has been shrinking in the past decades. Parlay because of lowering of groundwater and partly because of damming the rivers that used to flow into the lake, shrinkage of the Lake Urmia is another indicator of water stress in the region (Nikraftar et al, 2021).…”

Section: Nation-wide Identification Of Subsidence Zonesmentioning

confidence: 99%

Land Subsidence Hazard in Iran Revealed by Country-Scale Analysis of Sentinel-1 Insar

Haghighi

Motagh

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Many areas across Iran are subject to land subsidence, a sign of exceeding stress due to the over-extraction of groundwater during the past decades. This paper uses a huge dataset of Sentinel-1, acquired since 2014 in 66 image frames of 250 × 250 km, to identify and monitor land subsidence across Iran. Using a two-step time series analysis, we first identify subsidence zones at a medium scale of 100 m across the country. For the first time, our results provide a comprehensive nationwide map of subsidence in Iran and recognize its spatial distribution and magnitude. Then, in the second step of analysis, we quantify the deformation time series at the highest possible resolution to study its impact on civil infrastructure. The results spots the hazard posed by land subsidence to different infrastructure. Examples of road and railways affected by land subsidence hazard in Tehran and Mashhad, two of the most populated cities in Iran, are presented in this study.

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Lake Urmia restoration success story: A natural trend or a planned remedy?

Cited by 28 publications

References 39 publications

Lake Urmia level in 2020 based on satellite altimetry

Lake Urmia level in 2020 based on satellite altimetry

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

Land Subsidence Hazard in Iran Revealed by Country-Scale Analysis of Sentinel-1 Insar

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