A Future Study of an Environment Driving Force (EDR): The Impacts of Urmia Lake Water-Level Fluctuations on Human Settlements

Hamidi, Somayeh Mohammadi; Fürst, Christine; Nazmfar, Hossein; Rezayan, Ahad; Yazdani, Mohammad Hassan

doi:10.3390/su132011495

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…The nine stations, including Tabriz, Sahand, Urmia, Maragheh, Mianeh, Mahabad, Saqez in the Lake Urmia basin, Takab and Zanjan in the Sefidrood basin, were selected to investigate the application of machine learning in estimating and predicting ETo. The Urmia Lake basin is one of the important basins of Iran because the largest Lake of Iran (Urmia Lake) is located in this basin [63]. The Urmia basin area is about 51,460 km 2 .…”

Section: Study Area and Data Materialsmentioning

confidence: 99%

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

et al. 2022

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In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.

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Section: Study Area and Data Materialsmentioning

confidence: 99%

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

et al. 2022

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“…However, there was no significant difference between the mean perceived risks of these diseases. Studies have indicated adverse effects of LUD on hypertension ( 44 ), cancer ( 25 , 26 ), depression ( 41 ), stress/anxiety ( 41 ), cardiovascular diseases ( 29 , 42 , 43 , 49 ), and income and job loss ( 38 , 50 - 53 ). Moreover, several studies have shown the adverse effect of drying up of other lakes on cancer ( 11 , 54 ) and stress ( 55 ).…”

Section: Discussionmentioning

confidence: 99%

Public Perception on the Health Consequences of an Environmental Disaster: The Case of Lake Urmia Drying up

Jafari-Khounigh

Sadeghi‐Bazargani²,

Haghdoost

2022

Med. J. Islam. Republ. Iran

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“…There is a direct relationship between the scatter of salt particles in the air and the drying lake. The airborne particles are associated with serious illnesses 36 , as they can make their way into the airways and quickly reach the bloodstream. Aside from causing many diseases, salt release has also led to soil salinity, drying up the orchards in these areas (Fig.…”

Section: Study Areamentioning

confidence: 99%

Health effects of shrinking hyper-saline lakes: spatiotemporal modeling of the Lake Urmia drought on the local population, case study of the Shabestar County

Feizizadeh

Lakes

Omarzadeh

et al. 2023

Sci Rep

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Climate change and its respective environmental impacts, such as dying lakes, is widely acknowledged. Studies on the impact of shrinking hyper-saline lakes suggest severe negative consequences for the health of the affected population. The primary aim was to investigate the relationship between changes in the water level of the hyper-saline Lake Urmia, along with the associated salt release, and the prevalence of hypertension and the general state of health of the local population in Shabestar County north of the lake. Moreover, we sought to map the vulnerability of the local population to the health risks associated with salt-dust scatter using multiple environmental and demographic characteristics. We applied a spatiotemporal analysis of the environmental parameters of Lake Urmia and the health of the local population. We analyzed health survey data from local health care centers and a national STEPS study in Shabestar County, Iran. We used a time-series of remote sensing images to monitor the trend of occurrence and extent of salt-dust storms between 2012 and 2020. To evaluate the impacts of lake drought on the health of the residences, we investigated the spatiotemporal correlation of the lake drought and the state of health of local residents. We applied a GIScience multiple decision analysis to identify areas affected by salt-dust particles and related these to the health status of the residents. According to our results, the lake drought has significantly contributed to the increasing cases of hypertension in local patients. The number of hypertensive patients has increased from 2.09% in 2012 to 19.5% in 2019 before decreasing slightly to 16.05% in 2020. Detailed results showed that adults, and particularly females, were affected most by the effects of the salt-dust scatter in the residential areas close to the lake. The results of this study provide critical insights into the environmental impacts of the Lake Urmia drought on the human health of the residents. Based on the results we suggest that detailed socioeconomic studies might be required for a comprehensive analysis of the human health issues in this area. Nonetheless, the proposed methods can be applied to monitor the environmental impacts of climate change on human health.

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A Future Study of an Environment Driving Force (EDR): The Impacts of Urmia Lake Water-Level Fluctuations on Human Settlements

Cited by 13 publications

References 27 publications

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

Public Perception on the Health Consequences of an Environmental Disaster: The Case of Lake Urmia Drying up

Health effects of shrinking hyper-saline lakes: spatiotemporal modeling of the Lake Urmia drought on the local population, case study of the Shabestar County

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