Urmia Lake Basin Research Articles

Prediction of groundwater level variations based on gravity recovery and climate experiment (GRACE) satellite data and a time-series analysis: a case study in the Lake Urmia basin, Iran

Prediction of groundwater level variations based on gravity recovery and climate experiment (GRACE) satellite data and a time-series analysis: a case study in the Lake Urmia basin, Iran

Investigating the changes in agricultural land use and actual evapotranspiration of the Urmia Lake basin based on FAO’s WaPOR database

The Urmia Lake Basin (ULB) is a critical region in terms of water and environment in Iran. The purpose of this study was to investigate the relationship between agricultural land-use changes and the decrease of the Lake Urmia water level, calculating the actual evapotranspiration and interception (ETIa) in agricultural and non-agricultural lands, investigating the trend of temperature changes and its relationship with ETIa, determining the water balance, and estimating net and gross biomass water productivity in the ULB. So, Land Cover Classification (LCC) maps, ETIa and water productivity were prepared from FAO’s WaPOR product (FWP) from 2009 to 2020. Daily precipitation and temperature were collected from 24 meteorological stations in the ULB. To validate FWP land cover classification, 354 control points were collected and the error matrix and Kappa coefficient were used. Then, based on the ETIa and LCC estimated by FWP, the volume of water consumed in irrigated, rainfed, fallow and non-agricultural lands, as well as the water balance of the basin, was calculated. The overall accuracy (OA) and Kappa coefficient for LCC were 86% and 0.81, respectively. The results showed that until 2013, the area of irrigated and rainfed agricultural lands had been increased by 48%, which could be regarded as an influential factor in reducing the water level of Lake Urmia. The average annual precipitation and ETIa of the basin were 15.24 × 109 and 15.91 × 109 m3, respectively. Also, the average annual ETIa of agricultural lands, grasslands and evaporation from the surface of Lake Urmia were 4.88 × 109, 8.18 × 109 and 2.85 × 109 m3, respectively. Therefore, a negative balance of 0.67 × 109 m3 was observed. The results also showed that the average annual temperature had been increased by 1.1 °C from 2009 to 2020. Finally, the rise of the temperature and area of irrigated croplands, the decrease of precipitation and the negative water balance should be considered as warning signals calling for the proper management of water resources.

Linking Spatial–Temporal Changes of Vegetation Cover with Hydroclimatological Variables in Terrestrial Environments with a Focus on the Lake Urmia Basin

Investigation of vegetation cover is crucial to the study of terrestrial ecological environments as it has a close relationship with hydroclimatological variables and plays a dominant role in preserving the characteristics of a region. In Iran, the current study selected the watersheds of two rivers, Nazloo-Chay and Aji-Chay, to systematically investigate the implications and causes of vegetation cover variations under changing environments. These two rivers are among the essential inflows to Lake Urmia, the second largest saline lake on Earth, and are located on the west and east sides of the lake, respectively. There has been a debate between the people living in the rivers’ watersheds about who is responsible for the decline in the level of Lake Urmia—does responsibility fall with those on the east side or with those on the west side? In this study, the normalized difference vegetation index (NDVI) was used as a remotely sensed index to study spatial–temporal pattern changes in vegetation. Moreover, the temperature, precipitation, and streamflow time series were gathered using ground measurements to explore the causes and implications of changing vegetation cover. Discrete wavelet transform was applied to separate the different components of the time series. The Mann–Kendall (MK) test was applied to the time series on monthly, seasonal, and annual time scales. The connections and relationship between the NDVI time series and temperature, precipitation, and streamflow time series and any underlying causes were investigated using wavelet transform coherence (WTC). Land use maps were generated for different years using a support vector machine (SVM) in the final stage. The results indicated that the most dominant monthly, seasonal, and annual hydrological periodicities across the watersheds are 8 months, 6 months, and 2 years, respectively. The increasing vegetation cover during stable hydro-environmental periods revealed unusual conditions in the Aji-Chay watershed and reflected agricultural expansion. The WTC graphs indicated sudden changes in mutual periodicities and time-lags with different patterns between variables, which indicates the increasing anthropogenic activities in both watersheds. However, this was more dominant in the Aji-Chay watershed. The land use maps and investigation of the averaged NDVI maps also denoted that the areas of cultivated land have increased by 30% in the Aji-Chay watershed, and crop types have been changed to the crops with a higher demand for water in both watersheds.

Eco-hydro desktop assessment for determining rivers environmental flows in Urmia Lake Basin (case study: Simineh River)

Eco-hydro desktop assessment for determining rivers environmental flows in Urmia Lake Basin (case study: Simineh River)

Desiccation of a saline lake as a lock-in phenomenon: A socio-hydrological perspective

Understanding of how anthropogenic droughts occur in socio-hydrological systems is critical in studying resilience of these systems. This is especially relevant when a “lock-in” toward watershed desiccation occurs as an emergent outcome of coupling among social dynamics and surface and underground water processes. How the various processes collectively fit together to reinforce such a lock-in and what may be a critical or ignored feedback worsening the state of the socio-hydrological systems remains poorly understood. Here we tackle this gap by focusing on the case of Lake Urmia in Iran, a saline lake that faces the same fate as that of Aral Sea due to over-extraction of water sources that feed the lake. We develop an integrative, system-level understanding of how various anthropogenic, surface and underground environmental processes collectively generate the water scarcity and soil salinization issues in the study case. To this end, we investigate a paradoxical phenomenon wherein the increase of soil salinity has not noticeably affected the level of vegetation cover in Lake Urmia Basin. The outcome of our analysis may provide useful insights for informing policymakers how to cope with drought and water scarcity issues in many fragile saline lakes around the world that are currently under threat by overexploitation.

Evapotranspiration estimation of Urmia Lake Basin using GCOM-C thermal imagery

Basin-scale monthly and annual evapotranspiration (ET) is estimated for Urmia Lake Basin by applying the Global Change Observation Mission for Climate (GCOM-C) global ETindex estimation algorithm to thermal imagery observed by the GCOM-C satellite. In total, 297 satellite images acquired during 2018-2019 were used in this study. ET estimation accuracy was examined for an area dominated by apple fields using traditional surface irrigation. The estimated ET was 15% lower than the standard crop ET, which was computed using a procedure suggested by the Food and Agriculture Organization of the United Nations on a monthly timescale, and was 8% lower on an annual timescale. Comparison of estimated ET with a satellite-based ET map derived by using the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model showed a similar difference. The 8%–15% differences among the different sources of ET were small, given that a similar or wider range of uncertainty is frequently available even in ground-based ET measurements. Comparison between the estimated ET and the MODIS ET Product (MOD16) revealed a greater difference in the evaluated area of the apple fields. Given the climatic ET demands and the irrigation practices of the area, ET estimation accuracy is more likely to be higher using the dataset derived from this study than using MOD16. The GCOM-C satellite started routine surface observations in January 2018. Its contribution to agricultural water management, such as by estimating ET as presented in this study, will increase as the amount of historical data stored continues to accumulate.

Changing Causes of Drought in the Urmia Lake Basin—Increasing Influence of Evaporation and Disappearing Snow Cover

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC > 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 1995. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management.

Satellite-based monitoring of lake Urmia basin health with focus on its ecosystem regulating services by applying total ecosystem health (TEH) model

Satellite-based monitoring of lake Urmia basin health with focus on its ecosystem regulating services by applying total ecosystem health (TEH) model

Can climate-smart agriculture mitigate the Urmia Lake tragedy in its eastern basin?

Urmia Lake, which is one of the largest hypersaline lakes in the world, has experienced an excessive reduction of water level because of climatic change and anthropogenic influences. To mitigate this tragedy, several agricultural water management schemes have been initiated. However, many restoration programs have proven ineffective. To counter the lake’s disappearance, effective climate-smart agriculture (CSA) interventions are imperative. Nevertheless, the potential benefits, opportunities, risks, costs and incentive mechanisms of CSA interventions have not been well documented. Therefore, this qualitative research was performed in the eastern part of the Urmia Lake basin to identify the best CSA intervention for restoration of the lake and investigate the major drivers of CSA development. Using the “benefits, opportunities, risks, and costs” method, 14 influential factors were explored. Also, fuzzy VIKOR analysis indicated an urgent need of developing water-smart agriculture (WSA) in the Urmia Lake basin, where water scarcity, poor water governance and low water use efficiency are significant problems. Interpretive structural modeling and MICMAC analysis suggested that policy making and planning, research and development, monitoring and evaluation, education and extension services, supports and services, institutions and degree of their collaborations, knowledge and attitude, and infrastructures and farm structures can enhance the application of WSA interventions. Some recommendations and implications are offered to facilitate the adoption and development of WSA practices and technologies.

Assessing the effects of irrigated agricultural expansions on Lake Urmia using multi-decadal Landsat imagery and a sample migration technique within Google Earth Engine

Irrigated agricultural expansion is one of the main reasons for water scarcity in the Lake Urmia basin. Although previous studies have analyzed the impact of cropland expansion on the Lake Urmia Shrinkage, there is a lack of comprehensive annual assessment of historical irrigation expansion in the Lake Urmia basin and its impact on water resources of this region. In this study, we developed an automatic and efficient workflow using Landsat and Gravity Recovery and Climate Experiment (GRACE) data, GRACE Follow-On (GRACE-FO) data, and a sample migration technique within the Google Earth Engine cloud computing platform to comprehensively investigate the impact of irrigated agricultural expansion on the shrinkage of Lake Urmia, as one of the most severe environmental crisis in the world. Additionally, using the global surface water data, we proposed a fully automatic procedure to obtain reference samples from water bodies. The Lake Urmia basin was first classified into the water, irrigated, and Non-Water/Irrigated classes using the random forest algorithm. The average overall accuracy of the produced annual land cover maps during 1987–2020 was 92.2%, representing the great potential of the developed method for land cover mapping. We found that the irrigated lands expanded by nearly 890 km2 during the study period. Coincident with this change, although the area of water bodies in Lake Urmia partially recovered after 2015 (reached from 1,050 km2 in 2015 to 3,370 km2 in 2020), it is currently far beyond its original condition (i.e., ∼5,400 km2, average record during 1987–2000). Moreover, the information of the Terrestrial Water Storage (TWS) from the GRACE and GRACE-FO data between 2003 and 2020 showed a dramatic decrease in TWS level (∼−11.5 cm). The findings of this research will assist the local stakeholders and authorities to better understanding the environmental costs of irrigation expansion in the Lake Urmia basin.

Modelling of bivariate meteorological drought analysis in Lake Urmia Basin using Archimedean copula functions

AbstractThe Urmia Lake Basin as the world's second largest salt lake has experienced severe drought during recent years. The purpose of this study is to analyse the bivariate characteristics of drought (i.e., duration and severity) using two indices including SPI (standard precipitation index) and SPImod (modified SPI) associated with copula functions. For this purpose, rainfall data of six stations were used for the period of 1971–2017. At first, the characteristics of drought were extracted using the two indices. Then, through coding in the MATLAB software environment, eight families of Archimedean copula functions were applied. The simultaneous return period and conditional and Kendall returns were also investigated. The result showed that the Joe copula function was the best predictor for the analysis of both intensity and duration of drought for the study area. The correlation coefficients of Spearman, linear correlation and Tau Kendall computed for the SPI of stations were >0.65, >0.72 and >0.48, respectively, while all of them were significant. At a given critical probability level, t, the value of the Kendall return period was much greater than the standard return period, so that this difference increased with increasing t value. The results obtained from the time series of indices indicated that at least 40% of the months were dry, and the severity of droughts in the Urmia station was much higher than other stations during the studied period. Moreover, SPImod to a large extent eliminates the disadvantages of conventional SPI and takes into account seasonal variations of precipitation in the calculation of SPI.

Practical analysis of remote sensing estimations of water use for major crops throughout the Urmia Lake basin

Remote sensing techniques are used to estimate the high spatial and temporal hydrological variable of evapotranspiration. This is certainly considered a step forward, compared to alternate point measurements. It is important, however, to properly interpret these estimations to practical information for water managers. In this study, results of two separate remote sensing studies (FAO-IHE and RSRC) on estimation of actual evapotranspiration for major crops in the Urmia Lake basin were compared with widely accepted theoretical estimations of corresponding Irrigation Requirements (IR) using CROPWAT. CROPWAT estimations represent the irrigation requirements of crops under no stress, whereas RS techniques are used to estimate actual crop water use which may be under nonideal growing situations. Comparison of irrigation requirements and actual water use for different crops at various locations within the Urmia Lake basin can provide practical information on the status of field water managements, i.e., sufficiency of applied irrigation. Such analysis provides grounds for possible improvement on water management in a highly competitive water used region. The RS crop water estimations results were crosschecked with CROPWAT estimations of irrigation requirements, as the reference range, using ground data adopted from 21 scattered synoptic weather stations across the basin, representing the widely variable crop-water demand in the region and corresponding effective precipitations. FAO-IHE used the SEBAL and RSRC used the METRIC algorithm to produce ETa maps. The available land-use maps with 30-m by 30-m pixels were used for designation of the type of field crops. RSRC maps were produced using 1-km by 1-km daily satellite images. At this scale, a mixed combination of crops occurs in one pixel. To provide a more realistic comparison, the relative area of each crop cited from the land use maps was used to compare the ETa estimates by RSRC with the weighted average of irrigation requirements. The results showed the actual water use estimated from remote sensing images were generally higher than Irrigation Requirements. The difference can be attributed to overirrigation which is a common practice in the region. RS estimated actual crop-water use in rainfed lands was also compared with temporal local precipitations. In some areas, actual crop-water use in rainfed lands was more than the corresponding effective precipitation estimates. This observation cannot practically be justified and can be drawback in the preparation of land-use maps. Practical conclusions were drawn from comparison of actual crop-water use and theoretical estimates. Locations of possible over-/under-irrigation were identified, for which improved water management can provide means for saving water for the restoration of the desiccating Urmia Lake. The feasibility and versatility of RS techniques have motivated researchers to increase adoption of this method, provided that the uncertainties can be resolved.

Assessment of agricultural development preventive policies for restoration of Urmia Lake in Barandoz Basin, Iran

Urmia Lake (UL),located in northwestern Iran, is an important global biosphere; however, it has been experiencing a sharp drop in its water levels since 1995. While some measures have been taken since 2013 for the lake restoration, their impact on Land Use and Land Cover (LULC) changes in the UL basin have not been studied. In this study, we evaluated the restrictive policies effectiveness on agriculture development in the Barandoz basin, Iran. Therefore,the LULC changes of the study basin were analyzed using RS, GIS and Landsat satellite images from 1992 to 2018. The results showed an increase in rain-fed agricultural lands, garden, and built-up areas, which was about 268% (17248 ha), 160% (8934 ha), and 174% (1444 ha), respectively, during the period 1992–2018. On the other hand, rangelands, irrigated croplands, barren lands, and water bodies have decreased about 22% (19298 ha), 12% (1972 ha), 30% (5240 ha), and 99% (1117 ha), respectively. Our study also showed that gardens have increased about 40% (4174 ha), from 2013 to 2018.Analysis of the hydro-climatic parameters indicated no significant decrease in the precipitation, a significant decrease in the outflow from the basin, and a considerable increase in the groundwater depth in the Barandoz plain.We showed that the new restrictive policies adopted from 2013 have not prevented LULC changes and agricultural development in the studied basin. This study emphasizes the necessity of the integrated water resources management of basins based on an accurate understanding of the LULC changes.

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

Lake Urmia, one of the world’s largest salt lakes, is rapidly losing water and drying up. This environmental hazard has raised concerns about the consequences and impact on the surrounding communities. In this paper, we use a futuristic view (horizon of 10 years based on medium-term planning) to identify the main environmental drivers in the surrounding settlements of the Urmia Lake basin. A qualitative method, based on cross-impact analysis, was used as a means of future research. We also used a Delphi-based expert panel method to collect data and extract the environmental impacts of Urmia Lake. After the three rounds of the Delphi process, the expert panel reached a high level of agreement (100%) on the top 17 environmental consequences. Then, these consequences were classified by driving force and dependency using the MICMAC method. The results show that reducing pasture area, soil and water salinity, groundwater decline and depletion, and destruction of surrounding agricultural lands play a significant role in environmental change in Urmia Lake. Overall, any small change in these variables may lead to fundamental changes in the entire system.

Investigating the main reasons for the tragedy of large saline lakes: Drought, climate change, or anthropogenic activities? A call to action

Climate change, drought, and anthropogenic effects are among the main drivers causing alterations in natural resources one of which is the lake level (LL) of large saline lakes, which are mainly located in arid and semi-arid regions. As an urgent need to manage natural resources, this study focuses on determining the significant reasons for the shrinkage problem of saltwater lakes. To this end, the long-term severity and frequency of droughts from 2003 to 2020 and their spatiotemporal distribution in the Lake Urmia Basin (the second largest saline lake on Earth) were investigated based on the normalized difference vegetation index (NDVI) anomaly. Thereafter, monthly soil moisture, evapotranspiration, land surface temperature (LST), LL, NDVI, and precipitation time series were collected, Boolean images were used to generate drought frequency maps, and Mann-Kendall trend test techniques and wavelet transform coherence (WTC) were used to determine the impacts of anthropogenic activities and climate change on the region. The results indicated that the basin had only non-drought and mild drought conditions and did not experience moderate, severe, or very severe droughts. The results indicated that the mild drought indices covered 80% of the total area and were exposed for more than seven years. Pearson correlation analysis indicated that the primary reason for the drought was temperature anomalies (r = −0.68) in the basin. The annually changing drought conditions in the basin revealed human effects on the basin. The results indicated a statistically significant positive trend in evapotranspiration time series, which was a reason for the LL decline. Thereafter, wavelet coherence was employed to delve deeper into the correlation between LL and the hydro-environmental datasets. The increasing vegetation cover in the situation that precipitation, land surface temperature, and soil moisture had stable conditions as well as common periodicities between the hydro-environmental variables, and the abrupt change points in significant periodicities revealed that anthropogenic activities in terms of agricultural expansion had increased in the basin. Besides, the annually changing condition of drought in the basin is also an indicator of anthropogenic activities that affect the environment. The differences in the spatial distribution of the NDVI maps also indicated that people living in the western, eastern, and southern regions of the basin had cultivated more land.

Groundwater geochemistry, quality, and pollution of the largest lake basin in the Middle East: Comparison of PMF and PCA-MLR receptor models and application of the source-oriented HHRA approach

We evaluated groundwater quality, pollution, and its effects on human health in the eastern part of the Lake Urmia basin, the largest lake in the Middle East. Although groundwater quality is suitable for drinking and irrigation purposes, an index-based approach quantifying heavy metal pollution revealed that most sampling sites exhibited moderate to high pollution levels in the northern and southern regions. The positive matrix factorization (PMF) and principal component analysis-multi linear regression (PCA-MLR) receptor models suggest that the main contributors to the observed groundwater pollution, expressed as percentages by model, were: lake water infiltration and dissolution of minerals and fertilizers (46% and 63%), infiltration of leachates from solid wastes (29% and 15%), mixing with industrial-municipal wastewaters (18% and 13%), and vehicular emissions (7% and 9%). The PMF model indicated better correlations between observed and predicted concentrations (R2 = 0.96) than the PCA-MLR (R2 = 0.89). Our results from the human health risk assessments (HHRA) highlight non-carcinogenic and carcinogenic risks for Pb and Cr, respectively. Also, the PMF-based assessment of human health risk indicated that wastewaters and solid waste leachates are responsible for the cancer risk from Cr for children.

Multivariate analysis of rainfall and its deficiency signatures using vine copulas

AbstractIn the present study, a method based on the conditional density of vine copulas was used to drought monitoring and predicting the rainfall deficiency signature for a 60‐day duration in Dashband, sub‐basin of Lake Urmia basin. The annual rainfall and rainfall deficiency signatures at 10‐, 30‐ and 60‐day durations were considered as variables. D‐, C‐ and R‐vine copulas were used to represent the dependence among the variables, finding that D‐vine copula results to be more accurate for the case of interest. We found that, if the rainfall is less than the long‐term mean in the region, the rainfall deficiency signature for near future can be estimated by acceptable accuracy. Moreover, the results of the conditional probability analysis of rainfall deficiency signature for a 60‐day duration respect to the other variables showed that, on average, the probability of the occurrence of rainfall deficiency signature of 250 mm compared to the long‐term mean in the study area is more than 50% per year. The results showed that the proposed approach may facilitate the meteorological drought management in the considered sub‐basin.

Dried bottom of Urmia Lake as a new source of dust in the northwestern Iran: Understanding the impacts on local and regional air quality

Urmia Lake, the largest inland lake in Iran has lost most of its water surface area in the last 2 decades. The desertification caused by the drying of this unique lake may lead to dust storms from the lakebed and affect the air quality of the region. In this study, MODIS derived Aerosol Optical Depth (AOD) data observed between 2000 and 2019, Ultra Violet Aerosol Index (UVAI) data from Ozone Monitoring Instrument (OMI), meteorological data from MERRA-2 and ground-level PM10 data measured in the northwestern Iran and eastern Turkey were used to investigate the effects of the drying of Urmia Lake on local and regional aerosol pollution. The results revealed significant increase in aerosol pollution over the last 10 years. An inverse relationship was observed between water level fluctuations of Urmia Lake and aerosol pollution in the northwestern Iran. In total, 177 days (27 days in the period of 2000–2009 and 150 days in the period of 2010–2019) with mean daily AOD values equal to or greater than 1 were observed over the box covering Urmia Lake Basin (ULB). During 2000–2009, when the mean AOD was 0.25, the mean water surface area of the lake was observed to be 4000 km2. When the lake dried up faster during 2010–2019 (the mean water surface area was observed 2100 km2), the mean AOD increased to 0.42.The eastern part of the lake was more polluted than western part due to the extent of salinization and desertification in this part and the prevailing wind direction from west to east. The results indicated similar trends with PM10 concentration in the northwestern Iran and eastern Turkey but the classification of PM10 values based on wind direction and wind speed rejected the influence of the lakebed on aerosol pollution in the eastern Turkey except in special meteorological conditions. These conditions occur when a low-pressure system over central Iraq and a high-pressure system located in a more northerly part of the region, mainly over the Caucasian, form a dust channel. In addition to dust storms from Iraq, Syria, Arabian Desert and local emission sources, the lakebed contributes as a new source of aerosol pollution mainly in the northwestern Iran and close regions.

A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran

Urmia Lake in Northern Iran is drying up, which is causing significant environmental problems in the region, including saline storms that devastate agricultural land. We developed a remote sensing-based monitoring application to detect and map the location of saline flow sources with a novel automated deep learning convolutional neural network (DL-CCN). In order to train the model, we derived a normalised difference dust index (NDDI) from MODIS satellite images and collected ground control points (GCPs). These GCPs were randomly split for training (70%) and accuracy assessment (30%). We identified the following seven predisposing factors for saline flow source modelling: normalised difference vegetation index (NDVI), humidity percentage, temperature, wind speed, geomorphology, soil and land use/cover. In order to train the DL-CNN, we used ReLu, the root mean square error function, and Stochastic Gradient Descent (SGD) for the activation, loss/cost function, and optimization, respectively. Finally, we used the frequency ratio (FR) method to identify the most effective variable for the prediction of saline storm occurrences. The results reveal a high confidence (91.86% overall accuracy and a Kappa of 90.26) for the detection of saline flow sources. According to the FR model, the NDVI (0.982), humidity percentage (0.963), and land use/cover (0.925) are the most relevant factors for detecting the occurrence of saline storms in the Urmia Lake basin. In addition, we carried out a spatial uncertainty analysis of the results based on the Dempster Shafer Theory. The results will help the local stakeholders and decision-makers to better understating the saline flow sources and their respective environmental impacts.

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

AbstractAlthough the Urmia Lake has undergone remarkable drought conditions within the past decades mainly due to climate change, drought studies covering the entire Urmia Lake Basin and all drought aspects are lacking. The present study investigated the spatial and temporal drought conditions in the Urmia Lake Basin for the past (1988–2017) and future (2021–2050 and 2051–2080) periods using five general circulation models (GCMs) under the IPCC (Intergovernmental Panel on Climate Change) scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were compared. The SPEI predicted more drought events than the SPI, and it seemed to be a more suitable drought index than the SPI for the basin. In general, the future periods would encounter less drought conditions in terms of significant drought trends and duration than the observed period under all scenarios, but the frequency of quarterly severe drought events in the future periods would be higher than in the observed period. Furthermore, the stations Urmia (western bank) and Tabriz and Maragheh (eastern banks) would face the highest frequency of different types of quarterly drought events in the future periods compared with the observed period. The predicted high frequencies of drought events for the future periods can intensify the current low water level situation of the Urmia Lake, which seriously threatens all types of ecosystems in the basin. Therefore, serious actions need to be taken into account for efficient ecosystem and water resources management in the basin.