Urmia Lake Research Articles

A review and uncertainty analysis of Lake Urmia's hypsometric relationships

Lake Urmia, once the second-largest saline lake worldwide, has almost dried. Several studies have provided bathymetric maps for the lake using different data and techniques but rarely identified the associated uncertainties. This paper provides a comprehensive review of Lake Urmia bathymetric studies and discusses the underlying uncertainties. We developed an updated hybrid bathymetry and hypsometric relationships for Lake Urmia using in-situ and satellite data. The proposed hybrid bathymetry was validated against independent in-situ depth data (with an RMSE < 20 cm) and applied as a baseline for comparison with other available bathymetry models. Results revealed that uncertainties in lake depth measurement, water delineation algorithms from satellite data, spatial interpolation method, and sensor resolution have the highest uncertainty, respectively. Furthermore, the width of the uncertainty bands in the lake level-area relationships is not uniform and peaks at lake levels between 1270 and 1272 m, which are critical for conserving crucial ecosystem services of Lake Urmia, such as island conservation and dust control. Our synthesis indicated that the application of various Lake Urmia bathymetries could lead to about 0.5 and 1 km3differences in the estimated lake volumes for obtaining the ecological and dust control levels, respectively. Our findings provide useful insights for Lake Urmia managers, also applicable for other saline lakes, into considering thedynamic of lake topography and associated uncertainties in lake conservation/restoration programs.

Evaluation of the Water Policies of the Urmia Lake Basin: Has the Government Accurately Identified the Problem?

Evaluation of the Water Policies of the Urmia Lake Basin: Has the Government Accurately Identified the Problem?

U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model

Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.

Prolonged ecological changes can affect morphometrics and gene expression profile? Focusing on Hsp-70 and NLHS-induced Hsp-70 of Artemia urmiana

BackgroundIn recent years, many aquatic ecosystems, including Urmia Lake, have undergone severe ecological tensions. This lake, the largest natural habitat of the brine shrimp Artemia urmiana, has progressively desiccated and its salinity has dramatically increased over the last three decades. In the face of the long period environmental stresses, understanding the adaptation and ecological plasticity mechanisms is the most interesting challenges in genetic and applied ecology. These mechanisms may probably be driven by inducing expression of some genes involved in adaptation such as Hsp-70 and also adjusting morphological parameters. But they are yet to be understood. Hence, the present work aimed to study the mechanisms, along with testing the hypothesis that non-lethal heat shocked nauplii originating from drought period can evoke Hsp-70 expression more than those from rainy period. MethodsThis study measured and analyzed morphometrical characters of adult male and female Artemia urmiana over three decades. Then, the influence of three-decade ecological crisis on Hsp-70 and non-lethal heat shock (NLHS)-induced Hsp-70 expression levels of nauplii of Artemia urmiana habiting Urmia Lake using Real-time PCR technique, based on cyst collections in 1994 (rainy period) to 2020 (drought period), was evaluated. ResultsThe morphometrics results showed that the morphological characters were significantly shrunk in 2020 compared to 1994 (CI 95%, p < 0.05). Furthermore, our results depicted that, Hsp-70 expression level was significantly upregulated in response to the prolonged ecological crisis, (CI 95%, P < 0.0001), and also interestingly, the nauplii exposed to longe-term ecological crisis (belong to 2020) were able to increase Hsp-70 expression more than other ones in response to environmental stressors including heat. ConclusionsThe present results showed the involvement of Hsp-70 in the adaptation of Artemia urmiana to long term ecological alteration at the cost of shrinking morphometric parameters.

Quantifying the Effects of Climate Change on Simineh River Discharge in Lake Urmia Basin

The Simineh River is heavily reliant on water resources for agricultural aims in the Lake Urmia. However, the hydrological system of the Simineh basin is highly susceptible to the impacts of climate change scenarios, primarily due to the presence of diverse topographical features, limited availability of data, and the complex nature of the local climate. This study aimed to simulate the monthly discharge of the Simineh River using the SWAT and assess the effects of climate change on the monthly discharge. Future climate scenarios for the years 2011-2030 were generated using the HadCM3 weather models under the A2, B1, and A1B scenarios. After evaluating the performance of the LARS-WG model in producing precipitation, minimum and maximum temperatures for the Simineh River watershed, the output of the HadCM3 under the A1B, B1, and A2 scenarios reduced, and the desired meteorological parameters predicted. These predicted values used as inputs for the SWAT model. In this study, assuming no change in land use, the focus was solely on the impact of climate change scenarios. However, appropriate measures can be taken to save the Simineh River's water consumption by optimizing irrigation efficiency through innovative methods. This is crucial because the results indicate that a total reduction of up to 25% in discharge in the Lake Urmia basin under climate change leads to a significant decrease in the annual average inflow to the lake from 570 million cubic meters to 394, 398, and 440 million cubic meters under the A2, B1, and A1B scenarios, respectively. The Simineh River supplies 11% of the water in Lake Urmia, and taking necessary measures to conserve its water resources is essential.

Assessing water security and footprint in hypersaline Lake Urmia

The quantitative assessment of water security including its distribution helps plan and manage adequate amounts of quality water to sustain livelihoods. This research introduces a hydrological modeling framework designed to evaluate the spatial and temporal fluctuations of blue water and green water, aiming to quantify the water security status of Lake Urmia in Iran. This lake, recognized as the second largest hypersaline lake globally, has experienced a significant decline in its water storage in recent years. This model, based on water consumption and population size was used to investigate water security and footprint while quantifying the spatial and temporal changes in the availability of blue water and green water. As the results demonstrate, the monthly and annual coefficient of variation for blue water (41.1 % and 27.6%, respectively) was greater than for green water (32.3% and 21.7%, respectively), hence green water flow showed more homogeneous than blue water flow distribution among different sub-basins. At the same time, the coefficient of variation for the spatial distribution differs between various components of the water footprint. Agricultural use requires the most water in this basin, with a blue water scarcity of 0.27. The blue water vulnerability value for total water consumption indicates a lower risk of a water crisis under low-flow conditions. In addition, the freshwater provision indicator in each sub-basin varies between the middle (0.64) and lower (0.80) parts. Thus, in these parts, water can be consumed with less negative impact on the environment. In conclusion, the environmental situation of Lake Urmia and its water security has clearly decreased over the last decade which requires urgent management action (e.g., changing irrigation pattern) to mitigate an environmental crisis. This research offers precise and quantifiable references related to water resources and water security, and can enhance targeted water footprint management within the study area. Our hydrological modeling framework provides the basis to inform water management in various similar watersheds across the globe.

Predicting rainfall response to climate change and uncertainty analysis: Introducing a novel downscaling CMIP6 models technique based on the stacking ensemble machine learning

Abstract This study proposes a novel downscaling technique based on stacking ensemble machine learning (SEML) to predict rainfall under climate change. The SEML consists of two levels. Rainfall time series predicted by level 1 algorithms MLR, MNLR, MARS, M5, RF, LSBoost, LSSVM-GS, and a novel hybrid algorithm namely LSSVM-RUN) are used as inputs to the level 2 machine learning algorithm (MARS and LSSVM_RUN). Then, meta-algorithms of SEML predict rainfall based on eight predicted rainfall in level 1. This approach boosts prediction accuracy by utilizing the strong points of different machine learning (ML) algorithms. Results showed that MARS and LSSVM-RUN could be employed to improve the modeling results as meta-algorithms (level 2 of the SEML). Three global climate models (GCMs) in the historical period (1985–2014) and three SSP scenarios in the future period (2021–2050) were considered for downscaling and predicting rainfall at Lake Urmia and Sefidrood basins. Using meta-algorithms, the prediction results showed that rainfall in all scenarios and stations decreased between 0.02 and 0.20% (except Takab station in model CanESM5 scenarios). Hence, the proposed stacking ensemble ML has the potential for modeling and predicting precipitation with good accuracy and high reliability.

The rise and demise of Iran’s Urmia Lake during the Holocene and the Anthropocene: “what’s past is prologue”

The rise and demise of Iran’s Urmia Lake during the Holocene and the Anthropocene: “what’s past is prologue”

Evaluating the effects of reservoir outflow and land-use change on the Zarrineh River basin

ABSTRACT The rapid decline of Lake Urmia in northwest Iran, caused by both human and natural factors, motivated our utilization of the Soil and Water Assessment Tool (SWAT) model. The objective was to identify the most effective reservoir outflow simulation scheme and assess the influence of land use changes in the Zarrineh River basin on Lake Urmia. The target release approach for controlled reservoirs and the measured outflow schemes were evaluated. Despite improved calibration results in the latter method, the former demonstrated superior performance during the validation phase. Three land use scenarios – a model with the 2001 land use map, a model with the 2008 land use map, and a model with a gradually updating land use map – were considered. The gradually updating land use scenario exhibited a significant enhancement in validation results; it showed a 250% increase in water yield, while evapotranspiration declined by 3%.

Application of soft computing and evolutionary algorithms to estimate hydropower potential in multi-purpose reservoirs

Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor’s diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.

Application of a regional climate model on autumn dust events over the Urmia Basin

Dust events have occurred over the Urmia Lake Basin in northwestern Iran for many years. In this study, we identified the mechanism and source of dust events in the region and assessed the ability of the Regional Climate Model (RegCM) to simulate these events. The observation data includes horizontal visibility and present weather phenomena achieved in Tabriz, Urmia, and Saqez stations. The ERA5, MERRA-2, and CAMS data were prepared for this research. The long-term dusty day analysis was conducted from 2000 to 2019. The synoptic-dynamic analysis and RegCM simulations were done during dust events on 28–31 October 2017—the HYSPLIT model and MODIS image usage to consider more detail. The results show that the Urmia Basin experiences two peaks, dusty days in the May and autumn months, including September and October. The persistent and widespread dust event in Urmia resulted from the approaching trough at 500 hPa to the north of Saudi Arabia and Iraq, trigged low pressure and increasing wind speed over the area. Based on the synoptic-dynamic analysis, MODIS satellite images, and the outputs of HYSPLIT, Iraq deserts were the primary source of dust on 28–31 October 2017. The dust distribution in the RegCM model, MERRA-2 data, and MODIS image agree. The RegCM aerosol optical depth (AOD) output time series agreed with MERRA-2 data, with a correlation coefficient of 0.84 and an index of agreement (IOA) of 0.56 in Urmia stations. The local dust source detected by the surface emission flux in the eastern Lake Urmia close to Tabriz can be a reason for the dustiest day in Tabriz. The AOD diurnal variation obtained from RegCM, MERRA-2, and CAM are similar in Tabriz and Urmia. The AOD of RegCM is underestimated compared to MERRA-2 and CAM in three selected stations.

Assessing mountain block water storage changes in river basins using water balance and GRACE: A case study on Lake Urmia Basin of Iran

Study regionLake Urmia Basin (LUB), northwestern Iran, encloses the largest salt lake in Iran, where severe drawdown of the lake water level is causing salt storms that threaten the health of the basin's inhabitants. Study focusDifferent in-situ and RS-based datasets are used to estimate the water storage change (WSC) in LUB and partition it into the basin floor WSC and mountain block WSC components. Water balance components for LUB were estimated for the 2001–2011 period, using RS-based datasets for precipitation, actual evapotranspiration, soil moisture and snow storage. Ground-based data were used for validation of RS data as well as water balance closure purpose. GRACE terrestrial water storage data were also used to validate the water balance method results. New hydrological insights for the regionGRACE results confirmed an average WSC of − 921 million cubic meters per year (MCM/yr) (−17.6 mm over the basin area). WSC time series in the basin floor (average: −913 MCM/yr, or −53 mm over the basin floor area) was estimated by summing the related water storage components, and then subtracted from the basin WSC to estimate the mountain block WSC time series (average: −8 MCM/yr, or −0.2 mm over the mountain block area). To evaluate its authenticity, the estimated mountain block WSC time series was then compared to mountain block rivers’ base flow and springs' discharge, showing a considerable correlation between the time series. Considering the faster water resources depletion in the basin floor (where anthropological factors are significant) than in the mountain block (where anthropological factors are rare), one can conclude that anthropological factors may have the primary role in deteriorating water resources conditions in the basin.

Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques

Urmia Lake in Iran has been gradually drying up since the 2000 s, revealing soil that was once underwater. There is limited research on how this drying process affects the distribution of soil chemical ripening in the lakebed sediments. To investigate this, 131 surface soil samples were collected in July 2017 from six zones based on their distance from the lake's shoreline. Elemental concentrations in the samples were measured using a portable X-ray fluorescence (XRF) spectrometer. Five geochemical indices (GCIs) were calculated: Titanium/Zirconium, Barium/Strontium, Silicon/Aluminium, Silicon/R2O3, and the Darmody index. A remote sensing image (Landsat-8 OLI) from the same sampling period was used as environmental covariates to explain the spatial distribution of GCIs. Random forests (RF) and cubist tree models were employed to model and map these GCIs. The bootstrapping method was used to assess the uncertainty of the maps. Results indicated that the RF model performed better than the cubist model based on the concordance values in the validation dataset. Remotely sensed clay minerals ratio and normalized burn ratio were identified as the most significant covariates in making predictions. The study found that the distribution of all geochemical indices was directly related to their distance from the shoreline. Ba/Sr emerged as the best index for detecting soil chemical ripening in the study area. The index displayed the most significant differences between the drying zones. This research demonstrated that combining satellite imagery with GCIs using the digital soil mapping (DSM) technique can effectively detect soil ripening progression.

Operational policy development for dynamic restoration of lakes in a changing climate; application of innovative hedging rules in a system dynamics platform

Growing agricultural and domestic demand exacerbates water shortages globally, creating substantial environmental challenges, especially in lakes and wetlands ecosystems. This paper presents a stationary policy to restore and sustain the water level in natural lakes using a new version of hedging rule that accounts for time-dependent environmental demand and the water allocation to the agricultural and municipal sectors under different climate change projections. The proposed framework is demonstrated via operational policy derived to rehabilitate Lake Urmia in Iran. To simplify the modeling process, all reservoirs in the basin are replaced with an equivalent reservoir (ER) to allocate the available water between potential users. The ER is then operated using the set of hedging rules derived for staged restoration and sustainability of the lake level while meeting other stakeholder objectives. A system dynamics-based model is used to simulate the multi-sectoral system of the basin while using a built-in optimization algorithm to develop the most desirable multi-period operational policy. The lake-level condition is investigated by producing lake-level duration curves, while the reservoir performance indices (reliability, resiliency, and vulnerability) are used for assessing water supply in the basin. The results indicate that the proposed framework is highly effective in restoring lake level while meeting agricultural and municipal water demand in the basin. The proposed model provides a stationary policy for the lake restoration accounting for the dynamic variation of the lake level and fluctuations of the reservoirs inflow due to climate variability and change.

Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)

AbstractIn this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB) was obtained by using the GRACE satellites. The whole study area was covered by 10 GRACE/GFO pixels, and the TWSA value was calculated for these 10 pixels. Examining the changing trend showed that the value of TWSA in the LUB has a decreasing trend and fluctuates from approximately −200 to +200 compared to the average value. The TWSA was modelled using the group method of data handling (GMDH) by considering six different parameters obtained from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). Finally, the best models with two to six input variables were selected. Among the models with different inputs, the GMDH Model 2, with three inputs, had the best performance compared to the other models. After choosing the best inputs in calculating the TWSA value by the GMDH, the TWSA value was also modelled by using the Adaptive Neuro‐Fuzzy Inference System (ANFIS), the Extreme Learning Machine (ELM) and the Artificial Neural Network (ANN). The results showed that GMDH not only outperformed other models but also provided some simple equations to apply in practical tasks.

U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model

Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem.

Understanding saline lake sand dunes dynamics: Coupling remote sensing techniques and field studies

Large saline lakes, a benchmark for natural and anthropogenic fluctuations in the Earth's climate system, are drying up worldwide at accelerated rates. This study focuses on Lake Urmia, the world's second-largest hypersaline lake that indicates significant environmental challenges as it approaches near-complete dryness. Over the last decade, new aeolian features, including sand dunes, have emerged due to anthropogenic and natural factors. The present study documents the dynamics of recent aeolian processes on exposed dried-up lakebeds of saline lakes environments by investigating their mineralogy, geochemistry, and sedimentology to understand better dune morphology, evolution and migration rate. Quantitative analyses are performed on the dunes of Lake Urmia's western shores using ancillary data (meteorological and climate), field data, X-Ray Diffraction, Scanning Electron Microscopy and Energy Dispersive X-rays Spectroscopy (SEM - EDX), petrographic analysis, and supervised classification and change detection of satellite imagery from 2003 to 2019. The migration rates yielded high values, approximately above 20 m yr−1 on average, mostly related to (1) the rising drift potential (DP), (2) the percentage of wind speeds that are above the threshold velocity (V > Vt%), (3) decline in precipitation and unidirectional wind regime and (4) atmospheric circulations over the Mediterranean Sea. The results infer that local and global-scale factors significantly impact the migration rate of Lake Urmia dunes. The study outcomes are widely applicable to drying up saline lakes worldwide, enhancing the understanding of current aeolian processes and the evolution of dune fields/sand seas, building towards sustainable land management and policy-making.

Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring

Monitoring spatial and temporal trends of water use is of utmost importance to ensure water and food security in river basins that are challenged by water scarcity and climate change induced abnormal weather patterns. To quantify water consumption by the agriculture sector, continuous monitoring is required over different spatial scales ranging from field (< 1 ha) to basin. The demand driven requirement of covering large areas yet providing spatially distributed information makes the use of in-situ measurement devices unfeasible. Earth observation satellites and remote sensing techniques offer an effective alternative in estimating the consumptive use of water (Actual EvapoTranspiration (ETa) fluxes) by using periodic observations from the visible and infrared spectral region. Optical satellite data, however, is often hindered by noises due to cloud cover, cloud shadow, aerosols and other satellite related issues such as Scan Line Corrector (SLC) failure in Landsat 7 breaking the continuity of temporal observations. These gaps have to be statistically filled in order to compute aggregated seasonal and annual estimates of ETa. In this paper, we introduce an approach to develop a gap-filled multi-year monthly ETa maps at medium spatial resolution of 30 m. The method includes two major steps: (i) estimation of ETa using the python based implementation of surface energy balance model called PySEBAL and (ii) temporal interpolation using Locally Weighted Regression (LWR) model followed by spline based spatial interpolation to fill the gaps over time and space. The approach is applied to a large endorheic Lake Urmia Basin (LUB) basin with a surface area of ~ 52,970 km2 in Iran for the years 2013–2015 using Landsat 7 and 8 satellite data. The results show that the implemented gap filling approach could reconstruct the monthly ETa dynamics over different agriculture land use types, while retaining the high spatial variability. A comparison with a similar dataset from FAO WaPOR reported a very high correlation with R2 of 0.93. The study demonstrates the applicability of this approach to a larger basin which is extendible and reproducible to other geographical areas.

A scenario-based food security analysis and halophyte crop suitability assessment in dying lake environments impacted by climate change

Climate change has significantly impacted food shortages over the past decades, and predicted projections have also indicated potentially intensive consequences for species diversity and natural environments in the coming decades. Climate change in the form of drought has resulted in environmental impacts on global dying lakes (GDLs), such as water scarcity, land degradation and food shortage. Thus, new comprehensive environmental impact assessment methods are urgently needed for GDLs to inform decision-making and minimise the negative impacts on the environment and human life and wellbeing. As state of art and progressive research, the main objective of this research is to develop an approach using a comprehensive and transferable geographic information science scenario-based suitability assessment to analyse the capability of halophyte plants as alternatives for crop modification in GDL areas. The approach was developed and examined for Salicornia suitability mapping in the Lake Urmia Basin as one of the most critical GDL cases. To obtain the ecological requirement information, we performed a scenario-based cultivation experiment on Salicornia under different salinity conditions. Accordingly, we employed relevant ecological requirements and environmental indictors for the Salicornia plant (e.g. soil characteristics and climate) to identify suitable areas for cultivating this plant under scenario-based spatial modelling and performed an uncertainty analysis. The results were achieved and validated in a field operation and laboratory analysis. According to the results, the Salicornia plant can be efficiently considered an alternative crop in farmlands around Lake Urmia that are impacted by intensive soil, aquifer salinisation and food production issues. Results pointed out that about 4.6% of the area (2372 km2) is highly suitable for the cultivation of Salicornia. As state of the art, our approach was developed in a semi-automated context but can also be customised and applied to similar case studies in GDL environments. We expect that the information and insights gained from such assessments will support stakeholders, decision makers and authorities in the development of effective policies and plans for mitigating the impacts of climate change on the fragile ecosystems of GDLs.

Meteorological drought assessment and prediction in association with combination of atmospheric circulations and meteorological parameters via rule based models

The development of data-driven models along with the data that are recorded from satellites has made the study of water engineering more attractive. In this study, SPEI values were calculated with the meteorological variables and then a combination of the meteorological variables as well as atmospheric circulation values was considered as a data mining model input for estimating the droughts. The series of the SPEI values for 3-, 6-, 9-, 12-, 24- and 48-month time scales were obtained during 1988-2016. In this study, both M5 Tree model and Associate Rules were used to predict and analyze the meteorological drought at 6 synoptic stations in the basin considering both the atmospheric circulations (NAO, SO, MOgi, MOac, WeMO, Mediterranean, Red, Black, Caspian, and Persian Gulf SSTs) and the meteorological variables (lagged relative humidity, evapotranspiration, average temperature, minimum-maximum temperature and pressure). The results showed that making use of a combination of the atmospheric circulation indices and meteorological variables in the models increases the accuracy of the model and improves the results in a long term period. The best result in the study of drought in Urmia Lake basin is related to SPEI48 (R = 0.85, RMSE = 0.08, MAE = 0.11) and in the association rules the value of the lift index of best rule is 1.32. Although both approaches provided acceptable results, M5 Tree model had a comparative advantage due to simple and practical linear relationships.