Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images

Lake Chad vegetation cover and surface water variations in response to rainfall fluctuations under recent climate conditions (2000-2020)

Monitoring the evolution of the Sahelian environment is a major challenge because the great Sahelian droughts, marked by significant environmental consequences and social impacts, contributed, for example, to the drying up of Lake Chad. We combined remote sensing images with a water level database from the Hydroweb project to determine the response of Lake Chad vegetation cover and surface water variations to rainfall fluctuations in the Lake Chad watershed under recent climate conditions. The variance in lake surface water levels was determined by computing the monthly anomaly time series of surface water height and area from the Hydroweb datasets. The spatiotemporal variability of watershed rainfall and vegetation cover of Lake Chad was highlighted through multivariate statistical analysis. The spatial distribution of correlations between watershed rainfall and Lake Chad vegetation cover was investigated. The results show an increase in watershed rainfall, vegetation cover, and surface water area and height, as their slopes were all positive i.e., 5.1 10^-4 (mm/day); 4.26 10^-6 (ndvi unit/day); 1.2 10^-3 (km²/day) and 6 10^-5 (m/day), respectively. The rainfall variations in the watershed drive those of Lake Chad vegetation cover and surface water, as the rainfall trend was strongly and positively correlated with those of vegetation cover (0.79), surface water height (0.57), and area (0.53). The time lag between the watershed rainfall fluctuations and lake surface water variations corresponded to approximately ∼112 days. Between rainfall variations and vegetation cover changes, the spatial distribution of the time lag showed a response time of <16 days in the western shores of the lake and on both sides of the great barrier, about 16 days in the bare soils of the northern basin and the eastern part of the south basin, and >64 days in the marshlands of the southern basin. For the analysis of lakes around the world, this research provides a robust method that computes the spatiotemporal variances of their trends and seasonality and correlates these with the spatiotemporal variances of climate changes. The correlations obtained have strong potential for predicting future changes in lake surface water worldwide.

Identifying Seasonal and Diurnal Variations and the Most Frequently Impacted Zone of Aerosols in the Aral Sea Region

With the desiccation of the Aral Sea, salt-alkali dust storms have increased in frequency and the surrounding environment has deteriorated. In order to increase our understanding of the characteristics and potential impact zone of atmospheric aerosols in the Aral Sea region, we evaluated seasonal and diurnal variation of aerosols and identified the zone most frequently impacted by aerosols from the Aral Sea region using CALIPSO data and the HYSPLIT model. The results showed that polluted dust and dust were the two most commonly observed aerosol subtypes in the Aral Sea region with the two accounting for over 75% of observed aerosols. Occurrence frequencies of polluted dust, clean continental, polluted continental/smoke, and elevated smoke showed obvious seasonal and diurnal variations, while occurrence frequency of dust only showed obvious seasonal variation. Vertically, the occurrence frequencies of all aerosol subtypes except dust showed significant diurnal variation at all levels. The thickness of polluted dust layers and dust layers exhibited same seasonal and diurnal variations with a value of more than 1.0 km year-round, and the layer thickness of clean continental and polluted continental/smoke shared the same seasonal and diurnal variation features. The zone most severely impacted by aerosols from the Aral Sea region, covering an area of approximately 2 million km2, was mainly distributed in the vicinity of the Aral Sea region, including western Kazakhstan, and most of Uzbekistan and Turkmenistan. The results provide direct support for positioning monitoring of aeolian dust deposition and human health protection in the Aral Sea region.

A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation

Rainfall estimation using remote sensing products is an alternative to in situ measurement rainfall due to their high temporal and spatial resolution. Using satellite soil moisture (SM) observations in the SM to Rain (SM2RAIN) algorithm have a great potential to estimate rainfall. SMA2RAIN-NWF algorithm is a reinforced version of a SMA2RAIN algorithm which was developed to estimate rainfall through the integration of the SM2RAIN algorithm and the net water flux (NWF) model. A new release of SMA2RAIN-NWF algorithm uses the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM dataset as input datasets. The aim here is to assess the SMA2RAIN-NWF by using multiple SM products including ASCAT, and their integration in four aggregations (AGGR) periods (1, 7, 14, and 30 days) by comparing with rainfall observation of 15 stations over the Lake Urmia basin, Iran for the period January 2015 to December 2019. The Discrete Cosine Transform (DCT) method is applied to fill the gap in the satellite SM time series. Moreover, the effect of land cover classes (grasslands, croplands, and urban) on rainfall estimation is investigated. Considering the Kling-Gupta efficiency (KGE) and correlation coefficient (R) values in comparisons of calibration and validation revealed that urban areas experienced a minimum decrement rate (2-5 %). A comparison of three SM products (ASCAT, ASCAT+SMAP, and ASCAT+DCT) show that all products had a high performance on a daily time scale in term of the KGE and R. The results showed that algorithm performance gradually rose via an increase in AGGR levels, reaching KGE and R values of 0.8 and above. Furthermore, the comparison of SM2RAIN-NWF and SM2RAIN show an improvement of SM2RAIN-NWF performance across various AGGRs.

40-years of Lake Urmia restoration research: Review, synthesis and next steps

Public concern over environmental issues such as ecosystem degradation is high. However, restoring coupled human-natural systems requires integration across many science, technology, engineering, management, and governance topics that are presently fragmented. Here, we synthesized 544 peer-reviewed articles published through September 2020 on the desiccation and nascent recovery of Lake Urmia in northwest Iran. We answered nine questions of scientific and popular interest about causes, impacts, stabilization, recovery, and next steps. We find: (1) Expansion of irrigated agriculture, dam construction, and mismanagement impacted the lake more than temperature increases and precipitation decreases. (2) Aerosols from Lake Urmia's exposed lakebed are negatively impacting human health. (3) Researchers disagree on how a new causeway breach will impact salinity, evaporation, and ecosystems in the lake's north and south arms. (4) Most researchers tried to restore to a single, uniform, government specified lake level of 1274.1 m intended to recover Artemia. (5) The Iranian government motivated and funded a large and growing body of lake research. (6) Ecological and limnological studies mostly focused on salinity, Artemia, and Flamingos. (7) Few studies shared data, and only three studies reported engagement with stakeholders or managers. (8) Researchers focused on an integration pathway of climate downscaling, reservoirs, agricultural water releases, and lake level. (9) Numerous suggestions to improve farmer livelihoods and governance require implementation. We see an overarching next step for lake recovery is to couple human and natural system components. Examples include: (a) describe and monitor the system food webs, hydrologic, and human components; (b) adapt management to monitored conditions such as lake level, lake evaporation, lake salinity, and migratory bird populations; (c) improve livelihoods for poor, chronically stressed farmers beyond agriculture; (d) manage for diverse ecosystem services and lake levels; (e) engage all segments of society; (f) integrate across restoration topics while building capacity to share data, models, and code; and (g) cultivate longer-term two-way exchanges and public support. These restoration steps apply in different degrees to other Iranian ecosystems and lakes worldwide.

Halophytes play important role in phytoremediation of salt-affected soils in the bed of Urmia Lake, Iran

Soil salinity is a major threat in agriculture even in semi-arid regions of the world which can accelerate land degradation and desertification and decrease agricultural productivity and consequently jeopardize environmental and food security. Halophytes play important role in phytoremediation. This study is assessed the potential of Halocnemum strobilaceous, Atriplex verruciferae, Salsola crassae, and Salicornia europaeae in phytoremediation of saline soils occurred after water level desiccation of Urmia Lake. Three distances from the water body (500, 1000, and 1500 m) was selected for evaluating. Soils and plants were analyzed using standard methods. The mean values of salinity indices of the saline-sodic soil samples were identified as pH 8.6 and electrical conductivity (EC_e) 65.34 dS m⁻¹, also sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP) were higher than 13 and 15%, respectively. The maximum soil exchangeable Na⁺, K⁺, and Ca²⁺ concentrations (7200, 1900, and 1400 mg kg⁻¹, respectively), also the concentrations of Mn²⁺ (12.5 mg kg⁻¹), Fe²⁺ (5.5 mg kg⁻¹), and Cu²⁺ (1.5 mg kg⁻¹), were significantly different at various distances. However, the highest amounts were obtained at 500 m. In addition the concentration of Fe²⁺ (511.85 mg kg⁻¹), Zn²⁺ (99.97 mg kg⁻¹), and Na⁺ (25.65 mg kg⁻¹) was the highest, especially in shoots. Furthermore, Salicornia and Halocnemum were more effective in salinity-remediation in comparison to other halophytes. The maximum dry matter (38%), protein (16%), and oil percentage (3.5%) were found in Salicornia, followed by Halocnemum. The findings indicated that salt-accumulating halophytes could be considered as the suggestions for phytoremediation saline soils and desalinating soil in arid and semi-arid regions.

Monitoring of aquatic birds and surveillance of avian influenza and Newcastle disease of waterfowls at the National Park of Urmia Lake

BACKGROUND

Urmia lake, as a national park, is one of the most valuable aquatic ecosystems in the Middle East and quatitative and qualitative changes in Urmia lake water have a great impact on its ecological performance and in the region.

OBJECTIVES

This project was designed to study the effects of the extent of Urmia lake water surface area on the area size and on the number of aquatic birds of the six selected habitats in 2011-2019. The presence of avian influenza (AI) and Newcastle disease (ND) viruses in migratory aquatic birds together with their impacts on poultry farms as well as on rural birds was also under surveillance in 2018-2019.

METHODS

Changes of Urmia lake and its impacts on area size of the six selected birds habitats were monitored by GIS. The small monitoring program with circular plot point counts was used for counting of the number of birds of the six selected habitats. At least, 100 samples (oropharyngeal and cloacal swabs) were collected. each sample was placed in a sterile plastic tube containg transport media and assigned with an number and store untill used. Reverse transcription-polymerase chain reaction (RT-PCR) and real-time RT-PCR test were used for detection of AI and ND viruses in the samples.

RESULTS

The results revealed that changes in the water surface area of Urmia lake had a gsignificat impacts on area size and the number of aquatic birds of the six selected habitats. The surveillance results showed that 5% of the samples were AIV positvie while 25% of the samples were pasitive for NDV including 20% for non-virulent NDV (lNDV) and 5% for virulent NDV (vNDV) strains.

CONCLUSION

This study showed that fluctuation of Urmia lake's water surface area influenced (p < 0.05) the area size of the six selected aquatic birds' habitats and had a great impacts on the number of the migratory birds. Detection of AIV and vNDV emphesises that the seasonal migratory waterfowls spread AI and vND viruses to the ponds and estuaries as well as to the rural birds and industrialised poultry units around the Urmia lake. Potential public health treats were also discussed.

Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline

Anthropogenic activities, in addition to climate change caused the drying of Urmia Lake in Iran, since 2005. Dust storms blown from the dried lakebed have created serious environmental hazards in adjacent areas. These crises would jeopardise achieving United Nations Sustainable Development Goals (UN SDGs) and emphasise the need for evaluating the spatial distribution of soil enrichment of potentially toxic elements (PTEs) (As, Cr, Cu, Ni, Pb and Zn). Conventional assessment would require a costly sampling method to map potentially polluted areas. Digital soil mapping (DSM) has proved to be a cost-efficient method for soil mapping, however its application in mapping enrichment of PTEs in soil is still lacking. This study aims to map and project the potential pollution of PTEs in the Urmia Lake area using digital mapping techniques and Landsat-8 OLI satellite images. A total of 129 surficial soil samples were collected as ground control. Enrichment factors (EFs) of PTEs and the Modified Pollution Index (MPI) were spatially predicted using two machine learning models. Covariates were derived from a suite of Landsat-8 spectral indices. The bootstrapping method was used to analyse the uncertainties. The results showed that Random Forests performed well in estimating EFs of several PTEs. Spectral indices using NIR and SWIR bands were key to predict these PTEs and MPI. The digital maps demonstrated that the study area was enriched with As, Cu and Pb at moderate to significant levels. Regions under the lower ecological level (elevation <-1274 m) had significantly larger enrichment than those of higher elevation. Based on MPI, 43% of the area was categorised as moderately polluted, and 31% of the area was moderately-heavily polluted. Possible sources of PTEs were discharges from farmlands, landfills, and industries. Our results revealed that the Urmia Lake desiccating has caused severe environmental challenges and needs immediate restoration.

Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series

The sustainable development goals of the United Nations, as well as the era of pandemics have introduced serious challenges for agricultural production and management. Precise management of agricultural practices based on satellite-borne remote sensing has been considered an effective means for monitoring cropping patterns and crop-farming patterns. Therefore, we proposed a simple and generic approach to identify multi-year cotton-cropping patterns based on time series of Landsat and Sentinel-2 images, with few ground samples that covered many years, a simple classification algorithm, and had a high classification accuracy. In this approach, we extended the size of training samples using active learning, and we employed a random forest algorithm to extract multi-year cotton planting patterns based on dense time series of Landsat and Sentinel-2 data from 2014 to 2018. We created annual crop cultivation maps based on training samples with an accuracy greater than 95.69%. The accuracy of multi-year cotton cropping patterns was 96.93%. The proposed approach was effective and robust in identifying multi-year cropping patterns, and it could be applied in other regions.

Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm

Evaporation is a crucial factor in hydrological studies; its precise measurement has always been challenging due to the costly recording tolls. Therefore, machine learning models that can give reliable predictive results with the least information available have been recommended for evaporation prediction. This study was conducted in the central of Iran using the data related to the Doroudzan dam. Several hydrological and meteorological variables, including inflow and outflow of the reservoir, lake area behind the dam, temperature, overflow from the reservoir, precipitation, and evaporation at the previous month, were considered input data to predict the evaporation at the current month. Monthly data from October 1999 to September 2020 were used during the modeling. First, the single adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector regression (LS-SVR) models were evaluated for predicting the amount of evaporation using different scenarios defined based on the different combinations of input variables. The results showed that LS-SVR with RMSE = 2.77, MAPE = 2.48, and NSE = 0.93 provided a better prediction than ANFIS. Second, the Harris hawks optimization (HHO) algorithm was used to optimize the parameters of ANFIS to check for the possibility of performance improvement. The hybrid ANFIS-HHO model predicted the evaporation with RMSE = 2.35, MAPE = 1.55, and NSE = 0.95, respectively. The Taylor's diagram also demonstrated the superior performance of the hybrid ANFIS-HHO model than the LS-SVR and ANFIS models. The best scenario for all three models included all input variables but the area behind the dam into the models. The methodology proposed in this study is useful for predicting the evaporation from dam reservoirs under the influence of various dam variables.

Determination of nano and microplastic particles in hypersaline lakes by multiple methods

Microplastics and nanoplastics have a range of impacts on the aquatic environment and present major challenges to their mitigation and management. Their transport and fate depend on their composition, form, and the characteristics of the receiving environment. We explore the spatial and temporal dynamics of plastic particles in the world's second-largest hypersaline lake, combining information from microscopic, thermal gravimetric, and fractional methods. Studies on microplastic and nanoplastic pollution in these important environments are scarce, and there is limited understanding of their dynamics and fate. Our results for Urmia Lake (Iran) in 2016 and 2019 show a discrepancy in the composition and quantity of microplastics measured in river tributaries to the lake and the lake itself, suggesting an active microplastic sink. Potential sink mechanisms in hypersaline lakes are explored. The present study indicates that microplastics have different transport mechanisms and fate in these extreme environments, compared to lake and ocean environments.

The Salicornia europaea potential for phytoremediation of heavy metals in the soils under different times of wastewater irrigation in northwestern Iran

The use of wastewater for irrigation usually leads to the buildup of potentially toxic elements (PTEs) in soils. The objective of this study was to assess the capacity of Salicornia europaea L. to uptake heavy metal when irrigated with wastewater at the vegetative, flowering, and reproductive stages of S. europaea for 2 and 4 days (in each stage) in the coastal saline area of Lake Urmia. The concentrations of heavy metals were detected in irrigated water, soil, and plant samples, while transfer factor (TF), bioconcentration factor (BCF), and bioaccumulation factor (BAF) were calculated. The results revealed that metal concentrations in the wastewater were above the permissible limits. The wastewater irrigation caused higher shoot biomass despite the high uptake of PTEs. Levels of Fe and Cu in plants were higher when irrigated with wastewater at the reproductive stage as compared to flowering and vegetative stages. The TF of wastewater-irrigated plants was higher at the flowering stage. TF of different metals at the flowering stage were in order of Zn > Pb > Ni > Cd > Cu, while the BCF increased in the order Cd > Cu > Zn > Ni > Pb. The BAF of the investigated PTEs at the flowering stage increased in the order Cd > Pb > Ni > Zn > Cu. In conclusion, higher Pb and Zn in the shoot indicated that the plant exhibited the phytoextraction mechanism, while Salicornia used a phytostabilization mechanism for roots-Cu, Ni, and Cd.

Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status

European countries are obligated to monitor and estimate ecological status of lakes under European Union Water Framework Directive (2000/60/EC) for sustainable lakes’ ecosystems in the future. In large and shallow lakes, physical, chemical, and biological water quality parameters are influenced by the high natural variability of water level, exceeding anthropogenic variability, and causing large uncertainty to the assessment of ecological status. Correction of metric values used for the assessment of ecological status for the effect of natural water level fluctuation reduces the signal-to-noise ratio in data and decreases the uncertainty of the status estimate. Here we have explored the potential to create synergy between optical and altimetry data for more accurate estimation of ecological status class of lakes. We have combined data from Sentinel-3 Synthetic Aperture Radar Altimeter and Cryosat-2 SAR Interferometric Radar Altimeter to derive water level estimations in order to apply corrections for chlorophyll a, phytoplankton biomass, and Secchi disc depth estimations from Sentinel-3 Ocean and Land Color Instrument data. Long-term in situ data was used to develop the methodology for the correction of water quality data for the effects of water level applicable on the satellite data. The study shows suitability and potential to combine optical and altimetry data to support in situ measurements and thereby support lake monitoring and management. Combination of two different types of satellite data from the continuous Copernicus program will advance the monitoring of lakes and improves the estimation of ecological status under European Union Water Framework Directive.

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.