Identification of Landslide-prone Zones Using a GIS-based Multi-Criteria Decision Analysis and Region Growing Algorithm in Uncertain Conditions

Landslides are considered to be one of the most significant natural hazards. Detection of landslide-prone zones is an important phase in landslide hazard assessment and mitigation of landslide-related losses. AHP as one of the most effective methods for GIS-based multi-criteria decision analysis is increasingly being used in susceptibility mapping. However, its weights have some degree of uncertainty that interval comparison matrix (ICM) method can be used to deal with this problem. The importance of this study is to propose an interval number distance-based region growing (IDRG) method based on ICM for the identification of landslide-prone zones in the Urmia lake basin, Iran. To assess the capability of the proposed IDRG method, a landslide susceptibility map was produced using common AHP, too. To generate the maps, the weights of nine conditioning factors were determined using both traditional pairwise comparison matrices (PCM) of the AHP method and ICM. The accuracy of the produced maps was assessed through ROC (receiver operating curve) and using a dataset of known landslide occurrences. The results indicate an improvement in accuracy of about 11% by identifying the landslide-prone zones using the IDRG method. This improvement was achieved by minimizing the uncertainty associated with criteria ranking/weighting in a traditional AHP and identifying the prone zones as areas instead of pixels.

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 2010. 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.

Spatial and temporal assessment of groundwater quality and hydrogeochemical processes in Urmia Lake Basin, Iran

The evaluation of groundwater quality and geochemistry have an important role in the management of water resources in arid and semi-arid regions. In the present study, the spatio-temporal assessment of groundwater quality and hydrogeochemical processes, as well as, statistical analyses in the Azarshahr and Ajabshir planes located in the Urmia Lake basin were investigated. The results revealed that within six years (from 2014 to 2019), the value of total hardness was higher than the permissible level and the quality of groundwater for drinking was very hard and fresh in both planes. In 2019, 84 and 67% of the samples fell within the range of good to poor groundwater quality in the Azarshahr and Ajabshir planes, respectively. The temporal assessment with the help of water quality index values for both planes revealed a good groundwater quality for the Azarshahr plane and a good to poor groundwater quality for the Ajabshir plane. However, deterioration of water quality was observed in both planes from 2014 to 2019. The level of water quality for irrigation was better in the Azarshahr plane than in the Ajabshir plane, due to the presence of fewer salty sites. In addition, Ca–Mg–HCO3 and rock dominance were identified as hydrochemical facies and controlling factor in the groundwater of both planes, respectively. Multivariate statistical analyses indicated both natural and anthropogenic sources (such as weathering, fertilizers, and wastewater) for hydrochemical parameters. It was suggested to develop a comprehensive regulation to control the entry of pollutants into the groundwater of the study area.

Unraveling the Water-Energy-Food-Environment Nexus for Climate Change Adaptation in Iran: Urmia Lake Basin Case-Study

A holistic approach to the management of water, energy, food, and the environment is required to both meet the socioeconomic demands of the future as well as sustainable development of these limited resources. The Urmia Lake Basin has faced environmental, social, and economic challenges in recent years, and this situation is likely to worsen under the impacts of climate change. For this study, an adaptability analysis of this region is proposed for the 2040 horizon year. Two models, the water evaluation and planning (WEAP (Stockholm Environmental Institute, Stockholm, Sweden)) and the low emissions analysis platform (LEAP (Stockholm Environmental Institute, Boston, MA, USA)), are integrated to simulate changes in water, energy, food, and the environment over these 20 years. Two climate scenarios and nine policy scenarios are combined to assess sustainable development using a multi-criteria decision analysis (MCDA) approach. Results show that, through pursuing challenging goals in agricultural, potable water, energy, and industrial sectors, sustainable development will be achieved. In this scenario, the Lake Urmia water level will reach its ecological water level in 2040. However, social, technical, and political challenges are considered obstacles to implementing the goals of this scenario. In addition, industry growth and industry structure adjustment have the most impact on sustainable development achievement.

Bayesian Belief Networks for the metamodeling of simulation-optimization model to identify optimum water allocation scenario, Application in Miyandoab plain, Urmia Lake basin, Iran

<p>In this study, decision-making models in uncertain conditions are developed to identify optimal strategies for reducing competition between agricultural and environmental water demand. The decision-making models are applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Decision-making models are conceptualized based on static and dynamic Bayesian Belief Networks (BBN). The static BBN evaluates the effects of management strategies and drought conditions on environmental flow and agricultural profit at the annual scale, while the dynamic BBN accounts for monthly dynamics of water demand and conjunctive use. The reliability and performance of BBNs depend on the quantity and quality of data used to train the BBN and create conditional probability tables (CPTs). In this study, simulated outputs from a multi-period simulation-optimization model (Dehganipour et al., 2020) are used to populate the CPTs in each BBN and reduce the BBN training error. Cross-validation tests and sensitivity analysis are used to evaluate the effectiveness of the resulting BBNs. Sensitivity analysis shows that drought conditions have the most significant impact on environmental flow compared to other variables. Cross-validation tests show that the BBNs are able to reproduce outputs of the complex simulation-optimization model used for training, and therefore provide a computationally fast alternative for decision-making under uncertainty.</p><p><strong>Reference:</strong> Dehghanipour, A. H., Schoups, G., Zahabiyoun, B., & Babazadeh, H. (2020). Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management, 241, 106353.</p>

Amount and location of tectonic uplift in the Urmia region of northwest Iran from the Permian to the Neogene

The Urmia Lake Basin is located between the West and East Azerbaijan provinces in the northwest of Iran. Lake Urmia is the twentieth largest lake and second largest hypersaline lake in the world. Stratigraphic columns have been constructed, using published information, to compare the sedimentary units deposited from the Permian to the Neogene on the east and west sides of the lake, and to use these to quantity subsidence and uplift. East of the lake, the sedimentary section is more complete and has been the subject of detailed stratigraphic studies, including the compilation of measured sections for some units. West of the lake, the section is incomplete and less work has been done; three columns illustrate variations in the preserved stratigraphy for the time interval. In all cases, the columns are capped by the Oligocene–Miocene Qom Formation, which was deposited during a post-orogenic marine transgression and unconformably overlies units ranging from Precambrian to Cretaceous. Permian to Cretaceous stratigraphy is used to measure subsidence in the Lake Urmia basin up to the end of the Cretaceous, and then, the subsequent orogenic uplift, which was followed by further subsidence recorded by the deposition of the Qom Formation in the Oligocene–Miocene.