Urmia Lake Basin Research Articles (Page 1)

Climate Projection and Drought Assessment in the Lake Urmia Basin Using LSTM-Based Downscaling of GCM Models Under SSP Scenarios

ABSTRACT This research examines drought behavior in the Lake Urmia basin, Iran, over the years 1994 to 2023 using the SRI and a selection of statistical distribution models to improve drought assessment and monitoring. The suitability of each distribution was tested using standard performance measures such as RMSE, MSE, CC, and R2. Among the tested models, the Weibull distribution consistently gave the most reliable results, while the LogLogistic distribution also performed well across most criteria. In contrast, the Stable distribution showed weaker alignment with observed data. At the Chahrigh-Olia station, the LogLogistic model produced the most accurate SRI predictions, with an RMSE of 0.236, MSE of 0.052, CC of 0.993, and R2 of 0.989. The linear equation Y = –3.242 + 0.011X accounted for nearly 99.1% of the observed variation. To assess spatial drought conditions, a map was created using the IDW method for the year 2005, revealing areas under severe hydrological stress. The study supports the application of appropriate distribution functions to improve drought modeling in semi-arid environments. By comparing several distribution models and evaluating their performance across space and time, the analysis offers practical guidance for selecting reliable approaches to streamflow-based drought monitoring and water management planning.

Developing reservoir drought index and conducting copula-based frequency analysis for Lake Urmia basin in Iran

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area.

This study presents an explainable machine learning framework to forecast groundwater storage dynamics, quantified as the Lake Water Equivalent (LWE), in the Urmia Lake Basin from 2003 to 2023. Satellite-based observations (GRACE, GLDAS) and climatic variables were integrated to model LWE variability. An ensemble learning approach was employed, combining Ridge Regression and Random Forest enhanced through feature re-weighting based on XGBoost-derived importance scores. Model interpretability was addressed using SHapley Additive exPlanations (SHAP), offering transparent insights into the contributions of climatic drivers. Results demonstrated that the Random Forest model achieved superior performance (RMSE = 3.27; R2 = 0.89), with SHAP analysis highlighting the dominant influence of recent LWE values, temperature, and soil moisture. The proposed framework outperformed baseline models including Persistence, Standard Ridge Regression, and XGBoost in terms of both accuracy and explainability. The objectives of this study are (i) to forecast the LWE in the Urmia Lake Basin using an ensemble-based machine learning framework, (ii) to enhance predictive modeling through XGBoost-guided feature weighting, and (iii) to improve model transparency and interpretation using SHAP-based explainability techniques. By integrating ensemble learning with explainable AI, this work advances the transparent data-driven forecasting essential for sustainable groundwater management under climatic uncertainty.

Abstract The potential for sediments in the Lake Urmia basin to sorb and retain dissolved inorganic phosphorus (P) is unknown. Land use has impacted Sediment P sorption capacity along several tributaries to Lake Urmia and its wetlands. The following hypotheses were tested: (1) upstream agricultural P sources are more significant than downstream locations, (2) the storage of P and its subsequent release is controlled by potential differences in physicochemical properties of upstream versus downstream sediments, and (3) the differences in algae growth will correlate with its native adsorbed P (NAP) and equilibrium values (EPC0). We employed several strategies to link sediment physicochemical properties, sediment P adsorption characteristics, and potential algal response to understand upstream and downstream P cycling characteristics better. The results suggest that P sorption capacity was generally higher in the downstream sediments than in upstream locations. There was a significant correlation between sediment properties, mainly clay and organic matter, and sorption parameters. The equilibrium phosphorus concentration (EPC0) values were higher than the soluble reactive phosphorus (SRP) concentration in the water column, suggesting that sediment is a source of P in the water column. River sediments have a high potential for P sorption and, depending on the landscape and biogeochemical processes can be considered an internal source of pollution in the river system.

A comprehensive spatiotemporal and risk reduction drought assessment study utilizing SPEI index for Urmia Lake Basin, Iran

Misunderstanding complex environmental problems is often cited as a key reason for the failure to effectively address them. Those in charge of addressing these problems may be either unable or unwilling to acknowledge the problem's true nature. As one of the daunting environmental challenges in recent decades, the shrinkage of Urmia Lake has provoked global concern. Yet, despite substantial amounts of efforts and expenditure, the problem lingers and even worsens over time. This study aims to investigate the reasons behind the failure of restoration efforts in the Urmia Lake Basin by utilizing the concept of wicked problems as a fresh analytical framework. The paper also argues that the inherent characteristics of wicked problems provide a powerful tool for understanding complex water and environmental challenges, fostering new and insightful questions that pave the way for novel avenues of addressing the problem. The results revealed six characteristics of the problem that not only confirm its wicked nature but also suggest that it cannot be resolved using current approaches. These characteristics of the Urmia Lake issue include: (1) it manifests other problems; (2) it lacks clear boundaries; (3) it involves conflicting interests among stakeholders; (4) it has an evolving nature; (5) it is unique and specific to its location; and (6) it lacks a unified narrative or understanding of the problem. The paper concludes by offering suggestions tailored to these characteristics, serving as a starting point for rethinking the problem of Urmia Lake.

Abstract Determining the contribution of human activities to drought occurrence or aggravation of drought is an essential issue in drought risk management. Drought begins under the influence of meteorological processes and propagates to hydrological drought, which human activities affect. In this study, an Anthropogenic Drought Index (ADI) was developed using two meteorological indices (Standardized Precipitation Index [SPI] and Standardized Precipitation Evapotranspiration Index [SPEI]) and two hydrological indices (Standardized Streamflow Index [SSI] and Streamflow Drought Index [SDI]). The ADI considers the meteorological and hydrological drought characteristics of duration, severity, and frequency. To assess drought risk, the ADI was used as a hazard along with the vulnerability index, which also takes into account the socioeconomic and physical factors of the study area. The developed formulation was applied to the western part of the Lake Urmia basin, which has dried in recent years due to unsustainable water resources management. The study area covered 20 stations with 40‐year meteorological and hydrological data. A visual comparison of the meteorological and hydrological indices shows that human behavior has significantly increased the hydrological drought characteristics over the past 20 years. The ADI results indicate that in some stations, the ADI values exceed 2, highlighting the need for sustainable water withdrawal in these areas. The risk assessment results also classify the studied stations into five bands, prioritizing them from the viewpoint of drought risk management.

The Urmia Lake Basin has been severely affected by the unbalanced exploitation of water resources. To better manage the use of integrated water resources, the coupled SWAT-MODFLOW-NWT was adopted for the Mahabad Plain in the Urmia Lake Basin, N.W. Iran. The results indicated that a multifunctional calibration of SWAT and MODFLOW-NWT hydrological models in a large-scale irrigated area was necessary, using parameters such as evapotranspiration and crop yield in addition to the usual surface runoff and water table measures. The coupled model was then used to evaluate several water allocation scenarios, such as alternate proportions of irrigation water allocation from conjunctive water resources. The ultimate objective of adopting these scenarios was to increase the residual share of the water supply in order to compensate for the deprived share of Urmia Lake. The results of this study demonstrated that that the coupled SWAT-MODFLOW-NWT model was able to satisfactorily simulate the surface and groundwater balance components at different spatial and temporal dimensions. The results indicate that the Mahabad aquifer is capable of supplying irrigation water needs in the central and northern regions, with some limitations around running rivers. Furthermore, groundwater sustainability indicators showed that even with an additional 30% of the water supply from groundwater, the long-term sustainability of groundwater resources was preserved Ultimately, the findings indicated that a reduction in water allocation from surface waters can lead to an increase in water release to the lake of 16 million cubic meters (21%) to 18 million cubic meters (25%) in different years. The outcomes of this study can serve as a guiding principle for the optimal and sustainable allocation of surface and groundwater resources in highly competitive and fragile basins such as Lake Urmia.

In the original publication [...]

Comparison of kriging methods in rainfall estimation based on entropy-copula (case study: Simineh river, lake Urmia Basin, Iran)

Application of circular statistics in seasonality analysis of extreme precipitation occurrence time in Urmia Lake basin

Effects of land use changes on local dust event in Urmia Lake basin

Rising temperatures and sinking hopes: An in-depth analysis of the interplay between climate change, land use patterns, and the desiccation of a global biosphere reserve

Abstract Vegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

Although all of the existing water resources and lakes in the world are not suitable for agriculture,their preservation plays a very decisive role in preventing environmental disasters. Unfortunately, water sources in many countries are not strictly protected and are in danger of drying up as a result of artificial factors. Lake Urmia located between East and West Azerbaijani provinces in Iran is one of these lakes. Since the 1990s of the last century, the water level in Lake Urmia, which is considered one of the largest lakes in the world in terms of salinity, has decreased and is currently facing the threat of drying up. The complete drying of the lake will not only damage the ecosystem in the region, its side effects may also manifest in neighboring countries and the whole Middle East region. This process will also accelerate the migration flow from the region to other provinces and neighboring countries. Thus, the lake crisis in Urmia is hazardous for the entire region. The purpose of this study is to reveal the main causes of drying lake, predict threats emerging in the region and look for solutions to eliminate this crisis.

Spatio-temporal assessment of land use and land cover dynamics in Urmia lake basin of Iran: A bi-directional approach using optical and radar data on the Google Earth Engine platform

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability.

Evaluation of water resource balance in the Urmia Lake Basin: Integrating carrying capacity and water footprint model for sustainable management