Namak Lake Research Articles

Uncovering glacial footprints and identifying phylogeographic units in the freshwater crab Potamon elbursi Pretzmann, 1962 (Decapoda: Potamidae) based on mitochondrial data

The current patterns of biodiversity have significantly been affected by glacial-interglacial cycles during the Pleistocene period. True freshwater crabs are considered as poor dispersers and terrestrial barriers restrict gene flow between their local populations. Recent studies, however, suggest that certain environmental conditions, such as periods of heavy rainfall and humidity, can facilitate their between-drainage dispersal and will result in the evolution of homogenous genetic patterns among different drainage systems. Here we tested this hypothesis by comparing populations of the endemic freshwater crab Potamon elbursi Pretzmann, 1962 distributed in two drainage systems, the Caspian Sea and Namak Lake, in northern Iran. Our results based on the genetic analysis of 70 new and 61 previously published sequences of the mitochondrial cytochrome oxidase subunit I gene revealed a substantial haplotype diversity in some populations and high levels of local population structuring in others. Initially, we found mixed evidence of genetic differentiation and connectivity among drainages and populations. Genetic differentiation between the two drainages only became apparent after the Sepirdood population (which belongs to the Caspian Sea drainage) was excluded. Subsequently, the two drainages showed significant genetic distinctions, with a limited gene flow between them. Our demographic analyses supported recent population bottlenecks, followed by a rapid demographic and/or spatial expansion dating back to the Pleistocene climatic fluctuations. Species distribution modelling suggests that precipitation during warm weather conditions profoundly affects the distribution of P. elbursi. This study indicates that freshwater crabs can override short land barriers under favorable weather conditions and will have conservation implications in the face of contemporary climatic fluctuations.

Phylogeography of the freshwater crab Potamon persicum (Decapoda: Potamidae): an ancestral ring species?

The Zagros Mountains, characterized by complex topography and three large drainage systems, harbor the endemic freshwater crab Potamon persicum in Iran. Our study delves into the evolutionary history of P. persicum, utilizing two mitochondrial and one nuclear marker. We collected 214 specimens from 24 localities, identifying 21 haplotypes grouped into two major evolutionary lineages. Substantial differentiation exists between drainage systems and lineages. Historical demographic analysis revealed a significant decrease in population size during the late Holocene, accompanied by a recent population bottleneck. Species distribution modeling has revealed eastward shifts in suitable habitats between the last glacial maximum and the present day. Following the last glacial maximum, habitat fragmentation occurred, resulting in the establishment of small populations. These smaller populations are more vulnerable to climatic and geological events, thereby limiting gene flow and accelerating genetic differentiation within species. Historical biogeographic analysis traced the origin of P. persicum to the western Zagros Mountains, with major genetic divergence occurring during the Pleistocene. Our genetic analyses suggest that P. persicum may have shown a genetic pattern similar to a classical ring species before the Pleistocene. The Namak Lake sub-basin could have served as a contact zone where populations did not interbreed but were connected through gene flow in a geographic ring. Currently, genetic separation is evident between basins, indicating that P. persicum in the Zagros Mountains is not a contemporary ring species. Also, our biogeographical analysis estimated that range evolution may have been driven initially by dispersal, and only during the late Pleistocene by vicariance.

Temporal-spatial monitoring of the Namak Lake playa changes and its environmental impacts on the surrounding regions

Temporal-spatial monitoring of the Namak Lake playa changes and its environmental impacts on the surrounding regions

Correction to: Climate change and anthropogenic effects on the drying of a saline lake in an arid region (Namak Lake, Iran)

Correction to: Climate change and anthropogenic effects on the drying of a saline lake in an arid region (Namak Lake, Iran)

Climate change and anthropogenic effects on the drying of a saline lake in an arid region (Namak Lake, Iran)

Climate change and anthropogenic effects on the drying of a saline lake in an arid region (Namak Lake, Iran)

Prioritizing Water Resources for Conservation in a Land of Water Crisis: The Case of Protected Areas of Iran

This study examines the distribution of water resources in Protected Areas in Iran and their priority for conservation. The results show that most of the water resources are located in the north and northwest of Iran due to favorable climatic conditions, topography, ambient temperature, and annual rainfall levels. Conversely, the lowest amount of water resources are located in the center and southeast of the country. Water resources were prioritized based on expert ratings of indicators to determine their value for conservation. The wetland with the highest priority for conservation is the Anzali Wetland (Gilan province), which is an international Ramsar Wetland. Conversely, Namak Lake (Qom province) was deemed the least important due to its geographical location, biological sensitivity, and conservation status. Protected Areas were found to support more surface water resources and provide space for the largest percentage of water resources, demonstrating their great value for protecting water resources in Iran. However, the level of protection of these critical resources, although located in Protected Areas, was shown to be insufficient. Therefore, appropriate planning and integrated management approaches are urgently needed to protect water resources and aquatic habitats in Protected Areas in Iran to address the current water crisis.

Climate change impact assessment on low streamflows using cross-entropy methods

Climate change impacts on low streamflows provide a comprehensive picture of the state of surface and groundwater resources, particularly in arid and semi-arid regions. The objective of this study was to assess climate change impacts on low streamflow variations by detecting long-term spatio-temporal changes in climatic variables of rainfall and temperature, and their associations with low streamflow fluctuations. Seasonal variations in low streamflows (summer and winter) were examined at 18 hydrometric stations located in the Namak Lake basin, Iran, over 1970-2015, using the modified Mann-Kendall and Sen’s slope estimator methods. Seasonal low streamflow demonstrated a significant diminishing trend (more than 55% of the stations), while summer low streamflow showed a more drastic decreasing trend. Long-term changes in seasonal and annual rainfall/temperature also revealed a dominant decreasing trend in winter and spring rainfall (82% and 58% of all stations, respectively) and a dominant increasing trend in all temperature time scales (90% of all stations). The effects of climate variations on low streamflow were quantified by the Spearman’s rank correlation and Cross-SampEn methods. The results revealed that winter rainfall, and annual and summer temperatures have the strongest association with seasonal low streamflows, especially according to the entropy method. Although the relationships between low streamflows and temperature/rainfall are related to the different processes generating streamflows, particularly in heterogeneous locations, the results show that rainfall has a stronger influence on low streamflows in the study region than temperature does. The research findings indicate low streamflows are more nonlinearly related to climatic parameters, and Cross-SampEn robustness reflects degree of asynchrony for complex and non-stationary time series.

Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method

Abstract Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned factors, have been used to estimate River Water Withdrawal (RWW) for agricultural uses. Lavasanat and Qazvin are selected as study areas, located in the Namak Lake basin in Iran, with Bsk and Csa climate categories, respectively. Estimation of RWW is performed using single and Wavelet–hybrid (W-hybrid) data-driven methods, including Artificial Neural Networks (ANNs), Wavelet–ANN (WANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet–ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet–GEP (WGEP). Due to the evaluation criteria, the performance of the WGEP model is the best among the others for estimating RWW variables in both study areas. Considering the W-hybrid models with data de-noising for estimating RWW in the Lavasanat and Qazvin study areas, the obtained values of RMSE for WGEP11 to WGEP15 and WGEP21 to WGEP25 equal 67.268, 54.659, 80.871, 50.796, 15.676 and 105.532, 96.615, 105.018, 160.961, 44.332, respectively. The results indicate that WGEP and ANN are the best and poorest models in both study areas without regarding climate condition effects. Also, a combined factor which includes River Width (RW), minimum flow rate (QMin), average flow rate (QMean), Electrical Conductivity (EC), and Cultivated Area (CA) variables is introduced as the best factor to estimate RWW variables compared with the other factors in both the Bsk and Csa climate categories. On the other hand, qualitative hydrological and land use factors were the weakest ones to estimate RWW variables in the Bsk and Csa climate categories, respectively. Therefore, the current study explores how the mathematical relations for estimating RWW have a significant effect on water resources management and planning by policymakers in the future.

Improving spatial estimation of hydrologic attributes via optimized moving search strategies

The anisotropy and directional variation of spatial attributes is one of the most important challenges in spatial estimation of environmental features. This problem received a suitable response in the realm of stochastic modeling using directional variography; however, there is not any specific solution in the field of deterministic methods. In this research, Moving Least Square (MLS) and Moving Inverse Distance Weighting (MIDW) have been used to assess a moving domain as the remedy; although, there has been no research on the spatial estimation with the MLS method. The methods are evaluated in three case studies, (I) using anisotropic piezometric head data from the Wolfcamp aquifer in Texas, (II) long-term annual average precipitation measurements in Namak Lake Watershed (NLW) located in the central part of Iran, and (III) estimation of annual precipitation with remote-sensing products, TRMM, in NLW as well. To achieve the best performance of the methods, parameters have been optimized using the modified version of shuffled complex evolutionary method. Assessment of statistical metrics, Taylor Diagram, and numerical results showed that spatial interpolation using MIDW and MLS revealed better results, with an improvement of 45.3% on average in terms of Root Mean Square Error (RMSE), in comparison with original IDW as the benchmark evaluation approach. This shows the power of moving data partitioning on the performance of deterministic estimators. Additionally, MLS was more effective than MIDW with an average of 0.55, 10.53, and 17.19% reduction in RMSE of case I, II, and III, respectively.

Investigation on the Global Distribution of Invasive Fish Species, Convict Cichlid Amatitlania Nigrofasciata (Perciformes, Cichlidae) Over the Past Years with Emphasis on Iranian Inland Waters

Abstract The aim of this study was to investigate the global distribution of a non-indigenous and invasive fish species, the convict cichlid Amatitlania nigrofasciata, over time, with an emphasis on the inland waters of Iran. The results showed that the distribution range of the convict cichlid was initially based in Central America. Then, the distribution range of this species expanded to parts of North America. Finally, this fish species was introduced to other parts of the world such as Australia, Israel, Italy, Colombia, Germany, Peru, Slovakia, Japan, Indonesia, the Philippines, and Iran due to its extensive ability to spread. So far the presence of A. nigrofasciata has been reported in two domestic basins in Iran, namely Hormuz and Namak Lake. This study indicated that the distribution range of this species is not currently wide, but it is likely to expand towards the central plateau and southern parts of Iran.

Metal pollution assessment in surface sediments of Namak Lake, Iran.

Desiccation of the Namak Lake (NL) can result in the release of fine-grained dust contaminated with heavy metals, while there is little information available on the propagation of metals in the bed sediments of this lake. In this study, contamination of metals in the surface sediments of the NL was analyzed and the pollution status of sediments was assessed using geo-accumulation index (Igeo), enrichment factor (EF), the consensus-based sediment quality guidelines (CBSQGs), and mean probable effect concentration quotient (mPECQ). Results indicated that metal concentrations at the southern part were higher than at the middle and northern parts of the lake. Possible reasons are (i) pollution loads mainly entered the lake through the rivers at the west and northwest, but accumulated at the southern parts, (ii) hard layer of salt covering the bottom of the NL at the northern part suppresses adsorption of metals to the sediment, and (iii) the muddy nature of sediments at the southern part makes it easier for metals to be absorbed. EF results showed that sediments at the southern part of the lake were moderately enriched with lead (Pb). The low Igeo values suggested no pollution with the metals, and CBSQG values showed that the sediments of the NL were not toxic, while the mPECQ index suggested a toxicity probability of less than 25%. Cluster analysis classified the metals into two clusters. In general, the results showed that metal pollution in the surface sediments of NL was generally low although the concentration of Pb at the southern part of the lake was worrisome.

Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data

Accurate estimation of the spatial distribution of precipitation is crucial for hydrologic modeling. To achieve the realistic estimation of precipitation, developing a ground-based observatory system is a costly and time-consuming strategy compared with other solutions such as using a combination of satellite- and ground-based observations. In this paper, to improve the estimation accuracy of spatial precipitation variation, various linear regression methods were used that combine digital elevation model (DEM) data, rain gauge observations, and Tropical Rainfall Measuring Mission (TRMM) products. Specifically, fuzzy cluster-based linear regression (FCLR), local multiple linear regression using historical similarity (LMLR-HS), model tree (MT), and moving least squares (MLS) were used in the proposed methodology based on local data behavior. The results were compared with those obtained from multiple linear regression (MLR) methods including simple multiple linear regression (SMLR), robust multiple linear regression (RMLR), and generalized linear model (GLM) for monthly precipitation estimation. The study area was Namak Lake watershed, one of the largest watersheds in Iran. The results, estimated for wet and dry years (years 1999 and 2003, respectively), show superiority of local linear regression methods over the other linear methods. Based on the statistical metrics used for assessing the quality the results, FCLR and MLS outperformed other tested methods.

Length-weight relationship of ten species of Leuciscinae sub-family (Cyprinidae) from Iranian inland waters

In this study, the length-weight relationships (LWRs) were estimated for 697 specimens belonging to ten species of Leuciscinae subfamily, viz. Squalius berak, S. namak, S. turcicus, Acanthobrama marmid, A. microlepis, Chondrostoma esmaeilii, Leuciscus latus, Alburnus doriae, Leucaspius delineates and Blicca bjoerkna from Iranian inland waters, including the Persian Gulf, Caspian Sea, Namak Lake, Dasht-e Kavir, Hari River and Isfahan basins. Total length and total weight of the examined specimens ranged 3.8-27.8 cm and 0.59-247 g, respectively. Fishes were collected using electrofishing device between July 2010 and August 2017. This study represents the first reports of LWRs data for A. doriae, C. esmaeilii, L. latus and L. delineates. The length-weight parameter b found to be 2.75 (A. microlepis) - 3.44 (B. bjoerkna) with regression coefficients (r2) ranging 0.87 to 0.99 for the studied fishes. The student’s t-test showed all studied species have a positive allometric growth pattern except A. doriae (isometric) and A. microlepis (negative allometric).

Life history and other biological traits of Capoeta buhsei (Kessler,1877) in the Gharachay River, Saveh

Abstract: Capoeta buhsei is endemic to the Namak lake and littlee is known of its life history in Iranian waters. Life history characteristics we examined in 92 specimens collected from the Gharachay River. In july and January of 2011 and October of 2012. Sampling was by Electroshoker.Observed maximum age was +4 years in female. Sex Ratio (male: female) was 1:0/73, and length –weight relationships were BW=35/55.TL 32 in male and BW=14/8TL-30/7 in female. Mean egg diameter (ED) was 0/8726 0/12mm ranging from 0/67 to 1/1 mm. Mean absolute (AF)and relative. Fecundity (RF) were 4917/2157 ( 1690/04 SE) egg/female and 69/1( 16/15SE) eggs.g-1 body weight, respectively. AF and ED were found to increase significantly with increasing fish size, wherease RF increased significantly with both fish TL and weight.

Spatiotemporal changes of 7-day low flow in Iran’s Namak Lake Basin: impacts of climatic and human factors

Low flow is very sensitive to climate change and human intervention, especially in arid regions. In this study, changes of the 7-day low flow along the most important rivers of Iran’s Namak Lake Basin were investigated using nonparametric (Mann-Kendall and modified-Mann-Kendall) tests. A significant diminishing trend was observed in 72.2% of stations during the period of 1970–2012. The northern part of the basin lacked a significant trend, while in other parts of the basin, the descending trend was distributed uniformly. On the other hand, the changes of the annual rainfall during this period showed no clear trend (a significant trend in 36% and non-significant trend in 64% of stations), and the identified pattern of its changes was complicated on the basin scale and during the year. On a monthly scale, a significant decreasing trend was observed in March as one of the most productive months of the year in 49% of the stations. In addition, rainfall reduction was significant (over 35%) over the past 15 years in more than 71% of the stations. Also, changes in the proportion of seasonal rainfall and rainfall regime were considerable. The share of winter and spring rainfall showed a diminishing trend in 90% and 82% of stations, respectively. Also, rainfall regime based on precipitation concentration index (PCI) revealed a tendency to disorder (in 53% of stations). The annual temperature and temperature of October and February indicated a strong ascending trend in 92%, 71%, and 64% of stations, respectively, which can be effective during snow melting in basins with snow-rainy regimes and increasing evapotranspiration. Groundwater level changes also showed that, in the studied plains, the average water table drawdown was between 0.31 and 1.33 m/year. Therefore, the observed trend of low flow rates in this basin reflects the impact of climate change, where both direct and indirect human interference has led to the exacerbation of this situation.

Assessment of habitat suitability index of Capoeta species in the Caspian Sea and Namak Lake basins, Iran

Habitat suitability index (HSI) models are usually used to forecast habitat quality and species distributions and are used to develop biological studies, management priorities and anticipate possible changes under different management or climate change situations. This study was conducted to identify the habitat suitability index of three species namely, Capoeta buhsei , C. razii and C. alborzensis in the Kordan, Taleghan and Jajrood Rivers, respectively. At each station, environmental variables including temperature, dissolved oxygen, pH, EC, TDS and hydrological parameters such as flow velocity, depth, width, average diameter of stones and amount of phosphate, nitrate and ammonium were measured. The results showed that suitable habitats for these species are those with a high stone diameter, high temperature, low flow velocity and in areas where the width of the river is low. With respect to the abundance of fishes sampled in this study, the central and lower regions of the Jajrood and Kordan Rivers and the stations far from the dam on the Taleghan River are favorable habitats for the studied Capoeta species.

Assessment the Performance of Support Vector Machine and Artificial Neural Network Systems for Regional Flood Frequency Analysis (A Case Study: Namak Lake Watershed)

Assessment the Performance of Support Vector Machine and Artificial Neural Network Systems for Regional Flood Frequency Analysis (A Case Study: Namak Lake Watershed)

Challenge of rainfall network design considering spatial versus spatiotemporal variations

Precipitation data plays an important role in investigation of water-related fields of research such as water resources management, hydraulic structure design and groundwater quantity/quality parameters due to its high variability in space and time. To evaluate and investigate precipitation pattern, a set of well-designed rain gauge stations can substantially reduce the cost and increase the estimation accuracy. In the present research, a new methodology of spatiotemporal optimization is developed for a rain gauge monitoring network and the results are compared to the optimized network based on spatial variations of precipitation. The optimization process consists of two main steps of application of multi attribute decision making and heuristic approaches. The entropy is chosen as the multi attribute decision making approach to determine optimum number of stations. Then, the optimization process is implemented via coupling of Genetic Algorithm (GA) and geostatistical methods to identify the best rain gauge network configuration. To determine the spatiotemporal structure of precipitation, spatiotemporal variography and geostatistical methods known as Ordinary Kriging (OK) and Bayesian Maximum Entropy (BME) have been undertaken. Thirty years of annual precipitation data from 105 rain gauge stations within and near Namak Lake watershed in the central part of Iran are utilized in this research to optimize rain gauge stations spatially and temporally. Results showed that spatiotemporal network design considerably differs from spatial optimal rain gauge stations. Optimal locations of rain gauge stations resulted from spatiotemporal approach are almost two times better than spatial configuration to reduce rain gauge costs (installation, operation, maintenance, etc.) by avoiding data redundancy more efficiently. In addition, BME-based network design method outperformed OK-based network.

Revisited rainfall network design: evaluation of heuristic versus entropy theory methods

Rain gauges are installed to measure pointwise precipitation and provide a comprehensive perspective of its spatiotemporal variations. Selection of an efficient and reliable rainfall monitoring network is a key role to reduce its maintenance and handling cost. The main purpose of the current paper is to compare efficiencies of various network design methods. The applied methods are entropy theory (as probabilistic multi-criteria decision-making) and genetic algorithm (as one of the heuristic methods) with three objective functions. Also, two classical (ordinary kriging; OK) and modern (Bayesian maximum entropy; BME) spatial simulation methods were undertaken to provide a comprehensive spatial simulation of precipitation. The proposed assessment was applied on spatial mean annual precipitation variability in the Namak Lake watershed located in the central part of Iran. The final efficiency of developed network design methods is evaluated in terms of three criteria known as mass estimation error, total error, and spatial bias of estimated rainfall. Based on the results, different network distributions have been proposed by the methods. Despite the reliability of the heuristic approach in nonlinear optimization due to its mathematical principle, the results indicated that the network design based on entropy theory can be used to estimate long-term mean annual precipitation more reliably and accurately. Results of the mass estimation error have shown 78 and 83% superiority of the entropy theory approach from the worst approach obtained from the OK and BME methods, respectively.

Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran

ABSTRACTRegional flood frequency analysis (RFFA) was carried out on data for 55 hydrometric stations in Namak Lake basin, Iran, for the period 1992–2012. Flood discharge of specific return periods was computed based on the log Pearson Type III distribution, selected as the best regional distribution. Independent variables, including physiographic, meteorological, geological and land-use variables, were derived and, using three strategies – gamma test (GT), GT plus classification and expert opinion – the best input combination was selected. To select the best technique for regionalization, support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and nonlinear regression (NLR) techniques were applied to predict peak flood discharge for 2-, 5-, 10-, 25-, 50- and 100-year return periods. The GT + ANFIS and GT + SVR models gave better performance than the ANN and NLR models in the RFFA. The results of the input variable selection showed that the GT technique improved the model performance.