Orontes River Research Articles

Integrated time-series analysis of remote sensing imagery and crowdsourced data to monitor water hyacinth (Eichhornia crassipes) in the Orontes (asi) River

ABSTRACT Invasive plants are a serious global issue due to the damage they cause to aquatic ecosystems. In recent years, the Orontes River has been struggling with water hyacinth, an invasive species that completely covers the river’s surface. Within the scope of this study, the history of water hyacinth in the Orontes River was examined using remote sensing and crowdsourced data. Optical data with different spatial resolutions (Landsat-8 OLI and Sentinel-2 MSI) and Sentinel-1 SAR satellite data were used as remote sensing data. Google News, used as crowdsourced data, was compared with time series obtained from satellite data, emphasizing the complementary role of all three data types (optical, SAR and crowdsourced). Using PlanetScope Dove satellite data, the growth, development and extinction cycle of water hyacinth within a year were investigated according to water hyacinth density. In line with the results obtained, it is revealed that the water hyacinth began to appear after June, grew in the summer months, reached its peak in October–November, and disappeared after November with the increasing precipitation. This study is the first to examine water hyacinths in the Orontes River using remote sensing and crowdsourced data, and the results will guide decision-makers in the fight against water hyacinths.

Irrigation Water in Northwest Syria: Impact of the Recent Crisis and Drought

This study examines how Northwest Syria’s arable and irrigated areas are changing, with a particular emphasis on processes that have affected cultivation and water availability since 2011. A substantial decline in cultivated areas is revealed by the analysis of 502 samples from various stakeholders, including farmers, well owners, well-digging companies, agricultural pharmacies, and service offices. District-specific variations show a decrease of 15.3% and 8.5% in total cultivated land and irrigated areas, respectively. Particularly in areas known for their summer vegetables and winter crops, such Al-Bab and Jebel Saman, agricultural productivity has drastically decreased since 2011. Today, 67% of irrigation comes from groundwater wells, especially in Jabal Saman and Idleb, whereas Jisr-Ash-Shugur depends on the Orontes River. Due to drought and a lack of regulatory oversight, the number of irrigation wells has risen since 2011, severely reducing water supplies and driving up irrigation prices. High fuel and electricity costs, uncontrolled well drilling, broken machinery, and insufficient irrigation systems are some of the main obstacles. A lot of well owners would rather sell drinking water, which would cut down on irrigated land and crop cultivation. The needs of the stakeholders include subsidies for fuel and electricity, the development of alternative energy, well repairs, improvements to irrigation systems, the provision of agricultural inputs, and better local water management. This study emphasizes the critical need for focused interventions to support agriculture and livelihoods in Northwest Syria and offers significant insights for sustainable water management solutions.

First documented occurrence of the introduced Sailfin catfish Pterygoplichthys disjunctivus (Weber, 1991) in the inland waters of Syria (Orontes River)

First documented occurrence of the introduced Sailfin catfish Pterygoplichthys disjunctivus (Weber, 1991) in the inland waters of Syria (Orontes River)

State Production and Market at Ebla – Animal and Wool Values

The kingdom of Ebla covered an area extending around 200 km from north to south and from east to west; that is, from the modern Syrian-Turkish border down to the oasis of Ḥama in the south, and from the Amuq plain, where the delta of the Orontes River flows into the Mediterranean Sea, up to Emar, its allied city, whose territory reached the Euphrates. The Central Administration – the Palace – had at its disposal the revenues from its own administrative organization, with its workshops, as well as from the “village communities”, represented by a large number of “Elders”. Notwithstanding that, Ebla’s envoys also acquire several types of goods, such as mules, cattle and sheep, garments and wool, from the markets of the cities in its own kingdom, and in other city-states, primarily from its archival, Mari. Luxury goods, on the other hand, were mostly acquired on the basis of ceremonial gifts from court to court. Long-distance-trade, however, was not relevant in the formation of the Syrian regional states. Goods (including the tributary deliveries its officials owed the Palace) were given also values in silver, and small goods, such as aromatic essences, were valued in wool. The average value of a head of cattle was around 25 shekels of silver, while a sheep was worth between 1 and 1½ shekels. It is not possible to establish the exact value of the weight used for wool. These prices are in any case much lower compared to those used in Mesopotamia during the Ur III period.

A New Confirmation Record of the Introduced Sailfin Catfish Pterygoplichthys disjunctivus (Weber, 1991) in the Inland Waters of Syria (Orontes River)

A new occurrence of the sailfin catfish, Pterygoplichthys disjunctivus, has been documented in the inland waters of Syria, specifically in the Orontes River. The specimens measured between 23.5 and 27 cm in total length and weighed between 100 and 150 g. They were captured by artisanal fishermen using a trammel net at a depth of approximately 2-3 meters and recorded the surface temperature at 20°C. The reappearance of Pterygoplichthys disjunctivus indicates potential changes in freshwater biodiversity. Monitoring these developments is crucial for understanding the regional ecosystem. These findings underscore the necessity for further research to achieve a more comprehensive understanding of these emerging phenomena.

Antibiotic susceptibility profiles of Escherichia coli strains and fecal contamination in Orontes River, Turkey

This study was to evaluate the level of contamination of surface waters by coliform bacteria and Escherichia coli, as well as to detect the fecal pollution and the antibiotic resistance patterns of E. coli strains from recovered Asi (Orentes) River in the southern region of Turkey. Total and fecal coliform results were revealed as ˃1100 MPN/100 mL and 1100 MPN/100 mL for two stations, respectively. A total of 10 E. coli isolates were screened for antibiotic susceptibility by Kirby Bauer method. Resistance to cefotaxime, tobramycin, streptomycin, trimethoprim, cefepime, cefazolin, and ampicillin was determined in 20%, 20%, 40%, 20%, 20%, 20%, and 40%, respectively. These findings point out a widespread distribution of E. coli strains produced antibiotic resistance genes in the surface waters in the southern region of Turkey, suggesting an aquatic reservoir for resistance genes. Multiple antibiotic resistance (MAR) rates of %30 of E. coli isolates were greater than 0.200. This high index indicates exceeding the permissible water quality limits for human use and consumption and is the primary source of contamination in the discharges from domestic, agricultural, and urban.

Investigation of water-soil-plant relationships based on hazardous and macro-micro element concentrations on Orontes River, Türkiye

Arundo donax and Phragmites australis were examined in 4 different periods (June and October for 2 years), heavy metal and mineral element accumulations in plants were evaluated, and water-soil-plant relationships were revealed. Element distributions, bioaccumulation factors (BAF) and translocation factors (TF) in different parts of the investigated plant species were also determined. BAFs of elements calculated by using the concentration values in underground parts and sediment samples were between 1.02 and 4.96. While the highest TF was determined as 8.07 for Zn between washed leaf and stem in A. donax, the lowest TF was determined as 0.05 for Fe between stem and underground part. Corresponding highest and lowest TFs for P. australis were 11.80 for Cu between washed leaf and stem, and 0.02 for Fe between stem and underground part, respectively. The results were supported by MANOVA statistical analyzes. Additionally, the macro-micro elements and heavy metal accumulation levels in the parts of the Orontes River ecosystem were significantly higher in the fall periods compared to the spring periods. Our research revealed that the versatile accumulation properties and high accumulation ability of A. donax for Cd, Cr, and Ni and of P. australis for Cd, Co, Cu, Ni, Pb, and Zn.

Microplastic Composition, Load and Removal Efficiency from Wastewater Treatment Plants Discharging into Orontes River

Microplastic Composition, Load and Removal Efficiency from Wastewater Treatment Plants Discharging into Orontes River

Verification of MIKE 11-NAM Model for runoff modeling using ANN, FIS, and ARIMA methods in poorly studied basin

Hydrological information is the basis for conducting water balance studies in any region, and surface runoff is one of the most important hydrological parameters and one of the most difficult in the process of estimation and prediction. This study aims to verification of the MIKE 11-NAM Model for runoff modeling using artificial neural network (ANN), fuzzy inference system (FIS), and autoregressive integrated moving average (ARIMA) methods at Al-Jawadiyah hydrometric station on the Orontes River in Syria. MATLAB was used to build neural and fuzzy models, where many models were built with the change in all parameters, functions, and algorithms that can be used, and the Minitab was used to build ARIMA models. Many models were prepared with the addition of seasonal effect, and the comparison results showed an advantage for artificial neural network models in terms of evaluation parameters. After that, the artificial neural network models were adopted in the process of filling the gaps in the time series of surface runoff in the study area to be used in the Mike program for modeling the runoff and through the method of trial and error with a high number of iterative cycles, model parameters were calculated and runoff values estimated. Still, the results were not good, and there were significant differences between the measured values and the values simulated by the model, and this is due to the significant lack of available data. This study recommends the use of artificial intelligence and machine learning models in the field of estimation and prediction of hydrological parameters.

Predicting aluminium using full-scale data of a conventional water treatment plant on Orontes River by ANN, GEP, and DT

Predicting aluminium using full-scale data of a conventional water treatment plant on Orontes River by ANN, GEP, and DT

Comparison of FIS and ARIMA models in runoff estimating

The ability to surface runoff modelling plays an important role in the water resources management, and the possibility of estimating and predicting of runoff values takes on particular importance in the case of gaps in the recorded time series. Therefore, this study aims to compare between fuzzy inference system (FIS) models and autoregressive integrated moving average (ARIMA) models in estimating of the surface runoff at Al-Jawadiyah hydrometric station on the Orontes River in Syria. The MATLAB program was used to build the fuzzy inference models and the Minitab program to build the ARIMA models. A large number of fuzzy inference models were built with the change in the model parameters such as the type and number of membership functions and training algorithms. Likewise, a large number of ARIMA models were built with the change in autoregressive components, moving average components, and differences. The effect of seasonality on the model was also studied. Several criteria were used to compare the models and choose the best model, such as correlation coefficient and root mean square errors. The results showed that fuzzy inference models are superior to estimating surface runoff values with high reliability compared with ARIMA models. This study recommends creating complete databases for all factors related to water resources in the study area that can be relied upon in future studies.

Predicting aluminium using full-scale data of a conventional water treatment plant on Orontes River by ANN, GEP, and DT

Predicting aluminium using full-scale data of a conventional water treatment plant on Orontes River by ANN, GEP, and DT

Quantifying and Simplifying the 1997 UNWC’s Guidelines to Mitigate Hegemony in Transboundary Watercourse Negotiations

The 1997 United Nations Convention on the Law of the Non-navigational Uses of International Watercourses (UNWC) contains a negotiation framework for transboundary water rights. However, it is a subjective document open to a wide range of possibilities and interpretations. Water Rights Allocations (WRAs) as described by Dinar and Nigatu (2013) and Dinar and Tsur (2017) provide a limited number of quantifiable allocation possibilities based on the UNWC. It is suggested that this methodology streamlines the negotiation process and reduces the effects of hydro hegemony. These methodologies are explored and applied through a case study on the Orontes River Basin.

Evaluating performance of MIKE 11 NAM model for runoff modeling on upper basin of Orontes River in Syria

This study aims to evaluate the performance of MIKE 11 NAM model for runoff modeling in case of lack of data as a case study on the upper basin of the Orontes River in Syria. In this study, MIKE 11 NAM was relied on as one of the most important hydrological analysis and modeling models. At first, the data used was processed, and the gaps in the time series were filled; then, the data was entered into the model, and the trial-and-error method was used to adopt the model parameters that give the best results. By comparing the results with the measured real values of the flow, it was found that there are large errors and unreliability of the model, which is due to the lack and unreliability of the available data, and this is consistent with the results of other studies conducted in similar cases using the same model. Therefore, this study recommends expanding the possibility of using Mike's model and other models for hydrological analysis and modeling, especially in case of lack of data, because of this great importance in the preparation of hydrological studies, water balance studies, and sustainable development of water resources in the studied area.

Evaluating Different Machine Learning Models for Runoff Modelling

Estimation and forecasting of hydrological factors are of particular importance in hydrological modelling, and surface runoff is one of the most important of these factors. Machine learning (ML) models have attracted the attention of researchers in this field. So, this article aims to evaluate several types of ML models such as autoregressive integrated moving average (ARIMA), feed forward back propagation artificial neural network (FFBP-ANN), and adaptive neuro-fuzzy inference system (ANFIS) models in order to estimate runoff values at Al-Jawadiya meteostation in the Orontes River basin in Syria. A large number of ARIMA models were built and the seasonal effect on the models also verified. After that, FFBP-ANN models were used with the change in the number of inputs, the number of hidden layers, and the number of neurons in the hidden layer. Also, a large number of FIS models have been built and artificial neural algorithms have been used in the process of model parameters optimization. The results showed a preference for artificial intelligence models in general over ARIMA models, as well as a slight preference for FFBP-ANN models over ANFIS models. This study recommends expanding the use of ML models to reach the best models for forecasting hydrological factors.

Эффективность программы MIKE-NAM для моделирования стока с использованием моделей искусственного интеллекта

Introduction. The ability of runoff modeling is an essential step in the hydrologic modeling process and therefore water ba­lance studies, therefore, this study aims to verify the ability and reliability of the MIKE 11NAM program in modeling runoff, in the upper basin of Orontes River in Syria, with the use of artificial intelligence models to fill the gaps in runoff time series. Materials and methods. In this study, models of artificial neural networks and fuzzy inference models were used and they were compared with each other to determine the best model in order to fill the gaps in the surface runoff data at Al-Jawadiyah and Al-Amiri stations. Then the results were used in the modeling process using the MIKE 11 NAM program. Results. The results showed a high reliability of artificial intelligence models, whether neural networks or fuzzy inference models, with a relative preference for neural networks, and after using these results within the data required for modeling in the Mike program, it was found that there are large differences between the observed and simulated values due to the lack of existing data on the study area. Conclusions. This study recommends to continue research on the issue of hydrological modeling in case of lack of data and to compare between different models to find the best way to solve this problem.

Selection of suitable aggregation function for estimation of water quality index for the Orontes River

This study aims to select the most appropriate aggregation function to determine a preliminary classification of water quality according to special criteria for required use. The Orontes River was taken as a study case as an important water resource, to assess the suitability of its water for various uses by using water quality index. Four main steps were applied to build a water quality index; the first step is selecting parameters using the Delphi technique based on experts' opinion, where 5 parameters of the most important water pollutants were selected, the relative weight of each parameter was determined based on its significance to the final index using the weight criteria method. To standardize the range of pollution indicators, the quality rating curves for each parameter were drawn using regression analysis and extracting equations for the quality rating qi. The final step is aggregation Sub-indicators, about 20 aggregation functions were applied to select the most appropriate aggregation function for assessing the water quality of the Orontes River (ORSI) based on special criteria (Ambiguity, Eclipsing, Constant functional behavior, weight, sensitivity). After comparing the values of the indicators based on the criteria, the results showed that the weighted arithmetic mean (ORSIwa) is the best linear function for assessing the Orontes River water quality by the classifications of the main categories according to the required use.

Seasonal Differences in Lipid and Fatty Acid Composition of European Eels (Anguilla anguilla, Linnaeus 1758) from Orontes River, Turkiye

Seasonal differences in the lipid contents and fatty acid composition of European Eels (Anguilla anguilla, Linnaeus 1758) caught from the Orontes River (Hatay, Turkey) were determined. High lipid levels, as well as ω-3 and ω-6 fatty acids, are important factors in product quality. High-content lipid values of European eels from the Orontes River differed between seasons (p<0.05). The fatty acid compositions of eels ranged from 5.91-8.03 g/100 g in saturated fatty acids (SFA), 10.59-14.08 g/100 g in monounsaturated fatty acids (MUFAs), and 2.19-3.52 g/100 g in polyunsaturated fatty acids (PUFAs). Those present in the highest proportions were palmitic acid (C16:0, 62-67.51%), palmitoleic acid (C16:1, 15.67-19.07%), stearic acid (C18:0, 11.04-17.2%), oleic acid (C18:1ω-9, 70.67-72.44%), eicosapentaenoic acid (EPA) (C20:5ω-3, 12.77-22.83%), and docosahexaenoic acid (DHA, C22:6ω-3, 4.35-11.93%). Some fatty acids’ composition differed significantly (p<0.05) between seasons. In addition, the ratio of ω-3/ω-6 PUFAs varied between 1.14 and 1.72, reaching the highest value in autumn. The highest EPA+DHA contents were recorded during summer. In conclusion, analysis parameters show that commercially important European eels from the Orontes River are quite good sources of high-quality lipids and fatty acids. 

First record of microplastic occurence at the commercial fish from Orontes River

Freshwater environments are more sensitive to anthropogenic influences and usually contain higher concentrations of pollutants than marine environments. Microplastic pollution causes additional stress on freshwater animals; yet, studies evaluating the microplastic occurrence in freshwater biota are still limited. In this study, microplastic occurrence in the gastrointestinal tracts (GIT) and gill of commercial fish species (Prussian carp Carassius gibelio (Bloch, 1782); Abu mullet Planiliza abu (Heckel, 1843); Common carp Cyprinus carpio Linnaeus, 1758; European ell Anguilla Anguilla (Linnaeus, 1758); North African catfish Clarias gariepinus (Burchell, 1822); Goldfish Carassius auratus (Linnaeus, 1758) were reported from Orontes River. MPs abundance in the GIT and gill of six species were found as 5.1 ± 2 MPs fish−1 and 4.4 ± 2 MPs fish−1 with an occurrence of 95% and 74%, respectively. The majority of extracted microplastics were fiber, black and less than 1000 μm in size. FTIR analysis determined the main polymer types as polyester (50%), high-density polyethylene (HDPE) (10%), polypropylene (PP) (8%) and polyethylene terephthalate (PET) (5%). High MPs abundance and frequency of occurence indicate the exposure of microplastic pollution in freshwater biota which could threat the health of both individuals and consumers. Results obtained in this study will increase the acknowledgement of MPs pollution in the Orontes River. Also, this study will provide data to the administrators to set up necessary legislations in freshwater ecosystems.

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.