Gediz River Basin Research Articles

The critical need for long-term biomonitoring: The case study of a major river system in Anatolia.

The long-term monitoring of freshwater ecosystems has been globally recognized for its critical role in preserving natural biodiversity and human well-being over the past century, yet monitoring efforts remain heterogeneously distributed. The Anatolia region in Turkey is known to host three out of the world's 34 biodiversity hotspots, boasting high biodiversity, but is increasingly threatened by anthropogenic effects and invasive freshwater fish species. Although the introduction of non-native fish species and pollution has detrimental effects on different river catchments in this area, long-term biomonitoring studies in Anatolia remain scarce and inconsistent. Collecting information of the Gediz River basin, a major Anatolian river catchment as a model that has been significantly impacted by human activities, published between 1971 and 2023, a total of 21 fish species from 12 families were identified. The spatial and temporal occurrences of these species fluctuated significantly due to heterogeneous sampling efforts that did not follow a clear pattern, indicating a complex ecological and conservation scenario characterized by both spatial and temporal variability in species prevalence, endemism and abundance. The presented results emphasize the need for comprehensive long-term biodiversity monitoring for anthropogenically impacted catchments like the Gediz River basin. Only with a coherent basis of high-resolution monitoring data can the multiple stressors threatening Anatolia's freshwater biodiversity be disentangled and, subsequently, be mitigated. Using the Gediz River basin as an example, this study emphasizes the urgent need for comprehensive, long-term biodiversity monitoring in understudied regions worldwide to protect regional biodiversity and ensure ecosystem health.

Internal structure modification of a simple monthly water balance model via incorporation of a machine learning-based nonlinear routing

ABSTRACT Among various monthly water balance models, one of the models that has the simplest structure and offers a well-behaved conceptual platform is the GR2M. Despite the widespread use of the model with two-free parameters, the fact that it tends to produce relatively large errors in peak flow months necessitates some modifications to the model. The reason for the mentioned simulation deficiencies could be that the relationship between the routing reservoir and the external environment of the basin is controlled by a single parameter, making the storage–discharge relationship linear. Therefore, in this study, least squares support vector regression, one of the nonlinear data-driven models, has replaced the routing part of the GR2M to enhance the monthly runoff simulation. The performance of the three-parameter hybrid model (GR3M), which was developed by considering the parameter parsimony point of view and including a machine learning (ML)-based nonlinear routing scheme, was examined in some locations in the Gediz River Basin in western Turkey. Statistical performance measures have shown that GR3M, which both leverages the capabilities of an ML model and blends conceptual outputs within a nested scheme, clearly outperforms the original GR2M. The proposed modification has brought significant improvements, especially to high-flow simulations.

Determination of water quality profile of an urban stream in Gediz River Basin (West Anatolia, Türkiye) and comparative analysis of biotic indices

Determination of water quality profile of an urban stream in Gediz River Basin (West Anatolia, Türkiye) and comparative analysis of biotic indices

Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator

Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modeling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the “Kaplan–Meier estimator”, a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to apply this methodology to assess drought risk by treating past droughts as “events” and using drought indicators such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Mapping these results for a better understanding of the drought risks on larger spatial scales such as a river basin is also within the expected outcomes. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope gets steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps, which expose more vulnerable areas within the river basin.

Monitoring land subsidence induced by tectonic activity and groundwater extraction in the eastern Gediz River Basin (Türkiye) using Sentinel-1 observations

Alaşehir-Sarıgöl sub-basin (ASSB) in Türkiye faces severe water stress mainly due to intensive agricultural irrigation. This has led to declining groundwater levels, aquifer compaction, and subsequent land subsidence. This study aims to evaluate the roles of tectonic activity and groundwater withdrawal in land-subsidence and investigate additional factors like faults and soft soil thickness. The P-SBAS algorithm was applied, using 98 and 123 Sentinel-1 SAR images in ascending and descending orbits, respectively, from 2016 to 2020. Independent Component Analysis separated long-term displacements from seasonal variations in the InSAR time series data. InSAR analysis showed displacement rates up to −6.40 cm/year. Results reveal a direct correlation between InSAR displacement and soft soil thickness, highlighting aquitard layer compaction due to groundwater withdrawal and piezometric head depletion as the primary causes of land subsidence. It was not possible to disentangle the multi-year displacements with different rates, i.e., to isolate the displacements caused by tectonic activity from displacements due to the soft soil compaction induced by groundwater withdrawal. Nonetheless, the analysis identified two spatiotemporal displacement trends: one linked to long-term, linear plastic compaction of the aquitard due to groundwater withdrawal and the other involving long-term displacements with seasonal rebounds caused by yearly water level fluctuations.

GIS Analysis of spatial-temporal variation of the ecological risk caused by element and organic pollutants in Lake Marmara (TURKIYE)

Located in western Anatolia (Turkey), Lake Marmara is a wetland with high ecological value. Lake Marmara, which was exposed to intense anthropogenic effects after 1940, is experiencing ecological deterioration in recent years. This study aimed to analyze the ecological risk level of the lake by using Enrichment Factor (EF), Toxic Risk Index (TRI), Modified Potential Ecological Risk Index (mER) etc. The research hypothesized that the discharge of water from the Gediz River and Kum Stream to the lake after 1940 triggered ecological risk problems. Moderate toxic risk, low potential ecological risk, moderate element contamination were detected throughout the lake. It was determined that Mo, P and Hg of anthropogenic origin and As, Ni, Cr, Cu of lithological origin created ecological risk at a regional scale. Domestic-industrial wastes and agriculture in the Gediz River basin and around the lake were identified as the dominant anthropogenic activity. The temporal variation of the ecological risk indices based on the elements did not reflect the anthropogenic traces of water discharge into the lake from the Gediz River basin. However, the temporal variation of TOC, N, P and CDP showed the effects of anthropogenic interventions in the lake. According to the threshold values; P is highly contaminated and N is close to the severely contaminated limit. The findings, while confirming the research hypothesis, showed that the dominant ecological risk factor in Lake Marmara is not elemental contamination, but organic pollutants, nitrogen and phosphate.

Monthly Streamflow Prediction Using ANN, KNN and ANFIS models: Example of Gediz River Basin

Stream flow forecasting is very important in many aspects such as water supply, irrigation, building water infrastructures, and taking precautions against floods. The ability to forecast future streamflow helps us anticipate and plan for upcoming flooding, decreasing property destruction, preventing deaths and managing water in the best way possible. Different hydrological models have been developed for predicting streamflow and they have different characteristics, driven by the research area and available data. İn this study, three types of Artificial Intelligence models; K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) have been used to study the Gediz River Basin which is located in the Aegean region of western Turkey. The results varied due to the complication of the data and different parts of the study area as well as the structure of the models, over all, looking at Regression coefficient (R2), Root Mean Square Error (RMSE) and Wilcoxon (WT) values, ANFIS is more accurate compared to ANN and KNN models. Conversely, according to Taylor diagram, KNN is more accurate compared to ANN and ANFIS.

Biological assessment of Demirci Stream in Gediz River Basin (West Anatolia of Turkey) and comparative performance of benthic macroinvertebrate-based metrics

Biological assessment of Demirci Stream in Gediz River Basin (West Anatolia of Turkey) and comparative performance of benthic macroinvertebrate-based metrics

A comparison of growth of a vulnerable species, common carp, Cyprinus carpio (Actinopteri: Cyprinidae), from two different reservoir types in the same river basin

Context The mechanisms underlying regional differences in growth parameters of common carp, Cyprinus carpio Linnaeus, 1758 (Actinopteri: Cyprinidae), are not yet fully understood, because comparisons among them have been made post hoc. Aims To simultaneously compare growth of common carp between sexes and between regions: Demirköprü Reservoir (DR) and Marmara Lake (ML), which have different structures (lowland type vs highland type) and are proximate to each other in the same river basin (Gediz River Basin). Methods Length-frequency distributions, length–weight relationships and three growth models (von Bertalanffy growth function (VBGF), Gompertz growth function (GGF) and Robertson growth function (RGF)) were used for both sexes and all individuals in reservoirs. Key results As none of the best-fitting models used among these growth models clearly suggested a ‘clear winner’ (Akaike’s weight (wi) > 0.9), the model-averaged multi-model inference (MMI) was used for the first time for common carp. Conclusions Asymptotic length obtained from MMI for all individuals in ML (lowland type) was statistically higher than that in DR (highland type) (P < 0.05). Implications Based on the maximum observed length and the mean L∞ obtained from MMI, the common carp population in the DR can be considered threatened by overfishing.

A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model

In this study, a novel fully hybrid simulation-optimization approach is proposed for enhancing the prediction performance of the conceptual Technische Universität Wien (TUW) hydrological model. In the simulation part of the proposed approach, the TUW model is hybridized with a support vector regression (SVR) model where SVR directly uses the TUW model outputs as input and simulates the runoff process. This hybridized simulation model is then integrated to a hybrid optimization approach where heuristic harmony search (HS) and Nelder-Mead Simplex (NMS) optimization approaches are mutually integrated. The objective of the hybrid HS-NMS optimization approach is to calibrate the associated parameter values of both TUW and SVR models by maximizing the Nash-Sutcliffe efficiency (NSE) calculated between simulated and observed runoff values. The applicability of the proposed approach is evaluated on a Murat sub-basin of the Gediz River Basin (GRB) in Turkey for different simulation and optimization model combinations. Identified results indicated that the proposed fully hybrid simulation–optimization approach not only efficiently calibrates the associated solution parameters, but also improves the prediction performance of the TUW model.

The remarkable observation for a mallard (Anas platyrhynchos) food from estuarine/coastal area (İzmir/Turkey): A mosquitofish (Gambusia holbrooki)

The purpose of this study is to create awareness about the species of mosquito fishes (Gambusia holbrooki Girard, 1859) both vectors and ways of introduction to new aquatic habitats. Because the species creates risks, especially for the circumstance of the native and endemic fish fauna, studies about introduction stories of the species are quite important to prevent the risk. In the current report, mosquitofishes which are known as a highly invasive species were found in the crop content of a hen mallard duck (Anas platyrhynchos L., 1758) in an estuarine area, the Gediz River Basin (İzmir, Turkey). This report is the first record of a duck consuming mosquitofish. Although nevertheless, it is known that mallard duck feeding behaviour does not predominantly contain fish (they mainly feed on plant seeds), according to this observation, mosquitofish might be added to the prey list of mallards as a new type of food. Therewith, this feeding behavior also indicates that ducks might be a potential vector for the transport of invasive mosquitofishes.

Entropy Based Regional Precipitation Prediction in the Case of Gediz River Basin

The Shannon’s entropy concept is defined to measure the information content of hydrological processes in hydrology and water resources. Entropy concept has also provided an opportunity to solve several related topics in water resources engineering. The presented study aims to define the regional distribution of the expected long-term annual total precipitation, by using the entropy concept. For this purpose, the frequency analysis of the observed long-term total monthly precipitation data for each station is analyzed, and entropy values are calculated using the frequency histogram called " Intensity Entropy – IE". By using the IE values, it is possible to define the regional information of long-term expected precipitation even if the gauging stations have different observation periods without missing any information of available data. In addition, there is no need to complete the data of the missing observation years (months) to define the precipitation-elevation relations for producing an isometric map. In regional analysis, it is possible to create isoentropy map, by using the determined IE values for each gauging station. The IE method is performed and isoentropy map is created for Gediz Basin as a case study, and the a priori conditions of using IE method results for regional information are discussed in the paper.

Hermos: An annotated image dataset for visual detection of grape leaf diseases

Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)–based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset – namely, Hermos – for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data.

Stakeholders’ Perspective on Groundwater Management in Four Water-Stressed Mediterranean Areas: Priorities and Challenges

Recent studies highlight the fragility of the Mediterranean basin against climate stresses and the difficulties of managing the sustainable development of groundwater resources. In this work, the main issues related to groundwater management have been identified from the stakeholder’s perspective in the following four representative water-stressed Mediterranean areas: the coastal aquifer of Comacchio (Italy), the Alto Guadalentín aquifer (Spain), the alluvial aquifer of the Gediz River basin (Turkey), and the Azraq aquifer (Azraq Wetland Reserve, Jordan). This has been achieved by designing a methodology to involve and engage a representative set of stakeholders, including a questionnaire to learn their point of view concerning the current management of aquifer systems and their experience with the already available tools for groundwater resource management, such as monitoring networks and numerical models. The outcome of the survey has allowed us to identify both particular and common challenges among the four study sites and among the various groups of stakeholders. This information provides valuable insights to improve the transfer of scientific knowledge from the research centers to the authorities managing the groundwater resources and it will help to plan more effective research activities on aquifer management. The proposed methodology could be applied in other aquifers facing similar problems.

Length-Weight Relationship of 15 Different Freshwater Fish Species in the Gediz River Basin (Turkey) Lentic System

The aim of study is to determine the length-weight relationship of freshwater fish species in the Gediz River basin lentic system. Fish samples were collected between November 2016 and April 2017 using multi mesh gillnets and beach seine nets from six different locations in the Gediz River basin lentic system. Length–weight relationship was estimated for 15 fish species (Luciobarbus lydianus, Barbus pergamonensis, Cyprinus carpio, Carassius gibelio, Petroleuciscus smyrnaeus, Alburnus battalgilae, Squalius fellowesii, Chondrostoma holmwoodii, Vimba vimba, Rhodeus amarus, Pseudorasbora parva, Atherina boyeri, Cobitis kurui, Gambusia holbrooki and Knipowitschia mermere), belonging to 8 different families (Cyprinidae, Leuciscidae, Acheilognothidae, Gobionidae, Atherinidae, Cobitidae, Poeciliidae, Gobiidae). Computed exponent b and R2 values ranged from 1.9348 to 4.3466 and 0.7072 to 0.9986, respectively. In the study, a longer maximum length value was determined for the two species than reported in the literature. In addition, this study presents the first records of LWR parameters for four endemic species in the basin.

Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling

One of the frequently adopted hybridizations within the scope of rainfall-runoff modeling rests on directing various outputs simulated from the conceptual rainfall-runoff (CRR) models to machine learning (ML) techniques. In those coupled model exercises, after the parameter calibrations of the CRR models are made, their specific outputs constitute auxiliary inputs for the ML model training. However, in this parallel hybridization comprising two consecutive processes, performing the cascade calibration of CRR and ML models increases the computational complexity. Moreover, the mutual interaction between the parameters governing CRR and ML models is also not considered. In this study, to cope with the handicaps mentioned, artificial neural networks (ANN) and support vector regression (SVR) were separately embedded into a monthly lumped CRR model. The dynamic water balance model (dynwbm) was preferred as the CRR model. Then, all free parameters within these nested hybrid models were calibrated simultaneously. The ML parts within the nested schemes manipulate various output variants derived with three conceptual parameters for monthly runoff simulation. These new hybrid models equipped with an automatic calibration algorithm were applied at several locations in the Gediz River Basin of western Turkey. The performance measures regarding mean and high flows indicated that the nested hybrid models outperformed the standalone models (i.e., dynwbm, ANN, and SVR) and coupled model variants. Thus, the credibility of a novel modeling strategy, which takes advantage of the supplementary strengths of a conceptual model and different ML techniques, was demonstrated.

An approach for determining the nutrient sensitive areas: a case study for Gediz River Basin, Turkey.

This study presents an approach to the determination of nutrient sensitive areas (SA) based on National Urban Wastewater Treatment Regulation harmonized under the Water Framework Directive (WFD). A three-stage approach that covers designation of potential sensitive areas (PSA) which are under the risk of eutrophication, physicochemical and biological monitoring at PSA, and application of the suitable biotic indices is used for the determination of sensitive areas. PSA are identified as a result of either available monitoring data or if this is not available according to indexing method which developed in this study based on pressure and impact analysis. The Gediz River Basin, one of the most polluted basins in Turkiye, is chosen as a pilot area. According to the results of the study, 40% of the total drainage area of the basin was designated as sensitive area.

Assessment of river alteration using a new hydromorphological index.

Main river systems in large watersheds are mostly destroyed due to intense human activities. These rivers are modified by a number of water infrastructures such as dams, diversion weirs, flood control structures, and sediment traps. Such modifications alter the hydrology, continuity, and habitat quality of river waterbodies and degrade their overall ecological status. This study provides a systematic and quantitative assessment of river hydromorphology with a composite index based on four sets of criteria (i.e., hydrology, channel continuity, habitat quality, and bed modification) to assess the level of human intervention. The developed index is tested and implemented in Gediz River Basin in Western Anatolia (Turkey), which is one of the most important watersheds with regard to human settlements as well as agricultural and industrial production. The results of the developed index have revealed values between 42.36 and 88.14 on a 0-100 scale and a gradual decline in overall river hydromorphological quality along the flow path. The analysis has shown that barrier effects were found to be crucial in reduced river continuity and bed modification for flood control has resulted in degraded instream and riverbank habitat quality. The developed index methodology can serve as a systematic tool for assessing the hydromorphology and its associated influence in the ecological status of rivers. It can further assist the decision-makers in planning and prioritizing river restoration projects.

Assessing water quality in the Kelebek Stream branch (Gediz River Basin, West Anatolia of Turkey) using physicochemical and macroinvertebrate-based indices

This study was carried out in Kelebek, which is the most important stream in Gediz River Basin, Turkey, to determine water quality by using macroinvertebrate-based metrics and physicochemial variables. In addition, we also aimed to investigate the effects of anthropogenic pressure and dam construction on stream macroinvertebrates during the study period. In this study, following biotic indices are used: Saprobi Index (SI), Biological Monitoring Working Party (BMWP), Average Score per Taxon (ASPT), Family Biotic Index (FBI), Belgian Biotic Index (BBI), as well as diversity indices: Shannon-Weaver index (SWDI), Simpsons index (SDI) and Margalef index (MDI). Collection of macroinvertebrate samples and the physicochemical measurements were carried out monthly for a year. As a result of the identification, the most dominant macroinverterate group was Insecta. Our results show the presence of 9 taxonomic group in the stream which belong to nine groups: Oligochaeta, Mollusca, Crustacea, Ephemeroptera, Plecoptera, Trichoptera, Odonata, Coleoptera, Diptera. Canonical correlation analysis (CCA) indicates importance of physicochemial variables in the distribution of different macroinvertebrates groups (total variance 88%), species (total variance 86.2%) and biotic indices (total variance 88.2%). The water quality along the Kelebek Stream show variation from good class in station #1, #2 and #5, to moderate in station #3 and #4. We conclude that BMWP (Original), BMWP (Spanish), BMWP (Greek) and ASPT indices are suitable for assessing stream health by macroinvertebrates.

A strategy for the implementation of water-quality-based discharge limits for the regulation of hazardous substances.

Many developing countries apply technology-based discharge standards that set quantitative limits on pollutant discharges. These standards do not inherently consider ambient constraints and, therefore, cannot guarantee to protect aquatic life from hazardous pollutants. It is a challenge for developing countries to enforce water-quality-based limits for wastewater discharges and guarantee the intended use of water. This study aims to develop a strategy that suits the needs of developing countries for a transition from technology-based discharge standards to water-quality-based discharge limits. To this end, a pilot monitoring program was carried in the Gediz River Basin in Turkey. Surface water, industrial, and urban wastewater samples were collected and analyzed for 45 priority pollutants identified by the European Union and 250 national river basin specific pollutants. The monitoring results revealed that the environmental quality standards (EQSs) were exceeded for 8 priority, and 28 specific pollutants. This finding indicated that the existing technology-based discharge standards are not satisfactory to guarantee the intended water quality, and there is a need for adopting a new strategy for the implementation of water-quality-based discharge limits in Turkey. As a widely applied approach for determining water-quality-based discharge limits, firstly, conservative mass balance with and without consideration of mixing zone was evaluated. The results indicated that this approach was not applicable due to the receiving environment concentrations being higher than the EQSs. As an alternative approach, the dilution methodology, which considers the level of dilution occurring at the immediate discharge point, was tested. The results proved that the dilution methodology is the most appropriate strategy for developing countries with relatively poor surface water quality to improve the water quality to the level where the conservative mass balance approach can be applicable.