Browsing by Author "Yilmaz, Muhammet"

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Assessing the Main Drivers of Low Flow Series in Turkey
(Springer, 2023) Yilmaz, Muhammet; Tosunoglu, Fatih
Over the past decades, low flow has been highly impacted by climate change across Turkey, and it is important to investigate low flow trends and drivers of this change to guide water resources management. The standard normal homogeneity test (SNHT), the Pettitt test, and the Buishand range test were used for homogeneity analysis. A comprehensive assessment of trends and variability in low flows from 88 flow stations located in 26 river basins across Turkey and their attributions in a changing climate were presented. The Mann-Kendall (MK) and modified Mann-Kendall test (mMK) were utilized to detect the significance of trends, while Sen's slope method was used to identify the magnitude of trends. According to the trend test results, the low flow records of 34 stations indicated statistically decreasing trends, whereas those of four stations indicated a statistically increasing trend. We also analyzed how climate variables affect low flow variations. Correlations between climate variables (temperature and precipitation) and large-scale climate models with low flow were determined by Spearman's Rho test. North Atlantic Oscillation (NAO), Western Mediterranean Oscillation (WeMO), Southern Oscillation Index (SOI), and Pacific Decadal Oscillation (PDO) were investigated for their relation with the low flow variability. The results showed that low flow data were generally positively correlated with precipitation, and this result was particularly pronounced on the annual scale. Unlike precipitation, low flow series have a negative correlation with temperature data, and correlations are clearer during dry periods. In most areas, NAO and SO were negatively correlated with low flow patterns in Turkey, while PDO and WeMO were positively correlated with low flow series. The results indicate that low flows in most regions are more sensitive to precipitation than temperature and large-scale climate models. In addition, this research reported that the use of seasonal indices made some seasonal correlations more pronounced than their annual counterparts. These results suggest that seasonal climate indices can be potential candidates for low flow prediction.
Comparison of Conventional and Differential Evolution-Based Parameter Estimation Methods on the Flood Frequency Analysis
(Springer International Publishing AG, 2021) Yilmaz, Muhammet; Tosunoglu, Fatih; Demirel, Mehmet Cuneyd
Accurate estimation of flood frequency is an important task for water resources management. This starts with appropriate selection of probability distribution to flood samples (annual maximum flows) that is of great importance for flood frequency analysis (FFA). In order to reach the most precise estimation, the probability distribution of the considered time series should be well defined and its parameters should be more accurately estimated. First time in the FFA literature, a differential evolution-based parameter estimation method is applied to obtain the parameters of probability distribution functions and is compared with the traditional maximum likelihood method (MLM) in the present study. For this purpose, eleven distributions have been used to describe the annual maximum flood series of nine gauging sites, with the performance of each distribution being investigated based on six criteria. The results revealed that a single distribution cannot be specified as the best-fit distribution for the study area. Moreover, it has been found that the applied approach improves the probability prediction of floods better than MLM method for efficient design of hydraulic structures, risk analysis and floodplain management.
Evaluation of Nex-Gddp to Simulate Precipitation Using Multi-Criteria Decision-Making Analysis Over Türkiye
(Springer Wien, 2025) Yilmaz, Muhammet; Alemdar, Kadir Diler; Tosunoglu, Fatih
General Circulation Models (GCMs) are effective tools for understanding climate change and enabling possible future projections. Although there has been an increase in studies on GCMs assessment, there continues to be a lack of research determining the optimal number of GCMs to be included in a Best Multi-Model Ensemble (BMME). To address this gap, this study integrated the Multi-criteria decision-making methods (MCDM), and the K-means clustering Elbow Method. Firstly, the performance of 27 NEX-GDDP-CMIP6 models was assessed for the period of 1960-2014 with statistical metrics, including the correlation coefficient (CC), percentage bias (PBias), normalized root mean square error (nRMSE), Kling-Gupta efficiency (KGE), and mean absolute error (MAE), taking the fifth generation of European ReAnalysis Land Component (ERA5-Land) as reference. The results showed that NEX-GDDP-CMIP6 generally performs well but fails to capture precipitation characteristics in the northern parts of the country. Individual ranking for each basin was performed using the PROMETHEE method based on performance metric data. PROMETHEE results, in general, the MPI-ESM1-2-LR, MIROC-ES2L, INM-CM5-0, and ECEarth3- Veg-LR models provided sufficient performance in simulating the characteristics of precipitation. General assessment results derived from the integrated method that BMME is the best-performing model across all basins, consistently outperforming both individual NEX-GDDP-CMIP6 and Multi-Model Ensemble (MME) obtained by calculating the simple arithmetic mean of the 27 NEX-GDDP-CMIP6 models. Consequently, the proposed novel methodology can provide valuable information for climate change studies in the study area.
Evaluation of Trends and Dominant Modes in Maximum Flows in Turkey Using Discrete and Additive Wavelet Transforms
(ASCE-Amer Soc Civil Engineers, 2020) Yilmaz, Muhammet; Tosunoglu, Fatih; Kaplan, Nur Huseyin
This paper aims to define trends and dominant modes in annual instantaneous maximum flows (AIMF) covering the period 1961-2014 from 10 gauge stations located in different river basins in Turkey. To achieve this aim, discrete wavelet transform (DWT) and additive wavelet transform (AWT) in conjunction with the Mann-Kendall (MK) test are used and compared for the first time. Moreover, global wavelet spectrum (GWS) is employed to test the significance of the most effective periodic components. The sequential MK test is also used to determine the start or change points of trend in AIMF series. From the MK trend results, five stations showed a statistically significant (at a 5% level) negative trend for AIMF series and short-term periodic components (2 and 4 years) were found to be the most effective components, which are responsible for producing a real trend founded on the data series. The GWS analysis indicated that the most dominant components identified are significant. In addition, the MK-z values of the most effective periods derived from AWT generally showed a better agreement with MK-z value of original time series with higher correlation coefficient compared to those of DWT. The sequential MK graphs of the AWT-based time series also produced a better harmony with the sequential MK of the original data. Finally, the results showed AWT coupled with the MK provided a very efficient and accurate analysis for defining the most effective modes in the AIMF series and can be successfully used in any hydrological time series. (C) 2020 American Society of Civil Engineers.
Impact of Climate Change on Meteorological and Hydrological Droughts for Upper Coruh Basin, Turkey
(Springer, 2022) Yilmaz, Muhammet; Alp, Harun; Tosunoglu, Fatih; Asikoglu, Omer Levend; Eris, Ebru
Drought is a multifaceted natural hazard that occurs in almost every component of the hydrological cycle. This study investigated future hydro-meteorological droughts using climate projections from an ensemble of 13 European Coordinated Regional Downscaling Experiments (CORDEX) domain outputs under two alternative scenarios of representative concentration pathways (RCP 4.5 and RCP 8.5) for the 2030-2059 and 2070-2099 periods across Upper Coruh Basin, Turkey. The bias-corrected CORDEX climate projections were incorporated into the Soil and Water Assessment Tool (SWAT) hydrological model. In this study, two optimisation algorithms (the sequential uncertainty fitting algorithm of SWAT calibration and uncertainty and the shuffled complex evolution (SCE) algorithm in parameter estimation tool (PEST)) were tested for the automatic parameter calibration of a complex hydrologic model, SWAT, in the study area. Results show that SCE reached better parameter solutions than the other algorithm. This study investigated for the first time a comprehensive analysis of the projected droughts in the Upper Coruh Basin, Turkey. The standardised precipitation index and standardised streamflow index were used to evaluate the meteorological and hydrological droughts, respectively. Overall, the future annual precipitation and the maximum and minimum temperatures are projected to change from - 15.46% to 8.74%, 0.02 degrees C to 8.74 degrees C and - 2.69 degrees C to 5.27 degrees C, respectively. The results show that the frequency of hydrological drought durations will be higher under RCP4.5 and RCP8.5 during the period 2030-2059. In addition, the frequency of hydrological high-severity droughts (> similar to 5) and low-severity droughts (< similar to 2) will be more likely under RCP4.5 and RCP8.5 during the period 2030-2059 and 2070-2099, respectively. Other than this, not enough evidence exists to claim that hydrological and meteorological drought will become more significant in the twenty-first century.
An Integrated Methodology for Flood Risk Assessment of Road Networks: A Case Study of Ordu, Türkiye
(Springer Heidelberg, 2025) Yilmaz, Muhammet; Alemdar, Kadir Diler
Floods are among the most common and destructive natural disasters worldwide, causing both loss of life and serious economic damage. The effects of these disasters are critical to the functionality and safety of road networks. This study presents an integrated methodology to evaluate flood risk of road networks in Ordu, T & uuml;rkiye using a Geographic Information System based multi-criteria decision making (MCDM) methods, namely Analytical Hierarchy Process (AHP) and Elimination and Choice Translating Reality (ELECTRE). AHP-based GIS results highlighted the impact of flood risk on road networks is more evident in Alt & imath;nordu. According to ELECTRE results, the risk is higher in the road networks in Alt & imath;nordu, Kumru, and & Ccedil;atalp & imath;nar districts. Within the scope of the study, sensitivity analysis was also performed to evaluate the factors that trigger flood risk. The results showed that the Daily Maximum Rainfall, Proximity to River and Flow Accumulation criteria for hazard and Road Density, Road Networks and Population criteria for vulnerability are the most sensitive criteria for flood risk. The findings of the GIS-driven multi-criteria decision approach can be useful to decision makers and local governments for sustainable flood disaster planning and management of road networks in the study area, and the proposed method can be extended to different study areas to develop effective flood management strategies.
Inundation Risk Assessment in Urban Rail System of Mega-City via GIS-Based Multi Criteria Decision Approach
(Elsevier, 2025) Alemdar, Kadir Diler; Yilmaz, Muhammet
The urban rail system (URS) is a critical component of public transport, providing important social and economic services in megacities. Increasing frequency and severity of urban inundation may cause functional disruptions in URSs; therefore, understanding the inundation risk of URSs is a prerequisite for risk management in cities. This present study incorporates the Fuzzy Analytic Hierarchy Process (FAHP) into a Geographic Information System (GIS) and performs VIseKriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) approach, which uses the performance values of determined regions in the study area, to evaluate the inundation risk of the URSs in Istanbul. For the inundation risk analysis, 10 hazard criteria and 12 vulnerability criteria were identified. According to FAHP-based GIS results, the southeast of the European side and the southwest of the Asian side of the study area were determined to be the most sensitive regions. VIKOR results emphasized that inundation risk was higher in Atas,ehir, Kad & imath;koy, and Tuzla districts. Additionally, the results showed that more than 60 % of URSs were highly exposed to the risk of inundation, and this result was more pronounced in the Kad & imath;koy district. According to the sensitivity analysis results, it was determined that the most sensitive criteria in the inundation risk analysis were Daily Maximum Rainfall and Population Density, which had the highest weights for hazard and vulnerability. This research holds substantial importance regarding inundation warning and prevention in the Istanbul URSs, offering a theoretical framework for evaluating inundation risk in other metropolitan areas in terms of the data and methods used.
Investigation of the Low Impact Development Strategies for Highly Urbanized Area Via Auto-Calibrated Storm Water Management Model (SWMM)
(IWA Publishing, 2021) Ekmekcioglu, Omer; Yilmaz, Muhammet; Ozger, Mehmet; Tosunoglu, Fatih
This study aims to investigate the effectiveness of the low impact development (LID) practices on sustainable urban flood storm water management. We applied three LID techniques, i.e. green roof, permeable pavements and bioretention cells, on a highly urbanized watershed in Istanbul, Turkey. The EPA-SWMM was used as a hydrologic-hydraulic model and the model calibration was performed by the well-known Parameter ESTimation (PEST) tool. The rainfall-runoff events occurred between 2012 and 2020. A sensitivity analysis on the parameter selection was applied to reduce the computational cost. The Nash-Sutcliffe efficiency coefficient (NSE) was used as the objective function and it was calculated as 0.809 in the model calibration. The simulations were conducted for six different return periods of a storm event, i.e. 2, 5, 10, 25, 50 and 100-years, in which the synthetic storm event hyetographs were produced by means of the alternating block method. The results revealed that the combination of green roof and permeable pavements have the major impact on both the peak flood reduction and the runoff volume reduction compared to the single LIDs. The maximum runoff reduction percentage was obtained as 56.02% for a 10-years return period of a storm event in the combination scenario.
Mapping and Assessment of Flood Risk Based on Vulnerability and Hazard Factors in Urban Areas Through the Integration of Multi-Criteria Techniques and Gis: A Case Study in Yakutiye, Erzurum, Türkiye
(Springer, 2025) Yilmaz, Muhammet; Alemdar, Kadir Diler
Flood-related losses have prompted researchers to adopt comprehensive and scientific approaches to mitigate flood damages. Recently proposed multi-criteria decision making (MCDM) methods are used to perform flood risk analysis more participatory, multi-dimensional, and efficient. This study focuses on the flood risk analysis of Yakutiye District in Erzurum, T & uuml;rkiye, employing a multifaceted approach integrating Geographic Information System (GIS) and MCDM methods including Analytical Hierarchy Process (AHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and The Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE). A total of 20 flood indicators, including 11 hazard and 9 vulnerability indicators, were considered for evaluating flood risk maps of the study area. The criteria weights were derived from expert opinions along with a literature review. The results highlighted the importance of daily maximum rainfall, slope, and elevation criteria for hazard, and population density, bridges and culverts, and busy intersections criteria for vulnerability. In addition, the findings showed that 14.7% of Yakutiye district was at high and very high flood risk. Afterwards, in order to determine the flood risk priority of 44 neighborhoods determined in Yakutiye, TOPSIS and PROMETHEE approaches were used. Results revealed that the flood risk was higher in the southeastern part of the study area and also found that 8 out of 44 neighborhoods were located in high risk areas. The proposed generic framework provides solutions to specific problems in the field of flood risk and is a replicable approach in denser cities.
Multivariate Assessment of Low-Flow Hazards Via Copulas: The Case Study of the Coruh Basin (Turkey)
(MDPI, 2020) Tosunoglu, Fatih; Salvadori, Gianfausto; Yilmaz, Muhammet
Bivariate modeling and hazard assessment of low flows are performed exploiting copulas. 7-day low flows observed, respectively, in the upper, middle and lower parts of the coruh basin (Turkey) are examined, considering three pairs of certified stations located in different sub-basins. A thorough statistical analysis indicates that the GEV distribution can be used to model the marginal behavior of the low-flow. The joint distributions at each part are modeled via a dozen of copula families. As a result, the Husler-Reiss copula adequately fits the joint low flows in the upper part, while the t-Student copula turns out to best fit the other parts. In order to assess the low-flow hazard, these copulas are then used to compute joint return periods and failure probabilities under a critical bivariate "AND" hazard scenario. The results indicate that the middle and lower parts of the coruh basin are likely to experience the largest drought hazards. As a novelty, the statistical tools used allow to objectively quantify drought threatening in a thorough multivariate perspective, which involves distributional analysis, frequency analysis (return periods) and hazard analysis (failure probabilities).
Non-Stationary Low Flow Frequency Analysis Under Climate Change
(Springer Wien, 2024) Yilmaz, Muhammet; Tosunoglu, Fatih
Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.
Performance of Various Gridded Precipitation and Temperature Products Against Gauged Observations Over Turkey
(Springer Heidelberg, 2024) Yilmaz, Muhammet
Precipitation and temperature products play a critical role for hydrological, meteorological, and agricultures studies. The present study assessed the accuracy of eight gridded precipitation products (i.e., CHIRPS, CPC, CRU, ERA5-Land, GPCC, NCEP-NCAR R1, NCEP-DOE R2, and PREC/L) and three gridded temperature datasets (i.e. CPC, CRU, and, ERA5-Land) for Maximum temperature (TMAX), mean temperature (TMEAN), and minimum temperature (TMIN) over Turkey at monthly, seasonal and annual scales. Considering monthly gauge observation data from 105 stations as reference, the above precipitation and temperature products were evaluated based on using different performance indices. The results for precipitation showed that dataset performance varied by region and emphasized that GPCC is the most suitable dataset that can be used in Turkey for all evaluation criteria. CPC can be considered as the second most suitable datasets, while NCEP-NCAR R1 and NCEP-DOE R2 showed the weakest performance among the studied datasets. In addition, the findings of the study highlight that there is a better agreement between rain gauge observations and gridded products during the rainy seasons compared to the dry season. For TMAX, TMEAN, and TMIN, the regionally temporal evaluation results of the monthly scale show that in terms of overall statistical metrics, CPC generally performs better than the others. Comprehensive evaluation results showed that the gridded data showed poor performance in summer compared to other seasons in terms of Pearson coefficient of correlation (CC) and Normalized root mean square error (nRMSE), while they showed poorer skills based on Kling-Gupta efficiency (KGE) and Percentage Bias (PBias) in winter.
Predicting Monthly Streamflow Using Artificial Neural Networks and Wavelet Neural Networks Models
(Springer Heidelberg, 2022) Yilmaz, Muhammet; Tosunoglu, Fatih; Kaplan, Nur Huseyin; Unes, Fatih; Hanay, Yusuf Sinan
Improving predicting methods for streamflow series is an important task for the water resource planning, management, and agriculture process. This study demonstrates the development and effectiveness of a new hybrid model for streamflow predicting. In the present study, artificial neural networks (ANNs) coupled with wavelet transform, namely Additive Wavelet Transform (AWT), are proposed. Comparative analyses of Discrete wavelet transform (DWT) based ANN and conventional ANN techniques with the proposed method were presented. The analysis of these models was performed with monthly streamflow series for four stations on the coruh Basin, which is located in northeastern Turkey. The Bayesian regularization backpropagation training algorithm was employed for the optimization of the ANN network. The predicted results of the models were analyzed by the root mean square error (RMSE), Akaike information criterion (AIC), and coefficient of determination (R-2). The obtained revealed that the proposed hybrid model represents significant accuracy compared to other models, and thus it can be a useful alternative approach for predicting studies.
Trend Assessment of Annual Instantaneous Maximum Flows in Turkey
(Taylor & Francis Ltd, 2019) Yilmaz, Muhammet; Tosunoglu, Fatih
A comprehensive evaluation of trends in annual instantaneous maximum flows (AIMF) from 153 gauge stations located in 26 river basins in Turkey is presented. Two traditional non-parametric trend tests, the Mann-Kendall (MK) and Spearman's rho (SR), are used to quantify the significance of trends, while Sen's slope method is applied to determine the magnitude of trends. The traditional tests indicate that the AIMF records of 57 stations showed statistically decreasing trends, while those of six stations showed an increasing trend. Sen's trend method, which provides more detailed assessment of the trends in different clusters (low, medium and high), was applied to the AIMF series and the results were compared with traditional tests. Sen's trend method indicated that all flow clusters at nine stations have increasing or decreasing trends, although no significant trend was detected by the MK and SR tests.