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Analyzing temperature trends using innovative trend analyses in certain regions of Norway
(Springer Science and Business Media Deutschland GmbH, 2025) Tuğrul, Türker; Oruç, Sertaç; Hınıs, Mehmet Ali
A number of methods are used in the literature to track and monitor meteorological events in a region and make future predictions. Temperatures are one of the most important parameters that trigger changes in meteorological events. In this study, the trends in temperatures, which are a type of indicator of natural disasters in the Norwegian region, were examined. As trend analysis methods, the Innovative Trend Analysis (ITA), the Innovative Polygon Trend Analysis (IPTA), the Innovative trend pivot analysis method (ITPAM), and the Mann–Kendall Trend Test (MK) were preferred and data of monthly average temperature were collected from 4 different stations (Bodo, Karasjok, Oslo, and Tromsø) between 1948 and 2023. The results indicate the existence of increasing trends in all regions. This does not represent a risk or negativity for the region, but an advantage for this region. Furthermore, the results indicated that MK, in contrast to the other methods, was inadequate for identifying the specific trend and non-monotonic trend. Besides, in the annual MK analysis, Bodo, Oslo and Tromso displayed significant trends (p value < 0.05) with z-scores of 2.64, 2.48 and 2.07, respectively while with a z-score of 1.45, Karasjok did not exhibited a significant trend. In addition, one of the notable findings in this study is the demonstrated effectiveness of the graphical methods (ITA, ITPAM, and IPTA), as reflected in the trend results. The findings of this study are expected to support institutions or organizations in executing measures for natural disaster mitigation.
Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting
(Springer Science and Business Media Deutschland GmbH, 2025) Tuğrul, Türker; Hınıs, Mehmet Ali; Oruç, Sertaç
Many researchers are working to prevent, monitor and identify drought, which is one of the most insidious and dangerous natural disasters that negatively affects life. For this purpose, various drought indices are developed and new methods are proposed. One of the most widely used of these indexes is the Standard Precipitation Index (SPI). Since it is not known when the drought will begin, taking preventive measures is a difficult and challenging task. In the last decade, machine learning techniques have been preferred to increase success in predicting droughts. In this study, SPI was used as the drought index and Support Vector Machine (SVM) and Long-Short Term Memory Network (LSTM) methods, which are increasingly reliable among the most preferred machine learning and deep learning methods in drought predictions, were used as the prediction method, and furthermore, to increase the prediction power of these methods, new powerful models have been proposed using Wavelet transform and Variational mode transform. Support Vector Machines with Wavelet decomposition (SVM-W), Long-Short Term Memory Networks with Wavelet decomposition (LSTM-W), and Long Short Term Memory Networks with Variational Mode Decomposition (LSTM-VMD) were used as prediction models for drought analysis and the performances of these models were compared.
Performance enhancement of models through discrete wavelet transform for streamflow forecasting in Çarşamba River, Türkiye
(IWA Publishing, 2025) Tuğrul, Türker; Hınıs, Mehmet Ali
Streamflow forecasts play an active role in hydrological planning and taking precautions against natural disasters. Streamflow prediction models are frequently used by scientists, especially in dam management, sustainable agriculture, flood control, and flood mitigation. Hence, streamflow prediction modeling was performed in this study, and six models were employed through four different machine learning (ML) algorithms, namely, the artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision tree (DT) that are well known in the literature, in order to predict the monthly streamflow of Çarşamba River, Türkiye. To further enhance model performance, wavelet transform (WT) was applied to ML algorithms. In this study, monthly average streamflow and precipitation data between 1974 and 2015 was used, and the minimum redundancy maximum relevance method (MRMR) and the cross-correlation method were performed to determine model input data. Results of this study revealed that RF had superiority over the other models before WT, followed by the SVM model. The SVM after WT (W-SVM), M04 (r: 0.9846, NSE: 0.9695, and RMSE: 0.3536) gave the most effective performance results, while the W-ANN model (r: 0.9797, NSE: 0.9588, and RMSE: 0.4108) showed the second best performance.
Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tuğrul, Türker; Oruç, Sertaç; Hınıs, Mehmet Ali
Wind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific comunity. This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. In these analyses, five different models were created with the assistance of cross-correlation. The models obtained from the analyses were improved with the wavelet transformation (WT), and the results obtained were evaluated for the correlation coefficient (R), the Nash–Sutcliffe model efficiency (NSE), the Kling–Gupta efficiency (KGE), the performance index (PI), the root mean standard deviation ratio (RSR), and the root mean square error (RMSE). The results obtained from this study unveiled that LSTM emerged as the best performance metric in the M04 model among other models (R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248). Another notable finding obtained from this study was that the best performance values in analyses without WT were obtained with SARIMA. The results of this study provide information on forward-looking modeling for institutions and decision-makers related to energy and climate change.
The investigation of drum height effects on masonry domes
(Techno-Press, 2025) Fırat, Fatih K.; Tanrıverdi, Şükran
In this study, the effects of drum height on masonry dome behavior were examined experimentally and numerically by taking into account the domes with and without windows. Within the scope of the study, a total of eight domes, two of which were references (without drums) and six with different drum heights, were tested. One of the references was produced without windows and the other with windows. With window dome test elements having a drum height of 300 mm and 400 mm and windowless dome test elements with a drum height in the range of 100 mm, 200 mm, 300 mm, and 400 mm were examined. Numerical modeling of experimentally tested dome elements using the LUSAS analysis program was also examined and the experimental results were compared with the numerical results. As a result of the study, it was observed that the height of the drum significantly affected the load carrying capacity and horizontal displacement of the domes. It was determined that the lowest height drum application increased the load-carrying capacity of the dome by about 30% according to the drum-free reference test element. As the drum height increased, the load-carrying capacity and rigidity of the dome increased significantly, and the horizontal displacements decreased.
Web crippling of cold-formed steel with offset holes under ITF loading: Experimental, numerical study and new design equation
(Elsevier Ltd, 2025) Bölükbaş, Yakup; Oruç, Ramazan
This study investigates the web crippling behavior of cold-formed steel members with offset holes experimentally and numerically, proposing a new design equation. The experimental study includes a total of seven tests under the ITF loading condition, examining the parameters of hole size and distance from the holes to the loading plate. It was determined that hole size and the hole's distance from the loading plate play a significant role in determining web crippling capacity. To validate the experimental data and examine the effects of variables such as hole diameter to web depth, hole diameter to thickness, and distance from the holes to the loading plate to web depth on web crippling capacity, a total of 157 finite element models were developed. The results indicate that an increase in the hole size to web depth ratio and an increase in hole diameter leads to a reduction in web crippling capacity. When the hole is close to the loading point, an increase in hole diameter significantly affects the load-bearing capacity, whereas, at relatively greater distances, its effect is less pronounced. Based on experimental and numerical studies, a new design equation has been proposed, and its accuracy was evaluated through a reliability analysis, which revealed a mean deviation of just 0.2 %. The results indicate that the proposed equation provides consistent and reliable predictions for the web crippling capacity of members with holes. This work fills critical gaps in current design standards, which lack comprehensive guidelines for perforated CFS members under ITF loading.
The effect of window opening ratio on dome behavior in Ottoman historical masonry buildings
(Elsevier Ltd, 2025) Tanrıverdi, Şükran; Fırat, Fatih K.
In the present study, the effect of the window opening amount on the dome behavior was investigated experimentally and numerically. A total of three dome test elements were tested under the experimental setup that was prepared for this study to find the maximum horizontal loads and horizontal displacements corresponding to maximum load. The test domes were also analyzed numerically. Since the test results and the analysis results overlapped, an additional parametric study (in which variable drum height and opening amount were taken into account) was performed to examine the behaviors of the opening ratios on the dome with dome models that had different opening ratios (superficial 5 %, 7.5 %, 10 %, and 12.5 % openings). Analyzes were made for each opening ratio with twelve different dome models, one without a drum (pulley) and others with drum heights of 300 mm and 400 mm. The main purpose of this study is to investigate how window opening ratios and drum heights affect the strength and behavior of domes. At the end of the study, the results showed in the most general sense that the effect of the opening ratio on the dome behavior decreases when the height of the drum is increased. In addition, it was determined that as the window opening ratio in domes increases, the load carrying capacity decreases and the horizontal displacement increases.
Optimized water allocation with managed groundwater recharge and prioritized wetland deliveries to moderate human-nature water use tradeoffs under climate change
(Elsevier B.V., 2025) Li, Liying; Doğan, Mustafa S.; Maskey, Mahesh; Rodriguez-Flores, José M.; Vache, Kellie B.
Study region: California, United States. Study focus: In California, historical water system channelization disturbed the natural water system, making agricultural and wetland deliveries share the same water supply system. Climate change has intensified the competition between agricultural and environmental water uses. In the face of escalated climate change, this study tackles the critical challenge of optimizing water allocation to balance the needs of agriculture and the environment. A landscape-level, implicit stochastic deterministic linear hydro-economic optimization model is used with limited foresight to evaluate the combined impacts of climate change and water management policies on local water allocation decisions in California. The aim is to provide decision-support information for regional water cost-efficient water reallocation for climate change adaptation. New hydrological insights for the region: Climate change has reshaped water allocation ratios and caused agricultural water use to compromise with environmental water use. In water-scarce regions, the reduction of agricultural water use is most prominent in the wet years of the Mediterranean climate when both agricultural and environmental water use demands are high. The research identified when, where, and how much groundwater recharge benefit is acquired from prioritizing wetland deliveries to inform water use co-benefits and moderate conflicts. Climate change has also increased the overall value and variation across areas in the economic value of water, creating momentum for a cost-efficient market-based water reallocation approach.
Assessment of in-plane shear behavior of historical andesite stone–Khorasan mortar masonry walls strengthened with CFRP-based techniques and repair mortar
(Elsevier Ltd, 2025) Çelik, Tülin
Historic structures are highly vulnerable to horizontal forces from earthquakes due to their generally low tensile strength and rigid structures. Previous studies on these structures have revealed the need for retrofitting against earthquakes and led to the application of various methods. Carbon fiber reinforced polymer (CFRP) materials, with their superior strength-to-weight ratio, easy applicability and minimal intervention requirements, have recently emerged as an innovative technique preferred for the strengthening of historic masonry structures. In the study, the repair and strengthening of method of an unreinforced stone masonry wall with different materials was applied and the in-plane shear behavior of the wall was investigated by experimental and numerical analysis. An unreinforced stone masonry wall (URM), called the “reference specimen”, was produced as the URM-R specimen. Repair mortar (URM-M) was used in the repair and strengthening of a stone masonry wall. A stone wall specimen was placed with CFRP bars (URM-B), two pieces in each joint of the wall, for a total of 8 bars. Two specimens were strengthened with CFRP bars, one with Khorasan mortar (URM-BK) and one with repair mortar (URM-BM). CRFP laminate materials were also applied to two specimens either horizontally (URM-LH) or diagonally (URM-LD) on both surfaces of the URM wall. A comparison of experimental and numerical results showed that the URM-LD (CFRP laminate diagonal) specimen, the most effective of the methods tested, significantly increased maximum load, shear stress, ductility, and elastic stiffness. In addition, the URM-LD specimen reduced wall fragility and helped maintain wall integrity during fracture.
Influence of web holes on cold-formed steel beams: Experimental and numerical analysis
(Elsevier Ltd, 2025) Oruç, Ramazan
This research investigates the impact of web holes on the bending behaviour of beams, presenting both experimental and numerical investigations. It also introduces a new design equation. The experimental study involved subjecting 11 beams with and without holes to a four-point bending test, considering variables such as hole dimensions, number of holes, and spacing. The moment carrying capacity and failure modes of the beams with and without holes were thoroughly examined. It was established that hole dimensions play a crucial role in both the moment carrying capacity and failure mode of the beams. The numerical study developed 106 finite element models to validate the experimental data and explore various parameters. The validation models demonstrated good agreement with the experimental results. The parametric study analyzed variables such as hole shape, size, spacing, location, and section thickness. It was found that the moment carrying capacity decreased when the hole height/web height ratio exceeded 0.67, and the web height/thickness ratio increased. The numerical study results were compared with the local buckling strength equations proposed by AISI S100, AS/NZS 4600, and the design equation in the literature. The comparison revealed the inadequacy of these approaches as hole dimensions increased. Consequently, a new design equation for local buckling strength calculations was proposed and compared with the results. The proposed equation yielded more consistent results, especially for members with larger hole sizes.
Investigation of conical roof beam under vertical loads: Experimental and parametric studies
(Elsevier Ltd, 2025) Oruç, Ramazan; Kara, Mehmet Emin
This study presents experimental and numerical analyses to investigate the bending behavior of cold-formed steel (CFS) conical roof beams under vertical loads. The effects of radial beam orientation (face-to-face and face-to-back) and the spacing between circular beams (4500 mm, 1800 mm, 900 mm) on moment capacity, buckling modes, and deformation behavior were examined. Six single-span beam systems were tested, and it was observed that decreasing the spacing between circular beams increased the moment capacity and altered the buckling modes. The face-to-back orientation of radial beams reduced lateral displacement and rotation angles. Experimental results were validated using finite element analysis, and parametric studies were conducted. The parametric analyses revealed that decreasing the height-to-thickness (h/t) ratio of radial beams increased the moment capacity, while the thickness of circular beams had a limited effect. The results were compared with AISI S100–16 standards and an additional method to estimate local and distortional buckling capacities more accurately within the existing DSM approach.
Web crippling behaviour of cold-formed steel channels web holes under end two flange (etf) loading
(Hong Kong Institute of Steel Construction, 2024) Bölükbaş, Yakup
The design of the web-crippling behavior of cold-formed steel elements (CFS), which have been widely used in recent years, is essential. The concentrated loads acting on CFS members cause the section's web to crush and buckle. For this reason, it is necessary to calculate the web crippling strength correctly in the design of CFS sections. In order to observe the web-crippling behavior of CFS channel sections with holes drilled in the webs, this paper presents experimental and numerical experiments. Seven sections of the real-world system intended for End Two Flange (ETF) loading scenarios underwent testing. The tested cells were simulated by the finite element method with ABAQUS software. As a result of the numerical studies, 150 different model finite element analysis results are presented in the parametric study. In addition, the equations proposed by AISI and Eurocode 3 for the web-crippling design of CFS channel sections without web holes are analyzed. The findings of parametric investigations are compared with the design equation for sections with web holes presented by Uzzaman et al., and new coefficients are suggested for this equation. As a result of the study, the distance from the hole to the loading plate of CFS channel sections affects the section bearing capacity. Increasing the hole diameter drilled into the section web reduces the bearing capacity of the section. It is seen that h/t and N/t are more effective than R/t in the equation proposed by AISI for predicting the web-crippling strength of CFS channel sections.
Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Oruç, Sertaç; Hınıs, Mehmet Ali; Tuğrul, Türker
A serious natural disaster that poses a threat to people and their living spaces is drought, which is difficult to notice at first and can quickly spread to wide areas through subtle progression. Numerous methods are being explored to identify, prevent, and mitigate drought, and distinct metrics have been developed. In order to contribute to the research on measures to be taken against drought, the Standard Precipitation Evaporation Index (SPEI), one of the drought indices that has been developed and accepted in recent years and includes a more comprehensive drought definition, was chosen in this study. Machine learning and deep learning algorithms, including support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and Gaussian process regression (GPR), were used to model the droughts in six regions of Norway: Bodø, Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Four distinct model architectures were employed for this goal, and as a novel approach, the models’ output was enhanced by using discrete wavelet decomposition/transformation (WT). The model outputs were evaluated using the correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) as performance evaluation criteria. When the findings were analyzed, the GPR model (W-GPR), which was acquired after WT, typically produced the best results. Furthermore, it was discovered that, out of all the recognized models, M04 had the most effective model structure.
Beyond traditional metrics: exploring the potential of hybrid algorithms for Drought characterization and prediction in the Tromso Region, Norway
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Oruç, Sertaç; Tuğrul, Türker; Hınıs, Mehmet Ali
Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria.
Architectural and structural analysis of historical buildings: The case of Kırklareli Museum in Türkiye
(Techno-Press, 2024) Aksoy, Ercan; Ural, Ali
Traditional immovable cultural assets are significant in terms of societal memory and cultural continuity. Therefore, it is essential to preserve their original qualities without alteration while also assessing their resilience under various influences. This study aims to document the Kırklareli Museum building and conduct a performance analysis for potential earthquake scenarios. To this end, surveys of the structure were conducted, on-site inspections were carried out, and ground and material properties were determined for use in the analysis. The 3D model of the structure was prepared to understand its behavior during earthquakes. The analysis results indicate that there will be no damage to the structure. However, it should be noted that damage could occur in the event of a more severe earthquake than the design earthquake specified by the regulations. This study is significant not only for encompassing the museum structure but also for providing a comprehensive evaluation by determining all material properties.
Hydroeconomic optimization and eroperation of folsom reservoir for flood-managed aquifer recharge implementation
(ASCE: American Society of Civil Engineers, 2024) Erfani, Mahdi; Maskey, Mahesh L.; Doğan, Mustafa S.; Medellin-Azuara, Josue; Goharian, Erfan
The increasing occurrence of prolonged droughts and extreme wet events in California poses significant challenges to the management of the region's water resources. To address these challenges, the utilization of Flood-Managed Aquifer Recharge (Flood-MAR) has emerged as a potential solution. This study developed a multiobjective hydroeconomic model to assess the economic impact of implementing Flood-MAR and reoperation of Folsom reservoir within the American River Basin. The simulation module consists of a hydrological model and a linear programming groundwater recharge model. The economic impact evaluation considers three main components: the value of groundwater recharge, surface storage, and hydropower generation. The findings demonstrate that the adoption of Flood-MAR and reoperation of Folsom reservoir offer considerable economic benefits, with minimal adverse effects on the downstream system. Two different model solutions were analyzed: one that aimed to maximize recharge and storage benefits, and one that prioritized hydropower generation. The former exhibited an increase in reservoir storage compared with historical operation, along with increased water allocation for groundwater recharge during wet and normal years. The latter showed substantial gains in hydropower generation but occasional drops in reservoir storage below historical levels. Despite these differences, the solution emphasizing recharge and storage benefits was deemed to be more realistic, considering the risk of future droughts and uncertainties in climate and hydrological forecasts. Overall, this research provides a foundation for assessing the economic impact of Flood-MAR implementation in the Folsom reservoir system.
Integrated water operations under climate change: Uluova Micro Basin example
(IWA Publishing: International Water Association Publications, 2024) Şekerci, Kürsat; Tuna, Muhammed Cihat; Doğan, Mustafa Şahin
This study examines the impact of climate change on the Uluova Micro Basin, Turkey, employing an optimization model named ULUHEM across various water management and climate scenarios. With ULUHEM, the effects of different climate impact scenarios on agricultural water allocations, pumping costs, water scarcity, and scarcity costs were analyzed. The primary objective of this study is to identify gaps in demand within the current water supply infrastructure due to global warming and to develop adaptation strategies for basinwide water management operations. The research also emphasizes the importance of creating a basin-based hydroeconomic model that includes other surface water resources with a sustainable management approach to address the impact of climate change. In summary, the impacts of climate change on surface waters and groundwater in the Uluova Micro Basin include changes in water availability, water scarcity, and associated costs, and these have implications for agricultural water allocations and overall water management in the region. The study found that drier climate periods lead to reduced surface and groundwater input to farmland, resulting in increased water scarcity and scarcity costs. Conversely, periods characterized by wetter climates yield contrasting outcomes, alleviating water scarcity and its corresponding costs.
Trend analysis of hydrological and meteorological drought in Apa Dam, Türkiye
(Springer Science and Business Media Deutschland GmbH, 2024) Tuğrul, Türker; Hınıs, Mehmet Ali
Drought indices, such as the Standardized Precipitation Index (SPI) and the Stream Flow Drought Index (SDI), are mathematical indicators that represent an overall decrease in average amounts of rainfall over a specific period of time. The changing values of SPI and SDI can be determined by trend analysis and can help decision makers in estimating and managing the future values of water resources on issues such as dam management and energy production. In this study, in addition to SPI12 and SDI12, trend analyzes of monthly precipitation and stream flow data affecting drought were also conducted. Mann–Kendall Test (MK), Spearman's Rho test, Sen-Innovative Trend Analysis (ITA) were chosen as trend analysis. As a result of the analysis, in precipitation performed with MK, an increasing trend at 95% significance level was detected in January, while no trend was found in the other months. While increasing trends were found in all months using SPI12, no trend was detected in SDI12. In Spearman's Rho test, no trend was detected in SDI12 and precipitation for all months, whereas increasing trend for January, February and April were detected for SPI12 and January for streamflow data. The analysis made with ITA was evaluated in two parts, graphically and statistically. The graphical method was carried out for monthly data. In statistical evaluation of ITA for SPI12 and SDI12, increasing trends were detected for all monthly data, however, in the graphical analysis, different results were obtained for each month, which did not fully support the results of the statistical analysis.
Direct flame test performance of boards containing waste undersized pumice materials
(Elsevier B.V., 2024) Kalkan, Murat; Erenson, Can
This study investigates the thermal performance of boards containing waste undersize pumice material directly exposed to flame in terms of thermal conductivity coefficient (TCC) and thermal efficiency ratio (TER). In the study, the direct flame was applied for 60 s on the front face of the 400×400×25 mm boards whose weight and density values, void ratio, porosity, and water absorption rates by weight and volume were determined. Because moisture content and porosity are significant factors in defining temperature-related properties of construction materials. The thermal characteristics of the boards were interpreted in this regard in connection to their moisture content and void ratios. At the end of the tests, the temperature difference between the front and back surfaces of the boards containing 10%, 30%, and 50% by weight of pumice stone powder and pumice sand was measured using a laser pyrometer. Compared to the reference sample without pumice, the TCC values decreased to 1.653, 1.649, and 1.540 W/mK, respectively, as a result of the use of pumice products in building materials. Moreover, TER values for the same samples increased to 82.6 %, 83.5 %, and 85.3 %, respectively.
Using an integrated hydro-economic model to determine the drought and energy relationship in the Upper Euphrates Basin
(IWA Publishing, 2024) Aytaç, Ayça; Tuna, M. Cihat; Dogan, Mustafa Şahin
Decreasing precipitation in the Upper Euphrates Basin and the negative impact of climate change directly affect water resources and hydroelectricity generation in the basin. This basin, which contains the largest dams in terms of hydroelectricity generation potential, requires research studies to assess and characterize drought for risk prevention and mitigation applicable to water resources management. To better assess drought in the upper Euphrates Basin due to recent warming, FEHEM is developed, a hydro-economic optimization model of the integrated reservoir system of the Upper Euphrates Basin. Using a historical hydrological dataset, water management and hydroelectric operations are evaluated with a linear programming model at monthly time steps. This paper uses two different drought indices: (1) the standardized precipitation index, which is based on precipitation alone; and (2) the reconnaissance drought index, which takes into account both evaporation and precipitation. These indices were used to evaluate the impact of temporal drought characteristics in the Upper Euphrates Basin on the hydropower generation of 10 dams with a total installed capacity of over 50 MW in the basin, based on 45 years of precipitation data from more than a hundred measuring stations in the basin.

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