Comparison of Mann-Kendall and innovative trend method (Şen trend) for monthly total precipitation (Middle Black Sea Region, Turkey)

The objective of this study is to determine possible trend in annual total precipitation based on Mann–Kendall (MK) and a novel method lately published by Şen. The novel method is used for trend analysis of annual total precipitation data recorded at Sinop, Samsun, Ordu, Corum, Amasya, and Tokat provinces in Turkey. This provinces are located in the central Black Sea region of Turkey. The novel Şen’s trend method is applied to this data. According to the Şen’s trend method, peak and low values of annual total precipitation of the six provinces demonstrate same trends (increasing, decreasing, or trendless time series) with the MK test. The study demonstrates that the Şen method can be used for identifying trend analysis of peak and low values of annual total precipitation data. According to the MK trend test, annual total precipitations demonstrate increasing trend for Sinop, Ordu and Tokat provinces while Şen’s method indicates increasing trend in Sinop, Amasya and Tokat in Turkey. As a result, Şen’s method provides an important advantage in terms of especially in all ranges graphically clarification of the data evaluation phase

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D: A case study Samsun from Turkey

Određivanje Manningovih koeficijenata hrapavosti jedan je od najvažnijih koraka u modeliranju poplava. Koeficijenti hrapavosti u procesu modeliranja uzrokuju razlike u poplavnim područjima, razinama vode i brzinama. Cilj ove studije je utvrditi koeficijent hrapavosti u riječnim odsječcima te i izvan riječnih područja korištenjem Cowanove metode i tehnike daljinskog istraživanja. U modeliranju poplave korišten je program FLO-2D Pro koji može simulirati širenje poplave u dvije dimenzije. Za područje istraživanja odabrana je rijeka Mert u provinciji Samsun koja se nalazi u sjevernom dijelu Turske. Uzorci uzeti iz rijeke analizirani su pomoću sita, gdje su vrste sastavnog materijala određene prema srednjim promjerima, a koeficijenti hrapavosti dobiveni su Cowanovom metodom. Za područja izvan rijeke primijenjena je metoda najveće vjerojatnosti koja je jedna od kontroliranih metoda klasifikacije. Manningove vrijednosti hrapavosti dodijeljene su klasificiranim odsječcima slike. Tehnike daljinskih mjerenja pomno su korištene kako bi se procijenili koeficijenti hrapavosti u područjima izvan rijeke, a novi je pristup predložen u Manningovoj procjeni poplavnih područja kako bi se osigurala ujednačenost na istraživanom području. U klasifikaciji izvedenoj metodom najveće vjerojatnosti, ukupna točnost klasifikacije bila je 92,9%, a kappa omjer “κ” 90,64%. Rezultati su kalibrirani prema posljednjim slikama opasnih poplava 2012. godine i HEC-RAS 2D programom, koji je također program za modeliranje poplave.Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program

Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming

This study aims to carry out regional intensity - duration - frequency (IDF) equality using the relationship with IDF obtained from point frequency analysis. Eleven empirical equations used in the literature for seven climate regions of Turkey were calibrated using particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques and the obtained results were compared. In addition, in this study, new regional IDF equations were obtained for each region utilizing Multi-Gene Genetic Programming (MGGP) method. Finally, Kruskal-Wallis (KW) test was applied to the IDF values obtained from the methods and the observed values. As a result of the study, it was observed that the coefficients of 11 empirical equations calibrated with PSO, and GA techniques were different from each other. The mean absolute error (MAE), root mean square error (RMSE), mean absolute relative error (MARE), coefficient of determination (R-2), and Taylor diagram were used to evaluate the performances of PSO, GA, and MGGP techniques. According to the performance criteria, it has been determined that the IDF equations obtained by the MGGP method for the Eastern Anatolia, Aegean, Southeastern Anatolia, and Central Anatolia regions are more successful than the empirical equations calibrated with the PSO and GA method. The empirical IDF equations produced with PSO and the IDF equations acquired with MGGP have similar findings in the Mediterranean, Black Sea, and Marmara. In addition, the KW test results showed that the data of all models were from the same population

Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey

In this study, flood hazard maps were prepared for the Mert River Basin, Samsun, Turkey, by using GIS and Hydrologic Engineering Centers River Analysis System (HEC-RAS). In this river basin, human life losses and a significant amount of property damages were experienced in 2012 flood. The preparation of flood risk maps employed in the study includes the following steps: (1) digitization of topographical data and preparation of digital elevation model using ArcGIS, (2) simulation of flood lows of different return periods using a hydraulic model (HEC-RAS), and (3) preparation of flood risk maps by integrating the results of (1) and (2)

Forecasting of solar radiation using different machine learning approaches

In this study, monthly solar radiation (SR) estimation was performed using five different machine learning-based approaches. The models used are support vector machine regression (SVMR), long short-term memory (LSTM), Gaussian process regression (GPR), extreme learning machines (ELM) and K-nearest neighbors (KNN). Modeling of these approaches was carried out in two stages. In the first stage, VIF analysis was carried out to develop the model. Thus, the input parameters that decrease the performance of the model are removed. In the second stage, remaining input parameters such as meteorological data, station location data and spatial and temporal information were used in the forecasting modeling according to the correlation SR. In this study, the data set is divided into two parts as test and training. 30% was used in the testing phase, and 70% of the data was used in the training phase. When comparing models, the following error statistics were used: Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), mean absolute relative error (MARE), root-mean-square error (RMSE) and coefficient of determination (R-2). In addition, Taylor diagrams, violin plots, box error, spider plot and Kruskal-Wallis (KW) and ANOVA test were utilized to determine robustness of model's forecast. As a result of the study, the KW test and ANOVA test results showed that the data of many models were from the same population with observations, and it has proved that LSTM and GPR algorithms are applicable, valid and an alternative for SR forecasting in Turkey, which has arid and semi-arid climatic regions

Impacts of climatic variables on water-level variations in two shallow Eastern Mediterranean lakes

Variations in temperature and precipitation have direct impacts on the physical, chemical and biological characteristics of the shallow lakes. This paper examines the possible linkages between climate variables and the water levels of shallow interconnected Lakes Mogan and Eymir, located 20 km south of Ankara in Central Anatolia. The variations in the lakes' water levels during 1996-2015 are studied and the impacts of climate variables on the lake levels are assessed to address the long-term consequences. The nonparametric Mann-Kendall test was used to quantify trends in the climate variables and the lakes' level fluctuations between the observation periods 1998-2007 and 2008-2014. Statistical analyses results showed that precipitation and temperature have crucial influence on the variations in the lakes' levels. The projected increase in temperature and decrease in precipitation over the next century may produce substantial decreases in lake levels, with consequent drying of both lakes

Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey

Trend analyses of meteorological variables play an important role in assessing the long-term changes in water levels for sustainable management of shallow lakes that are extremely vulnerable to climatic variations. Lake Mogan and Lake Eymir are shallow lakes offering aesthetic, recreational, and ecological resources. Trend analyses of monthly water levels and meteorological variables affecting lake levels were done by the Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sen Trend (ST), and Linear trend (LT) methods. Trend analyses of monthly lake levels for both lakes revealed an increasing trend with the Mann-Kendall, Linear, and Sen Trend tests. The Modified Mann-Kendall test results were statistically significant with an increasing trend for Eymir lake levels, but they were insignificant for Mogan lake due to the presence of autocorrelation. While trend analyses of meteorological variables by Sen Test were significant at all tested variables and confidence levels, Mann-Kendall, Modified Mann-Kendall, and Linear trend provided significant trends for only humidity and wind speed. The trend analyses of Sen Test gave increasing trends for temperature, wind speed, cloud cover, and precipitation; and decreasing trends for humidity, sunshine duration, and pan evaporation. These results show that increasing precipitation and decreasing pan evaporation resulted in increasing lake levels. The results further demonstrated an inverse relationship between the trends of air temperature and pan evaporation, pointing to an apparent “Evaporation Paradox”, also observed in other locations. However, the increased cloud cover happens to offset the effects of increased temperature and decreased humidity on pan evaporation. Thus, all relevant factors affecting pan evaporation should be considered to explain seemingly paradoxical observations