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

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Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey water Article Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey Ozlem Yagbasan 1,*, Vahdettin Demir 2 and Hasan Yazicigil 3 1 Department of Geography Education, Gazi University, 06500 Ankara, Turkey 2 Department of Civil Engineering, KTO Karatay University, 42020 Konya, Turkey; [email protected] 3 Department of Geological Engineering, Middle East Technical University, 06800 Ankara, Turkey; [email protected] * Correspondence: [email protected]; Tel.: +90-312-202-1872 Received: 9 December 2019; Accepted: 31 January 2020; Published: 4 February 2020 Abstract: 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. Keywords: trend analyses; meteorological variables; lake levels; Central Turkey 1. Introduction Lakes are valuable water resources for humans, playing important roles in aquatic and terrestrial ecosystems and the environment [1–4]. Owing to the combined impact of anthropogenic activities and climatic variation, many lakes around the world are under threat [5,6]. Global climate change and anthropogenic activities adversely affect both water quantity and quality, having a critical influence on regional sustainable development. Water level fluctuation, a sensitive marker of change, is an important driver for lakes, with an impact on lake ecosystems [7]. Therefore, determination and understanding of long-term changes in water levels due to climatic variables are necessary to establish sustainable management of lakes. Shallow lakes Mogan and Eymir, located 20 km south of Ankara in Central Turkey, are important aesthetic, recreational, and ecological resources for the city of Ankara and the town of Gölba¸sı(Figure1). Water 2020, 12, 414; doi:10.3390/w12020414 www.mdpi.com/journal/water Water 2020, 12, 414 2 of 16 The drainage area of Lake Mogan is 926 km2 and that of Lake Eymir is 42 km2. Although both lakes and their surrounding areas mainly meet urban housing and recreational needs, the wildlife and biodiversity values of the lakes are high. These lakes, especially Lake Mogan, have wetlands housing more than 26 mammal species and 231 bird species, 76 of which are breeding. Hydrologically connected lakes are designated as “Specially Protected Areas”, and Lake Mogan was declared as an “Important Bird Area” in 1990 by the Ministry of Environment and Urbanization. Because of their importance, there is a growing concern about the possible impacts of climate change on the long-term sustainability of these lakes [8]. Although trends in lake levels of Turkey’s five largest lakes (Iznik,˙ E˘girdir, Tuz, Bey¸sehir, and Van lake) were analyzed using Mann-Kendall and Sen tests [9], there is no existing study on the hydrologic relationship between the trends of lake levels and meteorological variables. Therefore, the emphasis in this research is on the determination and quantification of trends in monthly lake water levels and hydrometeorological variables (average monthly pan evaporation, air temperature (◦C), precipitation (mm), humidity (%), wind speed (m/s), cloud cover (8 okta), and sunshine duration (h)). This will enable researchers to investigate the effect of changes in hydrometeorological variables on the water levels of Lake Mogan and Lake Eymir. The analyses were conducted by using four methods: Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sen Trend (ST), and Linear trend (LT). Trends at 90%, 95%, and 99% confidence levels were examined and compared. Understanding long-term trends in hydrometeorological variables are of high significance for sustainable water resource management [10]. Water 2020, 12, 414 3 of 16 Water 2020, 11, x FOR PEER REVIEW 3 of 17 FigureFigure 1. (a 1.) Location(a) Location of of the the study study area. area. ((b) Digital elevation elevation map map of ofthe the study study area area and andlocation location of the of the monitoringmonitoring stations. stations. 2. Materials and Methods Meteorological variables change in time and space for many reasons. These changes, based on observations, should be investigated through statistical methods. Trend analysis is the most important statistical method widely used around the world to assess the long-term changes in time series of meteorological variables, such as precipitation, temperature, evaporation, etc. [8,11–17]. Water 2020, 12, 414 4 of 16 The aim of this study was to evaluate the long-term trends of monthly water levels and meteorological variables affecting lake levels (average monthly pan evaporation, air temperature (◦C), precipitation (mm), humidity (%), wind speed (m/s), cloud cover (8 okta), and sunshine duration (h)) through Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sen Trend (ST), and Linear trend (LT) statistical methods at confidence levels of 90%, 95%, and 99%. The meteorological data and lake levels between 1997 and 2015 were obtained from Ankara Meteorology General Directorate and State Hydraulic Works, respectively. Unfortunately, these lake levels have not been monitored post 2015. Hence, the period selected for trend analysis was as per the lake monitoring duration. Table1 provides location information of the Ankara meteorological station and lake level monitoring stations utilized in the study. Table2 shows the statistical characteristics of the variables used in the study. Table 1. Location information of observation stations. Coordinates * Station Name Elevation (m) South (m) East (m) 17130/Ankara 488,396 4,424,913 891 Lake Eymir 485,674 4,408,599 968 Lake Mogan 482,787 4,402,980 972 * ED50/UTM Zone 36N. Table 2. Statistical characteristics of the variables. Station Parameters Min Max Mean Sx Csx Average Monthly Temperature ( C) 3.85 28.45 12.89 8.55 0.01 ◦ − Monthly Total Precipitation (mm) 0.00 167.60 34.41 27.07 1.23 Average Monthly Humidity (%) 28.07 87.07 59.74 12.90 0.25 − Ankara 17130 Average Monthly Wind Speed (m/s) 1.24 3.79 2.29 0.46 0.58 Monthly Total Sunshine Duration (h) 42.5 399.20 199.12 96.37 0.04 Monthly Total Evaporation (mm) 62.6 357.90 151.93 80.92 0.03 Average Monthly Cloud Cover (8 okta) 0.5 6.80 3.24 1.34 0.06 Eymir Average Monthly Lake Level (m) 966.38 969.5 968.24 0.78 0.37 − Mogan Average Monthly Lake Level (m) 971.79 974.3 972.81 0.46 0.10 Sx: Standard deviation, Csx: Skewness coefficient. 2.1. Mann-Kendall (MK) The non-parametric Mann-Kendall test may be used to detect trends that are monotonic, but not necessarily linear. This method tests if there is a trend in the time series data [18–20]. It is a non-parametric rank-based procedure, robust to the influence of extremes and suitable for application with skewed variables [11]. Test statistics are: 8 > 1; if xj > xj <> sgn(xj xi) = > 0; if xj = xi (1) − > : 1; if x < x − j i In Equation (1), xi and xj are the data values in time series i and j, respectively, and in Equation (2), n is the number of data points, sgn (x x ) is the sign function as: j − i n 1 n X− X S = sgn(x x ) (2) j − i i=1 j=i+1 The variance is computed as: PP n(n 1)(2n + 5) ti(ti 1)(2ti + 5) − − i=1 − Var(S) = (3) 18 Water 2020, 12, 414 5 of 16 In Equation (3), n refers to the number of data, P shows the number of tied groups, and ti indicates the number of ties of extent i. A tied group is a set of sample data and has the same value. In case sample size n > 10, the standard normal test statistic Z is calculated using Equation (4): 8 S 1 > − ; if S > 0 > pVar(S) <> Z = > 0 if S = 0 (4) > > S+1 if S < 0 : pVar(S) The computed standard Z value is compared with standard normal distribution according to two-tailed confidence levels (α = 10%, α = 5%, α = 1%). If the computed Z value is greater than |Z| |Z1 α/2|, the null hypothesis (H0) is rejected and thus the Ha (alternative hypothesis) hypothesis ≥ − is accepted. In this study, two-tailed confidence levels (α = 10%, α = 5% and α = 1%) are used for the Mann-Kendall trend test [12]. 2.2. Modified Mann-Kendall (MMK) The modified Mann-Kendall (MMK) method is a modified nonparametric trend method suitable for autocorrelated data based on the modified value in the variance of the test statistic.
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