Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns

This study develops three neural networks models for estimating daily pan evaporation (PE) in South Korea: multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and adaptive neuro-fuzzy inference system (ANFIS). Daily PE was estimated at Daegu and Ulsan stations using temperature-based, radiation-based, sunshine duration-based and merged input combinations under lag-time patterns. Daily evaporation values computed by the models using merged inputs agreed with observed values. Comparison was also made between the neural networks models and multiple linear regression model (MLRM), which showed the superiority of MLP-NNM, GRNNM, and ANFIS over MLRM. It is concluded that the applied neural networks models can be successfully employed for estimating daily PE in South Korea.     

Developing Equations for Estimating Reference Evapotranspiration in Australia

Quantifying reference evapotranspiration (ET₀) is essential in water resources management. Although, many methods have been developed with different level of accuracy, in this study, two new equations were developed and optimized for estimating ET₀ using Honey-Bee Mating Optimization (HBMO) algorithm. The first eq. estimates ET₀ from extraterrestrial radiation (R_a), relative humidity (RH) and mean daily temperature (T_mean), while the second uses the same parameters except that mean daily temperatures is replaced with maximum daily air temperature (T_max). Both equations were developed using climatic data from eight weather stations in Western Australia and subsequently verified using data from ten sites across Australia. The estimated ET₀ values from both equations versus the FAO56-Penman-Monteith have a coefficient of determination, R², of larger than 0.96. Moreover, the performance of six commonly used methods of estimating ET₀ including Hargreaves-Samani, Thornthwaith, Hamon, Mc Guinness-Bordne, Irmak and Jensen-Haise were assessed and the Hargreaves-Samani method performed better than others. An attempt was made to calibrate the Hargreaves-Samani equation; however, its overall performance did not improved and the two newly proposed equations are suggested to be used in Australia.     

Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E_pan). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R²), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E_pan. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data.     

A Comparative Assessment of Models to Predict Monthly Rainfall in Australia

Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the k-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy.     

Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks

This study is an attempt to find best alternative method to estimate reference evapotranspiration (ET_o) for the Mahanadi reservoir project (MRP) command area located at Raipur (Chhattisgarh) in India, when input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices. The statistical indices used in the analysis were the standard error of estimate (SEE), raw standard error of estimate (RSEE) and the model efficiency. Study was extended to identify the ability of Artificial Neural Networks (ANNs) for estimation of ET_o in comparison to climatic based methods. The networks, using varied input combinations of climatic variables have been trained using the backpropagation with variable learning rate training algorithm. ANN models were performed better than the climatic based methods in all performance indices. The analyses of results of ANN model suggest that the ET_o can be estimated from maximum and minimum temperature using ANN approach in MPR area.     

New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R²). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.     

Evaluation of Reference Crop Evapotranspiration Equations in Various Climates

Evapotranspiration is one of the most important elements for quantifying available water since it generally constitutes the largest component of the terrestrial water cycle. This study evaluated four models (Makkink, Turc, Priestley–Taylor and Hargreaves) commonly used to estimate monthly reference crop evapotranspiration (ET_o) values. The main aim of this study was to determine the model used to estimate ET_o with small data requirements and high accuracy for twelve synoptic stations in four climates of Iran. The results showed that the Turc model was the best suited model in estimating ET_o for cold humid and arid climates. The Hargreaves model turned out to be the most precise model under warm humid and semi-arid climatic conditions. In contrast, the Makkink model presented the poorest estimates in all of the climates exception for cold humid environment. In cold humid climate, the Hargreaves model was the least accurate model in estimating ET_o. In general, the results obtained from this study revealed very clearly that the Makkink and Priestley–Taylor models estimated ET_o values less accurately than Turc and Hargreaves models for the all climates.     

Evaluation of Radiation-Based Reference Evapotranspiration Models Under Different Mediterranean Climates in Central Greece

Eighteen radiation-based equations used to estimate reference evapotranspiration (ET_ref) were generalized into seven linear models. The general models were calibrated using the standard FAO-56 Penman-Monteith method. Model performance was evaluated under humid, sub-humid and semi-arid mediterranean climatic conditions in central Greece. Evaluation and comparison of the models was based on quantitative assessment of their ability to accurately estimate ET_ref values, generated by the FAO-56 Penman-Monteith equation. All models provided relatively accurate estimates of ET_ref. The Abtew model showed the best overall performance with respect to the data from all available climate stations of central Greece. The average error of the Abtew model in the monthly average daily ET_ref estimates was 0.24 mm, which corresponds to a relative error of 7.7 %. The Abtew method has not yet been tested under mediterranean climatic conditions. Based on our results, it seems to be a good choice for the estimation of monthly average daily ET_ref under different conditions in the mediterranean climate. An exception appears to be the mediterranean climate with relatively high humidity and low wind speed. Under these conditions the models of the Priestley-Taylor group, the Makkink group and the Jensen-Haise group performed better than the Abtew equation.     

Evaluation of Class A Pan Coefficient Models for Estimation of Reference Crop Evapotranspiration in Cold Semi-Arid and Warm Arid Climates

Evapotranspiration and evaporation measurements are important parameters for many agricultural activities such as water resource management and environmental studies. There are several models which can determine pan coefficient (K _Pan), using wind speed, relative humidity and fetch length conditions. This paper analyses seven exiting pan models to estimate K _Pan values for two different climates of Iran. Monthly mean reference crop evapotranspiration (ET₀) was calculated according to the pan-ET₀ model. The results showed that estimated pan coefficients by majority of the suggested models were not statistically accurate to be used in the pan-ET₀ conversion method. However, for the cold semi-arid climate condition, the best K _Pan models for estimation of ET₀ were Orang and Raghuwanshi–Wallender, respectively. Also, the Snyder and Orang models were best fitted models for warm arid climate, respectively. The mean annual value of K _Pan, determined by Penman–Monteith FAO 56 (PMF-56) standard model for warm arid sites, was approximately 32% higher than the corresponding value in the cold semi-arid climate. Similarly, the mean annual ET₀ in the warm arid sites was 66% higher, compared to the ET₀ of the cold semi-arid sites. These types of warm arid and semi-arid climates are found widely throughout the world.     

Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R²) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.     

Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques

An accurate prediction of pipes failure rate plays a substantial role in the management of Water Distribution Networks (WDNs). In this study, a field study was conducted to register pipes break and relevant causes in the WDN of Yazd City, Iran. In this way, 851 water pipes were incepted and localized by the Global Positioning System (GPS) apparatus. Then, 1033 failure cases were reported in the eight zones of understudy WDN during March-December 2014. Pipes break rate (BR_P) was calculated using the depth of pipe installation (h_P), number of failures (N_P), the pressure of water pipes in operation (P), and age of pipe (A_P). After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation. Results of the proposed relationships demonstrated that the MARS model with Coefficient of Correlation (R) of 0.981 and Root Mean Square Error (RMSE) of 0.544 provided more satisfying efficiency than the M5 model (R?=?0.888 and RMSE?=?1.096). Furthermore, statistical results indicated that MARS and GEP models had comparatively at the same accuracy level. Explicit equations by Artificial Intelligence (AI) models were satisfactorily comparable with those obtained by literature review in terms of various conditions: physical, operational, and environmental factors and complexity of AI models. Through a probabilistic framework for the pipes break rate, the results of first-order reliability analysis indicated that the MARS technique had a highly satisfying performance when MARS-extracted-equation was assigned as a limit state function.

     

Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region

The applicability of fuzzy genetic (FG) approach in modeling reference evapotranspiration (ET₀) is investigated in this study. Daily solar radiation, air temperature, relative humidity and wind speed data of two stations, Isparta and Antalya, in Mediterranean region of Turkey, are used as inputs to the FG models to estimate ET₀ obtained using the FAO-56 Penman–Monteith equation. The FG estimates are compared with those of the artificial neural networks (ANN). Root mean-squared error, mean absolute error and determination coefficient statistics were used as comparison criteria for the evaluation of the models’ accuracies. It was found that the FG models generally performed better than the ANN models in modeling ET₀ of Mediterranean region of Turkey.     

Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure

Potential evapotranspiration (ET_o) is an essential hydrologic parameter for having better understanding for hydrologic cycle in certain catchment area. In addition, ET_o is vital for calculating the agricultural demand. In fact, Penman-Monteith (PM) method is considered as reference method for estimating (ET_o), however, this method required a lot of data to be used which is not usually available in many catchment areas. Furthermore, there are several efforts that have been performed as competitor to reach accurate estimation of (ET_o) when there is lack of data to utilize (PM) method, but still required numerous research. Recently, methods based on Artificial Intelligence (AI) have been suggested to provide reliable prediction model for several application in engineering and especially for hydrological process. However, time series prediction based on Artificial Neural Network (ANN) learning algorithms is fundamentally difficult and faces problem. One of the major shortcomings is that the ANN model experiences over-fitting problem during training session and also occurs when a neural network loses its generalization. In this research a modification for the classical Multi Layer Preceptron- Artificial Neural Network (MLP-ANN) modeling namely; Ensemble Neural Network (ENN) is proposed and applied for predicting daily ET_o. The proposed model applied at two different region with two different climatic conditions, Rasht city located north part of Iran and Johor Bahru City, Johor, Malaysia using maximum and minimum daily temperature collected from 1975 to 2005. The result showed that the ENN outperformed the classical MLP-ANN method and successfully predict daily ETo utilizing maximum and minimum temperature only with satisfactory level of accuracy. In addition, the proposed model could achieve accuracy level better than the traditional competitor methods for ET_o.     

Changes of Pan Evaporation in the West of Iran

Evaporation is an important component of the hydrological cycle and its change would be of great significance for water resources planning, irrigation control and agricultural production. The main purpose of this study was to investigate temporal variations in pan evaporation (E_pan) and the associated changes in maximum (T_max), mean (T_mean) and minimum (T_min) air temperatures and precipitation (P) for 12 stations in Hamedan province in western Iran for the period 1982–2003. Significant trends were identified using the Mann–Kendall test, the Sen’s slope estimator and the linear regression. Analysis of the E_pan data revealed a significant increasing trend in 67% of the stations at the 95% and 99% confidence levels. To put the changes in perspective, the trend in E_pan averaged over all 12 stations was (+)160 mm per decade. Trend analysis of the meteorological variables for examination of causal mechanisms for E_pan changes showed warming trends in T_max, T_mean and T_min series in almost all the stations, which were significant over half of the total stations. On the contrary, no significant changes in precipitation were found approximately at all of the stations. Furthermore, a moderate positive correlation was observed between E_pan and T_max, T_mean and T_min, while a inverse correlation was found between E_pan and P data. The results indicated that the study area has become warmer and drier over the last 22 years, hence the evaporative demands of the atmosphere and thereby crop water requirements have increased.     

Are Weather Generators Robust Tools to Study Daily Reference Evapotranspiration and Irrigation Requirement?

Reference evapotranspiration (ETo) is one of the driving forces in crop simulation models and is very important to be estimated accurately. Moreover, weather generator (WG) models are widely used in combination with these crop models. As the quality of model output is related to the quality of weather data used as input, the evaluation of the sensitivity of model outputs to the quality of generated weather data is essential. In this study, eight different weather generator models were assessed and their outputs were used to estimate daily reference evapotranspiration and irrigation requirement. Two daily weather generator algorithms were combined with a monthly weather generator and/or an adjustment algorithm for low-frequency variances. Precipitation occurrence series was generated by an independent semi-empirical distribution. The daily weather generators outperformed the monthly models in reproducing daily statistics, while the monthly models performed better in simulating the monthly and yearly variations. After analyzing the model performances in simulating climatic variables, more assessments were carried out on ETo and irrigation requirement. The results depicted the strength of all the models in simulating daily ETo and irrigation requirement. Although all the studied models have comparable performances in simulating these two daily variables on daily and monthly scales, the monthly WGs outperform the daily models on yearly time scales and have better performances in simulating standard deviation values of yearly mean ETo and irrigation requirement. It can be concluded that WG models are robust tools for estimating these two daily variables if they can at least reproduce daily statistics (i.e. mean and standard deviation) well. But it must be taken in considerations that each WG model (including the one studied here) has different weaknesses and strengths and the best choice must be done according to the requirements.     

A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating

Streamflow estimation is highly significant for water resource management. In this work, we improve the accuracy and stability of streamflow estimation through a novel hybrid decompose-ensemble model that employs variational mode decomposition (VMD) and back-propagation neural networks (BPNN). First, the latest decomposition algorithm, namely, VMD, was used to extract multiscale features that were subsequently learned and ensembled by the BPNN model to obtain the final estimate streamflow results. The historical daily streamflow series of Laoyukou and Wushan hydrological stations in China were analysed by VMD-BPNN, by a single GBRT and BPNN model, ensemble empirical mode decomposition (EEMD) models. The results confirmed that the VMD outperformed a single-estimation model without any decomposition and EEMD-based models; moreover, ensemble estimations using the BPNN model development technique were consistently better than a general summation method. The VMD-BPNN model’s estimation performance was superior to that of five other models at the Wushan station (GBRT, BPNN, EEMD-BPNN-SUM, VMD-BPNN-SUM, and EEMD-BPNN) using evaluation criteria of the root-mean-square error (RMSE?=?2.62 m³/s), the Nash–Sutcliffe efficiency coefficient (NSE?=?0. 9792) and the mean absolute error (MAE?=?1.38 m³/s). The proposed model also had a better performance in estimating higher-magnitude flows with a low criterion for MAE. Therefore, the hybrid VMD-BPNN model could be applied as a promising approach for short-term streamflow estimating.

     

Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey

A regional flood frequency analysis based on the index flood method is applied using probability distributions commonly utilized for this purpose. The distribution parameters are calculated by the method of L-moments with the data of the annual flood peaks series recorded at gauging sections of 13 unregulated natural streams in the East Mediterranean River Basin in Turkey. The artificial neural networks (ANNs) models of (1) the multi-layer perceptrons (MLP) neural networks, (2) radial basis function based neural networks (RBNN), and (3) generalized regression neural networks (GRNN) are developed as alternatives to the L-moments method. Multiple-linear and multiple-nonlinear regression models (MLR and MNLR) are also used in the study. The L-moments analysis on these 13 annual flood peaks series indicates that the East Mediterranean River Basin is hydrologically homogeneous as a whole. Among the tried distributions which are the Generalized Logistic, Generalized Extreme Vaules, Generalized Normal, Pearson Type III, Wakeby, and Generalized Pareto, the Generalized Logistic and Generalized Extreme Values distributions pass the Z statistic goodness-of-fit test of the L-moments method for the East Mediterranean River Basin, the former performing yet better than the latter. Hence, as the outcome of the L-moments method applied by the Generalized Logistic distribution, two equations are developed to estimate flood peaks of any return periods for any un-gauged site in the study region. The ANNs, MLR and MNLR models are trained and tested using the data of these 13 gauged sites. The results show that the predicting performance of the MLP model is superior to the others. The application of the MLP model is performed by a special Matlab code, which yields logarithm of the flood peak, Ln(Q_T), versus a desired return period, T.     

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms

From a watershed management perspective, streamflow need to be predicted accurately using simple, reliable, and cost-effective tools. Present study demonstrates the first applications of a novel optimized deep-learning algorithm of a convolutional neural network (CNN) using BAT metaheuristic algorithm (i.e., CNN-BAT). Using the prediction powers of 4 well-known algorithms as benchmarks – multilayer perceptron (MLP-BAT), adaptive neuro-fuzzy inference system (ANFIS-BAT), support vector regression (SVR-BAT) and random forest (RF-BAT), the CNN-BAT model is tested for daily streamflow (Q_t) prediction in the Korkorsar catchment in northern Iran. Fifteen years of daily rainfall (R_t) and streamflow data from 1997 to 2012 were collected and used for model development and evaluation. The dataset was divided into two groups for building and testing models. The correlation coefficient (r) between rainfall and streamflow with and without antecedent events (i.e., R_t-1, R_t-2, etc.) (as the input variables) and Q_t (as the output variable) served as the basis for constructing different input scenarios. Several quantitative and visually-based evaluation metrics were used to validate and compare the model’s performance. The results indicate that R_t was the most effective input variable on Q_t prediction and the integration of R_t, R_t-1, and Q_t-1 was the optimal input combination. The evaluation metrics show that the CNN-BAT algorithm outperforms the other algorithms. The Friedman and Wilcoxon signed-rank test indicates that the prediction power of CNN-BAT algorithm is significantly/statistically different from the other developed algorithms.

     

Eutrophication modelling of Amirkabir Reservoir (Iran) using an artificial neural network approach

The complicated non‐linear relationships between water quality and environmental parameters involved in predicting algal blooms necessitate a new approach, using data‐driven modelling. Accordingly, a multilayer perceptron (MLP) and time delay neural network (TDNN) were used to predict the eutrophication status of two monitoring stations in the Amirkabir Reservoir in Iran. Six scenarios for each monitoring station were performed to select a significant, independent input using 12 years of monthly data. The final inputs were temperature, turbidity, phosphate (PO₄), nitrate (NO₃), nitrite (NO₂), ammonium (NH₃), dissolved oxygen (DO) and electrical conductivity (EC). Applying an MLP neural network to the upstream monitoring station with 21–38 neurons in the first and second hidden layers, the minimum mean squared errors (MSE ) in training, validating and testing were 0.083, 0.81 and 1.95 cells/100 ml, respectively. Further, when the TDNN network was used with the same neuron numbers in the hidden layer for the similar monitoring station, the minimum MSE values for model training, validating and testing were 0.06, 0.72 and 1.76 cells/100 ml, respectively. For the Beylaghan monitoring station, using the MLP neural network with 29–23 neurons in the first and second hidden layer, the minimum MSE values gained in training, validating and testing were 0.181, 0.58 and 0.95 cells/100 ml, respectively. Using the TDNN network with the same neurons in the hidden layers of the MLP neural network for the station, the minimum MSE values for training, validating and testing were 0.152, 0.43 and 0.84 cells/100mL, respectively. Thus, TDNN exhibited a high accuracy and workability, compared to the MLP. Sensitivity analysis of the Amirkabir Reservoir dataset indicated increasing the value of nitrate is the first factor, followed by turbidity and NH₃, having the greatest impacts on eutrophication prediction.     

Spatio-Temporal Analysis of Regional Trends and Shift Changes of Autocorrelated Temperature Series in Urmia Lake Basin

Recently, the Urmia Lake located in northwestern Iran which is the second largest hyper saline in the world suffers from the significant fluctuations of water level and surface area. The current study tries to investigate the spatiotemporal trends of mean (T_mean), maximum (T_max) and minimum (T_min) temperatures of monthly, seasonal and annual time-series. To do so, the data of 15 temperature gauge stations within the Urmia Lake basin, for the period 1972–2011 was employed. The pre-whitening approach was applied to remove the effects of serial correlation in the air temperature series based on the Mann-Kendall (MK) test. The results of Ljung-Box test showed positive serial correlation in the T_mean and T_max series for all of the stations at the 0.05 significance level. In the monthly series, the significant warming trends in the T_mean series were more perceptible than the same ones in T_max series; however, T_max trend was found more than T_min series. The Mann–Whitney (MW) test detected a significance upward shift changes in the annual T_mean, T_max and T_min series of about 86, 73 and 80 % of the stations, respectively. The average magnitude of significant warming trends in annual T_mean, T_max and T_min series were (+) 0.58 °C, (+) 0.52 °C and (+) 0.69 °C per decade, respectively. Furthermore, the interpolation maps showed that warming trends in the east and west of Urmia Lake were more than southern area. Therefore, the results showed that the basin has suffered from increasing trends in the T_mean, T_max and T_min over the recent decades. Finally, significant changes were found in 1980s and 1990s based on the Mann-Kendall ranks and change point tests. In this study, it is interesting that the period of significant changes in warming trends were close to the beginning of decreasing water level of the Lake.