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Modeling and forecasting of evaporation from the pan stations west of Lake Urmia

Number of pages: 106 File Format: word File Code: 32550
Year: 2013 University Degree: Master's degree Category: Agricultural Engineering
Tags/Keywords: Aquatic ecosystems - evaporation - Evaporation prediction - Hydrology - Lake Urmia - Modeling - solutes - water crisis - Water resources management - water vapor - West Station Pan
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  • Summary of Modeling and forecasting of evaporation from the pan stations west of Lake Urmia

    Dissertation for Master's Degree in Water Resources Engineering

    Abstract

    Today, due to the issue of water crisis and the need for accurate planning, the use of statistical models and methods is increasing. The research conducted in this field has also shown the usefulness of using statistical methods. The use of different forecasting methods has many advantages, including reducing costs, and in addition, in these methods, available and accessible information can be used. Considering the importance of Urmia lake problem, which is a social, political and cultural problem, and in order to provide a scientific tool to prepare the necessary preparations for the protection of the lake, finding a model for the evaporation data from the pan and its prediction was investigated. Evaporation from the pan is one of the most important and influential climatic parameters in the policy and optimal management of water resources, and the use of time series models is one of the practical ways to predict this hydrological component. In this research, the non-parametric tests of Kendall, seasonal Kendall and Spearman correlation coefficient at 95% confidence level have been performed to test the trend of evaporation data from the pans of Abajalu, Saro, Yalquz-Aghaj, Qamishlo, Muzaffar Abad and Urmia synoptic station. The results show the presence of decreasing and increasing trends in some stations. ADF and KPSS tests were used to check the stationarity of the data, and the results showed that after removing the instability factors and standardizing the data, the monthly and annual series became stationary. The BDS test has also been used to check the linearity or non-linearity of the data. Evaporation data from two stations, Abajalu and Saro, located in the west of Lake Urmia, have been used on monthly and annual scales with statistical periods of 34 years (1388-1355) to select and fit appropriate time series models. The aim of the current research is to produce the best time series model on the evaporation values ??and use it to predict the evaporation values ??from the west basin of Lake Urmia. After performing the BDS test, it was found that the evaporation data has a linear property and linear models can be used with acceptable reliability. The fitting results of common linear models showed that the AR(1) model is the selected model for the annual series of two stations. And the selected models for the monthly series of each station were different, ARMA(1,5) model was chosen for Seru station and ARMA(2,5) model was chosen for Abagalo station. rtl;">1-1 Introduction

    Laplace stated in 1776 that if we can identify the initial conditions of any phenomenon, we can accurately predict its future. This thinking was accepted by thinkers in the field of experimental sciences for a long time. But Poincaré stated in 1903 that small errors today lead to big errors in predicting tomorrow, and since accurate knowledge of the current situation is often not possible and is accompanied by error, prediction is also impossible. Anyway, even though today's dominant opinion is very close to Poincaré's opinion, thinkers of various sciences have focused a huge part of their studies on predicting the variables and phenomena investigated by their sciences. Among these, hydrological and climatic sciences can also be mentioned (Salami, 2010).

    Prediction of accidental processes is a key element in decision making. The final efficiency of any decision depends on the law of a set of events, which follows the decision. The basis of many decisions in hydrological and climatic processes is the decision to use water resources based on forecasting and analysis of time series. In fact, another important application of time series processes in hydrology is the prediction of the modeled hydrological variable for one or more time steps ahead (Malmir, 2015). Every year, thousands of billion cubic meters of fresh water evaporates from the reservoirs of the dams, which were collected at a great cost, and the salts and salts left by the evaporated water reduce the water quality.

    Evaporation from the pan is one of the important and influential components in the hydrological cycle and planning and management of water resources, and its estimation in different time scales, as one of the most important atmospheric parameters, is of special importance in irrigation planning in agriculture. On the other hand, Iran is considered a dry and semi-arid country, and the management of the correct use of water resources is essential (Kaherman 2018). Therefore, this hydrological phenomenon has been analyzed and investigated.

    In another definition, it can be said that evaporation is a physical process during which water or ice is transferred from a wet surface or a free surface to the atmosphere in the form of gases, and this transfer takes place in the form of water vapor at a temperature lower than the boiling point of water (Chacherlo, 2013).

    Therefore, the measurement of all these factors and their modeling is considered an important and big task, and the prediction of evaporation from the free surface of Lake Urmia, especially at the current time when it is drying up, is a necessary and necessary thing in order to manage the exploitation of water resources.

    1-2 Statement of the problem

    According to the ever-increasing downward trend of the water level of Lake Urmia and the special importance of this lake as one of the most important water ecosystems of Iran and one of the biophosphorous resources of the Man and Biophosphorus Program of UNESCO, and considering that evaporation is the main factor in the removal of water from existing water resources (Shir Gholami, 2013), therefore, the study of evaporation and the factors affecting them in the management issues of water resources of Lake Urmia, It is necessary.

    Evaporation from the surface of the evaporation pan can be considered as an indicator of the combination of the effects of radiation, temperature, humidity and wind on evaporation. The evaporation pan shows the actual amount of evaporation and with the help of observing the water level drop in it and the use of experimental coefficients, it can be used to estimate evaporation from free water levels. (Fernandes 2007) The pan coefficient changes between 0.65 and 0.75 depending on the humidity and the amount of daily wind. There will be water during periods of low water and eventually lowering the water level of the lake. The greatest effect of this factor is in the early summer and in the conditions when a large volume of water returns to the atmosphere from the surface of water bodies and soil (Malmir, 2015). The first step in the integrated management of water resources is to evaluate the available water resources, determine the flow rate and water level and its changes based on the evaporation rate and predict hydrological variables. The random nature of hydrological phenomena has caused hydrologists to take help from the concepts of random variables and time series in predicting hydrological variables. To model evaporation, stochastic models can be used, especially time series analysis, which is a suitable management tool for predicting the future values ??of hydrological processes. Many different models have been proposed to describe this process, which have different capabilities and complexities. Among these models are stochastic models (Hamidi, 1389).

    These stochastic models include common linear models of time series, which include AR autocorrelated model, ARMA autocorrelated moving average models, with fixed parameters and its derivatives including AR and MA. ARIMA cumulative autocorrelated moving average model, as well as PAR and periodic autocorrelated moving average PARMA models) and its nonlinear models (bilinear or nonlinear BL model, TAR threshold or limit autocorrelation model, and ARCH autocorrelated heterogeneous conditional variance model are rarely used in water resources. The most widely used are MA, AR, and ARMA. Providing a method that can predict gives a suitable and relatively accurate estimate of the amount of evaporation from the pan, it can be important in identifying the need for future developments, including the management and protection of water resources.

  • Contents & References of Modeling and forecasting of evaporation from the pan stations west of Lake Urmia

    List:

    1-1- Introduction. 3

    1-2- Statement of the problem. 4

    1-3-    Objectives. 5

    1-4- Thesis structure. 5

    2- The second chapter of the basics of time series. . 7

    2-1- Introduction. 7

    2-2-    Trend test. 8

    2-2-1- The advantages of non-parametric statistics. 8

    2-2-2- Disadvantages of non-parametric methods. 9

    2-3-    Static test. 9

    2-4- Non-linear BDS test. 10

    2-5-    Time series models. 10

    2-6-    Linear models. 12

    2-6-1-    Autocorrelation (AR) model. 13

    2-6-2-    Moving average process MA(q) 13

    2-6-3-    Cumulative autocorrelated moving average model ARIMA(p,d,q) 14

    2-6-4-    Fractional moving average cumulative autoreversion model (FARIMA) 15

    2-7-    Nonlinear models. 15

    2-7-1- Threshold or threshold autocorrelation model (TAR) 16

    2-7-2- ARCH correlated heterogeneous conditional variance model. 16

    2-7-3- Bilinear model. 17

    3-       The third chapter is a review of previous studies. 19

    3-1- Introduction. 19

    3-2-    Rainfall. 19

    3-3- Temperature. 20

    3-4-    Discharge and flow. 21

    3-5-    Evaporation. 22

    3-6- Reviewing previous studies on important characteristics of evaporation time series such as trends. 26

    4- Chapter 4 Materials and Methods 33

    4-1- The study area. 33

    4-1-1- Used evapotranspiration stations (west of Lake Urmia). 34

    4-1-2- Data used for trend analysis. 35

    4-2- Trend analysis. 38

    4-2-1- Mann-Kendall (MK) test 39

    4-2-2- Spearman correlation coefficient. 40

    4-2-3- Seasonal Kendall method. 41

    4-3-    Static test. 41

    4-3-1- Dickey-Fuller unit root test (ADF). 42

    4-3-2- KPSS test. 43

    4-4- Non-linear BDS test. 44

    4-5- Linear modeling. 45

    4-6-    Normalization and standardization of data 46

    4-6-1-    Normalization of time series variables. 46

    4-7- Model identification. 47

    4-8-    Estimation of parameters 49

    4-8-1-   Calculation of autocorrelation and partial autocorrelation functions. 50

    4-8-2- Determining the rank or order of the model. 50

    4-9- Best fit test. 51

    4-10- Analysis of residuals 51

    4-10-1 Using the sample autocorrelation function. 51

    4-10-2 Portmanteau test 52

    4-11- Model evaluation. 52

    4-12- Prediction. 53 5- Chapter 5 results and discussion 55 5-1 Introduction. 56

    5-2-    The results of the basic characteristics of time series. 56

    5-2-1- The results of data normality using the skewness test. 56

    5-2-2- Trend test results. 57

    5-3- Results of static test. 61

    5-4-    BDS test results. 62

    5-5- Results of linear modeling of time series. 64

    5-5-1- The results of the best fit test. 64

    5-6-    Prediction results. 75

    6- Chapter Six Summary of results and suggestions. 80

    6-1- Summary of results. 80

    6-2 -     Proposals. 82

    Sources   83

    Source:

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Modeling and forecasting of evaporation from the pan stations west of Lake Urmia
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