ارزیابی پارامترهای اقلیمی دریاچه اوان تحت تاثیر تغییر اقلیم

Abstract
An examination of the trend of climate data recorded in the past decades, as well as the output results of all climate models predicting the future climate, indicate the occurrence of negligible changes in the global climate. Therefore, long-term forecasting of climate variables has received much attention. In order to predict the climatic parameters in Evan Lake, downscaling of general circulation models (GCMs) was used by using the LARS-WG model. The General Circulation Model (GCM) is the most current method by using in climate change studies. They are a key to understanding changes in climate; Although GCMs are imperfect and uncertain. The LARS-WG model was implemented to predict climatic parameters and the necessary analyzes were performed on its results. After selecting the general circulation model and the scenario more in line with the climatic conditions of the region, the outputs of the selected model were compared with the base period to determine the trend of their changes. The results show a decrease in average rainfall (39.9 mm), an increase in minimum and maximum temperatures (0.4 ° C) and an increase in the number of wet days (7 days) and the number of frost days (5 days). Also, the number of dry days (7 days) and the number of hot days (5 days) will decrease in the climate period 2030-2020.

Introduction
Occurrence and intensification of extreme phenomena such as severe storms, severe droughts, untimely frosts, etc., is the result of global climate change that has convinced us in the face of a global threat (Ismaili et al., 2011). Therefore, long-term forecasting of climate variables has received much attention. There are various methods for predicting and simulating the future climate, the most valid of which is the use of climatic variables simulated by paired atmospheric-oceanic models. These models are able to model atmospheric and ocean parameters for a long period of time using IPCC-approved scenarios, but the main weakness of these models is the low spatial resolution and simplifications for processes. In order to overcome the weakness of low resolution, it is necessary to micro-scale the output of these models before using them in studies evaluating the effects of climate change (Haltiner and Williams, 1980). The statistical method of micro-scaling has more advantages and capabilities compared to dynamic methods, especially when lower costs and faster assessment of factors affecting climate change are required (Abbasi Et al., 2010). One of the statistical methods of microscaling is the LARS-WG model, which is one of the generators of random meteorological data, which is used to generate data on daily precipitation, daily radiation and maximum and minimum daily temperatures in a station under Current and future climatic conditions apply. Two important reasons for using LARS-WG model include the provision of a means of simulating synthetic weather time-series with certain statistical properties which are long enough to be used in an assessment of risk in hydrological or agricultural applications and providing the means of extending the simulation of weather time-series to unobserved locations. In fact, LARS-WG has been used in various studies, including the assessment of the impacts of climate change. In this study Changes in the climate variables are studied in Evan Lake located in Qazvin Province.

MATERIALS AND METHODS
Study area
Ovan lake is a small alpine lake in the Alamut region of the Alborz mountain range, in Qazvin province of Iran. The centre point of the lake is approximately located at 36°28′58.98″N 50°26′37.39″E. The only tributary that flows into the lake, is a stream with the same name, Ovan, coming down from northern mounts. The north of the lake is surrounded by three small villages, namely from east to west: Varbon, Avan and Zarabad.
Methodology
It was statistically evaluated the performance of the LARS-WG stochastic weather generator model by comparing the synthesized data with climatology period at selected synoptic stations, based on 2 GCMs models (MPEH5, HADCM3) and 2 scenarios (A2, B1). In this study, it has shown the period of base data contained precipitation, minimum and maximum temperatures and solar radiation. It can be divided the process of generating synthetic weather data into three distinct steps as follows; 1.Model Calibration, 2.Model Validation, and 3.Generation of Synthetic Weather Data Firstly, LARS-WG model was performed based on the historical climate data obtained for verification of the model. The model was performed after assessing the model ability in station for all 4 states (2 GCMs models based on 2 scenarios). Then the results were compared and the best model was chosen for evaluating the climate change in the study area.

Conclusion
Model validation is one of the most important steps of the process entire. The objective was to assess the performance of the model in simulating climate at the chosen site to determinate whether or not it is suitable for using. Firstly, LARS-WG model was performed based on the historical climate data obtained for verification of the model. A large number of years of simulated daily weather data were generated and were compared with observed data by using the t test. The mean monthly correlation of the precipitation, minimum and maximum temperature and solar radiation were acceptable in 0.05 level of confidence.
Then was performed for selecting the suitable GCM model, LARS-WG stochastic weather generator model for MPEH5 and HADCM3 models in A2 and B1 scenarios. Between these 4 states, MPEH5 model based on A2 scenario that has the least difference with the models mean has selected and used for predicting the future climate.
For evaluating the trend of changes between 2 periods, produced data based on selected model compared with the observed data. According to the results obtained in the next period, the average rainfall in Qazvin station will decrease by 39.9 mm. The minimum and maximum temperatures will increase by 0.4 degrees Celsius. The number of wet days will increase by 7 days and the number of dry days will decrease by 7 days. Also, the number of hot days will decrease by 5 days and the number of icy days will increase by 5 days. The sum of the results indicates that the region will move towards drought in the next decade.