Agricultural Meteorology
R. Maleki Meresht; B. Sobhani; M. Moradi
Abstract
Introduction: Heat waves (HWs) are one of the most important climatic disasters that have devastating environmental consequences in nature every year). The purpose of this study is investigation of the effect of heat waves on the intensification of thermal islands in Sanandaj city from 1989 to 2018. ...
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Introduction: Heat waves (HWs) are one of the most important climatic disasters that have devastating environmental consequences in nature every year). The purpose of this study is investigation of the effect of heat waves on the intensification of thermal islands in Sanandaj city from 1989 to 2018. The constant rise in temperature of the city as an urban heat island and the sudden occurrence of HW's as one of the major climatic hazards, is an important concern of urban management policy makers; because intensify heat of this city and cause a lot of environmental damage.
Materials and Methods: In order to identify HWs in Sanandaj city, from 1989 to 2018, by using Fumiaki Index and MATLAB software, days whit temperature above +2 standard deviation or above the mean Normalized Thermal Deviation (NTD) that lasted at least two days, were identified as the day with HWs and calculated by equation 1.
(1)
Where, T (i, j, n) is temperature of day ith from month jth in year nth indicates the average temperature of day i from month j. To eliminate the noise in the mean, a 9-day moving average filter was performed on these data three times and calculated by the following equation.
(2)
Where, ∆T= (i, j, n) indicates absolute deviation of temperature from the average on day jth of the month i th, in year n th compared to the average temperature of the same day. In order to the values of temperature deviation of different times and places to be comparable at a certain time and place, it is necessary to standardize these absolute values of temperature deviation by means of temperature diffraction. Like day-to-day changes, diffuse T∆ at 31 days for each day is calculated by the following equation.
(3)
The value is the average temperature deviation in 31 days that is calculated by the following equation.
(4)
Finally, (NTD) is calculated by the following equation.
(5)
Where .Then in MATLAB software, days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW.
(6)
Then the thermal island was calculated in Sanandaj city using Equation 7.
SUHI= MLSTurban –FLSTrural
(7)
Where, SUHI is the island surface heat index, MLSTurban and FLSTrural are the average surface temperature of urban and rural areas, respectively.
Results and Discussion: The results showed that, during the study period (1989-2008), the highest frequency of HW hazards in this city was in September, February, March, and October 1991. The maximum duration of HWs was 6 days, which occurred in December 2017 and 2005, therefore long-term HWs have been experienced in this city. Results also showed, in both HW and NHW conditions, in the hot and cold months of the year, often a cold island is formed in the city center during the day and a heat island is formed at night. Results also showed that short-time heat waves have been effective in intensifying heat islands. Examination of the intensity of thermal islands in this city showed that during the day in both HW and NHW conditions, which in the HW conditions dominance of the cold island compared to normal day, it has been reduced and in the last months of winter (February), even during the day, a heat island has been created in the center of the city. At night time, in both HW and NHW conditions, a heat island was created in Sanandaj center, but the intensity of night- time heat islands in HW conditions is often significantly higher than normal conditions especially in the winter. Investigation of the condition of thermal islands in the warm months of the year showed that in both HWs, a cold island has been created in the city center that the intensity of cold islands during the HW conditions, especially in the summer months, was often higher than NHW conditions. At night time, there was often a heat island in the city center that was more intense than normal day. Also, in HW conditions, wind speed and especially relative humidity has decreased significantly more than the cold months of the year.
Conclusion: According to the results the highest incidence of HW hazards occurred in the winter and early spring. Also, long-term (6-days) HW occurred in this period. The increasing trend, frequency and continuation of HW, especially in the cold months of the year, can be the effects of climate change and global warming. Severe and continuous HWs occurred in Sanandaj city, especially in late winter, can cause early germination and flowering of crops and gardens and it will negatively affect agriculture and horticulture and will lead to great economic losses. The effects of HWs on heat islands occurred in the suburbs due to having a clear sky without pollution, with minimal vegetation and lack of surface water resources and ground with low heat capacity is affected by HWs faster than the city center and as the land surface around the city becomes warmer than its center, a cold island is formed in the city center. At night, the suburbs due to low heat capacity, lose absorbed heat faster and as a result, the heat island is formed in the city center. In general, the occurrence of heat waves in the intensification of thermal islands in the Sanandaj city, especially in the warm months of the year, has a significant effect, and it is likely to intensify in the coming decades, especially at night during the hot months of the year.
B. Salahi; R. Maleki Meresht
Abstract
Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced ...
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Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced rainfall fluctuations and shortage of water supply. Precipitation is one of the most important features of the environment. The amount of precipitation over time and in different places is subject to large fluctuations which may be periodical. Studies show that, due to the certain complexities of rainfall, the models which used to predict future values will also need greater accuracy and less error. Among the forecasting models, Arima has more applications and it has replaced with other models.
Materials and Methods: In this research, through order 2 Autoregrressive, Winters, and Arima models, monthly rainfalls of Ardabil synoptic station (representing Ardabil province) for a 31-year period (1977-2007) were investigated. To assess the presence or absence of significant changes in mean precipitation of Ardabil synoptic station, rainfall of this station was divided into two periods: 1977-1993 and 1994-2010. T-test was used to statistically examine the difference between the two periods. After adjusting the data, descriptive statistics were applied. In order to model the total monthly precipitation of Ardabil synoptic station, Winters, Autoregressive, and Arima models were used. Among different models, the best options were chosen to predict the time series including the mean absolute deviation (MAD), the mean squared errors (MSE), root mean square errors (RMSE) and mean absolute percentage errors (MAPE). In order to select the best model among the available options under investigation, the predicted value of the deviation of the actual value was utilized for the months of 2006-2010.
Results and Discussion: Statistical characteristics of the total monthly precipitation in Ardabil synoptic station indicates that in May, the highest and in August, the lowest monthly total rainfall accounted in this station. Standard deviation of rainfall reached to the lowest level in August and its peak in November. Coefficients of skewness and kurtosis of total rainfall in all seasons, indicates a lack of compliance with normal distribution. From the view of the range of total monthly rainfall, October and August have highest and the lowest tolerance in these parameters, respectively. The results showed that the percentage of the mean absolute error for Arima, Winters and Autoregressive models was 61.82, 148.39 and 81.54 respectively and its R square came to be 88.28, 61.07 and 85.12 respectively. The comparison of the parameters is an indication of the fact that Arima has the highest R square and the lowest mean absolute error of 88.28 and 61.82 respectively than Winters and Autoregressive models. The presence or absence of significant changes in mean precipitation during 1977-1993 and 2010-1994 in Ardabil synoptic station shows that the difference of rainfall is not significant at the 5% error level from statistical point of view. The comparison between the monthly mean rainfall of Ardabil synoptic station in 1994-2010 and 1977-1993 indicates that rainfall has somewhat decreased in the former in recent years. Considering the low average monthly rainfall of Ardabil synoptic station in 1994-2010 compared to 1977-1993 (21.98 versus 26.11 mm), although no statistically significant difference was found in the average rainfall, low rainfall in this station would not be unexpected in the coming years. The comparison of predicted and actual values from 2011 to 2013 in Ardabil synoptic station showed that fitting real data with expected data was relatively acceptable. The observed differences between the actual and predicted values can be related to the influence of rainfalls and many local and dynamical factors of this area. Therefore, it is necessary for climatologists to better explain and predict phenomena besides statistical models and pay more attention to general circulation models (GCM) under different climate conditions.
Conclusion: Results of rainfall investigation by order 2 Autoregrressive, Winters, and Arima models showed a descending trend in monthly rainfalls in the coming years across the study location. The results of modeling and analysis of monthly rainfalls in Ardabil synoptic station showed that among these models, Arima was better than the other two because it enjoyed the lowest MAPE and the highest R2. AIC, RMSE and MAD scales of different patterns were calculated and finally, SARIMA(1,1,1)(2,0,1)12 pattern having the lowest AIC, RMSE and MAD was selected as the most appropriate pattern for monthly rainfall forecasting in Ardabil synoptic station.
