behrooz sobhani

Relative humidity is considered to be one of the most important climatic parameters and atmospheric phenomena. The purpose of the present study is to evaluate the regional algorithms for estimating relative humidity using remote sensing data in Hormozgan province. To this end, MOD05 and MOD07 products were employed to estimate total perceptible water, air temperature, and sea-level pressure Additionally, MOD35 was used for cloud verification, , resulting in the identification of 2190 cloudless images with 95% confidence level for analysis. radiosound data of Bandar Abbas ststion and synoptic stations Covering entire Hormozgan Province. were used to evaluate the results. The findings demonstrated high accuracy of the algorithms and experimental model, with acceptable R² and RMSE values between Modis product and ground data. These results align well with ground station measurements. The province's climate was determined to be semi-desert with a long warm season and a short cool period. Further analysis revealed a strong correlation between sea-level pressure and total perceptible water (TPW) with the region's topography. Maximum TPW and sea-level pressure values were recorded in coastal lowlands, while minimum values occurred in the highlands. Based on zoning maps, Hormozgan province can be divided into four regions based on relative humidity: from very dry conditions with less than 20% relative humidity in the highlands to humid areas with over 65% relative humidity along the coast.

Environmental changes The environmental environment of any environment can be changed. In case of achieving this goal, first the average daily statistics (2021-2009) were received from the General Meteorological Organization for Nair city. The maximum limit is determined by using 99% of Frein's threshold limit, then for Frein's days, the sea level pressure value was extracted from the Nova site, and on this model, patterning was done with Ward's hierarchical clustering method based on Euclidean distance. As a result, two major patterns were identified and one representative day of each pattern was included in the synoptic analysis. For this, NCEP-NCAR data were used to draw synoptic maps in the software environment. The results showed that the thermal low pressure systems that entered Iran from the south-east and south-west sides covered the entire geographical area of Iran and especially the studied area and brought an unstable atmosphere. At this time, the warm air from the southern latitudes that flowed through the Naveh channel in the west of Iran towards the northwest of Iran was blocked by the blocking barrier located in the western half of Iran. The anomalous state of air temperature showed that on the investigated day, temperature changes increased by 4 degrees Celsius compared to the length of the week. Geodetic maps of temperature and temperature advection at the level of 500 hectopascals also show that the origin of heat movement is from the north-east of Africa and has been drawn towards the study area, and heat convergence has occurred in the study area and has added to the temperature.

The aim of this study is simulation and forecasting of maximum temperature and heat waves in Urmia city from 2020 to 2050 in order to predict and reduce the negative effects of a sudden increase in temperature, which is simulated using SDSM software and CanESM2 model And is predicted using the most appropriate RCP scenario for the next 31 years. For this purpose, the maximum daily temperature data of Urmia from 1961 to 2005 were obtained from the Meteorological Organization and forecasted. The innovation of this study is using the least error RCP scenario for more accurate prediction. According to the results, RCP 8.5 scenario was selected as the least error scenario for forecasting. According to the results the average maximum temperature in Urmia will decrease from late winter to late spring compared to the base period and in mid-summer there will be a slight increase. In general, during the years 2020-2050, the maximum temperature trend of Urmia will be increasing. According to the results of the Baldi index, The heat waves will be short and maximum four days. One-day heat waves will have the highest frequency and will have a slight upward trend, Two, three and four day heat waves will have a decreasing trend. In general, short-term heat waves are more likely to occur than long-term heat waves. Also, since the detected heat waves often showed the highest frequency in autumn and winter, so the probability of this hazard occurring in cold seasons is higher than warm seasons.

The purpose of this research is to estimate and evaluate the future climate change of Iran using climatic elements (minimum temperature, maximum temperature and precipitation) until the year 2100 in Iran. For this purpose, in the current research, the innovative method and tools of the algorithm and coding and NASA data to evaluate and predict the aforementioned climate elements based on two intermediate scenario (RCP 4.5) and worst-case scenario (RCP 8.5) of the Canadian CanESM2 model from the system Google Earth Engine was used under the web. In order to better analyze, investigate and compare Iran's future climate changes, the studied period of 80 years, was divided into the first 40-year period (2021-2060) and the second 40-year period (2061-2100). The results of the current research based on the intermediate scenario (RCP 4.5) indicate that the minimum and maximum; minimum temperature of the second 40-year period compared to the first 40-year period is 2.69 and 0.62 degrees Celsius, respectively, and the minimum and maximum; maximum temperature The second 40-year period compared to the first 40-year period was 3.37 and 0.91 degrees Celsius respectively; Also, based on the worst-case scenario (RCP 8.5), the minimum and maximum; minimum temperature of the second 40-year period compared to the first 40-year period is 0.54 and 3.32 degrees Celsius, respectively, and the minimum and maximum; maximum temperature of the second 40-year period compared to the period An increasing trend was predicted for the first 40 years of 2.47 and 3.46 degrees Celsius respectively. Based on the results obtained from the present research, in the studied area, the frequency of rainfall decreases in the first 40-year period and increases in the second 40-year period.

Heat waves are one of the climate hazards. The purpose of this research is to investigate the heat waves of Ardabil city and its effect on urban heating from 2003 to 2018. For this purpose by using Fumiaki index, the days where the temperature was +2 standard deviations or higher than the NTD average and lasted for at least 2 days and was defined as a day with heat wave. In order to investigate the effect of heat waves on heat island in the hot and cold months, the heat island for days with a heat wave and a normal day without heat wave with the lowest and maximum temperature before the occurrence of each heat wave during the day and at night of Modis - Aqua was calculated. According to the results, during the studied period, the highest annual and monthly frequency of heat wave in Ardabil was in 2010 and 2016 and in March, April and July. The results also showed that the maximum duration of heat waves was 4 days and therefore short-term. According to the findings, both in the hot months and in the cold months, in both heat wave and no heat wave condition, a cold island is often formed during the day and a heat island is formed at night in Ardabil center. Heat waves, especially in the hot months of the year, have been more than no heat wave conditions. In the summer nights, in the heat wave conditions, the temperature increase up to 4 ºc has also been experienced in the heat island. In total, the results showed that the influence of the heat island on the occurrence of heat waves was higher in hot months than in cold months.

Heat waves are one of the most dangerous climate threats associated with global warming, affecting society, economy and environment today, one of the most important concerns from climatic hazards is the occurrence of heat waves, which widely affects human societies. The occurrence and intensity of heat waves have increased throughout the world in recent years. (Ding et al., 2006). An increase in average temperature can lead to hotter days and longer and more frequent heat waves. This, in turn, can cause an increase in heat-related diseases such as heatstroke, heat cramps, heat weakness, and even in advanced cases to die (Haidari et al., 2016). The stagnant atmospheric conditions resulting from heat waves trap pollutants in urban areas, and with the addition of the severe stress of toxic pollutants to the dangerous stress of hot air, it creates a large-scale environmental problem. Also, at night or in winter, the presence of cold air above causes the warmer air layer below it to remain constant along with the dust layer (Almusaed, 2011). Weather and its consequences during the next decades and centuries, the use of coupled atmosphere-ocean models has been considered. These modes are well-known tools for studying the future climate (Xu, 1999). These models are able to model climate variables for a long period using different scenarios (Abbasi et al., 2019).Therefore the purpose of this study is Identification and analysis of heat waves in Tabriz and Ardabil cities and its prediction with CanEsm2 climate model scenarios

In the present study to identify heat waves in Tabriz and Ardabil cites, Maximum daily temperature data of synoptic station of two studied cities, from 2003 to 2018, were obtained from the Meteorological Organization and then SDSM software and CanESM2 model and three RCP scenarios (2.6, 4.5 and 8.5) were used. Then using error measurement indicators maximum temperature was predicted and then the lowest risk scenario was selected for each city and the based on that scenario and using the Fumiaki index and through programming in MATLAB software, Days that had temperatures above +2 standard deviation or above average (NTD) and these conditions lasted for at least two days, were identified and selected as the day with heat waves. The Fumiaki index is obtained by relation 1:(1 Where T (i, j, n) temperature of day i th from month j th in year n th, 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 (Fujibi et al., 2007; Ismail Nejad et al. 2014). (2 Where ∆T= (i, j, n) absolute deviation of temperature from the average on day j th 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 30 days for each day is calculated by the following equation:(3 The value is the average temperature deviation in 30 days that is calculated by the following equation:(4 Finally, (NTD) is calculated by the following equation:(5 Where .Then days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW. (Ismail Nejad et al, 2014). Equation 6:(6 2 ≥ NTD (i+p) NTD (i+p-1) ≥ 2, NTD (i-1), NTD (i+1)… NTD (i), To evaluate the ability of CanESM2 model scenarios to predict the average maximum temperature for the next 31 years, the outputs of each scenario are averaged by the absolute error values (MAD), mean square error (MSE), root mean square error (RMSE) were compared and the most accurate and least error scenario was selected. Equation 7, 8, 9. (7 (8 (9

According to the presented results, during the recent decades, with the discussion of climate change, climate events, including the risk of heat waves, occur abundantly. Based on the results of the present research, heat waves during the past 16 years (2003-2018) in both cities of Ardabil and Tabriz were short-term and lasted for a maximum of 4 days, which occurred in December. According to the findings, in the next 30 years, as in the previous period, in both Tabriz and Ardabil, the maximum duration of the heat wave was predicted to be 4 days in Ardabil and 5 days in Tabriz. In terms of seasonal distribution, heat wave showed more frequency in both periods in spring and autumn. By comparing the duration of heat waves in the base period of 16 years with the predicted 30 years, it was concluded that in both periods and in both cities, heat waves with a duration of 2 days were more frequent. According to the results in Ardabil city, the continuation of heat waves is not much different compared to the base period, but it will increase in Tabriz city. Also, the maximum temperature in both stations will increase, and the highest increase will be in Tabriz. Since the maximum temperature also showed an increasing trend in both stations. Therefore, we can say that due to the frequent and continuous occurrence of heat waves and temperature increase, especially in the cold months of the year, early flowering and germination of agricultural and garden crops may occur, which is the result of the occurrence of late spring cold. After a heat wave, if there is no crisis management and insufficient information of farmers and horticulturists, we will see heavy damage to the agricultural economy in the study area. In addition, due to the tropical nature of the studied cities, the increase in temperature will definitely disturb the living comfort of the residents. Therefore, it is necessary to think about the necessary measures to deal with the negative effects caused by the increase in temperature. The findings of the present research, in the sense that heat waves will increase in the coming years and the most frequent heat waves will be two and three day waves and will often be more frequent in the cold seasons of the year. The opinions of most researchers were consistent.

Maximum temperature changes are one of the most important factors in plant growth and heat wave as one of the most harmful climatic hazards. The purpose of this study was to predict the maximum temperature and heat waves with the plant ecosystem approach in Sanandaj and Kermanshah from 2020 to 2050. For this purpose, the maximum daily temperature data were obtained from the Meteorological Organization from 1961-2005 and were simulated using SDSM software and CanESM2 model and under the most appropriate RCP scenario for the next 31 years was predicted. Then, by programming in MATLAB software and applying the Fumiaki index on the predicted maximum temperature data, the heat waves of these cities from 2020 to 2050 were identified and divided into two categories: long-term heat waves and short-term heat waves. According to the results, RCP4.5 and Rcp 8.5 scenarios were selected as the least error scenarios to predict the maximum temperature of Sanandaj and Kermanshah, respectively. According to the findings, the cities of Sanandaj and Kermanshah (2020-2050) will not experience long-term waves and the maximum duration of heat waves in Sanandaj will be 5 days and in Kermanshah will be 4 days and short-term heat waves of 2 days will be most frequent. Also, the trend of maximum temperature in both cities will increase and the trend of heat waves during the forecast period in Sanandaj will increase and in Kermanshah will decrease slightly. The highest frequency of heat waves occurred in these cities, often in the cold months.

Study of land use/cover changes is widely used in environmental planning. During the last decade, growing increase of aridity in Uromiyah Basin has become a major regional and even national problem. The purpose of this study is to reveal the changes in land use/cover in the southern and southeastern parts of the basin with using 2 images for month of July of 2000 to 2017. Landsat TM and OLI data and NDVI were used for classification this study. Land use/cover maps in the two studied years were provided using Maximum Likelihood Classifier (MLC) algorithm applied on two series data including spectral bands (data series 1) also spectral bands and filter texture layer (data series 2) and six categories of land use/cover containing Irrigated Farmland, Dry Farmland, garden, rangeland, bare land and water bodies were distinguished.. The accuracy of the produced maps were assessed and compared with the training samples derived from Google Earth images and Kappa Index, overral accuracy, producer accuracy and user accuracy. The results demonstrated that the maps produced using the data series 1 have higher accuracy and the overall accuracy of the maps of 2000 and 2017 using the data series 2 are 98.93 and 98.29 and these values for data series 1 were gained 99.28 and 91.45, respectively. In additional, texture filtering decreased amount of mixing between classes of rangeland, Irrigated Farmland and garden. The results of change detection showed considerable increase in the area of Irrigated Farmland (13.44) and garden 1.85 (27.24) an also at the studied period, the area of the water bodies and rangeland were decreased to 1.58 and 22.94%.

Dust in hazardous areas anywhere in the world is hazardous In different parts Life Organisms had. Dust Mineral Aerosols can significantly affect Earth's climate (Zhiyuan et al., 2019: 3). The prevalence of dust storms is devastating to human health and agricultural activities in Central Asia (Tiangang et al., 2019: 16). Dust it plays an important role in socio-economic development, but on the other hand, such supply can have a negative impact on the environment and the environment of the forest (Narayan et al, 2019: 4). According to the above studies, the importance of dust and the resulting hazard, in the case study, it can be admitted that the dust parameter is important for natural hazards. According to the studies, the existing methods for studying the dust that has been done so far have been general and have not adequately addressed the subject. Dust in the areas under its control anywhere in the world has had a dangers in various parts of life. Drought storms have also been growing in recent years (Mohammad Khan, 2017: 495). Drought phenomenon due to recent droughts has caused adverse biological effects and damages in agriculture, industry and transportation in the provinces of Khuzestan and other neighboring districts (Darvishi et al., 2017: 1). Today, dust is one of the common phenomena and is one of the major environmental problems in arid and semi-arid areas (Hejazi Zadeh et al., 2018: 108). The purpose of this study was to analyze the dust data first to address this issue Then, using ANFIS and RBF models, a modeling comparison was used and finally, for a better view of the dust situation for the future, in dusty regions of Iran, they were predicted.

In this study after analyzing 29-year-old dust data for 28 stations of dusty regions of Iran, they were first analyzed and then normalized, and non-normal stations were normalized. After normalizing the dust data using two new and powerful applied models for modeling and forecasting in climateology, the ANFIS and RBF models were modeled. and Then the two models were compared to accurate prediction for the future, and after training the dust data, they were predicted for the coming years. Finally, using the TOPSIS multivariate decision making model, regions are more involved with the priority hazardous dust hazard phenomenon and by utilizing ArcGIS software output data Zoned.RBF neural network model Neural networks with radial base function are widely used for nonparametric multi-dimensional functions through a limited set of educational information. Radial neural networks with a fast and comprehensive learning are very interesting and efficient, and they pay particular attention to it, Hartman et al. (1990). Gyrosy, Pogni, as well as Hartmann and Kepler, in the 1990s proved that radial-base grid networks are very powerful approximation devices, so that by having a sufficient number of hidden neurons, they can be able to approximate each function Continuously and accurately with every degree. These networks are often compared to the neural network back propagation error. The main architecture of the RBF consists of a two-tier network (Khanjani et al., 2016).ANFIS Neural Network Model In this step, it is possible to model and predict dust in the studied area using the ANFIS comparative neuro-fuzzy network model (Ansari, 2010: 29). In this study, the phenomenon of dust in a series of time of 276 months (23 × 12 276) in two ANFIS and RBF neural networks models in each station was considered. In a time series consisting of n examples x_1, 〖x〗 _ (2), ..., x_n is the next value of relation (6) of its previous value (Asghari Oskouei, 2002: 75). (6) x_k=f (〖 x〗_(k-1 ),〖 x〗_(k-2 ),…,x_(k-p ))The fuzzy system is a system based on the "conditional-result" logical rules that, using the concept of linguistic variables and fuzzy decision making process, depicts the space of input variables on the space of the output variables. The combination of fuzzy systems based on logical rules, and artificial neural network methods that enable the extraction of knowledge from numerical data, has led to the introduction of a comparative neural system inference. In Fig. 3, a sogeven fuzzy system with three inputs, one output and two laws and an equivalent ANFIS system were presented. This system has two inputs x and y and one f output.
Proximity to Ideal Mode (TOPSIS) Huang and Yun proposed TOPSIS in 1981. In this method, m options (A1, A2, ..., Am) were evaluated with n indices (C1, C2, ..., Cn) (Momeni, 2008). Solving this problem with this method was carried out in the following steps (Makvandi et al., 1391; Law and Order, 2014).

Comparison of two ANFIS and RBF neural network models According to comparisons of ANFIS and RBF neural network models, the two models were trained to predict dust. The results obtained from the training of the ANFIS neural network model at best, the RMSE value was 11.67 and the R2 value was 0/5879. But the results obtained from the training of the RBF neural network model, at best, were RMSE equal to 2.19 and the R2 value was 0.9854. By comparing these two models, it was finally concluded that the performance of the RBF neural network model was better. According to the modeling and the results obtained from the comparison of the models, the accuracy and reliability of the RBF neural network model was confirmed for prediction, then the prediction of the RBF neural network model was used.

The zoning of dust phenomena in dusty regions of Iran using TOPSIS The results of the implementation of the Topsis model, using the degree of importance of the criteria derived from the entropy method, indicate that, in terms of dust intensity, places more and less dusty for the next 14 years in dusty areas Iran, three stations of Abadan, Masjed Soleyman and Ahvaz were exposed to dust (1, 95%, and 81%), respectively, for simulated years. The northern stations of the study area including Khoy, Boroujen and Ahar showed a lower dust intensity with percentages (0.1, 0.4 and 0.6), respectively. According to TOPSIS model, south west and west of Iran were exposed to dust for simulated years.

Lake Urmia, as one of the most important lakes in the country, has an inappropriate environmental condition due to excessive consumption of water and climate change. The study of climate change and rainfall distribution in this area can improve water management in this basin. In this study, the information of 65 weather stations in the period of 1997-2016 were used for precipitation zoning of Urmia lake basin. For this purpose, the data of each month were standardized and arranged in a matrix with dimensions of (n*m) in which n is the number of stations (65) and m is the number of months (12). Principal Component Analysis (PCA) was performed on data matrix and the main components were determined according to their Eigen values greater than one. Then the principal component score (PCS) values were calculated for the selected components. These values were used as inputs in the Ward cluster analysis method. Then, the Procrustes method was used to determine the index stations. The results showed that the first two main components incorporated more than 87% of the all data variances. Based on the selected components, six distinct precipitation regions were identified throughout the basin. Moreover, it was found that four stations located in different points of the Urmia lake basin namely Mehmandar, Sarab, Babaroud and Santeh can be considered as indicator stations. These stations incorporated more than 84% of the all data variances of basin stations. The Mann-Kendall trend test showed that the rainfall in the autumn season has a significant increase trend, while annual precipitation has only a significant increase trend in one of the clusters.

Water and food security are serious challenges in climate change studies. the purpose of this study is to investigate the effects of climate change on water requirements of apple during the growing season. For this purpose, the ECMWF database has been used for observation data of Semirom and Urmia stations during the 20-year period (1996-2001).To check this quantity in the next 20 years, the daily  Downscaling dynamic data of the CORDEX project with a precision of 44% * 44% for the output of the ICHEC-EC-EARTH model under the two lines of 4.5 and 8.5 (RCP) was used for the period (2017-2037). In order to reduce the errors in the model estimates, the post-processing action of the estimated events was fulfilled. Then, minimum temperature data, maximum temperature, relative humidity, wind speed and radiation, potential evapotranspiration have been calculated using Penman-Monteith FAO method, which is more accurate than other models, and using the non-parametric Man-Kendall test and the Sen’s Slope estimator nonparametric Method in the confidence range, 95% evapotranspiration was determined. In the end, the water requirement was also examined during the growing season of apples. The results showed that evapotranspiration in both stations is increasing during the growing season. The ETo increase in the growing season of the apple tree stations was predicted from the base period for the trajectory of 4.5 and 8.5 for the Semirom 4.14.7 and 7.99.7, respectively, and for Orumiye Station, 26.5 and 11.8, respectively, and however, this increasing process will raise the apple's need for water. The highest amount of evaporation and water requirement in the observed and estimated data has occurred in the bud break periods, growth fruits. At this stage, the lowest effective rainfall and the highest average temperature have occurred but generally, the percentage of upcoming changes of germination stage was more than other stages.

In many parts of the world, there is not enough precise information about suitable land for cultivation. Understanding climate and the required parameters during the growth period of crops is the mostimportant factor in the zoning Agroclimate. In Iran, lentils with acreage of 240 thousand hectares and the production of 166 thousand tons per year after the peas in second place is important. Agro-ecological zoning is one of the land evaluating method that can be used to find better lands and improve the planning and management of land resources. The purpose of this research, zoning Agroclimatic Ardabil Province for the cultivation of lentils, In order to identify the characteristics of land resources for the planning and organize and optimal land, Assessing of land suitability, in any of the zone by using AHP is Agroclimatic. Ardabil Province is in northwestern Iran, with an area of 17953 square kilometers, 1.09 percent of the country's total area. The present study was conducted across Ardabil Province which is located in 37° 25' and 39° 42' N latitude and between 47° 03' and 48° 55' E longitude. Data used in this research; climatic factors such as rainfall, temperature, relative humidity, sunshine duration and degree-days during the growing season are the data of 37 synoptic and meteorological and data physiographic such as elevation, slope, aspect, soil and land use map of the study area were collected from relevant organizations. By performing the necessary calculations using SPSS and EXCEL software, the database was frormed in GIS environment. Then for each of the parameters in the GIS environment with generalized point data, linear and polygon surrounding surfaces (Interpolation) were performed, as the data layers and maps. Each of the layers of the study, based on favorable climatic requirements lentils were categorized based on AHP. According to the purpose of this study, the role of the main criteria such as Climate, Soil, Physiography and land use criteria selected under Options AHP were calculated and then using the weighted important factor, weight and final weight of all criteria were determined.
Lentil planting zoning based on climatic factors: Data of climatic elements during the growing season lentils have an influential role. The sudden change of climate data is effective on crop yield. In this study, the rate of climate data based on hierarchical, weight coefficient was determined as follows: Temperature 0.382, precipitation 0.250, humidity 0.160, sunshine hours 0.101, evaporation 0.064 and wind 0.043. By overlaying maps of climatic elements, about 20.42 percent with a very suitable, 37.75 percent was suitable, 24.47 percent average and 17.34 percent of conditions are unsuitable for cultivation of lentils.
Zoning planting lentils, based on physiographic factors: For evaluation the cultivation of lentils, map each datas, elevation, slope and aspect were prepared in GIS environment. Based on the favorable climatic lentils product to physiographic factors, weighting layers were based on AHP. Therefore, the height by a factor of 0.614, slope 0.268 and slope were determined 0.117. By combining maps of physiographic province, 22.68 percent has a very suitable, 21.11 percent sutitable, 17.33 percent average and 38.86 percent conditions are unsuitable for cultivation of lentils.
Zoning lentil cultivation, on the basis of physiographic and climatic elements: By combining all the maps of climatic and physiographic factors that were prepared based on hierarchical method weighting coefficient, the final zoning map Agroclimate lentil cultivation in this province was prepared. Based on the final map, in Ardebil province about 17% very suitable, 32% was suitable, 27% moderately and 24% had poor conditions for the cultivation of lentils. This study aimed to Agroclimateland zoning for planning agriculture in the province of Ardabil. Based on the favorable climate condition for lentil crop, land evaluation of each data was done for production of lentils. So the the main criteria including; climate 0.565 factor, physiographic by a factor of 0.262, soil 0.188 and land the highest weight by a factor of 0.055 respectively has an effective role in the lentils, as well as minimum temperature during the growing season and maximum temperatures and low rainfall (during seed germination and flowering) altitude and the high slope is the limiting factor for the cultivation of lentils in the province.

GIS.To achieve the land suitability assessment of the study area, climatological and Synoptic data at the provincial level since its establishment in 2011 were established for each of the phenological stages of rapeseed such as temperature, precipitation, daylight-hours, degree of day lgrowth. Sunny hours, number of frost days and relative humidity as well as environmental capability data, including soil type, land use, soil depth, elevation and slope were used. Depending on the purpose and nature of the research, the type of research is applied and the method of research is descriptive-analytical. collecting descriptive information was done by the library linguistic documentation method using library method and then in order to prioritize and evaluate the criteria related to rapeseed cultivation ، based on multi-criteria decision analysis method (MCDM) based on Hierarchical Analysis Process AHP) was used. Finally ، by ​​weighting the prepared layers according to the criteria and model ، the layers were combined and overlapped in GIS environment and the final land suitability assessment layer was prepared for rapeseed cultivation. The results of the discussions showed that the lands of the study area were 15.3% unlimited, 28.2% limited, 48.2% moderate and 8.3% unlimited.

With the seriousness of the climate change debate in the world, the study of parameters and elements of the climate has been widely considered. With changes in climate patterns and changes in temperature and precipitation patterns, other components such as runoff and soil moisture, which are important for natural and human systems, will undergo metamorphosis. Therefore, long-term prediction of climatic variables has been considered by many scientific communities worldwide in order to know about their changes and considering the necessary measures to moderate the adverse effects of climate change. The phenomenon of climate change is of increasing importance due to its scientific and practical dimensions, since human systems dependent on climatic elements such as agriculture, industry and the like are designed and operated on the basis of the stability and stability of the climate. Accordingly, general circulation models (GCMs) have been developed. Although these models represent significant results on the atmospheric and continental spatial scales, they combine a large part of the complexity of the planet's system, but they are inherently unable to control the dynamics and forms with a fine grid Local scalability. Therefore, an assessment of the effect of climate change on a local scale requires an interim and spatial gap between large-scale climatic variables and meteorological variables with local scale, in which case the main approach is the same downscaling models. The SDSM model is one of the most widely used statistical microscopic instruments, which has many uses in meteorological, hydrological, geographic and environmental studies. Because in this method, large-scale daily circulation patterns are used on a stationary scale; and when used for the rapid and cost-effective estimation of climate change, and for randomized meteorological generators and modified functions, have given acceptable results. Given that global models have generally simulated climatic elements until the year 2100, it is possible to use global model data to simulate the desired variables such as precipitation and temperature on a station scale. The Intergovernmental Panel on Climate Change (IPCC) has used its latest assessment report (AR5) on new scenarios for the RCP as representatives of different levels of greenhouse gas emissions. The new emission scenarios have four key paths RCP2.6, RCP4.5, RCP6 and RCP8.5, which are named after their radiation in 2100, Future Perspective. The variation of the maximum temperatures of the synoptic station of Urmia during the period (2021-2050) of the CanESM2 global model has been used under three scenarios RCP2.6, RCP4.5 and RCP8.5.

This study was performed to evaluate the extent of leaf area in Iran from (2002) to (2016) using data Remote sensing. For this purpose, we have attempted to extract data collection and leaf area index for the Iranian territory MODIS website. The database was established with the help of software programming in MATLAB to perform mathematical and Statistical calculations repeated. After the analysis of the data in this software was developed a long-term plan monthly average. The maps show that the central, East and South-East are almost empty of leaf area and leaf area or seen very sparse in some areas. In contrast with the northern half of the country's western areas of leaves are good, which generally includes fields, except forest Arasbaran and Hyrcany. Precipitation and the temperature, is the main factors for the growth and development of plants, these two conditions are enumerated in the West due to being in the westerly winds. Had the lowest leaf area index for January and February and the highest average leaf area May and June. Next, showed a study of 15 years of profile data range of leaf cluster analysis based on the calculation of Euclidean distance and Ward method, which fit all 12 months of the two main groups and, in fact, divided for two periods of strong and weak vegetation. In this analysis, they are located on a separate floor, April during the cold period and October in the warm period of the year as the transition months.

One of the main and known principles of sustainable development of agriculture is applying the agricultural lands based on their potential for the most appropriate use of agriculture. The aim of this research is zoning the capability of Mazandaran province's lands for cultivation of olive in relation with the desirable climatic needs of olive based on multi criteria decision making methods in GIS environment.The informative sources in this research are the climatic databases and physiographic resources. The climatic base data, including temperature, precipitation, freezing days, relative humidity, evaporation and transpiration were collected from the data of 28 synoptic, climatology and precipitation stations of the general offices of the concerned province were collected from the establishment time up to 2013 and the physiographic data include the elevation, slope , Direction, land use, soil and land suitability map. By using AHP method, the weighting criteria of each of the main data were determined: climate 0.590, topography 0.244, land capability of 0.112 and land use 0.044, and by overlapping them in GIS, the final map of olive cultivation for Mazandaran province was prepared. It was found that 21.2% is very suitable, 32.2% suitable, 34.2% medium and 12.3% unsuitable for olive cultivation. Therefore, the most suitable areas for cultivating olive, are the middle or foothills of Mazandaran province with height up to 900 meters From the sea level. The annual temperature of these areas is 17 to 20 degrees and their annual rainfall is 500 to 800 millimeters.

Rapeseed cultivation is growing to provide edible oils in temperate regions of Iran. In current study, environmental potential of Mazandaran Province was evaluated for rapeseed cultivation. The climatic data of the synoptic and climatological stations of the province such as temperature, rainfall, day-growth rate, sunshine, freezing days and relative humidity from 2001 to 2015 related to each phenological stage of rapeseed and environmental capability data such as land use capability, soil depth, elevation, slope and its direction were collected. To prioritize and evaluate the criteria for rapeseed cultivation, a hierarchical process analysis method was used. Then, weighing of the prepared layers based on the criteria and model, spatial analysis of information was carried out using TOPSIS model in GIS environment and a final evaluation of land suitability for canola cultivation was prepared. Finally, Mazandaran province was divided into four unlimited, low, moderate and high-limited regions for rapeseed. The results showed that a large part of Mazandaran Province has ecological potential for rapeseed cultivation.

Linking resolution global climate models with local scale is a micro climatic process that itself is a significant issue. Recently, attempts have been made by the climatology community to develop dynamics and statistical downscaling methods for expressing climate change has taken place at a local and regional scale. Two general techniques are used for downscaling of the output of general circulation models (GCM). The prior is using of statistical methods in which the output of a statistical model (MOS) and a planned approach to weather short-term numerical prediction is presented. The second is regional climate model (RCM), that same is limited GCM model in a subnet of the network global model and by dynamic method uses climatic conditions temporal changes according to GCM model. Both methods Play an important role in Determine the potential effects of climate change caused by increased greenhouse gas emissions. Much work is done to use this method for downscaling of the global model output in different areas In which the performance of the model is assessed and uncertainty analysis has been done on these methods or were compared by other statistical methods.
In this study, three approaches to statistical downscaling methods are provided. The first approach uses random generation of climate models based on time series and Fourier series delivers. LARS-WG statistical model(Rskv et al., 1991, 27) is one of the ways is built on the basis of this approach,. In this model, the empirical distribution of daily series of dry and wet precipitation and solar radiation use is desirable. The minimum and maximum daily temperatures as the daily stochastic process with mean and standard deviations are taken daily. Seasonal cycles by means of finite Fourier series are of the order of 3 models and model residuals (model errors) is approximated by a normal distribution.
The second approach is regression model or transfer function that is more used, which uses the relationship between atmospheric parameters and synoptic (predictor variables) and climatology Parameter that it is necessary to have a vision of the future(Instant predictor variable) is a transfer function is provided. One of the applications that combines these two approaches based on statistical downscaling model (SDSM) is. The meteorological station data required as input and output in seven steps GCM model on the basis of daily data in the area are downscaled.
The third downscaling model is artificial neural network (ANN), developed by Coulibaly et al., 2005. This model is a non-linear regression type in which a relationship is developed between a few selected large-scale atmospheric predictors and basin scale meteorological predictands. In developing that relationship a time lagged recurrent network is used in which inputs are supplied through tap delay line and the network is trained using a variation of backpropagation algorithm (Principle et al., 2000). A slightly different approach is used in selecting predictors for the case of neural network downscaling.
To compare data generated models and observations can be compared to an average of two non-parametric test Mann-Whitney society that is using correlation analysis. For the observed data and the model can be generated from correlation Spearman used. The basic correlation analysis based on linear correlation coefficient of the two variables. One of the important indicators that can be used for performance evaluation model, index model mean square error (RMSE) is defined as follows:The area North West of Iran, which includes the provinces of East and West Azerbaijan, Ardebil, Zanjan and a part of Kurdistan is the geographical coordinates '30 ˚49 '07 ˚44 to the East and the '00 ˚36 to '50 ˚39 North, is located. To study the effects of climate change in the region, using statistical models mentioned the need for a minimum period of 1961-1990 is based. In addition to the complete statistical period synoptic meteorological stations of old climate data confirmed the country's Meteorological Agency has been helping though some regional stations are multi-year statistical vacuum.
The results show that according to the Mann-Whitney test the performance of three models for minimum temperature in the study area are close. Spearman correlation test results for minimum temperature show that the number of correlation, in all stations for LARS-WG model is less than the other two and demonstrate low performance LARS-WG model is in this respect. The average number of months with significant correlation for ANN model with seven months of the year, the best performance among the three models in this respect. SDSM model with a four-month correlation table in the middle. In terms of RMSE index for the minimum temperature, LARS-WG and ANN models have average values are close together and show the error of sum of squares closer together the two models. RMSE values are less than the SDSM model and this shows the SDSM model is less than the other two models.
According to our evaluation, according to Mann-Whitney test data generated in which the difference between the observed and tested model placed, Parameters for minimum and maximum temperatures, three models have not different performance. But the results were somewhat different in different stations. Correlation data for SDSM and ANN models for maximum high temperature and minimum temperature for solidarity in SDSM model is less than ANN model. However, because the same structure prediction methods and large-scale use of such an outcome was not unexpected.
Mann-Whitney test for precipitation results show that significant differences observed and modeled data for ANN model is much more than the other two, which reflects the low performance of this model. SDSM and LARS-WG model and have similar good performance in this regard. The Spearman correlation test, all three models have a low correlation was observed and the model and represents the three models in the study area in this respect is low. According to the RMSE, the SDSM model is better than the other two models have average performance.

Rivers flow prediction in river basins has an important role in the operation and correct management of water resources. Determining type and number of estimator models inputs is one of the important steps in rivers flow prediction. Therefore, The Procrustes analysis (PA) method for determining the number of effective inputs was used. In this study, flow prediction was done using the flow data obtained from the Safakhaneh and Santeh hydrometric stations. The Artificial Neural Network (ANN) and The Support Vector Machine (SVM) models was used for flow prediction. The best estimation of flow is done using the MLP and SVM models in Safakhaneh hydrometric station with RMSE equal to 5.68 (m3/s) and 4.85 (m3/s), respectively, and CC equal to 0.73 and 0.78, respectively. While in Santeh hydrometric station RMSE was equal to 6.44 (m3/s) and 6.36 (m3/s) respectively, and CC was equal to 0.78 and 0.79 respectively for MLP and SVM models. PA-SVM model showed better results than SVM model in estimating Safakhaneh hydrometric stations flow with RMSE equal to 5.45 (m3/s) and CC equal to 0.73 during the test period. The results also indicated that SVM and PA-SVM models estimated the flow of Santeh station with RMSE equal to 6.85 (m3/s) and 7.03 (m3/s) respectively. Basically, results indicated that the Procrustes analysis method can be used as one of the Efficient and suitable methods for determining the number of effective inputs. Comparison of the ANN and SVM results indicated that ANN model has more accuracy than SVM model.

Flooding is a major natural disaster. This phenomenon becomes more important when it is occurring especially in the city. Therefore, prevention of the communities from the impacts is inevitable. The high level of awareness of potential runoff area for flood management is important. Given this importance, this paper deals with the calculation of runoff with SCS method in Meshkinshahr city and its surrounding area, For this purpose, we used GIS software and the accession of the ARC-HYDRO, especially Arc-CN-RUNOFF software. Finally, the layers of soil hydrologic group, the integration of land use and rainfall averages were compared with the index table, then the resulting map was obtained. Curve number Map (CN), which was proportional to the permeability of the 20 classes. Curve No (32) for areas with high permeability and low runoff and curve number is high (98) for areas with low permeability and high runoff. Dimensional map of the height map is runoff from zero to areas with very high permeability and high runoff starts 99/0, including the use of low permeability to continue. Check the map showed that both CN and RUN-OFF lot to do with the type of the user, so that the use of man-made and synthetic runoff curve number and height are more.

This paper presents a novel time-frequency analysis based technique for detection of synchronous generator islanding conditions. In the proposed method the output signals of Distributed Generation are measured locally and analyzed in time-frequency domain. In order to detection of islanding condition, the proposed method is used of energy variations of the rate of change of active power wavelet coefficients signal in a two-dimensional form. By considering the place of signals in any time, the islanding condition could be detected. At first, all possible linear and nonlinear load switching, motor starting, capacitor bank switching, and islanding conditions, are simulated and the required detection parameters measured. Using the discrete wavelet theory, the energy of any decomposition level of all mother wavelet for parameters detection is calculated. From of these signals, the best of them are selected for ANFIS training for islanding. Simulation results confirm the performance of the proposed detection algorithm in comparison with existing methods in islanding and switching conditions.

Linking resolution global climate models to local scale as a micro climatic process is a significant issue. Recently, attempts have been made by the climatology scientists to develop dynamics and statistical downscaling methods to express climate change at a local and regional scale. Two general techniques are been used for downscaling of the output of general circulation models (GCM). The further is by statistical methods in which the output of a statistical model (MOS) and a planned approach to weather short-term numerical prediction is presented. The later is regional climate model (RCM), same as limited GCM model in a subnet of network global model by dynamic method that uses climatic conditions temporal changes according to GCM model. Both methods play an important role to determine the potential effects of the climate change caused by increased greenhouse gas emissions. Much work is done to use this method for downscaling of the global model output in different areas in which the performance of the model is assessed. Uncertainty analysis has been done on these methods or compared by other statistical methods. In this study for more accurate validation of the two methods, uncertainty analysis is done on input data of daily temperature and precipitation. In uncertainty analysis of daily temperature data because of similarity of the statistical distribution to normal distribution, the monthly mean of downscaled data are statistically compared with observed data. In this case, parametric or non-parametric tests can be used to compare means. However, in uncertainty analysis of daily rainfall data because of lack of normality, comparison of the averages of downscaled data and observed data are not sufficient and should be compared with the dry and wet periods. In this method, the statistical distribution of downscaled dry and wet period durations is compared with the observed ones. Before the uncertainty analysis, first, an exploratory analysis is performed on the data in the data statistical condition and the approach to data analysis to be ascertained. This analysis is based on the study of statistical assumptions of the model. If these assumptions are not established, the statistical analysis parametric methods and respective tests lose their credibility and nonparametric methods must be used. These assumptions are: 1. Data come from normal or near to normal distribution. 2. Data standard average is close to zero or there is no outlier. 3. Data have little temporary correlation. Daily rainfall varies because of the skewness of the right (frequency of daily precipitation amounts toward zero), the normal assumption is questionable. Therefore, power transformations are used for normality of rainfall data in statistical analysis and modeling. On the other hand, the daily temperature data are normal in nature and there is not outlier. But, because of temporary correlation of daily temperature, the third assumption did not establish for them. Therefore, parametric methods are used for statistical analysis and comparative tests. According to the three basic assumptions of the model, daily rainfall data are far from a normal distribution and the data have a lot of outlier points. But, there is not significant correlation period. Unlike the daily maximum and minimum temperature data of relatively normal distribution, they have not had many outlier points. But, there is a significant correlation of the data with time. Therefore, it can be concluded that none of the data of temperature and precipitation have conditions of the three basic assumptions. Therefore, non-parametric methods must be used for statistical analysis and modeling. Or alternative parameters are used such as the number of days wet or dry. For the uncertainty analysis and comparison of the two models SDSM and LARS-WG we have used graphical and statistical methods. In this study, the absolute values of the differences between downscaled values and observed values in the 1961-1990 statistical periods are used as an indicator in the graphical analysis. Results of graphical analysis show that values of the absolute differences in different months of the SDSM model parameters are the minimum and maximum daily temperature LARS-WG is better than the model. The daily rainfall amounts during the months of absolute difference of the two models are relatively close to each other. Of course, the results of stations in Tabriz and Urmia are slightly different but are not significant. To evaluate the significance difference between observation and downscaled values in two models, we have used Mann-Whitney test. The results show that for the minimum temperature, in both models SDSM and LARS-WG almost in half of the months, the model error is significant, although the SDSM model is better. However, the tendency to work and choose the most appropriate model for large-scale predictor variables from the NCEP-NCAR data were obtained from the appropriate geographic region and it is possible to achieve better performance. Parameter maximum daily temperature for the SDSM model has better performance than the LARS-WG, which confirmed the results of the chart. The downscaled maximum daily temperature has less error than the minimum daily temperature. Especially in SDSM model in Tabriz station only January has a significant error in the model. It has good performance for daily precipitation models. Especially in Tabriz station the values of the model error is not significant in any of the months. In accordance with the similar results, the performance of the two models is similar for the daily rainfall. The results of this study indicates that in accordance with the results of the statistical downscaling SDSM and LARS-WG on stations of Tabriz and Urmia for daily minimum and maximum parameters, SDSM model has better performance than the LARS-WG. For daily precipitation performance, the two models are similar in the two stations. However, as the statistical distribution of daily rainfall data is not normal, the results of the models cannot be trusted. It is suggested that instead of precipitation, in the analysis we used the number of dry and wet days.

Canola is the world third leading oil seed after soybean and oil palm by producing 15 percent of the global plant oil. Climate، topography and lands capability are the most important environmental factors on which the crop production capability in a region depends. Therefore by evaluating these factors one can identify suitable lands for canola cultivation. In the present study، the climatological data such as temperature، precipitation، growth degree day، relative humidity، freezing days، and sunshine hours were collected from the West Azerbaijan province synoptic and rain gauge stations (since their installation untill 1388) which were associated with the phenologic stages of canola growth. In addition to the climatological data، earth resources like topographic layers، lands capability، soil depth and land-use were analyzed focusing on the climatological and ecological requirements of canola. After generalizing the data and processing by using ArcGIS، their corresponding information layers were derived. In order to prioritize and assess the criteria and information layers in relation to each other، the multi criteria decision method was employed based on analytical hierarchy process. Then، combination and spatial analysis of the information layers using TOPSIS model and GIS capabilities were done and the final capability ecological evaluation layer for canola cultivation was produced. Based on the obtained results، the province lands were divided into four categories of highly suitable (%18. 6)، suitable (%34. 4)، moderate (%32. 1) and weak (%14. 7) lands on the basis of the environmental and climatologocal potentials for canola cultivation.

Agricultural meteorology studies the effects of climatic factors on the living organisms (plants and animals) and interaction of climate and soil for maximum utilization of the environment. The purpose of this study is to determine Agroclimatic areas based on climatic indices including estimation of water, heat and effective precipitation requirements for wheat dry planting during the growth period. The data including average daily temperature, mean of maximum and minimum temperatures, monthly and annual precipitation, mean of monthly relative humidity, Evapotranspiration, transpiration, were collected from 10 synoptic meteorological stations in the province. Using FAO – Penman – Monteith was used to estimate the required water, temperature and density for wheaف dry planting. Descriptive calculation of data shows that the least water requirement is for Abatv Zarina station (352. 2) and the highest one is for Marivan and Sanandaj stations i. e. 388. 5 and 383. 3 mm, respectively. And the highest amount of Evapotranspiration and transpiration is for Sanandaj station (2. 47 mm per day) and Marivan (2. 52 mm per day) and and its lowest amount is for Zarina Abatv (2. 36 mm per day) and Bane (2. 43 mm per day). Marivan and Qorveh stations with 764. 9 mm and 314 mm, respectively, have the highest and lowest effective precipitation. Using GIS zoning maps, the data was located as the spot at each station of the province and by interpolation method, the maps related to the under study data were prepared. The results show that, about 25 percent of the area of Kurdistan, including the Northwest and West regions of the province due to its favorable climatic conditions is suitable for wheat dryland, and an area of about 47 percent including the east areas have a moderate condition and 28 percent of the province area i. e. Qorveh and Bijar cities have not suitable conditions for wheat rainfed.

The present paper introduces new drought indices with caption standardized precipitation evapotranspiration index (SPETI) that the first by Vicente-Serrano et al (2009) is presented. The SPETI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a log-logistic probability distribution. Mathematically, the SPETI is similar to the standardized precipitation index (SPI), but it includes the role of temperature. Because the SPETI is based on a water balance, it can be compared to the self-calibrated Palmer drought severity index (sc-PDSI). Time series of the three indices were compared for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions, only the sc-PDSI and SPETI identified an increase in drought severity associated with higher water demand as a result of evapotranspiration. Relative to the sc-PDSI, the SPETI has the advantage of being multiscalar, which is crucial for drought analysis and monitoring.
Material and We describe here a simple multiscalar drought index (the SPETI) that combines precipitation and temperature data. The SPETI is very easy to calculate, and it is based on the original SPI calculation procedure. The SPETI uses the monthly (or weekly) difference between precipitation and PET. This represents a simple climatic water balance (Thornthwaite, 1948) that is calculated at different time scales to obtain the SPETI. The first step, the calculation of the PET, is difficult because of the involvement of numerous parameters, including surface temperature, air humidity, soil incoming radiation, water vapor pressure, and ground–atmosphere latent and sensible heat fluxes. Different methods have been proposed to indirectly estimate the PET from meteorological parameters measured at weather stations. We followed the simplest approach to calculate PET (Thornthwaite 1948), which has the advantage of only requiring data on monthly-mean temperature.
With a value for PET, the difference between the precipitation P and PET for the month i is calculated using which provides a simple measure of the water surplus or deficit for the analyzed month. Tsakiris et al. (2007) proposed the ratio of P to PET as a suitable parameter for obtaining a drought index that accounts for global warming processes. This approach has some shortcomings: the parameter is not defined when PET = 0 (which is common in many regions of the world during winter), and the P/PET quotient reduces dramatically the range of variability and deemphasizes the role of temperature in droughts. The calculated values are aggregated at different time scales, following the same procedure as that for the SPI.
For calculation of the SPI at different time scales, a probability distribution of the gamma family is used (the two-parameter gamma or three-parameter Pearson III distributions), because the frequencies of precipitation accumulated at different time scales are well modeled using these statistical distributions. Although the SPI can be calculated using a two-parameter distribution, such as the gamma distribution, a three-parameter distribution is needed to calculate the SPETI. In two-parameter distributions, the variable x has a lower boundary of zero whereas in three-parameter distributions, x can take values in the range where is the parameter of origin of the distribution; consequently, x can have negative values, which are common in D series.
The probability density function of a three-parameter log-logistic distributed variable is expressed as where , , and are scale, shape, and origin parameters, respectively, for D values in the range ().
Parameters of the log-logistic distribution can be obtained following different procedures. Among them, the L-moment procedure is the most robust and easy approach (Ahmad et al. 1988). When L moments are calculated, the parameters of the Pearson III distribution can be obtained following Singh et al. (1993).
The probability distribution function of the D series, according to the log-logistic distribution, is given by The F(x) values for the D series at different time scales adapt very well to the empirical F(x) values at the different observatories, independently of the climate characteristics and the time scale of the analysis. With F(x) the SPETI can easily be obtained as the standardized values of F(x).
Where and P is the probability of exceeding a determined D value, P =1- F(x). If P >0.5, then P is replaced by 1- P and the sign of the resultant SPETI is reversed. The constants are ,,,,and .
The average value of SPETI is 0, and the standard deviation is 1. The SPETI is a standardized variable, and it can therefore be compared with other SPETI values over time and space. An SPETI of 0 indicates a value corresponding to 50% of the cumulative probability of D, according to a log-logistic distribution.
The result of monthly calculated of SPETI and SPI for the 12-month time scale in Tabriz station for period 1951-2010 shows despite the little difference between two indicies but in last 16 year the SPETI shows drought better than the SPI so the number of drought years in SPETI is greater than SPI due to using temperature in SPETI, the severe wet( severe drought) years that SPI shows because of high temperature( low temperature) are adjusted and the opposite is also true. This means that when the SPI shows normal or near normal condition the SPETI shows drought (wet) years because of high (low) temperature.
The last 16 years is an example of situation due to rising temperature which the SPETI the more drought year than SPI. For example, when the SPI is in normal condition in 2002 and 2007 years, the SPI indicates the drought condition for those years.
According to the results the SPETI has more advantages than SPI for monitoring drought because of being multiscalar and using a few meteorological parameters for calculation. Last 16 years shows this difference because of decreasing rainfall and rising temperature.