جستجوی مقالات مرتبط با کلیدواژه « wet » در نشریات گروه « جغرافیا »

Extreme rainfall fluctuations are one of Iran's environmental challenges. The heavy rains of 2019 and the relative drought of 2018 and 2021 show the unevenness of rainfall.

This study utilized the daily rainfall data from 360 weather stations spanning the years 1990 to 2021, along with the synoptic indices of the winter seasons of 2019 and 2021. The geographical range of the data, spanning from latitude N 10-90 to longitude E º 10-90, was chosen to provide a comprehensive view of the Iranian climate.

The probability of drought is 42%, drought is 35.5%, and normal conditions are 22.5%. Markov chain indicated the highest probability of normal drought to drought at 57% and consecutive drought at 46.2%. ANOVA showed a significant difference between the climatic variables of wet and dry years. Observational conditions showed that in the wet year (2019) sample year, the Siberian high-pressure center operator had more pressure (1054 megabytes) in 2019 and 1032 mb in 2021. In the wet year, the frequency of cyclones and the temperature difference at the level of 500-1000 are higher, but the omega values are lower. These conditions are for the drought (2021), the amount of pressure in Siberia, the temperature difference between 500 and 1000 is less, and Omega is more (stable air).

The amount of rainfall in Iran fluctuates from year to year, and low rainfall years occur more often than high rainfall years.

Innovation: 

Planners should increase the risk of water supply and development and plan based on the minimum amount of precipitation. The physical capacity of the country's ecology should be reviewed, and population concentration in limited areas should be avoided to reduce the vulnerability of the country's geographical environment.

The purpose of this research is to determine the best deployment position and stretching pattern of the side-to-side jet to create tarsal in the southwest of Iran, as well as to select the most frequent synoptic pattern in this region.

The daily rainfall data of stations in the southwest of Iran (Khuzestan, Chaharmahal, Bakhtiari, Kohgiluyeh, and Boyer Ahmad) during the statistical period in three solar cycles (1986-1997), (1997-2008) and (2008-2019) were received from the National Meteorological Organization. Orbital and meridional wind component data, geopotential height at 250 and 300 hPa levels, slp, specific humidity and omega from the US National Center for Oceanography and Environment (NCEP/NCAR) with a spatial resolution of 2.5 × 2.5 degrees. received. Then, using factor analysis and visual inspection, the most frequent patterns were determined for all three solar cycles.

The examination of the maps of the lower, border and middle level of the troposphere showed that in the severe droughts of southwest Iran, the establishment of high pressure Arabia over the Arabian and Oman seas and the movement of moisture from these seas to the low-pressure eastern slope of Sudan and the formation of a deep trough in the west of Iran from Europe to southern Sudan and Cold subpolar advection on the western low pressure slope of Sudan is the most suitable pattern for heavy rains and its continuation for several days in the southwest of Iran. The best location of the subtropical jet to strengthen the rain systems is the area between Egypt and the western coast of the Persian Gulf in southern Iraq, if the core of the jet is located in northwestern Saudi Arabia. And the main source of system moisture in all precipitation samples is the Arabian, Oman and Mediterranean seas.

In the study of the trend of dust storm index, the results showed that the study period of 2003-2007 in Semnan province has an increasing trend and has shown significant changes in the 95% confidence range, but the lack of significant changes in the last decade shows the effects of various events. In cross-cutting decisions in the field of dust in the region. The zoning of the DSI index changes in different regions of the province in a 15-year statistical period indicates that from the west to the east of the province due to the increase in the frequency of stormy days with moderate dust (MDS), dust has increased. The correlation between drought and DSI index in Semnan province showed that although DSI index increased during the period under analysis with increasing drought intensity and its correlation with drought during the 15-year period was not significant, but the pattern of DSI index is consistent with It is the pattern of the drought process. According to the results, it can be acknowledged that the dust situation has always been affected by climate, but the relationship between drought and the DSI index has always fluctuated with respect to droughts and wetlands. However, different climatic parameters are different and their impact is different. In addition to human activities, the main role of wind in the amount of dust or the existence of another source of dust should be considered.

Probability distributions and different mixed regression models are used to study of wet and dry spells. In this study, eight stations on the southern coast of the Caspian Sea were used for 55 years (1960 to 2015). One and two day wet spells are the most frequent in the area. Also, four and higher days wet spells in the western region is more than the east. The best probability distributions of wet spells are two-parameter gamma, two parameter normal log, two parameter logistic log, four-parameter generalized gamma, generalized three parameters gamma, two-parameter gamma, three-parameter gamma and a three-parameter logistic log. Also, the dry spells of the region are better fitted with generalized Parto, generalized logistics and generalized extreme value distributions. Due to the random behavior of the annual wet and dry spells, the best generalized linear mixed model for wet spells is the negative binomial log, Log Poisson, log normal and gamma logarithmic, also the Log Poisson, log normal and the negative binomial distribution was determined for the annual dry spells.</div>

Suspended load is one of the most important factors in taking decision to create aquatic construction and to determine useful life of dams. Lack of long time measurements of erosion, as well as insufficient researches in this field, limit the access to trustable data and therefore we usually use estimating methods. Whereas sediment rating curve and its adjustment are used as basic methods for estimating suspended sediment load, in this research we used these methods for both of regions. To determine most appropriate method for estimating the suspended load in part of west and north of Iran, flow discharge and corresponding sediment discharge of 12 (6 arid and 6 wet gauges station were analyzed. results showed correlation coefficient in mean load equations is more than single-linear equations in both of wet and arid regions. In the other hand results showed Root Mean Square Error of MVUE factor is less than other Correction factors in both of wet and arid regions.

Water, according to the climatic conditions in arid and semi-arid, in most areas of our country, the most vital matter. Humidity is an important factor in a rain Therefore, identification and knowledge of how to transfer moisture, vibration and affecting rainfall is in a critical area. Therefore, in this study re-analyzed data of precipitation ERA interim, geopotantial, not special and components (u,v) the wind in the period 1981-2011 with a spatial resolution of 1 arc in the first grade level climate prediction center of the mid-term Europe (ECMWF) were obtained. Using the Standardized Precipitation Index (ZSI) periods of drought and wet extraction, and moisture flux entering the area during this period were calculated. For comparison, circulation patterns and changes in the flux of moisture in dry and wet samples were taken. This variability makes changes in a rain storm and the subsequent occurrence of drought and wet years in the region. The amount of moisture flux and the total thickness of all layers of the atmosphere in the wet than dry years, respectively.

Drought means scarcity of water, which various sectors of human society, agriculture, water supply, industry and environment can adversely affect due to its occurrence. Drought is best characterized by multiple climatological parameters as a natural hazard. An understanding of the spatio-temporal dependence relationships between effective variables on drought is necessary to its description and analysis. The main purpose of this study is investigation of climatological drought in synoptic station of the Kurdistan Province, Iran, by using Triple Diagram Model (TDM). The data consists of monthly average temperature, precipitation, and also relative humidity from seven synoptic stations. The standardized precipitation index (SPI) was calculated for each station. The semivariogram of different used models have been analysed and were been chosen using the cross validation method. According to coefficient of determination and residual sum of squares indices appropriate method was selected for drawing triple diagram models. The triple diagram models have been prepared based on geostatistic principles by SPI, Temperature, and relative humidity variables. The results indicate that SPI classification is characterized by the scattering in two input variables can provide the analysis framework of the characteristics and variations of climatological factors. The spatial relationship between stations/regions can be carried out by comparison of the humidity conditions. Application of the triple diagram models can be recommended for drought analysis in other climatological stations. The triple diagram plots help to make interpretations in spite of extreme scatter points facilitate the explanations of the influencing trend among variables.

Variability and changes in precipitation amounts, including extreme precipitation and droughts, trigger relevant societal and environmental impacts. Atmospheric circulation strongly determines precipitation variability over southern half of Iran. The influence of prevalent synoptic situations on the distribution of precipitation amounts needs to be understood, as well as their frequency changes to appraise the impact of atmospheric circulation on intra- and inter-annual (decadal) fluctuations of precipitation amounts. On the other hand, the atmospheric moisture budget plays important role in the hydrology of this region. The aridity in the southwest and south of Iran is due to subsidence scale caused by Hadley circulation and a location far from oceanic sources of moisture. However the moisture and other properties of atmospheric quantities are transferred by local circulation from another source to the given region. Many investigations have been carried out on this subject. The consideration of the precipitation and its source region of moisture flux over south and southwest of Iran, during 1970-74 has been shown that 23 percent of the total low pressure system which passed over this area are from the southwest of the Red Sea (Faraji, 1982). In the investigation of moisture flux over Iran, which is carried out by Alijani (1995) noted that the Mediterranean Sea is the main source of moisture for precipitation over Iran. Evans, Smith & Oglesby (2004) have shown that the Persian Golf and the Caspian Sea are the main sources of moisture for precipitation over the Alborz and the Zagros mountains. They have shown that the subtropical jet current brings the warm and moist air from the South part of Saudi Arabia and the Aden golf to the Middle East in the middle and higher troposphere (Dayan &Abramski, 1983). The Red sea inverted trough at the surface with regard to its amplitude, is the Major source that transfer warm and moist air from the Aden golf to the East and North of the Arabian peninsula and also southwest and south of Iran (Alpert, 2001). The low pressure system which is formed at southwest of the Red sea is associated with high potential acceptability of moisture and warm air due to its characteristics of dynamical and thermo dynamical pressure for heavy rainfall over the study area. Rurerde (2006) using specific humidity map and its extension from the Caspian Sea to the North part of Iran derived a value of moisture quantities from each source region of moisture to the precipitation over Iran. The principal objective of this research was to understand the characteristics of moisture flux from each region of moisture source and their contribution to the rainfall during the above period.
Study Area : The study area for this study is southern half of Iran, which lies approximately between 25N and 35N in latitude and between 44_E and 64_E in longitude. The study area of this research contains some provinces such as: Ilam, Khozestan, Lorestan, CharmahalBakhtiyari, Kohkiloye and Boyerahmad, Bushehr, Esfehan, Yazd, Kerman, Hormozgan, Fars, and Khorasan Jonobi. Based on the Koppen climate classification, most parts of this area are categorized as generally having arid (BW) and semi-arid (BS) climates. The important mountain of this area is the Zagros, which play an important role in nonuniform spatial and temporal distribution of precipitation.
Material and Examination of precipitation properties needs long and high quality records of data. In the present study, the time series of precipitation data at 183 stations for the period 1974-2004 were collected from the Islamic Republic of Iran meteorological organization (IRIMO) and were analyzed. Data homogeneity is assessed by IRIMO previously. The Empirical orthogonal function (EOF) was applied to detect and describe spatial and temporal change in the precipitation. In addition, the principal component (PC) was applied to detect dry and wet years. In addition, to identify the spatial and temporal variations of the precipitation, we used the NCEP-NCAR reanalysis monthly mean values of the temperature, Geopotential height and pressure in 1000, 850, 500 and 300 hPa levels. These data have been widely used by many researchers over the last few years in tropical climate research. Finally, we calculated 30 years anomaly for these levels in dry and wet years.
The maps of the wettest months have shown the synoptic situation over study and its adjacent area during the above period. These figures have show the high pressure over the Oman and the Arabian seas, deep trough over the East of the Mediterranean and the west of the Red sea. In this case, eastern Saudi Arabia received more moisture and latent heat fluxes than the other parts of the area. Two moisture source regions as observed from the divergent flux, which are located over the Aden golf and Red sea, supply moisture to study region. Southern half of Iran receives precipitation during this study, mainly from the west of the Indian Ocean (the Arabian Sea, the Oman Sea, The Aden golf), the Red sea and the some extent from the Mediterranean. The divergent moisture and latent heat flux during wet cases were observed over north part of the Arabian Sea, the Aden golf, the central part of the Red sea and convergence was observed over South, central of the Arabian Peninsula, southern half of Iran. These processes are indicative of the upward motion of moist air, which in turn may release latent heat due to condensation. This available heat energy may be the source of low-level latent heat instability. The large amount of moisture and latent heat flux over Saudi Arabia in is transported, by rotational wind from the Red sea and the Mediterranean as mentioned above. Conversely, in dry years due to weak trough of East Mediterranean, systems move from latitudes upper than 40ºN and the cold air does not transfer from the southeast Europe to the Northeast of Africa resulting development of Sudan low. Overall, decreasing transfer of cold air will lead to decreasing of moisture and latent heat flux from the Indian Ocean to the southern half of Iran.
Atmospheric circulation strongly modulates precipitation patterns. Precipitation is one of the most important atmospheric variables in the global hydrological cycle and plays a key role in the Earth’s energy balance.The investigation of synoptic maps in 1000, 850 and 500 hPa levels in four systems shows that in wet years due to deep trough of East Mediterranean and West of the Red Sea, the cold air transfers from the southeast Europe to the Northeast of Africa resulting development of Sudan low. This low transfer to the center and north of the Arabian Peninsula, then move to the Southern half of Iran. The development of this low is due to release of sensible heat from the Arabian Desert and latent heat flux which transfer from the Indian Ocean, to this area. This low affects the southern half of Iran with significant value of perceptible water and low- level latent instability. It is seen that the transfer of moisture flux in the low-layer from the Indian Ocean plays an important role in precipitation over study region. Conversely, in dry years due to weak trough of the East Mesiterranean, systems move from latitudes upper than 40ºN and the cold air does not transfer from the southeast Europe to the Northeast of Africa resulting development of Sudan low. Overall, decreasing transfer of cold air will lead to decreasing of moisture and latent heat flux from the Indian Ocean to the southern half of Iran. The findings of this study confirmed the results of the previous studies (Alijani, 1995; Farajzadeh, 2007;Milind, 2006; Mofidi&Zarin, 2005).

IntroductionDrought is a temporary and recurring meteorological event which results from the lack of precipitation over an unusual extended period of time. Early indication of possible droughts can help set out drought mitigation strategies and measures, in advance. Therefore, the drought forecasting plays an important role in the planning and management of water resource systems. Stochastic models have been extensively used for forecasting hydrologic variables such as annual and monthly stream flow, precipitation, and etc. in the past. But they are basically linear models assuming that data are stationary, and have a limited ability to capture non-stationarities and nonlinearities in the hydrologic data. However, it is necessary to consider alternative models when nonlinearity and non-stationarity play a significant role in the forecasting. In the recent decades, artificial neural networks have shown great ability in modeling and forecasting nonlinear and non-stationary time series due to their innate nonlinear property and flexibility for modeling. The aim of this study is to compare the stochastic and artificial neural network models in forecasting the standardized precipitation index (SPI) in some stations of Iran. This is because of the multiplicity of drought occurrences in Iran and the necessity to determine the best forecasting model.MethodologyThe monthly total precipitation data (1973-2007) related to four synoptic stations of Iran including Bandar Anzali (with very wet climate), Hamedan Nojeh (with semi arid climate), and Bushehr (with arid climate) and Zahedan (with hyper arid climate) have been used after the homogeneity and adequacy of data have been confirmed by statistical tests. In the present study standardized precipitation index (SPI) time series (at 3-, 6- and 12- month timescales) have been calculated for the period of 1973-2007. The most suitable distribution function for precipitation at 3-, 6- and 12- month timescales has been determined by Easyfit software on the basis of kolmogorov-Smirnov statistic. This is performed separately for each month. Then, each cumulative probability density function is transformed into a cumulative standardized normal distribution. The SPI values for the period of 1973-2000 are used to calibrate the models and the rest of the data to be tested. Development of stochastic model consists of three stages of identification, estimation, and diagnostic checking (Box and Jenkins, 1976, 19). During the identification stage the candidate forms of the models are determined using the autoregressive function (ACF) and partial autoregressive function (PACF) and general forms of the models are determined on the basis of Schwarz Bayesian information criterion (Schwartz, 1978, 461–464) and Akaike information criterion (Akaike, 1974, 716–723). In the estimation stage the model parameters were calculated using Minitab14 software. Finally, diagnostic checks of the model are performed using kolmogorov-Smirnov (K-S) and Portmanteau test (Makridakis et al., 2003, 185) to reveal possible model inadequacies and to assist in selecting the best model. In the present paper two different approaches of neural networks including recursive multistep neural network approach (RMSNN) and direct multi-step neural network approach (DMSNN) are used for forecasting several time steps ahead. The RMSNN approach based on one output node forecasts a single step ahead, and the network is applied recursively, using the previous predictions as inputs for the subsequent forecasts. DMSNN is based on the multiple outputs, when several nodes are included in the output layer, and each output node represents one time step to be forecasted. The models are evaluated with statistical tests, correlation coefficient, and error index for 1- to 12-lead time ahead forecasting over the period of 2001- 2007. Also, the capability of the models in forecasting the SPI classes is investigated using Cohen’s Kappa statistic (Cohen, 1960, 37–46).Results and DiscussionThe results of stochastic modeling of SPI time series showed that the null hypothesis related to the normality of residuals is accepted for 3- and 6- month time scales but rejected for 12-month time scales at 1% significant level in all stations. The results of Portmanteau test signify that the chosen stochastic models are adequate on the available data at 1% significant level. The results of artificial neural networks (RMSNN and DMSNN) modeling of each SPI time series are presented as optimal architectures of the best number of input and hidden neurons. The significance lead times of drought forecasting are determined based on correlation coefficient and Kappa statistic between the observed and forecasted values of the SPI time series in the stations of interest. Accordingly, the most appropriate models for SPI values and classes have been determined by a comparison of three models for each time series.ConclusionThe results have revealed that generally, for 3-, 6- and 12-month time scales, stochastic models (with average error of 0.678, 0569 and 0.344 and average correlation coefficient of 0.682, 0.777 and 0.919, respectively) are more accurate than artificial neural network models to forecast SPI values. The comparison of models in forecasting SPI classes also showed that the most accurate model for forecasting SPI classes for 3-, 6- and 12-month time scales is DMSNN, RMSNN and stochastic model (with average Kappa of 0.397, 0530 and 0.750) in sequence.

Authors have studied the precipitation fluctuations and rainfall changes inAhar synoptic station in this paper, applying long term monthly records for the47 year period (1960-2006). Results of using Man-Kendal test indicatesignificant correlation between precipitation changes and tele-connectionpatterns in the study area. With regard to this matter, the relationship of autumnprecipitation with El-Nino atmospheric-oceanic pattern (so-called ENSO) duringwarm phase increases precipitation and the same linkages with La-Nina duringcold phases enhances droughts. The correlation of winter precipitation with teleconnectionpatterns of Northern Atlantic and Arctic Ocean has been alsosignificant in such a way that during the North Atlantic negative phases themagnitude of incoming cyclones to the area creates higher rates and on the otherhand, in positive phase causes a decrease in precipitation due to the dominanceof Azure Anticyclone. Testing of Standardized Precipitation Indexes (SPI) forevaluating monthly wet and dry periods also indicates that the occasionaloccurrence of drought periods in the area is irreversible. The independence ofmonthly wet and dry periods is also one of the main results of this study.

In order to analyze the precipitation of the Larestan area, the rain days with 0.1millimeter or more were obtained from the Iranian Meteorological Organization for the1960-2003 period. First the rainy periods with different lengths were identified andtheir monthly and seasonal frequencies were calculated. On the monthly basis Januaryhad the highest wet days frequency and winter was the wettest but the spring was thedriest season. The wettest year had 44 rain days while only 11 days were experiencedduring the dry year. The mean daily density of rain was 8.2 mm and the mean timeinterval between successive rainy periods was 6.2 days. On the average the rainyperiod begins each year on 8 of December and ends on 6 of April.The first order Markov chain was applied to the data series to forecast the wetperiods. The model responded well and was able to forecast significantly andprecisely. The model was fitted best for the runs of one to six days proving thehypothesis of the study.