Rainfall is considered as one of the most difficult and complex elements of the
hydrological cycle, to understand and model, due to the complexity of air operations that
generate rain. The importance of research comes from the direct relationship b
etween the
rainfall amount and economic & social activities of the population, planning scopes of the
water resources management, particularly with respect to the agricultural development.
The research aims to highlight the rainfall amounts in Tartous station which is
located in the southern part of the Syrian coast, and applying one model of Box-Jenkins
models for the purpose of predicting future rainfall amounts. Multiple Arima models have
been tested. The results showed that the model SARIMA (3,0,4) was the best one. Data
were divided into 43 years to build the model and eight years to test it. The test results
gave high accuracy in the performance, and the model was used to predict the values of
annual rainfall for the next twenty years.
Due to the importance of climate changes and their strong and increasing influences
on different human and ecological systems, It is necessary to study and understand these
changes. This research aims to determine the direction and magnitude of the
change in
temperatures and rainfall trend during 1978-2011 in Latakia, Kasaab and Slenfeh. The
study has been done through the analysis of annual, seasonal, and monthly rainfall amount
and temperature average, which showed a significant increasing in the average annual
temperature in the three regions and significant seasonally increasing except for Winter in
Latakia and Slenfeh, Autumn in Latakia. But the annual rainfall changes were nonsignificant,
while seasonally rainfall increased significantly in Slenfeh Winter and
decreased in Kasaab Winter. After dividing the entire study duration into two equal periods
1978-1995, 1995-2011 and comparing the second period with the first one, we found a
significant increasing in the average annual temperature in the three regions,
non- significant in the annual and seasonal rainfall changes. The highest significant in
seasonal temperature in Latakia, Kasaab and Slenfeh reached +1.5, +2.9, +1.8 during
September, August، and May respectively. While the annual drought showed increased
trend in Latakia and kasaab and decreased one in Slenfeh.
The research aims to study the impact of the change in rainfall on wheat Productivity
in al-Hasakah station in the Eastern Province. and to achieve the objectives of the research
were to adopt a time series first one from 1991 until 2010,was used a
nd divided by two
equal period stretching from 1991 until 2000, and the second period extends from 2001
until 2010, and so on quarterly and annual level for the amount of rain and the productivity
of wheat, and Study the effect of rainfall in both periods on wheat production.
This study was carried out at three different forest sites in Syria in order to
determine the effect of changing rainfall, temperature and soil on kernel
productivity of stone pine (Pinus pinea L.)> these sites included: Jabal Alnabi
Mata, (Tartou
s province, L1), Dahr Alkhoser (Homs province, L2) and E′en
Jron site (Idleb province, L3). Results showed that kernel productivity of stone
pine per tree was 236.3, 252.8, 143 g per tree, and 177, 162.3, and 86.98 kg per
hectare in L1, L2, and L3, respectively. These differences were attributed due
to the variation in the composition, textured and fertility of the soil available in
the three locations studied. It was concluded that trees of stone pine grow better
and superior in Kernel productivity in humid and super-humid bioclimatic
zone.
Due to the importance of water, and the increasing of demand at the present time due
to the tremendous development in all spheres of economic and social life, and as the
evaluation, planning and management of water sources, one of the important top
ics in
human life, especially in areas with scarce rainfall or where rainfall distribution is poor or
irregular so cannot be used for different purposes.
From here, the importance of the research in forecasting rainfall in the Husn
Suleiman station, comes, and to achieve this goal the data of time series for the average
annual rainfall precipitation been used in Husn Suleiman station which located in the
province of Tartous on longitude 36 ° 15 ' andlatitude 34 ° 56', for the period between
1959-2011,
The methodology of "Box – Jenkins" been used in the study, this methodology
relies on finding future forecasts from original data series.
Also,the applications “MINITAB, EXCEL” have been used in the statistical side and
the preparation of the study results.
As a result, the study found that rainfall value in the 'Husn Suleiman station'
decreasing, this decline amounted to 3.7 mm per year during the monitoring period.
Also, the appropriate (ARIMA) model for the series was build after it passed the
various statistical tests are required, and founded that ARIMA(1,0,0) model is a good
representation of the data and the ARIMA(4,1,5) model is the right model to forecast
future rainfall.
Rainfall is highly non-linear and complicated phenomena, which require nonlinear
mathematical modeling and simulation for accurate prediction. This study
comparing the performance of the prediction of one-day-ahead, where Two
Feed Forward Neural N
etwork FFNN models were developed and implemented
to predict the rainfall on daily for three months (December, January, February).
These models are Artificial Neural Network traditional (ANN) model and artificial
neural network technique combined with wavelet decomposition (Wavelet-
Neural) According to two different methods to build a model using two types of
wavelets of Daubechies family (db2, db5). In order to compare the performance
of the models in their ability to predict the rains on short-term (for one and two
and three-days-ahead) the last months of the period of study, used some statistical
standards, These parameters include the Root Mean Square Error RMSE,
Coefficient Of Correlation (R).
Weather forecasting (especially rainfall) is one of the most important and challenging
operational tasks carried out by meteorological services all over the world. Itis furthermore
a complicated procedure that requires multiple specialized fields o
f expertise.
In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform
is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of
Homs Meteorological Station on accounts of the preceding events of rainfall data.
The feed-forward neural network with back-propagation Algorithm is used in the
learning and forecasting, where the time series of rain that detailed transactions and the
approximate three levels of analysis using a Discrete wavelet transform (DWT).
The study found that the neural network WNN structured )5-8-8-8-1(, able to predict
the monthly rainfall in Homs station on the long-term correlation of determination and root
mean squared-errors (0.98, 7.74mm), respectively.
Wavelet Transform technique provides a useful feature based on the analysis of the
data, which improves the performance of the model and applied this technique in ANNmodels
for rain because it is simple, as this technique can be applied to other models.
The study shows the factors affecting rain precipitation, general
rate and annual, monthly and daily changes by calculating the
standard deviation and the annual fluctuation. The standard
deviation from the general average shows large values in th
e
stations located in the north of the study area. Stations in the
center and south, because of the nature of the dry climate, as well
as that the increase in the number of rainy days does not
necessarily mean an increase in the amount of precipitation, and a
difference in the amount of rainfall from one station to another
because of the difference in climatic factors affecting them .
This research aims at finding the model of rainfall carnal slope
in Kastal Mouaf-Coastal basin . Statistical analysis has been
performed during 48 year ( 1961-2009) using spss v.22 to
represent the relationship between the rainfall and the factors
whose impacts are shown by the passage time .
