River flows depend on precipitation in their catchments, where the flow is highly
correlated with precipitation, among many climatic and geographic factors. The
relationship between precipitation and runoff is of great importance in estimating flow
changes in The HWAIZ basin that is located between The-Zrod and The-Gelani basins.
The Al-HWAIZ Dam was built on the HWAIZ River with storage capacity of 16.5 MCM.
The purpose of this study is to find a relationship between rainfall and runoff in The
HWAIZ basin. This study depended on statistical analysis of rainfall and runoff data, and
the analytical study of the annual rainfall data (1959-2011), to guess the trend of rainfall
and its future changes and forecasting changes in the HWAIZ river flows. The study
showed that the runoff coefficient values ranged between (0.007-0.66). A mathematical
relationship was established that allows to estimate flow based on measured or predicted
precipitation values, as well as appraise missing or lacking data with accepted level of
accuracy.
The Alsafarqieh watershed is located on the western slopes of the coastal mountain range,
Its area is 132.58 km2, It forms a part of the Alros river basin, The river starts at a height of
1200 m, A group of tributaries meet and form the Alros River
, which flows into the
Mediterranean Sea. Salaheddin Dam was constructed to store 10 MCM on the riverbed at
the intersection of the Qurdaha River with the Shehada River. The study aims to determine
the rainfall- runoff relationship in The Alsafarqieh watershed. The solution depends on the
statistical analysis of precipitation and runoff data. Then the study found the mean annual
precipitation is 159.6 MCM/year, and the mean annual flow into the Salaheddin lake was
9.4 MCM during the study period (2010-2012), so the runoff coefficient is 0.06. This
indicates a significant water loss. A mathematical equation to predict the runoff quantities
depending on the values of precipitation, has been concluded. This is important to study
water projects for water storage and flood prevention.
This study has reached to that ANN (5-9-1) (five neurons in input
layer_nine neurons in hidden layer _ one neuron in output layer) is the
optimum artificial network that hybrid system has reached to it with
mean squared error equals (1*10^-4) (0.7
m3/sec), where this software
has summed up millions of experiments in one step and in limited time, it
has also given a zero value of a number of network connections, such as
some connections related of relative humidity input because of the lake
of impact this parameter on the runoff when other parameters are
avaliable.
This study recommend to use this technique in forecasting of
evaporation and other climatic elements.
the aim of this study is
determination of the most influential climatic factors in the rainfall
runoff relationship in Al-Kabir Al-shimalee river using artificial
neural networks. The inputs included Precipitation, runoff, in
different delays, in
addition on لاclimate factor in each network, to
determinate the best model.
This research was conducted in order to determine the impact
of raindrops in terms of force of impact and its relation to rain
intensity as well as the relay rain on the amount of soil eroded
and water drifting due to water erosion .
The effect of polymer (Carboxymethelcellulose ) treatment of different soils of 16 Tishreen lake basin on the Mean Diameter of Weighted Granulares by using 5 concentrations of polymer (0-5-10-20-25 )mg/L , and Relative Flocculation Index was studied
. The rate of runoff and the amount soil eroded were studied as well by exposing 3 soils treated with different concentrations of polymer ( 0-10-20 Kg/ha ) to rainfall intensity (30 mm/ hour) by using simulated rainfall .
The results showed significant increase of soils MWD treated with polymer compared to the control , and decrease of slaking values and light absorption which lead to increase Relative Flocculation Index .
The runoffrate of soils (2 ,3, 4 )treated with polymer decreased by 18% and 31.8% , 16.7% and 50% , and by 53.3% and 46.7% , respectively, compared to the control.
The amount of lossingsoil was diminished by treatment with polymer ( 10-20 Kg/ha) by 18.3% and 40.8% , 25.9% and 39.2% and by 52% and 53.4% in soils 2 , 3 and 4 , respectively .
Terracing is one of the oldest means for saving water and soil in Syria. This study
aims to evaluate the bench terraces efficiency in water and soil conservation in Salata
Village (30 km southeast of Lattakia). For this purpose, runoff and soil ero
sion were
estimated and compared between two treatments in one selected field, the first represent a
part of slope without terrace "witness", the second represents terraces "two adjacent plots
with terraces" Where three experimental plots, each one of 50 m2 were used for the
measurement of surface runoff and sediment concentration.
The study showed low values of runoff coefficient on "terraces" treatment compared
to the "witness", where its average value during the study period was 7.2% for "terraces",
and 27% for the "witness". As it turns out the low rate of soil loss during the same period,
from 79 t/ ha/year for the "witness" to 5.2 t/ ha/yeardown the "terraces".
The study confirmed the importance of terraces in water and soil conservation by
limiting soil erosion and reducing surface runoff.
The relation between rainfall and runoff forms one of the main hydrological cycle elements. It is one of the most complex hydrological phenomena because of the great numbers of parameters used in modeling the physical processes, the expansion of thei
r parameter space, and the temporary change in watershed specifications. Thus, modeling the relation between rainfall and runoff is necessary for hydrological and hydraulic engineering design, integrated management of water resourses, and forecasting flood and preventing its dangers. This research aims at modeling the relation between rainfall and runoff in Alkabeer Aljononbee catchment. It depends on the technique of Artificial Neural Network (ANN). The mathematical model was built by the ntstool and nntool available in the Matlab program. This model depends on daily rainfall, evaporation, air temperature, and relative humidity data taken from meteorological stations that are distributed in the watershed. The daily runoff data have also been used for checking the performance accuracy of the network, using the Simulink technique. The results of this research confirm that artificial neural network technology offers good results in modeling the relation rainfall-runoff, depending on the set of data used. So it could be a better alternative than traditional approaches.
Al-Kabeer Al-Shemale river rises from Aqraa Mountain and coastal mountains, it is considered one of the largest rivers in the coastal area.Its catchment area is 1097 km2, and empties into the sea to the southern of Lattakia.The study aims to determin
e the impact of climate change on the river discharge. Since the rainfall is the major factor in the runoff formation in the river catchment, the rainfall changes have been studied in climatic stations located within the catchment and its surroundings, and for a period of time exceeding thirty years. The study found that the general trend of rainfall change and runoff with time is decreasing, declining rainfall values ranged in the studied stations between (0.4-12.5) mm per year, and the runoff reached 0.08m3/s in the year. A mathematical equation, predict river discharge after knowing the values of daily precipitation, has been concluded.
