**1. Introduction**

Soil erosion by water is one of the most important land degradation processes in Mediterra‐ nean environments. This process is strongly linked to problems of flooding and channel management. The relationship between land use and erosion in mountainous forested wa‐ tersheds has been known in a qualitative sense for some time. Vegetation management, for‐ est road construction, and forest fires, impact basin sediment yield by increasing the amount of sediment available for transport and the amount of surface water available to transport it.For early flood warnings as well to get time for planning and operation of civil protection measures it has become very important that forecasts are made and simulation of floods is carried out. With the ever increasing demand for water resources, it has become very impor‐ tant that the natural processes of floods be predicted, so that current and future environ‐ mental issues can be addressed well in time. A simplified representation of the natural hydrological system is the hydrological model. In this model, different physical processes are represented at different time scales and at a wide range of times. This has basically been associated with a lack of appropriate observational data to constrain model states, increase in the number of model outputs [18] and lastly, the complexity of the model. Basically, the distributed hydrological models give us the opportunity to deal with forcing implement these models.The models will help provide the means by which important information re‐ garding existing and future stream flow conditions, very important information regarding hydrological state variables and the state of knowledge on basins of interest can easily be captured for use. Every entity in Iran has suffered great losses due o the floods together with socio-economic development. Among all the different kinds of natural disasters, flood is ranked first in terms of frequency, affected area, losses caused and the severity. On Au‐

© 2012 Haghizadeh; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Haghizadeh; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

gust 10th, 2001, a big flood took place in Golestan and Gorgan River with return period of 200 years and caused a lot of damage. The damages caused by the flood include 15,000 hec‐ tares damages to agricultural lands, 10,000 people rendered homeless, 10,000 hectares of damage to forests, and the greatest loss was the human death toll. 247 human beings had been killed in the disaster. The total damage to the entire province was a staggering 491 bil‐ lion Rials. Once again on July 29th, 1999 Neka city in Mazandaran province was hit by a flood similar to the one which had hit the Golestan and Gorgan Rivers. About a billion dol‐ lar worth of damage was caused, including more than 4000 shops and homes damaged to about 50% to 100 %, 400 km railway damaged. 33km of road and about 100 people injured. The Neka river basin is located in northern Iran. It is frequently affected by storms and heavy rain which causes inundation. Flood forecasting modelling is the most important component of the real-time flood forecasting system. This system can mitigate such natural disasters. Flood warning and forecasting systems mostly use hydrologic/hydraulic models. These models, when optimally validated and calibrated can be very effective in minimizing flood damage through non-structural means. In the early years, these flood models were very simple with sophistication in technology comes effectiveness. With advances in geo‐ graphic information systems and remote sensing theses models have now become more ef‐ fective. The advantage of these models is that spatially distributed basin characteristics on stream flow can be reflected by these models. There are various studies in which this partic‐ ular model has been applied, including the Alzette river basin in Luxembourg [9], Barebeek catchment in Belgium [3], the Hornad watershed in Slovakia [2], the Suoimuoi catchment in northwest Vietnam [8], the Simiyu river (Lake Victoria) in Tanzania [16] and the Suriname river basin, in central Suriname [14].

*<sup>d</sup> D PISERR*

including interception and depression losses, S (LT−1) is surface runoff or rainfall excess, E (LT−1) is evapotranspiration, R (LT−1) is percolation out of the root zone, and F (ms−1) is inter‐ flow. The assessment for excess rainfall is done by means of modification in a moisture-re‐ lated rational process with a latent runoff coefficient with due consideration to factors such

= -- - - - (1)

Daily Flow Simulation Using Wetspa Model with Emphasize on Soil Erosion...

http://dx.doi.org/10.5772/51420

L−3] is soil moisture, t (m) is time, I (LT−1) is initial abstraction

(2)

175

*dt* q

as land cover, slope, soil type, magnitude of rainfall, and pre soil moisture.

( )

æ ö = - ç ÷ è ø

<sup>1</sup> max 1, *run run <sup>K</sup> <sup>K</sup> P*

Where K*run* (-) is surface runoff exponent and P*max* (LT-1) is a rainfall intensity scaling factor. Is impacted by rainfall intensity and the effect is reflected by α (−). *Α* is greater when the rainfall intensity is not high and as a result, surface runoff is lower, and the approach is shifted towards high rainfall intensity leading to runoff and soil moisture's linear associa‐ tion. When the surface runoff exponent is 1, it implies Pmax parameter, which is threshold rainfall intensity, leading to linearity between actual runoff coefficient and comparative soil moisture content. In order to measure the scale for this parameter, we can compare observed and computed peak discharges when floods are high. The calculation of evapotranspiration from soil and vegetation is done on the basis of a relationship explored by Thornthwaite and Mather (1955) [17] as defined by probable growth level, evapotranspiration, vegetation type,

*S cP I*

This study utilizes these values. Rainfall excess coefficient, given by :

a

*s*

q

cient.A lookup table has been used for deriving the values of *C*, with associated values to slope, soil type and land use classes [10]. Literature was searched to obtain default values but they may be changed by the used if necessary as suitable according to a region's specific situation. Liu.et al (2005) [8] did this by creating a look up table for catchments, associating latent rainfall excess coefficient to various arrangements of slope, soil type and land use.

q

a

L−3) is soil porosity or saturated water content, *c* (-) is potential runoff coeffi‐

max

é ù æ ö - = + ê ú ç ÷ ë û è ø (3)

where D (m) is root depth, θ (L<sup>3</sup>

where θs(L<sup>3</sup>

and soil moisture content:
