In this article we will discuss about:- 1. Introduction to Soil Erosion Predictive Models 2. Importance of Erosion Predicting Models 3. Erosion Model Selection/Development.
Introduction to Soil Erosion Predictive Models:
There have been developed various soil erosion/soil loss predictive models/equations by several investigators, but they have their own limitations, hence do not provide suitable means for assessing the soil erosion/soil loss from all the areas. In 1940, Zing developed the first soil loss prediction equation for hill slope, considering the slope steepness and slope length. The equation is given as under –
Qs ∝ tanm θ. Ln
In which, Qs is the soil loss expressed as per unit area; θ is the gradient angle; L is the length of slope and m & n are the constants. In above equation, the value of m is 1.2 and n as 0.6. The computation of soil loss using above equation was limited only to the length and steepness of the field that is not justified.
Apart from slope steepness and slope length, the soil loss is also affected by several other factors, viz. climatic characteristics, soil characteristics, crop management and conservation practices; these factors must be considered for soil loss estimation.
In this regard, the effect of climatic factor in terms of rainfall erosivity index was considered by Musgrave (1947) and crop management factor taking into account the effectiveness of different growth stages of the crop on soil loss, was introduced by Smith (1958).
Similarly, the effects of conservation practices and soil erodibility on soil loss were also evaluated and incorporated in the predictive models, later on. Ultimately by taking all these factors into account, a predictive equation was developed by Wischmeir and Smith in the year, 1958 for estimating the soil-loss, called Universal Soil-Loss Equation.
Importance of Erosion Predicting Models:
There have been developed a host of erosion predicting models, world-wide. The soil erosion prediction models are very important for meeting practical needs of soil conservation goals and for scientific understanding of soil erosion processes.
Their importance is given as under:
i. They are used to select the suitable practices for reducing the soil erosion.
ii. The erosion prediction models can be used for assessing the erosion and also for preparing inventory to trace the temporal changes in erosion rates over large areas.
iii. Erosion models can be used for predicting the rate of sediment load entering the reservoir.
iv. Using erosion models and their results, a basis can be developed for regulating the conservation programs.
v. Models are very useful for predicting the soil erosion or soil loss, where their measurement is very costly and time taking or difficult.
Erosion Model Selection/Development:
In selecting a suitable erosion model, the decision must be taken whether the model is to be used for on-site concerns, off-site concerns or for the both. The on-site use is concerned, it is normally associated to the degradation of soil profile in the field, which could cause reduction in crop yield or soil productivity.
As per conservation point of view, it refers to the process of soil loss, in which the net amount of soil loss is counted only from a portion of field. The off-site soil loss is associated to the eroded soil mass or sediments that take place from the entire field.
The soil loss predicting models are of two categories, i.e.:
i. Material models; and
ii. Mathematical models.
The material models are also known as ‘formal’ model. These models are based on the physical representations of the system being modeled, and may be either iconic or analog. Iconic models are composed of same type of materials as the system, and in simpler form.
The development of model for soil erosion study through rainfall simulator in field or laboratory plot is the example of iconic model. The Analog models are composed of the parameters other than those, which are involved with the system. The application of electrical current for modeling water flow is an example of this type of model.
Analog models are not commonly used for soil erosion studies. Mathematical models of soil erosion by water are either empirical or process-based. The commonly developed models of soil erosion are the empirical, which are developed primarily from statistical analysis of erosion data.
The USLE is an example of the empirical soil loss model. In recent, several models have been developed on the basis of equations describing the physical, biological, and/or chemical processes associated to the soil erosion.
Overall, the development of suitable model depends on the following points:
(a) What type of information is required; and
(b) What type of data is available for the given site.
If the application of models is required for off-site conditions, then the development of process-based models that provide estimate on sediment yield from hill slope or watershed are being more suitable.
If the purpose of model is for obtaining the auxiliary informations about the specific choice on management strategy such as soil moisture or crop yield, then it should be for a process-based model that can provide such informations. Similarly, if the available data is limited for the given place/situation, then a simple empirical model might be the best choice to develop.
It is well known that the field studies for assessing the soil erosion of any particular area is very expensive, time taking and required to collect the data over several years’ time span. Also, for having a detail understanding about erosion processes, the field studies have limitations because of complexity in interactions and difficulty of generalizing the results.
Soil erosion prediction/assessment has been a challenge to the researchers. The soil erosion models can overcome the limitations by simulating the erosion processes, and enable to consider many of the complex interactions affecting the erosion rates. There have been developed a host of soil erosion predicting models, are categorized as empirical, semi-empirical and physical process-based models.
The empirical models are developed based on the data of a given place/region, and are usually statistical in nature. Semi-empirical models are between the physically process-based models and empirical models, and are based on the spatially lump forms of water and sediment continuity equations.
The physical process-based models are based on the essential mechanism controlling the erosion; and represent the synthesis of individual component, which affects the erosion, including the complex interactions between different factors and their spatial and temporal variability.
Few widely used erosion models are described as under:
In last 10 years there have been developed several process-based erosion models, such as EUROSEM in Europe; the GUEST model in Australia, and the WEPP model in the United States. The process-based models are also called physically based model. These models are based on the mass balance differential equation. The basic equation of mass balance of sediment in one dimension for a hill slope profile is mentioned as under –
c = sediment concentration (kg/m3)
q = unit discharge of runoff (m2/s)
h = depth of flow (m)
x = distance in the direction of flow (m)
t = time (s)
S = source/sink term for sediment generation [kg/ (m 2s)].
The equation (21.1) is exact, and is the basis for development of physically-based soil loss model.
The differences in different process-based erosion predicting models may be in following aspects:
(a) Whether the partial differential with respect to time is included, and
(b) Differences in representation of source/sink term (S).
In equation, if the terms of partial differential with respect to time are dropped, then the equation can be easily solved for steady-state condition. The term representing source/sink for sediment (S) is the greatest source of differences in soil erosion models might be due to incorporation of soil detachment, transport capacity and sediment deposition functions.
The disadvantages of process-based models are as under:
ii. Data requirement is more; and every new data element introduces uncertainty.
iii. Model structure interactions are also large.