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Hydrological modeling of the Congo River basin: Asoil-water balance approach

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par Bahati Chishugi Josue
University of Botswana - Masters of Sciences (M.Sc.) 2008
  

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CHAPTER THREE

3.0 LITERATURE REVIEW

3.1 Hydrological models

Hydrological model have been largely detailed and classified (Figure 1) based on multiple parameters such as the model input data sets types, and the physical characteristic of the model among others. However, Hydrological models have, traditionally, been modelled as physically-based or conceptual depending on the complexity and extent of completeness of the structure of the model (Beven, 1989; Refsgaard et al., 1989; Bergstrom, 1990; Refsgaard, 1996, 1998).

Hydrological
Models

 
 
 
 
 
 
 
 
 
 
 

Deterministic

 
 
 

Stochastic

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Grid based

 

Subwatershed

 

No Distribution

 
 
 
 

Figure 11 Hydrological Model Classification

Probabilistic

Time Series

Physical
Based

Distributed

Conceptual

Lumped

Empirical

In physically-based water balance models, all the physical phenomena like precipitation, evapotranspiration, ground water inflow, ground water outflow and storage need to be quantified and modelled. Models are further classified into lumped or distributed, based on basin terrain (Bergstrom and Graham, 1998). In lumped models, spatial variability in hydrologic parameters or meteorological related data are not accounted for, meaning that they are averaged or assumed uniform over the system, whereas, in distributed models spatial variability is explicitly accounted for by assuming uniformity over smaller modelling units by sub dividing the bigger system based on physical properties. In most of the distributed hydrologic models, these units are delineated by combining climatic components, topography, soil properties, land use properties and other pertinent properties. Distributed models are especially useful, for example, when impacts of land use change are to be studied or for analyzing spatially varying flood responses (Koka,

2004). The Lumped models are easy to implement, but do not account for terrain variability whereas Spatially-distributed models require sophisticated tools to implement, and account for terrain variability (Oliveira, 2002).

A statistical model derives an empirical relationship between precipitation, infiltration, flow and any other parameters that are included in the model. The relationship is derived based on observed data for all the dependent and independent parameters in the model. The best relationship is identified using suitable statistical parameters (Sukheswalla, 2003).

Large scale modeling of streamflow can be done efficiently using simple models (Becker and Braun, 1999; and Wolock and McCabe, 1999). Distributed models require high resolutions for efficient modeling like the MIKE SHE model (Ewen et al., 1999) and the TOPMODEL (Beven et al., 1994). However, for large scales such high resolution is not always available. Also, distributed models are generally not practical and efficient for large-scale modelling (Becker and Braun., 1999), while statistical lumped models that fulfil large scale modelling requirements of resolution and computation time are better (Becker and Pfützner, 1987).

Using the cell-to-cell model, a watershed can be represented as a single cell, a cascade of n equal cells, or a network of n equal cells (Singh, 1989). The storage in the cells is calculated as given below:

(1)

dS= -

dt

t I O

t t

where, St is the time-variant storage in a grid cell,

It is the summation of input coming into the cell from

upstream cells and the runoff generated in the cell, and

Qt is the outflow from the cell which is calculated by

various methods, e.g. the linear reservoir method.

Equation (1) is a generalized water balance model which can be applied in different situations: atmospheric, surface, soil-water, groundwater models. The types of input variables are defined by the researcher according to the problem. Several books, papers ad reports describing the application of equation (1) are available, viz Thornthwaite and Mather (1957), Chow et al (1988), Reed et al (1997) and Rasmusson (1997).

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