Y SCS I
3. THE SCS-ACRU APPROACH TO STORMFLOW MODELLING
3.1 The SCS Method and its Components
3.3.3 The SCS-ACRU Procedures to Account for Antecedent Soil Moisture
The SCS-ACRU procedures are conceptual approaches by which a catchment's antecedent soil moisture changes from initial values are computed with the ACRU model. The ACRU agrohydrological modelling system was developed around the following basic aims (Schulze,
1984; 1995a):
• It is aphysical conceptual model; physical in that physical processes are represented explicitly and conceptual in that it conceives of a system in which important processes are idealised.
• It is a daily time step model and uses daily input of climatic data. Certain more cyclic and less sensitive variables (e.g. temperature), for which values may have to be input at monthly level (if daily values are not available), are transformed internally inACRU to daily values by Fourier Analysis.
• Itis atwo layer soil water budgeting model which has been structured to be sensitive to climate and land use changes on soil water, actual evapotranspiration rates and runoff regimes (both stormflow and baseflow).
• It is a multi-purpose model which integrates the various water budgeting and runoff producing components of the terrestrial hydrological system with risk analysis and can be employed in design hydrology, crop yield modelling, reservoir yield simulation, irrigation water demand/supply, salinity simulation and regional water resources assessment.
• Itis a multi-level model, with either multiple options or alternative pathways available in many of its routines, depending on the level of input data available and detail output required.
• ACRU is not a parameter fitting or optimising model; parameters are estimated from physical characteristics of the catchment.
• ACRU can operate as a point or lumped model. However, for large catchments or in areas of complex land uses and soilsACRU can operate as a semi-distributed model.
A summary of the concepts of the ACRU model in terms of inputs, operational modes, simulation options and objectives is given in Figure 3.2. Figure 3.3 represents a schematic of the multi-layer soil water budgeting by partitioning and redistribution of soil water in the ACRU model. The model has been verified on a catchments in South Africa, Germany, the USA and Zimbabwe as reviewed by Schulze and Smithers (2004).
Figure 3.2
Figure 3.3
The ACRU agrohydrological modelling system: Concepts (after Schulze, 1995a)
The ACRU agrohydrological modelling system: General structure (after Schulze, 1995a)
The ACRU model simulates those components and processes of the hydrological cycle which affect the soil water budget and can output any of these components on a daily basis, or as monthly and annual totals of the daily values (Figure 3.2; Schulze, 1995a).
In its application for determining the change in soil moisture storage (LlS) for use in the SCS technique, two options are available to account for typical soil moisture conditions prior to design events, viz. the Median Condition Method and the Joint Association Method.
In the Median Condition Method (Schmidt and Schulze, 1987), initial soil water content was set equal to 50% plant available moisture (PAM) to comply with assumptions adopted in the SCS model, viz.that the initial CN (CNII) was representative of so-called "average" moisture conditions. The ACRU model was then used to compute LlS as the difference between prevailing soil water status immediately prior to a stormflow producing rainfall event and initial soil moisture conditions (for which the so-called "average" CNII was considered to be representative). The median, i.e. 50th percentile, condition ofLlSis then calculated from a long term (say 30 year) daily record and this statistically expected LlS is then used in Hawkins' equation (Equation 3.8) to compute the final CN (CNf). Results from gauged catchments have indicted the model to provide better estimates of soil water status and event responses for an assumed initial moisture status of 50% PAM when it used with a 3D-day antecedent period, as against a shorter antecedent period (Schulze, 1984).
In the Joint Association Method (Schmidt and Schulze, 1987), the ACRU model simulates antecedent actual evapotranspiration, stormflow and drainage for selected soil and land cover combinations, again using a 3D-day antecedent period for computing soil moisture conditions prior to a specific event. The computed LlS is then used directly in Equation 3.8 to adjust a given CNII(Schulze et al., 1992). Daily stormflow depths are computed for each rainfall event after adjusting CNII for prevailing ASM and a frequency analysis of the annual maximum stormflow depths gives approximations of design stormflow depths. This accounting by ACRU for the variation of the ASM between storms falling on a catchment implies that
j
rainfall of a given return period does not necessarily generate stormflow of equal return period, since a lesser rainfall event falling on a wet catchment could result in a bigger flood than a larger event falling on a dry catchment (Schmidt et al., 1986).
The outputs of the SCS-ACRU moisture budgeting procedure (both the Median Condition and Joint Association) have been verified under highly varying climatic regimes on gauged
catchments in South Africa and the USA (Schulze, 1984; 1989). Dunsmore (1985) and Schmidt et al. (1986) had previously concluded that simulating a flood of a given return period from the same return period of rainfall does not provide the hydrologist with a sound basis for analysis of design events from small catchments. It is clear that regional relationships between antecedent moisture condition and design rainfall depths are required for accurate stormflow estimations in hydrologically heterogeneous regions such as Eritrea.
Both the Median Condition Method and Joint Association Method are frequently used in applied design practice in southern Africa owing to the scarcity of any other information on associations between "extreme event" rainfall and corresponding catchment ASM (Schmidtet al.,1986, Schulzeet aI., 1992).
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So far in this chapter an attempt has been made to review the SCS stormflow approach and its components, followed by the description of the original SCS, the Hawkins and SCS-ACRU approaches to adjustment of CNs to ASM for stormflow modelling.
The next question that arises is to how to adapt some of these modelling approaches to be suitable for wider application in Eritrea. Determination of one day rainfall depth can be computed from the rainfall data available throughout most of the country and CNIJ can be obtained from field observation of soil properties, land uses and their treatment. The problem then arises how to estimate the LlSrequired for adjustment of CNIJ when using soil water budgeting procedures. As was mentioned in the introductory chapter, the main problem arises from limitations of long, adequate and accurate hydrological information needed to simulate LlS prior to flood producing rainfall events. In the next chapter, the possibility of using standardised climate classification systems for estimation of regional indices of LlS for adjustment of CNs are discussed as a possible solution to overcoming data limitations in developing countries. The Koppen climate classification, which has been tested in southern Africa with the 712 relatively homogeneous hydrological zones identified there, is eventually selected as the preferred climate classification.