Kienzle, formerly of the Department of Agricultural Engineering, University of Natal, for his co-supervision of the initial stages of this research. Doctor SA Lorentz, Department of Agricultural Engineering, University of Natal, for his guidance on the application of the sediment yield component of the model. The staff of the Computing Center for Water Research, especially Mr R Nundlall, for his help with data collection and data processing.
This study involves the application of ACRU's agrohydrological model to a selected study catchment in the Lower Mgeni catchment and its discretized sub-catchments immediately downstream of Inanda Dam. This aspect of the study involved, first, the creation of an input database for each distributed catchment within the catchment; second, the processes and techniques used to translate data into hydrological information; and finally the "running" of the hydrological model, which in turn "drives" the system and simulates the hydrology of the catchment. Specific objectives of the study involved simulation of hydrology, which focused on simulated runoff and streamflow; and sediment yield responses of the sub-catchments and the total Lower Mgeni study catchment in terms of gross volumes and sediment yields produced.
Modeling of hydrology in the Lower Mgeni is expected to contribute significantly to meeting river and estuarine ecological and geomorphological streamflow requirements. PIT DISCHARGE SIMULATION Estimation of peak discharge Estimation of catchment lag time SEDIMENT YIELD SIMULATION.
BACKGROUND
IMPACT OF DAM CONSTRUCTION
Mgeni Catchment
Study Area
- Ecological impacts
- Geomorphological impacts
- Impact on Sandwinning
- OBJECTIVES
- SELECTION AND DESCRIPTION OF THE ACRU MODEL
- HYDROLOGICAL MODELLING AND MODEL SELECTION
- ACRU CONCEPTS AND STRUCTURE
However, due to the construction of the dam, changes have resulted in the processes occurring within the system. Ecological reserve refers to the water needed to protect the aquatic ecosystems of the water source. Therefore, the long-term effect is to reduce the availability of sediment in that section of the river below the dam.
Dam construction inhibits sediment delivery along the river channel to the Indian Ocean in two ways. This is with the exception of the sediment yield generated in the Mgeni River catchment below the Inanda Dam. This study will focus on determining the sediment yield for the study area, which will be extrapolated to the rest of the Lower Mgenian, thus contributing to sustainable sand accumulation in the Lower Mgenian.
The main objectives of this study are to estimate streamflow and sediment yield of the Lower Mgeni study catchment. This chapter describes the concepts and structure of the ACRU model; and then the modeling of streamflow and sediment yield.
INPUTS
OPERATIONAL MODES
OPTIONS/
SPECIFIC OBJECTIVES I
COMPONENTS
STREAMFLOW SIMULATION
Storm surge generation is based on the principle that, after initial abstractions, runoff potential is a function of soil moisture (Schulze, 1989c and Schulze, 1995). Modification of the SCS equation, by Schulze (1989c) and (1995a), for use in the ACRU model has resulted in a number of conceptual differences. For example, for a catchment with predominantly short vegetation and shallow roots, a groundwater deficit equal to the depth of the topsoil horizon may be more representative of the runoff mechanism.
On the other hand, the critical water table depth for a watershed with tall, dense vegetation with deeper roots and thus relatively higher infiltrability may be deeper than the topsoil and should be considered. The base component of flow originates from groundwater supplies that are recharged by drainage from lower active soil horizons (Tarboton and Schulze, 1992). The first refers to the rate of drainage of water from the lowest storage of the subsoil horizon to the intermediate storage of groundwater when the water content of the soil exceeds the capacity of the field.
This response rate is a function of soil texture, and suggested values are given in Schulze et. The second response coefficient refers to the base flow release of water from the intermediate/groundwater storage in the stream.
PEAK DISCHARGE SIMULATION
- Estimation of peak discharge
- Estimation of catchment lag time
34;release" is expected to perform as a constant, experience has shown that baseflow release "decay" is not constant. It therefore consists of the peak discharge in m3s·1 calculated from the day's generated storm flow, as given by Eq. 2.2, superimposed on the mean base flow for the day in m3s·1 and transferred for the day of mean rapid flow from the previous day's storm flow, also in m3s·1." Three possible options, to determine catchment lag time for use in ACRU, are discussed and described in Schmidt and Schulze (1989); catchment delay time can be calculated using one of the following options available in ACRU:
The travel time in each flow reach is determined by dividing the reach length (in m) by the flow velocity as determined by the uniform flow equations (eg Manning's equation) for full flow conditions.
- SEDIMENT YIELD SIMULATION
- INTRODUCTION
- Operation in the distributed mode
- DATA AND INFORMATION REQUIREMENTS FOR ACRU STREAM FLOW SIMULATION Distributed modelling makes high demands on input because each subcatchment modelled requires
- Rainfall estimation method for the Lower Mgeni catchment
- Driver station rainfall estimation
- Rainfall data control variables
- Potential evaporation - some background
- Land cover
- Streamflow simulation control variables
This is due to the requirements of the input parameters, which can only be met for research catchments. The distributed version of the ACRU model was selected to model the Mgeni catchment (Tarboton and Schulze, 1992) and is used in this study. The extracted part of the menu containing information about the layout of the study catchment area is shown in Figure 3.3.
In addition, some of the input variables, as elaborated by Tarboton and Schulze (1992), are noted below:. That part of the menu that displays information about the layout of cells in the study catchment area is. In terms of the stated objectives of this study, the monthly summary statistics are considered sufficient and utilized in this study.
It is the author's opinion that this method is better than the one mentioned above. The information on land cover classes and sub-catchments proportional coverage of the Lower Mgeni study sub-catchments. Tarboton and Schulze (1992, p. 41) define the leaf area index (LAI) “as the planimetric area of the plant leaves relative to the soil surface area.
These input data for the respective sub-catchments of the study area are shown in table 3.5. These values for the 4 sub-catchments in the study area were entered (G.P. Jewitt, pers. comm., 1994) into the soil DSS to obtain a translation of the Land Type information into soil hydrologic variables required by the ACRU modeling system. The soil erodibility factor, one of the required inputs to ACRU, is the K-factor values for each.
The percentage of clay is taken as the average of the range (clay content) given in the computerized inventories. Some of the information used in the spreadsheet program is derived from computerized soil type inventories. The K-factor values of the different soil series must be calculated to determine the K-factors of the terrain unit.
Kienzle (pers. comm., 1994), these were rejected in favor of the procedure described in Lorentz and Schulze (1995). The C-factor information relevant to the 4 sub-basins of the study area is shown in Table 3.16.
RESULTS AND VALIDATION
- RESULTS AND VALIDATION OF SIMULATED RAINFALL OF THE LOWER MGENI
- Rainfall Results
- Rainfall Validation
- RESULTS AND VALIDATION OF SIMULATED STREAMFLOW OF THE LOWER MGENI
- Hydrology Results
- Streamflow Validation
- RESULTS AND VALIDATION OF SIMULATED SEDIMENT YIELD OF THE LOWER MGENI
- Sediment Yield Results
- Sediment Yield Validation
- OVERALL ACHIEVEMENTS
- STREAMFLOW AND SEDIMENT YIELD
- CONCLUDING REMARKS
A possible explanation for the difference is related to the differences in elevation of the sub-catchments. It can be argued that the rainfall is representative for the duration of the simulation. The estimation of streamflow production of the Lower Mgeni catchment is one of the main objectives of this study.
In sub-catchments 2 and 4, runoff production is consistently lower than the study catchment average. This streamflow simulation results subsection is a report on runoff coefficients for the 4 sub-catchments and the total study catchment. This value compares favorably with the runoff coefficient (stream versus precipitation) of 21.2% for the entire Lower Mgen study catchment.
It can be concluded that this is evidence of the impact of rainfall in the considered period. It is clear from the table above that there is considerable variation in the sediment yield between the sub-catchments in Lower Mgeni. - Surveys are carried out at intervals depending on the importance of the reservoir and the sediment yield of its catchment.
It is reasonable to assume, based on the studies reported, that the sediment yield results from this study provide a realistic simulation of sediment yield for the Lower Mgeni catchment. The overall objectives of this study were firstly to contribute to the completion of the development of the distributed hydrological modeling system for the Mgeni; and secondly, to test the sediment yield. Specific objectives of the study involved the simulation of hydrology, focusing on simulated runoff and streamflow; and sediment yield.
This contributes to the first objective of completing the modeling of the entire Mgeni catchment, including the Lower Mgeni. Although the simulated streamflow can be used with confidence, it is essential that the streamflow simulation of the Lower Mgeni is verified against observed data. This allows the use of the simulated values as a realistic approximation of sediment yield within the study area.
The difference between the sediment yield results from the simulation and other studies is that the "model". While the main objective of the study is to model streamflow and sediment yield for Lower Mgeni.
Regional lapse rates in southern Africa for monthly averages of daily maximum and minimum temperatures.
LANDUSES OF STUDY CATCHMENTS ,C":FACTOR
