For the Macaque model to be verified more extensively and for its use in operational .catchments, it will be necessary to improve the representation of spatial and temporal changes. Goodness-offfit statistics for simulating monthly totals of daily streamf10w from the Witklip catchment for the Macaque model.
INTRODUCTION
In this paper, aspects of the National Water Act (1998) are discussed to present the basics of how South Africa's water resources are likely to be managed and what kind of models water resource managers and planners require to accomplish this task. During the course of the research reported here, a number of problems were identified when the Macaque model was applied to South African catchments and these are discussed in detail in Chapter 6.
FORESTRY AND THE SOUTH AFRICAN NATIONAL WATER ACT
Scale considerations
Currently, the agreed scale for assessing forest impacts is that of a Quaternary basin (Van der Zel, 1995). However, the NWA proposes to divide the country into approximately eighteen water management zones (Forest Industries Association, 1998).
Critical Needs of the NWA
Chapter 2 discussed changes in regulatory controls over afforestation expansion and described the methods used to estimate the impact of afforestation on streamflow under the APS. The next chapter reviews progress in forest process research and approaches for modeling forest hydrology at the watershed scale.
HYDROLOGICAL PROCESSES DOMINANT IN FORESTED
Evapotranspiration
Dye (1996) concluded that transpiration from dry canopies was the dominant evaporation process in South African plantations. The relatively deep roots of trees also enable trees to extract groundwater from very deep in the soil profile (Dyeet al., 1997).
Runoff generation in forests and spatial variations in soil moisture
The following section discusses physics-based models and describes some of the problems encountered in their development and implementation. The following sections discuss the concept and theory of physically based modeling and consider the challenges and limitations of applying physically based models to land use change applications in South Africa.
Model theory
Therefore, the uncertainty of the model predictions is directly related to the uncertainty in the accuracy of the input data. As the models are being applied for non-research purposes, it is the responsibility of the individual user to apply the model to a.
THE ACRU MODEL
Background
- Runoff representation
- Riparian zone hydrology
This collective research contributed to the development of the ACRUjorest decision support system and an information base for use in ACR Uforest simulations. However, since the publication of the user manual has a value of 0.09 (9%) per day found more reasonable for most model applications (Pike, 2000, personal communication).
Vertical process representation
- Canopy interception
- Transpiration and soil evaporation
- Rooting patterns and soil water availability
At ACRU, tree evaporative demand is estimated from both atmospheric and LA demands. Allied to this concern is the fact that the transpiration component of the model has not been extensively verified.
THE MACAQUE MODEL
Model development history
From this review, it became clear that models suitable for large-scale and long-term modeling were limited. In particular, the requirement of the model to simulate changes in land cover eliminated most of the candidate models (Watson, 1999a). Although the selected models represented forest hydrologic processes, such as evaporation from complex forest canopies and the multi-layered, multi-dimensional flow of unsaturated and saturated water in soils in great detail, few models attempted to extend this approach to detailed modeling in large-scale applications.
The ladder "bridge" required careful consideration of issues of parsimoniousness and scale (Watson, 1999a), but was necessary if the model was to be applied for management purposes in operational catchments. Consequently, the development of a new model began that included complex forest processes, but had a very simplified soil moisture balance scheme, which would allow it to be applied on a larger scale.
Operating Environment
- Explicit Darcian lateral flow component
The Tarsier operating environment also allows easy model configuration and re-run of simulations. Horizontal structure refers to the spatial division of the watershed into elementary spatial units and the lateral movement of water between individual units. Previous versions of the model adopted lateral flow component parameters based on the TOPMODEL concept described in Chapter 3.
In the next section a comparison is made between the representations of these processes in the later version versus the earlier version of the model. Therefore, the saturation deficit of each ESU depended on the moisture index of the ESU and the average hillslope saturation deficit. 8 = factor controlling lateral redistribution of water within a hillslope Ssat, dist = soil saturation deficit, given by the previous distribution function Ssat = soil saturation deficit.
Initially the water table depth for each ESU is calculated as a function of the soil saturation deficit and porosity. Once the depth of the water table has been determined, the transmissivity of the soil is calculated by integrating from the bedrock depth (z) to the water table depth (d), as a function of the four parameters of the theoretical hydraulic conductivity variation curve of saturated, ie
Hillslope
Vertical process representation
- Soil water availability and rooting patterns
- Transpiration and soil evaporation
When water flows from the groundwater to the stream (so that base flow occurs), it does so as a gradient diffusion process, with the hydraulic gradient resulting in a driving force and resistive movement of the soil, as measured by hydraulic conductivity (Watson, 2000). , personal communication). Therefore, the developers of the Macaque model derived a hydraulic gradient parameter (PJatio_hydraulic_to_surface_gradient) defined as the ratio of the hydraulic gradient to the surface. Therefore, the saturated portion of the ESU will increase as the current saturation deficit continues to decrease beyond the zero saturation deficit (Ssat,o,surfilce).
The amount of baseflow released is calculated by multiplying the saturated portion of the ESU, the ratio of hydraulic to surface gradient, the surface saturated hydraulic conductivity, and the surface gradient of the ESU. The recharge of the saturated zone is calculated using the Clapp and Hornberger (1978) estimate of unsaturated hydraulic conductivity of the soil at the water table interface. The water available for transpiration in the unsaturated zone depends on the root depth and the actual volumetric water content of the soil.
If the tree roots extend beyond the water table, the extraction of water from the saturated zone depends on the depth of the roots beyond the water table and the maximum volumetric water content. The meteorological components of the equation are determined by the luminosity (specified as the vapor pressure deficit) and the net radiation.
APPLYING THE MACAQUE MODEL TO SOUTH AFRICAN CATCHMENTS
- Catchment selection
- The Maritsane Catchment
- Model configuration and input parameters
- Simulation results for the Witklip catchment
- Procedure in applying sensitivity analysis
- Parameters tested in a sensitivity analysis of Macaque
- Conclusions
The Maritsane catchment is located east of the town of Graskop in the Mpumalanga province (Figure 6.1). Three spatial levels were chosen for the configuration of the model for the Maritsane catchment. In the following section, the results of the macaque application on the Maritsane catchment are presented.
The results of the Maritsane simulation show that the Macaque model performed poorly on the daily time step and during periods of low flow. Section 6.4 tests the sensitivity of model output to changes in model input parameters and serves to highlight which parameters have the greatest influence on simulated streamflow output. In the following sections, the model configuration for Witklip Catchment V is described, and a brief overview of the Macaque model input is given.
Three spatial levels were selected for the configuration of the model for the Witklip catchment. The verification results of the ACRU simulation (Gush et al., 2001) indicated that the model performed well on a monthly basis in the Witklip catchment. Regarding the Maritsane river basin simulation, other than meteorological data, no data were available for internal verification of the Macaque model.
In the next section, the procedure followed in carrying out sensitivity analyzes of the Macaque model in the Maritsane and Witklip catchments is described.
DISCUSSION AND CONCLUSIONS
Furthermore, the scale of the available measurement technique may be smaller than the scale at which the parameter values are required by the model. Conversely, the ACRU model can accommodate a change in the spatial distribution of precipitation for each month of the year. It became apparent when the Macaque model was applied to South African catchments that the complexity of the model could not be supported by the available data.
This documentation provides the user with a detailed description of each of the parameters used, a description of processes represented in the model and recommended parameters for different climate regions and vegetation types. These assumptions and indications of where improvements are needed give the user an understanding of the model limitations and weaknesses. The verification of model components was part of the model development process and a significant amount of field measurements were made, although limited to the Maroondah catchments.
The simulation results of the Witklip and Maritsane catchments using the Macaque model showed that there is potential for incorporating a topography-based approach to model subsurface flow and base flow. These suggestions reflect some of the difficulties encountered in the application and verification of the ACRU and Macaque models.
RECOMMENDATIONS FOR FUTURE RESEARCH
It is only through cross-validation of simulated streamflow and a model's internal processes that greater confidence can be placed in model predictions used in water resources planning. Therefore, greater emphasis should be placed on testing the components of the new model to prevent "our modeling skills from exceeding our ability to gather meaningful information for model initialization and validation" (Vertessyet al., 1993).
Monitoring and modeling of water balance components in a grassland catchment in the summer rainfall area of South Africa. Streamflow responses to Eucalyptus grandis and Pinus patula afforestation and logging in the experimental catchment of Mokobulaan, Mpumalanga Province, South Africa.Journal ofHydrology. A re-analysis of experimental South African catchment afforestation data. · Water Research Commission, Pretoria, South Africa, Report 810/1/00, p. 13 8.
Unpublished contact report to the Chief Directorate of Forestry, Division of Forest Science and Technology, CSIR, Pretoria, South Africa.
APPENDICES
