DECLARATION 2: PUBLICATIONS
6. IMPACT OF MODEL CONFIGURATION AND PARAMETER ESTIMATION
6.6 Conclusions and Recommendations
the consequent land cover class selected. Similar to the use of default soils information, the use of default land cover maps and assigned land cover classes did not produce particularly good results, i.e. compared to those obtained from the Current CSM System. A degree of conservatism, however, was incorporated into the default land cover maps, as detailed in Section 6.5.1.3, which explains the deterioration in the results. This, similar to the results obtained from using national soils maps, indicates the importance of accurately estimating the actual land cover class for the catchment. In addition, based on the sensitivity of the results to the land cover class selected, the results, once again, possibly suggest that the changes in CN for each land cover class are too sensitive and abrupt, and that the CNs possibly need to be recalibrated for South Africa. Since the SCS CNs were derived using observed data it is, however, possible that such changes in stormflow response for corresponding changes in land cover classes and/or conditions are indeed correct. Once again, this can only be verified through further research, using observed data from catchments with specific land cover and soil combinations.
The final scenario assessed, was the SCS Lag Equation scenario. Since the lag equation only influences peak discharges, the NSE and MARE/MRE values, in terms of DyV and DnV respectively, are identical to those obtained for the Current CSM System scenario. In terms of the DyQp NSE and DnQp MARE/MRE values, however, the results are significantly worse for the SCS Lag Equation scenario. Therefore, for small catchments the Schmidt and Schulze (1984) lag equation produces better results. The results also, once again, indicate the sensitivity of the ACRU peak discharge computation to lag time estimates.
Chapter 4. The results indicated that the incremental UH approach generally performs substantially better than the single UH approach, or at least very similarly to the single UH approach, and should therefore be used as the default peak discharge computation procedure in the CSM system. Consequently, the incremental UH approach was applied in all subsequent assessments performed in Objective 2.
The second objective of this Chapter was to use the results obtained from Objective 1, referred to as the “Current CSM System” scenario (i.e. applying the incremental UH approach, site- specific land cover and soils information and the Schmidt and Schulze (1984) estimated lag time), and compare them to those obtained for several additional scenarios where different sources of input information are used. This included the default land cover and soils maps suggested for use with the CSM system in Chapter 3, i.e. when site-specific information is not available, as well as different options to estimate catchment lag time. The results indicated that:
(i) The Current CSM system, i.e. with site-specific land cover and soils information and the Schmidt and Schulze (1984) estimated lag time produced the best results.
(ii) When applying the ACRU National Soils scenario, i.e. where ACRU specific soils information was obtained from the most updated national soils map (Schulze and Horan, 2008), the results were slightly worse compared to those obtained from the Current CSM System, i.e. where default ACRU specific soils information has been assigned to SCS-SA soil groups. Therefore, when using the CSM system this default soils information must be used.
(iii) The results from the Schulze 2012 SCS Soils and Schulze and Schütte 2018 SCS Soils scenarios, where SCS-SA soil groups were estimated from national maps, were significantly worse compared to those obtained for the Current CSM System scenario.
The Schulze and Schütte 2018 SCS Soils scenario performed substantially better than the Schulze 2012 scenario overall. In general, however, both scenarios performed poorly. Ultimately the results indicate that the national soils maps poorly represent the actual SCS-SA soil group information at such localised scales. Therefore, further work on, or refinement of, the national SCS-SA soil group maps is required.
(iv) The NLC 2000 scenario also performed relatively poorly. A degree of conservatism, however, to rather overestimate daily and design values, was incorporated into the default land cover maps used for this scenario, which explains the deterioration in the results.
(v) The Schmidt and Schulze (1984) lag equation produced substantially better results compared to the SCS lag (1972) equation and must therefore be used to estimate lag time in the CSM system.
Based on the results obtained, as summarised above, the following final configuration for the CSM system has been proposed:
(i) The incremental UH approach is to be applied with the CSM system as the default option to simulate peak discharges.
(ii) Site-specific information related to land cover and soils should be used in preference to the national land cover and soils maps, where available. If the national soils maps are used, the Schulze and Schütte 2018 SCS Soils map must be used to estimate the SCS-SA soil group. When using NLC maps, validation of the land cover classes should be performed using globally available imagery such as Google Earth, or other means, to identify the most accurate land cover class for the catchment of interest.
(iii) The Schmidt and Schulze (1984) lag equation should be used as the default lag equation in the CSM system.
In addition to the results summarised above, it was noted that the CSM system is particularly sensitive to the land cover classes and SCS-SA soil groups selected. Therefore, an additional consideration for future research is to recalibrate or further verify the CNs for South Africa in order to verify that the changes in CN and consequent stormflow response, for changes in SCS- SA soil groups and land cover classes, are correct. As stated in Chapter 3, however, this will be challenging since there are very limited, if any, research catchment data to cover the wide range of soils and land cover combinations possible. In addition, mixes of land cover and soils classes in larger catchments, i.e. beyond the research catchments scale into the operational catchment scale, may further complicate the configuration. Further investigation of this, however, is recommended in future research.
In conclusion, although the results when using the default soils and land cover inputs were not particularly good, the CSM system provides a consistent and conceptually sound approach to estimate changes in streamflow response for different land cover and soils conditions. It is acknowledged that the CSM system has relied heavily on the SCS-SA land cover classification, and in the absence of observed data, the assumption has been made that the hydrological
responses from the SCS-SA model for these soils and land cover classes are reasonable.
Consequently, it is possible that the ACRU CSM system and event-based SCS-SA model may provide similar results. Therefore, an assessment of how the results from the CSM system developed compare to those obtained from the SCS-SA model is needed. Consequently, the next chapter will compare the performance of the Current CSM System, i.e. which provided the best results in this chapter, to the results from the SCS-SA model using the same input information. It is however, hypothesised that the CSM system will perform better since the approach accounts for the antecedent soil water conditions before each event and considers both stormflow and interflow/baseflow, none of which the SCS-SA model accounts for.