10.4 Suggestions for Future Research
10.4.1 Improve forecasting methodology used to generate runoff forecasts
Improved forecasts will enhance the reliability of forecasts rendering them useful to water resource users and managers. It is anticipated that the following recommendations would improve the forecast accuracy and skill:
i. Replace the forecasted seasonal runoff, which at present is derived from the median seasonal rainfall within a category of Above Normal, Near Normal or Below Normal, with a probability distribution of forecasted runoffs within each category.
ii. At present the ACRU model assessment at a Quaternary Catchment scale over South Africa is based on comparative simulations, i.e. the same model parameters for land use and hydrological response rates are used for simulations of median, actual and forecasted runoffs, assuming an unperturbed natural system. Because simulations of median, actual and forecasted runoffs are then compared in a simple win / lose benefit analysis using the same parameters, the final results are assumed to be reasonably correct on an individual Quaternary Catchment scale.
However, in future for critical areas of South Africa, the model will require:
a. Further verification studies against actual runoff observations under different hydrological regimes, with account taken of actual land uses, dam specifications, abstractions and return flows, as well as of irrigation demands/supplies and return flows, all of which perturb the natural hydrological responses, as have been undertaken already for the Mgeni, Mkomazi, Mvoti, Pongola, Sabie and Mbuluzi (Swaziland) catchments; and b. Cascading of runoff from one Quaternary Catchment in a nested systerT)
into the next downstream, to assess actual streamflow characteristics as they accumulate down the river system.
These facets of research still constitute a major collaborative undertaking of several years' duration. '
10.4.2 Extend the base data sets used to generate the forecasts and test the methodology
A quality checked, concurrent daily rainfall data set covering a period of 44 years from 1950 - 1993 for each Quaternary Catchment in South Africa was used in this analysis.
Unfortunately, the hind forecast data sets used in this project do not extend that far back, starting in the mid to early 1980s and extending beyond to beyond 1995. The result is that the overlap period is rather short and more representative results could be obtained by extending the daily rainfall database beyond the end of 1993. An extension of the daily rainfall database was not performed before the development and testing of the forecasting methodology as the data required to extend the database were not readily available at the time. In the meanwhile (2000 - 2002) a new initiative by the Water Research Commission (WRC) in South Africa titled "The Development of an Improved Gridded Database of Annual, Monthly and Daily Rainfall", will collate into a single database all the available daily rainfall observations from some 13000 rain gauges within South Africa and its neighbouring states (Lynch, 2001). The database will be more comprehensive than the existing daily rainfall database used in this study, including more stations and a longer concurrent record ending in 2000 or later. The data will be quality checked and missing or suspect data will be infilled via a hierarchical approach using a suite of different infilling techniques (Lynch, 2001). It is recommended that this database be used in further forecasting exercises and methodology testing.
10.4.3 Develop a system that automatically updates the hydrological forecasts
In the forecasting framework used in this study several different forecast time periods are used, ranging from four months down to 30 days. Tennant (1998b) produces even shorter duration forecasts of 7 and 14 days. To improve the application of forecasts in operational management of water resource systems, forecast reliability could be increased by developing a framework using several series of forecasts in conjunction with real-time observed data to continuously update the short (up to 14 days), median (up to 1 month) and longer range forecasts. Using such a structure, it is would be possible for forecasts to be updated, giving probabilities of fulfilling the original longer term forecasts. A sequence of imbedded forecasts coupled with real-time observed data could be created to run with the real data and different forecasts updating each other. The real data could be used to update the 7 day forecast and both the real data and the 7 day forecast could be used to
update the 14 day forecast, which in turn could be used to update the monthly forecasts which update the three monthly forecasts and finally the four monthly forecasts.
Probabilities of fulfilling the different forecasts could thus be generated and the water resource and risk managers could adjust their strategy accordingly, as longer range forecasts are updated. While, this technique would be quite valuable there may be problems with compounding errors cascading through the systems causing large in accuracies in the forecasts. This aspect therefore needs to be thoroughly investigated.
10.4.4 Identify areas where the application of forecasti n9 maybe more beneficial than elsewhere
The benefits that can be derived from forecasting are a composite of the level of risk associated with specific hazard occurrence in a particular area, and the ability to forecast the hazard occurrence in that area. The ability to reliably forecast a hazard in a particular area reduces the risk associated with that hazard, as mitigation and preventative actions can be taken to reduce losses (cf. Section 5.4.2). Risk, on the other hand, is a combination of the severity (variability or probability of occurrence) associated with particular hazard occurrence, such as a flood or drought, and the vulnerability either, or all, of the system, community or individual exposed to the hazard(cf. Chapter 4).
In the case of hydrological hazards, such as floods and droughts, the severity of the physical hazard is dependent on the particular phenomenon's sensitivity to causal phenomena such as rainfall and temperature. A direct correlation could thus be inferred, with sensitivity being used to represent severity or, alternatively, probability of occurrence.
The other dimension of risk is vulnerability, which was shown in Chapters 4, to be linked to different social, economic and physical elements such as education, poverty and population density (cf. Sections 4.2.3.2). Vulnerability indices could be created using criteria such as average income in the exposed communities, average level of education and population density in the exposed area. These criteria obviously change according to .the type of hazard that is occurring, Le. the vulnerability of people or a community to a
flood may be different to that of a drought.
The level of benefit that could be derived from a forecast could thus be derived as a combination of the forecast accuracy and skill, the sensitivity to causal phenomena and the vulnerability of that particular community/ person to the hazard occurrence. In Figure
10.1 a forecast benefit matrix is shown, which could be used to in an attempt to quantify the potential benefit derived from applying forecasts ov~r the whole of the South .Africa study region.
A forecast benefit score could be derived from the matrix by creating indices of forecast accuracy and skill, sensitivity and vulnerability. High levels of benefit would be derived from a forecast if the forecast accuracy and skill scores where high, the vulnerability of a particular community was high and the sensitivity was correspondingl'y high, Le. the score would occur in the upper right hand corner of the forecast benefit matrix shown in Figure 10.1 While the sensitivity and forecast accuracy results are relatively easy to obtain, as they are linked to physical attributes, the vulnerability may be difficult to determine and quantify as it is linked to social attributes (cf. Sections 4.2.3 and 5.3). Such matrices could be used to determ ine the forecast benefit over the South African study region. This type of research could rationalise forecasting efforts, concentrating forecasting activities in areas where the most benefit could be derived from forecasting .
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:JForecast Accuracy and Skill
. Figure 10.1 Forecast benefit matrix
10.4.5 Perform verification studies at a finer resolution on an actual