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7.1 Objectives
One of the primary objectives of this investigation was to assess the use of regional, categorical rainfall forecasts of four months, three months and one month over South Africa to develop corresponding forecasts of runoff using a daily time step hydrological simulation model (as a reminder for the purpose of this study South Africa is defined as the geographical entity comprising South Africa, Lesotho and Swaziland). To do this, historical forecasts for 1981 to 1995 were used for the four month forecast period, while historical forecasts from 1987 to 1996 were used for the three month forecasts. These periods were chosen as they correspond with the representative historical rainfall forecasts sets provided by Landman from the SAWS (Landman, 1998 pers comm.).
In order to assess the methodology used to translate categorical rainfall forecasts into runoff forecasts, techniques were developed
i. to first spatially downscale the four month, three month and one month categorical rainfall forecasts, which are given for large regions of the order of100 000 km2to be applicable to South Africa's operational Quaternary Catchments, which are generally of the order of 100 - 600 km2 in extent, with approximately a third covering areas of more than 600km2 in area; then
ii. to temporally downscale the four month, three month and one month forecasted categorised rainfall to representative data sets of daily rainfall for the corresponding forecast period for each Quaternary Catchment; thereafter
Problem Statement
Reliable, skillful hydrologicClI forecasts have the potential to prevent loss of life, spare considerClble hardship and save affected industry and commerce millions of Rands annually if applied operationally within the framework of water resources and risk management. Integrated model and database systems provide scientists and managers with tools to investigate the feasibility and applications of such systems. in operational forecasting activities.
Broad Objectives Place forecasting within a framework of water resources and risk management in South Africa.
(a)
Evaluate the hydrological forecasting techniques' performance over the South Africa study region using selected forecasting periods.
(c)
Discuss problems and shortfalls of the current methodology used to obtain the different runoff forl'lcasts. Recommend areas of potential future research that would enable operational application of these forecasts.
(d)
Specific Objectives i.
ii.
iii.
Review climate variability in South Africa and its associated effect on risk Review the current water resource situation in South Africa
Review risk evaluation and management strategies with specific attention given to the role of forecasting
i. Review current methodologies used to obtain hydrological forecasts
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i.
ii.
iii.
iv.
Use standard forecasting evaluation techniques to assess the performance of the hydrological forecasting techniques developed Compare different hydrological forecast performances
Attempt to identify trends that influence forecast performance
Provide explanations for the different trends influencing the forecast's performance
i.
ii.
Hi.
iv.
Identify shortfalls in the techniques used to translate rainfall forecasts into runoff forecasts Suggest areas where improvements could be made to increase the forecasts' reliability Suggest areas of future research that would lead to the operational use of the different runoff forecasts Identify key sectors of the study area where the application of forecasting may be particularly beneficial
Figure 08-7: Problem Statement, highlighting aspects covered in Chapter 7
iii. to simulate daily runoff with the ACRU model (Schulze, 1995) using the representative daily rainfall data sets for the forecast period; and finally
iv. to evaluate results of the forecasted runoff simulations for a selected forecast period, at both Quaternary Catchment and national scales, against runoff simulations from actual daily rainfall for the forecasted period.
The results are then used to assess where further research is needed in order to produce more reliable and applicable hydrological forecasts.
7.2 Defining forecast accuracy and skill
Forecasted predictions do not always convert to reality, and the forecasts' prediction ability has to be assessed. Therefore, before continuing to review the individual rainfall forecasts and evaluating the results of the runoff forecasts, it is important to define two terms used to assess the forecast performance, viz. forecast accuracyand forecasts skill.
i. Forecast accuracy is the average degree of correspondence between forecasts and observations and is defined as the number of times, or the percentage of occurrences, that the forecasts are correct. In terms of the categorical rainfall forecasts the number of times the forecast falls into the correct category could be defined as the forecast accuracy and for the purpose of this study will be termed the "hit rate".
ii. Forecast skill is a comparative measurement and is defined as the accuracy of a forecast relative to the accuracy of some other forecast produced by some standard procedu re, such as using the median value, or chance or persistence. In this study forecast skill would be the accuracy of the runoff forecast for a given time period, as derived by hydrological modelling using the rainfall forecasts, in comparison to the median runoff for the same period. This is based on the premise that water resource managers, in the absence of reliable runoff forecasts, assume
"future" runoff trends follow historical trends.
7.3 Categorical Rainfall Forecasts
The categorical seasonal rainfall forecasts in this study were obtained from the erstwhile South African Weather Bureau (SAWB), now the South African Weather Service (SAWS) (Land man, 1998 pers comm.). The forecasts have been generated using techniques described by Landman (1997). The forecasts were obtained for four ~onth, three month
and one month time periods. These forecasts place a season's rainfall into one of three categories, viz:
i. Above Normal, i.e. the upper (tercile) of the ranked accumulated rainfall for a specific season,
ii. Near Normal, represented by the middle tercile, and Hi. Below Normal, representing the lower tercile.
7.3.1 Procedure used to obtain operational categorical rainfall forecasts
Operational categorical rainfall forecasts ranging from one to three months have been available for ~everal years in South Africa. In order to obtain these forecasts, Landman (1997) set up the statistical fo'recasting model, which uses CCA. However, in order to obtain a forecasting model, Landman (1997) first had to obtain suitable predictor and predictand data sets. These data sets were then used to train, cross-validate and independentl y validate the categorical rainfall forecast model.
The Global Ocean Global Atmosphere (GOGA) sea surface temperature (SST) data (Pan and Oort, 1990; Lau and Nath, 1994; cited in Landman, 1997) of the oceans adjacent to southern Africa, and those in the equatorial Pacific Ocean, were used as the predictor data in the model. The predictor data obtained by Landman for the period 1946 to 1985 were then used as the training period to establish the categorical rainfall forecast model. A further ten years of Optimal Interpolated SST data (Reynolds and Smith, 1994; cited in Landman, 1997) were obtained for the period 1985 to 1995 for independent validation purposes. The predictor ocean regions used in the study as predictors in the model are:
i. Indian Ocean: 20.25° N to 38.250S,
, Iii. Atlantic Ocean: 7.750N to 38.250S, and iii. Equatorial Pacific Ocean: 1200
E to 85OW,11.250N to 11.250S (Landman, 1997).
The predictand data set was obtained from the district rainfall data of the South African Weather Bureau (van Rooy, 1972) for the same period as the predictor data set. In order . to simplify processing and eliminate unnecessary data, grouping and filtering techniques were applied to the predictand fields. A rotated principal component analysis was applied to the 80 South African rainfall districts to identify relatively homogeneous rainfall regions (Land man, 1997).
A principal component analysis using Empirical Orthogonal Functions was then used to identify the variables that explain the majority of the variation in both the predictor and the predictand data sets. This is a filtering technique that allows the user to identify the most physically meaningful variables that describe the majority of the variance and hence reduce the amount of data needed to produce the statistical model (Landman, 1997).
The technique of CCA was used in order to establish relationships between the filtered predictor (SST) and the predictand (rainfall) data sets. CCA is a generalised form of multiple regression analysis in which several predictor variables are simultaneously relJted to several predictand variables. It determines the optimal linear combinations of two data sets by maximising the correlation (Manly, 1986). A more detailed description of the statistical techniques used by Landman (1997) can be obtained from statistical texts dealing with multivariate analysis. One recommended text on the subject is that by Manly (1986).
Landman (1997) found that rainfall variability over South Africa may be explained by several different oceans' SST values, depending on the month of the year and which area in South Africa the prediction is for. The predictability of rainfall over South Africa varies from region to region and month to month. Predictability is determined by the influence a particular area of SST has on climate variability of a region for a specific period of time.
The length of a particular forecast may also affect the predictability, with periods extending for longer than a month being expected to be less predictable, since the month to month correlations are poor.
7.3.2 The four month categorical rainfall forecasts
The four month retro-active categorical rainfall forecasts were generated by Landman (1997) for six delimited forecast regions in the summer rainfall areas of South Africa.
These are regions A - C and F - H in Figure 7.1. The categorical forecasts used in this study were for the lJeriod from 1 December to 31 March for the seasons commencing . December 1981 to December1995. The categorical forecasts, as well as actual rainfall
categories which occurred historically in those seasons, are given in Table7.1.
Figure7.1 Regions delimited for the four month categorical rainfall forecast (after Landman, 1998pers comm.)