Abstract
Long sequences of rainfall at fine spatial and temporal detail are increasingly required, not only for hydrological studies, but also to provide inputs for models of crop growth, landfills, tailings dams, liquid waste disposal on land and other environmentally sensitive projects. Rainfall information derived from rain gauges, radar or satellites may not individually be adequate to meet the detail required by hydrological models or other water resource studies. Therefore, a suitable technique is required to estimate rainfall at finer spatial and temporal resolutions. Different techniques have been developed to merge rainfall information from rain gauges, radar and satellites in order to obtain the 'best' estimate of the 'true' rainfall field. However, the length of the radar and satellite estimated rainfall records is currently limited. In this study, the mean areal merged rainfall, derived from rain gauges and radar, was estimated for 26 subcatchments in the Liebenbergsvlei catchment, which is a research catchment, in South Africa for the period when radar data were available. By using the relationships derived between the merged rainfall and rain-gauge data, improved subcatchment rainfall may be estimated using the historical data from rain gauges located in and around the subcatchments. In most of the subcatchments the relationship between the daily mean areal merged rainfall of the subcatchment and the daily rainfall data from rain gauges is strong (R2 > = 0.5). The relationship between the daily rain gauges and mean areal merged rainfall of the subcatchments is used to adjust the historical rainfall data from the daily rain gauges in order to estimate long sequences of subcatchment rainfall for input to continuous simulation models (CSMs).
Highlights
In the application of information derived from rainfall data in the fields of hydrology, engineering and agriculture, it is becoming increasingly important to know, or at least to have a reasonable estimate, of rainfall both in space as well as time, and in more detail than it is possible to deduce from the data collected at rain gauges in a sparse network (Pegram and Seed, 1998)
Some of these methods generate synthetic rainfall values using statistical models (e.g. Pegram and Seed, 1998; Pegram and Clothier, 2001), or are models based on the physical properties of a rain cell or cloud (Gupta and Waymire, 1993), or are techniques that derive a sound relationship between the radar field and the rain-gauge data based on prior knowledge of radar values and rain-gauge data relationships (Todini, 2001; Ehret 2002)
Traditional mathematical interpolation techniques have been used to determine the spatial distribution of rainfall over an ungauged area from rain-gauge networks
Summary
In the application of information derived from rainfall data in the fields of hydrology, engineering and agriculture, it is becoming increasingly important to know, or at least to have a reasonable estimate, of rainfall both in space as well as time, and in more detail than it is possible to deduce from the data collected at rain gauges in a sparse network (Pegram and Seed, 1998). A relationship between the best estimate of the average daily rainfall depth in a subcatchment, obtained from merging the rain-gauge data with rainfall derived from a radar, and independent daily rainfall data from rain gauges, is developed .
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