Characterising rainfall using a high density rain gauge network in the Mooi River catchment

Abstract

Understanding and measuring rainfall is of critical importance for agriculture, disaster mitigation, drought relief and water resource management practises. With the advancement of technology, methods of earth observation have greatly improved. Satellite and weather radar can provide high resolution real-time measurements of rainfall. These instruments, however, need calibration and ground validation which can be accomplished with rain gauges. Therefore, the need to better understand and characterise spatial variability of rainfall using a rain gauge network is of great importance. The objectives of this study was, firstly, to evaluate the use syphon tipping bucket rain gauges to accurately characterise rainfall, secondly characterise the spatial variability of rainfall with a high density rain gauge network and finally to optimise the high density rain gauge network. A dense network of 15 syphon tipping bucket rain gauges spread out over the 3294 km2 Mooi River catchment in the North West and Gauteng provinces of South Africa, was used to characterise rainfall for the 2014/2015 rainfall season. Methods to evaluate the use of syphon tipping bucket rain gauges to determine the spatial variability of rainfall and to optimise a rain gauge network are described. Because tipping bucket rain gauges can produce misleading reports, this study suggests easily programmable diagnostic check that can be used in an operational environment to identify and eliminate any errors associated with rain gauge data. These checks were tested on the data obtained from the rain gauges in the Mooi River catchment and a variety of confirmed errors were found. The spatial variability of rainfall over the catchment was defined for rainfall accumulation periods ranging from 1 min to 14 days. It was evident that rainfall exhibits high spatial variability at shorter accumulation periods between 1 and 30 min with very poor correlation between gauges even at the shortest separation distances of 8 km. For longer accumulation periods, the daily scale for example, there is a good correlation between all the gauges of the network, even at 80 km separation distances. Therefore, the current rain gauge network in the Mooi River catchment is dense enough to provide information of rainfall at a daily resolution, but when high resolution rainfall data at a finer time scales are needed (e.g. 5-minute accumulations), the network is not dense enough. In order to accurately estimate the spatial variability of rainfall, a dense network of instruments is required, which will typically entail large installation and operational costs. Therefore, it is essential to optimise the number and distribution of rain gauges in a network. This in turn will make it possible to better estimate the rainfall at unrecorded locations from data recorded by an existing network of rain gauges. The optimal distribution of gauges for the Mooi River catchment was determined using a set of rules based on ordinary kriging error variance to assess the accuracy of rainfall estimation. Based on these rules, the total area in the network with acceptable estimation accuracy can be calculated. This study proposes a method to prioritise the existing rain gauges in the network, as well as identifying additional sites where rain gauges could be installed in order to improve the estimation accuracy of the Mooi River catchment rain gauge network. It was found that the current network of 15 rain gauges in the Mooi River catchment is not dense enough for high resolution rainfall estimates and an additional 13 rain gauges should be added to the existing rain gauge network. A total of 28 rain gauges are therefore required in the augmented network in order to obtain as much surface coverage of gauges over the catchment with acceptable accuracy as possible. The study contributes a great deal of knowledge to the scientific field through: a unique high-resolution rain gauge network. A new method of data quality control for syphon tipping bucket rain gauges is proposed. A better understanding and estimation method of the spatial variability of rainfall as well as a method to establish the optimal density of gauges in a network to capture the spatial variability of rainfall has been established.