Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

Abstract

Abstract:Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A welldesigned rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging-based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment.

Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

“…Exponential, Gaussian, and spherical are the most commonly used variogram models for kriging applications in hydrology (Adhikary et al, ), which are also used to model the experimental variogram. The functional forms these variogram models are given in Table .…”

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

“…This function represents the spatial dependence structure of the data. Exponential, Gaussian, and spherical are the most commonly used variogram models for kriging applications in hydrology [44].…”

Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite-based and ground-based data

“…Among the various theoretical semivariograms, an isotropic exponential model was selected following Adhikary et al . () and ignored the potential anisotropy in the semi‐variance to simplify the estimation.…”

“…After calculating the experimental semivariogram, it was fitted to theoretical semivariogram by using the weighted least squares to estimate the best variogram parameters such as nugget, sill and range (Robinson and Metternicht, 2006). Among the various theoretical semivariograms, an isotropic exponential model was selected following Adhikary et al (2014) and ignored the potential anisotropy in the semi-variance to simplify the estimation. In this study, the CM technique follows the procedure as follows:…”

“…Acquisition of precipitation data is conventionally limited to ground-based measurements via, for example, automatic weather stations. Ground-based systems can provide accurate measurements and reasonable areal estimation in the areas adjacent to them, but the quality of gauge networks over a large spatial extent is restricted by high spatial and temporal variability of precipitation, the influence of terrain and unrepresentative distribution of gauge network (Andréassian et al, 2001;Cheng et al, 2008;Adhikary et al, 2014). As a result, researchers have used various spatial interpolation techniques including Thiessen polygons, inverse distance weighting, ordinary kriging (OK), kriging with external drift (Hudson and Wackernagel, 1994;Goovaerts 2000) and cokriging (Bárdossy and Lehmann 1998;Lloyd, 2005), as well as other techniques based on splines (Hutchinson, 1998) in order to estimate spatial pattern of precipitation from rain gauge.…”