Abstract
Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological modelling, but without consensus. This paper contributes to the identification of the optimum rain gauge network density, using scaling laws and bias-corrected 1 km × 1 km grid radar rainfall records, covering an area of 28,371 km2 that hosts 315 rain gauges in south-east Queensland, Australia. Varying numbers of radar pixels (rain gauges) were repeatedly sampled using a unique stratified sampling technique. For each set of rainfall sampled data, a two-dimensional correlogram was developed from the normal scores obtained through quantile-quantile transformation for ordinary kriging which is a stochastic interpolation. Leave-one-out cross validation was carried out, and the simulated quantiles were evaluated using the performance statistics of root-mean-square-error and mean-absolute-bias, as well as their rates of change. A break in the scaling of the plots of these performance statistics against the number of rain gauges was used to infer the optimum rain gauge network density. The optimum rain gauge network density varied from 14 km2/gauge to 38 km2/gauge, with an average of 25 km2/gauge.
Highlights
Rainfall is a key forcing input for hydrological modelling, such as that used in studies on extreme events and climate impact analysis
In recent years, gridded radar and satellite products data have been processed to obviate the limitations of the gauges, but these approaches have their challenges, including the spatial scale, which ranges from 1 km2 to about 50 km2
A break in scaling, identified by plots of the performance statistics and the number of rain gauges, was used to infer the optimum rain gauge network density, which is the main aim of this paper
Summary
Rainfall is a key forcing input for hydrological modelling, such as that used in studies on extreme events and climate impact analysis. Gridded rainfall products (satellite, radar, general circulation models (GCMs), regional climate models (RCMs) are normally calibrated and validated using rain gauge data, but the poor network density introduces a high degree of uncertainty [6]. It is not just the density of the rain gauge networks, but their non-uniform (irregular) distribution over the catchments, due to issues of accessibility and topography, among other factors, contribute to the uncertainty [7], bearing in mind the high temporal and spatial variability [8]. A break in scaling, identified by plots of the performance statistics and the number of rain gauges, was used to infer the optimum rain gauge network density, which is the main aim of this paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
