Christiansen uniformity revisited: Re-thinking uniformity assessment in rainfall simulator studies

Abstract

Rainfall simulators have been used extensively for a variety of research applications. The uniformity and spatial distribution of simulated rainfall is an important metric to consider when conducting rainfall simulator experiments. Over the past 80 years, several methods of assessing rainfall distribution and uniformity over the plot surface below a rainfall simulator have been developed. However, the Christiansen Uniformity Coefficient (CU; Christiansen, 1942) is most frequently used. Here, we provide a critique of this established methodology and metric for quantifying rainfall distribution characteristics. Uniformity coefficients express the distribution of rainfall across the plot surface as a single percentage value. Therefore, if all collection beakers receive equal volumes of water during an experimental run, the rainfall simulator or irrigation system would be applying water with 100% uniformity. There are shortcomings with using a single percentage value to represent rainfall simulator rainfall distribution, especially over a larger plot surface, and CU is highly dependent on the sampling methodology employed. This paper assesses rainfall uniformity by conducting a series of controlled uniformity experiments and resampling different grid layouts of collection containers. Results demonstrate that the coarseness of the sampling methodology affects rainfall uniformity coefficient values. CU values of 45 – 51% were recorded under a dense (17 × 17) sampling grid layout, compared to CU values of 81% using a coarser (8 × 8) sampling methodology, despite being subjected to comparable rainfall events. This paper explores the sensitivity of CU to the resolution and spatial layout of the sampling methodology used and assesses whether the CU captures repeatability and localised variability of uniformity between experimental runs. A complementary statistical approach expressing variations in relation to their standard deviation from the mean is presented. Understanding how experimental setup (i.e. number, density, spatial configuration of collection containers) affects CU is critical when interpreting results from different rainfall simulator studies and an understanding of the factors which influence CU is critical to benchmark the results of uniformity testing across different rainfall simulator setups.