A comprehensive and systematic evaluation framework for a parsimonious daily rainfall field model

Abstract

The spatial distribution of rainfall has a significant influence on catchment dynamics and the generation of streamflow time series. However, there are few stochastic models that can simulate long sequences of stochastic rainfall fields continuously in time and space. To address this issue, the first goal of this study was to present a new parsimonious stochastic model that produces daily rainfall fields across the catchment. To achieve parsimony, the model used the latent-variable approach (because this parsimoniously simulates rainfall occurrences as well as amounts) and several other assumptions (including contemporaneous and separable spatiotemporal covariance structures). The second goal was to develop a comprehensive and systematic evaluation (CASE) framework to identify model strengths and weaknesses. This included quantitative performance categorisation that provided a systematic, succinct and transparent method to assess and summarise model performance over a range of statistics, sites, scales and seasons. The model is demonstrated using a case study from the Onkaparinga catchment in South Australia. The model showed many strengths in reproducing the observed rainfall characteristics with the majority of statistics classified as either statistically indistinguishable from the observed or within 5% of the observed across the majority of sites and seasons. These included rainfall occurrences/amounts, wet/dry spell distributions, annual volumes/extremes and spatial patterns, which are important from a hydrological perspective. One of the few weaknesses of the model was that the total annual rainfall in dry years (lower 5%) was overestimated by 15% on average over all sites. An advantage of the CASE framework was that it was able to identify the source of this overestimation was poor representation of the annual variability of rainfall occurrences. Given the strengths of this continuous daily rainfall field model it has a range of potential hydrological applications, including drought and flood risk.