Investigating rainfall models: Why a comprehensive and systematic approach is essential

In this article we take a look at why we need comprehensive and systematic evaluation of rainfall models. We also examine a new model evaluation framework, with examples of the framework in action.

Imagine a typical scene from a detective novel:

Sirens scream past – like every Tuesday in this forsaken town. I was about to close up shop for the night when a worried young man stepped sheepishly into my office. I couldn’t understand him at first. But through his mumblings it became clear that something was wrong. Something was wrong with … The Rain.

Actually, the work of a hydrologist is much like a detective.

Rainfall models and the character of rain

We rely on models of ‘fake’ rain. These rainfall models are relied upon to assess the hydrological impacts of droughts, floods, land-use and climate change. For example, to evaluate flood risk you can select a spatial rainfall model capable of generating long sequences of rainfall for the catchment.

But to provide robust assessments, the simulated rainfall must reproduce observed rainfall characteristics in space and time. And across a wide range of scales.

This is not a simple task. Many potential issues can arise – not enough data, model is too simple, etc.

When people think of ‘rain’ they think of one character, when actually there is a whole family. Some of the main characters include:

  • Daily rainfall amounts
  • Total annual rainfall
  • Inter-annual variability (i.e. year-to-year variability in the rainfall)
  • Wet/dry spell distributions
  • Seasonality
  • Spatial variability
  • Extremes

This Family of Rain Characters is complex. Each has its own personality. The problem is that when there is trouble reproducing an ‘observed rainfall’ characteristic, any one of these characters (or, perhaps, all of them) could be a culprit.

It is challenging because they are all interlinked. When we try to isolate ‘who’ caused what effect, they can provide alibis for each other! For example, an issue with low variability between years could actually be an issue with seasonality instead.

Imagine how our detective would tackle this challenge:

It smelt fishy to me. When dealing with a bunch of low lives like the Rainfall family you need to be thorough. It may be tempting to only interrogate a few key players and repeat offenders (inter-annual variability and wet-dry pattern come to mind). But going with your gut won’t cut it in this case. It occurred to me that any analysis of these slippery characters needs to be comprehensive and systematic. They need to be lined up side-by-side and interrogated to figure out who is pulling the strings and who is in cahoots.

In reality, past evaluations of rainfall models have presented performance in descriptive terms (e.g. words like ‘satisfactory’ or ‘well’). They have often used a set of selected statistics, sites or time periods. It is not systematic.

A new model evaluation framework

To address these issues, members of this research group have developed a new framework for evaluating rainfall model performance. The framework uses quantitative criteria to assess model performance across a comprehensive range of observed statistics of interest.

The framework is comprehensive. It plainly summarises performance across a range of time scales (years/months/days), and spatial scales (sites/fields). By using quantitative criteria (defined a priori) the evaluation is made transparent and avoids the need to frame performance results in purely descriptive terms.

These features of the framework help to identify model strengths and weaknesses, and to untangle the origin of deficiencies.

The framework in action

Let’s look at applying the framework to evaluate the performance of a rainfall model in simulating 100 realisations of daily rainfall for 73 years. We’ll look at this rainfall across 19 sites for a range of statistics, scales and seasons. The problem has many dimensions and needs to be tackled in a comprehensive and systematic fashion.

The performance criteria of the framework is used first, to assess the performance of each individual statistic of interest for each site/scale.  Then, the individual analyses can be summarised to provide an overview of model performance across a range of model properties.

The framework was applied to a case study of the Onkaparinga catchment in South Australia. You can see the application of the model, and an assessment of its performance, here.


[1] BENNETT, B., THYER, M., LEONARD, M., LAMBERT, M. & BATES, B. 2016. A comprehensive and systematic evaluation framework for a parsimonious daily rainfall field model, Journal of Hydrology, Available online 27 December 2016,

[2] MEHROTRA, R., & SHARMA, A. (2007). A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability.Journal of Hydrology,  335(1), 180-193,

[3] WILKS, D. S. (1999). Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology, 93(3), 153-169, DOI: 10.1016/S0168-1923(98)00125-7.

Tagged in Civil Environmental and Mining Engineering, Intelligent Water Decisions