Abstract
Long-term observations of daily rainfall are common and routinely available for a variety of hydrologic applications. In contrast, observations of 10 or more years of continuous hourly rainfall are rare. Yet, sub-daily rainfall data are required in rainfall-runoff models. Rainfall disaggregation can generate sub-daily time-series from available long term daily observations. Herein, the performance of Multiplicative Random Cascade (MRC) model at disaggregating daily-to-hourly rainfall was investigated. The MRC model was parameterized and validated with 15 years of continuous observed daily and hourly rainfall data at three weather stations in Oklahoma. Model performance, or degree to which the disaggregated rainfall time series replicated observations, was assessed using 46 variables of hourly rainfall characteristics, such as longest wet spell duration, average number of rainfall hours per year, and largest hourly rainfall. Findings include: a) average-type hourly rainfall characteristics were better replicated than single value characteristics such as longest, maximum, or peak hourly rainfall; b) the large number of sub-trace hourly rainfall values (<0.254 mm h-1) generated by the MRC model were not supported by observations; c) the random component of the MRC model led to a variation under 15% of the average value for most rainfall characteristics with the exceptions of the “longest wet spell duration” and “maximum hourly rainfall”; and d) the MRC model produced fewer persistent rainfall events compared to those in the observed rainfall record. The large number of generated trace rainfall values and difficulties to replicate reliably extreme rainfall characteristics, reduces the number of potential hydrologic applications that could take advantage of the MRC disaggregated hourly rainfall. Nevertheless, in most cases, the disaggregated rainfall generated by the MRC model replicated observed average-type rainfall characteristics well.
Highlights
Many of today’s hydrologic and agricultural crop models require sub-daily weather data to accurately simulate rainfall-runoff and crop growth processes
The three weather stations used in this rainfall disaggregation study were chosen based on the high quality and length (>10 years) of available overlapping daily and hourly rainfall records, on the broad range of annual rainfall (720 to 1170 mm/yr), and on a large number of hourly rainfall characteristics (Table 1)
The Relative Error (RE) between observed and disaggregated rainfall is shown in Figure 2 for the average and standard deviation of 26 rainfall characteristics for each of the weather stations
Summary
Many of today’s hydrologic and agricultural crop models require sub-daily weather data to accurately simulate rainfall-runoff and crop growth processes. Long-duration continuous sub-daily weather data at a location of interest are sparse and typically generated internally by the application model (Bisantino et al, 2015; Li et al, 2015; Moriasi et al, 2012; Malone et al, 2010; Semmens et al, 2008; Jones et al, 2003; Bingner at al., 2001; Laflen et al, 1991 and 1987). The need for long-duration hourly rainfall data by agricultural and hydrologic models is expected to increase as hillside, field, and hydrologic processes are investigated at an increasingly finer temporal resolution. The likelihood of a weather station with a long record of daily rainfall being located within a reasonable proximity of the site of investigation is high. Cascade disaggregation models represent a technology that may be useful to generate the desired hourly rainfall from available observed daily rainfall records
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