Disaggregation of daily rainfall by conditional simulation from a point-process model

Glasbey, C. A.; Cooper, G.; McGechan, M.B.

doi:10.1016/0022-1694(94)02598-6

Cited by 93 publications

(59 citation statements)

References 7 publications

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“…Lall and Sharma, 1996;Vogel and Shallcross, 1996), point process theory (e.g. Glasbey et al, 1995;Koutsoyiannis and Onof, 2001) and scale invariance theory (e.g. Schertzer and Lovejoy, 1987;Olsson, 1998;Serinaldi, 2010).…”

Section: Lisniak Et Al: Circulation Pattern Based Parameterizatiomentioning

confidence: 99%

Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

Lisniak

Franke

Bernhofer

2013

Hydrol. Earth Syst. Sci.

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Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969-1979 and 1989-1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989-1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice [1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999], as compared to the simple MRC. Despite similar results of both MRCs in the validation period of 1969-1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

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Section: Lisniak Et Al: Circulation Pattern Based Parameterizatiomentioning

confidence: 99%

Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

Lisniak

Franke

Bernhofer

2013

Hydrol. Earth Syst. Sci.

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“…Rodriguez-Iturbe et al (1988) further modified the 5-parameter BLRP model by introducing an additional parameter to enhance its ability to capture dry periods and called it the Modified BartlettLewis Rectangular Pulse (MBLRP) model. Among others, Islam et al (1990), Bo et al (1994), Onof and Wheater (1994a), and Chandler (1997) discuss the complexity of the model calibration; and Islam et al (1990), Bo et al (1994), Onof and Wheater (1994b), Glasbey et al (1995), Cowpertwait et al (1996a), Khaliq and Cunnane (1996), Onof et al (1996), and Verhoest et al (1997) assess the overall accuracy of its fundamental assumptions by comparing statistics and extreme values of observed and synthetic precipitation time series. Verhoest et al (1997), in particular, applied the MBLRP model to Uccle, Belgium, for which 27 years of 10-minute rainfall data were available.…”

Section: Introductionmentioning

confidence: 99%

Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model

Kim¹,

Olivera²,

Cho³

et al. 2013

Terr. Atmos. Ocean. Sci.

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Parameters of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall simulation model were regionalized across the contiguous United States. Three thousand four hundred forty-four National Climate Data Center (NCDC) rain gauges were used to obtain spatial and seasonal patterns of the model parameters. The MBLRP model was calibrated to minimize the discrepancy between the precipitation depth statistics between the observed and MBLRP-generated precipitation time series. These statistics included the mean, variance, probability of zero rainfall and autocorrelation at 1-, 3-, 12-and 24-hour accumulation intervals. The Ordinary Kriging interpolation technique was used to generate maps of the six MBLRP model parameters for each of the 12 months of the year. All parameters had clear to discernible regional tendencies; except for one related to rain cell duration distribution. Parameter seasonality was not obvious and it was more apparent in some locations than in others, depending on the seasonality of the rainfall statistics. Cross-validation was used to assess the validity of the parameter maps. The results indicate that the suggested maps reproduce well the observed rainfall statistics for different accumulation intervals, except for the lag-1 autocorrelation coefficient. The boundaries of the expected residual, with 95% confidence, between the observed rainfall statistics and the simulated rainfall statistics based on the map parameters were approximately ±0.064 mm hr -1 , ±1.63 mm 2 hr -2 , ±0.16, and ±0.030 for the mean, variance, lag-1 autocorrelation and probability of zero rainfall at hourly accumulation levels, respectively. The estimated parameter values were also used to estimate the storm and rain cell characteristics.

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“…To remedy this weakness, the authors examined another ad-hoc model that searches for best matching sequence on the basis of daily totals of 3 days. Despite the improvement in the statistics achieved, this method does not have the general character of the initial scheme, since a 3-day period cannot necessarily be considered as independent (Glasbey et al, 1995;Koutsoyiannis and Onof, 2001). …”

Section: Introductionmentioning

confidence: 99%

“…The literature reports numerous empirical investigations that examine the performance of the Poissoncluster models in a wide range of rainfall types and climatic conditions. The models have been successfully fitted to data of various fine time scales from England (Cameron et al, 2000;Cowpertwait, 1991;Entekhabi et al, 1989;Onof and Wheater, 1994a, 1994b, Scotland (Glasbey et al, 1995), Belgium (Verhoest et al, 1997), Switzerland (Paschalis et al, 2014), Germany (Kaczmarska et al, 2014), Spain (Khaliq and Cunnane, 1996), Ireland (Khaliq and Cunnane, 1996), South Africa (Smithers et al, 2002), New Zealand (Cowpertwait et al, 2007), Australia (Gyasi-Agyei and Willgoose, 1997;Gyasi-Agyei, 1999;Wasko et al, 2015), Greece (Derzekos et al, 2005;Kossieris et al, 2015Kossieris et al, , 2013, Italy (Bo et al, 1994;Islam et al, 1990), United States (Bo et al, 1994;Kim et al, 2016Kim et al, , 2013bRodriguez-Iturbe et al, 1988Velghe et al, 1994) and Korean Peninsula (Kim et al, 2014). Further to the single-site case, Poisson-cluster models have been also developed for the simulation of rainfall in space and time (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures

Kossieris

Makropoulos

Onof

et al. 2018

Journal of Hydrology

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Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 minute time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a 2 Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-minute time scale. The applicability of the methodology was assessed on a 5-minute rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.

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Disaggregation of daily rainfall by conditional simulation from a point-process model

Cited by 93 publications

References 7 publications

Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model

A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures

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