Stochastic Models for the Disaggregation of Precipitation Time Series on Sub-Daily Scale: Identiication of Parameters by Global Optimization

Villani, Veronica; Serafino, Daniela di; Rianna, Guido; Mercogliano, Paola

doi:10.2139/ssrn.2602889

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…The Bartlett-Lewis Rectangular Pulses (BLRP) model is used in many studies to disaggregate daily observed rainfall to finer scales. A member of general category of Poissoncluster models in which rectangular pulses occur in continuous time simulates rainfall events with clusters of these rectangular pulses [28,46,53,54,55].…”

Section: Disaggregationmentioning

confidence: 99%

“…The initially proposed model has 5 parameters [55,56] but Random Parameter Bartlett-Lewis Rectangular Pulse Model (BLRPR) which have 6 parameters: {λ, α, v, κ, φ, μX}, and is modified version of original model to enhance the model's flexibility in generating a greater diversity of rainfalls, is the preferred model in this study [44,45,53].…”

Section: Disaggregationmentioning

confidence: 99%

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Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

2022

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This study examines the potential impacts of climate change on extreme precipitation. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend.

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Section: Disaggregationmentioning

confidence: 99%

Section: Disaggregationmentioning

confidence: 99%

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

2022

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“…The rationale behind the rainfall disaggregation was to split the daily rainfall into hourly to sub-hourly [13,39,41,42,44,71,91] when the resolution of the available data is not fine enough. We derived MBLRP model parameters (Table 3) utilizing the statistical characteristics of historical monsoonal daily rainfall series for all eight stations and implemented the HyetosMinute software in R. We observed that the higher skewness value of the daily rainfall series was possibly due to strong variability in the daily rainfall time series, whereas the annual rainfall time series is rather smoothened out leading to low skewness, which was expected.…”

Section: Disaggregation Of Daily Rainfall Depthmentioning

confidence: 99%

Development of Monsoonal Rainfall Intensity-Duration-Frequency (IDF) Relationship and Empirical Model for Data-Scarce Situations: The Case of the Central-Western Hills (Panchase Region) of Nepal

et al. 2018

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Intense monsoonal rain is one of the major triggering factors of floods and mass movements in Nepal that needs to be better understood in order to reduce human and economic losses and improve infrastructure planning and design. This phenomena is better understood through intensity-duration-frequency (IDF) relationships, which is a statistical method derived from historical rainfall data. In Nepal, the use of IDF for disaster management and project design is very limited. This study explored the rainfall variability and possibility to establish IDF relationships in data-scarce situations, such as in the Central-Western hills of Nepal, one of the highest rainfall zones of the country (~4500 mm annually), which was chosen for this study. Homogeneous daily rainfall series of 8 stations, available from the government's meteorological department, were analyzed by grouping them into hydrological years. The monsoonal daily rainfall was disaggregated to hourly synthetic series in a stochastic environment. Utilizing the historical statistical characteristics of rainfall, a disaggregation model was parameterized and implemented in HyetosMinute, software that disaggregates daily rainfall to finer time resolution. With the help of recorded daily and disaggregated hourly rainfall, reference IDF scenarios were developed adopting the Gumbel frequency factor. A mathematical model [i = a(T)/b(d)] was parameterized to model the station-specific IDF utilizing the best-fitted probability distribution function (PDF) and evaluated utilizing the reference IDF. The test statistics revealed optimal adjustment of empirical IDF parameters, required for a better statistical fit of the data. The model was calibrated, adjusting the parameters by minimizing standard error of prediction; accordingly a station-specific empirical IDF model was developed. To regionalize the IDF for ungauged locations, regional frequency analysis (RFA) based on L-moments was implemented. The heterogeneous region was divided into two homogeneous sub-regions; accordingly, regional L-moment ratios and growth curves were evaluated. Utilizing the reasonably acceptable distribution function, the regional growth curve was developed. Together with the hourly mean (extreme) precipitation and other dynamic parameters, regional empirical IDF models were developed. The adopted approach to derive station-specific and regional empirical IDF models was statistically significant and useful for obtaining extreme rainfall intensities at the given station and ungauged Hydrology 2018, 5, 27 2 of 27 locations. The analysis revealed that the region contains two distinct meteorological sub-regions highly variable in rain volume and intensity.

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“…Some studies introduced a lognormal autoregressive fitting model to improve Bartlett–Lewis results (Gyasi‐Agyei, 1999; Gyasi‐Agyei and Willgoose, 1999). More recent studies used combined models including Bartlett–Lewis and global optimization models to solve deficiencies such as reproduction of several local minima in Bartlett–Lewis models (Vanhaute et al ., 2012) or to identify Bartlett–Lewis parameters (Villani et al ., 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the climate change impact on the extreme rainfall amounts using modified method of fragments for sub‐daily rainfall disaggregation

Rafatnejad

Tavakolifar

Nazif

2021

Intl Journal of Climatology

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Climate change has a dramatic effect on the hydrologic variables including extreme rainfall amounts. To evaluate the climate change effects, general circulation models (GCMs) have been developed. However, due to the daily temporal scale of GCM outputs which could be insufficient for some hydrological studies, disaggregation models are introduced. The available disaggregation models which are almost useful in producing time series of finer scale than a day, cannot accurately estimate some statistical characteristics such as extreme events. The method of fragments (MOF) is one of the disaggregation models which considers daily rainfall as the only input. In the present study, in addition to daily rainfall, other influential factors on the rainfall distribution during a day such as weather variables and sub‐daily characteristics have been considered to improve the disaggregation results especially extreme events estimation in the MOF model. The two introduced approaches have been examined for a case study in Tehran, Iran and indicated that weather variables and sub‐daily characteristics are effective in the daily rainfall disaggregation during the dry and wet seasons, respectively. These approaches seem to be much better than the basic MOF in sub‐daily rainfall disaggregation. Hence, the modified disaggregation approaches have been used to evaluate the climate change impacts on the sub‐daily rainfall distribution. The obtained results indicated an increase in the extreme value statistics such as mean and standard deviation of the 95th percentile data compared with the historical ones.

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Stochastic Models for the Disaggregation of Precipitation Time Series on Sub-Daily Scale: Identiication of Parameters by Global Optimization

Cited by 3 publications

References 19 publications

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

Development of Monsoonal Rainfall Intensity-Duration-Frequency (IDF) Relationship and Empirical Model for Data-Scarce Situations: The Case of the Central-Western Hills (Panchase Region) of Nepal

Evaluation of the climate change impact on the extreme rainfall amounts using modified method of fragments for sub‐daily rainfall disaggregation

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