Random cascade driven rainfall disaggregation for urban hydrology: An evaluation of six models and a new generator

Licznar, P.; Łomotowski, Janusz; Rupp, David E.

doi:10.1016/j.atmosres.2010.12.014

Cited by 57 publications

(51 citation statements)

References 29 publications

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“…Furthermore, if the objective were to have rainfall-dependent parameters, excluding the low intensity data would provide no guidance as to which parameter values to actually apply at these low rainfall intensities. In another example of censuring data, Licznar et al (2011a) simply eliminated what would be analogous in our study to all values of Y + = 1 from the empirical frequency distribution, under the assumption that most of these values were artifactual. A third procedure to deal specifically with recording precision is to add random noise to the rainfall observations, with the intent of replacing the information lost by round-off error and thus removing the discretization that leads to an excess of certain values of W + (or Y + ) (Licznar et al, 2011b).…”

Section: Hydrolmentioning

confidence: 99%

“…This makes characterization of the spatial distribution of rainfall at small time scales critical to evaluating the efficacy of urban stormwater drainage systems. Traditionally, design storms have been used to evaluate these systems in conjunction with rainfall-runoff and hydrodynamic models, but in recent years there has been a push towards stochastically downscaling long (e.g., multi-decadal) time series of coarse (e.g., daily) rainfall to higher resolution (e.g., minutes) with which to force models of stormwater drainage systems (e.g., Hingray and Ben Haha, 2005;Molnar and Burlando, 2005;Licznar et al, 2011a). Advantages of using long time series are that they allow for a statistical analysis of system performance and they eliminate the problem of defining the appropriate initial catchment water storage for a design storm (Hingray and Ben Haha, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data

Rupp

Licznar

Adamowski

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges is available, rather than from the full rainfall field. The number of gauges needs only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km 2 . MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density function of the random cascade generator to vary with spatial scale.

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Section: Hydrolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data

Rupp

Licznar

Adamowski

et al. 2012

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Güntner et al, 2001) or a quasi-daily starting timescale (e.g. Licznar et al, 2011). Since this was not an option in this work, the approach of a constant b = 2 was abandoned in favour of a mixed branching with b = 3 for the first cascade level (k = 1), resulting in a timescale of T 2 = 8 h on the second cascade level.…”

Section: Cascade Modelsmentioning

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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“…According to Berne et al (2004), rainfall data at a temporal resolution of a few minutes is required for hydrological applications in urban areas. However, for practical and financial reasons, there is a discrepancy between what would be optimal for model input and the data that are available from observations (Berne et al, 2004;Licznar et al, 2011). In situ measurements require a high density of rain gauges to capture the spatial variability, particularly for rain showers associated with local convection.…”

Section: Introductionmentioning

confidence: 99%

Impact of temporal resolution of precipitation forcing data on modelled urban‐atmosphere exchanges and surface conditions

Ward

Tan

Gabey

et al. 2017

Intl Journal of Climatology

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Representative precipitation data sets are very difficult to obtain due to the inherent spatial and temporal variability of rainfall. Gridded rainfall products exist at various scales, but temporal resolution is coarse (daily or, at best, a few hours). This study demonstrates the impact of low temporal resolution precipitation forcing data (PFD) on modelled energy fluxes, runoff and surface conditions, which could have implications for a range of applications including flood forecasting, irrigation scheduling and epidemiology. An evaporation-interception model originally developed for forests is applied here within the framework of the Surface Urban Energy and Water balance Scheme (SUEWS). The model is forced with rainfall data representative of a range of temporal resolutions (from 5 min to 3 h). Taking the highest resolution case as a reference, differences in model output are found as the temporal resolution of PFD decreases, depending on the timing of rainfall occurrence, intensity and duration. Modelled evaporation, runoff and surface wetness deviate from the reference case, which affect other variables such as the turbulent sensible heat flux. The largest impacts are seen on days with greatest daily total rainfall and, even on days with no rain, differences in antecedent conditions (soil moisture or surface wetness) can cause deviations from the reference case. Errors can be reduced by applying a disaggregation scheme that provides a more realistic distribution of rainfall, importantly, one that allows for intermittent rainfall.

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Random cascade driven rainfall disaggregation for urban hydrology: An evaluation of six models and a new generator

Cited by 57 publications

References 29 publications

Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data

Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data

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

Impact of temporal resolution of precipitation forcing data on modelled urban‐atmosphere exchanges and surface conditions

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