Imperfect scaling of time and space–time rainfall

Veneziano, Daniele; Furcolo, Pierluigi; Iacobellis, Vito

doi:10.1016/j.jhydrol.2005.02.044

Cited by 58 publications

(85 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the maximum observed value of each half year were extracted from both data sources to generate periods as long as possible. 20001929-20061952-2005Tetendorf 20041986-20001986-2000Reckershausen 20041964-20061974-2005 …”

Section: Runoff Datamentioning

confidence: 99%

“…For the sake of completeness it should be mentioned here that an unbounded cascade model has been used (Marshak et al, 1994). For all disaggregation steps with b=2, the same parameter set is applied due to the findings of 5 scale invariance over these temporal resolutions (see Veneziano et al (2006) and references therein).…”

Section: Rainfall Generation A) Rainfall Disaggregationmentioning

confidence: 99%

See 1 more Smart Citation

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Müller¹,

Wallner²,

Förster³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract.In this investigation, the influence of disaggregated rainfall data sets with different degrees of spatial consistence on rainfall runoff modeling results is analyzed for three meso-scale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values a multiplicative random cascade model is applied. The disaggregation is applied on a per station basis without consideration of surrounding stations, hence subsequent steps are then required to implement 15 spatial consistence. Spatial consistence is here represented by three bivariate spatial rainfall characteristics, complementing each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall data sets as input to hydrological modeling, 20 we hypothesize that derived runoff statistics are subject to similar differences as well. However, an impact on the runoff statistics summer and winter peak flows, monthly average discharge and flow duration curve of the simulated runoff time series cannot be detected. Several modifications of the investigation using rainfall runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall-runoff WaSiM-model is applied instead of the semi-distributed HBV-IWW-model, slight differences 25 regarding the seasonal peak flows can be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.Hydrol. Earth Syst. Sci. Discuss., https://doi

show abstract

Section: Runoff Datamentioning

confidence: 99%

Section: Rainfall Generation A) Rainfall Disaggregationmentioning

confidence: 99%

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Müller¹,

Wallner²,

Förster³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…For this paper, only the exterior process is considered for the bias-correction of the RCM scenario. For this kind of application, in fact, the coarse space-resolution of the RCM-pixel is unappealing for a complete representation of the rainfall space-time interior process (e.g., Veneziano et al, 2006). Size effects of the subdaily disaggregation of temporal rainfall will be the topic of future investigation.…”

Section: Rainfall Model and Stochastic Generation Of Local Precipitatmentioning

confidence: 99%

Stochastic bias-correction of daily rainfall scenarios for hydrological applications

Portoghese

Bruno

Guyennon

et al. 2011

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge.In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM) was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.

show abstract

“…Additionally, scaling behaviour was found to differ with the intensity of storms (Venugopal et al, 2006) and thus the nonraining intervals do not scale (Olsson, 1998). These deviations from perfect scaling are further examined in Veneziano et al (2006), Serinaldi (2010), and Rupp et al (2009), who showed that it is possible to model these imperfections in scaling through empirical functions of the parameters of various downscaling models.…”

Section: Introductionmentioning

confidence: 99%

“…Serinaldi, 2010;Rupp et al, 2009;Veneziano et al, 2006). Some other studies have investigated the dependence of breakdown coefficients, i.e.…”

Section: Introductionmentioning

confidence: 99%

Imperfect scaling in distributions of radar-derived rainfall fields

Berg

Delobbe

Verhoest

2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Fine-scale rainfall observations for modelling exercises are often not available, but rather coarser data derived from a variety of sources are used. Effectively using these data sources in models often requires the probability distribution of the data at the applicable scale. Although numerous models for scaling distributions exist, these are often based on theoretical developments, rather than on data. In this study, we develop a model based on the α-stable distribution of rainfall fields, and tested on 5 min radar data from a Belgian weather radar. We use these data to estimate functions that describe parameters of the distribution over various scales. Moreover, we study how the mean of the distribution and the intermittency change with scale, and validate and design functions to describe the shape parameter of the distribution. This information was combined into an effective model of the distribution.

show abstract

Imperfect scaling of time and space–time rainfall

Cited by 58 publications

References 34 publications

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Stochastic bias-correction of daily rainfall scenarios for hydrological applications

Imperfect scaling in distributions of radar-derived rainfall fields

Contact Info

Product

Resources

About