A spatial bootstrap technique for parameter estimation of rainfall  annual maxima distribution

Uboldi, Francesco; Sulis, A. N.; Lussana, Cristian; Cislaghi, M.; Russo, M.

doi:10.5194/hess-18-981-2014

Cited by 27 publications

(20 citation statements)

References 23 publications

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“…The index flood approach, which considers that only the location parameter varies in the region, is very popular (Gellens, 2000;Sveinsson et al, 2001;Rulfova et al, 2014). Uboldi et al (2014) used a bootstrap technique to randomly select data from neighbouring locations with a probability depending on the distance and altitude difference with the target location. The combined use of POT and RFA methods is recommended by Roth et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Goudenhoofdt

Delobbe

Willems

2017

Hydrol. Earth Syst. Sci.

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Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005-2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965-2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

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Section: Introductionmentioning

confidence: 99%

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Goudenhoofdt

Delobbe

Willems

2017

Hydrol. Earth Syst. Sci.

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“…The generation of a wide range of possible spatio-temporal precipitation distributions for a given total event precipitation enables an estimation of possible worst-case floods without using manually predefined distribution functions. This is clearly a benefit compared to standard precipitation generators (Leander et al 2005, Semenov 2008, Furrer and Katz 2008 or bootstrap techniques (Uboldi et al 2014). Uncoupling the precipitation distribution from the parameters of observed events allows for consideration of a great variety of possible patterns.…”

Section: Discussionmentioning

confidence: 97%

“…) and bootstrap techniques (Uboldi et al 2014). Such approaches can considerably improve the representation of spatio-temporal precipitation variability.…”

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confidence: 99%

An approach for the determination of precipitation input for worst-case flood modelling

Felder

Weingartner

2016

Hydrological Sciences Journal

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There is a lack of suitable methods for creating precipitation scenarios that can be used to realistically estimate peak discharges with very low probabilities. On the one hand, existing methods are methodically questionable when it comes to physical system boundaries. On the other hand, the spatio-temporal representativeness of precipitation patterns as system input is limited. In response, this paper proposes a method of deriving spatio-temporal precipitation patterns and presents a step towards making methodically correct estimations of infrequent floods by using a worst-case approach. A Monte Carlo approach allows for the generation of a wide range of different spatio-temporal distributions of an extreme precipitation event that can be tested with a rainfall-runoff model that generates a hydrograph for each of these distributions. Out of these numerous hydrographs and their corresponding peak discharges, the physically plausible spatio-temporal distributions that lead to the highest peak discharges are identified and can eventually be used for further investigations.

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“…To this end, first, the bootstrap method, which has the advantages of simplicity (i.e., no need to make any assumption of normality) and high-accuracy (i.e., asymptotically more accurate than the results obtained using sample variance and assumption of normality) [35][36][37], was introduced in this study to quantitatively analyze the spatial uncertainty of rainfall in river basins at different scales [38][39][40]. Second, by employing a distributed hydrological model on a high-performance computing (HPC) system, the uncertainty of simulated runoff was also analyzed using the bootstrap method.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Zhang

Shi

et al. 2018

Atmosphere

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Abstract:Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins.

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A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution

Cited by 27 publications

References 23 publications

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

An approach for the determination of precipitation input for worst-case flood modelling

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

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