Statistical analysis of error propagation from radar rainfall to hydrological models

Zhu, Dehua; Peng, Danling; Cluckie, ID

doi:10.5194/hess-17-1445-2013

Cited by 28 publications

(19 citation statements)

References 47 publications

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“…The soil within the catchment is prevalently clay loam but with a relatively high spatial variability represented by 29 soil units (polygons) of different size within the catchment. All these data are discretized to a spatial resolu-tion of 100 m × 100 m. Readers interested in more details on data set and the processing may refer to Kumar et al (2010), Samaniego et al (2010b) and Zink et al (2017). The spatial distributions of cumulative rain, potential evapotranspiration, land use and the mean annual leaf area index are shown in the Supplement (see Fig.…”

Section: Study Areamentioning

confidence: 99%

Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

Baroni

Zink

Kumar

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.

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Section: Study Areamentioning

confidence: 99%

Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

Baroni

Zink

Kumar

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Most of the study cases available in the literature deal with the propagation of RR uncertainty through large river catchments (see e.g. Borga, 2002;Vivoni et al, 2007;Collier, 2009;Zhu et al, 2013) and little is known about the way the RR uncertainty propagates on simulated flow peaks/volumes in urban drainage systems. Schellart et al (2012) showed that for a small urban catchment, large differences are observed in the flow peaks simulated by radar and raingauges due to the inherent uncertainties from both rainfall estimates.…”

Section: Introductionmentioning

confidence: 99%

Quantifying radar-rainfall uncertainties in urban drainage flow modelling

Rico‐Ramirez

Liguori

Schellart

2015

Journal of Hydrology

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a b s t r a c tThis work presents the results of the implementation of a probabilistic system to model the uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty propagates through the sewer system of an urban area located in the North of England. The spatial and temporal correlations of the RR errors as well as the error covariance matrix were computed to build a RR error model able to generate RR ensembles that reproduce the uncertainty associated with the measured rainfall. The results showed that the RR ensembles provide important information about the uncertainty in the rainfall measurement that can be propagated in the urban sewer system. The results showed that the measured flow peaks and flow volumes are often bounded within the uncertainty area produced by the RR ensembles. In 55% of the simulated events, the uncertainties in RR measurements can explain the uncertainties observed in the simulated flow volumes. However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows.

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“…Weather radar rainfall error modeling was well presented in the previous reports [16], [17], [18], [19], [20]. Hardware sources of errors are related to electronics stability, antenna accuracy, and signal processing accuracy studied by [21].…”

Section: Introductionmentioning

confidence: 84%

Error Modeling Radar Rainfall Estimation Through Incorporating Rain Gauge Data Over Upper Blue Nile Basin, Ethiopia

Birhan

Raju

Kenea

2019

IJCSAM

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Accurate and precise measurements of rainfall from weather radar reflectivity data is essential to supplement the limited characterization of spatial and temporal measurements provided by insufficient network and density of rain gauges. While weather radar has high spatial and temporal resolution, it contaminated with various sources of errors due to the conversion of reflectivity to rain rate and the projectile rainfall motion. Error modeling improvement with the application of projectile rainfall motion correction is essential to improve the radar data. However, stile is not well documented for over the world as well as Ethiopia. Therefore, the aim of this study was to generate an error model for weather radar rainfall estimation by incorporating gauge rainfall data over upper Blue Nile basin, Ethiopia. Projectile rainfall motion correction is considered on the data of reflectivity and rain rate to determine empirical error model parameter values. The model parameter values are found, multiplicative factor (a) was 55, the exponent factor (b) was 1.12, standard deviation of proportional error was 0.08 and standard deviation of random error was 0.07. The value of the total error varied from −0.45 to 1.16 mm and the domain of proportional error was greater than random error. After applying the projectile rainfall motion correction, the total error is reduced by 12%. In general, the assumption of projectile method is quite useful for improving the radar data over upper Blue Nile basin in Ethiopia as well as over the world. Hence, we wish to extend this method for other regions.

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Statistical analysis of error propagation from radar rainfall to hydrological models

Cited by 28 publications

References 47 publications

Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

Quantifying radar-rainfall uncertainties in urban drainage flow modelling

Error Modeling Radar Rainfall Estimation Through Incorporating Rain Gauge Data Over Upper Blue Nile Basin, Ethiopia

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