The effect of innovative rainfall data on urban drainage modelling by means of model uncertainty

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F16%3A00309747" target="_blank" >RIV/68407700:21110/16:00309747 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

The effect of innovative rainfall data on urban drainage modelling by means of model uncertainty

Popis výsledku v původním jazyce

When performing urban drainage modelling, it is crucial to deal with reliable rainfall information. Recently, microwave links (MWLs) have been introduced as a new potential source of rainfall data with high temporal-spatial resolution. We compare this novel rainfall data source with more conventional sources by the means of uncertainty analysis. We employ a method using Bayesian inference to calibrate our model and to estimate the uncertainty by statistically describing model bias. We use this method with rainfall data from 6 different sources in the examined catchment area. Results of the performed analyses show that MWL rainfall data, when a suitable adjustment is applied (e.g. using information from moderately remote rain gauges), can bring additional information about the rainfall event characteristics, especially if the given event is significantly dynamic. Based on these results, we see the microwave links as a promising rainfall data source and we encourage further research on this topic.

Název v anglickém jazyce

The effect of innovative rainfall data on urban drainage modelling by means of model uncertainty

Popis výsledku anglicky

When performing urban drainage modelling, it is crucial to deal with reliable rainfall information. Recently, microwave links (MWLs) have been introduced as a new potential source of rainfall data with high temporal-spatial resolution. We compare this novel rainfall data source with more conventional sources by the means of uncertainty analysis. We employ a method using Bayesian inference to calibrate our model and to estimate the uncertainty by statistically describing model bias. We use this method with rainfall data from 6 different sources in the examined catchment area. Results of the performed analyses show that MWL rainfall data, when a suitable adjustment is applied (e.g. using information from moderately remote rain gauges), can bring additional information about the rainfall event characteristics, especially if the given event is significantly dynamic. Based on these results, we see the microwave links as a promising rainfall data source and we encourage further research on this topic.

Druh

D - Stať ve sborníku

CEP obor

DA - Hydrologie a limnologie

OECD FORD obor

—

Projekt

<a href="/cs/project/GA14-22978S" target="_blank" >GA14-22978S: Predikce srážkového odtoku v urbanizovaných povodích na základě deštěm generovaného útlumu mikrovlnných spojů telekomunikační sítě</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Městské vody – Urban Water 2016

ISBN

978-80-86020-83-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

201-208

Název nakladatele

ARDEC s.r.o.

Místo vydání

Brno

Místo konání akce

Velké Bílovice

Datum konání akce

6. 10. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—