Predicting urban stormwater runoff with quantitative precipitation estimates from commercial microwave links

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F17%3A00313319" target="_blank" >RIV/68407700:21110/17:00313319 - isvavai.cz</a>

Výsledek na webu

<a href="http://meetingorganizer.copernicus.org/EGU2017/EGU2017-1525.pdf" target="_blank" >http://meetingorganizer.copernicus.org/EGU2017/EGU2017-1525.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting urban stormwater runoff with quantitative precipitation estimates from commercial microwave links

Popis výsledku v původním jazyce

Reliable and representative rainfall data are crucial for urban runoff modelling. However, traditional precipitation measurement devices often fail to provide sufficient information about the spatial variability of rainfall, especially when heavy storm events (determining design of urban stormwater systems) are considered. Commercial microwave links (CMLs), typically very dense in urban areas, allow for indirect precipitation detection with desired spatial and temporal resolution. Fencl et al. (2016) recognised the high bias in quantitative precipitation estimates (QPEs) from CMLs which significantly limits their usability and, in order to reduce the bias, suggested a novel method for adjusting the QPEs to existing rain gauge networks. Studies evaluating the potential of CMLs for rainfall detection so far focused primarily on direct comparison of the QPEs from CMLs to ground observations. In contrast, this investigation evaluates the suitability of these innovative rainfall data for stormwater runoff modelling on a case study of a small ungauged (in long-term perspective) urban catchment in Prague-Letňany, Czech Republic (Fencl et al., 2016). We compare the runoff measured at the outlet from the catchment with the outputs of a rainfall-runoff model operated using (i) CML data adjusted by distant rain gauges, (ii) rainfall data from the distant gauges alone and (iii) data from a single temporary rain gauge located directly in the catchment, as it is common practice in drainage engineering. Uncertainties of the simulated runoff are analysed using the Bayesian method for uncertainty evaluation incorporating a statistical bias description as formulated by Del Giudice et al. (2013). ...

Název v anglickém jazyce

Predicting urban stormwater runoff with quantitative precipitation estimates from commercial microwave links

Popis výsledku anglicky

Reliable and representative rainfall data are crucial for urban runoff modelling. However, traditional precipitation measurement devices often fail to provide sufficient information about the spatial variability of rainfall, especially when heavy storm events (determining design of urban stormwater systems) are considered. Commercial microwave links (CMLs), typically very dense in urban areas, allow for indirect precipitation detection with desired spatial and temporal resolution. Fencl et al. (2016) recognised the high bias in quantitative precipitation estimates (QPEs) from CMLs which significantly limits their usability and, in order to reduce the bias, suggested a novel method for adjusting the QPEs to existing rain gauge networks. Studies evaluating the potential of CMLs for rainfall detection so far focused primarily on direct comparison of the QPEs from CMLs to ground observations. In contrast, this investigation evaluates the suitability of these innovative rainfall data for stormwater runoff modelling on a case study of a small ungauged (in long-term perspective) urban catchment in Prague-Letňany, Czech Republic (Fencl et al., 2016). We compare the runoff measured at the outlet from the catchment with the outputs of a rainfall-runoff model operated using (i) CML data adjusted by distant rain gauges, (ii) rainfall data from the distant gauges alone and (iii) data from a single temporary rain gauge located directly in the catchment, as it is common practice in drainage engineering. Uncertainties of the simulated runoff are analysed using the Bayesian method for uncertainty evaluation incorporating a statistical bias description as formulated by Del Giudice et al. (2013). ...

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

<a href="/cs/project/GA17-16389S" target="_blank" >GA17-16389S: Odvození hydrologických veličin z šíření radiových vln v síti pevných mikrovlnných spojů</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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