CML precipitation estimates for hydrological modelling: A three-year experiment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F18%3A00322161" target="_blank" >RIV/68407700:21110/18:00322161 - isvavai.cz</a>

Result on the web

<a href="https://meetingorganizer.copernicus.org/EGU2018/EGU2018-18667.pdf" target="_blank" >https://meetingorganizer.copernicus.org/EGU2018/EGU2018-18667.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

CML precipitation estimates for hydrological modelling: A three-year experiment

Original language description

In a small urban catchment (1.3 km2) in Prague-Letňany, Czech Republic, we monitored 19 CMLs for a period spanning over three summer seasons. Furthermore, rainfall measurements from three traditional rain gauges (each in a distance of approximately 2.5 km from the catchment), as well as discharges at the outlet of the local stormwater drainage system, were monitored during the period. We compare the runoff measured at the drainage system outlet with outputs of a rainfall-runoff model of the studied catchment operated using the following rainfall data sets with one minute resolution: (i) QPEs from CMLs adjusted by distant rain gauges (mean over all 19 CMLs) and (ii) rainfall data from distant rain gauges alone. Our results show that QPEs from CMLs adjusted by distant rain gauges, when compared to distant rain gauge data, improve rainfall-runoff modelling results especially in terms of event dynamics. The improvement is demonstrated by the dQmax and NSE metrics, which both reach lower standard deviations and the mean is closer to 0 for the former and closer to 1 for the latter. On the other hand, we observe no conclusive improvement for the timing of the maximum and the dV metric reaches better values when using the rain gauge data alone. When analysing the results for events with various spatiotemporal variability, we observe no distinctive differences. This suggests that exploiting the adjusted CML data can improve urban rainfall data quality as well for rainfalls with lower spatiotemporal variability, for which the problem of insufficient representativeness of traditional rainfall measurements is not considered to be so pronounced.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10501 - Hydrology

Project

<a href="/en/project/GA17-16389S" target="_blank" >GA17-16389S: Hydrological estimates from radiowave propagation in terrestial microwave network</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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