Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation
Само за регистроване кориснике
2018
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Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-rec...ording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.
Кључне речи:
flood forecasting / HBV model / rainfall-runoff model / rainfall spatial distribution / sparse network / subdaily rainfall disaggregationИзвор:
Journal of Hydroinformatics, 2018, 20, 4, 784-797Финансирање / пројекти:
- Системи за одвођење кишних вода као део урбане и саобраћајне инфраструктуре (RS-37010)
- Оцена утицаја климатских промена на водне ресурсе Србије (RS-37005)
DOI: 10.2166/hydro.2018.053
ISSN: 1464-7141
WoS: 000436621300004
Scopus: 2-s2.0-85051377870
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