scholarly journals Reducing uncertainty bounds of two-dimensional hydrodynamic model output by constraining model roughness

2019 ◽  
Author(s):  
Punit Kumar Bhola ◽  
Jorge Leandro ◽  
Markus Disse

Abstract. The consideration of uncertainties in flood risk assessment has received increasing attention over the last two decades. However, the assessment is not reported in practice due to the lack of best practices and too wide uncertainty bounds. We present a method to constrain the model roughness based on measured water levels and reduce the uncertainty bounds of a two-dimensional hydrodynamic model. Results show that the maximum uncertainty in roughness generated an uncertainty bound in the water level of 1.26 m (90 % confidence interval) and by constraining roughness, the bounds can be reduced as much as 0.92 m.

2019 ◽  
Vol 19 (7) ◽  
pp. 1445-1457 ◽  
Author(s):  
Punit Kumar Bhola ◽  
Jorge Leandro ◽  
Markus Disse

Abstract. The consideration of uncertainties in flood risk assessment has received increasing attention over the last 2 decades. However, the assessment is not reported in practice due to the lack of best practices and too wide uncertainty bounds. We present a method to constrain the model roughness based on measured water levels and reduce the uncertainty bounds of a two-dimensional hydrodynamic model. Results show that the maximum uncertainty in roughness generated an uncertainty bound in the water level of 1.26 m (90 % confidence interval) and by constraining roughness, the bounds can be reduced as much as 0.92 m.


2020 ◽  
Vol 157 ◽  
pp. 02006
Author(s):  
Charith Dushyantha ◽  
Irina Ptuhina

Flood risk assessment curves were developed for a flood risk assessment carried out in Colombo, Sri Lanka. Annual maximum water levels at three gauging stations in Kelani Ganga were used as data to prepare the flood risk assessment. Information sources include the Ministry of Disaster Management, Irrigation department and Department of Metrology. Current approaches to risk assessment function development were improved by using the extreme value theory. The most suitable model from the extreme value theory was defined by the behaviour of the probabilistic density function. The probability of a threshold water level exceeding in a given year can be predicted by using the developed model. According to the results of the flood risk assessment at the three gauging stations along river Kelani Ganga, the Hanwella station is at an 89.3% high risk of inundating the area with the water level reaching up to 9.6m in 10 years of time. These results can be used to develop hazard maps for these areas as one of the criteria that must be taken into consideration when choosing the optimal site for construction.


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