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Uncertainty Analysis in Real- Time Flood Forecasting; a Case Study of Dender River Flooding, November – 2010, Belgium. Presented by Md. Ariful islam Date: September 21, 2018 SWAT International Conference 2018

New Uncertainty Analysis in Real- Time Flood Forecasting; a Case … · 2018. 9. 21. · Md. Ariful islam. Date: September 21, 2018. SWAT International Conference 2018. Content Introduction

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  • Uncertainty Analysis in Real-Time Flood Forecasting; a Case Study of Dender River Flooding,

    November – 2010, Belgium.

    Presented byMd. Ariful islam

    Date: September 21, 2018

    SWAT International Conference 2018

  • ContentIntroduction

    - General

    - Study Area

    - Objectives

    - Dender River Flood November, 2010

    Literature Review

    Materials and Methodology

    Results and discussions

    Conclusions

    Recommendations

  • Introduction Flood is the most devastating natural hazard

    It Causes of human sufferings and losses of properties

    Flood is an uncertain phenomenon,

    Flood forecasting model has involved different uncertainties

    (Input, Model parameters and structures)

    Uncertainty due to forecasted rainfall is the main issue

    Analysis of total uncertainty and part of uncertainty due to

    forecasted rainfall is the main thesis question

  • Study Area Is a tributary basin of the international Schelde (Escaut) basin

    Located at a mid distance between Brussels and Gent.

    The total area of the Dender basin is 1384 km², only the

    Flemish part (708 km²) is considered in this thesis.

    Very flat in northern part, steep slope (up to 20%) in southern

    part

    The dominant Soil type is loam.

    Topography is varying between 3 m to 112 m

  • Objectives

    To analyze the uncertainties of real-time flood forecasting at Dender river basin

    To analyze the part of total uncertainty due to uncertainty of forecasted rainfall in the same area

    To use some statistical performance indices

    To represent and communicate above uncertainties to water managers for application in flood forecasting model

  • Dender River Flood November, 2010

    Due to the heavy rainfall, the river Dender had been floodedquite heavily.

    Reached 46 cm above alarm level on Sunday, November 14,2010.

    The river was burst its banks at several locations and over200 houses were evacuated.

    The flow of water was estimated to be five times higher thannormal

    Four death have been attributed to the flooding

  • Literature review Uncertainty is an expression of some information deficiency

    The sources of uncertainties are model, input, parameters and natural & operational uncertainty.

    Impact of these uncertainties on the forecast will depend on the lead time needed and the response time of the river

    Rainfall uncertainty is the main concern to analyze the totaluncertainty

    MIKE 11 hydrodynamic model is used, developed by DHI(Based on Saint Venant equations)

  • Materials and methodologyMaterials;

    Water level data from the station of Overboelare for themonth of November, 2010 and January, 2011 (15 min

    resolution)

    Rainfall data from the stations of Ukkel, Dender belle andElst

    An existing MIKE 11 (HD) model is used with forecastedand observed rainfall

    MIKE 11 simulated results have been used (November2010 and January 2011).

  • Methodology

  • Methodology cont… Model simulations (November 2010 and January 2011)

    - Observed and forecasted rainfall- Water level measuring station of Overboelare- Rainfall from the stations of Ukkel, Dender belle and Elst- Four days (2 days forecast and 2 days hindcast)

    Calculation of Percentile of the Residuals- Residuals (Hsim-Hobs)- 100*)/)5.0( nip −=

  • Methodology cont… 95% confidence interval of residuals

    - Different water levels (interval 0.5m)- Difference between 97.5% and 2.5% percentile

    Percentile residuals (Hsim-Hobs) for the simulations ofobserved rainfall and forecasted rainfall

    Cumulative catchment rainfall- For catchments no 400 and 430- For forecasted period

    Confidence interval and bias correction with water level- Water level for January, 2011

  • Results and discussions2.5% percentile for observed rainfall at 16.5 to 17.0m WL

    Hsim (m)

    Time horizon (hrs)

    1 2 3 4 5 6 7 8 9

    0 - 3 3 - 9 9 - 15 15 - 21 21 - 27 27 - 33 33 - 39 39 - 45 45 - 48

    16.5-17.0 -1.48 -1.40 -0.27 -0.33 -0.34 -0.84 -0.28 -0.33 -0.33

    17.0-17.5 -0.16 -1.09 -0.96 -0.91 -0.89 -0.18 -0.06 0.08 0.04

    17.5-18.0 0.17 0.35 0.15 0.32 18.0-18.5 0.20 0.13 0.11 -0.07 -0.19 -0.37 -0.42

    18.5-19.0 0.51 0.15 0.02 -0.07 -0.09 0.67 0.73 0.57 0.58

    19.0-19.5 0.93 0.69 0.46 0.90 0.61 0.61 19.5-20.0 1.25 1.12 0.92 0.99 20.0-20.5 1.39 1.67 1.57

  • Results and discussions cont..Comparison of percentile and residual in different water level

  • Results and discussions cont…Comparison of 95% confidence interval

  • Results and discussions cont…Comparison of percentile residuals (Hsim-Hobs)

  • Results and discussions cont..Comparison of cumulative catchment rainfall

  • Results and discussions cont…Comparison of width of confidence interval with

    water level of January, 2011

  • Conclusions Total uncertainty and part of uncertainty due to forecasted

    rainfall have been analyzed

    Uncertainty is increasing with the increase of forecasted lead

    time

    Higher water level shows higher uncertainty

    Uncertainty due to forecasted rainfall is significant

    Communication to the water managers with uncertainty was not

    enough (only graphical presentation)

  • Conclusions cont.. Some statistical analysis tools (WETSPRO, ECQ) are not

    performed due to time series with one peak

    Flood forecasting model had the uncertainty from different

    sources but main uncertainty was rainfall forecast.

    Perfect precipitation forecasting is the precondition for accurate

    flood forecasting.

  • Recommendations Uncertainty analysis has been performed for considering

    water level only it is necessary to consider discharge also. Uncertainty due to rainfall forecast has only been analyzed,

    other sources of uncertainty in the HD model need toanalyze for further improvement of the flood forecastingsystem.

    For better statistical analysis (WETSRPO, ECQ) need toconsider long time series

    For further studies water level and rainfall data may beconsidered from more stations

    It is needed to consider other HD model for bettercomparison

    Communication to the water managers with uncertaintyanalysis need to be extended largely

  • 22

    Thank you for your attention

    Uncertainty Analysis in Real-Time Flood Forecasting; a Case Study of Dender River Flooding, November – 2010, Belgium.Content�Introduction�Study AreaObjectivesDender River Flood November, 2010Literature reviewMaterials and methodologyMethodology�Methodology cont…�Methodology cont…Results and discussionsResults and discussions cont..Results and discussions cont…Results and discussions cont…Results and discussions cont..Results and discussions cont…ConclusionsConclusions cont..RecommendationsThank you �for your attention