Uncertainty decomposition and reduction in river flood forecasting: Belgian case study
Uncertainty is a key factor to be taken into account in river flood forecasting. Every forecast is subject to several sources of uncertainty. Knowledge on the relative importance of the different sources would be useful to determine the most effective improvement actions to reduce the uncertainty. I...
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| Published in | Journal of flood risk management Vol. 8; no. 3; pp. 263 - 275 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Blackwell Publishing Ltd
01.09.2015
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1753-318X 1753-318X |
| DOI | 10.1111/jfr3.12093 |
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| Abstract | Uncertainty is a key factor to be taken into account in river flood forecasting. Every forecast is subject to several sources of uncertainty. Knowledge on the relative importance of the different sources would be useful to determine the most effective improvement actions to reduce the uncertainty. In this paper, three key uncertainty sources are studied for hydrological flood forecasting in the Belgian case study of the Rivierbeek: model uncertainty, forecasted rainfall uncertainty and uncertainty in the initial conditions. A non‐parametric data‐based approach is used to quantify the total uncertainty in the forecasts. By resimulating in the model historical forecasts with optimal initial conditions and observed rainfall, the uncertainty generated by each of the key sources could be identified. In order to reduce the model uncertainty, which was primarily identified as the most important source of uncertainty, a step‐wise physically based calibration technique was suggested. After recalibration of the model with this technique, a significant reduction of the contribution of the model uncertainty to the total forecast uncertainty could be achieved. Further improvement of the initial conditions, identified as the second most important uncertainty source for short lead times, could be obtained by applying data assimilation. Both uncertainty reduction techniques combined led to a reduction of the total forecast uncertainty with 30% to 40%. |
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| AbstractList | Uncertainty is a key factor to be taken into account in river flood forecasting. Every forecast is subject to several sources of uncertainty. Knowledge on the relative importance of the different sources would be useful to determine the most effective improvement actions to reduce the uncertainty. In this paper, three key uncertainty sources are studied for hydrological flood forecasting in the Belgian case study of the Rivierbeek: model uncertainty, forecasted rainfall uncertainty and uncertainty in the initial conditions. A non-parametric data-based approach is used to quantify the total uncertainty in the forecasts. By resimulating in the model historical forecasts with optimal initial conditions and observed rainfall, the uncertainty generated by each of the key sources could be identified. In order to reduce the model uncertainty, which was primarily identified as the most important source of uncertainty, a step-wise physically based calibration technique was suggested. After recalibration of the model with this technique, a significant reduction of the contribution of the model uncertainty to the total forecast uncertainty could be achieved. Further improvement of the initial conditions, identified as the second most important uncertainty source for short lead times, could be obtained by applying data assimilation. Both uncertainty reduction techniques combined led to a reduction of the total forecast uncertainty with 30% to 40%. |
| Author | Van Steenbergen, N. Willems, P. |
| Author_xml | – sequence: 1 givenname: N. surname: Van Steenbergen fullname: Van Steenbergen, N. email: niels.vansteenbergen@bwk.kuleuven.be organization: Hydraulics Division, KU Leuven, Leuven, Belgium – sequence: 2 givenname: P. surname: Willems fullname: Willems, P. organization: Hydraulics Division, KU Leuven, Leuven, Belgium |
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| Copyright | 2014 The Chartered Institution of Water and Environmental Management (CIWEM) and John Wiley & Sons Ltd Copyright © 2015 The Chartered Institution of Water and Environmental Management (CIWEM) and John Wiley & Sons Ltd |
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Rossa A., Liechti K., Zappa M., Bruen M., Germann U., Haase G., Keil C. & Krahe P. The COST 731 Action: a review on uncertainty propagation in advanced hydro-meteorological forecast systems. Atmos Res 2011, 100, 150-167. Aubert D., Loumagne C. & Oudin L. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. J Hydrol 2003, 280, (1-4), 145-161. Todini E. A model conditional processor to assess predictive uncertainty in flood forecasting. Int J River Basin Manag 2008, 6, (2), 123-137. Nielsen S.A. & Hansen E. Numerical simulation of the rainfall-runoff process on a daily basis. Nord Hydrol 1973, 4, 171-190. Freer J., Beven K. & Ambroise B. Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 1996, 32, (7), 2161-2173. Beven K.J. & Freer J. 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Hydrol Earth Sys Sci 2010, 14, 1881-1893. doi: 10.5194/hess-14-1881-2010. Chapman T.G. Comment on 'Evaluation of automated techniques for base flow and recession analyses' by R. J. Nathan and T. A. McMahon. Water Resour Res 1991, 27, (7), 1783-1784. Gupta H.V., Kling H., Yilmaz K.K. & Martinez G.F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling. J Hydrol 2009, 377, 80-91. Coccia G. & Todini E. Recent developments in predictive uncertainty assessment based on the model conditional processor approach. Hydrol Earth Sys Sci 2011, 15, 3253-3274. Van Steenbergen N., Ronsyn J. & Willems P. A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication. Environ Modell Softw 2012, 33, 92-105. Nash J.E. & Sutcliffe I.V. River flow forecasting trough conceptual models. J Hydrol 1970, 273, 282-290. Van Steenbergen N. & Willems P. Method for testing the accuracy of rainfall-runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context. J Hydrol 2012, 414-415, 425-434. Madsen H., Wilson G. & Ammentorp H.C. Comparison of different automated strategies for calibration of rainfall-runoff models. J Hydrol 2002, 261, (1-4), 48-59. Lee H., Seo D.-J. & Koren V. Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: effects of uncertainties in the data and initial model soil moisture states. Adv Water Resour 2011, 34, (12), 1597-1615. Meixner T., Gupta H.V., Bastidas L.A. & Bales R.C. Sensitivity analysis using mass flux and concentration. Hydrological Process 1999, 13, 2233-2244. Willems P. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environ Modell Softw 2009, 24, (3), 311-321. Koenker R. & Bassett G. Regression Quantiles. Econometrica 1978, 46, (1), 33-50. Cloke H.L. & Pappenberger F. Ensemble flood forecasting: a review. J Hydrol 2009, 375, 613-626. Gerard L., Piriou J.-M., Brožková R., Geleyn J.-F. & Banciu D. Cloud and precipitation parametrization in a meso-gamma-scale operational weather prediction model. Mon Weather Rev 2009, 137, 3960-3977. 2009; 24 2010; 14 2000; 235 2010 1995; 57 2009; 375 2007 2009; 377 2005 2011; 34 1993 2008; 6 2011; 15 2012; 414–415 2012; 33 1996; 32 2009; 137 2001; 249 1992; 6 2011; 401 1970; 273 1991; 27 2000; 36 2002; 261 2013; 53 1999; 35 1999; 13 2003; 280 2001; 15 1978; 46 1998; 204 1998; 34 2011; 100 1973; 4 |
| References_xml | – reference: Yapo P.O., Gupta H.V. & Sorooshian S. Multi-objective global optimization for hydrological models. J Hydrol 1998, 204, 83-97. – reference: Coccia G. & Todini E. Recent developments in predictive uncertainty assessment based on the model conditional processor approach. Hydrol Earth Sys Sci 2011, 15, 3253-3274. – reference: Madsen H., Wilson G. & Ammentorp H.C. Comparison of different automated strategies for calibration of rainfall-runoff models. J Hydrol 2002, 261, (1-4), 48-59. – reference: Chapman T.G. Comment on 'Evaluation of automated techniques for base flow and recession analyses' by R. J. Nathan and T. A. McMahon. Water Resour Res 1991, 27, (7), 1783-1784. – reference: Lee H., Seo D.-J. & Koren V. Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: effects of uncertainties in the data and initial model soil moisture states. Adv Water Resour 2011, 34, (12), 1597-1615. – reference: Van Steenbergen N., Ronsyn J. & Willems P. A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication. Environ Modell Softw 2012, 33, 92-105. – reference: Draper D. Assessment and propagation of model uncertainty. J Roy Stat Soc B 1995, 57, (1), 45-97. – reference: Willems P. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environ Modell Softw 2009, 24, (3), 311-321. – reference: Zappa M., Jaun S., Germann U., Walser A. & Fundel F. Superposition of uncertainties in operational flood forecasting chains. Atmos Res 2011, 100, 246-262. – reference: Krzysztofowicz R. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resour Res 1999, 35, 2739-2750. – reference: Rossa A., Liechti K., Zappa M., Bruen M., Germann U., Haase G., Keil C. & Krahe P. The COST 731 Action: a review on uncertainty propagation in advanced hydro-meteorological forecast systems. Atmos Res 2011, 100, 150-167. – reference: Aubert D., Loumagne C. & Oudin L. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. J Hydrol 2003, 280, (1-4), 145-161. – reference: Guinot V., Cappelaere B., Delenne C. & Ruelland D. Towards improved criteria for hydrological model calibration: theoretical analysis of distance- and weak form-based functions. J Hydrol 2011, 401, 1-13. – reference: Koenker R. & Hallock K.F. Quantile regression. J Econ Perspect 2001, 15, (4), 143-156. – reference: Meixner T., Gupta H.V., Bastidas L.A. & Bales R.C. Sensitivity analysis using mass flux and concentration. Hydrological Process 1999, 13, 2233-2244. – reference: Nielsen S.A. & Hansen E. Numerical simulation of the rainfall-runoff process on a daily basis. Nord Hydrol 1973, 4, 171-190. – reference: Freer J., Beven K. & Ambroise B. Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 1996, 32, (7), 2161-2173. – reference: Cloke H.L. & Pappenberger F. Ensemble flood forecasting: a review. J Hydrol 2009, 375, 613-626. – reference: Gupta H.V., Sorooshian S. & Yapo P.O. Towards improved calibration of hydrological models: multiple and noncommensurable measures of information. Water Resour Res 1998, 34, 751-763. – reference: Gupta H.V., Kling H., Yilmaz K.K. & Martinez G.F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling. J Hydrol 2009, 377, 80-91. – reference: Van Steenbergen N. & Willems P. Method for testing the accuracy of rainfall-runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context. J Hydrol 2012, 414-415, 425-434. – reference: Brocca L., Melone F., Moramarco T., Wagner W., Naemi V., Bartalis Z. & Hasenauer S. Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrol Earth Sys Sci 2010, 14, 1881-1893. doi: 10.5194/hess-14-1881-2010. – reference: Beven K.J. & Freer J. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol 2001, 249, (1-4), 11-29. – reference: Madsen H. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J Hydrol 2000, 235, 276-288. – reference: Gerard L., Piriou J.-M., Brožková R., Geleyn J.-F. & Banciu D. Cloud and precipitation parametrization in a meso-gamma-scale operational weather prediction model. Mon Weather Rev 2009, 137, 3960-3977. – reference: Dumedah G. & Coulibaly P. Evolutionary assimilation of streamflow data in distributed hydrologic modeling using in-situ soil moisture data. Adv Water Resour 2013, 53, 231-241. – reference: Koenker R. & Bassett G. Regression Quantiles. Econometrica 1978, 46, (1), 33-50. – reference: Nash J.E. & Sutcliffe I.V. River flow forecasting trough conceptual models. J Hydrol 1970, 273, 282-290. – reference: Krzysztofowicz R. & Kelly K.S. Hydrologic uncertainty processor for probabilistic river stage forecasting. Water Resour Res 2000, 36, (11), 3265-3277. – reference: Todini E. A model conditional processor to assess predictive uncertainty in flood forecasting. Int J River Basin Manag 2008, 6, (2), 123-137. – reference: Beven K. & Binley A. The future of distributed model: calibration and uncertainty prediction. Hydrological Process 1992, 6, 279-298. – volume: 14 start-page: 1881 year: 2010 end-page: 1893 article-title: Improving runoff prediction through the assimilation of the ASCAT soil moisture product publication-title: Hydrol Earth Sys Sci – volume: 35 start-page: 2739 year: 1999 end-page: 2750 article-title: Bayesian theory of probabilistic forecasting via deterministic hydrologic model publication-title: Water Resour Res – volume: 249 start-page: 11 issue: 1–4 year: 2001 end-page: 29 article-title: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology publication-title: J Hydrol – volume: 414–415 start-page: 425 year: 2012 end-page: 434 article-title: Method for testing the accuracy of rainfall‐runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context publication-title: J Hydrol – start-page: 351 year: 2005 – volume: 273 start-page: 282 year: 1970 end-page: 290 article-title: River flow forecasting trough conceptual models publication-title: J Hydrol – volume: 33 start-page: 92 year: 2012 end-page: 105 article-title: A non‐parametric data‐based approach for probabilistic flood forecasting in support of uncertainty communication publication-title: Environ Modell Softw – year: 2007 – volume: 100 start-page: 150 year: 2011 end-page: 167 article-title: The COST 731 Action: a review on uncertainty propagation in advanced hydro‐meteorological forecast systems publication-title: Atmos Res – volume: 137 start-page: 3960 year: 2009 end-page: 3977 article-title: Cloud and precipitation parametrization in a meso‐gamma‐scale operational weather prediction model publication-title: Mon Weather Rev – volume: 377 start-page: 80 year: 2009 end-page: 91 article-title: Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling publication-title: J Hydrol – volume: 280 start-page: 145 issue: 1–4 year: 2003 end-page: 161 article-title: Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall‐runoff model publication-title: J Hydrol – volume: 261 start-page: 48 issue: 1–4 year: 2002 end-page: 59 article-title: Comparison of different automated strategies for calibration of rainfall‐runoff models publication-title: J Hydrol – volume: 100 start-page: 246 year: 2011 end-page: 262 article-title: Superposition of uncertainties in operational flood forecasting chains publication-title: Atmos Res – start-page: 163 year: 1993 end-page: 180 – volume: 27 start-page: 1783 issue: 7 year: 1991 end-page: 1784 article-title: Comment on ‘Evaluation of automated techniques for base flow and recession analyses’ by R. J. Nathan and T. A. McMahon publication-title: Water Resour Res – volume: 13 start-page: 2233 year: 1999 end-page: 2244 article-title: Sensitivity analysis using mass flux and concentration publication-title: Hydrological Process – year: 2010 – volume: 15 start-page: 3253 year: 2011 end-page: 3274 article-title: Recent developments in predictive uncertainty assessment based on the model conditional processor approach publication-title: Hydrol Earth Sys Sci – volume: 34 start-page: 751 year: 1998 end-page: 763 article-title: Towards improved calibration of hydrological models: multiple and noncommensurable measures of information publication-title: Water Resour Res – volume: 235 start-page: 276 year: 2000 end-page: 288 article-title: Automatic calibration of a conceptual rainfall‐runoff model using multiple objectives publication-title: J Hydrol – volume: 4 start-page: 171 year: 1973 end-page: 190 article-title: Numerical simulation of the rainfall‐runoff process on a daily basis publication-title: Nord Hydrol – volume: 6 start-page: 279 year: 1992 end-page: 298 article-title: The future of distributed model: calibration and uncertainty prediction publication-title: Hydrological Process – volume: 24 start-page: 311 issue: 3 year: 2009 end-page: 321 article-title: A time series tool to support the multi‐criteria performance evaluation of rainfall‐runoff models publication-title: Environ Modell Softw – volume: 34 start-page: 1597 issue: 12 year: 2011 end-page: 1615 article-title: Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: effects of uncertainties in the data and initial model soil moisture states publication-title: Adv Water Resour – volume: 53 start-page: 231 year: 2013 end-page: 241 article-title: Evolutionary assimilation of streamflow data in distributed hydrologic modeling using in‐situ soil moisture data publication-title: Adv Water Resour – volume: 15 start-page: 143 issue: 4 year: 2001 end-page: 156 article-title: Quantile regression publication-title: J Econ Perspect – volume: 32 start-page: 2161 issue: 7 year: 1996 end-page: 2173 article-title: Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach publication-title: Water Resour Res – volume: 204 start-page: 83 year: 1998 end-page: 97 article-title: Multi‐objective global optimization for hydrological models publication-title: J Hydrol – volume: 36 start-page: 3265 issue: 11 year: 2000 end-page: 3277 article-title: Hydrologic uncertainty processor for probabilistic river stage forecasting publication-title: Water Resour Res – volume: 46 start-page: 33 issue: 1 year: 1978 end-page: 50 article-title: Regression Quantiles publication-title: Econometrica – volume: 375 start-page: 613 year: 2009 end-page: 626 article-title: Ensemble flood forecasting: a review publication-title: J Hydrol – volume: 6 start-page: 123 issue: 2 year: 2008 end-page: 137 article-title: A model conditional processor to assess predictive uncertainty in flood forecasting publication-title: Int J River Basin Manag – volume: 401 start-page: 1 year: 2011 end-page: 13 article-title: Towards improved criteria for hydrological model calibration: theoretical analysis of distance‐ and weak form‐based functions publication-title: J Hydrol – volume: 57 start-page: 45 issue: 1 year: 1995 end-page: 97 article-title: Assessment and propagation of model uncertainty publication-title: J Roy Stat Soc B |
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| SubjectTerms | Calibration Case studies data assimilation Data collection Decomposition Flood forecasting Floods hydrological modelling Meteorology River forecasting Rivers uncertainty decomposition Weather forecasting |
| Title | Uncertainty decomposition and reduction in river flood forecasting: Belgian case study |
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