Uncertainty Evaluation due to TIGGE Global System Precipitation Data for Flood Forecasting

Subject Areas : Hydrology, hydraulics, and water transfer buildings

Soudabeh Behiyan Motlagh ¹ , Afshin Honarbakhsh ² , Asghar Azizian ³

1 - PhD Student in Watershed Management, Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord.
2 - Associate Professor, Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord.
3 - Associate Professor, Water Sciences and Engineering Department, Faculty of Agriculture and Natural Resources ,Imam Khomeini International University, Qazvin.

Received: 2023-01-06 Accepted : 2023-04-17 Published : 2023-04-21

Keywords: Ensemble prediction system, Flood warning, HEC-HMS model, Numerical precipitation prediction models, TIGGE database,

Abstract :

Background and Aim: The occurrence of frequent floods in Iran necessitates a flood forecasting and warning system with a suitable lead time. The use of numerical rainfall forecasting models in flood forecasting and warning is one of the important measures taken by researchers in most parts of the world. The TIGGE database includes mid-term precipitation forecasts simulated by global forecast centers. The purpose of this research is to evaluate the efficiency and the degree of uncertainty caused by the rainfall forecasts of four numerical models of the TIGGE database (including CPTEC, ECCC, ECMWF, and KMA) for simulating floods with the HEC-HMS hydrological model.Methods: In this research, the precipitation data of seven meteorological stations were used to evaluate the uncertainty of discharge from TIGGE database precipitation prediction models in the Poldokhtar watershed. Also, three flood events on March 24, 2017, April 6, 2018, and April 15, 2018, were studied. At first, precipitation forecasts were extracted from four centers CPTEC, ECCC, ECMWF, and KMA. Due to the existence of systematic error in the forecasts, a bias correction was done on them, and to correct the bias, the Delta method was used. Processed and raw forecasts of four rainfall forecasting models were entered into the HEC-HMS model for flood forecasting, and in the next step, the flow uncertainty assessment of the HEC-HMS model was performed in all members of the four rainfall forecasting models. In this research, 5 factors P, R, S, T, and RD were used for uncertainty analysis.Results: The results indicate the significant superiority of the ECMWF model in predicting precipitation events. The use of all 4 rainfall sources led to an acceptable simulation of the flood peak flow in three different events. Also, the predicted peak discharge time had little difference from the observed data. According to the results of the uncertainty analysis, the ECMWF model was considered the best model based on P, R, S, T, and RD factors. The KMA model performed well in severe and very severe floods. The group prediction system of TIGGE models also had an acceptable performance in all events. Also, the hydrological-meteorological prediction model predicted the time of flood occurrence and the probability of occurrence well.Conclusion: The intended research investigates flood forecasting and warning in the Poldokhtar watershed using the meteorological-hydrological system, based on meteorological forecasts of the TIGGE database and flood simulation using the HEC-HMS hydrological model. The final product of this system is probable discharge and flood forecast. The results reveal the success of the TIGGE database in flood forecasting. The ECMWF model excelled in predicting peak discharge. The upper and lower band calculation method was used to determine the uncertainty, which showed the uncertainty well. This system displayed the time of peak discharge well and with a small time delay, which indicates its good performance. The predicted rainfall from the four centers used in this study (ECMWF, ECCC, CPTEC, and KMA) have significant differences. To reduce these differences, we used a multi-model group forecasting system that had encouraging results.

References:

Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., & Pappenberger, F. (2013). GloFAS–global ensemble streamflow forecasting and flood early warning. Hydrology and Earth System Sciences, 17(3), 1161–1175.
Aminyavari, S., Saghafian, B., & Sharifi, E. (2019). Assessment of precipitation estimation from the NWP models and satellite products for the spring 2019 severe floods in Iran. Remote Sensing, 11(23). https://doi.org/10.3390/rs11232741
Ayzel, G., Varentsova, N., Erina, O., Sokolov, D., Kurochkina, L., & Moreydo, V. (2019). OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia. Water (Switzerland), 11(8). https://doi.org/10.3390/w11081546
Bao, H. J., Zhao, L. N., He, Y., Li, Z. J., Wetterhall, F., Cloke, H. L., … Manful, D. (2011). Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast. Advances in Geosciences, 29(51509043), 61–67. https://doi.org/10.5194/adgeo-29-61-2011
Bartholmes, J., & Todini, E. (2005). Coupling meteorological and hydrological models for flood forecasting. Hydrology and Earth System Sciences Discussions, 9(4), 333–346.
Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55.
Beyer, R., Krapp, M., & Manica, A. (2020). An empirical evaluation of bias correction methods for palaeoclimate simulations. Climate of the Past, 16(4), 1493-1508.‏
Cai, C., Wang, J., & Li, Z. (2019). Assessment and modelling of uncertainty in precipitation forecasts from TIGGE using fuzzy probability and Bayesian theory. Journal of Hydrology, 577. https://doi.org/10.1016/j.jhydrol.2019.123995
Cloke, H. L., & Pappenberger, F. (2009). Ensemble flood forecasting: A review. Journal of Hydrology, 375(3–4), 613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005
Collischonn, W., Morelli Tucci, C. E., Clarke, R. T., Chou, S. C., Guilhon, L. G., Cataldi, M., & Allasia, D. (2007). Medium-range reservoir inflow predictions based on quantitative precipitation forecasts. Journal of Hydrology, 344(1–2), 112–122. https://doi.org/10.1016/j.jhydrol.2007.06.025
He, Y., Wetterhall, F., Cloke, H. L., Pappenberger, F., Wilson, M., & Freer, J. (2009). Tracking the uncertainty in flood alerts driven by grand. Meteorological Applications, 101(February), 91–101. https://doi.org/10.1002/met
Hopson, T. M., & Webster, P. J. (2010). A 1–10-day ensemble forecasting scheme for the major river basins of Bangladesh: Forecasting severe floods of 2003–07. Journal of Hydrometeorology, 11(3), 618–641.
Krishnamurti, T. N., Sagadevan, A. D., Chakraborty, A., Mishra, A. K., & Simon, A. (2009). Improving multimodel weather forecast of monsoon rain over China using FSU superensemble. Advances in Atmospheric Sciences, 26(5), 813–839.
Louvet, S., Sultan, B., Janicot, S., Kamsu-Tamo, P. H., & Ndiaye, O. (2016). Evaluation of TIGGE precipitation forecasts over West Africa at intraseasonal timescale. Climate Dynamics, 47(1), 31–47.
Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research. Cambridge University Press.‏
Mendez, M., Maathuis, B., Hein-Griggs, D., & Alvarado-Gamboa, L. F. (2020). Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica. Water (Switzerland), 12(2). https://doi.org/10.3390/w12020482
Räty, O., Räisänen, J., & Ylhäisi, J. S. (2014). Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations. Climate Dynamics, 42(9), 2287–2303.
Roulin, E. (2007). Skill and relative economic value of medium-range hydrological ensemble predictions. Hydrology and Earth System Sciences, 11(2), 725–737. https://doi.org/10.5194/hess-11-725-2007
Roulin, E., & Vannitsem, S. (2005). Skill of medium-range hydrological ensemble predictions. Journal of Hydrometeorology, 6(5), 729–744.
Rousset, F., Habets, F., Martin, E., & Noilhan, J. (2007). Ensemble streamflow forecasts over France, ECMWF Newsletter, 111, 21–27.
Saedi, A., Saghafian, B., & Moazami, S. (2020). Uncertainty of Flood Forecasts via Ensemble Precipitation Forecasts of Seven NWP Models for Spring 2019 Golestan Flood. Iran-Water Resources Research, 16(1). [in Persian]
Thiemig, V., Bisselink, B., Pappenberger, F., & Thielen, J. (2015). A pan-African medium-range ensemble flood forecast system. Hydrology and Earth System Sciences, 19(8), 3365–3385.
Thirel, G., Rousset-Regimbeau, F., Martin, E., & Habets, F. (2008). On the impact of short-range meteorological forecasts for ensemble streamflow predictions. Journal of Hydrometeorology, 9(6), 1301–1317. https://doi.org/10.1175/2008JHM959.1
Xiong, L., Wan, M., Wei, X., & O’Conno, K. M. (2009). Indices for assessing the prediction bounds of hydrological models and application by generalized likelihood uncertainty estimation. Hydrological Sciences Journal, 54(5), 852–871. https://doi.org/10.1623/hysj.54.5.852
Ye, J., Shao, Y., & Li, Z. (2016). Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast. Advances in Meteorology, 2016. https://doi.org/10.1155/2016/9129734