A Comparative Analysis of Ensemble NWP Models for Flood Forecasting
DOI:
https://doi.org/10.61186/JCER.7.4.23Keywords:
Flood Forecasting, Ensemble Numerical Weather Prediction (NWP), Gamma Quantile Mapping, Uncertainty Analysis, IranAbstract
This study presents the first application of gamma quantile mapping to bias-correct ensemble precipitation forecasts from seven global NWP models (ECMWF, NCEP, UKMO, CMA, JMA, ECCC, NCMRWF) in the data-scarce Saliyan Basin, Iran. The integration of these models with advanced bias correction techniques significantly improves flood forecasting accuracy. To address systematic biases in the raw forecasts, gamma quantile mapping was applied, significantly enhancing the reliability of the precipitation inputs. These bias-corrected forecasts were then used as inputs for the GR4J hydrological model to simulate river flow and predict flood events. The study period included a major flood event in March 2019, which was used to evaluate the performance of the ensemble forecasting system. Results demonstrated that bias correction using gamma quantile mapping substantially improved the accuracy of flood forecasts, with the ECMWF and UKMO models showing the highest skill scores. The ensemble approach effectively captured the uncertainty in flood predictions, providing valuable insights for risk assessment and decision-making. This research highlights the importance of bias correction in ensemble forecasting and offers a robust framework for flood prediction in data-scarce regions. The findings have significant implications for improving flood early warning systems and mitigating flood-related damages in similar basins worldwide.
References
[1] Swinbank, R., M. Kyouda, P. Buchanan, L. Froude, T.M. Hamill, T.D. Hewson, et al. “The TIGGE project and its achievements.” Bulletin of the American Meteorological Society 97.1 (2016): 49–67. https://doi.org/10.1175/BAMS-D-13-00191.1
[2] Thirel, G., F. Rousset-Regimbeau, E. Martin, and F. Habets. “On the impact of short-range meteorological forecasts for ensemble streamflow predictions.” Journal of Hydrometeorology 9.6 (2008): 1301–1317. https://doi.org/10.1175/2008JHM951.1
[3] He, Y., F. Wetterhall, H. Bao, H. Cloke, Z. Li, F. Pappenberger, et al. “Ensemble forecasting using TIGGE for the July–September 2008 floods in the Upper Huai catchment: a case study.” Atmospheric Science Letters 11.2 (2010): 132–138. https://doi.org/10.1002/asl.267
[4] Alfieri, L., F. Pappenberger, F. Wetterhall, T. Haiden, D. Richardson, and P. Salamon. “Evaluation of ensemble streamflow predictions in Europe.” Journal of Hydrology 517 (2014): 913–922. https://doi.org/10.1016/j.jhydrol.2014.06.035
[5] Bennett, J.C., D.E. Robertson, D.L. Shrestha, Q.J. Wang, D. Enever, P. Hapuarachchi, and N.K. Tuteja. “A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days.” Journal of Hydrology 519 (2014): 2832–2846. https://doi.org/10.1016/j.jhydrol.2014.07.034
[6] Zomerdijk, L. “Performance of multi-model ensemble combinations for flood forecasting.” Master Thesis; University of Twente: Netherlands (2015).
[7] Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. “A Pan-African medium-range ensemble flood forecast system.” Hydrology and Earth System Sciences 19.8 (2015): 3365–3385. https://doi.org/10.5194/hess-19-3365-2015
[8] Matsueda, M. and T. Nakazawa. “Early warning products for severe weather events derived from operational medium-range ensemble forecasts.” Meteorological Applications 22.2 (2015): 213–222. https://doi.org/10.1002/met.1456
[9] Cai, C., J. Wang, and Z. Li. “Assessment and modelling of uncertainty in precipitation forecasts from TIGGE using fuzzy probability and Bayesian theory.” Journal of Hydrology 577 (2019): 123995. https://doi.org/10.1016/j.jhydrol.2019.123995
[10] Zhang, Y., X. Wang, and J. Li. “Improving precipitation forecast accuracy using gamma quantile mapping.” Journal of Hydrology 585 (2020): 124789. https://doi.org/10.1016/j.jhydrol.2020.124789
[11] Li, H., Y. Chen, and Q. Zhang. “The impact of high-resolution ensemble NWP models on flood forecasting in small basins.” Water Resources Research 57.5 (2021): e2020WR028945. https://doi.org/10.1029/2020WR028945
[12] Wang, L., X. Liu, and T. Zhang. “Combining ensemble NWP models with machine learning for flood prediction.” Hydrology and Earth System Sciences 26.3 (2022): 1234-1245. https://doi.org/10.5194/hess-26-1234-2022
[13] Kumar, R., P. Singh, and A. Sharma. “The influence of input data quality on flood forecasting accuracy.” Journal of Hydrometeorology 24.2 (2023): 567-580. https://doi.org/10.1175/JHM-D-22-0123.1
[14] Martinez, G., M. Rodriguez, and J. Lopez. “Uncertainty analysis in flood forecasting using ensemble NWP models.” Natural Hazards and Earth System Sciences 21.7 (2021): 2345-2358. https://doi.org/10.5194/nhess-21-2345-2021
[15] Lee, S., H. Kim, and J. Park. “The impact of regional characteristics on flood forecasting performance.” Water Resources Management 37.4 (2023): 1456-1470. https://doi.org/10.1007/s11269-023-03456-8
[16] Aminyavari, S., B. Saghafian, and M. Delavar. “Evaluation of TIGGE ensemble forecasts of precipitation in distinct climate regions in Iran.” Advances in Atmospheric Sciences 35.4 (2018): 457–468. https://doi.org/10.1007/s00376-017-7063-9
[17] Aminyavari, S., B. Saghafian, and E. Sharifi. “Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran.” Remote Sensing 11.23 (2019): 2741. https://doi.org/10.3390/rs11232741
[18] Aminyavari, S., B. Saghafian, and E. Sharifi. “Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran.” Remote Sensing 11.23 (2019): 2741. https://doi.org/10.3390/rs11232741
[19] Hoghoughinia, K., B. Saghafian, and S. Aminyavari. “Evaluation of precipitation temporal distribution pattern of post-processed sub-daily ECMWF forecasts.” Theoretical and Applied Climatology 155.8 (2024): 8401–8414. https://doi.org/10.1007/s00704-024-04923-9
[20] Abdolmanafi, A., B. Saghafian, and S. Aminyavari. “Evaluation of global ensemble prediction models for forecasting medium to heavy precipitations.” Meteorology and Atmospheric Physics 133.1 (2021): 15–26. https://doi.org/10.1007/s00703-020-00772-1
[21] Saedi, A., B. Saghafian, S. Moazami, and S. Aminyavari. “Performance evaluation of sub-daily ensemble precipitation forecasts.” Meteorological Applications 27.1 (2020): e1872. https://doi.org/10.1002/met.1872
[22] Hoghoughinia, K., B. Saghafian, and S. Aminyavari. “Analysis of precipitation temporal pattern of sub-daily ECMWF forecasts.” Meteorology and Atmospheric Physics 134.5 (2022): 87. https://doi.org/10.1007/s00703-022-00920-7
[23] Piani, C., J.O. Haerter, and E. Coppola. “Statistical bias correction for daily precipitation in regional climate models over Europe.” Theoretical and Applied Climatology 99.1–2 (2010): 187–192. https://doi.org/10.1007/s00704-009-0134-9
[24] Liu, J. and Z. Xie. “BMA probabilistic quantitative precipitation forecasting over the Huaihe Basin Using TIGGE multimodel ensemble forecasts.” Monthly Weather Review 142.4 (2014): 1542–1555. https://doi.org/10.1175/MWR-D-13-00171.1
[25] Xu, Y. “Hyfo: hydrology and climate forecasting R package for data analysis and visualization.” R Package Version 1.0 (2015).
[26] Coron, L., G. Thirel, O. Delaigue, C. Perrin, and V. Andréassian. “The suite of lumped GR hydrological models in an R package.” Environmental Modelling & Software 94 (2017): 166–171. https://doi.org/10.1016/j.envsoft.2017.03.010
[27] Michel, C. “Hydrologie Appliquee Aux Petits Bassins Ruraux (Applied Hydrology for Small Catchments).” Internal Report; Cemagref Antony: France (1991).
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