Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model

Abstract

Long-term rainfall data are crucial for flood simulations and forecasting in karst regions. However, in karst areas, there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods. Quantitative precipitation forecasts (QPFs) and estimates (QPEs) could provide rational methods to acquire the available precipitation data for karst areas. Furthermore, coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas. This study served two main purposes. One purpose was to compare the performance of the Weather Research and Forecasting (WRF) QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System (PERSIANN-CCS) QPEs in rainfall forecasting in karst river basins. The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model. The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results. However, the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges, while the quantities forecasted by the PERSIANN-CCS QPEs were smaller. A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models. The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results. In particular, coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting, and a maximum lead time of 96 h is adequate for flood warnings and emergency responses, which is extremely important in flood simulations and forecasting.

Keywords

WRF QPF / PERSIANN-CCS QPEs / the Karst-Liuxihe model / flood simulation and forecasting / karst river basin

References

[1] Ahlgrimm M, Forbes R M, Morcrette J J, Neggers R A (2016). ARM’s Impact on Numerical Weather Prediction at ECMWF, Meteorol Monogr 57: 28.1–28.13

[2] Bartsotas N, Nikolopoulos E, Anagnostou E, Kallos G (2017). Improving satellite quantitative precipitation estimates through the use of high-resolution numerical weather predictions: similarities and contrasts between the Alps and Blue Nile region. In: EGU General Assembly Conference Abstracts, 19th EGU General Assembly, EGU 2017, Vienna, Austria

[3] Bitew M M, Gebremichael M (2011). Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resour Res, 47(6): 1–11

[4] Chapuis H, Jordan R B, Jolivet J, Paran F, Graillot D (2020). Karst–river interaction, elaboration of an indicator of the karst hydrological conditions applied to the Cèze River (Gard, France). In: Bertrand C, Denimal S, Steinmann M, Renard P, eds. Eurokarst 2018, Besançon, Advances in the Hydrogeology of Karst and Carbonate Resevoirs. Cham: Springer: 77–81

[5] Chen Y B (2009). Liuxihe Model. Beijing: China Science and Technology Press

[6] Chen Y, Zhou H, Zhang H, Du G, Zhou J (2015). Urban flood risk warning under rapid urbanization. Environ Res, 139(5): 3–10

[7] Chen Y, Li J, Xu H (2016). Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization. Hydrol Earth Syst Sci, 20(1): 375–392

[8] Chen Y, Li J, Wang H, Qin J, Dong L (2017). Large watershed flood forecasting with high-resolution distributed hydrological model. Hydrol Earth Syst Sci, 21(2): 735–749

[9] Chen Y (2018). Distributed Hydrological Models. Berlin: Springer

[10] Choi J, Harvey J W, Conklin M H (1999). Use of multi-parameter sensitivity analysis to determine relative importance of factors influencing natural attenuation of mining contaminants. In: The Toxic Substances Hydrology Program Meeting, Charleston, South Carolina,USA

[11] Chou M D, Suarez M J (1994). An efficient thermal infrared radiation parameterization for use in general circulation models, NASA Tech. Memo 104606, NASA: 1–92

[12] Delrieu G, Bonnifait L, Kirstetter P E, Boudevillain B (2014). Dependence of radar quantitative precipitation estimation error on the rain intensity in the Cévennes region, France. Hydrol Sci J, 59(7): 1308–1319

[13] Ek M B, Mitchell K E, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley J D (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res Atmos, 108(D22): 8851

[14] Fan K, Duan L, Zhang Q, Shi P, Liu J, Gu X, Kong D (2017). Downscaling analysis of TRMM precipitation based on multiple high-resolution satellite data in the Inner Mongolia, China. Sci Geograph Sin, 37(9): 1411–1421

[15] Fan Z, Hao Z, Chen Y, Wang J H, Huang F H (2012). The application and research of income flood simulation of the Baipenzhu Reservoir with the Liuxihe Model. Acta Sci Natur Univ Sunyatseni, 51(2): 113–118

[16] Faure D, Gaussiat N, Tabary P, Urban B (2015). Real time integration of foreign radar quantitative precipitation estimations (QPEs) in the French national QPE mosaic. In: 37th Conference on Radar Meteorology, AMS, Norman, OK, USA

[17] Ford D, Williams P W (1989). Karst Geomorphology and Hydrology. Netherlands: Springer

[18] Gebregiorgis A S, Hossain F (2013). Understanding the dependence of satellite rainfall uncertainty on topography and climate for hydrologic model simulation. IEEE Trans Geosci Remote Sens, 51(1): 704–718

[19] Goudenhoofdt E, Delobbe L (2009). Evaluation of radar-gauge merging methods for quantitative precipitation estimates. Hydrol Earth Syst Sci, 13(2): 195–203

[20] Higgins R W, Silva V B S, Shi W, Larson J (2007). Relationships between climate variability and fluctuations in daily precipitation over the United States. J Clim, 20(14): 3561

[21] Hong S, Lim J (2006). The WRF Single-Moment 6-Class Microphysics Scheme (WSM6). Asia-Pacific J Atmos Sci, 42: 129–151

[22] Hong Y, Gochis D, Cheng J, Hsu K, Sorooshian S (2007). Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network. J Hydrometeorol, 8(3): 469–482

[23] Hong Y, Hsu K L, Sorooshian S, Gao X (2004). Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J App Meteor, 43(12): 1834–1853

[24] Hsu K L, Gupta, H V, Gao X G, Sorooshian S (1999). Estimation of physical variables from multichannel remotely sensed imagery using a neural network: application to rainfall estimation. Water Resourc Res, 35(5): 1605–1618

[25] Hsu K, Braithwaite D, Imam B, Gao X, Sorooshian S (2008). Bias adjustment of PERSIANN rainfall for hydrologic applications. In: American Geophysical Union, Spring Meeting Abstracts, San Francisco, CA, USA

[26] Hu Q F, Yang D W, Wang Y T, Yang H B, Liu Y (2013). Characteristics and sources of errors in daily TRMM precipitation product over Ganjiang River Basin in China. Adv Water Sci, 24(6): 794–800

[27] Hui Z, Chen Y, Zhou J H (2015). Assessing the long-term impact of urbanization on run-off using a remote-sensing-supported hydrological model. Int J Remote Sens, 36(21): 1–17

[28] Johnson C M (2014). Influence of karst landscape on weather systems: a WRF model study on responses for different land and soil types. In: 28th Conference on Hydrology, Land-Atmosphere Interactions Part III, Atlanta, GA, USA

[29] Johnson C M, Fan X, Mahmood R, Groves C, Polk J S, Yan J (2018). Evaluating weather research and forecasting model sensitivity to land and soil conditions representative of karst landscapes. Boundary-Layer Meteor, 166: 1–28

[30] Kain J S (2004). The Kain–Fritsch convective parameterization: an update. J Appl Meteorol Climatol, 43(1): 170–181

[31] Kovacs A, Perrochet P (2011). Hydrograph analysis for parameter estimation of connected and karst systems. In: Proceedings of the 34th World Congress of the International Association for Hydro-Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water Engineering, Engineers Australia, Neuchatel, Switzerland

[32] Lahmers T M (2017). Enhancements to the WRF-Hydro hydrologic model structure for semi-arid environments. In: American Geophysical Union, Fall Meeting Abstracts, New Orleans, USA

[33] Leeper R, Mahmood R, Quintanar A I (2011). Influence of karst landscape on planetary boundary layer atmosphere: a weather research and forecasting (WRF) model-based investigation. J Hydrometeorol, 12(6): 1512–1529

[34] Li J, Chen Y, Wang H, Qin J, Li J, Chiao S (2017). Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model. Hydrol Earth Syst Sci, 21(2): 1279–1294

[35] Li J, Yuan D, Liu J, Jiang Y, Chen Y, Hsu K L, Sorooshian S (2019). Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model. Hydrol Earth Syst Sci, 23(3): 1505–1532

[36] Li J, Hong A, Yuan D, Jiang Y, Deng S, Cao C, Liu J (2020). A new distributed karst-tunnel hydrological model and tunnel hydrological effect simulations. J Hydrol (Amst), 593: 125639

[37] Li J, Hong A, Yuan D, Jiang Y, Zhang Y, Deng S, Cao C, Liu J, Chen Y (2021). Elaborate simulations and forecasting of the effects of urbanization on karst flood events using the improved Karst-Liuxihe Model. Catena, 197: 104990

[38] Li Y, Lu G H, Wu Z Y, Shi J (2015). Study of a dynamic downscaling scheme for quantitative precipitation forecasting, In: Remote Sensing and GIS for Hydrology and Water Resources. Proc. IAHS, 368: 108–113

[39] Mekonnen G, Hossain F (2010). Satellite Rainfall Applications for Surface Hydrology. Netherlands: Springer

[40] Mlawer E J, Taubman S J, Brown P D, Iacono M J, Clough S A (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-K model for the longwave. J Geophys Res Atmos, 102(D14): 16663–16682

[41] Molteni F, Buizza R, Palmer T N, Petroliagis T (1996). The ECMWF ensemble prediction system: methodology and validation. Q J R Meteorol Soc, 122(529): 73–119

[42] Moradkhani H, Hsu K, Hong Y, Sorooshian S (2006). Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting. Geophys Res Lett, 33(12): L12401

[43] Nguyen P, Thorstensen A, Sorooshian S, Zhu Q, Tran H. Ashouri H (2017). Evaluation of CMIP5 model precipitation using PERSIANN-CDR. J Hydrometeor, 18: JHM-D-16–0201.1

[44] Rafieei N A, Norouzi A, Kim B, Seo D (2014). Fusion of multiple radar-based quantitative precipitation estimates (QPE) for high-resolution flash flood prediction in large urban areas. In: AGU Fall Meeting Abstracts, AGU Fall Meeting, San Francisco, CA, USA

[45] Scanlon B R, Mace R E, Barrett M E, Smith B (2003). Can we simulate regional groundwater flow in a karst system using equivalent porous media models? Case study, Barton Springs Edwards aquifer, USA. J Hydrol (Amst), 276(1–4): 137–158

[46] Shuster E T, White W B (1971). Seasonal fluctuations in the chemistry of lime-stone springs: a possible means for characterizing carbonate aquifers. J Hydrol (Amst), 14(2): 93–128

[47] Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda G, Huang X, Wang W, Powers J G (2008). A Description of the Advanced Research WRF Version 3. NCAR Technical Note, NCAR/TN-468, STR. National Center for Atmospheric Research, Boulder, CO, Mesoscale and Microscale Meteorology Div, Denver, Colorado, USA

[48] Sorooshian S, Hsu K L, Gao X, Gupta H V, Imam B, Braithwaite D (2000). Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc, 81(9): 2035–2046

[49] Stenz R D (2014). Improving Satellite quantitative precipitation estimates by incorporating deep convective cloud optical depth. Dissertations for Master’s Degree. Grand Forks: University of North Dakota

[50] Takenaka H, Nakajima T Y, Higurashi A, Higuchi A, Takamura T, Pinker R T, Nakajima T (2011). Estimation of solar radiation using a neural network based on radiative transfer. J Geophys Res, 116(D8): D08215

[51] Tingsanchali T (2012). Urban flood disaster management. Procedia Eng, 32: 25–37

[52] Tong K, Su F, Yang D, Hao, Z (2014). Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modelling over the Tibetan Plateau. J Hydrol, 519: 423–437

[53] Wardhana A, Pawitan H, Dasanto B D (2017). Application of hourly radar-gauge merging method for quantitative precipitation estimates. In: IOP Conference Series: Earth Environ Sci, 58(1): 012033, IOP Publishing

[54] White W B (2002). Karst hydrology: recent developments and open questions. Eng Geo, 65(2–3): 85–105

[55] White W B, White E L (2005). Ground water flux distribution between matrix, fractures, and conduits: constraints on modelling. Speleogen Evol Karst Aquifers, 3(2): 1–6

[56] White W B (2007). A brief history of karst hydrogeology: contributions of the NSS. J Caves Karst Stud, 69(1): 13–26

[57] Williams P W (1978). Karst research in China. Beijing: Science Press

[58] Yang P H, Luo J Y, Peng W, Xia K S, Lin Y S (2008). Application of online technique in tracer test-a case in Qingmuguan subterranean river system, Chongqing, China. Carsologica Sinica, 27(3): 215–220 (in Chinese)

[59] Yáñez-Morroni G, Gironás J, Caneo M, Delgado R, Garreaud R (2018). Using the weather research and forecasting (WRF) model for precipitation forecasting in an andean region with complex topography. Atmosphere, 9(8): 304–327

[60] Zappa M, Beven K J, Bruen M, Cofiño A S, Kok K, Martin E, Nurmi P, Orfila B, Roulin E, Schröter K, Seed A, Szturc J, Vehviläinen B, Germann U, Rossa A (2010). Propagation of Uncertainty from observing systems and NWP into hydrological models: COST-731 Working Group 2. Atmos Sci Lett, 11(2): 83–91

[61] Zaripov R B, Martynova Y V, Krupchatnikov V N, Petrov A P (2016). Atmosphere data assimilation system for the siberian region with the wrf-arw model and three-dimensional variational analysis WRF 3D-VAR. Russ Meteorol Hydrol, 41(11–12): 808–815

[62] Zheng B R, Lan Z Y (2019). How beautiful is the karst in Southern China? The article was drawn in wechat official account: Institute for Planets

Acknowledgments

This study was supported by the National Science Foundation for Young Scientists of China (No. 42101031), Chongqing Natural Science Foundation (No. cstc2021jcyj-msxm0007), the Open Project Program of Guangxi Key Science and Technology Innovation Base on Karst Dynamics (KDL & Guangxi 202009, KDL & Guangxi 202012), and the National Natural Science Foundation of China (Grant No. 41830648).

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