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Frontiers of Earth Science

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Front. Earth Sci.    2022, Vol. 16 Issue (2) : 381-400    https://doi.org/10.1007/s11707-021-0909-6
RESEARCH ARTICLE
Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model
Ji LI1,2(), Daoxian YUAN1,2, Yuchuan SUN1, Jianhong LI2
1. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
2. Key Laboratory of Karst Dynamics (MNR & Guangxi) Institute of Karst Geology, CAGS, Guilin 541004, China
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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     
Corresponding Author(s): Ji LI   
About author: Tongcan Cui and Yizhe Hou contributed equally to this work.
Online First Date: 25 November 2021    Issue Date: 26 August 2022
 Cite this article:   
Ji LI,Daoxian YUAN,Yuchuan SUN, et al. Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model[J]. Front. Earth Sci., 2022, 16(2): 381-400.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0909-6
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/381
Fig.1  Sketch image of the Liujiang watershed. (a) Gauge distribution (modified from Yang et al., 2008); (b) the DEM image; (c) land use types; (d) soil types.
Fig.2  The karst landform evolution of the Liujiang Basin (modified from Zheng and Lan, 2019).
Thickness (h)/m Saturated water content (θsat)/(g·cm−3) Saturation permeability coefficient (θs)/(mm·h−1) Karst fissure width (wd)/cm Macro crack volume ratio (V)/% Field capacity (θfc)/mm
2.5–8 0.15–0.3.5 110–400 0.1–10 0.08–0.25 0.26–0.48
Tab.1  Initial hydrogeological parameters

(a) Initial parameter value range of the epikarst zone

Landforms Karst strongly developed Karst moderately developed Karst poorly developed
Closed depression 0.62–0.85 0.45–0.65 0.15–0.18
Not closed depression 0.34–0.67 0.32–0.55 0.18–0.28
Monadnock, platform 0.25–0.35 0.22–0.35 0.20–0.25
Gully, slope 0.01–0.25 0.01–0.24 0.01–0.25
  (b) The rainfall infiltration coefficient for different karst development characteristics
Fig.3  The flow chart of the parameter sensitivity analysis.
Fig.4  The rainfall results of the 3 precipitation products.
Fig.5  Karst flood simulation outputs through parameter optimization based on the improved PSO algorithm.
Floods Model parameter types Nash–Sutcliffe coefficient (C) Correlation coefficient (R) Process relative error (P%) Peak flow relative error (E%) Coefficient of water balance (W) Peak time error (T)/h
1994060700 Initial 0.65 0.58 37 32 0.76 −6
Optimized 0.93 0.87 14 2 0.95 −4
1995052100 Initial 0.58 0.61 35 33 0.57 −5
Optimized 0.85 0.9 20 3 0.81 −3
1996060600 Initial 0.61 0.62 35 35 0.74 −4
Optimized 0.9 0.93 18 5 0.86 −5
1997060400 Initial 0.59 0.6 31 34 0.72 −5
Optimized 0.84 0.95 13 2 0.95 −4
1998051600 Initial 0.62 0.58 25 35 0.76 −5
Optimized 0.83 0.95 10 2 1.05 −2
1999061700 Initial 0.62 0.55 27 38 0.56 −6
Optimized 0.89 0.93 15 3 0.8 −5
2000052100 Initial 0.54 0.68 26 31 0.58 −6
Optimized 0.89 0.89 8 2 0.83 −3
2001051500 Initial 0.6 0.65 26 35 0.65 −5
Optimized 0.91 0.89 12 2 0.82 −4
2002042600 Initial 0.65 0.68 28 29 0.67 −5
Optimized 0.86 0.9 14 4 0.87 −2
2003060600 Initial 0.63 0.65 24 26 0.65 −5
Optimized 0.92 0.85 9 3 0.86 −4
2004070300 Initial 0.58 0.62 25 39 0.67 −6
Optimized 0.88 0.92 13 4 0.85 −3
2005061400 Initial 0.54 0.65 38 45 0.52 −6
Optimized 0.87 0.92 10 3 1.08 −5
2006060400 Initial 0.66 0.71 28 43 0.61 −7
Optimized 0.91 0.89 11 5 0.92 −5
2007070800 Initial 0.63 0.66 32 38 0.71 −5
Optimized 0.89 0.93 14 4 1.12 −3
2008060900 Initial 0.65 0.68 29 32 0.67 −5
Optimized 0.96 0.94 5 3 0.94 −3
2009060908 Initial 0.67 0.61 28 34 0.79 −4
Optimized 0.95 0.92 17 4 0.9 −2
2010071208 Initial 0.6 0.65 26 36 0.83 −6
Optimized 0.88 0.91 15 2 0.89 −4
2011060109 Initial 0.65 0.83 25 21 0.89 −5
Optimized 0.95 0.92 16 3 1.02 −7
2012060220 Initial 0.69 0.54 31 27 0.75 −6
Optimized 0.93 0.91 8 5 0.89 −6
2013060114 Initial 0.7 0.84 28 38 0.79 −5
Optimized 0.95 0.94 7 6 0.92 −4
average value Initial 0.62 0.65 29 34 0.69 −6
Optimized 0.91 0.91 12 3 0.92 −3
Tab.2  Evaluation indices for the karst flood simulation outputs
Fig.6  The flood simulation results of flood based on the coupled model.
Floods Rainfall types Nash–Sutcliffe coefficient (C) Correlation coefficient (R) Process relative error (P)% Peak flow relative error (E)/% Coefficient of water balance (W) Peak time error (T)/h
200806090000 WRF 0.72 0.8 25 18 1.02 −9
PP-WRF 0.78 0.82 20 13 0.95 −7
PERSIANN 0.76 0.83 21 6 0.92 −10
PP-PERSIANN 0.83 0.88 18 5 0.94 −4
200906090800 WR 0.81 0.82 24 20 1.12 −6
PP-WRF 0.83 0.83 20 14 1.06 −4
PERSIANN 0.82 0.81 28 18 0.79 −6
PP-PERSIANN 0.85 0.87 22 12 0.85 −3
201106010900 WRF 0.79 0.81 26 14 1.15 −7
PP-WRF 0.83 0.83 20 10 1.08 −6
PERSIANN 0.85 0.85 21 12 0.92 −8
PP-PERSIANN 0.91 0.87 19 6 0.94 −6
201206022000 WRF 0.78 0.82 18 13 1.28 −10
PP-WRF 0.81 0.83 10 11 1.15 −8
PERSIANN 0.86 0.84 16 15 0.78 −7
PP-PERSIANN 0.92 0.89 9 6 0.85 −4
201306011400 WRF 0.78 0.82 13 21 1.2 −8
PP-WRF 0.82 0.85 9 12 1.12 −6
PERSIANN 0.82 0.89 12 17 0.85 −5
PP-PERSIANN 0.86 0.91 8 9 0.87 −4
average value WRF 0.78 0.81 21 17 1.15 −8
PP-WRF 0.81 0.83 16 12 1.07 −6
PERSIANN 0.82 0.84 20 14 0.85 −7
PP-PERSIANN 0.87 0.88 15 8 0.89 −4
Tab.3  The evaluation indices of karst flood simulations using the original WRF QPF and PERSIANN-CCS QPEs and their postprocessed values
Floods Type Average precipitation/mm Relative bias %
200806090200 Rain gauge 1.37
WRF QPF 1.55 13
PERSIANN-CCS QPEs 1.22 −11
200906090800 Rain gauge 0.74
WRF QPF 0.88 19
PERSIANN-CCS QPEs 0.62 −16
201106010900 Rain gauge 0.42
WRF QPF 0.46 10
PERSIANN-CCS QPEs 0.39 −7
201206022000 Rain gauge 0.78
WRF QPF 0.95 22
PERSIANN-CCS QPEs 0.63 −19
201306011400 Rain gauge 0.53
WRF QPF 0.65 23
PERSIANN-CCS QPEs 0.43 −20
average value Rain gauge 0.77
WRF QPF 0.9 17
PERSIANN-CCS QPEs 0.66 −14
Tab.4  Quantitative rainfall comparison results of the 3 precipitation products
Floods Rainfall types Nash–Sutcliffe coefficient (C) Correlation coefficient (R) Process relative error (P)/% Peak flow relative error (E)/% Coefficient of water balance (W) Peak time error (T)/h
200806090000 Rain gauge 0.96 0.94 5 3 0.94 −3
WRF 0.78 0.82 20 13 0.95 −7
PERSIANN 0.83 0.88 18 5 0.94 −4
200906090800 Rain gauge 0.95 0.92 17 4 0.9 −2
WRF 0.83 0.83 20 14 1.06 −4
PERSIANN 0.85 0.87 22 12 0.85 −3
201106010900 Rain gauge 0.95 0.92 16 3 1.02 −7
WRF 0.83 0.83 20 10 1.08 −6
PERSIANN 0.91 0.87 19 6 0.94 −6
201206022000 Rain gauge 0.93 0.91 8 5 0.89 −6
WRF 0.81 0.83 10 11 1.15 −8
PERSIANN 0.92 0.89 9 6 0.85 −4
201306011400 Rain gauge 0.95 0.94 7 6 0.92 −4
WRF 0.82 0.85 9 12 1.12 −6
PERSIANN 0.86 0.91 8 9 0.87 −4
average value Rain gauge 0.95 0.93 11 4 0.93 −3
WRF 0.81 0.83 16 12 1.07 −6
PERSIANN 0.87 0.88 15 8 0.89 −4
Tab.5  Evaluation indices of karst flood simulations using the three precipitation products
Flood Potential evaporation (Ep) Evaporation coefficient (λ) Wilting percentage (Cwl) Saturated water content (θsat) Saturation permeability coefficient (θs) Macro crack volume ratio (V)
2008060900 0.18 0.26 0.11 0.94 0.92 0.78
Field capacity (θfc) Soil layer thickness (z) Saturated hydraulic conductivity (Ks) Soil coefficient (b) Bottom slope (Sp) Bottom width (Sw)
0.88 0.73 0.82 0.64 0.53 0.6
Slope roughness (n) Channel roughness (n1) Depletion coefficient (ω) Permeability coefficient (K) Specific yield of the aquifer (χ) Thickness of the karst aquifer (h)
0.47 0.45 0.36 0.8 0.75 0.72
Tab.6  Parameter sensitivity analysis of the coupled model
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