To date, floods have become one of the most severe natural disasters on Earth. Flood forecasting with hydrological models is an important non-engineering measure for flood control and disaster reduction. The Xin'anjiang (XAJ) model is the most widely used hydrological model in China for flood forecasting, while the Soil and Water Assessment Tool (SWAT) model is widely applied for daily and monthly simulation and has shown its potential for flood simulation. The objective of this paper is to evaluate the performance of the SWAT model in simulating floods at a sub-daily time-scale in a slightly larger basin and compare that with the XAJ model. Taking Qilijie Basin (southeast of China) as a study area, this paper developed the XAJ model and SWAT model at a sub-daily time-scale. The results showed that the XAJ model had a better performance than the sub-daily SWAT model regarding relative runoff error (RRE) but the SWAT model performed well according to relative peak discharge error (RPE) and error of occurrence time of peak flow (PTE). The SWAT model performed unsatisfactorily in simulating low flows due to the daily calculation of base flow but behaved quite well in simulating high flows. We also evaluated the effect of spatial scale on the SWAT model. The results showed that the SWAT model had a good applicability at different spatial scales. In conclusion, the sub-daily SWAT model is a promising tool for flood simulation though more improvements remain to be studied further.
Keywords: sub-daily SWAT model; flood simulation; XAJ model; Qilijie basin
Flooding is one of the most common natural phenomena. With the economic and social development, the global climate and the underlying surface have changed, causing changes in the water circulation process of river basins and causing more serious and frequent floods. Severe flood disasters have become one of the most serious water issues, which have brought incalculable losses and caused serious threats to the safety of lives and properties.
Flood forecasting is an important non-engineering measure for flood control and disaster reduction. Timely and accurate flood forecasting is the most effective way to control flooding and reduce flooding damage. A hydrological model is a modern flood forecasting method developing with the rapid development of electronic computer technology. By simulating historical floods, we can evaluate the performance of these hydrological models and make full preparations for flood forecasting. Since the first watershed hydrological model—the Stanford model—was applied in hydrology research [[
The ability to simulate event-based floods is significant for hydrological models to sufficiently capture dynamic hydrological processes between short intervals. Therefore, more and more scholars have studied the application of the SWAT model for discharge simulation in a shorter time-step. Jeong et al. (2010) [[
To date, the XAJ model has been the most widely used hydrological model in flood forecasting in China, while the SWAT model has also proved its reasonable capability to simulate floods in several basins. The objectives of this study were threefold. The first objective was to evaluate the performance of the SWAT model in flood simulation at a sub-daily time-scale in a slightly larger basin and explore how this simulation improves our understanding of hydrological processes during a flood event. The second objective was to compare the results of sub-daily SWAT with the XAJ model and demonstrate the event-based flood simulation capability of the SWAT model. The third objective was to research the impact of spatial scale on the SWAT model and comprehensively estimate the applicability of the SWAT model.
The Minjiang River Basin is located in the north of Fujian province, between 116°23′ E and 119°43′ E longitude, 25°23′ N and 28°19′ N latitude. The study area of this paper is Qilijie Basin, a subbasin in the upper Minjiang River Basin and covers an area of 14,800 km
The XAJ model is the first watershed hydrological model in China. It is a semi-distributed, rain-runoff model proposed by Zhao et al. (1980) [[
XAJ model needs measured pan evaporation and precipitation inputs. Process of evapotranspiration in each computation unit is simulated using triplex evaporation method [[
The SWAT model is a basin-scale model which can predict the impact of management on water, sediment, and agricultural chemical pollutant loads. It is based on a strong physical mechanism that enables to simulate in the basins without observed data. In this model, the study area was first divided into multiple subbasins and then further divided into hydrological response units (HRUs) based on the unique combination of land use, soil and management measures. The SWAT model has been widely used in many fields and basins [[
SWAT model estimated potential evapotranspiration using the Penman-Monteith method [[
This paper developed sub-daily SWAT model for Qilijie Basin. The latest version of SWAT 2012 was employed in this study. Traditional DEM-based watershed delineation method had low precision in plain polders. In this research, "Burn-in" method [[
The data collected for SWAT model and XAJ model included geospatial input data, meteorological data and hydrological data. The geospatial input data are as follows: (
The meteorological data included precipitation, daily maximum and minimum air temperature, wind speed, relative humidity and solar radiation from 1980 to 2008 of 7 meteorological stations inside or near the study area. These data were prepared to generate weather parameters for Weather Generator.
Hydrological data included precipitation, discharge and measured pan evaporation. Precipitation data from 1988 to 1999 were collected from 43 rain gauges. Precipitation data were prepared in daily and hourly time-scale. Hourly time-scale precipitation data were only available during flood seasons, so hourly precipitation during non-flood seasons were estimated by assuming a uniform distribution in daily precipitation. Discharge data between 1988 and 1999 were collected from 3 hydrological stations, Qilijie (QLJ), Jianyang (JY) and Shuiji (SJ). They were also prepared in daily and hourly time-scale. QLJ station was used for model calibration and validation, while JY and SJ were used to evaluate the applicability of SWAT model on different spatial scales. All the data used in this paper were shown in Table 1.
There are many parameters in SWAT model, but only some of them are very sensitive to the operation of the model. Authors needed to perform sensitivity analysis and select the most sensitive parameters, and this could help us reduce time spent on model calibration. A global sensitivity analysis was conducted to identify the most sensitive parameters. The t-statistic and p-value were used to determine the sensitivity of the parameters. If the absolute value of t-statistic is large and p-value is small, then the parameter is classified to be sensitive. Table 2 listed the 16 parameters included in the calibration of sub-daily SWAT model and their detailed information.
Calibration and validation of SWAT model were based on Calibration and Uncertainty Procedures (SWAT-CUP) [[
The goodness-of-fit of the model is assessed by Coefficient of determination(R
(
where
is the i-th measured discharge,
is the i-th model simulated discharge.
is mean measured discharge for the entire time period,
is mean simulated discharge for the entire time period. R
NSE is calculated as:
(
NSE ranges between −∞ and 1 [[
The formula for calculating percent bias PBIAS is as follows:
(
PBIAS compares the average tendency of simulated output to observed data, and the optimal value is 0. Positive values mean that the simulated outputs are larger than observed data, while negative values are on the contrary [[
QLJ station was selected as the calibration station. For daily simulation, the period from 1988 to 1996 was selected as the calibration period and the first three years served as a warm up period. After that the model was validated using observed data between 1997 and 1999.
In this paper, calibration and validation of SWAT model for flood simulation were also based on SWAT-CUP. SWAT-CUP is often used to calibrate for daily, monthly and yearly time steps, so we need to modify it for hourly time step.
Besides R
(
(
where
is the measured runoff and
is the simulated runoff;
the measured peak flow and
is the simulated peak flow.
PTE is calculated as:
(
where
(h) represents the simulated occurrence time of peak flow and
(h) represents the measured occurrence time of peak flow. The positive value indicates that the simulated peak flow occurs later than the measured peak flow and the negative value indicates that the simulated peak flow occurs earlier than the measured peak flow.
In XAJ model, there are 15 important parameters, so authors calibrated the model automatically by matching simulated and observed values using an optimization algorithm and then manually adjusted them according to personal experience. The same indices as SWAT model for the goodness-of-fit of XAJ model were used.
Calibration for flood simulation was based on the calibration for daily simulation. Authors brought the decisive parameters calibrated in daily simulation back to the model to prepare the initial information for flood simulation. For flood simulation, the model was calibrated over flood periods only, so only discharge data during flood seasons were required. Twenty-two floods between 1991 and 1996 were used for calibration and 14 floods between 1997 and 1999 were chosen for validation.
The ranking of the most sensitive parameters obtained in daily simulation and flood simulation was listed in Table 3. In this paper, the parameter was considered to be significantly sensitive when the p-value was less than 0.03.
The significantly sensitive parameters of the SWAT model in daily simulation were baseflow alpha factor for bank storage (ALPHA_BNK), Manning's "n" value of the main channel (CH_N2), effective hydraulic conductivity in main channel alluvium (CH_K2), saturated hydraulic conductivity (SOL_K), threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN), groundwater "revap" coefficient (GW_REVAP) and initial SCS runoff curve number for moisture condition II (CN2). These parameters were related to flow routing and runoff generation. While in flood simulation, significantly sensitive parameters were ALPHA_BNK, CH_N2 and CH_K2. They were all connected with flow routing. In both daily and flood simulation, ALPHA_BNK, CH_N2 and CH_K2 were significantly sensitive which means that flow routing was very important for the simulation of the SWAT model in this study area, while their values were all larger in flood simulation. The larger ALPHA_BNK value meant flat recessions for bank flow and the larger CH_K2 value would cause more water loss to the groundwater within the stream bed. The fitted value of CH_N2 in flood simulation was 0.0847 (m
It is worth noting that the calibration and sensitivity analysis results were based on the data and model performance for this paper. The final result was influenced by all the parameters included. Many other factors, such as types of changes for parameters and the selection of objective function [[
The team developed a sub-daily SWAT model for daily discharge simulation and then compared it with the XAJ model. The results of calibration and validation for the XAJ and SWAT models were presented in Table 4, which shows separately the performances for the calibration and validation periods. After three iterations, a best iteration for sub-daily SWAT model was chosen. The p-factor and r-factor were 0.86 and 0.63 for the sub-daily SWAT model, respectively. So, the calibration of the SWAT model was considered to be satisfactory according to the criteria.
During the calibration period, the R
The performance of the SWAT model was nearly positively correlated with precipitation. The annual precipitation for 1992, 1995, 1997 and 1998 were all above 2000 mm. Simulation of daily discharge of these years was better in terms of R
Calibration and validation of the sub-daily SWAT model in flood simulation were conducted using SWAT-CUP and a few modifications were made to it to accommodate the hourly time-scale input. The results of calibration and validation for XAJ and the sub-daily SWAT model are listed in Table 5. During the calibration period, the sub-daily SWAT model performed better than the XAJ model with R
We also plotted flow duration curves (FDCs) in Figure 4 to evaluate the agreement between observations and simulations. Both the XAJ and SWAT models overestimated extreme high flows (<1% exceedance) during calibration and validation periods, and it was further obvious in the SWAT model. While the SWAT model could capture high flows (~2% exceedance) perfectly. During the calibration period, the SWAT model predicted better in terms of medium flows (~50% exceedance) but had a poor performance in terms of low flows. During the validation period, it was the opposite. The XAJ model performed satisfactorily in estimating medium flows while the SWAT model had better agreement in low flows. Both of the two models overestimated high flows and underestimated low flows as a whole. The models responded differently to extremely high and extremely low flows. Therefore, the responses of the models to different conditions [[
Table 5 summarizes the statistical performance measures for runoff, peak flow and occurrence time of peak flow. During the calibration period, the average RRE and RPE of the XAJ model were −8.78% and 11.59%, respectively, while the two values of the SWAT model were −13.06% and −7.82%, respectively. The SWAT model simulated better in terms of RPE. During the validation period, it showed the same conclusion. The distributed model consistently performed better than the lumped model in simulating peak flow [[
Figure 5 compared observed and simulated discharges by XAJ and SWAT models for eight event-based floods. The SWAT model had a poor performance in the beginning of most floods and it was particularly evident in the first flood of the year (Flood 910306, 920321 and 970605). It tended to underestimate the low flows. It is implied that antecedent conditions are very important for the model simulation [[
Due to the spatial heterogeneity of hydrological conditions of a river basin, such as topography, land use and soil type, hydrological phenomena also change accordingly. Research on the spatial scale is very important for the application of hydrological models. It is of great practical significance to utilize the hydrological data of a larger basin to deduce the hydrological characteristics of a smaller watershed.
To evaluate the effect of spatial scale on the SWAT model, the team took JY and SJ station as an example. Their locations are marked in Figure 1 and the basic information of these three stations is presented in Table 6. The catchment areas of JY and SJ station were much smaller than the area of QLJ station. There were also some differences in their land use and soil types. We simulated floods in JY and SJ stations using the parameters calibrated by QLJ station and calculated R
The qualification ratios of these three stations were also summarized according to the standard mentioned above. In QLJ station, there were 25 floods qualified among 36 floods, the qualified ratio was 69.4%. While there were 20 floods qualified among 33 floods in JY station and 20 floods qualified among 30 floods in SJ station. The qualified ratio was 60.6% and 66.7%, respectively. Among 25 floods qualified in QLJ station, 16 floods were likewise qualified in JY station and 14 floods were qualified in SJ station. About half of the floods qualified in QLJ station were qualified in JY and SJ station simultaneously. The results showed that the SWAT model had a good applicability at different spatial scales.
The SWAT model has been extensively used for long-term simulations with daily, monthly or yearly time-scales. This paper developed the sub-daily SWAT model for Qilijie basin. We evaluated the sub-daily SWAT model in flood simulation and compared it with the XAJ model.
Both the XAJ and SWAT models behaved satisfactorily in the simulation of event-based floods and had a similar qualified ratio. The XAJ model performed better than the sub-daily SWAT model in terms of RRE, but sub-daily SWAT had a better performance in reproducing peak flow and was good at capturing the occurrence time of peak flow. The sub-daily SWAT model had an improvement in simulating high and medium flows and had showed its capacity of simulating floods with multiple peaks accurately. Hence, the SWAT model has great potential for flood simulation.
The effect of spatial scale on the SWAT model was also evaluated in this research. The results showed that the SWAT model had a good applicability at different spatial scales and could deduce the hydrological characteristics of a smaller watershed using parameters from a larger basin. This is a valuable reference to research the effect of spatial scale on hydrological models.
However, the performance of the SWAT model in flood simulation was affected by precipitation data. Hourly time-scale precipitation data were only available during flood seasons in China and we estimated hourly precipitation during non-flood periods by assuming a uniform distribution in daily precipitation. Hence, the SWAT model simulated badly in the beginning of floods and it would perform better with accurate precipitation input. The SWAT model also performed poorly in estimating low flows and this might be attributed to the daily calculation of base flow. The feasibility of using the sub-daily SWAT model for flood simulation in large regulated regions remains to be further studied.
Graph: Figure 1 Location of Qilijie Basin and its stations.
Graph: Figure 2 Soil map (a)and land use (b) in Qilijie Basin.
Graph: Figure 3 (a) Comparisons between the observed and simulated daily discharge for the calibration period and (b) comparisons between the observed and simulated daily discharge for the validation period. For better readability, the maximal of Y axes in (b) was limited to 20,000 m3/s. So, some of the maximum discharge, i.e., 20,290 m3/s, were not shown in the figure.
Graph: Figure 4 Observed and simulated flow duration curves: (a) flow duration curves (FDCs) for high flows during calibration period; (b) flow duration curves (FDCs) for medium and low flows during calibration period; (c) flow duration curves (FDCs) for high flows during validation period; (d) flow duration curves (FDCs) for medium and low flows during validation period.
Graph: Figure 5 Comparison of simulation of floods conducted using XAJ and SWAT models.
Graph: Figure 6 Boxplots of R2, NSE and PBIAS. In each boxplot, the whisker ranges from the minimum to maximum, while the box ranges from the first quartile to the third quartile. The symbol cross represents the mean value and the symbol circle dot represents the outlier.
Table 1 Information on data collected for this research.
Category Data Type Data Series and Time Scale Usage Geospatial DEM 90 M (V4.1) Hydrological simulation 1:1,000,000 Soil map 2009 1:1,000,000 Landuse map 1995 Meteorological Air temperature 1980–2008/daily Weather Generator Precipitation 1980–2008/daily Humidity 1980–2008/daily Solar radiation 1980–2008/daily Wind speed 1980–2008/daily Hydrological Precipitation 1988–1999/daily&hourly Basic Hydrologic data Discharge 1988–1999/daily&hourly Pan evaporation 1988–1999/daily
Table 2 Parameters for daily and sub-daily SWAT models. "V" means the existing parameter will be replaced by a given value; "R" means that the existing parameter is multiplied by (1 ± a given value).
Parameter Input File Definition Type of Change CH_N2 .rte Manning's "n" value of the main channel (m−1/3s) V CH_K2 .rte Effective hydraulic conductivity in main channel alluvium (mm/h) V ALPHA_BNK .rte Baseflow alpha factor for bank storage V SOL_AWC .sol Available water capacity of soil (mm H2O/mm soil) R SOL_K .sol Saturated hydraulic conductivity (mm/h) R SOL_BD .sol Moist bulk density (g/cm3) R CN2 .mgt Initial SCS runoff curve number for moisture condition II R ALPHA_BF .gw Baseflow alpha factor V GW_DELAY .gw Groundwater delay time (days) V GW_REVAP .gw Groundwater "revap" coefficient V GWQMN .gw Threshold depth of water in the shallow aquifer required for return flow to occur (mm H2O) V SURLAG .bsn Surface runoff lag time V SFTMP .bsn Snowfall temperature (°C) V HRU_SLP .hru Average slope stepness (m/m) V SLSUBBSN .hru Average slope length (m) V ESCO .hru Soil evaporation compensation factor. V
Table 3 Parameter sensitivities for SWAT model in daily and flood simulation.
Parameter Daily Flood t-Test p-Value Fitted Value t-test p-Value Fitted Value ALPHA_BNK 10.81 0.000 0.113 3.39 0.001 0.518 CH_N2 −9.62 0.000 0.045 −3.19 0.002 0.085 CH_K2 −6.62 0.000 2.500 −2.57 0.011 23.250 SOL_K −4.37 0.000 −0.438 −1.36 0.174 −0.567 GWQMN −3.77 0.000 1604.000 −1.88 0.061 370.000 GW_REVAP −2.49 0.014 0.049 0.17 0.862 0.087 CN2 2.25 0.026 −0.195 0.50 0.615 −0.029 SOL_AWC −2.03 0.045 −0.119 −1.18 0.241 −0.097 ESCO 1.60 0.112 0.819 1.13 0.262 0.859 SURLAG 1.23 0.221 8.935 −0.34 0.734 13.823 ALPHA_BF −1.01 0.312 0.635 −1.27 0.207 0.686 SOL_BD −0.82 0.413 −0.235 −0.36 0.721 −0.032 SLSUBBSN −0.73 0.468 114.300 −0.76 0.447 25.750 SFTMP 0.61 0.543 −0.200 1.11 0.269 0.945 HRU_SLP −0.47 0.641 0.489 1.15 0.254 0.383 GW_DELAY −0.44 0.662 227.500 −1.45 0.150 111.000
Table 4 Results of calibration and validation for XAJ and SWAT models in daily simulation.
Period Year Precipitation R2 NSE PBIAS (mm) XAJ SWAT XAJ SWAT XAJ SWAT 1991 1289 0.74 0.63 0.65 0.53 −2.58 25.81 1992 2102 0.80 0.86 0.76 0.84 −3.12 18.48 1993 1721 0.74 0.91 0.70 0.83 −2.62 18.18 Calibration 1994 1826 0.72 0.84 0.68 0.83 3.40 −11.29 1995 2067 0.72 0.86 0.7 0.83 3.54 14.46 1996 1357 0.78 0.76 0.69 0.72 −3.10 22.02 average 1727 0.75 0.81 0.70 0.76 −0.75 14.61 1997 2165 0.75 0.84 0.69 0.77 −4.62 19.68 Validation 1998 2450 0.82 0.88 0.80 0.87 8.93 −14.4 1999 1920 0.69 0.81 0.65 0.77 2.34 20.59 average 2178 0.75 0.84 0.71 0.80 2.22 8.62
Table 5 Parameter sensitivities for SWAT model in daily and flood simulation.
Period Flood Code Runoff (mm) Peak(m3/s) RRE (%) RPE (%) PTE(h) Observed XAJ SWAT Observed XAJ SWAT XAJ SWAT XAJ SWAT XAJ SWAT 910326 78.9 76.2 69.8 3180 3530 2950 3.42 11.53 −11.01 7.23 42 39 910426 49.7 46.3 20.8 1730 1757 883 6.84 58.15 −1.58 48.96 148 −3 910617 12.7 12.0 12.9 958 1132 1080 5.51 −1.57 −18.19 −12.73 −22 −24 920321 130.7 85.9 116.1 4330 4351 3580 34.28 11.17 −0.48 17.32 −6 −5 920501 37.0 35.4 38.0 2210 2553 2620 4.32 −2.70 −15.50 −18.55 134 −13 920514 81.1 69.5 74.3 6920 7933 5680 14.30 8.38 −14.64 17.92 −5 −4 920616 80.3 77.1 68.3 6300 6811 5440 3.99 14.94 −8.10 13.65 −4 −1 920704 182.3 162.9 203.5 10,700 12,157 15,700 10.64 −11.63 −13.62 −46.73 −8 −9 920831 24.3 24.6 19.5 2080 2444 1810 −1.23 19.75 −17.50 12.98 7 −6 930502 76.5 44.7 64.8 3730 3629 3140 41.57 15.29 2.71 15.82 −8 −9 Calibration 930523 96.1 94.3 81.0 2740 3254 2250 1.87 15.71 −18.77 17.88 −23 −160 930615 198.3 190.4 145.6 9710 9509 8900 3.98 26.58 2.07 8.34 −7 −11 940425 95.3 85.5 118.2 7250 7668 10,900 10.28 −24.03 −5.77 −50.34 −4 −6 940521 62.4 62.8 54.1 4000 4727 2540 −0.64 13.30 −18.16 36.50 −4 8 940614 131.1 89.3 114.6 9240 11,787 9560 31.88 12.59 −27.57 −3.46 −6 −6 950424 168.2 147.6 138 4560 5248 3660 12.25 17.95 −15.09 19.74 −5 71 950603 96.5 91.2 87.2 6650 6989 5530 5.49 9.64 −5.09 16.84 93 −2 950614 187.9 146.3 165 7650 9934 7570 22.14 12.19 −29.86 1.05 50 −7 950625 137.3 133.1 120.3 11,100 12,795 12,500 3.06 12.38 −15.27 −12.61 −7 −7 950813 40.7 47.3 26.7 4310 4912 2330 −16.22 34.40 −13.98 45.94 −2 4 960317 82.0 85.1 86.1 4140 4281 3680 −3.78 −5.00 −3.40 11.11 −3 −4 960530 81.2 81.8 50.1 4620 4910 3460 −0.74 38.30 −6.27 25.11 32 30 average 96.8 85.9 85.2 5369 6014 5262 8.78 13.06 −11.59 7.82 18 −6 970605 63.8 68.2 66.0 5760 5247 5630 −6.90 −3.45 8.91 2.26 −7 −3 970620 59.5 45.1 64.0 3820 4463 3120 24.20 −7.56 −16.83 18.32 −3 −6 970702 134.5 130.9 108.9 7770 9081 7070 2.68 19.03 −16.87 9.01 −8 −6 970808 35.9 32.6 35.4 1490 1734 2090 9.19 1.39 −16.34 −40.27 −7 −9 980215 34.9 36.5 37.5 3790 3651 3860 −4.58 −7.45 3.68 −1.85 −7 −9 980301 137.6 133.8 115.1 5810 6943 5430 2.76 16.35 −19.49 6.54 −25 −9 980509 99.3 94.1 91.5 10,400 12,358 11400 5.24 7.85 −18.83 −9.62 −7 −4 Validation 980608 253.2 220 237.7 12,100 14,675 13400 13.11 6.12 −21.28 −10.74 62 59 980619 419.1 407 368.7 21,600 23,217 29600 2.89 12.03 −7.49 −37.04 −8 −10 990415 61.9 71.3 65.7 6360 6726 6570 −15.19 −6.14 −5.75 −3.30 −3 −3 990515 63.3 67.1 58.0 3950 4240 3300 -6.00 8.37 −7.34 16.46 −13 −16 990523 94.9 95.8 85.6 7910 9018 7780 -0.95 9.80 −14.01 1.64 −10 −7 990715 80.9 62.5 64.7 4420 5239 3070 22.74 20.02 −18.54 30.54 −6 −8 990825 83.9 91.3 68.6 3990 4600 2880 -8.82 18.24 −15.28 27.82 1 −1 average 115.9 111.2 104.8 7084 7942 7514 2.88 6.76 −11.82 0.70 −3 −2
Table 6 Basic information of QLJ, JY and SJ stations.
Category Data Type QLJ JY SJ Topography Catchment Area (km2) 14800 4846 3390 Slop (%) 28.4 28.6 27.6 Elevation (m) 423 397 460 Land use Forest (%) 65.17 70.53 64.65 Pasture (%) 14.45 10.04 10.39 Agriculture (%) 19.26 18.7 23.97 Soil Haplic Acrisols (%) 60.3 55.67 67.3 Cumulic Anthrosols (%) 15.6 16.7 15.5 Humic Acrisols (%) 12.2 12.99 9.8
P.S., D.L. and F.X. developed the hydrological model; X.C., J.G. and W.Z. calculated the input data; D.L. and S.Q. analyzed the results; D.L. and S.Q. wrote the paper.
The authors declare no conflict of interest.
The study is financially supported by the National Key Research and Development Program of China (2016YFC0402703), the National Natural Science Foundation of China (No. 41371048, 51479062, 40901015), the Fundamental Research Funds for the Central Universities (2017B10914).
By Dachen Li; Simin Qu; Peng Shi; Xueqiu Chen; Feng Xue; Jianfeng Gou and Wenhao Zhang