A PARAMETER ESTIMATE FOR THE LAND SURFACE MODEL VIC WITH HORTON AND DUNNE RUNOFF MECHANISM FOR RIVER BASINS IN CHINA

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1 A PARAMETER ESTIMATE FOR THE LAND SURFACE MODEL VIC WITH HORTON AND DUNNE RUNOFF MECHANISM FOR RIVER BASINS IN CHINA ZHENGHUI XIE Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing, , China FEI YUAN, QIAN LIU Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing, , China This paper presents a parameter estimate of the land surface model VIC to simulate streamflow for river basins in China by a methodology for model parameter transfer that limits the number of basins requiring direct calibration, where the new surface runoff parameterization that represents both Horton and Dunne runoff generation mechanisms with the framework of considering subgrid spatial scale soil heterogeneity in VIC is applied. The mainland area of China is represented by 4355 cells with a resolution of km 2 for each cell and grouped into climate zones. Initially, some of model parameters were calibrated for nine catchments, and those for the nine catchments were transferred within the climate zones. The transferred parameters were then used to simulate the water balance in six other catchments and river basins in China. The simulated daily runoff of VIC-3L with transferred and un-calibrated parameters is routed to the outlets of the catchments, and compared to the monthly-observed streamflow at the related gauge stations. As a whole, the parameter transfer approach reduced the bias and relative root mean squared error (RRMSE) and increased the Nash-Sutcliffe model efficiency coefficients for each individual catchment, and the parameter transfer scheme improved the streamflow simulation. Subsequent recalibration of all basins further enhanced the modeling performance. Results show that the model for the transferred parameters can simulate the observations accurately. INTRODUCTION As a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model [1,2,3,4] has been used to simulate runoff over large river basins. One practical problem in application of the model in river basins in China is the determination of model parameters. Studies have shown that land surface models could perform well if their model parameters were appropriately estimated on the basis of calibrations with observations but perform poorly if their model parameters are not calibrated properly. To better apply the VIC model to simulate streamflow for river basins in China, we present a parameter estimation of the land surface model VIC by a methodology of model

2 parameter transfer that limits the number of basins requiring direct calibration, where the new surface runoff parameterization that represents both Horton and Dunne runoff generation mechanisms with the framework of considering sub-grid spatial scale soil heterogeneity is applied. MODEL IMPLEMENTATION AND DATA SOURCES VIC-3L model Liang et al. [1] developed a two-layer Variable Infiltration Capacity (VIC-2L) model which includes two different time scales (i.e., fast and slow) for runoff to capture the dynamics of runoff generation. To better represent quick bare soil evaporation following small summer rainfall events, a thin soil layer is included in VIC-2L [2], and VIC-2L becomes VIC-3L. Liang and Xie [3] developed a new parameterization to represent the infiltration excess runoff mechanism in VIC-3L and combined it effectively with the original representation of saturation excess runoff mechanism [4]. To apply the VIC-3L including the new surface runoff mechanism to river basins in China, the model parameters have to be determined. Data and model parameters The VIC-3L model requires three types of data sets, which are vegetation, soil, and forcing data. In this study, vegetation, soil, and forcing data needed to apply VIC-3L are prepared at km 2 resolution for the river basins in China. Vegetation data sets are derived based on AVHRR [5] and LDAS information. Soil parameter sets are deduced from the NOAA global 5-min soil data and the work of Cosby et al. [6] and Rawls et al. [7]. For detailed information about thevic-3l vegetation and soil parameters, please refer to Su and Xie [8]. The forcing data are obtained through interpolation methods (minimum distance method and inverse distance square method) based on 740 meteorological stations, which contain 11 years of daily precipitation and air temperature data from 1980 to Some of forcings for two gauge stations are from 1990 to CALIBRATION AND PARAMETER TRANFER Before conducting numerical simulations, some model parameters of VIC-3L need to be calibrated since they cannot be determined well based on the available soil information. These are the depths of three soil layers (D 1, D 2 and D 3 ), the exponent (B) of the VIC-3L soil moisture capacity curve which describes the spatial variability of the soil moisture capacity, and the three parameters in the ARNO subsurface flow parameterization (i.e., D m, D s and W s ) [1]. Classification of climate zones Climatic characteristics were selected as the basis for the transfer of calibrated parameters 2

3 under the premise that hydrological processes and the parameters used to describe them are more similar within than between different climate zones. Based on the forcing data, the mainland area of China was grouped into six climate zones according to Köppen classification rules [9]. Figure 1 shows the main river basins in China and the climate zones. Figure 1. The river basins in China, and climate zones of China Table 1. Selected river basins River basin Catchment Predominant climate zones Area(km 2 ) upstream of gauge Category 1 Yellow River Qinan Continental climate with cool summer 9,805 Nanhechuan Continental climate with cool summer 1,3580 Haihe River Xiahui Continental climate with hot summer 5,340 Xiabao Continental climate with hot summer 4,040 Yangtze River Wuhouzhen Rainy, mid latitude climate 3,092 Madao Rainy, mid latitude climate 3,415 Huaihe River Xixian Rainy, mid latitude climate 10,190 Bantai Rainy, mid latitude climate 11,280 Heihe River Zhamashike Continental climate with short cool summer 4,589 Category 2 Yellow River Yangjiaping Continental climate with cool summer 14,124 Haihe River Xiangshuibao Continental climate with hot summer 14,140 Yangtze River Hanzhong Rainy, mid latitude climate 9,329 Chadianzi Rainy, mid latitude climate 1,683 Huaihe River Luohe Rainy, mid latitude climate 12,150 Heihe River Yingluoxia Continental climate with short cool summer 10,009 Selected catchments Table 1 lists the fifteen selected catchments in China for calibration and parameter transfer. Nine catchments in category 1 in Table 1, called the primary catchments, were 3

4 calibrated to produce parameter cluster for each climate zone. We tried to select at least one catchment for each climate zone, but due to the unavailability of streamflow data for dry and cold climate zone and for tropical climate zone, no catchments were selected for these two climate zones, and parameters for these zones were set to be default values without calibration. As shown in Table 1, six catchments in category 2 (secondary catchments) were initially modeled using parameters transferred from the calibrated primary catchments. Observed and simulated streamflow for these catchments were compared to determine the effectiveness of the parameter transfer method. The secondary catchments were then recalibrated in a second stage of the study. This second stage served two purposes. First, calibration of the secondary catchments served to further evaluate the effectiveness of the parameter transfer process. Ideally, this calibration should result in minimal improvement of model results, which would indicate that the parameter transfer process was highly successful. Second, the second stage calibration ensured that the estimates of water balance components were the best possible, given the model and meteorological forcings. In a final step, the calibrated parameters from all catchments were transferred to the remaining land surface grid cells in river basins in China to allow estimation of the continental and water balance. Model calibration In this study, model calibration focused on fitting streamflow data, since other model-predicted water storage and flux components (e.g. soil water storage, snow cover, evapotranspiration) are rarely observed at spatial and temporal scales suitable for direct comparison with the output from macroscale hydrological models. Calibration was performed manually and focused on matching the total flow volume and the shape of the monthly hydrograph. Relative error (E r ) between simulated and observed mean annual runoff, and the Nash-Sutcliffe coefficient (C e ) were selected as the criteria for model. Parameter transfer scheme The infiltration parameter (B) and the depths of the three-soil layer (D 1, D 2 and D 3 ), and the ARNO model parameters D m, D s and W s were calibrated and then transferred to the river basins in China. Parameters were transferred based on climate zone. The detail transfer scheme is described as follows: (1) Two catchments in the Yellow River Basin are selected to calibrate the above model parameters, the six parameters for the two catchments are averaged respectively as the corresponding parameters for the zone of continental climate with cool summer. (2) Two catchments in the Haihe River Basin are selected to calibrate, and the parameters for the two catchments are averaged respectively as the corresponding parameters for the zone of continental climate with hot summer. (3) Because of the unavailability of enough streamflow data, only one catchment in the Heihe River Basin is selected to calibrate, and the parameters for the catchment are set to those corresponding parameters for the zone of continental climate with hot summer. (4) Most of area in the Huaihe River Basin and the Yangtze River Basin belongs to the zone 4

5 of rainy, mid latitude climate. Two catchments in the Huaihe River Basin are selected to calibrate, and these parameters for the two catchments are averaged respectively as the corresponding parameters for the zone of rainy and mid latitude climate located in the Huaihe River Basin. Two catchments in the Yangtze River Basin are also selected to calibrate, and the parameters for the two catchments are averaged respectively as the corresponding parameters for the zone of rainy and mid latitude climate located in the Yangtze River Basin. Parameters for the rainy and mid latitude climate zone north of the Huaihe River Basin and the Yangtze River Basin are set to that for the Huaihe River Basin; parameter values for the climate zone south of these two river basins are equivalent to that for the Yangtze River Basin. (5) The zone of tropical climate has similar climatic characteristics as those in rainy and mid latitude climate zone. Therefore, the parameters for the zone of tropical climate are set to be the corresponding parameters for the Yangtze River Basin. (6) Since streamflow data for the zone of dry and cold climate is not available, default values of B, D 1, D 2, D m, D s and W s for the area are set to be 0.3, 0.1, 0.5, 2.0, 0.02, 8.0, and 0.8 respectively. To evaluate the effectiveness of the parameter transfer process and to provide the best possible water balance estimates, the secondary catchments were then further calibrated after transferring the parameters from all of the calibrated catchments to the remaining land surface grid cells. SIMULATED RESULTS Primary Catchments The VIC model also provides a default parameter set, namely base case parameter set, which can be a parameter substitute when no calibration is performed. Comparisons were made between the results of base case and calibration. Figure 2 shows the observed and the simulated mean monthly hydrographs for the nine primary catchments based on base case (no calibration) and calibrated parameter sets. The model performance was considerably better for the calibration parameters than the parameters without calibration. The model in base case commonly largely overestimated the streamflows for Qinan, Nanhechuan, Xiahui and Xiabao catchments and underestimated the streamflow for Zhamashike catchment, but the modeled streamflows using calibrated parameters fitted the observed well. The model in base case and calibration case both provided good simulation results for Wuhouzhen, Madao, Xixian and Bantai catchments, while the simulated streamflows in calibration case were closer to observed ones as compared with the simulated results in base case. Table 2 lists the results for the nine primary catchments based on base case and calibration. In general, calibration improved the results in all instances, although in some cases the final calibration was still unsatisfactory, especially for arid basins such as the Haihe River, which flows through a region with strong human activities. Calibration reduced the mean bias from to 111.1% to 9.1% and the relative root mean squared error (RRMSE) from 43.9% to 8.8%. The efficient coefficients (CE) for calibration are all higher than 70% except Xia Bao station (21.8%) in Haihe river basin. 5

6 These results indicated that after calibration the VIC model could perform good streamflow simulation for the nine primary basins and the calibrated parameters could be used reasonably to transfer over the secondary catchments. Figure 2. Mean monthly hydrographs of observed and simulated flow (base case and calibrated) for the primary river basins Table 2. Calibration and parameter transfer statistics River basin Gauge Base case Parameter transfer Calibration station CE a RRMSE b Bias c CE a RRMSE b Bias c CE a RRMSE b Bias c Category 1 Yellow Qinan Nanhechuan Haihe Xiahui Xiabao Yangtze Wuhouzhen Madao Huaihe Xixian Bantai Heihe Zhamashike Category 2 Yellow Yangjiaping Haihe Xiangshuibao Yangtze Hanzhong Chadianzi Huaihe Luohe Heihe Yingluoxia a CE - Nash-Sutcliffe model efficiency coefficient: = ( ( Q, ) (,, ) ) / o i Q o Q s i Q o i ( Q o, i Q o ) 100 % with Q s, i and Q o, i the simulated and observed flow in month i. n i =1 n CE, n b 1 2 RRMSE relative root mean squared error, defined as: RRMSE = ( Q s, i Q o, i ) / Q o 100 %. n i = 1 c bias, defined as: bias ( Q Q ) / Q 100% = s o o i = 1 n i = 1 6

7 Secondary Catchments Figure 3 shows the observed and the simulated mean monthly hydrographs for the six secondary catchments in base case, parameter transfer and recalibration. For the remaining six basins, the parameter transfer process improved the simulated flow volume in four cases (Yangjiaping, Xiangshuibao, Hanzhong, and Chadianzi) with the absolute value of bias being reduced from 132.4% to 11.8%, 437.6% to 87.8%, 28.1% to 2.3% and 26.5% to 9.8% respectively, and resulted in a little worse change in two cases (Luohe, and Yinghuoxia) with the absolute value of bias being increased from 14.8% to 18% and 10.9% to 73.6%. The transferred parameters reduced the relative (monthly) RRMSE for all cases except Luohe and Yingluoxia catchments, and increased all the efficiency coefficients(ce) other than that of Luohe catchment. As a whole, the parameter transfer scheme improved the runoff simulation for the secondary catchments. Subsequent recalibration of all basins further enhanced the modeling performance. Although Xiangshuibao catchment in the Haihe River Basin was involved in intense human activities where runoff simulation was a tough task, simulation for Xiangshuibao catchment in recalibration case was still improved. The recalibrated model reduced the average RRMSE from 22.0% to 8.9% and the average absolute value of bias from 33.9% to 7.3%, and increased the mean efficiency coefficient(ce) from 86.2% to 91.0%, where the CEs of Xiangshuibao catchment were not in statistics. Figure 3. Mean monthly hydrographs of observed and simulated flow (base case, parameter transfer and recalibrated) for the secondary river basins CONCLUSIONS In this paper, a parameter transfer scheme for VIC-3L is given to simulate streamflow for river basins in China, which is represented by 4355 cells with a resolution of km 2 and was grouped by climate zone, and model parameters were transferred within zones. The transferred parameters were then used to simulate the water balance in river basins in China. The simulated daily runoff of VIC-3L with transferred parameters and un-calibrated parameters was routed to the outlets of the river basins, and compared to the monthly-observed streamflow at the related catchments. Results show that the model 7

8 for the transferred parameters can simulate the observations well and the proposed parameter transfer framework is promising in estimating the VIC model parameters for data-sparse areas in China. ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (Grant Nos ), the National Key Planning Development Project for Basic Research (Grant Nos. 2001CB309404), the Hundred Talents Program of the Chinese Academy of Sciences, and the Knowledge Innovation Key Project of Chinese Academy of Sciences (Grant No. KZCX2-SW- 317). REFERENCES [1] Liang, X., Lettenmaier, D. P., Wood, E. F. and Burges, S. J., A simple hydrological based model of land surface water and energy fluxes for general circulation models, Journal of Geophysical Research. Vol. 99, No. D7, (1994), pp 14,415-14,428. [2] Liang, X., Wood, E. F. and Lettenmaier, D. P., Surface soil moisture parameterization of the VIC-2L model: Evaluation and modifications, Global and Planetary Change, Vol. 13, (1996), pp [3] Liang, X. and Xie Z., A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models, Advances in Water Resources, Vol. 24, (2001), pp 1,173-1,193. [4] Xie Z., Su. F., Liang, X., et al., Applications of a surface runoff model with Horton and Dunne runoff for VIC, Advances in Atmospheric Sciences. Vol. 20, No.2, (2003), pp [5] Hansen, M., DeFries, R., Townshend, J. R. G. and Sohlberg, R., Global land cover classification at 1km resolution using a decision tree classifier, International. Journal of Remote Sensing, Vol. 21, (2000), pp 1,331-1,365. [6] Cosby, B. J., Hornberger, G. M., Clapp, R. B. and Ginn, T. R., A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resources Research, Vol. 20, (1984), pp [7] Rawls, W. J., Ahuja, L. R., Brakensiek, D. L. and Shirmohammadi, A., Handbook of Hydrology, McGraw-Hill Inc., (1993). [8] Su, F. and Xie Z., A model for assessing effects of climate change on runoff of China, Progress in Natural Science, Vol.13, No. 9, (2003), pp [9] Hubert, B., Francois, L., Warnant, P. and Strivay, D., Stochastic generation of meteorological variables and effects on global models of water and carbon cycles in vegetation and soils, Journal of Hydrology, Vol , (1998), pp