Research Article  Open Access
Kairong Lin, Yanqing Lian, Yanhu He, "Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model", Mathematical Problems in Engineering, vol. 2014, Article ID 985054, 9 pages, 2014. https://doi.org/10.1155/2014/985054
Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model
Abstract
Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM). The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE) method with the shuffled complex evolution Metropolis (SCEMUA) sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high NashSutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.
1. Introduction
Since Crawford and Linsley developed the Stanford Watershed Model [1]; conceptual rainfallrunoff models have been widely used to tackle many practical and pressing issues in the planning, design, operation, and management of water resources. The successful application of hydrological models depends largely on whether or not the model is reasonably built and the selection of suitable models to represent the hydrological properties of study basins. Due to the complexity of hydrologic processes in watershed hydrology, hydrological models are often developed for specific problems and limited to the knowledge and experiences of model developers. Model uncertainty lies mainly in the inadequate knowledge and techniques and definite mathematic descriptions of the hydrological phenomena.
Model structure error associated with the mathematical representation or equation is an important cause for prediction uncertainty [2], but it is very difficult to quantify. Traditionally model calibration and validation are based on observed flow rates, while internally a number of additional states and fluxes are calculated. Many studies sought ways and measures to reduce prediction uncertainty in hydrological modeling by using other available information [3, 4]. For example, Gallart et al. used water table records to reduce the uncertainties of discharge and baseflow predictions [5]. Choi and Beven proposed a method by using multiperiod and multicriteria model conditioning to reduce the prediction uncertainty in TOPMODEL [6]. Maschio et al. dealt with uncertainty mitigation by using observed data integrated with uncertainty analysis and historymatching [7]. Schmittner et al. used isotope tracer observations to reduce the uncertainty of ocean diapycnal mixing and climatecarbon cycle projections [8]. Karasaki et al. tried to reduce the uncertainty of hydrologic models by using data from a surfacebased investigation in Hokkaido, Japan [9]. Lumbroso and Gaume used the analysis of various types of data that can be collected during postevent surveys and the consistency check to reduce the uncertainty in indirect discharge estimates [10]. Lin et al. used multisite evaluation to reduce parameter uncertainty in the Xinanjiang model within the GLUE framework [11].
Recently, Rouhani et al. adopted a graphical baseflow estimation method to calibrate and validate the SWAT (soil water assessment tool) model [12]. Ferket used a baseflow estimation method based on a physicallybased digital baseflow filter to validate the internal model dynamics of two widely used rainfallrunoff models [13]. However, most of the baseflow separation methods including the physicallybased digital baseflow separation algorithm are parametric methods [14], which often result in more uncertainty. While the smoothed minima method (SMM) [15] is a relatively objective method and is widely used in the world [14–16], which can obtain the continue baseflow processes and is easy to perform. Therefore, the aim of this paper is to study how well the uncertainty can be reduced by considering baseflow estimation information obtained from the SMM method in the Xinanjiang model, which is a conceptual model, and has been widely used in many regions of China and in some other regions of the world for flood forecasting and water resources planning and assessment [17]. The generalized likelihood uncertainty estimation (GLUE) method with shuffled complex evolution Metropolis (SCEMUA) sampling was used to analyze the uncertainties in hydrological modeling.
2. Methodology
2.1. The Xinanjiang Model
The core concept of the Xinanjiang model is to model the repletion of storage; in another word, the runoff is not produced until the soil moisture content reaches its field capacity, and thereafter the runoff equals the excessive rainfall without further loss [18]. The flow chart of the Xinanjiang model is shown in Figure 1. It can be seen from Figure 1 that the Xinanjiang model involves four major parts, that is, evapotranspiration, runoff production, runoff separation, and flow routing procedure. It is notable that two runoff components, surface runoff and groundwater flow, are used in the part of runoff separation. As listed in Table 1, there are 13 parameters in the Xinanjiang model, including four parameters (KE, , , and ) for evapotranspiration, three parameters (WM, , and IMP) for runoff production, four parameters (SM, EX, and KG) for runoff separation, and four parameters (CG, , and NK) for runoff concentration.

2.2. Model Calibration Method
One crucial step in hydrological modeling is model calibration, in which the closed values of model parameters are identified [19]. In this study, the SCEUA (shuffled complex evolution) method proposed by Duan et al. was selected to calibrate the model [20]. The NashSutcliffe efficiency coefficient was used to assess the effectiveness of model calibration. The NashSutcliffe efficiency index [21] is expressed as follows: where is the observed discharge (m^{3}/s), is the simulated discharge (m^{3}/s), and is the mean observed discharge in calibration period (m^{3}/s).
2.3. The Smoothed Minima Method
In practice, it is very difficult to separate a hydrograph into three components due to the lack of the observed data for a given basin. All available hydrograph separation methods including the SMM method used in this study attempt to separate a hydrograph into surface runoff and baseflow [14, 22, 23]. The baseflow separation procedure in the SMM method is described as follows [16].(a)Daily flows, are divided into nonoverlapping 5day blocks starting at the beginning of the daily flow time series. If is not a multiple of 5, then the final are ignored.(b)For each block, the minimum daily flow is identified, and these form the series of minima.(c)Turning points among are identified such that when the flow value is multiplied by 0.9, which is smaller than both neighbors; that is, is a turning point if and .(d)The turning points become baseflow ordinates, and baseflow values between turning points are linearly interpolated in time under the condition that the baseflow cannot exceed the total daily flow, since baseflow is part of the daily flow.
In order to compare the discharge components, an efficient index for the baseflow efficiency () is defined as where , , and denote the baseflow obtained by the smoothed minima method (SMM), the simulated baseflow from the Xinanjiang model, and the mean baseflow from the SMM method, respectively. is the size of the baseflow data.
2.4. The Uncertainty Estimated Method
GLUE is a parameter uncertainty estimation method proposed by Beven and Binley [24]. It has been widely used in many complex and nonlinear models [25, 26]. However, the Monte Carlo (MC) based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for highdimensional parameter estimation problems and in the case of complex simulation models that require significant computational time to produce the desired outputs [27]. In a separate line of research, Markov Chain Monte Carlo (MCMC) method has been developed to locate the high probability density (HPD) region of the parameter space efficiently. Therefore, Blasone et al. proposed a revised GLUE method by constructing the initial sample using the SCEMUA sampling algorithm and deriving the associated estimates of model outputs (as the median of the distribution) and uncertainty bounds (as percentiles of the output prediction) using the GLUE method [27]. The SCEMUA algorithm is a modified version of the original SCEUA global optimization algorithm [20]. This algorithm is Bayesian in nature and operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling to continuously update the proposal distribution and evolve the sampler to the posterior target distribution [28]. Blasone et al. found that the GLUE method with the SCEMUA sampling algorithm can find the behavioral simulations more efficiently [27]. A revised GLUE method with SCEMUA sampling algorithm was used to estimate the uncertainty by Lin et al. [11], which is also adopted to estimate the uncertainty in this study. The flow chart of this method is shown in Figure 2. In the SCEMUA sampling, a predefined number of different Markov Chains are initialized from the highest likelihood values of the initial population. Each chain evolves independently according to the Sequence Evolution Metropolis (SEM) algorithm, and this evolution is performed until the Gelman and Rubin convergence criteria [29] are satisfied. A detail description and explanation of this method can be found in Vrugt et al. [28]. The standard values of the parameters presented in Vrugt et al. [28] were adopted in this study.
Besides the NashSutcliffe efficiency coefficient for total flow (NE) and baseflow (), two other indices, that is, containing ratio (CR) and relative interval width (RIW), were adopted to evaluate uncertainty interval in this study. The definitions of these two indices are well introduced in the literatures [14, 24, 30–32] and can be calculated as follows: In which, where and denote the lower and upper uncertainty bounds at time , respectively. and denote the observed flow and its mean value, respectively. is the length of the series.
The NashSutcliffe efficiency index (NE) and the baseflow efficiency index () are used to evaluate the median values, MQ_{0.5}, against the observations of the total flow and baseflow.
3. Case Study: River Basin and Model Parameter Range
The Jiangkou basin was selected as case study, which is located in the upper Hanjiang River (Figure 3), which is one of the largest tributaries to the Yangtze River. The Hanjiang River is the headwater or sources water of the middle route for the SouthtoNorth water transfer in China (SNWTP). The Jiangkou basin drains 2803 km^{2} and the mean annual precipitation and runoff are 825 mm and 337 mm per unit area, respectively.
Daily rainfall and runoff data from 1980 to 1987 were used in this study. Based on the previous studies of the Xinanjiang model [18, 33, 34], the prior ranges of parameters for the Xinanjiang model were listed in Table 1.
4. Results and Discussions
4.1. Estimation of Baseflow
The Xinanjiang model for the study basin was first calibrated using the SCEUA method. The optimization parameters of the Xinanjiang model are listed in Table 2. Shown in Figure 4 are the simulated total flows, the baseflow estimated by the SMM method, and groundwater flow (QG) simulated by the Xinanjiang model. It showed that the SMM baseflow correlated well with QG.

The baseflow index (BFI), a volume ratio of baseflow to the total flow, is often used to evaluate the characteristic of baseflow. To validate the results of the SMM method, the digital filter and graphic approach were used for comparison in this study. In which, Arnold’s digital filter that used three passes of the filter and filter parameters of 0.925 [22], Spongberg’s digital filter that used two passes: forward and backward [23], and Graphical approach that adopted the oblique line separation method [35] were used to separate the baseflow in the study area. Table 3 listed the BFI values obtained by different methods. Referring to Table 3, the BFI indices for the baseflow obtained from SMM, QG calculated by the Xinanjiang model, Arnold’s digital filter, Spongberg’s digital filter, and Graphical approach were equal to 0.45, 0.46, 0.41, 0.47, and 0.49, respectively. Both Figure 4 and Table 3 showed that the baseflow obtained by the SMM method and the groundwater flow (QG) from the Xinanjiang model were comparable. Therefore, the groundwater flow (QG) was taken as baseflow obtained by the Xinanjiang model, so as to compare with the baseflow obtained by the SMM method.

4.2. Comparison of the Behavioral Parameter Sets
In order to assess the impact of baseflow simulation on model uncertainty, 18 scenarios were tested in this study, which were created by using the threshold values of 50%, 60%, and 70% for the NashSutcliffe efficiency index (NE) to be combined with no threshold and with different threshold values of 0%, 40%, 50%, 60%, and 70% for baseflow efficiency index (). The Xinanjiang model and the SCEMUA based GLUE method were used for uncertainty analysis. The total number of behavioral parameter sets for each scenario was listed in Table 4. It showed that the number of behavioral parameter sets decreased as increased.
 
* represents the scenario without threshold for the baseflow efficiency index. 
The mean and the standard deviation of behavior parameter sets and efficiency indices under different thresholds for the baseflow efficiency index were compared in Figure 5. Referring to Figure 5, the standard deviation of most behavior parameter sets decreased greatly with the increase of the threshold value of baseflow efficiency index and the same for the NashSutcliffe efficiency index and baseflow efficiency index. Results also showed that the mean of the NashSutcliffe efficiency index increased slightly with the increase of the threshold value of the baseflow efficiency index, while the mean of every behavior parameter sets varied differently. Figure 6 showed the scatter map between the NashSutcliffe efficiency index and the baseflow efficiency index when the threshold value for the NashSutcliffe efficiency index was at 70%. Referring to Figure 6, it can be seen that the high NashSutcliffe efficiency coefficients corresponded well with the high baseflow efficiency coefficients.
(a)
(b)
(c)
(d)
(a)
(b)
4.3. Comparison of Uncertainty Intervals
The results from Section 4.2 showed that baseflow has a great impact on behavioral parameter sets in hydrological modeling. This study also investigated the effect of baseflow on the uncertainty intervals in the Xinanjiang model. Four indices, that is, the containing ratio (CR), relative interval width (RIW), the NashSutcliffe efficiency index, and the baseflow efficiency index of the median value, MQ_{0.5} were selected to evaluate the efficiency of model uncertainty intervals. The uncertainty intervals of the 90% confidence level were obtained by the SCEMUAbased GLUE analysis. Table 5 compared the uncertainties evaluation of the Xinanjiang model with respect to different thresholds for the baseflow efficiency index. NE (MQ_{0.5}) and (MQ_{0.5}) in the table represented the NashSutcliffe efficiency index and the baseflow efficiency index for the median value, MQ_{0.5}, which was calculated from the uncertainty analysis by fitting the observed and simulated runoff series. Figures 7 and 8 illustrated the uncertainty intervals of total flow and baseflow for a sixmonth period in 1981 without the threshold and with the threshold value for the baseflow efficiency index at 50%, respectively.
 
* is for scenario without threshold for the baseflow efficiency index; RI is the change percentage of RIW between the scenarios with and without threshold for the baseflow efficiency index. 
(a)
(b)
(a)
(b)
As shown in Table 5, and in Figures 7 and 8, CR did not decrease by much as the threshold value increases, but there was a significant decrease for RIW, which implied the inclusion of baseflow efficiency in the proposed method can reduce model uncertainties. It can be also observed from Table 5, NE (MQ_{0.5}) and (MQ_{0.5}) increased when the thresholds of baseflow efficiency index increased, which indicated the simulated runoff from the Xinanjiang model with the behavioral parameter sets can fit the observed runoff series better when the baseflow efficiency index was considered.
5. Conclusions
Hydrologic and environmental models often face the problem with uncertainties in model results. Uncertainty reduction has both theoretical and practical importance in hydrological science. In this study, the baseflow estimated by the SMM method was used to validate the Xinanjiang model. The reduction in model uncertainty was evaluated through the GLUE method with the SCENUA sampling algorithm.
Uncertainty analysis from 18 scenarios showed that, under the same threshold of the NashSutcliffe efficiency index, the number and standard deviation of behavioral parameter sets decreased greatly with the increase of the threshold value of the baseflow efficiency index. It also showed that the inclusion of baseflow efficiency can reduce the modeling uncertainty in the Xinanjiang model and the simulated runoff from the Xinanjiang model with the behavioral parameter sets can fit the observed runoff better, which could mean the abstracted median value, MQ_{0.5}, can be improved for better runoff forecasts. This indicates that when taking the baseflow estimation information into consideration, the uncertainty in hydrological modeling can be reduced to some degree and more reasonable prediction bounds can be gained.
Furthermore, it is notable that the SMM method included in this study was just an alternative method for comparable baseflow processes to study the impact of baseflow separation on parameter uncertainty in hydrological modeling. In the future, with the development of the isotopes and distributed temperature sensing techniques, it would be possible to obtain a long enough time series of baseflow instead of the SMM result. The use of these methods, however, falls outside the scope of this paper.
Conflict of Interests
The authors declare that there is no conflict of interests regarding to the publication of this paper.
Acknowledgments
The authors would like to thank Lisa Sheppard from the Illinois State Water Survey for paper editing. The authors are grateful for Dr. Jasper A. Vrugt for developing code of SCEMUA. This study was financially supported by the National Natural Science Foundation of China (Grant no. 51379223).
References
 N. H. Crawford and R. S. Linsley, “Digital simulation in hydrology: the Stanford watershed model IV,” Tech. Rep. 39, Department of Civil Engineering, Stanford University, Palo Alto, Calif, USA, 1996. View at: Google Scholar
 J. Ewen, G. O’Donnell, A. Burton, and E. O’Connell, “Errors and uncertainty in physicallybasedrainfallrunoff modeling of catchment change effects,” Journal of Hydrology, vol. 330, pp. 509–529, 2006. View at: Google Scholar
 D. Goodman, “Extrapolation in risk assessment: improving the quantification of uncertainty, and improving information to reduce the uncertainty,” Human and Ecological Risk Assessment, vol. 8, no. 1, pp. 177–192, 2002. View at: Publisher Site  Google Scholar
 S. Uhlenbrook and A. Sieber, “On the value of experimental data to reduce the prediction uncertainty of a processoriented catchment model,” Environmental Modelling and Software, vol. 20, no. 1, pp. 19–32, 2005. View at: Publisher Site  Google Scholar
 F. Gallart, J. Latron, P. Llorens, and K. Beven, “Using internal catchment information to reduce the uncertainty of discharge and baseflow predictions,” Advances in Water Resources, vol. 30, no. 4, pp. 808–823, 2007. View at: Publisher Site  Google Scholar
 H. T. Choi and K. Beven, “Multiperiod and multicriteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework,” Journal of Hydrology, vol. 332, no. 34, pp. 316–336, 2007. View at: Publisher Site  Google Scholar
 C. Maschio, D. J. Schiozer, M. A. B. Moura Filho, and G. G. Becerra, “A methodology to reduce uncertainty constrained to observed data,” SPE Reservoir Evaluation and Engineering, vol. 12, no. 1, pp. 167–180, 2009. View at: Publisher Site  Google Scholar
 A. Schmittner, N. M. Urban, K. Keller, and D. Matthews, “Using tracer observations to reduce the uncertainty of ocean diapycnal mixing and climatecarbon cycle projections,” Global Biogeochemical Cycles, vol. 23, no. 4, pp. 19–32, 2009. View at: Publisher Site  Google Scholar
 K. Karasaki, K. Ito, Y. Wu et al., “Uncertainty reduction of hydrologic models using data from surfacebased investigation,” Journal of Hydrology, vol. 403, no. 12, pp. 49–57, 2011. View at: Publisher Site  Google Scholar
 D. Lumbroso and E. Gaume, “Reducing the uncertainty in indirect estimates of extreme flash flood discharges,” Journal of Hydrology, vol. 414, pp. 16–30, 2012. View at: Publisher Site  Google Scholar
 K. Lin, P. Liu, Y. He, and S. Guo, “Multisite evaluation to reduce parameter uncertainty in a conceptual hydrological modeling within the GLUE framework,” Journal of Hydroinformatics, vol. 16, no. 1, pp. 60–73, 2014. View at: Publisher Site  Google Scholar
 H. Rouhani, P. Willems, G. Wyseure, and J. Feyen, “Parameter estimation in semidistributed hydrological catchment modelling using a multicriteria objective function,” Hydrological Processes, vol. 21, no. 22, pp. 2998–3008, 2007. View at: Publisher Site  Google Scholar
 B. V. A. Ferket, B. Samain, and V. R. N. Pauwels, “Internal validation of conceptual rainfallrunoff models using baseflow separation,” Journal of Hydrology, vol. 381, no. 12, pp. 158–173, 2010. View at: Publisher Site  Google Scholar
 K. Lin, S. Guo, W. Zhang, and P. Liu, “A new baseflow separation method based on analytical solutions of the Horton infiltration capacity curve,” Hydrological Processes, vol. 21, no. 13, pp. 1719–1736, 2007. View at: Publisher Site  Google Scholar
 Institute of Hydrology, “Low flow studies,” Research Report 1, Institute of Hydrology, Wallingford, UK, 1980. View at: Google Scholar
 D. Mazvimavi, A. M. J. Meijerink, and A. Stein, “Prediction of base flows from basin characteristics: a case study from Zimbabwe,” Hydrological Sciences Journal, vol. 49, no. 4, pp. 703–715, 2004. View at: Publisher Site  Google Scholar
 S. Yang, G. Dong, D. Zheng, H. Xiao, Y. Gao, and Y. Lang, “Coupling Xinanjiang model and SWAT to simulate agricultural nonpoint source pollution in Songtao watershed of Hainan, China,” Ecological Modelling, vol. 222, no. 20–22, pp. 3701–3717, 2011. View at: Publisher Site  Google Scholar
 R. Zhao and X. Liu, “The Xinanjiang model,” in Computer Models of Watershed Hydrology, V.P. Singh, Ed., Water Resources Publication, Highlands Ranch, Colo, USA, 1995. View at: Google Scholar
 S. Sorooshian and V. K. Gupta, “Model calibration,” in Computer Models of Watershed Hydrology, V. P. Singh, Ed., Water Resources, Highlands Ranch, Colo, USA, 1995. View at: Google Scholar
 Q. Duan, S. Sorooshian, and V. Gupta, “Effective and efficient global optimization for conceptual rainfallrunoff models,” Water Resources Research, vol. 28, no. 4, pp. 1015–1031, 1992. View at: Publisher Site  Google Scholar
 J. E. Nash and J. V. Sutcliffe, “River flow forecasting through conceptual models part I—a discussion of principles,” Journal of Hydrology, vol. 10, no. 3, pp. 282–290, 1970. View at: Publisher Site  Google Scholar
 J. G. Arnold, P. M. Allen, R. Muttiah, and G. Bernhardt, “Automated baseflow separation and recession analysis techniques,” Ground Water, vol. 33, pp. 1010–1018, 1995. View at: Google Scholar
 M. E. Spongberg, “Spectral analysis of base flow separation with digital filters,” Water Resources Research, vol. 36, no. 3, pp. 745–752, 2000. View at: Publisher Site  Google Scholar
 K. Beven and A. Binley, “The future of distributed models: model calibration and uncertainty prediction,” Hydrological Processes, vol. 6, no. 3, pp. 279–298, 1992. View at: Publisher Site  Google Scholar
 C. E. McMichael, A. S. Hope, and H. A. Loaiciga, “Distributed hydrological modelling in California semiarid shrublands: MIKE SHE model calibration and uncertainty estimation,” Journal of Hydrology, vol. 317, no. 34, pp. 307–324, 2006. View at: Publisher Site  Google Scholar
 Y. Jung and V. Merwade, “Uncertainty quantification in flood inundation mapping using generalized likelihood uncertainty estimate and sensitivity analysis,” Journal of Hydrologic Engineering, vol. 17, no. 4, pp. 507–520, 2012. View at: Publisher Site  Google Scholar
 R. Blasone, J. A. Vrugt, H. Madsen, D. Rosbjerg, B. A. Robinson, and G. A. Zyvoloski, “Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling,” Advances in Water Resources, vol. 31, no. 4, pp. 630–648, 2008. View at: Publisher Site  Google Scholar
 J. A. Vrugt, H. V. Gupta, W. Bouten, and S. Sorooshian, “A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters,” Water Resources Research, vol. 39, no. 8, p. 1201, 2003. View at: Google Scholar
 A. Gelman and D. B. Rubin, “Inference from iterative simulation using multiple sequences,” Statistical Science, vol. 7, pp. 457–472, 1992. View at: Google Scholar
 A. Montanari, “Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfallrunoff simulations,” Water Resources Research, vol. 41, no. 8, Article ID W08406, 2005. View at: Publisher Site  Google Scholar
 L. Xiong and K. M. O'Connor, “An empirical method to improve the prediction limits of the GLUE methodology in rainfallrunoff modeling,” Journal of Hydrology, vol. 349, no. 12, pp. 115–124, 2008. View at: Publisher Site  Google Scholar
 K. Lin, Q. Zhang, and X. Chen, “An evaluation of impacts of DEM resolution and parameter correlation on TOPMODEL modeling uncertainty,” Journal of Hydrology, vol. 394, no. 34, pp. 370–383, 2010. View at: Publisher Site  Google Scholar
 R. Zhao, Y. Zhang, and L. Fang, “The Xinanjiang model,” IAHS Publications, vol. 129, pp. 351–356, 1980. View at: Google Scholar
 R.J. Zhao, “The Xinanjiang model applied in China,” Journal of Hydrology, vol. 135, no. 1–4, pp. 371–381, 1992. View at: Publisher Site  Google Scholar
 R. K. Linsley, M. A. Kohler, and J. L. H. Paulhus, Hydrology for Engineers, McGrawHill, 1982.
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Copyright © 2014 Kairong Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.