Kairong Lin Department of Water Resources and Environment Sun Yat-sen University

1. Strategic treatment of the hydrological uncertainty based on comparison of the separated runoff components Kairong Lin Department of Water Resources and Environment Sun Yat-sen University

2. Outline 1INTRODUCTION 2HYDROLOGICAL MODEL 3 RUNOFF SEPARATION METHOD 4 UNCERTAINTY ESTIMATION METHOD 5 STUDY REGIONS AND DATA 6 RESULTS AND DISCUSSIONS 7 CONCLUSION

3. 1 INTRODUCTION Hydrological Circles The upper layer factors, such as climate, the weather condition. The underlying factors, such as the geological condition, vegetation and soil condition

4. 1 INTRODUCTION Hydrological models have been widely used in the past to provide catchment management within formation on the interaction of water, energy and vegetation processes distributed over space and time. Hydrological phenomenon The determinate hydrological model The stochastic hydrological model The concept hydrological model Model’s character The systemic hydrological model Hydrological models The physical hydrological model Spatial variation of flow The lumped hydrological model The distributed hydrological model The hydrological model in period of time Time scale The monthly or daily hydrological model

5. 1 INTRODUCTION The hydrological model structure becomes more and more complicated which produces more uncertainty in order to study the environment problems more complicated, so we must consider this uncertainty seriously。 Uncertainty Hydrological phenomenon Hydrological model Model input Stochastic Fuzzy Model structure Model calibration Model applicability Precipitation Discharge Evapotranspiration Temperature

6. 1 INTRODUCTION Researches limited to the efficiency and precision of the parameters optimized arithmetic on the traditional parameter identification can’t satisfy the need of theories and practices (Beck, 1987). Widely and deeply Hydrological Model Water Resource Planning and Management Equifinality More Complex Model Structure Traditional optimal method high dimension space correlation efficiency Model Parameter sensitive less precision Uncertainty Study

7. 1 INTRODUCTION Recent years have witnessed an explosion of methods devoted to derive meaningful uncertainty bounds for hydrological model predictions (Beven and Binley, 1992; Vrugt et al., 2003; Moradkhani et al., 2005; Ajami and Duan, 2007; Li et al., 2010). • Tiwari (1978) applied Bayesian methods to the parameter identification of ecological models firstly； • RSA（Regionalized Sensitivity Analysis）of Horberger & Spear（1981） • The multi-parameter sensitivity analysis of Choi etal.(1999)； • In the 1990s, researchers have introduced the Markov Chain Monte Carlo method into the researches of parameter uncertainty； • The GLUE method which has been proposed by Beven & Binley (1992)（General Likelihood Uncertainty Estimation）。

8. 1 INTRODUCTION Prediction in Ungauged Basins (PUB) is an initiative that emerged out of discussions among IAHS members on the World-Wide Web and during a series of IAHS sponsored meetings in Maastricht (July 18-27, 2001), Kofu (March 28-29, 2002) and Brasilia (November 20-22, 2002) about the need to reduce the predictive uncertainty in hydrological science and practice. • One of the important aims is to develop new, innovative models and approaches to capture space-time variability of hydrological processes for predictions in ungauged basins, with a major reduction in predictive uncertainty as well. • The most efficient way in uncertainty reduction is to use all the available information. This paper focuses on how to reduce the hydrological uncertainty based on the TOPMODEL model, using the continuous base flow hydrograph separation method based on the Horton infiltration capacity curve to compare and analyze the separated runoff components.

9. P a i S S rz groundwater level Q uz s z i Q v Q b 2 hydrological model TOPMODEL • TOPMODEL (Beven & Kirkby, 1979) is a topographically based hydrological model that aims to reproduce the hydrological behaviour of watersheds in a semi-distributed way, in particular the dynamics of surface and subsurface contributing areas. TOPMODEL

10. 3 Runoff separation method BHSHIM • A runoff hydrograph separation method based on the Horton infiltration capacity curve (BHSHIM method) has been proposed by Lin et al. (Lin et al., 2007, published in Hydrological Processes). Fig. 1 shows the schematic diagram of the runoff process in the BHSHIM method.

11. 4 UNCERTAINTY ESTIMATION method • The generalized likelihood uncertainty estimation (GLUE) method is used to uncertainty estimation in this study. • The Nash-Sutcliffe efficiency index (R2) is selected as one of the likelihood functions, which is defined as: • To study the hydrological uncertainty reduction using comparing the separated runoff components needs to propose a new likelihood function called groundwater runoff efficiency index (Rg2 ) as another likelihood function, which is defined as:

12. 5 STUDY REGIONS AND DATA Catchment data • In this paper, Located in the upper Wulang River, a branch of the Jinshajiang River in China, the Yangping catchment is selected as the case study region .

13. 5 STUDY REGIONS AND DATA Catchment data • The Yangping catchment is mountainous, and is flourishing in natural vegetation. The data for hydrograph separation are hourly and daily rainfall and discharge covering the same period as June 1st to October 30th of 1970 to 1977. In which, the data from 1970 to 1975 are selected for the calibration of the model; and the data from 1976 to 1977 are used to validate the model. The characteristics of the Yangping catchment are shown in Table 1. Table 1 List of catchment characteristics in the study

14. 6 RESULTS AND DISCUSSIONS Runoff separation using the BHSHIM method • It includes five parameters in the BHSHIM method, i.e. the maximum soil water storage (Wm), the recession constant (KK), the coefficient of groundwater storage (K), the equilibrium infiltration rate (fc), the constant representing the rate of decrease (β). On the basis of the available hourly rainfall–runoff data in the Yangping catchment, flood events were selected to determine the parameter values; the results are shown in Table 2 (Lin et al., 2007) Table 2 List of parameter value in the BHSHIM method

15. 6 RESULTS AND DISCUSSIONS Comparison of the behavioral parameter sets • Based on the GLUE method, the uncertainty intervals of 90% confidence level are obtained by setting the likelihood function of the Nash-Sutcliffe efficiency index (R2) as 70%. Table 3 Parts of the behavioral parameter sets and associated likelihood function values

16. 6 RESULTS AND DISCUSSIONS Comparison of uncertainty intervals • The confidence interval at each time step is the major result by the GLUE method in terms of evaluations of the hydrological modelling uncertainty. In this study, interval width (IW) is adopted evaluating the uncertainty interval. IW is defined as the average width of the uncertainty intervals, which can be calculated by Discharge of the lower uncertainty bound Discharge of the upper uncertainty bound

17. 6 RESULTS AND DISCUSSIONS Comparison of uncertainty intervals • Table 4 lists the results of IW from the original and proposed methods in the calibration and validation periods. In which, the original method only sets the likelihood function of the Nash-Sutcliffe efficiency index (R2) as 70%, and the proposed method is improved by setting the likelihood function of the groundwater runoff efficiency index (Rg2) as 70% based on the original method. Table 4 Comparison of uncertainty interval width of original and proposed method • In which, RI is the percentage of IW decrease by the proposed method to the original method.

18. 7 CONCLUSION Hydrological models are based on determinate hydrological laws including physics and statistic laws, and researches on uncertainty is meaningless without researches on determinateness. Therefore, our purpose of studies on uncertainty is to reduce the uncertainty of hydrological forecast. • Based on the GLUE method, this study proposes a new method to reduce the hydrological uncertainty by using the continuous base flow hydrograph separation method based on the Horton infiltration capacity curve to compare and analysis the separated runoff components. The results show that the proposed method can reduce the uncertainty of hydrological modelling to some degree by comparing the separated runoff components.

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