祁连山排露沟水文动态HBV模型模拟参数检验及敏感性分析
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摘要
对流域水文动态及其过程的了解是预测和评价森林植被水源涵养功能的重要基础,而模型模拟是实现对水文动态规律描述的基本手段。然而,水文模型模拟和预测精度与模型参数的取值有很大关系,存在不确定性的问题。因此,如何选取适用于指定研究区域模型参数是确保水文模型预报精度的重要前提。
本研究将概念性半分布式模型HBV应用于祁连山黑河上游山区排露沟小流域,进行了4年(2000-2003年)逐日径流水分动态模拟。首先通过区域敏感性分析方法RSA(Regional Sensitivity Analysis),分析比较了基于传统统计、流量特征以及组合目标函数时HBV模型的参数敏感性。接着采用普适似然不确定估计GLUE(Generalized Likelihood Uncertainty Estimation)方法,利用覆盖率、中值宽度和流量累积差分析评价了HBV模型基于不同目标函数时模拟径流的不确定性。最后利用动态可识别方法DYNIA(Dynamic Identifiability Analysis)分析了模拟期间观测径流数据在识别各模型参数时信息量的变化、模型参数最优值随模拟时间的关系以及模型结构的完备性。
RSA结果表明HBV模型参数的敏感性随着选用的目标函数变化；当选取以流量为特征的目标函数以及组合目标函数时,能增加模型参数的敏感性,在一定程度上减少了模型参数的不确定性。GLUE结果表明HBV总体上可以很好地模拟排露沟流域的径流量,但低估了洪峰量。以流量为特征的目标函数以及组合目标函数时,大多数的模型参数显示了较高的识别性和低的不确定性。
DYNIA结果显示了模型参数有各自关键期,这些关键期参数的识别性较高并对模拟径流形成起着重要作用。在HBV模型中涉及到的21个参数中,共有11个参数(分别为：流域参数PCALT, TCALT;融雪模块参数CFMAX, SFCF;土壤模块参数FCshrub' LPshrub,LPgrass;响应模块参数PERC, UZL, K2;路径汇流模块参数MAXBAS)的最优值不随时间变化,在模拟过程中保持恒定值；5个参数(融雪模块参数TT；土壤模块参数FCforest,BETAshrub,BETAgrass;响应模块参数K1)的最优值变化幅度相对于各自参数的原始范围小于10%,可以认为这5个参数相对稳定其识别不随时间变化；而剩余5个参数(融雪模块参数CFR；土壤模块参数forest,BETAforest,FCgrass;响应模块参数Ko)的最优值在整个模拟过程中随着时间不断波动,难以识别率定。
综合所述,HBV模型可以很好地模拟排露沟流域出口断面的流量过程。基于水文特征的目标函数以及组合目标函数能有效增加模型参数的敏感性,减少模型参数的不确定性,可以作为模型参数率定评价时优先选取的目标函数。在21个模型参数中,共有16个参数得到了很好的识别,但CFR, LPforest,BETAforest,FCgrass,K05个参数难以有效识别,需要通过对其函数表达式作进一步修正和验证。
Understanding the catchment hydrological daynamics and processes is fundamental to predicting and evaluating the role of forest in water conservation, and model simulation is an important approach for describing the pattern of hydrological daynamics. However, the precision of simulation and prediction by using hydrological models is highly dependent of model parameterization and there exist uncertainties. Therefore, how to select the suitable model parameters for a given area is a prerequisite for assuring the accuracy of hydrological modeling and predictions.
In this study, the conceptual semi-distributed model HBV is applied in a small catchment, Pailugou, in the upper Heihe River basin in Qilian Mountains to simulate daily runoff daynamics over a period of four years (from2000through2003). Firstly, the Regional Sensitivity Analysis (RSA) is used to analyze the sensitivity of the HBV model parameters with objective functions based on statistical measures, runoff signatures, and combinations of different objective functions. Secondly, the Generalized Likelihood Uncertainty Estimation (GLUE) method with coverage, median width and cumulative runoff differences is used to analyze the uncertainty of modeled runoff. Finally, the Dynamic Identifiability Analysis (DYNIA) is used to analyze the changes in the information content of the observational runoff data when being used for identification of various model parameters, the behavours of parameters optima over the entire course of simulations, and the satisfaction of model structrues.
Results of RSA show that the parameter sensitivity varies with the objective functions used; most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used, which reduce the model parameter uncertainty to some degree. Results based on GLUE show that the HBV model is generally capable of simulating the runoff in the Pailugou catchment, although it underestimates the peak runoffs. Most parameters show higher parameter identifiability and lower model uncertainty when runoff signatures or the combined objective functions are used.
Results of DYNIA reveal that model parameters have key periods that show higher identifiability and play a crucial role in representing the predicted runoff. Among the21parameters in the HBV model, the optima for11of them (i.e. catchment parameters PCALT, TCALT; snow routine parameters CFMAX, SFCF; soil routine parameters FCshrub, LPshrub;response routine parameters PERC, UZL, K2; routing routine parameter MAXBAS) do not shift over the time domain and remain constant over the course of simulations; the optima for five parameters (i.e. snow routine parameters TT; soil routine parameters FCforest,BETAshrub, BETAgrass; response routine parameter K1) vary within10%of the original parameter ranges, indicating the possibility of a relatively stable and time-invariant parameter identification; and the optima for other five parameters (snow routine parameter CFR; soil routine parameters LPforest, BETAforest,FCgrass; response routine parameter Kl) shift over the time domain and are very difficult to identify.
In summary, the HBV model is highly capable of simulating the runoffs at the Pailugou catchment outlet. Objective functions based on runoff signatures or combinations of different objective functions can improve parameter sensitivity and reduce the model parameter uncertainty, which can be used as a priority objective function for model calibration. Sixteen out of the total21parameters are proven to be well identified, but the identifiability for five of them, including CFR, LPforest, BETAforest, FCgrass, and Kl, appears to be very difficult, suggesting that some modifications are needed for improving the model structure in applictions concerning specific catchements or watersheds.
本研究将概念性半分布式模型HBV应用于祁连山黑河上游山区排露沟小流域,进行了4年(2000-2003年)逐日径流水分动态模拟。首先通过区域敏感性分析方法RSA(Regional Sensitivity Analysis),分析比较了基于传统统计、流量特征以及组合目标函数时HBV模型的参数敏感性。接着采用普适似然不确定估计GLUE(Generalized Likelihood Uncertainty Estimation)方法,利用覆盖率、中值宽度和流量累积差分析评价了HBV模型基于不同目标函数时模拟径流的不确定性。最后利用动态可识别方法DYNIA(Dynamic Identifiability Analysis)分析了模拟期间观测径流数据在识别各模型参数时信息量的变化、模型参数最优值随模拟时间的关系以及模型结构的完备性。
RSA结果表明HBV模型参数的敏感性随着选用的目标函数变化；当选取以流量为特征的目标函数以及组合目标函数时,能增加模型参数的敏感性,在一定程度上减少了模型参数的不确定性。GLUE结果表明HBV总体上可以很好地模拟排露沟流域的径流量,但低估了洪峰量。以流量为特征的目标函数以及组合目标函数时,大多数的模型参数显示了较高的识别性和低的不确定性。
DYNIA结果显示了模型参数有各自关键期,这些关键期参数的识别性较高并对模拟径流形成起着重要作用。在HBV模型中涉及到的21个参数中,共有11个参数(分别为：流域参数PCALT, TCALT;融雪模块参数CFMAX, SFCF;土壤模块参数FCshrub' LPshrub,LPgrass;响应模块参数PERC, UZL, K2;路径汇流模块参数MAXBAS)的最优值不随时间变化,在模拟过程中保持恒定值；5个参数(融雪模块参数TT；土壤模块参数FCforest,BETAshrub,BETAgrass;响应模块参数K1)的最优值变化幅度相对于各自参数的原始范围小于10%,可以认为这5个参数相对稳定其识别不随时间变化；而剩余5个参数(融雪模块参数CFR；土壤模块参数forest,BETAforest,FCgrass;响应模块参数Ko)的最优值在整个模拟过程中随着时间不断波动,难以识别率定。
综合所述,HBV模型可以很好地模拟排露沟流域出口断面的流量过程。基于水文特征的目标函数以及组合目标函数能有效增加模型参数的敏感性,减少模型参数的不确定性,可以作为模型参数率定评价时优先选取的目标函数。在21个模型参数中,共有16个参数得到了很好的识别,但CFR, LPforest,BETAforest,FCgrass,K05个参数难以有效识别,需要通过对其函数表达式作进一步修正和验证。
Understanding the catchment hydrological daynamics and processes is fundamental to predicting and evaluating the role of forest in water conservation, and model simulation is an important approach for describing the pattern of hydrological daynamics. However, the precision of simulation and prediction by using hydrological models is highly dependent of model parameterization and there exist uncertainties. Therefore, how to select the suitable model parameters for a given area is a prerequisite for assuring the accuracy of hydrological modeling and predictions.
In this study, the conceptual semi-distributed model HBV is applied in a small catchment, Pailugou, in the upper Heihe River basin in Qilian Mountains to simulate daily runoff daynamics over a period of four years (from2000through2003). Firstly, the Regional Sensitivity Analysis (RSA) is used to analyze the sensitivity of the HBV model parameters with objective functions based on statistical measures, runoff signatures, and combinations of different objective functions. Secondly, the Generalized Likelihood Uncertainty Estimation (GLUE) method with coverage, median width and cumulative runoff differences is used to analyze the uncertainty of modeled runoff. Finally, the Dynamic Identifiability Analysis (DYNIA) is used to analyze the changes in the information content of the observational runoff data when being used for identification of various model parameters, the behavours of parameters optima over the entire course of simulations, and the satisfaction of model structrues.
Results of RSA show that the parameter sensitivity varies with the objective functions used; most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used, which reduce the model parameter uncertainty to some degree. Results based on GLUE show that the HBV model is generally capable of simulating the runoff in the Pailugou catchment, although it underestimates the peak runoffs. Most parameters show higher parameter identifiability and lower model uncertainty when runoff signatures or the combined objective functions are used.
Results of DYNIA reveal that model parameters have key periods that show higher identifiability and play a crucial role in representing the predicted runoff. Among the21parameters in the HBV model, the optima for11of them (i.e. catchment parameters PCALT, TCALT; snow routine parameters CFMAX, SFCF; soil routine parameters FCshrub, LPshrub;response routine parameters PERC, UZL, K2; routing routine parameter MAXBAS) do not shift over the time domain and remain constant over the course of simulations; the optima for five parameters (i.e. snow routine parameters TT; soil routine parameters FCforest,BETAshrub, BETAgrass; response routine parameter K1) vary within10%of the original parameter ranges, indicating the possibility of a relatively stable and time-invariant parameter identification; and the optima for other five parameters (snow routine parameter CFR; soil routine parameters LPforest, BETAforest,FCgrass; response routine parameter Kl) shift over the time domain and are very difficult to identify.
In summary, the HBV model is highly capable of simulating the runoffs at the Pailugou catchment outlet. Objective functions based on runoff signatures or combinations of different objective functions can improve parameter sensitivity and reduce the model parameter uncertainty, which can be used as a priority objective function for model calibration. Sixteen out of the total21parameters are proven to be well identified, but the identifiability for five of them, including CFR, LPforest, BETAforest, FCgrass, and Kl, appears to be very difficult, suggesting that some modifications are needed for improving the model structure in applictions concerning specific catchements or watersheds.
引文
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