基于GLUE的新安江模型参数不确定性分析及应用

结合新安江模型在东洋河流域的应用,提出了基于GLUE方法的新安江模型参数不确定性分析。采用GLUE算法抽样结果对东洋河流域进行不确定性预报,选用水文模拟中常用的确定性系数作为似然判据,通过设定0.7为阀值,得到的90%置信区间的流量过程,实例研究表明,以该结果进行不确定预报是可行的。     

GLUE法分析水文模型参数不确定性的研究

应用新安江模型,采用普适似然不确定估计(GLUE)方法,对梁辉水库洪水预报中参数的不确定性问题进行研究并对预报误差进行分析,结果发现,模型参数中存在大量的"异参同效"参数组,即存在许多可接受的似然估计值的参数值组合.据此,将参数分为敏感性参数和不敏感性参数两大类.深入了解新安江模型参数的意义及其敏感性问题为今后流域水文模拟提供参考,以助于新安江模型参数的率定和模型的推广应用.     

基于Copula-GLUE的新安江模型次洪参数不确定性分析

针对目前洪水预报存在的问题,引入Copula函数描述参数之间相关性,结合GLUE方法进行参数的不确定模拟,提出一种基于Copula-GLUE的新安江模型次洪参数不确定性分析的新方法。湖北陆水水库流域和浙江白水坑水库流域应用结果表明,在同样的采样次数下,相比于传统的GLUE方法,Copula-GLUE方法能够生成更多的有效参数组,避免了GLUE方法的不足,减小了由于参数不确定性导致的误差,从而提高了洪水预报的精度。     

基于GLUE的流域水文模型参数不确定性研究

通过对三水源新安江水文模型参数分析,针对模型中的自由水蓄水容量、壤中流消退系数、地下水消退系数和河网消退系数4个敏感参数,以淮河息县流域为例,应用基于Bayesian理论的GLUE方法对参数进行不确定性分析。结果表明,GLUE方法以确定性系数0.85为临界似然判据,得到90%置信区间的洪水过程概率分布,能够很好地模拟实况流量过程。     

GLUE方法分析新安江模型参数不确定性的应用研究

采用普适似然不确定估计（GLUE）方法,研究新安江流域水文模型参数的不确定性问题。通过江西乐安河流域资料的模拟分析表明,模型参数间存在“异参同效”现象,即存在许多可接受的似然估计值的参数值组合。利用这些参数组的概率分布,可以对模型模拟的不确定性范围进行分析。     

水文模型模拟水文极值的不确定性分析

以黄河源区为研究区,运用GLUE法分析了HBV模型和新安江模型模拟水文极值的不确定性。目标似然函数阈值分别选用0.7、0.6、0.5,将得到的洪水和枯水水文极值以及选出的模拟结果按丰水年、平水年和枯水年过程分别进行分析。结果表明:两个模型均擅长于模拟洪水,而且对洪水模拟的不确定性较对枯水模拟的不确定性低;从两个水文模型模拟日过程估计区间的差别看,HBV模型估计区间与实测相比,洪水年总体偏低,枯水年总体偏高,平水年不存在明显趋势,新安江模型则不存在这个特点;在相同的参数采样方法和策略下,HBV模型的不确定性比新安江模型更显著;两个水文模型对枯水指标Q_(90)和Q_(75)模拟的不确定性都较大;新安江模型对洪水指标Q_(25)和Q_(10)的估计区间小于HBV模型的,不确定性水平较低。     

新安江模型参数变化对流域降雨径流过程的影响分析

水文模型可以模拟自然流域的降雨径流过程,在降雨径流过程模拟中,水文模型参数的选择往往会影响模型对流域径流过程的模拟效果.针对新安江模型,以陕西黑河金盆水库流域为研究流域,分析新安江模型参数变化对模拟流域降雨径流过程的影响,以确定新安江模型参数的敏感程度,从而服务于研究流域的汛期水文预报参数修正.模拟结果表明:在研究流域新安江模型参数中KKSS、KKG、WLM、WDM、kg、k为敏感参数,对模拟流域降雨径流过程的影响较大;参数WUM、IMP、B、c、SM、EX为不敏感参数,对模拟流域降雨径流过程影响较小.     

水文模型不确定性研究方法耦合分析

为了探索解决在提高区间覆盖率的同时降低预测区间平均带宽的问题,将dual EnKF方法与GLUE方法2种水文模型不确定性研究方法进行了耦合分析,得到了dual E-G方法。以新安江模型为例,采用3个评价指标(覆盖率、平均带宽和平均不对称性)评价了dual E-G方法在3个流域(甲河流域、天河流域、灞河流域)的预测区间性质,并与GLUE方法进行了对比。结果表明：dual E-G方法可以同时改善3种不确定性指标。该成果可为水文模型预报、洪水决策风险分析等方面的深入研究提供有益参考。     

基于MCMC的新安江模型参数不确定性分析及应用

针对新安江模型参数不确定性分析过程中出现的参数多、收敛慢、计算负担重等问题，提出基于MCMC方法的新安江模型参数不确定性分析，该方法通过Markov链MonteCarlo抽样方法求解得到模型各参数后验分布，显著提高了算法的计算速度和求解质量。参数后验分布结果为区间预报提供了条件，实例研究表明，以该结果进行不确定预报是可行的。     

基于不同目标函数GLUE方法参数不确定性研究

水文模型在防洪预报中发挥着重要作用,然而由于模型参数具有不确定性,即使有实测的降雨资料,也可能会产生较大的预报误差。针对参数的不确定性问题,以半湿润的陈河流域为研究区域,基于单目标GLUE方法和多目标GLUE方法,分析不同目标函数GLUE方法对实测径流的模拟能力以及参数的敏感性。研究结果表明,参数可分为敏感性参数(SM,CS)和非敏感性参数(K,KG,KI,CG,CI),不同的似然目标函数对径流具有不同的模拟能力,相对于单目标函数,多目标函数预报的不确定性范围较大,区间覆盖率较大,洪水预报的精度更高。     

Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows

A growing number of investigations on uncertainty quantification for hydrological models have been widely reported in past years. However, limited studies are found on uncertainty assessment in simulating streamflow extremes so far. This article presents an intercomparison of uncertainty assessment of three different well-known hydrological models in simulating extreme streamflows using the approach of generalized likelihood uncertainty estimation (GLUE). Results indicate that: (1) The three modified hydrological models can reproduce daily streamflow series with acceptable accuracy. When the threshold value used to select behavioral parameter sets is 0.7, XAJ model generates the best GLUE estimates in simulating daily flows. However, the percentage of observations contained in the calculated 95 % confidence intervals (P-95CI) is low (<50 %) when simulating the high-flow index (Q10). (2) Decreasing average relative length (ARIL), P-95CI and increasing average asymmetry degree (AAD) are found, when the threshold value increases for both daily-flows and high-flows. However, there is a significant inconsistence between sensitivity of daily-flows and high-flows to various threshold values of the likelihood function. Uncertainty sources from parameter sets, model structure and inputs collectively accounts for above sensitivity. (3) The best hydrological model in simulating daily-flows is not identical under different threshold values. High P-95CIs of GLUE estimate for high-flows (Q10 and Q25) indicate that TOPMODEL generally performs best under different threshold values, while XAJ model produces the smallest ARIL under different threshold values. The results are expected to contribute toward knowledge improvement on uncertainty behaviors in simulating streamflow extremes by a variety of hydrological models.     

基于GLUE的垂向混合产流模型参数不确定性分析

水文模型能将复杂的水文现象和过程进行概化,其不确定性研究已成为模型研究的重要方向之一。以半湿润地区洛河流域为研究区域,构建了基于垂向混合产流模型的流域产流模拟模型,采用GLUE方法并结合5项相关指标对构建的模型进行不确定性分析。结果表明:使用GLUE方法对垂向混合产流模型进行不确定性评估和参数优选过程中,在置信度为95%的前提下,模型预报区间的覆盖率达到87.5%,对称度S和T接近于0,数据模拟对称度良好。     

通过流量放大减小水文模拟与预报不确定性的应用研究

应用TOPMODEL模型,采用普适似然不确定估计(GLUE)方法,以属于半干旱地区的东湾流域为例,探讨径流系数在水文模拟与预报中的影响作用。通过其影响来提高径流系数较低地区的模拟精度,减小水文模拟与预报的不确定性。研究发现,模拟精度随着径流系数增大而提高,并在径流系数为0.5附近达到峰值后逐渐降低。基于此通过流量放大的改进方法进行东湾流域的水文模拟,改进后的结果有较大改善。     

分布式水文模型结合气象预报方法初步探讨

为了尽量消除因流域空间非均一性引起的水文模拟不确定性,采用基于GBHM分布式水文模型以及具有明确物理意义的模型参数,利用三峡区间2011年5~6月期间的气象预报信息,探讨该区域实时洪水预报方法,以及不同预见期的洪水预报精度。结果表明,分布式水文模型与气象预报数据结合,能够较好地模拟该区间的洪水过程。该方法在一定预见期内能够对实时洪水过程进行预报,预报精度很大程度上取决于降水预报的准确性。     

贝叶斯概率水文预报研究进展与展望

水文预报不可避免地存在着输入、水文模型参数和结构等不确定性,导致预报结果也具有不确定性。因此,定量估计水文预报的不确定性,实现概率水文预报,不仅可得到比确定性预报更高的精度,而且还能为决策者提供更丰富的预报信息。本文根据不确定性来源的不同,从输入资料、模型结构、模型参数和综合不确定性等方面,详细综述了贝叶斯水文概率预报的研究进展,归纳了精度评定指标和效果检验方法,并展望了贝叶斯概率水文预报未来的研究重点和方向:(1)科学有效地解释、沟通和传播水文预报不确定性信息和概率水文预报产品;(2)建立水文集合概率预报框架,估计并降低水文预报的总不确定性;(3)开展考虑预报变量时空相关性的贝叶斯概率水文预报研究;(4)深入推动概率水文预报信息在风险决策中的应用。     

模糊神经网络水文预报模型在洺河流域的应用

本文选取雨强距离指数(IPD)及其他有关参数等作为自适应神经模糊推理系统(ANFIS)水文预报模型的输入,提出雨强距离指数的定义及其目的和意义。为剖析雨强距离指数在水文预报模型中的应用,分别建立不包含雨强距离指数的模型A与包含该指数的模型B进行对比评价。结果表明,将雨强距离指数作为ANFIS水文预报模型的输入能够提高模型的预报精度。     

Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes

Input error is one of the main sources of uncertainty in hydrological models. It mainly comes from the uncertainty of precipitation data, which is caused by inaccurate measurement at the point scale and imperfect representation at the regional scale. The structural error of the hydrological model is dependent on the input, and the uncertainty interaction between the model input and structural will increase the cumulative error of the hydrological process. Therefore, the objective of this study is to investigate the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes by setting nine input levels of rain gauge stations using three hydrological models (i.e., HyMod, XAJ and HBV). The variance decomposition method based on subsampling was used to dynamically quantify the contribution rates of rain gauge station input levels, hydrological models, and their interaction to the runoff simulation uncertainty. The results show that different rain gauge station input levels and hydrological models dynamically affected the hydrological simulation due to an uneven spatiotemporal distribution of precipitation. Moreover, the simulation accuracy was poor at low flow but better at high flow. Increasing the number of rainfall stations input under a certain threshold could significantly improve the hydrological simulation accuracy. In addition, the contributions of the uncertainties of the rain gauge station input levels and its interaction with the hydrological model to runoff were significantly enhanced in the flood season, but the contribution of the hydrological model uncertainty was still dominant. The results of this study can provide a decision-making basis and scientific guidance for the management and planning of water resources within basins under the influence of a changing environment.

     

Parameter identification and global sensitivity analysis of Xin’anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters’ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.     

SWAT模型径流模拟的校正与不确定性分析

为比较SUFI-2和GLUE两种常用的SWAT模型校正与不确定性分析方法的优劣,以三峡库区大宁河流域为研究对象,应用Arc SWAT 2012对大宁河流域进行径流模拟,并分别基于SUFI-2和GLUE方法进行校正和不确定性分析,进而从模型模拟结果的不确定性、拟合效果、运算效率和参数不确定性分析等方面对SUFI-2和GLUE进行比较。结果表明:SUFI-2和GLUE都可以较好地应用于大宁河流域SWAT模型的校正和不确定性分析,但SUFI-2方法能通过较少的模拟次数获得更小的模拟不确定性带以及更佳的拟合效果,而GLUE方法在参数不确定性分析方面的表现更佳。对于SWAT模型径流模拟,建议用SUFI-2方法进行校正和模拟结果不确定性分析,用GLUE方法进行参数不确定性分析。