水文模型不确定性分析的多准则似然判据GLUE方法

水文模型不确定性分析GLUE（Generalized Likelihood uncertainty Estimation）方法常用似然判据Nash-Sutcliffe系数（确定性系数）,这种判别标准侧重对整体过程误差的估计,对点状况如洪峰的表征不够,不能准确地反映模型的不确定性情况。本研究基于GLUE方法,建立了多准则似然判据,应用北方水库常用的大伙房模型,对碧流河水库洪水预报的不确定性进行研究,并给出预报误差分析。结果表明,将常用的确定性系数似然判据扩展为洪峰误差、洪量、峰现时间、确定性系数四个目标的多准则似然判据,能更好地反映模型的实际不确定情况,对模型参数的率定和不确定性研究具有重要意义。     

洪水预报不确定性分析及其在水库调度决策中的应用研究

洪水预报是汛期水库调度决策的重要支持,其不确定性将直接影响到水库调度运行.传统的模型参数优选方法未考虑不确定性,得到的最优参数在一些场次洪水预报中精度不高,难以满足汛期水库调度要求,需要研究洪水过程预报的不确定性辅助水库调度决策.本文基于常用的水文预报模型和水文不确定性GLUE分析方法,对洪水预报的不确定性进行分析,并将其引入水库调度决策中,建立了应用不确定性分析进行水库调度的决策模型.结果表明,考虑洪水预报不确定性,将能在期望意义上提高洪水预报水平,更好地为水库调度提供决策支持,发挥水库综合效益.     

Rejecting hydro-biogeochemical model structures by multi-criteria evaluation

This work presents a novel way for assessing and comparing different hydro-biogeochemical model structures and their performances. We used the LandscapeDNDC modelling framework to set up four models of different complexity, considering two soil-biogeochemical and two hydrological modules. The performance of each model combination was assessed using long-term (8 years) data and applying different thresholds, considering multiple criteria and objective functions. Our results show that each model combination had its strength for particular criteria. However, only 0.01% of all model runs passed the complete rejectionist framework. In contrast, our comparatively applied assessments of single thresholds, as frequently used in other studies, lead to a much higher acceptance rate of 40–70%. Therefore, our study indicates that models can be right for the wrong reasons, i.e., matching GHG emissions while at the same time failing to simulate other criteria such as soil moisture or plant biomass dynamics.     

A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model

Process based agricultural systems models allow researchers to investigate the interactions between variety, environment and management. The ‘Sugar’ module in the Agricultural Productions Systems sIMulator (APSIM-Sugar) currently includes definitions for 14 sugarcane varieties, most of which are no longer commercially grown. This study evaluated the use of two Bayesian approaches to calibrate sugarcane varieties in APSIM-Sugar: Generalized Likelihood Uncertainty Estimation (GLUE) and Markov Chain Monte Carlo (MCMC). Both GLUE and MCMC calibrations were able to accurately simulate green biomass and sucrose yield in both a theoretical and real world evaluation. In the theoretical evaluation GLUE and MCMC parameter estimates accurately reflected differences between two pre-defined sugarcane varieties. We found that the MCMC approach can be used to calibrate varieties in APSIM-Sugar based on yield data. With appropriate variety definitions, APSIM-Sugar could be used for early risk assessment of adopting new varieties.     

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。     

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。     

The sensitivity of fluvial flood risk in Irish catchments to the range of IPCC AR4 climate change scenarios

In the face of increased flood risk responsible authorities have set out safety margins to incorporate climate change impacts in building robust flood infrastructure. Using the case study of four catchments in Ireland, this study subjects such design allowances to a sensitivity analysis of the uncertainty inherent in estimates of future flood risk. Uncertainty in flood quantiles is quantified using regionalised climate scenarios derived from a large number of GCMs (17), forced with three SRES emissions scenarios. In terms of hydrological response uncertainty within and between hydrological models is assessed using the GLUE framework. Regionalisation is achieved using a change factor method to infer changes in the parameters of a weather generator using monthly output from the GCMs, while flood frequency analysis is conducted using the method of probability weighted moments to fit the Generalised Extreme Value distribution to ~ 20,000 annual maximia series. Sensitivity results show that for low frequency events, the risk of exceedence of design allowances is greater than for more frequent events, with considerable implications for critical infrastructure. Peak flows for the five return periods assessed were found to be less sensitive to temperature and subsequently to potential evaporation (PET) than to rainfall. The average width of the uncertainty range for changes in flood magnitude is greater for low frequency events than for high frequency events. In all catchments there is a progressive increase in the peak flows associated with the 5, 25, 50 and 100-year return periods when moving from the 2020s to the 2080s.