生物完整性指数(index of biotic integrity,IBI)是用以度量区域生物集群维持物种组成、多样性、结构和功能稳态能力的量化指标,经过30多年的发展,已成为水生态健康定量评价的热门方法。IBI是将具有不同敏感性的多项度量指标复合而得的...生物完整性指数(index of biotic integrity,IBI)是用以度量区域生物集群维持物种组成、多样性、结构和功能稳态能力的量化指标,经过30多年的发展,已成为水生态健康定量评价的热门方法。IBI是将具有不同敏感性的多项度量指标复合而得的一个数值,其理论基础是生态学与数学,涉及生物学和环境科学等其他多门学科。IBI作为一种定量分析方法,其理论技术体系仍在不断发展演化,关键技术环节为参照位点选取、度量指标筛选以及指标赋权和复合,各环节的实现存在多种观点和方法。基于大量监测数据的预测模型研究是目前国际学界的研究热点,但我国学界尚未见IBI预测模型的研究报道。除了传统的F-IBI(鱼类IBI)、B-IBI(底栖动物IBI)、A-IBI(固着藻类IBI)、P-IBI(浮游生物IBI)和AP-IBI(水生植物IBI),已有学者提出M-IBI(微生物IBI),基于上述单类群IBI(s-IBI)的研究成果,多类群IBI(m-IBI)将成为今后重要的研究方向。IBI的应用目的可分为水生态健康定量评价、水生态对人类干扰响应的定量分析和预测水生态健康状况。认为IBI具有定量化、对象依赖性、学科交叉性、标准化趋势和系统误差性的特点,IBI在农村河道、灌区和农田生态健康评价领域是一种极具前景的方法。展开更多
Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions b...Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.展开更多
The probability of quantitative precipitation forecast(PQPF)of three Bayesian Model Averaging(BMA)models based on three raw super ensemble prediction schemes(i.e.,A,B,and C)are established,which through calibration of...The probability of quantitative precipitation forecast(PQPF)of three Bayesian Model Averaging(BMA)models based on three raw super ensemble prediction schemes(i.e.,A,B,and C)are established,which through calibration of their parameters using 1-3 day precipitation ensemble prediction systems(EPSs)from the China Meteorological Administration(CMA),the European Centre for Medium-Range Weather Forecasts(ECMWF)and the National Centers for Environmental Prediction(NCEP)and observation during land-falling of three typhoons in south-east China in 2013.The comparison of PQPF shows that the performance is better in the BMA than that in raw ensemble forecasts.On average,the mean absolute error(MAE)of 1 day lead time forecast is reduced by 12.4%,and its continuous ranked probability score(CRPS)of 1-3 day lead time forecast is reduced by 26.2%,respectively.Although the amount of precipitation prediction by the BMA tends to be underestimated,but in view of the perspective of probability prediction,the probability of covering the observed precipitation by the effective forecast ranges of the BMA are increased,which is of great significance for the early warning of torrential rain and secondary disasters induced by it.展开更多
文摘生物完整性指数(index of biotic integrity,IBI)是用以度量区域生物集群维持物种组成、多样性、结构和功能稳态能力的量化指标,经过30多年的发展,已成为水生态健康定量评价的热门方法。IBI是将具有不同敏感性的多项度量指标复合而得的一个数值,其理论基础是生态学与数学,涉及生物学和环境科学等其他多门学科。IBI作为一种定量分析方法,其理论技术体系仍在不断发展演化,关键技术环节为参照位点选取、度量指标筛选以及指标赋权和复合,各环节的实现存在多种观点和方法。基于大量监测数据的预测模型研究是目前国际学界的研究热点,但我国学界尚未见IBI预测模型的研究报道。除了传统的F-IBI(鱼类IBI)、B-IBI(底栖动物IBI)、A-IBI(固着藻类IBI)、P-IBI(浮游生物IBI)和AP-IBI(水生植物IBI),已有学者提出M-IBI(微生物IBI),基于上述单类群IBI(s-IBI)的研究成果,多类群IBI(m-IBI)将成为今后重要的研究方向。IBI的应用目的可分为水生态健康定量评价、水生态对人类干扰响应的定量分析和预测水生态健康状况。认为IBI具有定量化、对象依赖性、学科交叉性、标准化趋势和系统误差性的特点,IBI在农村河道、灌区和农田生态健康评价领域是一种极具前景的方法。
基金supported by the Special Fund for Meteorological Scientific Research in the Public Interest (Grant No. GYHY201506002, CRA40: 40-year CMA global atmospheric reanalysis)the National Basic Research Program of China (Grant No. 2015CB953703)+1 种基金the Intergovernmental Key International S & T Innovation Cooperation Program (Grant No. 2016YFE0102400)the National Natural Science Foundation of China (Grant Nos. 41305052 & 41375139)
文摘Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.
基金This research was funded by the National Key R&D Program of China(No.2017YFC1502000)the Chinese Ministry of Science and Technology Project(No.2015CB452806)+1 种基金the National Natural Science Foundation of China(Grant No.41475044)National Key Technology Research and Development Program of the Ministry of Science and Technology of China(Grant No.2015BAK10B03).We gratefully acknowledge the anonymous reviewers for spending their valuable time and providing constructive comments and suggestions on this manuscript.
文摘The probability of quantitative precipitation forecast(PQPF)of three Bayesian Model Averaging(BMA)models based on three raw super ensemble prediction schemes(i.e.,A,B,and C)are established,which through calibration of their parameters using 1-3 day precipitation ensemble prediction systems(EPSs)from the China Meteorological Administration(CMA),the European Centre for Medium-Range Weather Forecasts(ECMWF)and the National Centers for Environmental Prediction(NCEP)and observation during land-falling of three typhoons in south-east China in 2013.The comparison of PQPF shows that the performance is better in the BMA than that in raw ensemble forecasts.On average,the mean absolute error(MAE)of 1 day lead time forecast is reduced by 12.4%,and its continuous ranked probability score(CRPS)of 1-3 day lead time forecast is reduced by 26.2%,respectively.Although the amount of precipitation prediction by the BMA tends to be underestimated,but in view of the perspective of probability prediction,the probability of covering the observed precipitation by the effective forecast ranges of the BMA are increased,which is of great significance for the early warning of torrential rain and secondary disasters induced by it.
