大型浅水湖泊沉积成岩模型不确定性与敏感性分析——以氮为例

随着太湖流域控源截污和面源整治的推行,底泥释放成为太湖不可忽视的污染源.本文基于EFDC模型构建太湖沉积成岩模型以动态模拟底泥释放过程,以氨氮和硝态氮为水质目标,采用拉丁超立方抽样抽取沉积成岩模型的18个参数进行不确定性分析,采用标准秩逐步回归法进行敏感性分析.结果表明：对于大型浅水湖泊,沉积物-水界面的硝化作用、反硝化作用和扩散过程对底泥氮的释放影响很大,太湖氮浓度的不确定性有明显的时空差异,并且受藻类生长影响;随藻类生长生化反应参数的敏感性逐渐减弱,动力参数的敏感性逐渐增强,氨氮的主要敏感参数为孔隙水扩散系数和最优硝化反应速率,贡献率分别是41.68%和37.82%,硝态氮的主要敏感参数为孔隙水扩散系数和表层反硝化作用反应速率,贡献率分别是29.15%和42.34%,这些参数的取值需予以着重考虑.本研究识别出太湖底泥氮释放的关键物化过程,为模型调参提供优先级并给出优化区间,对减小模型的不确定性、提高模型精度有参考意义,为定性指导大型浅水湖泊底泥释放的室内实验模拟提供依据.     

耦合敏感参数实时识别的新型数据同化算法研究——以湖泊藻类模拟为例

数据同化是提升复杂机理过程模型精度的关键技术之一,而湖泊藻类模型的敏感参数具有随时间动态变化的特征,导致数据同化过程中无法精准更新某一时段的敏感参数,影响数据同化的模型精度提升效果.针对上述问题,本研究耦合了参数敏感性分析与集合卡尔曼滤波,研发了一种能够实时识别模型敏感参数的新型数据同化算法;为验证研发算法的效率,依托巢湖的高频水质自动监测数据,测试算法对藻类动态模型的精度提升效果.测试结果表明：研发算法能够精准跟踪模型敏感参数的动态变化,并根据监测数据实时更新模型敏感参数,实现了水质高频自动监测数据与藻类动态模型的深度融合,藻类生物量模拟精度提升了55%,即纳什系数（NSE）从0.49提升到0.76,模拟精度提升效果也显著优于传统数据同化算法（NSE=0.63）.研发算法可应用于其它水生态环境模型的数据同化,为水生态环境相关要素的精准模拟预测提供关键技术支撑.     

贵州红枫湖越冬藻类的空间分布与实验室复苏实验

本文选取贵州省红枫湖这一典型的亚深水型湖泊作为研究对象,在8个代表性湖区开展了为期一年的表层水体藻类浮游植物分布的月定量监测,并在4个采样点采集新鲜沉积物进行了越冬藻类赋存与复苏模拟实验研究.研究表明,红枫湖表层水体藻类种群密度蓝藻绿藻>硅藻>甲藻,蓝藻为优势门类,水体藻类种群密度秋季初期最高,其次是春季初期和夏季,具有明显的季节性演化特征.水体中越冬藻类以蓝藻为主,其次是硅藻和绿藻,水深对水体中藻类的种群密度及组成没有显著影响.沉积物中越冬藻类以硅藻为主,基本不含蓝藻.模拟实验表明,水体中的光照条件对藻类的复苏和生长有重要影响,温度和沉积物中藻类的种群密度与组成同样影响藻类的复苏.     

青藏高原东北缘地震活动性广义帕累托模型的全域敏感性分析

基于广义帕累托分布构建地震活动性模型,因其输入参数取值难以避免不确定性,导致依据该模型所得的地震危险性估计结果具有不确定性。鉴于此,本文选取青藏高原东北缘为研究区,提出了基于全域敏感性分析的地震危险性估计的不确定性分析流程和方法。首先,利用地震活动性广义帕累托模型,进行研究区地震危险性估计;然后,选取地震记录的起始时间和震级阈值作为地震活动性模型的输入参数,采用具有全域敏感性分析功能的E-FAST方法,对上述两个参数的不确定性以及两参数之间的相互作用对地震危险性估计不确定性的影响进行定量分析。结果表明:地震危险性估计结果（不同重现期的震级重现水平、震级上限及相应的置信区间）对两个输入参数中的震级阈值更为敏感;不同重现期的地震危险性估计结果对震级阈值的敏感程度不同;对不同的重现期而言,在影响地震危险性估计结果的不确定性上,两个输入参数之间存在非线性效应,且非线性效应程度不同。本文提出的不确定性分析流程和方法,可以推广应用于基于其它类型地震活动性模型的地震危险性估计不确定性分析。      

武汉东湖主要湖区的藻类与营养型评价

对东湖９个湖区藻类的群落结构、生长潜力、初级生产力和营养状况进行了比较研究。结果表明,９个湖区藻类的种类组成无明显差异,绿藻为主,蓝藻和硅藻次之;藻类的生长潜力和初级生产力各湖区差异较大,均以茶港湾重污染区最高和牛巢湖最低。根据各项指标综合分析,９个湖区水质优劣的顺序是：牛巢湖、汤林湖、后湖、郭郑湖、菱角湖、筲箕湖、庙湖、喻家湖和茶港湾重污染区。对东湖的大水面郭郑湖４０年来藻类的有关参数进行比较发     

巢湖水华蓝藻原位生长率的时空变化及其环境影响因子

原位生长率是研究藻类生长、衰亡、种群演变、生产力估算,以及藻类对环境变化响应的重要指标,针对水华蓝藻原位生长率的测定,目前还缺乏成熟可靠的手段.本研究利用改进的原位培养法,根据培养前后藻蓝素浓度的变化,对巢湖东、中、西3个湖区水华蓝藻的原位生长率进行周年调查.结果表明,巢湖水华蓝藻的原位生长率变化范围在-1.16～0.69 d-1之间,表层的原位生长率最高,中层次之,底层大部分月份都为负值;原位生长率在空间分布上由高到低依次为西部湖区中部湖区东部湖区;在季节变化上,原位生长率春季(4 6月)相对较高,冬季(1 2月)相对较低.与环境因子进行相关分析后发现,原位生长率与溶解性总磷和温度正相关,这表明溶解性总磷和温度可能是影响巢湖水华蓝藻原位生长率的重要环境因子.     

热带气旋集合预报中的不确定性研究

综合考虑了数值天气预报过程中的两种不确定性：初值和模式的不确定性，建立了一个拥有20个成员的中尺度集合预报系统来模拟1997年热带气旋Danny的路径和对流系统.发现模拟气旋路径的集合平均误差在12 h以后比所有成员的误差都小.通过考察模拟结果对各种不确定性的敏感性，发现两种不确定性在模拟中都很重要,但不同的不确定性对模拟结果的贡献是不同的.初值的不确定性主要影响模式积分的前12 h，模式的不确定性在整个积分过程中始终存在.不确定性最敏感的区域主要分布在气旋附近的强天气区.     

地震资料精度和完整性对地震危险性参数的影响

针对现有地震目录中存在的不完备性问题，提出了考虑不同精度和完整性估计地震危险性参数的极大似然法．作为一个例子，计算了汾渭地震带和陕南地区的地震危险性参数．并在此基础上用单参数和多参数敏感性分析方法对参数的敏感性和结果的不确定性进行了分析和讨论．      

格点大气环流模式GAMIL2参数不确定性的量化分析与优化

物理过程参数化方案的不确定性是目前气候系统模式不确定性的重要来源之一.随着模式内在复杂度攀升,模拟场景多样化,参数化方案中基于先验的和人工的物理参数选取方法已经逐步成为限制模式模拟能力的瓶颈之一.为此,本文设计并提出了初选与寻优相结合的两步法参数优化方案.初选阶段用全因子采样方法对不确定参数空间进行初始敏感性分析,估计最优解所在区域;寻优步采用单纯型下山法,基于初选阶段确定的参数组合快速寻优.将两步法应用于中国科学院大气物理研究所(英文缩写:IAP)大气科学和地球流体力学数值模拟国家重点实验室(英文缩写:LASG)格点大气模式第2版:GAMIL2,选取其深对流方案和云量方案中的3个重要参数开展寻优,优化以综合减小模式降水、风场、温度、湿度、位势高度以及辐射通量的误差为目标.这些变量用GAMIL2标准版本标准化后形成单一的目标.结果显示,优化后的目标函数值比GAMIL2标准版本改进了7.5%.机理分析表明,调优后的参数优化了大气中的水汽凝结作用,进而减少模式的湿度偏差,改进云量的模拟效果;同时水汽凝结作用的变化通过大气内部动力和热力相互作用及响应影响温度、位势高度和风场的模拟.     

太湖水体中碱性磷酸酶的活性及磷的矿化速率

通过对2003年4月至2004年10月,太湖不同生态类型湖区水体中磷的形态、碱性磷酸酶活性及其它水环境因子的同步监测, 初步探讨了藻类不同生长时期、不同生态类型湖区水体中磷形态构成的差异及其与碱性磷酸酶活性之间的关系, 计算了水体中磷的矿化速率. 研究表明, 太湖水体中总磷的70.2%由悬浮态的颗粒磷构成, 溶解性反应磷(DRP)的含量不足7%; 而总磷中的58.2%可通过酶解生成DRP维持水体中藻类、细菌等的持续生长. 在不同生态类型湖区、以及藻类不同生长阶段, 水体中碱性磷酸酶的活性及动力学参数差异显著, 在酶的作用下, 磷的矿化速率也显示出明显的差异, 最短的磷周转时间仅需数分钟. 这种短时间尺度磷的快速循环, 在一定程度上可以解释藻类大量繁衍时, 所需磷酸盐的来源. 即当水体中DRP的浓度较低时, 藻类能通过酶解其它形态的磷,从而使得DRP得到及时的补充.     

Total and opportunistic algal cover in relation to environmental variables

Based on a large data set from the national Danish monitoring program, spatial and temporal variability in total algal cover and in the fraction of opportunistic macroalgae was analysed in relation to environmental variables. Variations in water clarity and salinity combined with information on geographical location of sampling areas were found to explain almost 80% of the large-scale variation in algal cover between areas. As water clarity was largely regulated by concentrations of total-nitrogen (TN), and TN-concentrations by TN-input from land, total algal cover at given water depths was partly related to TN-input from land. The fraction of opportunistic algae responded predominantly to differences in salinity, the highest fractions being found in the most brackish areas. Temporal variability in algal cover and fraction of opportunists over the 14-year investigation period was much smaller than the variability between areas and could not be predicted from variations in environmental variables. In order for macroalgal cover to become a more sensitive indicator of water quality it would be necessary to either increase the sensitivity of the method or identify and include supplementary regulating factors in the model.     

典型风力条件及水力调度下太湖藻华高频时空动态监测

湖泊藻华问题已成为全球水生态环境领域面临的长期挑战,风力条件变化和引调水工程的水力调度能改变湖体水动力结构,对藻类的生长和聚集过程产生影响,进行该过程的精细化监测和机制分析对于湖泊藻华预报预警和应急处置具有重要意义。本研究基于 Hiamwari-8/AHI 卫星遥感高频监测数据,对比分析了归一化差异植被指数(NDVI)、增强植被指数(EVI)和浮游藻类指数(FAI)3种不同指数对太湖藻华的反演效果,开展了典型风力条件下和水力调度下太湖藻华生消过程的持续监测分析。结果表明,FAI 对藻华区域和非藻华区域的区分更加明显,其阈值提取的藻华面积与基于 MODIS 图像解译的藻华面积的相对误差最低,为-2.27%。当营养盐充足且水温持续保持在蓝藻大量生长增殖的阈值以上时,风力条件是导致太湖藻类迁移聚集的关键因子,风向主要影响藻类的水平迁移,使其进行方向性迁移并逐渐形成大面积藻华区域。风速主要影响藻类的垂向迁移并存在临界阈值,当风速低于约2.5 m/s的临界风速时,藻华面积随风速增加而增加;当风速高于临界风速时,藻华面积随风速增加而降低。水力调度对距离较近的贡湖湾区域具有显著影响,主要通过水动力扰动来影响藻类的垂向迁移,使藻类沿水深方向强烈掺混,导致区域藻华面积下降。此外,持续且更大流量的调水将不断增强水动力扰动,减小区域藻华面积。本研究揭示了典型风力条件及水力调度下太湖藻华分布特征和迁移机理,可为湖泊藻华的精确动态监测预警和科学管控提供重要技术支撑。     

Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed

The increasing complexity of hydrological models results in a large number of parameters to be estimated. In order to better understand how these complex models work, efficient screening methods are required in order to identify the most important parameters. This is of particular importance for models that are used within an operational real‐time forecasting chain such as HQsim. The objectives of this investigation are to (i) identify the most sensitive parameters of the complex HQsim model applied in the Alpine Lech catchment and (ii) compare model parameter sensitivity rankings attained from three global sensitivity analysis techniques. The techniques presented are the (i) regional sensitivity analysis, (ii) Morris analysis and (iii) state‐dependent parameter modelling. The results indicate that parameters affecting snow melt as well as processes in the unsaturated soil zone reveal high significance in the analysed catchment. The snow melt parameters show clear temporal patterns in the sensitivity whereas most of the parameters affecting processes in the unsaturated soil zone do not vary in importance across the year. Overall, the maximum degree day factor (meltfunc_max) has been identified to play a key role within the HQsim model. Although the parameter sensitivity rankings are equivalent between methods for a number of parameters, for several key parameters differing results were obtained. An uncertainty analysis demonstrates that a parameter ranking attained from only one method is subjected to large uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

基于遥感反射率的太湖优势藻识别方法

我国淡水湖库频发水华,不同类群形成的水华特征、危害及其治理方法差异显著,因此,如何区分不同藻种的遥感反射率特征,获取湖泊优势种信息是一个亟待解决的科学问题。研究基于室内藻种培养实验,培养了富营养化湖泊中的典型蓝藻和绿藻藻种,其中,蓝藻包括铜绿微囊藻（Microcystis aeruginosa）、假鱼腥藻（Pseudanabaenasp．）和束丝藻（Aphanizomenonsp．）,绿藻包括小球藻（Chlorellasp．）以及四尾栅藻（Scenedesmus quadricauda）;基于实测的遥感反射率,经归一化处理后,分析了不同藻种的遥感反射率特征,构建了DI(difference index)指数以及ADI(algae distinguish index)指数,建立了藻种分类模型,利用验证集数据进行检验,整体识别精度达77.55%,Kappa系数为0.7178。将分类方法应用于太湖野外实测遥感反射率数据集中,结果与实测的生物量数据有较好的匹配;将模型应用于太湖OLCI(ocean and land colour instrument)影像数据,获得了2019年12月和2020...     

外源添加营养对安徽太平湖水库藻类增殖影响的研究

１９９２年１１月￣１９９３年１０月,在安徽太平湖水库,同时用藻类生长潜力测试法（ＡＧＰ试验）和外源添加营养的黑白瓶测定初级生产力法,对陵山站的水体分季度进行了分析测试。其结果表明,这两种方法对太平湖水库的营养评价均是行之有效的,而且二者的结果也能相互比较、相互验证;太平湖水库的不同季节,其主要营养限制性因子也各异。在枯水期,磷是藻类种群和密度的第一限制性营养元素;丰水期,由于各营养元素都相对缺乏,     

Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with KINEROS2 / Analyse d'incertitude et de sensibilité des simulations d'écoulement et de transport solide de KINEROS2 dans un petit bassin versant agricole

Abstract

Using the Monte Carlo (MC) method, this paper derives arithmetic and geometric means and associated variances of the net capillary drive parameter, G, that appears in the Parlange infiltration model, as a function of soil texture and antecedent soil moisture content. Approximate expressions for the arithmetic and geometric statistics of G are also obtained, which compare favourably with MC generated ones. This paper also applies the MC method to evaluate parameter sensitivity and predictive uncertainty of the distributed runoff and erosion model KINEROS2 in a small experimental watershed. The MC simulations of flow and sediment related variables show that those parameters which impart the greatest uncertainty to KINEROS2 model outputs are not necessarily the most sensitive ones. Soil hydraulic conductivity and wetting front net capillary drive, followed by initial effective relative saturation, dominated uncertainties of flow and sediment discharge model outputs at the watershed outlet. Model predictive uncertainty measured by the coefficient of variation decreased with rainfall intensity, thus implying improved model reliability for larger rainfall events. The antecedent relative saturation was the most sensitive parameter in all but the peak arrival times, followed by the overland plane roughness coefficient. Among the sediment related parameters, the median particle size and hydraulic erosion parameters dominated sediment model output uncertainty and sensitivity. Effect of rain splash erosion coefficient was negligible. Comparison of medians from MC simulations and simulations by direct substitution of average parameters with observed flow rates and sediment discharges indicates that KINEROS2 can be applied to ungauged watersheds and still produce runoff and sediment yield predictions within order of magnitude of accuracy.     

Parametric uncertainty and sensitivity analysis of hydrodynamic processes for a large shallow freshwater lake

Abstract

A parametric uncertainty and sensitivity analysis of hydrodynamic processes was conducted for a large shallow freshwater lake, Lake Taihu, China. Ten commonly used parameters in five groups were considered including: air–water interface factor, water–sediment interface factor, surrounding terrain factor, turbulent diffusion parameters and turbulent intensity parameters. Latin hypercube sampling (LHS) was used for sampling the parametric combinations, which gave predictive uncertainty results directly without using surrogate models, and the impacts of different parametric distribution functions on the results were investigated. The results showed that the different parametric distribution functions (e.g. uniform, normal, lognormal and triangular) for sampling had very little impact on the uncertainty and sensitivity analysis of the lake hydrodynamic model. The air–water interface factor (wind drag coefficient) and surrounding terrain factor (wind shelter coefficient) had the greatest influence on the spatial distribution of lake hydrodynamic processes, especially in semi-closed bays and lake regions with complex topography, accounting for about 60–70% and 20%, respectively, of the uncertainty on the results. Vertically, velocity in the surface layer was also largely influenced by the two factors, followed by velocity in the bottom layer; the middle velocity had minimal impact. Likewise, the water–sediment interface factor (i.e. bottom roughness height) ranked third, contributing about 10% to the uncertainty of the hydrodynamic processes of the lake. In contrast, turbulent diffusion parameters and turbulent intensity parameters in the lake hydrodynamic model had little effect on the uncertainty of simulated results (less than 1% contribution). The findings were sufficiently significant to reduce the parameter uncertainties and calibration workload of the hydrodynamic model in large shallow lakes.

Editor Z. W. Kundzewicz; Associate editor S. Grimaldi     

太湖藻类水平漂移特征及其影响因素

采用原位观测平台,研究分析了太湖竺山湾风速、湖流流速、波高以及藻类水平漂移特征及其影响因素,结果表明日间藻类水平漂移速率呈锯齿式交替特征,日内藻类水平漂移速率变化幅度大;藻类水平漂移速率与风速之间呈显著线性正相关;当0.02 m < 有效波高 < 0.1 m时,藻类水平漂移速率与有效波高呈显著线性负相关,当有效波高 > 0.1 m时,波浪会显著改变藻类运动方式,破坏藻类表层聚集形态;湖流对藻类水平漂移速率无显著性影响;藻类水平漂移方向受湖流流向和风向的共同作用;藻类水平漂移速率与风速、波高、湖流流速间关系可用多元线性方程表达,且拟合度良好,可为藻类水华预测模型构建提供依据.