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.     

大型浅水湖泊太湖波浪特征及其对风场的敏感性分析

以空间均匀的实际风场为驱动,利用SWAN模式模拟了太湖波浪场,结果表明:SWAN模式能够较好的模拟太湖波浪的生成与传播,适用于大型浅水湖泊(太湖);同时分析了该风场驱动下下太湖风浪谱,波浪的绝对频率主要集中在0.45～1.0 Hz的中高频率段;风向与波向具有高度一致性.在同一风速下,太湖不同区域波浪成长稳定时间不同,湖心区稳定的谱峰频率在0.342～0.585 Hz之间,湾区及西山岛附近狭长水域稳定的谱峰频率在0.447～0.765 Hz之间;在同一区域,风速增大,波浪稳定时间减少,谱峰频率沿低频推移,在湖心区谱峰频率最小不低于0.340 Hz,湾区、西山岛附近狭长水域最小不低于0.447 Hz;风向的改变对湾区及西山岛附近狭长水域的波浪频谱形状影响较大.     

地震危险性分析中的参数敏感性研究

强调地震危险性分析是包含多参数的复杂系统,其参数的敏感性应作为一个整体来研究．考察了参数连续变化对结果的影响以及超越概率和潜在震源区震级上限对参数敏感性的影响,得到了一些有益的认识．全面计算了包括地震空间分布函数在内的参数之间的交互作用,提出了相对交互作用的概念．得出在50年超越概率10%时相对交互作用不超过10%;在年超越概率10－4时,高震级潜在震源区的震级上限与年平均发生率、空间分布函数的相对交互作用分别可达到17%和22%,为实际应用时判断是否要考虑参数的交互作用提供了方便．指出了求解结果的概率分布的重要性和必要性,分析了各参数取不同水平的权重对结果分布的影响,揭示多参数敏感性的全貌,为进行合理的不确定性校正作了必要的准备．     

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.     

基于非线性模型的TLD参数优化

高柔结构在强风或地震等环境荷载作用下，往往会产生较大的变形和位移。采用调谐液体阻尼器对结构进行控制时，需要选择合适的水箱尺寸和水深，以期获得最好的减振效果。以往的调谐液体阻尼器参数优化往往基于等效线性模型或在小幅值激励下有较好精度的非线性浅水波动模型。采用了一种具有非线性阻尼和非线性刚度的等效调谐质量阻尼器模型对影响调谐液体阻尼器减振效率的主要参数进行了优化，该模型不再受小幅值激励的限制。优化结果表明，激励幅值对TLD的最优参数和减振效果有明显影响，同时水箱长度对TLD减振效果也有明显影响，这是基于线性模型TLD优化不能得到的结论。     

湖-气热传输模型及参数敏感性研究

在一维涡扩散模型的基础上,发展了考虑冬天结冰和夏天蒸发等水体相变问题及对流混合过程的湖-气热传输模型.模型采用焓代替温度作为预报变量,这也更加方便处理水的相变问题.模型的计算结果与以色列Kinneret湖的观测资料进行对比,验证了模型的合理性,说明了加入对流混合过程是符合湖-气热传输的实际物理机制的.对风速和湖面摩擦速度作了敏感性实验,证明他们对湖面温度和蒸发潜热有着重要的影响.     

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     

Parameter sensitivity analyses in seismic hazard

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Understanding the time‐varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis

Simulations from hydrological models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of such sources of uncertainty is essential to support model calibration, validation and diagnostic evaluation and to prioritize efforts for uncertainty reduction. It can also support the identification of ‘disinformative data’ whose values are the consequence of measurement errors or inadequate observations. Sensitivity analysis (SA) provides the theoretical framework and the numerical tools to quantify the relative contribution of different sources of uncertainty to the variability of the model outputs. In traditional applications of global SA (GSA), model outputs are aggregations of the full set of a simulated variable. For example, many GSA applications use a performance metric (e.g. the root mean squared error) as model output that aggregates the distances of a simulated time series to available observations. This aggregation of propagated uncertainties prior to GSA may lead to a significant loss of information and may cover up local behaviour that could be of great interest. Time‐varying sensitivity analysis (TVSA), where the aggregation and SA are repeated at different time steps, is a viable option to reduce this loss of information. In this work, we use TVSA to address two questions: (1) Can we distinguish between the relative importance of parameter uncertainty versus data uncertainty in time? (2) Do these influences change in catchments with different characteristics? To our knowledge, the results present one of the first quantitative investigations on the relative importance of parameter and data uncertainty across time. We find that the approach is capable of separating influential periods across data and parameter uncertainties, while also highlighting significant differences between the catchments analysed. Copyright © 2016 The Authors. Hydrological Processes. Published by John Wiley & Sons Ltd.     

Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty

Radar estimates of rainfall are being increasingly applied to flood forecasting applications. Errors are inherent both in the process of estimating rainfall from radar and in the modelling of the rainfall–runoff transformation. The study aims at building a framework for the assessment of uncertainty that is consistent with the limitations of the model and data available and that allows a direct quantitative comparison between model predictions obtained by using radar and raingauge rainfall inputs. The study uses radar data from a mountainous region in northern Italy where complex topography amplifies radar errors due to radar beam occlusion and variability of precipitation with height. These errors, together with other error sources, are adjusted by applying a radar rainfall estimation algorithm. Radar rainfall estimates, adjusted and not, are used as an input to TOPMODEL for flood simulation over the Posina catchment (116 km²). Hydrological model parameter uncertainty is explicitly accounted for by use of the GLUE (Generalized Likelihood Uncertainty Estimation). Statistics are proposed to evaluate both the wideness of the uncertainty limits and the percentage of observations which fall within the uncertainty bounds. Results show the critical importance of proper adjustment of radar estimates and the use of radar estimates as close to ground as possible. Uncertainties affecting runoff predictions from adjusted radar data are close to those obtained by using a dense raingauge network, at least for the lowest radar observations available. Copyright © 2004 John Wiley & Sons, Ltd.     

Uncertainty and sensitivity analysis of a travel-time distribution-based stream water electrical conductivity model

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2-year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute-balance approach and (2) a water age-based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age-based model than the solute-balance model; 4. using time-variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS-TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute-balance model, the rainfall concentration C_rain and in the age-based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute-balance model. Little change in parameter sensitivity over time was found for the age-based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute-balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.     

Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments

Abstract

The SWAT model was tested to simulate the streamflow of two small Mediterranean catchments (the Vène and the Pallas) in southern France. Model calibration and prediction uncertainty were assessed simultaneously by using three different techniques (SUFI-2, GLUE and ParaSol). Initially, a sensitivity analysis was conducted using the LH-OAT method. Subsequent sensitive parameter calibration and SWAT prediction uncertainty were analysed by considering, firstly, deterministic discharge data (assuming no uncertainty in discharge data) and secondly, uncertainty in discharge data through the development of a methodology that accounts explicitly for error in the rating curve (the stage?discharge relationship). To efficiently compare the different uncertainty methods and the effect of the uncertainty of the rating curve on model prediction uncertainty, common criteria were set for the likelihood function, the threshold value and the number of simulations. The results show that model prediction uncertainty is not only case-study specific, but also depends on the selected uncertainty analysis technique. It was also found that the 95% model prediction uncertainty interval is wider and more successful at encompassing the observations when uncertainty in the discharge data is considered explicitly. The latter source of uncertainty adds additional uncertainty to the total model prediction uncertainty.

Editor D. Koutsoyiannis; Associate editor D. Gerten

Citation Sellami, H., La Jeunesse, I., Benabdallah, S., and Vanclooster, M., 2013. Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean watersheds. Hydrological Sciences Journal, 58 (8), 1635?1657.     

太湖水质时空特性及其与蓝藻水华的关系

以太湖2005-2007年的连续监测资料为基础,运用聚类分析和自相关分析方法,针对总无机磷,总无机氮、水温等环境理化因素与叶绿素a进行时空序列分析,初步归纳了当前太湖水质指标变化的空间特点、时问周期性及其与蓝藻水华暴发的关系.结果表明,太湖水质的空间分布大致分为三个人湖河口、四个湖湾、湖心区、西部湖区、东部湖区等十个区...     

大型湖库滨岸带蓝藻水华堆积风险评估——以巢湖为例

淡水湖库富营养化与蓝藻水华是全球性的突出水环境问题,尤其是滨岸带严重蓝藻水华堆积甚至造成了水体黑臭、威胁饮用水安全等严重危害,科学评估滨岸带蓝藻水华堆积风险、精准识别蓝藻水华易堆积区域是水环境管理与研究中亟待解决的关键科学问题.本研究以我国长江中下游的大型浅水富营养化湖泊巢湖为研究对象,依托流域水文与湖泊水动力模拟、遥...