A Comparison Study of Sensitivity on PJ and NPS Models in China Seas

Evaporation duct is an ubiquitous natural phenomenon over the ocean and can be diagnosed by evaporation duct model. The model proposed by Paulus and Jeske and another model established by the American naval postgraduate school are the most widely used. They are called PJ model and NPS model, respectively. Two methods are used to investigate the global sensitivity of PJ model and NPS model in China Seas. The first method is based on meteorological and oceanographic observation data in China Seas. Considering the system random error caused by sensor measurement inaccuracies, the mean relative error and mean absolute error are used as criterion for sensitivity analysis. The second method, called Extended Fourier Amplitude Sensitivity Test(EFAST), takes into account the interaction between input parameters and is used for sensitivity analysis. The results show that NPS model is more sensitive to the random errors of sensors than PJ model. The mean relative errors of PJ model and NPS model are 11.43% and 14.81%, respectively. The results of global sensitivity parameter analysis indicate that wind speed is the key factor of PJ model, while all input parameter of NPS model have relatively large total sensitivity index. In addition, sensitivity analysis results confirm that wind speed is one of main driving factors for the formation of evaporation duct. These results are valuable for the selection of diagnosis models for evaporation duct, the evaluation of radio wave propagation in the marine atmospheric surface layer, and the prediction technique of evaporation duct based on numerical weather prediction(NWP) in China seas.     

Sensitivity analysis of the DeNitrification and Decomposition model for simulating regional carbon budget at the wetland-grassland area on the Zoige Plateau,china

Although mathematical models (e.g., DeNitrification and DeComposition (DNDC) provide a powerful tool to study regional carbon budget, it is still difficult to obtain accurate simulation results because there exists large uncertainties in modeling regional carbon budget. Through the investigation on the sensitivity of model output parameters to the input parameters, sensitivity analysis (SA) has been proved to be able to identify the key sources of uncertainties and be helpful to reduce the model uncertainties. However, some input parameters with discrete values (e.g., land use type and soil type) and the regional effect of the sensitive parameters were rarely examined in SA. In this paper, taking the Zoige Plateau as a case area, we combined the one-factor-at-a-time (OAT) with Extended Fourier Amplitude Sensitivity Test (EFAST) to conduct a SA of DNDC for simulating the regional carbon budget, including Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP), autotrophic respiration (Ra), soil microbial heterotrophic respiration (Rh) and ecosystem respiration (Re). The result showed that the combination of OAT and EFAST could test the contribution of the input parameters with discrete values to the output parameters. In DNDC model, land use type and soil type had a significant impact on the regional carbon budget of the Zoige Plateau, and daily temperature was also confirmed to be one of the most important parameters for carbon budget. For the other input parameters, with the change of land use type or soil type at regional scale, the sensitive parameters of carbon budget would vary accordingly. The SA results would provide scientific evidence to optimize DNDC model and they suggested that we should pay attention to the spatial/temporal effect of SA and try to use the appropriate data in simulation of the regional carbon budget.     

沉降观测AR模型的不确定性平差算法

在沉降观测过程中,获取的数据经常存在着不确定性,影响参数估计的可靠性和沉降预测的准确性。把不确定度作为参数融入AR模型,建立基于AR模型的不确定性平差模型。依据残差中不确定性传播规律,利用min-max估计准则,使得残差中的最大不确定度达到最小,从一个新的角度探讨不确定性观测数据的处理方法,得到基于不确定性算法的AR模型,扩展了现有误差理论。通过沉降实例进一步验证算法的优越性。     

用矩阵摄动法修改结构动力参数

作者应用模型简化技术中的矩阵摄动法来修改结构动力参数，从而避免了修改后结构动力参数的重计算，减少了系统仿真、分析和设计的计算量。算例表明，该方法有较好的计算效率。     

Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds(Case study: the Navrood watershed in Iran)

In watersheds that have not sufficient meteorological and hydrometric data for simulating rainfall-runoff events, using geomorphologic and geomorphoclimatic characteristics of watershed is a conventional method for the simulation. A number of rainfall-runoff models utilize these characteristics such as Nash-IUH, Clark-IUH, Geomorphologic Instantaneous Unit Hydrograph(GIUH), Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH), GIUH-based Nash(GIUH-Nash) and GcIUH-based Clark(GcIUH-Clark). But all these models are not appropriate for mountainous watersheds. Therefore, the objective of this study is to select the best of them for the simulation. The procedure of this study is: a) selecting appropriate rainfall-runoff events for calibration and validation of six hybrid models, b) distinguishing the best model based on different performance criteria(Percentage Error in Volume(PEV); Percentage Error in Peak(PEP); Percentage Error in Time to Peak(PETP); Root Mean Square Error(RMSE) and Nash-Sutcliffe model efficiency coefficient(ENS)), c) Sensitivity analysis for determination of the most effective parameter at each model, d) Uncertainty determination of different parameters in each model and confirmation of the obtained results by application of the performance criteria. For application of this procedure, the Navrood watershed in the north of Iran as a mountainous watershed has been considered. The results showed that the ClarkIUH and GcIUH-Clark are suitable models for simulation of flood hydrographs, while other models cannot simulate flood hydrographs appropriately. The sensitivity analysis shows that the most sensitive parameters are the infiltration constant rate and time of concentration in the Clark-IUH model. Also, the most sensitive parameters include the infiltration constant rate and storage coefficient in the GcIUHClark model. The Clark-IUH and GcIUH-Clark models are more sensitive to their parameters. The Latin Hypercube Sampling(LHS) on Monte Carlo(MC) simulation method was used for evaluation of uncertainty of data in rainfall-runoff models. In this method 500 sets of data values are produced and then the peak discharge of flood hydrographs for each produced data set is simulated with rainfall-runoff models. The uncertainty of data changes the value of simulated peak discharge of flood hydrograph. The uncertainty analysis shows that the observed peak discharges of different rainfall-runoff events are within the range of values of simulated by the six hybrid rainfall-runoff models and IUH that inputs of these models were the produced data sets. The range of the produced peak discharge of flood hydrographs by the Clark-IUH and GcIUH-Clark models is wider than those of other models.     

地表组合粗糙度不确定性引起的SAR反演土壤水分的不确定性分析

地表粗糙度的不确定性是引起SAR土壤水分反演结果不确定性的主要因素,现有研究大多着重于研究单个粗糙度参数（主要是相关长度）的不确定性,直接研究地表组合粗糙度不确定性的较少。本文使用偏度、峰度和四分位距3个指标来量化不确定性,通过在组合粗糙度中加入不同量级高斯噪声进行随机扰动的方法,研究组合粗糙度不确定性在反演过程中的传递,并对反演土壤水分的不确定性进行定量分析。进一步研究反演土壤水分的均方根误差对组合粗糙度不同比例误差范围的响应特征,得到满足反演精度要求的组合粗糙度误差控制范围。样区的实验分析结果表明：组合粗糙度高斯噪声标准差在0-0.045之间时,峰度取值从-0.1984到1.2501,偏度取值从0.0191到0.6791,四分位距取值从0.0018到0.0167,3个量化指标都随组合粗糙度高斯噪声量级的增大而增大,土壤水分反演值有集中在众数附近的趋势,土壤水分低估倾向比高估倾向更明显;本文提出的组合粗糙度误差控制范围可满足反演精度要求,误差控制范围与入射角负相关。     

下辽河平原浅层地下水脆弱性评价

以下辽河平原为研究对象,在DRASTIC模型基础上,结合RS技术建立了DRASTICL(DRASTIC land use type)模型。利用ArcGIS的水文分析工具对DEM影像进行子流域划分与数据提取。通过对参数进行不确定性表征,对三角模糊参数设定不同α截集,在此基础上将随机参数和模糊参数进行蒙特卡罗模拟。将不同α截集下模拟结果代入模糊模式识别模型,根据累积分布规律,选取不同百分位,从而得出不同α截集与不同百分位地下水脆弱性取值。结合ArcGIS数据可视化表达,得出不同α截集下下辽河平原浅层地下水脆弱性分布图,以此辨析下辽河平原浅层地下水不确定性与脆弱性程度。最后运用灵敏度分析辨别各参数对模拟结果的实际贡献程度。结果表明：（1）模糊模式识别模型用非线性的形式充分反映参数连续性变化对模拟结果产生的影响。（2）加入土地利用类型参数,结果更能反映人类活动影响下地下水脆弱程度。（3）不同α水平、不同百分位、与不同灵敏度系数3个层次的分析有效处理了参数不确定性问题,为制定相关政策提供更加准确的参考依据,对今后本地区的地下水环境开发利用和保护具有重要意义。     

一种基于M估计的Robust-ELM滑坡位移预测方法

采用传统ELM算法进行滑坡位移预测时,其网络输出权值由最小二乘估计得出,导致ELM抗差能力较差,从而造成网络训练参数不准确。为此,将M估计与ELM相结合,提出一种基于M估计的RobustELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值,以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例,将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明,该方法能够有效降低粗差对预测的影响,具有良好的抗差能力。     

基于手机定位数据的城市人口分布近实时预测

精细时空尺度下城市人口分布的近实时预测可为优化公共资源配置、协助城市交通诱导、制定公共安全应急预案、探索城市居民活动规律等提供重要科学依据。本文采用城市手机定位数据,基于时间序列分析方法,分别建立参数预测模型和非参数预测模型,对精细尺度下的城市人口空间分布开展近实时预测。预测结果表明,基于时间序列分析方法的预测模型可为精细尺度下的城市人口分布近实时预测提供方法支持;在本文实验条件下,从人口规模、时空分布、多时间尺度、特殊事件等多个角度评估模型精度,非参数预测模型其预测误差均小于参数预测模型,且预测结果更为稳定。     

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Uncertainty Assessment of Soil Erosion Model Using Particle Filtering

Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes.Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty.To deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the CSEM.The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets.PF provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set.CSEM-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events.Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.     

贝叶斯正则化的Elman神经网络电离层TEC预报模型

利用2017年中低纬电离层总电子含量、地磁活动指数、年积日等参数,首次建立基于贝叶斯正则化(Bayesian regularization)的Elman回归神经网络(BR-Elman)的电离层TEC预报模型。同时,根据地磁活动指数的变化特征,分别进行平静电离层和扰动电离层预报建模。实验结果表明,该方法在平静期5 d预测值的均方根误差为1.19 TECu,残差为1.03 TECu,相关系数为0.93;在扰动期5 d预测值均方根误差为1.34 TECu,残差为1.01 TECu,相关系数为0.91。贝叶斯正则化的BP神经网络模型以及传统BP神经网络模型在平静期与扰动期5 d的预测上,均方根误差最小为1.87 TECu,残差最小为1.50 TECu,相关系数最优为0.87。通过对比分析,该模型较其他2个模型的预报效果有明显改善。     

平面四参数坐标转换模型的改进与应用研究

采用数值分析理论讨论方阵奇异性判别的方法,针对平面四参数坐标转换模型法方程系数矩阵病态性的问题,提出中心化坐标与缩小误差方程系数相结合的方法,有效改善了法方程系数矩阵的结构与病态性,获取到稳定可靠的转换参数。通过理论与实例分析,验证了该方法简单可行,为区域工程测量中平面坐标转换提供了参考。     

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部分待估参数具有先验随机性的WTLS平差

部分待估参数具有先验随机信息,且误差方程系数矩阵含有观测误差,是一类新的平差问题。本文构造了部分待估参数含有先验随机信息的加权整体最小二乘平差函数模型,推导该模型参数估计与精度评定公式,给出计算步骤,适用于一般情形。实例对比分析证明,该算法正确可靠,迭代收敛速度较优。     

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Sensitivity analysis of thermal equilibrium parameters of MIKE 11 model: A case study of Wuxikou Reservoir in Jiangxi Province of China

Sensitivity analysis of thermal equilibrium parameters in the reservoir module of MIKE 11 model was conducted for the Wuxikou Reservoir in Jiangxi Province of China in order to apply the module to the environmental impact assessment to accurately predict water temperature of reservoirs.Results showed that radiation parameter A and evaporation-first parameter were much more sensitive than other parameters.The values of the radiation parameter A ranged from 0.10 to 0.34.The values of evaporation-first parameter varied from 0 to 10.The sensitivity of solar absorption parameters was less than that of evaporation parameter,of which light attenuation values ranged from 0.5 to 0.7,and this parameter would not impact model results if it was more than 2.Constants in Beer’s law ranged from 0.2 to 0.7.Radiation parameter B was not more sensitive than evaporation parameter and its reasonable range was higher than 0.48.The fitting curves showed consistent changing tendency for these parameters within the reasonable ranges.Additionally,all the thermal equilibrium parameters had much more important effects on surface water temperature than deep water temperature.Moreover,if no observed data could be obtained,the local empirical value would be used to input to the MIKE 11 model to simulate the changes in the discharged outflow-water temperature qualitatively.     

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Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallowwater model

Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) methods.A series of sensitivity experiments were designed to test the sensitivity of sensitive areas within the numerical model.The following results were obtained:(1) the effect of initial CNOP and FSV patterns in their sensitive areas is greater than that of the same patterns in randomly selected areas,with the effect of the initial CNOP patterns in CNOP sensitive areas being the greatest;(2) both CNOP- and FSV-type initial errors grow more quickly than random errors;(3) the effect of random errors superimposed on the sensitive areas is greater than that of random errors introduced into randomly selected areas,and initial errors in the CNOP sensitive areas have greater effects on final forecasts.These results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected areas.In addition,ideal hindcasting experiments were conducted to examine the validity of the sensitive areas.The results indicate that reduction(or elimination) of CNOP-type errors in CNOP sensitive areas at the initial time has a greater forecast benefit than the reduction(or elimination) of FSVtype errors in FSV sensitive areas.These results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.     

Landslide stability for Shuitianba in the Three Gorges area

According to the data of preliminary survey, the authors established a landslide geological model,on the basis of analyses on the sensitivity of landslide, tried to simulate and calculate the landslide stability of Shuitianba with the method of transfer coefficient when it is under different strength parameters, and study the landslide mechanism. The results show that it is sensitive to the effects of shear strength parameters of sliding zone and groundwater level on landslide stability safety coefficient, which provides reliable basis for calculation of landslide stability.