基于集合卡尔曼滤波的湖泊富营养化模型Delft3D-BLOOM数据同化

富营养化模型是进行湖泊水环境质量预测和管理的重要工具,然而模型客观存在的误差一直是应用者关心的重要问题.数据同化作为连接观测数据与数值模型的重要方法,可以有效提高模型的准确性.集合卡尔曼滤波(En KF)是众多数据同化算法中应用最为广泛的一种,可进行非线性系统的数据同化,并能有效降低数据同化的计算量.本研究以太湖作为具体实例,选择Delft3D-BLOOM作为富营养化模型,在数值实验确定En KF集合数为100、观测误差方差为1%、模拟误差方差为10%的基础上分别进行模型状态变量同化以及状态变量与关键参数同步同化.结果显示,仅同化状态变量时,模型预测精度有所增加;同时同化状态变量和关键参数时,可显著提升模型在湖泊水环境质量预测中的精度.该研究为应用集合卡尔曼滤波以提高复杂的湖库富营养化模型模拟精度提供了有效的方法.     

基于热层电离层耦合数据同化的热层参量估计

本文采用高效集合卡尔曼滤波（EnKF）算法和背景场热层电离层理论模式NCAR-TIEGCM,开发了热层电离层数据同化系统.基于全球空地基GNSS电离层斜TEC观测、CHAMP和TIMED/GUVI热层参量观测构型设计了系列观测系统模拟实验,对热层参量进行估计.实验结果表明,（1）通过集合卡尔曼滤波算法同化电离层TEC观测能够较好地优化热层参量.（2）中性质量密度优化效果在整个同化阶段均有提升,提升百分比能达到40%.（3）积分氧氮比在同化阶段也能得到较好的优化,但在电子密度水平梯度变化剧烈区域效果较差.最后本文对中性质量密度进行了预报评估,结果表明,由于中性成分优化,在地磁平静条件下其预报时间尺度可长达24 h.

     

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.     

基于微波亮温及集合Kalman滤波的土壤湿度同化方案

基于集合Kalman滤波及SCE-UA(shuffled complex evolution)算法发展了能够直接同化微波亮温的土壤湿度同化方案. 该方案以陆面过程模式CLM 3.0中的土壤水模型作为预报算子, 以辐射传输模型作为观测算子. 整个同化过程分为参数优化和土壤湿度同化两个阶段, 利用SCE-UA算法优化辐射传输模型中难以确定的植被光学厚度参数和地表粗糙度参数, 并利用优化参数作为观测算子的模型参数进行同化. 通过人工理想试验表明该同化方案可以明显改善表层土壤湿度的模拟精度, 并且对深层土壤湿度的模拟也有一定程度的改善; 利用AMSR-E亮温(10.65 GHz垂直极化)所进行的实际同化试验表明顶层(0~10 cm)土壤湿度同化结果与观测的均方根误差(RMSE)由模拟的0.05052减小到0.03355, 相对减小了33.6%, 而较深层(10~50 cm)平均减小了20.9%. 这些同化试验显示该同化方案的合理性.     

考虑记忆时间的LSTM模型在赣江流域径流预报中的应用

在气候变化条件下,准确的径流预测对水资源的规划与管理十分重要。本文基于长短时记忆神经网络(LSTM)模型,采用赣江流域外洲、峡江以及栋背水文站的逐日流量以及CN05.1日降水数据构建3个不同面积流域的径流预测模型,并通过设置不同情景分析:模型的有效预见期与不同流域平均产汇流时间之间的关系,有效预见期内LSTM径流预测模型精度与记忆时间之间的关系,不同长度的预见期与模型最佳记忆时间之间的关系,同时探讨LSTM径流预测所需的记忆时间与流域面积的关系。结果表明:(1)综合考虑降水和前期径流情景下的径流预测效果最好,当预见期为1 d时,外洲、峡江、栋背站的纳什效率系数(NSE)分别可达0.98、0.96以及0.90;且其有效预见期与仅考虑降水信息的有效预见期相同,均与流域平均产汇流时间相近。(2)随着预见期的延长,不同情景下的预测精度均有不同程度的下降,其中仅考虑前期径流情景的下降率最大,说明降水信息较前期径流对径流预测效果的提升更重要。同时,随着流域面积的增加,相同预见期内径流预测精度均有所提升。(3)当预见期相同时,随记忆时间的延长,不同径流预测模型的预测精度均先上升至最高,接着具有下降趋势,最后逐渐趋于稳定。且在有效预见期内,随着预见期的延长,最佳记忆时间均有增大趋势,当达到最长的有效预见期时,对应的最佳记忆时间均为14 d。此外,在赣江流域的模拟结果表明,随着流域面积的增大,LSTM的最佳记忆时间减小。研究结果可为赣江流域的径流预报提供参考,同时有助于推求其他流域采用机器学习进行径流预测所需的最佳记忆时间。     

基于四维变分同化法的巢湖流域南淝河水质模拟

针对河流模拟中未知不确定性源对模拟精度的影响,以巢湖流域南淝河为研究对象,建立了基于四维变分同化方法的南淝河干流水质模型,研究了含未知污染源的南淝河水质过程模拟.模型以未知污染负荷的动态变化过程为控制变量,通过同化沿河不同断面的逐日水质监测数据,识别不同河段的逐日入河污染负荷过程来实现水质过程的模拟,改变了常规模型模拟...     

考虑次网格变异性和土壤冻融过程的土壤湿度同化方案

集合Kalman滤波以其简单有效的特点在陆面数据同化中广泛应用,通常作为预报模型的陆面过程模式往往要考虑模式次网格变异性和土壤冻融过程,若对此不加考虑而直接对土壤湿度进行同化可能会使得同化结果发生偏差．将双集合Kalman滤波应用于土壤湿度的同化,基于NCAR／CLM陆面过程模式建立了一个考虑次网格变异性和土壤冻融过程的土壤湿度同化方案：在同一个时间步内用状态滤波对模式网格内某片上液态水分含量进行优化,用参数滤波对该片上的固态水分含量和其他片上的液态／固态水分含量进行优化,由此考虑模式次网格变异性和土壤冻融过程的影响,从而实现对整个模式网格上土壤湿度的同化．初步的同化试验表明：其同化效果在有、无土壤冻融阶段都优于一般的不考虑次网格变异性和土壤冻融变化的同化方案;该同化方案不仅能够提高那些有直接观测信息的土壤层的土壤湿度模拟精度,还能在一定程度上改善那些没有任何观测信息的土壤层的模拟效果;另外,土壤湿度同化结果的改善还能在一定程度上提高陆面模式对于土壤温度的模拟精度．     

开都河流域融雪径流模拟研究

高山融雪是塔里木河源流区重要的产流方式,4个山区流域具有面积大、测站稀少、降雨与融雪混合补给径流和显著局部降雨等特征.以开都河流域为研究区,分析流域特征对SRM融雪径流模型参变量的影响,确定相应选取策略以提高融雪径流模拟预报精度,为相似流域融雪径流模拟提供参考.研究结果表明(i)气温输入控制模拟径流的整体趋势,对模拟精度起决定性作用.但测站日均气温数据通常不能代表流域同高程的平均水平,直接作为输入会导致很大误差.基于遥感积雪图和模拟结果分析,开都河流域选择0.5倍巴音布鲁克站日最大气温作为流域平均气温较为合理.(ii)由于雨量站稀少和局部降雨特征显著,计算各高程分带平均降水并不现实.将测站降雨乘以放大系数,并借助参数"降雨径流系数"进行校正,可以满足模型对降雨输入的需求.(iii)根据融雪和降雨位置变化,调整参数"滞时"取值对提高局部洪峰过程的模拟精度非常重要.(iv)随气温升高,降雨增多,未能被有限测站完全监测,导致模拟精度逐步降低.     

开都河流域融雪径流模拟研究

高山融雪是塔里木河源流区重要的产流方式,4个山区流域具有面积大、测站稀少、降雨与融雪混合补给径流和显著局部降雨等特征.以开都河流域为研究区,分析流域特征对SRM融雪径流模型参变量的影响,确定相应选取策略以提高融雪径流模拟预报精度,为相似流域融雪径流模拟提供参考.研究结果表明:(i)气温输入控制模拟径流的整体趋势,对模拟精度起决定性作用.但测站日均气温数据通常不能代表流域同高程的平均水平,直接作为输入会导致很大误差.基于遥感积雪图和模拟结果分析,开都河流域选择0.5倍巴音布鲁克站日最大气温作为流域平均气温较为合理.(ii)由于雨量站稀少和局部降雨特征显著,计算各高程分带平均降水并不现实.将测站降雨乘以放大系数,并借助参数"降雨径流系数"进行校正,可以满足模型对降雨输入的需求.(iii)根据融雪和降雨位置变化,调整参数"滞时"取值对提高局部洪峰过程的模拟精度非常重要.(iv)随气温升高,降雨增多,未能被有限测站完全监测,导致模拟精度逐步降低.     

太湖西苕溪流域径流过程的模拟

西苕溪是太湖集水域的一个主要流域,研究西苕溪流域径流过程及污染物产出对了解太湖水文水质变化以及开展周围其它流域研究工作具有重要意义．作为研究的第一步,采用集总式模型LASCAM建立了西苕溪流域径流模型．以流域内2个水文观测站1968-1988年日径流观测数据对模型作了率定．率定效果满意,模拟日、年径流量与观测值吻合良好．在流域资料不够充分的情况下,模型能获得较为理想的模拟效果,说明所采用的模型适用于数据不足区域．模拟还揭示,西苕溪流域径流产生可能以饱和地面径流机制为主．近河道浅层饱和土体的水位与降雨量相关性好,呈现出明显的日波动周期;而深层地下水位呈年波动周期,在旱季和雨季,水位呈明显的降落和上升趋势．这些发现为进一步细化径流模型以及建立污染物输移模型奠定了基础．     

Review of the Kalman-type hydrological data assimilation

ABSTRACT

There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalman-type DA but also provide an important reference to other hydrological DA types.

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

Data assimilation in groundwater modelling: ensemble Kalman filter versus ensemble smoothers

Groundwater modelling calls for an effective and robust data integrating method to fill the gap between the model and observation data. The ensemble Kalman filter (EnKF), a real‐time data assimilation method, has been increasingly applied in multiple disciplines such as petroleum engineering and hydrogeology. In this approach, a groundwater model is updated sequentially with measured data such as hydraulic head and concentration. As an alternative to the EnKF, the ensemble smoother (ES) has been proposed for updating groundwater models using all the data together, with much less computational cost. To further improve the performance of the ES, an iterative ES has been proposed for continuously updating the model by assimilating measurements together. In this work, we compare the performance of the EnKF, the ES, and the iterative ES using a synthetic example in groundwater modelling. Hydraulic head data modelled on the basis of the reference conductivity field are used to inversely estimate conductivities at unsampled locations. Results are evaluated in terms of the characterization of conductivity and groundwater flow predictions. It is concluded that (a) the iterative ES works better than the standard ES because of its continuous updating and (b) the iterative ES could achieve results comparable with those of the EnKF, with less computational cost. These findings show that the iterative ES should be paid much more attention for data assimilation in groundwater modelling.     

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.     

Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems.     

Data assimilation methods for estimating a heterogeneous conductivity field by assimilating transient solute transport data via ensemble Kalman filter

An ensemble Kalman filter (EnKF) is developed to identify a hydraulic conductivity distribution in a heterogeneous medium by assimilating solute concentration measurements of solute transport in the field with a steady‐state flow. A synthetic case with the mixed Neumann/Dirichlet boundary conditions is designed to investigate the capacity of the data assimilation methods to identify a conductivity distribution. The developed method is demonstrated in 2‐D transient solute transport with two different initial instant solute injection areas. The influences of the observation error and model error on the updated results are considered in this study. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating solute concentration measurements. The larger area of the initial distribution and the more observed data obtained, the better the calculation results. When the standard deviation of the observation error varies from 1% to 30% of the solute concentration measurements, the simulated results by the data assimilation method do not change much, which indicates that assimilation results are not very sensitive to the standard deviation of the observation error in this study. When the inflation factor is more than 1.0 to enlarge the model error by increasing the forecast error covariance matrix, the updated results of the hydraulic conductivity by the data assimilation method are not good at all. Copyright © 2012 John Wiley & Sons, Ltd.     

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58 % of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and 3-h precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions.     

Initial ensemble generation and validation for ocean data assimilation using HYCOM in the Pacific

A method to initialize an ensemble, introduced by Evensen (Physica, D 77:108–129, 1994a; J Geophys Res 99(C5):10143–10162, 1994b; Ocean Dynamics 53:343–367, 2003), was applied to the Ocean General Circulation Model (OGCM) HYbrid Coordinate Ocean Model (HYCOM) for the Pacific Ocean. Taking advantage of the hybrid coordinates, an initial ensemble is created by first perturbing the layer interfaces and then running the model for a spin-up period of 1 month forced by randomly perturbed atmospheric forcing fields. In addition to the perturbations of layer interfaces, we implemented perturbations of the mixed layer temperatures. In this paper, we investigate the quality of the initial ensemble generated by this scheme and the influence of the horizontal decorrelation scale and vertical correlation on the statistics of the resulting ensemble. We performed six ensemble generation experiments with different combinations of horizontal decorrelation scales and with/without perturbations in the mixed layer. The resulting six sets of initial ensembles are then analyzed in terms of sustainability of the ensemble spread and realism of the correlation patterns. The ensemble spreads are validated against the difference between model and observations after 20 years of free run. The correlation patterns of six sets of ensemble are compared to each other. This study shows that the ensemble generation scheme can effectively generate an initial ensemble whose spread is consistent with the observed errors. The correlation pattern of the ensemble also exhibits realistic features. The addition of mixed layer perturbations improves both the spread and correlation. Some limitations of the ensemble generation scheme are also discussed. We found that the vertical shift of isopycnal coordinates provokes unrealistically large deviations in shallow layers near the islands of the West Pacific. A simple correction circumvents the problem.

基于Gauss-Markov卡尔曼滤波的电离层数值同化现报预报系统的构建—以中国及周边地区为例的观测系统模拟试验

本文给出了一个基于Gauss-Markov卡尔曼滤波的电离层数据同化系统的初步构建和试验结果.我们选择中国及周边地区部分涉及电离层观测的台站(包括子午工程台站、中国地壳形变网和部分IGS台站)作为观测系统进行模拟试验,背景场利用IRI模式,观测值则由NeQuick模式计算得到.我们的同化结果表明,采用Kalman滤波算法,把部分斜TEC同化到背景模式当中,能够获得较好的同化结果,说明我们设计的算法可行、所选择的各种参数比较合理,采用Gauss-Markov假设进行短期预报也取得了较合理的结果.本项研究经过进一步的改进和完善,可以用来对中国地区的电离层进行现报和短期预报,一方面满足相关空间工程应用,另一方面可以提升现有观测系统的科学意义.     

新安江模型在资料匮乏的长江中下游山区中小流域洪水预报应用

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