Multi‐scale assimilation of root zone soil water predictions

When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi‐scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi‐scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi‐scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi‐scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd.     

Time-domain identification of dynamic properties of layered soil by using extended Kalman filter and recorded seismic data

A novel time-domain identification technique is developed for the seismic response analysis of soil-structure interaction. A two-degree-of-freedom (2DOF) model with eight lumped parameters is adopted to model the frequency-dependent behavior of soils. For layered soil, the equivalent eight parameters of the 2DOF model arc identified by the extended Kalman filter (EKF) method using recorded seismic data. The polynomial approximations for derivation of state estimators are applied in the EKF procedure. A realistic identification example is given for the layered-soil of a building site in Anchorage, Alaska in the United States. Results of the example demonstrate the feasibility and practicality of the proposed identification technique. The 2DOF soil model and the identification technique can be used for nonlinear response analysis of soil-structure interaction in the time-domain for layered of complex soil conditions. The identified parameters can be stored in a database for use in other similar soil conditions. If a universal database that covers information related to most soil conditions is developed in the future, engineers could conveniently perform time history analyses of soil-structural interaction.     

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.     

Using a H∞ filter assimilation procedure to estimate root zone soil water content

Root zone soil water content impacts plant water availability, land energy and water balances. Because of unknown hydrological model error, observation errors and the statistical characteristics of the errors, the widely used Kalman filter (KF) and its extensions are challenged to retrieve the root zone soil water content using the surface soil water content. If the soil hydraulic parameters are poorly estimated, the KF and its extensions fail to accurately estimate the root zone soil water. The H‐infinity filter (HF) represents a robust version of the KF. The HF is widely used in data assimilation and is superior to the KF, especially when the performance of the model is not well understood. The objective of this study is to study the impact of uncertain soil hydraulic parameters, initial soil moisture content and observation period on the ability of HF assimilation to predict in situ soil water content. In this article, we study seven cases. The results show that the soil hydraulic parameters hold a critical role in the course of assimilation. When the soil hydraulic parameters are poorly estimated, an accurate estimation of root soil water content cannot be retrieved by the HF assimilation approach. When the estimated soil hydraulic parameters are similar to actual values, the soil water content at various depths can be accurately retrieved by the HF assimilation. The HF assimilation is not very sensitive to the initial soil water content, and the impact of the initial soil water content on the assimilation scheme can be eliminated after about 5–7 days. The observation interval is important for soil water profile distribution retrieval with the HF, and the shorter the observation interval, the shorter the time required to achieve actual soil water content. However, the retrieval results are not very accurate at a depth of 100 cm. Also it is complex to determine the weighting coefficient and the error attenuation parameter in the HF assimilation. In this article, the trial‐and‐error method was used to determine the weighting coefficient and the error attenuation parameter. After the first establishment of limited range of the parameters, ‘the best parameter set’ was selected from the range of values. For the soil conditions investigated, the HF assimilation results are better than the open‐loop results. Copyright © 2010 John Wiley & Sons, Ltd.     

Application of data assimilation with the Root Zone Water Quality Model for soil moisture profile estimation in the upper Cedar Creek,Indiana

Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field‐scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd.     

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.

     

Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter

Land data assimilation (DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations. Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus, improve the accuracy of state variables. It can also reduce uncertainties in the model by estimating some model parameters simultaneously. Among the various DA methods, the particle filter is free from the constraints of linear models and Gaussian error distributions, and can be applicable to any nonlinear and non-Gaussian state-space model; therefore, its importance in land data assimilation research has increased. In this study, a DA scheme was developed based on the residual resampling particle filter. Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously. Finally, to verify the scheme, a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona. The results show that the scheme can improve the accuracy of soil moisture estimations significantly. In addition, the three hydraulic parameters were also well estimated, demonstrating the effectiveness of the DA scheme.