A comparative modeling study of a dual tracer experiment in a large lysimeter under atmospheric conditions

In this paper, five model approaches with different physical and mathematical concepts varying in their model complexity and requirements were applied to identify the transport processes in the unsaturated zone. The applicability of these model approaches were compared and evaluated investigating two tracer breakthrough curves (bromide, deuterium) in a cropped, free-draining lysimeter experiment under natural atmospheric boundary conditions. The data set consisted of time series of water balance, depth resolved water contents, pressure heads and resident concentrations measured during 800 days. The tracer transport parameters were determined using a simple stochastic (stream tube model), three lumped parameter (constant water content model, multi-flow dispersion model, variable flow dispersion model) and a transient model approach. All of them were able to fit the tracer breakthrough curves. The identified transport parameters of each model approach were compared. Despite the differing physical and mathematical concepts the resulting parameters (mean water contents, mean water flux, dispersivities) of the five model approaches were all in the same range. The results indicate that the flow processes are also describable assuming steady state conditions. Homogeneous matrix flow is dominant and a small pore volume with enhanced flow velocities near saturation was identified with variable saturation flow and transport approach. The multi-flow dispersion model also identified preferential flow and additionally suggested a third less mobile flow component. Due to high fitting accuracy and parameter similarity all model approaches indicated reliable results.     

Parameter-independent model reduction of transient groundwater flow models: Application to inverse problems

A new methodology is proposed for the development of parameter-independent reduced models for transient groundwater flow models. The model reduction technique is based on Galerkin projection of a highly discretized model onto a subspace spanned by a small number of optimally chosen basis functions. We propose two greedy algorithms that iteratively select optimal parameter sets and snapshot times between the parameter space and the time domain in order to generate snapshots. The snapshots are used to build the Galerkin projection matrix, which covers the entire parameter space in the full model. We then apply the reduced subspace model to solve two inverse problems: a deterministic inverse problem and a Bayesian inverse problem with a Markov Chain Monte Carlo (MCMC) method. The proposed methodology is validated with a conceptual one-dimensional groundwater flow model. We then apply the methodology to a basin-scale, conceptual aquifer in the Oristano plain of Sardinia, Italy. Using the methodology, the full model governed by 29,197 ordinary differential equations is reduced by two to three orders of magnitude, resulting in a drastic reduction in computational requirements.     

Bayesian estimation of inflow hydrographs in ungauged sites of multiple reach systems

A Bayesian Geostatistical Approach to evaluate unknown upstream flow hydrographs in multiple reach systems is implemented. The methodology was, firstly, tested through three synthetic examples of river confluences, that differ in the available data, boundary conditions and number of the estimated inflow time series. Input discharge hydrographs were routed downstream by means of the widely known HEC-RAS river analysis system to obtain the downstream stage hydrographs used as known observations for the reverse procedure. In almost all cases, the observed water levels were corrupted with random errors to highlight the reliability of the methodology in preventing instabilities and overfitting. Then the procedure was applied to the real case study of the Parma–Baganza river confluence located at the city of Parma (Italy) to assess the tributary Baganza River inflow hydrograph (supposed completely ungauged) using water level data collected downstream on the main reach. The results show that the methodology properly reproduces the unknown inflows even in presence of errors affecting the downstream water levels. The practical applicability of the proposed approach is also demonstrated in complex river systems.     

Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.     

Short-term optimal operation of water systems using ensemble forecasts

Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.     

Response of hydrology to climate change in the southern Appalachian Mountains using Bayesian inference

Predicting long‐term consequences of climate change on hydrologic processes has been limited due to the needs to accommodate the uncertainties in hydrological measurements for calibration, and to account for the uncertainties in the models that would ingest those calibrations and uncertainties in climate predictions as basis for hydrological predictions. We implemented a hierarchical Bayesian (HB) analysis to coherently admit multiple data sources and uncertainties including data inputs, parameters, and model structures to identify the potential consequences of climate change on soil moisture and streamflow at the head watersheds ranging from low to high elevations in the southern Appalachian region of the United States. We have considered climate change scenarios based on three greenhouse gas emission scenarios of the Interovernmental Panel on Climate Change: A2, A1B, and B1 emission scenarios. Full predictive distributions based on HB models are capable of providing rich information and facilitating the summarization of prediction uncertainties. With predictive uncertainties taken into account, the most pronounced change in soil moisture and streamflow would occur under the A2 scenario at both low and high elevations, followed by the A1B scenario and then by the B1 scenario. Uncertainty in the change of soil moisture is less than that of streamflow for each season, especially at high elevations. A reduction of soil moisture in summer and fall, a reduction or slight increase of streamflow in summer, and an increase of streamflow in winter are predicted for all three scenarios at both low and high elevations. The hydrological predictions with quantified uncertainties from a HB model could aid more‐informed water resource management in developing mitigation plans and dealing with water security under climate change. Copyright © 2012 John Wiley & Sons, Ltd.     

上海及邻区尾波Q值研究

选取2011年1月~2012年12月上海台网数据库中记录的37次ML≥2.0地震的数字地震波资料,利用单次散射的Aki模型,计算上海及邻区尾波Q值,拟合Q值对频率的依赖关系,结果为Q(f)=(176.2±87.26)·f0.898 4±0.290 8。与国内其他区域相比,本区域Q值偏大,对频率依赖性低。结合上海及邻区地质特征及同期小震空间分布特点,将研究区域划分为以下4个小震密集区:苏中沿海区域、南黄海区域、苏州—东海区域、宁波—舟山区域,分区进行Q值统计分析,表明尾波Q值数据能够较好地反映该区域地震活动性。     

紫坪铺水库造成孔隙弹性耦合变化及其对2008年汶川地震触发作用

位于川西地区龙门山断裂带附近的紫坪铺水库于2005年9月开始蓄水.约2.7年后,2008年5月12日,M_w7.9级汶川地震在龙门山断裂带上发生,两事件在时间和空间上的接近,揭示其可能相互关联,但前人的诸多研究给出了不同甚至是相反的结果.本研究基于完全耦合孔隙弹性理论,利用二维有限元模型(FEM),模拟水库蓄水造成的区域孔隙压力场和应力场的演化过程,基于库仑应力演化探讨其对龙门山断裂带活动的影响.模拟结果显示紫坪铺水库蓄水打破了原来的区域孔隙压力平衡,形成孔隙压力梯度源,向周围地壳传播;进而造成龙门山断裂带上库仑应力正值范围不断扩大,由浅入深影响到整条断层,尤其对浅层范围的加载作用明显,达上百千帕,为整个断层面的失稳提供了基础.震源区域库仑应力呈持续增长趋势,汶川地震发震前,增长了约数千帕~数十千帕,即使初期库仑应力为负,在未来某时刻库仑应力仍可能由负转正,并不断增大.通过计算汶川地震震源及其附近区域内相对于多种可能断层倾角的库仑应力,发现库仑应力随断层倾角增大而增加.因此整体来看,紫坪铺水库蓄水对龙门山断裂带起加载作用,有可能触发地震.对紫坪铺库区周围的小震分析也显示,蓄水以来小震明显增多、且不同时段小震发生密集区与水库距离逐渐增大,与孔隙压力扩散趋势一致.以上结果表明紫坪铺水库的蓄水增加了汶川地震的危险性.     

基于入射角的两项流体阻抗反演方法

本文在孔隙弹性介质理论的指导下,基于入射角AVO近似方程推导了包含Russell流体项的两项AVO近似方程和相应的弹性阻抗方程,通过分析可知其精度符合反演要求.在贝叶斯理论框架下,建立了包含正则化约束的弹性阻抗反演方法,在此基础上直接提取Russell流体项.该方法可在缺少大角度叠前地震资料的情况下进行叠前直接反演得到流体因子,减少传统方法带来的累积误差.模型试算表明,该方法具有较好的准确度和稳定性.实际工区应用取得了良好的效果,表明该方法有实用性.     

基于新的非局部先验模型的Bayesian低剂量CT重建算法

为了改善低剂量CT重建图像质量,在传统非局部先验的基础上,提出了一种基于投影对称性的改进非局部先验模型。基于该先验模型构造了一种贝叶斯（Bayesian）重建算法,并将其应用到低剂量CT投影数据降噪中,通过滤波反投影算法重建出图像。仿真实验结果表明,本文所提出的算法较基于传统先验模型的重建算法,能在去除噪声与保持边缘之间取得较好的平衡。