Application of a data‐based mechanistic modelling (DBM) approach for predicting runoff generation in semi‐arid regions

This paper addresses the application of a data‐based mechanistic (DBM) modelling approach using transfer function models (TFMs) with non‐linear rainfall filtering to predict runoff generation from a semi‐arid catchment (795 km²) in Tanzania. With DBM modelling, time series of rainfall and streamflow were allowed to suggest an appropriate model structure compatible with the data available. The model structures were evaluated by looking at how well the model fitted the data, and how well the parameters of the model were estimated. The results indicated that a parallel model structure is appropriate with a proportion of the runoff being routed through a fast flow pathway and the remainder through a slow flow pathway. Finally, the study employed a Generalized Likelihood Uncertainty Estimation (GLUE) methodology to evaluate the parameter sensitivity and predictive uncertainty based on the feasible parameter ranges chosen from the initial analysis of recession curves and calibration of the TFM. Results showed that parameters that control the slow flow pathway are relatively more sensitive than those that control the fast flow pathway of the hydrograph. Within the GLUE framework, it was found that multiple acceptable parameter sets give a range of predictions. This was found to be an advantage, since it allows the possibility of assessing the uncertainty in predictions as conditioned on the calibration data and then using that uncertainty as part of the decision‐making process arising from any rainfall‐runoff modelling project. Copyright © 2001 John Wiley & Sons, Ltd.     

用方差分析法预测濮阳市冬小麦播种期降水量趋势

使用1971--2000年濮阳市冬小麦播种期降水量资料，采用方差分析法，按不同长度周期进行排列，求出各个周期的F值，进行F检验定出第一周期，其位相值为第一周期的第一次值。将原序列减去第一周期的位相值作为新序列1，对新序列1再进行不同长度周期排列，求出各个周期的F值，再进行F检验定出第二周期。对第一、二周期进行稳定性检查，采用经稳定后的第一、二周期的第2次值各自外推，求出预报初值，计算出剩余方差，建立预报方程，进而可计算出降水量预报值。经回代检验，历史拟合率为30／30=100％；2001—2005年试报准确率为4／5=80％。     

Real-time reservoir management: A multiscale adaptive optimization and control approach

We propose a decision-making approach for optimizing the profitability of hydrocarbon reservoirs. The proposed approach addresses the overwhelming complexity of the overall optimization problem by suggesting an oilfield operations hierarchy that entails different time scales. We discuss system identification, optimization, and control that are appropriate at various levels of the hierarchy and capitalize on the abilities of permanently instrumented and remotely actuated fields. Optimization is performed in real-time and is based on feedback. We provide details on real-time identification of hybrid models and their use at the scheduling and supervisory control levels. Case studies using field-calibrated simulation data demonstrate the applicability and value of the proposed approach. Directions for future development are given.     

Refinement of predictive reaeration equations for a typical Indian river

Dissolved oxygen mass balance has been computed for different reaches of River Kali in western Uttar Pradesh (India) to obtain the reaeration coefficient (K₂). A total of 270 field data sets have been collected during the period from March 1999 to February 2000. Eleven most popular predictive equations, used for reaeration prediction and utilizing mean stream velocity, bed slope, flow depth, friction velocity and Froude number, have been tested for their applicability in the River Kali using data generated during field survey. The K₂ values computed from these predictive equations have been compared with the K₂ values observed from dissolved oxygen balance measurements in the field. The performance of predictive equations have been evaluated using error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and correlation statistics. The equations developed by Smoot and by Cadwallader and McDonnell showed comparatively better results. Moreover, a refined predictive equation has been developed using a least‐squares algorithm for the River Kali that minimizes error estimates and improves correlation between observed and computed reaeration coefficients. Copyright © 2001 John Wiley & Sons, Ltd.     

星敏感器/GNSS定姿系统的联合非线性预测滤波算法

提出一种联合式非线性预测滤波算法,解决该系统在姿态动力学模型误差非高斯分布条件下的多敏感器信息融合问题。从算法结构和估计准则两个方面证明非线性预测滤波(NPF)与Kalman滤波的等效性,分析联合式NPF的算法流程,讨论模型误差方差矩阵的计算方法,给出加权系数矩阵的设计准则;介绍星敏感器和全球卫星导航系统(GNSS)的定姿原理,推导星敏感器/GNSS组合姿态确定系统的联合式NPF滤波模型,分析系统的算法实现流程;进行数值仿真试验,结果表明联合式NPF算法融合NPF与联邦滤波的优良品质,可有效解决姿态动力学模型误差非高斯分布条件下无陀螺姿态确定系统的多敏感器信息融合问题。     

山东夏季降水量场预测模型的改进及预测效果检验

本文对2002年建立的山东夏季降水量场预测模型进行了多方面的改进。预测因子的选取在原来以常规100、500hPa或海温状态场的关键区域平均值基础上,增加了能表征天气系统月际演变趋势及区域环流总体特征的遥相关型环流指数,建模资料的选取结合了环流突变的气候背景,以1977—2008年资料进行建模,建模方案在原来纯数学分析的基础上,增加了着重考虑因子物理意义的建模方案,用2009—2011年实况资料对两种建模方案及2002年建模方案的预测结果进行对比分析。结果表明,改进后预测模型较旧预测模型的预测效果有较大提高,而物理统计预测模型对降水的空间分布也具有较好的预测效果。     

A supplementary approach for estimating reaeration rate coefficients

Different commonly used predictive equations for the reaeration rate coefficient (K₂) have been evaluated using 231 data sets obtained from the literature and 576 data sets measured at different reaches of the River Kali in western Uttar Pradesh, India. The data sets include stream/channel velocity, bed slope, flow depth, cross‐sectional area and reaeration rate coefficient (K₂), obtained from the literature and generated during the field survey of River Kali, and were used to test the applicability of the predictive equations. The K₂ values computed from the predictive equations have been compared with the corresponding K₂ values measured in streams/channels. The performance of the predictive equations has been evaluated using different error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and coefficient of determination (r²). The results show that the reaeration rate equation developed by Parkhurst and Pomeroy yielded the best agreement, with the values of SE, NME, MME and r² as 33·387, 4·62, 3·58 and 0·95, respectively, for literature data sets (case 1) and 37·567, 3·57, 2·6 and 0·95, respectively, for all the data sets (literature data sets and River Kali data sets) (case 2). Further, to minimize error estimates and improve correlation between measured and computed reaeration rate coefficients, supplementary predictive equations have been developed based on Froude number criteria and a least‐squares algorithm. The supplementary predictive equations have been verified using different error estimates and by comparing measured and computed reaeration rate coefficients for data sets not used in the development of the equations. Copyright © 2003 John Wiley & Sons, Ltd.     

F-K变换与预测反褶积压制多次波效果对比

多次波压制是地震资料处理中一个重要步骤。根据多次波发生下行反射的位置,可以把多次波分为两类:自由界面多次波和层间多次波。不同的多次波有着不同特性,一种压制多次波的方法往往只能针对特定的某一种多次波进行压制。F-K域压制多次波和预测反褶积分别利用了多次波的可分性和周期性这两种具有代表性压制多次波的处理方法。这里首先利用声波时差有限差分的方法对模型进行了正演计算,得到了包含不同类型多次波的地震数据;然后对正演数据分别应用F-K域压制多次波和预测反褶积两种压制多次波技术对自由界面多次波和层间多次波进行处理;最后对比分析了两种方法的处理效果,总结了两种方法的适用条件。F-K域多次波压制手段适用于多次波与一次波时差差异较大的情况,预测反褶积对小炮检距附近严格满足周期性的多次波去除效果较好。     

多尺度气象干旱与土壤相对湿度的关系研究

通过分析我国东北、华北、西北地区东部、西南以及黄淮、江淮和江汉5个区域不同时间尺度气象干旱指数与20 cm土壤相对湿度的相关关系, 探讨了前期气象干旱对后期土壤湿度的影响, 并利用多元线性回归方法分区域、分季节建立了土壤湿度预测模型.结果表明: 春季, 东北地区土壤湿度主要受前5~6个月, 尤其是上年秋末冬初的降水的影响, 而其他4个区域土壤湿度主要受前1~2个月大气水分的影响;各区域夏季土壤湿度与前1~2个月时间尺度上的大气水分相关最密切;秋季, 东北地区20 cm土壤湿度主要受前2~4个月的气象干旱的影响, 其余区域土壤湿度仍与前1~2个月尺度的大气水分相关最密切.基于前期气象干旱指数建立的各区域、各季节的土壤湿度回归模型对当地土壤湿度具有一定的拟合能力, 平均估计偏差在10.1%~13.9%之间, 其中, 西北地区东部和华北地区春、夏季偏差较大, 2008-2011年间干旱等级拟合准确率在65%~74.9%之间;东北、西南、黄淮、江淮和江汉区域拟合较好, 拟合准确率在88%以上.     

A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland

This study compares the predictive accuracy of eight state‐of‐the‐art modelling techniques for 12 landforms types in a cold environment. The methods used are Random Forest (RF), Artificial Neural Networks (ANN), Generalized Boosting Methods (GBM), Generalized Linear Models (GLM), Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), Classification Tree Analysis (CTA) and Mixture Discriminant Analysis (MDA). The spatial distributions of 12 periglacial landforms types were recorded in sub‐Arctic landscape of northern Finland in 2032 grid squares at a resolution of 25 ha. First, three topographic variables were implemented into the eight modelling techniques (simple model), and then six other variables were added (three soil and three vegetation variables; complex model) to reflect the environmental conditions of each grid square. The predictive accuracy was measured by two methods: the area under the curve (AUC) of a receiver operating characteristic (ROC) plot, and the Kappa index (κ), based on spatially independent model evaluation data. The mean AUC values of the simple models varied between 0·709 and 0·796, whereas the AUC values of the complex model ranged from 0·725 to 0·825. For both simple and complex models GAM, GLM, ANN and GBM provided the highest predictive performances based on both AUC and κ values. The results encourage further applications of the novel modelling methods in geomorphology. Copyright © 2008 John Wiley & Sons, Ltd.