Trend analyses with river sediment rating curves

Sediment rating curves, which are fitted relationships between river discharge (Q) and suspended‐sediment concentration (C), are commonly used to assess patterns and trends in river water quality. In many of these studies, it is assumed that rating curves have a power‐law form (i.e. C = aQ^b, where a and b are fitted parameters). Two fundamental questions about the utility of these techniques are assessed in this paper: (i) how well to the parameters, a and b, characterize trends in the data, and (ii) are trends in rating curves diagnostic of changes to river water or sediment discharge? As noted in previous research, the offset parameter, a, is not an independent variable for most rivers but rather strongly dependent on b and Q. Here, it is shown that a is a poor metric for trends in the vertical offset of a rating curve, and a new parameter, â, as determined by the discharge‐normalized power function [C = â (Q/Q_GM)^b], where Q_GM is the geometric mean of the Q‐values sampled, provides a better characterization of trends. However, these techniques must be applied carefully, because curvature in the relationship between log(Q) and log(C), which exists for many rivers, can produce false trends in â and b. Also, it is shown that trends in â and b are not uniquely diagnostic of river water or sediment supply conditions. For example, an increase in â can be caused by an increase in sediment supply, a decrease in water supply or a combination of these conditions. Large changes in water and sediment supplies can occur without any change in the parameters, â and b. Thus, trend analyses using sediment rating curves must include additional assessments of the time‐dependent rates and trends of river water, sediment concentrations and sediment discharge. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Hydrological Processes published by John Wiley & Sons Ltd.     

基于阻尼最小二乘法的位场欧拉反演方法

欧拉反演方法是一种常用的估算场源位置的位场反演方法.在欧拉反演方法中如何获得稳定的解是一个还没有得到很好解决的难题,场源异常相互叠加干扰使反演系数矩阵极易奇异是造成反演结果发散的主要原因.我们基于阻尼最小二乘法对欧拉反演进行了进一步的分析研究,试图提高反演结果聚集度,获得可靠的反演结果.经过研究发现,阻尼因子的选取、系数矩阵的特征值、坐标系原点的位置设置等因素都会影响反演结果.阻尼系数大小与反演结果向原点压缩的程度呈正相关关系;系数矩阵特征值的大小与反演结果向原点压缩的程度呈负相关关系;并且特征值较小的系数矩阵对应的反演结果对坐标原点的位置变化较敏感.当阻尼系数确定后,采取变换坐标参考点的方法可以获得发散性小、稳定性好的反演结果,据此我们提出了变坐标系筛选方法.为了进一步地缩小反演结果的分布范围,我们根据反演结果与窗口中心点的空间关系对反演结果进行再次筛选.模型实验表明,阻尼最小二乘法欧拉反演方法和变坐标系筛选方法简单易行,可以明显地改善反演效果,增强反演结果的聚集程度.

     

地震序列较强余震最小二乘拟合预测的方法研究

本研究给出了针对地震序列较强余震发生时间进行快速预测的一种方法,即最小二乘拟合预测方法.该方法包括:最小二乘拟合预测模型的建立、预测模型的各种可信性检验及模型预测等内容.震例研究表明,该方法在大(强)地震现场震情分析中可望发挥积极作用.     

Time‐varying suspended sediment‐discharge rating curves to estimate climate impacts on fluvial sediment transport

This study presents time‐varying suspended sediment‐discharge rating curves to model suspended‐sediment concentrations (SSCs) under alternative climate scenarios. The proposed models account for hysteresis at multiple time scales, with particular attention given to systematic shifts in sediment transport following large floods (long‐term hysteresis). A series of nested formulations are tested to evaluate the elements embedded in the proposed models in a case study watershed that supplies drinking water to New York City. To maximize available data for model development, a dynamic regression model is used to estimate SSC based on denser records of turbidity, where the parameters of this regression are allowed to vary over time to account for potential changes in the turbidity‐SSC relationship. After validating the proposed rating curves, we compare simulations of SSC among a subset of models in a climate change impact assessment using an ensemble of flow simulations generated using a stochastic weather generator and hydrologic model. We also examine SSC estimates under synthetic floods generated using a peaks‐over‐threshold model. Our results indicate that estimates of extreme SSC under new climate and hydrologic scenarios can vary widely depending on the selected model and may be significantly underestimated if long‐term hysteresis is ignored when simulating impacts under sequences of large storm event. Based on the climate change scenarios explored here, average annual maximum SSC could increase by as much as 2.45 times over historical values.     

地震序列较强余震灰色及最小二乘拟合预测方法的应用研究

在收集、整理1966年～2002年8月我国(主要为大陆地区)183个5级以上地震序列资料的基础上，应用灰色预测和最小二乘拟合预测方法，对每一地震序列较强(显著)余震发震时间进行“硬性”检验预测。结果表明，2种预测方法对7级以上地震序列和6级地震序列的较强(显著)余震具有较好的预测效能，预测效能分别为76．2％和64．7％；而对5级地震序列预测效果则不甚明显。因此，灰色预测和最小二乘拟合预测方法主要着眼于对大(强)地震的较强(显著)余震预测，可望在地震现场大(强)震应急工作中发挥积极作用。     

利用最小二乘配置在球面上整体解算GPS应变场的方法及应用

通过对位移与应变微分公式的分析,给出了球坐标系下应用最小二乘配置法由GPS数据整体解算应变场的方法.首先,对模拟数据的理论应变场与采用全部采样数据和随机抽取50%限定采样数据的应变场结果的对比分析,初步验证了最小二乘配置球面整体解应变场方法的有效性.然后,利用1999~2001和2001~2004两期中国大陆区域网GPS速度场数据给出了基于最小二乘配置球面解获得的应变率场结果,概要分析了此期间中国大陆应变率场的动态变化.最后,讨论了最小二乘配置在球面上计算应变场需要注意的问题和建议.     

Time‐varying,nonlinear suspended sediment rating curves to characterize trends in water quality: An application to the Upper Hudson and Mohawk Rivers,New York

This work proposes two modelling frameworks for diagnosing temporal variations in nonlinear rating curves that describe suspended sediment–discharge relationships. A variant of the weighted regression on time, discharge, and season model is proposed and is compared against dynamic nonlinear modelling, a newly developed nonlinear time series filter based on sequential Monte Carlo sampling. Both approaches estimate a time series of rating curve parameters, with uncertainty, that can be used to diagnose variability in the sediment–discharge relationship over time. We evaluate the models with a variety of synthetic scenarios to highlight their ability to estimate signals of known rating curve change. Results reveal important bias‐variance trade‐offs unique to each approach, and in general, suggest that dynamic nonlinear modelling is better suited for rapid rating curve changes, whereas the weighted regression on time, discharge, and season variant more precisely estimates slow change. The techniques are then applied in two case studies in the Upper Hudson and Mohawk Rivers in New York. We conclude with a discussion of the implications of dynamic rating curves for the management of water quality in riverine and estuary systems.