Using multivariate statistical techniques and geochemical modelling to identify factors controlling the evolution of groundwater chemistry in a typical transitional area between Taihang Mountains and North China Plain

Identifying the key factors controlling groundwater chemical evolution in mountain-plain transitional areas is crucial for the security of groundwater resources in both headwater basins and downstream plains. In this study, multivariate statistical techniques and geochemical modelling were used to analyse the groundwater chemical data from a typical headwater basin of the North China Plain. Groundwater samples were divided into three groups, which evolved from Group A with low mineralized Ca-HCO₃ water, through Group B with moderate mineralized Ca-SO₄-HCO₃ water, to Group C with highly saline Ca-SO₄ and Ca-Cl water. Water-rock interaction and nitrate contamination were mainly responsible for the variation in groundwater chemistry. Groundwater chemical compositions in Group A were mainly influenced by dissolution of carbonates and cation exchange, and suffered less nitrate contamination, closely relating to their locations in woodland and grassland with less pronounced human interference. Chemical evolution of groundwater in Groups B and C was gradually predominated by the dissolution of evaporites, reverse ion exchange, and anthropogenic factors. Additionally, the results of the inverse geochemical model showed that dedolomitization caused by gypsum dissolution, played a key role in the geochemical evolution from Group A to Group B. Heavy nitrate enrichment in most groundwater samples of Groups B and C was closely associated with the land-use patterns of farmland and residential areas. Apart from the high loads of chemical fertilizers in irrigation return flow as the main source for nitrate contamination, the stagnant zones, flood irrigation pattern, mine drainage, and groundwater-exploitation reduction program were also important contributors for such high mineralization and heavy NO₃⁻ contents in Group C. The important findings of this work not only provide the conceptual framework for the headwater basin but also have important implications for sustainable management of groundwater resources in other headwater basins of the North China Plain.     

Serial dependence properties in multivariate streamflow simulation with independent decomposition analysis

Stochastic simulation of multivariate hydrologic variables has a key role in evaluating alternative designs and operation rules of hydrologic facilities. The recently developed decomposition analysis, Independent Component Analysis (ICA), allows us to apply the simple univariate time series model to each extracted component by: (1) decomposing multivariate time series into independent components with ICA; (2) modelling and generating each component independently; and (3) mixing the generated components to come back to observational domain. However, we illustrate in the current study that fitting a univariate time series model to each extracted component might end up with the underestimation of the serial dependence that the observation data might contain. A alternative for parameter estimation is suggested to preserve the serial dependence of the observation variable using the relationship between the observation variable and the decomposed variable. The case study of the Upper Colorado River basin shows that some improvement is made through the suggested alternative. Copyright © 2011 John Wiley & Sons, Ltd.     

13e SESSION DU COMITÉ CONSULTATIF KARACHI 4–15 NOVEMBRE 1957

Abstract

Event-based methods are used in flood estimation to obtain the entire flood hydrograph. Previously, such methods adopted in the UK have relied on pre-determined values of the input variables (e.g. rainfall and antecedent conditions) to a rainfall–runoff model, which is expected to result in an output flood of a particular return period. In contrast, this paper presents a method that allows all the input variables to take on values across the full range of their individual distributions. These values are then brought together in all possible combinations as input to an event-based rainfall–runoff model in a Monte Carlo simulation approach. Further, this simulation strategy produces a long string of events (on average 10 per year), where dependencies from one event to the next, as well as between different variables within a single event, are accounted for. Frequency analysis is then applied to the annual maximum peak flows and flow volumes.

Citation Svensson, C., Kjeldsen, T.R., and Jones, D.A., 2013. Flood frequency estimation using a joint probability approach within a Monte Carlo framework. Hydrological Sciences Journal, 58 (1), 1–20.     

基于三种多变量不安定指数分析模型的汶川县地质灾害易发性评价

采用多变量不安定指数分析法模型并加以改进,应用于汶川县地质灾害易发性评价。选取坡度、坡向、地层岩性、距断层距离、植被覆盖率及距水系距离六项影响因子,结合四川省自然资源厅发布的汶川县地质灾害隐患点数据,以幂次相乘、线性累加、幂次累加这三种不同的不安定指数分析法模型分别得到了研究区地质灾害易发性分区图,并用接受者操作特性曲线（Receiver Operating Characteristic curve, ROC curve）验证了各种模型的评价性能。结果表明:（1）对本案例而言,幂次相乘模型相较其它两种模型具有最高的精度;（2）汶川县地质灾害“极高”“高”“中”“低”“极低”易发区的面积占比分别为:19.3%、24.6%、19.2%、19.3%、17.6%,且研究区地质灾害易发性较高的区域多分布在断裂带附近。本研究成果可为区域地质灾害防治工作提供理论借鉴和技术参考。     

气象产品的广义联合偏差修正方法及其应用

卫星气象产品、气候模式预测数据通常与地面观测数据存在偏差,为保证数据的可靠性和合理性,需要对其进行偏差校正,但偏差校正过程往往受具体区域气象特征、方法本身假定等因素的影响,导致修正效果不佳。为此,本文提出一种广义联合偏差修正方法,相较于现有研究最常用的单变量QM修正方法以及固定多变量修正顺序的JBC修正方法,该方法充分考虑到流域尺度降水和气温双变量的时空相关性,并结合其对径流的主导作用对变量修正顺序进行动态调整,实现了QM法和JBC法的优势互补。在澜沧江-湄公河流域的应用表明：考虑降水、气温相关性可显著改善降水和温度极值的修正效果,尤其是5、6月份,修正后气象与实测数据的纳什系数提升了0.5以上;考虑气象要素的修正次序显著降低了修正后的降水和温度频率分布及均值偏差;利用修正后的气象数据驱动分布式水文模型时,部分月份的径流模拟精度提升了54.3%。     

节理粗糙度对岩块体积的影响规律研究

岩体结构直接控制着岩体力学、水力学特性,岩块体积分布规律可直接反映岩体结构特征。目前研究多将节理面简化为平面,忽略了粗糙度对岩块体积的影响。利用Hurst指数H和高度均方根R_q表征节理粗糙度,研究了节理粗糙度对岩块数量和体积分布规律的影响。结果表明：(1)H、R_q控制节理的粗糙起伏形态,粗糙度随着R_q的增大而增大、随着H的增大而减小;(2)块体的数量总体上与节理粗糙度正相关,即随着粗糙度的增大而增加;(3)R_q和H对岩块体积分布的影响,主要通过改变小体积块体所占比例实现,块体的平均体积和中位体积随着粗糙度的增大而减小;(4)当节理正交且间距相近时,块体数量随粗糙度的变化可以分为3个阶段,即稳定阶段、初始增长阶段和快速增长阶段,R_q和H共同控制着各区间的相对分布范围。最后,基于摄影测量采集的数据建立了岩体节理模型,对大连某公路边坡的岩块体积分布进行了研究,验证了上述结论的准确性。     

基于多元资料的成都市2049远景发展战略规划气候可行性论证

配合2015年成都市2049远景发展战略规划编制,利用成都市气象和空气质量观测数据、卫星遥感、地理信息及城市规划土地利用资料,对该市风、热/冷环境和大气环境进行计算分析,结果表明:①成都市主导风向为NE,年平均风速为1.2m/s,静风频率高达34%,冬季风速最小。市域内风速总体呈东北向西南递减趋势,西部沿龙门山岷江口和湔江口为市域2处主要通风口。21世纪00年代城市集中区风速较20世纪80、90年代显著减少,NE和NW风向上减少可达0.5m/s。②该市较强等级以上热岛面积由1992年的53.6km~2发展至2014年的798km~2,且热岛中心呈现多极化。高级别生态冷源集中于龙门山和龙泉山,大规模城市扩张与沿山坡耕地是热/冷环境恶化的重要原因。③成都大气环境状况不佳,空气污染程度呈现城区—近郊—远郊递减特征。2007—2014年城区PM10和NO2平均浓度分别为0.116mg/m~3和0.054mg/m~3,均未达国家环境空气质量二级标准。④绘制了成都市城市气候分析图,并结合实地调查、土地利用现状以及发展诉求,形成了城市规划气候建议图,将全市划分为6类规划分区,明确了对气候影响程度不同的各类空间,进而为成都市2049远景发展战略规划提出了气候可行性建议。     

钻井液连续循环钻井技术及自动化装备设计

钻井施工中,拆装钻杆需中止钻井液循环,但钻井液循环中止会导致孔内岩屑下沉、钻井液当量循环密度浮动变化,进而影响孔内安全。为避免上述情况的发生,钻井液不间断循环钻井技术的构想被提出。通过连续循环系统或连续循环接头进行钻井液分流,避开钻杆拆装部分,维持井内的钻井液循环,稳定钻井液当量循环密度。针对该技术原理,提出了一种自动化的钻井液不间断循环井口设备,通过背钳、自动钻井液切换系统和传扭动力卡瓦的配合,可实现钻井中拆卸、接装单根时钻井液不间断循环,有效避免因钻井液静置、沉砂过多导致的钻孔安全问题。     

联合反演模型中相对权比的约束反演

为确定联合反演模型中的相对权比,提出根据数据先验信息对相对权比进行一定约束,并将相对权比同其他模型参数一并作为未知参数参与反演的方法。数值实验表明,当先验信息可靠时,运用该方法可以获得较好的结果。     

大气校正对SPOT卫星遥测水质的影响

藉由卫星遥测进行河川水质监测，目前尚没有较明确可行之方法，如何利用较为简单且适当的SPOT卫星遥测大气校正方法，正确辨识水体水质，是本研究的主要目的。利用SPOT卫星作两阶段非监督式及监督式自动分类，确认卫星影像中水质测站对应之水体样本，并将所有样本依季节分群，俾让卫星监测水体水质样本较为均质。模拟方式采用多变量回归、类神经网络及判别分析3种模式，并比较4种不同之大气校正程序。结果发现。以水质及其指标整体预测来看，类神经网络预测结果较优于多变量回归及判别分析的结果，大气校正方法以直接采用灰度值并消除最暗像元灰度值之校正方法，即可达到不错之预测结果。综合而言，以SPOT或分辨率更高之卫星光谱遥测水质是简单可行，但仍需更多数据以验证其精确度。