Identification of the controlling factors for hydrological responses by artificial neural networks

Identifying the controlling factors for hydrological responses is of great importance for artificial neural network-based flood forecasting models, which are often hindered by the lack of physical mechanisms. To explore the first-order controlling factors of hydrograph patterns, a hybrid neural network was designed to analyse the impacts of potential driving variables with different temporal and spatial resolutions on hydrograph patterns. The Jinhua River Basin in Southeast China was used as an example in this study. Flood events with different hydrograph patterns and six external factors denoting potential controlling factors were individually classified into specific clusters using self-organizing maps (SOMs). Based on the back-propagation neural network (BPNN) and leave-one-out cross-validation methods, the controlling factors of different flood patterns were identified by comparing the performances of flood simulation models trained with datasets before and after the potential controlling factor classification. The results showed that (i) the classification of controlling factors indicating various runoff regimes significantly improved the performance of data-driven models in flood simulation in terms of correlation coefficient, Nash-Sutcliffe coefficient, and normalized root mean square error; (ii) the spatial distribution of antecedent soil moisture and vegetation conditions as well as the temporal distribution of rainfall dominated different hydrograph patterns; and (iii) the transition of dominant rainfall-runoff processes could be identified in an individual flood event using the hybrid SOM–BPNN model, indicating the varying influence of potential controlling factors on streamflow. Overall, the hybrid neural network models trained with datasets classified by controlling factors provide a general analytical framework to identify the governing dynamics for different flood patterns and improve the accuracy of flood simulations. Additionally, more attention should be devoted to improving the time to peak error of hydrological models, which cannot be settled by data-driven models trained with different data-splitting strategies.     

基于SOM和PSO的非监督地震相分析技术

地震相分析技术是储层预测的一种重要方法,可以用来描述有利沉积相带的分布规律.传统的地震相聚类分析方法对大数据的处理运算速度较慢,且容易陷入局部极小值,造成聚类分析的结构不准确.本文提出基于自组织神经网络(SOM)和粒子群优化方法(PSO)相结合的地震相分析技术,利用自组织神经网络能够保持原始地震数据的拓扑结构特性的特点,将大量冗余样本压缩为小样本数据,再通过粒子群的全局寻优能力改善K均值聚类的效果.理论模型和实际应用表明该方法能既有效实现数据压缩,又能提供较为准确的全局解,在地震相预测中兼顾计算效率和计算精度.     

中国近50年来群发性高温事件的识别及统计特征

本文提出一种简化的识别群发性气候事件方法,对近50多年来全国群发性高温事件识别结果表明该方法物理意义明确,客观有效,符合实际.通过对近50年来群发性高温事件统计发现,不论是从发生频数、持续时间、影响站数还是综合强度来看,2000年以后的群发性高温事件都是5个年代中最显著的.与此同时,群发性高温事件具有明显的区域特征,长江以南地区发生频率最高,其次为黄河下游华北地区,以新疆为主的西北地区亦是多发区,各个区域群发性高温事件在不同月份的分布差异体现了我国季风雨带移动及下垫面的影响.     

Assessment of chemical water types and their spatial variation using multi-stage cluster analysis, Queensland, Australia

A multivariate assessment has been adapted to the classification of a large, irregular dataset of approximately 34,000 surface water samples accumulated over more than 30 years. A two-stage K-means clustering method was designed to analyse chemical data in the form of percentages of major ions (Na, Mg, Ca, Cl, HCO₃ and SO₄); the first stage of clustering produced 347 groups, which were then re-clustered to generate the final nine water types. The analysis enabled the definition of provinces of water composition and highlighted natural processes influencing the surface water chemistry. On a statewide basis, sodium is the dominant cation and around 50% at all stream flows, while proportions of calcium and magnesium are about equal. Chloride and bicarbonate constitute the bulk of anions present, while sulfate occurs occasionally and tends to be localised. On a global basis, Queensland surface waters are relatively high in sodium, chloride and magnesium, and low in calcium and sulfate. It was also found that the geographical location has a greater impact on major ion ratios than does the stage of stream flow.

The regional chemical trends are consistent with geology and climate. Streams in northeast Queensland, with short, steep catchments and high rainfall, yield low salinity, sodium-dominated water; this is also the case for sandy southern coastal catchments. Both also reflect an oceanic influence. The proportions of sodium and chloride decrease westward; streams draining the western side of the Great Dividing Range or flowing into the Gulf of Carpentaria have low salinity but relatively hard water. Streams in western Queensland are higher in calcium and bicarbonate. In the large catchments flowing from Queensland into central Australia, the water composition is highly variable, commonly with elevated sulfate. Also in Queensland, there are several other clearly definable water provinces such as the high magnesium waters of basaltic areas.

The findings of this study confirm that the application of such analytical methods can provide a useful assessment of controls over water composition and support management at regional level; the approach used is shown and are applicable to large, disparate datasets.     

Singular spectrum analysis and forecasting of hydrological time series

The singular spectrum analysis (SSA) technique is applied to some hydrological univariate time series to assess its ability to uncover important information from those series, and also its forecast skill. The SSA is carried out on annual precipitation, monthly runoff, and hourly water temperature time series. Information is obtained by extracting important components or, when possible, the whole signal from the time series. The extracted components are then subject to forecast by the SSA algorithm. It is illustrated the SSA ability to extract a slowly varying component (i.e. the trend) from the precipitation time series, the trend and oscillatory components from the runoff time series, and the whole signal from the water temperature time series. The SSA was also able to accurately forecast the extracted components of these time series.     

海岛地震台网自然因素对地脉动干扰分析

利用海南地震台网宽频带地震仪连续观测资料,分析不同自然环境下地脉动扰动信号,结果显示:在不同自然环境下,地震仪记录的地脉动扰动信号的形态、振幅、频率、扰动强度不同,其中登陆海岛的热带气旋引起的震颤信号频带窄、能量大、扰动强度较大,对地脉动干扰影响大,其次是天文潮期,远离海岛的热带气旋对地脉动干扰影响较小;不同路径、距离的热带气旋引起的震颤信号频率带一致,但扰动强度区别明显;不同自然环境对不同观测台站记录的地脉动信号均存在影响,只是程度稍有不同。     

Simulation of probability distributions commonly used in hydrological frequency analysis

Random variable simulation has been applied to many applications in hydrological modelling, flood risk analysis, environmental impact assessment, etc. However, computer codes for simulation of distributions commonly used in hydrological frequency analysis are not available in most software libraries. This paper presents a frequency‐factor‐based method for random number generation of five distributions (normal, log–normal, extreme‐value type I, Pearson type III and log‐Pearson type III) commonly used in hydrological frequency analysis. The proposed method is shown to produce random numbers of desired distributions through three means of validation: (1) graphical comparison of cumulative distribution functions (CDFs) and empirical CDFs derived from generated data; (2) properties of estimated parameters; (3) type I error of goodness‐of‐fit test. An advantage of the method is that it does not require CDF inversion, and frequency factors of the five commonly used distributions involves only the standard normal deviate. Copyright © 2006 John Wiley & Sons, Ltd.     

Prediction of landslide occurrence based on slope-instability analysis and hydrological model simulation

Hillslope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed by a rising groundwater level during rainstorms. The present study adopted a slope-instability analysis and a hydrological model for landslide prediction during heavy rainstorms. Variation of the groundwater table on hillslope was simulated by using the hydrological model and then the temporal groundwater level at each grid was substituted into the slope-instability analysis to determine the instability of the grids in watersheds for prediction of massive landslides.Hydrological records from two landslide-prone areas in northern Taiwan were collected. Digital elevation model was adopted to obtain the geomorphologic factors required for the slope-instability analysis and the hydrological model. The spatial distribution of soil thickness required for performing the infinite slope model was estimated by using a wetness index. Results showed that the temporal variation of the percentage of unstable grids in the study watersheds basically followed the variation of rainfall hyetographs. The percentage of the unstable grids reached a maximum value when the centroid of the hyetograph passed. A comparison between the landslide records and the model analytical results revealed that a massive landslide might occur if more than 50% of the grids in the subwatershed were classified as unstable in the study areas. The predicted time and location of landslide occurrence were consistent with those obtained from field investigations. It is therefore considered promising to apply the developed analytical method for landslide warning to alleviate the loss of lives and property.     

对东北深震震相特征和地震参数的对比分析

对兰州地震台数字化和模拟记录并行期间,发生在中国东北的2个具有典型深震特征的中强地震,进行了数字与模拟(DK1)记录对比分析,以便快速、准确的速报其地震参数,提高兰州地震台对深震的快速反应能力.     

动态聚类分析与太湖水质分区

以总硬度、pH、溶氧、化学耗氧、总氮、总磷、浮游植物、透明度8项指标为依据,参照现行湖泊营养类型划分标准,应用fuzzy聚类分析方法对我国42个主要湖泊聚类并划分其营养类型,同时对各类型的指标特征进行了分析。结果表明:在相似水平λ=0.69下,42个湖泊共聚为13类。其中,中、富营养型有较强的相似性,其差异显著的指标为总氮、总磷和化学耗氧量;贫营养型湖泊相似性差,其共同特征是指标中一项或数项显著异于一般营养范围。聚类结果与传统分类基本吻合。     

南四湖底栖动物群落结构特征及其与环境因子的关系

为了解南四湖大型底栖动物群落结构特征及其与环境因子的关系,于2010年对南四湖15个采样点的底栖动物和生态环境进行4次调查研究.结果表明:共检出底栖动物23种,栖息密度和生物量为311.57 ind./m2和44.39 g/m2;群落物种优势度指数较高,优势种集中于羽摇蚊幼虫(Chironomus plumosus)和霍甫水丝蚓(Limnodrilus hoffmeisteri),分别占个体总数的77.45%和11.39%;水生植物生物量为1613 g/m2,盖度为33%,存在显著季节变化与空间差异,但底栖动物群落特征与之无显著相关性;南四湖水域主要污染物为氮,其次为磷,最后为高锰酸钾指数;2010年度除底栖动物物种数与水体CODMn含量呈显著负相关以及栖息密度与水体相关加权综合营养状态指数呈显著正相关外,其余底栖动物群落特征与水生植物、水质理化指标及营养状态间均未表现出显著相关性;依据底栖动物Goodnight-Whitley生物指数和水质相关加权综合营养状态指数评价结果,南四湖除局部区域受到严重的外源污染外,总体为清洁-中度营养类型.     

不同水文情景下洪泽湖二氧化碳排放通量特征及影响因素

内陆水域二氧化碳(CO₂)排放是全球碳平衡的重要组成部分,全球CO₂排放通量估算通常有很大不确定性,一方面源于CO₂排放数据观测的时空离散性,另一方面也是缺少水文情景与CO₂排放通量关联性的研究.本文观测了2018年洪泽湖不同水文情景表层水体CO₂排放通量特征,并探讨其影响因素.结果表明,洪泽湖CO₂排放通量为丰水期((106.9±73.4) mmol/(m²·d))>枯水期((18.7±13.6) mmol/(m²·d))>平水期((5.2±15.5) mmol/(m²·d)),且碳通量由丰(310.2~32.0 mmol/(m²·d))、枯(50.8~2.2 mmol/(m²·d))、平(-17.3~39.8 mmol/(m²·d))3种水文情景的交替表现出湖泊碳源到弱碳汇的转变,空间上CO₂排放通量总体呈现北部成子湖区低、南部过水湖区高的分布趋势.洪泽湖CO₂排放对水文情景响应敏感,特别是上游淮河流域来水量的改变,是主导该湖CO₂排放时空分异的重要因子.丰水期湖泊接纳了淮河更多有机和无机碳的输入,外源碳基质的降解和矿化显著促进了水体CO₂的生产与排放,同时氮、磷等营养物质的大量输入,加剧了水体营养化程度,进一步提高CO₂排放量,间接反映出人类活动对洪泽湖CO₂变化的深刻影响.平、枯水期随着上游淮河来水量的减少,驱动水体CO₂排放的因素逐渐由外源输入转变为水体有机质的呼吸降解.此外,上游河口区DOM中陆源类腐殖质的累积与矿化能够促进CO₂的排放,而内源有机质组分似乎并没有直接参与CO₂的排放过程.研究结果揭示了水文情景交替对湖库CO₂排放的重要影响,同时有必要进行高频观测以进一步明晰湖泊的碳通量变化及其控制因素.     

Time series analysis for evaluating hydrological responses of pore-water pressure to rainfall in a slope

Characterization of pore-water pressure at the soil–atmosphere interface is a major requirement in relation to slope instability. A rain-gauge and five piezometers (BH1–BH5) were installed on a slope located in a Korean military base. The upper slope (BH4, BH5) was covered with plastic sheets to prevent rainwater from percolating into the slope due to safety issues. Rainfall is matched by prompt changes in the pore-water pressure except at BH5. Due to the plastic cover, the pore water does not show any significant change in the early period by evapotranspiration. From correlation analysis, two wells (BH3, BH5) have longer memory effects due to matrix flow of past precipitation. Two principal components show hydrological responses of pore water to rainfall during intense rainfall, but PC2 does not indicate any important changes in low or no rainfall. This study suggests that correlation analysis with PCA can be a valuable tool for interpreting datasets consisting only of pore-water pressure.     

Application of PP cluster method in the earthquake swarm analysis

ＡｐｐｌｉｃａｔｉｏｎｏｆＰＰｃｌｕｓｔｅｒｍｅｔｈｏｄｉｎｔｈｅｅａｒｔｈｑｕａｋｅｓｗａｒｍａｎａｌｙｓｉｓＳｈｉ－ＹｏｎｇＺＨＯＵ；（周仕勇）Ｌｉｎｇ－ＲｅｎＺＨＵ；（朱令人）ａｎｄＣｈｕａｎ－ＬｉｎｇＤＥＮＧ（邓传玲）（Ｓｅｉｓｍｏｌｏｇｉｃ...     

黑河流域浮游植物群落特征与环境因子的关系

分别于2009年夏季和2010年夏、秋季对黑河流域进行了2次全面调查,共选取76个采样断面进行水样采集,鉴定出浮游植物242种,隶属于8门11纲25目45科94属.其中硅藻门为优势类群,占物种总数的38.43%,绿藻门和蓝藻门次之;黑河流域优势种为尺骨针杆藻(Synedra ulna)、无常蓝纤维藻(Dactylococcopsis irregularis)和尖针杆藻(S.acus),优势度分别为0.060、0.031和0.021,出现频度分别为43.42%、43.42%和46.05%.生物多样性指数及相关指数分析表明,黑河流域中、下游浮游植物群落结构的复杂程度和稳定性均高于上游;同时,综合生物多样性指数及相关指数以及水质理化指标表明,黑河上游水质为无污染或轻度污染,中、下游水质为轻中度污染.浮游植物丰度与环境因子的相关性分析表明,上游浮游植物丰度与水体硬度呈显著正相关;中游浮游植物丰度则与各环境因子无显著相关性;下游浮游植物丰度与水体硬度呈显著负相关,而与总氮×总磷呈显著正相关;总溶解性固体、pH值和水体硬度与全流域浮游植物丰度呈显著正相关.黑河流域浮游植物的空间分布具有与河水水文分带相对应的垂直地带性和水平地带性分异特征.     

宁静期间双峰电流片的观测研究

2001年8月19日2011～2030UT,AE指数相对较小(40～130 nT),Cluster 卫星穿越磁尾电流片.利用Cluster 观测资料分析,发现宁静期间有双峰电流片存在,这期间没有明显的高速流,没有明显的电流片振荡. 同时,进一步分析双峰电流片中的离子特性发现:质子数密度Np在中性线（ = 0）附近相对较大,呈非对称性分布;质子温度分布比较均匀;在中性线两侧,质子y方向上的流速Vy方向相反. 最后讨论LHDI（低混杂漂移不稳定性）的非线性演化可能是形成宁静期间双峰电流片的主要机制. 这些对进一步完善电流片形成机制可能有很重要的意义.     

太湖北部梅梁湾水域水质因子聚类

计算了沿梁溪河河口到太湖湖心断面上１０个监测点１７个水质因子９３组数据的Ｐｅａｒｓｏｎ相关系数和Ｋｅｎｄａｌｌ秩相关系数,进而运用最小距离法进行了因子聚类,正态分布检验和聚类结果表明,采用Ｋｅｎｄａｌｌ秩相关进行了聚类为宜,结果将诸因子聚为五大类：ＴＤＮ,ＴＮ,ＣＯＮ,ＮＯ２－Ｎ,ＮＨ４－Ｎ,ＯＨ和ＣＯＤＭｎ归为一类,ＴＤＰ,ＴＰ,ＰＯ＾３＋４和ｐＨ值归为一类;ＳＳ和ＳＤ归为一类,反映了该水域环境     

苏州平原河网区浅水湖泊叶绿素a与环境因子的相关关系

运用回归统计方法,研究苏州平原河网区60个浅水湖泊水体叶绿素a与水温、pH、Do、CODMn、TN、TP等环境因子的相关性,建立相应的同归方程,同时分析了湖泊水体叶绿素a的时空分布特征.研究表明,平原河网区浅水湖泊水体叶绿素a含量具有一定的时空差异性,冬季叶绿索a平均含量比夏季低,但冬、夏季叶绿素a含量空间分布具有一定相似性,整个区域呈现较明显的东高两低的分布趋势;湖泊水体叶绿素a含量与理化环境因子水温、pH、DO、CODMn呈显著正相关,水温可能是平原河网区浅水湖泊浮游植物生长的限制性因子:叶绿素a与NO2-N呈显著正相关,与NH4 -N无明显负相关,与NO3-N无显著正相关,与TN无显著相关,而叶绿素a的对数与TP的对数呈一定的正相关,与TN/TP的对数呈显著负相关.平原河网区浅水湖泊可能是一定程度的磷限制性湖泊.     

崇明岛"闸控型"河网水体富营养化特征及其影响因素

为研究崇明岛"闸控型"河网水体富营养化特征及其与环境因子的相互作用,以2010年各季节崇明岛河网水质数据为基础,探讨了环境因子与藻类的变化规律及其相互作用机制.结果表明:崇明岛河网水环境中营养盐水平较高,氮污染尤为严重.长江引水进入岛内河网水环境后,营养盐(除SiO₃-Si)、Chl.a含量和营养状态都出现了明显的升高.与长江引水相比,河网内藻类群落Chl.a贡献比例的变化主要表现为硅藻比例的下降以及蓝藻和绿藻比例的上升.尽管水温、pH、浊度以及营养盐中的TN、TP、NO₃^--N、DOC的变化均与Chl.a含量显著相关,但藻类群落对环境因子的响应关系存在较大差异.蓝藻的增加主要与水温和TP含量的升高有关;绿藻与TN、NO₃^--N的关系最为密切;硅藻的变化只与浊度存在明显正相关,营养盐并不是硅藻生长的促进因子.     

海南及邻近区域地震活动性聚类分析

采用数据挖掘技术中的聚类分析,综合考虑地震区域、震级、深度等因素,对海南及邻近地区地震数据进行聚类,通过聚类结果,分析各类特征,可发现该区域呈现较为规律的地震分布格局,在一定程度上为探讨该区域地震趋势、中长期地震预测等提供应用前景.