The limnogeology-ETH coring system

Various methods are available for coring in lakes. We have developed an integrated coring system based on a modified Kullenberg poston-corer principle which is particularly useful for multiple coring in deep lakes. The selection of sites is guided by concurrent high-resolution (3.5 kHz) continuous seismic profiling. The system is modular for simple transport, and includes a very reliable tripweight gravity corer which uses the same plastic liners as the piston corer. Piston-core lengths are variable in 2, 4, or 5 m sections up to 16 m. This system has been successfully deployed in glacially deepened perialpine lakes (Lakes Zürich, Zug, Greifensee, Lucerne, Walensee, Constance, Murten, Neuchâtel, Geneva, Thun, Brienz, Maggiore, Lugano, Iseo, Garda, d'Annecy, Bourget and Ammersee), deep rift lakes of Africa (Turkana, Albert, Edward, Kiwu), Lake Ohrid (Jugoslavia), Lake Van (Turkey), Qinghai Lake (China), and in very shallow hypersaline lakes Urmia (Iran) and Great Salt Lake (USA). Following numerous requests, this paper describes the system concept and constructional features that have been refined over the last 12 years. Development has stressed increasing simplification and increased reliability.     

应用核磁共振技术分析干盐湖地区地下卤水空间分布特征——以昆特依干盐湖为例

昆特依干盐湖位于柴达木盆地西北部,为特大型综合盐类矿床.大盐滩是昆特依干盐湖内最大的盐滩,地下赋存有一定量的卤水矿床,但该矿床的水文地质条件差,主要卤水矿层含水性弱,开采难度大.核磁共振找水方法作为当今世界上唯一的直接找水地球物理新方法,具有高分辨力、高效率、信息量丰富和解的唯一性等优点,本文运用该方法对昆特依干盐滩地区地下卤水空间分布特征进行研究,通过对核磁共振数据进行处理与反演,结合已有的地质与钻井资料,对测点进行综合地质-地球物理解释,获得以下认识:1)大盐滩0~130 m深度范围内,共存在3个卤水含水层,主要呈扁平状或漏斗状、近似层状展布,W1为晶间潜卤水层,渗透系数较大,颗粒较粗,单位体积含水量为0.4%~2.7%,W2和W3为晶间承压卤水层,渗透系数较小,颗粒较粗,单位体积含水量分别为0.2%~1.1%和0.1%~0.8%;2)大盐滩地区存在两个卤水富集区,分别为研究区西南部沉积盆地中心的Ⅰ号富卤区和盆地东北部的Ⅱ号富卤区;3)根据区域内卤水富集分布以及构造情况,划定大盐滩向斜沉积中心、大盐滩北侧F1~F8及遥F6断裂发育区和冷湖构造带为区域内主要的找矿找水远景区;4)GMR核磁共振系统在干盐滩地区理论探测深度为130 m,该系统不仅可以有效地探测自由水,而且可以依据束缚水的分布解译地下各类含水盐类矿物和含水黏土矿物的存在与分布.     

Measuring nonlinear dependence in hydrologic time series

It has been a common practice to employ the correlation dimension method to investigate the presence of nonlinearity and chaos in hydrologic processes. Although the method is generally reliable, potential limitations that exist in its applications to hydrologic data cannot be dismissed altogether. As for these limitations, two issues have dominated the discussions thus far: small data size and presence of noise. Another issue that is equally important, but less discussed in the literature, is the selection of delay time (τ _d ) for reconstruction of the phase-space, which is an essential first step in the correlation dimension method, or any other chaos identification and prediction method for that matter. It has also been increasingly recognized that fixing the delay time window (τ _w ) rather than just the delay time itself could be more appropriate, since the delay time window is the one that is of actual interest at the end to represent the dynamics. To this effect, Kim et al. (1998a) [Phys Rev E 58(5):5676–5682] developed a procedure for fixing the delay time window and demonstrated its effectiveness on three artificial chaotic series, and followed it up with the development of the C–C method to estimate both the delay time and the delay time window. The purpose of the present study is to test this procedure on real hydrologic time series and, hence, to assess their nonlinear deterministic characteristics. Three hydrologic time series are studied: (1) daily streamflow series from St. Johns near Cocoa, FL, USA; (2) biweekly volume time series from the Great Salt Lake, UT, USA; and (3) daily rainfall series from Seoul, South Korea. The results are also compared with those obtained using the conventional autocorrelation function (ACF) method.     

Improvement of robustness and stability in estimating Rayleigh–Schuster’s hodograph parameters using different procedures of vector normalization

This paper deals with Rayleigh–Schuster’s hodographs, intended for the detailed investigation of changes in the phase of quasiperiodic signals in time series. The hodographs are also known as the phasor-walkout method. A procedure of conditional vector normalization is proposed: it takes into account the vector amplitude for each period under consideration. The procedure considerably improves the robustness and stability of the hodograph approach to changes in the character of the processed data distributions and to various defects in the data. For example, when analyzing the earthquake catalog, the procedure strongly diminishes the influence of the event clustering caused, in particular, by swarms of earthquakes with comparable magnitude and the aftershock sequences of strong earthquakes. At the first stage, we calculate the vector sums (resulting vectors) for each period under investigation throughout the time series duration. For example, investigating diurnal periodicity of earthquakes, we first calculate the resulting vectors for each day of the observation. The further analysis of resulting vectors for each period throughout the time series duration can be performed with different procedures. We compare three procedures for normalization of the obtained resulting vectors, which are as follows: (1) th traditional one, preserving the real signal amplitude; (2) that with reduction of the obtained resulting vectors to the unit vector (phasor); and (3) that with conditional vector normalization, taking into account the amplitude of resulting vectors for each period throughout the time series duration. The third procedure diminishes the possible instability in some special distributions of the investigated data when the resulting vector for a period is close to zero. The procedures are compared using model signals and samples from real earthquake catalogs. All the procedures used give close results when processing random time series.     

基于小波变换和支持向量机的中国大陆强震预测

将小波变换和支持向量机用于中国大陆年度最大地震震级预测。 先用小波变换把中国大陆年度最大地震序列分解成几个不同尺度水平(频率)的子序列, 然后使用支持向量机对分解后的子序列分别进行预测, 最后通过重构几个子序列的支持向量机预测结果得到最终预测结果, 预测次年中国大陆最大地震震级。 与支持向量机和神经网络方法对比, 结果表明小波变换和支持向量机相结合方法具有更高的预测精度, 预测效果很好, 说明此方法可用于地震时间序列预测。     

基于长短时记忆神经网络的鄱阳湖水位预测

湖泊水位是维持其生态系统结构、功能和完整性的基础.鄱阳湖受流域"五河"和长江来水双重影响,水位变化复杂.为了准确预测鄱阳湖水位变化,采用长短时记忆神经网络方法(LSTM)构建了鄱阳湖水位预测模型.该模型以赣江、抚河、信江、饶河和修水"五河"入湖流量和长江干流流量作为输入条件,预测鄱阳湖湖区不同代表站(湖口、星子、都昌、吴城和康山)的水位过程.研究以1956—1980年的水文时间序列数据作为训练集,1981—2000年作为验证集,探讨了LSTM模型输入时间窗、隐藏神经元数目、初始学习率等模型参数对预测精度的影响,并确定了鄱阳湖水位预测模型的最优参数.结果表明,采用LSTM神经网络方法可基于流域"五河"和长江来水量历时数据合理预测鄱阳湖不同湖区的水位过程,五站水位预测的均方根误差为0.41～0.50 m,纳什效率系数和决定系数达0.96～0.98.为考察模型训练数据集对鄱阳湖水位预测结果的影响,进一步选取了随机5年(1956—1960年)的资料和5个典型水文年(1954年、1973年、1974年、1977年和1978年)的日均流量资料来训练模型.结果显示随机5年资料作为训练数据的预测精度要差于典型年水文资料训练得到的模型,尤其是洪、枯水位的预测;由于典型水文年数据量仍远低于20年的资料,故其总体预测精度要略低于采用20年资料训练的模型.建议应用这类基于数据驱动的模型时,应该尽可能多选取具有代表性的资料来训练.     

Geochemical simulation of the formation of brine and salt minerals based on Pitzer model in Caka Salt Lake

Copyright by Science in China Press 2004 Interactions between ions in natural waters have a great effect on the rates of redox processes[1], mineral solubility[2] and biochemical availability[3,4]. A model describing the variation of activity coefficients with ionic strength, temperature and composition of the solution is required to quantify those effects,thereby it can be used to make many geochemical calculations such as the aqueous speciation of elements, the min-eral sequence, mixing of …     

An assessment of water reserve changes in Salt Lake,Turkey, through multi‐temporal Landsat imagery and real‐time ground surveys

This paper focuses mainly on the investigation of water reserve changes in Salt Lake, Turkey, using remote‐sensing data. The study is performed in two stages: (1) correlation analysis for real‐time ground and satellite data and (2) assessment of water reserve changes using multi‐temporal Landsat imagery. First, correlation analysis is conducted to investigate the relationship between digital data from Landsat‐5 TM and spectral (in situ) measurements collected using a field spectroradiometer on the same day and time. A radiometric correction procedure, including conversions from digital numbers to radiance and from radiance to at‐satellite reflectance, is executed to make satellite data comparable to in situ measurements. This procedure show that simultaneous ground and satellite remote‐sensing data are highly correlated (0·84 > R² > 97) and the near‐infrared region (for this study TM4‐Landsat‐5 TM, band 4) is the best spectral range to distinguish salt and water on the satellite data for the multi‐temporal analysis of the water reserve in Salt Lake. It also shows that the use of shortwave infrared band(s) will result in confusion for the determination of the water reserve in this water‐covered study area. In a second and last phase, the water reserve change in the lake is examined using multi‐temporal Landsat imagery collected in 1990, 2001 and 2005. The remotely sensed, sampled and treated data show that the water reserve in the lake has decreased markedly between 1990 and 2005 due to drought and uncontrolled water usage. It is suggested that the use of water supplies around Salt Lake should be controlled and that the lake should regularly be monitored by up‐to‐date remote‐sensing data (at least annually) for better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd.     

Multivariate nonparametric resampling scheme for generation of daily weather variables

A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.     

基于支持向量机的地震序列分类

介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法——支持向量机算法。根据支持向量机线性分类和可以具有不同核函数的非线性分类两种算法,建立了地震序列分类模型。通过试算和分析比较得到了地震序列最佳分类模型,最佳模型的分类结果与实际地震序列分类基本一致。综合分析认为支持向量机算法无论在学习或者预测精度方面都具有很大的优越性,其获得的地震序列分类知识库可以较为准确地实现地震序列类型的分类,因此基于支持向量机理论建立的地震序列分类模型应该是可行的。     

内蒙古察干淖尔盐湖29-8ka时段的湖面波动

以内蒙古锡林郭勒盟苏尼特右旗的察干淖尔盐湖为研究对象,利用OSL(Optically Stimulated Luminescence)测年技术和DEM(Digital Elevation Model)数字高程模型,重建湖面波动历史,探讨湖泊形成与环境变化过程.通过对察干淖尔盐湖周边大量的野外考察,发现湖泊周围存在海拔高程为1020、978和973 m的三级古湖岸阶地,其OSL测年结果分别为29.2±1.3、18.4±0.8及8.2 8.0 ka.通过湖岸阶地高程恢复的上述3个时期的古湖面积分别为3600、500和400 km~2.与现今的干旱盐湖景观迥然不同.     

Potential of support vector regression for prediction of monthly streamflow using endogenous property

In the recent past, a variety of statistical and other modelling approaches have been developed to capture the properties of hydrological time series for their reliable prediction. However, the extent of complexity hinders the applicability of such traditional models in many cases. Kernel‐based machine learning approaches have been found to be more popular due to their inherent advantages over traditional modelling techniques including artificial neural networks(ANNs ). In this paper, a kernel‐based learning approach is investigated for its suitability to capture the monthly variation of streamflow time series. Its performance is compared with that of the traditional approaches. Support vector machines (SVMs) are one such kernel‐based algorithm that has given promising results in hydrology and associated areas. In this paper, the application of SVMs to regression problems, known as support vector regression (SVR), is presented to predict the monthly streamflow of the Mahanadi River in the state of Orissa, India. The results obtained are compared against the results derived from the traditional Box–Jenkins approach. While the correlation coefficient between the observed and predicted streamflows was found to be 0·77 in case of SVR, the same for different auto‐regressive integrated moving average (ARIMA) models ranges between 0·67 and 0·69. The superiority of SVR as compared to traditional Box‐Jenkins approach is also explained through the feature space representation. Copyright © 2009 John Wiley & Sons, Ltd.     

Space–time forecasting of groundwater level using a hybrid soft computing model

Forecasting of space–time groundwater level is important for sparsely monitored regions. Time series analysis using soft computing tools is powerful in temporal data analysis. Classical geostatistical methods provide the best estimates of spatial data. In the present work a hybrid framework for space–time groundwater level forecasting is proposed by combining a soft computing tool and a geostatistical model. Three time series forecasting models: artificial neural network, least square support vector machine and genetic programming (GP), are individually combined with the geostatistical ordinary kriging model. The experimental variogram thus obtained fits a linear combination of a nugget effect model and a power model. The efficacy of the space–time models was decided on both visual interpretation (spatial maps) and calculated error statistics. It was found that the GP–kriging space–time model gave the most satisfactory results in terms of average absolute relative error, root mean square error, normalized mean bias error and normalized root mean square error.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.     

Hydrological time‐series modelling using an adaptive neuro‐fuzzy inference system

Accurate forecasting of hydrological time‐series is a quite important issue for a wise and sustainable use of water resources. In this study, an adaptive neuro‐fuzzy inference system (ANFIS) approach is used to construct a time‐series forecasting system. In particular, the applicability of an ANFIS to the forecasting of the time‐series is investigated. To illustrate the applicability and capability of an ANFIS, the River Great Menderes, located in western Turkey, is chosen as a case study area. The advantage of this method is that it uses the input–output data sets. A total of 5844 daily data sets collected from 1985 to 2000 are used for the time‐series forecasting. Models having various input structures were constructed and the best structure was investigated. In addition, four various training/testing data sets were built by cross‐validation methods and the best data set was obtained. The performance of the ANFIS models in training and testing sets was compared with observations and also evaluated. In order to get an accurate and reliable comparison, the best‐fit model structure was also trained and tested by artificial neural networks and traditional time‐series analysis techniques and the results compared. The results indicate that the ANFIS can be applied successfully and provide high accuracy and reliability for time‐series modelling. Copyright © 2007 John Wiley & Sons, Ltd.     

Untersuchungen über den Schwermetallhaushalt in Seen

In the mesotrophic Lake of Lucerne (Bay of Horw) and in the eutrophic Lake of Alpnach concentration and sedimentation of iron, copper, zinc, cadmium and lead were investigated as a function of time and depth, and compared with a series of physical and chemical parameters. A metal balance was made for the Lake of Alpnach. A model for the distribution of the metals between biomass, allochthonous material and dissolved phase was employed. Phenomenology and model lead to the following conclusions: the sedimentation of the metals is dominated by allochthonous particles, the biogenic particularization is partially reversed by lake internal decomposition processes and the trace metals reaching the sediment are partially redissolved.     

Simulating seasonal to multi-decadal variation in lake thermal response to meteorological forcing using the UCLAKE 1-dimensional model code

Lake temperature responses to climate forcing are of interest on account of the important linkages between water temperature and ecosystem processes. This paper describes a new 1-dimensional (1D) numerical model code and its application to investigations of multi-scale linkages between the vertical temperature structure and meteorological forcing. UCLAKE is implemented as highly portable open-source software, based on computationally efficient algorithms, and able to resolve sub-daily (e.g., hourly) dynamics while retaining the efficiency to simulate multi-decadal time scales.A UCLAKE model is calibrated and validated against thermistor profile time series for a small upland lake in North Wales, UK. Some of the challenges in 1D model calibration are explored and a sensitivity analysis reveals a dependence of optimal parameter set values on water column depth and time. An exploratory 52-year hindcast simulation demonstrates the computational efficiency of UCLAKE for multi-decadal studies of trends in lake temperature that vary with depth. A supplementary application of UCLAKE to Windermere, in the English Lake District, demonstrates its performance for larger and deeper lakes.     

阿尔金断裂带中段乌苏肖地震形变带特征及地震事件参数

本文根据现场考察资料,论述阿尔金断裂带中段乌苏肖地震形变带特征及地震事件参数。据形变带特征及年代学研究,确定地震事件大约发生在1800年到1840年间,震级7.5～8级,震中位于乌苏肖盐湖西的库勒萨依一带。     

Statistical validation of GCM-simulated climates for the U.S. Great Lakes and the C.I.S. Emba and Ural River basins

Many researchers use outputs from large-scale global circulation models of the atmosphere to assess hydrological and other impacts associated with climate change. However, these models cannot capture all climate variations since the physical processes are imperfectly understood and are poorly represented at smaller regional scales. This paper statistically compares model outputs from the global circulation model of the Geophysical Fluid Dynamics Laboratory to historical data for the United States' Laurentian Great Lakes and for the Emba and Ural River basins in the Commonwealth of Independent States (C.I.S.). We use maximum entropy spectral analysis to compare model and data time series, allowing us to both assess statistical predictabilities and to describe the time series in both time and frequency domains. This comparison initiates assessments of the model's representation of the real world and suggests areas of model improvement.     

GEOHYDROLOGIC ANALOGIES BETWEEN THE JORDAN VALLEYS OF UTAH AND THE HOLY LAND

Abstract

The biblical Jordan River Valley, which extends from Lake Tiberias (the Sea of Galilee) to the Dead Sea, is decidedly similar to the Jordan River Valley of Utah, which joins Lake Utah and Great Salt Lake. Both Jordan Rivers drain relatively large fresh-water lakes and also are major sources of discharge into large salty lakes that have no outlets to the ocean.

The two Jordan River valleys and the highlands and mountains that surround them, have many physiographic, geologic, and hydrologic similarities as well as some noteworthy differences. For example, an hypothesis for the formation of the Dead Sea-Jordan Valley rift is that the east Jordan block slid northward with respect to the west Jordan block. The amount of displacement is estimated to be about 65 miles and took place partly in Miocene and possible Pliocène and partly in Pleistocene time. Tectonc activity has also been a major factor in the formation of the Jordan valley of Utah, but the movement here probably was along large normal faults in late Tertiary and Quaternary time. The sediments underlying both Jordan River valleys were deposited in ancestral lacustrine and fluvial environments. Abundant supplies of ground water are found under both valleys, but probably larger supplies of better quality water can be obtained in Utah. Both valleys contain numerous small nonthermal and a few large thermal springs.