黑河流域近60年来径流量变化及影响因素

基于莺落峡、正义峡水文站径流量数据及相关气候资料,借助小波分析、小波神经网络模型和GIS空间分析方法,研究黑河干流径流量变化周期、突变、趋势及原因。结果显示:1944~2005年莺落峡年均径流量变化长周期约7和25年,变化总体呈增加趋势,增加率约1.04m³/(s·10a);1954~2000年正义峡年均径流量变化长周期约6和27年,变化总体呈减少趋势,减少率约2.65m³/(s·10a);2006~2015年莺落峡年均径流量呈增加趋势,增加率约2.04m³/(s·10a);莺落峡径流量的增加主要与黑河流域上游气候向暖湿型转化有关,而正义峡径流量的减少与中游水资源利用量的迅速增加有关。     

基于R/S分析的黑河出山年径流量灰色预测

径流过程具有分形和灰色特征。基于此,将R/S分析与灰色系统理论相结合,提出了R/S灰色预测模型以预报黑河出山径流量。针对1949—2011年莺落峡水文站年径流量资料,首先进行R/S分析,确定径流量序列的Hurst指数和平均循环周期T;然后在一个周期内进行径流量灰色预测。结果表明:黑河出山径流量循环周期在20~25年之间,在进行R/S灰色预测时,取T=20为宜;R/S灰色预测结果的精度高于直接进行灰色预测。该方法拓宽了分形和灰色理论在径流过程研究的应用范围,为径流量的科学预测提供了一种新方法。     

1957-2008年黑河流域径流年内分配变化

根据黑河流域重点控制水文站莺落峡、正义峡实测月径流量观测资料,应用不均匀系数、集中度(期)、变化幅度等方法指标,分析了黑河流域径流年内变化规律;采用累积滤波器和Mann-Kendall秩相关法诊断了各月径流量的变化趋势.结果表明:①对于莺落峡站,总体上各个年代径流年内分配均呈明显的"单峰型"分布,其径流量7月达到极大值...     

黑河莺落峡站径流变化的影响因素分析

以黑河干流出山口径流控制站莺落峡水文站1960-2004年45年的径流序列为基础数据,采用相关分析、交叉谱分析、统计规律分析、降水—径流双累积曲线法等研究了全球变化、太阳黑子活动、ENSO循环和下垫面变化对黑河干流出山径流变化的影响。结果表明：①全球变化对莺落峡站径流变化影响较大;②莺落峡站年径流与太阳黑子相对数两序列在2年和3.3年两个振动周期上存在显著的相关关系,但在这两个存在显著相关关系的振动周期上太阳黑子相对数的周期波动变化都落后于莺落峡站年径流的周期波动变化;③ENSO循环对莺落峡站年径流变化有一定的影响,但是,El Nino事件和La Nina事件对莺落峡站年径流的影响时间持续较短,对事件发生当年的径流变化影响较大,对事件发生次年的径流变化影响较小;④流域下垫面变化对莺落峡站年径流量的变化影响较小。     

黑河莺落峡站年径流长期预报模型研究

通过分析黑河莺落峡站出山径流变化规律,并将年径流系列按照频率25%及75%为界,划分成3种情况:即多水年x_i > 55.0,中水年42.1≤x_i≤55.0,小水年x_i < 42.1。计算其状态转移概率得出:年径流过程从某一状态转移至其他状态的可能性都有,但其转移概率的最大值达81.8%。由此可知,年径流的变化过程不仅有随机性,而且有很强的相依性。通过多年对站年径流预报工作的实践,经过各种方法的比较检验,认为建立前期大气环流因子与年平均流量的预报模型和建立年平均流量的时间序列组合模型,其逐年预报的精度较高,经过误差评定分析,两个模型均为甲级方案,检验预报时性能较稳定,能对莺落峡站年平均流量进行有效预报,为黑河流域调水提供技术支撑。     

河流年径流量的GM(1,2)-Markov中长期预测模型

径流过程具有随机和灰色特征。基于此,将Markov预测与灰色GM(1,2)预测相结合,提出了GM(1,2)-Markov中长期河流年径流量预测模型。通过对黑河正义峡、莺落峡水文站65 a(1949-2014年)的年径流量资料分析,将莺落峡年径流量作为GM(1,2)预测的相关因素数据列,以1990-2009年的数据建立正义峡年径流量GM(1,2)-Markov模型,以2010-2014年的年径流量进行模型验证。结果表明:莺落峡、正义峡年径流量具有较强的相关性;建立的正义峡年径流量预测模型精度为83.65%;预测未来5 a的径流量,预测精度达到了97.12%;GM(1,2)-Markov、GM(1,1)和GM(1,2)模型的模型精度都符合建模的要求,但GM(1,2)-Markov模型的预测精度明显高于GM(1,1)模型和GM(1,2)模型。GM(1,2)-Markov模型为河流径流量的中长期预测提供了一种新方法。     

黑河上游多年基流变化及其原因分析

利用递归数字滤波法分割黑河上游主要水文站点的基流量,用移动平均、曼-肯德尔（Mann-Kendall）突变检验,小波分析方法分析研究区多年基流变化特点,并对基流影响因子进行分析。结果显示：黑河上游祁连、扎马什克、莺落峡三站多年平均基流指数均大于0．4;祁连与莺落峡多年基流量呈上升趋势,二者都在1979年发生增加到显著增...     

Kalman滤波方法在黑河出山径流年平均流量预报中的应用

应用Kalman滤波方法,以流域平均降水量、气温、太阳黑子相对数及莺落峡站6年显著周期序列等为控制参数,对黑河出山径流年平均流量的预测问题进行了初步研究和探讨。     

黑河流域莺落峡站河道内生态需水研究

生态需水是当前水问题研究中的一个热点问题, 对于生态环境急剧恶化的内陆干旱区来说, 这一研究尤为紧迫。气候变化及人类活动导致水文变异, 进而改变了当地生态系统的水文及生态平衡状况。文章采用黑河流域莺落峡站1947-2006 年月径流序列资料, 运用差积曲线-秩检验联合法来识别水文过程变异点并分析变异成因, 然后对各月平均流量序列用线性矩法推求GEV分布参数, 求出概率密度最大流量, 将其视为相应月河道内生态流量, 可以得到黑河莺落峡站河道内生态需水情况。结果表明, 基于水文变异情况分析河道内生态需水的计算方法在黑河上游是可行的;水文变异前后, 站点满足生态需水频率会发生显著变化, 因气候原因而导致的径流量变化是很难避免的, 科学合理计算生态需水对干旱区河流生态管理和经济社会可持续发展有重要作用和意义。     

基于小波变换和GRNN神经网络的黑河出山径流模型

对黑河山区流域月降水量和气温做Harr小波变换,并作为GRNN神经网络的输入,对黑河出山径流进行模拟和预测验证,效果较好。应用全球变化成果,在不同的气候情景下,对黑河出山径流进行预测。结果表明,黑河出山径流在未来一段时间内,径流量会有一定程度的增加,最终会减少。但模型对气温反应不敏感。去除气温重构的细节系数后,气温也成为一个敏感因素,但径流量却随气温的增加而增加。可推断,引进Haar小波变换的GRNN神经网络模型可应用于径流量对气温不敏感的流域。     

Earthquake prediction: the null hypothesis

The null hypothesis in assessing earthquake predictions is often, loosely speaking, that the successful predictions are chance coincidences. To make this more precise requires specifying a chance model for the predictions and/or the seismicity. The null hypothesis tends to be rejected not only when the predictions have merit, but also when the chance model is inappropriate. In one standard approach, the seismicity is taken to be random and the predictions are held fixed. 'Conditioning' on the predictions this way tends to reject the null hypothesis even when it is true, if the predictions depend on the seismicity history. An approach that seems less likely to yield erroneous conclusions is to compare the predictions with the predictions of a 'sensible' random prediction algorithm that uses seismicity up to time t to predict what will happen after time t. The null hypothesis is then that the predictions are no better than those of the random algorithm. Significance levels can be assigned to this test in a more satisfactory way, because the distribution of the success rate of the random predictions is under our control. Failure to reject the null hypothesis indicates that there is no evidence that any extra-seismic information the predictor uses (electrical signals for example) helps to predict earthquakes.     

The IASPEI procedure for the evaluation of earthquake precursors

Full scientific evaluation of proposed earthquake precursors for earthquake prediction is a problem because independent testing is difficult or impossible. To approach this difficulty, and to assess the current state of the art of earthquake prediction research, IASPEI has devised a peer-review procedure for precursor evaluation. The procedure does not consider predictions of impending earthquakes, but evaluates case histories of proposed precursors for past events according to stated validation criteria, which are specified in terms of guidelines concerning the hypothesized physical model, data quality, anomaly definition, the rules of association of precursor with earthquake, and statistical significance. So far, five precursors have been placed on a preliminary list of significant earthquake precursors, although none has satisfied the validation criteria well enough to ensure that their placement is permanent. Exclusion of a precursor from the list does not mean it is useless, but further work is required if it is to become convincing. The main objectives in producing the list are to establish a consensus on the criteria which a precursor must satisfy to be recognized as validated, and to find case histories which satisfy these criteria. Further nominations of precursor candidates are requested for evaluation by the IASPEI procedure.     

Lunar and solar tidal components in the occurrence of earthquakes in Italy

Summary. A method for the determination of lunar and solar daily components of earthquakes is proposed. Its application to the most intense earthquakes with epicentres in Italy (from 1900 to 1983) provides for the first time large and significant lunar and solar tidal terms. The absence of significant results from the analysis of 9157 world-wide large shocks processed according to the proposed method and to routine spherical harmonic techniques indicates that the Italian earthquakes are locally tidally triggered.     

Retrospective validation of the time association of precursors

This paper examines the problems raised by the retrospective association of events, a process which underlies any search for precursors to geophysical phenomena. Association is analysed here with respect to time only, disregarding the spatial component, so the results are more readily applicable to volcanic eruptions than to earthquakes. Two sources of bias are examined, viz. the time lag between the precursor and event series, and the tolerance or time window accepted on that lag, which is also linked to the number of associated events. These parameters are typically chosen a posteriori to minimize the apparent probability of random association in the given series. In general, given different input data, optimal association of the two series will be achieved for different values of these parameters. Thus, any valid system of significance testing must include a step which estimates the effect of retrospectively choosing optimal parameters rather than fixing them in advance. This paper shows that, as long as at least one of the two time-series—either the precursors or the events—is Poissonian, the correction factors for the significance level can be written in a simple closed form. The correction scheme is applied to a proposed association between seismic clusters and flank eruptions of Mount Etna. Although the association appears to be highly significant when assessed using simple standard tests, this significance is not confirmed when the effect of retrospective parameter optimization is taken into account. The key point is that retrospective studies can be investigated using standard techniques, but where such techniques are employed, the threshold for significance of association must be greatly lowered in order to account for the bias due to the optimal choice of parameters.     

基于Meta-Gaussian模型的中国农业干旱预测研究

在全球气候变化背景下,干旱愈加频发,有效且可靠的农业干旱预测对于保障粮食安全和水资源安全具有重要意义。以标准化降水指数（SPI）和联合标准化土壤湿度指数（JSSI）分别表征气象干旱和农业干旱,以前期的气象干旱和农业干旱指数作为预测因子,在1~3个月预见期下基于Meta-Gaussian（MG）模型对中国1961—2015年6—8月的农业干旱进行预测,并采用Brier Skill Score（BSS）和纳什效率系数（NSE）评价MG模型的预测性能。结果表明：① 将1个月、3个月、6个月、9个月和12个月时间尺度的标准化土壤湿度指数（SSI）结合起来得到的JSSI能够对中国农业干旱的综合状况进行客观评价。② 以中国2010年和2014年遭受严重的干旱事件为例,预见期为1~3个月时,除新疆南部、青海西部以及内蒙古西部等沙漠地区外,MG模型对6—8月农业干旱预测结果的分布范围与实际干旱的分布区域较吻合,预见期越短,吻合越好。③ 预见期为1个月时,6—8月BSS ≥ 0.5的面积比例分别为0.714、0.642和0.640,NSE ≥ 0.5的面积比例分别为0.903、0.829和0.837,表明MG模型能够对中国大部分区域的农业干旱作出可靠的预测。本文结果可为中国农业干旱的监测、预警及干旱决策提供科学指导。     

含HCl四元水盐体系溶解度预测及其在工艺上的应用：Pitzer 混合参数对HCl-MgCl2-H2O体系25°C时溶解度预测的影响

计算了 HCl- Mg Cl2 - H2 O体系 2 5°C时溶解度以及 θHMg和 ΨHMg Cl各以± 10 %变动时 ,HCl- Mg Cl2 - H2 O体系 2 5°C时的溶解度变化。计算的结果表明 ,H和 Mg之间的两离子相互作用和 H,Mg和 Cl之间的三离子相互作用对体系中 Mg Cl2 的溶解度有极大的影响。在 HCl的浓度最大时 ,这两种相互作用近似相等。当这两种相互作用同时增加或同时减小时 ,体系的溶解度几乎不发生变化 ;但当一种作用增加 ,另一种作用减小时 ,体系的溶解度会发生根本变化。该结果为后续工作—— H+ ,L i+ ,Mg2 + /Cl- - H2 O四元体系 2 5°C时组分溶解度预测奠定了基础 ,大大减少了锂资源开发中的实验工作量     

基于模糊综合评判的动态路径行程时间预测模型

针对城市交通路网的复杂性和不确定性,提出一种基于模糊综合评判的动态行程时间预测模型,将总行程时间分为行驶时间和交通延误时间两部分,分别介绍这两部分时间的预测模型,并利用该模型对一组模拟道路信息和路况信息进行实际预测,对预测结果进行比较和分析。研究表明该模型算法简捷实用,预测结果精度较高。