一次强降水过程涡旋状MCS结构特征及成因初步分析

利用新一代天气雷达资料分析了造成2011年6月18日湖北省江汉平原强降水涡旋状中尺度对流系统(MCS)发生发展过程的结构特征,联合常规观测、地面加密观测及雷达四维变分风场反演资料初步研究了MCS可能成因。结果表明:(1)成熟阶段的强降水涡旋状MCS回波表现为气旋性弯曲的多条螺旋对流回波带、周围被大片层状云回波所包裹的结构特征,后期因冷空气侵入演变出冷暖锋式结构。回波合并和旋转式列车效应是产生强降水的主要运动特征。(2)涡旋状MCS是在有利环境场下,主要由鄂西山地一江汉平原过渡带边界层中尺度涡旋系统强烈发展组织的结果。(3)中尺度涡旋系统形成发展与地面暖倒槽发展、西南低涡前侧降水和特殊地形作用有密切关系,来自不同方向气流形成的强烈辐合是其前期形成发展的主要机制,后期发展可能与潜热释放有关,涡旋环流向上发展到700 hPa。     

澜沧江-湄公河流域径流与气候因子变化的季节差异特征研究

根据DELWARE温度和降水数据、GLDAS蒸散发数据和湄公河干流9个水文站的实测径流,采用回归分析、均值T检验和低通滤波,分析了该流域气候和径流在1950-2017年间的变化情况,经分析表明流域内气候和径流在研究时段内有较大变化,而且在不同的月份呈现不同的变化特征。流域年平均温度整体呈增加趋势,2008年后的平均温度相对2008年前平均温度有显著增加;流域年平均降水的变化幅度不大;流域平均蒸散发在12月-次年2月呈下降趋势,其他月份呈增加趋势,2008-2017年月平均蒸散发与1950-2007年月平均蒸散发相比大幅提升,尤其是在6-10月;湄公河流域年径流没有显著变化,但径流在12月-次年4月呈上升趋势,7-10月呈下降趋势,其中,上升趋势比下降趋势显著,1-4月径流上升趋势在2008年之后更为显著;最小径流在2008年后有显著增加趋势,最大径流在2008年后呈下降趋势;年流量逆转次数自20世纪90年代起有明显升高趋势。通过比较温度、降水、蒸散发和径流在不同时间段的变化情况,可以看出径流在2008年后变化趋势和气候自然变化关系不显著,但可能跟大坝蓄水能力显著提高等人为活动有较大关系。     

我国东部4-9月大尺度南北旱涝的特征及变化

研究了我国东部4—9月逐月、逐季降水的跷跷板结构和气候特征,结果表明:不同时段跷跷板结构的位置和强度不同。当时间尺度加大时,南北旱涝特征更明显。定义并计算了1951—2003年我国的南北旱涝指数。对夏季和8月典型南北旱涝年进行差异的显著性t检验,表明南北旱涝年的划分是合理的。研究了1951—2003年我国南北旱涝特征的长期趋势和年代际变化;研究了南北旱涝指数与冷暖事件年的关系。结果表明,冷暖事件年对6月、6—7月以及夏季大尺度南北旱涝有影响,暖事件年有利于发生北旱南涝;而冷事件年则相反。结果还表明,冷暖事件仅是我国南北旱涝分布的一个影响因素。     

驻马店极端气温分析

利用驻马店10个站气象资料,分析了极端温度的时间变化特征、高温天气与6-8月降水量之间的关系和极端晴热高温、极端湿热高温天气形成的天气背景及影响因子,确定了高温出现的前期预报指标。     

降雨诱发浅层黄土滑坡变形破坏机制

由降雨引发的浅层黄土滑坡灾害具有致灾性强、范围广、影响面积大等特点,是黄土高原地区危害严重的地质灾害类型。经典算法采用安全系数描述坡体稳定性,难以对坡体变形破坏的起始位置和实际失效面加以识别,限制了滑坡变形过程的描述和滑坡有效预测。以非饱和土吸应力理论为指导,基于Hydrus中的Slope Cube模块,建立黄土斜坡水-力耦合模型,结合黄土地区易滑坡形态统计数据,针对凸型、凹型、直线型3种坡型与30°、40°、50°三种坡度组合,计算了不同降雨条件下的坡体稳定性响应。结果表明,不同坡型的黄土斜坡对降雨条件具有明显的响应。相同降雨量、相同坡度条件下直线型坡发生浅表层破坏的可能性最低,凹型坡次之、凸型坡的稳定性最差。与之相对应的,相同条件下凸型坡失稳时间最短、凹型坡次之、直线型坡最长。本研究可为浅层滑坡的早期识别和预报提供支撑。     

A nonstationary extreme value distribution for analysing the cessation of karst spring discharge

The effects of climate change and population growth in recent decades are leading us to consider their combined and potentially extreme consequences, particularly regarding hydrological processes, which can be modeled using a generalized extreme value (GEV) distribution. Most of the GEV models were based on a stationary assumption for hydrological processes, in contrast to the nonstationary reality due to climate change and human activities. In this paper, we present the nonstationary generalized extreme value (NSGEV) distribution and use it to investigate the risk of Niangziguan Springs discharge decreasing to zero. Rather than assuming the location, scale, and shape parameters to be constant as one might do for a stationary GEV distribution analysis, the NSGEV approach can reflect the dynamic processes by defining the GEV parameters as functions of time. Because most of the GEV model is designed to evaluate maxima (e.g. flooding, represented by positive numbers), and spring discharge cessation is a ?minima’, we deduced an NSGEV model for minima by applying opposite numbers, i.e. negative instead of positive numbers. The results of the model application to Niangziguan Springs showed that the probability of zero discharge at Niangziguan Springs will be 1/80 in 2025, and 1/10 in 2030. After 2025, the rate of decrease in spring discharge will accelerate, and the probability that Niangziguan Springs will cease flowing will dramatically increase. The NSGEV model is a robust method for analysing karst spring discharge. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Trend and extreme occurrence of precipitation in a mid‐latitude Eurasian steppe watershed at various time scales

The confounding effects of step change invalidate the stationarity assumption of commonly used trend analysis methods such as the Mann–Kendall test technique, so previous studies have failed to explain inconsistencies between detected trends and observed large precipitation anomalies. The objectives of this study were to (1) formulate a trend analysis approach that considers nonstationarity due to step changes, (2) use this approach to detect trends and extreme occurrences of precipitation in a mid‐latitude Eurasian steppe watershed in North China, and (3) examine how runoff responds to precipitation trends in the study watershed. Our results indicate that annual precipitation underwent a marginal step jump around 1995. The significant annual downward trend after 1994 was primarily due to a decrease in summer rainfall; other seasons exhibited no significant precipitation trends. At a monthly scale, July rainfall after 1994 exhibited a significant downward trend, whereas precipitation in other months had no trend. The percentage of wet days also underwent a step jump around 1994 following a significant decreasing trend, although the precipitation intensity exhibited neither a step change nor any significant trend. However, both low‐frequency and high‐frequency precipitation events in the study watershed occurred more often after than before 1994; probably as either a result or an indicator of climate change. In response to these precipitation changes, the study watershed had distinctly different precipitation‐runoff relationships for observed annual precipitations of less than 300 mm, between 300 and 400 mm, and greater than 400 mm. Copyright © 2013 John Wiley & Sons, Ltd.     

Analysis of the interannual variability of annual daily extreme water levels in the St Lawrence River and Lake Ontario from 1918 to 2010

We compared the interannual variability of annual daily maximum and minimum extreme water levels in Lake Ontario and the St Lawrence River (Sorel station) from 1918 to 2010, using several statistical tests. The interannual variability of annual daily maximum extreme water levels in Lake Ontario is characterized by a positive long‐term trend showing two shifts in mean (1929–1930 and 1942–1943) and a single shift in variance (in 1958–1959). In contrast, for the St Lawrence River, this interannual variability is characterized by a negative long‐term trend with a single shift in mean, which occurred in 1955–1956. As for annual daily minimum extreme water levels, their interannual variability shows no significant long‐term change in trend. However, for Lake Ontario, the interannual variability of these water levels shows two shifts in mean, which are synchronous with those for maximum water levels, and a single shift in variance, which occurred in 1965–1966. These changes in trend and stationarity (mean and variance) are thought to be due to factors both climatic (the Great Drought of the 1930s) and human (digging of the Seaway and construction of several dams and locks during the 1950s). Despite this change in means and variance, the four series are clearly described by the generalized extreme value distribution. Finally, annual daily maximum and minimum extreme water levels in the St Lawrence and Lake Ontario are negatively correlated with Atlantic multidecadal oscillation over the period from 1918 to 2010. Copyright © 2013 John Wiley & Sons, Ltd.     

Differing controls on river‐ and lake‐water hydrogen and oxygen isotopic values in the western United States

Surface water oxygen and hydrogen isotopic values are commonly used as proxies of precipitation isotopic values to track modern hydrologic processes while proxies of water isotopic values preserved in lake and river sediments are used for paleoclimate and paleoaltimetry studies. Previous work has been able to explain variability in USA river‐water and meteoric‐precipitation oxygen isotope variability with geographic variables. These studies show that in the western United States, river‐water isotopic values are depleted relative to precipitation values. In comparison, the controls on lake‐water isotopic values are not well constrained. It has been documented that western United States lake‐water input values, unlike river water, reflect the monthly weighted mean isotopic value of precipitation. To understand the differing controls on lake‐ and river‐water isotopic values in the western United States, we examine the seasonal distribution of precipitation, evaporation and snowmelt across a range of seasonality regimes. We generate new predictive equations based on easily measured factors for western United States lake‐water, which are able to explain 69–63% of the variability in lake‐water hydrogen and oxygen isotopic values. In addition to the geographic factors that can explain river and precipitation values, lake‐water isotopic values need factors related to local hydrologic and climatic characteristics to explain variability. Study results suggest that the spring snowmelt runs off the landscape via rivers and streams, depleting river and stream‐water isotopic values. By contrast, lakes receive seasonal contributions of precipitation in proportion to the seasonal fraction of total annual precipitation within their watershed. Climate change may alter the ratio of snow to rain fall, affecting water resource partitioning between rivers and lakes and by implication of groundwater. Paleolimnological studies must account for the multiple drivers of water isotopic values; likewise, studies based on the isotopic composition of fossil material need to distinguish between species that are associated with rivers versus lakes. Copyright © 2010 John Wiley & Sons, Ltd.