Long‐term variations and temporal scaling of hydroclimatic time series with focus on the German part of the Elbe River Basin

Long‐term variations and temporal scaling of mean monthly time series of river flow, precipitation, temperature, relative humidity, air pressure, duration of bright sunshine, degree of cloud cover, short wave radiation, wind speed and potential evaporation within or in vicinity of the German part of the Elbe River Basin are analyzed. Statistically significant correlations between the 2–15 year scale‐averaged wavelet spectra of the hydroclimatic variables and the North Atlantic Oscillation‐ and Arctic Oscillation index are found which suggests that such long‐term patterns in hydroclimatic time series are externally forced. The Hurst parameter estimates (H) based on the Detrended Fluctuation Analysis (DFA) indicate persistence for discharge, precipitation, wind speed, air pressure and the degree of cloud cover, all having an annual cycle and a broad low‐frequency distribution. Also, DFA H parameter estimates are higher for discharge than for precipitation. The major long‐term quasi‐periodic variability modes of precipitation detected using Singular Spectrum Analysis coincide with those detected in the discharge time series. Upon subtraction of these low‐frequency quasi‐periodic modes, the DFA H parameter estimates suggest absence of the persistence for both precipitation and 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.     

小冰期时中国南方地区降水模式的差异研究

小冰期是过去一千年中全球气候变化的重要事件之一。关于小冰期时中国季风区和西风影响区气候变化的对比研究众多,但是缺乏中国南方地区降水模式时空差异的研究,难以了解中国南方地区降水变化规律。为了系统地了解小冰期时中国南方地区降水的复杂性,本文将中国南方地区划分为东南—华南沿海地区、中部地区以及西南地区三个区域,总共选取了19条高分辨率的古气候记录进行对比研究,主要有以下几点认识:(1)相对于中世纪暖期而言,小冰期期间中国南方东南—华南沿海地区的气候偏湿,这可能与雨带在中国南方的滞留时间延长和沿海地区受台风的影响增强有关。(2)中国中部地区秦岭南麓和神农架高山林区在小冰期时期主要呈"冷湿"的模式,差异在于秦岭南麓区域主要在小冰期中后期偏湿,这与中部其他区域偏"冷干"的模式不同。这种区域差异可能是由于地形地势和大气环流的复杂性导致。(3)中国西南地区受印度夏季风和东亚夏季风的共同影响,且该区域地形复杂,其气候变化在小冰期时期存在更加明显的空间差异,没有呈现出比较一致的降水模式。与小冰期期间的降水变化不同的是,近30年东南—华南沿海地区除了台湾和雷州半岛,其他区域降水明显减少,可能受气温和人类活动等因素的影响。通过结合高分辨率的古气候记录,我们系统分析了中国南方小冰期的干湿模式在时空上的差异及其可能的影响因子,这对于认识小冰期时中国南方不同区域降水的复杂性及未来旱涝灾害的防控具有一定意义。     

岩溶泉补给地表溪流二氧化碳脱气作用研究

碳酸盐岩风化作用(即岩溶作用)能够吸收大气二氧化碳(CO_2),形成溶解无机碳(DIC,dissolved inorganic carbon),被认为是一种重要的陆地碳汇,其在全球碳收支平衡和未来陆地增汇中可能会有重要贡献。然而,目前对岩溶碳汇的稳定性还存在争议,一些学者认为岩溶地下水出露地表后会发生CO_2脱气,对岩溶碳汇通量估算带来不确定性。本文以广西桂林长流水表层岩溶泉补给的溪流(约2.7km长)为例,利用水化学和同位素质谱仪测试技术,研究了溪水水化学指标和溶解无机碳同位素(δ~(13)C_(DIC))沿流程变化,探讨了溪流CO_2脱气过程、通量及其影响因素,以更好地了解岩溶碳汇的稳定性。结果表明:从泉口向下游,在陡坡地段(C1～C14段,长约270m,坡度约10°),溪水pH值、方解石饱和指数和δ~(13)C_(DIC)沿流程分别升高了0.9、0.9和1.8‰,而CO_2分压、电导率、Ca~(2+)浓度和DIC浓度分别下降了85%、34μS/cm、0.2mmol/L和0.7mmol/L,说明溪水发生了显著的CO_2脱气,并伴随碳酸钙沉淀。而在平缓地段(C18～C26段,长约2.1km,坡度1°),溪水各水化学指标和δ~(13)C_(DIC)变化较小,指示CO_2脱气作用较弱。这些发现表明溪流CO_2脱气受到了地形决定的水动力条件控制。另外,在下游渠段,受支流汇入影响,溪水pH值和方解石饱和指数有所降低,在一定程度上抑制了CO_2脱气。溪流CO_2脱气能够抵消部分岩溶作用固定的大气CO_2量,但是在长流水这一高地势、低流量且有碳酸钙沉积的环境下,其抵消的量也仅占29%。对于在低缓地区受流量很大的岩溶泉/地下河补给的河流,其CO_2脱气作用对岩溶碳汇的影响有限,加之受可能增强的水生光合生物固碳效应影响,岩溶碳汇应具有很高的稳定性。     

雅鲁藏布江流域极端降水模拟及预估

为了解雅鲁藏布江流域汛期极端降水的变化规律,推算一定重现期的极端降水量分位数,通过百分位法、Hill图法、年交叉率法选取阈值,借助广义帕累托分布函数（GPD）对流域极端降水频率进行了分析。结果表明：99百分位时的阈值为流域内各站点的最佳阈值,且各站点超阈值序列通过了M-K的平稳性检验,无明显突变。拟合效果通过K-S检验,各站点拟合的极端降水理论频数和实测频数基本相符。尺度参数的大值区位于流域下游,表明该地区的极值波动大;形状参数正值区位于流域中上游地区,说明发生破纪录降水事件的概率较大,拟合结果与实际观测一致。从5年一遇和10年一遇的极端降水值来看,雅江流域除拉孜站外,其他地区降水极值均超过30 mm,日喀则地区的降水极值达50 mm;各地区20年一遇和30年一遇的降水极值增长的非常缓慢。通过与实际极端降水值对比分析得出,GPD拟合计算出的重现期水平基本符合实际,即具有一定的合理性。     

1960—2017年河西地区降水时空变化特征

利用1960—2017年日降水量资料,采用线性倾向趋势分析、滑动分析和泰森多边形法等,对河西地区多年降水时空变化特征及不同量级降水日数及降水强度的变化趋势进行了研究。结果表明：河西地区年均降水量为99.0 mm,呈现明显的逐年上升趋势,平均倾向率为8.72 mm?（10a）^-1,月降水量为单峰分布,5—10月夏秋汛期降水量占年降水量的89.2%,各季节降水量均呈现显著上升趋势;年均降水日数为36.7天,呈现明显的上升趋势,增幅为3.18 d?（10a）^-1,降水日数主要分布在夏季,约占总降水日数的54.6%;平均降水强度为2.70 mm?d^-1,呈现减弱趋势,变化速率为-0.04 mm?d^-1?（10a）^-1;零星小雨和小雨降水日数均呈现增加趋势,而二者平均降水强度均为下降趋势,小到中雨降水日数和降水强度呈现增加趋势,中雨及以上的降水变化趋势不明显。     

1961—2019年青藏高原中东部夏季强降水与大尺度环流的关系

采用一元线性回归、合成分析等方法对1961—2019年青藏高原中东部71个站点夏季强降水与大尺度环流进行了分析,研究结果表明,近年来青藏高原中东部强降水呈增加趋势。在强降水高值年时,青藏高原中东部水汽辐合加强,中纬度西风和热带地区东风带向极移动加强,高层辐散流场、水汽输送以及上升运动条件,共同作用导致了强降水的产生。在强降水低值年时,青藏高原中东部大部水汽异常辐散,区域内的季风水汽输送减弱,西风带和东风带均向赤道移动减弱,高层为气旋式环流异常。通过风暴轴、波作用通量和E-P通量进一步分析发现,当北大西洋地区风暴轴偏强（偏弱）时,瞬变扰动作用加强（减弱）,使得北大西洋地区高纬度西风加速（减弱）,急流出口区的不稳定能量激发了欧洲西北部的异常反气旋（异常气旋）,并通过Rossby波列调控季风输送,导致了青藏高原中东部地区强降水的变化。     

非平稳条件下北京市最大月降水量频率特征分析

为探究气候变化下极端降水的频率变化特征,基于北京市22个雨量站实测月降水量数据,以时间为协变量构建平稳和非平稳GEV模型,对北京市最大月降水量序列(极值降水序列)进行模拟和频率分析,并采用Bootstrap方法对频率分析结果的不确定性进行评价。结果表明:所有极值降水序列的最优概率分布模型均为非平稳GEV模型,该模型能够抓住序列随时间呈显著下降趋势的变化特征;由非平稳GEV模型估算得到的极值降水重现水平随时间呈减少趋势,这意味着未来极值降水导致洪涝灾害的风险在降低,但导致干旱的风险将增加;随着重现期的增加,极值降水重现水平估计值的不确定性也随之增大。     

2010—2016年江西省暖季短时强降水特征分析

利用江西省2010—2016年5—9月1597个观测站逐小时降水资料对江西省短时强降水进行统计分析。采用REOF将降水场划分为5个区域：赣北南部（Ⅰ区）,抚州市及赣州中部（Ⅱ区）,赣北北部（Ⅲ区）,赣南南部、北部（Ⅳ区）以及赣中西部（Ⅴ区）。短时强降水高频区主要分布在山地及河谷附近,分别为湘赣交界罗霄山脉东侧、武夷山西侧、信江河谷、乐安河谷和昌江河谷。河谷附近短时强降水频次以昌江河谷最高（16.9次/a）,山地附近最高在罗霄山脉东侧（12.6次/a）,极端短时强降水分别位于上饶市东北部山区（3.7次/a）及九岭山南侧的锦江河谷（3.3次/a）。短时强降水主要发生在5月第3候,6、7月第3~4候以及8月第2~3候。Ⅳ、Ⅴ区具有单峰型的日变化特征;Ⅰ、Ⅱ、Ⅲ区具有双峰型的日变化特征。主峰基本集中在下午17时;次峰在上午08—10时。短时强降水对暴雨贡献率基本在40%以上,Ⅰ、Ⅱ区的暴雨天气过程将近一半是由短时强降水贡献的。信江河谷是暴雨雨量中心,但并不是短时强降水雨量中心;昌江河谷与武夷山西麓既是暴雨中心也是短时强降水中心。