1958-2017年太湖蒸发量年际变化趋势及主控因子

湖泊蒸发对气候变化非常敏感,是水文循环响应气候变化的指示因子,因此研究湖泊蒸发的控制因素,对于理解区域水文循环有重要意义.本文利用太湖中尺度涡度通量网避风港站观测数据校正JRA-55再分析资料,驱动CLM4.0-LISSS模型,并利用2012-2017年涡度相关通量数据和湖表面温度数据检验模型模拟蒸发结果,验证了该模型在太湖的适用性;估算了1958-2017年间太湖的湖面蒸发量,并利用Manner-Kendall趋势检验分析了湖面蒸发的变化趋势,寻找太湖实际蒸发的年际变化的主控因子.结果如下:校正后的JRA-55再分析资料模拟的太湖蒸发与观测值之间存在季节偏差,但是季节偏差在年尺度上相互抵消,再分析资料可用于年际尺度太湖蒸发变化的模拟;1958-2017年间太湖蒸发量以1977年为界,先下降(-3.6 mm/a),后增加(2.3 mm/a);多元逐步回归结果表明,向下的短波辐射是太湖1958-2017年间太湖蒸发变化的主控因子,向下的长波辐射、气温、比湿也对湖泊蒸发年际变化有一定影响,但是风速对蒸发量的年际变化影响不大.     

松嫩平原近20年土壤盐渍化动态变化及驱动力分析

利用1986年（TM）和2001年（ETM）卫星遥感影像数据和RS-GIS集成技术,对松嫩平原盐渍化土地的现状、程度和发展趋势进行了量化分析.结果表明,这15年中,松嫩平原盐碱地面积增加了21.7112×10⁴ hm²,每年增加1.45×10⁴ hm²,年平均增长率为1.4%.其中轻度盐渍化土地增加8812 hm²,年平均增长0.16%;中度盐渍化土地增加3.8306×10⁴ hm²,年平均增长0.64%;重度盐渍化土地面积增加了16.9994×10⁴ hm²,年平均增长了4.2%.气候变暖,降水减少,人为活动增强是盐渍化程度加重的主要驱动力.     

Flood seasonality across Scandinavia—Evidence of a shifting hydrograph?

Fluvial flood events have substantial impacts on humans, both socially and economically, as well as on ecosystems (e.g., hydroecology and pollutant transport). Concurrent with climate change, the seasonality of flooding in cold environments is expected to shift from a snowmelt‐dominated to a rainfall‐dominated flow regime. This would have profound impacts on water management strategies, that is, flood risk mitigation, drinking water supply, and hydro power. In addition, cold climate hydrological systems exhibit complex interactions with catchment properties and large‐scale climate fluctuations making the manifestation of changes difficult to detect and predict. Understanding a possible change in flood seasonality and defining related key drivers therefore is essential to mitigate risk and to keep management strategies viable under a changing climate. This study explores changes in flood seasonality across near‐natural catchments in Scandinavia using circular statistics and trend tests. Results indicate strong seasonality in flooding for snowmelt‐dominated catchments with a single peak occurring in spring and early summer (March through June), whereas flood peaks are more equally distributed throughout the year for catchments located close to the Atlantic coast and in the south of the study area. Flood seasonality has changed over the past century seen as decreasing trends in summer maximum daily flows and increasing winter and spring maximum daily flows with 5–35% of the catchments showing significant changes at the 5% significance level. Seasonal mean daily flows corroborate those findings with higher percentages (5–60%) of the catchments showing statistically significant changes. Alterations in annual flood occurrence also point towards a shift in flow regime from snowmelt‐dominated to rainfall‐dominated with consistent changes towards earlier timing of the flood peak (significant for 25% of the catchments). Regionally consistent patterns suggest a first‐order climate control as well as a local second‐order catchment control, which causes inter‐seasonal variability in the streamflow response.     

Investigation into the daily precipitation variability in the Yangtze River Delta,China

In this article, by using the daily precipitation data measured at 58 meteorological stations, spatial and temporal variability of daily precipitation including zero rainfall values (called “precipitation”) and without zero rainfall values (called “rain”) and four precipitation extrema (P₀, P₂₀, P₅₀, and P₁₀₀ representing the daily precipitation with the magnitude smaller than 0.1 mm, bigger than 20 mm, 50 mm, and 100 mm per day, respectively) in the Yangtze River Delta (YRD) during 1958–2007 were analyzed, and the effects of urbanization were further investigated. Results indicate that (i) differing from the downward trends in 1958–1985, daily precipitation and rain in 1986–2007 show slowly downward trends in the mid YRD but show upward trends in the northern and southern YRD. (ii) Spatial and temporal variability of the rain is more complex than daily precipitation. Both of them become smaller but show more obvious fluctuations in 1986–2007. (iii) Urbanizations cause not only the urban rainfall island problem but also more obvious fluctuations of rain intensity in the mid YRD, reflecting more uncertainty of daily precipitation variability. (iv) Urbanizations have little effects on the variability of P₀ and P₁₀₀ but cause notable increases of P₂₀ and P₅₀. (v) The spatial variability of daily precipitation and precipitation extrema in 1958–1985 clearly shows a breakpoint at 30°20′N latitude, but the breakpoint disappears afterward because of the effects of urbanization. Copyright © 2012 John Wiley & Sons, Ltd.     

A study of variability of annual river flow of the southern African region

Abstract

Variability of river flow is investigated in 502 river flow gauging stations in nine countries of the southern African region with a view to document the spatial variability of the river flow regimes. Those regions where there is strong evidence of declining or increasing trend in annual runoff have been identified. The study has shown that runoff in the region ranges from over 320 mm year^?1 in the Lower Zambezi and the highlands of Tanzania to less than 10 mm year^?1 in the deserts of Namibia and the Kalahari. There is also evidence of declining runoff in parts of Zambia, Angola, Mozambique and the High Veld in South Africa. The recent decline seems to have started from around 1975.     

Low and high flow analyses and wavelet application for characterization of the Blue Nile River system

The low and high flow characteristic of the Blue Nile River (BNR) basin is presented. The study discusses low and high flow, flow duration curve (FDC) and trend analysis of the BNR and its major tributaries. Different probability density functions were fitted to better describe the low and high flows of the BNR and major tributaries in the basin. Wavelet analysis was used in understanding the variance and frequency‐time localization and detection of dominant oscillations in rainfall and flow. FDCs were developed, and low flow (below 50% exceedance) and high flow (over 75% exceedance) of the curves were analysed and compared. The Gravity Recovery and Climate Experiment (GRACE) satellite‐based maps of monthly changes in gravity converted to water equivalents from 2003 to 2006 for February, May and September showed an increase in the moisture influx in the BNR basin for the month of September, and loss of moisture in February and May. It was also shown that 2004 and 2005 were drier with less moisture influx compared to 2003 and 2006. On the basis of the Kolmogorov‐Smirnov, Anderson‐Darling and Chi‐square tests, Gen. Pareto, Frechet 3P, Log‐normal, Log‐logistics, Fatigue Life and Phased Bi‐Weibull distributions best describe the low and high flows within the BNR basin. This will be beneficial in developing flow hydrographs for similar ungauged watersheds within the BNR basin. The below 50% and above 75% exceedance on the FDC for five major rivers in addition to the BNR showed different characteristics depending on size, land cover, topography and other factors. The low flow frequency analysis of the BNR at Bahir Dar showed 0·55 m³/s as the monthly low flow with recurrence interval of 10 years. The wavelet analysis of the rainfall (at Bahir Dar and basin‐wide) and flows at three selected stations shows inter‐ and intra‐annual variability of rainfall and flows at various scales. Copyright © 2009 John Wiley & Sons, Ltd.     

Trend detection in river flow series: 1. Annual maximum flow / Détection de tendance dans des séries de débit fluvial: 1. Débit maximum annuel

Abstract

Results of a study on change detection in hydrological time series of annual maximum river flow are presented. Out of more than a thousand long time series made available by the Global Runoff Data Centre (GRDC) in Koblenz, Germany, a worldwide data set consisting of 195 long series of daily mean flow records was selected, based on such criteria as length of series, currency, lack of gaps and missing values, adequate geographical distribution, and priority to smaller catchments. The analysis of annual maximum flows does not support the hypothesis of ubiquitous growth of high flows. Although 27 cases of strong, statistically significant increase were identified by the Mann-Kendall test, there are 31 decreases as well, and most (137) time series do not show any significant changes (at the 10% level). Caution is advised in interpreting these results as flooding is a complex phenomenon, caused by a number of factors that can be associated with local, regional, and hemispheric climatic processes. Moreover, river flow has strong natural variability and exhibits long-term persistence which can confound the results of trend and significance tests.     

Discrete wavelet‐based trend identification in hydrologic time series

Trend identification is a substantial issue in hydrologic series analysis, but it is also a difficult task in practice due to the confusing concept of trend and disadvantages of methods. In this article, an improved definition of trend was given as follows: ‘a trend is the deterministic component in the analysed data and corresponds to the biggest temporal scale on the condition of giving the concerned temporal scale’. It emphasizes the intrinsic and deterministic properties of trend, can clearly distinguish trend from periodicities and points out the prerequisite of the concerned temporal scale only by giving which the trend has its specific meaning. Correspondingly, the discrete wavelet‐based method for trend identification was improved. Differing from those methods used presently, the improved method is to identify trend by comparing the energy difference between hydrologic data and noise, and it can simultaneously separate periodicities and noise. Furthermore, the improved method can quantitatively estimate the statistical significance of the identified trend by using proper confidence interval. Analyses of both synthetic and observed series indicated the identical power of the improved method as the Mann–Kendall test in assessing the statistical significance of the trend in hydrologic data, and by using the former, the identified trend can adaptively reflect the nonlinear and nonstationary variability of hydrologic data. Besides, the results also showed the influences of three key factors (wavelet choice, decomposition level choice and noise content) on discrete wavelet‐based trend identification; hence, they should be carefully considered in practice. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO ₂ are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO ₂ ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols c _i Scalar factor that minimizes the discrepancy between andU _i - D Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients - d Distance for which transition matrixU is estimated - d _ij Distance between observed valuesi andj - E Expected value - E _i Row vector of residuals in the standardized model - F(d _ij) Quadratic function of distanced _ij F(d _ij)=a+bd _ij+cd _ij ² - L Diagonal matrix of the eigenvalues ofU - _i Eigenvalue of the matrixU;ith diagonal element ofL - N Number of observations - p Number of variables - Q Total predictive power ofU - R Correlation matrix of the variables - R _0j Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m) - R _1j Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood - R _m ² Multiple correlation coefficient squared for themth variable - S _i Column vectori of the signal values - s _k ² Residual variance for variablek - T _i Amplitude vector corresponding toV _i;ith row ofT=V ^–1 - T Total variation in the system - U Nonsymmetric transition matrix formed by post-multiplyingR ₀₁ ^–1 byR _ij - U _i Componenti of the matrixU, corresponding to theith eigenvectorV _i;U _i= _iV_iT_i - U* _i ComponentU _i multiplied byc _i - U _ij Sum of componentsU _i+U _j - V _i Eigenvector of the matrixU;ith column ofV withUV=VL - w Weighting factor; equal to the ratio of two eigenvalues - X _i Random variable at pointi - x _i Value of random variable at pointi - y _i Residual ofx _i - Z _i Row vectori for the standardized variables - z _i Standardized value of variable     

When do we need a trend model in kriging?

Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option.