High-resolution flood hazard mapping based on nonstationary frequency analysis: case study of Ho Chi Minh City,Vietnam

Flood modelling inputs used to create flood hazard maps are normally based on the assumption of data stationarity for flood frequency analysis. However, changes in the behaviour of climate systems can lead to nonstationarity in flood series. Here, we develop flood hazard maps for Ho Chi Minh City, Vietnam, under nonstationary conditions using extreme value analysis, a coupled 1D–2D model and high-resolution topographical data derived from LiDAR (Light Detection and Ranging) data. Our findings indicate that ENSO (El Niño Southern Oscillation) and PDO (Pacific Decadal Oscillation) influence the magnitude and frequency of extreme rainfall, while global sea-level rise causes nonstationarity in local sea levels, having an impact on flood risk. The detailed flood hazard maps show that areas of high flood potential are located along river banks, with 0.60 km² of the study area being unsafe for people, vehicles and buildings (H5 zone) under a 100-year return period scenario.     

Analysis of Stationary-Wave Nonstationarity in the Northern Hemisphere 500-hPa Height Field

In this paper, the concept of stationary-wave nonstationarity is presented and elucidated in the framework of the Lorenz circulation decomposition. This concept indicates the relative magnitude of the zonal nonuniform abnormity to the intensity of stationary waves on the monthly mean scale. Based on the Lorenz circulation decomposition, the nonstationarity degree Ius(Ilus) of the global (local) stationary waves is defined, and then used to analyze the stationary-wave nonstationarity at 30° 60°N, where the intensity of stationary waves at 500 hPa in the Northern Hemisphere, as is well known, is very high. The following findings are obtained: (1) There exist seasonal southward and northward movements in the position of the nonstationarity zones of the global stationary waves. The steady stationary waves occur in midlatitudes (35°-55°N) in winter and in the subtropical region (south of 35°N) in summer, associated with the major troughs over East Asia and North America and the weak European trough in winter, and with the relatively steady subtropical high system in summer. A high value center of Ius is at 35°N in spring and 50°N in summer, which might be caused by the seasonal variation of stationary-wave intensity, particularly in association with the interannual variability of trough ridge positions of stationary waves on the monthly mean maps. (2) There exists obvious asymmetry in Ilus, with the steady zones always located in the areas controlled by strong troughs/ridges and the unsteady ones in the areas where the stationary-wave intensity is low. The Ilus in the subtropics (south of 35°N) is larger in winter than in summer, and vice versa in the midlatitude region (north of 35°N). The summertime distribution of Ilus on the whole shows a rather complicated structure. However, North Europe is the most unsteady area for local stationary waves, as represented by high values of Ilus in both summer and winter, while over the North American continent (about 120°E-60°W), the °Ilus is slightly less than 1 in summer, indicating that the stationary waves in this region are more steady than those over other mid and high latitude regions. (3) From North China to Northwest Pacific, there is a high value zone of Ilus in summer, with its center (45°N, 130°E) located in the east of Heilongjiang Province. This influences the summer climate of northern China, including Northeast, North, and Northwest China. It is obvious that the nonstationarity is an intrinsic attribute of stationary waves, and can be regarded as being of the same importance as the intensity and energy-spectrum structure of stationary waves in the studies of the general circulation system.     

混合地理加权回归模型算法研究

以迭代算法为基础，推导出混合地理加权回归模型的常系数（全局参数）和变系数（局域参数）的计算方法，并以上海市住宅小区楼盘销售平均价格为例进行验证。结果表明，混合地理加权回归模型的计算量略大于地理加权回归模型，但对样本数据的拟合更好，局域参数估计更稳健。     

The impact of climatic variability and change in the hydroclimatology of Lake Winnipeg watershed

The hydroclimatology of prairie‐dominated portions of the Lake Winnipeg watershed was investigated to determine the possible presence of trends and shifts in variables that may influence the streamflow regimes and water quality of Lake Winnipeg. The total annual streamflow, precipitation, runoff ratio and daily maximum streamflow in the two major tributaries of the Assiniboine River and Red River were analysed for a range of nonstationary behaviours. Each of these rivers has been gauged for more than 90 years. The methods used included a nonparametric Mann–Kendall test modified to account for diverse memory properties (i.e. short term versus long term) and a Bayesian change point detection model to identify possible segments of time series with inconsistent nonstationary behaviour. Although there is no evidence of statistically significant trends in precipitation and streamflow in the Assiniboine River watershed, a shift‐type nonstationarity in annual runoff and runoff ratio was observed in this area, which is manifested in the form of a sequence of wet and dry spells during the last century. Precipitation and runoff metrics in the American portion of the study area (i.e. Red River watershed) were characterised with both gradual and abrupt changes with an extremely increasing rate of streamflow beyond that of intensified precipitation. The nonproportional watershed runoff response is attributed to the dynamic nature of contributing areas that, together with the semiarid climate, leads to sudden changes of streamflow due to major or even some times minor changes in climate inputs. It is evident that streamflow in the depression‐dominated landscapes of the semiarid glaciated plains of North America is particularly sensitive and vulnerable to minor climate variability and change. This study provides valuable insights into the highly complex precipitation–runoff relationship in depression‐dominated landscapes and could have important implications for water management in this part of North America and comparable regions. Copyright © 2011 John Wiley & Sons, Ltd.     

Climate‐informed low‐flow frequency analysis using nonstationary modelling

Stationarity is often assumed for frequency analysis of low flows in water resources management and planning. However, many studies have shown that flow characteristics, particularly the frequency spectrum of extreme hydrologic events, were modified by climate change and human activities. Thus, the conventional frequency analysis that fails to consider the nonstationary characteristics may lead to costly design. The analysis presented in this paper was based on the more than 100 years of daily flow data from the Yichang gauging station 44 km downstream of the Three Gorges Dam. The Mann–Kendall trend test under the scaling hypothesis showed that the annual low flows had a significant monotonic trend, whereas an abrupt change point was identified in 1936 by the Pettitt test. The climate‐informed low‐flow frequency analysis and the divided and combined method were employed to account for the impacts from related climate variables and nonstationarities in annual low flows. Without prior knowledge of the probability density function for the gauging station, six distribution functions including the generalized extreme values (GEV), Pearson Type III, Gumbel, Gamma, Lognormal and Weibull distributions have been tested to find the best fit, in which the local likelihood method is used to estimate the parameters. Analyses show that GEV had the best fit for the observed low flows. This study has also shown that the climate‐informed low‐flow frequency analysis is able to exploit the link between climate indices and low flows, which would account for the dynamic feature for reservoir management and provide more accurate and reliable designs for infrastructure and water supply. Copyright © 2014 John Wiley & Sons, Ltd.     

Choosing an arbitrary calibration period for hydrologic models: How much does it influence water balance simulations?

The selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50-year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50-year mean monthly surface runoff simulations could vary by 23%–37% and 169%–209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes.     

地震动功率谱与反应谱的转换关系

本文详细评述了现今常用的几种加速度反应谱与功率谱的转换关系。对于小阻尼单质点体系而言，考虑到输入地震动是一非平稳的随机过程，又由于其传递函数的窄频带滤波特性，它的加速度反应将是一窄频带的非平稳随机过程。对于峰值系数水平的超越不是独立的，而是成群超越。据此本文考虑非平稳效应和对峰值系数水平的成群效应，对前人的转换关系进行了修正，并基于随机振动理论，给出了对工程上常用的频率平稳、强度非平稳的地震;动模型的功率谱和反应谱的转换关系。此转换关系对于长、短持时的地震动记录和反应谱长、短周期部分以及不同阻尼比的反应谱都能给出精度较高的结果。     

基于相位差谱的时─频非平稳人造地震动的生成

本文首先考察了地震动加速度时程在时域和频域上的非平稳性，通过实例分析说明地震动加速度时程 的非平稳性不能由相位谱的概率分布唯一决定，进而阐明了相位差谱是影响地震动非平稳的决定性因素。经 统计检验确定了脉动相位差的概率分布模型，利用相位差谱的数字特征与地震特性参数之间的统计关系，给 出了基于相位差谱的地震动时程生成方法。最后，通过对计算实例的分析，证实了此方法能够反映并模拟实 际地震动的时─频非平稳性。     

Analysing regional industrialisation in Jiangsu province using geographically weighted regression

 Industry is the most important sector in the Chinese economy. To identify the spatial interaction between the level of regional industrialisation and various factors, this paper takes Jiangsu province of China as a case study. To unravel the existence of spatial nonstationarity, geographically weighted regression (GWR) is employed in this article. Conventional regression analysis can only produce `average' and `global' parameter estimates rather than `local' parameter estimates which vary over space in some spatial systems. Geographically weighted regression (GWR), on the other hand, is a relatively simple, but useful new technique for the analysis of spatial nonstationarity. Using the GWR technique to study regional industrialisation in Jiangsu province, it is found that there is a significant difference between the ordinary linear regression (OLR) and GWR models. The relationships between the level of regional industrialisation and various factors show considerable spatial variability. Received: 4 April 2001 / Accepted: 17 November 2001     

地震动频谱非平稳性对结构非线性反应的影响

应用均匀调制随机过程模型和演变的随机过程模型合成了人工地震加速度过程。以此为输入，计算了一个三跨十层呆结构的地震反应，比较结果表明，考虑地震动的频谱非平稳稳征后，所计算结构的层位移，梁端塑性转角，柱的弯矩和剪力显著增大，结构在相对较小的地震动峰值下发生倒塌。