Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation

A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.     

Hierarchical decomposition of variance with applications in environmental mapping based on satellite images

A quadtree-based image segmentation procedure (HQ) is presented to map complex environmental conditions. It applies a hierarchical nested analysis of variance within the framework of multiresolution wavelet approximation. The procedure leads to an optimal solution for determining mapping units based on spatial variability with constraints on the arrangement and shape of the units. Linkages to geostatisiics are pointed out, but the HQ decomposition algorithm does not require any homogeneity criteria. The computer implementation can be parameterized by either the number of required mapping units or the maximum within-unit variance, or it can provide a spectrum of significances of nested ANOVA. The detailed mathematical background and methodology is illustrated by a salt-affected grassland mapping study (Hortobágy, Hungary), where heterogeneous environmental characteristics have been sampled and predicted based on remotely sensed images using these principles.     

Fitting the Linear Model of Coregionalization by Generalized Least Squares

In geostatistical studies, the fitting of the linear model of coregionalization (LMC) to direct and cross experimental semivariograms is usually performed with a weighted least-squares (WLS) procedure based on the number of pairs of observations at each lag. So far, no study has investigated the efficiency of other least-squares procedures, such as ordinary least squares (OLS), generalized least squares (GLS), and WLS with other weighing functions, in the context of the LMC. In this article, we compare the statistical properties of the sill estimators obtained with eight least-squares procedures for fitting the LMC: OLS, four WLS, and three GLS. The WLS procedures are based on approximations of the variance of semivariogram estimates at each distance lag. The GLS procedures use a variance–covariance matrix of semivariogram estimates that is (i) estimated using the fourth-order moments with sill estimates (GLS₁), (ii) calculated using the fourth-order moments with the theoretical sills (GLS₂), and (iii) based on an approximation using the correlation between semivariogram estimates in the case of spatial independence of the observations (GLS₃). The current algorithm for fitting the LMC by WLS while ensuring the positive semidefiniteness of sill matrix estimates is modified to include any least-squares procedure. A Monte Carlo study is performed for 16 scenarios corresponding to different combinations of the number of variables, number of spatial structures, values of ranges, and scale dependence of the correlations among variables. Simulation results show that the mean square error is accounted for mostly by the variance of the sill estimators instead of their squared bias. Overall, the estimated GLS₁ and theoretical GLS₂ are the most efficient, followed by the WLS procedure that is based on the number of pairs of observations and the average distance at each lag. On that basis, GLS₁ can be recommended for future studies using the LMC.     

地理国情各要素之间套合关系检查要点分析

将地理国情普查工作中地表覆盖和地理国情要素两个重要成果之间的套合关系进行梳理,依据各项规定和生产实践经验,对重点问题进行整理和分析,明确了两者相互关系并提供一些有益的建议,为提高地理国情普查成果质量提供帮助。     

Probabilistic Approach of Coastal Defense Against Typhoon Attacks for Nuclear Power Plant

With the global wanning and sea level rising, it is widely recognized that there is an increasing tendency of typhoon occurrence frequency and intensity. The defenses code against typhoon attacks for nuclear power plant should be calibrated because of the increasing threat of typhoon disaster and severe consequences. This paper discusses the probabilistic approach of definitions about "probable maximum typhoon" and "probable maximum storm surge" in nuclear safety regulations of China and has made some design code calibrations by use of a newly proposed Double Layer Nested Multi-objective Probability Model (DLNMPM).     

青藏高原月NDVI时空动态变化及其对气候变化的 响应

青藏高原脆弱的高寒植被对外界干扰十分敏感,使其成为研究植被对气候变化响应的理想区域之一。青藏高原气候变化剧烈,在较短的合成时间研究气候变化对植被的影响十分必要。因此,本文利用GIMMS NDVI时间序列数据集,研究了1982-2012年青藏高原生长季月尺度植被生长的时空动态变化,探讨了其与气温、降水量和日照时数等气候因子的响应关系。结果表明：在区域尺度上,除8月外,其他各月份植被均呈增加趋势,显著增加多发生在4-7月和9月;大部分月份的NDVI增加速率随着时段的延长显著减小,表明NDVI增加趋势放缓;在像元尺度上,月NDVI显著变化的区域多呈增加趋势,但显著减少范围的扩张多快于显著增加。4月和7月植被生长主要是受气温和日照时数共同作用,6月和9月受气温的控制,而8月则主要受降水量的影响。长时间序列NDVI数据集的出现为采用嵌套时段研究植被生长变化趋势奠定了前提,而植被活动变化趋势的持续性则有助于形象表征植被活动变化过程、深入理解植被对气候变化的响应和预测植被未来生长变化趋势。由此推测,青藏高原月NDVI未来增加趋势总体上趋于缓和,但在像元尺度显著变化的区域趋于增加。     

Numerical simulation of the Kuroshio using nested model of three-dimensional baroclinic circulation

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一种减少有限区气候模式嵌套误差积累的方法

介绍了一种用于减少有限区气候模式中嵌套误差积累的新方法——三维嵌套方法 ,详细阐述了这种方法提出的原因、必要性和它的理论基础。在有限区气候模式上进行三维嵌套与二维嵌套的对比试验 ,试验结果表明三维嵌套方案可减少嵌套预报误差的积累 ,并且对于降水的预报也有很大的改善。     

Connectivity of post-fire runoff and sediment from nested hillslopes and watersheds

Wildfire increases the potential connectivity of runoff and sediment throughout watersheds due to greater bare soil, runoff and erosion as compared to pre-fire conditions. This research examines the connectivity of post-fire runoff and sediment from hillslopes (< 1.5 ha; n = 31) and catchments (< 1000 ha; n = 10) within two watersheds (< 1500 ha) burned by the 2012 High Park Fire in northcentral Colorado, USA. Our objectives were to: (1) identify sources and quantify magnitudes of post-fire runoff and erosion at nested hillslopes and watersheds for two rain storms with varied duration, intensity and antecedent precipitation; and (2) assess the factors affecting the magnitude and connectivity of runoff and sediment across spatial scales for these two rain storms. The two summer storms that are the focus of this research occurred during the third summer after burning. The first storm had low intensity rainfall over 11 hours (return interval <1–2 years), whereas the second event had high intensity rainfall over 1 hour (return interval <1–10 years). The lower intensity storm was preceded by high antecedent rainfall and led to low hillslope sediment yields and channel incision at most locations, whereas the high intensity storm led to infiltration-excess overland flow, high sediment yields, in-stream sediment deposition and channel substrate fining. For both storms, hillslope-to-stream sediment delivery ratios and area-normalised cross-sectional channel change increased with the percent of catchment that burned at high severity. For the high intensity storm, hillslope-to-stream sediment delivery ratios decreased with unconfined channel length (%). The findings quantify post-fire connectivity and sediment delivery from hillslopes and streams, and highlight how different types of storms can cause varying magnitues and spatial patterns of sediment transport and deposition from hillslopes through stream channel networks.     

强震作用下松散海床地基的动力响应

海上和沿海区域砂性土地基在强震作用下需同时考虑液化、震陷以及流滑效应。选用杨朝晖叠套屈服面模型,提出了获取计算参数的简化方法,用OpenSees验证了该模型模拟液化的能力;开发了OpenSees与ANSYS的接口,对处于强震地区、地基土为松散砂性土的印尼某进水明渠堤坝进行了非线性有限元动力计算,判断了场地的液化情况,预测了堤坝及地基的震陷量和侧向流滑,计算结果对该类地基的加固处理具有一定的指导意义