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 I_（us）（I_（us）~1） 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 I_（us） 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 I_（us）~1,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 I_（us）~1 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 I_（us）~1 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 I_（us）~1 in both summer and winter,while over the North American continent （about 120°E-60°W）,the I_（us）~1 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 I_（us）~1 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.

Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Nonstationary sparsity-constrained seismic deconvolution

The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., sparsity-constrained deconvolution） generally attempt to suppress the problems associated with the first two assumptions but often ignore that seismic traces are nonstationary signals, which undermines the basic assumption of unchanging wavelet in reflectivity inversion. Through tests on reflectivity series, we confirm the effects of nonstationarity on reflectivity estimation and the loss of significant information, especially in deep layers. To overcome the problems caused by nonstationarity, we propose a nonstationary convolutional model, and then use the attenuation curve in log spectra to detect and correct the influences of nonstationarity. We use Gabor deconvolution to handle nonstationarity and sparsity-constrained deconvolution to separating reflectivity and wavelet. The combination of the two deconvolution methods effectively handles nonstationarity and greatly reduces the problems associated with the unreasonable assumptions regarding reflectivity and wavelet. Using marine seismic data, we show that correcting nonstationarity helps recover subtle reflectivity information and enhances the characterization of details with respect to the geological record.

The Prediction of Non-stationary Climate Series Based on Empirical Mode Decomposition

This paper proposes a new approach which we refer to as ＂segregated prediction＂ to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method （EMD）, which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.

Modeling nonstationary extreme wave heights in present and future climates of Greek Seas

In this study the generalized extreme value （GEV） distribution function was used to assess nonstationarity in annual maximum wave heights for selected locations in the Greek Seas, both in the present and future climates. The available significant wave height data were divided into groups corresponding to the present period （1951-2000）, a first future period （2001-2050）, and a second future period （2051-2100）. For each time period, the parameters of the GEV distribution were specified as functions of time-varying covariates and estimated using the conditional density network （CDN）. For each location and selected time period, a total number of 29 linear and nonlinear models were fitted to the wave data, for a given combination of covariates. The covariates used in the GEV-CDN models consisted of wind fields resulting from the Regional Climate Model version 3 （RegCM3） developed by the International Center for Theoretical Physics （ICTP） with a spatial resolution of 10 km ×10 km, after being processed using principal component analysis （PCA）. The results obtained from the best fitted models in the present and future periods for each location were compared, revealing different patterns of relationships between wind components and extreme wave height quantiles in different parts of the Greek Seas and different periods. The analysis demonstrates an increase of extreme wave heights in the first future period as compared with the present period, causing a significant threat to Greek coastal areas in the North Aegean Sea and the Ionian Sea.

Stochastic simulation of ground motions based on NGA-West2 strong motion records

Stochastic modeling of ground motion is a simple tool to predict ground shaking level for future earthquake and less time consuming than physics-based deterministic modeling.In this paper,a record-based stochastic method that considers the time-and frequency-evolution of ground motion is used to estimate ground motion for scenario earthquakes in tectonic active region.The stochastic method employs a time-domain modulation function to describe the temporal nonstationarity and a filter impulse response function that describe the evolution of frequency content.For characterizing the modulation function and the filter impulse function,six parameters(Ia,D5-95,tmid,ωmid,ω',ξf)are defined,and 2,571 pairs of ground motion recording in the NGA-west2 database are selected to identify the six parameters.Probabilistic density function is assigned to each of the parameter by fitting the frequency distribution histogram.The parameters are then transformed into standard normal space where regression analysis is performed by considering each parameter as function of moment magnitude,rupture distance,vS30(The time-averaged shear wave velocity of the top 30 m of soil).The prediction equations are used to generate ground motions for several scenario earthquakes and compared to NGA-West2 GMPEs.

Flood Risk for Embanked Rivers

Flood frequency analysis (FFA) concentrates on peak flows of flood hydrographs. However, floods that last years devastated large parts of Poland lead us to revision of the views on the assessment of flood risk in Poland. It turned out that it is the prolonged exposure to high water on levees that causes floods, not only the water overflowing the levee crest. This is because, the levees are weakened by water and their disruption occurs when it seems that the danger is over, i.e. after passing culmination. Two main causes of inundation of embanked rivers, namely over-crest flow and wash out of the levees, are combined to assess the total risk of inundation. Therefore the risk of inundation is the total of risk of exceeding embankment crest by flood peak and risk of washout of levees. Hence, while modeling the flood events in addition to the maximum flow one should consider also the duration of high water in a river channel, Analysis of the frequency of annual peak flows based on annual maxima and peaks over threshold is the subject of countless publications. Therefore we will here mainly modeling the duration of high water levels. In the paper the two-component model of flood hydrograph shape i.e. “duration of flooding-discharge- probability of nonexceedance” (DqF), with the methodology of its parameters estimation for stationary case was developed as a completion to the classical FFA with possible extension to non stationary flood regime. The model combined with the technical evaluation of probability of levees breach due to the d-days duration of flow above alarm stage gives the annual probability of inundation caused by the embankment breaking. The results of theoretical research were supplemented by a practical example of the model application to the series for daily flow in the Vistula River in Szczucin. Regardless promising results, this method is still in its infancy despite its great cognitive potential and practical importance. Therefore, we would like to point to the usefulness and necessity of the DqF models to the one-dimensional analysis of the peak flood hydrographs and to flood risk analysis. This approach constitutes a new direction in FFA for embanked rivers.

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas.This paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope.This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results.The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example.The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic regression.Moreover,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion morphology.Overall,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.

Spatiotemporal evolution of national development zones and their impact on urban land growth in China

Development zones are important growth poles for promoting regional economic development. However, the spatiotemporal relationship between development zone construction and urban land growth is still unclear. This paper analyzes the spatiotemporal changes of national-level development zones(NDZs), approximately 219 national economic development zones, and 156 high-tech development zones during 1990–2018 in China. The impact of development zone establishment on the growth of surrounding urban land was quantitatively explored using circle buffering analysis and time series comparative analysis. The results show that China's NDZs spread from the southeast coast to the inland area from 1990 to 2018, and the establishment of the development zones has an obvious promoting effect on the surrounding urban land growth. The scope and intensity of influences of the development zone established in different periods present distinct nonstationarity in space and time. Overall, the impact on urban land(IU) of China's NDZs established in different years was mostly highest at the 100 m buffer zone radius, while the slope of the IU was mostly negative, which meant that the 100 m buffer zone radius of the development zone center was the most efficient scale to promote urban land growth. In the meantime, the curve of IU of NDZs established before 1990, during 1996–2000 and 2001–2005 has a clear inflection point, which indicates that the most efficient scales of NDZs established before 1990, during 1996–2000, and 2001–2005 are 1300 m, 900–1000 m, and 800 m, respectively. NDZs established in other periods do not have the most obvious efficient scale. The development zone played the greatest role in promoting urban land growth from 2000 to 2010. Three association modes, including post-growth, pre-growth and steady-growth, were identified based on the differences in geographical location, establishment time, and type of development zones. We quantitatively identify the impact of the growth pole of NDZs on urban land growth from the perspective of spatiotemporal evolution. The findings would provide decision-making support for optimizing the spatial relationship between development zone construction and urban land growth.

Three-dimensional delineation of soil pollutants at contaminated sites: Progress and prospects

The precision remediation and redevelopment of contaminated sites are crucial issues for improving the human settlement and constructing a beautiful China. Three-dimensional delineation of soil pollutants at contaminated sites is a prerequisite for precision remediation and redevelopment. However, a contaminated site is a three-dimensional complex system coupling multiple spatial elements above-and under-ground. The complexity incurs high uncertainties about the three-dimensional delineation of soil pollutants based on sparse borehole and spatial statistics and inference models. This paper first systematically reviewed the objectives of fine three-dimensional delineation of soil pollutants, the sampling strategies for soil boring, the commonly used models for delineating soil pollutants, and the relevant cases of applying these models at contaminated sites. We then summarized the effects of borehole data and three-dimensional models on soil pollutants' delineation results from biased characteristics and nonstationary conditions. The present research status and related issues on correcting the biased characteristics and nonstationary conditions were analyzed. Finally, based on the problems and challenges, we suggested the three-dimensional delineation of soil pollutants in the underground “black box” for future research from the following six priority areas: multi-scenarios, nonstationary, non-linearity, multi-source data fusion, multiple model coupling, and the delineation of co-contaminated sites.

Detecting Non-Stationarity for Auscultation Signal of Traditional Chinese Medicine

The information about the nonstationarity of the aus-cultation signal is utilized in this paper to objectively and auto-matically identify healthy people and patients with qi-deficiency or yin-deficiency. In order to characterize the nonstationarity of the sound signal,the nonlinear cross-prediction method is used to extract features from the signal. A feature selection method based on conditional mutual information maximization criterion （CMIM） is implemented to find an optimal feature set. By means of the support vector machine （SVM） classifier,three common states （healthy,qi-deficiency and yin-deficiency） in traditional Chinese medicine are distinguished using the feature set,and a satisfactory classification accuracy of 80% is achieved in the experiment. In conclusion,the analysis based on the nonstationarity of the sound signal provides an alternative and outstanding approach to the objective auscultation of traditional Chinese medicine （TCM）.

Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China

Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location,scale, and shape was used for at-site precipitation forecasting. The results indicate that:(1) winter and spring precipitation processes at most stations are nonstationary,while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the studyregion;(2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation(PDO). The El Nin? o/Southern Oscillation(ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin;(3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and(4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation.