Spatially explicit assessment of land ecological security with spatial variables and logistic regression modeling in Shanghai,China

Ecological security is a fundamental component of regional security that has drawn increased attention worldwide over the past two decades. This paper presents a novel approach to assess the status of land ecological security (LES) in Shanghai, China from 1992 to 2011 using spatial variables and a logistic regression model. The LES status of 1745 points within the study area in 1992, 2001 and 2011 was sampled systematically using a 2 × 2 km grid sample frame and evaluated based on an expert method with ten experts from five fields. A five-point Likert scale was used to score the LES status as very insecure, insecure, neutral, secure or very secure. We identified several explanatory factors to the LES status, including distance-based variables describing the proximities to urban center, developed areas and sources of pollution, as well as variables regarding the density of built-up areas and the mean value of normalized difference vegetation index. A logistic regression model was used to quantify the relationship between LES scores and the spatial variables at each of the three time points, resulting in a series of maps illustrating the LES patterns of Shanghai in 1992, 2001 and 2011. The results show that LES is either very insecure or insecure at the center of Shanghai and at its district centers, and the LES of the entire Shanghai municipality has deteriorated significantly from 1992 to 2011. This research contributes to an enhanced understanding of LES changes resulting from rapid urbanization and industrialization of the Shanghai municipality and provides a methodological framework to study LES elsewhere.     

Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851–2006) over India

ABSTRACT

This study analysed long-term rainfall data (1851–2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) Bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the Bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through Bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871–1890, 1891–1910 and 1951–1970. Linear regression-based analysis revealed 1881–1910 and 1971–2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones.     

Seismic Microzonation: A Comparison between Geotechnical and Seismological Approaches in Pointe-à-Pitre (French West Indies)

Pointe-à-Pitre, the main city of Guadeloupe in the French West Indies, has on several occasions been partially destroyed by major historical earthquakes. Moreover, a post-seismic assessment of the damage from the 1985 Montserrat earthquake indicates that the town is prone to site effects. Consequently, from 1996 to 1998, BRGM conducted a seismic microzonation study based on geotechnical and geological data. At the same time, three seismological studies were being conducted – two based on earthquake recordings using a time-series analysis and the classical spectral ratio (CSR) method (CETE/LCPC and BRGM), and the third based on noise measurement at 400 points using the horizontal-to-vertical noise ratio (HVNR) method (CETE/LCPC). The objective of this paper is not to carry out a new microzonation study by taking into account all the results, but rather to show in what respects the results of these different methods are in agreement or not. A comparison of the results of the seismological studies with the geotechnical microzonation shows that they are in fairly good agreement, albeit with some discrepancies. The results indicate that the seismological methods and the geotechnical data are highly complementary and should be used together in compiling seismic transfer-function microzonation maps. This revised version was published online in July 2006 with corrections to the Cover Date.     

Water temperature modelling: comparison between the generalized additive model,logistic, residuals regression and linear regression models

Water temperature has a significant influence on aquatic organisms, including stenotherm fish such as salmonids. It is thus of prime importance to build reliable tools to forecast water temperature. This study evaluated a statistical scheme to model average water temperature based on daily average air temperature and average discharge at the Sainte-Marguerite River, Northern Canada. The aim was to test a non-parametric water temperature generalized additive model (GAM) and to compare its performance to three previously developed approaches: the logistic, residuals regression and linear regression models. Due to its flexibility, the GAM was able to capture some of the nonlinear response between water temperature and the two explanatory variables (air temperature and flow). The shape of these effects was determined by the trends shown in the collected data. The four models were evaluated annually using a cross-validation technique. Three comparison criteria were calculated: the root mean square error (RMSE), the bias error and the Nash-Sutcliffe coefficient of efficiency (NSC). The goodness of fit of the four models was also compared graphically. The GAM was the best among the four models (RMSE = 1.44°C, bias = ?0.04 and NSC = 0.94).     

Bayesian tsunami fragility modeling considering input data uncertainty

Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty.     

Integration of multiple soft data sets in MPS thru multinomial logistic regression: a case study of gas hydrates

A new approach is described to allow conditioning to both hard data (HD) and soft data for a patch- and distance-based multiple-point geostatistical simulation. The multinomial logistic regression is used to quantify the link between HD and soft data. The soft data is converted by the logistic regression classifier into as many probability fields as there are categories. The local category proportions are used and compared to the average category probabilities within the patch. The conditioning to HD is obtained using alternative training images and by imposing large relative weights to HD. The conditioning to soft data is obtained by measuring the probability–proportion patch distance. Both 2D and 3D cases are considered. Synthetic cases show that a stationary TI can generate non-stationary realizations reproducing the HD, keeping the texture indicated by the TI and following the trends identified in probability maps obtained from soft data. A real case study, the Mallik methane-hydrate field, shows perfect reproduction of HD while keeping a good reproduction of the TI texture and probability trends.     

Complex environmental gradients predict distributions of river-dependent plants in eastern Australia

We used principal components analysis and multiple logistic regression to investigate the relationships between environmental variables and the distributions of 71 species of river-dependent vascular plants in north-eastern New South Wales, Australia. Our analysis defined seven main environmental factors, summarised (in order of decreasing frequency of statistically significant association with species distributions) as exposure, salinity, stream size, stone scarcity, nutrient enrichment, grazing pressure and rockiness. The main environmental correlates of the presence or absence of macrophyte species in our study were broadly similar to those reported elsewhere, but the relatively low apparent importance of nutrients and grazing was unexpected. We were not able to fully separate the effects of climate-related and non-climatic environmental variables because variables of both types loaded strongly on some principal components, but we suggest that both types of variables should be included in models that aim to forecast potential shifts in plant distributions under projected climatic change. Vascular plants have been neglected in monitoring programs for Australian rivers and their conservation requires a better understanding of patterns and trends in distribution and abundance.     

Modelling for registration of remotely sensed imagery when reference control points contain error

1 Introduction The process of remotely sensed data acquisition isaffected by factors such as the rotation of the earth, finite scan rate of some sensors, curvature of the earth, non-ideal sensor, variation in platform altitude, attitude, velocity, etc.[1]. One important procedurewhich should be done prior to analyzing remotely sensed data, is geometric correction (image to map) or registration (image to image) of remotely sensed data. The purpose of geometric correction or registration is to e…     

Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran

Regional flood frequency analysis (RFFA) was carried out on data for 55 hydrometric stations in Namak Lake basin, Iran, for the period 1992–2012. Flood discharge of specific return periods was computed based on the log Pearson Type III distribution, selected as the best regional distribution. Independent variables, including physiographic, meteorological, geological and land-use variables, were derived and, using three strategies – gamma test (GT), GT plus classification and expert opinion – the best input combination was selected. To select the best technique for regionalization, support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and nonlinear regression (NLR) techniques were applied to predict peak flood discharge for 2-, 5-, 10-, 25-, 50- and 100-year return periods. The GT + ANFIS and GT + SVR models gave better performance than the ANN and NLR models in the RFFA. The results of the input variable selection showed that the GT technique improved the model performance.     

Probabilistic forecasting of hydrological events using geostatistical analysis / Prévision probabiliste d'événements hydrologiques par analyse géostatistique

Abstract

A method is introduced for probabilistic forecasting of hydrological events based on geostatistical analysis. In this method, the predictors of a hydrological variable define a virtual field such that, in this field, the observed dependent variables are considered as measurement points. Variography of the measurement points enables the use of the system of kriging equations to estimate the value of the variable at non-measured locations of the field. Non-measured points are the forecasts associated with specific predictors. Calculation of the estimation variance facilitates probabilistic analysis of the forecast variables. The method is applied to case studies of the Red River in Manitoba, Canada and Karoon River in Khoozestan, Iran. The study analyses the advantages and limitations of the proposed method in comparison with a K-nearest neighbour approach and linear and nonlinear multiple regression. The utility of the proposed method for forecasting hydrological variables with a conditional probability distribution is demonstrated.     

Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea

The aim of this study was to apply, verify and compare a multiple logistic regression model for landslide susceptibility analysis in three Korean study areas using a geographic information system (GIS). Landslide locations were identified by interpreting aerial photographs, satellite images and a field survey. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the logistic regression coefficient of each factor was computed. Landslide susceptibility maps were drawn for these three areas using logistic regression coefficients derived not only from the data for that area but also using those for each of the other two areas (nine maps in all) as a cross‐check of method validity. For verification, the results of the analyses were compared with actual landslide locations. Among the nine cases, the Janghung exercise using the logistic formula and the coefficient for Janghung had the greatest accuracy (88·44%), whereas Janghung results, when considered by the logistic formula and the coefficient for Boeun, had the least accuracy (74·16%). Copyright © 2007 John Wiley & Sons, Ltd.     

Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model： a case study of the Sabae Area, Japan

A number of statistical methods are typically used to effectively predict potential landslide distributions. In this study two multivariate statistical analysis methods were used （weights of evidence and logistic regression） to predict the potential distribution of shallow-seated landslides in the Kamikawachi area of Sabae City, Fukui Prefecture, Japan. First, the dependent variable （shallow-seated landslides） was divided into presence and absence, and the independent variables （environmental factors such as slope and altitude） were categorized according to their characteristics. Then, using the weights of evidence （WE） method, the weights of pairs comprising presence （w^＋（i）） or absence （w^-（i））, and the contrast values for each category of independent variable （evidence）, were calculated, Using the method that integrated the weights of evidence method and a logistic regression model, score values were calculated for each category of independent variable. Based on these contrast values, three models were selected to sum the score values of every gird in the study area. According to a receiver operating characteristic curve analysis （ROC）, model 2 yielded the best fit for predicting the potential distribution of shallow-seated landslide hazards, with 89% correctness and a 54.5% hit ratio when the occurrence probability （OP） of landslides was 70%. The model was tested using data from an area close to the study region, and showed 94% correctness and a hit ratio of 45.7% when the OP of landslides was 70%. Finally, the potential distribution of shallow-seated landslides, based on the OP, was mapped using a geographical information system.     

Suspended sediment concentration controlling factors: an analysis for the Argentine Pampas region

ABSTRACT

In the Argentine Pampas region, there is little information about sediment concentration in agricultural catchments. The aims of this work are: (1) to analyse fluctuations in sediment concentration and discharge, as a first attempt to characterize hysteresis patterns; and (2) to study sediment concentration controlling factors and to assess the importance of these factors using principal component analysis and a multiple regression model. Twenty-five events registered during 4 years in a 560 ha gauged basin of Argentina were studied. Analysis of data suggested a positive clockwise pattern. The multiple regression model was performed with three factors obtained by principal component analysis: runoff, precipitation and antecedent conditions. The model explained 83% of the variability of sediment concentration. The runoff factor contributed to modelled sediment concentration with the highest magnitude, followed by precipitation and antecedent condition factors. Although the watershed is under conservation tillage, rill erosion seems to be the main source of sediment concentration.

Editor M.C. Acreman; Associate editor X. Fang     

基于双通道参考技术的磁共振地下水探测电磁噪声分布规律研究

磁共振地下水探测(MRS)技术能直接测量水中氢质子的拉莫尔进动,可表征地下水含水量和介质孔隙度等信息,因而获得了广泛应用.然而,MRS信号微弱,实际探测中电磁噪声强度大,若噪声未被有效去除,会导致后续水文参数解释不准确,采用"8"字形线圈或运用带参考线圈的噪声抵消等方法可抑制噪声干扰,但这些方法消噪效果的好坏取决于电磁噪声的空间分布.另外,在强噪声或多噪声源环境下应用MRS方法,选择恰当的探测位置对提高探测效率以及增加探测结果的有效性具有重要意义.因此,本文使用双通道噪声采集装置在实验前记录环境中的电磁噪声,通过双通道参考技术推导同一时刻不同测点的噪声强度,再得到测区电磁噪声分布结果,该噪声分布情况对MRS方法探测方式的选取具有指导性的作用.同时,可根据噪声分布情况为探测线圈选择最佳测点以避开噪声强度大的区域,进而提高采集信号的信噪比.最后,通过采集室外环境下的电磁噪声,验证本文提出的基于双通道参考技术的电磁噪声分布规律研究方法的有效性,为可靠高效地开展磁共振地下水探测提供了新的技术手段.     

Prediction of Bulk Density of Soils in the Loess Plateau Region of China

Soil bulk density (BD) is a key soil physical property that may affect the transport of water and solutes and is essential to estimate soil carbon/nutrients reserves. However, BD data are often lacking in soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km²) at which we collected undisturbed and disturbed soil samples from two soil layers (0–5 and 20–25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson’s correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be given to the role of topography when developing PTFs for BD prediction. Testing of the developed PTF for use in other loess regions in the world is required.     

Spatial prediction on river networks: comparison of top‐kriging with regional regression

Top‐kriging is a method for estimating stream flow‐related variables on a river network. Top‐kriging treats these variables as emerging from a two‐dimensional spatially continuous process in the landscape. The top‐kriging weights are estimated by regularising the point variogram over the catchment area (kriging support), which accounts for the nested nature of the catchments. We test the top‐kriging method for a comprehensive Austrian data set of low stream flows. We compare it with the regional regression approach where linear regression models between low stream flow and catchment characteristics are fitted independently for sub‐regions of the study area that are deemed to be homogeneous in terms of flow processes. Leave‐one‐out cross‐validation results indicate that top‐kriging outperforms the regional regression on average over the entire study domain. The coefficients of determination (cross‐validation) of specific low stream flows are 0.75 and 0.68 for the top‐kriging and regional regression methods, respectively. For locations without upstream data points, the performances of the two methods are similar. For locations with upstream data points, top‐kriging performs much better than regional regression as it exploits the low flow information of the neighbouring locations. Copyright © 2012 John Wiley & Sons, Ltd.     

Analyzing spatial ecological data using linear regression and wavelet analysis

Spatial (two-dimensional) distributions in ecology are often influenced by spatial autocorrelation. In standard regression models, however, observations are assumed to be statistically independent. In this paper we present an alternative to other methods that allow for autocorrelation. We show that the theory of wavelets provides an efficient method to remove autocorrelations in regression models using data sampled on a regular grid. Wavelets are particularly suitable for data analysis without any prior knowledge of the underlying correlation structure. We illustrate our new method, called wavelet-revised model, by applying it to multiple regression for both normal linear models and logistic regression. Results are presented for computationally simulated data and real ecological data (distribution of species richness and distribution of the plant species Dianthus carthusianorum throughout Germany). These results are compared to those of generalized linear models and models based on generalized estimating equations. We recommend wavelet-revised models, in particular, as a method for logistic regression using large datasets.     

Comparison of parameter uncertainty analysis techniques for a TOPMODEL application

Parameter uncertainty in hydrologic modeling is crucial to the flood simulation and forecasting. The Bayesian approach allows one to estimate parameters according to prior expert knowledge as well as observational data about model parameter values. This study assesses the performance of two popular uncertainty analysis (UA) techniques, i.e., generalized likelihood uncertainty estimation (GLUE) and Bayesian method implemented with the Markov chain Monte Carlo sampling algorithm, in evaluating model parameter uncertainty in flood simulations. These two methods were applied to the semi-distributed Topographic hydrologic model (TOPMODEL) that includes five parameters. A case study was carried out for a small humid catchment in the southeastern China. The performance assessment of the GLUE and Bayesian methods were conducted with advanced tools suited for probabilistic simulations of continuous variables such as streamflow. Graphical tools and scalar metrics were used to test several attributes of the simulation quality of selected flood events: deterministic accuracy and the accuracy of 95 % prediction probability uncertainty band (95PPU). Sensitivity analysis was conducted to identify sensitive parameters that largely affect the model output results. Subsequently, the GLUE and Bayesian methods were used to analyze the uncertainty of sensitive parameters and further to produce their posterior distributions. Based on their posterior parameter samples, TOPMODEL’s simulations and the corresponding UA results were conducted. Results show that the form of exponential decline in conductivity and the overland flow routing velocity were sensitive parameters in TOPMODEL in our case. Small changes in these two parameters would lead to large differences in flood simulation results. Results also suggest that, for both UA techniques, most of streamflow observations were bracketed by 95PPU with the containing ratio value larger than 80 %. In comparison, GLUE gave narrower prediction uncertainty bands than the Bayesian method. It was found that the mode estimates of parameter posterior distributions are suitable to result in better performance of deterministic outputs than the 50 % percentiles for both the GLUE and Bayesian analyses. In addition, the simulation results calibrated with Rosenbrock optimization algorithm show a better agreement with the observations than the UA’s 50 % percentiles but slightly worse than the hydrographs from the mode estimates. The results clearly emphasize the importance of using model uncertainty diagnostic approaches in flood simulations.     

Regionalization of unit hydrograph parameters: 1. Comparison of regression analysis techniques

Hydrologic regionalization is a useful tool that allows for the transfer of hydrological information from gaged sites to ungaged sites. This study developed regional regression equations that relate the two parameters in Nash's IUH model to the basin characteristics for 42 major watersheds in Taiwan. In the process of developing the regional equations, different regression procedures including the conventional univariate regression, multivariate regression, and seemingly unrelated regression were used. Multivariate regression and seeming unrelated regression were applied because there exists a rather strong correlation between the Nash's IUH parameters. Furthermore, a validation study was conducted to examine the predictability of regional equations derived by different regression procedures. The study indicates that hydrologic regionalization involving several dependent variables should consider their correlations in the process of establishing the regional equations. The consideration of such correlation will enhance the predictability of resulting regional equations as compared with the ones from the conventional univariate regression procedure.