Integrative fuzzy set pair model for land ecological security assessment: a case study of Xiaolangdi Reservoir Region, China

Due to the increasingly serious ecological degradation of land systems, the land ecological security issues have attracted more and more attention of policy makers, researchers and citizens. Aiming at overcoming the disadvantages in subjectivity and complexity of the currently used assessment methods, an integrative fuzzy set pair model for assessing the land ecological security was developed by integrating fuzzy assessment and set pair analysis (SPA). The approximate degree of land ecological security to the optimal standard set was calculated to describe the secure level by combining multiple indices. The indices and weights were determined by a pressure-state-response model and the fuzzy analytic hierarchy process (AHP), respectively. Aided by a geographic information system, this model was applied to evaluate comprehensively the status of land ecological security in Xiaolangdi Reservoir Region, China, taking the administrative division as the assessment unit. The results showed that 20% of the total area maintained a slightly secure status, while 50% of the study area was of a middle or seriously low grade of land ecological security. The remaining portion (30%) was the most ecologically insecure. From the spatial prospective, obvious variations were observed. The land eco-security gradually decreased from the Xiaolangdi Dam to its surrounding regions. It was concluded that the status of the integral land ecological security of Xiaolangdi Reservoir Region was in the middle level, and increasingly intense human activities speeded up the degradation of regional land ecosystem in recent years and thus induced the crisis of land ecological security.     

地震数据存储的设备安全与应用安全

对于地震数据管理中心来说,地震数据的安全性和可用性极为重要,重要的数据(不管是在线的还是离线的)一旦被破坏或丢失,就会给我们的工作造成重大的影响,甚至是难以弥补的损失。将损失降至最小的最行之有效的办法莫过于地震数据的安全存储。地震数据的安全存储离不开数据的安全备份。数据的安全备份来自设备安全和应用安全两个方面。本文重点论述地震数据存储的设备安全与应用安全的要点和常见误区。     

The construction and application of a carrying capacity evaluation model in a national park

This work applies ecological footprint analysis to establish the framework of a carrying capacity model for a national park. The model estimates changes in ecological footprint and biocapacity indicators for Taroko National Park (Taiwan) from 2002 to 2011. The findings indicate that: (1) In the past 10 years, the biocapacity status of Taroko National Park has remained within the environmental capacity; however, the per-capita ecological reserve decreased by 73.38 % between 2002 and 2011, from 0.085010 global hectares to 0.022633 global hectares. Evaluation of the ecological footprint intensity index reveals that the park has maintained a level 1 status, the highest ecological security level; (2) Linear estimation undertaken in the study predicts that when the ecological footprint intensity index reaches 0.5—when the ecological security level falls from level 1 (good) to level 2 (fair)—the critical tourist-carrying capacity would be 3,380,351 visitors per year, or 1.18 times the number of tourists in 2011. Furthermore, when the ecological footprint intensity index reaches 0.8—when the ecological security falls to level 3 (poor)—the critical tourist-carrying capacity would be 5,408,561 visitors per year, or 1.88 times the number of tourists in 2011. This work adopted the ecological footprint method and the threshold ecological footprint intensity index value to estimate the critical number of tourists for the sustainable biocapacity of Taroko National Park. The results are intended to serve as a reference for national park administrators to develop policies on tourist carrying capacity.     

Effects of terrain attributes on snow-cover dynamics in parts of Chenab basin,western Himalayas

ABSTRACT

Terrain variables are the main factors affecting the spatial distribution of snow cover. This paper aims to find a relationship between snow-cover area (SCA) and topographic variables (elevation, slope and aspect), using MODIS Terra data (MOD09A1) in parts of the Chenab basin, western Himalayas. The inter-annual variability of SCA% for each month has been analysed for the years 2000 to 2011. The analysis reveals that mean annual SCA value was maximum (37.89%) in 2005 and minimum (32.07%) in 2001. The slope classes with maximum and minimum SCA% are 5°–10° and 30°–35°, respectively. Among the 16 aspect classes, the ESE-facing slope evinces maximum SCA%. During the snow accumulation period, the expanse at 3600–4300 m elevation, and in the depletion period, 4300–5000 m elevation are found to have maximum rate of change in SCA% per 100 m rise in elevation, i.e. 3.37% and 3.67%, respectively.

EDITOR Z.W. Kundzewicz; ASSOCIATE EDITOR not assigned     

太湖富营养化现状及原因分析

根据2005-2006年太湖湖泊生态系统研究站的监测结果,结合历史监测记录,评价了近5年来太湖富营养化的趋势.结果显示,从2000年以来,太湖的富营养化状况有加重趋势.主要表现在:1)与历史监测资料对比,近5年来无论梅梁湾还是湖心区,夏季水体TN、TP含量均呈增高趋势,如1992-2001年,太湖湖心区夏季(6-8月份)水体TN的平均值为1.706 mg/L (范围1.238-2.266mg/L),而2002-2006这5年间该平均值为2.344mg/L(范围1.924-2.717mg/L),明显高于前10年(p=0.005),另外,同期湖心区夏季的水体透明度则明显下降(1992—2001年夏季平均值为0.63 m,而2002-2006年则为0.34 m,p=0.003); 2)从野外调查看,太湖夏季水华暴发的范围越来越大,从2000年以前的梅梁湾、竺山湾及部分湖西区为主,发展到2006年的整个西太湖,夏季暴发水华的面积占太湖总面积的一半以上,且一年中出现水华的时间越来越长,水华出现的频率越来越高,微囊藻水华为特征的藻型生态系统在大太湖似乎越来越稳定;3)近年来太湖沉水植物分布区的面积有所下降.研究表明,太湖近年来富营养化的现状不容乐观,原因可能与近几年异常的气候和水文条件有关,也可能与水草区的不断破坏而减弱了微囊藻水华的生态竞争有关,应引起有关部门重视.     

Seasonal dynamics and stoichiometry of the planktonic community in the NW Mediterranean Sea: a 3D modeling approach

The 3D hydrodynamic Model for Applications at Regional Scale (MARS3D) was coupled with a biogeochemical model developed with the Ecological Modular Mechanistic Modelling (Eco3M) numerical tool. The three-dimensional coupled model was applied to the NW Mediterranean Sea to study the dynamics of the key biogeochemical processes in the area in relation with hydrodynamic constraints. In particular, we focused on the temporal and spatial variability of intracellular contents of living and non-living compartments. The conceptual scheme of the biogeochemical model accounts for the complex food web of the NW Mediterranean Sea (34 state variables), using flexible plankton stoichiometry. We used mechanistic formulations to describe most of the biogeochemical processes involved in the dynamics of marine pelagic ecosystems. Simulations covered the period from September 1, 2009 to January 31, 2011 (17 months), which enabled comparison of model outputs with situ measurements made during two oceanographic cruises in the region (Costeau-4: April 27–May 2, 2010 and Costeau-6: January 23–January 27, 2011).     

Modeling of the 2011 Japan Tsunami: Lessons for Near-Field Forecast

During the devastating 11 March 2011 Japanese tsunami, data from two tsunami detectors were used to determine the tsunami source within 1.5 h of earthquake origin time. For the first time, multiple near-field tsunami measurements of the 2011 Japanese tsunami were used to demonstrate the accuracy of the National Oceanic and Atmospheric Administration (NOAA) real-time flooding forecast system in the far field. To test the accuracy of the same forecast system in the near field, a total of 11 numerical models with grids telescoped to 2 arcsec (~60 m) were developed to hindcast the propagation and coastal inundation of the 2011 Japanese tsunami along the entire east coastline of Japan. Using the NOAA tsunami source computed in near real-time, the model results of tsunami propagation are validated with tsunami time series measured at different water depths offshore and near shore along Japan’s coastline. The computed tsunami runup height and spatial distribution are highly consistent with post-tsunami survey data collected along the Japanese coastline. The computed inundation penetration also agrees well with survey data, giving a modeling accuracy of 85.5 % for the inundation areas along 800 km of coastline between Ibaraki Prefecture (north of Kashima) and Aomori Prefecture (south of Rokkasho). The inundation model results highlighted the variability of tsunami impact in response to different offshore bathymetry and flooded terrain. Comparison of tsunami sources inferred from different indirect methods shows the crucial importance of deep-ocean tsunami measurements for real-time tsunami forecasts. The agreement between model results and observations along Japan’s coastline demonstrate the ability and potential of NOAA’s methodology for real-time near-field tsunami flooding forecasts. An accurate tsunami flooding forecast within 30 min may now be possible using the NOAA forecast methodology with carefully placed tsunameters and large-scale high-resolution inundation models with powerful computing capabilities.     

Overview and Assessment of Antarctic Ice-Sheet Mass Balance Estimates: 1992�C2009

Mass balance estimates for the Antarctic Ice Sheet (AIS) in the 2007 report by the Intergovernmental Panel on Climate Change and in more recent reports lie between approximately +50 to ?250 Gt/year for 1992 to 2009. The 300 Gt/year range is approximately 15% of the annual mass input and 0.8 mm/year Sea Level Equivalent (SLE). Two estimates from radar altimeter measurements of elevation change by European Remote-sensing Satellites (ERS) (+28 and ?31 Gt/year) lie in the upper part, whereas estimates from the Input-minus-Output Method (IOM) and the Gravity Recovery and Climate Experiment (GRACE) lie in the lower part (?40 to ?246 Gt/year). We compare the various estimates, discuss the methodology used, and critically assess the results. We also modify the IOM estimate using (1) an alternate extrapolation to estimate the discharge from the non-observed 15% of the periphery, and (2) substitution of input from a field data compilation for input from an atmospheric model in 6% of area. The modified IOM estimate reduces the loss from 136 Gt/year to 13 Gt/year. Two ERS-based estimates, the modified IOM, and a GRACE-based estimate for observations within 1992?C2005 lie in a narrowed range of +27 to ?40 Gt/year, which is about 3% of the annual mass input and only 0.2 mm/year SLE. Our preferred estimate for 1992?C2001 is ?47 Gt/year for West Antarctica, +16 Gt/year for East Antarctica, and ?31 Gt/year overall (+0.1 mm/year SLE), not including part of the Antarctic Peninsula (1.07% of the AIS area). Although recent reports of large and increasing rates of mass loss with time from GRACE-based studies cite agreement with IOM results, our evaluation does not support that conclusion.     

Evolution of earthquake losses in Portuguese residential building stock

The evolution of the expected earthquake losses in different Portuguese regions was studied in order to determine whether the natural regeneration of buildings could contribute to the mitigation of seismic risk, although the building exposure has increased over time in most analysed regions. To achieve this goal, five inter-related risk indicators were estimated, based on the Portuguese censuses’ surveys of residential buildings and inhabitants obtained in two different moments in time: 2001 and 2011. The FEMA (2008) approach was used to estimate the risk indicators, i.e., the Annualized Economic and Human earthquake Losses, either in absolute terms or normalised by building and population exposure. This study offers a perspective of the seismic risk in the Portuguese mainland, provides a comparison of risk levels between different epochs, and analyses the obtained results in order to compare the risk among Portuguese regions. It is important to mention that the comparison of economic losses based on the building stocks surveyed in 2001 and in 2011 took into account the Portuguese inflation rate in that time interval. The main findings of the study show that the natural regeneration of the residential housing stock contributes to the reduction of the expected seismic economic losses in Portugal, at an average rate of 14 % per 10 years, and to the reduction of the expected annualized human losses to almost one half after 10 years. The results support the conclusion that the natural regeneration of the residential housing stock contributed to the mitigation of the seismic risk in mainland Portugal, in the 10 year period under analysis, even though there was an increase of the building exposure in the region.     

Rupture pattern of the Oct 23, 2011 Van-Merkez,Eastern Turkey earthquake

High-frequency rupture process of the Oct 23, 2011 Van-Merkez earthquake is imaged by back-projection method using high-quality teleseismic P wave data from the US Array, and prestack Kirchhoff migration using P wave data from a subarray of global seismic networks. The rupture model with two asperities is confirmed by previous two methods. In low-frequency imaging, a large asperity derived from the migration method corresponds to the second one from the high-frequency P waves. The consistency of the locations of asperities from datasets with different frequency bands indicates that there is possible insignificance of the frequency-dependent feature for the earthquake. The resultant images illustrate the spatial and temporal evolution of the rupture, which mainly propagated WSW over a length of 33 km during the first 18 s, accompanying with bursts of two asperities at 3 and 11–13 s. The rupture direction is confirmed by the S wave corner frequency variations of strong ground accelerations. The rupture fronts are mainly located at the updip of the causative fault. Based on polarities of the P waveforms and focal mechanisms of the mainshock and aftershocks, the failure of these two asperities is determined to have occurred on a reverse fault with a dip angle of 47°. Hence, the rupture pattern of the 2011 Van-Merkez earthquake was dominated by a unilateral rupture toward the west-southwest direction.     

The 2010 and 2011 Tsunamis in French Polynesia: Operational Aspects and Field Surveys

We present a detailed timeline of the warning procedures as they unfolded at the Laboratoire de Géophysique in Papeete, Tahiti, during the nights of 26–27 February 2010 (Maule, Chile tsunami) and 10–11 March 2011 (Tohoku tsunami). In particular, we discuss how the flow of information available to the warning center (including seismic evaluations obtained both locally and from other warning centers, as well as maregraph and DART buoy data) built up and eventually led to red alerts, which the local authorities used in both cases to impose an evacuation of low-lying areas on 68 islands. While the alerts were successful in Polynesia, a difficulty arose in 2011 when the alert had to be reinstated immediately as the all clear was being declared, since the maximum amplitude was carried by the fourth wave packet. We also present a complete dataset of 119 values of run-up and inundation surveyed in the aftermath of the two tsunamis, principally in the Marquesas Islands where their effects were maximal, and on Tahiti and Moorea for the 2011 event. The highest run-up (4.45 m) was observed in 2011 in the Bay of Taipivai on Nuku Hiva, where seven houses were flooded. We find no clear correlation between run-up values at the same locations in 2010 and 2011, suggesting that local responses are controlled by details specific to each tsunami. In 2010, in the village of Puamau on Hiva Oa (Marquesas), a delayed harbor response, probably due to resonance of the bay upon arrival of short-period components dispersed outside the shallow-water approximation, flung a launch onto a wharf, 7 h after the first arrivals, and 2.5 h after issuance of the all clear.     

Simulation of maize (Zea mays L.) water use with the HYDRUS-1D model in the semi-arid Hailiutu River catchment,Northwest China

Groundwater provides an important source of water for maize cultivation where the water table is shallow in the semi-arid Hailiutu River catchment of the Maowusu Desert on the Erdos Plateau in Northwest China. A HYDRUS-1D model of the unsaturated flow beneath a maize (Zea mays L.) field was calibrated and validated with measured soil water contents at various depths during the maize growing period from 30 April to 1 October 2011, and from 23 May to 27 September 2012, respectively. The model computed the actual maize evapotranspiration (ET_a) as 580 mm during the whole growing period from 30 April to 1 October 2011. The groundwater contribution to ET_a was calculated to be 220 mm, accounting for 38% of maize water use during the growing season in 2011. When the groundwater level drops below a depth of 157 cm, maize can no longer use groundwater for transpiration. The irrigation water requirement increases with the increase of groundwater table depth. These results are very important for managing crop irrigation in the area.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR L. Ruiz     

A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278.  https://doi.org/10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32.  https://doi.org/10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.     

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.     

Management of Cultural Heritage Sites Using Remote Sensing Indices and Spatial Analysis Techniques

Sustainable management and exploitation policies as well as suitable conservation and mitigation strategies are mandatory to preserve cultural heritage and to reduce threats, weathering phenomena, and human actions that may produce significant deterioration and alteration of cultural heritage and “its environment”. In this context, remote sensing technologies can offer useful data to timely update information and documentation and set up reliable tools for systematic monitoring of cultural properties. In this study, multi-temporal and multi-sensor satellite data from Corona, Landsat, Spot, Quickbird, and Sentinel-2A have been exploited along with spatial analysis to investigate the area of the Theban temples at west Luxor (Egypt), severely threatened by uncontrolled urban sprawl. The results from our analyses showed that the urban expansion continuously occurred during the whole investigated period causing an increasing in urban areas around (1) 1.316 km² from 1967 to 1984, (2) 1.705 km² from 1984 to 2000, (3) 0.978 km² from 2000 to 2003, (4) 2.314 km² from 2003 to 2011, and (5) 1.377 km² from 2011 to 2017. The random urban expansion caused bad sewage networks and high groundwater depth which in turn affected the archaeological areas directly (as evident on a landscape view) and indirectly by causing changes (growing) in the level of ground water depth and increasing and accelerating weathering phenomena. The quantification and mapping of urban sprawl enabled us not only to quantify and spatially characterize urban sprawl but also to create a model to mitigate the impact and provide some operational recommendations to protect the archaeological site. Outcomes from our analysis pointed out that today the tremendous availability of advanced remote sensing data has opened new prospectives unthinkable several years ago.     

A statistical framework of data fusion for spatial prediction of categorical variables

With rapid advances of geospatial technologies, the amount of spatial data has been increasing exponentially over the past few decades. Usually collected by diverse source providers, the available spatial data tend to be fragmented by a large variety of data heterogeneities, which highlights the need of sound methods capable of efficiently fusing the diverse and incompatible spatial information. Within the context of spatial prediction of categorical variables, this paper describes a statistical framework for integrating and drawing inferences from a collection of spatially correlated variables while accounting for data heterogeneities and complex spatial dependencies. In this framework, we discuss the spatial prediction of categorical variables in the paradigm of latent random fields, and represent each spatial variable via spatial covariance functions, which define two-point similarities or dependencies of spatially correlated variables. The representation of spatial covariance functions derived from different spatial variables is independent of heterogeneous characteristics and can be combined in a straightforward fashion. Therefore it provides a unified and flexible representation of heterogeneous spatial variables in spatial analysis while accounting for complex spatial dependencies. We show that in the spatial prediction of categorical variables, the sought-after class occurrence probability at a target location can be formulated as a multinomial logistic function of spatial covariances of spatial variables between the target and sampled locations. Group least absolute shrinkage and selection operator is adopted for parameter estimation, which prevents the model from over-fitting, and simultaneously selects an optimal subset of important information (variables). Synthetic and real case studies are provided to illustrate the introduced concepts, and showcase the advantages of the proposed statistical framework.     

Empirical equations for the prediction of PGA and pseudo spectral accelerations using Iranian strong-motion data

A recently compiled, comprehensive, and good-quality strong-motion database of the Iranian earthquakes has been used to develop local empirical equations for the prediction of peak ground acceleration (PGA) and 5%-damped pseudo-spectral accelerations (PSA) up to 4.0 s. The equations account for style of faulting and four site classes and use the horizontal distance from the surface projection of the rupture plane as a distance measure. The model predicts the geometric mean of horizontal components and the vertical-to-horizontal ratio. A total of 1551 free-field acceleration time histories recorded at distances of up to 200 km from 200 shallow earthquakes (depth < 30 km) with moment magnitudes ranging from M_w 4.0 to 7.3 are used to perform regression analysis using the random effects algorithm of Abrahamson and Youngs (Bull Seism Soc Am 82:505–510, 1992), which considers between-events as well as within-events errors. Due to the limited data used in the development of previous Iranian ground motion prediction equations (GMPEs) and strong trade-offs between different terms of GMPEs, it is likely that the previously determined models might have less precision on their coefficients in comparison to the current study. The richer database of the current study allows improving on prior works by considering additional variables that could not previously be adequately constrained. Here, a functional form used by Boore and Atkinson (Earthquake Spect 24:99–138, 2008) and Bindi et al. (Bull Seism Soc Am 9:1899–1920, 2011) has been adopted that allows accounting for the saturation of ground motions at close distances. A regression has been also performed for the V/H in order to retrieve vertical components by scaling horizontal spectra. In order to take into account epistemic uncertainty, the new model can be used along with other appropriate GMPEs through a logic tree framework for seismic hazard assessment in Iran and Middle East region.     

Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, China

The integrated application of remote sensing, geographic information system and quantitative analytical modeling can provide scientific and effective methods for monitoring and studying urban heat island, based on land surface temperature (LST) retrieved from thermal infrared channel data of sensors. In this paper, LST is retrieved from Landsat TM6 and ETM + 6 data of Shanghai central city in 1989, 1997, 2000 and 2002, by using the mono-window algorithm. Based on the data, global and local spatial autocorrelation analysis, and geostatistical methods are adopted to quantitatively describe the characteristics of spatial heterogeneity and temporal evolution of land surface thermal landscape at different scales and periods in Shanghai central city, by utilizing exploratory spatial data analysis. Results show that LST field in Shanghai central city tends to fragmentize and complicate with the development of Shanghai, and its global spatial difference becomes greater gradually. The spatial variance pattern of the change of LST field from 1997 to 2002 indicates that the dynamic change of LST presents a tendency of increase in circularity. LST declines distinctly in the districts of Puxi and Pudong near and inside the inner ring road, while it rises obviously outside the central city and near the out ring road. The extrema of temporal change in LST field have a characteristic of spatial clustering. Besides, as the city of Shanghai expands in a circular pattern as a whole, the directional difference of dynamic change of urban surface thermal landscape exists but is not very obvious.     

Response of vegetation dynamics to climatic variables across a precipitation gradient in the Northeast China Transect

This study investigated the influence of climatic variables on the spatio-temporal variation of vegetation growth using normalized difference vegetation index (NDVI) data and climate data from 2000 to 2013 in the Northeast China Transect. Partial correlation and linear regression methods were applied to quantify the response of the growing season NDVI to climatic variables. Gradient analysis was used to investigate how the response changes across the precipitation gradient over the transect. The results show that, at the spatial scale, NDVI increases with precipitation in grassland, and the spatial sensitivity is 0.001/mm. At the temporal scale, grassland NDVI is less correlated with precipitation in wet areas where precipitation exceeds a threshold of 250 mm. The temporal sensitivity of grassland NDVI to precipitation is 0.0003–0.0006/mm. Positive correlations between NDVI and temperature dominate in forest areas, and forest NDVI is sensitive to temperature by 0.06–0.12/°C.