Early response of understory vegetation to the mass dieback of Norway spruce in the European lowland temperate forest

Spruce-dominated forests are commonly exposed to disturbances associated with mass occurrences of bark beetles.The dieback of trees triggers many physical and chemical processes in the ecosystem resulting in rapid changes in the vegetation of the lower forest layers.We aimed to determine the response of non-tree understory vegetation to the mass dieback of Norway spruce(Picea abies)in the first years after the disturbance caused by the European spruce bark beetle(Ips typographus)outbreak.Our study area was the Białowieża Biosphere Reserve covering the Polish part of the emblematic Białowieża Forest,in total 597km^(2).The main data source comprised 3,900 phytosociological relevés(combined spring and summer campaigns)collected from 1,300 systematically distributed forest sites in 2016–2018–the peak years of the bark beetle outbreak.We found that the understory responded immediately to mass spruce dieback,with the most pronounced changes observed in the year of the disturbance and the subsequent year.Shade-tolerant forest species declined in the initial years following the mass spruce dieback,while hemicryptophytes,therophytes,light-demanding species associated with non-forest seminatural communities,as well as water-demanding forest species,expanded.Oxalis acetosella,the most common understory species in the Białowieża Forest,showed a distinct fluctuation pattern,with strong short-term expansion right after spruce dieback,followed by a gradual decline over the next 3–4 years to a cover level 5 percentage points lower than before the disturbance.Thus,our study revealed that mass spruce dieback selectively affects individual herb species,and their responses can be directional and non-directional(fluctuation).Furthermore,we demonstrated that the mass dieback of spruce temporarily increases plant species diversity(α-diversity).

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.

Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan

Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.

Challenges in the Detection and Attribution of Northern Hemisphere Surface Temperature Trends Since 1850

Since 2007,the Intergovernmental Panel on Climate Change(IPCC)has heavily relied on the comparison between global climate model hindcasts and global surface temperature(ST)estimates for concluding that post-1950s global warming is mostly human-caused.In Connolly et al.,we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance(TSI)and ST data sets.We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST,one could(prematurely)conclude anything from the warming being“mostly human-caused”to“mostly natural.”Richardson and Benestad suggested our analysis was“erroneous”and“flawed”because we did not use a multilinear regression.They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC's attribution statement.They also objected that many of the published TSI data sets were out-of-date.However,here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series,the original conclusions of Connolly et al.are confirmed for all five ST data sets.Therefore,it is still unclear whether the observed warming is mostly human-caused,mostly natural or some combination of both.

Mapping species assemblages of tropical forests at different hierarchical levels based on multivariate regression trees

Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.Methods: We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve(WNNR), Hainan Island,China, using multivariate regression trees(MRT). The MRT was based on arboreal vegetation composition in 132plots of 20 m×20 m with a regular spacing of 1 km. Apart from the MRT, non-metric multidimensional scaling(NMDS) was used to evaluate vegetation-environment relationships.Results: The MRT model worked best when using 14 key environmental variables including topography, climate,latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil(especially available P), together with topographic variables, all influenced the distribution of formations and associations.Conclusions: While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices:Evidence from Guangdong-Hong Kong-Macao Greater Bay Area,China

Innovation capitalization is a new concept in innovation geography research.Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect.However,few studies investigate the spatial heterogeneity of innovation capitalization.Thus,case verification at the urban agglomeration scale is needed.Therefore,this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale.Examining the Guangdong-Hong Kong-Macao Greater Bay Area(GHMGBA),China as a case study,the study investigated the spatial heterogeneity of the influence of high-tech firms,representing innovation,on housing prices.This work verified the spatial heterogeneity of innovation capitalization.The study constructed a data set influencing housing prices,comprising 11 factors in 5 categories(high-tech firms,convenience of living facilities,built environment,the natural environment,and the fundamentals of the districts)for 419 subdistricts in the GHMGBA.On the global scale,the study finds that high-tech firms have a significant and positive influence on housing prices,with the housing price increasing by 0.0156%when high-tech firm density increases by 1%.Furthermore,a semi-geographically weighted regression(SGWR)analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity.The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the GuangzhouFoshan metropolitan area,western Shenzhen-Dongguan,north-central Zhongshan-Nansha district,and Guangzhou—all areas with densely distributed high-tech firms.These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations.The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.

Analysis of Weather Anomalies to Assess the 2021 Flood Events in Yaounde, Cameroon (Central Africa)

Extreme weather anomalies such as rainfall and its subsequent flood events are governed by complex weather systems and interactions between them. It is important to understand the drivers of such events as it helps prepare for and mitigate or respond to the related impacts. In line with the above statements, quarter-hourly data for the year 2021 recorded in the Yaounde meteorological station were synthesized to come out with daily and dekadal (10-day averaged) anomalies of six climate factors (rainfall, temperature, insolation, relative humidity, dew point and wind speed), in order to assess the occurrences and severity of floods to changing weather patterns in Yaounde. In addition, Precipitation Concentration Index (PCI) was computed to evaluate the distribution and analyse the frequency and intensity of precipitation. Coefficient of variation (CV) was used to estimate the seasonal and annual variation of rainfall patterns, while Mann-Kendall (MK) trend test was performed to detect weather anomalies (12-month period variation) in quarter-hourly rainfall data from January 1st to December 31st 2021. The Standard Precipitation Index (SPI) was also used to quantify the rainfall deficiency of the observed time scale. Results reveal that based on the historical data from 1979 to 2018 in the bimodal rainfall forest zone, maximum and minimum temperature averages recorded in Yaounde in 2021 were mostly above historical average values. Precipitations were rare during dry seasons, with range value of 0 - 13.6 mm for the great dry season and 0 - 21.4 mm for the small dry season. Whereas during small and great rainy seasons, rainfalls were regular with intensity varying between 0 and 50 mm, and between 0 and 90.4 mm, respectively. The MK trend test showed that there was a statistical significant increase in rainfall trend for the month of August at a 5% level of significance, while a significant decreasing trend was observed in July and December. There was a strong irregular rainfall distribution during the months of February, July and December 2021, with a weather being mildly wetted during all the dry seasons and extremely wetted in August. Recorded flooding days within the year of study matched with heavy rainy days including during dry seasons.

Stand structure and height-diameter relationship of a degraded Populus euphratica forest in the lower reaches of the Tarim River, Northwest China

Understanding stand structure and height-diameter relationship of trees provides very useful information to establish appropriate countermeasures for sustainable management of endangered forests. Populus euphratica, a dominant tree species along the Tarim River watershed, plays an irreplaceable role in the sustainable development of regional ecology, economy and society. However, as the result of climate changes and human activities, the natural riparian ecosystems within the whole river basin were degraded enormously, particularly in the lower reaches of the river where about 320 km of the riparian forests were either highly degraded or dead. In this study, we presented one of the main criteria for the assessment of vitality of P. euphratica forests by estimating the defoliation level, and analyzed forest structure and determined the height-diameter(height means the height of a tree and diameter means the diameter at breast height(DBH) of a tree) relationship of trees in different vitality classes(i.e. healthy, good, medium, senesced, dying, dead and fallen). Trees classified as healthy and good accounted for approximately 40% of all sample trees, while slightly and highly degraded trees took up nearly 60% of total sample trees. The values of TH(tree height) and DBH ranged from 0–19 m and 0–125 cm, respectively. Trees more than 15 m in TH and 60 cm in DBH appeared sporadically. Trees in different vitality classes had different distribution patterns. Healthy trees were mainly composed more of relatively younger trees than of degraded tress. The height-diameter relationships differed greatly among tress in different vitality classes, with the coefficients ranging from 0.1653 to 0.6942. Correlation coefficients of TH and DBH in healthy and good trees were higher than those in trees of other vitality classes. The correlation between TH and DBH decreased with the decline of tree vitality. Our results suggested that it might be able to differentiate degraded P. euphratica trees from healthy trees by determining the height-diameter correlation coefficient, and the coefficient would be a new parameter for detecting degradation and assessing sustainable management of floodplain forests in arid regions. In addition, tree vitality should be taken into account to make an accurate height-diameter model for tree height prediction.

Measuring the Internal Velocity of Debris Flows Using Impact Pressure Detecting in the Flume Experiment

Measuring the internal velocity of debris flows is very important for debris flow dynamics research and designing debris flow control works. However, there is no appropriate method for measuring the internal velocity because of the destructive power of debris flow process. In this paper, we address this problem by using the relationship between velocity and kinetic pressure, as described by surface velocity and surface kinetic pressure data. Kinetic pressure is the difference of impact pressure and static pressure. The former is detected by force sensors installed in the flow direction at the sampling section. Observations show that static pressure can be computed using the formula for static water pressure by simply substituting water density for debris flow density. We describe the relationship between surface velocity and surface kinetic pressure using data from seven laboratory flume experiments. It is consistent with the relationship for single phase flow, which is the measurement principle of the Pitot tube.

Paleoclimate and Paleoenvironment of Gonghe Basin, Qinghai-Tibet Plateau, During the Lastdeglacial: Weathering, Erosion and Vegetation Cover Affect Clay Mineral Formation

With the analysis of the sources and formation mechanism of the clay minerals in the sediment core from the Dalianhai lake in the Gonghe Basin, northeastern Tibet-Qinghai Plateau, clay mineral composition proxies, grain-size and carbonate contents have been employed for highresolution study in order to reconstruct East Asian Summer Monsoon(EASM) over the northeastern Tibet-Qinghai Plateau during the lastdeglacial. The study also extended to establish a relationship between vegetation cover changes and erosion during the last 14.5 ka with pollen record and clay mineral proxies. Smectite/kaolinite and smectite/(illite+chlorite) ratios allow us to assess hydrolysis conditions in lowlands and/or physical erosion process in highlands of the Gonghe Basin. Before 12.9 Cal ka BP, both mineralogical ratios show low values indicative of strong physical erosion in the basin with a dominant cold and dry phase. After 12.9 Cal ka BP, an increase in both mineralogical ratios indicates enhanced chemical weathering in the basin associated with a warm and humid climate. The beginning of Holocene is characterized by high smectite/(illite+chlorite) and smectite/kaolinite ratios that is synchronous as with deposition of many peat laminae, implying the best warm and humid conditions specifically between 8.0 to 5.5 Cal ka BP. The time interval after 5.0 Cal ka BP is characterized by a return to high physical erosion and low chemical weathering with dry climate conditions in the basin. Comparing variations of clay mineral assemblages with previous pollen results, we observe a rapid response in terms of chemical weathering and physical erosion intensity to a modification of the vegetation cover in the basin.

Driving Factors for Forest Fire Occurrence in Durango State of Mexico:A Geospatial Perspective

Forest fire is one of the major causes of forest loss and therefore one of the main constraints for sustainable forest management worldwide.Identifying the driving factors and understanding the contribution of each factor are essential for the management of forest fire occurrence.The objective of this study is to identify variables that are spatially related to the occurrence and incidence of the forest fire in the State of Durango,Mexico.For this purpose,data from forest fire records for a five-year period were analyzed.The spatial correlations between forest fire occurrence and intensity of land use,susceptibility of vegetation,temperature,precipitation and slope were tested by Geographically Weighted Regression(GWR) method,under an Ordinary Least Square estimator.Results show that the spatial pattern of the forest fire in the study area is closely correlated with the intensity of land use,and land use change is one of the main explanatory variables.In addition,vegetation type and precipitation are also the main driving factors.The fitting model indicates obvious link between the variables.Forest fire was found to be the consequence of a particular combination of the environmental factors,and when these factors coexist with human activities,there is high probability of forest fire occurrence.Mandatory regulation of human activities is a key strategy for forest fire prevention.

Quantitative Remote Sensing Study on Regional Soil Erosion——Bin County of Heilongjiang as an Example

Based on Chinese soil loss equation (CSLE) model, this paper utilized technical advantages of RS and geographic information system (GIS) on data access and erosion factors data-base building to study prediction methods of regional soil erosion. The spatial analysis module of ARCGIS platform was applied to study the spatial distribution of erosion and the inter-relations of the factors influencing regional soil erosion in the research area. As a result, the mean soil erosion modulus of Bin County is 3 555.42 t/(km2?a), which suggests moderate degree erosion. The mean soil erosion modulus of clayey meadow soil is higher than those of dark brown soil and black soil. Vegetation factor values are between 0.1-0.2. The mean slope gradient and slope length values are respectively 1.335 and 6.061 which shows slope length is a dominant factor. And soil type, vegetation coverage and to-pographic factors have remarkable relevance to each other. There-fore, RS, GIS and CSLE are applicable in regional scale to disclose spatial distribution characteristics of soil erosion and to analyze the characteristics of dominant soil erosion factor quantitatively.

Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements

The contents of nitrogen and organic carbon in an agricultural soil were analyzed using reflectance measurements (n=52)performed with an ASD FieldSpec-Ⅱspectroradiometer.For parameter prediction,empirical models based on partial least squares(PLS)regression were defined from the measured reflectance spectra(0.4 to 2.4μm).Here,reliable estimates were obtained for nitrogen content,but prediction accuracy was only moderate for organic carbon.For nitrogen, the real spatial pattern of within-field variability was reproduced with high accuracy.The results indicate the potential of this method as a quick screening tool for the spatial assessment of nitrogen and organic carbon,and therefore an appropriate alternative to time-and cost-intensive chemical analysis in the laboratory.

Machine learning and data-driven prediction of pore pressure from geophysical logs:A case study for the Mangahewa gas field,New Zealand

Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and direct down-hole pressure measurements.However,a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many wells.Applying machine learning(ML)algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is limited.In this research,several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field,New Zealand.Their predictions substantially outperform,in terms of prediction performance,those generated using a multiple linear regression(MLR)model.The geophysical logs used as input variables are sonic,temperature and density logs,and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions.A total of 25,935 data records involving six well-log input variables were evaluated across the four wells.All ML methods achieved credible levels of pore pressure prediction performance.The most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree(DT),adaboost(ADA),random forest(RF)and transparent open box(TOB).The DT achieved root mean square error(RMSE)ranging from 0.25 psi to 14.71 psi for the four wells.The trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other wellbores.For two wells(Mangahewa-03 and Mangahewa-06),semi-supervised prediction achieved acceptable prediction performance of RMSE of 130—140 psi;while for the other wells,semi-supervised prediction performance was reduced to RMSE>300 psi.The results suggest that these models can be used to predict pore pressure in nearby locations,i.e.similar geology at corresponding depths within a field,but they become less reliable as the step-out distance increases and geological conditions change significantly.In comparison to other approaches to predict pore pressures,this study has identified that application of several ML algorithms involving a large number of data records can lead to more accurate prediction results.

The Modern Problems of Sustainable Use and Management of Irrigated Lands on the Example of the Bukhara Region (Uzbekistan)

The Central Asian lowlands are characterized by an arid and continental climate. At the same time, the large streams and rivers have been providing water for the development of flourishing oases and extensive irrigated farming areas. Bukhara is one of those oases. The population of 1.7 mln. and especially the agricultural sector (with an irrigated area of 275,000 ha) use a considerable amount of water. But as the flat topography does not provide sufficient natural drainage, water logging and raising groundwater tables have become serious problems for the agricultural productivity. The combination of the high salinity of the irrigation water and the generous application of fertilizers leads to a widespread soil salinization. Excessive leaching is supposed to reduce the top soil salinity, but as the drainage system is only covering a small portion of the irrigated areas and is in need of maintenance, this process only contributes to the ongoing salinization and the reduction of soil fertility and crop yields. The data presented here for the years 2000 to 2013 indicate that the groundwater table is rising throughout the region while the groundwater salinity is decreasing. The soil salinity on the other hand is, after an improvement during the first half of the study period, slightly increasing since 2009, which also is reflected in the slight worsening of the condition of the reclaimed land during the same period.

Diagenetic and Depositional Impacts on the Reservoir Quality of the Upper Jurassic Arab Formation in the Balal Oilfield, Offshore Iran

The Kimmeridgian-Tithonian aged Arab Formation, as the main reservoir of the Jurassic succession in the Balal oilfield, located in the offshore region of the Iranian sector of the Persian Gulf, is investigated in this study. The formation is composed of dolomites and limestones with anhydrite interbeds. Based on detailed petrographic studies, six microfacies are recognized, which are classified in four sub-environments including supratidal, intertidal, lagoonal and the high energy shoal of a homoclinal carbonate ramp. The main diagenetic features of the studied succession include dolomitization, anhydritization, cementation, micritization, fracturing and compaction. Based on stable isotope data, dolomitization of the upper Arab carbonates is related to sabkha settings(i.e. evaporative type). In terms of sequence stratigraphy, three shallowing-upward sequences are recognized, based on core and wireline log data from four wells of the studied field. Considering depositional and diagenetic effects on the reservoir quality, the studied facies are classified into eight reservoir rock types(RRT) with distinct reservoir qualities. Dolomitization has played a major role in reservoir quality enhancement, whereas anhydritization, carbonate cementation, and compaction have damaged the pore throat network. Distribution of the recognized RRTs in time and space are discussed within the context of a sequence stratigraphic framework.

Seismic vulnerability assessment of urban buildings and traffic networks using fuzzy ordered weighted average

Urban buildings and urban traffic network are considered as the vital arteries of cities which have particular effects especially after the crisis in the search and rescue operations. The aim of this study is to determine the vulnerability of urban areas especially, buildings and traffic networks using multicriteria geographic information systems and decisionmaking methods. As there are many effective criteria on the seismic vulnerability that they have uncertain and vague properties, the method of this paper is applying fuzzy ordered weighted average(OWA) to model the seismic vulnerability of urban buildings and traffic networks in the most optimistic and pessimistic states. The study area is district 6 of Tehran that is affected by the four major faults, and thus will be threatened by the earthquakes. The achieved results illustrated the vulnerability with different degrees of risk levels including very high, high, medium, low and very low. The results show that in the most optimistic case 14% and in the pessimistic case 1% of buildings tolerate in very low vulnerability. The vulnerability of urban street network also indicates that in the optimistic case 12% and in the pessimistic case at most 9% of the area are in appropriate condition and the North and NorthEast of the study area are more vulnerable than South of it.