Examining Health of Wetlands with Multiple Ecosystem Services as Targets in China’s Coastal Regions

Coastal zones are key interconnectors of terrestrial and marine ecosystems.Due to the degradation and fragmentation of coastal wetlands,there is an urgent need to develop assessment methodology to compare the health of wetland ecosystems at different spatial scales.This will help efficiently develop and implement protections using easy-to-access ecosystem health data.This study aims to understand the spatial distribution of coastal and inland wetland health for China’s coastal regions.A Wetland Health Index(WHI)was developed to provide a basis for policy and decision making.Four utilization models—Long Term Model,Open Model,Nature Reserve Model,and Protected and Economic Model—were defined in the context of China’s coastal regions to specifically examine wetland health.Results show that the average WHI score was 63.6 with the range of 44.8–84.3 for 35 National Nature Reserves(NNRs),and the southern NNRs generally performed better than the northern NNRs.The wetlands in the southern provinces/municipalities are relatively healthier than their northern counterparts.The competent authority has slight influence on WHI scores but duration of conservation establishment does not show a clear correlation.With increasing economic activity,the differences in health conditions(WHI scores)of China’s coastal regions also increase.Gross Domestic Product(GDP)or economic intensification does not relate to WHI scores.Appropriate trade-offs between wetland management and economic development could contribute to improve health conditions,conservation and utilization of coastal and inland wetlands.

Spatial information recognizing of ocean eddies based on virtual force field and its application

A new approach to detecting ocean eddies automatically from remote sensing imageries based on the ocean eddy＇s eigen-pattern in remote sensing imagery and ＂force field-based shape extracting method＂ is proposed. First, the analysis on extracting eddies＇ edges from remote sensing imagery using conventional edge detection arithmetic operators is performed and returns digitized vector edge data as a result. Second, attraction forces and fusion forces between edge curves were analyzed and calculated based on the vector eddy edges. Thirdly, the virtual significant spatial patterns of eddy were detected automatically using iterative repetition followed by optimized rule. Finally, the spatial form auto-detection of different types of ocean eddies was done using satellite images. The study verified that this is an effective way to identify and detect the ocean eddy with a complex form.

Effects of land use change on the spatiotemporal variability of soil organic carbon in an urban-rural ecotone of Beijing,China

Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon （SOC） can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District -- an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred： Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland （e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg-l）. In contrast, the SOC content increased in region where the land use type changed to either orchard （excluding forestland） or forestland （e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg-1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg-1, respectively）. The organic carbon accumulation capacity per unit mass of the soil increased in the following order： grain cropland soil〈vegetable land/grassland soil〈orchard soil〈forestland soil. Therefore, to both secure supply of agricultural products and develop low carbon agriculture in a modern city, orchard has proven to be a good choice for land using.

Effects of topography and vegetation on distribution of rare earth elements in calcareous soils

This study investigated the impact of topography and vegetation on distribution of rare earth elements(REEs)in calcareous soils using methods of single extraction and mass balance calculation. The purposes of the study were to set a basis for further research on the biogeochemical REE cycle and to provide references for soil–water conservation and REE-containing fertilizer amendments. The results show a generally flat Post-Archean Average Australian Shale—normalized REE pattern for the studied calcareous soils. REE enrichment varied widely. The proportion of acidsoluble phases of heavy REEs was higher than that of light REEs. From top to bottom of the studied hills, dominant REE sources transitioned from limestone in-situ weathering to input from REE-containing phases(e.g., clay minerals,amorphous iron, REE-containing fluids). Our results indicate that the REE content of calcareous soils is mainly controlled by slope aspect, while the enrichment degree of REEs is related to geomorphological position and vegetation type.Furthermore, the proportion of acid-soluble phases of REEs is mainly controlled by geomorphological position.

Local perceptions of the conversion of cropland to forestland program in Jiangxi, Shaanxi, and Sichuan, China

Numerous land-use policies have been implemented in China in recent decades for ecological restoration and conservation to reduce environmental disasters and promote environmental sustainability.Many of these policies follow a top-down approach to implementation and as such,emphasize the hierarchical control within government structures.An understanding of local perceptions of land-use policies is important if the disconnect between policy makers and the target population is to be reduced and if program support is to improve.This study aimed to help improve local implementation,attitude toward,and engagement by examining the influence of socio-economic characteristics on the target population’s(local farmers)perception of the conversion of cropland to forestland program(CFPP)land use policy in Jiangxi,Sichuan,and Shaanxi provinces.It uses logistical regression models,with robust aspects of perception including confidence,support,transparency,prospects,fairness,and willingness to participate.Results indicate that social aspects as well as economic aspects are most important in influencing farmers’perceptions towards the CFPP.The farmers who have received technical support,rural male habitants,educated,and non-middle-aged farmers exhibit more positive perceptions of the program and are much more likely to support it,whereas farmers without any technical support or formal education,and female and middle-aged farmers are less likely to support the program.Importantly,this study also reveals the differences in responses,experiences and perceptions of the farmers living across different provinces.These empirical results provide insight into the influence of socio-economic characteristics on the perception of farmers towards land-use policies,which has important implications for designing targeted policy instruments and increasing farmer support for these policies.This knowledge can be harnessed and further evaluated in future research to improve citizen engagement,support,and understanding in order to help ecological restoration and conservation objectives be more effectively achieved.

Validation and application of multi-source altimeter wave data in China＇s offshore areas

Studies of offshore wave climate based on satellite altimeter significant wave height（SWH） have widespread application value. This study used a calibrated multi-altimeter SWH dataset to investigate the wave climate characteristics in the offshore areas of China. First, the SWH measurements from 28 buoys located in China＇s coastal seas were compared with an Ifremer calibrated altimeter SWH dataset. Although the altimeter dataset tended to slightly overestimate SWH, it was in good agreement with the in situ data in general. The correlation coefficient was 0.97 and the root-mean-square（RMS） of differences was 0.30 m. The validation results showed a slight difference in different areas. The correlation coefficient was the maximum（0.97） and the RMS difference was the minimum（0.28 m） in the area from the East China Sea to the north of the South China Sea.The correlation coefficient of approximately 0.95 was relatively low in the seas off the Changjiang（Yangtze River） Estuary. The RMS difference was the maximum（0.32 m） in the seas off the Changjiang Estuary and was0.30 m in the Bohai Sea and the Yellow Sea. Based on the above evidence, it is confirmed that the multialtimeter wave data are reliable in China＇s offshore areas. Then, the characteristics of the wave field, including the frequency of huge waves and the multi-year return SWH in China＇s offshore seas were analyzed using the23-year altimeter wave dataset. The 23-year mean SWH generally ranged from 0.6-2.2 m. The greatest SWH appeared in the southeast of the China East Sea, the Taiwan Strait and the northeast of the South China Sea.Obvious seasonal variation of SWH was found in most areas; SWH was greater in winter and autumn than in summer and spring. Extreme waves greater than 4 m in height mainly occurred in the following areas： the southeast of the East China Sea, the south of the Ryukyu Islands, the east of Taiwan-Luzon Island, and the Dongsha Islands extending to the Zhongsha Islands, and the frequency of extreme waves was 3%-6%. Extreme waves occurred most frequently in autumn and rarely in spring. The 100-year return wave height was greatest from the northwest Pacific seas extending to southeast of the Ryukyu Islands（9-12 m）, and the northeast of the South China Sea and the East China Sea had the second largest wave heights（7-11 m）. For inshore areas, the100-year return wave height was the greatest in the waters off the east coast of Guangdong Province and the south coast of Zhejiang Province（7-8 m）, whereas it was at a minimum in the area from the Changjiang Estuary to the Bohai Sea（4-6 m）. An investigation of sampling effects indicates that when using the 1°×1°grid dataset, although the combination of nine altimeters obviously enhanced the time and space coverage of sampling, the accuracy of statistical results, particularly extreme values obtained from the dataset, still suffered from undersampling problems because the time sampling percent in each 1°×1°grid cell was always less than33%.

Satellite-Based Estimation of Daily Average Net Radiation under Clear-Sky Conditions

ABSTRACT Daily average net radiation （DANR） is an important variable for estimating evapotranspiration from satellite data at regional scales, and is used for atmospheric and hydrologic modeling, as well as ecosystem management. A scheme is proposed to estimate the DANR over large heterogeneous areas under clear-sky conditions using only remotely sensed data. The method was designed to overcome the dependence of DANR estimates on ground data, and to map spatially consistent and reasonably distributed DANR, by using various land and atmospheric data products retrieved from MODIS （Moderate Resolution Imaging Spectroradiometer） data. An improved sinusoidal model was used to retrieve the diurnal variations of downward shortwave radiation using a single instantaneous value from satellites. The downward shortwave component of DANR was directly obtained from this instantaneous value, and the upward shortwave component was estimated using satellite-derived albedo products. Four observations of air temperature from MOD07_L2 and MYD07_L2 data products were used to derive the downward longwave component of DANR, while the upward longwave component was estimated using the land surface temperature （LST） and the surface emissivity from MOD1 l_L2. Compared to in situ observations at the cropland and grassland sites located in Tongyu, northern China, the root mean square error （RMSE） of DANR estimated for both sites under clear-sky conditions was 37 W m-2 and 40 W m-2, respectively. The errors in estimation of DANR were comparable to those from previous satellite-based methods. Our estimates can be used for studying the surface radiation balance and evapotranspiration.

Mapping Detailed Soil Property Using Small Scale Soil Type Maps and Sparse Typical Samples

Soil type maps at the scale of 1︰1 000 000 are used extensively to provide soil spatial distribution information for soil erosion assessment and watershed management models in China.However,the soil property maps produced through conventional direct linking method usually suffer low accuracy as well as the lack of spatial details within a soil type polygon.This paper presents an effective method to produce detailed soil property map based on representative samples which were extracted from each polygon on the 1︰1000 000 soil type map.The representative sample of each polygon is defined as the location that can represent the largest area within the polygon.The representativeness of a candidate sample is determined by calculating the soil-forming environment condition similarities between the sample and other locations.Once the representative sample of each polygon has been chosen,the property values of the existing typical samples are assigned to the corresponding representative samples with the same soil type.Finally,based on these representative samples,the detailed soil property map could be produced by using existing digital soil mapping methods.The case study in XuanCheng City,Anhui Province of China,demonstrated the proposed method could produce soil property map at a higher level of spatial details and accuracy:1)The soil organic matter(SOM)map produced based on the representative samples can not only depict the detailed spatial distribution of SOM within a soil type polygon but also largely reduce the abrupt change of soil property at the boundaries of two adjacent polygons.2)The Root Mean Squared Error(RMSE)of the SOM map based on the representative samples is1.61,and it is 1.37 for the SOM map produced by using conventional direct linking method.Therefore,the proposed method is an effective approach to produce spatial detailed soil property map with higher accuracy for environment simulation models.

Discussion on position of China’s north-south transitional zone by comparative analysis of mountain altitudinal belts

The Qinling Mountains has always been regarded as an essential dividing line between the warm temperate zone and the subtropical zone in eastern China and plays a vital role in the geoecological pattern of China.However,there is controversy about the specific location of this geographical boundary in the academic community.As a product of the combined effects of zonal and non-zonal factors,the mountain altitudinal belts(MABs)can reflect both the horizontal zonality and the vertical zonality of vegetation distribution.Using the MAB information,we can not only profoundly understand the complex mountain system of QinlingDaba Mountains but can also judge its nature as a geographical boundary more scientifically.Therefore,based on the comparative analysis of basal belt,dominant belt characteristics and belt structure characteristics of the MABs in Qinling-Daba Mountains,subtropical and temperate mountains,this paper analyzed the MAB differences and similarities among Qinling-Daba Mountains,subtropical and temperate typical mountains,to reveal the vegetation distribution characteristics in the north-south transitional zone.The results show that:(1)The MABs of the southern part of QinlingDaba Mountains(southern slope of the Daba Mountains)are the same or similar to those of the Subtropical Mountains,and the MABs of the northern part of Qinling-Daba Mountains(northern slope of the Qinling Mountains)are similar to those of the temperate mountains.While it shows obvious transitional characteristics in the vast area between the northern slope of the Daba Mountains and the southern slope of the Qinling Mountains:the basal belts gradually transit from the evergreen broadleaved forest belt(basal belt in subtropical mountains)to the evergreen and deciduous broad-leaved mixed forest belt,and the dominant belts also transit from the evergreen broad-leaved forest belt to the evergreen and deciduous broad-leaved mixed forest belt or the deciduous broad-leaved forest belt.(2)The transitional zone between the subtropical zone and the warm temperate zone is located between the northern slope of the Daba Mountains and the southern slope of the Qinling Mountains.The southern boundary of the transitional zone is along the northern slope of Shennongjia Mountain-the northern slope of Micang Mountain-Baishuijiang Nature Reserve,and the northern boundary is along the southern slope of Funiu Mountain-the southern slope of Taibai Mountain-Lianhua Mountain.Additionally,in the transitional zone,the average temperature in January is between-5°C and 1°C,the annual average temperature is between 10°C and 13°C except Hanzhong Basin and Hanshui Valley,and the accumulated temperature above 10°C ranges from 2000°C to 4000°C,the annual rainfall is about 800-1000 mm.The results provide a scientific basis for revealing the characteristics of China’s north-south transitional zone and scientific division of the boundary between the subtropical zone and warm temperate zone in China.

Monitoring Carbon Dioxide from Space：Retrieval Algorithm and Flux Inversion Based on GOSAT Data and Using CarbonTracker-China

Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

Uncertainty Representation of Ocean Fronts Based on Fuzzy-Rough Set Theory

Analysis of ocean fronts’ uncertainties indicates that they result from indiscernibility of their spatial position and fuzzi- ness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncer- tainties, then they are classified into three sets: with members one hundred percent belonging to the ocean front, belonging to the ocean front’s edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are util- ized to evaluate the ocean front’s degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts’ uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.

Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

The downward shortwave radiation(DSR)is a key input parameter for land surface models and climate models.Based on the daily averaged Global Land Surface Satellite downward shortwave radiation(GLASS-DSR)dataset over the Yunnan-Kweichow Plateau(YKP)from 1984 to 2018,this paper analyzes variation trend and breakpoints of DSR.The results show that:annual averaged DSR decreases at a decreasing rate of-1.84 W·m^(-2)·decade^(-1) over the YKP from 1984 to 2018;the overall distribution of interannual averaged DSR shows higher in the mid-west,and gradually decreasing from west to northeast over the YKP;the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon;monthly averaged DSR reaches its maximum in May and its minimum in December;breakpoints are found in the seasonal and trend components of daily averaged DSR.Eleven driving factors are examined for their effects on DSR variation,including annual average temperature,precipitation,10 m wind speed,aerosol optical thickness(AOT),total cloud cover,elevation,slope,aspect,longitude,latitude,and climate zones.According to thefindings,AOT predominates in the spatio-temporal distribution of DSR over the YKP.This study will contribute to studies related to climate change and highland radiation.

Combining LPJ-GUESS and HASM to simulate the spatial distribution of forest vegetation carbon stock in China

It is very important in accurately estimating the forests＇ carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the current estimation of forest carbon stock in the regional scale mainly depends on the forest inventory data, and the whole process consumes too much labor, money and time. And meanwhile it has many negative influences on the forest carbon storage updating. In order to figure out these problems, this paper, based on High Accuracy Surface Modeling （HASM）, proposes a forest vegetation carbon storage simulation method. This new method employs the output of LPJ-GUESS model as initial values of HASM and uses the inventory data as sample points of HASM to simulate the distribution of forest carbon storage in China. This study also adopts the seventh forest resources statistics of China as the data source to generate sample points, and it also works as the simulation accuracy test. The HASM simulation shows that the total forest carbon storage of China is 9.2405 Pg, while the calculated value based on forest resources statistics are 7.8115 Pg. The forest resources statistics is taken based on a forest canopy closure, and the result of HASM is much more suitable to the real forest carbon storage. The simulation result also indicates that the southwestern mountain region and the northeastern forests are the important forest carbon reservoirs in China, and they account for 39.82% and 20.46% of the country＇s total forest vegetation carbon stock respectively. Compared with the former value （1975-1995）, it mani- fests that the carbon storage of the two regions do increase clearly. The results of this re- search show that the large-scale reforestation in the last decades in China attains a signifi- cant carbon sink.

ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific

While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial accuracy. This is particularly true for research focused on the evaluation of adaptive forest management strategies. In this study, we developed an application, Climate AP, to generate scale-free(i.e., specific to point locations) climate data for historical(1901–2015) and future(2011–2100)years and periods. Climate AP uses the best available interpolated climate data for the reference period 1961–1990 as baseline data. It downscales the baseline data from a moderate spatial resolution to scale-free point data through dynamic local elevation adjustments. It also integrates and downscales the historical and future climate data using a delta approach. In the case of future climate data, two greenhouse gas representative concentration pathways(RCP 4.5 and 8.5) and 15 general circulation models are included to allow for the assessment of alternative climate scenarios. In addition, Climate AP generates a large number of biologically relevant climate variables derived from primary monthly variables. The effectiveness of the local downscaling was determined based on the strength of the local linear regression for the estimate of lapse rate. The accuracy of the Climate AP output was evaluated through comparisons of Climate AP output against observations from 1805 weather stations in the Asia Pacific region. The local linear regression explained 70%–80% and 0%–50% of the total variation in monthly temperatures and precipitation, respectively, in most cases. Climate AP reduced prediction error by up to27% and 60% for monthly temperature and precipitation,respectively, relative to the original baselines data. The improvements for baseline portions of historical and futurewere more substantial. Applications and limitations of the software are discussed.

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta,China

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

A ubiquitous knowledgeable data representation model(UKRM) for three-dimensional geographic information systems(3D GIS)

In the face of complicated, diversified three-dimensional world, the existing 3D GIS data models suffer from certain issues such as data incompatibility, insufficiency in data representation and representation types, among others. It is often hard to meet the requirements of multiple application purposes(users) related to GIS spatial data management and data query and analysis, especially in the case of massive spatial objects. In this study, according to the habits of human thinking and recognition, discrete expressions(such as discrete curved surface(DCS), and discrete body(DB)) were integrated and two novel representation types(including function structure and mapping structure) were put forward. A flexible and extensible ubiquitous knowledgeable data representation model(UKRM) was then constructed, in which structurally heterogeneous multiple expressions(including boundary representation(B-rep), constructive solid geometry(CSG), functional/parameter representation, etc.) were normalized. GIS's ability in representing the massive, complicated and diversified 3D world was thus greatly enhanced. In addition, data reuse was realized, and the bridge linking static GIS to dynamic GIS was built up. Primary experimental results illustrated that UKRM was overwhelmingly superior to the current data models(e.g. IFC, City GML) in describing both regular and irregular spatial objects.

Spatial, Temporal and Genetic Dynamics Characteristics of Influenza B Viruses in China, 1973–2018

Annual influenza B virus epidemics and outbreaks cause severe influenza diseases in humans and pose a threat to public health. China is an important epidemic area of influenza B viruses. However, the spatial, temporal transmission pathways and the demography history of influenza B viruses in China remain unknown. We collected the haemagglutinin gene sequences sampled of influenza B virus in China between 1973 and 2018. A Bayesian Markov chain Monte Carlo phylogeographic discrete approach was used to infer the spatial and temporal phylodynamics of influenza B virus. The Bayesian phylogeographic analysis of influenza B viruses showed that the North subtropical and South subtropical zones are the origins of the Victoria and Yamagata lineage viruses, respectively. Furthermore, the South temperate and North subtropical zones acted as transition nodes in the Victoria lineage virus dispersion network and that the North subtropical and Mid subtropical zones acted as transition nodes in the Yamagata lineage virus dispersion network. Our findings contribute to the knowledge regarding the spatial and temporal patterns of influenza B virus outbreaks in China.

The influence of urban three-dimensional structure and building greenhouse effect on local radiation flux

Accurate measurements of the three-dimensional structure characteristics of urban buildings and their greenhouse effect are important for evaluating the impact of urbanization on the radiation energy budget and research on the urban heat island(UHI)effect.The decrease in evapotranspiration or the increase in sensible heat caused by urbanization is considered to be the main cause of the UHI effect,but little is known about the influence of the main factor“net radiant flux”of the urban surface heat balance.In this study,experimental observation and quantitative model simulation were used to find that with the increase of building surface area after urbanization,the direct solar radiation flux and net radiation flux on building surface areas changed significantly.In order to accurately quantify the relationship between the positive and negative effects,this study puts forward the equivalent calculation principle of“aggregation element”,which is composed of a building’s sunny face and its shadow face,and the algorithm of the contribution of the area to thermal effect.This research clarifies the greenhouse effect of a building with walls of glass windows.Research shows that when the difference between absorption rates of a concrete wall and grass is−0.21,the cooling effect is shown.In the case of concrete walls with glass windows,the difference between absorption rates of a building wall and grass is−0.11,which is also a cooling effect.The greenhouse effect value of a building with glass windows reduces the cooling effect value to 56%of the effect of a building with concrete walls.The simulation of changes in net radiant flux and flux density shows that the greenhouse effect of a 5-story building with windows yields 15.5%less cooling effect than one with concrete walls,and a 30-story building with windows reduces the cooling effect by 23.0%.The simulation results confirmed that the difference in the equivalent absorption rate of the aggregation element is the“director”of cooling and heating effects,and the area of the aggregation element is the“amplifier”of cooling and heating effects.At the same time,the simulation results prove the greenhouse effect of glass windows,which significantly reduces the cold effect of concrete wall buildings.The model reveals the real contribution of optimized urban design to mitigating UHI and building a comfortable environment where there is no atmospheric circulation.

东西伯利亚地区植被对气温和降水交互作用的定量化响应

Significant changes to the world’s climate over the past few decades have had an impact on the development of plants.Vegetation in high latitude regions,where the ecosystems are fragile,is susceptible to climate change.It is possible to better understand vegetation’s phenological response to climate change by examining these areas.Traditional studies have mainly investigated how a single meteorological factor affects changes in vegetation phenology through linear correlation analysis,which is insufficient for quantitatively revealing the effects of various climate factor interactions on changes in vegetation phenology.We used the asymmetric Gaussian method to fit the normalized difference vegetation index(NDVI)curve and then used the dynamic threshold method to extract the phenological parameters,including the start of the season(SOS),end of the season(EOS),and length of the season(LOS),of the vegetation in this study area in the Tundra-Tagar transitional zone in eastern and western Siberia from 2000 to 2017.The monthly temperature and precipitation data used in this study were obtained from the climate research unit(CRU)meteorological dataset.The degrees to which the changes in temperature and precipitation in the various months and their interactions affected the changes in the three phenological parameters were determined using the GeoDetector,and the results were explicable.The findings demonstrate that the EOS was more susceptible to climate change than the SOS.The vegetation phenology shift was best explained by the climate in March,April,and September,and the combined effect of the temperature and precipitation had a greater impact on the change in the vegetation phenology compared with the effects of the individual climate conditions.The results quantitatively show the degree of interaction between the variations in temperature and precipitation and their effects on the changes in the different phenological parameters in the various months.Understanding how various climatic variations effect phenology changes in plants at different times may be more intuitive.This research provides as a foundation for research on how global climate change affects ecosystems and the global carbon cycle.

Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.