Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to...Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO_(2) concentrations. To investigate the impact of China's carbon neutrality goal on atmospheric CO_(2) concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing System Chemistry(GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO_(2) (XCO_(2) ) from Orbiting Carbon Observatory-2(OCO-2) and surface CO_(2) measurements in Obs Pack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO_(2) . The model exhibits a root mean square error(RMSE) of 1.51 ppm(R^(2)=0.89) for OCO-2 XCO_(2) in China and 2.65 ppm(R^(2)=0.75) for surface CO_(2) concentrations at the WLG station. Further, compared to 2.83 ppm yr^(-1)in the control experiment, we suggest that net-zero CO_(2) emissions in China decelerate the increasing trends of XCO_(2) to 1.81 ppm yr^(-1),making a decrease of approximately 35.89%. Meanwhile, the seasonal cycle amplitude(SCA) of XCO_(2) is moderately reduced from 7.39±0.81 to 6.75±0.70 ppm, representing a relative reduction of 9.91%. Spatially, net-zero CO_(2) emissions induce a more significant decrease in XCO_(2) trends over northern and southern China, while their impact on SCA is more evident in northern and northeastern China. Moreover, ideal experiments demonstrate that zero fossil CO_(2) emissions lead to a greater attenuation of the linear trends of XCO_(2) by 40.81%, while the absence of terrestrial CO_(2) sinks largely diminishes the SCA by 16.61%. Additionally,trends and SCA in surface CO_(2) concentrations exhibit almost identical decreasing responses to net-zero CO_(2) emissions but display greater sensitivities compared to XCO_(2) . Overall, our study underscores the potential of China's carbon neutrality goal in mitigating global warming, underscoring the need for concerted and collaborative efforts from nations worldwide.展开更多
Zinc(Zn),a widespread metal in the Earth’s crust,serves as a crucial nutrient in the Southern Ocean’s primary production.Studies on Zn in Antarctic snow and ice offer insights into the origins of this metal and its ...Zinc(Zn),a widespread metal in the Earth’s crust,serves as a crucial nutrient in the Southern Ocean’s primary production.Studies on Zn in Antarctic snow and ice offer insights into the origins of this metal and its transport routes,as well as its impact on the biogeochemical processes within the Antarctic atmosphere–land–ocean system.This review examines research on the spatial and temporal distribution of Zn in Antarctic snow and ice,as well as in Southern Ocean waters.It includes an overview of advanced methods for sampling and analyzing Zn,along with explanations for the observed variations.The review also discusses various sources of Zn as a nutrient to the Southern Ocean.Finally,it addresses prospective issues related to the use of Zn isotopes in identifying atmospheric sources and their biogeochemical effects on the development of the Southern Ocean ecosystem.展开更多
This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major find...This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, precipitation in flood season, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipi- tation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipita- tion showed little increase over the period of 1957-2002. By changing the time scale from 32-year to 4-year, we ob- served nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.展开更多
Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiomet...Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.展开更多
Using wavelet analysis,regression analysis and the Mann-Kendall test,this paper analyzed time-series(1959-2006) weather data from 23 meteorological stations in an attempt to characterize the climate change in the Tari...Using wavelet analysis,regression analysis and the Mann-Kendall test,this paper analyzed time-series(1959-2006) weather data from 23 meteorological stations in an attempt to characterize the climate change in the Tarim River Basin of Xinjiang Uygur Autonomous Region,China.Major findings are as follows:1) In the 48-year study period,average annual temperature,annual precipitation and average annual relative humidity all presented nonlinear trends.2) At the 16-year time scale,all three climate indices unanimously showed a rather flat before 1964 and a detectable pickup thereafter.At the 8-year time scale,an S-shaped nonlinear and uprising trend was revealed with slight fluctuations in the entire process for all three indices.Incidentally,they all showed similar pattern of a slight increase before 1980 and a noticeable up-swing afterwards.The 4-year time scale provided a highly fluctuating pattern of periodical oscillations and spiral increases.3) Average annual relative humidity presented a negative correlation with average annual temperature and a positive correlation with annual precipitation at each time scale,which revealed a close dynamic relationship among them at the confidence level of 0.001.4) The Mann-Kendall test at the 0.05 confidence level demonstrated that the climate warming trend,as represented by the rising average annual temperature,was remarkable,but the climate wetting trend,as indicated by the rising annual precipitation and average annual relative humidity,was not obvious.展开更多
Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the glo...Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.展开更多
Based on the time series data from the Aral hydrological station for the period of 1958-2005,the paper re-veals the long-term trend and fractal of the annual runoff process in the mainstream of the Tarim River by usin...Based on the time series data from the Aral hydrological station for the period of 1958-2005,the paper re-veals the long-term trend and fractal of the annual runoff process in the mainstream of the Tarim River by using the wavelet analysis method and the fractal theory.The main conclusions are as follows:1)From a large time scale point of view,i.e.the time scale of 16(24)years,the annual runoff basically shows a slightly decreasing trend as a whole from 1958 to 2005.If the time scale is reduced to 8(23)or 4(22)years,the annual runoff still displays the basic trend as the large time scale,but it has fluctuated more obviously during the period.2)The correlation dimension for the annual runoff process is 3.4307,non-integral,which indicates that the process has both fractal and chaotic characteris-tics.The correlation dimension is above 3,which means that at least four independent variables are needed to describe the dynamics of the annual runoff process.3)The Hurst exponent for the first period(1958-1973)is 0.5036,which equals 0.5 approximately and indicates that the annual runoff process is in chaos.The Hurst exponents for the second(1974-1989)and third(1990-2005)periods are both greater than 0.50,which indicate that the annual runoff process showed a long-enduring characteristic in the two periods.The Hurst exponent for the period from 1990 to 2005 indi-cates that the annual runoff will show a slightly increasing trend in the 16 years after 2005.展开更多
High-energy electron precipitation in the high latitude regions enhances the ionization of the atmosphere,and subsequently increases the atmospheric conductivities and the vertical electric field of the atmosphere nea...High-energy electron precipitation in the high latitude regions enhances the ionization of the atmosphere,and subsequently increases the atmospheric conductivities and the vertical electric field of the atmosphere near the ground as well.The High-Energy Electron Flux(HEEF) data measured by the Fengyun-3 meteorological satellite are analyzed together with the data of nearsurface atmospheric vertical electric field measured at the Russian Vostok Station.Three HEEF enhancements are identified and it is shown that when the HEEF increases to a certain level,the local atmospheric vertical electric field near the ground can increase substantially than usual.The response time of the electric field to HEEF enhancement is about 3.7 to 4 days.展开更多
Farmers’ perceptions, beliefs, adaptive strategies, and barriers regarding climate change are critical to promoting sustainable ecosystems and societal stability. This paper is based on an extensive survey of 1 500 f...Farmers’ perceptions, beliefs, adaptive strategies, and barriers regarding climate change are critical to promoting sustainable ecosystems and societal stability. This paper is based on an extensive survey of 1 500 farmers and their households in Henan Province in China during 2013–2014. Henan is the largest agricultural province in China with over 51 million farmers. The survey results showed that approximately 57% of the respondents perceived the direct impact of climate change during the past 10 years, with 70.3% believing that climate change posed a risk to their livelihood. Not surprisingly, most farmers reported that they have adopted new measures to mitigate the negative impacts of climate change. The main barriers hindering farmers’ adopting adaptation measures were lack of funds and timely information. A multinomial logit model revealed that land ownership, knowledge of crop variety and the causes of climate change, as well as the belief of climate change, were all positively related to the likelihood of employing adaptive strategies. Moreover, the percentage of households engaging in agriculture activity, and years of engaging in farming were both negatively correlated with famer’s likelihood of adopting adaptation strategies. More importantly, farmers with high incomes were less likely to adopt adaptive strategies and more willing to engage in other business activities. In conclusion, it is important to communicate climate change related information and government policies in rural areas, promote farmer associations and other educational outreach efforts to assist Chinese farmers to deal with climate change.展开更多
The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analy...The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index(API) in four cities(Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.展开更多
With the rapid development of population and urbanization and the progress of lighting technology, the influence of artificial light sources has increased.In this context, the problem of light pollution has attracted ...With the rapid development of population and urbanization and the progress of lighting technology, the influence of artificial light sources has increased.In this context, the problem of light pollution has attracted wide attention.Previous studies have revealed that light pollution can affect biological living environments, human physical and mental health, astronomical observations and many other aspects.Therefore, organizations internationally have begun to advocate for measures to prevent light pollution, many of which are recognized by the International Dark-Sky Association(IDA).In addition to improving public awareness, legal protections, technical treatments and other means, the construction of Dark Sky Reserves(DSR) has proven to be an effective preventive measure.So far, as a pioneer practice in this field, the IDA has identified 11 DSRs worldwide.Based on the DA requirements for DSRs, this paper utilizes NPP-VIIRS nighttime light data and other multi-source spatial data to analyze possible DSR sites in China.The land of China was divided into more than ten thousand 30 km × 30 km fishnets, and constraint and suitable conditions were designated, respectively, as light and cloud conditions, and scale, traffic and attractiveness conditions.Using a multiple criteria evaluation, 1443 fishnets were finally selected as most suitable sites for the construction of DSRs.Results found that less than 25% of China is not subject to light pollution, and less than 13% is suitable for DSR construction, primarily in western and northern areas, including Tibet, Xinjiang, Qinghai, Gansu and Inner Mongolia.展开更多
This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China.To identify the impact of elevated surface ozone concentrations,we constructed an economet...This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China.To identify the impact of elevated surface ozone concentrations,we constructed an econometric model by controlling the impact of climate variables and related economic variables.This study also considered the potential spatial correlation in the measurement of the impact of surface ozone on maize yield.Results confirmed that the increase of ozone concentration decreased the maize yield.Moreover,maize was found to be the most sensitive to ozone at the end of the second month of the growing season.The average annual loss of maize caused by ozone pollution is about 4.234 million tons in 2013–2015,accounting for 1.9%of the average output.展开更多
Jiangsu has the most abundant tidal flat resources among China＇s coastal provinces. To ease the conflict between the growing population and shrinking usable land and to promote regional sustainable development, large...Jiangsu has the most abundant tidal flat resources among China＇s coastal provinces. To ease the conflict between the growing population and shrinking usable land and to promote regional sustainable development, large-scale coastal reclamation development activities have been performed in Jiangsu Province since 1949. The present study has integrated multi-source remote sensing images during 1974 to 2012 by using a Multi-point Fast Marching Method （MFMM） to extract the Jiangsu coastal reclamation areas for different time periods. The temporal and spatial patterns of the extent and elevation of the reclamation areas were analysed in order to determine the unused potential for future reclamation. It will provide information necessary to support the development and construction of tidal flats in Jiangsu. Results show that： （1） the reclaimed tidal fiats along the Jiangsu coast cover more than 19.86×10^4 hm^2, of which 13.97×10^4 hm^2 is located in Yancheng, 4.84×10^4 hm^2 in Nantong, and 1.05×10^4 hm^2 in Lianyungang; （2） the average elevation of the reclaimed Jiangsu tidal flats has gradually decreased over the last 40 years, while those in Dongtai and Rudong have showed particularly accelerated decrease since the 1990s; and （3） in 2012, very few unused tidal flats two meters above the sea level are left along the Jiangsu coast, and mainly concentrated in Yancheng. As there are still reserving some coastal land resources between 0-2 m, providing us with a potential for future development. All of these findings may be useful for researchers and local authorities for the development and utilization of tidal flat resources in Jiangsu.展开更多
Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time ar...Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.展开更多
Reactive nitrogen(Nr)emission from soils,e.g.,nitrous acid(HONO)and nitric oxide(NO),is a key process of the global nitrogen(N)cycle and has significant implications for atmospheric chemistry.To understand the underly...Reactive nitrogen(Nr)emission from soils,e.g.,nitrous acid(HONO)and nitric oxide(NO),is a key process of the global nitrogen(N)cycle and has significant implications for atmospheric chemistry.To understand the underlying mechanisms of soil Nr emissions,air-dried or oven-dried soils are commonly used in the laboratory.To date,few studies have compared the effects of different drying methods on soil Nr gas fluxes and N fractions.Here,the authors studied soil water content,pH,(in)organic N content,and Nr gas fluxes of air-dried,freeze-dried,oven-dried,and fresh soils from different land-use types.The results showed that the soil pH of air-dried and oven-dried samples was significantly lower compared with fresh soil from farmland and grassland,but higher compared with forest soil.The difference in soil pH between freeze-dried and fresh soil(mean±standard deviation:0.52±0.31)was the lowest.In general,all drying methods increased the soil NH4+-N,NO3−-N,and dissolved organic N contents compared with fresh soil(P<0.05).The maximum HONO and NO flux and total emissions during a full wetting–drying cycle of fresh soil were also increased by air-drying and oven-drying(P<0.001),but comparable with freeze-dried soil(P>0.2).In conclusion,all drying methods should be considered for use in studies on the land–atmosphere interface and biogeochemical N cycling,whereas the freeze-drying method might be better for studies involving the measurement of soil Nr gas fluxes.展开更多
Annual Land Use/Land Cover(LULC)change information at medium spatial resolution(i.e.,at 30 m)is used in applications ranging from land management to achieving sustainable development goals related to food security.How...Annual Land Use/Land Cover(LULC)change information at medium spatial resolution(i.e.,at 30 m)is used in applications ranging from land management to achieving sustainable development goals related to food security.However,obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities,training data,and workflow design.Using the Google Earth Engine(GEE),which provides a catalog of multi-source data and a cloud-based environment,we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery.Our major contribution is a hybrid approach that includes three major components:1)a high-quality training dataset derived from multi-source LULC products,filtered by k-means clustering analysis;2)a yearly 39-band stack feature space,utilizing all available Landsat data and DEM data;and 3)a self-adaptive Random Forest(RF)method,introduced for LULC classification.Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33%in the entire Delta.The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability.In addition,as the proposed workflow is based on open source data and the GEE cloud platform,it can be used anywhere by anyone in the world.展开更多
Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics(spectral, temporal, and spatial) ...Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics(spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hypertemporal, and large-volume light detection and ranging(Li DAR) and multispectral data to(i) identify the best machine learning method and prediction stage for wheat yield estimation,(ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and(iii) elucidate the contribution of time-series data fusion and 3 D spatial information to yield estimation. Wheat yield could be accurately(R^(2)= 0.891) and timely(approximately-two months before harvest) estimated from fused Li DAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits(such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits(such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3 D points than from canopy surface points and from integrated multi-stage(especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and timeseries information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.展开更多
The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the infl...The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate(ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate(ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest(training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China.The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small(<1%), whereas the ICR values are obviously more in line with general recognition(usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the MLbased natural hazard susceptibility assessments.展开更多
1.Introduction The solar flux is considered the fundamental energy source of earth’s climate system,and the earth’s motion greatly influences climate change over long time scales(Imbrie and Imbrie 1980;Ruddiman 2001...1.Introduction The solar flux is considered the fundamental energy source of earth’s climate system,and the earth’s motion greatly influences climate change over long time scales(Imbrie and Imbrie 1980;Ruddiman 2001).Modern global climate change is one of the core issues in research on climate change.The degree to which astronomy and earth motion factors,which are characterized by quite weak and slow variations,展开更多
While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly,aerosol ammonium nitrate remains high in East China.As the high nitrate abundances are...While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly,aerosol ammonium nitrate remains high in East China.As the high nitrate abundances are strongly linked with ammonia,reducing ammonia emissions is becoming increasingly important to improve the air quality of China.Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions,long-term surface observation of ammonia concentrations are sparse.In addition,there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget.Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policymakers,but existing methods have not been well validated.Revisiting the concentration measurements and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions.展开更多
基金supported by the National Key Research and Development Program of China (Grant No. 2022YFB3904801)the National Natural Science Foundation of China (Grant No. 42475129)+2 种基金the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20221449)the Xizang Science and Technology Innovation Base Construction Project (Grant No. XZ202401YD0008)the National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility” (Grant No. 2023-EL-ZD-00022)。
文摘Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO_(2) concentrations. To investigate the impact of China's carbon neutrality goal on atmospheric CO_(2) concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing System Chemistry(GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO_(2) (XCO_(2) ) from Orbiting Carbon Observatory-2(OCO-2) and surface CO_(2) measurements in Obs Pack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO_(2) . The model exhibits a root mean square error(RMSE) of 1.51 ppm(R^(2)=0.89) for OCO-2 XCO_(2) in China and 2.65 ppm(R^(2)=0.75) for surface CO_(2) concentrations at the WLG station. Further, compared to 2.83 ppm yr^(-1)in the control experiment, we suggest that net-zero CO_(2) emissions in China decelerate the increasing trends of XCO_(2) to 1.81 ppm yr^(-1),making a decrease of approximately 35.89%. Meanwhile, the seasonal cycle amplitude(SCA) of XCO_(2) is moderately reduced from 7.39±0.81 to 6.75±0.70 ppm, representing a relative reduction of 9.91%. Spatially, net-zero CO_(2) emissions induce a more significant decrease in XCO_(2) trends over northern and southern China, while their impact on SCA is more evident in northern and northeastern China. Moreover, ideal experiments demonstrate that zero fossil CO_(2) emissions lead to a greater attenuation of the linear trends of XCO_(2) by 40.81%, while the absence of terrestrial CO_(2) sinks largely diminishes the SCA by 16.61%. Additionally,trends and SCA in surface CO_(2) concentrations exhibit almost identical decreasing responses to net-zero CO_(2) emissions but display greater sensitivities compared to XCO_(2) . Overall, our study underscores the potential of China's carbon neutrality goal in mitigating global warming, underscoring the need for concerted and collaborative efforts from nations worldwide.
基金supported by the National Natural Science Foundation of China(Grant nos.42176240 and 42101142).
文摘Zinc(Zn),a widespread metal in the Earth’s crust,serves as a crucial nutrient in the Southern Ocean’s primary production.Studies on Zn in Antarctic snow and ice offer insights into the origins of this metal and its transport routes,as well as its impact on the biogeochemical processes within the Antarctic atmosphere–land–ocean system.This review examines research on the spatial and temporal distribution of Zn in Antarctic snow and ice,as well as in Southern Ocean waters.It includes an overview of advanced methods for sampling and analyzing Zn,along with explanations for the observed variations.The review also discusses various sources of Zn as a nutrient to the Southern Ocean.Finally,it addresses prospective issues related to the use of Zn isotopes in identifying atmospheric sources and their biogeochemical effects on the development of the Southern Ocean ecosystem.
基金Under the auspices of Second-stage Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-XB2-03)the major direction of Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW- 127)Shanghai Academic Discipline Project (Human Geography) (No. B410)
文摘This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, precipitation in flood season, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipi- tation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipita- tion showed little increase over the period of 1957-2002. By changing the time scale from 32-year to 4-year, we ob- served nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.
基金Project supported by the National Natural Science Foundation of China (No. 40571130)the Natural Science Foundation of Shanghai, China (No. 07ZR14032)
文摘Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.
基金Under the auspices of the Second-stage Knowledge Innovation Programs of Chinese Academy of Sciences (No KZCX2-XB2-03,KZCX2-YW-127)National Natural Science Foundation of China (No 40671014)Shanghai Academic Discipline Project (Human Geography) (No B410)
文摘Using wavelet analysis,regression analysis and the Mann-Kendall test,this paper analyzed time-series(1959-2006) weather data from 23 meteorological stations in an attempt to characterize the climate change in the Tarim River Basin of Xinjiang Uygur Autonomous Region,China.Major findings are as follows:1) In the 48-year study period,average annual temperature,annual precipitation and average annual relative humidity all presented nonlinear trends.2) At the 16-year time scale,all three climate indices unanimously showed a rather flat before 1964 and a detectable pickup thereafter.At the 8-year time scale,an S-shaped nonlinear and uprising trend was revealed with slight fluctuations in the entire process for all three indices.Incidentally,they all showed similar pattern of a slight increase before 1980 and a noticeable up-swing afterwards.The 4-year time scale provided a highly fluctuating pattern of periodical oscillations and spiral increases.3) Average annual relative humidity presented a negative correlation with average annual temperature and a positive correlation with annual precipitation at each time scale,which revealed a close dynamic relationship among them at the confidence level of 0.001.4) The Mann-Kendall test at the 0.05 confidence level demonstrated that the climate warming trend,as represented by the rising average annual temperature,was remarkable,but the climate wetting trend,as indicated by the rising annual precipitation and average annual relative humidity,was not obvious.
基金Under the auspices of Asia Pacific Network for Global Change Research(APN)Global Change Fund Project(No.ARCP2015-03CMY-Li)+2 种基金National Natural Science Foundation of China(No.41271361,41501575)National Key Research and Development Project(No.2018YFD0800201)Key Project of Chinese National Programs for Fundamental Research and Development(No.2010CB950702)
文摘Understanding the net primary productivity(NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach(CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia(1737.23 × 104 km^2), while the grassland area in Europe was relatively small(202.83 × 10~4 km^2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas(560.10 g C/(m^2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m^2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation(AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.
基金Under the auspices of the Second-stage Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-XB2-03)Major Direction of Knowledge Innovation Progromt of Chinese Academy of Sciences (No. KZCX2-YW-127)Shanghai Academic Discipline Project (Human Geography) (No. B410)
文摘Based on the time series data from the Aral hydrological station for the period of 1958-2005,the paper re-veals the long-term trend and fractal of the annual runoff process in the mainstream of the Tarim River by using the wavelet analysis method and the fractal theory.The main conclusions are as follows:1)From a large time scale point of view,i.e.the time scale of 16(24)years,the annual runoff basically shows a slightly decreasing trend as a whole from 1958 to 2005.If the time scale is reduced to 8(23)or 4(22)years,the annual runoff still displays the basic trend as the large time scale,but it has fluctuated more obviously during the period.2)The correlation dimension for the annual runoff process is 3.4307,non-integral,which indicates that the process has both fractal and chaotic characteris-tics.The correlation dimension is above 3,which means that at least four independent variables are needed to describe the dynamics of the annual runoff process.3)The Hurst exponent for the first period(1958-1973)is 0.5036,which equals 0.5 approximately and indicates that the annual runoff process is in chaos.The Hurst exponents for the second(1974-1989)and third(1990-2005)periods are both greater than 0.50,which indicate that the annual runoff process showed a long-enduring characteristic in the two periods.The Hurst exponent for the period from 1990 to 2005 indi-cates that the annual runoff will show a slightly increasing trend in the 16 years after 2005.
基金Supported by the National Basic Research Program of China(2011CB811404)the Natural Science Foundation of China(40774081)+1 种基金the Specialized Research Fund for State Key LaboratoriesCAS-NSSC-135 project
文摘High-energy electron precipitation in the high latitude regions enhances the ionization of the atmosphere,and subsequently increases the atmospheric conductivities and the vertical electric field of the atmosphere near the ground as well.The High-Energy Electron Flux(HEEF) data measured by the Fengyun-3 meteorological satellite are analyzed together with the data of nearsurface atmospheric vertical electric field measured at the Russian Vostok Station.Three HEEF enhancements are identified and it is shown that when the HEEF increases to a certain level,the local atmospheric vertical electric field near the ground can increase substantially than usual.The response time of the electric field to HEEF enhancement is about 3.7 to 4 days.
基金supported by the National Natural Science Foundation of China (41301149)the National Major Scientific Research Project, China (2012CB955800)+3 种基金the China Postdoctoral Science Foundation of the Fifty-Seventh Batch of Funds (2015M570626)the Open Research Fund of the Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, China (JOF 201601)the Open Research Funding Program of Key Laboratory of Geographic Information Science, Ministry of Education (KLGIS2014A03)the Science and Technology Innovation Team Support Plan Funded for University in Henan Province, China (16IRTSTHN012)
文摘Farmers’ perceptions, beliefs, adaptive strategies, and barriers regarding climate change are critical to promoting sustainable ecosystems and societal stability. This paper is based on an extensive survey of 1 500 farmers and their households in Henan Province in China during 2013–2014. Henan is the largest agricultural province in China with over 51 million farmers. The survey results showed that approximately 57% of the respondents perceived the direct impact of climate change during the past 10 years, with 70.3% believing that climate change posed a risk to their livelihood. Not surprisingly, most farmers reported that they have adopted new measures to mitigate the negative impacts of climate change. The main barriers hindering farmers’ adopting adaptation measures were lack of funds and timely information. A multinomial logit model revealed that land ownership, knowledge of crop variety and the causes of climate change, as well as the belief of climate change, were all positively related to the likelihood of employing adaptive strategies. Moreover, the percentage of households engaging in agriculture activity, and years of engaging in farming were both negatively correlated with famer’s likelihood of adopting adaptation strategies. More importantly, farmers with high incomes were less likely to adopt adaptive strategies and more willing to engage in other business activities. In conclusion, it is important to communicate climate change related information and government policies in rural areas, promote farmer associations and other educational outreach efforts to assist Chinese farmers to deal with climate change.
基金Under the auspices of Special Research Fund of the Ministry of Land and Resources for the Non-Profit Sector(No201411014-03)National Key Technology Research and Development Program of China(No.2012BAH28B04)
文摘The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index(API) in four cities(Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.
基金Under the auspices of the National Natural Science Foundation of China(No.41871162)
文摘With the rapid development of population and urbanization and the progress of lighting technology, the influence of artificial light sources has increased.In this context, the problem of light pollution has attracted wide attention.Previous studies have revealed that light pollution can affect biological living environments, human physical and mental health, astronomical observations and many other aspects.Therefore, organizations internationally have begun to advocate for measures to prevent light pollution, many of which are recognized by the International Dark-Sky Association(IDA).In addition to improving public awareness, legal protections, technical treatments and other means, the construction of Dark Sky Reserves(DSR) has proven to be an effective preventive measure.So far, as a pioneer practice in this field, the IDA has identified 11 DSRs worldwide.Based on the DA requirements for DSRs, this paper utilizes NPP-VIIRS nighttime light data and other multi-source spatial data to analyze possible DSR sites in China.The land of China was divided into more than ten thousand 30 km × 30 km fishnets, and constraint and suitable conditions were designated, respectively, as light and cloud conditions, and scale, traffic and attractiveness conditions.Using a multiple criteria evaluation, 1443 fishnets were finally selected as most suitable sites for the construction of DSRs.Results found that less than 25% of China is not subject to light pollution, and less than 13% is suitable for DSR construction, primarily in western and northern areas, including Tibet, Xinjiang, Qinghai, Gansu and Inner Mongolia.
基金the financial support by the National Natural Science Foundation of China (71673137)the Nanjing Agricultural University, China (Y0201400037, SKCX2015004)+4 种基金the Education Department of Jiangsu Province, China (2014SJD069)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)the China Center for Food Security Studies at Nanjing Agricultural UniversityJiangsu Rural Development and Land Policy Research InstituteJiangsu Agriculture Modernization Decision Consulting Center, China
文摘This study investigated the adverse effect of surface ozone on the maize yield using a unique panel from 880 counties in China.To identify the impact of elevated surface ozone concentrations,we constructed an econometric model by controlling the impact of climate variables and related economic variables.This study also considered the potential spatial correlation in the measurement of the impact of surface ozone on maize yield.Results confirmed that the increase of ozone concentration decreased the maize yield.Moreover,maize was found to be the most sensitive to ozone at the end of the second month of the growing season.The average annual loss of maize caused by ozone pollution is about 4.234 million tons in 2013–2015,accounting for 1.9%of the average output.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.41471068,41171325,41230751,and J1103408)the Program for New Century Excellent Talents in University(Grant No.NCET-12-0264)+2 种基金the Fundamental Research Funds for the Central Universitiesthe Priority Academic Program Development of the Jiangsu Higher Education Institutions(PAPD)the National Key Project of Scientific and Technical Supporting Programs funded by the Ministry of Science&Technology of China(Grant No.2012BAH28B04)
文摘Jiangsu has the most abundant tidal flat resources among China＇s coastal provinces. To ease the conflict between the growing population and shrinking usable land and to promote regional sustainable development, large-scale coastal reclamation development activities have been performed in Jiangsu Province since 1949. The present study has integrated multi-source remote sensing images during 1974 to 2012 by using a Multi-point Fast Marching Method （MFMM） to extract the Jiangsu coastal reclamation areas for different time periods. The temporal and spatial patterns of the extent and elevation of the reclamation areas were analysed in order to determine the unused potential for future reclamation. It will provide information necessary to support the development and construction of tidal flats in Jiangsu. Results show that： （1） the reclaimed tidal fiats along the Jiangsu coast cover more than 19.86×10^4 hm^2, of which 13.97×10^4 hm^2 is located in Yancheng, 4.84×10^4 hm^2 in Nantong, and 1.05×10^4 hm^2 in Lianyungang; （2） the average elevation of the reclaimed Jiangsu tidal flats has gradually decreased over the last 40 years, while those in Dongtai and Rudong have showed particularly accelerated decrease since the 1990s; and （3） in 2012, very few unused tidal flats two meters above the sea level are left along the Jiangsu coast, and mainly concentrated in Yancheng. As there are still reserving some coastal land resources between 0-2 m, providing us with a potential for future development. All of these findings may be useful for researchers and local authorities for the development and utilization of tidal flat resources in Jiangsu.
基金The National Key Research and Development Program of China under contract No.2016YFC1402609the Open Fund of the Key Laboratory of Marine Hazards Forecasting+1 种基金Ministry of Natural Resources under contract No.LOMF 1804the National Natural Science Foundation of China under contract No.42077438。
文摘Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.
基金This work was sponsored by the National Natural Science Foundation of China[grant numbers 41807449,41761144062,and 41730646]the Shanghai Pujiang Program[grant number 18PJ1403500]the Fundamental Research Funds for the Central Universities.
文摘Reactive nitrogen(Nr)emission from soils,e.g.,nitrous acid(HONO)and nitric oxide(NO),is a key process of the global nitrogen(N)cycle and has significant implications for atmospheric chemistry.To understand the underlying mechanisms of soil Nr emissions,air-dried or oven-dried soils are commonly used in the laboratory.To date,few studies have compared the effects of different drying methods on soil Nr gas fluxes and N fractions.Here,the authors studied soil water content,pH,(in)organic N content,and Nr gas fluxes of air-dried,freeze-dried,oven-dried,and fresh soils from different land-use types.The results showed that the soil pH of air-dried and oven-dried samples was significantly lower compared with fresh soil from farmland and grassland,but higher compared with forest soil.The difference in soil pH between freeze-dried and fresh soil(mean±standard deviation:0.52±0.31)was the lowest.In general,all drying methods increased the soil NH4+-N,NO3−-N,and dissolved organic N contents compared with fresh soil(P<0.05).The maximum HONO and NO flux and total emissions during a full wetting–drying cycle of fresh soil were also increased by air-drying and oven-drying(P<0.001),but comparable with freeze-dried soil(P>0.2).In conclusion,all drying methods should be considered for use in studies on the land–atmosphere interface and biogeochemical N cycling,whereas the freeze-drying method might be better for studies involving the measurement of soil Nr gas fluxes.
基金Under the auspices of the National Key Research and Development Program of China(No.2017YFB0504205)National Natural Science Foundation of China(No.41571378)Natural Science Research Project of Higher Education in Anhui Provence(No.KJ2020A0089)。
文摘Annual Land Use/Land Cover(LULC)change information at medium spatial resolution(i.e.,at 30 m)is used in applications ranging from land management to achieving sustainable development goals related to food security.However,obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities,training data,and workflow design.Using the Google Earth Engine(GEE),which provides a catalog of multi-source data and a cloud-based environment,we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery.Our major contribution is a hybrid approach that includes three major components:1)a high-quality training dataset derived from multi-source LULC products,filtered by k-means clustering analysis;2)a yearly 39-band stack feature space,utilizing all available Landsat data and DEM data;and 3)a self-adaptive Random Forest(RF)method,introduced for LULC classification.Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33%in the entire Delta.The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability.In addition,as the proposed workflow is based on open source data and the GEE cloud platform,it can be used anywhere by anyone in the world.
基金supported by the Jiangsu Agricultural Science and Technology Independent Innovation Fund Project (CX(21)3107)the National Natural Science Foundation of China(32030076)+4 种基金High Level Personnel Project of Jiangsu Province(JSSCBS20210271)China Postdoctoral Science Foundation(2021 M691490)Jiangsu Planned Projects for Postdoctoral Research Funds (2021K520C)Strategic Priority Research Program of the Chinese Academy of Sciences (XDA24020202)the Jiangsu 333 Program。
文摘Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics(spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hypertemporal, and large-volume light detection and ranging(Li DAR) and multispectral data to(i) identify the best machine learning method and prediction stage for wheat yield estimation,(ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and(iii) elucidate the contribution of time-series data fusion and 3 D spatial information to yield estimation. Wheat yield could be accurately(R^(2)= 0.891) and timely(approximately-two months before harvest) estimated from fused Li DAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits(such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits(such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3 D points than from canopy surface points and from integrated multi-stage(especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and timeseries information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.
文摘The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate(ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate(ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest(training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China.The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small(<1%), whereas the ICR values are obviously more in line with general recognition(usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the MLbased natural hazard susceptibility assessments.
基金supported by the National Basic Research Program of China[grant number 2012CB957800],[grant number2012CB957804]
文摘1.Introduction The solar flux is considered the fundamental energy source of earth’s climate system,and the earth’s motion greatly influences climate change over long time scales(Imbrie and Imbrie 1980;Ruddiman 2001).Modern global climate change is one of the core issues in research on climate change.The degree to which astronomy and earth motion factors,which are characterized by quite weak and slow variations,
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC0210100)National Research Program for Key Issues in Air Pollution Control(Grant No.DQGG0208)+1 种基金the National Natural Science Foundation of China(Grant No.41405144)WWW acknowledges support from the Atmospheric and Geospaces Sciences U.S.National Science Foundation(Grant No.AGS 1351932)。
文摘While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly,aerosol ammonium nitrate remains high in East China.As the high nitrate abundances are strongly linked with ammonia,reducing ammonia emissions is becoming increasingly important to improve the air quality of China.Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions,long-term surface observation of ammonia concentrations are sparse.In addition,there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget.Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policymakers,but existing methods have not been well validated.Revisiting the concentration measurements and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions.
