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Multiscale spatiotemporal meteorological drought prediction:A deep learning approach

Advances in Climate change Research 2024年第2期15卷 211-221页

作者： Jia-Li ZHANG Xiao-Meng HUANG Yu-Ze SUN department of earth system science Ministry of Education Key Laboratory for Earth System ModellingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related *** utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological ***,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time *** address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple *** formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis *** indicators span time scales of approximately 30 d and one ***-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future *** focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal *** model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought *** results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of *** of the four drought indicators accurately delineates specific facets of the meteorological drought ***,three representative drought events,namely flash drought,sustained drought and severe drought,

关键词： Meteorological drought Spatiotemporal prediction Multiscale Swim transformer Deep learning

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MEIC-global-CO_(2):A new global CO_(2)emission inventory with highly-resolved source category and sub-country information

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science China earth sciences 2024年第2期67卷 450-465页

作者： Ruochong XU Dan TONG Qingyang XIAO Xinying QIN Cuihong CHEN Liu YAN Jing CHENG Can CUI Hanwen HU Wenyu LIU Xizhe YAN Huaxuan WANG Xiaodong LIU Guannan GENG Yu LEI Dabo GUAN Kebin HE Qiang ZHANG department of earth system science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Chinese Academy of Environmental Planning Beijing100041China institute for Carbon Neutrality Tsinghua UniversityBeijing100084China

CO_(2)emission inventory provides fundamental data for climate research and emission ***,most global CO_(2)emission inventories were developed with energy statistics from International Energy Agency(IEA)and were available at country level with limited source ***,as the first step toward a high-resolution and dynamic updated global CO_(2)emission database,we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories,797 sub-country administrative divisions in 29 countries,42 fuel types,and 52 sectors,with the fusion of activity data from 24 international statistics and 65 regional/local *** CO_(2)emissions from fossil fuel combustion and cement production in 1970–2021 were then estimated with highly-resolved source category(1,484 of total)and sub-country information(797 of total).Specifically,73%of global CO_(2)emissions in 2021 were estimated with sub-country information,providing considerably improved spatial resolution for global CO_(2)emission *** the support of detailed information,the dynamics of global CO_(2)emissions across sectors and fuel types were presented,representing the evolution of global economy and progress of climate *** differences of sectoral contribution were found across sub-country administrative divisions within a given country,revealing the uneven distribution of energy and economic structure among different *** estimates were generally consistent with existing databases at aggregated level for global total or large emitters,while large discrepancies were observed for middle and small *** database,named the Multiresolution Emission Inventory model for Climate and air pollution research(MEIC)is publicly available through http://*** with highly-resolved information and timely update,which provides an independent carbon emission accounting data source for climate rese

关键词： CO_(2)emissions Data-driven approach Highly-resolved source category Sub-country information

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Reply to “Beyond microbial carbon use efficiency”

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National science Review 2024年第4期11卷 35-37页

作者： Feng Tao Johannes Lehmann Ying-Ping Wang Lifen Jiang Bernhard Ahrens Kostiantyn Viatkin Stefano Manzoni Benjamin Z.Houlton Yuanyuan Huang Xiaomeng Huang Yiqi Luo department of Ecology and Evolutionary Biology Cornell University Soil and Crop sciences Section School of Integrative Plant ScienceCornell University CSIRO Environment Max Planck institute for Biogeochemistry department of Physical Geography and Bolin Centre for Climate Research Stockholm University department of global Development Cornell University Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences department of earth system science Ministry of Education Key Laboratory for Earth System Modelling Institute for Global Change Studies Tsinghua University

In their commentary, Xiao et al.[1]cautioned that the conclusions on the critical role of microbial carbon use efficiency (CUE) in global soil organic carbon (SOC) storage in the paper by Tao et al.[2] might be too *** claimed that Tao et al.’s study lacked mechanistic consideration of SOC formation and excluded important data *** et al. brought up important points,which can be largely reconciled with our findings by understanding the differences in expressing processes in empirical studies and in models.

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research institute Chinese Academy of SciencesBeijing 100094China department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of global change and earth system science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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Advances in Atmospheric sciences 2024年第7期41卷 1326-1341页

作者： Lu LI Yongjiu DAI Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI School of Atmospheric sciences Sun Yat-sen UniversityGuangzhou 510275China Southern Marine science and Engineering Guangdong Laboratory(Zhuhai) Guangzhou 510275China Guangdong Province Key Laboratory for Climate change and Natural Disaster studies Guangzhou 510275China institute of Surface-earth system science School of Earth System ScienceTianjin UniversityTianjin 300072China College of Computer science and Technology Changchun Normal UniversityChangchun 130123China

Accurate soil moisture(SM)prediction is critical for understanding hydrological ***-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface *** addition to PB models,deep learning(DL)models have been widely used in SM predictions ***,few pure DL models have notably high success rates due to lacking physical ***,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM *** this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the global Forecast system to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM *** performances of the proposed hybrid models were investigated and compared with two existing hybrid *** results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid ***,the ensemble model performed best among all hybrid models at all forecast time scales and different soil *** is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day *** findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

关键词： soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism

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Uncertainties of ENSO-related Regional Hadley Circulation Anomalies within Eight Reanalysis Datasets

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Advances in Atmospheric sciences 2024年第1期41卷 115-140页

作者： Yadi LI Xichen LI Juan FENG Yi ZHOU Wenzhu WANG Yurong HOU International Center for Climate and Environment sciences(ICCES) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of sciences Beijing 101408China College of global change and earth system science(GCESS) Beijing Normal UniversityBeijing 100875China

El Nino-Southern Oscillation(ENSO),the leading mode of global interannual variability,usually intensifies the Hadley Circulation(HC),and meanwhile constrains its meridional extension,leading to an equatorward movement of the jet *** studies have investigated the response of HC to ENSO events using different reanalysis datasets and evaluated their capability in capturing the main features of ENSO-associated HC ***,these studies mainly focused on the global HC,represented by a zonal-mean mass stream function(MSF).Comparatively fewer studies have evaluated HC responses from a regional perspective,partly due to the prerequisite of the Stokes MSF,which prevents us from integrating a regional *** this study,we adopt a recently developed technique to construct the three-dimensional structure of HC and evaluate the capability of eight state-of-the-art reanalyses in reproducing the regional HC response to ENSO *** show that all eight reanalyses reproduce the spatial structure of HC responses well,with an intensified HC around the central-eastern Pacific but weakened circulations around the Indo-Pacific warm pool and tropical *** spatial correlation coefficient of the three-dimensional HC anomalies among the different datasets is always larger than ***,these datasets may not capture the amplitudes of the HC responses *** uncertainty is especially large for ENSO-associated equatorially asymmetric HC anomalies,with the maximum amplitude in Climate Forecast system Reanalysis(CFSR)being about 2.7 times the minimum value in the Twentieth Century Reanalysis(20CR).One should be careful when using reanalysis data to evaluate the intensity of ENSO-associated HC anomalies.

关键词： regional Hadley circulation ENSO atmosphere-ocean interaction reanalysis data

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Data error propagation in stacked bioclimatic envelope models

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Integrative Zoology 2024年第2期19卷 262-276页

作者： Xueyan LI Babak NAIMI Peng GONG Miguel B.ARAÚJO Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information system Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of ScienceGuangzhouChina ‘Rui Nabeiro’Biodiversity Chair CHANGE-MED InstituteUniversity ofÉvoraÉvoraPortugal department of Geography and department of earth sciences University of Hong KongHong KongChina department of Biogeography and global change National Museum of Natural SciencesCSICMadridSpain

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species *** data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in *** fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)*** species predictions were stacked to generate species richness ***,we show that different data sources and BEMs can generate spatially varying gradients of species *** environmental predictors that best explained species distributions also differed between data *** using citizen-based data had the highest accuracy,whereas those using range data had the lowest *** richness patterns estimated by GAM and BRT models were robust to data *** multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.

关键词： richness patterns species distribution stacked bioclimatic envelope models uncertainty

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An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

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Computer Modeling in Engineering & sciences 2024年第2期138卷 1549-1561页

作者： Jialun Lin Qiong Chen College of Biomedical Information and Engineering Hainan Medical UniversityHaikou571199China TJ-YZ School of Network science Haikou University of EconomicsHaikou571127China department of earth system science Ministry of Education Key Laboratory for Earth SystemModelingInstitute forGlobal Change StudiesTsinghua UniversityBeijing100084China

Watermarks can provide reliable and secure copyright protection for optical coherence tomography(OCT)fundus *** effective image segmentation is helpful for promoting OCT image ***,OCT images have a large amount of low-quality data,which seriously affects the performance of ***,this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network(RCNN).First,the rough-set-based feature discretization module is designed to preprocess the input ***,a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select important information for ***,the refinement module for enhancing the extraction power of multi-scale information is added to improve the edge accuracy in *** is compared with CE-Net and MultiResUNet on 83 gold standard 3D retinal OCT data *** average dice similarly coefficient(DSC)obtained by RCNN is 6%higher than that of *** average 95 percent Hausdorff distance(95HD)and average symmetric surface distance(ASD)obtained by RCNN are 32.4%and 33.3%lower than those of MultiResUNet,*** also evaluate the effect of feature discretization,as well as analyze the initial learning rate of RCNN and conduct ablation experiments with the four different *** experimental results indicate that our method can improve the segmentation accuracy of OCT fundus images,providing strong support for its application in medical image watermarking.

关键词： Watermarks image segmentation rough convolutional neural network attentionmechanism feature discretization

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changes in albedo and its radiative forcing of grasslands in East Asia drylands

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Ecological Processes 2024年第1期13卷 237-251页

作者： Qingsong Zhu Jiquan Chen Liangxu Wu Yuting Huang Changliang Shao Gang Dong Zhe Xu Xianglan Li State Key Laboratory of Remote Sensing science College of Global Change and Earth System Science Faculty of Geographical ScienceBeijing Normal University department of Geography Environment and Spatial Sciences Michigan State University State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China National Hulunber Grassland Ecosystem Observation and Research Station Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences School of Life science Shanxi University

Background Grasslands in drylands are increasingly influenced by human activities and climate change, leading to alterations in albedo and radiative energy balance among others. Surface biophysical properties and their interactions change greatly following disturbances. However, our understanding of these processes and their climatic impacts remains limited. In this study, we used multi-year observations from satellites and eddy-covariance towers to investigate the response of albedo to variables closely associated with human disturbances, including vegetation greenness（EVI） and surface soil volumetric water content（VWC）, as well as snow cover and clearness index（Ta） for their potential *** EVI and VWC during the growing season were the primary factors influencing albedo. EVI and VWC were negatively correlated with albedo, with VWC's total direct and indirect impacts being slightly smaller than those of EVI. During the non-growing season, snow cover was the most influential factor on albedo. VWC and Ta negatively affected albedo throughout the year. We estimated the impact of variations in EVI and VWC on climate to be in the range of 0.004 to 0.113 kg CO2m-2yr-1in *** This study indicates the significant impacts of climate change and human disturbances on vulnerable grassland ecosystems from the perspective of altered albedo. changes in vegetation greenness and soil properties induced by climate change and human activities may have a substantial impact on albedo, which in turn feedback on climate change, indicating that future climate policies should take this factor into consideration.

关键词： Albedo global warming potential Grassland Disturbance

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Decoding the inconsistency of six cropland maps in China

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The Crop Journal 2024年第1期12卷 281-294页

作者： Yifeng Cui Ronggao Liu Zhichao Li Chao Zhang Xiao-Peng Song Jilin Yang Le Yu Mengxi Chen Jinwei Dong institute of Geographic sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of sciences Beijing 100049China department of Geographical sciences University of MarylandCollege ParkMD 20740USA College of Grassland science and Technology China Agricultural UniversityBeijing 100193China Ministry of Education Ecological Field Station for East Asian Migratory Birds Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(department of earth system science)-Xi’an institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China institute of Remote Sensing and Geographic Information system Peking UniversityBeijing 100871China department of Forest and Conservation sciences University of British ColumbiaVancouverBCV6T 1Z4Canada

Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like *** widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these *** factors influencing their inconsistency are also *** this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural *** using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as *** work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.

关键词： Consistency and accuracy 10-and 30 m Cropland mapping Agricultural management China

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