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Understanding and improving Yangtze River Basin summer precipitation prediction using an optimal multi-Physics ensemble

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Frontiers of Earth science 2024年第1期18卷 256-277页

作者： Yang ZHAO Fengxue QIAO Xin-Zhong LIANG Jinhua YU Climate Environment and Sustainability CenterNanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of geographic information science(Ministry of Education) East China Normal UniversityShanghai 200241China School of geographic sciences East China Normal UniversityShanghai 200241China Department of Atmospheric and Oceanic science University of MarylandCollege Park MD 20740USA Earth System science Interdisciplinary center University of MarylandCollege Park MD 20740USA Key Laboratory of Meteorological Disaster(KLME)(Ministry of Education)/Collaborative Innovation center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science and TechnologyNanjing 210044China

This study employs the regional Climate-Weather Research and Forecasting model(CWRF)to first investigate the primary physical mechanisms causing biases in simulating summer precipitation over the Yangtze River Basin(YRB),and then enhance its predictive ability through an optimal multi-physics ensemble *** CWRF 30-km simulations in China are compared among 28 combinations of varying physics parameterizations during 1980−***-term average summer biases in YRB precipitation are remotely correlated with those of large-scale *** teleconnections of biases are highly consistent with the observed correlation patterns between interannual variations of precipitation and circulations,despite minor shifts in their primary action *** YRB precipitation aligns with a southward shifted East Asian westerly jet,an intensified low-level southerly flow south of YRB,and a south-eastward shifted South Asian high,alongside higher moisture availability over ***,decreased YRB precipitation corresponds to an opposite circulation *** CWRF control configuration using the ensemble cumulus parameterization(ECP),compared to other cumulus schemes,best captures the observed YRB precipitation characteristics and associated circulation *** ECP with the Morrison or Morrison-aerosol microphysics and the CCCMA or CAML radiation schemes enhances the overall CWRF *** to the control CWRF,the ensemble average of these skill-enhanced physics configurations more accurately reproduces YRB summer precipitation’s spatial distributions,interannual anomalies,and associated circulation *** Bayesian Joint Probability calibration to these configurations improves the ensemble’s spatial distributions but compromises its interannual anomalies and teleconnection *** findings highlight substantial potential for refining the representation of climate system physics to improve YRB precipitation *** is notabl

关键词： physics parameterization regional climate model downscaling skill enhancement multi-physics ensemble teleconnection bias reduction

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ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

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Computers, Materials & Continua 2024年第8期80卷 1907-1925页

作者： Jing Wang Chen Zhang Tianwen Lin geographic information and Tourism College Chuzhou UniversityChuzhou239000China Anhui Province Key Laboratory of Physical geographic Environment Chuzhou UniversityChuzhou239000China

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low *** propose the introduction of spatial and channel attention modules to the convolutional neural network ***,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise *** on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample *** results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for *** with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing *** also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

关键词： Deep learning semantic segmentation remote sensing imagery road extraction

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FIBTNet:Building Change Detection for Remote Sensing Images Using Feature Interactive Bi-Temporal Network

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Computers, Materials & Continua 2024年第9期80卷 4621-4641页

作者： Jing Wang Tianwen Lin Chen Zhang Jun Peng School of geographic information and Tourism Chuzhou UniversityChuzhou239000China Anhui Province Key Laboratory of Physical geographic Environment Chuzhou UniversityChuzhou239000China

In this paper,a feature interactive bi-temporal change detection network(FIBTNet)is designed to solve the problem of pseudo change in remote sensing image building change *** network improves the accuracy of change detection through bi-temporal feature *** designs a bi-temporal feature exchange architecture(EXA)and a bi-temporal difference extraction architecture(DFA).EXA improves the feature exchange ability of the model encoding process through multiple space,channel or hybrid feature exchange methods,while DFA uses the change residual(CR)module to improve the ability of the model decoding process to extract different features at multiple ***,at the junction of encoder and decoder,channel exchange is combined with the CR module to achieve an adaptive channel exchange,which further improves the decision-making performance of model feature *** results on the LEVIR-CD and S2Looking datasets demonstrate that iCDNet achieves superior F1 scores,Intersection over Union(IoU),and Recall compared to mainstream building change detectionmodels,confirming its effectiveness and superiority in the field of remote sensing image change detection.

关键词： Change detection change residual module feature exchange mechanism feature fusion

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Modelling and validation of flash flood inundation in drylands

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Journal of geographical sciences 2024年第1期34卷 185-200页

作者： GAO Dan YIN Jie WANG Dandan YANG Yuhan LU Yi CHEN Ruishan School of geographic sciences East China Normal UniversityShanghai 200241China Key Laboratory of geographic information science of Ministry of Education East China Normal UniversityShanghai 200241China National Disaster Reduction center of China Ministry of Emergency Management of People's Republic of ChinaBeijing 100124China School of Design Shanghai Jiao Tong UniversityShanghai 200240China

In the context of climate change and human activities,flood disasters in arid mountainous areas have become increasingly frequent,and seriously threatened the safety of people's lives and *** and accurate flash flood inundation modelling is an essential foundational research area,which can aid in the reduction of casualties and the minimization of disaster losses;however,this modelling is also very difficult,and models need to be urgently developed to address flash flood forecasting and *** objective of this study is to construct a numerical modelling method for flash floods in *** on a 2D high-resolution flood numerical model(Flood Map-Hydro Inundation2D),we hindcasted the dynamic process of flash flooding and show the spatio-temporal characteristics of flash flood inundation for the“8·18”flash flood disaster that occurred in Datong county,Qinghai *** results showed that the model output effectively agreed with the observed inundation after the event in terms of both spatial extent and temporal *** flooding mainly occurred between 00:00 and 01:00 on August 18,***,Hejiazhuang and Longwo villages were affected most *** further conducted model sensitivity analysis and found that the model was highly sensitive to both roughness and hydraulic conductivity in drylands,and the effect of hydraulic conductivity was more *** study confirmed the good performance of our model for the simulation of flash flooding in arid areas and provides a potential method for flash flood assessment and management in arid areas.

关键词： flash flood numerical modelling arid areas Datong county Flood Map-Hydro Inundation2D

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2SWUNet: small window SWin UNet based on tansformer for building extraction from high-resolution remote sensing images

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Optoelectronics Letters 2024年第10期20卷 599-606页

作者： YU Jiamin CHAN Sixian LEI Yanjing WU Wei WANG Yuan ZHOU Xiaolong College of Computer science and Technology Zhejiang University of Technology Key Laboratory of Meteorological Disaster(KLME) Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science &Technology College of geographic information Modern Industry Zhejiang University of Technology College of Electrical and information Engineering Quzhou University

Models dedicated to building long-range dependencies often exhibit degraded performance when transferred to remote sensing images. Vision transformer(Vi T) is a new paradigm in computer vision that uses multi-head self-attention(MSA) rather than convolution as the main computational module, with global modeling capabilities. However, its performance on small datasets is usually far inferior to that of convolutional neural networks(CNNs). In this work, we propose a small window SWin UNet(2SWUNet) for building extraction from high-resolution remote sensing images. Firstly, the 2SWUNet is trained based on swin transformer by designing a fully symmetric encoder-decoder U-shaped architecture. Secondly, to construct a reasonable U-shaped architecture for building extraction from high-resolution remote sensing images, different forms of patch expansion are explored to simulate up-sampling operations and recover feature map resolution. Then, the small window-based multi-head self-attention(W-MSA) is designed to reduce the computational and memory burden, which is more appropriate for the features of remote sensing images. Meanwhile, the pre-training mechanism is advanced to make up for the lack of decoder parameters. Finally, comparison experiments with other mainstream CNNs and Vi Ts validate the superiority of the proposed model.

关键词： A

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Determinants of malaria from environmental and poverty aspects in Indonesia:A spatiotemporal perspective,2016-2020

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Asian Pacific Journal of Tropical Medicine 2024年第6期17卷 256-267页

作者： Afi Nursafingi Prima Widayani Sidiq Purwoko Andy Bhermana Research center for Public Health and Nutrition Research Organization for HealthNational Research and Innovation AgencyJakartaIndonesia Department of geographic information science Faculty of GeographyUniversitas Gadjah MadaYogyakartaIndonesia center for Food Crops Research Organization for Agriculture and FoodNational Research and Innovation AgencyJakartaIndonesia

Objective:To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to ***:Random forest regression was used to analyse the independent variables contributing to malaria *** conditions were extracted from remotely sensed data,including vegetation,land temperature,soil moisture,precipitation,and *** contrast,the social aspects of poverty were obtained from government statistical ***:From 2016 to 2020,the contribution of each environmental and social aspect of poverty to malaria incidence fluctuated ***,the top three essential variables were people aged 15 years and above,experiencing poverty(variable importance/VI=32.0%),people experiencing poverty who work in the agricultural sector(VI=14.4%),and precipitation(VI=9.8%).It was followed by people experiencing poverty who are unemployed(VI=9.2%),land temperature(VI=5.2%),people experiencing poverty who have low education(VI=8.0%),soil moisture(VI=7.4%),elevation(VI=6.0%),and vegetation(VI=3.8%).Conclusions:Poverty and variables related to climate have become the crucial determinants of malaria in *** government must strengthen malaria surveillance through climate change mitigation and adaptation programs and accelerate poverty alleviation programs to support malaria elimination.

关键词： Malaria Poverty Climate Random forest Indonesia

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MGCPN:An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

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Journal of Meteorological Research 2024年第4期38卷 693-707页

作者： Mingyue LU Zhiyu HUANG Manzhu YU Hui LIU Caifen HE Chuanwei JIN Jingke ZHANG Key Laboratory of Meteorological Disaster Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Department of Geography Pennsylvania State UniversityUniversity ParkPA 16802USA ZhenhaiDistrict Meteorological Bureau Ningbo315200China geographic science College Nanjing University of Information Science&TechnologyNanjing 210044China School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044China

The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast *** address such a challenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM) for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement(IMERG) data,offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 *** results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast *** issue is a common challenge in precipitation forecasting ***,we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model *** model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecasting in the TP *** metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success index,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33%,19%,and 8% compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet) models.

关键词： precipitation nowcasting Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM)for Precipitation Nowcasting(MGCPN) Integrated Multi-satellite Retrievals for globalprecipitation measurement(IMERG) deep learning Tibetan Plateau

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Morphological characteristics and evolution model of slope units along loess gully cross section

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Frontiers of Earth science 2024年第1期18卷 17-29页

作者： Lulu LIU Fayuan LI Xue YANG Jianhua CHENG School of Geography Nanjing Normal UniversityNanjing 210023China Jiangsu center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023China Key Laboratory of Virtual geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China

Gully morphology is an important part of loess landform *** with gully development,the variation of its cross section is the most significant,and it can intuitively reflect the characteristics of the lateral widening of the gully ***,in-depth research of the variation of the cross-sectional morphology of the gully is helpful to understanding the development process of the loess *** on the DEMs(Digital Elevation Model)of nine periods of an indoor simulated loess small watershed,this paper studies the evolution model of a complete branch ditch in the watershed using the digital terrain analysis theory and *** show that with the development of the gully,the average gradient of the gully slope continuously decreases,and the slope morphology is mostly a concave slope along the slope *** degree of downward concave first increases and then gradually tends to be *** gully erosion mode is gradually transformed from downward cutting erosion to lateral *** more mature the gully development,the lower the depth of gully bottom cutting is compared with the width of gully ***,the surface cutting depth tends to be stable and the slope is ***,the transformation law of the slope morphology of the gully cross section with the development of the gully is studied,and the prediction model of the transformation of the slope morphology of the gully cross section is established by using the Markov *** Markov model can better reflect the dynamic change of the slope morphology of the gully cross section,which is of great significance to revealing the external performance and internal mechanism of the gully morphology.

关键词： gully cross section slope morphology Markov chain digital elevation model

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Geomorphology-oriented theoretical framework and construction method for value-added DEM

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Journal of geographical sciences 2024年第1期34卷 165-184页

作者： ZHANG Haiping TANG Guoan XIONG Liyang YANG Xin LI Fayuan Key Laboratory of Virtual geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China School of Geography Nanjing Normal UniversityNanjing 210023China Jiangsu center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023China

Digital elevation model(DEM)plays a fundamental role in the study of the earth system by expressing surface configuration,understanding surface process,and revealing surface *** is widely used in analysis and modeling in the field of ***,traditional DEM has the defect of single attribute,which is difficult to support the research in earth system science oriented to geoscience process and mechanism ***,realizing the value-added data model on the basis of traditional DEM is necessary to serve digital elevation modeling and terrain analysis under the background of a new geomorphology research paradigm and earth observation technology.A theoretical framework for value-added DEM that mainly includes concept,connotation,content,and categories,is constructed in this *** relationship between different types of value-added DEMs as well as the research significance and application category of this theoretical framework are also *** following are different methods of value-added DEMs:(1)value-added methods of DEM space and time dimensions that emphasize the integration of the ground and underground as well as coupling of time and space,(2)attribute-based value-added methods composed of material(including underground,surface,and ground)and morphological properties,and(3)value-added methods of features and physical elements that consider geographical objects and landform features formed by natural processes and artificial *** digital terrace,slope,and watershed models are used as examples to illustrate application scenarios of the three kinds of value-added *** study aims to improve expression methods of DEMs under the background of new surveying and mapping technologies by adding value to the DEM at three levels of dimensions,attributes,and elements as well as support knowledge-driven digital geomorphological analysis in the era of big data.

关键词： spatial data model value-added DEM digital terrain analysis geomorphological ontology geomorphology

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Monitoring Multi-Temporal Changes of Lakes on the Tibetan Plateau Using Multi-Source Remote Sensing Data from 1992 to 2019: A Case Study of Lake Zhari Namco

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Journal of Earth science 2024年第5期35卷 1679-1691页

作者： Juan Wu Chang-Qing Ke Yu Cai Zheng Duan Jiangsu Provincial Key Laboratory of geographic information science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing University Collaborative Innovation center of Novel Software Technology and Industrialization Guangxi Water and Power Design Institute Co. Ltd Collaborative Innovation center of South China Sea Studies Department of Physical Geography and Ecosystem science Lund University

Lake level, area and volume are sensitive indicators of climate change. At present, many studies have focused on the interannual water balance of lakes, but lake level and area can change remarkably with seasons, especially for lakes with seasonal ice cover. Zhari Namco, a seasonal frozen lake, was selected as an example to investigate its seasonal water balance. Multi-source altimetry and Landsat data were used to obtain the seasonal lake level and area from 1992 to 2019, and seasonal lake volume variations were also estimated. The results indicated the average lake level, area and volume in autumn were the largest. The lake level, area, and volume experienced three turning points approximately in 2000, 2010, and 2016, and showed an overall increasing trend from 1992 to 2019, with slopes of 0.15 m/year, 2.17 km2/year, and 0.14 km3/year, respectively. The lake area expanded significantly in autumn, which was related to the abundant precipitation. Delay time of land surface runoff, increased temperature, and evaporation may be the reason for the low lake level and volume in summer. The precipitation was the dominant factor of water balance, which explained 62.09%, 62.43%, and 62.10% of the variations in lake level, area, and volume, respectively.

关键词： lake level lake area lake volume satellite altimetry landsat Zhari Namco hydrogeology

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