<div style="text-align:justify;"> Precipitation is an important part of the global hydrological cycle. The large- scale, high-precision continuous precipitation data obtained by satellite remote sensin...<div style="text-align:justify;"> Precipitation is an important part of the global hydrological cycle. The large- scale, high-precision continuous precipitation data obtained by satellite remote sensing detection technology has become an important source of spatial precipitation data. However, because the spatial resolution of remote sensing precipitation data is still low, it is difficult to meet the needs of hydrological research, which restricts their application in drought and flood analysis, hydrological simulation, etc. In response to this problem, this paper takes the Beijing-Tianjin-Hebei region as the research area, downscaling the TRMM data and the GPM data space of the continuation plan, and increasing the spatial resolution of the data to 1 km. Compared with the original data, spatial downscaling data not only greatly improves the spatial resolution, but also increases the accuracy of the data, which has better applicability. </div>展开更多
Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and ...Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and highquality land assimilation products prior to 2008.To address this issue,this paper proposes a deep learning model based on the Hybrid Attention Transformer(HAT),aiming to improve the downscaling accuracy of high speed winds in the CLDAS2.010-m wind field from 6.25 to 1 km by(1)incorporating digital elevation information(DEM),(2)enhancing the loss function,and(3)employing a prediction error method.We utilized data in 2020–2021 for training and validation,and data in 2019 for testing,conducted ablation experiments to verify the effectiveness of each module,while comparing the results with those of the traditional bilinear interpolation method and the UNET model coupled with a dual cross-attention mechanism.The ablation experiment results indicate that in terms of wind speed categories,HAT with DEM performs the best for wind speeds below level 3 on the Beaufort scale,while HAT with DEM,loss function,and prediction error improvements excels for wind speeds above level 4.Specifically,for wind speeds above level 6,the HAT with all the three improvement measures achieves decent results,with mean absolute error(MAE),probability of detection(POD),and threat score(TS)of 0.825 m s-1,0.813,and 0.607,respectively,when evaluated against CLDAS3.0 as the ground truth.The model performs better in March–May and November,while its performance is the weakest in June–August;it also performs better during the day than at night and shows suboptimal performance over the plains.The model is closer to the ground truth in reconstructing the structural details of wind fields and outperforms the annual average during most high wind weather events,indicating better predictive capability and adaptability for such events.Overall,the HAT with all the three proposed improvements demonstrates significant progress in downscaling predictions of high winds and provides insights into generation of high-resolution historical meteorological gridded data.展开更多
The mass elevation effect(MEE)is a thermal effect,in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift,which has a profound impact on the hydrothermal conditions and natu...The mass elevation effect(MEE)is a thermal effect,in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift,which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas.Based on multi-source remote sensing data and field observations,a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted,and the MEE was estimated and a spatio-temporal analysis was conducted.The Geo Detector model(GDM)and a geographically weighted regression(GWR)model were applied to explore the spatial and temporal heterogeneity of the study area.Four key results can be obtained.(1)The temperature pattern is complex and diverse,and the overall temperature presented a pattern of high in the south and east,but low in the north and west.There were clear zonal features of temperature that were negatively correlated with altitude,and the temperature difference between the internal and external areas of the mountains.(2)The warming effect of mountains was prominent,and the temperature at the same altitude increased in steps from west to east and north to south.Geomorphological units,such as large valleys and intermontane basins,weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude,with the warming effect of mountains in the southern Tianshan Mountains.(3)The dominant factors affecting the overall pattern of the MEE were topography and location,among which the difference between the internal and external areas of the mountains,and the absolute elevation played a prominent role.The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors,and there was a strong interaction between terrain and climate,precipitation,nthe normalized difference vegetation index(NDVI),and other factors.(4)There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE.Absolute elevation was significantly positively correlated with the change of MEE,while precipitation and the NDVI were dominated by negative feedback.In general,topography had the largest effect on the macroscopic control of MEE,and coupled with precipitation,the underlying surface,and other factors to form a unique mountain circulation system and climate characteristics,which in turn enhanced the spatial and temporal heterogeneity of the MEE.The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.展开更多
National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies a...National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.展开更多
Global climate and environmental change studies require detailed land-use and land-cover(LULC)information about the past,present,and future.In this paper,we discuss a methodology for downscaling coarse-resolution(i.e....Global climate and environmental change studies require detailed land-use and land-cover(LULC)information about the past,present,and future.In this paper,we discuss a methodology for downscaling coarse-resolution(i.e.,half-degree)future land use scenarios to finer(i.e.,1 km)resolutions at the global scale using a grid-based spatially explicit cellular automata(CA)model.We account for spatial heterogeneity from topography,climate,soils,and socioeconomic variables.The model uses a global 30 m land cover map(2010)as the base input,a variety of biogeographic and socioeconomic variables,and an empirical analysis to downscale coarse-resolution land use information(specifically urban,crop and pasture).The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100(with four representative concentration pathway(RCP)scenarios--RCP 2.6,RCP 4.5,RCP 6.0,and RCP 8.5)at a 1 km resolution within a globally consistent framework.The data are freely available for download,and will enable researchers to study the impacts of LULC change at the local scale.展开更多
The Council on Environmental Quality’s Climate and Economic Justice Screening Tool defines“disadvantaged communities”(DAC)in the USA,highlighting census tracts where benefits of climate and energy investments are n...The Council on Environmental Quality’s Climate and Economic Justice Screening Tool defines“disadvantaged communities”(DAC)in the USA,highlighting census tracts where benefits of climate and energy investments are not accruing.We use a principal component generalized linear model(PCGLM),which addresses the intertwined nature of economic factors,income and employment and model their relationship to DAC status.Our study(1)identifies the most significant income groups and employment industries that impact DAC status(2)provides the probability of DAC status across census tracts and compares the predictive accuracy with widely used machine learning(ML)approaches,(3)obtains historical predictions of the probability of DAC status,(4)obtains spatial downscaling of DAC status across block groups.Our study provides valuable insights for policymakers and stakeholders to develop strategies that promote sustainable development and address inequities in climate and energy investments in the USA.展开更多
We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface ...We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.展开更多
文摘<div style="text-align:justify;"> Precipitation is an important part of the global hydrological cycle. The large- scale, high-precision continuous precipitation data obtained by satellite remote sensing detection technology has become an important source of spatial precipitation data. However, because the spatial resolution of remote sensing precipitation data is still low, it is difficult to meet the needs of hydrological research, which restricts their application in drought and flood analysis, hydrological simulation, etc. In response to this problem, this paper takes the Beijing-Tianjin-Hebei region as the research area, downscaling the TRMM data and the GPM data space of the continuation plan, and increasing the spatial resolution of the data to 1 km. Compared with the original data, spatial downscaling data not only greatly improves the spatial resolution, but also increases the accuracy of the data, which has better applicability. </div>
基金Supported by the Project of“Advanced Research on Civil Space Technology during the 14th Five-Year Plan”of National Meteorological Information Centre of China Meteorological Administration(NMICJY202305)National Natural Science Foundation of China(42205153,42430602,and 92037000)。
文摘Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and highquality land assimilation products prior to 2008.To address this issue,this paper proposes a deep learning model based on the Hybrid Attention Transformer(HAT),aiming to improve the downscaling accuracy of high speed winds in the CLDAS2.010-m wind field from 6.25 to 1 km by(1)incorporating digital elevation information(DEM),(2)enhancing the loss function,and(3)employing a prediction error method.We utilized data in 2020–2021 for training and validation,and data in 2019 for testing,conducted ablation experiments to verify the effectiveness of each module,while comparing the results with those of the traditional bilinear interpolation method and the UNET model coupled with a dual cross-attention mechanism.The ablation experiment results indicate that in terms of wind speed categories,HAT with DEM performs the best for wind speeds below level 3 on the Beaufort scale,while HAT with DEM,loss function,and prediction error improvements excels for wind speeds above level 4.Specifically,for wind speeds above level 6,the HAT with all the three improvement measures achieves decent results,with mean absolute error(MAE),probability of detection(POD),and threat score(TS)of 0.825 m s-1,0.813,and 0.607,respectively,when evaluated against CLDAS3.0 as the ground truth.The model performs better in March–May and November,while its performance is the weakest in June–August;it also performs better during the day than at night and shows suboptimal performance over the plains.The model is closer to the ground truth in reconstructing the structural details of wind fields and outperforms the annual average during most high wind weather events,indicating better predictive capability and adaptability for such events.Overall,the HAT with all the three proposed improvements demonstrates significant progress in downscaling predictions of high winds and provides insights into generation of high-resolution historical meteorological gridded data.
基金National Natural Science Foundation of China,No.41761108。
文摘The mass elevation effect(MEE)is a thermal effect,in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift,which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas.Based on multi-source remote sensing data and field observations,a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted,and the MEE was estimated and a spatio-temporal analysis was conducted.The Geo Detector model(GDM)and a geographically weighted regression(GWR)model were applied to explore the spatial and temporal heterogeneity of the study area.Four key results can be obtained.(1)The temperature pattern is complex and diverse,and the overall temperature presented a pattern of high in the south and east,but low in the north and west.There were clear zonal features of temperature that were negatively correlated with altitude,and the temperature difference between the internal and external areas of the mountains.(2)The warming effect of mountains was prominent,and the temperature at the same altitude increased in steps from west to east and north to south.Geomorphological units,such as large valleys and intermontane basins,weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude,with the warming effect of mountains in the southern Tianshan Mountains.(3)The dominant factors affecting the overall pattern of the MEE were topography and location,among which the difference between the internal and external areas of the mountains,and the absolute elevation played a prominent role.The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors,and there was a strong interaction between terrain and climate,precipitation,nthe normalized difference vegetation index(NDVI),and other factors.(4)There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE.Absolute elevation was significantly positively correlated with the change of MEE,while precipitation and the NDVI were dominated by negative feedback.In general,topography had the largest effect on the macroscopic control of MEE,and coupled with precipitation,the underlying surface,and other factors to form a unique mountain circulation system and climate characteristics,which in turn enhanced the spatial and temporal heterogeneity of the MEE.The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.
基金funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101036458.
文摘National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.
基金partially supported by the National Natural Science Foundation of China(41301445)Research Grant from Tsinghua University(20151080351)a Meteorological Public Benefit project of China(GYHY201506010)
文摘Global climate and environmental change studies require detailed land-use and land-cover(LULC)information about the past,present,and future.In this paper,we discuss a methodology for downscaling coarse-resolution(i.e.,half-degree)future land use scenarios to finer(i.e.,1 km)resolutions at the global scale using a grid-based spatially explicit cellular automata(CA)model.We account for spatial heterogeneity from topography,climate,soils,and socioeconomic variables.The model uses a global 30 m land cover map(2010)as the base input,a variety of biogeographic and socioeconomic variables,and an empirical analysis to downscale coarse-resolution land use information(specifically urban,crop and pasture).The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100(with four representative concentration pathway(RCP)scenarios--RCP 2.6,RCP 4.5,RCP 6.0,and RCP 8.5)at a 1 km resolution within a globally consistent framework.The data are freely available for download,and will enable researchers to study the impacts of LULC change at the local scale.
基金supported by the Agile Initiative,a multi-disciplinary Pacific Northwest National Laboratory(PNNL)initiative.PNNL is operated by Battelle Memorial Institute under Contract DE-AC06-76RL01830.
文摘The Council on Environmental Quality’s Climate and Economic Justice Screening Tool defines“disadvantaged communities”(DAC)in the USA,highlighting census tracts where benefits of climate and energy investments are not accruing.We use a principal component generalized linear model(PCGLM),which addresses the intertwined nature of economic factors,income and employment and model their relationship to DAC status.Our study(1)identifies the most significant income groups and employment industries that impact DAC status(2)provides the probability of DAC status across census tracts and compares the predictive accuracy with widely used machine learning(ML)approaches,(3)obtains historical predictions of the probability of DAC status,(4)obtains spatial downscaling of DAC status across block groups.Our study provides valuable insights for policymakers and stakeholders to develop strategies that promote sustainable development and address inequities in climate and energy investments in the USA.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41930647, 41590844, 41421001 & 41971358)the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (Grant No. XDA20030203)+1 种基金the Innovation Project of LREIS (Grant No. O88RA600YA)the Biodiversity Investigation, Observation and Assessment Program (2019–2023) of the Ministry of Ecology and Environment of China。
文摘We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.
