Forests exert significant biogeophysical cooling effects(CE)through processes such as increased evapotranspiration,reduced albedo,and enhanced surface roughness.However,little is known about the extent to which elevat...Forests exert significant biogeophysical cooling effects(CE)through processes such as increased evapotranspiration,reduced albedo,and enhanced surface roughness.However,little is known about the extent to which elevation-induced temperature differences bias the observed CE and how this bias interacts with the underlying biogeophysical mechanisms.In this study,we integrated multisensor remote sensing products and Shuttle Radar Topography Mission(SRTM)elevation data on the Google Earth Engine(GEE)platform,and applied a spatial-temporal window regression approach to quantify and correct the sensitivity of land surface temperature(LST)to elevation for forest pixels across China from 2001 to 2022.First,we found that forest LST exhibited a significant negative relationship with elevation,leading to systematic CE overestimation by 0.61 K during the day and 0.60 K at night compared with altitudecorrected CE values.Second,after correction,the CE showed clear spatial heterogeneity,with stronger daytime cooling in tropical(-0.54 K)and temperate forests(-0.24 K),and warming in cold(+0.11 K)and arid regions(+0.53 K),while most regions experienced nighttime warming.Among forest types,evergreen needleleaf forests(ENF)exhibited the strongest daytime cooling(-0.36 K),whereas deciduous broadleaf(DBF)and open shrublands(OS)tended to warm.Third,mechanism analysis revealed that elevation correction strengthened the correlations of CE with leaf area index(LAI)and evapotranspiration,while maintaining a significant negative correlation with albedo,indicating that both radiative and non-radiative processes jointly shape the unbiased CE.These findings provide a more accurate quantification of forest CE by eliminating elevation-induced bias,which providing a more accurate assessment of the climate mitigation potential of forests,which is crucial for developing more effective forest management and ecological restoration strategies.展开更多
A 3-craft formation configuration is proposed to perform the digital elevation model （DEM） for the distributed spacebome interferometric synthetic aperture radar （InSAR）, and it is optimized by the modified ant co...A 3-craft formation configuration is proposed to perform the digital elevation model （DEM） for the distributed spacebome interferometric synthetic aperture radar （InSAR）, and it is optimized by the modified ant colony algorithm to have the best compatibility with J2 invariant orbits created by differential correction algorithm. The configuration has succeeded in assigning the across-track baseline to vary periodically and with its mean value equal to the optimal baseline determined by the relative height measurement accuracy. The required relationship between crafts＇ magnitudes and phases is formulated for the general case of interferometry measure from non-orthographic and non-lateral view. The J2 invariant configurations created by differential correction algorithm are employed to investigate their compatibility with the required configuration. The colony algorithm is applied to search the optimal configuration holding the near-constant across-track baseline under the J2 perturbation, and the absolute height measurement accuracy is preferable as expected.展开更多
To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with dif...To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases.展开更多
Local scour, a non-negligible factor in hydraulic engineering, endangers the safety of hydraulic structures. In this work, a numerical model for simulating local scour was constructed, based on the open source code co...Local scour, a non-negligible factor in hydraulic engineering, endangers the safety of hydraulic structures. In this work, a numerical model for simulating local scour was constructed, based on the open source code computational fluid dynamics model Open FOAM. We consider both the bedload and suspended load sediment transport in the scour model and adopt the dynamic mesh method to simulate the evolution of the bed elevation. We use the finite area method to project data between the three-dimensional flow model and the two-dimensional(2D) scour model. We also improved the 2D sand slide method and added it to the scour model to correct the bed bathymetry when the bed slope angle exceeds the angle of repose. Moreover, to validate our scour model, we conducted and compared the results of three experiments with those of the developed model. The validation results show that our developed model can reliably simulate local scour.展开更多
基金Under the auspices of National Social Sciences Foundation of China(No.21BJY114)。
文摘Forests exert significant biogeophysical cooling effects(CE)through processes such as increased evapotranspiration,reduced albedo,and enhanced surface roughness.However,little is known about the extent to which elevation-induced temperature differences bias the observed CE and how this bias interacts with the underlying biogeophysical mechanisms.In this study,we integrated multisensor remote sensing products and Shuttle Radar Topography Mission(SRTM)elevation data on the Google Earth Engine(GEE)platform,and applied a spatial-temporal window regression approach to quantify and correct the sensitivity of land surface temperature(LST)to elevation for forest pixels across China from 2001 to 2022.First,we found that forest LST exhibited a significant negative relationship with elevation,leading to systematic CE overestimation by 0.61 K during the day and 0.60 K at night compared with altitudecorrected CE values.Second,after correction,the CE showed clear spatial heterogeneity,with stronger daytime cooling in tropical(-0.54 K)and temperate forests(-0.24 K),and warming in cold(+0.11 K)and arid regions(+0.53 K),while most regions experienced nighttime warming.Among forest types,evergreen needleleaf forests(ENF)exhibited the strongest daytime cooling(-0.36 K),whereas deciduous broadleaf(DBF)and open shrublands(OS)tended to warm.Third,mechanism analysis revealed that elevation correction strengthened the correlations of CE with leaf area index(LAI)and evapotranspiration,while maintaining a significant negative correlation with albedo,indicating that both radiative and non-radiative processes jointly shape the unbiased CE.These findings provide a more accurate quantification of forest CE by eliminating elevation-induced bias,which providing a more accurate assessment of the climate mitigation potential of forests,which is crucial for developing more effective forest management and ecological restoration strategies.
基金supported by the National Natural Science Foundation of China (10702003)
文摘A 3-craft formation configuration is proposed to perform the digital elevation model （DEM） for the distributed spacebome interferometric synthetic aperture radar （InSAR）, and it is optimized by the modified ant colony algorithm to have the best compatibility with J2 invariant orbits created by differential correction algorithm. The configuration has succeeded in assigning the across-track baseline to vary periodically and with its mean value equal to the optimal baseline determined by the relative height measurement accuracy. The required relationship between crafts＇ magnitudes and phases is formulated for the general case of interferometry measure from non-orthographic and non-lateral view. The J2 invariant configurations created by differential correction algorithm are employed to investigate their compatibility with the required configuration. The colony algorithm is applied to search the optimal configuration holding the near-constant across-track baseline under the J2 perturbation, and the absolute height measurement accuracy is preferable as expected.
基金supported by the National Natural Science Foundation of China(grant number 42271438)the Shan-dong Provincial Natural Science Foundation of China(grant no.ZR2020YQ26)a project of the Shandong Province Higher Educational Youth Innovation Science and Technology Program(grant number 2019KJH007).
文摘To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases.
基金the State Key Laboratory of Hydraulic Engineering Simulation and Safety Foundation (No. HESS-1412)the National Science Fund (No. 51179178)the 111 Project (No. B14028)
文摘Local scour, a non-negligible factor in hydraulic engineering, endangers the safety of hydraulic structures. In this work, a numerical model for simulating local scour was constructed, based on the open source code computational fluid dynamics model Open FOAM. We consider both the bedload and suspended load sediment transport in the scour model and adopt the dynamic mesh method to simulate the evolution of the bed elevation. We use the finite area method to project data between the three-dimensional flow model and the two-dimensional(2D) scour model. We also improved the 2D sand slide method and added it to the scour model to correct the bed bathymetry when the bed slope angle exceeds the angle of repose. Moreover, to validate our scour model, we conducted and compared the results of three experiments with those of the developed model. The validation results show that our developed model can reliably simulate local scour.
