Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in ...Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world.This study,using multisource datasets(including satellite data and meteorological observations and reanalysis data)revealed the mutual feedback mechanisms between changes in climate(temperature and precipitation)and vegetation coverage in recent decades in the Hengduan Mountains Area(HMA)of the southeastern TP and their influences on climate in the downstream region,the Sichuan Basin(SCB).There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA,which is most significant during winter,and then during spring,but insignificant during summer and autumn.Rising temperature significantly enhances local vegetation coverage,and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere.The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure.The high pressure anomaly disperses downstream via the westerly flow,expands across the SCB,and eventually increases the SCB temperature.This effect lasts from winter to the following spring,which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring.These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios,as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate.展开更多
This study uses <span style="font-family:Verdana;">an empirical</span><span style="font-family:Verdana;"> analysis to quantify the downstream analysis effects of data pre-processi...This study uses <span style="font-family:Verdana;">an empirical</span><span style="font-family:Verdana;"> analysis to quantify the downstream analysis effects of data pre-processing choices. Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE). Results of the risk function decomposition are used to measure the effects of model development choices on </span><span style="font-family:Verdana;">model</span><span style="font-family:Verdana;"> bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development. Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices. </span><span style="font-family:Verdana;">In addition</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;">, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library. Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities. Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.</span>展开更多
基金the National Natural Science Foundation of China(Grant Nos.42205059 and 42005075)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA23090303 and XDB40010302)+1 种基金the State Key Laboratory of Cryospheric Science(Grant No.SKLCS-ZZ-2024 and SKLCS-ZZ-2023)the Key Laboratory of Mountain Hazards and Earth Surface Processes.
文摘Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world.This study,using multisource datasets(including satellite data and meteorological observations and reanalysis data)revealed the mutual feedback mechanisms between changes in climate(temperature and precipitation)and vegetation coverage in recent decades in the Hengduan Mountains Area(HMA)of the southeastern TP and their influences on climate in the downstream region,the Sichuan Basin(SCB).There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA,which is most significant during winter,and then during spring,but insignificant during summer and autumn.Rising temperature significantly enhances local vegetation coverage,and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere.The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure.The high pressure anomaly disperses downstream via the westerly flow,expands across the SCB,and eventually increases the SCB temperature.This effect lasts from winter to the following spring,which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring.These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios,as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate.
文摘This study uses <span style="font-family:Verdana;">an empirical</span><span style="font-family:Verdana;"> analysis to quantify the downstream analysis effects of data pre-processing choices. Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE). Results of the risk function decomposition are used to measure the effects of model development choices on </span><span style="font-family:Verdana;">model</span><span style="font-family:Verdana;"> bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development. Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices. </span><span style="font-family:Verdana;">In addition</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;">, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library. Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities. Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.</span>
