应澳大利亚联邦科学与工业研究组织(Commonwealth scientific and Industrial Research Organisation, CSIRO)邀请,2018年4月清华大学组织科技考察团赴澳大利亚墨累-达令河流域开展为期一周的科学考察。考察团从墨累河出海口逆流而上,...应澳大利亚联邦科学与工业研究组织(Commonwealth scientific and Industrial Research Organisation, CSIRO)邀请,2018年4月清华大学组织科技考察团赴澳大利亚墨累-达令河流域开展为期一周的科学考察。考察团从墨累河出海口逆流而上,通过学习交流、现场考察和访问农场等方式,与澳大利亚同行们进行了深入交流,对澳大利亚墨累-达令河流域的气候变化与水的影响与适应对策研究,尤其是陆面水文-气候、极端水文研究、水资源管理体系的最新动态,及其气候变化应对与减缓、极端水文事件风险管理等进一步了解。此次考察对认识多时空尺度的气候-陆面-水文相互作用机理及其对自然强迫和人类活动(含人为强迫和下垫面人类活动)的响应机制,揭示全球气候系统能量-水循环动态演变规律和极端水文事件变化成因,构建全球增暖背景下应对极端水文事件的风险管理体系,提出中国适应性对策具有重要的借鉴意义。展开更多
A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trend...A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air （TDSA） are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.展开更多
Based on the Beijing Climate Center’s land surface model BCC_AVIM(Beijing Climate Center Atmosphere-Vegetation Interaction Model),the ensemble Kalman filter(EnKF)algorithm has been used to perform an assimilation exp...Based on the Beijing Climate Center’s land surface model BCC_AVIM(Beijing Climate Center Atmosphere-Vegetation Interaction Model),the ensemble Kalman filter(EnKF)algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer(MODIS)land surface temperature(LST)product to study the influence of satellite LST data frequencies on surface temperature data assimilations.The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System(GLDAS)LST products.The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions.Diurnal variation information in the observation data has a significant effect on the assimilation results.Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis.Overall,when assimilation is performed using observation data at 6-hour intervals,a relatively good assimilation result can be obtained,indicated by smaller bias(<2.2K)and root-mean-square-error(RMSE)(<3.7K)and correlation coefficients larger than 0.60.Conversely,the assimilation using 24-hour data generally showed larger bias(>2.2K)and RMSE(>4K).Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space.The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa(both<1K);the use of 24-hour interval observation data for assimilation produces the smallest bias(<2.2K)in March,April and July.展开更多
文摘应澳大利亚联邦科学与工业研究组织(Commonwealth scientific and Industrial Research Organisation, CSIRO)邀请,2018年4月清华大学组织科技考察团赴澳大利亚墨累-达令河流域开展为期一周的科学考察。考察团从墨累河出海口逆流而上,通过学习交流、现场考察和访问农场等方式,与澳大利亚同行们进行了深入交流,对澳大利亚墨累-达令河流域的气候变化与水的影响与适应对策研究,尤其是陆面水文-气候、极端水文研究、水资源管理体系的最新动态,及其气候变化应对与减缓、极端水文事件风险管理等进一步了解。此次考察对认识多时空尺度的气候-陆面-水文相互作用机理及其对自然强迫和人类活动(含人为强迫和下垫面人类活动)的响应机制,揭示全球气候系统能量-水循环动态演变规律和极端水文事件变化成因,构建全球增暖背景下应对极端水文事件的风险管理体系,提出中国适应性对策具有重要的借鉴意义。
文摘A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air （TDSA） are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.
文摘Based on the Beijing Climate Center’s land surface model BCC_AVIM(Beijing Climate Center Atmosphere-Vegetation Interaction Model),the ensemble Kalman filter(EnKF)algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer(MODIS)land surface temperature(LST)product to study the influence of satellite LST data frequencies on surface temperature data assimilations.The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System(GLDAS)LST products.The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions.Diurnal variation information in the observation data has a significant effect on the assimilation results.Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis.Overall,when assimilation is performed using observation data at 6-hour intervals,a relatively good assimilation result can be obtained,indicated by smaller bias(<2.2K)and root-mean-square-error(RMSE)(<3.7K)and correlation coefficients larger than 0.60.Conversely,the assimilation using 24-hour data generally showed larger bias(>2.2K)and RMSE(>4K).Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space.The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa(both<1K);the use of 24-hour interval observation data for assimilation produces the smallest bias(<2.2K)in March,April and July.
