Microwave brightness temperature(TB)can be used to retrieve lake ice thickness in the Arctic and subarctic regions.However,the accuracy of the retrieval is affected by the physical properties of lake ice.To improve th...Microwave brightness temperature(TB)can be used to retrieve lake ice thickness in the Arctic and subarctic regions.However,the accuracy of the retrieval is affected by the physical properties of lake ice.To improve the understanding of how lake ice affects TB,numerical modeling was applied.This study combined a physical thermodynamic ice model HIGHTSI with a microwave radiation transfer model SMRT to simulate the TB and lake ice evolution in 2002-2011 in Hulun Lake,China.The reanalyzed meteorological data were used as atmospheric forcing.The ice season was divided into the growth period,the slow growth period,and the ablation period.The simulations revealed that TB was highly sensitive to ice thickness during the ice season,especially vertical polarization measurement at 18.7 GHz.The quadratic polynomial fit for ice thickness to TB outperformed the linear fit,regardless of whether lake ice contained bubbles or not.A comparison of the simulated TB with space-borne TB showed that the simulated TB had the best accuracy during the slow growth period,with a minimum RMSE of 4.6 K.The results were influenced by the bubble radius and salinity.These findings enhance comprehension of the interaction between lake ice properties(including ice thickness,bubbles,and salinity)and TB during ice seasons,offering insights to sea ice in the Arctic and subarctic freshwater observations.展开更多
径流是水循环中的重要过程,流域中河流的分布信息对该区域的水资源、生态、环境及其社会经济活动具有重要意义。我国广西、云南、贵州和东盟十国属于多云雨、山地特点突出的区域,对比已有的河流面域数据产品,发现对该区域的河流信息表...径流是水循环中的重要过程,流域中河流的分布信息对该区域的水资源、生态、环境及其社会经济活动具有重要意义。我国广西、云南、贵州和东盟十国属于多云雨、山地特点突出的区域,对比已有的河流面域数据产品,发现对该区域的河流信息表征不足。为获得更为精确的河流分布数据,满足水资源及生态环境评估,本文采用欧亚大陆河流矢量数据(2010)及欧空局(ESA)全球土地分类数据(2020)陆表水体产品,通过融合形成综合河流矢量数据,再采用膨胀和收缩缓冲分析方法,解决河流不连续问题,并制作了中国广西、云南、贵州及东盟十国河流面域矢量数据。通过对ESA土地分类数据的进一步处理,获得了同区域的湖泊面域矢量数据。最终,本文计算了河流和湖泊的覆盖率,并生成1 km格网的河流和湖库覆盖率数据集。与Hydro RIVERS数据集,以及4种水体遥感数据集(Global Surface water,Esri Land Cover,Dynamic World V1,GRWL)的对比结果显示,本数据集在研究区对河流水系的表征能力更强,比原始输入数据集具有更丰富的细节,表现为:本数据集填补了欧亚大陆河流矢量数据(2010)中缺少的山区支流部分,解决了河流不连续、缺失等问题。本数据集为我国广西、云南、贵州及东盟十国的水体提取提供基础的先验数据,在洪水预报、水资源管理等方面具有重要价值,可服务于生态环境、交通运输、农业灌溉、能源等社会经济活动。展开更多
基金supported by the National Science and Technology Major Project(Grant no.2022ZD0117202)the National Natural Science Foundation of China(Grant no.42101389)CAS President's International Fellowship Initiative(Grant no.2021VTA0007).
文摘Microwave brightness temperature(TB)can be used to retrieve lake ice thickness in the Arctic and subarctic regions.However,the accuracy of the retrieval is affected by the physical properties of lake ice.To improve the understanding of how lake ice affects TB,numerical modeling was applied.This study combined a physical thermodynamic ice model HIGHTSI with a microwave radiation transfer model SMRT to simulate the TB and lake ice evolution in 2002-2011 in Hulun Lake,China.The reanalyzed meteorological data were used as atmospheric forcing.The ice season was divided into the growth period,the slow growth period,and the ablation period.The simulations revealed that TB was highly sensitive to ice thickness during the ice season,especially vertical polarization measurement at 18.7 GHz.The quadratic polynomial fit for ice thickness to TB outperformed the linear fit,regardless of whether lake ice contained bubbles or not.A comparison of the simulated TB with space-borne TB showed that the simulated TB had the best accuracy during the slow growth period,with a minimum RMSE of 4.6 K.The results were influenced by the bubble radius and salinity.These findings enhance comprehension of the interaction between lake ice properties(including ice thickness,bubbles,and salinity)and TB during ice seasons,offering insights to sea ice in the Arctic and subarctic freshwater observations.
文摘径流是水循环中的重要过程,流域中河流的分布信息对该区域的水资源、生态、环境及其社会经济活动具有重要意义。我国广西、云南、贵州和东盟十国属于多云雨、山地特点突出的区域,对比已有的河流面域数据产品,发现对该区域的河流信息表征不足。为获得更为精确的河流分布数据,满足水资源及生态环境评估,本文采用欧亚大陆河流矢量数据(2010)及欧空局(ESA)全球土地分类数据(2020)陆表水体产品,通过融合形成综合河流矢量数据,再采用膨胀和收缩缓冲分析方法,解决河流不连续问题,并制作了中国广西、云南、贵州及东盟十国河流面域矢量数据。通过对ESA土地分类数据的进一步处理,获得了同区域的湖泊面域矢量数据。最终,本文计算了河流和湖泊的覆盖率,并生成1 km格网的河流和湖库覆盖率数据集。与Hydro RIVERS数据集,以及4种水体遥感数据集(Global Surface water,Esri Land Cover,Dynamic World V1,GRWL)的对比结果显示,本数据集在研究区对河流水系的表征能力更强,比原始输入数据集具有更丰富的细节,表现为:本数据集填补了欧亚大陆河流矢量数据(2010)中缺少的山区支流部分,解决了河流不连续、缺失等问题。本数据集为我国广西、云南、贵州及东盟十国的水体提取提供基础的先验数据,在洪水预报、水资源管理等方面具有重要价值,可服务于生态环境、交通运输、农业灌溉、能源等社会经济活动。
