The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SW...The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SWE). The product of snow density and depth provides an estimate of SWE. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antenna are between 50 and 300. A snow-covered prairie grass field experiment shows potential for inferring snow water equivalent using GPS-IR. For this case study, the average inferred snow depth (17.9 cm) is within the in situ measurement range (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.13 g.cm-3) overestimates the in situ measurements (0.08 g.cm-3 ± 0.02 g.cm-3). Consequently, the average inferred SWE (2.33 g.cm-2) also overestimates the in situ calculations (1.38 g.cm-2 ± 0.36 g.cm-2).展开更多
雪水当量的监测对于气候变化的预测、水资源管理、农业生产规划具有重要意义。GPS干涉反射(GPS interferometric reflectometry,GPS-IR)技术是一种十分有效的地表积雪监测技术,基于GPS-IR技术提出了一种雪水当量的快速估计方法。首先基...雪水当量的监测对于气候变化的预测、水资源管理、农业生产规划具有重要意义。GPS干涉反射(GPS interferometric reflectometry,GPS-IR)技术是一种十分有效的地表积雪监测技术,基于GPS-IR技术提出了一种雪水当量的快速估计方法。首先基于GPS-IR技术获取美国板块边界观测(plate boundary observatory,PBO)GPS站的雪深时间序列;然后利用美国积雪遥测(SNowTELemetry,SNOTEL)站观测数据构建雪水当量转换模型;最后以北美历史与预测气候数据项目(historical and projected climate data for North America,ClimateNA)的气候预测数据作为参数约束,将GPS日雪深快速转化为雪水当量,并对雪水当量估计与验证过程的影响因素进行评价。实验结果表明,基于GPS-IR技术得到的雪深序列具有良好可靠性,与观测值的相关系数(R^(2))达到0.98,均方根误差(root mean square error,RMSE)为11.1 cm,偏差(Bias)为-3.7 cm;快速转化模型对雪水当量估计具有较高精度(R^(2)=0.98,RMSE=4.2 cm,Bias=-2.5 cm)与稳定性;转化模型时空稳定性较高,残差集中在5 cm内;气候预测数据的引入、积雪分布差异对雪水当量估计与验证影响较小。所提方法在积雪监测设备缺乏区域可实现雪水当量快速估计,同时也为现有积雪观测网络增强、积雪产品改善等研究提供参考。展开更多
文摘The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SWE). The product of snow density and depth provides an estimate of SWE. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antenna are between 50 and 300. A snow-covered prairie grass field experiment shows potential for inferring snow water equivalent using GPS-IR. For this case study, the average inferred snow depth (17.9 cm) is within the in situ measurement range (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.13 g.cm-3) overestimates the in situ measurements (0.08 g.cm-3 ± 0.02 g.cm-3). Consequently, the average inferred SWE (2.33 g.cm-2) also overestimates the in situ calculations (1.38 g.cm-2 ± 0.36 g.cm-2).
文摘雪水当量的监测对于气候变化的预测、水资源管理、农业生产规划具有重要意义。GPS干涉反射(GPS interferometric reflectometry,GPS-IR)技术是一种十分有效的地表积雪监测技术,基于GPS-IR技术提出了一种雪水当量的快速估计方法。首先基于GPS-IR技术获取美国板块边界观测(plate boundary observatory,PBO)GPS站的雪深时间序列;然后利用美国积雪遥测(SNowTELemetry,SNOTEL)站观测数据构建雪水当量转换模型;最后以北美历史与预测气候数据项目(historical and projected climate data for North America,ClimateNA)的气候预测数据作为参数约束,将GPS日雪深快速转化为雪水当量,并对雪水当量估计与验证过程的影响因素进行评价。实验结果表明,基于GPS-IR技术得到的雪深序列具有良好可靠性,与观测值的相关系数(R^(2))达到0.98,均方根误差(root mean square error,RMSE)为11.1 cm,偏差(Bias)为-3.7 cm;快速转化模型对雪水当量估计具有较高精度(R^(2)=0.98,RMSE=4.2 cm,Bias=-2.5 cm)与稳定性;转化模型时空稳定性较高,残差集中在5 cm内;气候预测数据的引入、积雪分布差异对雪水当量估计与验证影响较小。所提方法在积雪监测设备缺乏区域可实现雪水当量快速估计,同时也为现有积雪观测网络增强、积雪产品改善等研究提供参考。
