Assimilating snow observations to snow interception process simulations
Assimilating snow observations to snow interception process simulations
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DOI：
10.1002/hyp.13720
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发表时间：
2020-02
影响因子：
3.2
通讯作者：
Zhibang Lv;J. Pomeroy
中科院分区：
文献类型：
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作者：
Zhibang Lv;J. Pomeroy
积雪截留是寒冷地区针叶林的一个重要的水文过程，但很少被直接测量。可以通过测量风暴过程中森林地面和附近空地之间积雪增加的差异来间接估计积雪的截留量。如果可以可靠地估计积雪密度，带有声学积雪深度传感器的自动气象站对提供了估计这一点的机会。研究了三种估计新雪密度的方法：基于物理的Snobal模型的加权风暴后密度增量，根据气温经验估计的新雪密度(Hedstrom，N.R.，等)。[1998]。水文过程，12,1611-1625)，以及根据气温和风速经验估计的新雪密度(Jordan，R.E.，等人)。[1999]。地球物理研究杂志，104,7785-7806)。在加拿大阿尔伯塔省落基山脉的亚高山森林中，利用来自邻近森林和空地的自动积雪深度观测和估计的积雪密度来确定暴风雪的拦截。然后，利用寒区水文模拟平台(CRHM)，通过集合卡尔曼滤波或基于规则的简单直接插入法，将估计的积雪截留量和来自称重的悬挂树和延时相机的测量截留量信息同化成一个模型。用Hedstrom-Pomeroy新雪密度方程的密度估计值确定的截留量与观测结果最吻合。与由数值天气模式驱动的开环模拟相比，同化来自自动积雪深度测量的积雪拦截信息改善了模型的积雪拦截时间7%和幅度13%；其精度与使用当地观测气象数据模拟的结果接近。将树木测得的积雪拦截同化后，积雪拦截模拟的时间和幅度分别提高了18%和19%。延时相机积雪拦截信息同化使积雪拦截模拟的时间和幅度分别提高了32%和7%。同化效果受同化频率和强迫资料质量的影响较大。
Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post‐storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time‐lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule‐based direct insertion method. Interception determined using density estimates from the Hedstrom‐Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree‐measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time‐lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.