Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.     

巴基斯坦近40a气温时空变化特征分析

基于巴基斯坦不同气候区4个代表站(卡拉奇、雅各布阿巴德、奎达、德罗什)1979—2018年地面观测资料和ERA-Interim气温资料,利用一元线性回归、Mann-Kendall突变检验和Morlet小波分析,分析其气温时空变化特征。结果表明:(1)巴基斯坦过去40 a经历了先降温后增温的过程,除北部山区年平均气温增温趋势显著,四季总体都有增温,春秋两季增温趋势显著。巴基斯坦于1998年左右发生气温突变,年平均气温存在4~5、12、20~22和32 a的周期。(2)ERAInterim再分析资料与地面观测数据拟合分析显示两者相关性达到极显著水平,除北部山区外,在大部分区域误差较小,能够较好地反映巴基斯坦气温的变化特征。(3)巴基斯坦年、季平均气温空间分布地带性明显,高温区位于南部印度河平原、三角洲地区,低温区分布在北部山区,年、春季、秋季平均气温变化在空间上总体均呈现明显增长趋势.