Snow accumulation patterns in a high mountain Andean catchment from optical tri-stereoscopic remote sensing

<strong>1) DBSM_Data_RioYeso'</strong> = Automatic weather station (AWS) data from Yeso Embalse and Termas del Plomo meteorological stations (available from Chilean Water Directorate, 'Dirección General de Aguas' or 'DGA' http://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=d508beb3a88f43d28c17a8ec9fac5ef0), used to force a distributed blowing snow model of Essery et al. (1999) to derive spatial snow depth of the Rio del Yeso catchment, Chile. The format is as follows: <em>{'Year','Month','Day','Hour','Incoming shortwave radiation (Wm2)','Incoming longwave radiation (Wm2)','SnowfallRate(mm/hr)','RainfallRate(mm/hr)','Air temperature (celsius)','Relative humidity (%)','Wind speed (m s-1)','Compass wind direction','Air pressure (hPa)'};</em> <strong>2) 'snowHeightPleiadesREG' </strong>= A snow depth map (horizontal resolution 4m) derived from triplets of high resoution stereo optical satellite images (Pléiades) following the methodology of Marti et al. (2016). The snow depth map is derived for a high mountain catchment (Rio del Yeso) of the central Chilean Andes (see Burger et al., 2018). <strong>3) 'L2_LiDAR_4m'</strong> = A LiDAR (Light detection and Ranging) spatial snow depth map at a horizontal resolution of 4 m. The data were captured by a Reigl VZ-6000 LiDAR scanner and generated from the difference of two constructed digital elevation models (DEMs) between the dates 13th September, 2017 (with snow) and 12th December, 2017 (without snow). <strong>4) 'L2_Pleiades_SDLidar_NEW' </strong>= The Pléiades snow depth map as described in <strong>2)</strong>, extracted by the areas of LiDAR scan described in <strong>3)</strong>. <strong>5) 'SnowDepthResults'</strong> = A folder containing a corrected and gap-filled Pléiades snow depth map (<strong>'SD_PleiadesCORR'</strong>) and for comparison: <strong>'SD_TOPO'</strong>, a statistical estimation of snow depth using topographic parameters and the regression equation of Grünewald et al. (2013) and; The physically based estimates of snow depth using the DBSM model as in <strong>1)</strong> without snow transport for the 4th September, 2017 (<strong>'SD_EXTP_Sep04'</strong>) and 13th September, 2017 ('<strong>SD_EXTP_Sep13'</strong>) and with snow transport for those dates (<strong>'SD_Wind_Sep04','SD_Wind_Sep13'</strong>). <strong>6) 'rdyDEM'</strong> = An independent ASTER GDEM (https://asterweb.jpl.nasa.gov/gdem.asp) cut to the area of the study catchment (horizontal resolution = 30 m). <strong>7) '</strong><strong>PlanetScope_20170907_TPK' </strong>= An stitched optical PlanetScope image of the catchment (horizontal resolution of 3.25 m) derived from access under the research and teaching iniative (planet.com). <strong>Cited work:</strong> <strong>Burger, F. et al.</strong> (2018) ‘Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment : understanding the role of debris cover in glacier hydrology’, Hydrological Processes, pp. 1–16. doi: 10.1002/hyp.13354. <strong>Essery, R</strong>., Li, L. and Pomeroy, J. (1999) ‘A distributed model of blowing snow over complex terrain’, Hydrological Processes, 13(14–15), pp. 2423–2438. doi: 10.1002/(SICI)1099-1085(199910)13:14/15&lt;2423::AID-HYP853&gt;3.0.CO;2-U. <strong>Grünewald, T. et al.</strong> (2013) ‘Statistical modelling of the snow depth distribution in open alpine terrain’, Hydrology and Earth System Sciences, 17(8), pp. 3005–3021. doi: 10.5194/hess-17-3005-2013. <strong>Marti, R. et al</strong>. (2016) ‘Mapping snow depth in open alpine terrain from stereo satellite imagery’, The Cryosphere, pp. 1361–1380. doi: 10.5194/tc-10-1361-2016.<strong>1) DBSM_Data_RioYeso</strong> = 来自耶索水库（Yeso Embalse）与普拉莫温泉（Termas del Plomo）气象站的自动气象站（Automatic Weather Station, AWS）数据，数据由智利水务局（Dirección General de Aguas, DGA，http://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=d508beb3a88f43d28c17a8ec9fac5ef0）提供，用于驱动Essery等人1999年提出的分布式吹雪模型，以推导智利Rio del Yeso流域的积雪深度空间分布。数据格式如下： <em>{'年','月','日','时','入射短波辐射(W·m⁻²)','入射长波辐射(W·m⁻²)','降雪速率(mm/hr)','降雨速率(mm/hr)','气温(摄氏度)','相对湿度(%)','风速(m·s⁻¹)','罗盘风向','气压(hPa)'};</em> <strong>2) 'snowHeightPleiadesREG'</strong> = 积雪深度图（水平分辨率4m），基于Marti等人2016年的方法，由高分辨率立体光学卫星影像（Pléiades）三联组生成，覆盖智利安第斯山脉中部的高海拔流域Rio del Yeso（详见Burger等人2018年研究）。 <strong>3) 'L2_LiDAR_4m'</strong> = 水平分辨率4m的激光雷达（Light Detection and Ranging, LiDAR）积雪深度空间分布图。该数据由Reigl VZ-6000型激光雷达扫描仪采集，通过对比2017年9月13日（有积雪）与2017年12月12日（无积雪）的两幅数字高程模型（Digital Elevation Model, DEM）的差值生成。 <strong>4) 'L2_Pleiades_SDLidar_NEW'</strong> = 即前文2)中描述的Pléiades积雪深度图，提取自前文3)所述的激光雷达扫描区域。 <strong>5) 'SnowDepthResults'</strong> = 包含以下内容的文件夹：经过校正与间隙填充的Pléiades积雪深度图（'SD_PleiadesCORR'），以及用于对比的以下数据集：'SD_TOPO'——基于地形参数与Grünewald等人2013年的回归方程进行积雪深度统计估算的结果；以及基于1)中所述DBSM模型的物理模拟积雪深度结果：2017年9月4日与13日无积雪输运条件下的结果（分别为'SD_EXTP_Sep04'与'SD_EXTP_Sep13'），以及对应日期考虑积雪输运的结果（'SD_Wind_Sep04'、'SD_Wind_Sep13'）。 <strong>6) 'rdyDEM'</strong> = 裁剪至研究流域范围的独立ASTER GDEM（https://asterweb.jpl.nasa.gov/gdem.asp），水平分辨率为30m。 <strong>7) 'PlanetScope_20170907_TPK'</strong> = 该流域的拼接式光学PlanetScope影像（水平分辨率3.25m），数据通过研究与教学计划（planet.com）获取。 <strong>引用文献:</strong> <strong>Burger, F. 等人</strong> (2018) 《高海拔安第斯流域冰川径流的年际变化：冰川碎屑覆盖层在冰川水文学中的作用》，《水文过程》，第1–16页，doi: 10.1002/hyp.13354。 <strong>Essery, R.</strong>、Li, L. 与Pomeroy, J. (1999) 《复杂地形下的分布式吹雪模型》，《水文过程》，13(14–15)，第2423–2438页，doi: 10.1002/(SICI)1099-1085(199910)13:14/15&lt;2423::AID-HYP853&gt;3.0.CO;2-U。 <strong>Grünewald, T. 等人</strong> (2013) 《开放高山地形积雪深度分布的统计建模》，《水文与地球系统科学》，17(8)，第3005–3021页，doi: 10.5194/hess-17-3005-2013。 <strong>Marti, R. 等人</strong> (2016) 《基于立体卫星影像绘制开放高山地形的积雪深度》，《冰冻圈》，第1361–1380页，doi: 10.5194/tc-10-1361-2016。