Girotto Manuela, Formetta Giuseppe, Azimi Shima, Bachand Claire, Cowherd Marianne, De Lannoy Gabrielle, Lievens Hans, Modanesi Sara, Raleigh Mark S, Rigon Riccardo, Massari Christian
Environmental Science and Policy Management, University of California, Berkeley, CA, USA.
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy.
Sci Total Environ. 2024 Jan 1;906:167312. doi: 10.1016/j.scitotenv.2023.167312. Epub 2023 Sep 25.
Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.
山区降水变化极大且测量不足，给水资源管理带来了重大挑战。传统的降水估算方法包括实地测量仪、多普勒天气雷达、卫星雷达和辐射计、数值模拟以及再分析产品。这些方法中的每一种都无法充分捕捉复杂的地形降水。在此，我们提出一种新方法来描述山区的地形降雪。我们使用粒子批量平滑器，利用哨兵 -1 反演的积雪深度获取卫星信息，并校正各种网格化降水产品。我们使用一个简单的积雪模型对位于意大利特伦蒂诺上阿迪杰的一个高山盆地进行了测试。我们量化了整个盆地的降水偏差，发现同化方法（i）校正了降雪偏差和不确定性，（ii）使降水产品之间的累积降雪海拔模式保持一致，并且（iii）总体上改善了全流域的积雪变量（积雪深度和积雪覆盖面积）以及流域径流估计。
