Leveraging data from NASA􀁁s snowex campaign, this terrestrial hydrology investigation will resolve the influence of forest canopies on snow depth distributions across energy gradients. Our study…

Boise State University was awarded a $288,856.00 grant from the National Aeronautics and Space Administration (NASA) to conduct a terrestrial hydrology investigation leveraging data from NASA's SnowEx campaign. The research will focus on resolving the influence of forest canopies on snow depth distributions across energy gradients in the Grand Mesa, Colorado region. Key objectives include evaluating how forest cover and canopy characteristics impact snow depth distributions from the mesa-scale down to individual tree branches, as well as improving the representation of canopy processes in physically-based snow models. The research team will utilize ground-based terrestrial lidar scanning (TLS) data, along with other SnowEx observations, to validate airborne and satellite remote sensing techniques for capturing in- and sub-canopy snow depths. This work is an integral part of the broader SnowEx campaign and aims to support NASA's global snow monitoring efforts.

Leveraging data from NASA􀁁s snowex campaign, this terrestrial hydrology investigation will resolve the influence of forest canopies on snow depth distributions across energy gradients. Our study will focus on the grand mesa (mesa), CO, snowex site where the energy conditions range from wind-dominated redistribution with sparse vegetation cover to low wind, interception-dominated regions with dense forest cover and variable snow depth distributions. We will use snowex􀁁s ground-based terrestrial lidar (TLS) and airborne lidar from the airborne snow observatory (ASO) along with snowex snow PIT, snow transect, and meteorological observations, to quantify the in- and sub-canopy snow distribution from fine (individual branches and trees) to coarse (landscape) scales. Our project is an integral part of snowex because IT represents a crucial component of ground-truth for the snowex aerial campaign snow-on and off observations. Our TLS dataset spans 13 sites across grand mesa and can be used for validation of ASO, uavsar, glistin-a, and projects using the suite of snowex sensors. In this study, we will evaluate how forest-cover/canopy influences snow depth distributions at the mesa-scale (470 KM2) down to the scale of individual tree branches (CM2). Of paramount importance to this effort are the TLS observations which penetrate and measure in- and sub-canopy snow depths in the densest of forests; no other instrument in the snowex arsenal captured in- or sub-canopy snow depths with the resolution, accuracy, and precision as TLS. The comparisons between our TLS data and other snowex observations will provide vital improvements to airborne and satellite remote sensing and snow modeling. We will also test how variations in driving mechanisms (wind-driven redistribution VS. Interception and longwave ablation) influence snow depth distributions and how accurately ASO and other observations capture these variations compared to TLS. Ultimately these comparisons will be used to drive improvements in how physically-based snow models (snowmodel) represent canopy structure and the processes controlling accumulation and ablation in forested areas. We will address the following scientific questions: Q1. With what accuracy do satellite and airborne platforms reflect in- and sub-canopy snow distributions and how can physicallybased modelling approaches improve the ability of airborne and satellite techniques to accurately reflect forest snow depth distributions? Q2. How do in- and sub-canopy snow depth distributions change with canopy characteristics and forcing mechanisms (e.g. from winddominated to interception- and longwave ablation-dominated) and how do these relationships/distributions change with spatial scale? Q3. What parameterizations are required to improve how snow models represent in- and sub-canopy snow distributions across heterogeneous canopy conditions and forcing mechanisms? The results of this project area vital to NASA􀁁s global snow mission in a number of ways. First our study addresses the key knowledge GAP of how well and with what uncertainty do airborne techniques capture in- and sub-canopy snow depth distributions across canopies with different densities and varying forcing mechanisms. Secondly, resolving the influence of forest canopy parameters at the range of scales in this study will help determine the appropriate scale(s) and associated errors for snow parameter retrieval from a global snow mission. The research team is comprised of PIS with expertise in hydrologic modeling, ecosystem science, and remote sensing of ecosystems and snow. The PIS represent the institutions that performed the snowex TLS data acquisition and processing at grand mesa, CO.