UNM Grad Student Studying Life Cycle of New Mexico Mountain Snowpacks

UNM Grad Student Studying Life Cycle of New Mexico Mountain Snowpacks by Catherine Ortega Klett It's well known that water from melting snow is a critical resource in New Mexico. The seasonal snow cover is the natural reservoir that stores water from winter storms for later release during the warm seasons. Accordingly, it is very important to be able to assess and predict the lifecycles of these snowpacks. The climatic warming that has been occurring in recent years will likely increase the stress on available water supply, which further underscores the need for better monitoring and understanding of the behavior of these snowpacks. An ambitious combined field and modeling study of this problem is currently underway. Chad Mickschl, a master’s student in the Civil Engineering (Hydraulics and Water Resources) Master’s Program at UNM with faculty advisor Dr. Mark Stone, is studying the snowpacks in the Alamo Creek watershed in the Jemez mountains of New Mexico. The heating and cooling of snowpacks is driven primarily by the energy exchange processes that occur at the interfaces of the snowpacks with air and the ground. The main focus of the present study is on the contribution of the ground heat flux on snowmelt, including an assessment of whether near surface ground temperatures can be used as a reliable indicator of the onset of snowmelt. The pattern of snow deposition and melt in semi-arid mountainous watersheds will, of course, depend strongly on altitude, and the exposure or shelter provided by topography and vegetative cover. In this study, relatively open areas with north and south facing slopes at various altitudes were chosen. To capture the energy fluxes, two portable weather stations have been set up to provide time series data on air temperature, pressure, relative humidity, wind speed and direction, precipitation, snow depth, soil moisture, and net thermal radiation. Energy fluxes beneath the snowpack are monitored using temperature sensors buried at four different shallow depths below the ground surface. These ground temperatures are being monitored at five distributed "footprints," each of about 10 square meters in extent, in order to capture small scale variability. In addition, two snow profile plots have been established, wherein the structure, temperature, and density of the snowpack is monitored as a function of time. This provides a closer look at snowpack properties, including snow grain size and shape and temperature, which in turn helps determine the direction of the water vapor flux and the likely changes in snow properties, which also affects the transport of heat in the snow. Finally, additional observations are being carried out in the form of five snow course survey sites, at which the snowpack depth and liquid water content is tracked. In addition to the above comprehensive measurement program, there is an accompanying modeling study underway, the goal of which is ultimately to provide better estimates of the timing and quantity of snowmelt. To this end, a state-of-the-art energy and mass balance simulation model known as SNOBAL is being used to predict snowpack properties from the meteorological and other snowpack data gathered by the field study. The SNOBAL model is initiated by using measurements of snow depth, density, temperature and net water content. Following these initial inputs, their change with time is determined by various forcing variables involving information provided by net solar radiation, precipitation mass, temperature, and estimated density. The model output from this includes the energy, mass balance, and runoff from the snow cover. It is hoped these simulations will support the initial conjecture that near surface ground temperature measurements may provide a reliable indicator of the onset and rate of snowmelt and the amount of energy supplied to the snowpack during melt in this semi-arid mountain climate. Chad Mickschl received an NM WRRI Student Water Research Grant for this study and his final report will be posted on the institute’s website in July 2016.