Monitoring snow depth by Integrating in an optimal way citizen science and other techniques

The snow depth (SD) is an excellent indicator of climate, yet a poorly monitored variable in many mountain ranges. A novel integrated approach is proposed for optimal monitoring of SD dynamics in the 5 National Parks located in Alpine (NPA) zones of Spain (i.e., Sierra Nevada, Guadarrama, Picos de Europa, Ordesa y Monte Perdido, and Aigüestortes i Estany de Sant Maurici). It will leverage the existing infrastructure of snow poles installed by the Snow Monitoring National Program in Spain (ERHIN). This program obtains SD measurements by direct observation from helicopter flights (1-3 per year). This monitoring activity has been drastically reduced in some mountain ranges during the economic crisis. The objective of this current work is to avoiding potential gaps in the valuable long-term SD timeseries of the pole measurements. An innovative Citizen Science Activity (CSA) methodology is being implemented to engage volunteers to collect the maximum number of photos of the snow poles. It is designed as a sports challenge, in which ranking and awards will be given to the most active participants. It aims to enhance the project with a minimum economic cost, and has the additional objective of raising awareness and encouraging responsible visits to these NPA. It has been tested in Sierra Nevada National Park, where we have identified the necessity to combine the information obtained from this CSA with other approaches to maximize the amount of useful information collected, and in order to reduce the uncertainty in snow distribution. A number of automatic point sensors have been installed to collect additional snow depth data at snow poles with a high number of days with snow, as identified from a historical analyses of snow cover area (SCA). These locations also have higher uncertainty SD measurements, and thus far, there have been less opportunity for the citizen science collection of photos. In order to identify the most relevant snow poles, we have used a regression model that estimates the spatial distribution of snow depth and its uncertainty from snow cover area and snow depth data. since the high cost of this complementary monitoring actions needs to be considered. a multi-sensors experiment is being performed to identify the best cost-benefit automatic sensors (ultrasound sensors, time-lapse cameras, etc). Drone field campaigns will be also performed, together with distributed information from airborne LIDAR and high resolution Pléiades satellite imagery. Such field campaigns there are costly, and thus the CSA has been also extended to the other 4 NPA. We are using a variety of media (e.g., social networks, TV, radio, and newspapers) to disseminate and communicate the CSA activity in order to maximize participation.