Advances in Snow Hydrology Using a Combined Approach of GNSS In Situ Stations, Hydrological Modelling and Earth Observation—A Case Study in Canada

Abstract

The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, spatially distributed information on snow is potentially available, but often lacks the required spatial or temporal requirements for hydrological applications. For the assurance of a high spatial and temporal resolution, however, it is often necessary to combine several methods like Earth Observation (EO), modelling and in situ approaches. Such a combination was targeted within the business applications demonstration project SnowSense (2015–2018), co-funded by the European Space Agency (ESA), where we designed, developed and demonstrated an operational snow hydrological service. During the run-time of the project, the entire service was demonstrated for the island of Newfoundland, Canada. The SnowSense service, developed during the demonstration project, is based on three pillars, including (i) newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS); (ii) EO snow cover products on the snow cover extent and on information whether the snow is dry or wet; and (iii) an integrated physically based hydrological model. The key element of the service is the novel GNSS based in situ sensor, using two static low-cost antennas with one being mounted on the ground and the other one above the snow cover. This sensor setup enables retrieving the snow parameters SWE and liquid water content (LWC) in the snowpack in parallel, using GNSS carrier phase measurements and signal strength information. With the combined approach of the SnowSense service, it is possible to provide spatially distributed SWE to assess run-off and to provide relevant information for hydropower plant management in a high spatial and temporal resolution. This is particularly needed for so far non, or only sparsely equipped catchments in remote areas. We present the results and validation of (i) the GNSS in situ sensor setup for SWE and LWC measurements at the well-equipped study site Forêt Montmorency near Quebec, Canada and (ii) the entire combined in situ, EO and modelling SnowSense service resulting in assimilated SWE maps and run-off information for two different large catchments in Newfoundland, Canada.

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

“…In fact, the presented approach was already successfully tested within the European Space Agency (ESA) business applications demo project SnowSense (2015‐2018) at lower elevation sites, for example, at the Forêt Montmorency study site (673 m above sea level) in Quebec, Canada, as well as at several locations in Newfoundland, Canada (Appel et al, ). Based on the presented measurement algorithms and the described installation concept at the study site Weissfluhjoch, we furthermore developed self‐supplied snow measurement stations including an integrated communication unit, which are ready to be used at further sites.…”

“…As this GPS approach represents a point‐scale measurement, combining in situ data with remote sensing data and/or modeling methods should provide improved spatial information on SWE, HS, and LWC (Foppa et al, ; Magnusson et al, ; Pulliainen, ). Appel et al () recently presented promising results to improve hind‐ and forecast hydrological modeling as well as hydropower forecasts with such a combination. Moreover, such in situ measurements have the potential for serving as valuable ground truth for various remote sensing and modeling approaches for dry‐snow and the even more challenging wet‐snow conditions, for example, for microwave remote sensing approaches.…”

“…As we use low‐cost GPS sensor components, sensor networks become feasible, which offer a better spatial and temporal coverage. As shown within the SnowSense project (Appel et al, ), the in situ data can be assimilated into modeling or remote sensing snow products to, for example, improve the prediction of melt water runoff and hydropower forecasts for large catchments. Moreover, buried GPS sensors in slopes may support snow management in ski resort or local avalanche forecasting.…”

Near Real-Time Measurement of Snow Water Equivalent in the Nepal Himalayas

“…Automated methods of SWE measurement can increase the ease with which seasonal SWE patterns can be monitored and, unlike manual sampling techniques, do not invasively disturb a snowpack's internal structure (Kinar and Pomeroy, 2015). Many automatic ground-based methods of measuring SWE exist, including weighing techniques (e.g., Serreze et al, 1999;Johnson et al, 2015), radiation-based methods (e.g., Kodama et al, 1979;Choquette et al, 2008;Martin et al, 2008;Rasmussen et al, 2012), technologies that measure the reflectance of acoustic impulses (Kinar and Pomeroy, 2007) and methods that utilize the Global Navigation Satellite System (Henkel et al, 2018;Appel et al, 2019). However, there is no ideal method of automatically measuring SWE (Egli et al, 2009), and installation and maintenance of gauging stations at elevations where the majority of Himalayan snow cover resides and melts (4000-5000 m a.s.l.)…”

Special Issue on Remote Sensing of Snow and Its Applications

“…They presented that the use of observation-based SDC (they derived the new SDC from the MODIS snow cover fraction and SNOTEL snow water equivalent (SWE) observations) showed improvement over the default model-based snow cover fraction (SCF) forecasts and snow state analysis. Appel et al [25] used the in situ and EO information to assimilate the input and the parameters of the applied hydrological model PROMET (Processes of Mass and Energy Transfer) to calculate SWE, snowmelt onset, and river run-off in catchments as spatial layers. They used newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS) and Sentinel-1A and -1B EO data in interferometric wide (IW) swath mode on the snow cover extent and on information whether the snow is dry or wet.…”