The seasonal snow cover has an important role as hydrological storage for the Earth’s fresh water resources. The amount of water stored in the snowpack as snow and ice is expressed as snow water equivalent (SWE) and is a key variable in water resources management, which is an essential component within the Earth’s climate system [1
]. The amount of water, which is released seasonally (or in events) as snowmelt in the rivers, as well as the timing of the water release, mainly in spring, is relevant for numerous hydrological applications, such as hydropower production, irrigation, and fresh water supply. In addition, knowledge about the snow situation is a concern of many safety related institutions and businesses, such as avalanche warning centres and (re-)insurance companies. The onset of snowmelt and its intensity are major drivers for flood forecasting, especially in mountainous areas, and are, besides the knowledge on the total amount of water stored as snow, a very valuable information for hydropower companies.
Regarding in situ snow measurements, until now, SWE is mainly measured manually by weighing a given volume of snow, which is cut out of the snowpack with tubes [2
]. This approach is reliable but can provide only a snapshot in time and in space, and in addition, it is labour-intense and destructive. Automatic SWE measurements are mainly performed by weighing systems like snow pillows and snow scales [3
]. These methods provide time series, but the instruments and their installation and operation are quite costly, and their results might be affected by ice-bridging effects of thermal fluxes resulting in potential over- or underestimations of SWE [4
]. Alternative SWE in situ observation systems make, e.g., use of cosmic rays or neutron rays [5
] or apply a passive gamma monitoring sensor (GMON) [6
]. However, the reliability of those sensors depends strongly on the underlying surface conditions, the measurements are limited to a certain amount of SWE and thus are only applicable at certain locations [7
As an alternative to the standard in situ methods, L-band Global Navigation Satellite (GNSS) signals can be used to derive snow cover properties. Different methods were developed within the last years. As an advantage, it is possible to apply these methods globally as the GNSS signals can be tracked almost any place on Earth, they are non-destructive and can be used even for large amounts of snow [8
]. Snow height (HS), for example, can be determined by the reflection of GNSS signals on the snow–air interface [9
]. Liquid water content (LWC) can be derived by GNSS signal strength attenuation through a snowpack of a given volume [10
]. Henkel et al. [11
] presented for the first time the possibility to derive SWE for dry snow conditions using two low-cost GNSS sensors for a carrier phase-based approach to detect signal changes within the snowpack. Steiner et al. [12
] confirmed this by using a similar technique with geodetic sensors and applying different ambiguity resolution strategies and wideline combinations. Finally, we developed a novel approach combining GNSS signal attenuation and time delay by combining information on GNSS carrier phases and signal strengths. We accomplished deriving the three snow cover parameters SWE, LWC, and snow height in parallel, as recently demonstrated in Koch et al. [8
Spatially distributed snow information such as the snow cover extent, information weather the snow is dry or wet, and dry snow SWE or snow height, can be derived from Earth Observation (EO) data, based on different remote sensing techniques using active or passive microwaves, or optical, infrared or thermal approaches. An overview is given, for example, by Hall [13
] and Tedesco [14
]. In recent years, especially the freely accessible Sentinel-1, -2 and -3 data are a useful source for determining the above-mentioned snow parameters, like snow extent, or wet snow [15
]. Besides ‘raw’ satellite data, also different, often project-based, internet portals like GlobSnow, CryoLand, Google Earth Engine, or EUMETSAT H-SAF are providing already processed satellite-based snow parameter products. However, in general, all remote sensing products are often not available in high-temporal resolution or may lack the required spatial resolution. This is especially the case for optical images, e.g., from MODIS, which face potential cloud cover issues [16
]. Active microwave products are often restricted due to foreshortening or layover effects, in particular in mountain regions and passive microwave products are very coarse regarding their spatial resolution. Recent approaches tend to apply more and more multi-sensor techniques to overcome some of these limits, which is, e.g., currently a big aim of the NASA SnowEx campaign [19
]. Additionally, the combination of EO and hydrological model approaches helps to increase the temporal and spatial availability of snow or run-off information as e.g., presented by Cline et al. [20
] and Immerzeel et al. [21
In the current study, we present a comprehensive overview of a combined approach on using in situ measurements, EO, and hydrological modelling to derive continuous information on snow parameters and run-off. The applied methods and sensors of the in situ component as well as hydrological services were designed, developed and demonstrated in the framework of the business applications demonstration project SnowSense (2015–2018), which was co-funded by the European Space Agency (ESA). The SnowSense service mainly targets snow hydrological applications and is based on three pillars, including (i) a newly developed SnowSense in situ snow monitoring stations based on GNSS signals, (ii) EO products of the snow cover extent and information if and where the snow is dry or wet, and (iii) an integrated physically-based hydrological model.
The in situ and EO information are used to assimilate the input and the parameters of the applied hydrological model PROMET (Processes of Mass and Energy Transfer) [22
] to calculate SWE, snowmelt onset, and river run-off in catchments as spatial layers. Those data layers contain the relevant information for flood forecasts and hydropower plant management, particularly for so far non- or sparsely equipped catchments in remote areas. Within the project demonstration phase, we validated the GNSS in situ snow stations and the first run-off results of the combined approach were already provided as an operational service for a commercial hydropower plant company and the administration of the island of Newfoundland, Canada, being our first demo users.
3. Results and Discussion
In the first part of this section, we present the results and the validation of the GNSS in situ station for the well-equipped study site NEIGE at Forêt Montmorency. In the second part, we focus on the results and the validation of the hydrological service of the combined and assimilated run-off at, in total four, gauges of the Humber and Exploit catchments in Newfoundland.
3.1. Station Performance at the Forêt Montmorency NEIGE Site near Quebec
The SnowSense station was installed in October 2017 at the NEIGE site at Forêt Montmorency and was operational during the entire snow-covered winter season 2017/18. The power supply and the communication unit resisted the cold and windy environment without damage or failure. The station delivered continuous daily SWE and LWC measurements without any significant discontinuities (Figure 5
In general, the SWE values derived by the SnowSense GNSS in situ station are in good agreement with the provided reference measurements by the two GMON CS725 sensors and the manual snow pit measurements (Figure 5
The GMON sensor as well as the GNSS sensor are both non-destructive measurement methods and are largely capable of deriving SWE. Both sensors were already validated against other sensors like snow pillows and manual measurements (e.g., [7
]). As stated by Choquette et al. [6
], at the observed study site NEIGE, an average error of 18% between manual measurements and the GMON sensor is reasonable, and, for SWE levels less than 400 mm w.e., the estimation is inside the 5–10% range.
At the time the GNSS measurements started in early winter 2017, the GNSS-derived SWE as well as the measurements of the two GMON sensors lay in a similar range at approximately 100 mm w.e. At the end of April 2018, the maximum amount of SWE with approximately 500 mm w.e. was reached, which was indicated by the GNSS solution as well as the manual measurements and one GMON sensor. Comparing the two GMON sensors (blue solid and blue dashed line, Figure 5
b), however, an offset of the SWE measurement of up to 30% occurred between them. This offset was low in the beginning of the time series and increased during the winter continuously. The reasons for this might originate in different sensor locations e.g., with slightly different wind conditions, and are still under discussion, but are out of the scope of this paper. The manual measurements were performed 16 times on a weekly basis during the snow season. On each day, three to four snow pits were analyzed. The resulting averaged SWE measurements from the snow pits lie well in between the range of the two GMON sensors. The SWE results of the SnowSense GNSS station (black solid line) follows in the beginning of the season the lower GMON sensor (CS725_TL, blue dashed line). Since 20 February, after a heavy precipitation event, the GNSS derived SWE jumps to the level of the GMON sensor with the higher SWE values (CS725_K, blue solid line). The coefficient of determination (R2
) between the GNSS data and the data from the GMON sensors is 0.53 for CS725_TL and 0.93 for CS725_K, respectively.
Throughout the entire season, the GNSS measurements agree very well with the manual measurements from snow pits including their minimum and maximum values. Here, the coefficient of determination (R2
) is 0.64. For all R2
and root mean square errors (RMSE) errors of the validation study, we refer to Table 1
The range of the SWE validation results presented in this study for the Forêt Montmorency are in good accordance with previously conducted studies validating the GNSS in situ SWE component successfully at the high-alpine study site Weissfluhjoch in the Swiss Alps as presented in Henkel et al. [11
] and Koch et al. [8
]. One key result of their validation is a very good performance of the SWE determination capabilities of the in situ GNSS approach: the inter-comparison of the SnowSense GNSS station with a snow pillow and manual measurements show a very high agreement indicated by the values of R2
close to 1 [11
]. The root mean square errors (RMSE) between the measurements from GNSS, snow pillow, and from snow pits fit very well; they lay in the range of 11–24 mm w.e. [11
] but might be higher regarding wet snow conditions, e.g., due to an increase in GNSS signal attenuation (approx. 45 mm w.e.) [8
Until mid-April 2018, the snowpack was predominantly dry at the study site Forêt Montmorency. In January, February and March, only single wet-snow events of up to two to three days occurred. The occurrence of wet snow presented by the GNSS-derived LWC is shown in Figure 5
a. As no further sensors for a comparison with the GNSS-derived LWC were available at this site, we compared the GNSS-derived LWC with meteorological parameters. In general, the measured LWC is in a good temporal correspondence with rainfall events and goes along with warm air temperatures (Figure 6
), which was also demonstrated in Koch et al. [10
]. However, as shown in other previous studies, we were able to successfully validate the GNSS LWC measurements with other sensors like an upward-looking ground-penetrating radar and capacity probes at the study site Weissfluhjoch [45
3.2. SnowSense Service for the Island of Newfoundland
For the entire SnowSense service based on the GNSS in situ measurements, EO and modelling, spatially-distributed maps of the SWE were generated and run-off estimates were derived for several river locations in the two main catchments Humber River and Exploit River. Figure 2
gives an example of a SWE map, which was modelled and assimilated for the entire island of Newfoundland for the 15 March 2018. Such a SWE map was provided for each day.
The simulated and assimilated run-off results were validated against four run-off gauge measurements in total. Measurements (raw data) are made available by the Water Resources Management Divisions. As for the catchments of the Humber River and the Exploits River, a strong interest in flood forecast was predominating; the run-off results are presented for the gauge stations at the lower parts of the two catchments.
3.2.1. Humber River
The run-off results for Humber Village for the winter season December 2017–May 2018 show very good agreement with the measured run-off reference values (Figure 7
). Assimilation of the in situ information, mainly adjusting the solid precipitation, provide a perfect match of the volume of water. For the entire region, the amount of water stored as snow was perfectly represented by the model. The volume of total water released in the winter season 2017/18 was modelled by 95% (80% without assimilation). The coefficient of determination (R2
) reaches 0.90 at Humber Village. The results for the upper part of the catchment down to Reidville also show a very reasonable accordance between measured and modelled run-off behaviour (Figure 8
). Due to invalid run-off measurements during the first flood peak in January 2018, no full analyses of the period were performed. However, the volume of total water released in up to Reidville was modeled by 90% (65% without assimilation). The coefficient of determination (R2
) reaches 0.80 at Reidville.
3.2.2. Exploits River
The analyzed run-off results for the down-stream gauges “Below Noel Pauls Brook” and “Charlie Edwards Point” at the Exploits River show a very notable agreement regarding timing and volume with the measured values in the winter season from December 2017–May 2018 (Figure 9
and Figure 10
). Due to the strong influence of the controlled water release at Millertown Dam, and the not daily adapted model parameters during the demo, the results are a little below the ones for Humber River. The coefficient of determination (R2
) reaches 0.80 at Noel Paul Brook. For the downstream section of the Exploits River, after the conjunction with Badger River, the occurrence of river ice impeded the full evaluation of the results. Within the period of mid-December to late April, the measurement point was affected by ice jams, which resulted in an increased water level and therefore a false determination of the run-off. Due to the thawing in April, the flood peak from the snow melt could be well compared (Figure 10
3.3. Advantages and Potential Limitations
Regarding the SnowSense in situ station, it is capable of measuring reliably SWE and LWC using freely available GNSS signals and low- cost GNSS sensors. Besides the derivation of SWE and LWC, Koch et al. [8
] recently presented an approach to additionally derive snow height, which might even extend the range of application, not only for hydrological targets. A great advantage of the SnowSense in situ station is its light-weight design making an easy transportation and installation possible, which is highly valuable especially for remote and difficult to access areas. In total, only two people are needed for the set-up and all components can be carried in a big backpack. As the stations have an integrated on-board-processing module and satellite communication capabilities, the results can be transmitted (sub-)daily to the users. This makes the station autonomous and guarantees low maintenance. In general, the in situ station can either be used as a stand-alone component for snow cover property determination or can easily be integrated in the entire service encompassing the EO and modelling components.
As GNSS signals are globally available, the application of such in situ stations is potentially possible all over the world. However, a potential restriction of the in situ stations might be the availability of satellite reception in extreme locations, e.g., in narrow alpine valleys or in dense forests, with reduced GNSS signal reception. As presented in Lamm et al. [26
], the integration of Galileo satellites besides GPS satellites increases satellite availability markedly, which increases also the availability in potentially difficult areas. Further studies will focus in more detail on, e.g., tilted terrain like avalanche-prone slopes, different climate and altitudinal ranges as well as more challenges on the station design regarding its protection from wildlife or its mounting on top of bare rocks and ice. Until now, we were able to test the GNSS SWE-derivation for quite huge amounts of snow with up to 1000 mm w.e. in high alpine regions [8
]. In this study, we reached SWE values of up to 500 mm w.e. However, further studies on even more extreme amounts of snow and especially wet snow will be conducted in the future, as the limited operational time span of the demonstration project did not provide an opportunity to test the sensor performance for such extreme events performance. This is also true for further tests on extreme temperatures for the entire sensor hardware, which is designed for minimum temperatures of −40
C, though temperatures down to −35
C were reached in December 2017 and January 2018 at the NEIGE testsite at Forêt Montmorency (Figure 6
Regarding the different SWE measurement techniques applied at the NEIGE study site at Forêt Montmorency, the GMON sensor is based on passive gamma rays, whereas the GNSS based measurements are based on electromagnetic waves. Both techniques are capable of deriving SWE in good accordance with standard measurement techniques like manual measurements or snow pillows [6
]. Of course, slight differences in the derivation of SWE might occur considering these two relatively new sensors, however, as the sensors are not installed at the exact same place and are located up to 25 m from each other and have a distance (up to 150 m) to the manual measurements. The main differences might originate in different amounts of snow at each location, e.g., due to different wind effects and the different physical principles of the measurements.
Regarding the spatially distributed components EO and the hydrological model, it is another big advantage that both can also be potentially used worldwide and are often free of charge as, e.g., the Sentinel data. Of course, remote sensing products might be restricted in temporal and spatial resolution and face, depending on the wavelength and if the systems are active or passive, different limitations as, e.g., cloud cover or foreshortening effects. Therefore, it is often more difficult to apply EO in mountainous terrain. The applied hydrological model PROMET was already tested and validated for various applications for small and large catchments (e.g., [22
]) and also globally (e.g., [48
]) in different temporal and spatial resolutions. Although there are a few limitations in the model setup like, for example, the difficulty of implementing small-scale features regarding snow variability or the run-off generation in extreme alpine surroundings, the modelled output provides very good results for different scales, also in case of sparse input data as it is the case in Newfoundland. Especially in those remote areas with sparse data, real-time information and forecasts of run-off, fresh water availability, SWE, snow extent and the snowmelt onset can be significantly improved. Up to now, a limitation in our hydrological model setup is the lake ice formation. In general, SWE is calculated from the snow cover on the ground. However, until now, snow accumulation on top of frozen lakes is not implemented, although this SWE also contributes to the run-off after the onset of snow- and ice-melt. Moreover, we aim to describe river ice formation as an additional feature in the model since it can build up to ice dams with a subsequent banking up of water masses. These two model improvements will be implemented in the future, e.g. as suggested in [49
The big advantage of the entire combined SnowSense service is that it picks up the advantages of all three components to deliver a reliable, assimilated product of snow and hydrology. In case data of one component is missing, the service can still rely on two other pillars and is therefore less vulnerable to data losses or other failures. The service can be used as entire combined system relying on the three pillars in situ measurements, EO, and hydrological modelling, but each of these pillars can also be applied as a modular stand-alone solution if desired. It therefore enhances and combines existing solutions and is, due to its modularity, a customer friendly approach.
3.4. Demo User Feedback
The feedback from the two demo users, Nalcor/NL Hydro and the Water Resources Management Division in the Department of Municipal Affairs and Environment (WRMD) of the Government of Newfoundland and Labrador, confirmed the significant importance of snow and run-off monitoring, which could be improved by using such a combined approach like the SnowSense service. They underlined that this is especially important for remote locations, as many regions in Newfoundland are only accessible by helicopter or snowmobiles and face limited access possibilities due to poor weather conditions. From their experience, standard in situ measurements sometimes fail, as the snowpack is often icy with a varying snow density and snow hardness, which makes it difficult to measure SWE with manual snow core equipment. Normally, manual snow surveys are performed three times per winter, which is expensive and is difficult to be completed under bad weather conditions, and causes labour safety risks. Existing automatic instrumentation (GMON sensors) is not providing enough information. The demo users state a need for accurate, reliable SWE information for each hydrological watershed and runoff forecasting that affects the real-time scheduling of hydrological assets and minimizes the use of thermal heat generation. From their point of view, a network of stations across the island of Newfoundland and Labrador would be the ideal scenario to give a province wide estimate of SWE for all users. In addition, they confirmed that SnowSense has the potential to fill a gap regarding the provision of spatial and temporal data at a high resolution by applying EO and modelling for spatial SWE maps of the entire region and run-off information at specific points of interest for real-time and forecasts. The received products matched with other sources of information, which they had for comparison and could even provide insights in hydrological processes. Both users stated that the in situ stations are competitive in their operation compared to other SWE monitoring technologies and therefore they have the potential to replace existing SWE monitoring stations like snow pillows or manual field observations.
In general, the feedback provided by the demo users was very positive, which encourages us in our further developments and improvements.
Within the ESA business applications demonstration project SnowSense (2015–2018), we successfully demonstrated a large scale snow hydrological monitoring service, by combining newly developed in situ stations based on signals of the Global Navigation Satellite System (GNSS), Earth Observation (EO) and hydrological modelling. With this combined approach, we present a reliable, and cost-efficient tool for the determination of snow cover properties like snow water equivalent (SWE), snow liquid water content (LWC), snow extent as well as run-off assessment, for real-time and forecast applications.
The GNSS in situ component was successfully applied and validated at the well-equipped study site NEIGE at Forêt Montmorency, Quebec, Canada. Furthermore, the entire SnowSense service providing modelled, in situ-, and EO-assimilated run-off was applied and validated at four run-off gauges within the Humber River and the Exploit River catchments on the island of Newfoundland, Canada.
The entire SnowSense service solution driven with an integrated numerical weather prediction (NWP) model for its application in this study in Newfoundland is capable of providing detailed knowledge on water stored as snow over large spatial scales. It is able to provide real-time and forecasted snow and run-off information and, if desired, also on reservoir status, which might be of great interest for hydropower plant operators. This information, which can be provided in various time steps, e.g., hourly up to daily, is especially needed in regions or catchments where in situ stations are only sparsely or non equipped catchments. The service is applicable at almost any location and was especially designed for remote locations, where access is limited and snow and run-off measurements were difficult up to now.
Within the project, the SnowSense service already reached a market dedicated design, based on the identification of potential customers (i.e., hydropower plants) and use cases (i.e., weather and climate observations, e.g., by national weather services).