Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay
TL;DR: In this paper, a non-destructive approach based on Global Positioning System (GPS) signals was developed to derive SWE, snow height (HS), and snow liquid water content (LWC) simultaneously using one sensor setup only.
Abstract: For numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validationmeasurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.
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TL;DR: In this article, a review of state-of-the-art data assimilation methodologies used to optimally combine measurements with snow cover models in order to reduce uncertainties is presented.
Abstract: The snow cover is a key component of land surface hydrology, especially in mountain areas where it governs the amount and timing of water availability in downstream areas. It is involved in relevant climate feedbacks and natural hazards such as avalanches and floods. Monitoring and forecasting snow cover characteristics is challenging. While snow cover extent is relatively easy to retrieve from satellite data, remote sensing retrievals of the snow water equivalent (SWE) is often inaccurate, particularly in complex mountainous terrain. Model-based snow cover estimates, driven by meteorological data, often bear significant uncertainties due to both input data and model errors. Data assimilation can combine both approaches to improve SWE estimates. In this paper, we review current state-of-the-art data assimilation methodologies used to optimally combine measurements with snow cover models in order to reduce uncertainties. The suitability of a given data assimilation method varies with the numerical complexity of snow models as well as the availability and the type of observations. This review describes the issues and challenges associated with data assimilation applied to the mountain snow cover, providing recommendations for existing and upcoming monitoring and prediction systems of snow hydrology in mountainous regions.
42 citations
Cites methods from "Retrieval of Snow Water Equivalent,..."
...SWE measurements are also accessible through the attenuation of the Global Navigation Satellite System signal (e.g., Koch et al., 2019) with reasonable accuracy (41–73 kg m−2 depending on snow wetness)....
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TL;DR: In this article, the authors presented the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation.
Abstract: . Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ± 6 % and ± 9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.
32 citations
Cites background or methods from "Retrieval of Snow Water Equivalent,..."
...Recent studies present sub-snow low-cost GPS as a promising method to continuously derive SWE (Steiner et al., 2018; Henkel et al., 2018; Steiner et al., 2019; Koch et al., 2019)....
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...GPS signals are freely available but the signal strength may be limited in high mountain regions depending on slope aspect and location (Koch et al., 2019)....
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TL;DR: In this article, the authors analyzed the correlation between the multitemporal SAR backscattering and the snowmelt dynamics, and found that the C-band SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff.
Abstract: . Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
29 citations
27 citations
Cites methods from "Retrieval of Snow Water Equivalent,..."
...Correlation coefficient (R), root mean square error (RMSE), NSE, and RB are commonly used in snow related evaluations (Chen et al., 2017; Han et al., 2019; Henn et al., 2018; Koch et al., 2019), and therefore, they are used in the snow simulation evaluations in this study:...
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...…root mean square error (RMSE), NSE, and RB are commonly used in snow related evaluations (Chen et al., 2017; Han et al., 2019; Henn et al., 2018; Koch et al., 2019), and therefore, they are used in the snow simulation evaluations in this study: RMSE…...
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22 citations
References
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TL;DR: In this article, an ambiguity decorrelation approach is introduced to flatten the typical discontinuity in the GPS-spectrum of ambiguity conditional variances and return new ambiguities that show a dramatic improvement in correlation and precision.
Abstract: The GPS double difference carrier phase measurements are ambiguous by an unknown integer number of cycles. High precision relative GPS positioning based on short observational timespan data, is possible, when reliable estimates of the integer double difference ambiguities can be determined in an efficient manner. In this contribution a new method is introduced that enables very fast integer least-squares estimation of the ambiguities. The method makes use of an ambiguity transformation that allows one to reformulate the original ambiguity estimation problem as a new problem that is much easier to solve. The transformation aims at decorrelating the least-squares ambiguities and is based on an integer approximation of the conditional least-squares transformation. This least-squares ambiguity decorrelation approach, flattens the typical discontinuity in the GPS-spectrum of ambiguity conditional variances and returns new ambiguities that show a dramatic improvement in correlation and precision. As a result, the search for the transformed integer least-squares ambiguities can be performed in a highly efficient manner.
1,562 citations
"Retrieval of Snow Water Equivalent,..." refers background or methods in this paper
..., 2018) with an integer least squares estimator (Teunissen, 1995b)....
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...4472 possible to jointly estimate SWE and the carrier phase ambiguities (Henkel et al., 2018) with an integer least squares estimator (Teunissen, 1995b)....
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...Besides the snow‐related term, all other variables are part of standard RTK positioning algorithms, described, for example, in Talbot (1993), Teunissen (1995a, 1995b), Henkel and Cárdenas (2014), and Henkel et al. (2016)....
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Book•
01 Jan 1990
972 citations
"Retrieval of Snow Water Equivalent,..." refers background or methods in this paper
...The wavelengths of the GPS signals are in comparison to the size of snow grains significantly smaller with approximately 1 mm (Fierz et al., 2009)....
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...Furthermore, we used information from snow pit measurements (Marty, 2017), taken weekly or biweekly, which were performed according to Fierz et al. (2009)....
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TL;DR: In this paper, a calibration curve for the TDR method is presented which is not restricted to specific soil conditions, based on the dielectric mixing model of Dobson et al. (1985).
Abstract: Time domain reflectometry (TDR) has been developed to an operational level for the measurement of soil water content during the past decade. Because it is able to provide fast, precise and nondestructive in situ measurements, it has become an alternative to the neutron scattering method, in particular for monitoring water content under field conditions. One of the major disadvantages of the neutron scattering technique is that, due to the relatively high sensitivity of the signal to factors other than water content, site-specific calibration is usually required. In this paper a calibration curve for the TDR method is presented which is not restricted to specific soil conditions. The calibration is based on the dielectric mixing model of Dobson et al. (1985). Measurements of volumetric water content and dielectric number at eleven different field sites representing a wide range of soil types were used to determine the parameter of the model by weighted nonlinear regression. The uncertainty (root mean square error) of water content values calculated with the optimized calibration curve was estimated not to exceed 0.013 cm3/cm3. This value is comparable to the precision of the thermogravimetric method. From a sensitivity analysis it was determined that the temperature dependence of the TDR signal may have to be corrected to obtain optimum accuracy.
895 citations
"Retrieval of Snow Water Equivalent,..." refers background in this paper
...Following Schmid et al. (2015), we applied the dielectric three‐phase mixing model after Roth et al. (1990) defining the real part of the complex permittivity of snow ε′s as ε 0 s ¼ 0:01 LWC ffiffiffiffiffi ε0w q þ ρs;d ρi ffiffiffi ε0i q þ 1− ρs;d ρi −0:01 LWC ffiffiffiffi ε0a q 2 (2) with the…...
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..., Denoth, 1989; Sihvola & Tiuri, 1986) or by three‐phase mixing models (e.g., Lundberg & Thunehed, 2000; Roth et al., 1990)....
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...The permittivity of dry and wet snow might be either estimated empirically (e.g., Denoth, 1989; Sihvola & Tiuri, 1986) or by three‐phase mixing models (e.g., Lundberg & Thunehed, 2000; Roth et al., 1990)....
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Book•
01 Jan 2008
TL;DR: The next generation of positioning models for positioning and data processing will depend on the design of the satellite itself, as well as on the satellite orbits it is placed in.
Abstract: Reference systems.- Satellite orbits.- Satellite signals.- Observables.- Mathematical models for positioning.- Data processing.- Data transformation.- GPS.- Glonass.- Galileo.- More on GNSS.- Applications.- Conclusion and outlook.
893 citations
TL;DR: In this paper, a spatially explicit, global typology of the so-called "water towers" at the 0.5° × 0. 5° resolution is proposed to identify critical regions where disproportionality of mountain runoff as compared to lowlands is maximum, and an Earth systems perspective is considered with incorporation of lowland climates, distinguishing four different types of water towers.
Abstract: [1] Mountains are important sources of freshwater for the adjacent lowlands. In view of increasingly scarce freshwater resources, this contribution should be clarified. While earlier studies focused on selected river systems in different climate zones, we attempt here a first spatially explicit, global typology of the so-called “water towers” at the 0.5° × 0.5° resolution in order to identify critical regions where disproportionality of mountain runoff as compared to lowlands is maximum. Then, an Earth systems perspective is considered with incorporation of lowland climates, distinguishing four different types of water towers. We show that more than 50% of mountain areas have an essential or supportive role for downstream regions. Finally, the potential significance of water resources in mountains is illustrated by including the actual population in the adjacent lowlands and its water needs: 7% of global mountain area provides essential water resources, while another 37% delivers important supportive supply, especially in arid and semiarid regions where vulnerability for seasonal and regional water shortage is high.
871 citations
"Retrieval of Snow Water Equivalent,..." refers background in this paper
...Regarding hydrological aspects, snow is particularly important in mountainous regions, which are considered as the water towers of the adjacent lowlands (Viviroli et al., 2007)....
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