In Québec, Eastern Canada, snowmelt runoff contributes more than 30% of 17 the annual energy reserve for hydroelectricity production, and uncertainties in annual 18 maximum snow water equivalent (SWE) over the region are one of the main 19 constraints for improved hydrological forecasting. Current satellite-based methods for 20 mapping SWE over Québec's main hydropower basins do not meet Hydro-Québec 21 operational requirements for SWE accuracies with less than 15% error. This paper 22 assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines 23 microwave satellite data and in situ measurements, for hydrological applications in 24Québec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with 25 space-and time-matched values from a comprehensive dataset of in situ SWE 26 measurements (a total of 38 990 observations in Eastern Canada). The root mean 27 square error (RMSE) of the GS-2 SWE product is 94.1 ± 20.3 mm, corresponding to an 28 overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are 29 wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover 30 type. However, compared to a typical stand-alone brightness temperature channel 31 https://ntrs.nasa.gov/search.jsp?R=20170003585 2020-07-04T11:59:55+00:00ZRemote Sensing of Environment. 2016 2 of 32 difference algorithm, the assimilation of surface information in the GS-2 algorithm 32 clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of 33 trends in annual mean and maximum SWE between surface observations and GS-2 34 over 1980-2009 showed agreement for increasing trends over southern Québec, but 35 less agreement on the sign and magnitude of trends over northern Québec. Extended 36 at a continental scale, the GS-2 SWE trends highlight a strong regional variability. 37Keywords: GlobSnow-2, passive microwave, in situ SWE measurements, Eastern 38Canada, land cover, water resources. 39 40 41 (Brown and Tabsoba, 2007). Optimal management of the snowmelt contribution to 57 hydroelectric production requires accurate estimates of peak snow accumulation prior 58 to spring melt (Turcotte et al. 2010). This is one of the main challenges for hydrological 59 forecasting particularly over large remote watersheds. Current operational runoff 60 forecast systems typically rely on surface snow surveys to determine pre-melt SWE, 61 which can be supplemented with geostatistical interpolation procedures to provide a 62 more detailed estimate of the spatial pattern (e.g. Tapsoba et al 2005). 63However, manual snow surveys are time-consuming and expensive which make 64 SWE estimation from satellite passive microwave (PMW) sensors an attractive option. 65 PMW sensors also offer advantages of all weather and all year coverage at good 66 temporal (daily) and moderate spatial (~25 km) resolution. The basic physics behind 67 PMW SWE retrievals is that the natural emission measured by satellite-borne 68 microwave radiometers, expressed as brightness temperature (TB), is characterized ...

Abstract. Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer – Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ω-τopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012–2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 68 % compared to the original SWE simulations, from 23.7 kg m−2 without assimilation to 7.5 kg m−2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1 % (19 % without assimilation) for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50 % of the PMW signal.

Understanding the hydrological dynamics of boreal wetland ecosystems (peatlands) is essential in order to better manage hydropower inter-annual productivity at the La Grande basin (Northern Quebec, QC, Canada). Given the remoteness and the huge dimension of the La Grande basin, it is imperative to develop remote sensing monitoring techniques to retrieve hydrological parameters. The main objective of this study is to find out if multi-date and multi-polarization Radar Satellite 2 (RADARSAT-2) (C-band) image analysis could detect seasonal variations of surface soil moisture conditions of the acrotelm. A change detection approach through the use of multi temporal indexes was chosen based on the assumption that the temporal variability of surface roughness and natural vegetation biomass is generally at a much longer time scale than that of surface soil moisture (Δ-Index is based on a reference image that represents dry soil, in order to maximize the sensitivity of σ° to changes in soil moisture with respect to the same location when soil is wet). The Δ-Index approach was tested with each polarization: σ° for fully polarimetric mode (HH, HV, VV) and the cross-polarization coefficient (HV/HH). Results show that the best regression adjustment with regard to surface soil moisture content in boreal wetlands was obtained with the cross-polarization coefficient. The cross-polarization OPEN ACCESSRemote Sens. 2013, 5 4920 multi-temporal index enables precise volumetric surface soil moisture estimation and monitoring on boreal wetlands, regardless of the influence of vegetation cover and surface roughness conditions (bias was under 1%, standard deviation and RMSE were under 10% for almost all estimation errors). Surface soil moisture estimation was more precise over permanently flooded areas than seasonally flooded ones (standard deviation is systematically greater for the seasonally flooded areas, at all analyzed scales), although the overall quality of the estimation is still precise. Cross-polarization ratio image analysis appears to be a useful mean to exploit radar data spatially, as we were able to relate changes in wetland eco-hydrological dynamics to variations in the intensity of the ratio.

Over northern snowmelt‐dominated basins, the snow water equivalent (SWE) is of primary interest for hydrological forecasting. This paper evaluates first the performance of a detailed multilayer snowpack model (Crocus), driven by meteorological predictions generated by the Canadian Global Environmental Multiscale model, for hydrological applications. Simulations were compared to daily snow depth and SWE measurements over Québec, northeastern Canada (56–45°N), for 2012–2016, highlighting an overestimation of the annual maximum snow depth (35%) and of the annual maximum SWE (16%), which is not accurate enough for hydrological applications. To improve SWE simulations, a chain of models is implemented to simulate and to assimilate passive microwave satellite observations. The snowpack model is coupled to a microwave snow emission model (Dense Media Radiative Transfer‐Multilayers model, DMRT‐ML), and the comparison of simulated brightness temperatures (TBs) with surface‐based TB measurements (at 11, 19 and 37 GHz) shows best results when the snow stickiness parameter is set to 0.17 in DMRT‐ML. The overall root‐mean‐square error (RMSE) obtained by the calibrated coupling reaches 27 K, significantly better than the RMSE obtained by considering nonsticky spheres in DMRT‐ML (43.0 K). The relevance of TB assimilation is tested with synthetic observations to evaluate the information content of each frequency for SWE estimates. The assimilation scheme is a Sequential Importance Resampling Particle filter using an ensemble of perturbed meteorological forcing data. The results show a SWE RMSE reduced by 82% with TB assimilation compared to without assimilation.

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