The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during winter 2011 at the research site of the National Weather Service office, Caribou ME, USA. In this ground experiment, dual polarized microwave (37 and 89 GHz) observations are conducted along with detailed synchronous observations of snowpack properties. The objective of this long term field experiment is to improve our understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations in the microwave spectral range. In this paper, we presented the overview of field experiment and preliminary analysis of the microwave observations for the first year of experiment along with support observations of the snowpack properties obtained during the 2011 winter season. SNTHERM and HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures respectively. Simulated brightness temperatures were compared with observed brightness temperature from radiometer under different snow conditions. On the time series, large difference in the brightness temperature were observed for fresh compared to aged snow even under the same snow depth, suggesting a substantial impact of other parameters such as: snow grain size and density on microwave observations. A large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization factor was observed due to cold nights and warm days, which caused a cycling between wet snow and ice-over-snow states during the early spring. Scattering analysis of microwave brightness temperatures from radiometers were performed to distinguished different snow conditions developed through the winter season

Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during January-March 2011 at the research site of the National Weather Service office, Caribou, ME, USA. In this experiment dual-polarized microwave (37 and 89 GHz) observations were accompanied by detailed synchronous observations of meteorology and snowpack physical properties. The objective of this long-term field experiment was to improve understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations. In this paper we present an overview of the field experiment and comparative preliminary analysis of the continuous microwave and snowpack observations and simulations. The observations revealed a large difference between the brightness temperature of fresh and aged snowpack even when the snow depth was the same. This is indicative of a substantial impact of evolution of snowpack properties such as snow grain size, density and wetness on microwave observations. In the early spring we frequently observed a large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization corresponding to daytime snowmelt and nighttime refreeze events. SNTHERM (SNow THERmal Model) and the HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures, respectively. Simulated snow depth and snowpack temperature using SNTHERM were compared to in situ observations. Similarly, simulated microwave brightness temperatures using the HUT model were compared with the observed brightness temperatures under different snow conditions to identify different states of the snowpack that developed during the winter season.

The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research.

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Soil moisture is placed at the interface between land and atmosphere which influences water and energy flux. However, soil moisture information has a significant importance in hydrological modelling and environmental processes. Recent advances in acquiring soil moisture from the satellite and its effective utilization provide an alternative to the conventional soil moisture methods. In this study, an attempt is made to apply physically based, distributed-parameter, Soil and Water Assessment Tool (SWAT) to validate Advanced Microwave Scanning Radiometer (AMSR2) soil moisture in parts of Puerto Rico. For this, calibration is performed for the years 2010 to 2012 with known observed discharge sites, Rio Guanajibo and Rio Grande de Añasco in Puerto Rico and validation, with the observed stream flow for the year 2013 using the AMSR2 soil moisture. Moreover, the SWAT and AMSR2 soil moisture outcome are compared on a monthly basis. The model capability and performance in simulating the stream flow are evaluated utilizing the statistical method. The results indicated a negligible difference in SWAT soil moisture and AMSR2 soil moisture for stream flow estimation. Finally, the model retrievals show a satisfactory agreement between observed and simulated streamflow.