Abstract
This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> ] and without [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> ] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> . Comparison of SWE estimates from Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. River discharge using Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.
Highlights
This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the NoahMultiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates
River discharge using Sentinel-1 DAv1 improved the Nash-Sutcliffe efficiency at all available stations. These results suggest that physicallyconstrained SVM can serve as an efficient observation operator for snow mass data assimilation (DA) through explicit consideration of the firstorder C-band scattering mechanisms over different terrestrial snow conditions
The study presented here assimilates C-band synthetic aperture radar (SAR) backscatter observations collected by Sentinel-1 into the Noahmultiparametrization (Noah-MP) Land surface models (LSMs) to improve the accuracy of the terrestrial snow mass
Summary
UANTIFYING the snow mass (e.g., snow water equivalent [SWE] and snow depth) across the globe is essential for understanding the terrestrial water and energy cycles as well as in aid of efficient water resources management efforts [1]–[3]. When the area is completely snow-covered, sequential assimilation of SCA provides no additional information to SWE via DA Another option to update snow mass is to utilize the Tb observations (or spectral difference, ∆Tb ) observed from the Advanced Microwave Scanning Radiometer for EOS (AMSRE) [5], [27], [28], AMSR2 [29], or the Special Sensor Microwave/Imager (SSM/I) [30], [31]. The study presented here assimilates C-band SAR backscatter observations collected by Sentinel-1 into the Noahmultiparametrization (Noah-MP) LSM to improve the accuracy of the terrestrial snow mass. The main scientific inquiry addressed in this study is whether integration of C-band backscatter observations into a LSM using a machine learning algorithm and an ensemble-based DA framework results in better characterization of terrestrial snow mass estimates. 3) Does the improvement in snow mass estimates via assimilation translate into improvements in river discharge estimates?
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