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Estimating The Spatial Distribution of Snow Water Equivalent Using in situ and Remote Sensing Observations
Mountain snowpack is one of the primary surface water sources for about one-sixth of the global population. More than 75% of the total runoff originates from mountain snowpacks in the Western U.S. Snowmelt water recharges reservoirs and aquifers gradually in the melting season, providing vital water supplies for urban and agricultural areas. Therefore, accurately monitoring the spatial and temporal distribution of mountain snowpack – often measured as snow water equivalent (SWE) – is crucial for effective water management. While existing SWE estimation approaches remain highly uncertain, particularly when applied over large mountainous regions, the remotely-sensed snow data provide new opportunities to better characterize the spatial distributions of mountain snowpack.
This dissertation investigates the approaches that optimally blend satellite, airborne, and ground snow observations to improve (near) real-time SWE estimation over mountainous terrain. The second chapter of this dissertation evaluates the accuracy of existing SWE estimation models in Sierra Nevada California. Five large-scale SWE datasets at fine spatial resolutions (<= 1000 m) are comprehensively validated and compared with the Airborne Snow Observatory (ASO) SWE data in the Tuolumne River Basin (2013-2017), and ground snow pillow and snow course SWE observations across the Sierra Nevada (2004-2014). These SWE datasets include REC-INT, REC-ParBal, a Sierra Nevada SWE reanalysis (REC-DA), and two operational SWE datasets from the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE), respectively. The results show that the REC-DA overall provides the most accurate SWE estimates across the Sierra Nevada (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%), followed by the REC-ParBal (R2 = 0.73, MAE = 83 mm, PBIAS = -6.4%), which is the least biased SWE estimates. Generally, SNODAS (R2 = 0.66, MAE = 106 mm, PBIAS = 9.3%) and REC-INT (R2 = 0.61, MAE = 131 mm, PBIAS = -28.3%) exhibit comparable but lower accuracy than the earlier mentioned two datasets, while NWM-SWE (R2 = 0.49, MAE = 142 mm, PBIAS = -25.2%) shows the least accuracy among the five SWE datasets.
Given that REC-DA is not applicable in real-time, in the third chapter, a SWE data-fusion framework is developed, which integrates the historical SWE patterns derived from REC-DA into a statistically-based linear regression model (LRM) to estimate SWE in real-time. To investigate the influence of satellite-observed daily mean fractional snow-covered area (DMFSCA) on SWE estimation accuracy, two LRMs are compared: a baseline regression model (LRM-baseline) in which physiographic data and historical SWE patterns are used as independent variables, and an FSCA-informed regression model (LRM-FSCA) in which the DMFSCA from MODIS satellite imagery is included as an additional independent variable. By incorporating DMFSCA, LRM-FSCA outperforms LRM-baseline with improved R2 from 0.54 to 0.60, and reduced PBIAS from 2.6% to 2.2% in snow pillow cross-validation. The improvement in LRM-FSCA’s performance is more significant during snow accumulation periods than during the snowmelt seasons. Compared to the ASO SWE, the LRM-FSCA explains 85% of the variance on average, which is at least 21% higher than the operational SNODAS (R2 = 0.64) and NWM-SWE (R2 = 0.33) in comparison.
In chapter 4, a SWE bias correction framework (SWE-BCF) is developed that incorporates the ASO SWE and machine learning (ML) algorithms to further improve LRM SWE estimates in real-time. The performance of a wide range of commonly used machine learning algorithms is examined in the SWE-BCF including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results indicate that all ML algorithms are capable of improving LRM-SWE accuracy substantially. While no single model performs significantly better than others, GPR, overall, shows the best performance with a 20% (0.14) increase in mean R2 value, a 31% (51 mm) reduction in mean RMSE, and a 61% (18.0%) reduction in absolute PBIAS compared with the original LRM using ASO SWE data for model validation. RF shows the most robust and stable performance in SWE bias correction with a 10% (0.08) increase in median R2 and a 41% (50 mm) reduction in median RMSE compared with the original LRM.</p
Effect of snow microstructure and subnivean water bodies on microwave radiometry of seasonal snow
Remote sensing using microwave radiometry is an acknowledged method for monitoring various environmental processes in the cryosphere, atmosphere, soil, vegetation and oceans. Several decades long time series of spaceborne passive microwave observations can be used to detect trends relating to climate change, while present measurements provide information on the current state of the environment. Unlike optical wavelengths, microwaves are mostly insensitive to atmospheric and lighting conditions and are therefore suitable for monitoring seasonal snow in the Arctic.
One of the major challenges in the utilization of spaceborne passive microwave observations for snow measurements is the poor spatial resolution of instruments. The interpretation of measurements over heterogeneous areas requires sophisticated microwave emission models relating the measured parameters to physical properties of snow, vegetation and the subnivean layer. Especially the high contrast in the electrical properties of soil and liquid water introduces inaccuracies in the retrieved parameters close to coastlines, lakes and wetlands, if the subnivean water bodies are not accounted for in the algorithm. The first focus point of this thesis is the modelling of brightness temperature of ice- and snow-covered water bodies and their differences from snow-covered forested and open land areas. Methods for modelling the microwave signatures of water bodies and for using that information in the retrieval of snow parameters from passive microwave measurements are presented in this thesis.
The second focus point is the effect of snow microstructure on its microwave signature. Even small changes in the size of scattering particles, snow grains, modify the measured brightness temperature notably. The coupling of different modelled and measured snow microstructural parameters with a microwave snow emission model and the application of those parameters in the retrieval of snow parameters from remote sensing data are studied