Snow is an important aspect of hydrologic and climatic cycle.

There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ̃0.09; mean error ̃0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA.

Based on a study of Tb spectra, we proposed a new snow depth retrieval algorithm for mountainous deep snow using airborne multifrequency PM radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly- proposed method utilized a single flight observation and deployed the snow hydrologic models as a basis for a “snapshot” retrieval algorithm. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Our approach consists of a particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7-, 18.7-, 37.0-, and 89.0 GHz), the root mean square error (RMSE) values were reduced by 62% at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a higher sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter) except the forest cover density and precipitation bias. The prior RMSE values at the forest-covered areas were reduced by 27 - 41% even in the presence of forest cover.

Improved knowledge of the cryosphere state is paramount for continued model development and for accurate estimates of fresh water supply. This work focuses on evaluation and potential improvements of current snow datasets over the United States. Snow in mountainous terrain is most difficult to quantify due to the slope, aspect, and remote nature of the environment. Due to the difficulty of measuring snow quantities in the mountains, the initial study creates a new method to upscale point measurements to area averages for comparison to initial snow quantities in numerical weather prediction models. The new method is robust and cross validation of the method results in a relatively low mean absolute error of 18% for snow depth (SD). Operational models at the National Centers for Environmental Prediction which use Air Force Weather Agency (AFWA) snow depth data for initialization were found to underestimate snow depth by 77% on average. Larger error is observed in areas that are more mountainous. Additionally, SD data from the Canadian Meteorological Center, which is used for some model evaluations, performed similarly to models initialized with AFWA data. The use of constant snow density for snow water equivalent (SWE) initialization for models which utilize AFWA data exacerbates poor SD performance with dismal SWE estimates. A remedy for the constant snow density utilized in NCEP snow initializations is presented in the next study which creates a new snow density parameterization (SNODEN). SNODEN is evaluated against observations and performance is compared with offline land surface models from the National Land Data Assimilation System (NLDAS) as well as the Snow Data Assimilation System (SNODAS). SNODEN has less error overall and reproduces the temporal evolution of snow density better than all evaluated products. SNODEN is also able to estimate snow density for up to 10 snow layers which may be useful for land surface models as well as conversion of remotely-sensed SD to SWE. Due to the poor performance of previously evaluated snow products, the last study evaluates openly-available remotely-sensed snow datasets to better understand the strengths and weaknesses of current global SWE datasets. A new SWE dataset developed at the University of Arizona is used for evaluation. While the UA SWE data has already been stringently evaluated, confidence is further increased by favorable comparison of UA snow cover, created from UA SWE, with multiple snow cover extent products. Poor performance of remotely-sensed SWE is still evident even in products which combine ground observations with remotely-sensed data. Grid boxes that are predominantly tree covered have a mean absolute difference up to 87% of mean SWE and SWE less than 5 cm is routinely overestimated by 100% or more. Additionally, snow covered area derived from global SWE datasets have mean absolute errors of 20%-154% of mean snow covered area.

The past few decades have seen decreasing trends of snow-covered regions in the Northern Hemisphere. It remains unknown how these trends affect the spatial and temporal variability of snowpack water storage, a variable with significant implications for managing water resources to meet agricultural, municipal, and hydropower demands. To improve snowpack estimates, this dissertation developed a new snow data assimilation system (SNODAS) through multi-institutional collaborations. The new SNODAS consists of coupling of the Community Land Model version 4 (CLM4) and the Data Assimilation Research Testbed (DART), which is capable of assimilating multi-sensor satellite observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) anomalies. This dissertation describes the new SNODAS, presents the results of the data assimilation of MODIS SCF and GRACE TWS observations, and assesses the influence of uncertainties from multiple sources on the SNODAS performance. The first two studies compared the open loop run and the assimilation runs to evaluate the data assimilation (DA) performance. Data assimilation results were also evaluated against other independent observation-based snow data on daily and monthly timescales. Both assimilations can improve the snowpack simulations in CLM4; their strengths and drawbacks were discussed. When only MODIS SCF is assimilated, the innovation (i.e. the difference between analysis and forecast) is marginal in the regions where the snow cover extent reaches 100% regardless of snow mass changes. Further assimilation of GRACE TWS anomalies, however, can adjust the modeled snowpack, resulting in noteworthy improvements over the MODIS-only run in high-latitude regions. The effectiveness of the assimilation was analyzed over several Arctic river basins and various land covers. The third study discussed the influences of atmospheric forcing, model structure, DA technique, and satellite remote sensing product within the framework of SNODAS. The atmospheric forcing uncertainty is found to be the largest among the various uncertainty sources examined, especially over the Tibetan Plateau and most of the mid- and high-latitudes. The uncertainty of model structure as represented by two different parameterizations of SCF is the second largest. DA methods and products of GRACE TWS data have relatively less impacts. This study also showed that CLM4.5 produces better TWS anomalies than CLM4, which would have implications for improving the performance of GRACE TWS data assimilation.