Using Remote Sensing Data Fusion Modeling to Track Seasonal Snow Cover in a Mountain Watershed

Seasonal snowfall is the largest component of the water budget in many mountain headwater regions around the world. In addition to sustaining biological water needs in drier, lower elevation areas throughout the year, mountain snowpack also provides essential water inputs to the Critical Zone (CZ) - the outer layer of the Earth’s surface, which hosts a variety of biogeochemical processes responsible for transforming inorganic matter into forms usable for life. Water is a known driver of CZ activity, but uncertainty exists in its spatial and temporal interactions with CZ processes, particularly in the complex terrain of heterogeneous mountain areas. Increasing pressure on the CZ due to climate change and human land use needs creates an urgency to better understand the CZ system and how it may change in the future. An important variable for water driven CZ behaviors in mountain areas is the spatial extent of snow, also known as snow-covered area (SCA). SCA in mountain areas can change quickly over small scales of time and space with large impacts on the rest of the system. It has been difficult historically, however, to measure snowpack extent for large areas on very fine spatial and temporal scales due to a lack of remote sensing datasets with both of these fine scale characteristics. In this study we use the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to fill this historic knowledge gap for the East River watershed in Colorado, USA. By fusing low spatial and high temporal resolution data from MODIS (500-m, daily) with high spatial and low temporal resolution data from Landsat (30-m, 16 days), a fine resolution, 30-m daily dataset can be created. This study is one of the first to use this model with the primary intent of monitoring SCA in a mountain watershed. The first component of the study in this thesis presents a comprehensive validation of STARFM for use in monitoring snow cover in mountain areas. Normalized Difference Snow Index (NDSI) values from MODIS and Landsat are used as input to the STARFM model, and synthetic NDSI values at 30-m resolutions are obtained for days without Landsat data acquisitions. After converting NDSI to binary snow cover, we then examine the temporal performance of STARFM for an entire calendar year. The model’s performance is also analyzed for different landscape features known to influence snow cover. Accuracy, precision, recall, and F-score values indicate that the model is able to successfully predict the location of SCA in the landscape when validated with Landsat data. The second component of the study describes the process of creating the daily, 30-m NDSI dataset with STARFM for 20 water years of analysis and provides examples of how these data can be used to monitor SCA in a mountain watershed. We examine patterns of percent annual snow cover for three of the water years from the dataset, a dry, average, and wet water year. Here we find that predictable patterns of SCA occur over those years, with the highest percent annual snow cover occurring during the wet year and the lowest occurring during the dry year. Despite these differences, however, elevation is clearly the dominating factor in determining the spatial variability of snow cover in the landscape for all three water years. We also connect our snow cover analysis back to CZ processes by examining the timing of snow cover disappearance with the peak of annual stream discharge at the watershed outlet. The results of this work provide a multi-decadal dataset of snow cover information for the East River that can be used for future research into snowpack and streamflow forecasting, modeling of the movement of water through the CZ, and the effects that climate change may have on these processes. This study also provides examples of methods that can be used for further snow monitoring work in the East River watershed and other snow-dominated mountain catchments similar to it.

Status of springs in mountain watershed of western Nepal

The study, conducted in western hilly areas of Nepal, inventoried and mapped over 4,222 springs from five different watersheds. The study showed that more than 50% of the spring sources were found under natural conditions, i.e., open spring whereas 15% of them were of pond type. Similarly, the other 15% spring was recorded as a concrete structure or tank while 1% was determined to be a well. Attempts were made to identify if a change in water discharge from springs relates to rainfall patterns. The inter-annual variability analysis shows a significant fluctuation suggesting variation in water discharge across spring sources. The lowest amount of yearly rainfall received in the river basin is governed by decreasing water flow from the springs in the upper and mid-hills of Nepal. Besides, the intra-annual variation (i.e., seasonal and concentrative nature of rainfall only during monsoon) leads to shortage of drinking water and other domestic purposes (e.g., cooking, cleaning) during the dry months of the year. This study, based on the estimation of discharge flow in these springs, revealed that about 70% were decreasing and, in particular, the flow over the recent ten years decreased significantly.

Significant stream chemistry response to temperature variations in a high-elevation mountain watershed

High-elevation mountain regions, central to global freshwater supply, are experiencing more rapid warming than low-elevation locations. High-elevation streams are therefore potentially critical indicators for earth system and water chemistry response to warming. Here we present concerted hydroclimatic and biogeochemical data from Coal Creek, Colorado in the central Rocky Mountains at elevations of 2700 to 3700 m, where air temperatures have increased by about 2 °C since 1980. We analyzed water chemistry every other day from 2016 to 2019. Water chemistry data indicate distinct responses of different solutes to inter-annual hydroclimatic variations. Specifically, the concentrations of solutes from rock weathering are stable inter-annually. Solutes that are active in soils, including dissolved organic carbon, vary dramatically, with double to triple peak concentrations occurring during snowmelt and in warm years. We advocate for consistent and persistent monitoring of high elevation streams to record early glimpse of earth surface response to warming.