Mountain Scholar :: Browsing by Author "Fassnacht, Steven, advisor"

Browsing by Author "Fassnacht, Steven, advisor"

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Open Access
Evaluating and correcting sensor change artifacts in the SNOTEL temperature records, southern Rocky Mountains, Colorado
(Colorado State University. Libraries, 2017) Ma, Chenchen, author; Fassnacht, Steven, advisor; Kampf, Stephanie, advisor; Wei, Yu, committee member
In many high elevation mountain regions, documented warming rates have been greater than the global surface average. These warming rates directly affect the snowpack, runoff, ecosystems, agriculture and species that rely on a high elevation snowpack. Temperature records from the snow telemetry (SNOTEL) network across the Southern Rocky Mountains in the western United States have high warming rates, which may have been affected by systematic inhomogeneities in the temperature data caused by sensor changes. This study evaluates the maximum, average, and minimum temperature trends from 68 long-term SNOTEL stations across Colorado for the period from the 1980s through 2015 using the non-parametric Mann-Kendall/Theil-Sen's analyses before and after the temperature records were corrected for the sensor-caused inhomogeneities. Three homogenization methods were tested using a simple temperature index snow accumulation and melt model. Results show that the significant warming trends found in the original datasets, especially in minimum temperature (average increase of 1.2 °C per decade), decreased (to an average of 0.5 °C per decade) after homogenization. Step-like shifts in temperature datasets were observed in SNOTEL temperature records at the time of temperature sensor change, which created a discontinuity in the temperature dataset. The temperature-index snow model simulated snow water equivalent (SWE) well (more than 93% of the calibrated stations within the "good" and "very good" performance category for all three statistical-evaluation periods based on the Nash-Sutcliffe coefficient of efficiency, NSCE) using the new temperature sensor dataset. However, these models did not perform as well when using the original (pre-sensor change) and homogenized temperatures, with 23% of stations for the original temperature data and 44-69% of stations for two homogenized temperature datasets within the "good" and "very good"temperature data, but they did not fully correct for the effects of sensor change on the temperature records. The NSCE and bias statistics from SWE modeling using the original and homogenized datasets suggest that the homogenization methods evaluated in this study are applicable for many of the SNOTEL stations in Colorado but not all, and need to be applied with caution. Potential users of temperature products from the SNOTEL network should also be very careful when choosing time periods for future climate change research and assessments. More long-term climate monitoring stations should be installed in high elevation mountain regions to document and investigate elevation-dependent warming.
Open Access
Evaluating the spatial variability of snowpack properties across a northern Colorado basin
(Colorado State University. Libraries, 2012) Sexstone, Graham Andrew, author; Fassnacht, Steven, advisor; Laituri, Melinda, committee member; Sibold, Jason, committee member
Knowledge of seasonal mountain snowpack distribution and estimates of its snow water equivalent (SWE) can provide insight for water resources forecasting and earth system process understanding, thus, it is important to improve our ability to describe the spatial variability of SWE at the basin scale. The objectives of this thesis are to: (1) develop a reliable method of estimating SWE from snow depth for the Cache la Poudre basin, and (2) characterize the spatial variability of SWE at the basin scale within the Cache la Poudre basin. A combination of field and Natural Resource Conservation Service (NRCS) operational-based snow measurements were used in this study. Historic (1936 - 2010) snow course data were obtained for the study area to evaluate snow density. A multiple linear regression model (based on the historical snow course data) for estimating snow density across the study area was developed to estimate SWE directly from snow depth measurements. To investigate the spatial variability and observable patterns of SWE at the basin scale, snow surveys were completed on or about April 1, 2011 and 2012 and combined with NRCS operational measurements. Bivariate relations and multiple linear regression models were developed to understand the relation of SWE with physiographic variables derived using a geographic information system (GIS). SWE was interpolated across the Cache la Poudre basin on a pixel by pixel basis using the model equations and masked to observe SCA (from an 8-day MODIS product). The independent variables of snow depth, day of year, elevation, and UTM Easting were used in the model to estimate snow density. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance and model verification suggests the model is transferable to independent data within the bounds of the original dataset. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time consuming measurements of SWE are often not feasible. Bivariate relations of SWE and snow depth measurements (from WY 2011 and WY 2012) with physiographic variables show that elevation and location (UTM Easting and UTM Northing) are most strongly correlated with SWE and snow depth. Multiple linear regression models developed for WY 2011 and WY 2012 include elevation and location as independent variables and also include others (e.g., eastness, slope, solar radiation, curvature, canopy density) depending on the model dataset. The final interpolated SWE surfaces, masked to observed SCA, generally show similar patterns across space despite differences in the 2011 and 2012 snow years and differing estimation of SWE magnitude between the combined dataset of field-based and operational-based measurements (modelO+F) and the dataset of operational-based measurements only (modelO). Within each of the model surfaces, interpolated volume of SWE was greatest within Elevation Zone 5 (3,043 - 3,405 m). The percentage of the total interpolated SWE volume for each model was distributed similarly among elevation zones.
Embargo
Inter-daily temperature variability in the southern Rocky Mountains of Colorado
(Colorado State University. Libraries, 2024) Steen, Brian, author; Fassnacht, Steven, advisor; Barnard, David, committee member; Ronayne, Michael, committee member
While daily temperature variability has decreased in northern latitudes, variability across the western United States has increased. Changes in temperature variability can influence hydrological and earth system processes that could have severe ecological impacts. Mountainous areas are more sensitive to warming trends, but daily temperature variability in the Rocky Mountains is unknown. We investigated daily temperature trends across the Yampa and Rio Grande watersheds of the Southern Rocky Mountains in Colorado using 23 Snow Telemetry (SNOTEL) stations at high elevation, snow-covered regions (2521-3536m) and ten Cooperative Observer Program (COOP) stations at lower elevations (1961-2840m). SNOTEL data were homogenized to account for temperature sensor changes in 2003-2006, with five possible bias correction combinations compared. Daily data were detrended using the long-term and annual means, so that the day-to-day variability could be quantified. Trends were analyzed from the mid-1980s to 2022 using the Mann‐Kendall significance test and Theil‐Sen's rate of change. Inter-daily temperature variability (ITV) changed over the 30+ year period of evaluation with mixed increases and decreases based on location and time period. Variability in the spring at 26 stations has increased upwards of 0.8°C per 30 years in the spring. Ninety percent of stations have increased in variability up to 1.0°C per 30 years in the fall. In the summer, Yampa area stations decreased in variability while the Rio Grande area stations increased, both significantly. Low elevation COOP stations demonstrated smaller increases in variability than high elevation SNOTEL stations in the Rio Grande watershed throughout all seasons. The Yampa watershed showed no similar elevational patterns, but rather decreased variability for SNOTEL stations with little change for variability for COOP stations. The scattered decreases in the Yampa area and at lower elevations emphasize the spatiotemporal variability of montane climatology and suggest increased ITV trends across the Rocky Mountain West are watershed and station specific.
Open Access
Local understanding of hydro-climate changes in Mongolia
(Colorado State University. Libraries, 2012) Sukh, Tumenjargal, author; Fassnacht, Steven, advisor; Laituri, Melinda, committee member; Fernandez-Gimenez, Maria, committee member; Butters, Greg, committee member
Air temperatures have increased more in semi-arid regions than in many other parts of the world. Mongolia has an arid/semi-arid climate where much of the population is dependent upon the limited water resources, especially herders. This paper combines herder observations of changes in water availability in streams and from groundwater with an analysis of climatic and hydrologic change from station data to illustrate the degree of change of Mongolian water resources. We find that herders' local knowledge of hydro-climatic changes is similar to the station based analysis. However, station data are spatially limited, so local knowledge can provide finer scale information on climate and hydrology. We focus on two regions in central Mongolia: the Jinst soum in Bayankhongor aimag in the desert steppe region and the Ikh-Tamir soum in Arkhangai aimag in the mountain steppe. As the temperatures have increased significantly (more in Ikh-Tamir than Jinst), precipitation amounts have decreased in Ikh-Tamir which corresponds to a decrease in streamflow, in particular, the average annual streamflow and the annual peak discharge. At Erdenemandal (Ikh-Tamir) the number of days with precipitation has decreased while at Horiult (Jinst) it has increased. Herders observed that the amount of precipitation has decreased (71% in Jinst; 100% in Ikh-Tamir) in recent years. The long-term average streamflow of the Tuin River at Jinst has not changed significantly while the herders have seen a depletion of water resources (73% of respondents). The Khoid Tamir River at Ikh-Tamir has seen a statistically significant decline in the average annual streamflow and the annual maximum daily discharge, which was also observed by all herders surveyed.
Open Access
Motorized winter recreation impacts on snowpack properties
(Colorado State University. Libraries, 2011) Heath, Jared Tucker, author; Fassnacht, Steven, advisor; Elder, Kelly, committee member; Stednick, John, committee member; Wilson, Kenneth, committee member
Winter recreation, consisting of snowshoeing, skiing, snowboarding, and snowmobiling, has been increasing annually in Colorado's forests. This increase in recreational activity creates direct and indirect wildlife interactions. Motorized winter recreation in the backcountry compacts the snow possibly influencing the physical and mechanical properties of the snowpack. Snow depth, density, stratigraphy and grain characteristics control to the insulating properties of the snowpack and create habitat for small non-hibernating mammals. Changes to these physical properties and compaction of the subnivean space may be detrimental to these species. Two hypotheses were formulated: (1) a snowpack compacted by motorized winter recreation will result in changes to physical and mechanical properties of the snowpack; and (2) the amount of motorized winter recreation and the depth of snow when motorized winter recreation begins affects the physical properties of the snowpack. During the 2009-2010 winter season snow compaction plots near Rabbit Ears Pass and Fraser Experimental Forest, Colorado were manipulated with varying use of motorized winter recreation (low, medium and heavy use) beginning on different snow depths, shallow (30 cm) and deep (120 cm). Physical and mechanical properties of the snowpack, including snow density, temperature, snow depth, snow water equivalent, stratigraphy, hardness and ram resistance were measured and used to examine the statistical difference between no use and varying degrees of motorized winter recreation (low, medium and heavy use). The results were used to infer implications on changes to the insulative value of the subnivean space and the potential for movement by subnivean mammals. The largest differences in snowpack properties were associated with motorized winter recreation beginning on a shallow snowpack. Compaction from motorized winter recreation that began on a shallow snowpack increased both mean and subnivean density, hardness, and ram resistance, which resulted in significant differences (p<0.10) between varying use of motorized winter recreation and no use. Snow depth and basal temperatures (ground/snow interface) decreased as a result of motorized winter recreation beginning on a shallow snowpack (p<0.10), while temperature gradients were unaffected throughout the duration of the winter season. Implications to changes in these snowpack properties could decrease the insulative value of the snowpack and make movement by small mammals that utilize the subnivean space more difficult. On the contrary, motorized winter recreation that began on a deep snowpack showed no significant difference suggesting later initiation of use minimizes changes to snowpack properties from compaction.
Open Access
Patterns of dust-enhanced absorbed energy and shifts in melt timing for snow of southwestern Colorado
(Colorado State University. Libraries, 2020) Duncan, Caroline R., author; Fassnacht, Steven, advisor; Kampf, Stephanie, committee member; Ham, Jay, committee member
Deposited dust layers reduce the surface albedo of snow and accelerate melt by this change to the snowpack energy balance. Senator Beck Study Basin in the San Juan Mountains of southwestern Colorado monitors the effects of dust on midlatitude continental snowpack. Continuous automated measurements include shortwave and longwave radiation in addition to conventional micrometeorological variables. Dust layer characteristics and snow properties are collected during snow pit excavation throughout each ablation period. Both sets of data were used to simulate snowpack under observed and dust-free conditions with the snow energy balance model SNOBAL for WY2007 to WY2019. Across the 13 years, dust concentrations ranged from 0.16 to 4.80 mg g-1 resulting in a range of daily mean dust-enhanced absorbed visible energy from 31 to 50 W m-2 during ablation, with hourly peaks up to 347 W m-2. We found snow melt accelerated by 11 to 31 days in a logarithmic response to end-of-year dust concentration modified by seasonal variations in snow amount and cloud cover.
Open Access
Practical snow depth sampling around six snow telemetry (SNOTEL) stations in Colorado and Wyoming, United States
(Colorado State University. Libraries, 2012) Kashipazha, Amir, author; Fassnacht, Steven, advisor; Kampf, Stephanie, committee member; Laituri, Melinda, committee member; Arabi, Mazdak, committee member
Across the Western United States, the Natural Resources Conservation Service (NRCS) operates about 700 automated snowpack telemetry (SNOTEL) measurement stations. These stations measure snow depth (SD), snow water equivalent (SWE), air temperature and precipitation. To assess how representative the stations are of the surrounding 1 km2 area, a set of approximately 200 snow depth measurement were taken using ten 1000-m transects sampled at 50-m intervals. This sampling was undertaken at the Dry Lake, Joe Wright, Lizard Head, Niwot, (in Colorado) South Brush Creek, and Togwotee Pass (in Wyoming) SNOTEL stations during the winters of 2008, 2009, and 2010. Various sampling patterns were employed at each sampling point, such as three depth measurements in a row parallel or perpendicular to a transect, and five in a row or five in a plus pattern. We used these patterns and various sub-sets of the 1 km2 surrounding area to assess suitable and practical sampling strategies, to determine the minimum number of transects need for measuring the average SD of each station, to evaluate if each station represent the SD average of its 1 km2 area surrounding, and to investigate inter- and intra-annual variations of SD for each station. Statistical analysis used the least-significant-based analysis of variance with a 95 percent confidence level. Statistical analyses showed snow depth averages of incorporated sampling methods were not significantly difference at the 95 percent confidence level. Therefore, any sampling method could be used for SD measurement based on sampling constraints. We recommend measuring three to five snow depths at each sampling spot and the distance between sampling spots should be less than 200m. The minimum number of transects needed for each station was not the same and it depended upon the physiographic and vegetation heterogeneity of the area surrounding a station. Snow depth varied within a 1 km2 area surrounding of SNOTEL station and we did not find two sampling methods that had the same average SD. However, this did not mean that the average SD using a variety of sampling methods was significantly different at the 95 percent confidence level. A heterogeneous snowpack is caused variations in precipitation, wind patterns, solar radiation, etc. Physiographic and vegetation characteristics can be used as surrogates for these meteorological factors that vary at the small and large scale. The effect of these factors on snowpack heterogeneity is more likely greater when the distance of sampling spots is more than 1 km. The correlation between snowpack heterogeneity and the surrogate characteristics varied in spatially and temporally, and from location to location. The Dry Lake, Joe Wright, Lizard Head, and Niwot SNOTEL stations represented the SD average of their 1 km2 area surrounding while Lizard Head station represented the SD average of its 0.36 km2 area surrounding, all at the 95 percent confidence level. However, the Togwotee Pass and South Brush Creek stations did not represented the SD average of their surrounding area. Whether a SNOTEL station does or does not represent the SD average of its surrounding area is related to the complexity of the terrain. For example, the area surrounding the Joe Wright station has complex terrain but represented the station SD while the South Brush Creek terrain was more homogeneous and did not represent station SD. The performance of the SD sensor at the SNOTEL station can be affected by the interaction of meteorology, physiography, vegetation, and possibly human influences, that can produce an highly varying snow pack under and/or around a SD sensor and led to a lack of sensor representivity or sensor error. Due to potential SD sensor and sampling errors a reasonable amount of error for snow samples, such as 5-10% should be considered.
Open Access
Precipitation and temperature changes and their effect on groundwater along the Kona coast of Hawai'i
(Colorado State University. Libraries, 2015) Stevenson, Sharla Ann, author; Fassnacht, Steven, advisor; Kampf, Stephanie K., committee member; Butters, Gregory, committee member
Water resources are an important part of the Hawaiian cultural tradition, and a shift to a warmer, dryer climate may initiate physical and biological changes that would inhibit the practice of Native Hawaiian cultural traditions by altering the coastal ecosystem resources such as those found within Kaloko-Honokōhau National Historical Park. The high degree of spatial heterogeneity and numerous microclimates on the Island of Hawai'i motivated an in-depth analysis of changes in precipitation and temperature occurring during the time since the park was established in 1978 up to the year 2010 at stations located within the regional recharge area for the Kona aquifer system. The potential long-term implications of changes in climate to groundwater recharge were also modeled using stochastic techniques. A statistical analysis was conducted on annual, winter, and summer precipitation and minimum and maximum temperature climate records using the Mann-Kendall test to detect the presence of a monotonic increasing or decreasing trend at significance levels of alpha = 0.1, 0.05, 0.01, and 0.001. The similarities and differences between station records were further evaluated by a double mass analysis of the same precipitation datasets. The changes identified during trend analysis were used to create synthetic realizations of temperature and rainfall patterns 50 years into the future using stochastic modeling techniques. The future realizations were analyzed to evaluate changes in net precipitation and the potential effect on groundwater recharge. Within the Kona aquifer recharge area there is evidence of diverse changes in rainfall that have taken place over recent decades. 13 out of 15 stations evaluated for changes in rainfall have decreasing trends during the 1978 to 2010 time period and over their entire observation record. Decreases in annual rainfall range from 30mm to 250mm per decade with the majority of declines occurring in the summer season. Almost half of the stations had significant changes in rainfall during the summer season, but none of the changes in winter rainfall were significant. The trends displayed in both rainfall and temperature when modeled 50 years into the future indicate declines in net precipitation ranging from 6 to 48% compared to the modeled stationary 50 year mean. All of the modeled scenarios indicated a decline in the number of days with rainfall for all of the locations with the decline resulting in four locations having a season with no rainfall at all. Large declines in modeled net precipitation such as these would affect the overall amount of recharge to the regional aquifer. In an island ecosystem, the constant pressure of saltwater intrusion and the input of freshwater recharge creates a delicate balance of fresh and saline water underground. Any change in net precipitation that affects recharge could disrupt that delicate balance allowing increased saltwater intrusion along the coastline and within the Park.
Open Access
Snow depth measurement via automated image recognition
(Colorado State University. Libraries, 2019) Brown, Kevin S. J., author; Fassnacht, Steven, advisor; Ham, Jay, committee member; McGrath, Dan, committee member; Ross, Matt, committee member
Seasonal snow is a significant contributor to the water supply of nearly 2 billion people in semi-arid regions around the world. Quantification of this resource is critical to planning sustainable water and food supplies in these regions. While Snow Water Equivalent (SWE) is the most common parameter used to estimate snow water storage, snow depth has often been used as a proxy since it is much simpler to measure and can be converted to SWE if density can be estimated. Depth of snow varies greatly at the regional, watershed, and plot scales and better quantification of this variability can improve water storage estimates. Installation and maintenance of new snow measurement sites is typically expensive and time consuming, so a technology that could produce high temporal resolution snow depth data for a low cost would be useful. Manual reading of snow depth from graduated staffs driven into the ground has been used by the Natural Resources Conservation Service (NRCS) for operational and research purposes. The amount of data available from this method has traditionally been limited by the time-consuming step of manually reading snow depths in images. The central objective of this research was to automate this process in order to reduce the time requirement and allow this technology to be deployed more widely. Five sites were established with time lapse cameras and a set of snow depth staffs around the state of Colorado. Several image recognition methods were considered, and the Aggregate Channel Features technique was used to detect snow depths based on images of the depth staffs. At the most successful sites, absolute error was close to 20 cm, while at less successful sites consistent errors as high as 100 cm made the data unusable. The variety of site configurations examined allowed factors which increased error such as forested backgrounds, close staff placement, and poor camera mounting, to be identified. Additional studies could take advantage of new, cloud-based image recognition technologies in order to allow anyone with a camera and an internet connection to measure snow depth automatically from pictures taken at specific locations.
Open Access
Snowfall-driven topographic evolution: impacts on snow distribution patterns
(Colorado State University. Libraries, 2023) Olsen-Mikitowicz, Alexander Richard, author; Fassnacht, Steven, advisor; McGrath, Daniel, committee member; Leisz, Stephen, committee member
This study develops a scalable meteorologically independent snow accumulation model to better estimate snowpack depth using an enhanced representation of actual processes. Current snow accumulation models incorporate bare or snow-free surface properties derived from elevation, aspect, vegetation, and prevailing wind characteristics to determine the drivers of snow distribution yet neglect to consider how subsequent snowfalls can reshape the initial terrain conditions. We hypothesize that a snow depth model that accumulates snowfall while accounting for the antecedent snow-affected surface characteristics is more representative of natural processes and will therefore yield more accurate depth estimates than models that reference a snow-free topographic surface. To address this premise, the research explores (1) conducting a sensitivity analysis to evaluate the behavior of both models, (2) determining the differences between the two snow accumulation modeling approaches, and (3) assessing each model's performance in different location, scale, and temporal resolution conditions to determine their resiliency and transferability. Terrestrial LiDAR was employed at two field sites following snow deposition events and captured a range of spatial extents and resolutions. The Upper Piceance Creek (UPC) site near Meeker, CO covered approximately 10 m2 at centimeter resolution; the Izas Experimental Catchment in the Spanish Pyrenees covered 1 km2 at meter resolution. A regression tree machine learning model was utilized to estimate snow depth based on 14 topographic features. This process engaged in two mechanisms: 1. Static method, where snow depth (dst) determined from the bare earth digital terrain model (ds0) was estimated with snow-free topographic features and 2. Dynamic method, where snow depth (dst) determined from the previous snow surface height (dst-1) was estimated with the dst-1 snowfall affected surface. The analyses found that the models were resilient to changes in training allocations under a random sampling method, but sensitive to both the prevailing wind direction used for feature creation and the overall resolution used to represent surface features. The primary difference between the static and dynamic models for snow depth estimates was the number of features used and their relative importance. The static method had a higher overall median importance and relied mainly on Directional Relief and Relative Topographic Position for snow depth estimates, while the dynamic method displayed lower overall median importance but utilized more surface features over a single accumulation season. The dynamic method outperformed the static method at UPC by approximately 0.07 in a Nash-Sutcliffe efficiency comparison, and only 0.01 at Izas Experimental Catchment suggesting issues with process-scale representation of snow accumulation at the Izas site.
Open Access
Social perceptions versus meteorological observations of snow and winter along the Front Range
(Colorado State University. Libraries, 2013) Milligan, William James, author; Fassnacht, Steven, advisor; DiEnno, Cara, committee member; De Miranda, Michael, committee member
This research aims to increase understanding of Front Range residents' perceptions of snow, winter and hydrologic events. This study also investigates how an individual's characteristics may shape perceptions of winter weather and climate. A survey was administered to determine if perceptions of previous winters align with observed meteorological data. The survey also investigated how individual characteristics influence perceptions of snow and winter weather. The survey was conducted primarily along the Front Range area of the state of Colorado in the United States of America. This is a highly populated semi-arid region that acts as an interface between the agricultural plains to the east that extend to the Mississippi River and the Rocky Mountains to the west. The climate is continental, and while many people recreate in the snowy areas of the mountains, most live where annual snowfall amounts are low. Precipitation, temperature, and wind speed datasets from selected weather stations were analyzed to determine correct survey responses. Survey analysis revealed that perceptions of previous winters do not necessarily align with observed meteorological data. The mean percentage of correct responses to all survey questions was 36.8%. Further analysis revealed that some individual characteristics (e.g. winter recreation, source of winter weather information) did influence correct responses to survey questions.
Open Access
Spatial precipitation trends and effects of climate change on the Hawai'ian Hualalai aquifer
(Colorado State University. Libraries, 2015) Hendricks, Alyssa Danielle, author; Fassnacht, Steven, advisor; Laituri, Melinda, committee member; Arabi, Mazdak, committee member
While trends in temperature are well studied and understood spatially and temporally at a multitude of scales, trends in precipitation are less understood. As the predominant source of groundwater recharge in Western Hawai'i, precipitation plays a vital role in maintaining tourism and industry throughout the Kona Region. Kaloko-Honokohau National Historical Park was established in 1978 to perpetuate and maintain traditional native Hawai'ian culture and the surrounding ecosystem, which is dependent on freshwater from the surrounding Hualalai Aquifer. Precipitation increases with elevation from the coast to approximately 1500 meters up the slope of Hualalai Volcano and then decreases to approximately 2000 meters. Western Hawai'i has a dense rain gauge network and changes in precipitation in the last several decades have been observed, though the rate sand significance of change is unclear. This study introduces a new method of integrated spatial analysis aimed at representing spatial trends in more detail. Using the Rainfall Atlas of Hawai'i, produced by the University of Hawai'i at Manoa, spatial trends from 1978-2007 were studied by annually adjusting the 30-year climate normal and calculating residuals between adjusted and observed precipitation. The Mann-Kendall and Sen's Slope statistical tests were used spatially to determine the rate and significance of change. This method was then compared with spatial interpolation by inverse distance weighting (IDW) and ordinary kriging to assess the differences in methods. Results from the integrated spatial analysis show an annual decrease of -8.42 x 10⁶ m³/year across the entire study area and a decrease of -4.62 x 10⁶ m³/year when only significant areas are considered. This can be compared with -10.8 x 10⁶ m³/year total and -0.64 x 10⁶ m³/year in significant areas from IDW and -8.41 x 10⁶ m³/year and -1.31 x 10⁶ m³/year respectively from ordinary kriging. On a monthly basis, both the integrated spatial analysis and IDW yield similar trends regarding an increase or decrease in the net volume entering the aquifer, however IDW underestimates the overall magnitude. The introduced integrated spatial analysis method provides an improved assessment of spatial trends that, while not limited to precipitation, can assist in broadening the limited knowledge of spatial precipitation trends across the globe.
Open Access
Spatial variability of snow depth measurements at two mountain pass snow telemetry stations
(Colorado State University. Libraries, 2012) Blumberg, Evan J., author; Fassnacht, Steven, advisor; Laituri, Melinda, committee member; Butters, Greg, committee member
Much of the Western United States relies heavily on spring snow melt runoff to meet its industrial, agricultural, and household water needs. Water professionals use the network of snowpack telemetry (SNOTEL) stations to help forecast spring melt water runoff. These stations only represent a small area and across a watershed, the variability in snowpack properties can be large. Properties such as snow depth can vary substantially even over distances as short as a meter. Previous studies have examined how snow depth is distributed across the landscape and how terrain and vegetation parameters can be used as surrogates for the meteorological variables that drive the distribution of snow. The parameters are derived from a digital elevation model (DEM) that is now at a 30x30m resolution, and they include elevation, aspect, slope angle, and canopy cover, as well as clear sky solar radiation and the maximum upwind slope. Typically three to five snow depth measurements are taken to represent each 30x30m DEM pixel. This study examines the distribution of variability in snow depth within a pixel. Snow depth surveys were conducted around the Joe Wright SNOTEL station near Cameron Pass in northern Colorado on May 1st, 2009 and May 1-2, 2010 and around the Togwotee Pass SNOTEL station in north-central Wyoming on March 17th 2009. Surveys were performed by taking snow depth measurements in a 1 x 1 kilometer block around each SNOTEL station. Due to the logistics of sampling these two locations that both have dense forests and steep terrain, three different sampling methods were employed based on a standard of three points in a row spaced 5 meters apart. To examine the variability at a location (pixel), at least eight additional measurements were taken between the three points (11 points were taken on May 1st, 2009 at Joe Wright). At Togwotee Pass, 10 additional depth measurements were taken about the mid-point, perpendicular to the main transect, yielding 21 points. For the 2010 survey at Joe Wright, the 11 points in a row were supplemented by two points at the beginning, middle and end (three standard points) to yield 17 measurements at a location. From these data the parameters most strongly correlated with the average snow depth, the standard deviation of snow depth, and the coefficient of variation were computed. Binary regression trees were used to further explore the relation between the average and variability and the terrain and canopy parameters. The statistics (average and standard deviation) from the standard three points was compared to all the points (11, 17or 21) measured at a location. Data were sub-set from all the points to determine the average difference and subsequently an appropriate number of depth measurements that should be taken to represent a location. Key variables were not consistent for the 2009 and 2010 Joe Wright SNOTEL surveys, and also varied when looking at standard deviation or coefficient of variation. Among many surveys, canopy cover, elevation, and sin of slope were key variables, but to different degrees. Investigation into survey efficiency show that taking between 3 to 6 data points per pre-determined sample point is suitable to be within 5% of the overall average, whether it be the 11, 17, or 21 point survey scheme.
Open Access
Streamflow forecasting in a snow-dominated river of Chile
(Colorado State University. Libraries, 2021) Pérez Peredo, Felipe Andrés, author; Fassnacht, Steven, advisor; Sibold, Jason, committee member; Barnard, Dave, committee member
The combination of 10 years of drought in the Chilean Andes and an increased demand water supply and agricultural activities has created the need for better forecasts to inform water management and decision making. The existing water supply forecasts have been insufficient for the snow-dominated systems originating in the mountains, especially under the new drought conditions. Future climate change and inter-annual variability will further require the use of more detailed snowpack information to create better water supply forecasts. This research focuses on the monthly water supply forecast for the basin upstream the flow gauging station called Río Aconcagua en Chacabuquito, in central Chile. This basin is located in the Mediterranean climate zone, originating at the highest peak in the Andes, Aconcagua. Meteorological data are collected at several stations in the lower elevations, and snowpack information, specifically monthly snow water equivalent (SWE) has been collected at the higher elevation Portillo snow course since 1951. Here, a new methodology is created to improve the seasonal volume and the monthly distribution streamflow forecasts, using available information from operational and more representative stations. Results are being evaluated for the current snowmelt period (September 2020 to March 2021), with monthly updates. Improvements have been seen in the seasonal volume, due the use of historical data and because the new methodology also incorporates the recent dry years, unlike the previous forecast model. Improvement in the monthly distributions are seen due the newly adopted methodology distribution.
Open Access
The dynamic nature of snow surface roughness
(Colorado State University. Libraries, 2022) Sanow, Jessica, author; Fassnacht, Steven, advisor; Sexstone, Graham, committee member; McGrath, Dan, committee member; Bauerle, William L., committee member
Throughout the winter season, the snowpack becomes the surface-atmosphere boundary for the energy balance within the hydrologic cycle and is key for understanding and modeling meltwater availability, streamflow, and groundwater recharge. The aerodynamic roughness length, z0, is one metric to quantify the roughness characteristics of the snowpack surface. Roughness is a key component when analyzing the snowpack surface energy exchange because it exerts a strong influence on turbulent energy exchanges between the snowpack and atmosphere. Snow surface roughness fluctuates throughout the winter season due to snowpack accumulation and melt, redistribution, ecological, and meteorological influences. However, current hydrologic and energy balance models use a static z0 value despite the snowpack surface, and resulting z0 value, being spatially and temporally dynamic throughout the winter. Inclusion of a site specific, spatially, and temporally variable z0 is expected to improve hydrologic and energy balance models. Therefore, the following research investigates 1) comparing the anemometric and geometric methods of measuring z0, 2) the correlation between z0 and snow depth, 3) spatial and temporal variability of z0, 4) post-processing effects on z0 measurements, and 5) application of a variable z0 within the SNOWPACK model. Results of this study indicate a strong correlation when comparing geometric versus anemometrical methods of calculation. 30 wind profiles were compared to 30 corresponding geometrically calculated surface measurements using a terrestrial based LiDAR. These combined profiles had a Nash-Sutcliffe Coefficient of Efficiency of 0.75, an r2 of 0.96, a best fit slope of 0.98, and a Root Mean Square Error of 8.9 millimeters. The correlation between snow depth and z0 is variable depending on periods of melt, accumulation, and the initial snow-free roughness. The z0 was shown to be spatially and temporally variable across study sites. Interpolation resolution during post processing of z0 was found to modify z0 by several orders of magnitude. Variable z0 values were found to alter SNOWPACK model results within several of the output variables. The most sensitive output variables were sublimation, latent, and sensible heat due to the direct use of z0 within the calculations. These key findings highlight the importance of a variable z0. Inclusion of a variable z0 parameterization within models should be site specific, spatially and temporally dynamic, with special attention to post-processing steps.
Open Access
The effects of temperature-elevation gradients on snowmelt in a high-elevation watershed
(Colorado State University. Libraries, 2022) Sears, Megan G., author; Fassnacht, Steven, advisor; Kampf, Stephanie, committee member; Rasmussen, Kristen, committee member
The majority of snowmelt in the western U.S. occurs at high elevation where hydrometeorological measurements needed for monitoring snowpack processes are often in complex terrain. Data are often extrapolated based on point measurements at lower elevation stations and the elevation to be modeled. In this study, we compute near-surface air temperature-elevation gradients and dew point temperature-elevation gradients (TEG and DTEG, respectively) and compare values to widely accepted rates (e.g., environmental lapse rate). Further, the implications on snowmelt modeling of TEG and DTEG versus accepted temperature-elevation gradients are quantified using two index snowmelt models, 1) temperature and 2) temperature and radiation. TEG and DTEG were found to be highly variable and during nighttime often influenced by cold air drainage. Several modeling scenarios were applied that manipulated air temperature and dew point temperature, via incoming longwave radiation. When compared to the control scenario, these scenarios ranged in snow-all-gone date by -1 to +6 days. The model utilizing observed air temperature and an estimated DTEG performed most similarly to the control scenario. Thus, the estimated DTEG is adequate for index snowmelt models used in similar domains; however, further investigation should be done prior to applying the environmental lapse rate or other estimated TEG values.
Open Access
Uncertainty in hydrological estimation
(Colorado State University. Libraries, 2021) Hultstrand, Douglas Michael, author; Fassnacht, Steven, advisor; Hiemstra, Christopher, committee member; Laituri, Melinda, committee member; Stednick, John, committee member
Detailed hydrometeorologic analyses and uncertainty assessments are needed to aid water resources decision-making, to account for upstream-downstream linkages and dominant process scale for integrated land and water resources management and planning. The water balance is a fundamental concept in hydrology that inspires many tools for predicting the specific components including precipitation, streamflow, soil moisture, and groundwater storage. A water balance is typically expressed as an equation that relates water inputs, outputs, and storage of a system. The water balance model is applied to analyze the allocation of water among components of the hydrologic system. Knowledge on the components composing inputs and outputs in a water balance are essential to understanding watershed processes. While methods to measure and model water balance components continue to improve, all components of the balance have substantial uncertainty. Methods to analyze a water balance should acknowledge these uncertainties and consider how they propagate through water balance calculations in order to better assist water resources decisions. This research investigated four water balance components: (1) snowpack sublimation, (2) precipitation as snow, (3) precipitation as rain, and (4) stream discharge in mountainous watersheds in order to examine and build our knowledge of uncertainty in the water balance for mountainous environments. The research presented in this dissertation supports a theme that hydrology is a highly uncertain science, where uncertainty is a result of the hydrologic community's knowledge gap to accurately model physics of atmospheric and hydrologic processes. A finding of this work is that no component of the water balance can be quantified at watershed scale without estimating he associated uncertainty. Results highlight that mean cumulative snowpack sublimation uncertainty is 41% with individual input variable uncertainties in the range of 1 to 29%; simulated to observed basin mean snow depth was estimated within 15% for 10-years while extreme dry and wet years were within 5%; and forcing precipitation datasets used in hydrologic models to estimate streamflow have cumulative uncertainties in the range of 30 to 60%. Results of this dissertation identify the importance to account for uncertainty in water resources, i.e., Monte Carlo methods, to properly account for and quantify associated risks in water management and design infrastructure decisions.
Open Access
Using snow telemetry (SNOTEL) data to model streamflow: a case study of three small watersheds in Colorado and Wyoming
(Colorado State University. Libraries, 2013) Deitemeyer, David C., author; Fassnacht, Steven, advisor; Laituri, Melinda, committee member; Arabi, Mazdak, committee member
The use of operational snow measurements in the Western United States is instrumental in the successful forecasting of water supply outlooks. The focus of this study is to determine if hydro-meteorological variables available from Snow Telemetry (SNOTEL) stations could successfully estimate the annual total runoff (Q100) and components of the hydrograph, in particular, the date of the passage of 20% of the Q100 (tQ20), 50% of Q100 (tQ50), 80% of Q100 (tQ80), and the peak runoff (Qpeak). The objectives are to: (1) determine the correlation between streamflow and hydro-meteorological variables (from SNOTEL station data); (2) create a multivariate model to estimate streamflow runoff, peak streamflow, and the timing of three hydrograph components; (3) run calibration/testing on the model; and (4) test the transferability to two other locations, differing in catchment area and location. Snow water equivalent (SWE) data from the Natural Resources Conservation Service (NRCS) Joe Wright Snow Telemetry (SNOTEL) was correlated to streamflow at the United State Geological Survey (USGS) Joe Wright Creek gauging station. This watershed is located between the Rawah and Never Summer Mountains in Northern Colorado and has a drainage area of 8.8 km2. Temperature data were not used due to non-stationarity of this time series, while the SWE data were stationary over the 33-year period of record. From the SNOTEL SWE data, peak SWE, date of peak SWE, and number of consecutive days with snow on the ground up to the date of peak SWE had the strongest correlation to streamflow (R2 = 0.19 to 0.58). A collection of models runs were tested with various SNOTEL variables to develop optimal models for each of the five hydrograph components (tQ20, tQ50, tQ80, Q100, Qpeak). Five of the six estimates of were made at the date of Peak SWE. A refined estimate was made for the Q100 at melt-out, when the SWE equaled zero at the SNOTEL station. For the model development, most of the model trials (78%) had a Nash-Sutcliffe coefficient of efficiency (NSCE) value of greater than 0.50. The variables were analyzed for collinearity through a Variance Inflation Factor (VIF). Models with low collinearity (VIF < 5) and greatest accuracy from the calibration and testing periods were selected as optimal model configurations for each of the hydrograph components. The optimal model configuration in the Joe Wright Creek watershed had strong performance for the tQ20, tQ50, Q100 and Qpeak (NSCE > 0.50). The tQ80 model was the least accurate model (NSCE = 0.32). Applying the optimal model equation to the two larger watersheds; Shell Creek is located in Big Horn Mountains of Northern Wyoming (with a drainage area of 59.8 km2) and Booth Creek is located north of Vail in Central Colorado (with a drainage area of 16.0 km2). Basin specific coefficients were generated for a calibration period (1980 to 1996), and evaluated for a testing period (1997 to 2012). A majority of the model outcomes were considered good, with 72% of the outcomes having NSCE > 0.50. The Q100 at melt-out model performed the best (NSCE = 0.62 to 0.94). In a final analysis, the Joe Wright Creek coefficients were applied directly to the two larger watersheds to test model transferability. The location specific model coefficients did not perform well for the other two basins. However, for the Shell Creek watershed, results were still good for the following variables: tQ20, Q100 (using data up to peak SWE and using all SWE data including melt-out) and Qpeak, with NSCE values of 0.45, 0.46, 0.47, and 0.37, respectively. The similar results between Joe Wright Creek and Shell Creek watersheds suggest comparable physiographic characteristics between the two watersheds. An earlier observed onset of snowmelt (as indicated by tQ20) at the Booth Creek watershed influenced the overall accuracy of the model transferability. Despite the differences in the transferability of the model, the optimal configured models derived from accessible SNOTEL data and basin specific coefficients serve as a beneficial tool to water managers and water users for the forecasting of hydrograph components.
Open Access
Variable fresh snow albedo: how snowpack and sub-nivean properties influence fresh snow reflectance
(Colorado State University. Libraries, 2021) Reimanis, Danielle C., author; Fassnacht, Steven, advisor; Butters, Gregory, committee member; McGrath, Daniel, committee member
The understanding of albedo, or ratio of outgoing to incoming shortwave radiation, is necessary for modeling the melt characteristics of a snowpack in snow-dominated areas. The timing and supply of meltwater downstream is influenced by the energy balance, and albedo is used in those calculations. Current snow albedo models range from simple models that only reset albedo with new snowfall to complex models that are not feasible for most applications. We present a variable fresh snow model that enhances a simple albedo model, initially created by the U.S. Army Corps of Engineers, and used extensively in the Canadian LAnd Surface Scheme (CLASS). The new approach considers conditions prior to and during a snowfall event to improve fresh snow albedo estimates, instead of resetting to a static value; it also considers differences in the albedo decay rate.Hourly shortwave radiation (incoming and outgoing), snow depth, temperature, and other meteorological data from two stations at the Senator Beck Basin in the San Juan Mountains of Southwest, Colorado were used for the period from 2005 to 2014. We evaluated changes in albedo of a high-elevation seasonal snowpack during fresh snow events and apply a set of multivariate regressions to recreate values of broadband albedo. The variable fresh snow albedo model approaches the Visible and Near-Shortwave Infrared portion of the electromagnetic spectrum differently and groups values by temperature. The model needs few inputs, specifically measurements of depth and temperature, an estimation of ground albedo, and for increased accuracy, a quantification of the number of aeolian dust deposition events on the snowpack every year. This variable fresh snow model showed higher accuracy in albedo values, both of fresh and decayed snow (R2 of 0.77 and Nash Sutcliffe Efficiency, NSE of 0.75) than of CLASS (R2 of 0.67 and NSE of 0.62). When isolating fresh snow events, the variable fresh snow albedo model was much more accurate than the single-reset albedo provided by CLASS but still had a weak correlation to measured values (R2 of 0.38). The variable fresh snow albedo model especially outperformed CLASS during the melt period, with ~24% more accurate absorption values to measured values than CLASS. Since fresh snow albedo is primarily weighted by albedo from the timestep before, we suggest this model also be used to correct erroneous values of albedo given incorrect sensor measurements, such as due to snow accumulation on the upward looking shortwave radiation sensor (pyranometer).
Open Access
Water quality benefits of wetlands under historic and potential future climate in the Sprague River Watershed, Oregon
(Colorado State University. Libraries, 2013) Records, Rosemary M., author; Fassnacht, Steven, advisor; Arabi, Mazdak, advisor; Duffy, Walter, committee member; Butters, Greg, committee member
An understanding of potential climate-induced changes in stream sediment and nutrient fluxes is important for the long-term success of regulatory programs such as the Total Maximum Daily Load and sustainability of aquatic ecosystems. Such changes are still not well characterized, particularly in the Pacific Northwest, although shifts in stream flow associated with warming temperatures have already been observed in the region. Conservation practices such as wetland restoration are often regarded as important in watershed-scale management of water quality. However, the potential of wetland gains or losses to alter future stream water quality conditions has received relatively little study. The primary goal of this research is to assess the basin-scale regulation of sediment, nitrogen and phosphorus provided by variable wetland extent under current climate and potential mid-21st century climate. Specific objectives of the study are (1) to evaluate the effects of present-day wetlands on stream water quality under current climate; (2) to identify direction and magnitude of potential changes in stream flow, sediment, and nutrient loads under present-day wetlands and potential future climate; and (3) to determine how wetland gain or loss might exacerbate or ameliorate climate-induced changes in future water quality. These objectives are investigated with the Soil and Water Assessment Tool (SWAT) hydrologic model in the Sprague River watershed in southern Oregon, United States, which has been historically snowmelt dominated and where elevated nutrient loads in the 20th century have contributed to decline of fish species downstream. Results suggest that present-day wetlands under current climate may result in substantially lower nitrogen and phosphorus loads at the Sprague River watershed outlet. SWAT simulations forced with precipitation and temperature from six General Circulation Model (GCM) derived climate projections for 2030-2059 suggest uncertainty in magnitude and direction of both precipitation and stream flow changes on an average annual and monthly basis. Under present-day wetland extent, long-term average annual runoff for 2030-2059 decreased by 4% under one projection relative to a baseline period of 1954-2005, but increased by 6-31% under other projections. However, change in future annual runoff was statistically different from baseline for only two of six climate projections. Late spring and summer stream flow was lower in all simulations but significantly different from baseline in only some cases; for simulations driven with wetter future climate projections average monthly flow increased significantly from approximately October through March, and peak average monthly flow increased from 3-36% but timing did not alter. A simulation driven with a drier future climate projection showed decreases in average flow for most months, but was not significantly different from baseline. Simulated average annual sediment and nutrient loads generally tracked flow seasonality and decreased by 6% (sediment), 8% (TN) and 11% (TP) under one projection, but increased from 7-52% (sediment), 4-37% (TN) and 1-38% (TP) under other projections. Findings suggest that nutrient loads at the Sprague River outlet under future climate and scenarios of wetland change could vary significantly from baseline, or could be similar to the historic period. However, a threshold of wetland loss may exist beyond which large increases in nutrient loads could occur, and wetland gain might do little to ameliorate climate impacts to stream water quality in the Sprague River watershed.