Department of Ecosystem Science and Sustainability

Permanent URI for this community

https://hdl.handle.net/10217/100401

These digital collections include theses, dissertations, faculty publications, and datasets from the Department of Ecosystem Science and Sustainability. Due to departmental name changes, materials from the following historical departments are also included here: Cooperative Watershed Management Unit; Earth Resources; Geology.

Of special note are digital copies of materials referenced by emeritus Earth Resources professor James R. Meiman in a literature review titled Little South Poudre Watershed and Pingree Park Campus (Colorado State University, College of Forestry and Natural Resources, 1971). These materials can be found here (re watershed science) and in departmental collections related to forestry, geology, and wildlife biology.

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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
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
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.

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Browsing Department of Ecosystem Science and Sustainability by Author "Arabi, Mazdak, committee member"

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