Poster

Rainfall and Streamflow Analysis of Depression Losses at the Rocky Flats National Wildlife Refuge

Anonymous — Wed, 10 Apr 2024 17:39:47 +0000

Rainfall and Streamflow Analysis of Depression Losses at the Rocky Flats National Wildlife Refuge Anonymous (not verified) Wed, 04/10/2024 - 11:39 Eric Balderrama

This study is centered around the analysis of rainfall and streamflow data collected from a watershed located within the Rocky Flats National Wildlife Refuge. The data was systematically filtered and cleaned to avoid skewness and potential error. Afterwards, a statistical analysis of the data was conducted which led to the creation of detailed relationships between rainfall and streamflow with the intention of updating historical depression loss values. Mile High Flood District (MHFD) gives up depression loss values of 0.2-0.6 in. for open fields, with a recommended value of 0.4 in. This means that we can expect 0.2-0.6 in. to get temporarily captured in depression storage, preventing it from becoming runoff. Now, when we look at the highest and lowest (non-zero) rainfall depths captured by the closest rain gage, in relative proximity to the watershed, which did not result in a flow event, we see these values are 2.48 in. and 0.04 in., respectively. This is significant because the largest value that did not result in a streamflow event is magnitudes larger than that of the recommended value of 0.4 in, indicating much of the captured rainfall must have been lost elsewhere. The relevance of this analysis is not to be underestimated, as it allows for the understanding of the threshold at which a flow event occurs. Additionally, there were 13 streamflow events in the 5-years’ worth of data that were captured with a delayed start time. Of the 13 events, the average delay time from the first instance of rainfall to the first detection of streamflow was 04:51 [hh:mm] with a standard deviation of 04:26 [hh:mm]. The data was further filtered and any time delay above 300 minutes was removed. This led to only 9 rainfall events that led to streamflow being analyzed. The time delay average and standard deviation of these events was 02:16 [hh:mm] and 01:21 [hh:mm], respectively. The investigation of depression losses in open fields presented a unique opportunity to examine the intricate relationship between rainfall depth and flow events. It was recognized that just before the threshold for flow events was reached, a significant quantity of rainfall was lost in depression storage. Therefore, the rainfall quantity was recorded for these 9 specific instances of delay time. The rainfall depth average and standard deviation are 0.64 in. and 0.50 in., respectively. These values allow for a much clearer comparison between the depression loss values given by MHFD and the data from the observed watershed.

Eric Balderrama · CEAE · BS Student

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Water Supply Prediction in Unmonitored Basins: Integrating Statistical Models and Remotely Sensed Snow Data

Anonymous — Tue, 09 Apr 2024 18:01:00 +0000

Water Supply Prediction in Unmonitored Basins: Integrating Statistical Models and Remotely Sensed Snow Data Anonymous (not verified) Tue, 04/09/2024 - 12:01 Kaitlyn Bishay

Accurate predictions of seasonal water supply are vital to all communities – regardless of their size, population, or location – as they are the basis for informed water resource decisions. Throughout the western U.S., predictions of total annual streamflow often rely upon spatially limited in situ snow measurements, which may not be available in all watersheds. However, previous work by the author team showed that these in situ measurements can be supplemented (or even replaced) by remotely sensed snow timing data. Initial findings for fifteen snow-dominated basins during the years 2001-2019 indicate the existence of a significant (p ≤ 0.05) predictive linear relationship between remotely sensed day of snow disappearance (DSD) and seasonal water supply, with mean DSD explaining roughly half of the variance in AMJJ total flow volume. This work expands on the spatial and temporal extents of previous research, describing the skill of these remotely sensed variables as predictors of water supply in over one hundred basins with varied watershed characteristics (elevation, SWE/P ratio, etc.) Further, we are particularly interested in the utility of remotely sensed snow disappearance in basins that lack in situ monitoring. By comparing the skill of watershed scale Monte Carlo linear regression models across monitored and unmonitored basins, this analysis provides new insight into the potential for remotely sensed data-driven models across the western U.S.

Kaitlyn Bishay · CEAE · PhD Student

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Comparative Analysis of Snow-Water Equivalent Measurements: Insights from Niwot Ridge

Anonymous — Tue, 09 Apr 2024 17:59:10 +0000

Comparative Analysis of Snow-Water Equivalent Measurements: Insights from Niwot Ridge Anonymous (not verified) Tue, 04/09/2024 - 11:59 Samuel Fitterman , Drake Stasyshyn , Eva Ramm , Jennifer Frances Morse

This study investigates the correlation between snow: depth, water equivalent and density measurements taken by the SNOTEL 663 site and those obtained through the Federal Snow Sampler at C1 on Niwot Ridge from 2016 - 2023. Utilizing linear regression analysis, we examined the relationship between the two measurement techniques to assess their comparability and reliability. Our study identified moderate positive correlations between SNOTEL and Federal Sampler measurements for snow depth and snow water equivalent (SWE) on Niwot Ridge, demonstrating that SNOTEL data can partially explain the variability in Federal Sampler readings. The regression analysis yielded a correction equation for Federal Sampler density measurements based on SNOTEL data, facilitating further insight into achieving accurate water availability forecasting. Niwot Ridge is located in the Front Range of the Colorado Rockies and is a designated United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserve. The C1 site is characterized by its relative shelter within a subalpine forest on a ridge with an elevation of 3022 meters. The SNOTEL site is approx. 262 meters to the WNW of the C1 pit site. By analyzing the correlations between these methodologies and employing linear regression to establish a correction equation, this research aims to enhance data comparability and reliability, thereby improving water resource management and predictive modeling in snow hydrology within the subalpine ecosystems of the Colorado Rockies.

Samuel Fitterman, Drake Stasyshyn, Eva Ramm, · GEOG · BA Students

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Communicating Nature Based Solutions to Reduce Urban Runoff within the Colorado Front Range

Anonymous — Tue, 09 Apr 2024 17:54:35 +0000

Communicating Nature Based Solutions to Reduce Urban Runoff within the Colorado Front Range Anonymous (not verified) Tue, 04/09/2024 - 11:54 Leanna Johnson

·According to the Water Education organization in Colorado, the Colorado population is increasing at a rapid rate and is expected to grow to 8.1 million by the year 2050. At the same time. Freshwater resources will continue to decline as the impacts of climate change continue. This leads to an impending water crisis in Colorado, so water conservation and protection strategies have been created.
Specifically, within the Urban environment, the natural water cycle is being disrupted. In the urban landscape, there are hard surfaces such as roads, sidewalks, and houses that cause increased amounts of runoff. The runoff collects pollutants that run into local waterways harming fish, wildlife, plants, and humans. Strategies, such as low-impact development (LID), Sustainable Urban Drainage Systems (SUDS), and Water Sensitive Urban Design (WSUD), have been created to manage wet weather flows and provide habitat, flood protection, cleaner air, and cleaner water. Our project’s goal was to create a way to communicate these strategies through a visual display to the public. We aimed to make the diorama relevant to the Front Range by deriving our data directly from major cities along the Front Range. The data consists of precipitation amounts, snow, and minimum and maximum temperatures. The diorama shows four scenarios that undergo a similar amount of rain. Natural grassland represents undeveloped land in Colorado, which consists only of native Colorado grass. Traditional Development represents a current house in Colorado with impervious surfaces and conventional landscaping, such as turf which is water intensive. Low Impact Development shows the ideal housing development with green infrastructure implemented. Post-wildfire is meant to show the impact of wildfires on a region's soil and water quality as wildfires are becoming more frequent.
Environmental problems are often viewed as problems that require large-scale solutions that the general public does not have access to implement. As the population of Colorado continues to increase so does the demand for fresh water. Climate change is decreasing the availability of freshwater at the same time. To prevent the loss of water resources both large-scale and local-scale solutions should be implemented. The general public can only help with this issue if they know these solutions. These decentralized nature-based solutions are how the public can get involved in protecting the Front Range for years to come.

Leanna Johnson · CEAE · BS Student

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Image analysis of stream channels for flow presence monitoring

Anonymous — Tue, 09 Apr 2024 17:53:21 +0000

Image analysis of stream channels for flow presence monitoring Anonymous (not verified) Tue, 04/09/2024 - 11:53 Junwon Lee

Efficient and accurate analysis of large amounts of images is critical for water monitoring studies. This study proposes the use of open-source deep learning, RGB and HSL-based OpenCV Python code as a solution to overcome the limitations of manually analyzing large numbers of images by humans. Previous studies have already utilized these technologies for water monitoring and analysis, but which technology is most suitable and efficient is not yet clear. In this study, RGB-based images and HSL images, and we plan to analyze them using RGB-based OpenCV Python code, HSL-based OpenCV Python code, and deep learning techniques. Through this, we will evaluate which technology produces the most accurate results and suggest a direction for developing efficient program tools for water monitoring research. This will support more accurate and reliable data acquisition in the field of water monitoring. And if this technology develops further, it will be possible to detect not only natural river flows but also urban floods using various crowdsource within the city.

Junwon Lee • CEAE• BS Student

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Quantifying Baseflow Using Groundwater Levels in The Upper Colorado River Basin

Anonymous — Tue, 09 Apr 2024 17:52:35 +0000

Quantifying Baseflow Using Groundwater Levels in The Upper Colorado River Basin Anonymous (not verified) Tue, 04/09/2024 - 11:52 Corrine Liu

The vitality of the Colorado River faces uncertainty in light of frequent and prolonged droughts induced by climate change. Progressing knowledge concerning the role of groundwater and surface water interactions is critical in informing water resource managers and Colorado River water users—namely, the mechanism of baseflow accounts for a substantial portion of streamflow. Baseflow is considered a proxy for groundwater discharge to streams. Groundwater is vital in sustaining streamflow via baseflow, particularly during periods of low precipitation and overland flow. Limited baseflow studies within the Upper Colorado River Basin indicate that approximately half of streamflow is accounted for by baseflow. This study aims to quantify baseflow to the Roaring Fork River, a major tributary within the Upper Colorado River Basin. The Roaring Fork River flows along the western margins of Colorado’s Southern Rocky Mountains physiographic province.

This study employed a new approach based on groundwater level data from the Colorado Division of Water Resources (CDWR). Groundwater level observations were compiled between 2000 and 2022 from over 150 wells in the Roaring Fork subbasin to ultimately interpolate static groundwater level elevations. Hydraulic gradients near the Roaring Fork River were elucidated from contoured groundwater levels. Existing estimates of hydraulic conductivity were analyzed using empirical pumping test formulae. On the basis of hydraulic gradient and hydraulic conductivity, a mean annual groundwater discharge of 1.57 m3/s to the Roaring Fork River is estimated. In parallel, baseflow separation using a graphical method was conducted, which yields a similar magnitude of baseflow. This study, for the first time, demonstrates the potential of utilizing existing groundwater level data to supplement the study of baseflow. Enriching the arsenal of baseflow analysis will help contribute to sustainable and informed water resource management.

Corrine Liu • GEOL • MS Student

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Determining the Distribution and Volume of Water in Arctic Surge Glaciers for Understanding Surge Processes: A Combined Approach via Image Classification and ICESat-2 Altimetry Data

Anonymous — Tue, 09 Apr 2024 17:51:36 +0000

Determining the Distribution and Volume of Water in Arctic Surge Glaciers for Understanding Surge Processes: A Combined Approach via Image Classification and ICESat-2 Altimetry Data Anonymous (not verified) Tue, 04/09/2024 - 11:51 Rachel Middleton

Surge glaciers have a unique type of glacial acceleration, surging, in which the glacial system leaves a period of quiescence and experiences velocities that are up to 200 times the non-surge velocities. Surge events play a critical role in sea level rise (SLR), as the mass loss from even a single marine-terminating glacier during a surge has been estimated to be upwards of 0.5 percent of annual global SLR. The glacial hydrologic system, the water that flows above, within and below the glacier, plays a critical role in surge evolution and initiation, as the initiation of a surge requires decoupling of the glacier from the bed via reduction of basal friction, which is directly related to the subglacial water pressure at the basal boundary. This work establishes a simple framework for improving the estimation of basal water content in surge glaciers with a data-driven and model-based approach by combining image classification techniques with processing of ICESat-2 altimetry data to realistically estimate changes in volume of supraglacial water during a surge. This can then be related to the physical changes in the englacial hydrologic system.
Distribution of glacial surface water is determined by spectral classification of satellite imagery. The volume of surface water is determined by estimating water and ice surface elevation for each water feature with the Density-Dimension Algorithm for ice surfaces. The DDA-ice-2 determines ice surface height, crevasse morphology of wet and dry crevasses and water depth from ICESat-2 ATLAS data. The DDA-bifurcate algorithm determines ice surface height, melt pond morphology, and water depth from ICESat-2 ATLAS data. An idealized system of drainage through the glacier is established to constrain a numerical 3D model. The primary objective of this work is to establish a framework for determining and modeling the hydrologic conditions of a surge-type glacier. Furthermore, this framework was used to investigate the dynamics of the Negribreen Glacier System in Svalbard, Norway.

Rachel Middleton • CEAE • PhD student

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Fusing airborne lidar snow depth snapshots to generate long-term spatial SWE estimates

Anonymous — Tue, 09 Apr 2024 17:49:23 +0000

Fusing airborne lidar snow depth snapshots to generate long-term spatial SWE estimates Anonymous (not verified) Tue, 04/09/2024 - 11:49 Lena Nyblade , Jordan Herbert , Mark Raleigh , Eric Small

Seasonal snowpack is an important runoff source in many mountain watersheds, making accurate snowpack water storage data essential to streamflow prediction. Traditional ground-based observations of snow water equivalent (SWE), however, are insufficient in number and distribution to characterize the water supply from snowpack at larger spatial scales, from watersheds to global regions. Satellite, airborne, and drone-based remote sensing provide additional observations of snowpack variables at different spatial, temporal resolutions and accuracies to supplement ground-based observations. Airborne lidar provides spatially extensive and detailed but infrequent snapshots of snow depths across a basin. There is high utility in developing techniques that can integrate these infrequent snapshots with in situ SWE time series and snowpack models to improve SWE estimation.
Here, we describe a data assimilation approach to combine spatially-extensive snow observations from lidar with snowpack models to better quantify SWE in mountain watersheds. Using lidar flights over different Colorado basins, we compare snow depth from lidar with outputs from model ensembles (SNOW 17) to define weights based on the accuracy of each run relative to the Lidar snow depths for each model parameter set. We then extend through time yielding a lidar-informed SWE and depth data cube. Finally, the lidar informed weights and evaluate strategies for combining these disparate weights to generate long-term gridded daily datasets of SWE and snow depth.

Lena Nyblade • GEOL • MS Student

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A Distributed Flood Monitoring And Forecasting System: Development And Application In China

Anonymous — Tue, 04 Sep 2018 16:01:36 +0000

A Distributed Flood Monitoring And Forecasting System: Development And Application In China Anonymous (not verified) Tue, 09/04/2018 - 10:01 Ziyue Zeng

Zeng, Ziyue ¹ ; Hong, Yang ² ; Shen, Xinyi ³

¹ Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO
² School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK
³ Civil and Environmental Engineering, University of Connecticut, CT

Due to complex hydrometeorological and geographic conditions, China is continuously affected by severe floods, which often lead to significant losses on human lives and property. Aiming to support the progressive forecasting, analysis and evaluation of flood disasters, we developed a distributed high-resolution Flood Monitoring and Forecasting System and applied it at global, national and regional scales on the platform of China Meteorological Administration (CMA). In this system, based on the SMAP satellite soil moisture data and gauge-combined GSMaP satellite precipitation product, the annual global runoff (April 2015-March 2016, 0.1°×0.1°) is estimated using a new version of global Curve Number (CN) dataset. The estimated monthly runoff shows consistency with the observed data in Jialing River Basin. Furthermore, a distributed hydrological model, the Coupled Routing and Excess STorage (CREST) version 2.1, was been used to realize systematical and dynamical simulation of hydrological processes in a fine resolution in China (0.125?×0.125?and daily for the nation, 1km×1km and hourly for basins). Embedded a global geomorphology variable database, an Inundation Mapping module (iMap) using CREST simulated streamflow as the main input to calculate flood areas and depths was developed, dependent on which the time series of spatial and temporal dynamic inundation became available. Driven by the merged precipitation product of CMORPH and observations from automatic rainfall stations, simulation results demonstrate good skills in forecasting storm-triggered floods. The performance of iMap also indicated that this system is capable of estimating flood process in Gan river basin and Jialing river basin, thus offering guidance in flood disaster prevention and mitigation for users in China and even in the whole Asia. Enhanced by this system in the ability of flood forecasting and risk assessment modelling, CMA has adopted it as an operational system since 2013. Despite positive performance, more accurate forecasting precipitation data (e.g. Quantitative Precipitation Estimation products) and social-economic data (e.g. GDP, population, prevention measures) are need to improve the predictive capability and robustness of this system.

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An Integrated Approach To Risk Analysis And Spatio-Temporal Trend Analysis Of Hydraulic Fracturing Chemicals Utilizing The FracFocus Database

Anonymous — Tue, 04 Sep 2018 15:58:06 +0000

An Integrated Approach To Risk Analysis And Spatio-Temporal Trend Analysis Of Hydraulic Fracturing Chemicals Utilizing The FracFocus Database Anonymous (not verified) Tue, 09/04/2018 - 09:58 John Stults

STULTS, John ¹

¹ CU Boulder

The last decade of oil and gas exploration in the United States has been characterized by a sharp increase in the amount of oil and gas acquired from low-permeability shale formations. These low-permeability formations are non-traditional oil and gas reservoirs, which require hydraulic fracturing to stimulate oil and gas production. Many current and planned hydraulic fracturing operations now encroach on urban and peri-urban areas, raising alarm over the potential groundwater contamination threat posed by chemicals in hydraulic fracturing fluids. The purpose of this study was to develop a risk analysis metric that could be cross-referenced with hydraulic fracturing data to evaluate spatio-temporal trends related to the safety of chemicals used in hydraulic fracturing. A risk analysis metric was developed using available transport, persistence, and toxicity data on chemicals found in hydraulic fracturing fluids. Chemicals with available data were given a “combined risk score” based on their transport time, persistence, and toxicity data. Our team compiled PDF and machine-readable data from FracFocus, the largest publicly available database on hydraulic fracturing, into one machine readable database. The FracFocus data used in this study spanned from January 1st, 2011 through data posted as of February 22nd, 2018, and contained 116231 hydraulic fracturing job records. there were 302 chemicals found in FracFocus with available combined risk score data. Every chemical used in the 116,231 hydraulic fracturing jobs was evaluated, and each job was given a “job combined risk score” based on combined risk score of chemicals with available risk used in the job. Out of the 116,231 jobs analyzed, 106,691 (91.8%) had available combined risk score data. There is quasi-significant trend observed of increasing job combined risk scores from 2011 through the end of 2016. There are several spatial regions (i.e. states, sedimentary basins, or shale plays) which have significantly higher job combined risk scores, and several spatial regions which demonstrate significant trend toward increasing job combined risk scores over time. This integrated risk and data analysis approach to evaluating the potential groundwater threat of hydraulic fracturing chemicals is one of the largest and most comprehensive analysis of hydraulic fracturing chemicals ever attempted. Overall, this analysis framework is a valuable tool for oil and gas industry professionals and regulators to assess trends related to the use of hydraulic fracturing fluid chemicals on national and regional scales.

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