Poster

Evaluating Meteorological Controls on Wet Bulb Globe Temperature Sensitivity and Implications for Urban Heat Stress

Sarah Rogers — Fri, 27 Mar 2026 21:05:35 +0000

Evaluating Meteorological Controls on Wet Bulb Globe Temperature Sensitivity and Implications for Urban Heat Stress Sarah Rogers Fri, 03/27/2026 - 15:05 Cambria Danielski

Urban heat stress poses a direct threat to the public health of urban communities in arid and semi-arid regions. Heat stress can be determined using Wet Bulb Globe Temperature (WBGT), which is calculated by integrating meteorological data including wet bulb temperature, globe temperature, and dry bulb temperature. By incorporating these variables, WBGT captures the “feel like” temperature perceived by the human body, allowing heat stress to be assessed on an index scale. Over the course of a three-month urban heat field campaign (June-August 2025), a Kestrel 5400 heat sensor measurement tool was utilized to measure WBGT across five varying landscape ground cover types - traditional grass, native grass, artificial turf, gravel, and wood mulch - at five parks throughout the Denver, Colorado area. I performed a sensitivity analysis by developing meteorological baseline conditions derived from a single representative field day, adjusting air temperature and relative humidity across a ±30% range and wind speed across a -100/+300% range to isolate and better understand their individual influence on WBGT. Initial results indicate that air temperature influences WBGT most substantially. Vegetation landscape cover types play a critical role in urban heat stress, highlighting the need for future heat mitigation strategies to be assessed.

Cambria Danielski · CEAE · Undergraduate Student

Off

Traditional

White

Comparison of human perceived temperature between in-situ and reanalysis datasets in Denver, Colorado

Sarah Rogers — Fri, 27 Mar 2026 20:43:15 +0000

Comparison of human perceived temperature between in-situ and reanalysis datasets in Denver, Colorado Sarah Rogers Fri, 03/27/2026 - 14:43 Nicholas Guthro

Urban heat stress can be caused by transitioning from natural, non-urbanized environments to dense urban environments, as this increases the number of buildings, roads, and other infrastructure. A commonly used metric to study heat stress is Wet Bulb Globe Temperature (WBGT), which is a “feels like” temperature that incorporates wind speed, relative humidity, cloud cover, and temperature. Over a three-month field campaign in Denver, Colorado, in the summer of 2025, Kestrel 5400 heat sensors were used to measure WBGT over grass, native grass, wood mulch, gravel, and artificial grass in five parks. To better understand the individual effects of landscapes, a regional WBGT must be calculated from ERA5 climate and weather data. This study uses wind speed, dew point temperature, air temperature, and net solar radiation to determine a regional WBGT value for each park examined. This WBGT can then be compared with the value observed in the field, allowing comparison of values across different parks and days. This work is important for assessing the environmental effects of waterwise landscapes that may be implemented in arid or semi-arid cities to reduce municipal outdoor water use.

Nick Gurthro · CEAE · PhD Student

Off

Traditional

White

Temperature Analysis for Common Waterwise Landscape Types in Urban Heat Island for a Denver Park

Sarah Rogers — Fri, 27 Mar 2026 20:29:58 +0000

Temperature Analysis for Common Waterwise Landscape Types in Urban Heat Island for a Denver Park Sarah Rogers Fri, 03/27/2026 - 14:29 Leanna Johnson

Urban heat islands intensify thermal stress in cities by elevating surface and air temperatures relative to surrounding areas. In semi-arid cities such as Denver, landscape design is an important tool in moderating these effects because the thermal behavior of urban surfaces strongly influences both daytime heating and increasing levels of stored heat. As municipalities increasingly promote waterwise landscaping to reduce irrigation demand, there is a growing need to understand how common low-water landscape alternatives compare thermally with conventional surfaces. This study evaluates the temperature behavior of representative landscape types in an urban park setting to assess their relative contributions to surface heating and potential heat island mitigation.

The temperature data was collected during the summer of 2025 at Denison Park in Denver, Colorado. The six land-cover types were turf grass in full sun, turf grass in full shade, pavement, wood mulch, native grass, and rock mulch/squeegee, with all non-shaded surfaces located in full sun. Measurements were obtained using two complementary technologies. A Kestrel instrument was used to measure atmospheric conditions, including air temperature and wet bulb temperature, while an infrared device was used to measure surface temperature. A time-series analysis of air and surface temperatures revealed clear differences in thermal behavior among the six landcover types. Pavement consistently reached higher temperatures and remained warm for longer periods than vegetated surfaces, demonstrating its strong role in amplifying local heat exposure and prolonging thermal stress after peak heating hours. In contrast, grassed surfaces generally exhibited surface temperature patterns that more closely tracked air temperature, suggesting lower heat storage and faster cooling relative to hardscape materials. This tendency indicates that vegetated surfaces may reduce the persistence of elevated surface temperatures and therefore lessen the localized thermal burden associated with urban heat islands.

Leanna Johnson · CEAE · Undergraduate Student

Off

Traditional

White

Preparing for the impacts of Colorado's 2026 Snowpack

Sarah Rogers — Fri, 27 Mar 2026 20:24:02 +0000

Preparing for the impacts of Colorado's 2026 Snowpack Sarah Rogers Fri, 03/27/2026 - 14:24 Angelina Kondrat

We will be focusing on analyzing the quantity of this year's snowpack by demonstrating data collection, adding visual demonstrations, and interviewing professionals working in Front Range water, fire, and avalanche safety.

To incorporate a visual component to our presentation, we will create maps, and include photos to connect our data to physical locations. Our main map will be of the Mountain Research area with sites C1, Tundra, fed-sample area, elevation, and stream location (?). We will show the paths we take to sample areas as well as photos of us working in the field. This map and photos will be a great reference when distinguishing between cites. We will include graphs to visually compare previous years historical records to our data collected this semester allowing us to track trends.

We are scheduling interviews with Leila Parker, a Water Resources Senior Project Manager at City of Boulder, Ryan Zarter at CDOT/CAIC, and Eric Kennedy, a Wildland Coordinator and Captain at Four Mile Fire Protection District. The purpose is to add a qualitative perspective to our data collection in the field and learn how different local agencies might use the data we are collecting in real application for their mitigation, prevention and allocation strategies. We also want to understand how these agencies are reacting to this year’s record low snowpack.

For data collection, we are comparing measurements made at the C1 site and our single tundra lab measurement to previous snow intern data. Specifically comparing our data to SNOTEL to identify how consistent they are with each other. Furthermore, we are looking at previously record low year of runoff and comparing it to this year in adding SWE numbers and temperatures at the MRS to other parts of Colorado.

Angelina Kondrat · GEOG · Undergraduate Student

Off

Traditional

White

Evaluating Hydro-Climatic Variability within the Prairie Pothole Region

Sarah Rogers — Fri, 27 Mar 2026 19:22:01 +0000

Evaluating Hydro-Climatic Variability within the Prairie Pothole Region Sarah Rogers Fri, 03/27/2026 - 13:22 Ayon Saha

The Prairie Pothole Region (PPR) is defined by a dense network of shallow lakes and wetlands that are fundamental to regional hydrology, aquatic biodiversity, and outdoor recreation. While climate variability dictates the behavior of these systems through snowmelt, infiltration, and "fill-and-spill" dynamics, the specific drivers of streamflow during wet periods remain insufficiently documented. This study utilizes the Cold Region Hydrologic Model (CRHM) to analyze hydrological shifts across select PPR subbasins from Water Year 2001 to 2023. By evaluating key state variables, including Snow Water Equivalent (SWE) and annual evaporation, alongside watershed storage factors like depressional connectivity, we assessed the primary contributors to lake volume fluctuations.

Our results demonstrate that annual evaporation rates fluctuate significantly, underscoring the heavy influence of year-to-year climate shifts. Spatially, the model reveals a distinct latitudinal gradient, with higher evaporation in the southern PPR than in the north due to elevated temperatures and solar radiation. A longitudinal gradient is also apparent, as more arid western zones experience steeper water losses. Temporally, three hydroclimatic phases were identified: (1) two evaporation-heavy periods (2004-2008 and 2014-2021), and (2) a wet interval (2009-2013) marked by snowmelt-induced flooding and fill-and-spill dynamics. These findings provide a necessary framework for water resource managers to maintain lake health and recreational value in the face of ongoing environmental change.

Ayon Saha · ERTH · PhD Student

Off

Traditional

White

Hydroclimatic Regime Conditioning of Seasonal Streamflow Forecasts in the Western US

Sarah Rogers — Fri, 27 Mar 2026 16:26:12 +0000

Hydroclimatic Regime Conditioning of Seasonal Streamflow Forecasts in the Western US Sarah Rogers Fri, 03/27/2026 - 10:26 Olivia Stanley

Seasonal water supply forecasts in snow-dominated basins of the western United States rely on statistical regression models that treat all years as drawn from a single climatological population. We evaluate whether k-means clustering of cold-season hydroclimatic predictors can improve April–July streamflow forecasts by conditioning separate regressions on data-driven regimes, extending the stratified-training approach of Modi et al. (2022) to 29 CAMELS basins across five regions.

Under standard frozen-cluster leave-one-out cross-validation (FC-LOOCV), the regime model improves on a SWE-plus-precipitation baseline in 27 of 29 basins. Nested re-clustering within each fold (NR-LOOCV), which prevents information leakage from withheld years into cluster assignment, reduces genuine improvement to 2 of 29 basins, with median leakage of ΔR² = 0.21 and extreme cases exceeding 2.0. Leakage magnitude is predicted by a minimum-cluster residual degrees-of-freedom (DOF) criterion: basins where the smallest cluster supports fewer than five residual DOF cannot sustain stable regime-specific regressions, and standard cross-validation cannot detect this failure. In failing basins, skill loss concentrates in years that fall far from both cluster centroids; a distance-based fallback to the baseline forecast recovers skill in these cases. The k-means clusters recover physically interpretable warm/cold hydroclimatic regimes, and the method produces genuine skill gains where cluster balance and record length provide sufficient DOF. Honest evaluation of regime-conditioned forecasts requires nested re-clustering; frozen-cluster cross-validation is structurally biased and should not be used to claim skill improvement.

Olivia Stanley · ERTH · PhD Student

Off

Traditional

White

How can automated observational snow networks be used to accurately determine snow-rain temperature thresholds in the western U.S.?

Sarah Rogers — Fri, 27 Mar 2026 16:14:10 +0000

How can automated observational snow networks be used to accurately determine snow-rain temperature thresholds in the western U.S.? Sarah Rogers Fri, 03/27/2026 - 10:14 Cayden Stratford

Snowpack is critical for water supply in the western U.S. but challenging to physically model. The snowline, the average elevation at which snow will persist in a given region, is of interest because it enables us to identify changes in both water supply timing and in habitats for snow-adapted species like the Canadian lynx. Here, we focus on a hydrometeorological approach to map the snowline. In this approach, a series of snow-rain thresholds are tested using surface air temperature observations, validated against in situ observations of snow. Given snowfall dependencies on temperature and relative humidity, we expect that the rain-snow partition threshold will vary regionally. Prior analyses have sought to map the rain-snow partition such as Jennings et al. 2018, Sims & Liu, 2015, and Klos et al., 2014. However, their data included limited observations at low surface pressures (i.e. high elevation), warranting further investigation. We therefore assess whether rain-snow temperature thresholds can be more accurately characterized by using a more extensive network of in-situ observations.
This study uses the NRCS SNOw TELemetry (SNOTEL) automated observational network’s data from 1990-2020, from 947 sites across the western U.S. We use historical records of daily precipitation, snow-water equivalent (SWE), and air temperature to align actual SWE against the SWE estimated using the amount of precipitation that accumulates below each tested temperature threshold. Here, we identify the optimal temperature threshold which minimizes errors between observed and estimated SWE. Preliminary results find that the average partition temperature is greater than 4.5 °C across most of Colorado, with a median of 5.5 °C for all years. We can also delineate by wet and dry years (wet being those with peak SWE above the long-term mean, and dry those below). For example, a band of high (>4 °C) thresholds in the Northern Rockies encompasses more stations across wet years than across dry. We aim to identify the cause of this, and di`erences by basin, elevation, and wet / dry years. We anticipate that these findings will help validate further analyses of the snowline, and aid in the understanding of the evolving habitats in the mountainous U.S.

Cayden Stratford, Gillian Gallagher, and Ben Livneh · CEAE · Undergraduates and Faculty

Off

Traditional

White

Augmenting Flume Experiment Data Using a Convolutional Neural Network to Track Wood in Flow

Sarah Rogers — Thu, 26 Mar 2026 22:21:10 +0000

Augmenting Flume Experiment Data Using a Convolutional Neural Network to Track Wood in Flow Sarah Rogers Thu, 03/26/2026 - 16:21 Josie Welsh

Large wood (LW; >10 cm in diameter and >1 m in length) within river corridors - including channels and adjacent floodplains - plays a key role in shaping hydraulic conditions, sediment deposition and erosion, nutrient cycling, and habitat availability for diverse aquatic and terrestrial species. Thus, to fully understand how a river system functions, we must understand when, how, and why LW is stored or transported.

Here, we present the results of initial tests that use a CNN called You Only Look Once (YOLO) v26 to track wood pieces in videos of a set experiments investigating wood transport in forested river valleys. To create training data, we extracted and annotated wood in 800 video frames from 16 representative experiments. We then retrained the out-of-box YOLOV26n model to detect wood pieces. We investigated different data augmentation schemes, such as altering image color and image mosaicing. Once this model was trained and validated, we used a tracker algorithm called BOTsort to relate wood detections across frames to create traces for each piece of wood.

We found that image mosaicing and color augmentations improved detection precision, recall, and mean average precision by 10-15%. Additionally, BOTsort produced fragmented traces, which could be stitched together in a post processing step to create much more reliable traces. These results represent a step towards using advanced machine learning techniques to support LW research. Successful wood trace extraction will likely result in new insights into how wood is transported in complex environments and how wood jams accumulate. Understanding these complex processes will improve our understanding of wood in river systems and help river managers make informed decisions about wood in their systems.

Josie Welsh · GEOG · PhD Student

Off

Traditional

White

Monitoring Changing Lag Time and Flow Duration at a Developing Site in Parker, Colorado

Sarah Rogers — Thu, 26 Mar 2026 22:18:02 +0000

Monitoring Changing Lag Time and Flow Duration at a Developing Site in Parker, Colorado Sarah Rogers Thu, 03/26/2026 - 16:18 Halee Workman

Urban development can have a large impact on watershed hydrology as pervious surfaces are replaced by impervious surfaces. This can cause noticeable changes in runoff and the shape of a watershed hydrograph in response to storm events. We are monitoring West Stroh Gulch, a 0.6 mi2 watershed in Parker, CO, as it develops into residential housing. As construction has progressed, we have observed decreasing lag time and increasing flow duration.

To track these changes, we are using precipitation data obtained from Gauge-Adjusted Radar Rainfall (GARR) data and time series flow data from a streamgauge on site, paired with photographs from flow-monitoring cameras set up along the watershed. Generally, the lag times have decreased over time. The average in 2023, when development activities began, was 5.72 hours. Comparatively, the average lag time in 2025 was 2.18 hours, after the main stormwater infrastructure was installed and connected. Furthermore, the flow duration has increased from an average of 5.88 hours in 2023 to 24.13 hours in 2025. This monitoring will be continued through post-development of the site in order to identify changes in watershed hydrology, and its response to rain events.

Halee Workman · CEAE · Undergraduate Student

Off

Traditional

White

Using Photogrammetry to Monitor Hydrologic and Geomorphic Changes in West Stroh Gulch

Sarah Rogers — Tue, 01 Apr 2025 20:00:00 +0000

Using Photogrammetry to Monitor Hydrologic and Geomorphic Changes in West Stroh Gulch Sarah Rogers Tue, 04/01/2025 - 14:00 Eric Balderrama Sanchez

Urban development can have a large impact on streams, and tracking these changes over time is important for future urban planning. This project focuses on West Stroh Gulch, a non-perennial stream in Parker, Colorado, where new housing development is underway. To monitor how the landscape and stream morphology change, we are using drone-based photogrammetry to create high-resolution Digital Elevation Models (DEMs) and orthomosaic maps. By flying a drone every few months and processing aerial imagery, we can generate a visual timeline of how urbanization affects the stream channel and surrounding area. These models will be compared over time to highlight topographic changes and help improve our understanding of development-driven hydrologic shifts.

Eric Balderrama Sanchez · CVEN · BS Student

Off

Traditional

White