ABSTRACT:

The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body. In this investigation, we seek to answer the following Research Question:

What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river?

For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U.

To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.

ABSTRACT:

The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body. In this investigation, we seek to answer the following research question:

What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river?

For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U.

To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.

ABSTRACT:

The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body. In this investigation, we seek to answer the following research question:

What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river?

For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U.

To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.

ABSTRACT:

Climate change and a long history of fire suppression have contributed to an increase in the frequency and extent of high-severity wildfires across the western United Sates. Forest treatments such as mechanical thinning and prescribed burning are commonly used strategies to reduce wildfire severity and improve forest resilience. Because of the importance of vegetation cover on the timing and quantity of snow accumulation and melt, both forest treatment and wildfire can affect snowpack behavior in important ways. Here, we develop SNOW-17(VEG), an adaptation of the widely used and validated SNOW-17 temperature index model that incorporates vegetation structure. Statistical evaluation of SNOW-17(VEG) indicated strong agreement between simulated and observed snowpack conditions in a seasonal snowpack when comparing dense forest to a high severity burn scar in Colorado (NSE: 0.95, 0.94; Pbias: 3.4, -2.6; R: 0.97, 0.94). In an ephemeral snowpack in New Mexico, while performance was weaker in terms of absolute magnitude, the model reproduced key differences in snow accumulation and melt timing among dense forest, thinned forest, and high severity burn scar conditions (NSE: 0.84, 0.86, 0.92; Pbias: 56.3, 57.7, 26.7; R: 0.91, 0.90, 0.95). SNOW-17(VEG) provides a low-data framework for evaluating the influence of forest management and wildfire disturbance on snow accumulation and melt in mountain watersheds with available temperature and precipitation observations. The model may support scenario analysis for forest management, water resources planning, and wildfire-related risk assessment.

ABSTRACT:

Provided is an ArcGIS tool and python code to create automated vegetation density zones for use in SNOW-17(VEG) snow model. SNOW-17(VEG) is an adaptation of the widely used and validated SNOW-17 temperature index model that incorporates vegetation structure. The method creates an EVI2 layer which it bins into vegetation zone categories. It then separates categories by elevation to create final elevation zones. Required inputs are a watershed shapefile or feature class, dem, and remotely sensed imagery that has Red and NIR bands.

ABSTRACT:

Snowpack is a critical component of water resources in the southwestern United States, however, it is becoming increasingly vulnerable due to climate change. Changes in forest structure resulting from wildfire and forest treatments further influence snow accumulation and melt processes. Existing snow models that explicitly represent forest structure’s influence on snowpack are often data intensive and therefore not applicable for many watersheds, especially in the southwestern United States. In this study we apply a relatively low data requirement, forest structure driven, snow model called SNOW-17(VEG). We develop an automated methodology for delineating vegetation-density zones, integrating stochastic temperature and precipitation scenarios, and operationalizing the model through a graphical interface. Model calibration shows strong agreement with observed snowpack, especially in the snow-dominated portions of the watershed (NSE: 0.96 , Pbias: 8.8, R: 0.97). Projected increases in temperatures reduced snowpack accumulation and accelerated snowmelt across simulation scenarios. This study demonstrates a transferable, low-data framework for integrating vegetation structure and climate uncertainty into snowpack modeling and provides a practical approach for evaluating the combined effects of vegetation change and climate uncertainty on snowpack dynamics in semi-arid watersheds.

ABSTRACT:

Climate change and a long history of fire suppression have contributed to an increase in the frequency and extent of high-severity wildfires across the western United Sates. Forest treatments such as mechanical thinning and prescribed burning are commonly used strategies to reduce wildfire severity and improve forest resilience. Because of the importance of vegetation cover on the timing and quantity of snow accumulation and melt, both forest treatment and wildfire can affect snowpack behavior in important ways. We developed SNOW-17(VEG), an adaptation of the widely used and validated SNOW-17 temperature index model that incorporates vegetation structure. Statistical evaluation of SNOW-17(VEG) indicated strong agreement between simulated and observed snowpack conditions in a seasonal snowpack when comparing dense forest to a high severity burn scar in Colorado (NSE: 0.95, 0.94; Pbias: 3.4, -2.6; R: 0.97, 0.94). In an ephemeral snowpack in New Mexico, while performance was weaker in terms of absolute magnitude, the model reproduced key differences in snow accumulation and melt timing among dense forest, thinned forest, and high severity burn scar conditions (NSE: 0.84, 0.86, 0.92; Pbias: 56.3, 57.7, 26.7; R: 0.91, 0.90, 0.95). We also developed an automated methodology for delineating vegetation-density zones, integrating stochastic temperature and precipitation scenarios, and operationalizing the model through a graphical interface. Model calibration shows strong agreement with observed snowpack, especially in the snow-dominated portions of the watershed (NSE: 0.96 , Pbias: 8.8, R: 0.97). Projected increases in temperatures reduced snowpack accumulation and accelerated snowmelt across simulation scenarios. SNOW-17(VEG) provides a low-data framework for evaluating the influence of forest management and wildfire disturbance on snow accumulation and melt in mountain watersheds with available temperature and precipitation observations.

ABSTRACT:

Collins Lake Ranch is a private ranch located in Mora County, northern New Mexico. It specializes in residential living and education for adults with disabilities (Collins Lake Ranch, 2024). In 2022, part of the ranch was impacted by the Hermit’s Peak Calf Canyon (HPCC) wildfire. New Mexico Highlands University commonly uses this ranch as a research site for forestry management and have treated multiple areas on the ranch, saving it from more extreme burning from the HPCC fire. This study area has three study locations, totaling six plots. Due to active thinning on this ranch, we moved our dense location for the 2024-2025 snow season, however, we tried to maintain as similar a forest structure and elevation as possible. Collins Lake Ranch represents lower elevation mid latitude locations that receive substantial snowfall, however, the snowpack is not seasonally persistent and is therefore classified as ephemeral. Each plot has a Wingscapes TimelapseCam Pro camera, a two and a half meter by one-inch diameter snow pole attached to a U-post, and temperature loggers distributed on the ground surface. The time-lapse camera takes a picture twice a day of the snow pole which is painted in five-centimeter increments. Daily snow depth was recorded from the pictures by a minimum of two people and vetted by a third. SWE was calculated using methodology from Hill et al (2019). 10 Temperature loggers are placed on the ground in a spoke-like pattern around the snow pole. There are 4 buttons at N, S, E, W 4 ft from the pole. There are 4 buttons at N, S, E, W 8 ft from the pole. There is a button at NE, and SW 8 ft from the pole.

ABSTRACT:

The Valles Caldera National Preserve is located in northern New Mexico in the Jemez Mountains outside of Los Alamos, NM. It is a 35,977 hectare (88,900 acre) park, surrounding a volcanic caldera formed by a super volcano that erupted 1.2 million years ago. The 2013 Thompson Ridge wildfire affected a portion of the caldera where one of our locations exist (VCM). These study locations were already established prior to this study to examine energy and water exchanges. Elevation ranges from 2,438 meters to 3,430 meters, largely within Mixed Conifer and Ponderosa Pine forest. There are four study locations and eight plots within this study area. The Valles Caldera represents higher to mid elevation locations at mid latitudes in the southwestern United States that normally have a seasonal to marginal snowpack. Each plot has a Wingscapes TimelapseCam Pro camera, a two and a half meter by one-inch diameter snow pole attached to a U-post, and temperature loggers distributed on the ground surface. The time-lapse camera takes a picture twice a day of the snow pole which is painted in five-centimeter increments. Daily snow depth was recorded from the pictures by a minimum of two people and vetted by a third. SWE was calculated using methodology from Hill et al (2019). 16 HOBO Pendant MX Water Temperature Data Loggers temperature loggers are placed on the ground in a spoke-like pattern around the snow pole. There are 4 buttons at N, S, E, W 4 ft from the pole. There are 4 buttons at N, S, E, W 8 ft from the pole. There are 4 buttons at NE, SE, SW, and NW 8 ft from the pole. here are 4 buttons at NE, SE, SW, and NW 16 ft from the pole.

ABSTRACT:

Banded Peak Ranch is a private 20,477 hectare (50,600 acre) ranch that lies at the headwaters of the Navajo River. The Navajo River is a main tributary of the San Juan Chama diversion project which is an inter-basin water transfer that provides water to the Rio Grande River Basin. Banded Peak Ranch is managed for ecological sustainability and improved wildlife habitat for large game hunting (elk and mule deer). Vegetation at the ranch spans from oak groves, ponderosa forests, and meadows at 2,400 meters, through aspen forest, to mixed conifer in the high country at over 3,800 meters. There are two study locations with contrasting forest structure in this study area: one in dense Mixed Conifer with some Aspen and one in relatively open areas with some nearby Mixed Conifer and Aspen trees. Banded Peak Ranch represents mid elevations at higher latitudes of the southwestern United States with seasonal to marginal snowpack. Each plot has a Wingscapes TimelapseCam Pro camera, a two and a half meter by one-inch diameter snow pole attached to a U-post, and temperature loggers distributed on the ground surface. The time-lapse camera takes a picture twice a day of the snow pole which is painted in five-centimeter increments. Daily snow depth was recorded from the pictures by a minimum of two people and vetted by a third. SWE was calculated using methodology from Hill et al (2019). 16 HOBO Pendant MX Water Temperature Data Loggers temperature loggers are placed on the ground in a spoke-like pattern around the snow pole. There are 4 buttons at N, S, E, W 4 ft from the pole. There are 4 buttons at N, S, E, W 8 ft from the pole. There are 4 buttons at NE, SE, SW, and NW 8 ft from the pole. here are 4 buttons at NE, SE, SW, and NW 16 ft from the pole.