The project looks to connect Earth Observations with Decision Making. The key innovation is the novel inclusion of human system models in land cover land use change (LCLUC) modeling that should help make better future predictions.
The project is funded by the NASA LCLUC program grant number 80NSSC23K0507.
More details are on the project web page https://sites.google.com/asu.edu/lcluc-el-paso/home.
TMDL report selection tool (https://occviz.com/tmdl/) is a web-based software tool for TMDL report selection based on different water management criteria. The tool uses an automated search method based on the frequency of common water body impairments and models to categorize and select TMDL reports.see more >>
The primary goal of this project is to establish methods that may be used to estimate reliable ET from aerial thermal imaging. The project is collecting temperature and other weather data for some felids in the El Paso region. This will highlight the crop stress within a field that may be used to identify areas within a field that may need interventions.
System dynamics modeling is designed to enable decision making. It starts with a system diagram for the complex non-linear system under investigation. The dynamic behavior of interacting systems may be simulated by mathematically representing the flows from key stocks (reservoirs) in the system diagram and interactions between stocks.This project aims to better represent the complex interactions for the water-salt-agriculture linked system and enable decision making for enhancing the overall sustainability of irrigated agriculture in the arid region. This modeling will directly aid growers to assess the varying impacts of salt in irrigation water on soil and crops. See SMITUV publication
There has never been a time with more interest and investment in nutrient control. Yet, in many cases, we have been unable to achieve the water quality improvements expected due to limitations in the availability of data and information on effective practices; poor engagement with important stakeholders; and insufficient mechanistic understanding of linkages between management practices and water quality and ecosystem responses. The proposed project will address an important information gap in nutrient management by collecting available information on nutrient controls, implementation of total maximum daily loads (TMDLs), water quality, and ecosystem responses. In doing so, this project will not only facilitate access to existing data sources but also reveal data gaps that could be filled with improved data collection during nutrient management projects.
The project is funded by Water Research Foundation (WRF). Hazen and Sawyer is the partners and lead agency for the project.
The research group is conducting aerial hyperspectral (400-1700nm) surveys of three fields in El Paso. The main goal of this project is to develop best practices of aerial hyperspectral analysis and develop new deep neural network-based methods (beyond the current index-based approach) for detecting on-field heterogeneity in crop stress.
The objective of this study is to obtain an accurate assessment of water balance for a flood irrigated Pecan Orchard. We have sap flow sensors on some trees, an eddy covariance flux tower for average ET, equipment to measure the water applied, passive-wick lysimeters for flow out of the field, LAI measurements, and UAV-based thermal/multispectral imaging to understand spatially distributed crop use.
OccViz is an ongoing attempt to develop a web-based platform to visualize and derive knowledge from the water resources datasets. The OccViz user portal is designed to be interactive and useful for stakeholders to retrieve near-real-time data from several water resources monitoring sites in a watershed. The OccViz system also enables data curation, integrates data from several sources into a single database, and provides tools to analyze data irregularities in massive real-time datasets, which are essential functionalities for data managers.
Best Management Practices (BMPs) are designed to control non-point (diffused) water pollution and are crucial for meeting goals of the Clean Water Act. One of the biggest obstacles to achieving long-term water quality and ecological objectives is the lack of follow-up monitoring and analysis after BMPs have been implemented. We are trying to develop models to use daily PlanetScope earth observation to identify BMPs. An experiment with annual PlanetScope data coupled with 2D-CNN-based image analysis shows over 95% accuracy for the identification of wet ponds in the Northern Virginia region; part of larger Chesapeake Bay. These models are being extended to understand the efficiency of BMP-enabled mitigation of water quantity and quality issues.
Groundwater in Ogallala has been declining. Northwest Texas is the southern end of the Ogallala Aquifer which has seen steady groundwater decline. In this project, we are trying to understand how a transition out of plentiful groundwater may look like, based on what we have observed in the last 50 years on the edges of the Ogallala. We are developing Bayesian belief network models to quantify landuse and other changes. The model may answer the critical questions on the drivers of transition.
Transboundary aquifers are a critical source of water for both the United States and Mexico that have been catering to increasing demands often with degrading quality of waters. The existence of communities all along the US-Mexico border depends on these aquifers. We are working on Hueco Bolson [about Huceo Bolson] near El Paso. Hueco is relevant to the region as a critical water supply for municipal use and irrigation. Together with partners from Mexico, our team is leading the efforts on developing numerical and system models for the water quality and quantity in Hueco Bolson. The hope is that these models will allow us to manage the aquifer better into the future. See details at https://webapps.usgs.gov/taap/index.html
A set of grower tools designed for nursery growers to manage water. The project was funded by USDA as part of CleanWater3 research lead by Clemson University.
The project with partners from UTEP and TXDOT analyzed over 10,000 crashes in North Texas involving pedestrians or pedalcyclists. We analyzed the crashes and associated conditions to develop countermeasures that may be used to make roadways safer for pedalcyclists and pedestrians. As part of the project, a web-based software Crash Data Analysis and Visualization Application (CDAVA) was developed. We are working on making CDAVA open source and available to the wider community.
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