Location: Water Management and Conservation Research
Project Number: 2020-13660-009-000-D
Project Type: In-House Appropriated
Start Date: Jan 16, 2022
End Date: Jan 15, 2027
Objective:
The overall goals of this project are to 1) improve knowledge of water, nutrient, and crop growth processes in arid agroecosystems, 2) to develop sensing, computing, and decision support technologies that improve water and nutrient use efficiency for crop production, and 3) provide science-based data to ensure that treated municipal wastewater used for irrigation poses minimal threat to people and the environment.
Objective 1: Design, test, improve and integrate sensor data and simulation models to improve irrigation and fertilization decision support for irrigated cropping systems.
Objective 2: Create and evaluate suites of satellite-based hydrology models that enable accurate monitoring and forecasting of evapotranspiration and other soil water balance components over irrigated agriculture, leading to improved irrigation scheduling.
Objective 3: Design, test and/or improve sensors and technologies for optimizing surface irrigation systems.
Objective 4: Determine the processes that govern the environmental fate and transport of emerging contaminants and other constituents found in treated wastewater used for irrigation to provide a research basis for potential regulation of these constituents.
Objective 5: Develop and optimize low input treatment systems to reduce emerging contaminants and nutrients found in degraded waters to increase water resources used for food production.
Approach:
Objective 1: Design, test, improve and integrate sensor data and simulation models to improve irrigation and fertilization decision support for irrigated cropping systems. Sub-objective 1A: Develop remote, proximal, and in-situ sensing technologies for estimating crop, water, and nutrient status of irrigated agroecosystems. Sub-objective 1B: Develop and evaluate simulation models, machine learning algorithms, and data integration strategies that better inform crop management decisions. Sub-objective 1C: Develop and field-test decision support tools that integrate data and models for improving in-season crop management. Sub-objective 1D: Develop irrigation guidelines, tools, and models for direct-seeded guayule.
Objective 2: Create and evaluate suites of satellite-based hydrology models that enable accurate monitoring and forecasting of evapotranspiration and other soil water balance components over irrigated agriculture, leading to improved irrigation scheduling. Sub-objective 2A: Develop and test crop coefficient models driven by remote sensing data. Sub-objective 2B: Develop and test algorithms that use remote sensing to track water budgets across multiple cropping seasons.
Objective 3: Design, test and/or improve sensors and technologies for optimizing surface irrigation systems. Sub-objective 3A. Evaluate and improve infiltration modeling approaches for irrigation design and management, tied to the Natural Resources Conservation Service (NRCS) soils database. Sub-objective 3B. Develop design and management strategies that account for the spatial and temporal variability of conditions, including infiltration, hydraulic resistance, and flow rate.
Objective 4: Increase water supplies available for irrigation and managed aquifer recharge through safely reusing treated wastewater. Sub-objective 4A will determine the effect of temperature on the sorption, fate, and transport of pharmaceuticals in soil. Sub-objective 4B will determine the effects of long-term wastewater irrigation on soil contaminant concentration and the soil microbiome. Sub-objective 4C will optimize removal of pharmaceuticals from water.
Objective 5 Will be a modeling exercise to determine the potential for using reclaimed wastewater as a supplemental irrigation source in rainfed agricultural systems. The volume of produced wastewater in regions of the Midwest will be spatially matched to potential crop needs. Crop irrigation needs will be modeled using historical weather data and then spatially matched to wastewater availability.