Skip to main content
ARS Home » Pacific West Area » Davis, California » Sustainable Agricultural Water Systems Research » Research » Research Project #438377

Research Project: A Systems Approach to Improved Water Management for Sustainable Production

Location: Sustainable Agricultural Water Systems Research

Project Number: 2032-13220-001-000-D
Project Type: In-House Appropriated

Start Date: Jul 20, 2020
End Date: Mar 16, 2022

Objective 1: Develop technologies to enhance the sustainability of water resources for crop production that account for the processes governing movement, storage, quantity and quality of water and water re-use. Objective 2: Develop a production system decision support toolkit for water management by producers that accounts for interacting GxExMxP factors. Objective 3: Quantify and enable management of the effects of new water management strategies on crop production responses by assessing genotype x environment x limited-water and water re-use management (GxExMxP) interactions for different land use, management, and climate scenarios at field to watershed scales.

Objective 1 will be addressed by developing, implementing, and overcoming challenges associated with Managed Aquifer Recharge (MAR) technologies to store episodic excess surface water supplies in aquifers to mitigate flooding and groundwater depletion, and for later use by agriculture. Other degraded water resources may also be considered for MAR operations. Potential MAR techniques that will be studied include Ag or flood MAR, subsurface leach fields (reverse of tile drains), drywells, infiltration basins, and injection well approaches. Research will address ways to mitigate clogging, optimize treatment of source water, and operate MAR sites to ensure recharge water quality over a range of soil types and climatic conditions. Field sites will be characterized for soil hydraulic properties, and equipped to monitor water inputs, infiltration, recharge, and soil and water quality parameters. Complementary laboratory studies will be conducted to better infer underlying mechanisms controlling MAR performance. Collected data streams will be used in conjunction with mathematical modeling to inversely determine parameters, design improved MAR strategies that optimize water quantity and quality, and to predict long-term performance of MAR on the sustainability of groundwater and irrigated agriculture. Calibrated models will in turn be used to develop meaningful predictions of risk, management, and future performance. These models may include conventional deterministic models, stochastic models, empirical algorithms, and/or machine learning approaches to account for groundwater-surface water interactions at various scales. Objectives 2 and 3 will be accomplished using a combination of airborne and satellite remote sensing data, field measurements of micrometeorological (e.g., Eddy covariance towers) and biophysical data during different phenological stages, and modeling to estimate spatial and temporal variations in evapotranspiration (ET), crop stress, and irrigation requirements in high-value crops like vineyards and orchards. This information is expected to improve field-scale irrigation efficiency and thereby reduce water demands and increase crop quality and productivity. These research activities will continue over the long-term and be expanded to maximize yield and/or crop quality, efficient use of water on farms, and to assess impacts of irrigation practices on groundwater recharge at different scales. Decision support toolkits will be developed for growers to improve field-scale crop and irrigation management. The wide-spread acceptance and application of such tools are critical to ensure the long-term sustainability of irrigated agriculture and groundwater resources, especially during periods of drought. Economic analyses will be employed to study long-term implications of MAR strategies and remote-sensing based irrigation management tools on the food-energy-water nexus, groundwater sustainability, and the long-term viability of irrigated agriculture. This research will support objectives 1-3.