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ARS Home Pacific West Area Davis, California Sustainable Agricultural Water Systems Research Research





- Bradford, Scott
- Knipper, Kyle
- Levers, Lucia
- Osterman, Gordon
- Roby, Matthew
- Meles, Menberu
- 2023
- 2022
- Publications
- Water Availability and Watershed Management (211)
- Sustainable Agricultural Systems Research (216)

Research Project: Improved Agroecosystem Efficiency and Sustainability in a Changing Environment **Location:
                            Sustainable Agricultural Water Systems Research** 2023 Annual Report **Objectives**
                                

The availability of surface water and groundwater supplies for irrigated agriculture in California are adversely impacted by droughts, groundwater depletion and degradation, and increasing water demands. The overall aim of this project is to increase the efficiency and sustainability of irrigated agriculture, with a special focus on vine and tree orchard crops in the Central Valley, CA. This will be accomplished by: (i) improving irrigation efficiency; (ii) optimizing on-farm strategies for managed aquifer recharge (MAR); and (iii) assessing the long-term impacts on the sustainability of crop production, soil health, and groundwater quantity and quality. Specific objectives and subobjectives for this project are given below.

Objective 1: Improve irrigation efficiency in agroecosystems by providing growers with accurate and timely estimates of spatial and temporal variations of crop ET in the field.
Sub-objective 1A: Develop and validate an ET modeling framework designed to address the unique and highly structured canopy cover associated with California specialty crops.
Sub-objective 1B: Improve irrigation efficiency for woody perennial crops in California by developing techniques to produce near-real-time estimates of ET from satellite data.
Sub-objective 1C: Pair ET with quantified soil hydraulic and bio-meteorological properties at field-scale for better information on plant-available water within the soil column.

Objective 2: Increase groundwater sustainability for irrigated agriculture by optimizing MAR strategies to capture excess surface water supplies that episodically occur during winter storms and store them into aquifers for later use.
Sub-objective 2A: Quantify subsurface heterogeneity in soil properties to identify optimal locations and to better monitor and predict performance of MAR.
Sub-objective 2B: Monitor and simulate the performance of MAR sites.
Sub-objective 2C: Develop approaches to predict and minimize clogging and pathogen contamination at MAR sites.
Sub-objective 2D: Develop and apply a computationally efficient watershed model to predict impacts of MAR on water quantity and quality.

Objective 3: Assessment of the long-term impacts of irrigated agriculture on crop production, soil health, and groundwater quantity and quality under changing environmental conditions by monitoring atmospheric and subsurface fluxes and properties of soils and plants.
Sub-objective 3A: Use economic analyses to assess impacts of MAR and improved irrigation efficiency on groundwater sustainability and the food-energy-water nexus. **Approach**
                                

Objective 1 will be accomplished using a combination of satellite remote sensing data, modeling, and field measurements of micrometeorological (e.g., Eddy covariance towers) and biophysical data during different phenological stages to estimate spatial and temporal variations in evapotranspiration (ET), crop stress, and irrigation requirements in vine and tree orchards crops at multiple sites in the Central Valley of California. The collected soil and bio-meteorological data will be used to validate and refine model estimates of ET from satellite imagery. Improved algorithms will be developed for near real-time ET estimates and their spatial variability in the field that can be used to improve irrigation efficiency.

Objective 2 will be addressed by developing, implementing, and overcoming challenges associated with Managed Aquifer Recharge (MAR) technologies. MAR techniques that will be studied include Ag or flood MAR and drywells. Geophysical methods will be employed to identify optimal locations for MAR, to better characterize subsurface heterogeneity, and to monitoring infiltration and recharge behavior. 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 and pore-network modeling studies will be conducted to better infer underlying mechanisms controlling MAR performance, including clogging and pathogen transport and fate parameters. 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 at particular sites. A computationally efficient watershed scale model will be developed to rigorously simulate exchange of water and contaminants between surface water, the vadose zone, and groundwater. Numerical experiments will be conducted to test specific hypotheses and generalize results to other sites, water management practices, climatic conditions, and watersheds.

Objective 3 involves the extension of Objectives 1 and 2 to include economic analyses to study long-term implications of remote-sensing based irrigation management tools and MAR strategies on the food-energy-water nexus, groundwater sustainability, and the long-term viability of irrigated agriculture. It also includes economic analyses of various land management practices (e.g., land fallowing), policies (e.g., the sustainable groundwater management act), and impacts on endangered species. **Progress Report**
                                

Field and modeling research activities have been conducted to accomplish Objective 1. A network of Eddy-covariance towers has been instrumented, installed, and/or maintained in specialty crop fields (10 in wine grapes, four in oil olives, three in almonds, and two in pistachios) in the Central Valley of California. These towers and sensors are being used to measure atmospheric fluxes of water, energy, and carbon. This information is being employed to assess the impacts of various management practices (e.g., regenerative farming practices such as the use of cover crops) and environmental stresses (e.g., heat waves and advection) on evapotranspiration (ET) and greenhouse gas emissions. It is also being used to validate and refine predicted ET estimates based on satellite and/or drone remote sensing and ALEXI-DisALEXI modeling. Model validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T), but an understanding of each is imperative in cropping systems that employ cover crops within the inter-row. The DisALEXI model was modified to evaluate E and T estimates using the Priestley-Taylor or the Penman-Monteith models. Results suggest different formulations of DisALEXI can have large effects on estimated E and T values, while playing a smaller role in the magnitude of total ET estimates. Growers use various forms of ET to guide irrigation management, but operational applications remain hindered by latency in satellite product delivery. Approaches have been developed to estimate ET in near real time to improve irrigation management. Specifically, machine learning algorithms were employed to determine a pseudo-atmospherically corrected Land Surface Temperature (LST) that is used in the ALEXI-DisALEXI model. A random forest machine learning approach provided adequate LST estimates within eight hours of satellite overpass, but results appear to vary geographically.

To accomplish Objective 2, we have initiated a range of research projects to optimize and evaluate managed aquifer recharge (MAR) approaches (drywells and flooded agricultural fields) in the Central Valley of California. Time lapsed electrical resistivity tomography (ERT) was used to monitor changes in the evolution of the wetting front from drywells, whereas other sensors allowed us to assess changes in water levels, water saturation and pressure, electrical conductivity, and temperature in the vadose zone down to the water table (250 ft deep). A towed-transient electromagnetic (tTEM) surveying tool was used to map spatial variability in resistivity at potential MAR sites. tTEM resistivity data was analyzed using a Latin-hypercube sampling script that was developed to determine optimal sampling sites to relate resistivity to sediment properties. Supporting data has been acquired to further relate resistivity to sediment texture, water content, and salinity. This information will be used to translate our spatially extensive resistivity data sets (from ERT and/or tTEM) to estimate sediment texture and relevant hydraulic parameters in the field. Research has also been conducted to develop a computationally efficient integrated hydrologic model. Initial efforts have focused on coupling water flow and solute transport at the surface-subsurface boundary of a hillslope but are now being extended to the catchment scale and to include groundwater. A high-resolution groundwater flow model is also being developed for the Turlock-Modesto groundwater subbasins to assess groundwater sustainability under changing climatic, MAR, and land management conditions.

An economic optimization model was developed in support of Objective 3. This model can evaluate adaptations to reduced water availability on croplands in California, including MAR and irrigation systems. We have analyzed costs and benefits of different MAR types (e.g., flood-MAR, recharge basins, drywells, and aquifer storage and recovery) and used the model to assess groundwater management plans developed under the Sustainable Groundwater Management Act. We have also developed a nonlinear optimization model for irrigated croplands in the Ogallala aquifer, another arid region with over-pumping of groundwater. Nonlinear yield functions that account for crop type, location, salinity, and evaporation are included which allow us to predict crop yield as a function of applied water for irrigation systems with different efficiencies. Cost parameters for irrigation systems including furrow, flood, micro-sprinkler, sprinkler, and drip are estimated and included along with cost parameters for all MAR types. This modelling provides a framework upon which irrigation methods can be assessed, accounting for both their physical effects and costs of operation. It can also assess the efficiency of MAR methods and predict optimal locations in California for MAR to achieve sustainability under changing climatic conditions. **Accomplishments**
                                

1. Estimating specialty crop evapotranspiration in near-real time to improve irrigation management. Operational applications of satellite-based evapotranspiration (ET) to guide irrigation management are hindered by latency in satellite product delivery, due in part to computationally expensive atmospheric corrections. A machine learning model was developed by ARS researchers in Davis, California, to derive atmospherically corrected Land Surface Temperature (LST) in near-real-time (NRT) for ingestion into the ALEXI-DisALEXI ET model. The purpose was to estimate ET in NRT for irrigation management. Results indicate the model can derive reliable ET estimates in NRT and is currently being implemented operationally as part of a collaboration with stakeholders. **Review Publications**
                            *Chen, L., Simunek, J., Bradford, S.A., Ajami, H., Meles, M.B. 2022. A computationally efficient hydrologic modeling framework to simulate surface-subsurface hydrological processes at the hillslope scale. Journal of Hydrology. 614. Article 128539. https://doi.org/10.1016/j.jhydrol.2022.128539.*
                                *Gomez-Flores, A., Bradford, S.A., Cai, L., Urík, M., Kim, H. 2022. Prediction of attachment efficiency using machine learning on a comprehensive database and its validation. Water Research. 229. Article 119429. https://doi.org/10.1016/j.watres.2022.119429.*
                                *Knipper, K.R., Anderson, M.C., Bambach, N., Kustas, W.P., Gao, F.N., Zahn, E., Hain, C., McElrone, A.J., Rosario Belfiore, O., Castro, S., Alsina, M.M., Saa, S. 2022. Evaluation of partitioned evaporation and transpiration estimates within the DisALEXI modeling framework over irrigated crops in California. Remote Sensing. 15(1). Article 68. https://doi.org/10.3390/rs15010068.*
                                *Gomez-Flores, A., Bradford, S.A., Hong, G., Kim, H. 2023. Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems. Journal of Hazardous Materials. 454. Article 131482. https://doi.org/10.1016/j.jhazmat.2023.131482.*
                                *Perzan, Z., Osterman, G.K., Maher, K. 2023. Controls on flood managed aquifer recharge through a heterogeneous vadose zone: Hydrologic modeling at a site characterized with surface geophysics. Hydrology and Earth System Sciences. 27(5):969-990. https://doi.org/10.5194/hess-27-969-2023.*
                                *Wang, L., Hu, Z., Yin, H., Bradford, S.A., Luo, J., Hou, D. 2022. Aging of colloidal contaminants and pathogens in the soil environment: Implications for nanoplastic and COVID-19 risk mitigation. Soil Use and Management. 39(1):70-91. https://doi.org/10.1111/sum.12849.*
                                *Knipper, K.R., Yang, Y., Anderson, M.C., Bambach, N., Kustas, W.P., McElrone, A.J., Gao, F.N., Alsina, M. 2023. Decreased latency in landsat-derived land surface temperature products: A case for near-real-time evapotranspiration estimation in California. Agricultural Water Management. 283. Article 108316. https://doi.org/10.1016/j.agwat.2023.108316.*
                                *Gao, R., Torres-Rua, A., Nieto, H., Zahn, E., Hipps, L., Kustas, W.P., Alsina, M., Ortiz, N., Castro, S., Prueger, J., Alfieri, J.G., McKee, L.G., White, W.A., Gao, F.N., McElrone, A.J., Anderson, M.C., Knipper, K.R., Coopmans, C., Gowing, I., Agam, N., Sanchez, L., Dokoozlian, N. 2023. ET partitioning assessment using the TSEB model and sUAS information across California Central Valley vineyards. Remote Sensing. 15(3). Article 756. https://doi.org/10.3390/rs15030756.*
                                *Elias, E.H., Tsegaye, T.D., Hapeman, C.J., Mankin, K.R., Kleinman, P.J., Cosh, M.H., Peck, D.E., Coffin, A.W., Archer, D.W., Alfieri, J.G., Anderson, M.C., Baffaut, C., Baker, J.M., Bingner, R.L., Bjorneberg, D.L., Bryant, R.B., Gao, F.N., Gao, S., Heilman, P., Knipper, K.R., Kustas, W.P., Leytem, A.B., Locke, M.A., McCarty, G.W., McElrone, A.J., Moglen, G.E., Moriasi, D.N., O'Shaughnessy, S.A., Reba, M.L., Rice, P.J., Silber-Coats, N., Wang, D., White, M.J., Dobrowolski, J.P. 2023. A vision for integrated, collaborative solutions to critical water and food challenges. Journal of Soil and Water Conservation. 78(3):63A-68A. https://doi.org/10.2489/jswc.2023.1220A.*


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