UC Davis

Due to population growth and progress made in sustainable water management, water resources availability to California’s croplands is becoming more scarce. Besides, due to climate change, year-to-year water supplies are becoming increasingly unpredictable. As 80% of all human use of water is diverted for farmland irrigation, spatial estimates of crop evapotranspiration with high accuracy, from the field to watershed scale, have become increasingly important for water resources management. The present work aims to improve and evaluate an algorithm for estimating spatial crop evapotranspiration and explore its application to water resource management. The generated knowledge and tools from this study can provide data-driven guidance for water resources planning at the watershed level, contributing to a more sustainable use of water in California’s agricultural regions. The first chapter focused on improving and evaluating a semi-empirical model to map crop evapotranspiration at a 30m resolution using Landsat satellite images and concurrent spatial meteorological data. The algorithm was optimized and cross-validated with available field measurements from over 22 sites in California’s Central Valley for major crops, including alfalfa, almond, citrus, corn, pasture, and rice. The evapotranspiration estimates explained more than 70% variance in daily measurements with an RMSE of 0.88 mm day-1. The site-level evaluation was repeated with the estimates from another remote sensing algorithm, PT-JPL, which is developed by scientists at the NASA Jet Propulsion Laboratory. The two algorithms share a similar theoretical basis and design, and an improvement over a PT-JPL was observed when the proposed approach is crop-specifically calibrated. This chapter demonstrated that land use information and field measurements are useful for reducing the uncertainty of spatial crop evapotranspiration estimates. The model presented in the first chapter can be applied for continuous monitoring of the dynamics and spatial heterogeneity of crop consumptive water use in California at a field scale. Chapter 2 scaled up the proposed algorithm to estimate annual evapotranspiration at 30m resolution across the entire Central Valley for water years 2014 and 2016. It examined the spatial pattern of crop water consumptive use at a regional level and a local scale of Groundwater Sustainability Agencies (GSAs). We estimated a mean (x̅) evapotranspiration of 820 mm yr-1 with a standard deviation (σ) of 290 mm yr-1in 2014. Compared to 2014, the total estimated evapotranspiration in 2016 increased by 9.6%, mostly because of land-use conversion from fallow/idle land to cropland. Large variation of agricultural water use was found across different and within the same crop types, with a coefficient of variation ranging from 8% for Rice (x̅ = 1110, σ = 85 mm yr-1) to 59% for Pistachio (x̅ = 592, σ = 352 mm yr-1). Besides, tree crops, such as Almonds, were found to have a large variation of evapotranspiration among and within GSAs. To further understand the spatial variability of almond’s evapotranspiration at the GSAs scale, an empirical model using normalized difference vegetation index (NDVI) and orchard age was optimized and evaluated. Orchard age was shown to be an important factor contributing to the variability of evapotranspiration across GSAs. However, spatial variation of other physical factors that could affect NDVI, such as salinity at the root zone, will likely have stronger control over almond’s evapotranspiration, explaining its spatial variation within and among GSAs. Overall, this chapter demonstrates how spatial estimates of evapotranspiration can be used to provide data-driven guidance for more effective land use and water planning across regional and local GSA scales. In chapter 3, the proposed algorithm was implemented to estimate daily crop evapotranspiration at 30m resolution in the Pajaro Valley, CA for 2009 and 2011. The objective of the study was to develop and evaluate methods to estimate agricultural applied water from field to local agency scales. To estimate applied water at the field scale, the daily spatial evapotranspiration was coupled with soil water balance for simulation of daily fluxes in a water balance at 30m resolution. This coupled model requires a crop-specific optimization on application efficiency, using a limited number of field-scale applied water records. When the model estimates are spatially aggregated by fields and temporally aggregated to the annual total, the evaluation showed that our applied water estimates agreed well with the pumping records at the field scale, explaining 86% of variability with an RMSE of 39.6 million liters. When aggregated over the Pajaro Valley, we estimated a total of agricultural groundwater pumping ranging from 53.9 in 2009 to 42.6 billion liters in 2011, which deviated from the sum of all agricultural pumping and surface water delivery by less than 4.6%. Over 16 subregions in the valley, our estimates explained more than 89% variance in pumping records with an RMSE of 1.2 billion liters. Our estimates of water balance fluxes were also compared with that by USGS, to evaluate the model’s ability in representing the hydrological process of the Valley. Results from chapter 3 demonstrated that remote sensing evapotranspiration estimates can be used to estimate regional agricultural applied water with reasonable accuracy yet in a cost-efficient way, over areas where only a limited number of field-scale applied water data is available for calibrating a soil water balance model.