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1. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

   https://doi.org/10.3390/rs12142328  

   Ketchum, David ; Jencso, Kelsey ; Maneta, Marco P. ; Melton, Forrest ; Jones, Matthew O. ; Huntington, Justin ( July 2020 , Remote Sensing)

   High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California. 

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2. Irrigation intensification impacts sustainability of streamflow in the Western United States

   https://doi.org/10.1038/s43247-023-01152-2  

   Ketchum, David ; Hoylman, Zachary H. ; Huntington, Justin ; Brinkerhoff, Douglas ; Jencso, Kelsey G. ( December 2023 , Communications Earth & Environment)

   Quantifying the interconnected impacts of climate change and irrigation on surface water flows is critical for the proactive management of our water resources and the ecosystem services they provide. Changes in streamflow across the Western U.S. have generally been attributed to an aridifying climate, but in many basins flows can also be highly impacted by irrigation. We developed a 35-year dataset consisting of streamflow, climate, irrigated area, and crop water use to quantify the effects of both climate change and irrigation water use on streamflow across 221 basins in the Colorado, Columbia, and Missouri River systems. We demonstrate that flows have been altered beyond observed climate-related changes and that many of these changes are attributable to irrigation. Further, our results indicate that increases in irrigation water use have occurred over much of the study area, a finding that contradicts government-reported irrigation statistics. Increases in crop consumption have enhanced fall and winter flows in some portions of the Upper Missouri and northern Columbia River basins, and have exacerbated climate change-induced flow declines in parts of the Colorado basin. We classify each basin’s water resources sustainability in terms of flow and irrigation trends and link irrigation-induced flow changes to irrigation infrastructure modernization and differences in basin physiographic setting. These results provide a basis for determining where modern irrigation systems benefit basin water supply, and where less efficient systems contribute to return flows and relieve ecological stress.

    

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3. Using Machine Learning to Integrate On-Farm Sensors and Agro-Meteorology Networks into Site-Specific Decision Support

   https://doi.org/10.13031/trans.13917  

   Kelley, Jason ; McCauley, Dalyn ; Alexander, G. Aaron ; Gray, Wilton F. ; Siegfried, Rylie ; Oldroyd, Holly J. ( January 2020 , Transactions of the ASABE)

   null (Ed.)

   Highlights Machine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements. On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements. Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management. Abstract . One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation. 

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4. An Observational, Irrigation-Sensitive Agricultural Drought Record from Weather Data

   https://doi.org/10.1175/JHM-D-23-0026.1  

   Tang, Lois I. ; McColl, Kaighin A. ( November 2023 , Journal of Hydrometeorology)

   The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land–atmosphere coupling—surface flux equilibrium (SFE) theory—to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century.

   Irrigation grew profoundly in the United States over the twentieth century, increasing the resilience of American agriculture to drought. Yet observational records of agricultural drought, and its response to irrigation, are limited to the satellite era. Here, we show that a common measure of agricultural drought (the evaporative fraction, EF) can be estimated using widespread weather data, extending the agricultural drought record decades further back in time. We show that EF estimated using our approach is both sensitive and specific to the occurrence of irrigation, unlike an alternative derived from a reanalysis.

    

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5. Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

   https://doi.org/10.3390/land12081500  

   Igwe, Kelechi ; Sharda, Vaishali ; Hefley, Trevor ( August 2023 , Land)

   Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

    

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