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1. 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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2. Modeling tree radial growth in a warming climate: where, when, and how much do potential evapotranspiration models matter?

   https://doi.org/10.1088/1748-9326/ac1292  

   Dannenberg, Matthew P (July 2021, Environmental Research Letters)

   Abstract Process-based models of tree-ring width are used both for reconstructing past climates and for projecting changes in growth due to climate change. Since soil moisture observations are unavailable at appropriate spatial and temporal scales, these models generally rely on simple water budgets driven in part by temperature-based potential evapotranspiration (PET) estimates, but the choice of PET model could have large effects on simulated soil moisture, moisture stress, and radial growth. Here, I use four different PET models to drive the VS-Lite model and evaluate the extent to which they differ in both their ability to replicate observed growth variability and their simulated responses to projected 21st century warming. Across more than 1200 tree-ring width chronologies in the conterminous United States, there were no significant differences among the four PET models in their ability to replicate observed radial growth, but the models differed in their responses to 21st century warming. The temperature-driven empirical PET models (Thornthwaite and Hargreaves) simulated much larger warming-induced increases in PET and decreases in soil moisture than the more physically realistic PET models (Priestley–Taylor and Penman–Monteith). In cooler and more mesic regions with relatively minimal moisture constraints to growth, the models simulated similarly small reductions in growth with increased warming. However, in dry regions, the Thornthwaite- and Hargreaves-driven VS-Lite models simulated an increase in moisture stress roughly double that of the Priestley–Taylor and Penman–Monteith models, which also translated to larger simulated declines in radial growth under warming. While the lack of difference in the models’ ability to replicate observed radial growth variability is an encouraging sign for some applications (e.g. attributing changes in growth to specific climatic drivers), the large differences in model responses to warming suggest that caution is needed when applying the temperature-driven PET models to climatic conditions with large trends in temperature. 

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3. An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

   https://doi.org/10.3390/su151712908  

   Zhao, Haoteng; Di, Liping; Guo, Liying; Zhang, Chen; Lin, Li (September 2023, Sustainability)

   Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making. 

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4. Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand

   https://doi.org/10.1038/s41467-021-25254-7  

   Zhang, Jingwen; Guan, Kaiyu; Peng, Bin; Pan, Ming; Zhou, Wang; Jiang, Chongya; Kimm, Hyungsuk; Franz, Trenton E.; Grant, Robert F.; Yang, Yi; et al (December 2021, Nature Communications)

   Abstract Irrigation is an important adaptation to reduce crop yield loss due to water stress from both soil water deficit (low soil moisture) and atmospheric aridity (high vapor pressure deficit, VPD). Traditionally, irrigation has primarily focused on soil water deficit. Observational evidence demonstrates that stomatal conductance is co-regulated by soil moisture and VPD from water supply and demand aspects. Here we use a validated hydraulically-driven ecosystem model to reproduce the co-regulation pattern. Specifically, we propose a plant-centric irrigation scheme considering water supply-demand dynamics (SDD), and compare it with soil-moisture-based irrigation scheme (management allowable depletion, MAD) for continuous maize cropping systems in Nebraska, United States. We find that, under current climate conditions, the plant-centric SDD irrigation scheme combining soil moisture and VPD, could significantly reduce irrigation water use (−24.0%) while maintaining crop yields, and increase economic profits (+11.2%) and irrigation water productivity (+25.2%) compared with MAD, thus SDD could significantly improve water sustainability. 

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5. Assessment of the long-term hydrological performance of a green roof system in stormwater control

   https://doi.org/10.1016/j.jenvman.2024.122831  

   Dong, Zhaokai; Bain, Daniel J; Buck, John K; Ng, Carla (November 2024, Journal of Environmental Management)

   Green roof systems (GRs) are effective tools for urban stormwater management. However, there is limited documentation of the long-term hydrological performance of GRs to support decision-making. This study evaluated long-term field monitoring records (7 years) from a 12-year-old GR, situated in a Moist Subtropical Mid- Latitude Climate, to analyze seasonality in and evolution of hydrological performance. The monitoring system was built within a pan lysimeter buried under substrate layers matching the surrounding GR. The monitoring results highlight the efficacy of this GR in long-term stormwater runoff control. The GR can retain 87% of the annual precipitation and return 54% of the precipitation to the atmosphere through evapotranspiration (ET) and sustain long-term event-based mean runoff volume reductions, peak flow reductions, and flow delays of 82%, 93%, and 4.3 h, respectively. The initial moisture content prior to events was highly correlated with hydrological performance, with a seasonal mean Spearman correlation coefficient of 0.47, suggesting the potential of enhancing ET from the GR to improve performance. Substrate water holding capacity increased over time, but no obvious changes in water retention performance were observed. These monitoring results from the aging GR demonstrate the effectiveness of GR systems for long-term stormwater management. 

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