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Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain

Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPD k , where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or k Dynamic , which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and k Dynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, k Dynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that k Dynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.

New Mexico

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California