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Abstract Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply‐demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi‐arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices. 

Abstract Atmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate. 

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data. 

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics. 

Abstract Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Significance Statement Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.