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Abstract Human heat stress depends jointly on atmospheric temperature and humidity. Wetter soils reduce temperature but also raise humidity, making the collective impact on heat stress unclear. To better understand these interactions, we use ERA5 to examine the coupling between daily average soil moisture and wet-bulb temperature (T_w) and its seasonal and diurnal cycle at global scale. We identify a global soil moisture–T_wcoupling pattern with both widespread negative and positive correlations in contrast to the well-established cooling effect of wet soil on dry-bulb temperature. Regions showing positive correlations closely resemble previously identified land–atmosphere coupling hotspots where soil moisture effectively controls surface energy partition. Soil moisture–T_wcoupling varies seasonally closely tied to monsoon development, and the positive coupling is slightly stronger and more widespread during nighttime. Local-scale analysis demonstrates a nonlinear structure of soil moisture–T_wcoupling with stronger coupling under relatively dry soils. Hot days with highT_wvalues show wetter-than-normal soil, anomalous high latent and low sensible heat flux from a cooler surface, and a shallower boundary layer. This supports the hypothesis that wetter soil increasesT_wby concentrating surface moist enthalpy flux within a shallower boundary layer and reducing free-troposphere-air entrainment. We identify areas of particular interest for future studies on the physical mechanisms of soil moisture–heat stress coupling. Our findings suggest that increasing soil moisture might amplify heat stress over large portions of the world including several densely populated areas. These results also raise questions about the effectiveness of evaporative cooling strategies in ameliorating urban heat stress. Significance StatementThe purpose of this study is to provide a global picture of the relationship between soil moisture anomalies and a heat stress metric that includes the joint effects of temperature and humidity. This is important because a better understanding of this relationship will help improve the prediction of extreme heat stress events and inform strategies for ameliorating heat stress. We find a widespread positive correlation between soil moisture and heat stress, in contrast to studies relying on temperature alone. This raises the possibility that, over much of the world, and in the most populous regions, strategies like irrigation or “greening” that can reduce temperature might be ineffective or even harmful in reducing heat stress with humidity incorporated. 

Abstract Meeting the United Nation’ Sustainable Development Goals (SDGs) calls for an integrative scientific approach, combining expertise, data, models and tools across many disciplines towards addressing sustainability challenges at various spatial and temporal scales. This holistic approach, while necessary, exacerbates the big data and computational challenges already faced by researchers. Many challenges in sustainability research can be tackled by harnessing the power of advanced cyberinfrastructure (CI). The objective of this paper is to highlight the key components and technologies of CI necessary for meeting the data and computational needs of the SDG research community. An overview of the CI ecosystem in the United States is provided with a specific focus on the investments made by academic institutions, government agencies and industry at national, regional, and local levels. Despite these investments, this paper identifies barriers to the adoption of CI in sustainability research that include, but are not limited to access to support structures; recruitment, retention and nurturing of an agile workforce; and lack of local infrastructure. Relevant CI components such as data, software, computational resources, and human-centered advances are discussed to explore how to resolve the barriers. The paper highlights multiple challenges in pursuing SDGs based on the outcomes of several expert meetings. These include multi-scale integration of data and domain-specific models, availability and usability of data, uncertainty quantification, mismatch between spatiotemporal scales at which decisions are made and the information generated from scientific analysis, and scientific reproducibility. We discuss ongoing and future research for bridging CI and SDGs to address these challenges. 

Abstract Wet‐bulb globe temperature (WBGT) is a widely applied heat stress index. However, most applications of WBGT within the heat stress impact literature that do not use WBGT at all, but use one of the ad hoc approximations, typically the simplified WBGT (sWBGT) or the environmental stress index (ESI). Surprisingly, little is known about how well these approximations work for the global climate and climate change settings that they are being applied to. Here, we assess the bias distribution as a function of temperature, humidity, wind speed, and radiative conditions of both sWBGT and ESI relative to a well‐validated, explicit physical model for WBGT developed by Liljegren, within an idealized context and the more realistic setting of ERA5 reanalysis data. sWBGT greatly overestimates heat stress in hot‐humid areas. ESI has much smaller biases in the range of standard climatological conditions. Over subtropical dry regions, both metrics can substantially underestimate extreme heat. We show systematic overestimation of labor loss by sWBGT over much of the world today. We recommend discontinuing the use of sWBGT. ESI may be acceptable for assessing average heat stress or integrated impact over a long period like a year, but less suitable for health applications, extreme heat stress analysis, or as an operational index for heat warning, heatwave forecasting, or guiding activity modification at the workplace. Nevertheless, Liljegren's approach should be preferred over these ad hoc approximations and we provide a fast Python implementation to encourage its widespread use. 

The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics. 

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.