We synthesize the interconnected impacts of Texas’ water and energy resources and infrastructure including the cascading effects due to Winter Storm Uri. The government’s preparedness, communication, policies, and response as well as storm impacts on vulnerable communities are evaluated using available information and data. Where knowledge gaps exist, we propose potential research to elucidate health, environmental, policy, and economic impacts of the extreme weather event. We expect that recommendations made here — while specific to the situation and outcomes of Winter Storm Uri — will increase Texas’ resilience to other extreme weather events not discussed in this paper. We found that out of 14 million residents who were on boil water notices, those who were served by very small water systems went, on average, a minimum of three days longer without potable water. Available county-level data do not indicate vulnerable communities went longer periods of time without power or water during the event. More resolved data are required to understand who was most heavily impacted at the community or neighborhood level. Gaps in government communication, response, and policy are discussed, including issues with identifying — and securing power to — critical infrastructure and the fact that the state’s Emergency Alert System was not used consistently to update Texans during the crisis. Finally, research recommendations are made to bolster weaknesses discovered during and after the storm including (1) reliable communication strategies, (2) reducing disproportionate impacts to vulnerable communities, (3) human health impacts, (4) increasing water infrastructure resilience, and (5) how climate change could impact infrastructure resilience into the future.

Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass‐yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county‐level wild hay yield reported by the National Agricultural Statistics Service (NASS). We next evaluated the performance of mechanistic, evapotranspiration (ET)‐driven grassland productivity models with and without assimilation of measured FAW into the models’ water balance routines. Models were calibrated by comparing estimated ET with ET measured using eddy covariance, and calibration proved essential for accurate ET estimates. Models were validated by comparing NASS county‐level hay yields to the modeled yields, which were the product of normalized transpiration estimates (the ratio of transpiration to reference ET) and an empirically derived grassland water productivity (the ratio of accumulated biomass to normalized transpiration) estimate. The mechanistic model produced more accurate estimates of wild‐hay yields with soil moisture data assimilation (Nash–Sutcliffe efficiency [NSE] = 0.55) than without (NSE = 0.10). These results suggest that improved estimates of grassland productivity could be achieved using in situ soil moisture, which could benefit grazing management decisions, wildfire preparedness, and disaster assistance programs.

Groundwater supplies ?20% of global freshwater withdrawals, and accurate information regarding groundwater recharge rates is needed for sustainable groundwater management. Recharge rates are often limited by the rates of drainage from the soil profile, which are influenced by soil moisture conditions. Soil moisture monitoring has expanded dramatically in recent decades with the advent of large-scale networks like the Oklahoma Mesonet, which has monitored soil moisture statewide since 1996. Using those data with site-specific soil hydraulic properties and a unit-gradient assumption, we estimated daily drainage rates at 60 cm for 78 sites for up to 17 yr. Our working hypothesis was that these drainage rates are indicative of potential groundwater recharge rates. Mean annual drainage rates ranged from 6 to 266 mm yr −1 , with a statewide median of 67 mm yr −1 . These rates agreed well with prior recharge estimates for major Oklahoma aquifers. To provide a further independent check on our results, drainage was modeled using HYDRUS-1D for four focus sites across 17 yr. Soil-moisture-based drainage rates and HYDRUS-1D drainage rates agreed to within 10 mm yr −1 at the drier two sites but had discrepancies of > 150 mm yr −1 at two sites with > 1000 mm yr −1 precipitation. Simulations also showed that for a semiarid site the unit-gradient assumption was likely violated at the 60-cm depth, highlighting the need for deeper soil moisture monitoring. Despite these limitations, this simple method for estimating drainage through long-term soil moisture monitoring shows unique potential to provide valuable information for hydrology and groundwater management.Abbreviations: NSE, Nash-Sutcliffe efficiency; RSR, root mean square error observations standard deviation ratio.

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The recent novel coronavirus pandemic led to global changes in higher education as universities transitioned to online learning to slow the spread of the virus. In the United States, this transition occurred during the spring of 2020, and the compulsory shift to online learning led to frustrations from students and instructors alike. I studied student participation during the online portion of a university‐level soil physics course taught in Spring 2020. Participation was quantified using the number of student posts in weekly discussion boards, the number of student views of asynchronous videos, and the number of video views during each week of online instruction. Relationships between video length and number of student views and between student participation and final exam grades were also examined. My findings show that student views of mini‐lecture videos were low and decreased throughout the online learning period. Conversely, views of example problem videos and the number of posts on graded discussion boards were high and remained high throughout the online learning period, suggesting that students were more engaged with online material that affected their grades. I also found that the level of student engagement in online material was positively correlated with higher final exam scores. The findings presented here may be used to improve the development and delivery of online coursework in natural science disciplines, both during current and future emergencies.