Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.

Scholars have identified a ‘usability gap’ between science and its ability to inform real-world decisions as well as a range of factors that facilitate or impede attempts to span the usability gap with information products. However, most attention has focused on barriers related to information users; much less research focuses on the unique institutional and organizational barriers experienced by creators of decision support tools. To address this gap, we used semi-structured interviews to investigate the perspectives and experiences of practitioners holding scientific or technology roles, including their goals for their tools, their perceptions of success in meeting those goals, and the barriers and opportunities they encountered. We find that there is often a mismatch between what tool creators know is necessary to achieve success for their tools and what is actually possible given various constraints. Our results suggest that knowledge may be a less important barrier to conducting actionable science through creating decision support tools than the institutional context in which tool creators work.

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

The future of dry forests around the world is uncertain given predictions that rising temperatures and enhanced aridity will increase drought-induced tree mortality. Using forest management and ecological restoration to reduce density and competition for water offers one of the few pathways that forests managers can potentially minimize drought-induced tree mortality. Competition for water during drought leads to elevated tree mortality in dense stands, although the influence of density on heat-induced stress and the durations of hot or dry conditions that most impact mortality remain unclear. Understanding how competition interacts with hot-drought stress is essential to recognize how, where and how much reducing density can help sustain dry forests in a rapidly changing world. Here, we integrated repeat measurements of 28,881 ponderosa pine trees across the western US (2000–2017) with soil moisture estimates from a water balance model to examine how annual mortality responds to competition, temperature and soil moisture conditions. Tree mortality responded most strongly to basal area, and was elevated in places with high mean temperatures, unusually hot 7-year high temperature anomalies, and unusually dry 8-year low soil moisture anomalies. Mortality was also lower in places that experienced unusually wet 3-year soil moisture anomalies between measurements. Importantly, we found that basal area interacts with temperature and soil moisture, exacerbating mortality during times of stress imposed by high temperature or low moisture. Synthesis and applications. Our results imply that a 50% reduction in forest basal area could reduce drought-driven tree mortality by 20%–80%. The largest impacts of density reduction are seen in areas with high current basal area and places that experience high temperatures and/or severe multiyear droughts. These interactions between competition and drought are critical to understand past and future patterns of tree mortality in the context of climate change, and provide information for resource managers seeking to enhance dry forest drought resistance.