Energy and Resources Group

The State of Bay–Delta Science 2016 (SBDS) is a collection of papers that summarizes the scientific understanding of the Sacramento–San Joaquin Delta, emphasizing progress made during the past decade.It builds on the first SBDS edition (Healey et al. 2008). Paper topics for this edition address the most relevant scientific issues in the Delta identified by senior scientists and managers. The topical papers cover issues ranging from contaminants in the Delta to levee stability, and from Delta food webs to recent discoveries about salmon migration. These papers are written for a scientific audience. Two additional papers, one describing the challenges of managing water and ecosystems in the Delta and another that discusses policy implications of the recent scientific findings, are written for a general audience. The papers will be published in at least two issues of San Francisco Estuary and Watershed Science and will be available as a set electronically.

The State of Bay–Delta Science 2008 highlighted seven emerging perspectives on science and management of the Delta. These perspectives had important effects on policy and legislation concerning management of the Delta ecosystem and water exports. From the collection of papers that make up the State of Bay–Delta Science 2016, we derive another seven perspectives that augment those published in 2008. The new perspectives address nutrient and contaminant concentrations in Delta waters, the failure of the Delta food web to support native species, the role of multiple stressors in driving species toward extinction, and the emerging importance of extreme events in driving change in the ecosystem and the water supply.

The scientific advances that underpin these new perspectives were made possible by new measurement and analytic tools. We briefly discuss some of these, including miniaturized acoustic fish tags, sensors for monitoring of water quality, analytic techniques for disaggregating complex contaminant mixtures, remote sensing to assess levee vulnerability, and multidimensional hydrodynamic modeling.

Despite these new tools and scientific insights, species conservation objectives for the Delta are not being met. We believe that this lack of progress stems in part from the fact that science and policy do not incorporate sufficiently long-term perspectives. Looking forward half a century was central to the Delta Visioning process, but science and policy have not embraced this conceptual breadth. We are also concerned that protection and enhancement of the unique cultural, recreational, natural resource, and agricultural values of the Delta as an evolving place, as required by the Delta Reform Act, has received no critical study and analysis. Adopting wider and longer science and policy perspectives immediately encourages recognition of the need for evaluation, analysis, and public discourse on novel conservation approaches. These longer and wider perspectives also encourage more attention to the opportunities provided by heavily invaded ecosystems. It is past time to turn scientific and policy attention to these issues.

Ecosystems in the Sacramento–San Joaquin Delta are changing rapidly, as are ecosystems around the world. Extreme events are becoming more frequent and thresholds are likely to be crossed more often, creating greater uncertainty about future conditions. The accelerating speed of change means that ecological systems may not remain stable long enough for scientists to understand them, much less use their research findings to inform policy and management. Faced with these challenges, those involved in science, policy, and management must adapt and change and anticipate what the ecosystems may be like in the future. We highlight several ways of looking ahead—scenario analyses, horizon scanning, expert elicitation, and dynamic planning—and suggest that recent advances in distributional ecology, disturbance ecology, resilience thinking, and our increased understanding of coupled human–natural systems may provide fresh ways of thinking about more rapid change in the future. To accelerate forward-looking science, policy, and management in the Delta, we propose that the State of California create a Delta Science Visioning Process to fully and openly assess the challenges of more rapid change to science, policy, and management and propose appropriate solutions, through legislation, if needed.

Uncertainties in understanding ecosystems increase the risk that management will fail to achieve desired results. Adaptive management is a structured, iterative application of science-based knowledge to reduce uncertainties and build flexibility into decision-making. However, adaptive management is more easily planned than implemented, and it is only beginning to be applied in the California’s Sacramento–San Joaquin Delta. We draw from two assessments of adaptive management in the Delta and examples of its use elsewhere to suggest how the process can be facilitated. Although a highly structured adaptive-management process may not always be needed, several elements are essential. Adaptive management should begin by clearly identifying the problem, goals, and objectives; recognizing uncertainties; identifying decision points and alternative approaches; recognizing when adjustments are needed and having the flexibility to make them; and considering societal and political constraints. Model complexity should be matched to that of the system and management needs; experiments can help unravel causal relationships. Monitoring, analyses, and syntheses require comprehensive data-management systems. More frequent and organized communications among scientists, managers, stakeholders, and decision-makers are necessary. We propose the establishment of an “Adaptive Management Team” to coordinate efforts across the management spectrum of the Delta and to provide guidance and link individual projects to shared approaches and experiences. Reliable long-term support will be needed to assess results of management actions, adjust approaches where improvement is likely, and strive toward the legislated goals of enhancing the Delta ecosystem while also providing reliable water supplies to much of California, and doing both these things in a manner that protects values of the Delta as a place where people live and work.

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.

Abrupt snowmelt, triggered by rain-on-snow events or "snow-eater heat waves," can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850-2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks.Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions. Plain language The rate of growth in global CO2 emissions has been in decline over the last decade following efforts to enact climate policy, shift to clean energy, and mitigate leaks from oil and gas facilities. This is good progress, but with emissions of both CO2 and methane (CH4) on the rise, time is running out to meet temperature targets. Spaceborne greenhouse gas (GHG) observations can help monitor emissions/removals and track progress at the urban- to country- level, but currently lack the frequency, coverage, and precision to do so. Representatives from the carbon cycle community convened in September 2024 to discuss viable options for addressing these limitations. We recommend a unified GHG observing system leveraging frequent (daily) sampling and vertical profiles of atmospheric GHGs to more accurately track carbon gains and losses, and harmonized maps of biomass to track growth, recovery, and disturbance. Expanded coverage of climate sensitive tropical, polar, and ocean regions is needed to monitor unexpected changes in the natural carbon cycle. Complementary data from airplanes and surface networks is needed to fill observational and science gaps. This system should prioritize timely delivery of carbon flux and stock information to support carbon management efforts.

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

Data centers powering artificial intelligence are growing rapidly across the United States, raising concerns among policymakers and local communities about their environmental and social costs. These costs depend on where data centers locate, and strategic siting has been proposed as a way to limit them. Using a comprehensive facility-level dataset of past, operational, and announced U.S. data centers, we characterize how the environmental and community characteristics of data center locations evolve over 2010–2030. We quantify how much of the projected growth in environmental damages can be attributed to changes in data center locations versus growth in power requirements. Decomposing projected carbon emissions and monetized local air pollution damages into scale and location-driven composition effects, we find that over 2010–2025, composition changes reduced carbon and local air pollution damages by 4 and 12%, respectively. Over 2024–2030, scale accounts for approximately 97% of projected pollution growth, with composition changes contributing around 3%. We further find that data centers consistently locate in less densely populated areas, with planned facilities entering census tracts roughly five times less dense than tracts without data centers. Contrary to patterns documented for other disamenities, data centers are not systematically located in lower-income, higher-poverty, or higher nonwhite-share communities. Our results imply that policies nudging the location of data centers would do little to mitigate their aggregate environmental footprint, which is governed instead by the scale of buildout, and that permitting and community negotiations over siting will increasingly concentrate in the nation’s least population dense communities.

Measurement of methane fluxes (FCH₄) from natural systems, such as wetlands, has lagged far behind carbon dioxide fluxes. Short and fragmented wetland FCH₄ data limit our ability to assess its long-term dynamics and potential climate feedbacks. Extrapolating short-term FCH₄ records to recent decades remains challenging for both process-based models and data-driven machine learning (ML) approaches. Here, we develop a knowledge-guided ML framework that integrates eddy covariance (EC) FCH₄ observations, field warming experiments, and biogeochemical knowledge to reconstruct the long-term FCH₄ budgets and trends. Focusing on the 11 longest EC monitoring sites in the AmeriFlux network, we found considerable variability in multi-decadal trends of wetland FCH₄, with increases up to 14% per decade from 2000 to 2024. We also found that the strength of these increasing trends declines from high to low latitudes, highlighting the vulnerability of northern wetlands. This work presents novel and robust reconstructions of long-term wetland FCH₄, offering critical benchmark datasets for bottom-up ecosystem models and advancing fundamental understanding of wetland biogeochemistry.