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Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes. [ABSTRACT FROM AUTHOR]

Extreme weather variability has long posed difficulties for food, energy, and water systems in the Western US and is only projected to become more variable in a changing climate. The key to understanding weather in the West lies in understanding variability of large-scale weather regimes that dictate regional weather patterns. In this study, we propose a novel, multi-objective optimization and regression-based framework that reconstructs the annual frequency of regional weather regimes based on a gridded, tree-ring based reconstruction of cold season precipitation. The approach optimally smooths and selects the tree-ring based information used in the weather regime reconstruction to enhance out of sample performance and minimize model complexity. Multiple objectives are considered within the optimization to balance the preservation of low and high frequency modes of variability in the multivariate weather regime dynamics. We reconstruct weather regime frequencies back to 1400 CE and show that the reconstructed weather regimes are consistent with previously identified megadroughts and pluvials. Further, the reconstructed weather regimes exhibit significant variability in the 3–15-year frequency band and extend far beyond the bounds of the instrumental period. Overall, the weather regime reconstructions developed here provide important insight into the extent of natural atmospheric variability that can influence Western US weather. [ABSTRACT FROM AUTHOR]

Assessing impacts on coupled food-water systems that may emerge from water policies, changes in economic drivers and crop productivity requires an understanding of dominant uncertainties. This paper assesses how a candidate groundwater pumping restriction and crop prices, crop yields, surface water price, electricity price, and parametric uncertainties shape economic and groundwater performance metrics from a coupled hydro-economic model (HEM) through a diagnostic global sensitivity analysis (GSA). The HEM used in this study integrates a groundwater depth response, modeled by an Artificial Neural Network (ANN), into a calibrated Positive Mathematical Programming (PMP) agricultural production model. Results show that in addition to a groundwater pumping restriction, performance metrics are highly sensitive to prices and yields of perennial tree crops. These sensitivities become salient during dry years when there is a higher reliance on groundwater. Furthermore, results indicate that performing a GSA for two different water baseline conditions used to calibrate the production model, dry and wet, result in different sensitivity indices magnitudes and factor prioritization. Diagnostic GSA results are used to understand key factors that affect the performance of a groundwater pumping restriction policy. This research is applied to the Wheeler Ridge-Maricopa Water Storage District located in Kern County, California, region reliant on groundwater and vulnerable to surface water shortages. [ABSTRACT FROM AUTHOR]

The United Nations Framework Convention on Climate Change agreed to “strengthen the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty” (UNFCCC 2015). Designing a global mitigation strategy to support this goal poses formidable challenges. For one, there are trade-offs between the economic costs and the environmental benefits of averting climate impacts. Furthermore, the coupled human-Earth systems are subject to deep and dynamic uncertainties. Previous economic analyses typically addressed either the former, introducing multiple objectives, or the latter, making mitigation actions responsive to new information. This paper aims at bridging these two separate strands of literature. We demonstrate how information feedback from observed global temperature changes can jointly improve the economic and environmental performance of mitigation strategies. We focus on strategies that maximize discounted expected utility while also minimizing warming above 2 °C, damage costs, and mitigation costs. Expanding on the Dynamic Integrated Climate-Economy (DICE) model and previous multi-objective efforts, we implement closed-loop control strategies, map the emerging trade-offs and quantify the value of the temperature information feedback under both well-characterized and deep climate uncertainties. Adaptive strategies strongly reduce high regrets, guarding against mitigation overspending for less sensitive climate futures, and excessive warming for more sensitive ones. [ABSTRACT FROM AUTHOR]

Satellite services are fundamental to the global economy, and their design reflects a tradeoff between coverage and cost. Here, we report the discovery of two alternative 4-satellite constellations with 24- and 48-hour periods, both of which attain nearly continuous global coverage. The 4-satellite constellations harness energy from nonlinear orbital perturbation forces (e.g., Earth's geopotential, gravitational effects of the sun and moon, and solar radiation pressure) to reduce their propellant and maintenance costs. Our findings demonstrate that small sacrifices in global coverage at user-specified longitudes allow operationally viable constellations with significantly reduced mass-to-orbit costs and increased design life. The 24-hour period constellation reduces the overall required vehicle mass budget for propellant by approximately 60% compared to a geostationary Earth orbit constellation with similar coverage over typical satellite lifetimes. Mass savings of this magnitude permit the use of less expensive launch vehicles, installation of additional instruments, and substantially improved mission life. Telecommunication, navigation and remote sensing services are highly dependent on how well satellites provide global coverage. Here the authors show a pair of four-satellite low-cost longer-life constellations that provide nearly continuous global coverage to support Earth observation. [ABSTRACT FROM AUTHOR]