Regional Analysis

Nexus vs. silo investment planning under uncertainty

Abstract: Water, energy, and agricultural infrastructure investments have important inter-relations fulfilling potentially competing objectives. When shaping investment plans, decision makers need to evaluate those interactions and the associated uncertainties.

We compare planning infrastructure under uncertainty with an integrated water-energy-food nexus framework and with sector-centered (silo) frameworks. We use WHAT-IF, an open-source hydroeconomic decision support tool with a holistic representation of the power and agriculture sectors. The tool is applied to an illustrative synthetic case and to a complex planning problem in the Zambezi River Basin involving reservoirs, hydropower, irrigation, transmission lines and power plant investments. In the synthetic case, the nexus framework selects investments that generate more synergies across sectors. In sector-centered frameworks, the value of investments that impact multiple sectors (like hydropower, bioenergy, and desalinization) are under- or overestimated. Furthermore, the nexus framework identifies risks related to uncertainties that are not linked to the investments respective sectors.

In the Zambezi river case, we find that most investments are mainly sensitive to parameters related to their respective sectors, and that financial parameters like discount rate, capital costs or carbon taxes are driving the feasibility of investments. However, trade-offs between water for irrigation and water for hydropower are important; ignoring trade-offs in silo frameworks increases the irrigation expansion that is perceived as beneficial by 22% compared to a nexus framework that considers irrigation and hydropower jointly. Planning in a nexus framework is expected to be particularly important when projects and uncertainties can considerably affect the current equilibrium.

Moving ecological and biogeochemical transitions across the North Pacific

Abstract: In the North Pacific Ocean, nutrient rich surface waters flow south from the subpolar gyre through a transitional region and into the subtropics. Along the way, nutrients are used, recycled, and exported, leading to lower biomass and a commensurate change in ecosystem structure moving southward. We focus on the region between the two gyres (the Transition Zone) using a coupled biophysical ocean model, remote sensing, floats, and cruise data to explore the nature of the physical, biogeochemical, and ecological fields in this region.

Nonlinear interactions between biological processes and the meridional gradient in nutrient supply lead to sharp shifts across this zone. These transitions between a southern region with more uniform biological and biogeochemical properties and steep meridional gradients to the north are diagnosed from extrema in the first derivative of the properties with latitude. Some transitions like that for chlorophyll a (the transition zone chlorophyll front [TZCF]) experience large seasonal excursions while the location of the transitions in other properties moves very little. The seasonal shifts are not caused by changes in the horizontal flow field, but rather by the interaction of seasonal, depth related, forcing with the mean latitudinal gradients. Focusing on the TZCF as a case study, we express its phase velocity in terms of vertical nutrient flux and internal ecosystem processes, demonstrating their nearly equal influence on its motion.

This framework of propagating biogeochemical transitions can be systematically expanded to better understand the processes that structure ecosystems and biogeochemistry in the North Pacific and beyond.

Predictability of U.S. regional extreme precipitation occurrence based on large-scale meteorological patterns (LSMPs)

Abstract: In this study, we use our analogue method and Convolutional Neural Networks (CNNs) to assess the potential predictability of extreme precipitation occurrence based on Large-Scale Meteorological Patterns (LSMPs) for the winter (DJF) of Pacific Coast California (PCCA) and the summer (JJA) of Midwestern United States (MWST). We evaluate the LSMPs constructed with a large set of variables at multiple atmospheric levels and quantify the prediction skill with a variety of complementary performance measures.

Our results suggest that LSMPs provide useful predictability of extreme precipitation occurrence at a daily scale and its interannual variability over both regions. The 14-year (2006-2019) independent forecast shows Gilbert Skill Scores (GSS) in PCCA range from 0.06 to 0.32 across 24 CNN schemes and from 0.16 to 0.26 across 4 analogue schemes, in contrast to those from 0.1 to 0.24 and from 0.1 to 0.14 in MWST.

Overall, CNN is shown to be more powerful in extracting the relevant features associated with extreme precipitation from the LSMPs than the analogue method, with several single-variate CNN schemes achieving more skillful prediction than the best multi-variate analogue scheme in PCCA and more than half of CNN schemes in MWST. Nevertheless, both methods highlight that Integrated Vapor Transport (IVT, or its zonal and meridional components) enables higher prediction skill than other atmospheric variables over both regions. Warm-season extreme precipitation in MWST presents a forecast challenge with overall lower prediction skill than in PCCA, attributed to the weak synoptic-scale forcing in summer.

Thoughts on a federal government stimulus package for buildings

Abstract: Residential buildings account for 22% of U.S. carbon emissions and there is widespread consensus that these carbon emissions can only be reduced if buildings become both more efficient and switch all of their fuel sources to electricity. We have developed an outline for an economic recovery package that would support energy retrofits for the 84 million detached single-family homes in the U.S. These retrofits would lead to a 31% to 48% reduction in U.S. residential carbon emission while creating well-paying clean tech jobs distributed across the nation. The goal of the program is to raise the nationwide retrofitting rate to 4% by 2030 so that all single-family homes will be retrofitted by 2050. Ultimately, the goal of the program is to provide a base of support so that market forces can sustain a 4% retrofitting rate after the ten year program phases out.

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