ABSTRACT
Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 264 suitability layers for use with 56 power plant technologies in a harmonized format that can be easily ingested by geospatially-enabled modeling software allowing for customization to robustly address science objectives when evaluating varying future conditions.
ABSTRACT
The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant's reported annual generation using proxies of monthly generation-namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset-named RectifHyd-provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001-2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales.
ABSTRACT
Drinking water supplies of cities are exposed to potential contamination arising from land use and other anthropogenic activities in local and distal source watersheds. Because water quality sampling surveys are often piecemeal, regionally inconsistent, and incomplete with respect to unregulated contaminants, the United States lacks a detailed comparison of potential source water contamination across all of its large cities. Here we combine national-scale geospatial datasets with hydrologic simulations to compute two metrics representing potential contamination of water supplies from point and nonpoint sources for over a hundred U.S. cities. We reveal enormous diversity in anthropogenic activities across watersheds with corresponding disparities in the potential contamination of drinking water supplies to cities. Approximately 5% of large cities rely on water that is composed primarily of runoff from non-pristine lands (e.g., agriculture, residential, industrial), while four-fifths of all large cities that withdraw surface water are exposed to treated wastewater in their supplies.
