Data Methods: Solar & Wind Resource Droughts

The steps below describe how the values shown in the tool were calculated, from raw climate model output to the final data product.

Input data

Climate model simulations

Description: Ensemble of 4 bias-adjusted climate model simulations, dynamically downscaled using the WRF model for the state of California: EC-Earth3-r1i1p1f1, MPI-ESM1-2-HR-r3i1p1f1, TaiESM1-r1i1p1f1, MIROC6-r1i1p1f1.
Spatial resolution: 3 kilometers
Temporal resolution: Hourly
Time period: 1980-2100: the modeled historical period (1980-2014), followed by the SSP3-7.0 scenario (2015-2100)
Download: Browse in the Cal-Adapt data catalog (STAC), or download directly from cloud storage (AWS).

Output data

Resource droughts

Description: The multi-model mean count of resource drought days per month, for each year of the 30-year period at global warming levels of 0.8, 1.5, 2.0, 2.5, and 3.0 °C
Shown in: Renewables Visualizer
Format: Zarr stores at 3 kilometer resolution
Download: Download directly from cloud storage (AWS): utility solar, distributed solar, onshore wind, offshore wind.

Generation profiles

Description: Generated power (MW) and capacity factor for each simulation, hourly and daily, for the historical and SSP3-7.0 periods
Shown in: None (intermediate product)
Format: Zarr stores at 3 kilometer resolution
Download: Browse in the Cal-Adapt data catalog (STAC), or download directly from cloud storage (AWS):

Solar: data catalog, cloud storage (utility, distributed)

Wind: data catalog, cloud storage (onshore, offshore)

Calculation steps

A solar resource drought refers to periods of daytime cloudiness which result in a decrease in the solar radiation used by solar panels to produce photovoltaic energy. A wind resource drought refers to periods of still or calm winds which result in a decrease in the generated wind power. Under guidance from Rinaldi et al., 2021 ¹, both are defined using the following procedure.

Each global warming level (GWL) is represented by the 30 year time window around the year (+/- 15 years) in which each simulation reaches that warming level.

Reference production

Calculate the 30-year (centered around the 0.8 °C global warming level) average generation potential at each grid cell for each day of the year. This average daily quantity is referred to as the reference production.

Resource drought days

If any day's total generation potential is less than 50% of the reference production for that day of the year, it is flagged as a resource drought day.

Monthly totals

All resource drought days are summed over each month.

Solar generation profiles

Generation profiles: Hourly photovoltaic energy generation potential profiles were simulated with the National Laboratory of the Rockies' (NLR, formerly NREL) PVWattsv8 software, run through the Renewable Energy Potential (reV) model, version 0.9.4.
System design: For most design characteristics, information from configuration files found in NLR's Renewable Energy Potential (ReV) GitHub repo were utilized. Other design characteristics (array type and capacity) are informed by an NLR benchmark report ².

Parameter	Utility (front-of-meter)	Distributed (behind-the-meter)
Description	Typical utility-company installation	Typical residential rooftop installation
Array type	Single-axis tracking	Fixed rooftop mount
Installation capacity	100 MW	10 kW
Tilt	None (single-axis tracking)	20°

Shared by both installation types: azimuth 180° (south-facing), no losses except snow (snow loss enabled), 96% inverter efficiency, DC to AC ratio of 1.3, 40% ground cover ratio, standard module type.

Wind generation profiles

Generation profiles: Hourly wind energy generation potential profiles were simulated with the National Laboratory of the Rockies' (NLR, formerly NREL) Windpower software.
Turbine design: Turbine power curves from NLR's Renewable Energy Potential (ReV) GitHub repo were utilized.

Parameter	Onshore	Offshore
Nameplate capacity	3 MW	8 MW
Hub height	100 m	120 m
Rotor diameter	127.5 m	180 m

Shared turbine characteristics: turbine spacing is 8× rotor diameter in the x and y directions, with every other row offset by ½ spacing. Max coefficient of power: 0.474457866.

The following figures show the power curves and farm layouts for both the onshore and offshore wind power modules.

Onshore (3 MW)Offshore (8 MW)

Onshore (1,020 m spacing)Offshore (1,440 m spacing)

Land use exclusions

Generation potential was not simulated at all grid points in the domain due to land use or land cover restrictions. The following tables summarize the relevant land use exclusions for the PVWattsv8 and Windpower simulations under each installation type. In other words, if a grid cell meets any of these criteria, it was excluded from the indicated simulations.

Solar

Restriction	Utility PV	Distributed PV
Federal and state protected lands	✓	✓
Slope > 20%	✓
High intensity urban (row houses, apartment buildings, commercial buildings)	✓
Medium intensity urban (single family homes)
Open water	✓	✓

Wind

Restriction	Onshore	Offshore
Federal and state protected lands	✓	✓
Slope > 20%	✓	✓
High intensity urban (row houses, apartment buildings, commercial buildings)	✓	✓
Medium intensity urban (single family homes)	✓	✓
Open water	✓

Geospatial data sources used to build the exclusion masks:

Federal and state protected lands: Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023)
Slope > 20%: LandFire, Version 2.2.0, Slope Percent Rise
High/medium intensity urban: NLCD land cover
Open water: masked with the WRF domain's land-sea mask

The exclusion datasets are available from cloud storage (AWS):

The geospatial files (.gpkg format) used to create the land use and land cover exclusion masks
The gridded datasets with the binary land use and land cover restriction masks (coord_ds_d03_all_restrictions.zarr)

References

Rinaldi, K. Z., Dowling, J. A., Ruggles, T. H., Caldeira, K., & Lewis, N. S. (2021). Wind and Solar Resource Droughts in California Highlight the Benefits of Long-Term Storage and Integration with the Western Interconnect. Environ. Sci. Technol., 55(9), 6214–6226 https://doi.org/10.1021/acs.est.0c07848.

Ramasamy, V., Feldman, D., Desai, J., & Margolis, R. (2021). U.S. Solar Photovoltaic System and Energy Storage Cost Benchmarks: Q1 2021. National Lab of the Rockies. https://docs.nlr.gov/docs/fy22osti/80694.pdf.

For attribution, please cite this work as:

Cal-Adapt. (2026). Data Methods: Solar & Wind Resource Droughts. Cal-Adapt. https://cal-adapt.org/dashboard/data-methods/renewables