The ClimAVA_SWE data set — where ClimAVA stands for Climate Data for Adaptation and Vulnerability Assessments — provides high-resolution (4 km) future climate projections derived from 13 CMIP6 General Circulation Models (GCMs). It focuses on Snow Water Equivalent (SWE), a crucial indicator of water availability, hydrologic extremes, and climate-related vulnerability, and includes projections for three Shared Socioeconomic Pathways (SSP245, SSP370, and SSP585) at a daily temporal scale. The initial release of ClimAVA_SWE covers the entire western United States. ClimAVA_SWE is produced using the newly developed Spatial Interactions Downscaling (SPID) method, which ensures high-quality downscaling through advanced machine learning techniques. SPID captures the relationship between large-scale spatial patterns at GCM resolution and fine-scale pixel values. For each pixel, two Random Forest models (one for the accumulation period and one for the ablation period) were trained using fine-resolution reference data as the predictand, and nine neighboring pixels from a spatially resampled (coarser) version of the reference data as predictors. These trained models are then applied to bias-corrected GCM data to generate the downscaled projections. The resulting dataset maintains strong climate realism and effectively represents extreme events.
Khoshnood Motlagh, Sajad; de Lima Moraes, Andre Geraldo; Smith, Kayla (2025). Climate data for adaptation and vulnerability assessments (SWE) – west. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/ClimAVA-SWE
Utah Agricultural Experiment Station, Utah State University Grant/Award No: Grant/Award No: UTA01726 - UAES grants
Scientific Quality Assurance (SQA)
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2025-11-27
Technical Quality Assurance (TQA)
TQA - Technical Quality Assurance 'approved by WDCC'
Description
1. Number of data sets is correct and > 0: passed; 2. Size of every data set is > 0: passed; 3. The data sets and corresponding metadata are accessible: passed; 4. The data sizes are controlled and correct: passed; 5. The spatial coverage description (metadata) is consistent to the data: passed; 6. The format is correct: passed; 7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
[1] DOIde Lima Moraes, Andre Geraldo; Khoshnood Motlagh, Sajad. (2024). The Climate Data for Adaptation and Vulnerability Assessments and the Spatial Interactions Downscaling Method. doi:10.1038/s41597-024-03995-6
Is derived from
[1] DOIBroxton, P.; Zeng, X.; Dawson, N. (2019). Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US. (NSIDC-0719, Version 1). doi:10.5067/0GGPB220EX6A