Gridded data is calculated from the UCLA Western U.S. Snow Reanalysis (WUS-SR), which is available at a 16 arc-second (500m) resolution for WY1985-2021 (Fang et al., 2022). Here, we utilize their daily median estimates of posterior snow water equivalent (SWE) and fractional snow-covered area (fSCA) to identify dates of snow onset (DSO) and disappearance (DSD) for each grid cell and water year. Processed snow timing files are available on the same 1° by 1° grid as the reanalysis, for 31°-48°N and 103°-124°W, and are georeferenced to the World Geodetic System 1984 spatial grid (EPSG:4326).
A dataset containing the list and shapefiles of basins used in the analysis of intermittency-flooding relationship analyzed in Livneh et al. 2024 (https://www.sciencedirect.com/science/article/pii/S0048969724039718). 108 watersheds were analyzed, during the period 1950-2022. Basins were filtered from GAGES II dataset using a python script (included in data).
Information on the 245 burned basins, 293 unburned basins, and 356 associated fires from across the U.S. West which were used in statistical analyses of post-wildfire water quality response. Included are physiographic characteristics, as well as ESRI Shapefile polygons representing delineations for each basin and fire. Additionally, daily carbon, nitrogen, phosphorus, sediment, and turbidity data sampled from the basins' outlets are provided from 1974-2022. R programming scripts used in data processing and modeling also included.
The Ensemble Streamflow Prediction (ESP) framework combines a probabilistic forecast structure with process-based models for water supply predictions. However, process-based models require computationally intensive parameter estimation, increasing uncertainties and limiting usability. Motivated by the strong performance of deep learning models, we seek to assess whether the Long Short-Term Memory (LSTM) model can provide skillful forecasts and replace process-based models within the ESP framework.
Matthew is currently pursuing his MS in civil engineering, with a focus in Hydrology, Water Resources, and Environmental Fluid Mechanics. His research currently focuses on how drought propagates through meteorological, hydrological, and agricultural systems. Previously, Matthew graduated from Colby College in 2021, with a BA in Biology. Before starting his MS, he worked for an EPA contractor on water quality and watershed health projects, performing data analysis and developing web applications.
Jeremy Barroll is currently pursuing an MS in civil engineering. Jeremy’s research presently focuses on small-scale variability in the prediction of snowpack in mountainous regions using remotely-sensed sea surface temperature data. The goal of this is to increase the predictive accuracy of large lead time water supply forecasting for small watersheds in the Western United States. Jeremy previously received a BS in civil and environmental engineering from UC Berkeley in 2016.
Gillian is a Ph.D. student at the University of Colorado Boulder. Her current research is focused on the influence of seasonal snowpack on thresholds related to species' habitat in the western United States. Gillian completed her M.S. in Civil Engineering at Purdue University in 2023, and earned a graduate certificate in GIS. Her previous research involved evaluating methods to improve the simulation of urban flooding using hydrologic models. She also graduated with a B.S. in Civil Engineering from the University of Vermont in 2020.
Melanie received her Ph.D. and M.S. in Civil Engineering from the University of Colorado Boulder. In her graduate studies, Melanie developed and validated monitoring technologies to improve water management and support local policy compliance in California and Colorado. Using a combination of land-surface modeling, in situ groundwater extraction monitoring, and remote sensed data, Melanie’s research focused on developing a groundwater abstraction forecast model to aid agencies in complying with groundwater regulations in the drought-prone western United States.
Justin is a CIRES affiliate and an Assistant Research Scientist with the University of Maryland and NASA Goddard. Prior to this, Justin was a CIRES Visiting Postdoctoral Fellow in the Water and Climate Research Group, where his research focused on investigating the sensitivity of wildlife habitat estimates to snow modeling decisions.
Fangfang is a postdoctoral fellow in Cooperative Institute for Research in Environmental Sciences (CIRES). He holds a Ph.D. in Physical Geography from Kansas State University and an M.S. in Geographic Information Systems (GIS) from the University of the Chinese Academy of Sciences. His research explores the interaction among lakes, climate change and human activities with a primary focus on the terrestrial water cycle, climate and human impacts on natural and manmade lakes, and the environmental impact of dams.