A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021.

Water stored in mountain snowpacks (i.e., snow water equivalent, SWE) represents an important but poorly characterized component of the terrestrial water cycle. The Western United States snow reanalysis (WUS-SR) dataset is novel in its combination of spatial resolution (~500 m), spatial extent (31°-49° N; 102°-125° W), and temporal continuity (daily over 1985-2021). WUS-SR is generated using a Bayesian framework with model-based snow estimates updated through the assimilation of cloud-free Landsat fractional snow-covered area observations. Over the WUS, the peak SWE verification with independent in situ measurements show correlation coefficient, mean difference (MD), and root mean squared difference (RMSD) of 0.77, -0.15 m, and 0.28 m, respectively. The effects of forest cover and Landsat image availability on peak SWE are assessed. WUS-SR peak SWE is well correlated (ranging from 0.75 to 0.91) against independent lidar-derived SWE taken near April 1st, with MD <0.15 m and RMSD <0.38 m. The dataset is useful for characterizing WUS mountain snow storage, and ultimately for improving snow-derived water resources management.

A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model.

Hydrologic models predict the spatial and temporal distribution of water and energy at the land surface. Currently, parameter availability limits global-scale hydrologic modelling to very coarse resolution, hindering researchers from resolving fine-scale variability. With the aim of addressing this problem, we present a set of globally consistent soil and vegetation parameters for the Variable Infiltration Capacity (VIC) model at 1/16° resolution (approximately 6 km at the equator), with spatial coverage from 60°S to 85°N. Soil parameters derived from interpolated soil profiles and vegetation parameters estimated from space-based MODIS measurements have been compiled into input files for both the Classic and Image drivers of the VIC model, version 5. Geographical subsetting codes are provided, as well. Our dataset provides all necessary land surface parameters to run the VIC model at regional to global scale. We evaluate VICGlobal's ability to simulate the water balance in the Upper Colorado River basin and 12 smaller basins in the CONUS, and their ability to simulate the radiation budget at six SURFRAD stations in the CONUS.

Stay-at-Home Orders during the COVID-19 Pandemic Reduced Urban Water Use.

In response to the outbreak of the COVID-19 pandemic, many governments instituted "stay-at-home" orders to prevent the spread of the coronavirus. The resulting changes in work and life routines had the potential to substantially perturb typical patterns of urban water use. We present here an analysis of how these pandemic responses affected California's urban water consumption. Using water demand modeling that fuses an integrated water use database, we first simulated the water use in a business-as-usual (non-pandemic) scenario for essentially all urban areas in California. We then subtracted the business-as-usual water use from the actual use to isolate the changes caused solely by the pandemic response. We found that the pandemic response decreased California's urban water use by 7.9%, which can be largely attributed to an 11.2% decrease in the commercial, industrial, and institutional sector that more than offset a 1.4% increase in the residential sector. The influence of the stay-at-home practices on urban water use is slightly stronger than the combined influences of all non-pandemic factors. This study covers both metropolitans and suburbs; therefore, the results could also be useful for analysis of the impacts of COVID-19 on water use in other urban areas.

Potential for western US seasonal snowpack prediction.

Western US snowpack-snow that accumulates on the ground in the mountains-plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.