Extreme Value Snow Water Equivalent and Snowmelt for Infrastructure Design Over the Contiguous United States

Abstract

Snowmelt-driven floods result in large societal and economic impacts on local communities including infrastructure failures in the United States. However, the current U.S. government standard design precipitation maps are based on liquid precipitation data (e.g., National Oceanic and Atmospheric Administration's Precipitation-Frequency Atlas 14; NOAA Atlas 14) with very limited guidance on snowmelt-driven floods. In this study, we developed 25-and 100-year return level design maps of snow water equivalent (SWE) and 1-and 7-day snowmelt including precipitation events (e.g., rain-on-snow) using long-term observation-based gridded SWE developed by University of Arizona (UA) incorporating the national snow model product (SNOw Data Assimilation System; SNODAS) over the contiguous United States (CONUS). For the 44 U.S. states where the NOAA Atlas 14 maps are available, the design snowmelt values from this study exceed the standard design values in 23% of the total extent. The snowmelt values exceed the NOAA Atlas 14 design precipitation by up to 171 and 254 mm in the northeastern United States; 127 and 225 mm in the north central United States; and 191 and 425 mm in the western mountain United States for the 25-and 100-year return levels, respectively. A comparison of 7-day design snowmelt between with and without precipitation shows that including precipitation results in an average increase of 42 and 68 mm for 25-and 100-year return levels, respectively, over snowmelt that do not include precipitation. The design snowmelt maps from this study complement the NOAA Atlas 14 design precipitation and provide additional guidance on infrastructure design for snowmelt-driven floods in the CONUS.

Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds

“…In our case, the forecasted streamflow using RF does not yield a standard error comparable to that provided by a traditional regression model, and hence there is no way to provide probabilistic confidence intervals for predictions. Methods to estimate confidence intervals have been proposed by Wager et al (2014), Mentch and Hooker (2016), and Coulston et al (2016), but they are not widely applied. For future work, the computation of confidence intervals in RF prediction will be useful in addressing and understanding uncertainty.…”

Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes Over North America

“…In the late 21st century, SWE no longer exists in the regions. Because ephemeral snowpack changes in these regions are important for hydrologic and ecosystem processes (Cho & Jacobs, 2020;Friggens et al, 2018), further study to better understand the source of these uncertainties is warranted. There are also large RP variations among RCMs in the Pacific Northwest and California where large shifts in precipitation partitioning from snowfall to rain as well as increases in extreme atmospheric river-induced precipitation are expected (Huang et al, 2020).…”

“…The current U.S. government standard design maps such as the National Oceanic and Atmospheric Administration's National Weather Service Precipitation-Frequency Atlas 14 (NOAA Atlas 14) are based solely on liquid precipitation data with very limited guidance on snowmelt events (Fassnacht & Records, 2015;Harpold & Kohler, 2017;Yan et al, 2019). Recently, Cho and Jacobs (2020) developed 25 and 100-year return level (also known as a recurrence interval that is an estimated average time between the events to occur) snow water equivalent (SWE) and snowmelt maps using reliable long-term gridded SWE products and compared these to the NOAA Atlas 14 standard design maps over the continental United States. They found that their snowmelt design values exceeded the standard rainfall design values in 23% of the area in the 44 United States where the standard maps are available.…”

“…In this study, we create and compare spatial maps of three extreme design metrics, SWE, snowmelt, RP (snowmelt plus precipitation), for the historical, mid, and late 21st centuries, Writing - review & editing: Eunsang Cho, Rachel R. McCrary, Jennifer M. Jacobs respectively, using NA-CORDEX. The RP is the actual amount of water available for runoff originating from melting snow and precipitation (including snowfall, rainfall absorbed by the snowpack, and percolated rain water through the snowpack) for a given time period (Cho & Jacobs, 2020;Yan et al, 2018Yan et al, , 2019. We aim to answer the following three research questions: (a) How much will the magnitude of extreme SWE, snow melt, and RP change by the mid and late 21st century?…”

Monitoring and evaluation of the passage of high water and floods in 2018 in the Amur region

“…Due to climate change, the frequency of natural disasters is increasing [1][2][3][4][5]. Such hazardous hydrological phenomena are associated with an increase in the water level in water bodies during the period of snowmelt and heavy rains [6][7][8][9]. In the future, the risk of floods and other negative impacts of water will persist and intensify due to the increase in hazardous hydrological phenomena [10,11].…”