The goal of this project is to characterize and constrain the physical mechanisms that control snowmelt delivery to streams in headwater basins. This project leverages new observation and modeling techniques to quantify and simulate the snow distribution, water holding capacity, snowmelt production, and dynamic flowpaths. This is achieved through state-of-the-science observation techniques including ground penetrating radar (GPR), Terrestrial LiDAR Scanning (TLS), global positioning system (GPS) instrumentation, a network of sensor nodes continuously measuring soil moisture and snow depth, and a weir to monitor streamflow. Finally, hydrologic modeling will be conducted with the Structure for Unified Multiple Modeling Alternatives (SUMMA) model to assess the impact of modeling decisions and the ability to simulate snowmelt dynamics. The overarching research question of this project is: How do snowpack liquid water storage and through-snow hydrologic flowpaths affect hillslope-stream connectivity, and how do these processes evolve throughout the snowmelt season? This research question will be investigated in a snow-dominated headwater catchment. This work will observe and simulate the spatially and temporally variable snowmelt season to complete the following project objectives: O1) Map the dynamics of catchment snow water equivalent (SWE) using TLS surveys, GPR surveys, a network of sensor nodes, and manual observations. O2) Monitor the spatial and temporal progression of snowpack liquid water content and transport using combined TLS and GPR surveys, automated GPS signal attenuation, soil moisture sensors, and catchment streamflow response. O3) Evaluate the skill of hydrologic models to simulate the observed dynamics of the snowpack, soil, and streamflow response by systematically analyzing multiple model representations of hydrologic processes and scaling behavior. The work builds upon decades of local research in hydrology, biogeochemistry, and ecological processes.
more »
« less
Investigating the Role of Snow Water Equivalent on Streamflow Predictability during Drought
Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (
Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an “adaptive sampling” application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts.
- Award ID(s):
- 2009922
- NSF-PAR ID:
- 10376352
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Hydrometeorology
- Volume:
- 23
- Issue:
- 10
- ISSN:
- 1525-755X
- Page Range / eLocation ID:
- p. 1607-1625
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract The western United States (US) is a hotspot for snow drought. The Oregon Cascade Range is highly sensitive to warming and as a result has experienced the largest mountain snowpack losses in the western US since the mid‐20th century, including a record‐breaking snow drought in 2014–2015 that culminated in a state of emergency. While Oregon Cascade snowpacks serve as the state's primary water supply, short instrumental records limit water managers' ability to fully constrain long‐term natural snowpack variability prior to the influence of ongoing and projected anthropogenic climate change. Here, we use annually‐resolved tree‐ring records to develop the first multi‐century reconstruction of Oregon Cascade April 1st Snow Water Equivalent (SWE). The model explains 58% of observed snowpack variability and extends back to 1688 AD, nearly quintupling the length of the existing snowpack record. Our reconstruction suggests that only one other multiyear event in the last three centuries was as severe as the 2014–2015 snow drought. The 2015 event alone was more severe than nearly any other year in over three centuries. Extreme low‐to‐high snowpack “whiplash” transitions are a consistent feature throughout the reconstructed record. Multi‐decadal intervals of persistent below‐the‐mean peak SWE are prominent features of pre‐instrumental snowpack variability, but are generally absent from the instrumental period and likely not fully accounted for in modern water management. In the face of projected snow drought intensification and warming, our findings motivate adaptive management strategies that address declining snowpack and increasingly variable precipitation regimes.
-
more » « less
Abstract Interannual variability of mountain snowpack has important consequences for ecological and socioeconomic systems, yet changes in variability have not been widely examined under future climates. Physically based snowpack simulations for historical (1970–1999) and high‐emission scenario (RCP 8.5) mid‐21st century (2050–2079) periods were used to assess changes in the variability of annual maximum snow water equivalent (SWEmax) and SWEmaxtiming across the western United States. Models show robust declines in the interannual variability of SWEmaxin regions where precipitation is projected to increasingly fall as rain. The average frequency of consecutive snow drought years (SWEmax< historical 25th percentile) is projected to increase from 6.6% to 42.2% of years. Models also project increases in the variability of SWEmaxtiming, suggesting reduced reliability of when SWEmaxoccurs. Differences in physiography and regional climate create distinct spatial patterns of change in snowpack variability that will require adaptive strategies for environmental resource management.
-
more » « less
Abstract The impacts of warming temperatures and declining snowpack on seasonal water yields in the Missouri River Headwaters are not well understood, revealing a gap in our understanding of regional hydroclimate and drivers of streamflow within the Upper Missouri River basin. This study presents the first annually resolved tree‐ring reconstruction of spring precipitation for the Missouri River Headwaters. This reconstruction along with existing tree‐ring reconstructions of 1 April snow water equivalence (SWE) and water year streamflow are used to detect variable influences of winter and spring precipitation on streamflow over past centuries, and relative to the modern period. The results suggest that spring precipitation has been a more consistent influence on water year streamflow in the Missouri River Headwaters over past centuries than winter snowpack. The strong relationship between 1 April SWE values and water year streamflow in the Missouri River Headwaters observed over much of the 20th century is not found to be a consistent feature of these multicentury paleorecords. Instead, the climatic influences on streamflow within the Missouri River Headwaters are likely more variable than 20th‐century instrumental records indicate.
-
Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region.more » « less
