Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt, and multiple studies have found that snow loss leads to earlier snowmelt timing and declines in annual streamflow. However, hydrologic responses to snow loss are heterogeneous, and not all areas experience streamflow declines. This research examines whether streamflow generation is different for rainfall versus snowmelt inputs. We compiled a sample of 57 small U.S. Geological Survey watersheds in the western United States containing a Natural Resource Conservation Service Snow Telemetry site and having ratios of mean annual peak snow water equivalent to precipitation ratios >0.25. Daily streamflow was separated into quickflow and baseflow using a digital filter, and quickflow was then divided into quickflow response intervals using thresholds in quickflow slope. Each quickflow response interval was categorized by its fraction of input from snowmelt. Most sites exhibited two streamflow generation peaks each year, with one peak in the winter when runoff efficiency is greatest, and the second in the spring during peak snowmelt input. On average, study watersheds were dominated by snowmelt inputs (70%), and snowmelt and mixed inputs usually generated greater streamflow than rainfall because of higher inputs and longer durations. However, rainfall produced high streamflow generation in winter, when watersheds have their highest runoff efficiency (81%) across all input types. We demonstrate that while snowmelt is important for streamflow generation due to high input over long periods, increases in rain and mixed input during wet winter periods can countervail tendencies for reduced streamflow with declining snowpacks.
Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone
Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.
more »
« less
- Award ID(s):
- 1653998
- NSF-PAR ID:
- 10337000
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 26
- Issue:
- 10
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 2779 to 2796
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
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
-
The Yukon River basin encompasses over 832,000 km2 of boreal Arctic Alaska and northwest Canada, providing a major transportation corridor and multiple natural resources to regional communities. The river seasonal hydrology is defined by a long winter frozen season and a snowmelt-driven spring flood pulse. Capabilities for accurate monitoring and forecasting of the annual spring freshet and river ice breakup (RIB) in the Yukon and other northern rivers is limited, but critical for understanding hydrologic processes related to snow, and for assessing flood-related risks to regional communities. We developed a regional snow phenology record using satellite passive microwave remote sensing to elucidate interactions between the timing of upland snowmelt and the downstream spring flood pulse and RIB in the Yukon. The seasonal snow metrics included annual Main Melt Onset Date (MMOD), Snowoff (SO) and Snowmelt Duration (SMD) derived from multifrequency (18.7 and 36.5 GHz) daily brightness temperatures and a physically-based Gradient Ratio Polarization (GRP) retrieval algorithm. The resulting snow phenology record extends over a 29-year period (1988–2016) with 6.25 km grid resolution. The MMOD retrievals showed good agreement with similar snow metrics derived from in situ weather station measurements of snowpack water equivalence (r = 0.48, bias = −3.63 days) and surface air temperatures (r = 0.69, bias = 1 day). The MMOD and SO impact on the spring freshet was investigated by comparing areal quantiles of the remotely sensed snow metrics with measured streamflow quantiles over selected sub-basins. The SO 50% quantile showed the strongest (p < 0.1) correspondence with the measured spring flood pulse at Stevens Village (r = 0.71) and Pilot (r = 0.63) river gaging stations, representing two major Yukon sub-basins. MMOD quantiles indicating 20% and 50% of a catchment under active snowmelt corresponded favorably with downstream RIB (r = 0.61) from 19 river observation stations spanning a range of Yukon sub-basins; these results also revealed a 14–27 day lag between MMOD and subsequent RIB. Together, the satellite based MMOD and SO metrics show potential value for regional monitoring and forecasting of the spring flood pulse and RIB timing in the Yukon and other boreal Arctic basins.more » « less
-
null (Ed.)An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this presentation is to introduce a novel method that combines light detection and ranging (LiDAR) with ground-penetrating radar (GPR) to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring melt. This method was developed at multiple plots in Colorado in 2017 and applied at the small catchment scale in 2019. We developed this method in a manner to observe rapid changes that occur at subdaily timescales. Observed volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters during method development. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for studies to estimate the complex spatio-temporal dynamics of liquid water in snow. During the spring snowmelt season of 2019, we applied this method to a small headwater catchment in the Colorado Front Range. A total of 9 GPR surveys of approximately 3 km in length were conducted over a six-week period. Additionally, five LiDAR scans occurred over the same area. Using this technique, we identify locations that melting snow accumulates and is stored as liquid water within the snowpack. This work shows that the vadose zone may be conceptualized, during snowmelt, as extending above the soil-snow interface to include variably saturated flow processes within the snowpack.more » « less
-
more » « less
Abstract In mountainous river basins of the Pacific Northwest, climate models predict that winter warming will result in increased precipitation falling as rain and decreased snowpack. A detailed understanding of the spatial and temporal dynamics of water sources across river networks will help illuminate climate change impacts on river flow regimes. Because the stable isotopic composition of precipitation varies geographically, variation in surface water isotope ratios indicates the volume‐weighted integration of upstream source water. We measured the stable isotope ratios of surface water samples collected in the Snoqualmie River basin in western Washington over June and September 2017 and the 2018 water year. We used ordinary least squares regression and geostatistical Spatial Stream Network models to relate surface water isotope ratios to mean watershed elevation (MWE) across seasons. Geologic and discharge data was integrated with water isotopes to create a conceptual model of streamflow generation for the Snoqualmie River. We found that surface water stable isotope ratios were lowest in the spring and highest in the dry, Mediterranean summer, but related strongly to MWE throughout the year. Low isotope ratios in spring reflect the input of snowmelt into high elevation tributaries. High summer isotope ratios suggest that groundwater is sourced from low elevation areas and recharged by winter precipitation. Overall, our results suggest that baseflow in the Snoqualmie River may be relatively resilient to predicted warming and subsequent changes to snowpack in the Pacific Northwest.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-26-2779-2022
