Linking quickflow response to subsurface state can improve our understanding of runoff processes that drive emergent catchment behaviour. We investigated the formation of non‐linear quickflows in three forested headwater catchments and also explored unsaturated and saturated storage dynamics, and likely runoff generation mechanisms that contributed to threshold formation. Our analyses focused on two reference watersheds at the Coweeta Hydrologic Laboratory (CHL) in western North Carolina, USA, and one reference watershed at the Susquehanna Shale Hills Critical Zone Observatory (SHW) in Central Pennsylvania, USA, with available hourly soil moisture, groundwater, streamflow, and precipitation time series over several years. Our study objectives were to characterise (a) non‐linear runoff response as a function of storm characteristics and antecedent conditions, (b) the critical levels of shallow unsaturated and saturated storage that lead to hourly flow response, and (c) runoff mechanisms contributing to rapidly increasing quickflow using measurements of soil moisture and groundwater. We found that maximum hourly rainfall did not significantly contribute to quickflow production in our sites, in contrast to prior studies, due to highly conductive forest soils. Soil moisture and groundwater dynamics measured in hydrologically representative areas of the hillslope showed that variable subsurface states could contribute to non‐linear runoff behaviour. Quickflow generation in watersheds at CHL were dominated by both saturated and unsaturated pathways, but the relative contributions of each pathway varied between catchments. In contrast, quickflow was almost entirely related to groundwater fluctuations at SHW. We showed that co‐located measurements of soil moisture and groundwater supplement threshold analyses providing stronger prediction and understanding of quickflow generation and indicate dominant runoff processes.
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.
more » « less- NSF-PAR ID:
- 10446498
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 56
- Issue:
- 4
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract -
Abstract In this study, we characterize the snowmelt hydrological response of nine headwater watersheds in southeast Wyoming by separating streamflow into three components using a combination of tracer and graphical approaches. First, continuous 15‐min records of specific conductance (SC) from 2016 to 2018 were used to separate streamflow into annual contributions, representing water that contributes to streamflow in a given year that entered the watershed in the same year being considered, and perennial contributions, representing water that contributes to streamflow in a given year that entered the watershed in previous years. Then, diurnal streamflow cycles occurring during the snowmelt season were used to graphically separate annual contributions into rapid diurnal snowmelt contributions, representing water with the relatively fastest hydrological response and shortest residence time, and delayed annual contributions, representing water with relatively longer residence time in the watershed before becoming streamflow. On average, mean annual total streamflow was comprised of between 22% and 46% perennial contributions, 7% and 14% rapid diurnal snowmelt contributions, and 46% and 55% delayed annual contributions across the watersheds. A hysteresis index describing SC‐discharge patterns indicated that, annually, most watersheds showed negative, concave, anti‐clockwise hysteretic direction suggesting faster flow pathways dominate streamflow on the rising limb of the annual hydrograph relative to the falling limb. At the daily timescale during snowmelt‐induced diurnal streamflow cycles, hysteresis was negative, but with a clockwise direction, implying that rapid diurnal snowmelt contributions generated from the concurrent daily snowmelt, with lower SC, arrived after delayed annual contribution peaks and preferentially contributed on the falling limb of diurnal cycles. South‐facing watersheds were more susceptible to early season snowmelt at slower rates, resulting in less annual and more perennial contributions. Conversely, north‐facing watersheds had longer snow persistence and larger proportions of annual contributions and rapid diurnal snowmelt contributions. Watersheds with surficial geology dominated by glacial deposits had a lower proportion of rapid diurnal snowmelt contributions compared to watersheds with large percentages of bedrock surficial geology.
-
null (Ed.)Abstract. In the past decades, data-driven machine-learning (ML) models have emerged as promising tools for short-term streamflow forecasting. Among other qualities, the popularity of ML models for such applications is due to their relative ease in implementation, less strict distributional assumption, and competitive computational and predictive performance. Despite the encouraging results, most applications of ML for streamflow forecasting have been limited to watersheds in which rainfall is the major source of runoff. In this study, we evaluate the potential of random forests (RFs), a popular ML method, to make streamflow forecasts at 1 d of lead time at 86 watersheds in the Pacific Northwest. These watersheds cover diverse climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes based on the timing of center-of-annual flow volume: rainfall-dominated, transient, and snowmelt-dominated. RF performance is benchmarked against naïve and multiple linear regression (MLR) models and evaluated using four criteria: coefficient of determination, root mean squared error, mean absolute error, and Kling–Gupta efficiency (KGE). Model evaluation scores suggest that the RF performs better in snowmelt-driven watersheds compared to rainfall-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.more » « less
-
Abstract In the western United States, water supplies largely originate as snowmelt from forested land. Forests impact the water balance of these headwater streams, yet most predictive runoff models do not explicitly account for changing snow‐vegetation dynamics. Here, we present a case study showing how warmer temperatures and changing forests in the Henrys Fork of the Snake River, a seasonally snow‐covered headwater basin in the Greater Yellowstone Ecosystem, have altered the relationship between April 1st snow water equivalent (SWE) and summer streamflow. Since the onset and recovery of severe drought in the early 2000s, predictive models based on pre‐drought relationships over‐predict summer runoff in all three headwater tributaries of the Henrys Fork, despite minimal changes in precipitation or snow accumulation. Compared with the pre‐drought period, late springs and summers (May–September) are warmer and vegetation is greener with denser forests due to recovery from multiple historical disturbances. Shifts in the alignment of snowmelt and energy availability due to warmer temperatures may reduce runoff efficiency by changing the amount of precipitation that goes to evapotranspiration versus runoff and recharge. To quantify the alignment between snowmelt and energy on a timeframe needed for predictive models, we propose a new metric, the Vegetation‐Water Alignment Index (VWA), to characterize the synchrony of vegetation greenness and snowmelt and rain inputs. New predictive models show that in addition to April 1st SWE, the previous year's VWA and summer reference evapotranspiration are the most significant predictors of runoff in each watershed and provide more predictive power than traditionally used metrics. These results suggest that the timing of snowmelt relative to the start of the growing season affects not only annual partitioning of streamflow, but can also determine the groundwater storage state that dictates runoff efficiency the following spring.
-
Abstract The glacial meltwater streams in the McMurdo Dry Valleys (MDVs), Antarctica only flow during the austral summer and contain abundant algal mats which grow at the onset of flow. Their relative abundance in stream channels of this polar desert make the streams biogeochemical hot spots. The MDVs receive minimal precipitation as snow, which is redistributed by wind and deposited in distinct locations, some of which become persistent snow patches each year. Previous studies identified that MDV streamflow comes from a combination of glacier ice and snow, although snow was assumed to contribute little to the overall water budget. This study uses a combination of satellite imagery, terrain analysis, and field measurements to determine where snow patches accumulate and persist across MDV watersheds, and to quantify the potential hydrologic and biogeochemical contributions of snow patches to streams. Watersheds near the coast have the highest snow‐covered area and longest snow persistence. Many of these snow patches accumulate within the stream channels, which results in the potential to contribute to streamflow. During the summer of 2021–2022, stream channel snow patches had the potential to contribute anywhere between <1% and 90% of the total annual discharge in Lake Fryxell Basin streams, and may increase with different hydrometeorological conditions. On average the potential inputs from snow patches to streamflow was between 12% and 25% of the annual discharge during the 2021–2022 season, as determined by snow area and SWE. Snow patches in the majority of the watersheds had higher nitrogen and phosphorous concentrations than stream water, and six streams contained snow with higher N:P ratios than the average N:P in the stream water. This suggests that if such patches melt early in the summer, these nutrient and water inputs could occur at the right time and stoichiometry to be crucial for early season algal mat growth.