Snow-dominated Watersheds Research Articles

Potential Changes in Runoff of California’s Major Water Supply Watersheds in the 21st Century

This study assesses potential changes in runoff of California’s eight major Central Valley water supply watersheds in the 21st century. The study employs the latest operative climate projections from 10 general circulation models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) under two emission scenarios (RCP 4.5 and RCP 8.5) to drive a hydrologic model (VIC) in generating runoff projections through 2099. Changes in peak runoff, peak timing, seasonal (major water supply season April–July) runoff, and annual runoff during two future periods, mid-century and late-century, relative to a historical baseline period are examined. Trends in seasonal and annual runoff projections are also investigated. The results indicate that watershed characteristics impact runoff responses to climate change. Specifically, for rain-dominated watersheds, runoff is generally projected to peak earlier with higher peak volumes on average. For snow-dominated watersheds, however, runoff is largely projected to peak within the same month as historical runoff has, with little changes in peak volume during mid-century but pronounced decreases during late-century under the higher emission scenario. The study also identifies changes that are common to all study watersheds. Specifically, the temporal distribution of annual runoff is projected to change in terms of shifting more volume to the wet season, though there is no significant changing trend in the total annual runoff. Additionally, the snowmelt portion of the total annual runoff (represented by April–July runoff divided by total annual runoff) is projected to decline consistently under both emission scenarios, indicative of a shrinking snowpack across the study watersheds. Collectively, these changes imply higher flood risk and lower water supply reliability in the future that are expected to pose stress to California’s water system. Those findings can inform water management adaptation practices (e.g., watershed restoration, re-operation of the current water system, investing in additional water storage) to cope with the stress.

The Value of Accurate High-Resolution and Spatially Continuous Snow Information to Streamflow Forecasts

Abstract Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

Hydrologic responses to climate warming for a snow‐dominated watershed and a transient snow watershed in the California Sierra

AbstractClimate warming will have substantial impacts on hydrological fluxes in the California Sierra. A commonly used approach for assessing these impacts, particularly in mountain watersheds, is to substitute space for time. This conceptual model assumes that with warming, the hydrologic behaviour of higher elevation snow dominated watersheds (SDWs) will converge to the hydrologic behaviour of lower elevation transient snow watersheds (TSWs). To investigate the efficacy of this conceptual model, a process‐based model (RHESSys) was applied to a TSW and a SDW with a mean annual temperature 2 °C lower than the TSW in the Sierra National forest, California. This study investigated the effect of climate warming (2 and 4 °C) on the model estimates of snow water equivalent (SWE), streamflow, evapotranspiration (ET), and moisture deficit in the two watersheds. Modelling results show that SDW under 2 °C warming scenarios generates monthly SWE similar in magnitude and timing as TSW under historic conditions. However, SDW under 2 °C warming scenarios generates higher annual and summer streamflow due to shallower groundwater storage and experiences less water limitation due to lower ET, compared with TSW under historical climate conditions. In both watersheds, leaf area index and wetness index are primary factors controlling spatial patterns of seasonal ET under both historical climate conditions and warming scenarios. Climate warming increases the spatial variability in monthly ET, especially in the summer period. These modelling results suggest that vegetation structure and subsurface properties may be as important as climate in explaining hydrologic response to climate warming in small Sierra Nevada watersheds.

A methodological framework to assess PMP and PMF in snow-dominated watersheds under changing climate conditions – A case study of three watersheds in Québec (Canada)

Climate change will affect precipitation and flood regimes. It is anticipated that the Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) will be modified in a changing climate. This paper aims to quantify and analyze climate change influences on PMP and PMF in three watersheds with different climatic conditions across the province of Québec, Canada. Output data from the Canadian Regional Climate Model (CRCM) was used to estimate PMP and Probable Maximum Snow Accumulation (PMSA) in future climate projections, which was then used to force the SWAT hydrological model to estimate PMF. PMP and PMF values were estimated for two time horizons each spanning 30 years: 1961–1990 (recent past) and 2041–2070 (future). PMP and PMF were separately analyzed for two seasons: summer-fall and spring. Results show that PMF in the watershed located in southern Québec would remain unchanged in the future horizon, but the trend for the watersheds located in the northeastern and northern areas of the province is an increase of up to 11%.

Effects of climate change on hydrology and water resources in the Blue Mountains, Oregon, USA

In the semi-arid environment of the Blue Mountains, Oregon (USA), water is a critical resource for both ecosystems and human uses and will be affected by climate change in both the near- and long-term. Warmer temperatures will reduce snowpack and snow-dominated watersheds will transition to mixed rain and snow, while mixed rain and snow dominated watersheds will shift towards rain dominated. This will result in high flows occurring more commonly in late autumn and winter rather than spring, and lower low flows in summer, phenomena that may already be occurring in the Pacific Northwest. Higher peak flows are expected to increase the frequency and magnitude of flooding, which may increase erosion and scouring of the streambed and concurrent risks to roads, culverts, and bridges. Mapping of projected peak flow changes near roads gives an opportunity to mitigate these potential risks. Diminished snowpack and low summer flows are expected to cause a reduction in water supply for aquatic ecosystems, agriculture, municipal consumption, and livestock grazing, although this effect will not be as prominent in areas with substantial amounts of groundwater. Advanced planning could help reduce conflict among water users. Responding pro-actively to climate risks by improving current management practices, like road design and water management as highlighted here, may be among the most efficient and effective methods for adaptation.

Topography significantly influencing low flows in snow-dominated watersheds

Abstract. Watershed topography plays an important role in determining the spatial heterogeneity of ecological, geomorphological, and hydrological processes. Few studies have quantified the role of topography in various flow variables. In this study, 28 watersheds with snow-dominated hydrological regimes were selected with daily flow records from 1989 to 1996. These watersheds are located in the Southern Interior of British Columbia, Canada, and range in size from 2.6 to 1780 km2. For each watershed, 22 topographic indices (TIs) were derived, including those commonly used in hydrology and other environmental fields. Flow variables include annual mean flow (Qmean), Q10 %, Q25 %, Q50 %, Q75 %, Q90 %, and annual minimum flow (Qmin), where Qx % is defined as the daily flow that occurred each year at a given percentage (x). Factor analysis (FA) was first adopted to exclude some redundant or repetitive TIs. Then, multiple linear regression models were employed to quantify the relative contributions of TIs to each flow variable in each year. Our results show that topography plays a more important role in low flows (flow magnitudes ≤ Q75 %) than high flows. However, the effects of TIs on different flow magnitudes are not consistent. Our analysis also determined five significant TIs: perimeter, slope length factor, surface area, openness, and terrain characterization index. These can be used to compare watersheds when low flow assessments are conducted, specifically in snow-dominated regions with the watershed size less than several thousand square kilometres.

Future snow? A spatial-probabilistic assessment of the extraordinarily low snowpacks of 2014 and 2015 in the Oregon Cascades

Abstract. In the Pacific Northwest, USA, the extraordinarily low snowpacks of winters 2013–2014 and 2014–2015 stressed regional water resources and the social-environmental system. We introduce two new approaches to better understand how seasonal snow water storage during these two winters would compare to snow water storage under warmer climate conditions. The first approach calculates a spatial-probabilistic metric representing the likelihood that the snow water storage of 2013–2014 and 2014–2015 would occur under +2 °C perturbed climate conditions. We computed snow water storage (basin-wide and across elevations) and the ratio of snow water equivalent to cumulative precipitation (across elevations) for the McKenzie River basin (3041 km2), a major tributary to the Willamette River in Oregon, USA. We applied these computations to calculate the occurrence probability for similarly low snow water storage under climate warming. Results suggest that, relative to +2 °C conditions, basin-wide snow water storage during winter 2013–2014 would be above average, while that of winter 2014–2015 would be far below average. Snow water storage on 1 April corresponds to a 42 % (2013–2014) and 92 % (2014–2015) probability of being met or exceeded in any given year. The second approach introduces the concept of snow analogs to improve the anticipatory capacity of climate change impacts on snow-derived water resources. The use of a spatial-probabilistic approach and snow analogs provide new methods of assessing basin-wide snow water storage in a non-stationary climate and are readily applicable in other snow-dominated watersheds.

Developing a Snowmelt Forecast Model in the Absence of Field Data

In data poor regions predicting water availability is a considerable challenge for water resource managers. In snow-dominated watersheds with minimal in situ measurements, satellite imagery can supplement sparse data networks to predict future water availability. This technical note presents the first phase of an operational forecast model in the data poor Elqui River watershed located in northern Central Chile (30°S). The approach applies remotely-sensed snow cover products from the Moderate Resolution Imaging Spectrometer (MODIS) instrument as the first order hydrologic input for a modified Snowmelt Runoff Model. In the semi-arid Elqui River, snow and glacier melt are the dominant hydrologic inputs but precipitation is limited to up to six winter events annually. Unfortunately winter access to the Andean Cordillera where snow accumulates is incredibly challenging, and thus measurements of snowpack are extremely sparse. While a high elevation snow monitoring network is under development, management decisions regarding water resources cannot wait as the region is in its eighth consecutive year of drought. Our model applies a Monte Carlo approach on monthly data to determine relationships between lagged changes in snow covered area and previous streamflow to predict subsequent streamflow. Despite the limited data inputs the model performs well with a Nash-Sutcliffe Efficiency and R2 of 0.830 and 0.833 respectively. This model is not watershed specific and is applicable in other regions where snow dominates hydrologic inputs, but measurements are minimal.

Exploration of sequential streamflow assimilation in snow dominated watersheds

This paper evaluates Ensemble Kalman filter (EnKF) sequential data assimilation on a semi-distributed hydrological model implementation on two snow-dominated watersheds, focussing strictly on snow accumulation and melt periods while assimilating streamflow for the updating of various state variables combinations. Three scenarios are explored in depth: (1) updating the three state variables that were previously identified pertinent for snow-free hydrological processes: soil moisture in the intermediate layer, soil moisture in the deep layer, and the overland routing reservoir, (2) updating the snow water equivalent, and (3) updating all of the above state variables. Inputs (precipitation and temperature) and output (streamflow) perturbation factors are first identified for each scenario, based on their performance and reliability for simulation with assimilation. The three EnKF implementations are next compared to one another and to an open-loop run, in an ensemble forecasting context. The third scenario outperforms the others in most situations and provides the largest gain in reliability. The ensemble size may also be reduced, from 1000 to 50 members, without much loss in performance or reliability.

Exploration of sequential streamflow assimilation in snow dominated watersheds

This paper evaluates Ensemble Kalman filter (EnKF) sequential data assimilation on a semi-distributed hydrological model implementation on two snow-dominated watersheds, focussing strictly on snow accumulation and melt periods while assimilating streamflow for the updating of various state variables combinations. Three scenarios are explored in depth: (1) updating the three state variables that were previously identified pertinent for snow-free hydrological processes: soil moisture in the intermediate layer, soil moisture in the deep layer, and the overland routing reservoir, (2) updating the snow water equivalent, and (3) updating all of the above state variables. Inputs (precipitation and temperature) and output (streamflow) perturbation factors are first identified for each scenario, based on their performance and reliability for simulation with assimilation. The three EnKF implementations are next compared to one another and to an open-loop run, in an ensemble forecasting context. The third scenario outperforms the others in most situations and provides the largest gain in reliability. The ensemble size may also be reduced, from 1000 to 50 members, without much loss in performance or reliability.

A hydrogeologic framework for characterizing summer streamflow sensitivity to climate warming in the Pacific Northwest, USA

Abstract. Summer streamflows in the Pacific Northwest are largely derived from melting snow and groundwater discharge. As the climate warms, diminishing snowpack and earlier snowmelt will cause reductions in summer streamflow. Most regional-scale assessments of climate change impacts on streamflow use downscaled temperature and precipitation projections from general circulation models (GCMs) coupled with large-scale hydrologic models. Here we develop and apply an analytical hydrogeologic framework for characterizing summer streamflow sensitivity to a change in the timing and magnitude of recharge in a spatially explicit fashion. In particular, we incorporate the role of deep groundwater, which large-scale hydrologic models generally fail to capture, into streamflow sensitivity assessments. We validate our analytical streamflow sensitivities against two empirical measures of sensitivity derived using historical observations of temperature, precipitation, and streamflow from 217 watersheds. In general, empirically and analytically derived streamflow sensitivity values correspond. Although the selected watersheds cover a range of hydrologic regimes (e.g., rain-dominated, mixture of rain and snow, and snow-dominated), sensitivity validation was primarily driven by the snow-dominated watersheds, which are subjected to a wider range of change in recharge timing and magnitude as a result of increased temperature. Overall, two patterns emerge from this analysis: first, areas with high streamflow sensitivity also have higher summer streamflows as compared to low-sensitivity areas. Second, the level of sensitivity and spatial extent of highly sensitive areas diminishes over time as the summer progresses. Results of this analysis point to a robust, practical, and scalable approach that can help assess risk at the landscape scale, complement the downscaling approach, be applied to any climate scenario of interest, and provide a framework to assist land and water managers in adapting to an uncertain and potentially challenging future.

Anomalous trend in soil evaporation in a semi-arid, snow-dominated watershed

Soil evaporation in arid and semi-arid regions is generally moisture-limited. Evaporation in these regions is expected to increase monotonically with increase in precipitation. In contrast, model simulations in a snow-dominated, semi-arid Reynolds Mountain East (RME) watershed point to the existence of an anomalous trend in soil evaporation. Results indicate that soil evaporation in snow-dominated watersheds first increases and then subsequently decreases with increasing precipitation. The anomalous variation is because of two competing evaporation controls: (a) higher soil moisture in wetter years which leads to larger evaporation, and (b) prolonged snow cover period in wetter years which shields the soil from the atmosphere, thus reducing soil evaporation. To further evaluate how the competition is mediated by meteorological and hydrogeological characteristics of the watershed, changes in the trend due to different watershed hydraulic conductivity, vegetation cover, and snowfall area fraction are systematically studied. Results show considerable persistence in the anomalous trend over a wide range of controls. The controlling factors, however, have significant influence both on the magnitude of the WY evaporation and the location of the inflection point in the trend curve.

An Evaluation of Multiscalar Drought Indices in Nevada and Eastern California

AbstractNevada and eastern California are home to some of the driest and warmest climates, most mountainous regions, and fastest growing metropolitan areas of the United States. Throughout Nevada and eastern California, snow-dominated watersheds provide most of the water supply for both human and environmental demands. Increasing demands on finite water supplies have resulted in the need to better monitor drought and its associated hydrologic and agricultural impacts. Two multiscalar drought indices, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI), are evaluated over Nevada and eastern California regions of the Great Basin using standardized streamflow, lake, and reservoir water surface stages to quantify wet and dry periods. Results show that both metrics are significantly correlated to surface water availability, with SPEI showing slightly higher correlations over SPI, suggesting that the inclusion of a simple term for atmospheric demand in SPEI is useful for characterizing hydrologic drought in arid regions. These results also highlight the utility of multiscalar drought indices as a proxy for summer groundwater discharge and baseflow periods.

Spawning site selection in interior Fraser River coho salmon Oncorhynchus kisutch: an imperiled population of anadromous salmon from a snow-dominated watershed

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 16:249-260 (2012) - DOI: https://doi.org/10.3354/esr00401 Theme Section: Endangered river fish: threats and conservation options Spawning site selection in interior Fraser River coho salmon Oncorhynchus kisutch: an imperiled population of anadromous salmon from a snow-dominated watershed Crystal J. McRae1,2,*, Kyla D. Warren1,3, J. Mark Shrimpton1 1Ecosystem Science and Management (Biology) Program, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada 2Present Address: Committee for General Education, National Dong Hwa University, Shoufeng, Hualien, 97401 Taiwan 3Present address: Rescan Environmental Services Ltd., Smithers, British Columbia V0J 2N0, Canada *Email: mcraec@unbc.ca ABSTRACT: Understanding habitat requirements for spawning is important for species at risk of extirpation to define areas for protection of this critical life stage. Interior Fraser coho salmon (IFC) Oncorhynchus kisutch spawn in tributaries of the upper Fraser River, British Columbia, Canada. They are regionally isolated, genetically distinct, and have been listed as endangered. Recent enumeration efforts have shown declining escapement and range contraction. IFC spawn in snow-dominated watersheds, yet much of our understanding of spawner habitat requirements is based on information for coho from coastal rain-dominated systems. Consequently, we examined the relative influence of habitat features on spawning site selection. Our sampling focused on why fish chose particular locations and not others in what seemed to be suitable habitat. We defined several logistic regression models, including various combinations of physical, chemical and hydrological microhabitat features, and used an information-theoretic approach to evaluate the relative plausibility of these models. Models combining intragravel measures of electrical conductance, dissolved oxygen, temperature and specific discharge were the best models associated with spawning microhabitat selection, and jackknife validations showed that these models had good predictive abilities. Intragravel variables, therefore, appear to play a prominent role in spawning site selection for IFC. Understanding the influence of the intragravel environment on spawning site selection will help identify habitat requirements as well as potential threats to fish populations and will contribute to the development of comprehensive conservation initiatives. KEY WORDS: Interior Fraser coho · Spawning · Site selection · Intragravel habitat Full text in pdf format PreviousNextCite this article as: McRae CJ, Warren KD, Shrimpton JM (2012) Spawning site selection in interior Fraser River coho salmon Oncorhynchus kisutch: an imperiled population of anadromous salmon from a snow-dominated watershed. Endang Species Res 16:249-260. https://doi.org/10.3354/esr00401 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 16, No. 3. Online publication date: March 22, 2012 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2012 Inter-Research.

Seasonal versus transient snow and the elevation dependence of climate sensitivity in maritime mountainous regions

[1] In maritime mountainous regions, the phase of winter precipitation is elevation dependent, and in watersheds receiving both rain and snow, hydrologic impacts of climate change are less straightforward than in snowmelt-dominated systems. Here, 29 Pacific Northwest watersheds illustrate how distribution of seasonal snow, transient snow, and winter rain mediates sensitivity to 20th century warming. Watersheds with >50% of their area in the seasonal snow zone had significant (α ≤ 0.1) trends towards greater winter and lower summer discharge, while lower elevations had no consistent trends. In seasonal snow-dominated watersheds, runoff occurs 22–27 days earlier and minimum flows are 5–9% lower than in 1962, based on Sen's slope over the period. Trends in peak streamflow depend on whether watershed area susceptible to rain-on-snow events is increasing or decreasing. Delineation of elevation-dependent snow zones identifies climate sensitivity of maritime mountainous watersheds and enables planning for water and ecosystem impacts of climate change.

Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

The National Weather Service (NWS) uses the SNOW17 model to forecast snow accumulation and ablation processes in snow-dominated watersheds nationwide. Successful application of the SNOW17 relies heavily on site-specific estimation of model parameters. The current study undertakes a comprehensive sensitivity and uncertainty analysis of SNOW17 model parameters using forcing and snow water equivalent (SWE) data from 12 sites with differing meteorological and geographic characteristics. The Generalized Sensitivity Analysis and the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm are utilized to explore the parameter space and assess model parametric and predictive uncertainty. Results indicate that SNOW17 parameter sensitivity and uncertainty generally varies between sites. Of the six hydroclimatic characteristics studied, only air temperature shows strong correlation with the sensitivity and uncertainty ranges of two parameters, while precipitation is highly correlated with the uncertainty of one parameter. Posterior marginal distributions of two parameters are also shown to be site-dependent in terms of distribution type. The SNOW17 prediction ensembles generated by the DREAM-derived posterior parameter sets contain most of the observed SWE. The proposed uncertainty analysis provides posterior parameter information on parameter uncertainty and distribution types that can serve as a foundation for a data assimilation framework for hydrologic models.

The influence of forest and topography on snow accumulation and melt at the watershed-scale

Summary Snow accumulation and melt are the two most important processes contributing to the spring freshet, the main hydrological event in snow-dominated watersheds. The magnitude and timing of the spring freshet is determined by the spatial variability of melt rates and peak snow water equivalent (SWE). Although it is known that the spatial variability of SWE is controlled by topography and vegetation, their combined influence on snow accumulation and melt at the watershed-scale has only been addressed in a few studies. The high small-scale variability of a snowpack combined with the classical sampling technique along transects makes it difficult to estimate how much of the observed spatial variability in SWE can be attributed to topographic and vegetative controls such as elevation, aspect, wind, and forest type, and how much of it is local variability. A nested stratified sampling design was developed for the snow courses in this study, where small-scale variability at the plot-scale was integrated by 12 snow density and 60 snow depth measurements, while the variability at the watershed-scale was captured by 19 strata, defined by elevation, aspect, and forest cover (either clearcut or forest). Snow course sampling was carried out during the spring of 2005, a year with 25% below average snow accumulation, and in spring of 2006, a year with 25% above average snow accumulation for the region. Snow accumulation in both years and snowmelt for a period in the spring of 2006 are analysed with multiple linear regressions and spatial autocorrelation of SWE with variogram analysis. The combination of elevation, aspect, and forest cover explained about 80–90% of the large-scale variability in snow accumulation for both years. At the plot-scale the coefficient of variation was higher under the forest canopy than in clearcuts, but did not relate to topographic controls. Elevation had the greatest influence on SWE in both years, with a steeper gradient in the snow rich year, whereas aspect had a similar effect in both years. In the mild winter of 2005, forests accumulated 39% less snow than clearcuts, while in 2006, forests had 27% less snow than clearcuts. Relative to the mean, aspect had a greater influence on snow accumulation than forest cover in the mild winter of 2005. Elevation played the most important role in snowmelt, while both aspect and forest cover had a comparable effect with slightly less influence than elevation. Almost all variograms show a clear sill, confirming that in the majority of cases, a 60 × 60 m plot size was adequate in capturing the variability of the plot-scale. In general, the degree of spatial autocorrelations is persistent in the snowmelt season for both years. This study has shown that it is possible to separate large-scale variability created by topography and forest cover from plot-scale variability through specifically designed snow surveys. The methods and data set of this study are useful in testing spatially-distributed snowmelt models and in developing new algorithms that reflect the relationships between the factors controlling the spatial variability of SWE.