Snowmelt Runoff and Water Yield Along Elevation and Temperature Gradients in California’s Southern Sierra Nevada1

Hunsaker, Carolyn T., Thomas W. Whitaker, and Roger C. Bales, 2012. Snowmelt Runoff and Water Yield Along Elevation and Temperature Gradients in California’s Southern Sierra Nevada. Journal of the American Water Resources Association (JAWRA) 48(4): 667‐678. DOI: 10.1111/j.1752‐1688.2012.00641.x Abstract: Differences in hydrologic response across the rain‐snow transition in the southern Sierra Nevada were studied in eight headwater catchments – the Kings River Experimental Watersheds – using continuous precipitation, snowpack, and streamflow measurements. The annual runoff ratio (discharge divided by precipitation) increased about 0.1 per 300 m of mean catchment elevation over the range 1,800‐2,400 m. Higher‐elevation catchments have lower vegetation density, shallow soils with rapid permeability, and a shorter growing season when compared with those at lower elevations. Average annual temperatures ranged from 6.8°C at 2,400 m to 8.6 at 1,950 m elevation, with annual precipitation being 75‐95% snow at the highest elevations vs. 20‐50% at the lowest. Peak discharge lagged peak snow accumulation on the order of 60 days at the higher elevations and 20 to 30 days at the lower elevations. Snowmelt dominated the daily streamflow cycle over a period of about 30 days in higher elevation catchments, followed by a 15‐day transition to evapotranspiration dominating the daily streamflow cycle. Discharge from lower elevation catchments was rainfall dominated in spring, with the transition to evapotranspiration dominance being less distinct. Climate warming that results in a longer growing season and a shift from snow to rain would result in earlier runoff and a lower runoff ratio.     

FOREST GROWTH IMPACT ON PEAK SNOW PACK MEASUREMENTS AT LONG TERM SNOW COURSES1

ABSTRACT: Snow course surveys in late winter provide stream‐flow forecasters with their best information for making water supply and flood forecasts for the subsequent spring and summer runoff period in mountainous regions of western North America. Snow survey data analyses are generally based on a 30‐year “normal” period. It is well documented that forest cover changes over time will affect snow accumulation on the ground within forests. This paper seeks to determine if forest cover changes over decades at long term snow courses decrease measured peak snow water equivalent (SWE) enough to affect runoff prediction. Annual peak SWE records were analyzed at four snow courses in two different forest types having at least 25 years of snowpack data to detect any decreases in SWE due to forest growth. No statistically significant decreases in annual peak SWE over time were found at any of these four snow courses. The wide range of annual winter precipitation and correspondingly highly variable peak snowpack accumulation, as well as many other weather and site variables, masked any minor trends in the data.     

Effect of Snow Cover Conditions on the Hydrologic Regime: Case Study in a Pluvial‐Nival Watershed,Japan1

Abstract: Hydrologic monitoring in a small forested and mountainous headwater basin in Niigata Prefecture has been undertaken since 2000. An important characteristic of the basin is that the hydrologic regime contains pluvial elements year‐round, including rain‐on‐snow, in addition to spring snowmelt. We evaluated the effect of different snow cover conditions on the hydrologic regime by analyzing observed data in conjunction with model simulations of the snowpack. A degree‐day snow model is presented and applied to the study basin to enable estimation of the basin average snow water equivalent using air temperature at three representative elevations. Analysis of hydrological time series data and master recession curves showed that flow during the snowmelt season was generated by a combination of ground water flow having a recession constant of 0.018/day and diurnal melt water flow having a recession constant of 0.015/hour. Daily flows during the winter/snowmelt season showed greater persistence than daily flows during the warm season. The seasonal water balance indicated that the ratio of runoff to precipitation during the cold season (December to May) was about 90% every year. Seasonal snowpack plays an important role in defining the hydrologic regime, with winter precipitation and snowmelt runoff contributing about 65% of the annual runoff. The timing of the snowmelt season, indicated by the date of occurrence of the first significant snowmelt event, was correlated with the occurrence of low flow events. Model simulations showed that basin average snow water equivalent reached a peak around mid‐February to mid‐March, although further validation of the model is required at high elevation sites.     

PREDICTING WILDERNESS SNOW WATER EQUIVALENT WITH NONWILDERNESS SNOW SENSORS1

ABSTRACT: Ten pairs of snow sensors were analyzed to investigate the feasibility of predicting snow water equivalent at high-elevation, telemetered snow sensor sites from lower-elevation sensors. The need for this analysis stems from an agreement between the California Department of Water Resources and the USDA Forest Service to temporarily allow snow sensors in California's wilderness areas so that a predictive relationship can be developed. After 10 or 15 years, the agreement calls for the sensors to be removed. Initial efforts to a priori select sensor pairs were based on proximity, colocation within a basin, and annual precipitation amount, but regression yielded poor fits (R² < 0.65) and high standard errors in eight of the ten cases. Analysis of the results suggested that eleva-tional similarity was the most important selection criteria, and that all available sensors near the target site should be analyzed via a regression screening. Using elevation for selection and the regression screening, five sensors that initially had poor fits were reanalyzed. Each of the five sensors was paired with between two and five new sensors, and R² values improved between 27 and 46 percent. Various data smoothing and editing algorithms were evaluated, but they rarely resulted in improved fits.     

APPLICATION OF THE SNOWMELT RUNOFF MODEL USING MULTIPLE-PARAMETER LANDSCAPE ZONES ON THE TOWANDA CREEK BASIN,PENNSYLVANIA1

ABSTRACT: The Snowmelt Runoff Model (SRM) is designed to compute daily stream discharge using satellite snow cover data for a basin divided into elevation zones. For the Towanda Creek basin, a Pennsylvania watershed with relatively little relief, analysis of snow cover images revealed that both elevation and land use affected snow accumulation and melt on the landscape. The distribution of slope and aspect on the watershed was also considered; however, these landscape features were not well correlated with the available snow cover data. SRM streamflow predictions for 1990, 1993 and 1994 snowmelt seasons for the Towanda Creek basin using a combination of elevation and land use zones yielded more precise streamflow estimates than the use of standard elevation zones alone. The use of multiple-parameter zones worked best in non-rain-on-snow conditions such as in 1990 and 1994 seasons where melt was primarily driven by differences in solar radiation. For seasons with major rain-on-snow events such as 1993, only modest improvements were shown since melt was dominated by rainfall energy inputs, condensation and sensible heat convection. Availability of GIS coverages containing satellite snow cover data and other landscape attributes should permit similar reformulation of multiple-parameter watershed zones and improved SRM streamflow predictions on other basins.     

iTree‐Hydro: Snow Hydrology Update For The Urban Forest Hydrology Model1

Yang, Yang, Theodore A. Endreny, and David J. Nowak, 2011. iTree‐Hydro: Snow Hydrology Update for the Urban Forest Hydrology Model. Journal of the American Water Resources Association (JAWRA) 47(6):1211–1218. DOI: 10.1111/j.1752‐1688.2011.00564.x Abstract: This article presents snow hydrology updates made to iTree‐Hydro, previously called the Urban Forest Effects—Hydrology model. iTree‐Hydro Version 1 was a warm climate model developed by the USDA Forest Service to provide a process‐based planning tool with robust water quantity and quality predictions given data limitations common to most urban areas. Cold climate hydrology routines presented in this update to iTree‐Hydro include: (1) snow interception to simulate the capture of snow by the vegetation canopy, (2) snow unloading to simulate the release of snow triggered by wind, (3) snowmelt to simulate the solid to liquid phase change using a heat budget, and (4) snow sublimation to simulate the solid to gas phase via evaporation. Cold climate hydrology routines were tested with research‐grade snow accumulation and weather data for the winter of 1996‐1997 at Umpqua National Forest, Oregon. The Nash‐Sutcliffe efficiency for open area snow accumulation was 0.77 and the Nash‐Sutcliffe efficiency for under canopy was 0.91. The USDA Forest Service offers iTree‐Hydro for urban forest hydrology simulation through http://www.iTreetools.org .     

Hydrologic Calibration and Validation of SWAT in a Snow‐Dominated Rocky Mountain Watershed,Montana, U.S.A.1

Abstract: The Soil and Water Assessment Tool (SWAT) has been applied successfully in temperate environments but little is known about its performance in the snow‐dominated, forested, mountainous watersheds that provide much of the water supply in western North America. To address this knowledge gap, we configured SWAT to simulate the streamflow of Tenderfoot Creek (TCSWAT). Located in central Montana, TCSWAT represents a high‐elevation watershed with ～85% coniferous forest cover where more than 70% of the annual precipitation falls as snow, and runoff comes primarily from spring snowmelt. Model calibration using four years of measured daily streamflow, temperature, and precipitation data resulted in a relative error (RE) of 2% for annual water yield estimates, and mean paired deviations (Dv) of 36 and 31% and Nash‐Sutcliffe (NS) efficiencies of 0.90 and 0.86 for monthly and daily streamflow, respectively. Model validation was conducted using an additional four years of data and the performance was similar to the calibration period, with RE of 4% for annual water yields, Dv of 43% and 32%, and NS efficiencies of 0.90 and 0.76 for monthly and daily streamflow, respectively. An objective, regression‐based model invalidation procedure also indicated that the model was validated for the overall simulation period. Seasonally, SWAT performed well during the spring and early summer snowmelt runoff period, but was a poor predictor of late summer and winter base flow. The calibrated model was most sensitive to snowmelt parameters, followed in decreasing order of influence by the surface runoff lag, ground water, soil, and SCS Curve Number parameter sets. Model sensitivity to the surface runoff lag parameter reflected the influence of frozen soils on runoff processes. Results indicated that SWAT can provide reasonable predictions of annual, monthly, and daily streamflow from forested montane watersheds, but further model refinements could improve representation of snowmelt runoff processes and performance during the base flow period in this environment.     

USE OF TIME-LAPSE PHOTOGRAPHY TO ASSESS POTENTIAL INTERCEPTION IN ARIZONA PONDEROSA PINE1

ABSTRACT: The behavior of intercepted snow on a stand of uneven-aged ponderosa pine in east-central Arizona was evaluated with the use of a super 8-mm time lapse movie camera to determine the relative significance of snowfall interception in the water yield of this type forest. A snow load index was developed to estimate interception storage for two trees in the field of view for discrete time periods. The snow load index. photographs, and climatic data were combined to describe accumulation and to identify and rank according to relative magnitudes the basic processes of canopy snow removal. The rate of snow accumulation was nonlinear with initial storage being rapid. then slowing with time. Most of the intercepted snow eventually reached the snowpack on the ground by snowslide and wind erosion or by snowmelt and subsequent stemflow and drippmg of meltwater, and was therefore not considered a significant loss to the water budget on site. Some water apparently was disposed of by the evaporation of meltwater and sublimation of canopy snow, but these losses appeared to be comparatively minor.     

Projections of 21st Century Sierra Nevada Local Hydrologic Flow Components Using an Ensemble of General Circulation Models1

Abstract: Sierra Nevada snowmelt and runoff is a key source of water for many of California’s 38 million residents and nearly the entire population of western Nevada. The purpose of this study was to assess the impacts of expected 21st Century climatic changes in the Sierra Nevada at the subwatershed scale, for all hydrologic flow components, and for a suite of 16 General Circulation Models (GCMs) with two emission scenarios. The Soil and Water Assessment Tool (SWAT) was calibrated and validated at 35 unimpaired streamflow sites. Results show that temperatures are projected to increase throughout the Sierra Nevada, whereas precipitation projections vary between GCMs. These climatic changes drive a decrease in average annual streamflow and an advance of snowmelt and runoff by several weeks. The largest streamflow reductions were found in the mid‐range elevations due to less snow accumulation, whereas the higher elevation watersheds were more resilient due to colder temperatures. Simulation results showed that decreases in snowmelt affects not only streamflow, but evapotranspiration, surface, and subsurface flows, such that less water is available in spring and summer, thus potentially affecting aquatic and terrestrial ecosystems. Declining spring and summer flows did not equally affect all subwatersheds in the region, and the subwatershed perspective allowed for identification for the most sensitive basins throughout the Sierra Nevada.     

PROBABLE MAXIMUM PRECIPITATION FOR THE LOWER RIO GRANDE1

To aid in planning and design of additional flood protection on the Lower Rio Grande, the Hydroraeteorological Branch prepared a probable maximum precipitation study for the International Boundary and Water Commission (United States and Mexico) and the Republic of Mexico. Five drainages from 2,000 to over 17,000 square miles in area between Falcon and Anzalduas Dams including Rio San Juan and Rio Alamo in Mexico are the areas of concern. The great rains of hurricane Beulah, September 19–24, 1967 verified that additional protection is needed. Procedures for estimating probable maximum precipitation (PMP) are described. A particular problem was to estimate rainfall potential for the Sierra Madre Oriental in Rio San Juan and Alamo drainages. These mountains form a north-south windward-facing slope and barrier of over 7000 feet in elevation. A detailed study was made of rains from hurricane Beulah. The storm produced the greatest known rain depths in North America for 50,000 square miles or greater, and durations longer than 48 hours.     

Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.     

CLIMATE,SNOW COVER,GLACIERS, AND RUNOFF IN THE TIEN SHAN,CENTRAL ASIA1

ABSTRACT: The Pica Shan, a mountainous region located on the northern periphery of central Asia, has a wide range of climatic and hydrological conditions. On the basis of long term data from 348 meteorological and glaciological stations, the annual distribution of precipitation in different regions and elevational zones of the Tien Shan was calculated. Major climatic features are the entrance of moisture during spring-summer, small winter precipitation, decrease of precipitation towards the east and the center of the mountains or with distance up valleys, and increase of precipitation with altitude up to crest-lines of ranges. Annual total evaporation from snow can be 50–60 mm per year, reaching 30 percent of snow accumulation. Four main groups of rivers were identified: rivers with mainly snow nourishment, rivers with mainly glacial nourishment, rivers with mainly rain nourishment, and rivers with mainly ground water nourishment. Coefficient of runoff variation in Tien Shan's rivers is about 0.20, and coefficient of glacial runoff variation is about 0.15. Glacial runoff is 15–20 percent of the total volume of river runoff.     

Spatiotemporal Variability of Snow Depletion Curves Derived from SNODAS for the Conterminous United States, 2004‐2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental‐scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental‐extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1‐km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed‐scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed‐scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national‐scale categorization of snowmelt processes.     

Winter Hydrology and NO3− Concentrations in a Forested Watershed: A Detailed Field Study in the Adirondack Mountains of New York1

Abstract: More than 85% of NO₃^? losses from watersheds in the northeastern United States are exported during winter months (October 1 to May 30). Interannual variability in NO₃^? loads to individual streams is closely related to interannual climatic variations, particularly during the winter. The objective of our study was to understand how climatic and hydrogeological factors influence NO₃^? dynamics in small watersheds during the winter. Physical parameters including snow depth, soil temperature, stream discharge, and water table elevation were monitored during the 2007‐2008 winter in two small catchments in the Adirondack Mountains, New York State. Snowpack persisted from mid‐December to mid‐April, insulating soils such that only two isolated instances of soil frost were observed during the study period. NO₃^? export during a mid‐winter rain‐on‐snowmelt event comprised between 8 and 16% of the total stream NO₃^? load for the four‐month winter study period. This can be compared with the NO₃^? exported during the final spring melt, which comprised between 38 and 45% of the total four‐month winter NO₃^? load. Our findings indicate that minor melt events were detectable with changes in soil temperature, streamflow, groundwater level, and snow depth. But, based on loading, these events were relatively minor contributors to winter NO₃^? loss. A warmer climate and fluctuating snowpack may result in more major mid‐winter melt events and greater NO₃^? export to surface waters.     

A NEW METHOD FOR SAMPLING SNOW MELT AND RAINFALL IN FORESTS1

ABSTRACT: Monitoring snow melt rates in high elevation, high snowfall forest stands is difficult mechanically and often impossible due to winter inaccessibility. A method for continuous unattended measurements of melt rate is described. With individual lysimeter pans connected to a common collector, any reasonable number of pans can be installed at each site.     

DEVELOPMENT AND TESTING OF A SNOWMELT-RUNOFF FORECASTING TECHNIQUE1

ABSTRACT: The snowmelt-runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April-September) for ten years on the Rio Grande Basin (3419 km²) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi-forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six-month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi-tation data, SRM produced a forecast hydrograph that had a r²= 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R²= 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.     

Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI). The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.     

DISTRIBUTION AND STOCHASTIC GENERATION OF ANNUAL AND MONTHLY PRECIPITATION ON A MOUNTAINOUS WATERSHED IN SOUTHWEST IDAHO1

ABSTRACT: The 18-year precipitation record from the dense gage network on the Reynolds Creek Experimental Watershed located in southwest Idaho was used to determine the spatial distribution of annual and monthly precipitation on a mountainous watershed. Analyses of these data showed a linear relationship between annual amounts and elevation. This relationship was best when the gages were grouped into downwind and upwind sites. This grouping was appropriate because most of the winter storms moved over the watershed from the west and southwest, and the heaviest precipitation was on the west (downwind) side of the watershed. Gage sites along the western and southern watershed borders were most representative of the upwind gages on the east side, because they measured the precipitation from the air moving upwind onto the watershed. The maximum annual precipitation on the watershed was just leeward of the western watershed boundary. The monthly precipitation and elevation relationship was also best represented by grouping the gage sites into upwind and downwind sites. However, during the summer when there are only small amounts of pre cipitation and thunderstorms are the source of most precipitation, one equation can be used to represent the elevation relationship. This study also showed that the log-normal distribution could be used to generate the annual synthetic series, and the cube-root-normal distribution could be used to generate monthly synthetic series for all locations on the watershed.     

ESTIMATED RUNOFF FROM MAN-MADE SNOW1

ABSTRACT: The consumptive loss from man-made snowmaking at six Colorado ski areas is calculated. The focus of the procedures in this investigation is on the consumptive loss that occurs to man-made snow particles during the period they reside on or in the snowpack until spring snowmelt (termed the watershed loss). Calculated watershed losses under a variety of precipitation and temperature conditions at six ski areas varied from 7 to 33 percent. These calculations were made using the calibrated Subalpine Water Balance Simulation Model (Leaf and Brink, 1973a, 1973b). The watershed loss of 7 to 33 percent indicates the range of likely watershed losses that can be expected at Colorado ski areas. A previous paper by the authors (Eisel et al., 1988) provided estimates of the mean consumptive loss during the snowmaking process (termed initial loss) for conditions existing at Colorado ski areas to be 6 percent of water applied. Therefore, based on the mean initial loss, the total consumptive loss from man-made snowmaking under conditions found at Colorado ski areas could be expected to range from 13 to 37 percent. These results demonstrate the range of total consumptive losses that could be expected in various years and for various watershed conditions. These total percentage losses cannot be extrapolated directly to other specific sites because the total consumptive loss is dependent on temperature during actual snowmaking, temperature and precipitation throughout the winter at the specific ski area, and watershed conditions at the ski area. Consumptive losses to man-made snow for a specific ski area should be estimated using the handbook procedures developed especially for this purpose (Colorado Ski Country USA, 1986b).     

OPERATIONAL APPLICATIONS OF SATELLITE SNOW COVER OBSERVATIONS1

ABSTRACT: Several federal and state water resources agencies and NASA have recently completed an Applications Systems Verification and Transfer (ASVT) project on the operational applications of satellite snow cover observations. When satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error from 15 percent to 10 percent on three study basins; and modeling studies on the Boise River basin in Idaho indicated that satellite snow cover could be used to reduce short term forecast error by up to 9.6 percent (5 day forecast). Potential benefits from improved satellite snow cover based predictions across the 11 western states total 10 million dollars for hydropower and 28 million dollars for irrigation annually. The truly operational application of the new technology in the West, however, will only be possible when the turnaround time for all data is reduced to 72 hours, and the water management agencies can be assured of a continuing supply of operational snow cover data from space.