SNOW: NATURE'S RESERVOIR1

ABSTRACT: Snow, one of Nature's greatest reservoirs, supplies most of the usable water in the Western United States. Reliable predictions of the quantity and timing of the release of this water are used in making management decisions involving irrigation, stock water and municipal water supplies, hydro-power generation, recreation, navigation, and pollution control Practically oriented research is vital for the proper development and management of this resource. In southwestern Idaho, the Northwest Watershed Research Center, ARS, USDA, is conducting intensive investigations for assessing snow Volumes, snow water content, and snow-melt over a watershed. Application of these research findings will result in better development and management of the water stored as snow in Nature's reservoir.     

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.     

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.     

Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1

Abstract: Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short‐term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km²) located in Southern Québec, Canada. Streamflow (Q), precipitations (rain R and snow S, or total P), temperature (T) and snow lying (A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow (Q_SM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long‐term averages, seasonal data, and Q_SM failed to improve the networks.     

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.     

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).     

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

Modeling Hydrology in a Small Rocky Mountain Watershed Serving Large Urban Populations1

Abstract: This article describes the development of a calibrated hydrologic model for the Blue River watershed (867 km²) in Summit County, Colorado. This watershed provides drinking water to over a third of Colorado’s population. However, more research on model calibration and development for small mountain watersheds is needed. This work required integration of subsurface and surface hydrology using GIS data, and included aspects unique to mountain watersheds such as snow hydrology, high ground‐water gradients, and large differences in climate between the headwaters and outlet. Given the importance of this particular watershed as a major urban drinking‐water source, the rapid development occurring in small mountain watersheds, and the importance of Rocky Mountain water in the arid and semiarid West, it is useful to describe calibrated watershed modeling efforts in this watershed. The model used was Soil and Water Assessment Tool (SWAT). An accurate model of the hydrologic cycle required incorporation of mountain hydrology‐specific processes. Snowmelt and snow formation parameters, as well as several ground‐water parameters, were the most important calibration factors. Comparison of simulated and observed streamflow hydrographs at two U.S. Geological Survey gaging stations resulted in good fits to average monthly values (0.71 Nash‐Sutcliffe coefficient). With this capability, future assessments of point‐source and nonpoint‐source pollutant transport are possible.     

ESTIMATING REGIONAL SNOW WATER EQUIVALENT WITH A SIMPLE SIMULATION MODEL1

A simple simulation model designed to monitor snow-packs of the central Sierra Nevada is described. The model estimates average snow water equivalent for rectangular subregions in the area. Static subregion characteristics, daily precipitation and mean and minimum air temperatures measured at three index stations are the only needed input values. A water balance technique simulates daily snowpack changes in each subregion. Reasonable basinwide water equivalent values are produced. The procedure should be useful for estimating snow water distribution in large mountainous watersheds.     

ENERGY BALANCE OF A CORN RESIDUE-COVERED FIELD DURING SNOWMELT1

ABSTRACT: Transport of agricultural chemicals in runoff and recharge waters from snowmelt and soil thawing may represent a significant event in terms of annual contaminant loadings in temperate regions. Improved understanding of the melt dynamics of shallow snowpacks is necessary to fully assess the implications for water quality. The objective of this study was to measure the energy balance components of a corn (Zea mays L.) stubble field during the melting of its snowcover. Net radiation (Rn), soil (G), sensible (H), and latent (Q) heat fluxes were measured in a field near Ames, Iowa, during the winter of 1994–1995. Energy consumed by melting including change in energy storage of the snowpack was determined as the residual of the measured energy balance. There was continuous snowcover at the field site for 71 days (maximum depth = 222 mm) followed by an open period of 11 days before additional snowfall and a second melt period. The net radiation and snow melt/energy storage change (5) terms dominated the energy balance during both measurement intervals. Peak daily sensible and latent heat fluxes were below 100 W m^?2 on all days except the last day of the second melt period. There was good agreement between predicted and measured values of H and Q during the melting of an aged snow layer but poorer agreement during the melt of fresh snow. Both snowpacks melted rapidly and coincident changes in soil moisture storage were observed. Improved estimates of Q and H, especially for partially open surfaces, will require better characterization of the surface aerodynamic properties and spatially-representative surface temperature measurements.     

A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

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.     

Evaluation of Short-to-Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM1

Harshburger, Brian J., Karen S. Humes, Von P. Walden, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2010. Evaluation of Short-to-Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM. Journal of the American Water Resources Association (JAWRA) 46(3):603-617. DOI: 10.1111/j.1752-1688.2010.00437.x Abstract: As demand for water continues to escalate in the western United States, so does the need for accurate streamflow forecasts. Here, we describe a methodology for generating short-to-medium range (1 to 15 days) streamflow forecasts using an enhanced version of the Snowmelt Runoff Model (SRM), snow-covered area data derived from MODIS products, data from Snow Telemetry stations, and meteorological forecasts. The methodology was tested on three mid-elevation, snowmelt-dominated basins ranging in size from 1,600 to 3,500 km². To optimize the model performance and aid in its operational implementation, two enhancements have been made to SRM: (1) the use of an antecedent temperature index method to track snowpack cold content, and (2) the use of both maximum and minimum critical temperatures to partition precipitation into rain, snow, or a mixture of rain and snow. The comparison of retrospective model simulations with observed streamflow shows that the enhancements significantly improve the model performance. Streamflow forecasts generated using the enhanced version of the model compare well with the observed streamflow for the earlier leadtimes; forecast performance diminishes with leadtime due to errors in the meteorological forecasts. The three basins modeled in this research are typical of many mid-elevation basins throughout the American West, thus there is potential for this methodology to be applied successfully to other mountainous basins.     

A 21st-Century perspective on snow drought in the Upper Colorado River Basin

In the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term “snow drought” has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin-wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—“warm,” “dry,” and “warm & dry”—that minimize within-group variance. Over the period 1916–2018, we identify 14 warm years ($\overline{P}$ = 160 mm; $\overline{\mathrm{SWE}/P}$ = 0.24), 24 dry years ($\overline{P}$ = 117 mm; $\overline{\mathrm{SWE}/P}$ = 0.35), and 21 warm & dry years ($\overline{P}$ = 94 mm; $\overline{\mathrm{SWE}/P}$ = 0.23). An elevation-based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas “dry” scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management.     

Characterizing Runoff and Water Yield for Headwater Catchments in the Southern Sierra Nevada

In a Mediterranean climate where much of the precipitation falls during winter, snowpacks serve as the primary source of dry season runoff. Increased warming has led to significant changes in hydrology of the western United States. An important question in this context is how to best manage forested catchments for water and other ecosystem services? Answering this basic question requires detailed understanding of hydrologic functioning of these catchments. Here, we depict the differences in hydrologic response of 10 catchments. Size of the study catchments ranges from 50 to 475 ha, and they span between 1,782 and 2,373 m elevation in the rain‐snow transitional zone. Mean annual streamflow ranged from 281 to 408 mm in the low elevation Providence and 436 to 656 mm in the high elevation Bull catchments, resulting in a 49 mm streamflow increase per 100 m (R² = 0.79) elevation gain, despite similar precipitation across the 10 catchments. Although high elevation Bull catchments received significantly more precipitation as snow and thus experienced a delayed melt, this increase in streamflow with elevation was mainly due to a reduction in evapotranspiration (ET) with elevation (45 mm/100 m, R² = 0.65). The reduction in ET was attributed to decline in vegetation density, growing season, and atmospheric demand with increasing elevation. These findings suggest changes in streamflow in response to climate warming may likely depend on how vegetation responds to those changes in climate.     

A COMPARISON OF GEOSTATISTICAL METHODOLOGIES USED TO ESTIMATE SNOW WATER EQUIVALENT1

ABSTRACT: The need to monitor and forecast water resources accurately, particularly in the western United States, is becoming increasingly critical as the demand for water continues to escalate. Consequently, the National Weather Service (NWS) has developed a geostatistical model that is used to obtain areal estimates of snow water equivalent (the thtal water content in all phases of the snowpack), a major source of water in the West. The areal snow water equivalent estimates are used to update the hydrologic simulation models maintained by the NWS and designed to produce extended streamflow forecasts for river systems throughout the United States. An alternative geostatistical technique has been proposed to estimate snow water equivalent. In this research, we describe the two methodologies and compare the accuracy of the estimates produced by each technique. We illustrate their application and compare their estimation accuracy using snow data collected in the North Fork Clearwater River basin in Idaho.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

A Simple Process‐Based Snowmelt Routine to Model Spatially Distributed Snow Depth and Snowmelt in the SWAT Model1

Abstract: We present a method to integrate a process‐based (PB) snowmelt model that requires only daily temperature and elevation information into the Soil and Water Assessment Tool (SWAT) model. The model predicts the spatiotemporal snowpack distribution without adding additional complexity, and in fact reduces the number of calibrated parameters. To demonstrate the utility of the PB model, we calibrate the PB and temperature‐index (TI) SWAT models to optimize agreement with stream discharge on a 46‐km² watershed in northwestern Idaho, United States, for 10 individual years and use the calibrated parameters for the year with the best agreement to run the model for 15 remaining years. Stream discharge predictions by the PB and TI model were similar, although the PB model simulated snowmelt more accurately than the TI model for the remaining 15‐year period. Spatial snow distributions predicted by the PB model better matched observations from LandSat imagery and a SNOTEL station. Results for this watershed show that including PB snowmelt in watershed models is feasible, and calibration of TI‐based watershed models against discharge can incorrectly predict snow cover.     

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.