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.     

EVALUATION OF PRACTICES IN WATER SUPPLY FORECASTING1

ABSTRACT: Snowmelt runoff is a primary source of water supply in much of the Western United States. Multipurpose planning requires long-range forecasts and the accuracy of the forecasts has a significant effect on economic benefits. In an effort to increase the accuracy of snowrnelt runoff forecasts, selected practices in water supply forecasting were evaluated. These practices include 1) using multiple regression in developing forecasting models;2) using a model that was calibrated to make forecasts an April 1 for making forecasts at other times;3) using maximum snow water equivalent measurements in forecast equations; and 4) using weighted snow water equivalent measurements for making forecasts. The results of a case study indicate that forecasting accuracy is significantly affected by these practices. Goodness-of-fit statistics may not be indicative of the accuracy of forecasts when the prediction equations are used to make forecasts for dates other than that used in calibration. The use of maximum snow water equivalentmeasurements and weighted averages did not improve forecast accuracy.     

POTENTIAL CLIMATE CHANGE IMPACTS ON MOUNTAIN WATERSHEDS IN THE PACIFIC NORTHWEST1

ABSTRACT: Global climate change due to the buildup of greenhouse gases in the atmosphere has serious potential impacts on water resources in the Pacific Northwest. Climate scenarios produced by general circulation models (GCMs) do not provide enough spatial specificity for studying water resources in mountain watersheds. This study uses dynamical downscaling with a regional climate model (RCM) driven by a GCM to simulate climate change scenarios. The RCM uses a subgrid parameterization of orographic precipitation and land surface cover to simulate surface climate at the spatial scale suitable for the representation of topographic effects over mountainous regions. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO₂ concentration. The RCM results indicate an average warming of about 2.5°C, and precipitation generally increases over the Pacific Northwest and decreases over California. These simulations were used to drive a distributed hydrology model of two snow dominated watersheds, the American River and Middle Fork Flathead, in the Pacific Northwest to obtain more detailed estimates of the sensitivity of water resources to climate change. Results show that as more precipitation falls as rain rather than snow in the warmer climate, there is a 60 percent reduction in snowpack and a significant shift in the seasonal pattern of streamflow in the American River. Much less drastic changes are found in the Middle Fork Flathead where snowpack is only reduced by 18 percent and the seasonal pattern of streamflow remains intact. This study shows that the impacts of climate change on water resources are highly region specific. Furthermore, under the specific climate change scenario, the impacts are largely driven by the warming trend rather than the precipitation trend, which is small.     

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.     

GENERALIZED WATERSHED LOADING FUNCTIONS FOR STREAM FLOW NUTRIENTS1

ABSTRACT: Loading functions are proposed as a general model for estimating monthly nitrogen and phosphorus fluxes in stream flow. The functions have a simple mathematical structure, describe a wide range of rural and urban nonpoint sources, and couple surface runoff and ground water discharge. Rural runoff loads are computed from daily runoff and erosion and monthly sediment yield calculations. Urban runoff loads are based on daily nutrient accumulation rates and exponential wash off functions. Ground water discharge is determined by lumped parameter unsaturated and saturated zone soil moisture balances. Default values for model chemical parameters were estimated from literature values. Validation studies over a three-year period for an 850 km² watershed showed that the loading functions explained at least 90 percent of the observed monthly variation in dissolved and total nitrogen and phosphorus fluxes in stream flow. Errors in model predictions of mean monthly fluxes were: dissolved phosphorus - 4 percent; total phosphorus - 2 percent; dissolved nitrogen - 18 percent; and total nitrogen - 28 percent. These results were obtained without model calibration.     

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.     

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.     

SPATIAL VARIATIONS OF WATER LOSS DURING DROUGH A CASE STUDY1

ABSTRACT: A regional water conservation system for drought management involves many uncertain factors. Water received from precipitation may stay on the ground surface, evaporate back into the atmosphere, or infiltrate into the ground. Reliable estimates of the amount of evapotranspiration and infiltration are not available for a large basin, especially during periods of drought. By applying a geographic information system, this study develops procedures to investigate spatial variations of unavailable water for given levels of drought severity. Levels of drought severity are defined by truncated values of monthly precipitation and daily streamflow to reflect levels of water availability. The greater the truncation level, the lower the precipitation or streamflow. Truncation levels of monthly precipitation are recorded in depth of water while those of daily streamflow are converted into monthly equivalent water depths. Truncation levels of precipitation and streamflow treated as regionalized variables are spatially interpolated by the unbiased minimum variance estimation. The interpolated results are vector values of precipitation and streamflow at a grid of points covering the studied basin. They are then converted into raster‐based values and expressed graphically. The image subtraction operation is used to subtract the image of streamflow from that of precipitation at their corresponding level of drought severity. It is done on a cell‐by‐cell basis resulting in new attribute values to form the spatial image representing a spatial distribution of potential water loss at a given level of drought severity.     

Observed Changes in Climate and Streamflow in the Upper Rio Grande Basin

Observed streamflow and climate data are used to test the hypothesis that climate change is already affecting Rio Grande streamflow volume derived from snowmelt runoff in ways consistent with model‐based projections of 21st‐Century streamflow. Annual and monthly changes in streamflow volume and surface climate variables on the Upper Rio Grande, near its headwaters in southern Colorado, are assessed for water years 1958–2015. Results indicate winter and spring season temperatures in the basin have increased significantly, April 1 snow water equivalent (SWE) has decreased by approximately 25%, and streamflow has declined slightly in the April–July snowmelt runoff season. Small increases in precipitation have reduced the impact of declining snowpack on trends in streamflow. Changes in the snowpack–runoff relationship are noticeable in hydrographs of mean monthly streamflow, but are most apparent in the changing ratios of precipitation (rain + snow, and SWE) to streamflow and in the declining fraction of runoff attributable to snowpack or winter precipitation. The observed changes provide observational confirmation for model projections of decreasing runoff attributable to snowpack, and demonstrate the decreasing utility of snowpack for predicting subsequent streamflow on a seasonal basis in the Upper Rio Grande Basin.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

DEVELOPMENT AND VERIFICATION OF A WATER BALANCE MODEL FOR SUBSURFACE DRAINAGE DESIGN1

ABSTRACT: A water balance model was developed to predict daily water table depths in some corn fields with or without subsurface drainage systems, using pertinent soil and water properties and weather data. The model outputs were compared with the recorded data of observed water table depths. Some statistical parameters such as the mean, standard deviation, the coefficient of correlation, the sum of the squares of deviations, and a nonparametric statistical test were used to study the extent of agreement between the observed and the predicted water table depths. No significant difference was found between the distributions of the observed and the predicted water table depths at the 99% confidence level. The study was conducted on some sand and clay soils of the Ottawa-St. Lawrence Lowlands region in Canada where there is a cool, moist climate and poor natural drainage.     

The Sensitivity of California Water Resources to Climate Change Scenarios1

Abstract: Using the latest available General Circulation Model (GCM) results we present an assessment of climate change impacts on California hydrology and water resources. The approach considers the output of two GCMs, the PCM and the HadCM3, run under two different greenhouse gas (GHG) emission scenarios: the high emission A1fi and the low emission B1. The GCM output was statistically downscaled and used in the Variable Infiltration Capacity (VIC) macroscale distributed hydrologic model to derive inflows to major reservoirs in the California Central Valley. Historical inflows used as inputs to the water resources model CalSim II were modified to represent the climate change perturbed conditions for water supply deliveries, reliability, reservoir storage and changes to variables of environmental concern. Our results show greater negative impacts to California hydrology and water resources than previous assessments of climate change impacts in the region. These impacts, which translate into smaller streamflows, lower reservoir storage and decreased water supply deliveries and reliability, will be especially pronounced later in the 21st Century and south of the San Francisco bay Delta. The importance of considering how climate change impacts vary for different temporal, spatial, and institutional conditions in addition to the average impacts is also demonstrated.     

Daily Updating of Operational Statistical Seasonal Water Supply Forecasts for the western U.S.1

Abstract: Official seasonal water supply outlooks for the western United States are typically produced once per month from January through June. The Natural Resources Conservation Service has developed a new outlook product that allows the automated production and delivery of this type of forecast year‐round and with a daily update frequency. Daily snow water equivalent and water year‐to‐date precipitation data from multiple SNOTEL stations are combined using a statistical forecasting technique (“Z‐Score Regression”) to predict seasonal streamflow volume. The skill of these forecasts vs. lead‐time is comparable to the official published outlooks. The new product matches the intra‐monthly trends in the official forecasts until the target period is partly in the past, when the official forecasts begin to use information about observed streamflows to date. Geographically, the patterns of skill also match the official outlooks, with highest skill in Idaho and southern Colorado and lowest skill in the Colorado Front Range, eastern New Mexico, and eastern Montana. The direct and frequent delivery of objective guidance to users is a significant new development in the operational hydrologic seasonal forecasting community.     

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.     

CONTINENTAL SCALE SIMULATION OF THE HYDROLOGIC BALANCE1

This paper describes the application of a continuous daily water balance model called SWAT (Soil and Water Assessment Tool) for the conterminous U.S. The local water balance is represented by four control volumes; (1) snow, (2) soil profile, (3) shallow aquifer, and (4) deep aquifer. The components of the water balance are simulated using “storage” models and readily available input parameters. All the required databases (soils, landuse, and topography) were assembled for the conterminous U.S. at 1:250,000 scale. A GIS interface was utilized to automate the assembly of the model input files from map layers and relational databases. The hydrologic balance for each soil association polygon (78,863 nationwide) was simulated without calibration for 20 years using dominant soil and land use properties. The model was validated by comparing simulated average annual runoff with long term average annual runoff from USGS stream gage records. Results indicate over 45 percent of the modeled U.S. are within 50 mm of measured, and 18 percent are within 10 mm without calibration. The model tended to under predict runoff in mountain areas due to lack of climate stations at high elevations. Given the limitations of the study, (i.e., spatial resolution of the data bases and model simplicity), the results show that the large scale hydrologic balance can be realistically simulated using a continuous water balance model.     

LOADING FUNCTIONS FOR PREDICTING NUTRIENT LOSSES FROM COMPLEX WATERSHEDS1

ABSTRACT: A loading function methodology is presented for predicting runoff, sediment, and nutrient losses from complex watersheds. Separate models are defined for cropland, forest, urban and barnyard sources, and procedures for estimating baseflow nutrients are provided. The loading functions are designed for use as a preliminary screening tool to isolate the major contributors in a watershed. Input data sources are readily available and the functions do not require costly calibrations. Data requirements include watershed land use and soil information, daily precipitation and temperature records and rainfall erosivities. Comparison of predicted and measured water, sediment, and nutrient runoff fluxes for the West Branch Deleware River in New York, indicated that runoff was underpredicted by about 14 percent while dissolved nutrients were within 30 percent of observed values. Sediment and solid-phase nutrients were overpredicted by about 50 percent. An annual nutrient budget for the West Branch Delaware River showed that cornland was the major source of sediment, solid phase nutrients, and total phosphorus. Waste water treatment plants and ground water discharge contributed the most dissolved phosphorus and dissolved nitrogen, respectively.     

GENERAL-CIRCULATION-MODEL SIMULATIONS OF FUTURE SNOWPACK IN THE WESTERN UNITED STATES1

ABSTRACT: April 1 snowpack accumulations measured at 311 snow courses in the western United States (U.S.) are grouped using a correlation-based cluster analysis. A conceptual snow accumulation and melt model and monthly temperature and precipitation for each cluster are used to estimate cluster-average April 1 snowpack. The conceptual snow model is subsequently used to estimate future snowpack by using changes in monthly temperature and precipitation simulated by the Canadian Centre for Climate Modeling and Analysis (CCC) and the Hadley Centre for Climate Prediction and Research (HADLEY) general circulation models (GCMs). Results for the CCC model indicate that although winter precipitation is estimated to increase in the future, increases in temperatures will result in large decreases in April 1 snowpack for the entire western U.S. Results for the HADLEY model also indicate large decreases in April 1 snowpack for most of the western US, but the decreases are not as severe as those estimated using the CCC simulations. Although snowpack conditions are estimated to decrease for most areas of the western US, both GCMs estimate a general increase in winter precipitation toward the latter half of the next century. Thus, water quantity may be increased in the western US; however, the timing of runoff will be altered because precipitation will more frequently occur as rain rather than as snow.     

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.     

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.