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.     

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.     

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.     

SNOTEL,the Soil Climate Analysis Network,and water supply forecasting at the Natural Resources Conservation Service: Past,present, and future

The Snow Survey and Water Supply Forecasting (SSWSF) Program and the Soil Climate Analysis Network (SCAN) of the United States Department of Agriculture's Natural Resources Conservation Service (NRCS) generate key observational and predictive information for water managers. Examples include mountain climate and snow monitoring through manual snow surveys and the SNOw TELemetry (SNOTEL) and SNOtel LITE networks, in situ soil moisture data acquisition through the SCAN and SNOTEL networks, and water supply forecasting using river runoff prediction models. The SSWSF Program has advanced continuously over the decades and is a major source of valuable water management information across the western United States, and the SCAN network supports agricultural and other water users nationwide. Product users and their management goals are diverse, and use-cases range from guiding crop selection to seasonal flood risk assessment, drought monitoring and prediction, avalanche and fire prediction, hydropower optimization, tracking climate variability and change, environmental management, satisfying international treaty and domestic legal requirements, and more. Priorities going forward are to continue innovating to enhance the accuracy and completeness of the observational and model-generated data products these programs deliver, including expanded synergies with the remote sensing community and uptake of artificial intelligence while maintaining long-term operational reliability and consistency at scale.     

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.     

UNCERTAINTIES IN ESTIMATING THE WATER BALANCE OF LAKES1

ABSTRACT: Evaluation of hydrologic methodology used in a number of water balance studies of lakes in the United States shows that most of these studies calculate one or more terms of the budget as the residual. A literature review was made of studies in which the primary purpose was error analysis of hydrologic measurement and interpretation. Estimates of precipitation can have a wide range of error, depending on the gage placement, gage spacing, and areal averaging technique. Errors in measurement of individual storms can be as high as 75 percent. Errors in short term averages are commonly in the 15-30 percent range, but decrease to about 5 percent or less for annual estimates. Errors in estimates of evaporation can also vary widely depending on instrumentation and methodology. The energy budget is the most accurate method of calculating evaporation; errors are in the 10–15 percent range. If pans are used that are located a distance from the lake of interest, errors can be considerable. Annual pan-to-lake coefficients should not be used for monthly estimates of evaporation because they differ from the commonly used coefficient of 0.7 by more than 100 percent. Errors in estimates of stream discharge are often considered to be within 5 percent. If the measuring section, type of flow profile, and other considerations, such as stage discharge relationship, are less than ideal errors in estimates of stream discharge can be considerably greater than 5 percent. Errors in estimating overland (nonchannelized) flow have not been evaluated, and in most lake studies this component is not mentioned. Comparison of several lake water balances in which the risdual consists solely of errors in measurement, shows that such a residual, if interpreted as ground water, can differ from an independent estimate of ground water by more than 100 percent.     

Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow1

Miller, W. Paul and Thomas C. Piechota, 2011. Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow. Journal of the American Water Resources Association (JAWRA) 47(6):1197–1210. DOI: 10.1111/j.1752‐1688.2011.00565.x Abstract: Water resource managers in the Western United States (U.S.) are currently faced with the challenge of adapting to unprecedented drought and uncertain impacts of climate change. Recent research has indicated increasing regional temperature and changes to precipitation and streamflow characteristics throughout the Western U.S. As such, there is increased uncertainty in hydroclimatological forecasts, which impact reservoir operations and water availability throughout the Western U.S., particularly in the Colorado River Basin. Previous research by the authors hypothesized a change in the character of precipitation (i.e., the frequency and amount of rainfall and snowfall events) throughout the Colorado River Basin. In the current study, 398 snowpack telemetry stations were investigated for trends in cumulative precipitation, snow water equivalent, and precipitation events. Observations of snow water equivalent characteristics were compared to observations in streamflow characteristics. Results indicate that the timing of the last day of the snow season corresponds well to the volume of runoff observed over the traditional peak flow season (April through July); conversely, the timing of the first day of the snow season does not correspond well to the volume of runoff observed over the peak flow season. This is significant to water resource managers and river forecasters, as snowpack characteristics may be indicative of a productive or unproductive runoff season.     

PROBABLE MAXIMUM FLOOD ESTIMATION-EASTERN UNITED STATES1

In 1982, the National Weather Service (NWS) published criteria for developing the spatial and temporal precipitation distribution characteristics of Probable Maximum Storms. The criteria, which are intended for use in the United States east of the 105th meridian, involve four variables: (1) location of the storm center, (2) storm-area size, (3) storm orientation, and (4) temporal arrangement of precipitation amounts. A computer program has been developed which applies the NWS criteria to produce hyetographs of spatially-averaged precipitation for a basin, or for each subbasin if the basin is subdividided. The basis and operational characteristics of the program are described, and an application is illustrated in which the program is used in conjunction with a precipitation-runoff simulation program (HEC-1) to compute a Probable Maximum Flood.     

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.     

AUTOMATED METHODS FOR ESTIMATING BASEFLOW AND GROUND WATER RECHARGE FROM STREAMFLOW RECORDS1

ABSTRACT: To quantify and model the natural ground water recharge process, six sites located in the midwest and eastern United States where previous water balance observations had been made were compared to computerized techniques to estimate: (1) base flow and (2) ground water recharge. Results from an existing automated digital filter technique for separating baseflow from daily streamflow records were compared to baseflow estimates made in the six water balance studies. Previous validation of automated baseflow separation techniques consisted only of comparisons with manual techniques. In this study, the automated digital filter technique was found to compare well with measured field estimates yielding a monthly coefficient of determination of 0.86. The recharge algorithm developed in this study is an automated derivation of the Rorabaugh hydrograph recession curve displacement method that utilizes daily streamflow. Comparison of annual recharge from field water balance measurements to those computed with the automated recession curve displacement method had coefficients of determination of 0.76 and predictive efficiencies of 71 percent. Monthly estimates showed more variation and are not advocated for use with this method. These techniques appear to be fast, reproducible methods for estimating baseflow and annual recharge and should be useful in regional modeling efforts and as a quick check on mass balance techniques for shallow water table aquifers.     

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.     

A SURVEY OF THE U.S. NATIONAL PRECIPITATION NETWORK1

ABSTRACT: Numbers and record lengths of precipitation stations were surveyed in the conterminous United States using climatological data published in 1975 by the National Weather Service (NWS). The total numbers of nonrecording (8247) and recording (3036) gages were about the same as in the 1940s and less than in the late 1950s; about 70 percent of the nonrecording gages have record lengths of 25 years or more. State network densities were increased exponentially with population density and long term precipitation average. Except for a few states, precipitation stations maintained by the NWS are adequate in numbers to ensure a 95 percent statistical probability that state sample means will estimate true means within ± 5 percent.     

APPLICATION OF KRIGING TO ESTIMATING MEAN ANNUAL PRECIPITATION IN A REGION OF OROGRAPHIC INFLUENCE1

ABSTRACT: Estimates of mean annual precipitation (MAP) over areas are the starting point for all computations of water and chemical balances for drainage basins and surface water bodies. Any errors in the estimates of MAP are propagated through the balance computations. These errors can be due to: (1) failures of individual gages to collect the amount of precpitation that actually falls; (2) operator errors; and (3) failure of the raingage network to adequately sample the region of interest. This paper attempts to evaluate the last of these types of error by applying kriging in two different approaches to estimating MAP in New Hampshire and Vermont, USA. The data base is the 1951–1980 normal precipitation at 120 raingages in the two states and in adjacent portions of bordering states and provinces. In the first approach, kriging is applied directly to the MAP values, while in the second, kriging is applied to a “precipitation delivery factor” that represents the MAP with the orographic effect removed. The first approach gives slightly better kriged estimates of MAP at seven validation stations that were not included in the original analysis, but results in an error surface that is highly contorted and in larger maximum errors over most of the region. The second approach had a considerably smoother error surface and, thus, is generally preferable as a basis for point and areal estimates of MAP. MAP estimates in the region have 95 percent confidence intervals of about 20 cm/yr at low and moderate elevations, and up to 35 cm/yr at high elevations. These uncertainties amount to about 20 percent of estimated MAP values.     

Defining the Diurnal Pattern of Snowmelt Using a Beta Distribution Function

Snow is an important component of the hydrologic cycle for many regions worldwide. In addition to vital water resources, snowmelt can be important for forest ecosystem dynamics and flood risk. However, standard design events in the United States lack a design snowmelt event, including only precipitation events, though snowmelt has been shown to be larger than rainfall. In this article, we present a method using hourly snow water equivalent data to develop and test a function for representing the diurnal pattern of snowmelt. A two‐parameter beta distribution function is modified for the purposes of this study and found to fit the pattern of snowmelt well with a root mean squared error of 0.008. Soil moisture sensors were additionally utilized to assess the timing of the snowmelt water outflow from the base of the snowpack that supports the shape of the function, but suggests that the timing of losses recorded on snow pillows lag as much as 3 h. Further testing of the function showed the shape of the function to be accurate. The methods developed and tested in this paper can be applied for design purposes comparing snowmelt and rainfall events or to improve hydrological models investigating processes such as streamflow or groundwater recharge.     

Estimating Hydraulic Properties of Volcanic Aquifers Using Constant‐Rate and Variable‐Rate Aquifer Tests1

Abstract: In recent years the ground‐water demand of the population of the island of Maui, Hawaii, has significantly increased. To ensure prudent management of the ground‐water resources, an improved understanding of ground‐water flow systems is needed. At present, large‐scale estimations of aquifer properties are lacking for Maui. Seven analytical methods using constant‐rate and variable‐rate withdrawals for single wells provide an estimate of hydraulic conductivity and transmissivity for 103 wells in central Maui. Methods based on constant‐rate tests, although not widely used on Maui, offer reasonable estimates. Step‐drawdown tests, which are more abundantly used than other tests, provide similar estimates as constant‐rate tests. A numerical model validates the suitability of analytical solutions for step‐drawdown tests and additionally provides an estimate of storage parameters. The results show that hydraulic conductivity is log‐normally distributed and that for dike‐free volcanic rocks it ranges over several orders of magnitude from 1 to 2,500 m/d. The arithmetic mean, geometric mean, and median values of hydraulic conductivity are respectively 520, 280, and 370 m/d for basalt and 80, 50, and 30 m/d for sediment. A geostatistical approach using ordinary kriging yields a prediction of hydraulic conductivity on a larger scale. Overall, the results are in agreement with values published for other Hawaiian islands.     

MEAN AREAL PRECIPITATION FOR DAILY HYDROLOGIC MODELING IN MOUNTAINOUS REGIONS1

ABSTRACT: A procedure using detrended kriging has been developed to calculate daily values of mean areal precipitation (MAP) for input to hydrologic models. The important features of this procedure that overcome weaknesses in existing MAP procedures are: (1) specific precipitation-elevation relationships are determined for each time period as opposed to using relationships based on climatological averages, (2) spatial variability is incorporated by estimating precipitation for each grid cell over a watershed, (3) the spatial correlation structure of precipitation is explicitly modeled, and (4) station weights for precipitation estimates are determined objectively and optimally. Detailed cross-validation testing of the procedure was done for the Reynolds Creek research watershed in southwestern Idaho. The procedure is suitable for use in operational streamflow forecasting.     

Comparative Analysis of New Zealand and US Approaches for Agricultural Nonpoint Source Pollution Management

The role of the central government in New Zealand is generally limited to research and policy development, and regional councils are responsible for most monitoring and management of the problem. The role of the federal government in the United States includes research and monitoring, policy development, and regulation. States also have a significant management role. Both countries rely on voluntary approaches for NPS pollution management. Very few national water quality standards exist in New Zealand, whereas standards are widely used in the United States. Loading estimates and modeling are often used in the United States, but not in New Zealand. A wide range of best management practices (BMPs) are used in the United States, including buffer strips and constructed/engineered wetlands. Buffer strips and riparian management have been emphasized and used widely in New Zealand. Many approaches are common to both countries, but management of the problem has only been partly successful. The primary barriers are the inadequacy of the voluntary approach and the lack of scientific tools that are useful to decision-makers. More work needs to be performed on the evaluation of approaches developed in both countries that could be applied in the other countries. In addition, more cooperation and information/technology transfer between the two countries should be encouraged in the future.     

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.     

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.     

A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION1

One of the problems which often arises in engineering hydrology is to estimate data at a given site because either the data are missing or the site is ungaged. Such estimates can be made by spatial interpolation of data available at other sites. A number of spatial interpolation techniques are available today with varying degrees of complexity. It is the intent of this paper to compare the applicability of various proposed interpolation techniques for estimating annual precipitation at selected sites. The interpolation techniques analyzed include the commonly used Thiessen polygon, the classical polynomial interpolation by least-squares or Lagrange approach, the inverse distance technique, the multiquadric interpolation, the optimal interpolation and the Kriging technique. Thirty years of annual precipitation data at 29 stations located in the Region II of the North Central continental United States have been used for this study. The comparison is based on the error of estimates obtained at five selected sites. Results indicate that the Kriging and optimal interpolation techniques are superior to the other techniques. However, the multiquadric technique is almost as good as those two. The inverse distance interpolation and the Thiessen polygon gave fairly satisfactory results while the polynomial interpolation did not produce good results.