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.     

WATER RESOURCE MANAGEMENT AND DROUGHT FORECASTS IN SOUTH CAROLINA1

ABSTRACT: Despite potential benefits for resource planning, community water systems managers have not used seasonal climate forecasts extensively. Obstacles to forecast use include a lack of awareness of their existence, distrust of their accuracy, perceived irrelevance to management decisions, and competition from other technological innovations. In this paper, ways in which seasonal forecasts might be extended to address more directly some concerns of South Carolina community water systems managers are explored. From May 1998 through August 2002, this group experienced drought conditions that threatened water quality and supply and required restrictions on water consumption. Methods for incorporating long lead forecasts with joint probabilities of monthly temperature and precipitation to produce drought forecasts are demonstrated. When tailored to specific places, such forecasts show the likelihood of exceeding drought thresholds that would trigger water use restrictions. The methods illustrate how long lead forecasts can be extended and customized into secondary products that address issues of greater relevance to water resource managers.     

CLIMATE FORECASTS IN FLOOD PLANNING: PROMISE AND AMBIGUITY1

ABSTRACT: Recent technical and scientific advances have increased the potential use of long term, seasonal climate forecasts for improving water resource management. This paper examines the role that forecasts, in particular those based on the El Niño Southern Oscillation (ENSO) cycle, can play in flood planning in the Pacific Northwest. While strong evidence exists of an association between ENSO signals and flooding in the region, this association is open to more than one interpretation depending on: (a) the metric used to test the strength of the association; (b) the definition of critical flood events; (c) site specific features of watersheds; and (d) the decision environment of flood management institutions. A better understanding and appreciation of such ambiguities, both social and statistical, will help facilitate the use of climate forecast information for flood planning and response.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

What Influences Innovation Adoption by Water Managers? Climate Information Use in Brazil and the United States1

Abstract: This paper examines the use of climate forecasting in water management in Brazil and the United States (U.S.). Specifically, it seeks to understand how different institutional arrangements shape the willingness and ability of water managers to incorporate technoscience, especially seasonal climate forecasting (SCF), in their decision‐making process. It argues that among the many factors shaping the willingness of water managers to use SCF, institutional design and change is critical to explain different patterns in Brazil and the U.S. Moreover, factors related to individual flexibility, discretion, and accountability also affect the ability of managers to use climate information in water management. This paper finds that while water managers in the U.S operate in a mostly fragmented and risk‐averse system – which constrains the adoption of innovation – decision makers in Brazil can afford more flexibility to introduce new decision tools as a result of widespread water management reforms initiated in the 1990s.     

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.     

WATER EXCHANGES: TOOLS TO BEAT EL NIÑO CLIMATE VARIABILITY IN IRRIGATED AGRICULTURE1

ABSTRACT: Using a case study of the Yakima River Valley in Washington State, this paper shows that relatively simple tools can be used to forecast the impact of the El Niño phenomenon on water supplies to irrigated agriculture, that this information could be used to estimate the significantly shifted probability distribution of water shortages in irrigated agriculture during El Niño episodes, and that these shifted probabilities can be used to estimate the value of exchanges of water between crops to relieve some of the adverse consequences of such shortages under western water law. Further, recently devised water‐trading tools, while not completely free under western water law to respond to forecasted El Niño episodes (ocean circulation patterns), are currently being employed during declared drought to reduce the devastating effects of water shortages in junior water districts on high valued perennial crops. Additional institutional flexibility is needed to take full advantage of climate forecasting, but even current tools clearly could prove useful in controlling the effects of climate variability in irrigated agriculture. Analysis shows the significant benefit of temporarily transferring or renting water rights from low‐value to high‐value crops, based on El Niño forecasts.     

WATER CONSERVATION IN CASABLANCA,MOROCCO1

ABSTRACT: This study examines water consumption characteristics in Casablanca and analyzes approaches for sustainable water demand management. Research procedures involve the development and estimation of water demand models for the residential/commercial, industrial, and institutional sectors; forecasts of water demand to 2010; and simulation of the effects of a complex of water conservation methods on the forecasted demands. The results indicate that residential/commercial water demand is weakly responsive to price changes (elasticity =?0.448) while institutional water demand is slightly more responsive (elasticity =?0.648). The conservation approaches used in the simulations included public education, plumbing code revisions to require use of water conservation devices, leak detection and repair, pricing policy, metering, and pressure reduction. The results indicate that considerable saving in water use can be attained through a comprehensive water demand management program.     

THE ACCURACY OF WATER USE FORECASTS: EVALUATION AND IMPLICATIONS1

ABSTRACT: Forecasts of 1980 river basin water use presented in the reports of the 1960 Senate Select Committee on National Water Resources and in the Water Resources Council's First National Water Assessment of 1968 were compared to estimates of actual use in 1980 to assess the accuracy of efforts to forecast future water use. Results show that the majority of the forecasts were substantially in error. In general, the First National Assessment forecasts erred by a smaller margin, but tended to repeat the regional patterns of overestimation (underestimation) exhibited in the Senate Select Committee forecasts. Moreover, forecasts of the two groups that came within 20 percent of the 1980 withdrawals, in general were accurate, not because of superior prediction, but because of offsetting errors in forecast components. This performance leads us to conclude that water use forecasts, regardless of the time-frame or the forecast method employed, are likely to always be highly inaccurate. Accordingly, if such forecasting efforts are to be of value in contemporary water resources planning, forecasters should direct their attention toward methods which will illuminate the determinants of the demand for water.     

MULTIPLICATIVE,SEASONAL ARIMA MODELS FOR LAKE ERIE AND LAKE ONTARIO WATER LEVELS1

ABSTRACT: Federal agencies in the U.S. and Canada continuously examine methods to improve understanding and forecasting of Great Lakes water level dynamics in an effort to reduce the negative impacts of fluctuating levels incurred by interests using the lakes. The short term, seasonal and long term water level dynamics of lakes Erie and Ontario are discussed. Multiplicative, seasonal ARIMA models are developed for lakes Erie and Ontario using standardized, monthly mean level data for the period 1900 to 1986. The most appropriate model identified for each lake had the general form: (1 0 1)(0 1 1)₁₂. The data for each lake were subdivided by time periods (1900 to 1942;1 943 to 1986) and the model coefficients estimated for the subdivided data were similar, indicating general model stability for the entire period of record. The models estimated for the full data sets were used to forecast levels 1,2,3, and 6 months ahead for a period of high levels (1984 to 1986). The average absolute forecast error for Lake Erie was 0.049m, 0.076m, 0.091 m and 0.128m for the 1, 2,3, and 6 month forecasts, respectively. The average absolute forecast error for Lake Ontario was 0.058m, 0.095m, 0.120m and 0.136m for the 1,2,3, and 6 month forecasts, respectively. The ARIMA models provide additional information on water level time series structure and dynamics. The models also could be coordinated with current forecasting methods, possibly improving forecasting accuracy.     

Using Instream Water Temperature Forecasts for Fisheries Management: An Application in the Pacific Northwest1

Huang, Biao, Christian Langpap, and Richard M. Adams, 2011. Using Instream Water Temperature Forecasts for Fisheries Management: An Application in the Pacific Northwest. Journal of the American Water Resources Association (JAWRA) 47(4):861‐876. DOI: 10.1111/j.1752‐1688.2011.00562.x Abstract: Water temperature is an important factor affecting aquatic life within the stream environment. Cold water species, such as salmonids, are particularly susceptible to elevated water temperatures. This paper examines the potential usefulness of short‐term (7 to 10 days) water temperature forecasts for salmonid management. Forecasts may be valuable if they allow the water resource manager to make better water allocation decisions. This study considers two applications: water releases from Lewiston Dam for management of adult Chinook salmon (Oncorhynchus tshawytscha) in the Klamath River and leasing water from agriculture for management of steelhead trout (Oncorhynchus mykiss) in the John Day River. We incorporate biophysical models and water temperature distribution data into a Bayesian framework to simulate changes in fish populations and the corresponding opportunity cost of water under different levels of temperature forecast reliability. Simulation results indicate that use of the forecasts results in increased fish production and that marginal costs decline as forecast reliability increases, suggesting that provision and use of such stream temperature forecasts would have potential value to society.     

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.     

A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

FORECASTING DRY SEASON STREAMFLOW ON THE PEACE RIVER AT ARCADIA,FLORIDA, USA1

ABSTRACT: The Peace River at Arcadia, Florida, is a municipal water supply supplement for southwestern Florida. Consequently, probabilities of encountering low flows during the dry season are of critical importance. Since the association between Pacific Ocean sea surface temperatures (SSTs) and seasonal streamflow variability in the southeastern United States is well documented, it is reasonable to generate forecasts based on this information. Here, employing historic records of minimum, mean, and maximum flows during winter (JFM) and spring (AMJ), upper and lower terciles define “above normal,”“normal,” and “below normal” levels of each variable. A probability distribution model describes the likelihood of these seasonal variables conditioned upon Pacific SSTs from the previous summer (JAS). Model calibration is based upon 40 (of 50) years of record employing stratified random sampling to ensure equal representation from each decade. The model is validated against the remaining 10 samples and the process repeated 100 times. Each conditional probability distribution yields varying probabilities of observing flow variables within defined categories. Generally, a warm (cold) Pacific is associated with higher (lower) flows. To test model skill, the forecast is constrained to be the most probable category in each calibration year, with significance tested by chi‐square frequency tables. For all variables, the tables indicate high levels of association between forecast and observed terciles and forecast skill, particularly during winter. During spring the pattern is less clear, possibly due to the variable starting date of the summer rainy season. This simple technique suggests that Pacific SSTs provide a good forecast of low flows.     

LONG-RANGE STREAMFLOW FORCASTING USING NONPARAMETRIC REGRESSION1

ABSTRACT: A class of nonparametric procedures is developed for producing long-range streamflow forecasts. The forecasting procedures, which are based solely on daily streamflow data, utilize nonparametric regression to relate a forecast variable to a covariate variable. The forecast variable is a function of future streamflow and can take a wide variety of forms. The covariate variable is a function of antecedent streamflow. The forecasting procedures are quite flexible, both in terms of the duration of the forecast period and the types of forecast variables that can be considered. The procedures are used to develop long-term (1–4 months) forecasts of minimum daily flow of the Potomac River at Washington, D.C. This forecast information is an integral component of water management activities for the Washington, D.C. metropolitan area.     

DOWNSCALED CLIMATE AND STREAMFLOW STUDY OF THE SOUTHWESTERN UNITED STATES1

ABSTRACT: Downscaling coarse resolution climate data to scales that are useful for impact assessment studies is receiving increased attention. Basin-scale hydrologic processes and other local climate impacts related to water resources such as reservoir management, crop and forest productivity, and ecosystem response require climate information at scales that are much finer than current and future GCM resolutions. The Regional Climate System Model (RCSM) is a dynamic downscaling system that has been used since 1994 for short-term precipitation and streamflow predictions and seasonal hindcast analysis with good skill. During the 1997–1998 winter, experimental seasonal forecasts were made in collaboration with the NOAA Climate Prediction Center and UCLA with promising results. Preliminary studies of a control and 2°CO₂ perturbation for the southwestern U.S. have been performed.     

MONTHLY RUNOFF PREDICTIONS BASED ON RAINFALL FORECASTS IN A SMALL OKLAHOMA WATERSHED1

ABSTRACT: Conditions under which monthly rainfall forecasts translate into monthly runoff predictions that could support water resources planning and management activities were investigated on a small watershed in central Oklahoma. Runoff response to rainfall forecasts was simulated using the hydrologic model SWAT. Eighteen scenarios were examined that represented combinations of wet, average, and dry antecedent rainfall conditions, with wet, normal, and dry forecasted rainfall. Results suggest that for the climatic and physiographic conditions under consideration, rainfall forecasts could offer potential application opportunities in surface water resources but only under certain conditions. Pronounced wet and dry antecedent rainfall conditions were shown to have greater impact on runoff than forecasts, particularly in the first month of a forecast period. Large forecast impacts on runoff occurred under wet antecedent conditions, when the fraction of forecasted rainfall contributing to runoff was greatest. Under dry antecedent conditions, most of the forecasted rainfall was absorbed in the soil profile, with little immediate runoff response. Persistent three‐month forecasts produced stronger impacts on runoff than one‐month forecasts due to cumulative effects in the hydrologic system. Runoff response to antecedent conditions and forecasts suggest a highly asymmetric utility function for rainfall forecasts, with greatest decision‐support potential for persistent wet forecasts under wet antecedent conditions when the forecast signal is least dampened by soil‐storage effects. Under average and dry antecedent conditions, rainfall forecasts showed little potential value for practical applications in surface water resources assessments.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

MANAGING THE NATION'S WATER IN A CHANGING CLIMATE1

ABSTRACT: Among the many concerns associated with global climate change, the potential effects on water resources are frequently cited as the most worrisome. In contrast, those who manage water resources do not rate climatic change among their top planning and operational concerns. The difference in these views can be associated with how water managers operate their systems and the types of stresses, and the operative time horizons, that affect the Nation's water resources infrastructure. Climate, or more precisely weather, is an important variable in the management of water resources at daily to monthly time scales because water resources systems generally are operated on a daily basis. At decadal to centennial time scales, though, climate is much less important because (1) forecasts, particularly of regional precipitation, are extremely uncertain over such time periods, and (2) the magnitude of effects due to changes in climate on water resources is small relative to changes in other variables such as population, technology, economics, and environmental regulation. Thus, water management agencies find it difficult to justify changing design features or operating rules on the basis of simulated climatic change at the present time, especially given that reservoir-design criteria incorporate considerable buffering capacity for extreme meteorological and hydro-logical events.     

Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1

Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August.