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.     

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.     

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 Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.     

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.     

USE OF STREAMFLOW INCREASES FROM VEGETATION MANAGEMENT IN THE VERDE RIVER BASIN1

ABSTRACT: Although the effects of vegetation management on streamflow have been studied in many locations, the effects of augmented streamflow on downstream water users have not been carefully analyzed. This study examines the routing of streamflow increases that could be produced in the Verde River Basin of Arizona. Reservoir management and water routing to users in the Salt River Valley around Phoenix were carefully modeled. Simulation of water routing with and without vegetation modification indicates that, under current institutional conditions, less than one-half of the streamflow increase would reach consumptive users as surface water. Most of the remainder would accumulate in storage until a year of unusually heavy runoff, when it would add to reservoir spills. Under alternative scenarios, from 39 to 58 percent of the streamflow increase was delivered to consumptive users.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

Models,Assumptions, and Stakeholders: Planning for Water Supply Variability in the Colorado River Basin1

Abstract: Declining reservoir storage has raised the specter of the first water shortage on the Lower Colorado River since the completion of Glen Canyon and Hoover Dams. This focusing event spurred modeling efforts to frame alternatives for managing the reservoir system during prolonged droughts. This paper addresses the management challenges that arise when using modeling tools to manage water scarcity under variable hydroclimatology, shifting use patterns, and institutional complexity. Assumptions specified in modeling simulations are an integral feature of public processes. The policymaking and management implications of assumptions are examined by analyzing four interacting sources of physical and institutional uncertainty: inflow (runoff), depletion (water use), operating rules, and initial reservoir conditions. A review of planning documents and model reports generated during two recent processes to plan for surplus and shortage in the Colorado River demonstrates that modeling tools become useful to stakeholders by clarifying the impacts of modeling assumptions at several temporal and spatial scales. A high reservoir storage‐to‐runoff ratio elevates the importance of assumptions regarding initial reservoir conditions over the three‐year outlook used to assess the likelihood of reaching surplus and shortage triggers. An ensemble of initial condition predictions can provide more robust initial conditions estimates. This paper concludes that water managers require model outputs that encompass a full range of future potential outcomes, including best and worst cases. Further research into methods of representing and communicating about hydrologic and institutional uncertainty in model outputs will help water managers and other stakeholders to assess tradeoffs when planning for water supply variability.     

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.     

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.     

Scenario‐Based and Scenario‐Neutral Assessment of Climate Change Impacts on Operational Performance of a Multipurpose Reservoir

Scenario‐based and scenario‐neutral impacts assessment approaches provide complementary information about how climate change‐driven effects on streamflow may change the operational performance of multipurpose dams. Examining a case study of Cougar Dam in Oregon, United States, we simulated current reservoir operations under scenarios of plausible future hydrology. Streamflow projections from the CGCM3.1 general circulation model for the A1B emission scenario were used to generate stochastic reservoir inflows that were then further perturbed to simulate a potentially drier future. These were then used to drive a simple reservoir model. In the scenario‐based analysis, we found reservoir operations are vulnerable to climate change. Increases in fall and winter inflow could lead to more frequent flood storage, reducing flexibility to store incoming flood flows. Uncertainty in spring inflow volume complicates projection of future filling performance. The reservoir may fill more or less often, depending on whether springs are wetter or drier. In the summer, drawdown may occur earlier to meet conservation objectives. From the scenario‐neutral analysis, we identified thresholds of streamflow magnitude that can predict climate change impacts for a wide range of scenarios. Our results highlight projected operational challenges for Cougar Dam and provide an example of how scenario‐based and scenario‐neutral approaches may be applied concurrently to assess climate change impacts.     

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.     

Methodology for Developing Flood Rule Curves Conditioned on El Niño‐Southern Oscillation Classification1

Lee, Se‐Yeun, Alan F. Hamlet, Carolyn J. Fitzgerald, and Stephen J. Burges, 2011. Methodology for Developing Flood Rule Curves Conditioned on El Niño‐Southern Oscillation Classification. Journal of the American Water Resources Association (JAWRA) 47(1):81‐92. DOI: 10.1111/j.1752‐1688.2010.00490.x Abstract: Regional climate varies on interannual and decadal time scales that in turn affect annual streamflows, flood risks, and reservoir storage deficits in mid‐summer. However, these variable elements of the climate system are generally not included in water resources operating policies that attempt to preserve a balance between flood risk and other water resources system objectives. A methodology for incorporating El Niño‐Southern Oscillation (ENSO) information in designing flood control curves is investigated. An optimization‐simulation procedure is used to develop a set of ENSO‐conditioned flood control rule curves that relate streamflow forecasts to flood control evacuation requirements. ENSO‐conditioned simulated flood risk and storage deficits under current operating policy are used to calibrate a unique objective function for each ENSO classification. Using a case study for the Columbia River Basin, we demonstrate that ENSO‐conditioned flood control curves constructed using the optimization‐simulation procedure consistently reduce storage deficits at a number of interrelated projects without increasing flood risk. For the Columbia Basin, the overall improvements in reservoir operations are relatively modest, and (in isolation) might not motivate a restructuring of flood control operations. However, the technique is widely applicable to a wide range of water resources systems and/or different climate indices.     

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.     

CONSUMPTIVE USE OF STREAMFLOW INCREASES IN THE COLORADO RIVER BASIN1

ABSTRACT: This study examined the disposition of streamflow increases that could be created by vegetation management on forest land along the upper reaches of the Colorado River. A network optimization model was used to simulate water flow, storage, consumptive use, and loss within the entire Colorado River Basin with and without the flow increases, according to various scenarios incorporating both current and future consumptive use levels as well as existing and potential institutional constraints. Results indicate that very little of the flow increases would be consumptively used at current use levels, or even at future use levels, if water allocation institutions remain unchanged. Given future use levels and economically based water allocation institutions, up to one-half of the flow increases could be consumptively used. The timing of streamflow increases, and the institutional constraints on water allocation, often limit the potential for consumptive use of flow increases.     

Loss Rates from Lake Powell and Their Impact on Management of the Colorado River

As demand for water in the southwestern United States increases and climate change potentially decreases the natural flows in the Colorado River system, there will be increased need to optimize the water supply. Lake Powell is a large reservoir with potentially high loss rates to bank storage and evaporation. Bank storage is estimated as a residual in the reservoir water balance. Estimates of local inflow contribute uncertainty to estimates of bank storage. Regression analyses of local inflow with gaged tributaries have improved the estimate of local inflow. Using a stochastic estimate of local inflow based on the standard error of the regression estimator and of gross evaporation based on observed variability at Lake Mead, a reservoir water balance was used to estimate that more than 14.8 billion cubic meters (Gm³) has been stored in the banks, with a 90% probability that the value is actually between 11.8 and 18.5 Gm³. Groundwater models developed by others, observed groundwater levels, and simple transmissivity calculations confirm these bank storage estimates. Assuming a constant bank storage fraction for simulations of the future may cause managers to underestimate the actual losses from the reservoir. Updated management regimes which account more accurately for bank storage and evaporation could save water that will otherwise be lost to the banks or evaporation.     

COMPUTATIONAL IMPROVEMENT FOR STOCHASTIC DYNAMIC PROGRAMMING MODELS OF URBAN WATER SUPPLY RESERVOIRS1

ABSTRACT: This paper describes two methods that are introduced to improve the computational effort of stochastic dynamic programming (SDP) as applicable to the operation of multiple urban water supply reservoir systems. The stochastic nature of streamflow is incorporated explicitly by considering it in the form of a multivariate probability distribution. The computationally efficient Gaussian Legendre quadrature method is employed to compute the conditional probabilities of streamflow, which accounts for the serial correlation of streamflow into each storage and the cross correlation between the streamflow into various storages. A realistic assumption of cross correlation of streamflow is introduced to eliminate the need to consider the streamflow combinations which are unlikely to occur in the SDP formulation. A “corridor” approach is devised to eliminate the need to consider the infeasible and/or inferior storage volume combinations in the preceding stage in computing the objective function in the recursive relation. These methods are verified in terms of computational efficiency and accuracy by using a hypothetical example of three interconnected urban water supply reservoirs. Therefore, it can be concluded that these methods allow SDP to be more attractive for deriving optimal operating rules for multiple urban water supply reservoir systems.     

ASSESSING INSTREAM FLOWS AND RESERVOIR OPERATIONS ON AN EASTERN IDAHO RWER1

ABSTRACT: A methodology for assessing reservoir management was applied to the historical conflict between winter fish and wildilife flows below Island Park Reservoir on Henrys Fork of the Snake River and the fulfillment of storage water rights. The methodology consists of (1) identifying impacts of flow regulation, (2) quantifying relationships among variables affecting physical reservoir fill, and (3) assessing effects of these discharges on the fulfillment of water rights in the context of a larger system of interrelated reservoirs. Winter (storage season) flows are critical to management of fish and wildlife populations below Island Park Dam, but flow regulation has resulted in decreased winter discharge. Allowable winter flows are a function of inflow, length of storage season, reservoir content at the start of storage season, and potential for downstream capture of excess storage season water discharged at Island Park. Modeling results indicate that winter flows in the range of those recommended for fish and wildlife management are attainable during average years but not during years when initial reservoir content is low. The methodology was successful in quantifying information useful to decision makers in a variety of agencies and disciplines and could be applied to solve water management problems on other regulated river systems.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

Reservoir Operation with Feedback in a Coupled Land Surface and Hydrologic Model: A Case Study of the Huai River Basin,China

Regarding emerging large‐scale reservoir operation models, reports of reservoir operation feedback for hydrologic modeling are rare, and little attention has been paid to flood control. An operation scheme considering multilevel flood control (MLFC) was first proposed in this study, but more reservoir information was needed. Thus, an alternative scheme was proposed that consisted of a modified version of the reservoir operation scheme in the Soil and Water Assessment Tool Model (MSWAT scheme). These schemes were coupled to a land surface and hydrologic model system with feedback, i.e., a system in which reservoir operation can affect the subsequent simulation, and were investigated in the Huai River Basin. The results show reservoir storage and peak flow were generally overestimated by the original SWAT reservoir scheme (SWAT scheme). Compared with the SWAT scheme, the MSWAT scheme successfully reduced the simulated storage and peak flow at the reservoir stations. For the downstream stations, the streamflow simulations were improved at a significance level of 5%. The performances of the MSWAT and MLFC schemes at the reservoir stations were nearly equivalent. Importantly, reservoir operation feedback to hydrologic modeling was necessary because the reservoir operation effects could not be transferred downstream without it. The streamflow simulation of a reservoir station located on a flat plain was less sensitive to feedback than that of a mountain reservoir station.