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.     

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.     

Sustainable Water Resources Management in a Conflict Resolution Framework1

Abstract: A decision support system for sustainable water resources management in a water conflict resolution framework is developed to identify and evaluate a range of acceptable alternatives for the Geum River Basin in Korea and to facilitate strategies that will result in sustainable water resource management. Working with stakeholders in a “shared vision modeling” framework, sustainable management strategies are created to illustrate system tradeoffs as well as long‐term system planning. A multi‐criterion decision‐making (MCDM) approach using subjective scales is utilized to evaluate the complex water resource allocation and management tradeoffs between stakeholders and system objectives. The procedures used in this study include the development of a “shared vision model,” a simulated decision‐making support system (as a tool for sustainable water management strategies associated with water conflicts, management options, and planning criteria), and the application of MCDM techniques for evaluating alternatives provided by the model. The research results demonstrate the utility of the sustainable water resource management model in aid of MCDM techniques in facilitating flexibility during initial stages of alternative identification and evaluation in a basin suffering from severe water conflicts.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

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.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

IMPACTS OF A SEVERE SUSTAINED DROUGHT ON COLORADO RWER WATER RESOURCES1

ABSTRACT: The impacts of a severe sustained drought on Colorado River system water resources were investigated by simulating the physical and institutional constraints within the Colorado River Basin and testing the response of the system to different hydrologic scenarios. Simulations using Hydrosphere's Colorado River Model compared a 38-year severe sustained drought derived from 500 years of reconstructed streamflows for the Colorado River basin with a 38-year streamflow trace extracted from the recent historic record. The impacts of the severe drought on streamflows, water allocation, storage, hydropower generation, and salinity were assessed. Estimated deliveries to consumptive uses in the Upper Basin states of Colorado, Utah, Wyoming, New Mexico, and northern Arizona were heavily affected by the severe drought, while the Lower Basin states of California, Nevada, and Arizona suffered only slight shortages. Upper Basin reservoirs and streamflows were also more heavily affected than those in the Lower Basin by the severe drought. System-wide, total hydropower generation was 84 percent less in the drought scenario than in the historical stream-flow scenario. Annual, flow-weighted salinity below Lake Mead exceeded 1200 ppm for six years during the deepest portion of the severe drought. The salinity levels in the historical hydrology scenario never exceeded 1100 ppm.     

Climate Change Impacts on Water Resources in the Upper Blue Nile River Basin,Ethiopia1

Kim, Ungtae and Jagath J. Kaluarachchi, 2009. Climate Change Impacts on Water Resources in the Upper Blue Nile River Basin, Ethiopia. Journal of the American Water Resources Association (JAWRA) 45(6):1361‐1378. Abstract: Climate change affects water resources availability of international river basins that are vulnerable to runoff variability of upstream countries especially with increasing water demands. The upper Blue Nile River Basin is a good example because its downstream countries, Sudan and Egypt, depend solely on Nile waters for their economic development. In this study, the impacts of climate change on both hydrology and water resources operations were analyzed using the outcomes of six different general circulation models (GCMs) for the 2050s. The outcomes of these six GCMs were weighted to provide average future changes. Hydrologic sensitivity, flow statistics, a drought index, and water resources assessment indices (reliability, resiliency, and vulnerability) were used as quantitative indicators. The changes in outflows from the two proposed dams (Karadobi and Border) to downstream countries were also assessed. Given the uncertainty of different GCMs, the simulation results of the weighted scenario suggested mild increases in hydrologic variables (precipitation, temperature, potential evapotranspiration, and runoff) across the study area. The weighted scenario also showed that low‐flow statistics and the reliability of streamflows are increased and severe drought events are decreased mainly due to increased precipitation. Joint dam operation performed better than single dam operation in terms of both hydropower generation and mean annual storage without affecting the runoff volume to downstream countries, but enhancing flow characteristics and the robustness of streamflows. This study provides useful information to decision makers for the planning and management of future water resources of the study area and downstream countries.     

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.     

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.     

滇池流域管理体制机制改革方案初探

滇池是中国重点治理的"三湖"之一。自"九五"以来国家已投入大量资金治理滇池水环境,但收效缓慢。流域综合管理是现代流域管理体制改革和发展的方向。面对流域社会经济的快速发展带来的污染压力和治理效果的不理想,流域管理体制机制的改革成为加快滇池流域水环境治理的必然选择和迫切需求。本文通过部门调研、专家访谈、信息综合分析等方法,剖析了滇池流域管理体制的问题,并提出了滇池流域管理体制机制改革方案。滇池流域的管理呈现出以部门为基础的横向管理和以行政单位为基础的纵向管理相交叉的条块分割局面,导致流域多头管理、多重目标。同时利益相关方参与广度和深度均不足。最后建议滇池流域的管理从管理机构、管理方式以及利益相关方参与等方面进行改革,分为近期、远期分步实施,最终实现一方主导、以流域为管理单元、利益相关方充分参与的流域综合管理体制机制。本文可为滇池流域管理体制改革提供有力参考,也可为其他流域管理体制的完善提供借鉴。     

Water Prism: A Tool to Assess Water Availability Risk and Investigate Water Management Strategies

Water availability risk is a local issue best understood with watershed‐scale quantification of both withdrawal and consumptive demands in the context of available supply. Collectively, all water use sectors must identify, understand, and respond to this risk. A highly visual and computationally robust decision support tool, Water Prism, quantitatively explores mitigation responses to water risk on both a facility‐level and basin‐aggregated basis. Water Prism examines a basin water balance for a 40‐ to 60‐year planning horizon, distinguishes among water use sectors, and accounts for ecosystem water needs. The 2012 Texas State Water Plan was used to apply Water Prism to the Big Cypress‐Sulphur Basin (Texas). The case study showed Water Prism to be an accurate and convenient tool to provide fine‐scale understanding of water use in the context of available supply, evaluate multi‐sector combinations of conservation strategies, and quantify the effects of future demands and water availability. Analyses demonstrated water availability risks for rivers and reservoirs can vary within a basin and must be calculated independently, simulation of water balance conditions can help illuminate potential impacts of increasing demands, and scenario simulations can be used to evaluate relative conservation efficacy of different water resource management strategies for each sector. Based on case study findings, Water Prism can serve as a useful assessment tool for regional water planners.     

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.     

Salt-induced land and water degradation in the Aral Sea basin: A challenge to sustainable agriculture in Central Asia

Expansion of irrigated agriculture in the Aral Sea Basin in the second half of the twentieth century led to the conversion of vast tracks of virgin land into productive agricultural systems resulting in significant increases in employment opportunities and income generation. The positive effects of the development of irrigated agriculture were replete with serious environmental implications. Excessive use of irrigation water coupled with inadequate drainage systems has caused large‐scale land degradation and water quality deterioration in downstream parts of the basin, which is fed by two main rivers, the Amu‐Darya and Syr‐Darya. Recent estimates suggest that more than 50% of irrigated soils are salt‐affected and/or waterlogged in Central Asia. Considering the availability of natural and human resources in the Aral Sea Basin as well as the recent research addressing soil and water management, there is cause for cautious optimism. Research‐based interventions that have shown significant promise in addressing this impasse include: (1) rehabilitation of abandoned salt‐affected lands through halophytic plant species; (2) introduction of 35‐day‐old early maturing rice varieties to withstand ambient soil and irrigation water salinity; (3) productivity enhancement of high‐magnesium soils and water resources through calcium‐based soil amendments; (4) use of certain tree species as biological pumps to lower elevated groundwater levels in waterlogged areas; (5) optimal use of fertilizers, particularly those supplying nitrogen, to mitigate the adverse effects of soil and irrigation water salinity; (6) mulching of furrows under saline conditions to reduce evaporation and salinity buildup in the root zone; and (7) establishment of multipurpose tree and shrub species for biomass and renewable energy production. Because of water withdrawals for agriculture from two main transboundary rivers in the Aral Sea Basin, there would be a need for policy level interventions conducive for enhancing interstate cooperation to transform salt‐affected soil and saline water resources from an environmental and productivity constraint into an economic asset.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Exploring Persistence in Streamflow Forecasting

In this study, the authors explore three persistence approaches in streamflow forecasting motivated by the need for forecasting model skill evaluation. The authors use streamflow observations with 15 min resolution from the year 2008 to 2017 at 140 United States Geological Survey streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 to 37,000 km². The study explores three approaches: simple persistence, gradient persistence, and anomaly persistence. The study shows that persistence forecasts skill has strong dependence on basin scales and weaker but non‐negligible dependence on geometric properties of the river network for a given basin. Among the three approaches explored, anomaly persistence shows highest skill especially for small basins, under about 500 km². The anomaly persistence can serve as a benchmark for model evaluations considering the effect of basin scales and geometric properties of river network of the basin. This study further reiterates that persistence forecasts are hard‐to‐beat methods for larger basin scales at short to medium forecast range.     

Water Use for Hydraulic Fracturing of Oil and Gas in the South Platte River Basin,Colorado

Water use for oil and gas development (i.e., hydraulic fracturing) is a concern in semiarid basins where water supply is often stressed to meet demands, and oil and gas production can exacerbate the situation. Understanding the impacts of water use for hydraulic fracturing (HF) on water availability in semiarid regions is critical for management and regulatory decisions. In the current work, we quantify water use for HF at several scales — from municipal to state‐wide — using the IHS Enerdeq database for the South Platte Basin. In addition, we estimate produced water (a by‐product of oil and gas production), using data from the Colorado Oil and Gas Conservation Commission to explore reuse scenarios. The South Platte River Basin, located in northeastern Colorado, encompasses the Denver‐Metro area. The basin has one of the most productive oil and gas shale formations in Colorado, with much of the production occurring in Weld County. The basin has experienced higher horizontal drilling rates coupled with an increasing population. Results show water use for horizontal and vertical wells averages 11,000 and 1,000 m³, respectively. Water use for HF in the South Platte Basin totaled 0.63% of the basin's 2014 total water demand. For Weld County, water use for HF was 2.4% of total demand, and for the city of Greeley, water use was 7% of total demand. Produced water totaled 9.4 Mm³ in the basin for 2014, which represents 42% of the total water used for HF.     

A DECISION SUPPORT TOOL FOR THE MANAGEMENT OF MULTI‐RESERVOIR SYSTEMS1

ABSTRACT: A decision support tool is developed for the management of water resources, focusing on multipurpose reservoir systems. This software tool has been designed in such a way that it can be suitable to hydrosystems with multiple water uses and operating goals, calculating complex multi‐reservoir systems as a whole. The mathematical framework is based on the parameterization‐simulation‐optimization scheme. The main idea consists of a parametric formulation of the operating rules for reservoirs and other projects (i.e., hydropower plants). This methodology enables the radical decrease of the number of decision variables, making feasible the location of the optimal management policy, which maximizes the system yield and the overall operational benefit and minimizes the risk for the management decisions. The program was developed using advanced software engineering techniques. It is adaptable in a wide range of water resources systems, and its purpose is to support water and power supply companies and related authorities. It already has been applied to two of the most complicated hydrosystems of Greece, the first time as a planning tool and the second time as a management tool.     

RESERVOIR WATER LEVEL IMPACTS ON RECREATION,PROPERTY, AND NONUSER VALUES1

ABSTRACT: Wise interbasin management of Southeastern U.S. water resources is important for future development. Alabama‐Coosa‐Tallapoosa and Apalachicola‐Flint‐Chattahoochee River basins' water usage has evolved from power generation to multiple uses. Recreation and housing have become increasingly valuable components. Changing use patterns imply changing resource values. This study focused on six Alabama reservoirs, using contingent valuation questions in on‐site, telephone, and mail surveys to estimate impacts on lakefront property values, recreational expenditures, and preservation values for scenarios of permanent changes to reservoir water quantity. As summer full‐pool duration decreased, lakefront property value decreased, and as duration increased, property values increased, but at a lesser rate. Similar findings occurred for winter draw down alternatives. Permanent one‐foot reductions in summer full‐pool water levels resulted in a 4 to 15 percent decrease in lakefront property values. Recreational expenditures decreased 4 to 30 percent for each one‐foot lowering of reservoir water levels. Current nonusers of the six reservoirs showed strong preferences for protecting study reservoirs with willingness to pay values of 47 per household or approximately 29 million for the entire six‐reservoir watershed basin area. Resource management based on historic use patterns may be inappropriate and more frequent and comprehensive valuation of reservoir resources is needed.     

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.