Modeling River Temperatures - An Annotated Bibliography

A considerable amount of work is reported in the literature on modeling river water temperature. Various approaches are available that have the potential of better predictability than the current regression model used by Brookfield at the Deep Creek Hydroelectric facility.

This section of the website collects some of the most relevant reports dealing with river temperature predictions on an hourly basis with emphasis on preserving fish habitat.

Physical Modeling

1.M. C. Westhoff, H. H. G. Savenije, W. M. J . Luxemburg, G. S. Stelling, N. C. van de Giesen, J. S. Selker, L. Pfister, and S. Uhlenbrook, “A distributed stream temperature model using high resolution temperature observations,” Hydrol. Earth Syst. Sci., 11, 1469–1480, 2007 and Corrigendum

Abstract: Distributed temperature data are used as input and as calibration data for an energy based temperature model of a first order stream in Luxembourg. A DTS (Distributed Temperature Sensing) system with a fiber optic cable of 1500 m was used to measure stream water temperature with 1 m resolution each 2 min. Four groundwater inflows were identified and quantified (both temperature and relative discharge). The temperature model calculates the total energy balance including solar radiation (with shading effects), long-wave radiation, latent heat, sensible heat and river bed conduction. The simulated temperature is compared with the observed temperature at all points along the stream. Knowledge of the lateral inflow appears to be crucial to simulate the temperature distribution and conversely, that stream temperature can be used successfully to identify sources of lateral inflow. The DTS fiber optic is an excellent tool to provide this knowledge.

2.GEORGE W. BROWN , “PredictingTemperatures SmallStreams,” Forest ResearchLaboratory, School of Forestry OregonState University, Corvallis 97331, WATER RESOURCES RESEARCH, VOL. 5, NO. 1 , FEBRUARY 1969

Abstract: Hourly temperatures of small streams can be accurately predicted using an energy balance. Micrometeorological measurements are required to assess the environment of the small stream accurately. The temperature-prediction technique was tested on three streams in Oregon. On unshaded stretches, net all-wave radiation is the predominant energy source during the day; evaporation and convection account for less than 10% of the total energy exchange. Conduction of heat into the stream bottom is an important energy balance component on shallow streams having a bed rock bottom. Up to 25% of the energy absorbed by such a stream may be transferred into the bed. Hourly temperature changes of 0-16 ℉ were predicted to within 1 ℉ more than 90% of the time. This technique permits foresters to control water temperature through manipulation of stream-side vegetation.

3.Cindie Hébert , “MODELING OF HOURLY STREAM TEMPERATURES WITHIN TWO FORESTED CATCHMENTS,” PhD Thesis, Dalhousie University, Halifax, Nova Scotia, 2013.

Abstract: Water temperature is a key physical habitat determinant in lotic ecosystems as it influences many physical, chemical and biological properties of rivers. Hence, a good understanding of the thermal regime of rivers is essential for effective management of water and fisheries resources. This study deals with the modeling of hourly stream water temperature using a deterministic model, an equilibrium temperature model and an artificial neural network model. The water temperature models were applied on two thermally different streams, namely, the Little Southwest Miramichi River (LSWM) and Catamaran Brook (Cat Bk) in New Brunswick, Canada.

4.Stephen P. Schreiner and Geoffrey D. Birky, (Versar Inc.) “A TEMPERATURE SIMULATION MODEL OF THE YOUGHIOGHENY RIVER FROM DEEP CREEK STATION TO SANG RUN”, PPRP-DC-1, June 1997 See also pprp-dc-2.pdf, pprp-dc-3.pdf, pprp-dc-4.pdf, pprp-dc-5.pdf and pprp-dc-6.pdf.

Abstract: Deep Creek Lake Hydroelectric Station discharges into the Youghiogheny River (MD) in a peaking mode, resulting in rapid and dramatic changes in flow and temperature in the river. During low flow periods in summer, cold water releases from the project can provide a benefit to the trout fishery in the river by moderating otherwise unfavorable low flows and high temperatures. We developed a temperature model of the river using CE-QUAL-RIV1 to evaluate the effectiveness of various release scenarios for maintaining water temperature below 25EC, a critical value for brown trout. Temperatures were recorded continuously at 10-30 minute intervals for several summers at various locations in the river to provide data for model calibration and verification. The model was modified to include benthic conduction and shading subroutines to improve simulation results. Model simulations and test releases included both full and partial generation releases of 1-3 hours in duration at mid-day and during several continuous low flow releases. Results were then used to estimate the relative cost of various release scenarios to the utility and other users of river flows. Model results were also used in determining a means of triggering releases when required, based on daily meteorological forecasts, flows, and temperature conditions in the river.

5.Penn State Integrated Hydrologic Modeling System (PIHM); accessed 17 April 2014.

Abstract: The Penn State Integrated Hydrologic Model (PIHM) is a multiprocess, multi-scale hydrologic model where the major hydrological processes are fully coupled using the semi-discrete finite volume method. The model itself is “tightly-coupled” with PIHMgis, an open-source Geographical Information System designed for PIHM. The PIHMgis provides the interface to PIHM, access to the digital data sets (terrain, forcing and parameters) and tools necessary to drive the model, as well as a collection of GIS-based pre- and post-processing tools. Collectively the system is referred to as the Penn State Integrated Hydrologic Modeling System. The modeling system has been written in C/C++, while the GIS interface is supported by Qt. The Penn State Hydrologic Modeling System is open source software, freely available for download at this site along with installation and user guides.

6.Hauser, G. E. and G. A. Schohl. “River Modeling System V4 — User Guide and Technical Reference,‘ Report No. WR28-1-164, TVA River System Operations and Environment, Norris, TN, May 2002.

Abstract: RMS is a longitudinal, dynamic model of river system hydrodynamics, water quality, and fish growth. The hydrodynamic component solves the one dimensional, longitudinal, equations for conservation of mass and momentum (St. Venant equations) using a four-point implicit finite difference scheme with weighted spatial derivatives. Major inputs to the hydrodynamic component include channel geometry, roughness coefficients, upstream and lateral inflows, boundary rating curves, and initial water surface elevations and discharges throughout the modeled reach. The water quality component solves the mass transport equation with a choice of two different numerical schemes (4-point implicit, Holly-Priessmann). Major inputs include meteorology, inflow water quality, and initial conditions. The bioenergetics fish component simulates fish growth as a function of food availability, temperature, and dissolved oxygen. Inputs to the fish component include temperature and dissolved oxygen over time throughout the modeled.

7.Colin W. Krause, Tammy J. Newcomb, Donald J. Orth, “Thermal habitat assessment of alternative flow scenarios in a tailwater fishery”, River Research and Applications 21(6):581 - 593 · July 2005; Accessed 9/23/2017.

Abstract: Tailwaters below hydropower dams can create desirable coldwater trout fisheries; however, a flow regime ideal for hydropower often presents challenges for management of the fishery. The Smith River tailwater (Henry County, VA) offers a self-sustaining brown trout fishery managed for trophy trout (≥ 406 mm), yet trophy-sized fish are rare. Slow growth and small size are likely caused by any one or a combination of thermal habitat, limited food resources, and/or physical habitat. To evaluate the potential for thermal habitat improvement, temperature changes resulting from alternative flows were assessed with a one-dimensional hydrodynamic model coupled with a water temperature model. Simulated temperatures from each flow scenario were assessed every 2 river kilometres over a 24 kilometre river section below the dam for occurrence of optimal growth temperatures, as well as compliance with Virginia Department of Environmental Quality hourly temperature change and daily maximum temperature standards. The occurrence of optimal growth temperatures increased up to 11.8% over existing conditions by releasing water in the morning, decreasing the duration of release, and not increasing baseflow. Incidences of hourly temperature changes greater than 2°C were reduced from 4% to 0–1.2% by non-peaking releases, increasing baseflow, morning releases, and decreasing the duration of release. Maximum temperature occurrence (> 21°C) decreased from 1.3% to 0–0.1% by releasing flows daily to prevent elevated temperatures on non-generation days, increasing baseflow, increasing duration of release, and releasing in the morning rather than evening. Despite conflicting adjustments to best improve all thermal criteria concurrently, a 7-day/week, morning, one hour release regime was determined to improve all criteria throughout the tailwater compared to existing conditions. Copyright © 2005 John Wiley & Sons, Ltd.

8.Donald J. Orth, Tammy J. Newcomb, C. Andrew Dolloff, Panos Diplas, Colin W. Krause, M. Anderson, A. Hunter, Y. Shen, “Influences of Fluctuating Releases on Stream Habitats for Brown Trout in the Smith River below Philpott Dam,” Annual Report Year 4, August 31, 2003

Abstract: This project consists of three job elements. Job 1 focuses on Characteristics of Spawning and Rearing Habitats for Brown Trout. The objectives are to characterize instream habitat conditions in areas where successful spawning and juvenile rearing of brown trout occurs. Job 2 focuses on Determinants of Brown Trout Growth and Abundance. The objectives of Job 2 are (1) To collect biological data to quantify relative abundance of trout in the Smith River from Philpott Dam to Martinsville and monitor annual variation in brown trout recruitment success; (2) To assess longitudinal and seasonal shifts in brown trout diet composition, and (3) To evaluate the bioenergetic constraints on trout growth under existing temperature regimes. Job 3 focuses on Hydraulic Model Development and Application to Smith River Tailwater. The objective is to design a field survey and modeling protocol to measure effects of varying flows on the shear stress, mobilization of stream bed gravels, and relate discharge to the amount of red scouring or brown trout fry displacement that would occur at sites in the tailwater. This information coupled with flow records should permit prediction of catastrophic year-class failures and flow ranges that provide for acceptable reproduction.

9.Yong G. Lai and David Mooney, “On a Two-Dimensional Temperature Model: Development and Verification,” ASCE World Environmental and Water Resources Congress, Kansas City, Missouri, May 17-21, 2009

Abstract: Government regulators on many rivers have specified acceptable temperatures based upon habitat and biological criteria. These temperature thresholds impose constraints on reservoir operations and can limit water deliveries and power generation. Existing tools based on low-order modeling simplify a river to a simple line with limited spatial distribution of inputs and poorly represent physics of the river processes. The limited spatial extents restrict the usefulness of low-order modeling for such features as agricultural returns, gravel pits, groundwater upwelling, side channel activation, and stream side vegetation. It also imposes limitation on fish habitat assessment and reapportion outside the range of the calibration datasets. This study develops a two-dimensional (2D) temperature module for an existing 2D hydraulic model, SRH-2D version 2. The 2D model incorporates data with both lateral and longitudinal geographic extents rather than lumping results into a point-to- point or uni-directional representation. The objective was to improve the representation of spatial features where low-order models resort to empiricism for a lumped treatment. Better representation of processes leads to increased accuracy and higher confidence. The SRH-2D temperature model utilizes meteorological data as inputs (solar radiation, cloud cover, air temperature, dew point temperature and wind speed). Physical processes modeled include solar radiation, terrain and vegetation shade, atmospheric radiation, water back radiation, heat exchange between water and river bed, water surface evaporative and conductive losses. The model formulation, along with governing and process equations, is discussed first. The model is then tested and verified with simple cases having analytical solutions. The model is finally verified by applying to flows on the McKay Creek downstream of the McKay Dam.

10.Mike Deas and Limor Geisler, “Completion of the Shasta River Flow and Temperature Modeling Phase,”

Abstract: This document is a review of the Shasta River modeling project status as of August, 2004. The implementation, calibration, and validation of the model with respect to hydrodynamics and temperature are complete. Three seven day periods were modeled for flow and temperature. The period from 9/17/2002 to 9/23/2002 was used for calibration of the model, and the other two periods were modeled using the same input parameters, for the purpose of validation. These periods approximately represent early-summer, mid-summer, and late- summer/early-fall conditions in the Shasta River and include a sufficient range of flows and water temperatures to test the model.

11.Jianchun Huang and Jennifer Bountry, “Temperature Simulation of a Reach of the Methow River Near Winthrop, Washington,” 2012

Abstract: In this paper, a two-dimensional (2D) temperature model is tested within a reach of the Methow River near Winthrop, WA. The reach has a warm water tributary entering on river left and a cold water spring entering from river right. The 2D temperature model is spatially distributed in the lateral and longitudinal geographic extents, allowing for more accurate simulation of lateral changes in temperature across the channel than a 1D representation. The SRH-2D temperature model utilizes meteorological data as inputs (solar radiation, cloud cover, air temperature, dew point temperature, air pressure, and wind speed). Physical processes modeled include solar and atmospheric heating, effects of terrain and vegetation shading, heat exchange between water column and bed substrate, and losses due to evaporation, conduction, and back radiation. Two sets of data are used to test the model. Test one uses steady solution to simulate the lateral temperature mixing zone when warmer water from the Chewuch River enters from river left and colder water from Spring Creek enters from river right. Test two uses unsteady solution to simulate the continuous temperature change due to various water heat gains and losses.

12.Sarah E. Null, Michael L. Deas and Jay R. Lund, “Flow and Water Temperature Simulation for Habitat Restoration in the Shasta River, California,” River. Res. Applic. (2009)

Abstract: Low instream flows and high water temperatures are two factors limiting survival of native salmon in California’s Shasta River. This study examines the potential to improve fish habitat conditions by better managing water quantity and quality using flow and water temperature simulation to evaluate potential restoration alternatives. This analysis provides a reasonable estimate of current and potential flows and temperatures for a representative dry year (2001) in the Shasta River, California. Results suggest restoring and protecting cool spring-fed sources provides the most benefit for native salmon species from a broad range of restoration alternatives. Implementing a combination of restoration alternatives further improves instream habitat. Results also indicate that substituting higher quality water can sometimes benefit native species without increasing environmental water allocations. This study shows the importance of focusing on the limitations of specific river systems, rather than systematically increasing instream flow as a one size fits all restoration approach.

13.Werner Meier, Cyrill Bonjour, Alfred Wuest, and Peter Reichert, “Modeling the Effect of Water Diversion on the Temperature of Mountain Streams”, Journal of Environ- mental Engineering, Vol. 129, No. 8, August 1, 2003

Abstract: Water diversion for hydroelectric power generation impacts the temperature of mountain streams. Such changes are estimated by using a coupled one-dimensional dead-zone heat balance model. In very steep river sections, the dissipation of kinetic energy is the dominant heat source. For such streams, water diversion has only a minor effect on water temperature, because dissipation-induced temperature changes are independent of discharge. In contrast, in river sections of gradual slope, the influence by solar radiation, long-wave radiation, and heat exchange with the streambed is stronger. In such cases, a discharge reduction can lead to significant temperature changes. For a small stream in the southern Swiss Alps, model results show that diversion increases temperature by about 3.7 0.9 °C in a 21 km long river section under high solar radiation during summer. During a cold winter episode, water temperature is estimated to be about 1.8 0.8 °C lower compared to natural conditions. This heat balance model can also be used to simulate the effect of different measures to reduce water temperature changes in affected streams.

14.“Application of a 1-D Heat Budget Model to the Columbia River System,” EPA Region 10 Publication Number May 2001

Abstract: from the Introduction: …When setting priorities for attaining established water quality standards for temperature, the first step is to assess the importance of sources that may significantly affect the thermal energy budget. Changes in the thermal energy budget of the Snake and Columbia rivers, relative to the natural unregulated river system, are due primarily to advected thermal energy from point sources, surface water, and ground water, as well as modification of river geometry and hydraulics due to the construction and operation of hydroelectric facilities. The goal of this work is to support the priority-setting phase of the TMDL process by assessing the impacts of the principal sources of thermal energy.

15.M. T. H. van Vliet, J. R. Yearsley, W. H. P. Franssen, F. Ludwig, I. Haddeland1, D. P. Lettenmaier, and P. Kabat, “Coupled daily streamflow and water temperature modelling in large river basins”, Hydrol. Earth Syst. Sci., 16, 4303–4321, 2012

Abstract: Realistic estimates of daily streamflow and water temperature are required for effective management of water resources (e.g. for electricity and drinking water production) and freshwater ecosystems. Although hydrological and process-based water temperature modeling approaches have been successfully applied to small catchments and short time periods, much less work has been done at large spatial and temporal scales. We present a physically based modeling framework for daily river discharge and water temperature simulations applicable to large river systems on a global scale. Model performance was tested globally at 1/2 × 1/2-degree spatial resolution and a daily time step for the period 1971– 2000. We made specific evaluations on large river basins situated in different hydro-climatic zones and characterized by different anthropogenic impacts. Effects of anthropogenic heat discharges on simulated water temperatures were incorporated by using global gridded thermoelectric water use datasets and representing thermal discharges as point sources into the heat advection equation. This resulted in a significant increase in the quality of the water temperature simulations for thermally polluted basins (Rhine, Meuse, Danube and Mississippi). Due to large reservoirs in the Columbia which affect streamflow and thermal regimes, a reservoir routing model was used. This resulted in a significant improvement in the performance of the river discharge and water temperature modeling. Overall, realistic estimates were obtained at daily time step for both river discharge (median normalized BIAS = 0.3; normalized RMSE = 1.2; r = 0.76) and water temperature (median BIAS = −0.3 ℃; RMSE = 2.8 ℃; r = 0.91) for the entire validation period, with similar performance during warm, dry periods. Simulated water temperatures are sensitive to headwater temperature, depending on resolution and flow velocity. A high sensitivity of water temperature to river discharge (thermal capacity) was found during warm, dry conditions. The modeling approach has potential to be used for risk analyses and studying impacts of climate change and other anthropogenic effects (e.g. thermal pollution, dams and reservoir regulation) on large rivers.

16.John M. Bartholow, “The Stream Segment and Stream Network Temperature Models: A Self-Study Course, Version 2.0”, Open-File Report 99-112, U.S. DEPARTMENT OF THE INTERIOR U.S., GEOLOGICAL SURVEY, March 2000.

Abstract: This course’s goal is to prepare the student, self-study or otherwise, to comprehend the basics of stream temperature modeling, to apply two specific models, and to evaluate the appropriateness of these models for real-world, biological problem solving. After completing this course, the dedicated student should be able to:
a. Understand the theoretical basis for model, including its assumptions and limitations.
b. Be fluent in the stream geometry, hydrology, and meteorology components of the model, and how combining these components creates a stream system description.
c. Understand how to enter data, run, and interpret results from the network and stream reach versions of model.
d. Be capable of calibrating the SNTEMP model given typical constraints, e.g., some data are missing.
e. Be capable of using the model to estimate unknown temperatures for the baseline condition and predict water temperatures under altered conditions.
f. Depending on the needs of individual students, he or she will be prepared to either:

  1. Conduct a “live” temperature investigation, including how to plan a cost-effective study, gather needed input data, assemble that data into appropriate formats, and display results in a communicative manner; or
  2. Be able to review a completed study performed by another individual or organization, to assure its quality by critically analyzing it’s modeling components and evaluating the achievement of study objectives.

To accomplish the goal and objectives for this class, the self-study student must be willing to independently read portions of complex technical material, work autonomously to comprehend examples provided, and test his or her knowledge by hands-on application to lab problems. Participating in a classroom setting will provide the additional dimension of collaborative, small- team problem solving, and offer a wider perspective of viewpoints and approaches through classroom discussions, and, occasionally, offer the opportunity for classroom presentations and/or field trips.

17.Ali Erturk, “A Simple Stream Water Quality Modeling Software for Educational and Training Purposes”, Turkish Journal of Fisheries and Aquatic Sciences 10: 61-70 (2010)

Abstract: Water quality models are important decision support system tools for water pollution control, study of the health of aquatic ecosystems and assessment of the effects of point and diffuse pollution. However, water quality models are usually comprehensive software, which are usually not easy to learn and apply. Thus extensive training is needed before scientists and engineers can use most of the water quality models effectively. In this study; a new, easy to use and simple stream water quality modeling software is developed. The model underwent an extensive testing period that includes education/training oriented applications and a real world application as well. The software is easy to learn and the model is simple enough to be used in advanced undergraduate and introductory graduate level courses in aquatic sciences and environmental science, engineering or management programs. It can also be used for institutional training in state offices that are dealing with water pollution control and integrated water management as well. All the modeling system is developed according to free/open software philosophy so that advanced level users such as trainers are able to modify it according to the training/institutional needs.

18.Matthew Boyd & Brian Kasper, “Analytical Methods for Dynamic Open Channel Heat and Mass Transfer”, Methodology for the Heat Source Model Version 7.0, February 2007

Abstract: This document is intended to serve as a reference for the stream heat and mass transfer analytical methodology Heat Source1 . Chapters that follow describe in detail the mathematics and solution techniques suited for heat and mass transfer quantification. Simulation of water temperature and flow dynamics over various scales (i.e. reach, watershed to basin scales) is made possible with high resolution spatially continuous data, coupled with deterministic modeling of hydrologic and landscape processes. These processes are often interrelated and occur simultaneously (and can amplify or mask the effect of other processes). The methods presented in this paper are predicated foremost on data accuracy and resolution, and then analytical methodology robustness.

19.John M. Bartholow, “STREAM TEMPERATURE INVESTIGATIONS: FIELD AND ANALYTIC METHODS”, U.S. Fish and Wildlife Service, Fort Collins, CO, Biological Report 89(17), June 1989

Abstract: Extract from Summary: This document provides guidance to the user of the U.S. Fish and Wildlife Service’s Stream Network Temperature Model (SNTEMP). Planning a temperature study is discussed in terms of understanding the management objectives and ensuring that the questions will be accurately answered with the modeling approach being used. A sensitivity analysis of SNTEMP is presented to illustrate which input variables are most important in predicting stream temperatures. This information helps prioritize data collection activities, highlights the need for quality control, focuses on which parameters can be estimated rather than measured, and offers a broader perspective on management options in terms of knowing where the biggest temperature response will be felt… Alternative public domain stream and reservoir temperature models are contrasted with SNTEMP. A distinction is made between steady-flow and dynamic-flow models and their respective capabilities. Regression models are offered as an alternative approach for some situations, with appropriate mathematical formulations suggested.

20.[Rosealea M. Bonda, Andrew P. Stubblefield, Robert W. Van Kirk, “Sensitivity of summer stream temperatures to climate variability and riparian reforestation strategies”, Journal of Hydrology: Regional Studies 4 (2015) 267–279]((/river_temperature/bibliography/1-s2.0-S2214581815000865-main.pdf)

Abstract: The Salmon River is the second largest tributary of the Klamath River in northern California, USA. It is a region of steep mountains and diverse conifer forests. Historical land uses including logging, flow diversions, and hydraulic gold mining, have resulted in altered sediment transport regimes, diminished riparian cover and reduced large woody debris. These in turn have altered the thermal regime of the river. Summer stream temperatures commonly exceed salmonid (specifically Oncorhynchus spp.) temperature thresholds. Study focus: Thermal dynamics of a one-kilometer reach of the Salmon River was quantified using distributed temperature sensing fiber-optics (DTS) and Heat Source modeling. Stream thermal responses to scenarios of air temperature increase and flow reduction were compared with riparian reforestation simulations to estimate benefits of reforestation. New hydrological insights: Elevated air temperatures (2 ℃, 4 ℃, 6 ℃) increased mean stream temperature by 0.23℃/km, 0.45℃/km and .68℃/km respectively. Reforestation low- ered temperatures 0.11–0.12℃/km for partial and 0.26–0.27℃/km for full reforestation. Reduced streamflow raised peak stream temperatures in all simulations. Warming could be mitigated by reforestation, however under severe flow reduction and warming (71.0 % reduction, 6℃ air temperature), only half of predicted warming would be reduced by the full reforestation scenario. Land managers should consider reforestation as a tool for mitigating both current and future warming conditions.

Review Papers/Reports

1.D. CAISSIE, “The thermal regime of rivers: a review,”Freshwater Biology (2006) 51, 1389–1406, <The thermal regime of rivers: a review - CAISSIE - 2006 - Freshwater Biology - Wiley Online Library>, viewed 17 April 2014.

Abstract: 1) The thermal regime of rivers plays an important role …. 2) This study reviews the different river thermal processes responsible for water temperature variability on both the temporal (e.g. diel, daily, seasonal) and spatial scales, as well as providing information related to different water temperature models currently found in the literature. 3) Water temperature models are generally classified into three groups: regression, stochastic and deterministic models. Deterministic models employ an energy budget approach to predict river water temperature, whereas regression and stochastic models generally rely on air to water temperature relationships. 4) Water temperature variability can occur naturally or …

2.Loubna Benyahya, Daniel Caissie, André St-Hilaire, Taha B.M.J. Ouarda and Bernard Bobée, “A Review of Statistical Water Temperature Models ,” Canadian Water Resources Journal, Vol. 32(3): 179-192 (2007)

Abstract: This chapter deals with thermal regime of rivers and therefore the basic thermodynamic relations for river waters are described. At first a detailed description of the fundamental laws of thermodynamics applied to rivers is given and then some examples of how to use these laws to solve thermal problems are presented. Description of the first law of thermodynamics requires introducing some fundamental variables like the internal energy, conduction, convection, enthalpy, and so on. All these thermodynamic variables are described in detail. One example is related to the thermal problem of mixing two rivers of different temperatures and others are related to a case of free or natural convection. These examples are often met in environmental engineering practice. The most important issue in river thermodynamics is the heat exchange through the water-air interface. The physical processes involved in the heat transfer through a water surface are extremely complex and dependent upon a variety of hydrodynamical and meteorological factors. The following processes are described: the net solar radiation flux, the net atmospheric radiation flux, the water surface radiation flux, the evaporation heat flux and the sensible heat flux. These heat fluxes are defined and some approximation formulae to calculate them are given. Also the problem of the heat exchange between water and river bed is briefly discussed. A case study is presented. It comprises two engineering problems related to calculation of depth-averaged temperature in rivers. The first one is related to the calculation of the temperature field in a given river reach downstream of an outlet of a thermal discharge. The river reach is wide and shallow with bends of moderate curvature, where centrifugal forces are negligibly small. Flow is steady and both the river depth and the depth-averaged velocity can be varied in both horizontal directions. The main mechanism that determines the river temperature is the mixing of discharge warm waters into a river. The second problem is related to calculation of the water temperature field of a given river reach under extreme meteorological conditions. These conditions (three consecutive hottest days) are chosen based on historical data. The main mechanism that determines the river temperature, in this case, is surface heat exchange through the water-air interface. This mechanism is discussed and the problem of calculation of river temperature distribution in these extreme conditions is solved and the results are discussed in detail.

3.W. Czernuszenko, “THERMODYNAMICS OF RIVERS,” FRESH SURFACE WATER – Vol. II.

Abstract: This chapter deals with thermal regime of rivers and therefore the basic thermodynamic relations for river waters are described. At first a detailed description of the fundamental laws of thermodynamics applied to rivers is given and then some examples of how to use these laws to solve thermal problems are presented. Description of the first law of thermodynamics requires introducing some fundamental variables like the internal energy, conduction, convection, enthalpy, and so on. All these thermodynamic variables are described in detail. One example is related to the thermal problem of mixing two rivers of different temperatures and others are related to a case of free or natural convection. These examples are often met in environmental engineering practice. The most important issue in river thermodynamics is the heat exchange through the water-air interface. The physical processes involved in the heat transfer through a water surface are extremely complex and dependent upon a variety of hydrodynamical and meteorological factors. The following processes are described: the net solar radiation flux, the net atmospheric radiation flux, the water surface radiation flux, the evaporation heat flux and the sensible heat flux. These heat fluxes are defined and some approximation formulae to calculate them are given. Also the problem of the heat exchange between water and river bed is briefly discussed. A case study is presented. It comprises two engineering problems related to calculation of depth-averaged temperature in rivers. The first one is related to the calculation of the temperature field in a given river reach downstream of an outlet of a thermal discharge. The river reach is wide and shallow with bends of moderate curvature, where centrifugal forces are negligibly small. Flow is steady and both the river depth and the depth-averaged velocity can be varied in both horizontal directions. The main mechanism that determines the river temperature is the mixing of discharge warm waters into a river. The second problem is related to calculation of the water temperature field of a given river reach under extreme meteorological conditions. These conditions (three consecutive hottest days) are chosen based on historical data. The main mechanism that determines the river temperature, in this case, is surface heat exchange through the water-air interface. This mechanism is discussed and the problem of calculation of river temperature distribution in these extreme conditions is solved and the results are discussed in detail.

Genetic Programming

1.M. Arganis, R.Val, J. Prats, K. Rodríguez, R. Domínguez and J. Dolz, “WATER TEMPERATURE MODELING BY MEANS OF GENETIC PROGRAMMING,” 11th International Conference of Urban Drainage, Edinburgh, Scotland, UK., 2008.

Abstract: In this work, an application of Genetic Programming (an evolutionary computational tool) is presented with the aim of modeling the behavior of the water temperature in a river. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the model developed when data are standardized. This improvement is reflected in a reduction of the mean square error.

2.CE-QUAL-RIVI: A Dynamic, One-Dimensional (Longitudinal) Water Quality Model for Streams,User’s Manual, US Army Corps of Engineers, 1990.

Abstract: A dynamic, one-dimensional (longitudinal), water quality model for unsteady flows in rivers and streams, CE-QUAL-RIVi, is presented. CE-QUAL-RIVI is developed in two parts, hydrodynamic and water quality. Output from the hydrodynamic solution is used to drive the water quality model. The hydrodynamic code uses a four-point implicit Newton-Raphson procedure to solve the nonlinear St. Venant equation. Numerical accuracy for the advection of sharp gradients is preserved in the water quality code through the use of the explicit two-point, fourth-order accurate, Holly-Preissmann scheme. Water quality constituents include temperature, dissolved oxygen, carbonaceous bio- chemical oxygen demand, organic nitrogen, ammonia nitrogen, nitrate nitrogen, ortho- phosphate phosphorus, coliform bacteria, dissolved iron, and dissolved manganese. The effects of algae and macrophytes are also included. The model allows simulation of branched river systems with multiple hydraulic control structures, such as run-of-the-river dams, waterway locks and dams, and reregulation dams. The model was developed to simulate the transient water quality conditions associated with highly unsteady flows that can occur on regulated streams.

3.W.A. Perkins, M. C. Richmond, “Long-term, One-dimensional Simulation of Lower Snake River Temperatures for Current and Unimpounded Conditions,” PNNL-13443, February 2001.

Abstract: The objective of the study was to compare water temperatures in the Lower Snake River for current (impounded) and unimpounded conditions using a mathematical model of the river system. A long-term analysis was performed using the MASS1 one-dimensional (1D) hydrodynamic and water quality model. The analysis used historical flows and meteorological conditions for a 35-year period spanning between 1960 and 1995. Frequency analysis was performed on the model results to calculate river temperatures at various time exceedance levels. Results were also analyzed to compute the time when, during the year, water temperatures rose above or fell below various temperature levels.

The long-term analysis showed that the primary difference between the current and unimpounded river scenarios is that the reservoirs decrease the water temperature variability. The reservoirs also create a thermal inertia effect which tends to keep water cooler later into the spring and warmer later into the fall compared to the unimpounded river condition. Given the uncertainties in the simulation model, inflow temperatures, and meteorological conditions the results show only relatively small differences between current and unimpounded absolute river temperatures.

4.Michael L. Deas, Cindy L. Lowney, “Water Temperature Modeling Review”, California Water Modelling Forum, September 2000.

Abstract: Construction of dams on the Sacramento and San Joaquin River, as well as on most major tributaries to these rivers, has blocked passage for anadromous fishes that historically spawned in these watersheds. Additionally, the impoundment of waters and operation of reservoirs has altered both the flow regime and water quality in downstream river reaches. Downstream river reaches are further impacted by diversions and return flows. Of particular concern is the effect that impoundments and water resources development along river reaches have on water temperature. In response to the interest and concern associated with selection and application of temperature models and the biological and ecological effects of temperature regimes, the Bay Delta Modeling Forum (BDMF) sponsored two preliminary assessments: this review of temperature modeling for Central Valley water management and a review of temperature effects on anadromous Central Valley salmonids. As such, the report objective is: To provide an overview of stream and reservoir water temperature modeling, review historic and current temperature modeling work in the Central Valley, identify basic temperature prediction concepts, present the required field and other physical data, and define the role of temperature modeling in addressing current biological problems.*

5.Ruochuan Gu, “A simplified river temperature model and its application to streamflow management,” Journal of Hydrology (NZ) 37(1):35-54, 1998.

Abstract: Relations between water temperatures and river discharge or flow depth are developed from a simplified model, using an analytical solution and the concept of surface heat flux and equilibrium temperature. The effects of streamflow and weather conditions on summer riser temperatures are analyzed and quantified. The model relationships are compared with 5 summers of field data for river temperature (measured hourly), weather variables (hourly), and discharge (daily) for the central Platte River, Nebraska, USA. The method is applied to the Platte River to derive weather- related flow requirements for controlling summer river temperature maxima through streamflow management. Above a critical discharge, increasing the discharge has little effect on reducing water temperature.

6.V. Kothandaraman and R.L. Evans, “Use of Air-Water Relationships for Predicting Water Temperature,” Report of Investigation 69, State of Illinois, 1972.

Abstract: Application of harmonic analysis to average daily air and water temperature records for a given location indicates that the first harmonic accounts for a major portion of the total variance in the records. Water temperature residuals are well correlated with air temperature residuals. Parametric values of a mathematical model for predicting water temperatures from air temperature records are stable from year to year. The water-air temperature relationship appears to be a stationary linear process. Consequently, it is possible to predict water temperatures at a specific location from the air temperature records, provided both water and air temperature records are available for another similarly situated water body. Air and Illinois River water temperature data at Peoria and Havana, Illinois, were used for the study.

7.Corbitt Kerr, Joe Kasprzyk, David Vargas, and Keith Sawicz, “Integrated Modeling Using PIHM GIS for the Shavers Creek Watershed,” CE 555- Final Report, Penn State University, 12/19/07.

Abstract: The purpose of this project is to demonstrate the capabilities and sensitivity of the Pennsylvania State University’s PIHMgis hydrologic modeling system. PIHMgis was created as a means to model the full hydrologic cycle through a GIS interface. The integration of surface and groundwater hydrology into a single platform allows for a greater understanding of the processes that occur within geo-hydrologic systems. To assess the sensitivity and capability of PIHMgis, the Shavers Creek watershed was selected as an appropriate study site.

Tennessee Valley Authority River Modeling System which consists of the ADYN flow model and the RQUAL water quality model developed by the Tennessee Valley Authority (TVA). RMS is a one-dimensional(longitudinally), physically based numerical model composed of modules for hydrodynamics (ADYN) and water quality (RQUAL) (Hauser and Schohl, 2002). This application used a 1 h time step, and a variable spatial scale with node spacing ranging from 10–660 m to accommodate the sinuosity of the stream. ADYN (hydrodynamics module). ADYN simulates hydrodynamic flows in main stem and tributary channels, channel junctions and distributed or point lateral inflows at tributaries (Hauser and Schohl, 2002). The Shasta River was modeled as one continuous reach with tributaries as point inflows, large diversions as point diversions and smaller accretions and depletions as distributed flows. ADYN solves one-dimensional forms of conservation of mass and momentum equations (St. Venant equations for unsteady flow) for depth and velocity using a four-point implicit finite difference scheme with weighted spatial derivatives. Governing equations and associated details for ADYN are found in Hauser and Schohl (2002).

WATER RESOURCES RESEARCH, VOL. 47, W10508,doi:10.1029/2010WR009767, 2011

More to Come

  1. Youghiogheny River Water Temperature Enhancement Plan, Deep Creek Station, Youghiogheny River, May 1995

  2. “Estimating Water Temperatures in Small Streams in Western Oregon Using Neural Network Models,” John C. Risley, U.S. Geological Survey, Edwin A. Roehl, Jr., Advanced Data Mining, LLC, and Paul A. Conrads, U.S. Geological Survey, U.S. GEOLOGICAL SURVEY, Water-Resources Investigations Report 02-4218, Portland, Oregon 2003.

  3. “Short term streamflow forecasting using artificial neural networks,” Cameron M. Zealand, Donald H. Burn, , Slobodan P. Simonovic, Department of Civil and Geological Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6, Journal of Hydrology, Volume 214, Issues 1–4, January 1999, Pages 32–48.

  4. Chenard, J.-F. and Caissie, D. (2008), Stream temperature modeling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada. Hydrol. Process., 22: 3361–3372. FEB 2008.

  5. Pennsylvania Electric Company, SUPPORT DOCUMENT FOR PERMIT APPLICATION TO APPROPRIATE AND USE WATERS OF THE STATE DEEP CREEK STATION, PENNSYLVANIA ELECTRIC COMPANY, Revised Section 4.0, April 1994.

  6. Stephen P. Schreiner and Geoffrey D. Birky, (Versar Inc.) “A TEMPERATURE SIMULATION MODEL OF THE YOUGHIOGHENY RIVER FROM DEEP CREEK STATION TO SANG RUN”, PPRP-DC-1, June 1997

  7. Stephen P. Schreiner Versar, Inc., “YOUGHIOGHENY RIVER TEMPERATURE ENHANCEMENT PROTOCOL: MODEL DEVELOPMENT AND RESULTS FOR 1995 AND 1996 , PPRP-DC-2 , 1998

  8. Stephen P. Schreiner, “YOUGHIOGHENY RIVER TEMPERATURE ENHANCEMENT PROTOCOL FOR OPERATING DEEP CREEK HYDROELECTRIC STATION: MODEL DEVELOPMENT AND RESULTS FOR 1995-2000, PPRP-DC-4, February 2001

  9. Stephen P. Schreiner Jodi R. Dew, Craig M. Bruce, Versar, Inc., “YOUGHIOGHENY RIVER TEMPERATURE ENHANCEMENT PROTOCOL FOR OPERATING DEEP CREEK HYDROELECTRIC STATION: MODEL DEVELOPMENT AND RESULTS FOR 1995-2005, PPRP-DC-5, 15 August 2006

  10. Stephen P. Schreiner, Jodi Dew-Baxter, Versar Inc, and Alan W. Klotz, DNR, “TEMPERATURE AND TROUT HABITAT ENHANCEMENT FOR OPERATING DEEP CREEK HYDROELECTRIC STATION: OPERATING PROTOCOL DEVELOPMENT AND RESULTS FOR 1995-2008”, PPRP-DC-6, March 2011.

  11. M. Arganis1, R.Val, J. Prats, K. Rodríguez, R. Domínguez1and J. Dolz, “WATER TEMPERATURE MODELING BY MEANS OF GENETIC PROGRAMMING,” 11th International Conference of Urban Drainage, Edinburgh, Scotland, UK., 2008

  12. Ami Preis 1, Avi Ostfeld, “A coupled model tree–genetic algorithm scheme for flow and water quality predictions in watersheds,” Journal of Hydrology (2008) 349, 364–375.

  13. Avi Ostfeld and Shani Salomons, “A hybrid genetic—instance based learning algorithm for CE-QUAL-W2 calibration,” Journal of Hydrology 310 (2005) 122–142.

  14. Misgana K. Muleta and John W. Nicklow, Using Genetic Algorithms and SWAT to Minimize Sediment Yield From an Agriculturally Dominated Watershed

  15. A. Doglioni, O. Giustolisi, D. A. Savic, B. W. Webb, “An investigation on stream temperature analysis based on evolutionary computing,” Hydrological Processes, Volume 22, Issue 3, pp 315–326, January 2008

  16. D. CAISSIE, “The thermal regime of rivers: a review,”Freshwater Biology (2006) 51, 1389–1406, The thermal regime of rivers: a review - CAISSIE - 2006 - Freshwater Biology - Wiley Online Library j.1365-2427.2006.01597.x.pdf

  17. Loubna Benyahya, Daniel Caissie, André St-Hilaire, Taha B.M.J. Ouarda and Bernard Bobée, “A Review of Statistical Water Temperature Models ,” Canadian Water Resources Journal, Vol. 32(3): 179-192 (2007)

  18. Stephen P. Schreiner, Versar, Inc., “SURVEY OF NONCOMMERCIAL RECREATIONAL USE OF WHITEWATER IN THE UPPER YOUGHIOGHENY RIVER, 1996-1997,” PPRP-DC-3, June 1998.

  19. GEORGE W. BROWN , “PredictingTemperatures SmallStreams,” Forest ResearchLaboratory, School of Forestry OregonState University, Corvallis 97331, WATER RESOURCES RESEARCH, VOLL. 5, NO. 1 , FEBRUARY 1969

  20. Ibrahim Reda and Afshin Andreas, Solar Position Algorithm for Solar Radiation Applications, NREL/TP-560-34302, January 2008

  21. Pennsylvania Electric Company, Deep Creek Station Support Document for Application to Appropriate and Use Waters of the State, August 1993

  22. John M. Bartholow, The Stream Segment and Stream Network Temperature Models: A Self-Study Course, Version 2.0”, Open-File Report 99-112, U.S. DEPARTMENT OF THE INTERIOR U.S., GEOLOGICAL SURVEY, March 2000.

  23. Werner Meier; Cyrill Bonjour; Alfred Wuest; and Peter Reichert, “Modeling the Effect of Water Diversion on the Temperature of Mountain Streams”, Swiss Federal Institute of Technology Zurich, DOI 10.1061(ASCE)0733-9372(2003)129:8(755)

  24. Ali Erturk, “A Simple Stream Water Quality Modelling Software for Educational and Training Purposes”, Turkish Journal of Fisheries and Aquatic Sciences 10: 61-70 (2010)

  25. Michael L. Deas and Cindy L. Lowney, “Water Temperature Modeling Review”, Central Valley, California Water Modeling Forum

  26. Cindie Hébert, “MODELING OF HOURLY STREAM TEMPERATURES WITHIN TWO FORESTED CATCHMENTS”, PhD Thesis, Dalhousie University Halifax, Nova Scotia, February 2013.

  27. Matthew Boyd & Brian Kasper, “Analytical Methods for Dynamic Open Channel Heat and Mass Transfer”, Methodology for the Heat Source Model Version 7.0, February 2007

  28. John M. Bartholow, “STREAM TEMPERATURE INVESTIGATIONS: FIELD AND ANALYTIC METHODS”, U.S. Fish and Wildlife Service, Fort Collins, CO, Biological Report 89(17), June 1989

  29. Werner Meier; Cyrill Bonjour; Alfred Wuest; and Peter Reichert, Modeling the Effect of Water Diversion on the Temperature of Mountain Streams”

  30. Michael L. Deas and Cindy L. Lowney, “Water Temperature Modeling Review,” Central Valley, September 2000.

  31. EPA, “Application of a 1-D Heat Budget Model to the Columbia River System,” Publication Number May 2001.

  32. W. Czernuszenko, “THERMODYNAMICS OF RIVERS,” FRESH SURFACE WATER – Vol. II

  33. M. C. Westhoff, H. H. G. Savenije, W. M. J . Luxemburg, G. S. Stelling, N. C. van de Giesen1, J. S. Selker, L. Pfister, and S. Uhlenbrook,, “A distributed stream temperature model using high resolution temperature observations,” Hydrol. Earth Syst. Sci., 11, 1469–1480, 2007.

  34. M. C. Westhoff, H. H. G. Savenije, W. M. J . Luxemburg, G. S. Stelling, N. C. van de Giesen1, J. S. Selker, L. Pfister, and S. Uhlenbrook, Corrigendum to “A distributed stream temperature model using high resolution temperature observations” published in Hydrol. Earth Syst. Sci., 11, 1469–1480, 2007



PLV
First Published: 9/23/2017
NOTE: These references have been collected over a period of approximately 5 years.