Effects of Climate and Dam Operations on Reservoir Thermal Structure

Home , Blue Mesa Dam, Blue Mesa Reservoir

Brett M. Johnson1; Laurel Saito, M.ASCE2; Mark A. Anderson, A.M.ASCE3; Paul Weiss4; Mary Andre5; and Darrell G. Fontane, M.ASCE6

Abstract: Recently, the United States Bureau of Reclamation agreed to increase spring releases from Upper Colorado River Basin reservoirs to create a more natural flow regime in the lower Gunnison River and Upper Colorado River. Fishery managers have expressed concern that new operations could change reservoir conditions and jeopardize popular and economically important reservoir sport fisheries. This study attempts to predict how one aspect of reservoir conditions, thermal structure, might respond to new dam operations at Blue Mesa Reservoir aimed at addressing downstream ecological concerns. A one-dimensional thermal model ͑CE-THERM͒ is applied to simulating thermal effects of ‘‘traditional’’ and proposed ‘‘new’’ dam operation scenarios. To evaluate the relative importance of climate and dam operations a sensitivity analysis of hydrologic ͑i.e., inflows and starting reservoir elevation͒ and meteorologic ͑i.e., air temperature, cloud cover, and dew point temperature͒ inputs was conducted along with an ‘‘extreme’’ dam operation scenario. Results indicate that reservoir managers at Blue Mesa Reservoir have considerable latitude for new operations without negative thermal consequences. The natural variability of climate and hydrology appear to exert stronger control over reservoir thermal structure than reservoir operations at Blue Mesa. DOI: 10.1061/͑ASCE͒0733-9496͑2004͒130:2͑112͒ CE Database subject headings: Dams; Reservoirs; Simulation models; Climatic changes; Colorado River; Thermal factors.

Introduction Recently, river ecologists have advocated the use of new dam operations to create a more natural flow regime for restoration of In addition to their intended purposes, dams have a variety of regulated rivers ͑National Research Council ͑NRC͒ 1991; Stan- physical, chemical, and biological effects on the rivers they im- ford et al. 1996; Poff et al. 1997͒. One important element of a pound ͑Baxter 1977; Ward and Stanford 1979; Collier et al. natural flow regime that is most often constrained by dams is the 1996͒. Increasing public concern over adverse, downstream effects of dams has prompted debate about the efficacy of dam amplitude of annual peak flows. In free-flowing rivers, these peak removal for regulated river restoration. However, many dams now flows are part of the natural channel maintenance process, and provide indispensable services to society such as water supply, they maintain connections between the channel and its floodplain hydropower, and flood control. Efforts to mitigate the ecological habitats. Natural variation in flow also plays an important role in ͑ ͒ effects of many dams will focus, at least in the short term, on the life cycle processes of stream biota Allan 1995 . changing dam operations to minimize riverine impacts. To achieve a more natural flow regime downstream, dam operators must depart from their historic operating regimes. In the 1 Rocky Mountain West, where natural hydrographs are character- Associate Professor, Dept. of Fishery and Wildlife Biology, ͑ ͒ Colorado State Univ., Fort Collins, CO 80523-1474. E-mail: ized by a spring snowmelt peak Van Steeter and Pitlick 1998 , [email protected] dams have traditionally been used to capture high spring inflows 2Assistant Professor, Dept. of Natural Resources and Environmental for consumptive uses during drier summer months. Operations to Science/186, Univ. of Nevada-Reno, 1000 Valley Rd., Reno, NV 89512. achieve this goal are constrained by other needs, such as the need E-mail: [email protected] to maintain adequate storage capacity for flood control, to main- 3 Associate Engineer, CH2M Hill, 825 NE Multnomah, Suite 1300, tain minimum downstream flows, to reach elevation targets for Portland, OR 97232-2146. E-mail: [email protected] recreational and aesthetic purposes, and to generate hydropower 4Engineer, Riverside Technology, 2209 E. Prospect Rd., Suite l, Fort during periods of increased demand. Many large western dams Collins, CO 80525. E-mail: [email protected] 5Project Engineer, Civil Design Consultants, Inc., P.O. Box 775167, were constructed with hypolimnetic outlets to facilitate hydro- 405 S. Lincoln Ave., Steamboat Springs, CO 80477-5167. E-mail: power generation and maximize water available for release. [email protected] New dam operations on the Colorado River system have re- 6Professor, Dept. of Civil Engineering, Colorado State Univ., Fort cently focused on two areas: the construction of selective with- Collins, CO 80523-1372. E-mail: [email protected] drawal devices for temperature control ͑e.g., at Flaming Gorge Note. Discussion open until August 1, 2004. Separate discussions Dam, Utah; and Glen Canyon Dam, Arizona͒ and altered release must be submitted for individual papers. To extend the closing date by schedules to manage the downstream hydrograph ͑e.g., Blue one month, a written request must be filed with the ASCE Managing Mesa Dam, Colorado, as well as at Flaming Gorge and Glen Editor. The manuscript for this paper was submitted for review and pos- ͒ sible publication on March 8, 2002; approved on April 2, 2003. This Canyon Dams . Historic operations of dams on the Colorado paper is part of the Journal of Water Resources Planning and Manage- River system appear to have had adverse effects on rare and en- ment, Vol. 130, No. 2, March 1, 2004. ©ASCE, ISSN 0733-9496/2004/2- dangered fishes by creating hydrologic conditions unfavorable for 112–122/$18.00. spawning or recruitment ͑Tyus 1991; Stanford 1994; Osmundson

112 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 and Burnham 1998͒. In recent consultations between the United States Fish and Wildlife Service ͑USFWS͒ and the United States Bureau of Reclamation ͑USBR͒ under Section 7 of the Endan- gered Species Act, the USFWS Upper Colorado River Endan- gered Fish Recovery Program requested new dam operations that would create a more natural flow regime in the lower Gunnison River and Upper Colorado River ͑McAda 2003͒. To achieve this, USBR agreed to implement increased spring releases from Upper Colorado River Basin reservoirs. However, little was known about potential in-reservoir impacts of increased spring releases from western reservoirs, and reservoir fishery managers expressed concern that new operations could jeopardize popular and economically important reservoir sport fisheries. Changing the schedule of releases could alter seasonal reservoir elevations and reservoir thermal structure ͑Thornton et al. 1990͒. Changes to thermal structure can have far-reaching direct ͑on physiological rates of organisms͒ and indirect ͑on water column stability and nutrient cycling, and prey production͒ consequences for system productivity ͑Wetzel 2001͒. Studies on Lake Granby, a 3,000 ha montane reservoir near the headwaters of the Colorado River, show a strong correlation between reservoir content ͑volume of water in the reservoir͒ in July and the thermal structure of the reservoir ͑Martinez and Wiltzius 1995͒. Changes in thermal structure have several important biological consequences affecting the distribution of biota in the reservoir, zooplankton production, and sport fish growth rates. However, that study failed to reveal the extent to which reservoir operations versus weather and hydrologic variables contribute to the observed patterns in thermal structure and biological indicators. This study attempts to predict how reservoir thermal structure might respond to new dam operations at Blue Mesa Reservoir that were aimed at addressing downstream concerns. The range of potential dam operations is constrained by a variety of socioeco- nomic factors that may be in conflict with river restoration goals. Fig. 1. ͑A͒ Location and configuration of Blue Mesa Reservoir. Dam operation regimes that are likely to occur are simulated and Sampling stations ͑1–3͒ are also indicated. ͑B͒ Schematic of Blue compared to predictions from an extreme scenario. Since reser- Mesa Dam and reservoir in cross section ͑USBR 1975͒. voir heat budgets are sensitive to climatic and hydrologic effects, the relative sensitivity of reservoir thermal structure to variations in dam operations and climate/hydrologic factors is also evalu- ated. Ultimately, the interest is in evaluating the degree to which the annual inflow occurs in spring in a pattern characteristic of a ͑ ͒ human-induced i.e., dam operations factors alter reservoir ther- snowmelt hydrograph. The regional climate is semi-arid with mal structure, and thus the likelihood that changing dam opera- about 260 mm of precipitation annually. tions might indirectly alter biological conditions in the reservoir.

Description of Thermal Model Study Area: Blue Mesa Reservoir CE-THERM is a one-dimensional model of reservoir thermal Blue Mesa Reservoir is a 25 km long, 3,700 ha impoundment on structure. The model is an independent program within CE- the Gunnison River in southwest Colorado ͓Fig. 1͑A͔͒. The Gun- QUAL-R1, a reservoir water quality model developed by the U.S. nison River flows into the Colorado River at Grand Junction, Army Corps of Engineers ͑USACE͒ at the Engineering Research Colorado. As part of the USBR’s Wayne N. Aspinall Unit of the and Development Center and originally released in the 1970s. The Colorado River Storage Project, Blue Mesa Reservoir serves model simulates reservoir thermal dynamics over a series of well- hydropower, flood control, irrigation, and recreation uses. mixed horizontal layers. As such, longitudinal or lateral variations The reservoir has a maximum storage of about in thermal conditions are not predicted, and all inflow quantities 9 3 1.16ϫ10 m (940,700 acre ft) at 2,292 m elevation, and a maxi- and constituents are instantaneously distributed throughout the mum depth of 101 m. The intake structure is located 50 m below horizontal layers. Mass balance calculations are made for each the surface at full pool ͓Fig. 1͑B͔͒. Maximum discharge from the layer, and layer sizes are adjusted once each computational inter- outlet works is 142 cm s ͑5,000 cfs͒ at maximum water surface val to minimize problems of resolution or numerical errors. Heat elevation ͑USBR 1975͒; hydraulic residence time is about 0.6 fluxes with the exception of solar radiation are combined for the year. Limnologically, Blue Mesa Reservoir stratifies every sum- surface layer by putting the following equation into solution form mer, beginning in early May. It is considered mesotrophic, and in the subroutine RADIATE: mean July secchi depths ͑a measure of water transparency͒ have ϭ Ϫ Ϫ Ϫ averaged about 4.1 m since 1983 ͑Johnson et al. 1997͒. Most of Q* QNA Qb Qe Qc (1)

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 / 113 Table 1. Inputs Used in Thermal Simulations of Blue Mesa Reservoir Using CE-THERM. Page Numbers and Equation Numbers in ‘‘Source’’ Refer to CE-THERM User Manual ͑USACE 1995͒. Variable Calibration Validation Final Variable description name run run value Source Day of initial conditions ͑day of year͒ IFIRST 144 154 121 By design Last day of simulation ͑day of year͒ ILAST 304 273 304 By design Computation interval ͑hours͒ NHOI 24 24 24 By design Output interval ͑hours͒ IPRT Variable Variable Variable By design First day of simulation ͑day of year͒ ISTART 144 154 121 By design Year of simulation IYEAR 1994 1996 N/A N/A Mode of outflow MODE NORMAL NORMAL NORMAL Assume continuous flow through port Structural type STRUCT PORT PORT PORT Assume all withdrawals are made through port Flow amounts through ports CHOICE SPECIFY SPECIFY SPECIFY By design Temperature profiles for calibration? CALBRAT YES YES YES Profiles were used for calibration Number of tributaries NTRIBS 2 2 2 Cudlip et al. ͑1987͒ Initial number of layers NUME 60 60 Variable By design Latitude ͑deg͒ XLAT 38.50 38.50 38.50 Longitude ͑deg͒ XLON 107.3 107.3 107.3 Dust attenuation coefficient* TURB 0.80 0.80 0.80 p pp. 220 and 364, accounts for topographic shading and cloud cover Empirical wind coefficient* AA 0.00Eϩ00 0.00Eϩ00 0.00Eϩ00 p. 221, values taken from Lake Hefner study Empirical wind coefficient* BB 0.10EϪ08 0.10EϪ08 0.10EϪ08 p. 221, values taken from Lake Hefner study Elevation above sea level for pool bottom ͑m͒ ELEMSL 2,194.56 2,194.56 2,194.56 Reservoir length ͑m͒ RLEN 32,190 32,190 32,190 USBR ͑1975͒ Minimum layer thickness ͑m͒ SPZMIN 0.5 0.5 0.5 p. 222, Ͼ0.4 m Maximum layer thickness ͑m͒ SPZMAX 3.0 3.0 3.0 p. 222, Ͼ2ϫSPZMIN Number of outlets NOUTS 1 1 1 USBR ͑1975͒ Elevation of outlet center from bottom ͑m͒ ELOUTS 50.9 50.9 50.9 USBR ͑1975͒ Vertical dimension of port ͑m͒ PVDIM 4.88 4.88 4.88 USBR ͑1975͒ Horizontal dimension of port ͑m͒ PHDIM 5.49 5.49 5.49 USBR ͑1975͒ Area coefficient ACOEF͑1͒ 30,650.510 30,650.510 30,650.510 Fit to morphometric data Eq. ͑8͒,p.40 Area coefficient ACOEF͑2͒ 2,009.614 2,009.614 2,009.614 Fit to morphometric data Eq. ͑8͒,p.40 Area coefficient ACOEF͑3͒ 3,832.564 3,832.564 3,832.564 Fit to morphometric data Eq. ͑8͒,p.40 Width coefficient WCOEF͑1͒ 5.387 5.387 5.387 Eq. ͑190͒, p. 231 and bathymetric maps Width coefficient WCOEF͑2͒ 0.962 0.962 0.962 Eq. ͑190͒, p. 231 and bathymetric maps Sheltering coefficient* SHELFCF 0.95 0.95 0.95 p. 232, calibrated to observed profiles Penetrative convection fraction* PEFRAC 0.50 0.50 0.50 p. 233, within recommended range Eddy diffusion parameter* CDIFW 0.10EϪ4 0.10EϪ4 0.10EϪ4 p. 233, calibrated to observed profiles Eddy diffusion parameter* CDIFF 0.20EϪ05 0.20EϪ05 0.20EϪ05 p. 233, calibrated to observed profiles Critical density ͑kg/m3͒ CDENS 0.05 0.05 0.05 p. 234, within recommended range Extinction coefficient ͑1/m͒* EXCO 0.38 0.38 0.38 p. 237, Eq. ͑191͒ using Secchi depth data Surface radiation fraction* SURFRAC 0.34 0.34 0.34 p. 236, Fig. 80 using Secchi depth data

114 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 Table 1. ͑Continued) Variable Calibration Validation Final Variable description name run run value Source Self-shading coefﬁcient ͑1/m–mg/L͒* EXTINS 0.25 0.25 0.25 TSS includes organic matter, therefore EXTINS includes all particulates, p. 238 Suspended solid settling velocity ͑m/day͒* TSSETL 1 1 1 p. 309, within recommended range Note: Asterisk ͑*͒ indicates calibration parameters.

where all terms are in units of kcal mϪ2 sϪ1 and Q*ϭcombined warmer water above the penstocks was unlikely, and confirming heat flux excluding solar radiation; QNAϭatmospheric longwave the applicability of a one-dimensional model at Blue Mesa Res- ϭ ϭ radiation; Qb water backwave radiation; Qe evaporative heat ervoir. ϭ loss; and Qc conductive heat transfer. Shortwave solar radiation Meteorologic data were recorded at a National Weather Ser- calculated in subroutine RADIATE is absorbed exponentially with vice station at the Gunnison Airport, about 25 km upstream of the depth in subroutine HEAT. The distribution of thermal energy is reservoir. Based on a comparison of daily wind measurements at then calculated using the conservation of thermal energy for each the airport and at the reservoir in 1996 ͑the only year when wind layer according to a differential mass balance equation summa- data existed at the reservoir͒, airport-measured wind speeds were rized in Eq. ͑2͒͑all terms are in units of kcal hϪ1͒: increased by a factor of 1.7 to account for local topographic conditions that resulted in higher wind speeds near the reservoir ͑the Rate of Internal absorption ͫ ͬϭ͓ ͔ϩͫ ͬϩ͓ ͔ reservoir is located within a relatively narrow wind channel cut change Diffusion of solar radiation Advection into the surrounding high plateau by the Gunnison River͒. Cloud (2) cover data were converted from categorical to numerical propor- where the internal absorption of solar radiation is from subroutine tions using the following convention: ‘‘overcast’’ϭ0.95, HEAT. For the surface layer, a term for Q* is added to the right- ‘‘broken’’ϭ0.75, ‘‘scattered’’ϭ0.3, and ‘‘clear’’ϭ0.05 ͑Ahrens hand side of Eq. ͑2͒. Internal fluxes among layers include entrain- 1994͒. Hourly observations were averaged over the entire day to ment, convection and diffusion processes. The program and user’s provide a daily input for the model. As is typical of many auto- manual were updated in 1995 ͑USACE 1995͒. mated weather stations, cloud cover data at Gunnison were mea- To implement the model, several kinds of input data are re- sured with a ceilometer that only detected clouds up to an altitude quired, including reservoir bathymetry, latitude and longitude, of 3,658 m. However, clouds above this level would still affect and configuration of the outlet works. An air turbidity constant insolation to the reservoir. Therefore, recorded cloud cover was ͑TURB͒ is specified to simulate light attenuation due to dust in adjusted by a factor of 1.25 ͑to a maximum cloud cover value of the atmosphere. Initial reservoir conditions ͓starting elevation, 1.0͒ to account for clouds above the detection limit of the ceilo- water transparency, temperature profile, and total dissolved solids meter ͑Nolan Doesken, Assistant Colorado State Climatologist, ͑TDS͔͒ must be specified. Daily values for the quantity, tempera- personal communication͒. ture, and TDS of inflows, as well as meteorologic data ͑cloud Tributary inflows were obtained from USBR databases. Be- cover, air temperature, dew point, barometric pressure, and wind cause the model allowed inputs for only two tributary inflows speed͒ are necessary to perform simulations on a daily time step. ͑TRIB1 and TRIB2 parameters͒ 57% of the total inflow was ap- Finally, daily outflows and discharge depths are specified. With portioned into TRIB1 ͑the Gunnison River; Cudlip et al. 1987͒ this information, the model calculates the temperature profile and and 43% to TRIB2 ͑sum of remaining tributary inflows͒. Inflow water budget for each time step. temperatures (T j) were generated using an air-stream temperature ͑ ͒ regression Bartholow 1989 developed from daily air (Taj) and stream temperature measurements made in 1997: Model Calibration and Validation 2␲ j 2␲ j T ϭA ϩA T ϩA ln͑Q ͒ϩA sinͩ ͪ ϩA cosͩ ͪ j 0 1 aj 2• j 3• 365 4• 365 Data were gathered during an intensive limnological study of the (3) reservoir in 1994 ͑Johnson et al. 1995͒ and from government da- ϭ ϭ ϭϪ ϭ tabases for model calibration. Secchi depths and temperature pro- where A0 9.26879; A1 0.29580; A2 0.72847; A3 Ϫ ϭϪ ϭ ϭ files were measured biweekly during May 24–September 16, 3.58261; and A4 2.51847; j day of year; and Q j daily 1994, with the former used to compute the light extinction param- stream discharge. Coefficient of determination of the regression eters ͑EXCO and SURFRAC; Table 1͒. Conductivity measure- was high, r2ϭ0.98. ments from the reservoir and the inflows in mS mϪ1 ͑Cudlip et al. Calibration and validation simulations were performed on a 1987͒ were used to estimate TDS in mg LϪ1 according to the daily time step from May 24, 1994 and May 21, 1996 through formula TDSϭ0.6*Conductivity ͑USACE 1995͒. Temperature October 31, 1994 and September 30, 1996, respectively ͑Table 1͒. measurements at three stations along the longitudinal axis of the Initial thermal conditions were temperature profiles measured on reservoir ͓Fig. 1͑A͔͒ showed only minor differences ͑Ͻ2°C͒ in the first model day of each year. To calibrate the model, several temperature in the epilimnion ͑the surface layer of a stratified light and mixing parameters ͑Table 1͒ were adjusted to maximize water body͒͑Johnson et al. 1995͒. Further, temperature profiles the fit between observed and predicted temperature profiles. The measured in front of the dam and at a mid-lake station during light parameters controlled how much light energy reached the unusually high releases in 1995 were nearly indistinguishable surface of the reservoir, and how that radiation was distributed ͑Johnson et al. 1996͒, suggesting that local entrainment of through the water column. For some of these parameters empiri-

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 / 115 would likely be observed at Blue Mesa. Due to the inability to adjust any parameters in the long-wave radiation equation to calibrate the model, adjustments were made in short-wave radiation reaching the reservoir surface. To compensate for higher than expected long-wave radiation calculated by the model, the incoming short-wave radiation was reduced by using a calibrated TURB value of 0.8, instead of a value within the 0–0.13 range that was recommended by USACE ͑1995͒. To validate the model, hydrologic and meteorologic data measured in May–September 1996 were provided as inputs, along with parameters derived from the 1994 calibration, to predict 1996 temperature profiles. Again, the fit between observed and predicted temperature profiles was well within measurement error ͓Fig. 2͑B͒; average RMSEϭ0.58°C͔ suggesting that the model represents the system well, despite the compensatory calibration using the TURB coefficient.

Fig. 2. ͑A͒ Observed ͑solid lines͒ and predicted ͑dotted lines͒ temperature profiles for Blue Mesa Reservoir in 1994 after calibration of CE-THERM reservoir thermal model. ͑B͒ Observed ͑solid lines͒ and Climate and Operational Scenarios predicted ͑dotted lines͒ temperature profiles generated using all parameters fit during calibration and 1996 hydrologic and meteorologic Once the model was calibrated so that it accurately described inputs for validation of thermal model. observed conditions at Blue Mesa Reservoir, operational scenarios were developed to represent ‘‘traditional,’’ ‘‘new,’’ and ‘‘extreme’’ dam operations to evaluate the effects of dam opera- cal values can be found ͑e.g., using relationships to measurements tions on thermal conditions. To evaluate the relative importance such as secchi depth͒, but others ͑e.g., TURB, dust attenuation of climate and dam operations on reservoir thermal structure, a coefficient͒ serve as ‘‘dials’’ to adjust the model to fit measured sensitivity analysis of hydrologic ͑inflows and starting reservoir calibration data. Furthermore, the internal mixing parameters elevation͒ and meteorologic ͑air temperature, cloud cover, and ͑e.g., eddy diffusion constants͒ are difficult to measure in the dew point temperature͒ inputs was conducted. Predictions from field. Mixing parameters and other dials were modified to adjust the scenario that incorporated traditional ͑i.e., historical͒ opera- the shape and slope of the generated temperature profiles to match tions and long-term average hydrologic ͑Fig. 3͒ and meteorologic existing data. Verification of the accuracy of model assumptions conditions were used as the baseline output. related to reservoir morphometry and predictions of reservoir Realistic dam operations regimes were developed by review- water mass balance ͑inflow, outflow, storage, and evaporative ing historic operations data and consulting with USBR hydrolo- losses͒ is based on measured or modeled data from the USBR. gists and USFWS biologists about expected new operations ͑final The root mean square error ͑RMSE͒ provides a measure for flow recommendations are forthcoming, Charles McAda, comparing the fit between observed ͑or baseline͒ and predicted USFWS, personal communication͒. The traditional operations re- temperature profiles on a particular date: gime ͓Fig. 3͑B͔͒ emphasizes stable releases for hydropower gen- ͑ ͒ ͚n ͑ Ϫ ͒2 eration 57 cm s maximum , while attempting to fill the reservoir iϭ1 Ti ti ͑ ͒ RMSEϭͱ (4) by about July 15 Coll Stanton, USBR, personal communication . n Given a May 1 starting elevation and historical inflows, the vol- ϭ ume above that necessary to reach the fill target was computed where Ti predicted water temperature in model layer i; ϭ ͑ ͒ and released evenly over the simulation period. Regardless of ti observed or baseline water temperature in layer i; and nϭnumber of layers in each temperature profile. inflows, releases of at least 8.5 cm s were maintained for instream An adequate fit between observed and predicted profiles was flow requirements. After July 15, releases increased if necessary observed ͓Fig. 2͑A͔͒ with an average RMSE of 0.71°C during the to meet downstream user allocations ͑totaling 37 cm s͒. Tradition- simulated period. After calibration, all but one parameter ͑TURB͒ ally, operators have tried to draw the reservoir down to 2,283 m were found to lie within the suggested ranges for the model by December 31 to allow them to capture the following spring ͑USACE 1995͒. runoff and to prevent ice damage along the river above the reser- The TURB parameter was adjusted to compensate for an ap- voir. parent shortcoming in the method for computing long-wave ra- Under new operations, it is assumed that inflows greater than diation as implemented in the model. Long-wave radiation is cal- the quantity needed to achieve the in-reservoir goals explained culated using an empirical relationship developed by the above can be used to generate an 8-day ‘‘spring peak hy- Tennessee Valley Authority ͑TVA 1972͒ that assumes total energy drograph’’ downstream. The peak is generated using a 4-day reaching the ground by long-wave radiation is dependent on air ramping up period with outflows increasing at р14 cm s/day, fol- temperature and relative humidity ͑the TVA relationship uses lowed by a 4-day period of outflows decreasing at р11 cm s/day. cloud cover as a surrogate for relative humidity͒. Data for this Thus, average releases before July 15 need to be lower than under empirical relationship were collected in the southeast United traditional operations so that the spring peak can be generated States, where relative humidity is much higher for a given cloud ͓Fig. 3͑B͔͒. After July 15, releases are the same under traditional cover than would be likely in high desert regions such as the area and new operations regimes. surrounding Blue Mesa Reservoir. Thus, incoming long-wave ra- Also simulated is an ‘‘extreme’’ operational scenario that diation calculated by the model was considerably higher than simulates the farthest departure from traditional operations that

116 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 Fig. 3. ͑A͒ Average daily inflow and low inflow ͑mean Ϫ1 standard deviation͒ to Blue Mesa Reservoir. ͑B͒ Three outflow operational scenarios under average inflow conditions: traditional operations that simulated historic operational policies, new operations that created spring peak hydrograph downstream after operational constraints were satisfied, and extreme operations scenarios where outflows matched inflows. could occur without removing the dam; i.e., operating the reser- To conduct the sensitivity analysis, average daily values were voir as if it were run-of-the river ͓Fig. 3͑B͔͒. This scenario uses computed for all input variables over the period of record, along average climatic conditions, including the average starting eleva- with the associated standard deviation ͑SD͒. Inputs were then tion with the associated initial temperature profile, and average perturbed one variable at a time by Ϯ1 SD and the model run inflows. Outflows are adjusted so that the starting elevation is from May 1 through October 31. The goal was not to evaluate the maintained throughout the simulation ͓Fig. 4͑A͔͒. importance of uncertainty in hydrometeorologic inputs per se, but Simulations were of a six month duration from May 1 through rather to compare the relative influence of variation in each of October 31 since this is the period in which operational policies these inputs on predicted thermal structure. are expected to change, and since this is the period that the res- Some model inputs are dependent on perturbed inputs ͑e.g., ervoir exhibits thermal stratification. Boundary conditions are stream temperatures are computed from air temperatures and in- therefore required for May 1. This requires adjusting the initial flow volumes, starting elevations are based on inflow quantities, water temperature profile in the model based on measured water and target fill elevations are based on starting elevations͒. Thus, temperatures on May 24, 1994 and May 21, 1996 to May 1. This dependent inputs were adjusted as necessary in each simulation is accomplished by adjusting an initial profile on May 1 until the using the same relationships used to compute the dependent in- modeled profile on May 21 matches the mean of the measured puts in the baseline run ͑Table 2͒. Perturbations to air tempera- profile on May 21 and May 24 for the 2 years. ture, cloud cover and dew point temperature were simulated in-

Fig. 4. Predicted reservoir volume (106 m3) under ͑A͒ three operational regimes and ͑B͒ two hydrologic and two starting elevation scenarios

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 / 117 Table 2. Average of Root Mean Squared Errors ͑RMSEs͒ of Differences from Baseline in Predicted Water Temperatures Computed on Model Days 149, 177, 205, 233, 261, and 289 for Three Limnetic Strata Defined as Epilimnion ͑top 10 m͒, Metalimnion ͑10–40 m͒, Hypolimnion ͑у40 m͒, and Whole Profile. Baseline Profile was Scenario with All Input Variables Set to Historical Average Values, and Traditional Outflows. Average RMSE ͑°C͒ Epilimnion Metalimnion Hypolimnion Variable ͑0–10 m͒ ͑10–40 m͒ ͑у40 m͒ Whole profile Inflows Ϫ1SDa 0.30 2.58 0.60 1.60 Inflows ϩ1SDa 0.41 0.96 1.60 1.36 Air temperature Ϫ1SDb 1.19 0.76 0.13 0.62 Air temperature ϩ1SDb 1.10 0.88 0.15 0.66 Start elevation Ϫ1SDc 0.41 0.66 0.54 0.62 Start elevation ϩ1SDc 0.30 0.58 0.67 0.64 Cloud cover Ϫ1 SD 0.32 0.34 0.09 0.24 Cloud cover ϩ1 SD 0.62 0.42 0.19 0.37 Dew point Ϫ1 SD 0.64 0.39 0.10 0.33 Dew point ϩ1 SD 0.72 0.37 0.08 0.34 New operations 0.28 0.31 0.09 0.23 Extreme operations 0.31 0.64 0.50 0.56 Note: SD is standard deviation of long-term mean. aAdjusting inflow also changed stream temperatures, starting and target elevations. bAdjusting air temperature also changed stream temperatures. cAdjusting starting elevation also changed target fill elevations. dependently from each other. There was a positive relationship erations and these constraints took precedence over goals for new between air temperature and dew point temperature, but air tem- release regimes that would alter downstream flows. Because of perature and cloud cover data were not correlated, and dew point these operational constraints, ‘‘new’’ operations did not differ and cloud cover data were not correlated. greatly from historic release patterns in many of the simulations. Average daily inflows and standard deviations were computed In wet years ͑mean inflow ϩ1SD͒, operation of the reservoir was from USBR records measured during 1969–1997. Starting eleva- dominated by flood control considerations and thus, release re- tion, stream temperatures, and reservoir outflows were adjusted gimes for traditional and new operations were not different. according to simulated inflows. Average starting elevation ͑May Under conditions of low inflow ͑mean Ϫ1SD͒, the operating 1͒ and standard deviation were computed from USBR records criteria prohibited the generation of a spring peak in releases be- measured during 1969–1997. Average daily air and dew point cause of insufficient inflow to meet in-reservoir requirements. temperatures and their daily standard deviations were computed However, fishery biologists would be unlikely to request high from Colorado Climate Center records measured during 1900– spring flows for endangered fishes downstream in dry years be- 1997. Corresponding inflow water temperatures were adjusted ac- cause such flows would depart from their goal of re-establishing a cording to simulated air temperatures. Daily cloud cover averages more natural hydrograph below the reservoir. Thus, the pattern of and standard deviations were determined from data over 1992– releases under new operations differed from traditional operations 1997. only in years with intermediate inflows. Sensitivity of model parameters was assessed by computing Because release patterns were closely tied to inflows and fill the average RMSE between water temperature profile predictions targets, reservoir content was relatively unaffected by the imple- Ϯ under baseline conditions with temperatures predicted from 1 mentation of new operations ͓Fig. 4͑A͔͒. Under new operations, SD scenarios. A mixing index was used to compare the heat bud- reservoir content dropped slightly below the baseline run in late get for each of the scenarios, where the index was defined as the May, when the spring release period began, and content remained shallowest depth below the water surface at which the water tem- slightly lower until the mid summer target elevation was reached. perature was 1°C colder than the water temperature at 0.5 m ͑ ͒ The extreme operations scenario produced the greatest departure depth Brett et al. 1998 . This index is usually equivalent to the from the historic pattern in reservoir content, maintaining a fixed depth of the thermocline ͑sensu Birge, where change in tempera- reservoir volume throughout the simulation period. Changes in ture Ͼ1°C/m; Cole 1988͒, but occasionally a very gradual change release patterns necessitated by variations in hydrologic condi- in temperature with depth resulted in highly variable thermocline tions ͑inflows or starting elevations͒ had a much greater effect on depths across dates. The mixing index provided a more stable and reservoir content ͓Fig. 4͑B͔͒ than would the prescribed attempts consistent descriptor of thermal conditions in the upper portion of to create a more natural hydrograph in ‘‘average’’ hydrologic the reservoir water column. These statistics were calculated for years. model output at 4-week intervals on model days 149 ͑May 29͒, Operational constraints on release patterns and reservoir eleva- 177 ͑June 26͒, 205 ͑July 24͒, 233 ͑August 21͒, 261 ͑September tions also meant that water residence time was similar under tra- 18͒, and 289 ͑October 16͒. ditional and new operations ͓Fig. 5͑A͔͒, generally remaining between 100 and 200 days. Water residence time under new Model Predictions operations was generally slightly higher than under traditional operations before the midsummer elevation target was reached, The new operations regime was constrained by the same fill target except during the 8-day spring peak release. Thereafter, water and minimum release requirements that governed traditional op- residence time did not differ between the two operating regimes.

118 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 Fig. 5. Predicted water residence time under ͑A͒ three operational regimes and ͑B͒ two hydrologic and two starting elevation scenarios

The extreme scenario produced a pattern in water residence time mixing, but average water temperatures above the index were that was roughly the inverse of the seasonal pattern in inflow/ slightly higher than with average inflows. outflow, with lowest water residence time ͑56 days͒ during peak inflow/outflow, and gradually increasing water residence time to a maximum of almost 500 days at the end of October. Realistic Discussion variation in hydrologic conditions produced a range in water residence time of about 100–400 days ͓Fig. 5͑b͔͒. CE-THERM proved to be a useful tool for evaluating the effects The new dam operational regime had little influence on reser- of alternative dam operation regimes on in-reservoir thermal con- voir thermal structure. Comparison of water temperature predic- ditions. The model underwent a thorough calibration and valida- tions for ‘‘traditional’’ versus ‘‘new’’ operational regimes with tion process that provided reasonable evidence that the model baseline hydrologic and meteorologic conditions showed very accurately captured the essential elements of the physical system. slight changes in epilimnetic and metalimnetic water temperatures A potential shortcoming of the method the model uses for com- ͑RMSEр0.31°C͒, and virtually no change in hypolimnetic water puting long-wave radiation was uncovered; refinements to that temperatures throughout the modeled period ͑Table 2͒. Even the subroutine should be made to enhance the capabilities of the extreme release scenario had a relatively minor warming effect on model over a wider geographic area and range of climatic condi- temperature profiles with an average RMSE over the water column of 0.56°C. Perturbations to hydrologic and meteorologic variables, particularly changes in inflows, starting elevations, and air temperatures, produced much greater changes in water temperatures than changing reservoir operations ͑Table 2, Fig. 6͒. Because increased air temperature and dew point temperatures each independently caused some warming, and because these inputs were positively correlated with each other, actual effects of increased air temperatures or dew points would likely be greater than predicted because of compounded effects of the two variables in concert. Inspection of RMSE values over the vertical regions of the reservoir further revealed that the largest effect of meteorologic variables and reservoir operations occurred in the upper 40 m of the reservoir, and virtually no effect occurred in the hypolimnion. The opposite was true for the hydrologic variables, which gener- ͑ ͒ ally showed the smallest effect on water temperatures in the top Fig. 6. Deviation mean root mean square error, °C of temperature ͑ Ϯ 10 m of the reservoir. profiles from baseline run resulting from changes mean 1 standard ͒ The mixing index was greater for cooler meteorologic condi- deviation, SD in hydrologic and meteorologic input conditions. tions ͑Table 3͒, e.g., for lower dew point and air temperatures, and Change in temperature profiles resulting from new operations under ͑ ͒ higher cloud cover values. Corresponding average water tempera- average meteorologic and hydrologic conditions dotted line , and change in temperature profiles resulting from extreme operations tures above the index were lower. Thus, the overall heat content ͑ ͒ of the upper reservoir appeared to decrease under cooler meteo- under average meteorologic and hydrologic conditions dashed line are also shown. rologic conditions. Lower inflows also resulted in deeper thermal

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 / 119 Table 3. Change in Mixing Index ͑Mixing Index from Baseline Run under these conditions because of reduced epilimnetic volume. In Minus Index from Run with Altered Input Variable͒ and Change in fact, when starting elevations alone were reduced, epilimnetic Average Water Temperature Above Index between Results from Al- temperatures fell by a greater amount than when starting eleva- tered Input Variables and Baseline Profile ͑Scenario with All Input tions were increased because of the lower volume of water in the ͒ Variables Set to Historical Average Values, and Traditional Outflows reservoir. Thus, climate and water management acted together to Change in Change in average water control heat content in the upper layer of the reservoir, with im- mixing index temperature above index portant biological implications. Variable ͑m͒ ͑°C͒ The epilimnion in Blue Mesa Reservoir contains the bulk of Inflows Ϫ1SDa Ϫ0.500 Ϫ0.043 the zooplankton biomass that forms the energetic basis for large Inflows ϩ1SDa 0.000 0.054 populations of kokanee and rainbow trout ͑Johnson et al. 2002͒. Air temperature Ϫ1SDa Ϫ0.567 1.114 In cooler years that start out wetter, the greater heat content in the Air temperature ϩ1SDa 1.267 Ϫ1.178 upper layer of the reservoir could allow greater biological produc- Start elevation Ϫ1SDa 0.783 0.086 tion. This phenomenon was demonstrated by model simulations at Start elevation ϩ1SDa Ϫ0.300 0.032 Shasta Lake where greater net primary production was predicted Cloud cover Ϫ1SDa 0.583 Ϫ0.259 under hydrologically wet conditions ͑Saito et al. 2001͒. Interest- Cloud cover ϩ1SDa Ϫ1.733 0.544 ingly, simulations with reduced inflows ͑which would correspond Dew point Ϫ1SDa Ϫ1.767 0.622 with drier conditions throughout the year͒ indicated that epilim- Dew point ϩ1SDa 0.900 Ϫ0.737 netic volume was greater and water temperatures were slightly New operations 0.133 Ϫ0.017 higher, which could drive greater epilimnetic production. While Extreme operations 0.500 Ϫ0.019 the effect of reduced inflows appears to contradict the results of Note: Negative values indicate greater mixing index depth or higher av- altering starting elevations alone, the reduction of inflows also erage water temperatures in run with altered input variable. For example, incorporated increased stream temperatures throughout the year when air temperature was higher, average water temperature above index according to Eq. ͑1͒. Under wetter conditions ͑i.e., increased in- depth was 1.178°C higher than in baseline run. flows͒, the largest change in water temperatures occurred in the a Standard deviation. hypolimnion, indicating that more of the cooler inflow water was reaching the bottom of the reservoir. tions. By adjusting the short-wave radiation to compensate for The new release regime that was simulated, while producing a excessive long-wave radiation values, some erroneous model pre- small spring peak in the downstream hydrograph under average dictions were observed when comparing with measured data. For hydrologic conditions, constituted a relatively minor departure example, goodness of fit varied by Julian day, as would be ex- from historic operations. Operations were identical in both dry pected for short-wave radiation errors. Because long-wave and and wet years and after July 15 in average years because of op- short-wave radiation distribute differently in the thermal profile, a erational constraints that insured meeting in-reservoir content and too-warm epilimnion was predicted by the model when USACE downstream delivery objectives. While these simulated opera- ͑1995͒ recommended TURB values were used, or a too-cool hy- tional scenarios were the most realistic operations, it was hypoth- polimnion was predicted by the model when using the calibrated esized that larger departures from traditional operations could high TURB values. Model calibration and validation would likely have a much larger effect on reservoir thermal structure. How- be improved by using a regionally calibrated relationship for ever, the results of the extreme scenario indicated that extreme long-wave radiation in lieu of the TVA relationship, or by replac- operations did affect reservoir structure more strongly than with ing the embedded assumption in the TVA equation with a region- the more realistic ‘‘new’’ operations, but even the extreme opera- ally sensitive equation to create a more physically accurate model tions did not have as large of an effect on reservoir thermal struc- that would be applicable for a wider variety of climatic condi- ture as changes in inflows, air temperatures, or starting elevations tions. Future users of the model should consider modifying the ͑Tables 2 and 3, Fig. 5͒. Thus, our model results indicate that source code if similar circumstances are encountered. natural variability of climate and hydrology controls reservoir The largest changes in thermal patterns occurred for changes thermal structure more strongly than do reservoir operations at in inflows, starting elevations, and air temperatures. One of the Blue Mesa. reasons the model was particularly sensitive to these variables Effects of new dam operations on nutrient dynamics and en- was because each of them affected other input variables as well. trainment have not been addressed. However, based on work else- For example, calculation of inflow temperatures was dependent where ͑Hanna et al. 1999; Bartholow et al. 2001͒ it is unlikely upon inflow quantities and air temperatures. Thus, changing air temperatures also changed inflow temperatures. Changing the that the slight thermal effects that were predicted would alter starting elevation or changing inflow quantities caused the target exchange of nutrients between the hypolimnion and epilimnion. fill elevation to change, which also led to changes in outflow Further, dam operators have been employing the new operating distributions. While these relationships were mathematically in- regime at Blue Mesa since 1993 and limnological studies in dur- ͑ ͒ duced in the model, the interdependencies of these input variables ing 1993–1997 Johnson et al. 1995, 1996, 1997 detected no are real phenomena. changes in the biomass of phytoplankton and zooplankton nor in The observations for the changes in the index of thermal mix- planktivorous fish growth rates, suggesting that reservoir nutrient ing and the average water temperature above the index depth status has not been altered by the change in dam operations. Be- indicate that in years that start out drier, or that are warmer, large cause currently projected new dam operations represent a rela- spring releases draw the reservoir down and reduce the volume of tively minor departure from historical release patterns and there- the epilimnion. Although warmer meteorological conditions re- fore water residence times, large increases in entrainment losses sulted in higher water temperatures in the epilimnion, the heat of reservoir biota in response to the new releases in most years content in the surface layer of the reservoir could actually be less were not expected.

120 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 Conclusions T ϭ water temperature predicted from Ϯ1 standard deviation run; ϭ The overall results indicate that reservoir thermal regime was Taj air temperature on day j; ϭ relatively insensitive to changes in dam operations under ‘‘nor- Tavg water temperature predicted from ‘‘baseline’’ run; mal’’ climatic and hydrologic conditions. Changes in weather or and ϭ hydrologic variables such as inflows, starting elevations, and air T j stream temperature on day j. temperatures had a much larger effect on reservoir thermal patterns. The average difference in temperature profiles between baseline runs and either of the new operations were similar in References magnitude to those arising from model prediction errors quanti- fied during calibration and validation. This suggests that realistic Ahren, C. D. ͑1994͒. Meteorology today, West Publishing, St. Paul. Minn. changes to dam operations at Blue Mesa Reservoir may produce Allan, J. D. ͑1995͒. Stream ecology: Structure and function of running changes in thermal regime that are too small to model accurately waters, Kluwer Academic, Dordrecht, The Netherlands. and are probably of limited ecological significance. Bartholow, J. ͑1989͒. ‘‘Stream temperature investigations: Field and ana- If modest changes in seasonal discharge volumes alone are not lytic methods.’’ Instream Flow Information Paper No. 13, Biol. Rep. sufficient to restore more natural and favorable conditions down- 89(17), U.S. Fish and Wildlife Service, Grand Junction, Colo. stream for endangered fish then larger releases may be required. Bartholow, J., Hanna, B., Saito, L., Lieberman, D., and Horn, M. ͑2001͒. Even the extreme operations scenario, with a run-of-the-river re- ‘‘Simulated limnological effects of the Shasta Lake temperature con- ͑ ͒ lease pattern, produced small changes in reservoir thermal regime trol device.’’ Environ. Manage. (N.Y.), 27 4 , 609–626. Baxter, R. M. ͑1977͒. ‘‘Environmental effects of dams and impound- relative to those induced by the climatic and hydrologic variables. ments.’’ Annu. Rev. Ecol. Syst., 8, 255–283. It appears that water managers at Blue Mesa Reservoir have con- Brett, M. T., Sarsfield, C., DeStaso, J., Duffy, S., and Heyvaert, A. C. siderable latitude for implementing new operations without nega- ͑1998͒. Physical forcing of the phytoplankton bloom dynamics in tive thermal consequences to the reservoir. The generality of this Shasta Lake, California: A Progress Rep. on the study of limnological conclusion for other reservoirs should be investigated because of effects following installation of a temperature control device, U.S. growing interest in mitigating the downstream effects of dams by Bureau of Reclamation, Redding, Calif. changing dam operations. Cole, G. A. ͑1988͒. Textbook of limnology, Waveland Press, Prospect Heights, Ill. Collier, M., Webb, R. H., and Schmidt, J. C. ͑1996͒. Dams and rivers: A Acknowledgments primer on the downstream effects of dams, U.S. Geological Survey, Denver. Cudlip, L. S., French, R. D., and Hickman, D. ͑1987͒. ‘‘Blue Mesa Res- This study was supported by funds from the U.S. Bureau of ervoir, CO: A historical review of its limnology, 1965–1985.’’ Rep. Reclamation and logistical support from the Colorado Division No. REC-ERC-87-3, U.S. Bureau of Reclamation, Denver. of Wildlife and the National Park Service. The writers appreciate Hanna, R. B., Saito, L., Bartholow, J. M., and Sandelin, J. ͑1999͒. ‘‘Re- the able field and lab assistance of Pat Martinez, Mike Wise, sults of simulated temperature control device operations on in- Jason Stockwell, and Krista Bonfantine. They also thank reservoir and discharge water temperatures using CE-QUAL-W2.’’ Ron Sutton, Steve McCall, Chris Karas, Coll Stanton, Matt Lake Reserv. Manage., 15͑2͒, 87–102. ͑ ͒ Malick, and John Carron. This research was supported in part Johnson, B. M., Martinez, P. J., and Stockwell, J. D. 2002 . ‘‘Tracking by Nevada Agricultural Experiment Station, publication trophic interactions in coldwater reservoirs using naturally occurring stable isotopes.’’ Trans. Am. Fish. Soc., 131, 1–13. No. 5202377. Johnson, B. M., Stockwell, J. D., and Bonfantine, K. ͑1997͒. ‘‘Ecological effects of reservoir operations on Blue Mesa Reservoir.’’ Annual Progress Rep., U.S. Bureau of Reclamation, Salt Lake City. Notation Johnson, B. M., Wise, M. J., Counard, C. J., and Szerlong, R. G. ͑1995͒. ‘‘Ecological effects of reservoir operations on Blue Mesa Reservoir.’’ The following symbols are used in this paper: Annual Progress Rep., U.S. Bureau of Reclamation, Grand Junction, ϭ Colo. A0 regression coefficient for stream temperature equation; Johnson, B. M., Wise, M. J., Herwig, B., Szerlong, R. G., Faber, D., and ͑ ͒ A ϭ regression coefficient for stream temperature Byall, B. 1996 . ‘‘Ecological effects of reservoir operations on Blue 1 Mesa Reservoir.’’ Annual Progress Rep., U.S. Bureau of Reclamation, equation; ϭ Grand Junction, Colo. A2 regression coefficient for stream temperature Martinez, P. J., and Wiltzius, W. J. ͑1995͒. ‘‘Some factors affecting a equation; hatchery-sustained kokanee population in a fluctuating Colorado res- ϭ A3 regression coefficient for stream temperature ervoir.’’ N. Am. J. Fish. Manage., 15, 220–228. equation; McAda, C. W. ͑2003͒. ‘‘Flow recommendations to benefit endangered ϭ A4 regression coefficient for stream temperature fishes in the Colorado and Gunnison Rivers.’’ Final Rep., Recovery equation; Program Project 54, U.S. Fish and Wildlife Service, Grand Junction, j ϭ day of year; Colo. n ϭ number of predictions compared; National Research Council ͑NRC͒. ͑1991͒. Colorado River ecology and Q* ϭ combined heat flux excluding solar radiation; dam management, National Academy Press, Washington, D.C. ϭ Osmundson, D. B., and Burnham, K. P. ͑1998͒. ‘‘Status and trends of the Qb water backwave radiation; ϭ endangered Colorado squawfish in the upper Colorado River.’’ Trans. Qc conductive heat transfer; Am. Fish. Soc., 127, 957–970. ϭ Qe evaporative heat loss; Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., ϭ Q j stream discharge on day j; Richter, B. D., Sparks, R. E., and Stromberg, J. C. ͑1997͒. ‘‘The QNA ϭ atmospheric longwave radiation; natural flow regime: A paradigm for river conservation and restora- RMSE ϭ root mean squared error; tion.’’ BioScience, 47, 769–784.

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / MARCH/APRIL 2004 / 121 Saito, L., Johnson, B. M., Bartholow, J., and Hanna, R. B. ͑2001͒. ‘‘As- Battle against extinction: Native fish management in the American sessing ecosystem effects of reservoir operations using food web- West, W. L. Minckley and J. E. Deacon, eds., Univ. of Arizona Press, energy transfer and water quality models.’’ Ecosystems, 4, 105–125. Tucson, Ariz., 379–402. Stanford, J. A. ͑1994͒. ‘‘Instream flows to assist the recovery of endan- U. S. Army Corps of Engineers ͑USACE͒. ͑1995͒. ‘‘CE-QUAL-R1: A gered fishes in the upper Colorado River basin.’’ Biol. Rep. 24, Na- numerical one-dimensional model of reservoir water quality; User’s tional Biological Survey, U.S. Department of Interior, Washington, manual.’’ Instruction Rep. No. E-82-1, Revised Ed., Waterways Ex- D.C. periment Station, Vicksburg, Miss. U. S. Bureau of Reclamation ͑USBR͒. ͑1975͒. ‘‘Blue Mesa Dam and Stanford, J. A., Ward, J. V., Liss, W. J., Frissell, C. A., Williams, R. N., powerplant.’’ Technical record of design and construction—Colorado Lichatowich, J. A., and Coutant, C. C. ͑1996͒. ‘‘A general protocol for River Storage Project, Gunnison Division—Curecanti Unit, Colorado, restoration of regulated rivers.’’ Regul. Rivers: Res. Manage, 12, 391– Denver. 413. Van Steeter, M. M., and Pitlick, J. ͑1998͒. ‘‘Geomorphology and endan- ͑ ͒ ͑ ͒ Tennessee Valley Authority TVA . 1972 . ‘‘Heat and mass transfer be- gered fish habitats of the upper Colorado River 1. Historic changes in tween a water surface and the atmosphere.’’ TVA Lab Rep. No. 14, streamflow, sediment load, and channel morphology.’’ Water Resour. Report No. 0-6803, Norris, Tenn. Res., 34, 287–302. Thornton, K. W., Kimmel, B. L., and Payne, F. E. ͑1990͒. Reservoir Ward, J. V., and Stanford, J. A., eds. ͑1979͒. The ecology of regulated limnology: Ecological perspectives, Wiley, New York. streams, Plenum, New York. Tyus, H. M. ͑1991͒. ‘‘Ecology and management of Colorado squawfish.’’ Wetzel, R. G. ͑2001͒. Limnology, 3rd Ed., Academic, San Diego.