| CRiSP1.6 Theory & Calibration Manual: II.6 - Dam Passage | |
II.6 - Dam Passage
Fish enter the forebay of a dam from the reservoir and experience predation during transit time and during delays due to diel and flow related processes. They leave the forebay and pass the dam mainly at night through spill, bypass or turbine routes, or are diverted to barges or trucks for transportation. Each route leaving the forebay has an associated mortality, and fish returning to the river are exposed to predators in the tailrace before they enter the next reservoir. The details of passage through the regions of the dam are illustrated schematically in Fig. 46.
Fig. 46 Dam processes showing passage routes and mortality. Forebay delay is further illuminated in Fig. 47.
The movement and allocation of fish through the forebay is illustrated in Fig. 47. Fish exiting the reservoir in each reservoir time slice, currently two slices per day, are evenly allocated as input to the forebay across the dam time slices, currently four slices per day. Fish entering from the reservoir are subjected to possible predation for the duration of the forebay transit. The forebay transit only affects mortality modeling, not travel time. Next, fish are either passed (through dam or spillway) to the tailrace or are delayed for one dam time slice in the forebay. Delayed fish are combined in the next dam time slice with fish completing the forebay transit. These fish are passed or are delayed and the cycle repeats.
Output from the forebay in each dam time slice depends on flow and diel illumination. Allocation to the passage routes depends on spill schedules and passage efficiencies through the routes.
Fig. 47 Transfer of fish from reservoir to forebay to dam. Diagram shows allocation of fish from a reservoir time slice of 12 hours to dam time slices of 6 hours each. Mortality is associated with dam and spill passage as well as forebay transit and delay.
II.6.1 - Forebay Delay
Studies of the timing of fish passage at dams indicate that passage occurs mostly at night, with fish delaying passage during daylight hours. This delay process is represented in CRiSP.1 as a simple input-output submodel. Fish enter the forebay at a rate determined by reservoir passage factors. Fish are assumed to be more susceptible to being drawn into turbine intakes or spill at night than during the day. Susceptibility is also determined by flow, spill, and julian date; expressing the propensity of the fish to pass dams as the season progresses.
Dam Delay Model
t = instantaneous probability of passage
- p = proportion of time step during day
- (1-p) = proportion of time step during night
- Vt = upstream river velocity in mi/day
- SPt = proportion of river spilled
- Dt = julian date
's and
's = parameters that vary by dam and species.
The probability of remaining during a single time step is:
Fig. 48 Cumulative passage versus dam delay in days at Little Goose Dam
II.6.2 - Spill
The spill algorithm represents allocations of spill from hydroregulation models (HYDROSIM or HYSSR) through flow archive files or the Spill Schedule window under the Dam menu.
Flow Archive Spill
When spill is allocated from flow archive files, it is identified as a percent of daily averaged flow over multi-day periods. Consequently, for use in CRiSP.1, archive derived spill must be allocated to specific days and hours of the day. CRiSP.1 considers three types of spill: Planned Fish Spill, Overgeneration Spill, and Forced Spill.
- Planned Fish Spill is requested by the fisheries agencies. The schedule for this can be obtained from the Flow Archive files or can be set in the Spill Schedule window.
- Overgeneration Spill occurs when electrical generation demand is less than that available in flow. This is obtained from the flow archive file only.
- Forced Spill occurs when river flow exceeds powerhouse capacity. This is calculated by CRiSP.1.
CRiSP.1 allocates spill flows in the following order.
First, Planned Fish Spill is allocated. For each period, planned spill is distributed over scheduled spill days and fish spill hours (within those days) using the following steps.
- Total modulated flow in the period that occurs in fish spill hours on planned spill days is calculated and designated
- The requested spill in a period is designated
- Percent spill during Fish Hours is calculated as
- If
spill_daily_percent> 100%
- Overgeneration spill is added to Planned Fish Spill in Fish Hours every day in a period to yield total spill.
- If total spill in Fish Hours is now greater than the total flow over the hours then the excess is distributed over the rest of the day.
- If total spill for the entire day is greater than the total daily modulated flow then the spill is set to the total daily modulated flow.
- Calculate the quantity
- Then, if possible forced spill > total fish & overgeneration spill
- Otherwise, the forced spill is assimilated into fish and overgeneration spills.
Spill from Spill Schedule Tool
Planned spill can be set by specifying spill information with the Spill Schedule window. The following information is entered:
- fraction of flow spilled
- days over which the spill fraction applies
- days in which actual spill occurs, i.e. the planned spill
- hours of planned spill for the indicated days.
Overgeneration spill is only applied if Monte Carlo Mode is used. Forced spill is calculated as described above and is applied in both Scenario and Monte Carlo Modes.
Spill Caps
The maximum allowable planned spill is set by the spill capacity (cap) at each dam. If planned spill exceeds the cap then spill is limited to spill cap. Forced spill can exceed the spill cap.
Spill Efficiency
The fraction of fish passed with spilled water is defined by one of nine possible empirical equations that can be selected by the user. The following are the spill efficiency equations:
(120)
- Y = fraction of total fish passed in spill
- X = fraction of water spilled
- a and b = regression coefficients
- e = error term (var) selected from random distribution.
The equations and parameters defining spill efficiency (often called "effectiveness" in the literature) are indicated in Table 33. These values were used beginning with the SOR (System Operation Review) screening runs of CRiSP.1.
Table 33 Spill efficiency (% fish passed in spillway /% flow passed in spillway). Dam Spill equation Reference Wells zero1 Erho et al. 1988; Kudera et al. 1991 Rocky Reach % pass = 0.65 * (% spill) Raemhild et al. 1984b Rock Island % pass = 0.94 * (% spill) + 11.3 Ransom et al. 1988 Wanapum % pass = 15.42 * ln (% spill)
Dawson et al. 1983Priest Rapids % pass = (% spill) ^ 0.82 L. Monumental % pass = 1.2 * (% spill) Johnson et al. 1985; Ransom and Sullivan 1989 The Dalles % pass = 2 * (% spill) Parametrix, Inc. 1987 all other dams % pass = (% spill)
1 Wells Dam is designed to pass smolts preferentially through the spillway system: about 96% of all smolts pass via the spillway. This is modeled by assigning an FGE value of 96% (range 95-97%) at Wells with a zero spill efficiency for years 1991 on, except as specified in Table 34.
II.6.3 - Fish Guidance Efficiency (FGE)
Guidance of fish into the bypass systems of dams is achieved by diverting fish into a bypass slot. Individual FGEs are specified for day and night at each dam and for each species. In addition, CRiSP.1 can treat FGE as constant over time or vary FGE with the age of the fish relative to the onset of smoltification.
Constant FGE
When age dependent fge is turned off, the model will use the constant FGE condition. Day and night fish guidance efficiency then vary randomly on each dam time step according to a fixed probability distribution, i.e. the distribution has no seasonal trend. FGE is specific to a given dam and species and its random variations occur for each dam time step (6 hours).
The probability distribution of constant FGE is defined by a piecewise linear distribution within the range identified by the low and high values. When the low and high values are set to zero, or when the low and high are set to the mean value, CRiSP.1 uses the mean value at all times (the term becomes deterministic). When the low and high values are not equal, CRiSP.1 uses the mean, low and high values to randomly generate a value when executed with variance suppression turned off. With variance suppression turned on, CRiSP.1 uses the mean value and ignores the high and low values. In either case, the mean value must lie within the central two quartiles of the distribution (i.e., the middle 50%).
Age Dependent FGE
Studies on fish guidance at several dams in the Columbia system indicate that FGE varies with seasons from a number of factors including the water quality and the degree of smolt development in the fish, which changes with age. When the model is run under this condition (age dependent fge turned on), day and night FGE change randomly for each dam time step (6 hours) according to probability distributions that change with fish age and reservoir elevation. Variations in FGE from the initial condition depends on julian day, the day since the onset of smoltification, and reservoir elevation for each day. If the age dependent option is selected, fish depth in the forebay varies with age, which in turn alters the FGE. The algorithm assumes that fish above some critical depth z enter the bypass system and fish below z enter the turbine (Fig. 49). Thus, to define age dependent FGE, fish depth in the forebay is defined as a function of age. If the surface drops below the level of the bypass orifice, then fish bypass goes to zero.
Fig. 49 Critical parameters in fish guidance are fish forebay depth z, screen depth D and elevation drop E. Only fish above z are bypassed. Bypass stops when the surface is below the bypass orifice depth.
The FGE submodel is based on the FGE model of Anderson (1991). Behavioral and hydraulic factors affecting FGE are combined into a calibration factor Dc. In addition, the affect of drawdown on FGE can be expressed in terms of screen depth relative to the surface. The modified equation is:
- fge = fish guidance efficiency
- z = median depth of fish in the forebay at a distance from the dam where fish are susceptible to being drawn into the intake
- D = screen depth relative to full pool forebay elevation
- Dc = FGE calibration parameter
- E = amount the pool is lowered below full pool elevation.
Thus, changes in FGE result from changes in fish depth and changes in reservoir elevation. The parameter Dc depends on physical and hydraulic properties of a dam, and behavioral properties of fish. As such, the term is specific to both a given species and a given dam. In addition, separate coefficients are defined for day and night dam passage.
Changes in FGE with fish age are represented by changes in fish forebay depth which is described by a linear equation:
To implement the FGE equation, we define the calibration coefficient:
Combining equations (121), (122) and (123), the final FGE equation is:
- t = fish age since the onset of smoltification
- t0 = onset of change in FGE relative to the onset of smoltification, set in the Release window
t = increment of time over which FGE changes
- z0 = initial mean fish depth (at age t equals 0) in the forebay
- z1= final mean fish depth (at age t equals t0 +
- fge0 = FGE at onset of smoltification
- E(t) = elevation drop.
The resulting FGE and depth are illustrated in Fig. 50.
Fig. 50 FGE and fish depth over fish age
Parameter Determination
Nearly all federal projects on the Columbia and Snake rivers have undergone considerable change since their initial construction. Most have added bypass systems or other mechanisms to provide improved juvenile passage; consequently, FGE has improved over time. We use current estimates of FGE as determined by the PATH process (L.D. Krasnow, National Marine Fisheries Service, NWFSC, pers. com., 2000). These FGE values are adjusted for sensitivity #1 analysis, where the effectiveness of extended-length submersible-bar screen is assumed to be better than standard screens. Estimated historical FGE values for CRiSP.1 for all species are given in Table 34.
Table 34 Historical FGE values for each dam, by species as determined by PATH and used for CRiSP.1 (L.D. Krasnow, National Marine Fisheries Service, NWFSC, pers. com., 2000). Dam Year yearling chinook subyearling chinook steelhead Bonneville 1 1971-1983 1984-1987 1988-1998 Bonneville 2 1982-1988 1989-1992 1993-1998 The Dalles 1957-1974 1975-1998 John Day 1968-1984 1985 1986 1987-1998 McNary 1979 1980 1981 1982 1983-1989 1990 1991-1992 1993 1994 1995 1996 1997-1998 Wells1 1991-1992 1993-1999 Rocky Reacha 1993-1998 Rock Island 1a 1994-1998 Ice Harbor 1967-1979 1980-1982 1983-1992 1993-1995 1996-1998 Lower Monumental 1969-1991 1992-1999 Little Goose 1970 1971-1972 1973-1975 1976 1977 1978-1987 1988-1992 1993-1994 1995 1996 1997-1999 Lower Granite 1975-1976 1977-1990 1991-1994 1995 1996-1999
1 Whitney et al. 1997.
Time Variable FGE
The calibration of time varying FGE is not available for CRiSP1.6.
Bypass Orifice and FGE
Fish guidance goes to zero when the surface elevation drops below the bypass orifice elevation (Fig. 49). This parameter, designated
bypass_elevation, is set in the columbia.desc file. Ifbypass_elevationis not specified, then the bypass elevation is set to the poolfloor_elevationand bypass will occur for all reservoir elevations. This function applies with or without selection of age dependent FGE.Bypass Elevations
The bypass elevations and forebay elevations in feet above sea level (obtained from the U.S. Army Corps of Engineers) are set in the columbia.desc file for each dam where a bypass system exists.
Multiple Powerhouses
Bonneville Dam and Rock Island Dam each have two powerhouses that can be operated independently to optimize survival during the fish passage season since each project has a single spillway. Multiple powerhouse dams can be represented schematically as shown in Fig. 51.
Fig. 51 Multiple powerhouse configuration showing allocation of spill and powerhouse flows.
For multiple powerhouse dams, flow is allocated fractionally as follows:
- Flows are first allocated to planned spill in fish passage hours.
- Remaining flow is partitioned between the primary and secondary powerhouses and additional spill as follows:
Fig. 52 Flow allocation through two powerhouse projects.
An example of flow allocations is described as follows (Fig. 52):
- At level ¿: 4 units of flow are put to Fish Spill and 2 units are put through the First Powerhouse.
- At level² ¡: Fish Spill has four units of flow, the First Powerhouse is run at its hydraulic capacity, which is 4 flow units, and the spillway has 3 units of additional spill.
- At level ¬: the First Powerhouse is at hydraulic capacity, spill flow includes Fish Spill and additional spill up to the spill threshold, and 2 units of flow pass the Second Powerhouse.
Fish Passage Efficiency (FPE)
Fish passage efficiency (FPE) is the percent of fish that pass a project by non-turbine routes (spill, bypass, and sluiceway passage). FPE considers that fish pass mostly during the night, and spill generally occurs at night. The simple fish routing is illustrated in Fig. 53. A fraction of the fish are first diverted in to spill water. The fish that remain are diverted into the turbine intake and a fraction of this flux is diverted into the fish bypass system.
Fig. 53 Routing of fish for calculation of FPE
The formula expressing FPE considers these independent diversions and accounts for the fact that fish may be attracted to spill flow over flows into the turbine. The simplified formula for FPE which considers spill occurs at night and most of the fish pass at night can be expressed:
- D = fraction of fish that pass dam during spill hours
- Fsp = fraction of daily flow that passes in spill
- SE = fraction of fish that pass in spill relative to the fraction of flow passing in spill
- FGE = fraction of fish passing into turbine intake that are bypassed.
The spill flow, in percent of the total flow, required to generate a given FPE can be expressed by arranging eq (125) to give:
Dam Passage Survival
Fish passing through the dams can take several routes (depicted in Fig. 46). Equations describing the number of fish that pass through each route in terms of the number that enter the dam from the forebay in a particular dam time step are given in the following sections. In each case, the mortality and passage efficiencies have deterministic and stochastic parts.
For mortalities and FGE, the random elements are represented by additive deterministic and stochastic parts in:
- x = deterministic part of the random parameter fixed for each species and dam
- x' = stochastic part of the parameter taken from a broken-stick distribution (see Section II.7.1 Stochastic Parameter Probability Density) over each dam time slice.
For spill efficiency, each equation contains a random term. A typical equation is:
- y = spill efficiency
- x = percent flow
- a and b = deterministic parameters
- e = stochastic parameter selected from a normal distribution.
Turbine Survival
The equation for turbine survival can be expressed:
- Ntu = number of fish passing in a time increment (6 hours)
- Nfo = number of fish in forebay ready to pass in the increment
- p = probability of passing during the increment (1 - P1 from eq (119))
- mfo = mortality in forebay (see Section II.4.2 Predation Mortality)
- mtu = mortality in turbine passage
- fge = fish guidance efficiency for a day or night period
- Y = proportion of fish passage in spill defined by spill efficiency equation (see eq (120)).
Bypass Survival
The equation for bypass survival is:
Transport Survival
The equation for transport survival with fixed transport mortality is:
Spill Survival
The equation for spill survival is:
Parameter Determination for Passage Mortality
Turbine mortalities are based on the following studies:
- Direct measure estimates
- Oligher and Donaldson 1966
- Weber 1954
- Indirect measure estimates
- Holmes 1952
- Schoeneman et al. 1961
- Long 1968
- Long et al. 1975
- Raymond 1979
- Raymond and Sims 1980
- Ledgerwood et al. 1990.
The recent measurements of turbine survival with inflated tags and PIT tags are given in Table 36.
Bypass and spill mortalities are based on the following studies:
- Ceballos et al. 1991
- Ledgerwood et al. 1990
- Ledgerwood et al. 1991
- Muir et al. 1996.
Passage mortalities used in calibration, including mean, low and high values, are given in Table 37. The mortalities are used for all species but most of the data was from studies involving spring chinook. The estimates are weighted towards the more recent studies. High estimates of dam passage mortality in 1970s are used to represent documented problems in Snake River dam passage in these years (Raymond 1979; Raymond and Sims 1980). The high mortalities were assigned to both turbine and bypass routes.
II.6.4 - Transport Parameters
The direct transport survival in barging is set at 98%. The transport effectiveness expressed by "D" is not included in the passage model.
Transportation schedule
The schedule of transporting fish from each transport dam depends on the flow, number of each species passing the dam, and the efficiency of separating fish for return back into the river. The schedules for transportation, compiled from the annual reports from various sources on the juvenile fishery operation of the Columbia Basin and transportation plans and studies, for the historical years 1975-1999 are given in Table 38.
Table 38 Historical transport operations, 1975-1999, at Lower Granite (LWG), Little Goose (LGS), Lower Monumental (LMN), and McNary (MCN) dams. Year Dam Start Date Stop Date Ref. 1975 LGS 4/10 6/15 1 1976 LWG 4/15 6/15 Park et al. 1977 LGS 4/16 6/15 1977 LWG 4/25 6/17 Park et al. 1978 LGS 4/29 6/16 1978 LWG 4/4 6/21 Park et al. 1979 LGS 4/6 6/15 1979 LWG 4/11 7/4 Smith et al. 1980 LGS 4/17 7/4 MCN 4/16 8/24 Park et al. 1980 1980 LWG 4/3 7/7 Smith et al. 1981 LGS 4/7 7/4 MCN 4/9 9/5 Park et al. 1981 1981 LWG 4/2 7/30 Athearn 1985 LGS 4/7 7/25 MCN 3/27 9/11 1982 LWG 4/4 7/29 Basham et al. 1983 LGS 4/8 7/22 MCN 3/30 9/24 1983 LWG 4/2 7/30 Delarm et al. 1984 LGS 4/4 7/8 MCN 4/2 9/22 1984 LWG 4/1 7/26 Koski et al. 1985 LGS 4/5 7/28 MCN 4/16 9/28 1985 LWG 3/28 7/23 Koski et al. 1986 LGS 3/30 7/23 MCN 4/6 9/26 1986 LWG 3/27 7/24 Koski et al. 1987 LGS 3/29 7/3 MCN 3/27 9/26 1987 LWG 3/28 7/31 Koski et al. 1988 LGS 4/2 7/9 MCN 3/27 10/29 1988 LWG 3/25 7/31 Koski et al. 1989 LGS 4/7 7/15 MCN 3/25 9/21 1989 LWG 3/25 7/27 Koski et al. 1990 LGS 4/4 7/11 MCN 3/24 9/19 1990 LWG 3/27 7/26 Ceballos et al. 1991 LGS 4/12 7/21 MCN 4/2 9/14 1991 LWG 3/27 10/31 Ceballos et al. 1992 LGS 4/3 8/21 MCN 4/2 9/14 1992 LWG 4/1 10/31 Ceballos et al. 1993 LGS 4/12 10/31 MCN 3/25 12/7 1993 LWG 4/14 11/1 Hurson et al. 1995 LGS 4/15 11/1 LMN 5/3 11/1 MCN 4/14 10/30 1994 LWG 4/6 11/1 Hurson et al. 1996 LGS 4/5 11/1 LMN 4/6 11/1 MCN 4/8 12/6 1995 LWG 3/28 11/1 Baxter et al. 1996 LGS 4/5 11/1 LMN 4/1 11/1 MCN 6/20 12/8 1996 LWG 3/27 10/31 Spurgeon et al. 1997 LGS 4/1 10/28 LMN 4/1 10/28 MCN 6/4 11/22 1997 LWG 3/26 11/1 Hetherman et al. 1998 LGS 4/1 11/1 LMN 4/1 11/1 MCN 5/30 12/14 1998 LWG 3/26 11/1 Hurson et al. 1999 LGS 4/1 11/1 LMN 4/1 11/1 MCN 6/1 12/15 1999 LWG 3/25 11/10 a LGS 4/1 11/4 LMN 4/1 10/31 MCN 6/22 12/14
1 Dave Hurson, U.S. Army Corps of Engineers, Walla Walla District. Telephone conversation with author, 6 July 2000.
Transportation Separation
Transportation separation criterion indicates conditions under which collected fish are separated and returned to the river. Transportation studies indicate that transportation always benefits juvenile steelhead. Many people believe that smaller migrants (chinook, coho, sockeye) benefit from transportation when flows are low, but are better off in the river when flows are higher and conditions are presumably better. If a dam has a Separation Trigger, when flows exceed that value, smaller fish are separated from the larger steelhead smolts and are returned to the river. This separation continues according to the criterion given in Table 40. For example, the criterion "full transport @ 80% yearlings" means that fish are separated under high flow conditions until it is estimated that 80% of yearlings have already passed the dam. After that point, all collected fish are transported regardless of flow condition.
Table 39 Smolt Index passage data1 used to determine high flow percent hfl_pass_perc at McNary Dam based on the separation criterion. Date when Chin0 > Chin1 Chinook 1 (yearling) # passed by Date total # passed % of run passed by Date 6/17/1993 1687884 1729010 98% 6/17/1994 2511366 2572338 98% 6/02/1995 2759231 2879069 96% 5/25/1996 1059141 1240878 85% 5/20/1997 894421 1184530 76% 5/28/1998 1572715 1727071 91% 6/01/1999 3605974 3692944 98% 6/06/2000 1868078 1986380 94%
1 Columbia Basin Research, ed. Columbia River Data Access in Real Time (DART). Hp. 3 Dec. 2000 [last update]. Online. Columbia Basin Research, School of Aquatic & Fishery Sciences, University of Washington. Available: http://www.cbr.washington.edu/dart/dart.html. 4 Dec. 2000.
Table 40 contains the transportation separation parameters used for the historical data files at the transportation projects. The transportation separation criterion is compiled from the annual reports from various sources on the juvenile fishery operation of the Columbia Basin and transportation plans and studies for the years 1975-1999. These criterion are used in conjunction with the transport operation dates in Table 38 to create transportation records for each transport dam in the yearly input data files (.dat). In CRiSP.1,
high_flowis set to 0 when no flow criterion is specified for separation. This forces separation to occur under all flow conditions until separation is terminated by thehfl_pass_percof the indicator species (always set toChinook_1). When the transport operations criterion specifies transport all with no separation specifications, thenhigh_flowis set to 1. During a model run, this forces no separation of the collected fish to occur, and as a result, all fish collected are transported.
Table 40 Transport separation parameters1 for historical data files, 1975-1999, at Lower Granite (LWG), Little Goose (LGS), Lower Monumental (LMN), and McNary (MCN) dams. Year Dam Separation @ kcfs Criterionb Ref. hfl_pass_percc high_flow 1975 LGS transport all d 0 0 1976 LWG transport to 50% of run Park et al. 1977 1 0 LGS transport to 50% of run 1 0 1977 LWG transport to 65% of run Park et al. 1978 0 0 LGS transport to 65% of run 0 0 1978 LWG transport all 0 0 LGS transport all 0 0 1979 LWG transport all;
control by spillCOFO 1980 0 0 LGS transport all;
control by spill0 0 MCN transport all;
control by spill0 0 1980 LWG transport all;
control by spillCOFO 1981 0 0 LGS transport all;
control by spill0 0 MCN transport all;
control by spill0 0 1981 LWG transport all;
control by spillCOFO 1982 0 0 LGS transport all;
control by spill0 0 MCN transport all;
control by spill0 0 1982 LWG full trans @ 80% yearlings COFO 1983 0.8 0 LGS full trans @ 80% yearlings 0.8 0 MCN transport all 0 0 1983 LWG none sep by size full trans @ 80% yearlings COFO 1984 0.8 0 LGS none sep by size full trans @ 80% yearlings 0.8 0 MCN none sep by size full trans @ 80% yearlings 0.8 0 1984 LWG full trans @ 80% yearlings BPA 1984 0.8 0 LGS full trans @ 80% yearlings 0.8 0 MCN full trans @
yearlings <
subyearlings0.95 0 1985 LWG full trans @ 80% yearlings Karr and Mather 1985 0.8 0 LGS full trans @ 80% yearlings 0.8 0 MCN full trans @
yearlings <
subyearlings0.95 0 1986 LWG full trans @ 80% yearlings CBFWA 1986 0.8 0 LGS full trans @ 80% yearlings 0.8 0 MCN full trans @
yearlings <
subyearlings0.95 0 1987 LWG full trans @ 80% yearlings CBFWA 1987 0.8 0 LGS 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.95 220 1988 LWG full trans @ 80% yearlings CBFWA 1988 0.8 0 LGS 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.95 220 1989 LWG transport all USACE 1989c 0 0 LGS 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.95 220 1990 LWG transport all USACE 1990 0 0 LGS 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.95 220 1991 LWG transport all USACE 1991 0 0 LGS 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.95 220 1992 LWG transport all USACE 1992a 0 0 LGS 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.95 220 1993 LWG transport all USACE 1993 0 0 LGS 100 full trans @ 80% yearlings 0.8 100 LMN 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.98 220 1994 LWG transport all USACE 1994b 0 0 LGS 100 full trans @ 80% yearlings 0.8 100 LMN 100 full trans @ 80% yearlings 0.8 100 MCN 220 full trans @
yearlings <
subyearlings0.98 0 1995 LWG transport all USACE 1995 0 0 LGS 100 full trans @ 80% yearlings 0.8 100d LMN 100 full trans @ 80% yearlings 0.8 100d MCN full trans @
yearlings <
subyearlings0.96 0 1996 LWG transport all d 0 0 LGS 100 full trans @ 80% yearlings 0.8 100d LMN 100 full trans @ 80% yearlings 0.8 100d MCN full trans @
yearlings <
subyearlings0.85 0 1997 LWG transport all d 0 0 LGS 100 full trans @ 80% yearlings 0.8 100d LMN 100 full trans @ 80% yearlings 0.8 100d MCN full trans @
yearlings <
subyearlings0.76 0 1998 LWG transport all d 0 0 LGS transport all 0 0 LMN transport all 0 0 MCN full trans @
yearlings <
subyearlings0.91 0 1999 LWG transport all USACE 1999a 0 0 LGS transport all 0 0 LMN transport all 0 0 MCN full trans @
yearlings <
subyearlings0.98 0
1 High Flow Species (high_flow_species) is set to Chinook 1 for all dams for all transportation years.
b. Criterion Definitions:
transport all: transport all fish that are collected; does not mean that all fish passing a dam are transported
full trans @ 80% yearling: transport all collected fish after a date when it is estimated that 80% of the yearling chinook run has passed the dam
full trans @ yearlings < subyearlings: transport all collected fish after a date when it is determined that the majority of the yearling chinook run has passed the dam and subyearling chinook are the dominate species in the collection
transport all; control by spill: transport all fish that are collected with collection at the dam controlled by spill
c. High Flow Percent (hfl_pass_perc) at McNary Dam is set to the median value 95% from Table 39 for the years 1984-1992 for which there is no Smolt Index passage data.
d. Dave Hurson, U.S. Army Corps of Engineers, Walla Walla District. Telephone conversation with author, 6 July 2000.
The goal of separation is to retain steelhead for transport and return the other, smaller fish to the river. The parameter separtion probability (
separation_prob), as used in CRiSP.1, represents the percent of the collected fish that will be returned to the river. Separation probability is species-specific and set for each dam to represent the ability of the dam to separate individuals of that species during bypass. Estimates of separation probability are based on the total number of fish collected and the total number of fish transported from each transportation dam as reported in the annual transportation reports.1 These estimates are included in Table 41.
Table 41 Separation Probability (separation_prob) estimates as used in CRiSP.1, based on the total number of fish collected and the total number of fish transported from each transportation dam. Year Dam (report comment) Species Total Number Collected Total Number Transported Separation Probability 1982 (Basham et al. 1983) LWG (no recorded bypass) chin1 361369 356952 0.01 chin0 110367 110415 (0.00) steelhead 1458060 1373312 0.06 LGS (no recorded bypass) chin1 230104 224425 0.02 chin0 121612 107864 0.11 steelhead 908541 897460 0.01 MCN (no recorded bypass) chin1 822009 789918 0.04 chin0 1696104 1600708 0.06 steelhead 364174 353492 0.03 1983 (Delarm et al. 1984) LWG (no recorded bypass) chin1 900210 862160 0.04 chin0 239904 235256 0.02 steelhead 1326091 1265283 0.05 LGS (no recorded bypass) chin1 275109 166983 0.39 chin0 27925 24960 0.11 steelhead 689119 673646 0.02 MCN (no recorded bypass) chin1 720756 10710 0.99 chin0 4389357 4222176 0.04 steelhead 338267 55368 0.84 1984 (Koski et al. 1985) LWG (no recorded bypass) chin1 828332 824464 0.00 chin0 97639 96925 0.01 steelhead 1114740 1113675 1.00 LGS chin1 786583 488499 0.38 chin0 243668 157596 0.35 steelhead 1695494 1617549 0.05 MCN chin1 1261187 292572 0.77 chin0 4098004 3909983 0.05 steelhead 610511 366647 0.40 1985 (Koski et al. 1986) LWG chin1 1742244 1730180 0.01 chin0 44008 42817 0.03 steelhead 2689579 2679990 0.00 LGS chin1 1114640 905272 0.19 chin0 28175 27094 0.04 steelhead 1124082 1073809 0.04 MCN chin1 2952613 902123 0.69 chin0 6562483 6411493 0.02 steelhead 840493 547710 0.35 1986 (Koski et al. 1987) LWG chin1 1625352 1572408 0.03 chin0 51628 50435 0.02 steelhead 3089551 3052991 0.01 LGS chin1 722867 694044 0.04 chin0 2644 2595 0.02 steelhead 1365409 1353341 0.01 MCN chin1 2486407 289768 0.88 chin0 6135379 5848547 0.05 steelhead 716335 344854 0.52 1987 (Koski et al. 1988) LWG (no juvenile bypass) chin comb 2497635 2466595 0.01 steelhead 3013986 3003262 0.00 LGS chin comb 1021760 987722 0.03 steelhead 953917 914724 0.04 MCN chin1 3450113 1689419 0.51 chin0 7029401 6665048 0.05 steelhead 1004967 690179 0.31 1988 (Koski et al. 1989) LWG (no juvenile bypass) chin comb 2790395 2775282 0.01 steelhead 4741920 4727691 0.00 LGS (no juvenile bypass) chin comb 828016 816661 0.01 steelhead 896311 889348 0.01 MCN chin1 2971263 2852953 0.04 chin0 6884478 6696264 0.03 steelhead 822944 815716 0.01 1989 (Koski et al. 1990) LWG chin1 2585531 2320084 0.10 chin0 steelhead 5246843 4447768 0.15 LGS chin1 1367170 1049898 0.23 chin0 steelhead 1601833 1255389 0.22 MCN chin1 2332718 624845 0.73 chin0 5019631 4574417 0.09 steelhead 943347 672196 0.29 1990 (Ceballos et al. 1991) LWG chin1 3200401 3187485 0.00 chin0 steelhead 6139402 6133053 0.00 LGS chin1 1379295 1362693 0.01 chin0 steelhead 952899 949249 0.00 MCN chin1 2344063 1854828 0.21 chin0 7099003 6997022 0.01 steelhead 620526 546444 0.12 1991 (Baxter et al. 1996) LWG chin1 2295306 2270166 0.01 chin0 15599 15196 0.03 steelhead 6282557 6112540 0.03 LGS chin1 1133986 1123886 0.01 chin0 4106 4024 0.02 steelhead 1110651 1104467 0.01 MCN chin1 1870638 735990 0.61 chin0 4017330 3673677 0.09 steelhead 549080 326148 0.41 1992 (Baxter et al. 1996) LWG chin1 2496805 2465920 0.01 chin0 6054 6011 0.01 steelhead 4406612 4291805 0.03 LGS chin1 1010333 1002191 0.01 chin0 3001 2914 0.03 steelhead 781074 771540 0.01 MCN chin1 2554039 2458090 0.04 chin0 6193658 5780411 0.07 steelhead 557989 538008 0.04 1993 (Baxter et al. 1996) LWG chin1 1782168 1684228 0.05 chin0 16469 16263 0.01 steelhead 6223636 5864193 0.06 LGS chin1 842973 497114 0.41 chin0 10042 9510 0.05 steelhead 1157983 826265 0.29 LMN chin1 540277 372484 0.31 chin0 76745 76416 0.00 steelhead 719493 536374 0.25 MCN chin1 1216056 558147 0.54 chin0 4239846 4019359 0.05 steelhead 450863 339958 0.25 1994 (Hurson et al. 1999) LWG chin1 2179329 2155140 0.01 chin0 6769 6725 0.01 steelhead 4701402 4653482 0.01 LGS chin1 696298 662504 0.05 chin0 4168 4028 0.03 steelhead 802378 774772 0.03 LMN chin1 1054265 895057 0.15 chin0 6459 5897 0.09 steelhead 570297 536374 0.06 MCN chin1 2217602 1913653 0.14 chin0 5079565 4621965 0.09 steelhead 562268 483206 0.14 1995 (Hurson et al. 1999) LWG chin1 3780519 3493439 0.08 chin0 31019 28855 0.07 steelhead 5915634 5522860 0.07 LGS chin1 1839335 1486521 0.19 chin0 19571 15345 0.22 steelhead 1212063 897354 0.26 LMN chin1 1485757 930707 0.37 chin0 12101 8750 0.28 steelhead 1234106 716598 0.42 MCN chin1 1739833 19301 0.99 chin0 6124925 5446950 0.11 steelhead 431125 1272 1.00 1996 (Hurson et al. 1999) LWG chin1 589890 516539 0.12 chin0 17346 16742 0.03 steelhead 4586509 4551937 0.01 LGS chin1 332765 329401 0.01 chin0 10008 9777 0.02 steelhead 1536236 1530064 0.00 LMN chin1 350398 322974 0.08 chin0 8755 8663 0.01 steelhead 966165 923598 0.04 MCN chin1 568781 23664 0.96 chin0 3309408 2921165 0.12 steelhead 370239 9626 0.97 1997 (Hurson et al. 1999) LWG chin1 281825 278121 0.01 chin0 90910 87012 0.04 steelhead 4322725 4205576 0.03 LGS chin1 194472 87600 0.55 chin0 60742 57011 0.06 steelhead 1928202 459160 0.76 LMN chin1 234739 119954 0.49 chin0 18848 18277 0.03 steelhead 1672872 591396 0.65 MCN chin1 458705 26207 0.94 chin0 5587014 5209575 0.07 steelhead 481333 26417 0.95 1998 (Hurson et al. 1999) LWG chin1 1604689 1491722 0.07 chin0 81806 78810 0.04 steelhead 5085525 4956044 0.03 LGS chin1 900122 888412 0.01 chin0 52896 50848 0.04 steelhead 1505203 1500648 0.00 LMN chin1 492765 487277 0.01 chin0 22953 21428 0.07 steelhead 949322 935917 0.01 MCN chin1 1045547 37341 0.96 chin0 8290717 7948235 0.04 steelhead 327396 10960 0.97 19991 LWG chin1 2173493 2044080 0.06 chin0 253340 250143 0.01 steelhead 3355165 3087680 0.08 LGS chin1 3532362 3489662 0.01 chin0 197307 192502 0.02
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