Independent Scientific Advisory Board

for the Northwest Power and Conservation Council,

Columbia River Basin Indian Tribes, 

and National Marine Fisheries Service

 851 SW 6th Avenue, Suite 1100

Portland, Oregon 97204

ISAB@nwcouncil.org

Review of the COMPASS Model 

Robert Bilby 

Susan Hanna 

Nancy Huntly, Chair 

Stuart Hurlbert 

Roland Lamberson 

Colin Levings 

William Pearcy 

Thomas Poe 

Peter Smouse 

Richard Alldredge, Ad Hoc Member, ISRP 

ISAB 2006-2 

March 15, 2006
ISAB Review of the COMPASS Model 

Contents 

Executive Summary........................................................................................ 1 

I. Introduction and Background...................................................................... 4 

Background on SIMPAS – the Currently Used Hydrosystem Passage Model............... 4 

Past ISAB and ISRP Comments on SIMPAS................................................................. 4 

Development of a New Passage Survival Model - COMPASS...................................... 6 

Review Request .............................................................................................................. 6 

II. Review Results........................................................................................... 7 

Model Capabilities.......................................................................................................... 7 

Model Complexity .......................................................................................................... 9 

Data-Based Model .......................................................................................................... 9 

Statistical Methods.......................................................................................................... 9 

Documentation.............................................................................................................. 13 

III. Concluding Comment............................................................................. 13 

References..................................................................................................... 14 
ISAB 2006-2 COMPASS Review

ISAB Review of the COMPASS Model 

Executive Summary 

NOAA Fisheries, along with federal, state, tribal agencies, and the University of 

Washington, are developing a new comprehensive fish passage model (COMPASS) that 

is intended to replace the currently used simulated fish passage model (SIMPAS). NOAA 

Fisheries has indicated that the new COMPASS model will be used extensively in the

new Federal Columbia River Power System Biological Opinion (BiOp) and that it should 

be subjected to thorough and transparent scientific review. On January 12, 2006, NOAA 

Fisheries requested that the ISAB review the partially completed COMPASS model 

specifically addressing several questions regarding the model capabilities, complexity, 

data usage, statistical protocols, documentation, and graphical interface. 

On January 27, 2006, following a briefing describing the development and status of the 

COMPASS model, the ISAB accepted this assignment. A summary of the ISAB’s 

findings and answers to questions posed by NOAA Fisheries are provided in this 

Executive Summary. Full explanations of the ISAB’s findings with technical details 

intended for the model developers and users are in the main body of the report. 

Capabilities of the COMPASS Model 

(a) How realistically does COMPASS portray the hydrosystem and variable river 

conditions? 

Since the model uses a daily time step, it should have the potential to portray the 

downstream movement of fish through the hydrosystem realistically and with sufficient 

detail to capture the impact of hydrosystem and river variability. It divides the model into 

individual modules for passage through the individual reservoirs and dams. It accounts 

for the spread of passage times through reservoirs. It separates the various modes of dam

passage (bypass, spill, turbine) and will account for day/night differences. It separately 

tracks transported fish. These are all critically important functions and all are treated 

pretty much as one would imagine them being treated. 

(b) Does the model allow users to simulate the effects of management actions? 

It provides for user input of fish release schedules, dam operation and fish transportation 

scenarios, and flow and temperature files (scenarios). One should be able to create any 

reasonable management scenario with these inputs. The real issue is how easy it will be 

to develop and input new scenarios. Our assessment of that will have to await the final 

model, examination of a complete manual, and a trial of the user interface. 

1 
ISAB 2006-2 COMPASS Review

(c) Does the model accurately reflect available data, particularly PIT-tag data? 

The in-river part of the model seems to be in place, but the part of the model that 

accounts for survival from below Bonneville Dam to the ocean and the return is not 

completed yet. To deal with a major limitation of two alternative passage models, 

SIMPAS and CRiSP, that component of COMPASS needs to be completed. 

(d) Does the model adequately characterize uncertainty in predictions? 

To account for uncertainty in the predictions, a stochastic version of the model will be 

needed, the lack of which was a major criticism of the SIMPAS model. At the time of

writing this review, the stochastic version was not yet available. 

(e) Does the model adequately account for hydrosystem effects occurring outside the 

hydrosystem? 

This is a major component of the model that was not completed at time of our review. In 

the model overview, the authors state that the downstream passage model and postBonneville mortality module operate independently and that they “would like” to 

integrate these into one modeling platform. It is imperative that they link these models 

into one platform. 

Model Complexity 

The SIMPAS model is too simplistic, treating all species, life history types, sizes of 

anadromous juveniles the same. By contrast, the CRiSP model is too complex, with the 

assumptions based too much on theory, with a lack of empirical evidence. COMPASS is 

similar to but less complex than CRiSP, providing a reasonable level of complexity, with 

sufficient detail to capture what is happening, without being overly demanding of 

knowledge that does not exist. In this vein, the ISAB recommends that a list of the model 

assumptions should be put together and placed up front in the manual. 

Statistical Methods 

The ISAB recommends several alternative estimation techniques that should improve the 

estimates (and thus) the parameters of the model. It is possible to over-fit a regression 

model to data, and while there are standard criteria for deciding “when to stop”, the real 

issue is that one wants a model that will generally behave well, under a wide variety of

circumstances. In that vein, for predictive power and for transportability from one site to 

another, a model should include only mechanisms that we understand, in mechanistic 

terms, and the model should be economical of explanation, wherever possible. At the 

same time one should be very wary of eliminating data sets because of small sample size. 

Instead data can be weighted to reflect the fact that some data is better than others. 

2 
ISAB 2006-2 COMPASS Review

Data Representation, Documentation, and User Interface 

These elements of the model are incomplete and in various stages of development. The 

model is designed to incorporate all the available data (but see pages 9-13, below). The 

plans for the stochastic model suggest that it will eventually reflect variability in the data 

and in the hydrosystem. The documentation is reasonable for this stage of development, 

but is not yet sufficient for complete review, nor is it currently adequate for operation of 

the model by the many new users who will need it. The graphical user interface has not 

yet been developed. 

Concluding Comment 

This new COMPASS model will be heavily used by many people in the Columbia River 

Basin and should prove to be a welcome addition to the analytical tools available to both 

scientists and managers alike. Our critique here is voluminous, but is explicitly intended 

to provide a series of strong but constructive suggestions to facilitate the continuing 

development of what we feel will be a valuable new modeling tool for the region. The 

model is still under active development, particularly the components for stochasticity and 

the Bonneville-to-ocean-Bonneville segment, and will profit from another review when it 

is complete. We look forward to that review. 

3 
ISAB 2006-2 COMPASS Review

ISAB Review of the COMPASS Model 

I. Introduction and Background 

Background on SIMPAS – the Currently Used Hydrosystem Passage Model 

The Simulated Passage (SIMPAS) model is a spreadsheet-based Excel module, produced 

in the mid-1990’s to evaluate the effects of various structural or operational management 

measures on passage and survival of juvenile salmonids through the mainstem Columbia 

and Snake River dams. Since its development, SIMPAS has been used extensively for a 

variety of hydroelectric system operational management decisions and structural fish 

passage improvements. SIMPAS was also recently used for analyses in development of 

the 2004 BIOP. Lately, SIMPAS has received a number of regional criticisms, among 

which are that: (1) it only produces seasonal survival predictions; (2) it is not stochastic; 

and (3) it does not adequately deal with effects expressed outside the hydrosystem. 

Past ISAB and ISRP Comments on SIMPAS 

While the ISAB and the ISRP have not done a specific in-depth review of SIMPAS, we 

have periodically commented on the model’s strengths and weaknesses (mostly its 

weaknesses), as documented below: 

(1) The ISRP Reimbursable Review of the Bonneville I. Decision Document (ISRP 

2001-11) was the first opportunity to comment on SIMPAS, as follows: 

To develop estimates of relative survival benefits as called for in the BiOp, the 

Decision Document employs the SIMPAS model. The ISRP referred to Appendix 

D of the BiOp for description of the SIMPAS model. The SIMPAS model 

consists of a simple set of deterministic mathematical calculations of by-reach and 

overall survival estimates of juvenile salmonids as they pass downstream in the 

Columbia and Snake rivers. Only the Bonneville portion was used in preparing 

the Decision Document. The model was used for analysis and evaluation of 

alternative devices intended to improve survival of juvenile salmonids at 

Bonneville Dam. The model requires explicit specification of fewer assumptions 

and is more straightforward than the models reviewed by the ISAB in ISAB 2001-

1 (www.nwcouncil.org/library/isab/isab2001-1.pdf), but of course the simplicity 

of this model constitutes a far-reaching implicit (and possibly unexamined) 

assumption. For example, the SIMPAS model includes no provision for 

addressing the impacts of fish passage routes on biodiversity of anadromous or 

resident species. The estimates of survival by size and life cycle stage for multiple 

resident and anadromous species are well beyond the information available and 

the capabilities of the model. 

4 
ISAB 2006-2 COMPASS Review

(2) From the ISAB Review of Giorgi et al. (ISAB 2002-1, pp 17-18): 

Giorgi et al. (2002) suggest employment of mathematical models, namely 

computer based models such as CRiSP or SIMPAS to estimate or predict the total 

mortality experienced by smolts in passing either a series of projects or a single 

dam. They attempt to identify key issues applying to the use of passage models in 

spill analysis. They identify a primary concern as being the criteria adopted for 

selecting the best estimate from a pool of estimates of spill efficiency and survival 

in spill, among other inputs that will be employed in the model analysis. "There 

will surely be factions that take exception to the resultant set of estimates." It 

seems more appropriate for a diverse technical group to develop and apply criteria 

that would be used for assembling the most representative set of inputs that could 

be applied as a standard. It is not clear that NMFS employment of the SIMPAS 

model to evaluate changes in expected smolt survival under four spill scenarios 

accurately depicted spill related survival, particularly under the extreme low flow 

conditions experienced in 2001. The only analysis they found that attempts to 

assess the change in survival for the smolt population subjected to different spill 

scenarios is the 2001 model analysis conducted by NPPC staff. It may not be 

totally satisfactory because the SIMPAS model used in the analysis probably 

needs updating. A number of improved estimates have become available since 

SIMPAS was constructed. Giorgi et al. (2002) doubt that it would be practical to 

attempt to design manipulative experiments that could isolate spill effects in the 

complex Columbia River system. 

 

The ISAB finds that the SIMPAS model is being used in the region for several 

kinds of applications, some of which are inappropriate or questionable. This 

model can be an important and useful tool, but it should be the subject of an 

update and verification, as suggested by Giorgi et al. (2002). In particular, input 

values for the SIMPAS model are most often ad hoc guesses, suffering from a 

lack of real data. It is important to build regular updates of the necessary 

parameter values into an ongoing, long-term monitoring and evaluation plan. As 

above, the effects of speed in passing smolts through the concrete when water is 

spilled should be factored into modeling. 

(3) From the ISAB Flow Update and Clarification Report (ISAB 2003-1): 

Bruce Suzumoto used SIMPAS to estimate changes in survival that might result 

from the Council's proposed modifications. But then he offers, "SIMPAS is not 

highly sensitive to changes in flows. Another regional model might be better to 

estimate the effect of flow changes on total system survival." (Suzumoto, B. 2002. 

Mainstem juvenile survival: CRiSP modeling results. Memorandum of December 

2, 2002 to Council members) 

5 
ISAB 2006-2 COMPASS Review

(4) The ISAB Flow Symposium Report (ISAB 2004-2) offered the following comment 

about the strengths and weaknesses of available (SIMPAS and CRiSP) models: 

No present model is capable of an accurate, precise analysis of the effects of the 

Montana proposal. The two process models we examined, CRiSP and SIMPAS, 

contain unrealistic or untested assumptions. The models include an a priori effect 

of flow on survival based on simplified conditions, which to some extent 

predetermines the result. Nonetheless, both models suggest that the effect of the 

Montana System Operations Request will be small. The regression models we 

saw often contain so much data scatter that model fitting is itself uncertain. 

Because the regression models use the same data with the same scatter, however, 

comparisons can be made among flow conditions. The results to date from the 

Fish Passage Center and NOAA Fisheries regression models suggest the Montana 

System Operations Request would result only in small changes. 

Development of a New Passage Survival Model - COMPASS 

In light of the above criticisms of SIMPAS, NOAA Fisheries, along with federal, state, 

tribal agencies, and the University of Washington, are developing a new comprehensive 

passage model, COMPASS, that is intended to replace SIMPAS. Prior to initiating model 

development, NOAA Fisheries reviewed and analyzed all existing passage models to 

determine if components of one or more of these models could be used in the new model 

that would address the concerns elaborated above. The results of that review and analysis 

are described in a NOAA, Northwest Fisheries Science Center Report (NOAA Fisheries 

Memo, 2005). 

The results of that comparative review indicated the CRiSP model (developed by the 

University of Washington, with funding from BPA) had a number of desirable features 

that have now been incorporated into the COMPASS model. 

Review Request 

NOAA Fisheries has indicated that the new COMPASS model will be used extensively in 

the new Federal Columbia River Power System Biological Opinion (BiOp) and that it 

should be subjected to thorough and transparent scientific review. On January 12, 2006, 

NOAA Fisheries requested the ISAB to review the partially completed, new fish passage 

survival model, COMPASS, specifically addressing the following questions: 

(1) Does the model successfully perform the desired capabilities, as listed below? 

a) realistically portray the hydrosystem and variable river conditions 

b) allow users to simulate the effects of management actions 

c) accurately reflect available data, particularly PIT-tag data 

d) characterize uncertainty in predictions

e) account for hydro-system-related effects that occur outside the hydrosystem 

6 
ISAB 2006-2 COMPASS Review

(2) Is the model too complex or too simple?

(3) Does the model realistically represent the data and its variability?

(4) Are the statistical methods sound?

(5) Is the documentation adequate?

(6) Is the graphical user interface easy to understand, intuitive, and transparent? 

On January 27, 2006, following a briefing describing the development and status of the 

COMPASS model, the ISAB accepted this assignment with no alterations to the above 

review questions. 

II. Review Results 

Model Capabilities 

(a) How realistically does it portray the hydrosystem and variable river conditions? 

Since the model uses a daily time step, it should have the potential to portray the 

downstream movement of fish through the hydrosystem realistically and with sufficient 

detail to capture the impact of hydrosystem and river variability. It divides the model into 

individual modules for passage through the individual reservoirs and dams. It accounts 

for the spread of passage times through reservoirs. It separates the various modes of dam

passage (bypass, spill, turbine) and will account for day/night differences. It separately 

tracks transported fish. These are all critically important functions and all are treated 

pretty much as one would imagine them being treated. 

(b) Does it allow users to simulate the effects of management actions? 

It provides for user input of fish release schedules, dam operation and fish transportation 

scenarios, and flow and temperature files (scenarios). One should be able to create any 

reasonable management scenario with these inputs. The real issue is how easy it will be 

to develop and input new scenarios? Our assessment of that will have to await the final 

model, examination of a complete manual, and a trial of the user interface. 

(c) Does the model accurately reflect available data, particularly PIT-tag data? 

The downstream part of the model seems to be in place, but the part of the model that 

accounts for survival from below Bonneville Dam to the ocean and the return is not 

7 
ISAB 2006-2 COMPASS Review

completed yet. To deal with a major limitation of the SIMPAS and CRiSP models, that 

component of the model needs to be completed. 

The fish travel time model presented graphically in Figure 13 (from Zabel and Anderson, 

1997) indicates that the model systematically underestimates the mode of the actual 

distribution of the data. This could lead to overestimating the frequency with which long 

travel times occur, because the right tail of the modeled distribution is overestimated. 

Furthermore, the travel time models in Zabel and Anderson (1997) exhibited a significant 

lack of fit (p < 0.05) in 22 out of 46 data sets. 

The authors state in their Proposal for Final Data Set for Passage Models that they will 

use the harmonic mean as a measure of travel time, because it behaves more like the 

median than the arithmetic mean. Why not just use the median travel time directly? 

In their Passage Model Design Considerations, Figure 2 shows system survival, plotted 

against water travel time. It is clear that the 2001 data point is different from the other 

data, but the statement following the figure, “The relationship indicates that in years 

of poor flow (high water travel times) fish survive at relatively low levels" seems too 

strong, inasmuch as it is based on one anomalous observation. 

There are some statistical issues for the translation of real data into model inputs (see 4, 

below) that may have some impact on the accuracy of the chosen model values, but the 

model is designed to take advantage of the real data, as they come in. 

(d) Does the model adequately characterize uncertainty in predictions? 

To account for uncertainty in the predictions, a stochastic version of the model will be 

needed, the lack of which was a major criticism of the SIMPAS model. At time of writing 

this review, the stochastic version was not yet available. There are not enough details 

presented to evaluate stochasticity in reservoir survival adequately. For example, it is not 

clear what statistical distribution is assumed when sampling from an error term with 

variance based on the error term from the regression equation used to predict –ln(Sg.p). 

Nor is it clear how the assumed distribution will be justified. 

There is a distinction to be made between varying the parameters over some range, even 

if drawing from a distribution, and varying the outcome for individual fish, given a set of 

fixed parameter values for the model, determined via multiple sampling runs of any 

particular model. Our sense is that parametric uncertainty will yield a greater degree of 

outcome variability, and that the dam passage algorithm will be “stochasticized” by the 

imposition of variability on top of the deterministic models, but exactly how this is to be

done is not entirely clear from the write-up of COMPASS. For the travel time parts of the 

model, it reads as though variability will be imposed on individual fish transit times, but 

again, the report is unclear on this aspect of the problem. 

8 
ISAB 2006-2 COMPASS Review

(e) Does the model adequately account for hydrosystem effects occurring outside the 

hydrosystem? 

There are two major pieces of the model that were not completed at time of review. The 

stochastic element was one of them. This is the other. In the model overview, the authors 

state that the downstream passage model and post-Bonneville mortality module operate 

independently and that they “would like” to integrate these into one modeling platform. 

It is imperative that they link these models into one platform. 

Model Complexity 

The SIMPAS model is too simplistic, treating all species, life history types, sizes of 

anadromous juveniles the same. By contrast, the CRiSP model is too complex, with the 

assumptions based too much on theory, with a lack of empirical evidence. COMPASS is 

similar to but less complex than CRiSP, providing a reasonable level of complexity, with 

sufficient detail to capture what is happening, without being overly demanding of 

knowledge that does not exist. In this vein, we would recommend that a list of the model 

assumptions should be put together and placed up front in the manual. 

Data-Based Model 

The downstream migration model is designed to incorporate the available data (but see 

below). The plans for the stochastic model suggest that it will eventually reflect 

variability in the data and in the hydrosystem. The state of development of the model is 

not yet sufficient, nor is it well enough documented to say more at this time. 

A late-breaking missive came in from the COMPASS team, just before submission of this 

report, describing the progress on the stochastic version of the model. We had insufficient 

time to evaluate that addition, before submission of our report. We suggest that a careful 

review of the stochastic aspects of the COMPASS model should be dealt with at the same

time as the below-Bonneville portion of the model (see just below), when the work is 

finally completed and available for another review. 

Statistical Methods 

The current version of COMPASS incorporates laudable attempts to connect the model 

parameters with the real data, enabling adjustment of parameters as real world experience 

accumulates. There are some guiding principles that should govern any effort to estimate 

parameters for such a complex model, and there are improvements in estimation 

techniques that should improve the estimates (and thus) the parameters of the model. In 

the spirit of improving statistical performance, the ISAB would recommend the 

following: 

(a) It is possible to over-fit a regression model to data, and while there are standard 

criteria (R2

-values, AIC and the more conservative AICC criteria, likelihood ratios) 

9 
ISAB 2006-2 COMPASS Review

for deciding “when to stop”, the real issue is that one wants a model that will 

generally behave well, under a wide variety of circumstances. In that vein, 

For predictive power and for transportability from one site to another, a model should 

include only mechanisms that we understand, in mechanistic terms. And while we may 

be able to improve performance ever so slightly at one site by adding another variable, 

the model should be economical of explanation, wherever possible. It is important to 

allow testing of more elaborate models, where and when it becomes appropriate, but 

parsimony of explanation should be the goal, everything else being equal. 

(b) Log-linear models facilitate the use of simple statistical families for purposes of

fitting. Estimated coefficients can be expected to vary from place to place and year to 

year, and we need to allow for that, but they should behave coherently. For example, 

if a variable increases survival in general, we should expect it to yield a variable (but 

generally positive) β-coefficient, not sometimes positive and sometimes negative. 

Higher order interactions that are significant in one reservoir, but not in the next, or that 

are strongly positive in one reservoir and strongly negative in the next, are an indication 

that one has over-fit noisy data, and that the effect is not well estimated in general. It is

not generally a good idea to turn sampling noise into “signal.” 

(c) A log-linear regression model (reflecting non-linear survival probabilities) which also 

employs high order polynomial functions of its predictive x-variables is a statement of 

ignorance about the variables in question, and is not mechanistically credible. It is 

good to have a model that allows the users to evaluate higher order functions, but for 

routine use, it is better to use defensible log-linear functions. We suspect that the 

authors are systematically over-fitting their data (Tables 3 & 4, page 15). 

We also note that the log-linear models presented do not (currently) have an intercept 

term. That creates unnecessary statistical limitations later. For example, R2

- values are 

presented as indicators of model fit in Tables 3, 4, 5 and 6, as well as in Figures 6, 7, 8 

and 9. Unless these R2

-values are adjusted for the lack of intercept terms, they cannot be 

interpreted in the usual manner. A proper cascade of log-linear models would be the 

following (using the model in Figure 4 to illustrate): 

= −β + ε 0 ln(S )

t (log-intercept model) 

S = −β −β t + ε t 0 1 ln( ) (log-linear model)

 S = −β −β t −β t + ε , (log-quadratic model) t

2

0 1 2 ln( )

with the understanding that even if t = 0 (no elapsed time), survival probability may be 

less than ‘1’, and that one should probably not extend beyond the second (log-linear) 

10 
ISAB 2006-2 COMPASS Review

version of the model without compelling evidence of cubic effects, in which case, one 

probably has the wrong mechanistic description, and needs to reconsider the model. 

(d) Many of the regression models used in COMPASS are an attempt to predict survival, 

as functions of some set of predictive variables. Survival is bounded by both ‘0’ and 

‘1’, and the authors realize that one cannot model S as a linear equation near the 

boundaries, so they use a set of exponential models of the general form: 

S( ) X = exp{− fn(θ , X )} or lnS(X ) = − fn(θ, X ) , 

where fn(θ, X) is some regression function of the measured x- (hopefully predictive) 

variables, and θ is a set of parameters (regression coefficients, to be estimated). We

have commented on the choice of those models above. 

The authors have recognized that the measured survival estimates from the field are 

themselves of variable reliability, some having much higher variance than others. To a 

first approximation, the variance of an observed value of S is S• (1 – S)/N. Given that 

small sample sizes yield less reliable data, for any given value of S, the authors have 

chosen to suppress (ignore) data sets for which the sample sizes were “too small”, but 

have chosen to weight the other data points equally. As a matter of standard operating 

procedure, there is almost no justification for eliminating data for any reason other than 

verifiably mistaken values. To eliminate substantial portions of the project survival 

estimates because of large standard errors is especially troubling. 

We would suggest that all of the data should be used, preferably with inverse variance 

weighting (see below), and that some compelling justification be provided before data are 

removed. The data are never perfect, and some data are better than others, but they are all 

we have that connects the model with the real world! 

Standard theory in binomial regression also suggests that one should weight all the data 

by the inverses of their respective variances. When conducting a binomial regression of S

on X, the values near ‘0’ or ‘1’ should receive substantial weight (small variances), and 

values near ‘ 1⁄2’ should receive less weight (large variances), for any given sample size. 

Weights of wi = Ni / Si•(1 – Si) are appropriate. 

The authors are regressing lnS on a set of x-variables, and the weighting scheme has to 

change from that described above (see Logit Regression, below). In particular, under that 

type of analysis, the survival values near ‘ 1⁄2’ acquire high weight, and those near ‘0’ or 

‘1’ acquire substantially less weight, for any given sample size. In either case, ignoring 

the profound differences in precision of the S-values near ‘ 1⁄2’ and those near ‘0’ or ‘1’ is 

not making good use of (hard to come by) data. Differences in sample size are only part 

of the estimation strategy. The plots on pages 17 & 18 of the preliminary report indicate 

what happens when relative precision is ignored. The authors can do much better. 

(e) The plots on pages 17 and 18 are revealing in yet another way. We understand that 

Cormack-Jolly-Seber estimates of survival can occasionally exceed ‘1’, particularly 

11 
ISAB 2006-2 COMPASS Review

when the parametric value of S is near ‘1’ and the sample size is small. The best 

interpretation is that the estimates “cannot be distinguished from complete survival”, 

but it makes no sense to fit a regression model that attempts to mimic an S-value of 

1.02, simply because that is what we “poorly perceive” in the data. Some of the 

regression models in the preliminary report provide estimates of S that exceed ‘1’, 

which requires that fn(θ , X) < 0, rendering exp{−fn(θ, X)}> 1, some β-coefficients 

being positive and some negative. The regression has become pathological. 

There is an easy (and standard) way to deal with this particular problem. Instead of using 

lnS(X ) = − fn(θ, X ) to represent the relationship, we use a logit regression instead, 

Y = ( , ) 1 ( )

( ) ln fn X

S X

S X = − θ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− , 

which is sigmoid, asymptotically approaching both S → ‘0’ and S → ‘1’. It has all the 

nice features of lnS(X ) = − fn(θ, X ) , uses data weights of wi = Ni•Si•(1 – Si), and (most 

importantly) stays bounded between ‘0’ and ‘1’, whether the β-coefficients are positive 

or negative, and whether exp{ − fn(θ, X ) } is > or < ‘1’. We can also write the model as 

0 ≤

1 exp{ ( , )}

exp{ ( , )} (X) fn X

fn X S

θ

θ

+ −

− = ≤ 1. 

By virtue of the weighting scheme, most of the weight goes to the S-values near ‘ 1⁄2’. To 

allow for the fact that we have some “observed” values of S ≥ 1, we adjust the Y = logit 

(S) values as follows. If Si = si ÷ Ni, then we set Yi = (si + 1)/(Ni – si + 1), which - for the 

case of observed Si ≥ 1 - becomes Yi = logit (Si) = (Ni + 1)/1. We have simultaneously 

constrained the predicted survival values between ‘0’ and ‘1’ and have weighted the 

observations appropriately, with more weight going to the values near ‘ 1⁄2’. In other 

words, instead of using the cascade of models shown above, we would use 

= − − = −β + ε 0 Y ln(S ) ln(1 S ) t t t (logit intercept model) 

Y = S − − S = −β −β t + ε t t t 0 1 ln( ) ln(1 ) (logit-linear model)

 Y = S − − S = −β − β t − β t + ε , (logit-quadratic model) t t t

2

0 1 2 ln( ) ln(1 )

Converting to logit regression models (with an intercept, β0) will cost the authors very

little estimation or programming time, and the modeling results should improve. 

(f) The preliminary report also provides estimation procedures for spill, water flow, and 

travel times. We see mixtures of different types of models, some linear (but with 

quadratic and cubic terms), some exponential, some logistic, and so on. 

12 
ISAB 2006-2 COMPASS Review

Some effort to establish a relatively coherent modeling and estimation philosophy would 

seem to be in order, based on what the theory tells us, and based on the best of modern

statistical practice. Our intent is not so much to fine tune as to recommend “an approach.” 

The users of the COMPASS model will need something relatively simple, coherent, and 

well behaved, while retaining the flexibility that is currently being introduced. Our earlier 

comments on fish travel time distribution modeling are appropriate in this vein. 

Documentation 

The documentation is reasonable for this stage of development, but is not yet sufficient 

for complete review, nor is it currently adequate for operation of the model by the many 

new users who will need it. 

Graphical User Interface 

Not yet developed. 

III. Concluding Comment 

This new COMPASS model will be heavily used by many people in the Columbia River 

Basin and should prove to be a welcome addition to the analytical tools available to both 

scientists and managers alike. Our critique here is voluminous, but is explicitly intended 

to provide a series of strong but constructive suggestions to facilitate the continuing 

development of what we feel will be a valuable new modeling tool for the region. The 

model is still under active development, particularly the components for stochasticity and 

the Bonneville-to-ocean-Bonneville segment, and will profit from another review when it 

is complete. We look forward to that review. 

13 
ISAB 2006-2 COMPASS Review

References 

Giorgi, A., M. Miller, and J. Stevenson. 2002. Mainstem passage strategies in the 

Columbia River System: Transportation, spill, and flow augmentation. Report 2002-03. 

Northwest Power Planning Council, Portland, OR. 

ISAB Report 2002-1. Review of Giorgi et al. Report “Mainstem Passage Strategies in the 

Columbia River system: Transportation, Spill, and Flow Augmentation”. 

 

ISAB Report 2003-1. Review of Flow Augmentation:Update and Clarification. 

ISAB Report 2004-2. ISAB Findings from the Reservoir Operations/Flow Survival 

Symposium.

ISRP Report 2001-11. ISRP Preliminary Review of the Corps of Engineers’ Bonneville 

Decision Document – Juvenile Fish Passage Recommendations October 2001. 

NOAA Fisheries Memo. 2005. Review of Fish Passage Models. 

Zabel, R.W., and J.J. Anderson. 1997. A model of travel time of migrating juvenile 

salmon, with an application to Snake River spring Chinook. North American Journal of 

Fisheries Management 17:93-100. 

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