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International Journal of Water Sciences

Assessment of Spring Chinook Salmon Habitat Suitability in the San Joaquin River Using a 2-D Depth-Averaged Model Regular Paper

Lubo Liu1,* and Joaquin Ramirez1

1 Department of Civil and Geomatics Engineering, Lyles College of Engineering, California State University Fresno, Fresno, US * Corresponding author E-mail: [email protected]

Received 9 Sep 2013; Accepted 22 Nov 2013 DOI: 10.5772/57437 © 2013 Liu and Ramirez; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract The San Joaquin River Restoration Program (SJRRP) provides adult Chinook salmon with a passage to upstream spawning beds and a safe route for juveniles returning to the Delta. A two-‐‑ dimensional depth-‐‑averaged hydrodynamic model based on the RMA10 scheme was developed to simulate the hydraulic properties (current velocities, depth, water surface elevation) of three proposed alternative migration pathways to explore flow patterns and offer useful insights into the effects of hydrodynamic alterations of the channel, a critical capability for determining the best passage for migration. The finite element model reasonably described the hydrodynamic conditions and calculated a Suitability Index (SI) for the habitat for spring-‐‑run Chinook salmon, with a Nash-‐‑Sutcliffe Coefficient (NSC) of 0.75 for discharge and 0.56 for water surface elevation (WSE) respectively. The alternatives analysed were found to be characterized by similar SI distributions under the same boundary conditions. Alternatives 2 and 3 had higher overall Weighted Area Habitat Suitability Index (WAHSI) values and would thus be expected to provide better environments for salmon migration than Alternative 1.

Normalized cross-‐‑ correlation calculations revealed fair correlations between the WAHSI values and upstream discharge or downstream water surface elevation. The hydrodynamic model may also provide a reference for similar suitability studies of salmon habitat in other inland rivers.

Keywords Hydrodynamic Model, Habitat Suitability, Salmon Migration, Correlation

1. Introduction

As the second longest river in California, the San Joaquin River (SJR) is a vital natural resource for numerous residents and industries. It provides an array of utilities within the Central Valley and is home to some of California’s most productive agricultural areas [1]. Headwaters for the river originate in the high Sierra Nevada, mainly from snowmelt and runoff [2]. Eventually the SJR conjoins with the Sacramento River to form the largest river delta on the west coast of North America [3].

1Lubo Liu and Joaquin Ramirez: Assessment of Spring Chinook Salmon Habitat Suitability in the San Joaquin River Using a 2-D Depth-Averaged Model

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ARTICLE

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The river is crucial for the propagation and survivability of Chinook salmon and other aquatic species and wildlife, but over the years it has experienced considerable hydrologic disconnection along its reaches due to extensive water diversion. Indigenous salmon populations have suffered as a result and their numbers have decreased significantly due to insufficient flows and anthropogenic activities [4]. In order to restore salmon and other fish populations to a point of self-‐‑sustainment, the San Joaquin River Restoration Program (SJRRP) was established in 2006 to maintain a continuous flow from the Friant Dam to its confluence with the Merced River. Due to practical limitations, routing the flow along several alternative pathways has been considered [5]. A critical task for the SJRRP, the so-‐‑called “Reach 4B Project”, was to modify and improve the channel capacity of Reach 4B (which is separated into 4B1 and 4B2, shown in Figure 1) of the SJR. Multiple scenarios for the restoration of the river and for modifications of existing SJR channels were designed and studied to ensure fish passage and adequate flow throughout the study area [6].

Natural Chinook salmon runs along the SJR (above the Merced River Confluence) originally occurred in fall, spring and in late autumn for some species. However, all runs had ceased by the late 1940s due to water diversion [7]. In a natural river system, salmonids have evolved to exploit natural flow patterns in streams so that migrations can take place when water characteristics are ideal [8]. However, anthropogenic activities alter natural settings and offset the timing of advantageous river conditions and hence salmonid migration [9]. As part of the effort to restore the river’s natural conditions, a great deal of research has been devoted to the study of habitat and flow relationships in recent years [9-‐‑11]. In relation to California’s waterways, researchers have mainly focused on the delta region with only limited investigations conducted for the SJR, especially for the middle section of the river [12]. When salmon return to their spawning grounds, they must complete their migration within a certain amount of time and with adequate reserves of energy in order to complete their life cycle [13]. Hydrodynamic conditions affecting salmon passage include the water velocity, depth and water quality, all of which are important factors for their migration. Sustained water velocity and water depth provide opportune passage conditions for the successful upstream migration of adult salmon [14, 15]. An in-‐‑depth hydrodynamic investigation is therefore essential to support efforts to better delineate the impact of flow characteristics on salmon migration and habitat conditions.

Modelling methods have been very effective tools for this type of riverine study and several hydrodynamic models have been constructed for the SJR. However, most have

been one-‐‑dimensional [12, 16], providing a large-‐‑scale overview of the river network. Considering the complexity and heterogeneous properties of the SJR, a two-‐‑dimensional model is more suitable for describing detailed local conditions such as those critical for the progress of salmon migration [17-‐‑20].

The goal of the SJRRP’s Reach 4B was to provide a passage for adult Chinook salmon to spawning beds further upstream and a safe route for juveniles to the delta [5]. To this end, the objective of this research was therefore to model the stream conditions, including current velocity, depth and water surface elevation (WSE), for each of the three alternatives proposed in Project 4B under the same hydrologic/hydraulic boundary conditions. A two-‐‑dimensional depth-‐‑averaged model incorporating disconnected portions of the SJR was developed based on the RAM10 scheme and used to simulate these local river characteristics and conditions to further explore the correlations between river flow and salmon migration under the different alternatives proposed. The model facilitates the development of a better understanding of the effects of different boundary conditions, both upstream and downstream, on salmon habitat suitability, survival and migration conditions. Model simulations allow the exploration of flow patterns and enable users to compare alternative scenarios. Modelling results also provides insights into the hydrodynamic behaviour that would result from proposed river alterations and support the prediction and analysis of the consequent impact on the conditions for Chinook salmon runs.

2. Description of study area

The study area lies within the Middle San Joaquin-‐‑Lower Chowchilla watershed and extends approximately 57.6 river miles (92.2 km) from monitoring stations SJR near Dos Palos (SDP) to SJR at the Fremont Ford Bridge (FFB) near California Highway 140. The SJR is divided into different river segments in this area, designated Reaches 4A, 4B1, 4B2 and 5, Eastside Bypass and Mariposa Bypass (see Figure 1). Initially, the channel of pathway of the SJR consisted of Reaches 4A, 4B1, 4B2 and 5. Descriptions of each river reach are listed in Table 1. The original Eastside Bypass and Mariposa Bypass were utilized as flood control channels. However, the Eastside Bypass now conveys all water from upstream as a portion of the river and Reach 4B1 is hydraulically disconnected. The portion of the Eastside Bypass within our scope of study begins directly downstream of Reach 4A near SWA and extends to Reach 5 of the SJR. Mariposa Bypass is another channel designed to convey flood flow and connects the Eastside Bypass to Reach 4B2. Under normal flow conditions, the river flows from Reach 4A to the Eastside Bypass and re-‐‑enters the SJR at Reach 5.

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121.0 120.8 120.6 120.436.9

37.0

37.1

37.2

37.3

37.4

Longitude, oW

Lati

tud

e, o

N

Mariposa Bypass

San Joaquin River

Eastside Bypass

Reach 4B1

Reach 4B2

SDP

SWA

EBM

FFB

Reach 4A

Reach 5

J2

J1

5 miles

Alt1/2/3

Alt1/3

Alt1

Alt2

Alt2/3

Alt1/2/3

Alt3

North

Figure 1. Geographic map showing the reaches in the modelling domain of the San Joaquin River (SJR) (Alt – Alternative, 1 mile = 1.6 km). Monitoring stations (SDP, SWA, EBM and FFB) are described in Table 2.

Reach/bypass Length (miles)

Flow capacity (cfs)

Connections Usage CurrentStatus

Reach 4A 13.5 4500 SDP** – SWA** River flow F

Reach 4B1 21.3 Unknown SWA – J1*Runoff, receiving water from agricultural practices and rain events

NF

Reach 4B2 11.4 10000 J1 – J2 Occasional flood water received from the Eastside Bypass and backflow from Reach 5

F

Reach 5 17.5 26000 J2 – FFB**

River flow received from the Eastside Bypass, Reach 4B2 and agricultural return flows

F

Eastside Bypass 21.8 15700 (average) SWA–EBM**–J2 Flood control and SJR flow F MariposaBypass

3.4 8000 EBM–J1 Flood flow, transporting water from the Eastside Bypass to the SJR

F

F: Functional; NF: Not Functional; *: J1 and J2 -‐‑ Junction points; **: SDP -‐‑ SJR near Dos Palos, EBM -‐‑ Eastside Bypass below Mariposa Bypass, SWA -‐‑ SJR near Washington Rd, FFB -‐‑ SJR near Washington Rd.

Table 1. Reaches and bypasses in the middle of the San Joaquin River (SJR) (1 mile = 1.6 km; 1 cfs = 0.028 cms)


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Station Description Station ID Reach/confluence Station Location,

NAD83 Agency

SJR near Dos Palos SDP 4A 36.9949, -‐‑120.501 CADWR*

SJR near Washington Rd SWA 4A 37.1114, -‐‑120.591 CADWR Eastside Bypass below Mariposa Bypass

EBM Eastside Bypass 37.2060, -‐‑120.697 CADWR

SJR at Fremont Ford Bridge FFB Reach 5 37.3099, -‐‑120.931 USGS**: CADWR -‐‑ California Department of Water Resources, USGS -‐‑ U. S. Geological Survey

Table 2. Monitoring stations in the study river reaches

The data used for the model development and calibration, including bathymetry, channel flow rate and WSE, were obtained from the U.S. Geological Survey (USGS), the U.S. Bureau of Reclamation (USBR) and the California Department of Water Resources (CADWR). Bathymetry data were collected during 2010 and 2011 by USBR using GPS and the Acoustic Doppler Current Profiler (ADCP) at a spatial interval of 20 feet. Flowrate and river stage data were collected for the year of 2011 (from January 1st to September 30th) by the four CADWR and USGS in-‐‑situ river gauge stations located in the study area at 15 minute intervals. Station descriptions are listed in Table 2. The year 2011 was selected for model calibration because adequate interim flows were released from upstream and the data for this period are quality-‐‑assured by reporting agencies. The geographic boundary of the SJR was determined using coordinates from Google Earth based on the WGS84 global reference system. The data collected from multiple government agencies were converted and georeferenced using the same coordinate system and reference datum, namely the North American Vertical Datum NAVD 88 and California State Plane, Zone 3, North American horizontal Datum NAD 83.

3. Hydrodynamic model

A vertically-‐‑integrated hydrodynamic model was developed using the finite element scheme RMA10 [21] to describe the flow velocity, water depth and WSE. The governing equations in the x and y directions are as follows:

0)( =!!+

!!+

!!+

!!+

!!

y

V

x

Uh

y

hV

x

hU

t

h

(1)

2 2 22

1/3

1 ( ) ( )

( )( ) cos

xx xy

U U Uh hU hV fVht x y

U Uh hx x y y

Ugn U Va hgh Wx x h

" "#

$ %

! ! !+ + & =! ! !

! ! ! != +! ! ! !

+! !& + & +! !

(2)

2 2 22

1/3

1 ( ) ( )

( )( ) sin

yx yy

V V Vh hU hV fUht x y

V Vh hx x y y

Vgn U Va hgh Wy y h

" "#

$ %

! ! !+ + + =! ! !

! ! ! != +! ! ! !

+! !& + & +! !

(3)

Where VU , are the depth-‐‑averaged velocities in the yx, directions; h is the water depth and a is the

bottom surface elevation; g is the acceleration due to

gravity; W is the wind velocity; % is the wind direction; $ is an empirical wind coefficient; and f is the Coriolis parameter. n denotes the Manning’s roughness coefficient and " is the depth-‐‑averaged eddy viscosity. Horizontal mixing was described using the Smagorinsky eddy parameterization:

!!+

!!+

!!+

!!== )(

212

22

x

V

y

U

y

V

x

UAA

mS'"

(4)

where S" is the eddy viscosity; ' is a constant in the

range 0.01-‐‑0.5 ( 05.0=' was used for this study) and A is the area of the current element. The horizontal turbulent mixing of momentum was typically ignored in some previous hydrodynamic models of the SJR [22, 23]. Our results suggest that the model is insensitive to the eddy viscosity within a range from 0 to 10 m2/s, so a constant value of 1.0 m2/s was used for the minimum eddy viscosity. Values in the different regions were varied depending on the element size and the velocity gradients according to Equation 4. Wind stress and Coriolis force, both of which typically play a critical role in large water bodies such as oceans or lakes, were neglected in this model of a small-‐‑scale river section.

For the initial conditions for the model, it was assumed that the river was at rest at the start and it took a considerable time (ten days in this case) for the model to


Alternative 1 Alternative 2 Alternative 3 Element # 2483 1841 2134 Node # 9568 7056 8209

Reaches included (Figures 1 and 2)

Reach 4A Eastside Bypass Reach 5

Reach 4A Reaches 4B1 and 4B2 Reach 5

Reach 4A Eastside Bypass Mariposa Bypass Reach 4B2 Reach 5

Table 3. Finite Element Model (FEM) information

Salmon Species Swimming Speed Minimum Depth

Cruising Velocity1* Sustained Velocity2* Darting Velocity3*

Spring Salmon 0 – 3.41 (ft/s) 0 – 1.04 (m/s)

3.41 – 10.79 (ft/s) 1.04 – 3.29 (m/s)

10.79 – 22.41 (ft/s) 3.29 – 6.83 (m/s)

0.80 (ft) 0.24 (m)

Autumn Salmon 0 – 3.41 (ft/s) 0 – 1.04 (m/s)

3.41 – 10.79 (ft/s) 1.04 – 3.29 (m/s)

10.79 – 22.41 (ft/s) 3.29 – 6.83 (m/s)

0.80 (ft) 0.24 (m)

1*: Cruising speed is the speed at which a fish can swim for an extended period of time, usually hours. 2*: Sustained is a speed that can be maintained for minutes. 3*: Darting represents a single effort or burst which is not maintainable.

Table 4. Salmon swimming capabilities (velocity, depth) reported in the literature [9, 13, 15]

reach the actual initial conditions. The boundary conditions for the hydrodynamic simulation included a no leakage condition across the surface and the bottom, no wind stress and zero pressure at the free water surface, a drag stress condition at the bottom of the river, a discharge condition at the upstream and a WSE condition at the downstream.

In order to accurately delineate the complex physical boundaries of the SJR, a finite-‐‑element mesh was used (see Table 3) for this model. The sizes of the non-‐‑uniform elements were between 1 and 100 feet (Figure 2d).

4. Hydrodynamic considerations for salmon migration

To address the hydrodynamic impact on salmon in a river, Bell [15] divided the swimming capabilities of salmon into three speed categories (Table 4). Fish normally travel at a cruising speed for several hours during migration, at a sustainable speed over a few minutes for navigation through difficult areas and at a darting speed for feeding or escape [15]. Based on this behaviour, velocity can be manipulated for use as either an artificial barrier or as a means to attract fish. Ideally, cruising speed can be considered attractive, while sustained speed can become a barrier over an extended distance and darting speed an immediate barrier if the transition is rapid [15]. The ranges of velocity values of these three categories are listed in Table 4. A suitable minimum depth of 0.8 ft (0.244m) has also been recommended for the passage of adult spring Chinook salmon (Table 4) [14]. Although salmon have been observed travelling at depths less than those indicated in Table 4, at depths of less than 0.8 feet fish may suffer

injuries and compromise their migration [24]. In addition, when fish are not fully submerged they partially lose the ability to generate thrust [25]. In Thompson’s model, it was assumed that a safe passage depth of greater than 0.8 ft must be maintained over 25% of the stream width and must remain continuous for 10% of the cross section [14].

5. Results and discussion

The model simulated hydrodynamic flow characteristics under the three alternative water pathways for the spring Chinook salmon run. The two-‐‑dimensional finite meshes for the three alternatives are shown in Figure 2. Figure 2a shows the Alternative 1 which has flow from Reach 4A through the Eastside Bypass (passing through stations SWA and EBM) to Reach 5 and the RMA10 model for Alternative 1 does not include any finite-‐‑element mesh for Reaches 4B1 and 4B2. Alternative 2 (Fig. 2b) has flow from Reach 4A through the original course of the SJR (Reaches 4B1 and 4B2) to Reach 5 and the model for Alternative 2 does not include any mesh for the Eastside Bypass. Figure 2c includes all sections of the study domain. The currently preferred option (Alternative 3) consists of conveying a small amount of flow through reach 4B1, with the remaining restoration flow continuing down the Eastside Bypass, transferring into the Mariposa Bypass and re-‐‑entering the SJR in Reach 4B2 [26]. Therefore, the major fish route in Alternative 3 follows Reach 4A, Eastside Bypass, Mariposa Bypass, Reach 4B2 and Reach 5. Figure 2d shows an enlarged portion of the two-‐‑dimensional mesh in the area around station SWA.

The model was calibrated using the 2011 data set and deemed applicable for modelling the investigation of


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hydrodynamic conditions affecting salmon migration in the SJR. The discharge data were used for the upstream boundary condition at station SDP and the WSE data were used for the downstream boundary condition at station FFB. These data sets were recorded at 15-‐‑minute intervals between January 1st and September 1st 2011, by CADWR and USGS. Figure 3 shows these boundary conditions. In the convergence test, 0.1% and 0.05% were set as the convergence criteria for current velocities and WSE respectively, for each iteration within the same time

level. The data indicate that little flow (less than 100 cfs) occurred upstream at SDP in early spring until late March 2011 (before the 80th day of the simulation period). In 2011, the most abundant flow occurred from late spring to late summer, the period when the adult Chinook salmon in the spring run enter the freshwater to spawn in the autumn. The model simulated the discharge and the WSE in this period utilizing a time step (15 minutes) that was the same as the data collection interval for calibration.

SJR

EBM

FFB

SWA

SDP

Eastside Bypass

FFB

(a)

SJR

EBM

FFB

SWA

SDP

4B2

Eastside Bypass

4B1

(c)

SJR

EBM

FFB

SWA

SDP

(b)

SWA

Eastside Bypass4B1

(d)

Figure 2. Finite element meshes of three flow pathways: (a) Alternative 1; (b) Alternative 2; (c) Alternative 3; and (d) Enlarged 2-‐‑dimensional finite element meshes in the SJR near Washington Rd (SWA) region.


0 20 40 60 80 100 120 140 160 180 2000

1000

2000

3000

4000

5000Upstream condition: Discharge at SDP

(a)

Dis

cha

rge

(cfs

)

0 20 40 60 80 100 120 140 160 180 20050

60

70

80Downstream condition: WSE at FFB

(b)

Julian days, 2011

WS

E (

ft)

Figure 3. Boundary conditions: (a) Upstream condition: Discharge at the SJR near Dos Palos (SDP); and (b) Downstream condition: Water Surface Elevation (WSE) at the SJR at Fremont Ford Bridge (FFB). (1ft = 0.3048 m, 1 cfs = 0.0283 cms)

Data Stations SWA (Discharge) SWA (WSE) EBM (WSE) SDP (WSE)

MinimumRMSE 0.47 0.017 0.014 0.064

NSC 0.75 0.56 0.42 0.34

Table 5. RMSE and NSC results

The Manning roughness coefficient of the channel was manually adjusted to calibrate the model using the method of minimum normalized Root Mean Squared Error (RMSE), which is defined as follows:

o

N

t

i

o

i

m

X

XXN

RMSE=

!= 1

2)(1 (5)

where i

mX and i

oX are the modelled and observed

discharge/WSE at time it while N is the number of

observations. oX is the average of the observed values.

By varying the values of the roughness coefficient in Equations 2 and 3, we obtained the optimum value of 0.035, which resulted in the minimum RMSE .

In addition, to quantitatively describe the accuracy of model output, the Nash-‐‑Sutcliffe Coefficient (NSC) was calculated as follows,

2

1

2

1

)(

)(1

o

N

i

i

o

i

m

N

i

i

o

XX

XX

NSC

=

=

!

!!= (6)

where, NSC is the Nash-‐‑Sutcliffe model efficiency Coefficient. The error results including the minimum RMSE and the corresponding NSC are reported in Table 5.

Figure 4 compares the observed data for the discharge (Figure 4a) and WSE (Figure 4b) at SWA with the modelling results. Figures 5a and 5b compare observed WSE at SDP and at EBM, respectively, with the modelling results. The results obtained from the hydrodynamic model were generally found to be consistent with the observed data and found to reasonably describe both the WSE and the discharge at the observing stations. While the WSE values (Figures 4b, 5a and 5b) were simulated fairly accurately, simulating the discharge was relatively challenging due to the lack of discharge data for several minor tributaries along the river in the study area. The water surface elevation abruptly dropped from 93 ft (28.35 m) to 57 ft (17.37 m) and then rose up to 117 ft (35.66 m) between days 22 and 35 at Station EBM in Figure 5; these results appear to represent equipment malfunction or error. The water surface elevation (WSE) fluctuation of more than 50 ft (15.24 m) during these days was not reflected by the nearest functional gauge stations (see Figure 4b for upstream at SWA and Figure 3b for downstream at FFB).


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0 20 40 60 80 100 120 140 160 180 2000

2000

4000(a)

SJR Discharge at SWA

Dis

charg

e (c

fs)

observed

modeling

0 20 40 60 80 100 120 140 160 180 20090

100

110

120SJR WSE at SWA

Julian days, 2011

WS

E (

ft)

observed

modeling

Figure 4. Comparison of observed and modelling results of: (a) San Joaquin River (SJR) discharge at the SJR near Washington Rd (SWA); and (b) SJR Water Surface Elevation (WSE) at SWA. (1ft = 0.3048 m, 1 cfs = 0.0283 cms)

120 130 140 150 160 170 180 190 20090

100

110

120

130

(a)

SJR WSE at SDP

WS

E (

ft)

observed

modeling

0 20 40 60 80 100 120 140 160 180 200

60

80

100

120SJR WSE at EBM

(b)

Julian days, 2011

WS

E (

ft)

observed

modeling

Figure 5. Comparison of observed and modelling results for: (a) San Joaquin River (SJR) Water Surface Elevation (WSE) at the SJR near Dos Palos (SDP); and (b) SJR WSE at Eastside Bypass below Mariposa Bypass (EBM). (1ft = 0.3048 m)

The output from the validated and calibrated model was used to assess the habitat suitability of the river channel for the spring Chinook salmon run. Historically, there were four distinct salmon runs in the Sacramento-‐‑San Joaquin River system, designated according to the season in which the majority of the run entered the freshwater as adults [27]. The spring-‐‑run Chinook salmon entered the water system from late March through September, with adults staying in cool water habitats through the summer

and then spawning in the autumn from mid-‐‑August through early October. For this run, therefore, the hydrodynamic scenario in the summer season is especially critical for the salmon migrating from the ocean to upstream spawning grounds. In this research, the hydrodynamic model simulated water depth and velocity between late March and late June (between day 90 and day 181 of the simulation period) of 2011 and the corresponding habitat suitability for salmon was


quantified using the habitat Suitability Index (SI) [27]. Figures 6a and 6b were developed by the California Department of Fish and Wildlife (CADFW) [28, 29] and show the standard habitat SI for Chinook salmon for velocity (Velocity SI) and depth (Depth SI), respectively. The range of the dimensionless SI at any location in a river is between 1 and 0, representing the best and the worst habitat quality, respectively. To align with the standard

convention in the literature, metric units were used for the SI calculations, so velocities between 12.2 cm/s and 21.3 cm/s and depths between 30.5 cm and 61 cm constitute the best ranges for Chinook salmon (Figure 6). The best velocities in this method all support cruising velocity, which is consistent with the literature (Table 4). Figures 6a and 6b show that the best velocity ranges of velocity and depth are 12 – 22 cm/s and 35 – 60 cm, respectively.

0 10 20 30 40 50 60 70 80 90 1000.0

0.2

0.4

0.6

0.8

1.0

1.2(a)

SI

Velocity (cm/s)

0 20 40 60 80 100 120 140 160 180 2000.0

0.2

0.4

0.6

0.8

1.0

1.2(b)

SI

Depth (cm)

Figure 6. Suitability Index (SI) curves for Chinook salmon: (a) Velocity; and (b) Depth suitability (By California Department of Fish andWildlife (CADFW) [28]).

80 100 120 140 160 180 2000.0

0.2

0.4

0.6

0.8

1.0(a)

Vel

oci

ty W

AH

SI

Alternative 1

Alternative 2

Alternavite 3

80 100 120 140 160 180 2000.0

0.2

0.4

0.6

0.8

1.0(b)

Dep

th W

AH

SI

Alternative 1

Alternative 2

Alternavite 3

80 100 120 140 160 180 2000.0

0.4

0.8

1.2

1.6

2.0(c)

Julian days, 2011

Ov

era

ll W

AH

SI

Alternative 1

Alternative 2

Alternavite 3

Figure 7. Model Weighted Area Habitat Suitability Index (WAHSI) for three alternatives for: (a) Velocity suitability; (b) Depth suitability; and (c) Overall suitability.


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The representative values of velocity and depth for each element were the values at its centre, which were calculated by interpolating the values at all nodes of the element to the centre using Inverse Distance Weighted method (IDW). The calculated velocity and depth for each element were used for determining its SIs at different times using Figures 6a and 6b. The values of velocity SI, depth SI and the overall SI (velocity SI +depth SI) for the entire domain under investigation can be calculated using the following weighted average habitat suitability index (WAHSI ),

=

=

!

!"=

M

i

i

M

i

iji

j

A

ASI

WAHSI

1

1, )(

(7)

where j =1 for velocity, 2 for depth and 3 for the combined WAHSI for velocity and depth; M is the total number of wetted finite elements; and A is the area of element.

Figures 7a, 7b and 7c show the time variation of WAHSI values derived from the velocity SI, depth SI and the overall SI, respectively, for all three of the proposed alternatives. In the late spring (day 90) and early summer (day 181), neither velocity (with WAHSI around 0.3) nor depth (with WAHSI around 0.1) was deemed suitable for salmon migration. Under the conditions obtaining in the summer of 2011, the SIs of all the proposed alternatives increased from Day 90 to Day 145, then maintained these peak values for about 30 days. After this point, the hydrodynamic conditions for salmon fluctuate and deteriorate, so the period between mid-‐‑May and mid-‐‑June of that year would have been the best period for Chinook salmon migration. Generally, the impact of velocity is more stable than that of depth. Among the three proposed alternatives, the WAHSI values of Alternatives 2 and 3 were generally equal to or higher than those of Alternative 1, which incorporates the Eastside Bypass. The similar shapes of the depth (Figure 7b) and overall (Figure 7c) WAHSIs indicate that the overall WAHSI in this case is controlled by water depth, which was thus more critical for aquatic life than the velocity under the insufficient discharge conditions experienced during the modelling period.

0.200.160.140.120.100.080.060.040.020.00

SWA4B1

(e) Depth SI forAlternative 2

0.200.160.140.120.100.080.060.040.020.00

SWA

Eastside Bypass

(f) Depth SI forAlternative 3

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.30

SWA4B1

(b) Velocity SI forAlternative 2

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.30

SWA

Eastside Bypass

(c) Velocity SI forAlternative 3

0.200.160.140.120.100.080.060.040.020.00

SWA

Eastside Bypass

(d) Depth SI forAlternative 1

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.30

SWA

Eastside Bypass

(a) Velocity SI forAlternative 1

Figure 8. Spatial distribution of Suitability Index (SI) in the region at the SJR near Washington Rd (SWA) at day 130 for: (a) Velocity SIfor Alternative 1; (b) Velocity SI for Alternative 2; (c) Velocity SI for Alternative 3; (d) Depth SI for Alternative 1; (e) Depth SI for Alternative 2; and (f) Depth SI for Alternative 3.


0.200.160.140.120.100.080.060.040.020.00

SWA4B1

(e) Depth SI forAlternative 2

0.200.160.140.120.100.080.060.040.020.00

SWA

Eastside Bypass

(f) Depth SI forAlternative 3

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.30

SWA4B1

(b) Velocity SI forAlternative 2

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.30

SWA

Eastside Bypass

(c) Velocity SI forAlternative 3

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.30

SWA

Eastside Bypass

(a) Velocity SI forAlternative 1

0.200.160.140.120.100.080.060.040.020.00

SWA

Eastside Bypass

(d) Depth SI forAlternative 1

Figure 9. Spatial distribution of Suitability Index (SI) in the region at the SJR near Washington Rd (SWA) at day 170 for: (a) Velocity SIfor Alternative 1; (b) Velocity SI for Alternative 2; (c) Velocity SI for Alternative 3; (d) Depth SI for Alternative 1; (e) Depth SI for Alternative 2; and (f) Depth SI for Alternative 3.

Since the SWA confluence region (Figure 2) is common to all three alternative flow paths, it was used to compare the spatial distributions of the SIs at specific times. Figure 8 (a through f) shows the distribution of SI in the SWA confluence region on Day 130, when SI started to rise, for velocity SI (Figures 8a, 8c and 8e for Alternatives 1, 2 and 3 respectively) and for depth SI (Figures 8b, 8d and 8f for Alternatives 1, 2 and 3 respectively). Figure 9 shows the corresponding distributions on day 170, when the SIs started to fall near the end of the most suitable period. All SIs generally increased over the period from Day 130 to Day 170. Figure 10 shows the corresponding velocity vectors and depth distributions at day 130. Figures 8 and 9 show the significant improvement of the SIs for all three alternatives during the period. The areas with higher SIs increased in both velocity SIs and depth SIs. Most locations in the river reach on day 170 have higher

velocity SIs (greater than 0.6) than those on day 130. The improvement of the depth SI was not as significant as that of the velocity SI. Figures 8 and 9 show an improvement of 0.12 -‐‑ 0.2 for depth SI and 0.2 – 0.4 for velocity SI in many regions during this period. These observations were consistent with Figure 7.

Interestingly, there is some similarity between the time variation trend for the boundary conditions (Figure 3) and that of the WAHSIs (Figure 7) during the salmon migration period between Day 90 and Day 180, with an expected lag time. For example, the peak values of the discharge at SDP and the WSE at FFB occurred at around Day 100 and Day 87, respectively. To better understand how the boundary conditions impact salmon migration and thus predict the suitability for salmon migration (WAHSI) based on the upstream incoming flow or


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downstream WSE, it is necessary to identify any cross-‐‑correlations. The normalized cross-‐‑correlation between discharge or WSE and WAHSI was defined as follows [30]:

[ ] [ ]==

!

=

"

+=

N

i

N

i

kN

n

iuiw

nwknu

NCC

1

2

1

2

1

)()(

)()(

(8)

where NCC is the normalized cross-‐‑correlation function and has a value between -‐‑1 and 1, with 0 being completely unrelated and 1 or -‐‑1 highly correlated;

meanwhile )(iw and )(iu are the WAHSI and discharge

or WSE at the time step, respectively; i is the number of model data points; and n is the number of lag time steps between two correlated parameters.

100 cm/s

SWA4B1

(e) Velocity vector forAlternative 2

100 cm/s

SWA

Eastside Bypass

(f) Velocity vector forAlternative 3

5.00 m4.00 m3.50 m3.00 m2.50 m2.00 m1.50 m1.00 m0.50 m

SWA4B1

(b) Depth forAlternative 2

5.00 m4.50 m4.00 m3.50 m3.00 m2.50 m2.00 m1.50 m1.00 m0.50 m

SWA

Eastside Bypass

(c) Depth forAlternative 3

5.00 m4.50 m4.00 m3.50 m3.00 m2.50 m2.00 m1.50 m1.00 m0.50 m

SWA

Eastside Bypass

(a) Depth forAlternative 1

100 cm/s

SWA

Eastside Bypass

(d) Velocity vector forAlternative 1

Figure 10. Spatial distribution of depth and velocity vector in the region at the SJR near Washington Rd (SWA) at day 130 for: (a) Depth for Alternative 1; (b) Depth for Alternative 2; (c) Depth for Alternative 3; (d) Velocity vector for Alternative 1; (e) Velocity vector for Alternative 2; and (f) Velocity vector for Alternative 3.

Parameter Upstream Discharge (SDP) Downstream WSE (FFB)

Velocity Depth Overall Velocity Depth Overall

NCC 0.834 0.895 0.855 0.926 0.765 0.895 Lag Time (days) -‐‑50.7 -‐‑47.6 -‐‑47.8(53) 0.0 0.0 0.0

Table 6. Maximum and corresponding lag time for three alternatives


!2500 !2000 !1500 !1000 !500 0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

(a)

NN

C (

Vel

oci

ty)

Alternative 1

Alternative 2

Alternative 3

!2500 !2000 !1500 !1000 !500 0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

(b)

NN

C (

Dep

th)

Alternative 1

Alternative 2

Alternative 3

!2500 !2000 !1500 !1000 !500 0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

(c)

Lag time (Hours)

NN

C (

Over

all

)

Alternative 1

Alternative 2

Alternative 3

Figure 11. Correlations between upstream discharge and Model Weighted Area Habitat Suitability Index (WAHSI) for the three alternatives for: (a) Velocity WAHSI; (b) Depth WAHSI; and (c) Overall WAHSI

!2500 !2000 !1500 !1000 !500 0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

(a)

NN

C (

Vel

oci

ty)

Alternative 1

Alternative 2

Alternative 3

!2500 !2000 !1500 !1000 !500 0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

(b)

NN

C (

Dep

th)

Alternative 1

Alternative 2

Alternative 3

!2500 !2000 !1500 !1000 !500 0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

(c)

Lag time (Hours)

NN

C (

Over

all

)

Alternative 1

Alternative 2

Alternative 3

Figure 12. Correlations between downstream Water Surface Elevation (WSE) and Weighted Area Habitat Suitability Index (WAHSI) of three alternatives for: (a) Velocity WAHSI; (b) Depth WAHSI; and (c) Overall WAHSI.


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Figure 11 shows the correlations represented by the different lag times between WAHSIs (Figures 11a, 11b, 11c for velocity, depth and the overall WAHSI, respectively) and the discharge boundary condition (incoming flow). The figures demonstrate fairly similar correlations for each of the three alternatives. The values for the best correlations and their corresponding lag times are listed in Table 6. All the best values are greater than 0.8, indicating reasonably good correlations between the upstream flow condition at SDP and the salmon habitat suitability expressed by the WAHSI values. However, the best correlations occur at a time lag of about 50 days (1200 hours) between these two parameters. For example, the salmon habitat suitability based on velocity considerations (represented by the velocity WAHSI) fully responds to upstream discharges in about 50 days. The second peak values (all less than 0.5) occurring near a zero time lag represent only the local best values and are not optimized correlations on a global scale.

Figure 12 shows the correlations represented by the NCC between the WAHSI and the downstream WSE values at FFB for different lag times. The velocity WAHSI (Figure 12a) correlates better with downstream WSE, exhibiting a higher value (0.926) than either the depth WAHSI (= 0.765, Figure 12b) or the overall WAHSI (= 0.895, Figure 12c). All the best correlations are observed at a zero lag time for each of the three alternatives. This indicates almost synchronized responses between the downstream WSE and the WAHSI values.

6. Conclusions

The two-‐‑dimensional hydrodynamic model using a finite element scheme developed in this study reasonably described the hydrodynamic conditions in the middle reaches of the San Joaquin River (SJR). It can be used to calculate the habitat suitability in terms of Suitability Index (SI) for the spring Chinook salmon run within the investigation domain of the SJR. Three proposed alternatives for the San Joaquin River Restoration Program (SJRRP) were compared based on both their hydrodynamic and SI aspects. All three alternatives showed similar SI distributions under the same boundary conditions. Alternatives 2 and 3 produced higher overall Weighted Area Habitat Suitability Index (WAHSI) values than Alternative 1, indicating that these alternatives could lead to a better environment for salmon migration. There exist fair correlations between the WAHSI values and the boundary conditions. The lag time that produced the best correlation between salmon habitat suitability and upstream discharge was around 50 days based on the cross-‐‑correlation calculations. The WAHSI and the downstream Water Surface Elevation (WSE) values change synchronically. This study demonstrates that the boundary conditions may help predict habitat suitability

for salmon by using the hydrodynamic model for the SJR. The modelling method, together with the correlation results reported here, may provide a reference for similar suitability studies of salmon habitat in other inland rivers.

7. Acknowledgements

The observed data, including the discharge, water surface elevation and bathymetry data, used in this paper for model validation and calibration were provided by the California Department of Water Resources, the U.S. Geological Survey (USGS) and the U.S. Bureau of Reclamation (USBR), respectively. Special thanks go to our colleague, Dr John Suen of the Department of Earth and Environmental Sciences of California State University, Fresno, whose help in reviewing the manuscript and the many stimulating exchanges we have enjoyed during the course of this project have greatly improved the outcome.

8. References

[1] California Water Plan Update 2009 Integrated Water Management (2009) San Joaquin River Hydrological Region Vol. 3 Available:

http://www.waterplan.water.ca.gov/. Accessed 2013 April 10.

[2] Gronberg JM, Dubrovsky NM, Kratzer CR, Domagalski JL, Brown LR, Burow KR (1998) Environmental Setting of the San Joaquin-‐‑Tulare Basins, California. U.S. Geological Survey Water-‐‑Resources Investigations Report 97-‐‑4205, 45 p.

[3] Herbold B, Moyle PB (1989) The Ecology of the Sacramento-‐‑San Joaquin Delta: A Community Profile. Report prepared for the U.S. Department of the Interior, Fish and Wildlife Service. Biological Report 85(7.22). 107 p.

[4] Yoshiyama R, Fisher F, Moyle P (1998) Historical Abundance and Decline of Chinook Salmon in the Central Valley Region of California. North American Journal of Fisheries Management, 18(3): 487-‐‑521

[5] California Department of Water Resources (2013) San Joaquin River Restoration Program Overview. Available: http://www.water.ca.gov/rivers/sanjoaquin/program/. Accessed 2013 Mar 15.

[6] San Joaquin River Restoration Project (2011) Reach 4B, Eastside Bypass and Mariposa Bypass Channel and Structural Improvements Project Initial Alternatives Technical Memorandum. Available: http://restoresjr.net/activities/site_specific/R4B/index.html. Accessed 2012 Sept 4.

[7] Yoshiyama RM, Gerstung ER, Fisher FW, Moyle PB (2001) Historical and Present Distribution of Chinook Salmon in the Central Valley Drainage of California. Contributions to the Biology of Central Valley Salmonids, Fish Bulletin, 179: 71-‐‑176.


[8] Quinn TP (2005) The Behaviour and Ecology of Pacific Salmon and Trout. University of Washington Press, Seattle. 378 p.

[9] Reiser D, Huang C, Beck S, Gagner M, Jeanes E (2006) Defining Flow Windows for Upstream Passage of Adult Anadromous Salmonids at Cascades and Falls. Transactions of the American Fisheries Society, 135(3): 668-‐‑679.

[10] Geist D, Jones J, Murray C, Dauble D (2000) Suitability Criteria Analyzed at the Spatial Scale of Redd Clusters Improved Estimates of Fall Chinook Salmon (Oncorhynchus Tshawytscha) Spawning Habitat Use in the Hanford Reach, Columbia River. Canadian Journal of Fisheries and Aquatic Sciences, 57(8): 1636-‐‑1646.

[11] Hanrahan T, Dauble DD, Geist DR (2004) An Estimate of Chinook Salmon (Oncorhynchus Tshawytscha) Spawning Habitat and Redd Capacity Upstream of a Migration Barrier in the Upper Columbia River. Canadian Journal of Fisheries and Aquatic Sciences, 61(1): 23.

[12] Kimmerer WJ, Nobriga ML (2008) Investigating Particle Transport and Fate in the Sacramento-‐‑San Joaquin Delta Using a Particle Tracking Model. San Francisco Estuary and Watershed Science, 6(1). Available: http://www.escholarship.org/uc/item/547917gn.Accessed 2013 Sept 14.

[13] Bjornn T, Reiser DW (1991) Habitat Requirements of Salmonids in Streams. American Fisheries Society Special Publication, 19: 83-‐‑138.

[14] Thompson K (1972) Determining Stream Flows for Fish Life. Pacific Northwest River Basins Commissions, 20 p.

[15] Bell MC (1990) Fisheries Handbook of Engineering Requirements and Biological Criteria. Corps of Engineers, Portland OR, North Pacific Division.

[16] Huang J, Greimann BP (2010) Sediment Transport and Channel Morphology Model in the San Joaquin River From Friant Dam to Mendota Dam, California. 2nd Joint Federal Interagency Conference, Las Vegas, NV, June 27 -‐‑ July 1, 2010.

[17] Shen Y, Diplas P (2008) Application of Two-‐‑ and Three-‐‑dimensional Computational Fluid Dynamics Models to Complex Ecological Stream Flows. Journal of Hydrology, 348(1): 195-‐‑214.

[18] Crowder DW, Diplas P (2000) Using Two-‐‑dimensional Hydrodynamic Models at Scales of Ecological Importance. Journal of Hydrology, 230(3): 172-‐‑191.

[19] Hardy TB (1998) The Future of Habitat Modelling and Instream Flow Assessment Techniques. Regulated Rivers: Research & Management, 14(5): 405-‐‑420.

[20] Ghanem A, Steffler P, Hicks F, Katopodis C (1996) Two-‐‑dimensional Hydraulic Simulation of Physical Habitat Conditions in Flowing Streams. Regulated Rivers: Research & Management, 12(2-‐‑3): 185-‐‑200.

[21] King IP (2011) User Guide of RMA10 -‐‑ A Finite Element Model for Three-‐‑dimensional Density Stratified Flow.

[22] Chua VP, Fringer OB (2011) Sensitivity Analysis of Three-‐‑dimensional Salinity Simulations in North San Francisco Bay Using the Unstructured-‐‑grid SUNTANS Model. Ocean Modelling, 39(3): 332-‐‑350.

[23] Gross ES, MacWilliams ML, Kimmerer WJ (2010) Three-‐‑dimensional Modelling of Tidal Hydrodynamics in the San Francisco Estuary. San Francisco Estuary and Watershed Science, 7(2). Available:

http://www.escholarship.org/uc/item/9rv243mg. Accessed 2013 Sept 14.

[24] Mosley MP (1982) Critical Depths for Passage in Braided Rivers, Canterbury, New Zealand. New Zealand Journal of Marine and Freshwater Research, 16(3-‐‑4): 351-‐‑357.

[25] Powers PD (1984) Analysis of Barriers to Upstream Fish Migration. Master'ʹs thesis, Washington State University.

[26] San Joaquin River Restoration Program (SJRRP) (2012) Record of Decision -‐‑San Joaquin River Program. Available: http://restoresjr.net/. Accessed 2013 Jan. 1.

[27] California Department of Fish and Wildlife (CADFW) (2012) Central Valley Spring-‐‑run Chinook Salmon. Available:

http://www.dfg.ca.gov/fish/Resources/Chinook/.Accessed 2013 Jun. 15.

[28] Raleigh RF, Miller WJ, Nelson PC (1986) Habitat Suitability Index Models and In stream Flow Suitability Curves: Chinook Salmon. U.S. Fish Wildl. Serv. Biol. Rep. 82(10.122). 64 pp.

[29] Armour CL, Fisher RJ, Terrell JW (1984) Comparison of the Use of the Habitat Evaluation Procedures (HEP) and the Instream Flow Incremental Methodology (IFIM) in Aquatic Analyses. U.S. Fish Wildlife Services, FWS/OBS-‐‑84/11. 30 pp.

[30] Orfanidis SJ (1996) Optimum Signal Processing [M]. 2nd Ed, Englewood Cliffs, NJ: Prentice-‐‑Hall.


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