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CENTRAL VALLEY PROJECT IMPROVEMENT ACT FISHERIES INVESTIGATIONS

Annual Progress Report Fiscal Year 2018 U.S. Fish and Wildlife Service Lodi Fish and Wildlife Office 850 S. Guild Avenue, Suite 105 Lodi, California 95240 Prepared by staff of The Anadromous Fish Restoration Program

USFWS, LFWO, Anadromous Fish Restoration Program FY 2018 Annual Report December 7, 2018

PREFACE The following is the Annual Progress Report, Central Valley Project Improvement Act Fisheries Investigations. The purpose of these investigations is to provide scientific information to other CVPIA programs to use in assessing fisheries restoration actions. The purpose of this report is to provide an update on the Anadromous Fish Restoration Program’s CVPIA-funded activities and accomplishments during fiscal year 2018 to interested stakeholders. The field work described herein was conducted by Mark Gard, Rick Williams, Kes Benn, Paul Cadrett, Shane Abeare, Mary Kate Swenarton, Tricia Parker-Hamelberg, Kathryn Sykes, and Tanya Sheya. Written comments or questions can be submitted to: Mark Gard, Senior Biologist Anadromous Fish Restoration Program U.S. Fish and Wildlife Service Lodi Fish and Wildlife Office 2800 Cottage Way, Room W-2605 Sacramento, California 95825

[email protected] Electronic versions of our previous years’ annual reports are available on our website:

https://www.fws.gov/lodi/instream-flow/instream_flow_reports.htm Cover photo: August 2018: assessing geomorphic changes to Deer Creek resulting from the installation of a new fish ladder at Lower Deer Creek Falls in 2016-2017.

https://www.fws.gov/lodi/instream-flow/instream_flow_reports.htm


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OVERVIEW In response to substantial declines in anadromous fish populations, the Central Valley Project Improvement Act provided for enactment of all reasonable efforts to double sustainable natural production of anadromous fish stocks including the four runs of Chinook salmon (fall, late-fall, winter, and spring), steelhead trout, white and green sturgeon, American shad and striped bass. In 2018, the following fisheries investigation tasks (Figure 1) were selected for study: 1) North Cow Creek upstream passage study; 2) Mill Creek Ward Dam fish ladder passage assessment; 3) Antelope Creek floodplain feasibility study; 4) Sacramento River stranding site evaluation; 5) Yuba River Narrows as-built survey; 6) North Fork Cottonwood Creek water temperature model; 7) North Cow Creek Cook Butcher Diversion Dam data collection; 8) Paynes Creek Bend Irrigation District Diversion Dam data collection; 9) Lower Deer Creek Falls post-restoration monitoring; 10) Tuolumne River Bobcat East data collection; 11) Butte Creek Weir 1 hydraulic modeling; and 12) Central Valley Structured Decision Model technical support. We performed the following fisheries investigations to assess fisheries restoration actions:

1) In FY 2018, we developed relationships between stream flows and upstream passage on North Cow Creek.

2) We developed a hydraulic model at the Mill Creek Ward Dam fish ladder project to develop preliminary alternatives that can be further engineered to provide upstream passage at this location.

3) We conducted a feasibility study of potential floodplain restoration actions on Antelope Creek.

4) We collected topographic and hydraulic data to use in designing solutions to juvenile salmonid stranding at nine sites on the Sacramento River.

5) We conducted an as-built survey for the Yuba River Narrows restoration site. 6) We developed a water temperature model on North Fork Cottonwood Creek. 7) We collected topographic data to use in designing a solution to the upstream passage

barrier on North Cow Creek at the Cook Butcher Diversion Dam. 8) We collected topographic data to use in designing a solution to the upstream passage

barrier on Paynes Creek at the Bend Irrigation District Diversion Dam. 9) We conducted post-restoration monitoring of the Lower Deer Creek Falls fish ladder. 10) We collected topographic data to use in designing the Tuolumne River Bobcat East

restoration site. 11) We developed a hydraulic model of Weir 1 on Butte Creek to identify the effect of

removing Weir 1 on water surface elevations at an upstream weir. 12) We reviewed the Central Valley Structured Decision Model to identify needed changes

to the model and sources of data that could be used to improve the parameterization of the model.


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Figure 1

Fiscal Year 2018 Fisheries Investigation Tasks


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The results of these scientific investigations were provided to other CVPIA programs. The following sections summarize the twelve project activities that were performed between October 2017 and September 2018.

FISHERIES INVESTIGATIONS

North Cow Creek Upstream Passage Assessment Methods The purpose of this task was to assess flows needed for upstream passage of anadromous salmonids through critical riffles in North (or Little) Cow Creek, using the methods of CDFW (2015). This methodology was selected because preliminary information indicated that upstream passage is likely the critical life stage for instream flows in North Cow Creek. The concern is that some upstream migrating fish may not be able to migrate to suitable spawning habitat if they are blocked by shallow water at a riffle. Information from aerial imagery was used to identify critical riffles. After obtaining landowner permission, three riffles (Figure 2) were selected for study. Separate flow measurements were made for Riffle 18 (located upstream of Cook Butcher Diversion Dam) and Riffles 33 and 34 (located downstream of Cook Butcher Diversion Dam). Data for the critical riffle analysis (CRA) were collected as a five-part field sampling series, mostly on the receding limb of the hydrograph as flows declined. The sampling events were timed to capture the full range of discharges necessary to adequately bracket and identify passage flows for fall-run Chinook salmon and steelhead adults and juvenile salmonids on North Cow Creek (Table 1). Once a riffle was identified for critical riffle analysis, the transect was established, marked on each bank with flagging and rebar, and photographed. A discharge measurement was taken onsite. Onsite discharge measurements were made following procedures of Rantz (1982). A transect was established at each critical riffle running along the shallowest course of the riffle from bank to bank using a measuring tape. The transects are not linear, but instead follow the contours of the riffle along its shallowest course from bank to bank. The critical riffle transect was established during the first sampling event, and then was used repeatedly for each subsequent sampling event at different flows. Staff waded the riffle and determined the shallowest course from bank to bank. Several 2.5 ft pieces of 0.5 inch diameter rebar were hammered into the riffle contour, and a wind-up, light-weight measuring tape was attached to the rebar. The headpin (rebar) for each critical riffle transect is located on the left bank of the river looking upstream, and the tail pin (rebar) on right bank looking upstream. The headpin serves as the starting point for each critical riffle water depth measurement, starting from zero feet, and the tail pin serves as the end point of the measurements. Once head- and tail pins are in place, a wind-up, light-weight measuring tape is attached to the base of the headpin. The tape is then extended working across the riffle, following the contour of shallowest course until reaching the tail pin, where the tape is then attached to the tail pin. This process is followed for each subsequent sampling event.


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Figure 2

North Cow Creek Critical Riffles, Scale 1” = 0.55 mi

Table 1 Adult migration and juvenile emigration timing for salmonids in North Cow Creek. Shading

indicates timing span, with darker shading indicating months of peak movement.

Species and Life Stage Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fall Run Chinook Salmon

Adult

Juvenile

Steelhead

Adult

Juvenile

Source: Matt Johnson, California Department of Fish and Wildlife, 2016.


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The Cow Creek flow gage (USGS No. 11374000) is downstream but in the same watershed. North Cow Creek is one of five tributaries in the Cow Creek watershed near Palo Cedro in Shasta County, California. The Cow Creek flow gage (which has a high correlation with North Cow Creek flows) was used to assess whether flow levels changed during the data collection. Specifically, a regression equation of North Cow Creek flows with flows from the Cow Creek gage had an R2 value of 0.89. Depths were measured every two feet along the transect. A stadia rod (with scale to 100ths of a foot) was used to measure depth along each transect. After measuring water depths, the data were recorded in a field notebook for each distance across the transect. Careful attention was taken to record water depths at individual locations as the fish would encounter and use them. In accordance with CDFW (2015), depth and velocity criteria were used to assess critical riffles; criteria are presented below in Table 2. A site is deemed passable when a combination of minimum stream flow depths and wetted widths are greater than conditions specified by two evaluation parameters: the percentage of the total transect width meeting the life stage-specific depth criteria and the contiguous percentage of the transect width meeting the life stage-specific depth criteria (Thompson 1972). The more stringent of the two criteria are used to establish passage flows. Passage velocities have been established based on the perceived swimming abilities of salmon and trout to pass over barriers. A maximum passage velocity of 8.0 feet per second (ft/s) is considered appropriate for adult Chinook salmon and steelhead (Thompson 1972; Table 2). The minimum depth criteria used in CRA is based on the water depth needed for a salmonid to adequately navigate over a critical riffle with sufficient clearance underneath it, so that contact with the streambed and abrasion are minimized (R2 Resource Consultants 2008). The minimum depth passage criteria for adult Chinook salmon, adult Steelhead, and juvenile salmonids are 0.9 ft, 0.7 ft, and 0.3 ft, respectively (CDFW 2015; Table 2). Where migration timing overlaps (see Table 1), the deeper body depth criteria must take precedence to protect all species and life stages present.

Table 2 Depth and velocity criteria for adult and juvenile salmonid passage

Species (Life stage) Minimum depth (ft) Maximum Velocity (ft/s)

Chinook Salmon (adult) 0.9 8.0

Steelhead (adult) 0.7 8.0

Salmonid (young-of-year/juvenile) 0.3 ---

Source: Thompson 1972; R2 Resource Consultants 2008; CDFW 2015. Results The three riffles were sampled five times between January 23 and July 12, 2018 at flows ranging from 0.92 to 232 cfs (representing the 15th to 85.8th percentile annual flows for North Cow


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Creek). The fastest velocity measured for any of the riffles was 3.67 ft/s. High flows between the second and third measurements shifted the rating curve for Riffle 18, as a result of deposition of material downstream of the riffle. As a result, we shifted the rating curve downward 0.24 feet for measurements made after the high flows to account for this deposition. Photos, including latitudes and longitudes of the riffles, and regressions are shown in Appendix A. For Riffles 33 and 34, there were multiple flows with zero width for depths ≥ 0.9 feet. Accordingly, we only used the highest flow with a zero width in the regressions for those criteria. The relationships between width and flow were not linear for the entire range of measured data for percent total width ≥ 0.7 feet at Riffle 34, and for percent total and continguous width ≥ 0.3 feet for both Riffles 33 and 34; accordingly, we only used the data from the linear portion of the range in those cases. For Riffle 18, we were unable to use the regression equations to predict the flow meeting the criteria due to the shift in the rating curve. For those cases, we used an alternative but equally valid method by developing a stage-discharge relationship (rating curve) using the flows from the field data points collected and the depth profile measured at the highest flow level sampled. The rating curve was then used to calculate water surface elevation (WSELs) at each flow of interest for each critical riffle transect, as it is analogous to an empirical version of two-dimensional hydraulic habitat modeling, and similar to the methodology employed in Physical Habitat Simulation (PHABSIM) systems (Waddle 2012). The benefit of using a method that is similar to PHABSIM is the defensibility of the method, due to the widespread use of PHABSIM in instream flow studies. Depths measured for each critical riffle transect, along with the associated WSEL, are used to calculate the bed elevations across the transect. The depths at flows below those measured at the critical riffles are then calculated by subtracting the bed elevations from the WSELs derived from the rating curve. The resulting depths can then be used to determine the total and contiguous widths meeting the depth criteria at each flow of interest. As shown in Table 3, Riffle 18 (with a length of 168 feet) was the most critical riffle for upstream passage, while Riffle 33 was the least critical riffle for upstream passage. Mean monthly flows (based on the 8 year period of record (1957-1965) of daily average flows for USGS Gage 113733001) and known diversions and diversion rates for North Cow Creek, as per SWRCB (2015), are shown in Table 4. The above results can be used to establish flows for upstream passage in North Cow Creek. Discussion Riffle 18 is very long and needs very high flows to ensure adult Chinook upstream passage that correspond to a 93.6 percentile exceedance flow. At flows of less than 180 cfs, the entire length of the riffle had depths less than 0.9 feet, while at the highest measured flow only four feet of the riffle had depths ≥ 0.9 feet. There is more uncertainty in the Riffle 18 adult Chinook upstream

1 https://waterdata.usgs.gov/ca/nwis/dv?cb_00060=on&format=gif_default&site_no=11373300 &referred_module=sw&period=&begin_date=1957-10-01+&end_date=1965-09-27

https://waterdata.usgs.gov/ca/nwis/dv?cb_00060=on&format=gif_default&site_no=11373300%20&referred_module=sw&period=&begin_date=1957-10-01+&end_date=1965-09-27

https://waterdata.usgs.gov/ca/nwis/dv?cb_00060=on&format=gif_default&site_no=11373300%20&referred_module=sw&period=&begin_date=1957-10-01+&end_date=1965-09-27


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passage flow than for the other riffles and species/life stages, since this flow is more than 2.5 times the highest measured flow (201 cfs). A general rule of thumb is to not extrapolate rating

Table 3 Flow needed at the 3 study riffles to ensure passage of adult and juvenile salmonids

Criterion Riffle 18 Riffle 33 Riffle 34 Adult Chinook Total Width 440 58 98 Adult Chinook Contiguous Width 530 48 59 Adult Steelhead Total Width 220 35 39 Adult Steelhead Contiguous Width 270 5 16 Juvenile Salmonid Total Width 30 7 10 Juvenile Salmonid Contiguous Width 45 3 3

Table 4 North Cow Creek Mean Monthly Flows and Diversions

Month Mean Monthly Flows (cfs) Mean Monthly Diversions (cfs) January 240 1.1 February 410 1.1 March 225 1.6 April 289 4.8 May 108 5.9 June 39 7.1 July 13 7.1

August 9.4 6.9 September 8.4 6.0

October 48 4.5 November 84 3.1 December 224 0.9

curves to more than 2.5 times the highest measured flow. The mean monthly flows in Table 4 likely represent unimpaired flows, since USGS Gage 11373300 was located upstream of all diversions. However, it would not take into account tributary flows downstream of the gage, which could be significant during high flows. For example, at our highest sampled flow, the flow at Riffles 33 and 34 was 31 cfs higher than the flow at Riffle 18, reflecting tributary inflow. Both total width, representing all of the passage opportunities through a critical riffle, and continguous width, representing a single path through the riffle that is passable, are considered in evaluating upstream passage. The next step in developing instream flow requirements for North Cow Creek is to address water temperature data, since the availability of suitable water temperatures is a consideration in developing instream flow requirements.


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Mill Creek Ward Dam Fish Ladder Passage Assessment

Methods The goal of this task was to develop a solution to the upstream passage impediment caused by the deposition of a large cobble bar2 upstream and inside of the new 2015 Ward Dam fish ladder. In FY 2018, we used the SRH-2D model described in our FY 2017 annual report to simulate 21 different alternatives, involving different configurations of bed deflectors, bank modifications, fish ladder modifications and sluiceways. The design criterion used to evaluate the alternatives was no deposition of bedload at the cobble bar location after a five year flow event, and thus maintenance of at least 10 percent of the total flow of Mill Creek through the fish ladder. Two alternatives (a large sluiceway, and Bendway Weirs/cut bank) were selected for further evaluation based on this criterion. On July 10, 2018, we collected 2,270 topography data points on and downstream of Ward Dam using two survey-grade Real Time Kinematic Global Positioning System (RTK GPS) units (Figure 3) to extend the SRH-2D model further downstream. The downstream end of the survey was the downstream-most transect, located 220 feet downstream of Ward Dam, of a HEC-RAS model developed for the design of the new fish ladder. The survey below the dam included,

Figure 3

Ward Dam topographic survey

2 The cobble bar seems to have been formed during a five-year flow event in March 2016, and reformed during an additional five-year flow event in February 2017.


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going from downstream to upstream, a run, high gradient riffle and pool. Data collection on the north floodplain was limited by trees interfering with operation of GPS units. As a result, data from the HEC-RAS model were used to supplement the topography data for the north floodplain. A comma delimited file of topography data was produced to input into the Surface-water Modeling System (SMS, ver. 12.2.6 64 bit) software. For the proposed alternatives, the topography data were modified to incorporate the selected structures. A computational mesh was developed in SMS by first defining polygons based on aerial photography. Five material types were defined for the polygons, per Pasternack (2011): 1) left overbank; 2) right overbank; 3) glide/run; 4) pool and 5) high gradient riffle. Manning’s n values for the first three polygons were taken from the HEC-RAS model, while Manning’s n values for the last two polygons were based on visual observation of substrate size during data collection (Table 5). An additional material type was defined for the Bendway weirs, with a Manning’s n value based on the proposed sizing of the material to be used to construct the weirs (boulders). Paving meshes with 3 feet by 3 feet square mesh elements were used for the in-channel polygons, while 10 feet by 10 feet square paving meshes were used for the overbank polygons. The scatter dataset was interpolated to the computational mesh using the inverse distance weighted interpolation option in SMS. The resulting computational mesh was used as an input to SRH-2D Version 3.2.0 (USBR, Denver, CO), with a downstream boundary condition from the HEC-RAS model. The fixed-bed version of SRH-2D was run at the five-year flow of 9,180 cfs, for existing conditions and the two selected alternatives.

Table 5 Manning’s n Values for Ward Dam SRH-2D model

Polygon Roughness Element Manning’s n

Left Overbank Vegetation 0.05

Right Overbank Vegetation 0.05

Glide and Run Small Cobble 0.035

Pool Sand 0.03

High Gradient Riffle Large Cobble 0.05

Flow Deflection Structures Boulder 0.10

Parameters for the mobile bed version of SRH-2D were based on pebble count data in NHC (2016). The Wilcock equation was used for sediment capacity. Since sediment supply data is not available for Mill Creek, we used the sediment capacity option for the upstream sediment input boundary condition, where the amount of sediment coming in at the upstream boundary is set equal to the capacity of the channel at that location to carry sediment. The output of the fixed bed version of SRH-2D was used as the initial condition for the mobile bed model. Additional information about the gravel bar deposition is given in NHC (2016).


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Results The simulations with the extended downstream boundary predicted similar patterns of erosion and deposition downstream of Ward Dam for existing conditions and the two alternatives that were selected for further evaluation. These simulations also illustrated the limitations of the SRH-2D model. Specifically, it was unable to reach a stable solution, and thus had unrealistic results, using the natural, rapidly-varying hydrograph. Another limitation of the modeling with the extended downstream boundary was predicted erosion of the dam face due to the inability to spatially vary erosion potential. With erosion turned off, to eliminate the artifact of erosion of the dam face, little to no sediment transport was predicted, presumably because most of the sediment deposition predicted was of material that eroded within the site, rather than being transported from upstream of the model domain. Discussion The hydraulic modeling has helped in selecting a preferred alternative long-term solution to upstream fish passage at Ward Dam. After the preferred alternative is selected by the Ward Dam Fish Passage Technical Team, a 30 percent basis of design report can be completed and environmental compliance and permitting can start.

Antelope Creek Floodplain Feasibility Study

Methods The goal of this task was to develop a HEC-RAS model to assess the current extent of floodplain inundation in the Antelope Creek watershed and to develop alternatives to improve aquatic and terrestrial habitat while developing feasible solutions to the flooding problem on Antelope Creek that are sensitive to the needs and values of the local landowners. HEC-RAS models are typically developed using a combination of in-channel cross-sections and Light Detection and Ranging (LIDAR) data. The resulting HEC-RAS model is then used with LIDAR data to generate floodplain inundation. The first step in this task was to determine what existing sources of data were available. Inquiries with CDWR, CALTRANS and FEMA indicated that there were no existing HEC-RAS models for Antelope Creek. We obtained LIDAR data from CDWR, but this data was limited to the portion of the watershed downstream of Cone Grove Road. USGS Digital Elevation Model (DEM) data was used for a preliminary representation of the topography upstream of Cone Grove Road3. The geographic extent of the Antelope Creek HEC-RAS model will be from the Sacramento River to upstream of all distributaries (Figure 4). Antelope Creek has four distributaries: Butler Slough, Craig Creek, Mill Race Creek and New Creek (Stillwater Sciences et al. 2011). In turn, Mill Race and New Creeks are tributaries of Salt Creek. Antelope

3 USGS DEM data has limited utility for floodplain delineation because the vertical accuracy is 5 ft and the horizontal resolution is 25 ft. In contrast, LIDAR data typically has a vertical accuracy of 0.5 ft and a horizontal resolution of 3 ft.


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Figure 4

Geographic Extent of Antelope Creek HEC-RAS Model Data Collection

Creek, Butler Slough, Craig Creek and Salt Creek flow directly into the Sacramento River. Little Antelope Creek will be treated in the HEC-RAS model as an additional flow source to Antelope Creek. The number of river miles for each stream in the HEC-RAS model are shown in Table 6.


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Table 6 Length of Streams to be Modeled for Antelope Creek HEC-RAS model

Stream

Stream Length (Miles)

Antelope Creek 10 Butler Slough 5.4 Craig Creek 2 Mill Race Creek 4.3 New Creek 3.2 Salt Creek 2

Our investigations indicated that there were limited sources of in-channel cross-sections for the Antelope Creek watershed, specifically: 1) as-built surveys for bridge crossings; 2) topography data that we collected in 2012 at the Antelope Creek/Craig Creek junction; and 3) cross-section data currently being collected by Stillwater Sciences for a critical riffle study of Antelope and Craig Creeks. We obtained as-built surveys from CALTRANS and the Tehama County Department of Public Works. For the Stillwater Science data to be usable for a HEC-RAS model, we needed to shoot in their controls with our RTK-GPS equipment. We can use the LIDAR data as a data source for the Mill Race Creek in-channel cross-sections, since Mill Race Creek was entirely dry at the time that the LIDAR data was collected. We sent out letters to all of the landowners on Antelope, Craig, New and Salt Creeks and Butler Slough requesting permission for access to collect in-channel cross-sections. Bed elevation profiles were measured for each cross-section using standard surveying techniques (differential leveling), and a tape to record stations. We used our RTK GPS unit to establish vertical control for each transect, as well as to record the horizontal locations of the ends of the cross-sections. Additional data that were recorded for each cross-section were the main channel left bank and right bank stations and the Manning’s n values for the left overbank, main channel and right overbank. Results In FY-18, we completed data collection for portions of the streams where we have approval for access. Discussion For land parcels whose owners did not grant us survey access, the only option we will have is to interpolate cross-sections between the areas we have been able to survey. As a result, the accuracy of the model will be diminished in those areas. The largest data gap is for Antelope Creek upstream of Cone Grove Park and New Creek upstream of Cone Grove Road, where we did not have landowner permission for access. Three options were identified to address this data gap: 1) have the upstream extent of the HEC-RAS model be at Cone Grove Road/Cone Grove Park; 2) use data collected by Davids Engineering in the vicinity of Edwards Dam for the in-


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channel portions of the HEC-RAS transects, with interpolation used for the intervening area; and 3) have CDFW staff collect HEC-RAS data upstream of Cone Grove Road/Cone Grove Park. The first alternative would not allow for development of alternatives upstream of Cone Grove Road/Cone Grove Park. The second alternative would result in reduced accuracy of the HEC-RAS model for most of the area upstream of Cone Grove Road/Cone Grove Park due to interpolation. Further, the first two alternatives would not allow for Mill Race Creek to be included in the HEC-RAS model, since the inflow to Mill Race Creek could not be calculated without inclusion of the distributary junction in the HEC-RAS model. Implementation of the third alternative is based on the assumption that CDFW could get landowner permission for access to Edwards Ranch. After this data gap is addressed, the HEC-RAS model will be developed in FY-19. LIDAR data may be collected in FY-19 for the portion of the watershed upstream of Cone Grove Road through the USGS 3DEP program, but would likely require $1,000 of CVPIA seed funding.

Sacramento River Stranding Site Evaluation

Methods The goal of this task was to develop solutions to juvenile salmonid stranding at nine sites on the Sacramento River near Redding, CA. This uppermost portion of the mainstem Sacramento is critically important to the early life stages of all salmonids. Prior to fixes being implemented, it is important to collect data to determine the best locations to seek funding, designs, permitting, etc for these sites. The following data were collected at the nine stranding sites: Three sets of water surface elevations, stage of zero flow value (thalweg bed elevation at the stranding site), stranding site connection point bed elevation, and topographic data for stranding sites. Water surface elevations and stranding site connection point bed elevation were measured with an autolevel and stadia rod. The stage of zero flow value was determined from Acoustic Doppler Current Profiler (ADCP) data, together with the water surface elevation at the time the ADCP data was collected. Topographic data were collected using survey-grade RTK GPS units, and covered both the stranding area and the existing connection channels to the Sacramento River. This data was used to: 1) determine at what flow each site currently strands; 2) develop a reconnaissance-level design for solutions to the stranding problems; and 3) estimate the cost to solve the stranding problems. For all nine sites, the design consisted of a three foot wide channel, with a bed elevation that would result in a depth of 0.5 feet at 3,250 cfs, and with 3:1 side slopes. An alignment was selected for the channel that would minimize the volume of material that would need to be excavated. This alignment generally followed the natural connection channel. Results The stranding flow and estimated volume of material that would need to be excavated for each site is given in Table 7.


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Table 7 Stranding Site Characteristics

Site Name River Mile Stranding Flow (cfs) Excavation volume (ft3) Turtle Bay Side Channel 1 296.5 6,785 4,130 Turtle Bay Side Channel 2 296.5 3,250 2,384 Shea 289.4 5,950/4,350 21,463 Mouth Clear Creek 289.25 3,250 120 Clear Creek Side Channel 1 289.35 5,250 4,055 Clear Creek Side Channel 2 289.8 4,500 10,179 Clear Creek Side Channel 3 290 5,200 982 Elks Pool 244.5 37,000 84,960 East Sand Slough 244.2 19,950 1,387,873 For two of the sites (Turtle Bay Side Channel 1 and Shea), which had multiple stranding pools within them, the most effective design was to lower the entrance to the side channels so there would be flow through the side channels at a total Sacramento River flow of 3,250 cfs. To develop these designs, River2D (Steffler and Blackburn 2002) hydraulic models were constructed using the topography data collected on the sites, as modified by the designed channels. The model inflow boundary condition was calculated, using Manning’s equation, from the design channel width, depth and slope (Table 8). Water surface elevations measured at the downstream end of the side channels at a Sacramento River flow of 3,250 cfs were used as the outflow boundary conditions for the hydraulic models. Uniform bed roughness heights of 0.12 and 0.195 meters (based on the dominant substrate size present in each site) were used, respectively, for Turtle Bay Side Channel 1 and Shea, as the final input for the hydraulic models. Figures 5 and 6 show the predicted depths of these two sites at a Sacramento River flow of 3,250 cfs after construction of the designed channels. The Shea site has two inflow boundaries; the two stranding flows in Table 7 are the stranding flows for each of the inflow boundaries. Green lines represent the inflow boundaries while blue lines represent the outflow boundaries. For Shea, there was an additional stranding area within the site (isolated area in Figure 6); connecting this stranding area to the side channel would require an additional excavation volume of 1,144 cubic feet, in addition to the volume shown in Table 7. The data collected for the East Sand Slough stranding site was provided to Nancy Snodgrass, Engineer of CDWR to be used in designing the b(13) restoration project in East Sand Slough. Discussion The Mouth of Clear Creek stranding site would be the most cost effective site to solve the stranding problem because it has the least excavation volume, while the East Sand Slough and Elks Pool stranding sites would be the most expensive sites to address. A better alternative for East Sand Slough would be to address the stranding issue through the East Sand Slough b(13)


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Figure 5 Turtle Bay Side Channel 1 (RM 296.5) Depths

Table 8

Stranding Site Inflow Channel Design Parameters

Site Name Inflow (cfs) Bottom Width (ft) Depth (ft) Slope (%) Turtle Bay Side Channel 1 30.5 14 1.0 0.3%

Shea Inflow Channel 1 24.9 20 0.5 0.98% Shea Inflow Channel 2 5.5 10 0.5 0.17%


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Figure 6

Shea Stranding Site Depths restoration project. A better alternative for the Elks Pool stranding site would be to reconstruct the levee at this location to isolate this site from the Sacramento River even at very high flows. The above cost effectiveness estimates only consider excavation volume – other factors, such as ease of access, could have a significant effect on the cost of implementing a solution to the stranding problem. Permission for access to the sites is unknown at this time, and would be a consideration in the ability to implement solutions to the stranding problems. It might be possible to use hand tools to address the Mouth of Clear Creek site, while the other sites would likely require the use of heavy equipment. The Turtle Bay Side Channel 1 and Shea stranding sites would have additional benefits, compared to the other stranding sites, in providing high quality juvenile rearing habitat for anadromous salmonids. The Clear Creek Side Channel 2 and


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3 stranding sites are also located at the upstream end of side channels that stop flowing at the flows in Table 7. The proposed connection channels might result in these channels flowing at 3,250 cfs, and thus creating similar benefits as for the Turtle Bay Side Channel 1 and Shea stranding sites; however, additional data collection and hydraulic modeling would be needed to make that determination.

Yuba Narrows As-built Survey

Methods The purpose of this task was to conduct an as-built survey for the Yuba Narrows restoration project. Topographic data were collected using survey-grade Real Time Kinematic Global Positioning System (RTK GPS) units for the dry and shallow portions of the site, and with a combination of an Acoustic Doppler Current Profiler (ADCP) and a survey-grade RTK GPS unit for the deeper portions. For each traverse with the ADCP, the RTK GPS was used to record the horizontal location and WSEL at the starting and ending location of each traverse, while the ADCP provided depths and distances across the traverse. The WSEL of each ADCP traverse is then used together with the depths from the ADCP to determine the bed elevation of each point along the traverse. We also collected a WSEL profile throughout the site to use in calibrating a two-dimensional hydraulic model of the site, and used the RTK GPS units to make the locations of spring-run Chinook salmon redds within the site. Results Topographic data were collected on September 24-27, 2018. A total of 13,258 data points were collected. The as-built topography is shown in Figure 7. There were 158 spring-run Chinook salmon redds in the site. Discussion The as-built topography shown in Figure 7 can be compared to the design for the Narrows restoration project to assess how close construction was to the design specifications. The redd locations can be used as the initial data source for post-project monitoring.

North Fork Cottonwood Creek Flows Investigation

Methods The purpose of this task was to develop a Stream Network Temperature Model (SNTEMP) water temperature model for North Fork Cottonwood Creek (Figure 8) to use in identifying opportunities for restoration or negotiations on flow management in North Fork Cottonwood Creek to improve summer water temperatures for spring-run Chinook salmon adult holding. Shade and mesohabitat mapping data for the water temperature model were collected for portions of North Fork Cottonwood Creek where we were able get landowner permission for


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Figure 7

Narrows As-built Topography

access. Pressure transducers were installed at the upstream and downstream end of the area to be modeled to collect flow data for the water temperature model. Three flows were measured at each pressure transducer to develop the rating curves for these gauges. Temperature loggers were placed at the upstream end of the area to be modeled (as an input to the water temperature model), and at the downstream end of the area to be modeled (to calibrate the water temperature model). Representative transects (one each in a pool, riffle, and run) were selected, and bed cross-sections and three sets of water surface elevations were measured to develop physical geometry data to use as inputs to the water temperature model. Weather data were downloaded from the internet as the final set of information needed for the water temperature model. In addition, an Onset Corporation Hobo weather station, measuring air temperature, wind speed and relative humidity, was installed adjacent to North Fork Cottonwood Creek to correct internet weather data for local conditions.


22

Figure 8

North Fork Cottonwood Creek Flows Investigations Map

To develop the water temperature model, four stream reaches were established based on the locations of stream water diversions, stream hydrology and gradient. Reach 1 is downstream of Rainbow Lake, but upstream of the Igo Ono Community Service District diversion. Reach 2 is the portion of North Fork Cottonwood Creek downstream of the diversion but upstream of Sunny Hill Road, whereas Reach 3 is the portion of North Fork Cottonwood Creek between Sunny Hill Road and Jerusalem Creek. Reach 4 extends from the confluence of Jerusalem and North Fork Cottonwood Creeks to the waterfall that is the upstream barrier for anadromous salmonids. All data were compiled and checked before entry into a SEFA4 data file. All of the measured WSELs were checked to make sure that water did not appear to be flowing uphill due to measurement errors or other factors. A total of three WSEL sets at low, medium, and high flows 4 SEFA is commercially produced software (Jowett et al. 2013) that incorporates the modeling procedures used in Physical Habitat Simulation (PHABSIM) software.


23

were used. Calibration flows in the data files were the flows measured in the reach. An important parameter used in calibrating the stage discharge relationship is SZF, which was determined and entered for each transect. In habitat types without backwater effects (e.g., riffles and runs), this value generally represents the lowest point in the streambed across a transect. However, if a transect directly upstream contains a lower bed elevation than the adjacent downstream transect, the SZF for the downstream transect applies to both. The first step in the calibration procedure was to determine the best approach for WSEL simulation. Initially, the IFG4 hydraulic model (Milhous et al. 1989) was run on each dataset to compare predicted and measured WSELs. This model produces a stage-discharge relationship using a log-log linear rating curve calculated from at least three sets of measurements taken at different flows. Besides IFG4, two other hydraulic models are available in PHABSIM to predict stage-discharge relationships. These models are: 1) MANSQ, which operates under the assumption that the condition of the channel and the nature of the streambed controls WSELs; and 2) WSP, the water surface profile model, which calculates the energy loss between transects to determine WSELs. MANSQ, like IFG4, evaluates each transect independently. WSP must, by nature, link at least two adjacent transects. IFG4, the most versatile of these models, is considered to have worked well if the following criteria are met: 1) the beta value (a measure of the change in channel roughness with changes in streamflow) is between 2.0 and 4.5; 2) the mean error in calculated versus given discharges is less than 10%; 3) there is no more than a 25% difference for any calculated versus given discharge; and 4) there is no more than a 0.1 foot (0.031 m) difference between measured and simulated WSELs5. MANSQ is considered to have worked well if the second through fourth of the above criteria are met, and if the beta value parameter used by MANSQ is within the range of 0 to 0.5. The first IFG4 criterion is not applicable to MANSQ. WSP is considered to have worked well if the following criteria are met: 1) the Manning's n value used falls within the range of 0.04 - 0.07; 2) there is a negative log-log relationship between the reach multiplier6 and flow; and 3) there is no more than a 0.1 foot (0.031 m) difference between measured and simulated WSELs. The first three IFG4 criteria are not applicable to WSP. After the transect stage-discharge relationships were calibrated, the SEFA file was used to generate wetted width and average depth for flows of 3.2 to 455 cfs (Appendix B). The flow ranges were selected to go from 40% of the flow for the lowest measured WSEL to 2.5 times the flow for the highest measured WSEL. Overall flow-width and flow-depth relationships were then generated from the individual transects by weighting the transects based on the mesohabitat composition of each reach. Log-log regression of wetted width versus flow was then used to compute the width parameters (width A constant, width B coefficient and maximum width7) used

5 The first three criteria are from U.S. Fish and Wildlife Service (1994), while the fourth criterion is our own criterion. 6 The reach multiplier is used to vary Manning’s n as a function of discharge. 7 The maximum width used in SNTEMP was calculated from the log-log regression equation using the highest simulated flow.


24

in SNTEMP, whereas a plot of depth versus flow was used to extrapolate the residual depth parameter in SNTEMP (the average depth present at a flow of zero). The initial value of Manning’s n for each reach used in SNTEMP was the default value of 0.035.

Average vegetation shade angle and vegetation density values for the left and right banks of each reach were calculated in Excel from the field data. The shade angle data was converted to vegetation heights using the following formula: Vegetation height = ½ x wetted width x tan (vegetation shade angle) Vegetation crown widths were calculated using the following equation:

Vegetation crown width = maximum width – wetted width Data to compute topographic shade angles were developed from a digital terrain model in GIS. Elevations of the stream channel and the topographic horizon (such as ridge tops) were recorded from the digital terrain model, while the horizontal distance from the stream channel to the topographic horizon was measured in GIS. The topographic shade angles for left and right banks of each reach were then computed from the following formula: Topographic shade angle = Arctan (elevation difference/distance) The reach azimuth for each reach was measured and the latitude of each reach was determined in Google Earth. The length of each reach was computed from the mesohabitat polyline shapefiles, whereas the elevation at the downstream end of each reach and at the upstream end of Reach 1 was interpolated from USGS quad map elevation contour lines in GIS. Daily average air temperatures, percent humidity, wind speed and cloud cover data for Redding (KCAREDDI69) were downloaded from the Weather Underground website. Cloud cover data were converted to percent possible sun (the input variable for SNTEMP), using the equation: Percent possible sun = 100 x (1 – 0.125 x cloud cover) The first step in entering data into StreamTemp8 data files was to define network streams, nodes and reaches. Nodes were defined at the upstream and downstream end of each reach. The next step in entering data into StreamTemp data files is to input the reach channel geometry parameters for each reach. Next, a time series of hydrology data was entered into the StreamTemp model, including diversion flows at the Igo Ono Community Service District diversion from SWRCB (2015). The time series of hydrology data also included the average

8 StreamTemp is commercially produced software (Payne and Associates 2005) that incorporates the modeling procedures used in SNTEMP.


25

daily water temperature9 at the upstream end of Reach 1 (an input to the model), and average and maximum daily water temperatures at the downstream end of Reaches 2 and 4 from the temperature loggers and pressure transducers (used to calibrate the model). Finally shade data for each reach and a time series of weather data were entered into StreamTemp. The first step in the SNTEMP model calibration was to run the StreamTemp model and compare the model output to the measured water temperature data at the downstream end of Reaches 2 and 4 for the period of June 16 to November 310. The calibration of the model was evaluated based on the following criteria: 1) average error for each reach less than 1.8 degrees Fahrenheit (1 degree Celsius); and 2) maximum error for each reach less than 2.7 degrees Fahrenheit (1.5 degree Celsius) from Kimmerer and Carpenter (1989). Calibration was also evaluated by the following criteria identified in the StreamTemp help files: 1) Correlation Coefficient (R-Squared) as close to 1.0 as possible; 2) Mean Error as close to zero as possible; 3) Probable Error equal to or less than 0.5; 4) Maximum Error equal to or less than 1.5 degrees; 5) Number of Predicted Errors Greater than 1.0 degrees less than 10%; and 6) Bias minimal. Parameters were then varied to improve the agreement between simulated and measured water temperatures. Water temperatures were simulated for a 44 year period (January 2, 1973 through October 1, 2017) for both impaired and unimpaired flows. Unimpaired flows were calculated by setting Igo Ono Community Service District diversion flows equal to zero. The starting end of this time period was when meteorological data were first available. The Dry Creek SNTEMP model will be validated in FY-19 by comparing measured and simulated water temperatures at the downstream end of Reach 4 for the period of June 16 to November 3, 2018. Flow data for both the validation and simulation periods were computed from a linear regression of North Fork Cottonwood Creek versus Cottonwood Creek flows. Results Data collection was completed in early FY-18. No unreasonable model behavior with water appearing to flow uphill due to measurement error or inaccuracies was found for any of the transects. A total of three WSEL sets at low, medium, and high flows were used for all transects. IFG4 did not meet all of the criteria described in the methods for IFG4 (Appendix B). All IFG4 construction and calibration parameter results were within acceptable ranges for beta values, mean error in calculated and given discharges, percent difference in calculated and given discharge, and difference in measured and simulated WSELs (Appendix A), with the exception

9 The values used were from a linear regression of mean daily air temperatures versus mean daily water temperatures measured on June 16 to November 3, 1999 at a temperature logger at the upstream end of Reach 1. Water temperature data were collected by Crane Mills. 10 Calibration was restricted to this time period to correspond to the time period for the upstream temperature boundary condition data. Reach 2 was only calibrated with data from 2017, which Reach 4 was calibrated with data from 2012 through 2017 (collected by CDFW staff).


26

of beta values for Transect 2, mean error and percent difference in calculated and given discharge for one of the three flows for Transect 1 and 2, and difference in measured and simulated WSELs for two flows for Transect 1 and 2. The regression equation (R2 = 0.24) to predict the daily average water temperature at the upstream end of Reach 1 was as follows: Water Temperature (℉) = 46.7 + 0.2049 x Air Temperature (℉) The flow-width relationships for each reach are shown in Appendix B. The initial values of universal parameters and reach-specific parameters are shown in Appendix B (Tables B-1 and B-2). The calibrated values of universal parameters and reach-specific parameters are shown in Appendix B (Tables B-3 and B-4). The calibrated model met the average error criterion but did not meet the maximum error criterion from Kimmerer and Carpenter (1989) for mean daily average water temperature, nor did it meet the criteria for 7 Day Average Daily Maximum (7DADM) (Appendix B, Table B-5). The calibrated model had a correlation coefficient of 0.9046, a mean error of -0.036, a probable error of 1.7416, a maximum error of -9.9214, 70.94 % of the predicted errors greater than 1.0, and a bias of 0.0606. For model simulations, the regression equation (R2 = 0.91) predicting North Fork Cottonwood Creek flows at the downstream end of Reach 4 from Cottonwood Creek flows is as follows:

North Fork Cottonwood Creek flow = 31.38 + 0.136 x Cottonwood Creek flow

The regression equation (R2 = 0.976) predicting Reach 2 flows from Reach 4 flows, developed from the Reach 2 and 4 pressure transducer data, is as follows:

Reach 2 flow = 0.871 x Reach 4 flows For impaired flows, there were 30 days during the 44 year period of record when maximum daily temperatures at the downstream end of Reach 4 exceeded 76 degrees F11. These days fell between June 25 and August 15. In contrast, with unimpaired flows there were no days when maximum daily temperatures at the downstream end of Reach 4 exceeded 76 degrees F. Discussion

Access and high flows in FY-17 complicated development of the water temperature model. We were unable to get access to the portion of North Fork Cottonwood Creek upstream of Sunny Hill Road (approximately one mile downstream of the diversion shown in Figure 8). As a result, we used Crane water temperature monitoring data for the upstream boundary condition of the 11 This is the highest daily maximum water temperature where adult spring-run are observed holding in Beegum Creek, a tributary to Middle Fork Cottonwood Creek (D. Killam, CDFW, personal communication).


27

model. High flows resulted in an incomplete dataset and loss of some temperature loggers; however, we had sufficient data to still develop the model. Performance of SEFA likely reflected channel changes at Transects 1 and 2 during high flows (reaching 2,270 cfs) during the winter of 2017; one set of WSELs were collected prior to high flows, while the other two sets of WSELs were collected after high flows. While the SNTEMP model did not meet the maximum error criterion from Kimmerer and Carpenter (1989), nor did it meet the average error criterion for 7DADM, we conclude that it is still sufficiently accurate to compare the water temperature regimes that would result from different flow regimes. Performance of SNTEMP likely reflected errors in the upstream boundary condition water temperature, since this was based on a regression with air temperatures. The performance of SNTEMP in simulating maximum daily water temperatures is a limitation of the daily time step of the SNTEMP model. The SNTEMP model should be useful in identifying opportunities for restoration or negotiations on flow management in North Fork Cottonwood Creek to improve summer water temperatures for spring-run Chinook salmon adult holding. For example, curtailment of Igo Ono Community Service District diversions from June 25 to August 15 would result in attainment of the 76 degree F criterion. Alternatively, dredging Rainbow Lake to increase storage capacity might provide the ability to supplement impaired flows during this time period without the need to curtail diversions.

Cook Butcher Diversion Dam Data Collection

Methods The purpose of this task was to collect topographic data at the Cook Butcher Diversion Dam on North Cow Creek to develop a reconnaissance-level design to solve the upstream passage barrier at this location. Data were collected for the site using RTK GPS units. Results Topographic data were collected on July 11, 2018. A total of 2,658 data points were collected, extending from 620 feet upstream to 660 feet downstream of the dam (Figure 9). The topographic data were transmitted to Ross Perry of the Western Shasta Resource Conservation District to be used in developing a design to solve the upstream passage barrier at this location. Discussion The data from this effort should serve as a good baseline topographic survey for developing a design to solve the upstream passage barrier at this location.


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Figure 9

Cook Butcher Diversion Dam Topographic Data


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Bend Irrigation District Diversion Dam Data Collection

Methods The purpose of this task was to collect topographic data at the Bend Irrigation District Diversion Dam on Paynes Creek to develop a reconnaissance-level design to solve the upstream passage barrier at this location. Data were collected for the site using RTK GPS units for areas with adequate satellite coverage and a total station, stadia rod and prism for areas with heavy canopy. Results Topographic data were collected on May 15 and July 9, 2018. A total of 562 data points were collected, in both Paynes Creek, extending from 110 feet upstream to 100 feet downstream of the dam, and in the diversion canal (Figure 10). The topographic data were transmitted to Tricia Bratcher of CDFW to be used in developing a design to solve the upstream passage barrier at this location. In fall/winter 2018-2019, funds from Proposition One will be used by Trout Unlimited/NHC to initiate a fish passage improvement project for this site. Discussion The data from this effort should serve as a good baseline topographic survey for developing a design to solve the upstream passage barrier at this location.

Lower Deer Creek Falls Fish Ladder Post-restoration Monitoring

Methods The goal of this task was to collect information to assess geomorphic changes to Deer Creek resulting from the installation of a new fish ladder at Lower Deer Creek Falls (LDCF) in 2016-2017. On August 6-9, 2018, we resurveyed a total of 6 transects (two upstream of Lower Deer Creek Falls and four downstream of Lower Deer Creek Falls, Figure 11). Bed elevation profiles were measured for each transect using standard surveying techniques (differential leveling), and a wind-up, light-weight measuring tape to record stations. We also conducted a pebble count at each of the 6 transects to estimate how the grain size distribution on the creek bed near the monitoring sections changed as a result of the installation of a new fish ladder at Lower Deer Creek Falls, and took 4 post-restoration photographs (looking from left bank to right bank, looking from right bank to left bank, looking upstream, and looking downstream) of each of the 6 transects. A minimum of 100 pebbles (Harrelson et al. 1994) were counted per cross-section. We used the stake-out feature of our survey-grade RTK GPS equipment to resurvey the longitudinal profile of the study area that we had originally surveyed in 2015. The transect data were entered into an Excel spreadsheet to generate bed elevation profiles. We also collected preliminary data on the WSELs in the fish ladder – efforts were limited by access and time.


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Figure 10

Paynes Diversion Dam Topographic Data


31

Figure 11

Lower Deer Creek Falls Transects


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Results We were unable to resurvey most of the longitudinal profile downstream of the new fish ladder due to poor GPS satellite configuration during data collection. The longitudinal profile (Figure 12) showed only minor changes for the portion we were able to survey in FY-18. The pebble count data (Table 9) showed only minor changes between pre and post-restoration. Cross-section profiles and photographs are shown in Appendix C. There were more changes for the transects downstream of the new fish ladder than for the transects upstream of the fish ladder. For the fish ladder, there was a 1.42 foot drop in WSEL across the downstream-most weir and a 2.32 foot drop in WSEL across the upstream-most weir of the new fish ladder.

Figure 12

Lower Deer Creek Falls Longitudinal Profile. The falls and fish ladder are at Station 600. Blue lines are pre-restoration profile, red line and marker are post-restoration profile.

Table 9

Lower Deer Creek Falls Pebble Count Data12

D16, inches D50, inches D84, inches D100, inches Section Pre Post Pre Post Pre Post Pre Post

1 2.4 1.8 10.1 10.1 < 14.3 < 14.3 bedrock bedrock 2 3.5 1.8 10.1 10.1 bedrock bedrock bedrock bedrock 3 1.8 1.8 5.0 10.1 < 14.3 < 14.3 bedrock bedrock 4 2.4 5.0 5.0 10.1 < 14.3 < 14.3 bedrock < 14.3 5 3.5 2.4 10.1 5.0 < 14.3 < 14.3 bedrock bedrock 6 0.3 2.4 5.0 7.1 < 14.3 < 14.3 bedrock bedrock

12 Sections 1-4 are downstream of Lower Deer Creek Falls and Sections 5-6 are upstream of Lower Deer Creek Falls.


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Discussion

Changes between 2015 and 2018 likely reflect effects of high flows rather than an effect of the new fish ladder. Larger changes as a result of high flows would be expected for the transects downstream of the fish ladder, versus upstream of the fish ladder, due to the greater channel confinement of these transects by the vertical bedrock walls. Further, the larger changes to transects 1-3, versus transect 4, is inconsistent with any expected effect of the new fish ladder, since transect 4 is closest to the new fish ladder. After demolition of the bedrock to chisel the channel in for the precast concrete pieces, some of the fractured rock was flown off site, but some was volitionally added to the stream channel during the high flow event of 2016. However, we were unable to detect this in the post-restoration monitoring. Continued monitoring in FY-19 will focus on developing a rating curve for Deer Creek at the fish ladder exit and assessments of the fish ladder performance, as well as completing the post-restoration longitudinal profile.

Bobcat Flat East Topographic Data Collection

Methods The purpose of this task was to collect topography data to use in designing a floodplain and in-channel restoration site. Topographic data were collected using the same methods described above for the Yuba Narrows project. We also installed a permanent control on site, consisting of a copper-weld 18" x 1 1/2" brass marker embedded in concrete. Coordinates for this control were established by setting up the RTK base on the following known control: DESIGNATION - T 679, PID - HS2178, NAVD 88 ORTHO HEIGHT - 218.feet, SPC CA 3 - 2,057,178.85 6,528,865.16 sFT. Coordinates for this control were from the National Geodetic Survey Data Sheet. Results Topographic data were collected on October 24-26 and November 27-30, 2017. A total of 24,287 data points were collected. The data were transmitted to Fred Meyer of McBain and Associates to be used in developing a floodplain and in-channel restoration design at this location. Discussion The data from this effort should serve as a good baseline topographic survey for developing a floodplain and in-channel restoration design at this location. A static survey of the Bobcat East control network suggested that there may be a 5.5 foot horizontal error and 0.9 foot vertical error in the published coordinates for the known control. Accordingly, we plan to conduct a static survey in FY-19 on the known control to assess the accuracy of the published coordinates for this control.

https://www.ngs.noaa.gov/datums/vertical/index.shtml#NAVD88


34

Weir 1 Hydraulic Modeling

Methods The purpose of this task was to develop a hydraulic model to evaluate the effect of removal of Weir 1 on the Guisti Weir, located 3.75 miles upstream of Weir 1. Both weirs are in the downstream-most portion of Butte Creek, in the west borrow channel of the Sutter Bypass. On November 6, 2017, we collected data for three HEC-RAS transects (located upstream, on and below Weir 1), plus an additional HEC-RAS transect at Guisti Weir, using the methods described above for Antelope Creek. We derived a HEC-RAS model of the west borrow channel of the Sutter Bypass, extending from 0.6 miles downstream of Weir 1 to 1.6 miles upstream of Guisti Weir, from the CDWR Central Valley Flood Evaluation and Delineation (CVFED) Program HEC-RAS models (CDWR 2015). After we added the above four transects to the resulting HEC-RAS model, we ran the model at flows of 50 to 250 cfs with and without the transect on Weir 1. Results There was no difference in the water surface elevation at Guisti Weir with versus without Weir 1 at any of the simulated flows. Discussion We attribute the lack of effect of the removal of Weir 1 on WSELs at Guisti Weir to the elevation of the west borrow channel between the weirs. Specifically, the thalweg elevation (78.06 feet) of the HEC-RAS transect 200 feet upstream of Weir 1 was high enough that the backwater effect of removing the weir only extended 200 feet upstream of Weir 1. The water surface elevations upstream of this transect were controlled by the elevation of this transect, with or without Weir 1.

Central Valley Structured Decision Model Evaluation

Methods The purpose of this task was to develop new sources of data to improve the parameterization of the Central Valley Structured Decision Model (Peterson et al. 2014). Efforts on this task in FY-18 focused on floodplain habitat for the 26 streams in the Structured Decision Model (SDM). The original SDM had floodplain habitat values that were largely based on expert opinion and did not vary with flow. In previous years, we used simplified HEC-RAS models that were extracted from the CDWR Central Valley Flood Evaluation and Delineation (CVFED) Program HEC-RAS models (CDWR 2015) to develop flow-floodplain area relationships. We also sought out other sources of HEC-RAS models to develop flow-floodplain area relationships for streams that were not modeled by the CVFED program.


35

Results In FY-18, we continued developing a flow-floodplain area relationship for the Cosumnes River and started developing a flow-floodplain area relationship for the Mokelumne River. In FY-19, we will complete the flow-floodplain area relationships for the Cosumnes and Mokelumne Rivers. The flow-floodplain area relationships for the Cosumnes River will be based on a HEC-RAS model developed by UC Davis. The flow-floodplain area relationships for the Mokelumne River will be developed with a SRH2D model, using a topographic dataset provided by East Bay Municipal Utility District, and the same methods described above for Ward Dam. Discussion This information should be useful to the CVPIA fisheries Science Integration Team in their efforts to refine the Central Valley structured decision model.

REFERENCES California Department of Fish and Wildlife (CDFW). 2015. Critical Riffle Analysis for Fish

Passage in California. California Department of Fish and Wildlife Instream Flow Program Standard Operating Procedure DFW-IFP-001, 26 pp. October 2012, updated February 2015.

CDWR. 2015. Central Valley Flood System Conservation Strategy, Appendix H Central Valley

Chinook salmon rearing habitat needed to satisfy the anadromous fish restoration program doubling goal. CDWR: Sacramento, CA.

Environmental Protection Agency (EPA). 2003. EPA Region 10 Guidance for Pacific Northwest

State and Tribal Temperature Water Quality Standards. EPA 910-B-03-002. Region 10, Office of Water, Seattle, WA.

Graham Matthews and Associates. 2003. Hydrology, geomorphology, and historic channel

changes of Lower Cottonwood Creek, Shasta and Tehama Counties, California. Prepared by Graham Matthews and Associates, Weaverville, CA for National Fish and Wildlife Foundation, San Francisco, CA. CALFED Bay-Delta Program Project #97-N07 Final Report.

Harrelson, Cheryl C; Rawlins, C. L.; Potyondy, John P. 1994. Stream channel reference sites: an

illustrated guide to field technique. Gen. Tech. Rep. RM-245. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 61 p.

Jowett, I., T. Payne and R. Milhous. 2013. SEFA System for Environmental Flow Analysis.

Software Manual Version 1.2. Pukekohe, New Zealand.


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Kimmerer, W. and J. Carpenter. 1989. Desabla-Centerville project (FERC 803) Butte Creek interim temperature modeling study. Prepared for Pacific Gas and Electric by BioSystems Analysis, Inc. Tiburon, CA.

Milhous, R.T., M.A. Updike, and D.M. Schneider. 1989. Physical habitat simulation system

reference manual. Version II. Instream Flow Information Paper No. 26 U.S. Fish and Wildlife Service Biological Report 89(16).

NHC. 2016. Mill Creek, Ward Dam Fish Passage – DRAFT Cobble Bar/Bed Mobility Study

Memorandum – July 2016. NHC, Sacramento, CA. Pasternack, G.B. 2011. 2D Modeling and Ecohydraulic Analysis. University of California at

Davis, Davis, California. 158 pp. Payne and Associates. 2005. StreamTemp Version 1.0.4 and TRPA Stream Temperature User

Manual. Arcata, CA: Thomas R. Payne and Associates. Peterson, J.T., K. McDonnell and M. C. Colvin. 2014. Coarse Resolution Planning Tools for

Prioritizing Central Valley Project Improvement Act Fisheries Activities. Draft Progress Report. Oregon State University: Corvallis, OR. July 28, 2014.

Rantz, S. E. 1982. Measurement and computation of streamflow, Volume 1, Measurement of

stage and discharge, U.S. Geological Survey. Water Supply Paper 2175. 14pp. R2 Resource Consultants, 2008. Appendix G: Approach for Assessing Effects of Policy Element

Alternatives on Upstream Passage and Spawning Habitat Availability. R2 Resource Consultants, Inc. March 14, 2008 Administrative Draft prepared for the California State Water Resources Control Board, Division of Water Rights as part of the North Coast Instream Flow Policy: Scientific Basis and Development of Alternatives Protecting Anadromous Salmonids. Accessed from: http://www.swrcb.ca.gov/waterrights/water_issues/programs/instream_flows

Steffler, P. and J. Blackburn. 2002. River2D: Two-dimensional Depth Averaged Model of River

Hydrodynamics and Fish Habitat. Introduction to Depth Averaged Modeling and User’s Manual. University of Alberta, Edmonton, Alberta. 120 pp. http://www.River2D.ualberta.ca/download.htm

Stillwater Sciences, Tehama County Resource Conservation District, and Matt Kondolf. 2011.

Assessment of hydrology and geomorphology related to fish passage in lower Antelope Creek. Stillwater Sciences: Arcata, CA. February 4, 2011.

SWRCB (State Water Resources Control Board). 2015. 2010-2013 Average Demand Dataset -

Updated February 20, 2015. Accessed from: http://www.waterboards.ca.gov/waterrights/water_issues/programs/drought/analysis/

http://www.swrcb.ca.gov/waterrights/water_issues/programs/instream_flows

http://www.waterboards.ca.gov/waterrights/water_issues/programs/drought/analysis/docs/Demand_Database_2015-02-20.xlsx

http://www.waterboards.ca.gov/waterrights/water_issues/programs/drought/analysis/


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Thompson, K.E. 1972. Determining streamflows for fish life. In: Proceedings of the Instream Flow Requirement Workshop. Pacific N.W. River Basins Commission. Portland, OR. P. 31-50.

Waddle, T.J. (ed.). 2012. PHABSIM for Windows user's manual and exercises. Open-

File Report 2001-340. Fort Collins, CO: U.S. Geological Survey. 288 p.


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Appendix A North Cow Creek Critical Riffle Study Photos and Regressions


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North Cow Creek Riffle 18 at 201 cfs looking upstream

North Cow Creek Riffle 18 at 8.5 cfs looking upstream


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North Cow Creek Riffle 33 at 41.5 cfs looking upstream

North Cow Creek Riffle 33 at 0.92 cfs looking downstream


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42

North Cow Creek Riffle 18 Flow-Width Relationships


43

North Cow Creek Riffle 33 Regressions


44

North Cow Creek Riffle 34 Regressions


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Appendix B North Fork Cottonwood Creek Water Temperature Model Development and Calibration


46

Transect Stage-Discharge Calibration Parameters Used

Transect

Flow Range

Calibration Flows

SZF 1 3.2 - 455 8.1, 53.8, 182 96.5 2 3.2 - 455 8.1, 53.8, 182 96.7 3 3.2 - 455 8.1, 53.8, 182 95.4

BETA

%MEAN

Calculated vs Given13 Discharge (%)

Difference14 (measured vs. pred. WSELs)

XS COEFF. ERROR 8.1 53.8 182 8.1 53.8 182

1 2.31 19.4 6.6 32.5 19.1 0.02 0.21 0.24

2 1.92 24.3 24.2 31.4 17.4 0.06 0.25 0.23

3 4.02 8.0 3.9 12.6 7.7 0.01 0.07 0.06

13 Given refers to measured flows. 14 Units of Difference are feet.


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Downstream Pressure Transducer Rating Curve


48

Upstream Pressure Transducer Rating Curve


49

Reach 1/2/3 Flow-Width Relationship

Reach 4 Flow-Width Relationship


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Table B-1. Initial values of global SNTEMP parameters.

Parameter/Variable Value Constant Coefficient

Average Annual Air Temperature 63.09

Bowen Ratio 0.000619

Evaporation Factor A 40

Evaporation Factor B 15

Evaporation Factor C 0

Dust 4

Ground Reflection 17

Air Temperature -6 1.0444

Wind Speed 0 0.1746

Relative Humidity 0 0.9933

Percent Sunshine 0 1

Solar Radiation 0 1


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Table B-2. Initial values of reach-specific SNTEMP parameters.

Parameter Reach 1 Reach 2 Reach 3 Reach 4

Thermal gradient 1.65 1.65 1.65 1.65

Left bank vegetation height 51 51 51 27

Right bank vegetation height 43 43 43 19

Left bank vegetation density 65 65 65 54

Right bank vegetation density 65 65 65 51

Left bank topographic shade 7 7 14 9

Right bank topographic shade 6 9 10 8

Left bank vegetation crown width 29 29 29 15

Right bank vegetation crown width 25 25 25 11

Width A constant 31.80 31.80 29.47 34.89

Width B coefficient 0.269 0.269 0.095 0.083

Maximum width 33.05 41.01 33.05 33.91

Residual Depth 1 1 1 1

Manning’s n 0.035 0.035 0.035 0.035


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Table B-3. Calibrated values of universal SNTEMP parameters

Parameter/Variable Value Constant Coefficient

Average Annual Air Temperature 63.09

Bowen Ratio 0.000619

Evaporation Factor A 40

Evaporation Factor B 15

Evaporation Factor C 0

Dust 4

Ground Reflection 17

Air Temperature -6 1.0444

Wind Speed 0 0.1746

Relative Humidity 0 0.9933

Percent Sunshine 0 1

Solar Radiation 0 1


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Table B-4. Calibrated values of reach-specific SNTEMP parameters.

Parameter Reach 1 Reach 2 Reach 3 Reach 4

Thermal gradient 1.65 1.65 1.65 1.65

Left bank vegetation height 51 51 51 27

Right bank vegetation height 43 43 43 19

Left bank vegetation density 32.5 32.5 77.5 77.5

Right bank vegetation density 32.5 32.5 77.5 77.5

Left bank topographic shade 7 7 14 9

Right bank topographic shade 6 9 10 8

Left bank vegetation crown width 22.4 22.4 40.8 21.6

Right bank vegetation crown width 18.9 18.9 34.4 15.2

Width A constant 31.80 31.80 29.47 34.89

Width B coefficient 0.269 0.269 0.095 0.083

Maximum width 33.05 41.01 33.05 33.91

Residual Depth 1 1 1 1

Manning’s n 0.03 0.03 0.03 0.03


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Table B-5. Calibrated SNTEMP model performance.

Parameter (° F) Reach 2 Reach 4

Mean error for daily mean temp -0.3 0.0

Error range for daily mean temp -5.9 – 7.3 -9.9 – 8.4

Mean error for 7DADM -1.1 -1.9

Error range for 7DADM -5.8 – 5.8 -6.5 – 5.9


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Appendix C Lower Deer Creek Falls Cross-sectional Profiles15 and Photos16

15 Blue lines are pre-restoration profiles, while red lines are post-restoration profiles. 16 Photos with latitudes, longitudes and elevations of zero are from when the GPS in the camera was not working.


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Lower Deer Creek Falls Cross-Section 1



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Lower Deer Creek Falls Section 1 - View of Right Bank from Left Bank

Lower Deer Creek Falls Section 1 - View of Upstream from Mid-channel


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Lower Deer Creek Falls Section 1 - View of Downstream from Mid-channel

Lower Deer Creek Falls Section 1 - View of Left Bank from Right Bank


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central valley project improvement act fisheries … · 2019-08-16 · central valley project...

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