Current Status of the SCHISM-CoSiNEModel in San Francisco Bay

Richard Dugdale, Estuary and Ocean Science CenterRomberg Tiburon Campus, SFSU

Eli Ateljevich, Department of Water ResourcesFei Chai, Qianqian Liu, Zhengui Wang,

University of MaineYi Chao, Remote Sensing Solutions and UCLA

Curtiss Davis, Oregon State University

NASA NNX11AP11Gand NNX14AD79G

California Water and Environmental Modeling Forum Annual Meeting 2018

• Our modeling, data acquisition and remote sensing team

• Our tool box of models• California ROMS model• Semi-implicit Cross-scale Hydroscience Integrated

System Model (SCHISM), previously SELFE• Coupling CA ROMS and SCHISM and validation • Carbon, Silicate, Nitrogen Ecosystem model (CoSiNE)• Coupling CA ROMS, SCHISM and CoSiNE and

validation • Future model improvements

Outline

SCHISMSemi-implicit Cross-scale Hydroscience Integrated

System Model(Yi Chao, RSS

Eli Ateljevich, DWR)

CoSiNE ecological model (Fei Chai, U. Maine)

Field Observations(Dick Dugdale, RTC

NRL and OSU)

Project - Impacts of Population Growth on the SF Bay and Delta Ecosystem

Remote sensing(Curt Davis, OSU)

Resultsmerging data and

models to understand SFE

Role of modeling

NASA Grant NNX14AD79G Impacts of Population Growth on the San Francisco Bay and Delta Ecosystem

Usefulness of Remote Sensing – can get spatial coverage.

Aim is to combine models with these data as verification

Toolbox of Simple Models

ClamFlow

NAMFlow

Toolbox of open source modelsCA ROMS, SCHISM and CoSiNE

Bathymetry with 2 black boxes showing 2 nested finer resolution model domains

Operational 3km resolution CA ROMS

CeNCOOSwww.cencoos.org/data/models/roms/ca

San Francisco Bay SCHISM model linked with California coastal ocean ROMS

(SCHISM - Semi-implicit Cross-scale Hydroscience Integrated System Model)

(http://oceanview.pfeg.noaa.gov/sesame/estuarycompare.html)

ROMS-SCHISM Bay model validation

blue data red- model

CoSiNE (Carbon, Silicate, Nitrogen Ecosystem) Model to understand/predict the ecosystem

Boundary Conditions: 3-km ROMS-CoSiNE; Rivers with discharge, dissolved inorganic nutrients – using USGS Menlo Park data.

Sources: Nutrients from 18 WWTPS (green circles)

Model Configuration (Zhang, Virginia Institute of Marine Science)

Coupled SCHISM-CoSiNE model

San Francisco Bay Nutrients and Plankton Dynamics as Simulated by a Coupled Hydrodynamic-Ecosystem Model

Qianqian Liua, Fei Chaia,, Richard Dugdaleb, Yi Chaoc,d, Huijie Xuea, Shivanesh Raoe, Frances Wilkersonb, John Farrarac, Hongchun Zhangd, Zhengui Wanga, YinglongZhangf

[a] School of Maine Sciences, University of Maine, Orono, ME 04469, USA[b] Romberg Tiburon Center, San Francisco State University, Tiburon, CA, 94920, USA[c] Remote Sensing Solutions, Inc., Monrovia, CA 91016, USA

[d] Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA 90095, USA[e] School of Mathematics and Statistics, University of New South Wales, Sydney NSW, 2052, Australia[f] Virginia Institute of Marine Science, Gloucester Point, VA 23062, USA

Paper accepted by Continental Shelf Research

SCHISM-CoSiNE Validation of temperature and salinity

Central Bay, RTC site

SCHISM-CoSiNE model - Validation of ammonium inhibition function

Prop

ortio

n of

NO

3up

take

:tota

lN u

ptak

e

inhibition factor varied

Ammonium concentration Ammonium concentration, µmol/LN

O3

upta

ke, µ

mol

/L/d

SCHISM-CoSiNE model - validation of flow and ammonium flux at the entrance to Suisun Bay

Modeled flux of ammonium and nitrate to the ocean and between bays

INFLOW

OUTFLOW

OUTFLOW

SCHISM-CoSiNE output - partitioned into four box regions

Suisun Bay (Box 1) depth-averaged nutrients and chlorophyll

Comparisons of NO3, NH4 and Chlorophyll concentrations over the USGS stations in Suisun Bay. Lines for model results; Stars for observations.

Nitrate

Ammonium

Chlorophyll

control experiment (R1)

Sediment Model to Regulate Light

Attenuation by SPM

Improvements – apply sediment model to SCHISM-CoSiNE

(use sediment transport model in SCHISM in collaboration with Joseph Zhang and Eli Ateljevich)

Note: biology and heating are fully coupled with EcoLight-S (biology affects heating and heating affects biology)

from Mobley et al., J. Geophys. Res. (2015)

Differences in Chl after 14 days when EcoLight-S is used rather than an approximate analytic light model.

Improvement – link Ecolight model of Curt Mobley with CoSiNE to improve optics

Analytic Model Ecolight Model

What uses can be made of the model?

1) Planning and analyzing experimental manipulations

2) Adaptive management tool

3) Anticipating large events, e.g. change in sewage

treatment, climate change effects

4) Other research opportunities and questions

Uses of this ROMS-SCHISM-CoSiNE model

We are focusing on:

Predicting the occurrence, location and magnitude of both fall and spring phytoplankton blooms

Sorting out the roles of grazing and phytoplankton sinking on bloom dynamics

Current Efforts

CA ROMS- SCHISM Yi Chao (Remote Sensing Solutions) Carrie Zhang (UCLA)

SCHISM hydrodynamics: Joseph Zhang (VIMS)Eli Ateljevich (CA DWR)

CoSiNE Ecosystem: Fei Chai (Univ. Maine)Zhengui Wang (Univ. Maine)Shivanesh Rao (Univ. Maine)Qianqian Liu (Univ. Maine

NASA Grant- NNX14AD79G: impacts of population growth on the San Francisco Bay and Delta ecosystem

22

Acknowledgments

End

Parameters Symbol Value Units

Light attenuation due to water k1 0.75 m−1

Light attenuation due to phytoplankton k2 0.03 m−1(mmol m−3)−1

Light attenuation due to suspended particular matter k3 0.066 m−1(mg m−3)−1

A constant suspended particular matter concentration k3 20 mg m−3

Initial slope of P-I curve α 0.1 day−1(W m−2)−1

Nitrification rate γ7 0.25 day−1

Maximum specific growth of small phytoplankton µ1max 2.0 day−1

Maximum specific growth of diatom µ2max 2.5 day−1

Ammonium inhibition parameter ψ 1.5 (mmol m−3)−1

Half-saturation for nitrate uptake by small phytoplankton Kno3s1 1.0 mmol m−3

Half-saturation for ammonium uptake by small phytoplankton Knh4s1 0.1 mmol m−3

Half-saturation for nitrate uptake by diatom Kno3s2 3.0 mmol m−3

Half-saturation for ammonium uptake by diatom Knh4s2 0.3 mmol m−3

Small phytoplankton specific mortality rate γ3 0.2 day−1

Diatom specific mortality rate γ4 0.1 day−1

Mesozooplankton maximum grazing rate G2max 0.65 day−1

Half-saturation for mesozooplankton ingestion K2gr 0.25 mmol m−3

Microzooplankton excretion rate to ammonium reg1 0.2 day−1

Mesozooplankton excretion rate to ammonium reg2 0.2 day−1

Microzooplankton assimilation rate γ1 0.75 day−1

Mesozooplankton assimilation rate γ2 0.75 day−1Microzooplankton (meso-) specific mortality rate γ 0.15 day−1

Grazing preference for diatoms ρ5 0.70Grazing preference for microzooplankton ρ6 0.20Grazing preference for detritus ρ7 0.10

Table 1. Model parameters used in all experiments except as noted in the manuscript for the process-oriented experiments.

Monthly Mean USGS (red stars) and Modeled Chlorophyll over the Four Boxes for the Year of 2011

A bloom occurs afterthe turbidity becomessmall.

Affected by the Upstream, high SPM Regions

NO3, NH4, and Diatom in 2011

Diatom and nutrients (NO3 and NH4) at DWR-D7 in 2011 and 2012

NO3, NH4, and Diatom in 2012

Still to do - improve modeling the light field: High modeled summer bloom is likely due to modeled water

clarity with insufficient SPM (TSS) or turbidity