Spatially-explicit Hydrodynamic and Water Quality Modeling of the A.R.M. Loxahatchee National Wildlife Refuge Part I - Model Setup and Calibration

Transcription

1 Spatially-explicit Hydrodynamic and Water Quality Modeling of the A.R.M. Loxahatchee National Wildlife Refuge Part I - Model Setup and Calibration Chufang Chen 1, Ehab Meselhe 2, Michael Waldon 3, Hongqing Wang 2, and Matthew Harwell 3 1 University of Florida, School of Natural Resources and Environment 2 Center for Louisiana Water Studies, University of Louisiana at Lafayette, Lafayette, LA 3 DOI Everglades Program Team USFWS, Boynton Beach, FL

2 Background

3 Background Changes in water quantity, timing and quality are impacting the Refuge s ecosystem. It is a priority for the Refuge to ensure appropriate water management decision rules (regulation schedule) Fish and Wildlife Nutrients Loading Flood Control Water Supply

4 Marsh Bathymetry

5 L-7 & L-39 Canal Bathymetry Elevation (ft NGVD29) L-7 Canal L-39 Canal Canal Mile Sed. Surf. Ele. Channel Bot. Elev. Obtained from University of Florida - IFAS

6 L-40 Canal Bathymetry Elevation (ft NGVD29) Canal Mile Sed. Surf. Ele. Channel Bot. Elev. Obtained from University of Florida - IFAS

7 G-301 Spillway G-300 Spillway G-310 PS G-251 PS S-362 Pump Station ACME 1 Pump Station G 94D Pump Station G-94C Culvert G-338 PS S-6 PS S-10E Culvert G-94B Culvert G-94A Culvert S-10D Cul S-10C Cul S-10A Cul S-39 Culvert

8 Precipitation and Evapotranspiration

9 Evapotranspiration Reduction factor f ET ET = f ET * ET obs f ET = Maximum fet min Minimum, 1, H H ET f ETmin is the minimum percentage that ET can be reduced H is the water depth H ET is the depth below which ET is reduced ET is reduced to 20% when the depth = 0 and is 100% when the depth is 0.20 m.

10 MIKE FLOOD Dynamic model coupling MIKE21 with MIKE11 Finite difference solver Flooding and drying capabilities Groundwater losses in canal and marsh can be included Spatially variable marsh resistance, precipitation, ET and dispersion coefficient can be included Control structure can be used to access alternative of Regulation Schedule Developed by DHI Water & Environment (DHI, 2008)

11 Grid for Marsh Simulation

12 Canal in MIKE L40 chainage L7 chainage Elevation (ft) Elevation (ft) Distance from left levee (ft) Distance from left levee (ft)

13

14 Model Setup Period of study calibration validation , Lateral cell link of MIKE21 to MIKE11 Initial water level uniform in the marsh and canal Initial concentration spatially varied in marsh based on measurements using inverse distance, uniform in canal Time integration method Euler Time step 5 min for hydrodynamics, variable for water quality ranging from 1 min to 3 min

15 Model Calibration - Hydrodynamics Parameters: Marsh and canal roughness: bed resistance is calculated based on Manning's equation Wetting and drying depths Coefficients for ET reduction - f ETmin and H ET Seepage rate in the marsh and the canal

16 Gage North (Soil Level m) Calibration Validation 12/31/ /31/ /31/ /30/ /30/ /30/2006 Observed Model Prediction 12/31/2000 1/1/2000 Stage (m)

17 12/30/ /30/ Gage 1-7 (Soil Level m) 12/31/ /31/ /31/ /30/2004 Observed Model Prediction 12/31/2000 1/1/2000 Stage (m)

18 12/30/ /30/ Gage 1-8T (Soil Level m) 12/31/ /31/ /31/ /30/2004 Observed Model Prediction 12/31/2000 1/1/2000 Stage (m)

19 12/30/ /30/ Gage 1-9 (Soil Level m) 12/31/ /31/ /31/ /30/2004 Observed Model Prediction 12/31/2000 1/1/2000 Stage (m)

20 12/30/ /30/ Gage South (Soil Level m) ` 12/31/ /31/ /31/ /30/2004 Observed Model Prediction 12/31/2000 1/1/2000 Stage (m)

21 12/30/ /30/ Gage 1-8C 12/31/ /31/ /31/ /30/2004 Observed Model Prediction 12/31/2000 1/1/2000 Stage (m)

22 Calibration Statistics ( ) Parameter North T 1-9 South 1-8C Bias (m) RMSE (m) Variance Reduction 46% 66% 82% 77% 80% 74% R (Correl Coef) Nash-Sutcliffe Eff

23 Validation Statistics ( ) Parameter North T 1-9 South 1-8C Bias (m) RMSE (m) Variance Reduction 88% 89% 91% 93% 91% 83% R (Correl Coef) Nash-Sutcliffe Eff

24 Hydrodynamic Animation I 06/ /2006

25 Hydrodynamic Animation II 06/ /2006

26 Water Quality Modeling Modules: AD and ECO Lab - AD: Standard advection-dispersion module - ECO Lab Open process module for ecological modeling Template independent of grid system Components - state variables, constants, forcings, auxiliary variables, processes, and derived outputs

27 ECO Lab MIKE FLOOD ECO Lab Equations: Rate of mass accumulation = Mass inflow - Mass outflow + Dispersion in Dispersion out + Production - Disappearance A cell dhc dt = Q C b i i Q o C o Disp Source Mass inflow aerial deposition wet deposition = rain rate * rain concentration dry deposition = loading rate Mass outflow evaporation = does not transport mass transpiration = ET * % trans * C + + K s C

28 ECO Lab (cont.) Chloride (CL) is modeled as conservative tracer Sulphate is modeled using Monod relationship with half saturation constant disappearance rate = k c + c Total Phosphorus (TP) is modeled following DMSTA dynamics (Walker and Kadlec, 2005) ( - water column storage - biomass storage 0 k 1 2

29 DMSTA TP cycling processes

30 Auxiliary variables Concentration multiplier F c C Depth function F z 1 min(1, depth/ Z x ) Z x 0 otherwise

31 DMSTA Calibration Parameters Maximum Uptake Rate Recycle Rate Burial Rate Depth Scaling Factor K m 3 /mg-year K m 2 /mg-year K /year Z x 0.6

32 Initial P storage in the sediment layer

33 Conclusions Model results in good agreement with observations. Statistics are encouraging that model would meet project objectives. Model is computationally efficient (Intel (R) T GHz, 3.25GB RAM) - to run 1 year of hydrodynamic requires 0.75 CPU hours - to run 1 year for CL requires 2.0 CPU hours New model of 400m resolution available for Refuge restoration planning applications and the Everglades simulation.

34 Future/Ongoing Developments Validate models for the Period of Record between 1995 and 1999 Ground water seepage will be enhanced by MIKE SHE. Regulation Schedule and management scenarios are being assessed.

35 Questions?