Skill Assessment of Multiple Hypoxia Models in the Chesapeake Bay and Implications for Management Decisions
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1 Skill Assessment of Multiple Hypoxia Models in the Chesapeake Bay and Implications for Management Decisions Isaac (Ike) Irby - Virginia Institute of Marine Science Marjorie Friedrichs VIMS Carl Friedrichs - VIMS Cathy Feng VIMS Raleigh Hood UMCES Jeremy Testa UMCES
2 Project Group U.S. IOOS Coastal Ocean Modeling Testbed (COMT)Support for Scenario-based Ecological Forecasts To improve understanding and operational forecasts of extreme events and chronic environmental conditions affecting the U.S. Five Science Teams Chesapeake Bay Estuarine Hypoxia Forecasting Integration of West Coast Operational Coastal & Ocean Models Puerto Rico/U.S. Virgin Islands Inundation & Wave Forecasting Northern Gulf of Mexico Ecological Forecasting Cyberinfrastructure
3 Project Group U.S. IOOS Coastal Ocean Modeling Testbed (COMT)Support for Scenario-based Ecological Forecasts To improve understanding and operational forecasts of extreme events and chronic environmental conditions affecting the U.S. Five Science Teams VIMS: Chesapeake Marjy Friedrichs Bay Estuarine (lead PI) Hypoxia UMCES: Forecasting Carl Friedrichs (VIMS-PI) Ike Irby (funded student) Aaron Bever (consultant) Jian Shen (collaborator) Cathy Feng (collaborator) WHOI: Malcolm Scully (WHOI-PI) Raleigh Hood (UMCES-PI) Hao Wang (funded student) Jeremy Testa (collaborator) Wen Long (collaborator) NOAA-CSDL: Lyon Lanerolle (NOAA-PI) Frank Aikman (collaborator)
4 Chesapeake Hypoxia Objective Assess the readiness and maturity of a suite of existing estuarine ecological community models for determining past, present, and future hypoxia events within the Chesapeake Bay, in order to accelerate the transition of hypoxia model formulations and products from academic research to operational centers. Operational Centers: Chesapeake Bay Ecological Prediction System (CBEPS) Short-term forecasts (R. Hood) NOAA/NOS/CO-OPS Short-term forecasts (L. Lanerolle) EPA Chesapeake Bay Program (CBP) Scenario-based forecasts (M. Friedrichs, C. Friedrichs, I. Irby)
5 Scenario-based Forecasting Project Can community-based models achieve a similar skill to the Chesapeake Bay Program s regulatory model in representing dissolved oxygen concentrations? Would a similarly skilled model predict similar water quality standard attainment as the Chesapeake Bay Program s regulatory model from a given level of nutrient reduction? How can these models be used to define uncertainty in regulatory model predictions?
6 Hypoxia Model Comparisons Statistically compare output from four Bay models Three ROMS-based models with varying biological complexity ROMS RCA ChesNENA ChesROMS BGC CBP regulatory/operational biologically sophisticated model CH3D ICM Examine how well each model reproduces the mean and spatial/seasonal variability of: Temperature Salinity Stratification Dissolved Oxygen Biological Complexity Chlorophyll-a Nitrate
7 !! Chesapeake Bay! Hypoxia Model Comparisons Compare simulations to observations at 10 main-stem stations for ~16 cruises in 2004 and 2005 BDK0000 WIL0013 POT2386 POT2766 SAV0000 NBP0326 NBP0103 GEO0009 NBP0534 NBP0023 TOW0030 NBP0689 NBP0461 WEST VIRGINIA TBK01 PMS01 TUT01 ANA01 PMS02 AAG01 PMS03 ANA04 TFB01 AAG02 PMS05 ANA06 PMS11 THR01 TNA01 PMS10 ANA07 ANA05 TWB03 PMS07 PMS12 TWB01 TWB02 PMS08 TCO01 ANA08 PMS13 KNG02 TWB04 TWB06 PMS09 ANA09 PTB01 TPB01 PMS16 ANA14 ANA10 TWB05 TDU01 ANA11 ANA19 TFC01 PMS18 KNG01 PWC04 ANA17 ANA13 ANA12 PMS21 ANA20 PMS23 ANA22 ANA16 TFD01 ANA15 ANA18 ANA24 TTX27 PMS25 ANA23 TFS01 PMS27 ANA25 ANA29 ANA21 PMS29 ANA26 PMS31 ANA27 PMS33 TOR01 PMS35 PMS37 PMS39 PMS41 PMS44 Washington, DC PMS46 Inset PMS Miles PMS51 SMNT05 SMNT06 ELI1 SMNT07 SMNT04 SMNT08 SMNT03 SMNT09 LAF1 LFA01 SMNT09.5 LE5.6 SMNT10 SMNT12 LFB01 SMT01 ELI2 Potomac TDA01 TCO06 SMT05 SMT09 SMNT01 SMNT14 SMT08 St. George Cr. St. Mary's River Inset Miles SMT06 SMT13 River RCR01 RCR07 RCR09 RCM0111 RCR04 ANA03 SMT04 SMSMC WBB05 ANA0082 ANA30 ANA02 SMNT11 SMNT02 SMT02 ELD01 SMNT13 SMT03 ELI3 ELE01 St. Mary's R. Potomac R. SMT10A SMT10 SMT10B SMT12 SMT11 SMT07 Elizabeth R. Western Br. Elizabeth River Inset Miles Lafayette R. WBE1 EBE1 SBE1 EBE1-E SBA1 SBE2 SBC1 SBE3 Southern Br. SBD1 SBE4 SBE5 SBD4 Eastern Br. EBE2 EBB01 TF5.0J CON0180 POT1830 CON0005 VIRGINIA James CAC0148 POT1595 ANT0203 ANT0044 POT1472 POT1471 TF3.1D River TF3.1W TF3.0 TF5.0A ANT0366 CAC0031 POT1596 TF3.1 TF3.1A TF5.2 TF5.2A TF5.3 TF5.4 IH3 TF3.1C MON0269 MON0155 MON0020 CJB0005 IH5 TF3.1E SEN0008 TF2.4 TF4.0P POT1184 IH1 TF5.5 TF2.3 RET2.1 RET2.3 RET2.2 TF5.5A MON0528 BPC0035 MARYLAND TF3.1B TF3.2 TF2.1 TF2.2 IH4 TF4.0M MAT0078 MAT0016 IH2 IH6 Rappahannock TF4.1A TF4.2 TF5.5AN TF5.5AS TF5.6 PXT0972 PXT0809 See DC Inset TF3.2A TF4.4 XFB1986 RET2.4 RET4.1 TF3.3 RET3.1 RET4.3S Maryland and Virginia Water Quality Monitoring Stations PAT0285 TF5.6A TF4.4A TF1.2 WXT0001 PIS0033 RET4.3 RET5.1 RET5.2S NPA0165 JON0184 GWN0115 TF1.0 Potomac TF1.7 RET3.1S RET5.1A RET3.1N River RET4.2 GUN0476 WT4.1 PAT0176 WT5.1 TF1.3 TF1.4 Patuxent TF1.5 LE5.2 WT7.1 WT8.2 TF1.6 LE2.2 RET3.2 RET4.3N LE3.2 LE4.1 LE4.2 LE5.1 LE3.1 WT6.1 LE1.1 LE1.3 WT8.1 WT8.3 LE1.4 LE4.2S LE4.3N RET5.2N LE4.3 RET5.2 LE3.2S York R. LE5.2S GUN0258 CB4.2W CB4.3W RET1.1 River FRG0018 HOK0005 CB4.1W LE3.2N LE4.2N LE5.2N LE1.2 LE4.3S LE5.3 LE5.4 GUN0125 Patapsco R. CB3.3W CB4.1C CB4.2C CB4.3C LE3.3 LE3.3A CB5.4W LE3.4 CB4.2E LE3.6 LE3.7 LE5.5 WT3.1 CB5.1W WE4.3 WT2.1 WT1.1 CB3.3C CB4.1E CB4.3E See St. Mary's Inset River LE2.3 WE4.1 WE4.2N WE4.2S NOM0007 MDR0028 EE2.2 CB4.4 CB3.2 CB3.3E WE4.2 WE4.4 EE2.1 CB5.1 LE3.6N LE3.6S CB6.3 LE5.5A LE5.5B See Elizabeth Inset Susquehanna R. CB3.1 ET4.2 EE1.1 CB5.2 CB1.0 DER0015 CB2.2 XGG8251 TRQ0146 CB5.5 CB6.4 CB6.1 EE3.0 CB5.3 CB5.4 CB6.2 H-1A CB7.2 CB7.2E CB7.3E C-1 OP-1 CB8.1 CB7.3 EE3.1 CB7.4N CB7.4 CB8.1E CB1.1 Chester R. ET5.2 CB7.1N H-2 C-2 C-3 OP-3 OP-2 ET2.3 CB2.1 CB7.1S ET2.2 ET6.2 CB7.1 H-3 H-1 ET1.1 ET4.1 Choptank ET5.0 ET5.1 XDJ9007 TRQ0088 Nanticoke ET2.1 ET9.1 ET3.1 ET8.1 OC-3 ET6.1 CCM0069 R. River EE3.5 ET7.1 XCI4078 POK0087 EE3.2 EE3.3 EE3.4 - DELAWARE CS-3 ON-3 Legend WIW0141 MNK0146 ET10.1 BXK0031 XAK7810 Fall Line Miles
8 Model Skill Assessment
9 Model Skill Assessment
10 Model Skill Assessment
11 Model Comparison Results ROMS - RCA! 2004 Bias% 1 Surface Nitrate"! CH3D - ICM! ChesNENA! Unbiased% RMSD% ChesROMS - BGC! ROMS - RCA! 1 as% Bottom Temperature" Bottom Salinity" Stratification" Bottom Dissolved Oxygen" Surface Chlorophyll" Surface Nitrate"! Overall skill of all four models (temporal + spatial variability) are: highest in terms of temperature similar to each other in terms of T, S, stratification and DO Different in terms of chlorophyll and nitrate
12 Model Comparison Results CH3D - ICM! ChesNENA! ChesROMS - BGC! ROMS - RCA! High Flow as% Bottom Temperature" Bottom Salinity" Stratification" Bottom Dissolved Oxygen" Surface Chlorophyll" Surface Nitrate"! Normal Flow
13 Summary of Results Regardless of complexity, models do similarly well in terms of reproducing observations of T, S, and DO, and similarly poorly in terms of stratification All models reproduce DO better than variables that are typically thought to be primary influences on DO (stratification, chlorophyll, and nitrate) This is because seasonal DO variability is sensitive to T (solubility effect), and the models reproduce T very well Modeled DO simulations may be very sensitive to any future increases in Bay temperature à Hypoxia scenario forecasting is possible with simple biological formulations
14 Moving to Scenario-based Forecasts EPA/CBP model" ChesNENA" EPA/CBP Projected Water Quality " Nutrient Reduction Scenario" Ensemble of Implementations" Model Ensemble Projected " Water Quality" Conducted with assistance from the CBP! Estimate Uncertainty in Projections"
15 Session goals how we can make the various ecological models/ forecasts that are being created through R&D more sustainable and accessible for application to important ecosystem-based management decisions and to be improved through time Community-based models to support and define uncertainty in regulatory models Easy to run/can be quickly updated to tell us when the regulatory model needs be updated
16 Thank You
17 Skill Assessment Project Results: 2004 Seasonal Variability Surface Chlorophyll Surface Nitrate CBP A CBP A B C B C
18 Skill Assessment Project Results: 2004 Seasonal Variability CBP A CBP A B C B C
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