Lake Clarity Model: Development of Updated Algorithms to Define Particle Aggregation and Settling in Lake Tahoe

Transcription

1 Lake Clarty Model: Development of Updated Algorthms to Defne Partcle Aggregaton and Settlng n Lake Tahoe Goloka B. Sahoo S. Geoffrey Schladow John E. Reuter Danel Nover Davd Jassby

2 Lake Clarty Model Weather Precptaton Trbutares Land Use Groundwater Atmospherc Deposton Shorelne Eroson Total Pollutant Load to Lake Tahoe CDOM Nutrents (N P) Mneral Partcles Lght Scatterng and Absorpton Phytoplankton Growth Zooplankton Growth Loss (coagulaton and settlng) Secch Depth Detrtus Loss Death Loss Sedment Hydrodynamc Model

3 LCM Modfcaton after Lake Clarty Model (Sahoo G. B. Schladow S.G. and Reuter J. E. () Effect of Sedment and Nutrent Loadng on Lake Tahoe (CA-NV) Optcal Condtons and Restoraton Opportuntes Usng a Newly Developed Lake Clarty Model. Water Resources Research do:.9/9wr87) Lahontan and Nevada Dvson of Envronmental Protecton (NDEP). Lake Tahoe Total Maxmum Daly Load Techncal Report. p. Introducton of Turbulent Dffuson Model to LCM (Sahoo G. B. Schladow S.G. and Reuter J. E. () Dynamcs and Hydrologc Budget of a Large Olgotrophc Lake to Hydro-meteorologcal Inputs usng Predctve Model under Revson for Journal of Hydrology Updated stream partcles usng measured data - (D. Nover ). Fractal partcle aggregaton model (D. Jassby 6 and Sahoo after 6). Probablty of aggregaton

4 Swft () and Swft et al. (6) Fracton of scatterng or ab sorp ton % Organc partcles % Inorganc partcles 8% b (chl) scatterng b (norganc) scatterng Water molecules and CDOM 7% a(w+cdom) absorpton Jan Ap r Jul O ct Jan Ap r Jul O ct Jan Ap r Jul O ct Jan Ap r Jul O ct Tme a(w+cdo M) b (water) b (norganc) a* chl b (chl)

5 Partcle Aggregaton Theores. Sold Partcle Aggregaton (SPA) Model (O Mela 98). Fractal Partcle Aggregaton (FPA) Model (Jackson 99 )

6 Prevous Partcle Model. Sold Partcle Aggregaton (SPA) Model. Constant value for probablty of aggregaton (α) n m l n n n l n l m l n z c z n E z z c w c m l c c c m l t c ) ( ) ( ) ( where c l c m and c n are number concentraton of partcles (# m - ) of sze l m and n respectvely s a collson effcency factor reflectng the stablty of the partcles and the surface chemstry of the system (l m) s a collson frequency that depends on the nter-partcle (partcles of sze l and m) contacts w n (m s - ) s the settlng velocty of partcles of sze n and E(n z) s an exchange coeffcent accountng for turbulent and molecular effects. The expresson l + m n under the summaton denotes the condton that M l + M m = M n thus ensurng conservaton of mass.

7 New algorthms. Fractal Partcle Aggregaton (FPA) Model (modfed Jackson ). Varable probablty of partcle aggregaton (α) We postulated that probablty of aggregaton s functon of partcle sze dstrbuton partcle concentraton and phytoplankton concentraton.

8 Modfcaton contd. Chlorophyll a: Lterature (Passow ) suggests that Transparent Exopolymerc Partcles (TEP) hghly correlates wth Chl a. TEP accounts for partcles stckness. Partcle Concentraton: The probablty of aggregaton ncreases as the concentraton of partcles ncreases. Partcle sze dstrbuton: as smaller partcles concentraton s hgher to large partcles α s nversely proportonal to partcle sze (r) The constant (C a ): Calbrated. Both SPA and FPA conserve mass though the area avalable for collson s more for the case of FPA (Lee et al. ; Burd and Jackson 9). The new α was used for both SPA and FPA.. Stoke s law estmates settlng velocty for SPA. For FPA settlng velocty s based on fractal dmenson. Both use the three dfferent processes: Brownan dffuson flud shear and dfferental settlng for collson frequency.

9 Secch depth (m) Results (Annual Average SD) Measured constant coag SPA FPA

10 Partcles ( m - ) (a) Surface (.-mm) Partcles ( m - ) Partcles ( m - ) Results (Lake Partcle.-μm) (b) 6 m from surface (.-mm) 7 8 (c) m from surface (.-mm)

11 Results (Lake Partcle -μm) Partcles ( 9 m - ) Partcles ( 9 m - ) Partcles ( 9 m - ) 6 (b) m from surface (-mm) 6 (a) Surface (-mm) (c) m from surface (-mm)

12 Results (Lake Partcle -μm) Partcles ( 8 m - ) 8 6 (a) Surface (-mm) Partcles ( 8 m - ) (b) Partcles ( 8 m - ) 6 m from surface (-mm) (c) m from surface (-mm)

13 Results (Lake Partcle -8μm) Partcles ( 8 m - ) (a) Surface (-8mm) Partcles ( 8 m - ) Partcles ( 8 m - ) (b) 6 m from surface (-8mm) 7 8 (c) m from surface (-8mm)

14 Results (Lake Partcle 8-6μm) Partcles ( 7 m - ) (a) Surface (8-6mm) DLM-WQ: Sold DLM-WQ: Fractal Partcles ( 7 m - ) (b) Partcles ( 7 m - ) m from surface (8-6mm) (c) m from surface (8-6mm) DLM-WQ: Sold DLM-WQ: Fractal

15 6 7 8 Secch depth (m) Secch depth (m) Results usng new α and Sold Partcle Algorthm Model Annual Average Measured Daly LCM Measured LCM

16 Secch Depth (m) Secch Depth (m) Results usng new α and Sold Partcle Algorthm Model Wnter (Dec-Mar) Measured LCM Seasonal trend Summer (June-Sep) Measured LCM

17 Summary Long term measured lake and stream partcle data helps to estmate the trend and calbrate the model well The new probablty of aggregaton term captures well the seasonal and nterannual Secch depth varaton compared to constant number. Both FPA and SPA conserve mass though area avalable for collson s more for FPA case. So smaller partcles are aggregated at hgher rate for the case of FPA. Because of that predcted Secch depth usng FPA s lttle hgher to usng SPA. Ths s not the end of modfcaton. Avalablty of new dataset wll help to fnd the ground truths of many processes and wll ask for modfcaton.

18 ACKNOWLEDGEMENTS Ths research s supported by Unversty of Calforna Davs and grants from the USDA Forest Servce Pacfc Southwest Research Staton usng funds provded by the Bureau of Land Management through the sale of publc lands as authorzed by the Southern Nevada Publc Land Management Act.