1 / 12

Lake Clarity Model

Lake Clarity Model. Inputs. Weather, Precipitation. Tributaries. Land Use. Atmospheric Deposition. Groundwater. Shoreline Erosion. Total Pollutant Load to Lake Tahoe. Modules. CDOM. Nutrients (N, P). Mineral Particles. Light Scattering and Absorption. Zooplankton Growth.

isaura
Télécharger la présentation

Lake Clarity Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Lake Clarity Model Inputs Weather, Precipitation Tributaries Land Use Atmospheric Deposition Groundwater Shoreline Erosion Total Pollutant Load to Lake Tahoe Modules CDOM Nutrients (N, P) Mineral Particles Light Scattering and Absorption Zooplankton Growth Phytoplankton Growth Output Detritus Death Secchi Depth Loss (coagulation and settling) Loss Loss One Dimensional Hydrodynamic Model

  2. Input files to LCM LCM requires all weather forcing data, pollutant loads, lake and stream information, initial condition of lake, and parameters controlling the bio-chemical reactions.

  3. Stream Inputs LSPC outputs : Flow volume and nutrients Particle counts : Rabidoux’s regression equation and LCPC flow Stream temperature : Artificial neural network and weather data

  4. Stream Inputs Rabidoux (2005) developed statistically unbiased regression equations: P = 1  Q* + 0 Punbiased = exp(P)  g(z) Where, P and Q are natural logarithms of particle flux (#/s) and stream flow (cfs), respectively; 0, and 1 are interception and slope of the log-log linear regression equation; g(z) is the Bradu and Mundlak (1970) correction factor for statistically unbiased estimation. Example

  5. Storm water mean particle concentration (Source: Allan Heyvaert, DRI)

  6. Most Urban Areas in the Intervening Zones

  7. Multiplication Factor to Estimate Urban Particles Using Regression Equations Allan Heyvaert field data Average particle concentration for 0.49 - 16m = 3.4694  107 #/ml Rabidoux (2005) Average particle concentration for 0.49 - 16m = 1.0881  105 #/ml (Annual Average for the period 1994-2004) Multiplication factor for 0.5-16 m = (3.47  107)/ (1.09 105) = 318.9 Similarly, Multiplication factor for 16-63 m = (7.75  103)/ (3.54 102) = 21.9

  8. Input files to LCM

  9. Time-independent pollutant loadings to LCM Atmospheric load : LTADS report (CARB, 2006), UC Davis – TERC, DRI, UC Davis DELTA Group Groundwater load : USACOE [2003] Shoreline erosion : Adams and Minor [2002] estimates

  10. Precipitation year selection

  11. Precipitation year selection (cont)

  12. Precipitation year selection (cont) Time dependent inputs Stream inputs Meteorological inputs Outflow inputs 1999 2000 2001 2002 … Precipitation year selection Time independent inputs • Atmospheric depositions • Groundwater loading • Shoreline erosion • Model parameters

More Related