Automatic calibration of water quality models for reservoirs and lakes. Nasim Shojaei Portland State University Supervisor: Prof.

Transcription

1 Automatic calibration of water quality models for reservoirs and lakes Nasim Shojaei Portland State University Supervisor: Prof. Scott Wells

2 Drafts Ecosystem Services - Water quality models - Calibration Particle swarm optimization algorithm The Study Area CE-QUAL-W2 model s

3 Ecosystem Services Ecosystem Services: (ES) Water quality Water Quantity Many regulating, provisioning, supporting, and cultural services are related to water. Some of the Ecosystem services that people benefit from most directly include the provision of drinking water, irrigation water, hydropower, fish, opportunities for recreation. Water temperature is a major concern in our study area because its threatened salmonid species are vulnerable to high water temperatures. Nutrients concentration are major concerns because of irrigation and drinking purposes. Water quality models are increasingly developed to achieve water quality goals and to evaluate the impacts of climate, land use, and land on the quantity and quality of land and water resources. Calibration and validation of these models are critical steps in the overall model development before using them in research and/or real-world applications. With a proper water quality model, we can develop different scenarios to find effects of management plans on water bodies.

4 Similature CE-QUAL-W2 :(Cole and Wells 23) 2-dimensional, hydrodynamic, and water quality model (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body The parameters and coefficients affecting model calibration included: T (Temperature), WSE(Water Surface Elevation) Chlr a NH4 NO3 PO4

5 Particle Swarm Optimization (PSO) PSO is a robust population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO applies the concept of social interaction to problem solving It was developed in 1995 by James Kennedy (social psychologist) and Russell Eberhart (electrical engineer)

6 Particle Swarm Optimization Equation i i v [ t 1] wv [ t ] i, best i c11 r x t x t 2 2 [] [] gbest i c r x [] t x [] t PERSONAL BEST x i [ t 1] x i [ t ] v i [ t 1] NEW POSITION PRESENT POSITION GLOBAL BEST

7 Flowchart for automatic calibration model

8 The Study A rea The reservoir is located on the Karkheh River in the southeast region of Iran, draining approximately 5 Km^2 of mountainous and plain area. The Karkheh Reservoir has been designed to provide drinking water for few cities and agricultural water for nearby 18 thousand hectares of irrigable area The Karkheh Reservoir has 5x1^9 m^3 capacity, 64-Km length, and 162 Km^2 surface areas in normal water level, with maximum depth of 117 m. the project is quite strategic for supplying reliable and high quality water for the irrigation and municipal needs.

9 The Study A rea

10 The Study A rea

11 The Study A rea

12 Vertical view of Karkheh reservoir with monitoring stations

13 Karkheh reservoir model segmentation and location of monitoring stations

14 MODEL APPLICATION calibration was accomplished during May to November 25: For a one year simulation period, thermal simulation of Karkheh reservoir with 55 layers and 64 sections requires approximately 24 seconds on a CORE 5 duo CPU, 4Gb RAM, 2.67 GHz desktop computer. The proposed model converged to near optimal solution after approximately 8 hours in 12 function evaluation with 3 particles and 4 iterations.

15 Comparing water surface elevation simulation with observed data 28 Water Surface elevation (cm) Data Julian Day

16 Comparing chlr a simulation with observed data 5 Chlr (MicroGr/Lit) Data Julian Day

17 Comparing temperature simulation with observed data May 15, 25 (m) (m) July 2, (m) -1-2 July 25, Temperature (ºC) -5-6 Temperature (ºC) Temperature (ºC)

18 Comparing temperature simulation with observed data August 15, September 4, 25 (m) October 2, Temperature (ºC) (m) (m)

19 Comparing PO4 simulation with observed data

20 Comparing PO4 simulation with observed data September4, Data PO4 Concentration October2,25 Data PO4 Concentration Novamber1,25 Data P O4 Concentration

21 Comparing NH4 simulation with observed data May 15, Data N H4 Concentration July 25, July 2, N H4 Concentration August 15, Data N H4 Concentration Data N H4 Concentration

22 Comparing NH4 simulation with observed data September 4, Data N H4 Concentration -1 Novamber 1, October 2, Data N H4 Concentration Data -4-5 N H4 Concentration

23 Comparing NO3-NO2 simulation with observed data May, Data -5-6 No3 Concentration 2 July, No3 Concentration Data August, No3 Concentration Data

24 Comparing NO3-NO2 simulation with observed data September, No3 Concentration Data October,25 No3 Concentration Data November, 25 No3 Concentration Data

25 References Cole TM, Wells SA (23) CE-QUAl-W2: a twodimensional, laterally averaged, hydrodynamic and water quality model, version 3.. Instruction Report EL-2. US Army Engineering and Research Development Center, Vicksburg Coello Coello CA, Pulido GT, Lechuga MS (24) Handling multiple objective with particle swarm optimization. IEEE Transaction on Evolutionary Computation 8(3): Eberhart RC, Kennedy J (1995) A new optimizer usingparticle swarm theory. Proceedings Sixth Symposium on Micro Machine and Human Science,Nagoya Japan 39-43

26 Thank you For Your Notation