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Development of Remote Sensing-based Predictive Models for the Management of Taste and Odor Events in Kansas Reservoirs Dr. Mark Jakubauskas Kansas Biological.

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Presentation on theme: "Development of Remote Sensing-based Predictive Models for the Management of Taste and Odor Events in Kansas Reservoirs Dr. Mark Jakubauskas Kansas Biological."— Presentation transcript:

1 Development of Remote Sensing-based Predictive Models for the Management of Taste and Odor Events in Kansas Reservoirs Dr. Mark Jakubauskas Kansas Biological Survey 2101 Constant Avenue Lawrence, Kansas Dr. Mark Jakubauskas Kansas Biological Survey 2101 Constant Avenue Lawrence, Kansas 66047

2 Development of Remote Sensing-based Predictive Models for the Management of Taste and Odor Events in Kansas Reservoirs Project Tasks: –A remote sensing-based predictive model providing an advance warning of potential drinking water taste and odor problems. –Remote sensing-based seasonal and year-to-year reservoir condition maps showing sediment plumes and algae bloom patterns within large reservoirs. Project Tasks: –A remote sensing-based predictive model providing an advance warning of potential drinking water taste and odor problems. –Remote sensing-based seasonal and year-to-year reservoir condition maps showing sediment plumes and algae bloom patterns within large reservoirs.

3 Work Item: Develop Advance Warning Model A predictive model providing an advance warning of potential drinking water taste and odor problems will be developed. This model will use remotely sensed and other data to develop predictive relationships between surrogates of watershed landscape condition (Normalized Difference Vegetation Index, Vegetation Phenological Metrics), reservoir condition, and reported taste and odor events. Reservoirs for which the most complete and extensive data sets are available will be selected for this analysis. A predictive model providing an advance warning of potential drinking water taste and odor problems will be developed. This model will use remotely sensed and other data to develop predictive relationships between surrogates of watershed landscape condition (Normalized Difference Vegetation Index, Vegetation Phenological Metrics), reservoir condition, and reported taste and odor events. Reservoirs for which the most complete and extensive data sets are available will be selected for this analysis.

4 Traditional approaches: Land use / Land Cover

5 Alternate approaches: Land surface state or condition Landsat satellite imagery Clinton Lake watershed, 2002 March 21 July 24 September 26 Land cover and use, as traditionally defined, have not changed during the course of the year. Land state or condition, however, changes dramatically.

6 Quantifying vegetation condition – The Normalized Difference Vegetation Index (NDVI)

7 Corn phenology as measured by NDVI (April 30-Sept. 24) Bare soil to near-complete canopy in 20 days 5/26 6/2 6/15

8 Seasonal changes in NDVI Early October, 1999 March 1999 July 1999 Late May, 1999

9 Remote sensing of watershed conditions: Clinton Lake watershed, 1997 and 1998 Mid-July 1997: Springtime rainfall – 10.5 in. Mid-July 1998: Springtime rainfall = 3.9 in. Topeka Vegetation in a watershed is highly sensitive to year-to-year variations in temperature and rainfall, changes that can predict water quality impairment.

10 Average growing season NDVI as a function of precipitation for a fifteen- month precipitation period (current growing season plus the seven preceding months). Satellite NDVI is responsive to climate variations

11 Vegetation Phenological Metrics

12 Temporal Metrics: Time of onset of greenness Time of end of greenness Duration of greenness Time of maximum greenness NDVI-value Metrics: Value of onset of greenness Value of end of greenness Value of maximum NDVI Range of NDVI Derived Metrics: Accumulated NDVI Rate of green up Rate of senescence Mean daily NDVI Beginning of photosynthetic activity End of photosynthetic activity Length of photosynthetic activity Time when photosynthesis is maximum Level of photosynthesis at start Level of photosynthesis at end Level of photosynthesis at maximum Range of measurable photosynthesis Net Primary Production Acceleration of increasing photosynthetic activity Acceleration of decreasing photosynthetic activity Mean daily photosynthetic activity

13 Watershed NDVI and water quality relationships Predicting potential total phosphorus using satellite-derived vegetation indices

14 Average Time of Onset of Greenness ( )

15 Average Time of Maximum Greenness ( )

16 D. Develop Advance Warning Model TASK ITEMS: 1. Select reservoirs for analysis 10/15/06 2. Obtain and analyze satellite imagery 12/15/06 - Watershed landscape condition - Reservoir condition 3. Analyze existing data from Item A-4 12/15/06 4. Develop model 2/1/07 5. Deliverable: Advance Warning Model 4/30/07

17 E. Develop Alternative Monitoring Methods Many reservoirs exhibit relatively rapid changes in water quality conditions, sediment plumes and algae blooms that are difficult and expensive to detect using traditional sampling techniques. Multitemporal MODIS satellite imagery will be used to develop seasonal and year-to-year reservoir condition maps showing sediment plumes and algae bloom patterns within large reservoirs. As a prototype, Kansas’ 10 largest reservoirs providing a significant source of drinking water will be mapped. Many reservoirs exhibit relatively rapid changes in water quality conditions, sediment plumes and algae blooms that are difficult and expensive to detect using traditional sampling techniques. Multitemporal MODIS satellite imagery will be used to develop seasonal and year-to-year reservoir condition maps showing sediment plumes and algae bloom patterns within large reservoirs. As a prototype, Kansas’ 10 largest reservoirs providing a significant source of drinking water will be mapped.

18 1 mile Cheney Reservoir, June-July, 2003 Red areas on this Landsat Thematic Mapper image indicates high chlorophyll (blue-green algae) concentrations in the water. Examples of satellite imagery of reservoir algae blooms

19 Red areas on these Landsat Thematic Mapper images indicate high chlorophyll (blue-green algae) concentrations in the water. “Following a three-week ordeal with anabaena algae in the Marion Reservoir, the water plants in Hillsboro and Marian were able to restore service in early July.” Kansas Municipal Utilities Newsletter, August 2003 April 21, 2003July 10, 2003 Examples of satellite imagery of reservoir algae blooms

20 Identifying spatial and temporal patterns: Similarities and differences in turbidity among reservoirs within the same region ClintonLake John Redmond Reservoir PerryLake PomonaLake Low turbidityHigh turbidity

21 TASK ITEMS: 1. Select 10 reservoirs for analysis 10/15/06 2. Obtain and process MODIS satellite imagery 12/15/06 3. Produce Produce seasonal and year-to-year reservoir condition maps2/1/07 4. Evaluate feasibility of satellite early warning and monitoring system for reservoir condition monitoring 4/30/07 E. Develop Alternative Monitoring Methods

22 Reservoir Bathymetry – some brief notes In May 2006, the Kansas Biological Survey acquired a Biosonics DT-X digital acoustic echosounding system. The system, sampling methodologies, and data processing approaches are currently being tested. Lone Star, Olathe, and Carbondale Lakes have been surveyed under the testing phase (Gardner Lake scheduled for July 26, 2006) In May 2006, the Kansas Biological Survey acquired a Biosonics DT-X digital acoustic echosounding system. The system, sampling methodologies, and data processing approaches are currently being tested. Lone Star, Olathe, and Carbondale Lakes have been surveyed under the testing phase (Gardner Lake scheduled for July 26, 2006)

23 Intensity and timing of the signal received back by transducer is a function of depth, intervening medium (water), and the characteristics of the bottom sediments. “Echogram” Acoustic Remote Sensing for Reservoir Studies Depth = ½ (Speed of sound in water x time)

24 Olathe Lake, Kansas Bathymetry survey tracks, June 2, 2006

25 Lone Star Lake – First test, KBS bathymetric mapping system

26 Olathe Lake Bathymetry Map

27 Carbondale Lake – KBS bathymetry overlaid on pre- impoundment topographic contour map

28 Carbondale Lake – Preliminary bottom type classification map

29 Development of Remote Sensing-based Predictive Models for the Management of Taste and Odor Events in Kansas Reservoirs Dr. Mark Jakubauskas Kansas Biological Survey 2101 Constant Avenue Lawrence, Kansas Dr. Mark Jakubauskas Kansas Biological Survey 2101 Constant Avenue Lawrence, Kansas 66047


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