1. Approach in developing PnET-BGC model inputs for Smoky Mountains
to simulate response of soil and stream chemistry to the elevated acid deposition

2. Outline Major data sources used Developing atmospheric drivers
Developing soil chemistry inputs
Model calibration

3. Major data sources used in this study

4. Developing atmospheric drivers
Atmospheric monitoring sites near GRSM
Regression models
Dry/Wet deposition ratio
Weathers et al (2006) model
CMAQ estimations for S and N deposition
Background deposition

5. Atmospheric monitoring stations inside and near the GRSM

6. National Park Service sites in GRSM
Site name
Start year
End year
Elevation (m)
Cades Cove
1999
present
564
Clingmans Dome
1993 (May-Oct.)
2021
Look Rock
1991
793
Cove Mountain
1996
present (Solar:2006)
1243

7. Regional spatial pattern for solar radiation
Multiple linear regression of solar radiation data against longitude, latitude and elevation (based on 10 sites of national solar radiation data base (NSRDB) )
After including 4 more climate sites from NPS:
Units:
radiation in μmol m–2 s–1
longitude and latitude in decimal degree
elevation in meter

8. Regression model developed based on NSRDB overestimated mean monthly solar radiation at NPS sites

9. Regional spatial pattern for precipitation, tmax and tmin based on long term measurements ( ) in 100 NCDC sites around GRSM
Precipitation (cm):
Maximum monthly temperature (˚C):
Minimum monthly temperature(˚C):

10. Cross validation of monthly precipitation

11. Cross validation of monthly maximum temperature

12. Cross validation of monthly minimum temperature

13. Regional spatial pattern for ion concentrations in deposition
Regression equations describing regional patterns of ion concentrations in precipitation (in mg/L, n = 14) based on mean measured values in 14 NADP sites around GRSM.
Since above regression equations describing N and S concentrations are not significant, Weathers et al. (2006) model was used to develop spatial pattern for N and S (see slides 11-13).

14. Scaling factor developed based on Weathers et al
Scaling factor developed based on Weathers et al. (2006) model for total N and S deposition compare to TN11

15. Total S deposition in the Great Smoky Mountain national park
2000

16. Total S deposition in the Great Smoky Mountain national park
2007

17. Total S deposition in the Great Smoky Mountain national park
2012

18. Total N deposition in the Great Smoky Mountain national park
2000

19. Total N deposition in the Great Smoky Mountain national park
2007

20. Total N deposition in the Great Smoky Mountain national park
2012

21. Dry to wet deposition ratio in two nearest sites to Smoky Mountains

22. Dry to wet deposition ratio at CASTNET sites near Smoky Mountains

23. Comparing scale up factor estimated by Weathers et al
Comparing scale up factor estimated by Weathers et al. (2006) with the measured values by UT

24. Temporal trend of total S deposition in NLD

25. Total S deposition based on a hybrid approach with CMAQ model and monitoring data
2010

26. CMAQ estimate for atmospheric deposition in GRSM

27. Total N deposition based on a hybrid approach with CMAQ model and monitoring data
2010

28. Background deposition
Ten-years ( ) average of SO42- and NO3- concentration in TN11 is 19 and 11 µeq/l, respectively
Galloway et al (1984) reported 3-10 µeq/l for SO42- and 2-5 µeq/l for NO3- at the remote sites.
Assuming 35% of current concentration will be in the middle of the range observed for remote sites .

29. Soil chemistry inputs DDRP sites Soil-mass weighted average
Sulfate adsorption isotherm
Lab experiment

30. DDRP sites

31. Location of soil dataset (DDRP) near GRSM
Among 37 DDRP sites (844 soil horizons) in Southern Blue Ridge Province (SBRP) 5 sites (70 soil horizons) are located inside Smoky Mountain

32. DDRP data analysis
 DDRP dataset includes 213 sites and 561 pedons. Each pedon was sampled in several horizons. Soil chemistry of each site was estimated from mass-weighted averaging of measured values in soil horizons as follows (for example for CEC estimation):
𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠( 𝑘𝑔 𝑚 2 ) ℎ𝑜𝑟𝑖𝑧𝑜𝑛 =𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ( 𝑔 𝑐𝑚 3 ) ℎ𝑜𝑟𝑖𝑧𝑜𝑛 ∗ 𝐷𝑒𝑝𝑡ℎ 𝑐𝑚 ℎ𝑜𝑟𝑖𝑧𝑜𝑛 ∗ (1 – CFV)
𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠( 𝑘𝑔 𝑚 2 ) 𝑝𝑎𝑑𝑜𝑛 = 𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠( 𝑘𝑔 𝑚 2 ) ℎ𝑜𝑟𝑖𝑧𝑜𝑛
𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠( 𝑘𝑔 𝑚 2 ) 𝑠𝑖𝑡𝑒 = 𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠( 𝑘𝑔 𝑚 2 ) 𝑝𝑒𝑑𝑜𝑛 𝑛 𝑝𝑒𝑑𝑜𝑛
𝐶𝐸𝐶 𝑝𝑒𝑑𝑜𝑛 = 𝐶𝐸𝐶 ℎ𝑜𝑟𝑖𝑧𝑜𝑛 ∗ 𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠 ℎ𝑜𝑟𝑖𝑧𝑜𝑛 𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠 𝑝𝑎𝑑𝑜𝑛
𝐶𝐸𝐶 𝑠𝑖𝑡𝑒 = 𝐶𝐸𝐶 𝑝𝑎𝑑𝑜𝑛 ∗ 𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠 𝑝𝑎𝑑𝑜𝑛 𝑆𝑜𝑖𝑙𝑀𝑎𝑠𝑠 𝑠𝑖𝑡𝑒

33. DDRP raw data

34. For each DDRP site mass-weighted chemical properties of soil were calculated
SoilMass (kg/m2)= Bulk Dens. (g/cm3)* Thick. (cm)* (1-CFV)*10

35. Sulfate adsorption isotherms were prepared for each horizon
No Langmuir Isotherm can be fitted to some DDRP Data.

36. DDRP analysis results

37. Sulfate adsorption capacity based on DDRP data

38. Soil lab experiment Objectives:
Developing SO4 adsorption isotherm for three sampled sites in Smoky Mountains
Parameterizing pH dependent adsorption of DOM on soil
Initial results: (extracted DOC from three organic horizons sampled in Smoky Mountains) :

39. Model calibration
Hydrology
Chemistry

40. USGS stream stages (used to evaluate model output flow)

41. Calibrating hydrology of the model
Data from NCDC climate sites used as model input for precipitation.
Three NCDC-USGS pairs were assessed.
I chose three pairs of NCDC and USGS stations which were close to each other: Waterville with Cosby Creek, Cataloochee with Cataloochee creek, Gultinburg with Little River

42. For how long common data between NCDC precipitation and USGS stream flow is available?
Then I looked at the time period that these couples are common

43. Model Inputs: To check model outputs: Bars=95%CI
Top graph shows that 1) even ncdc sites are from different location of GRSM their monthly values are not too different,2)summer has higher ppt
Below graph: 1) the three site shows vey similar seasonal trend, 2) almost for all months the values are in the same range for the 3 sites, so we may conclude that using similar hydrological coefficients in model are fine for all sites, model hydrological parameters should be calibrated in a way to simulate runoff of e.g. March in 12cm and Sep. in 5cm , 3) in summer flow reduced because of evaporation and in spring (March) increased because of snow melt.
Compare above to below graph: spatial variability of ppt is greater that spatial variability of runoff, make sense since run off represent run off of an area but ppt represent ppt of a point.

44. Comparison between model outputs and USGS data
(after hydrological parameters of the model were calibrated):
Model calibrated to observed runoff from the 3 sites, each site has its own model with specific ppt and temp. Evaporation factor was calibrated, WHC and fast flow coeff did not help.

45. Stream water chemistry (STORET-EPA) is utilizing for chemistry calibration