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

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

Major data sources used in this study

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

Atmospheric monitoring stations inside and near the GRSM

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

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

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

Regional spatial pattern for precipitation, tmax and tmin based on long term measurements (1981-2010) in 100 NCDC sites around GRSM Precipitation (cm): Maximum monthly temperature (˚C): Minimum monthly temperature(˚C):

Cross validation of monthly precipitation

Cross validation of monthly maximum temperature

Cross validation of monthly minimum temperature

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).

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

Total S deposition in the Great Smoky Mountain national park 2000

Total S deposition in the Great Smoky Mountain national park 2007

Total S deposition in the Great Smoky Mountain national park 2012

Total N deposition in the Great Smoky Mountain national park 2000

Total N deposition in the Great Smoky Mountain national park 2007

Total N deposition in the Great Smoky Mountain national park 2012

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

Dry to wet deposition ratio at CASTNET sites near Smoky Mountains

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

Temporal trend of total S deposition in NLD

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

CMAQ estimate for atmospheric deposition in GRSM

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

Background deposition Ten-years (2003-2012) 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 .

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

DDRP sites

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

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

DDRP raw data

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

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

DDRP analysis results

Sulfate adsorption capacity based on DDRP data

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) :

Model calibration Hydrology Chemistry

USGS stream stages (used to evaluate model output flow)

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

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

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.

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.

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