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ISCMEM Groups 2 and 4 Joint Webinar Presentations. Training Range Environmental Evaluation and Characterization System (TREECS™) Applications June 19, 2012. Outline. Overview of TREECS – Billy Johnson TREECS Application to Borschi Watershed

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iscmem groups 2 and 4 joint webinar presentations
ISCMEM Groups 2 and 4 Joint Webinar Presentations

Training Range Environmental Evaluation and Characterization System (TREECS™) Applications

June 19, 2012

  • Overview of TREECS – Billy Johnson
  • TREECS Application to Borschi Watershed
    • Overview of the Chernobyl Accident and BorschiWatershed – Boris Faybishenko
    • Modeling of 90Sr Transport for the BorschiWatershed – Mark Dortch
  • TREECS Application to Artillery Impact Area (AIA) for RDX – Mark Dortch
overview training range environmental evaluation and characterization system
Overview - Training Range Environmental Evaluation and Characterization System

Billy E. Johnson, ERDC

treecs problem statement
TREECS Problem Statement
  • Residues and disturbances from range operations can impact the environment, including human and ecological health off-range. Such impacts can impact environmental compliance and range sustainment.
  • Army live fire training and test ranges have unique environments in which low-order and unexploded ordnance (dud munitions) are likely to cause random and highly uncertain sources of munitions constituents (MC) contamination.
  • An assessment tool is needed to forecast if, when, and at what level MC concentrations in off-range media (groundwater, surface water, and sediment) may exceed protective health benchmarks.
treecs solution approach
TREECS Solution / Approach

Training Range Environmental Evaluation and Characterization System (TREECS) is a client-based system that provides forecasts of Munitions Constituents (MC) fate on and off range based on munitions use on range.

Development Approach:

Formulate and couple models of reduced form for MC fate/transport-transformation-sequestration in an integrated framework for fast assessments with a minimal amount of user input.



treecs components
TREECS Components
  • Framework for Tier 1 and 2 assessments
  • Constituent databases
  • Health Benchmark database
  • Munitions database
  • MC residual mass loading module based on munitions use
  • GIS module
  • Hydro-geo-characteristics toolkit (HGCT) for estimating input parameters
  • Models for soil, surface water, vadose zone, and groundwater
  • Simplified user input interfaces for models (GUIs)
  • Viewers for results
  • Sensitivity and uncertainty module for Tier 2 assessments
treecs gis components
TREECS GIS Components

- For opening individual GIS files

- For measuring length and area in the workspace

- For saving individual GIS files

- For converting a shapefile to a grid

- For resampling a grid

- For extracting a subset of a grid

- For zooming into an area in the workspace

- For zooming out of an area in the workspace

- For creating slope grid from DEM and performing simple arithmetic operations on a grid

- For panning in the workspace

- For creating a rectangular AOI shapefile in the workspace

- For creating a polygon AOI shapefile in the workspace

treecs hgct
  • To aid the user in determining input variables required by TREECS models
    • Soil Properties
    • Soil erosion rate
    • Hydrology (infiltration, runoff, ET, etc.)
    • Darcy velocity
  • Allows point (single value) and spatially-varying composite estimates
  • Spatial option requires use of GIS module in TREECS or externally developed map files (grids)
tiered approach
Tiered Approach
  • Tier 1 (screening)
    • Steady-state, no degradation, worse case, highly conservative
    • Requires little data
    • Can be applied very quickly
    • Indicates whether a problem could ever potentially exist; if so, proceed to Tier 2
  • Tier 2 (more comprehensive)
    • Time-varying, much more realistic and accurate
    • Requires more data
    • Requires more time to set up and apply, but still can be done relatively quickly
    • Can be used to determine when benchmark exceedence may occur
    • Useful for evaluating range management strategies
constituent selection
Constituent Selection

Select MC

Select Database

View MC Properties

Can use a user defined database (Create under Tools)

mc residue mass loading module operational inputs
MC Residue Mass Loading Module (Operational Inputs)

Pulled from MIDAS Extract DB

Constant in Tier 1

Can use a User Defined munitions database

Provided by user

mc residue mass loading
MC Residue Mass Loading

Li,k = Loading for constituent I for year k, g/yr

DUDj,k = percent of duds for munitions item j for year k

HOj,k = percent of high order detonations for munitions item j for year k

LOj,k = percent of low order detonations for munitions item j for year k

Mi,j = mass of constituent i in munitions item j delivered to impact area, g/item

Nj,k = number of munitions item j fired for year k

n = total number of munitions items used at AOI

SYMj,k = percent of sympathetic detonation of duds for munitions item j for year k

YHOj,k = percent yield of munitions item j due to high order detonation for year k

YLOj,k = percent yield of munitions item j due to low order detonation for year k

YSYMj,k = percent yield of munitions item j due to sympathetic detonation for year k

advanced tier 2
Advanced Tier 2
  • Multiple AOIs
  • Complex Pathways
  • Multiple Receptors

Future capabilities will include remediation and

BMP modules

benefits of using treecs
Benefits of using TREECS
  • Allows the user to project future conditions
    • Answers the question of whether there will be a problem in the future
  • Can be used to develop and assess mitigation scenarios
  • Can be used to help optimize and prioritize data collection sites for future assessment activities
  • Can be used in designing new training ranges to help minimize migration of MC

Overview of Chernobyl Accident

Boris Faybishenko, LBNL

  • Radioactive gases, aerosols and finely fragmented fuel were releases to the atmosphere:
  • Fuel particles—finely dispersed, low volatility, settled primarily within the ChEZ
  • Condensed components—from radioactive gases, settled primarily along the atmospheric flow pathways
  • Hot particles—fuel particles, uranium dioxide, with a specific activity >105Bq/g, size 1 to 100 µm, surface density ~ 1,600 per m2, to ~0.5 m depth





Example of the autoradiography film of fuel particles in wetland sediment (Freed et al., 2003)


90Sr in the Dnieper River reservoirs is still above of pre-accident levels. 137Cs in the water at the lowest reservoir returned to its pre-accidental.

There is a wealth of data within the Chernobyl Exclusion Zone and the cascade of 6 water reservoirs along the Dnieper River, which can provide important information for the testing and validation of models, parameter estimation, uncertainty quantification, and understanding conceptual models of flow and transport processes in surface water, vadose zone, and groundwater.

  • .
borschi watershed monitoring
BorschiWatershed Monitoring
  • 3 km south of ChNNP
  • Area 8.5 km2
  • Flows into the Chernobyl Cooling Pond drainage ditch and then into the Pripyat River
  • Sandy soil underlain by clay marl
  • Primarily formerly cultivated land and planted forests with two seasonal wetlands

Freed et al., Seasonal Changes of the 90Sr Flux in the Borschi Stream, Chernobyl, ERSP, 2003


Post-Chernobyl Radionuclide Transport Processesin Surface Water Reservoirs

Microbial communities

Suspended sediments



Radionuclides in

suspended sediments



Dissolved radionuclides






Radionuclides in bottom sediments

Hot particles

Modified after M.Zheleznyak

application of treecs to borschi watershed for 90 sr and aia for rdx
Application of TREECS™ to Borschi Watershed for 90Sr and AIA for RDX

Mark Dortch, PhD, PE

Contractor to the Environmental Lab, ERDC

model inputs in general
Model Inputs in General
  • MCs of interest and their properties
  • Source zone inputs (e.g., range munitions use and associated parameters, or constituent loading rate)
  • Average annual hydrology and soil erosion (from HGCT)
  • Site specific media inputs (soil, vadose, groundwater, surface water, sediment)
borschi watershed
Borschi Watershed
  • 3 km south of CNNP
  • 8.5 km2
  • Sandy soil underlain by clay marl
  • Primarily formerly cultivated land and planted forests with two seasonal wetlands
  • Flows into a cooling pond drainage ditch and then into the Pripyat River
strontium 90
  • Half life = 29 years
  • Specific radioactivity = 143 Ci/g
  • Approximate watershed inventory for year 2000 = 1.0 E13 Bq
  • Watershed stream exit activity conc. (Bq/L) was monitored to estimate watershed export of 1.27 E10 to 1.62 E10 Bq/yr for 1999 – 2001, with average = 1.43 E10 Bq/yr, or annual export of 0.14% of 2000 inventory
watershed hydrology freed 2002
Watershed Hydrology (Freed 2002)
  • Average annual precipitation = 0.6 m/yr
  • Rainfall is approximately 0.47 m/yr based on snow melt = 22 % of stream flow
  • Stream flow = 15 – 20 % of precipitation
  • Stream flow for 1999 – 2000 = 0.097 m/yr, or 16 % of average precip
  • 80 % of stream flow is base flow from subsurface, or 0.078 m/yr; results in runoff = 0.019 m/yr
  • Groundwater recharge had to be estimated from water balance
conceptual site model
Conceptual Site Model

AOI = Borschi Watershed

Runoff (Q) & Erosion


Watershed Outlet

Surface Soil

Vadose Zone

Base flow = interflow



Infiltration = P – ET – Q

Infiltration = Recharge + Interflow

Model requires infiltration and % of infilt. going to interflow

required treecs models
Required TREECS™ Models
  • Only the Tier 2 soil model was needed (includes interflow pathway)
  • Vadose and aquifer models were not used since recharge was assumed to be lost
  • No surface water model was used since watershed outlet is considered to be part of AOI or location of soil model export flux
  • No AOI loading, only initial inventory in 2000
soil model inputs
Soil Model Inputs
  • Area = 8.5 E6 m2
  • Active soil layer = 0.2 m
  • Soil – water temperature = 7.7 deg C
  • Mobile Sr(90) Inventory conc. = 6.3 E-7 mg/kg (assumed 20% non-exchangeable adsorbed); did not matter whether in solid or non-solid form
  • Assumed loamy sand: 12 % water content, 1.49 g/cc bulk density, 44 % porosity
  • Erosion rate = 2.5 E-6 m/yr based on USLE (insignificant amount)
soil model inputs cont
Soil Model Inputs, cont.
  • Precip = 0.6 m/yr; rainfall = 0.47 m/yr
  • Runoff = 0.019 m/yr
  • Infiltration = 0.097 m/yr with 80% of infiltration diverted to interflow (uncertain, so varied, i.e., doubled and halved, for sensitivity)
  • Kd = 76 L/kg (initial value based on one sediment measurement; treated as uncertain)
  • t1/2 = 29 yr
  • Mw = 90 g/mol
baseline results
Baseline Results

Compared to 1.43 E10

Year 2000

evaluation of uncertainties
Evaluation of Uncertainties
  • Kd: conducted Monte Carlo simulation assuming normal distribution and varying between 100 and 300 L/kg with mean of 200 and Std of 33
  • % non-exchangeable adsorbed Sr: did not vary and kept fixed at 20%; recognize that varying this has linear inverse affect on export
  • Infiltration & % interflow: doubling/halving improved estimate of ET but did not affect export in 2000; does slightly decrease export vs time
uncertainty analysis for varying k d
Uncertainty Analysis for Varying Kd

Compared to 0.14 % observed

  • Results are insensitive to distribution of inventory between solid- and non-solid-phases due to relatively fast dissolution rate of Sr particles.
  • Initial export (year 2000) is insensitive to the amount of infiltration and recharge as long as the same amount of interflow is used (0.078 m/yr)
  • Export of 90Sr from the Borschi watershed to surface water is predominantly a result of soil pore water containing dissolved Sr being diverted to surface waters that eventually flow out of the watershed
conclusions continued
Conclusions, continued
  • The percentage of non-exchangeable adsorbed Sr and the soil-water Kd are the two most sensitive and uncertain factors affecting the amount of export
  • This application demonstrates how TREECS™ can be applied for a radionuclide. Such an application is accomplished by:
    • Converting radioactivity to mass units for model input using the specific radioactivity of the radionuclide
    • Modeling the constituent mass as normally done
    • Converting the output mass concentration/flux values to radioactivity using the specific radioactivity of the radionuclide
conclusions continued1
Conclusions, continued
  • Application of TREECS could be useful for the larger Pripyat and Dnieper River watersheds during the remediation evaluation process and for the feasibility study of the remediation and decommissioning of the Chernobyl cooling pond
tier 2 application to an artillery impact area aia for rdx
Tier 2 Application to an Artillery Impact Area (AIA) for RDX

Terrain drains into small stream that flows into a creek; glacial till over bedrock

Beginning of stream model

Modeled creek extended 4.4 km to a usage point


3 ranges fire 105, 60, and 81 mm artil./mortars into AOI

conceptual site model1
Conceptual Site Model

Target receptor

Erosion and Runoff

AOI Soil



4.4 km

All net infiltration is diverted to soil interflow that exits to surface water

Assumption: Runoff from AOI moves quickly and un-attenuated to creek

sources of model input data
Sources of Model Input Data
  • Google Earth™ for watershed and AOI delineation and areas
  • NRCS Web Soil Survey (WSS) for AOI soil characteristics
  • Daily local air temperature and precip data for period of record from a met station near the site (used by HGCT)
  • Training ammunition usage reports for 2 years for estimating munitions constituents (MC) loadings
  • Application of HGCT within TREECS
estimating mc loading rate
Estimating MC Loading Rate


Computed loading rate of RDX = 50 kg/yr (mostly from 105 mm low orders). Assumed a constant loading for 70 years

key inputs for soil model
Key Inputs for Soil Model
  • Soil characteristics (sandy loam, glacial till on top of bedrock)
    • Bulk density = 1.48
    • Avg. vol. moisture content = 17%
    • Porosity = 44%
  • Average annual hydrology/erosion from HGCT
    • Precip/rainfall =1.1 and 1.0 m/yr
    • Infiltration = 0.25 m/yr
    • Runoff = .52 m/yr
    • 141 rainfall events/yr
    • ULSE estimated erosion = 0.0025 m/yr
key inputs for soil model cont
Key Inputs for Soil Model (cont.)
  • RDX F/T parameters
    • Soil Kd = 0.42 L/kg (from GUI utility)
    • Particle average initial diameter = 12 mm
    • No degradation
  • RDX chemical-specific properties
    • Solubility at 10.9 deg C = 29.3 mg/L
    • Henry’s law const. = 6.23E-8 atm m3/mol
    • Molecular Wt. = 222.12
    • Solid phase density = 1.8 g/cm3
key inputs for stream model used cms
Key Inputs for Stream Model (used CMS)
  • Model parameters
    • 40 computational segments for 4.4 km
    • Flow Dispersion = 1 m2/sec
    • TSS = 3 mg/L
    • Sediment foc = 0.02
    • Active sediment layer porosity = 0.7
  • Hydraulics
    • Estimated width = 10 m and depth = 0.3 m
    • Average flow = 0.27 m3/sec based on runoff depth and watershed area; resulting travel time of ½ day
key inputs for stream model
Key Inputs for Stream Model
  • RDX chemical-specific properties
    • Same as for soil for MW, He
    • Molecular diffusivity = 5.9E-6 cm2/sec
    • Kow = 7.41 ml/ml (used to estimate Kd)
  • Sedimentation
    • Assumed equilibrium or zero burial; assumed settling rate = 2 m/day resulting in computed resuspension = 2.5E-5 m/day
Ran both soil and stream model for 200 years with RDX loading extending over first 70 years, from approx. 1940 to 2010
comparison with data measured in creek in 2003
Comparison with Data Measured in Creek in 2003
  • Sediment: measured < RL (RL = 0.05 mg/kg) compared to model value of 0.007 mg/kg or also < RL
  • Water: measured = 1.1 ppb compared to model value of 2.5 ppb

Encouraging considering: assumed constant firing rate and assumed low order rate for 70 yrs, no loss between AOI-creek, no loss to GW, no degradation loss, constant and estimated creek flow rate

other validation applications
Other Validation Applications

All model-computed results were within a factor of 10 of those observed. Agreement for HE was much better than for metals.

conclusions recommendations
  • With these models of reduced form, applications require a couple of days to discover info & set-up and less than one minute to simulate centuries
  • Validation application results indicate TREECS™ can provide insightful predictions regarding MC fate down-gradient of ranges with predictions within an order of magnitude (or closer) of observed data
  • The Monte Carlo uncertainty analysis feature in TREECS™ can be used to provide confidence bounds in predictions due to imprecise input estimates