Evaluation of GIS-based erosion-deposition modeling at military

installations  Helena Mitasova, Anna Petrasova and Vaclav Petras,  

Center for Geospatial Analytics, North Carolina State University, Raleigh    

Steven D. Warren, U.S. Forest Service, Rocky Mountain Research Station, Provo, UT    

Thomas Ruzycki, Center for Environmental Management of Military Lands, Colorado State University, Ft. Collins, CO  

 Niels Svendsen and Matthew Hohmann, US Army ERDC-CERL, Champaign IL  

 Robert Vaughan, U.S. Forest Service, Remote Sensing Application Center, Salt Lake City,

UT  

Soil erosion on installations

Soil erosion on military training facilities is due to disturbances by tracked and wheeled vehicles, exploding munitions, and construction Damage is expensive to repair and diminishes the realism of the training experience, and jeopardizes the safety of soldiers and equipment. Sediment transport from installations also creates off-site impacts - sediment is the single largest contributor to non-point source pollution. Limited field data combined with imagery and RUSLE model estimates indicated reduction of soil erosion since 50ies thanks to conservation programs

Soil disturbances from training

Tracked and wheeled vehicles compact the soil and damage vegetation

Gully erosion

Soil erosion may accelerate as the soil surface becomes increasingly disturbed and protective vegetation is lost as a result of the cumulative impacts of military training. Extensive damage from gullying may occur

Erosion modeling for installations

Erosion modeling has been used to support land management, conservation and sediment control Spatially aggregated USLE or RUSLE, only potential sediment sources mapped no deposition considered Very limited experimental spatially distributed data available Simplicity important at installation scale GIS-based models developed in 90s to support land management systems: mapping erosion and deposition, process-based simulations

GIS based erosion models

Landscape scale mapping of potential sediment sources (and sinks) Identification of locations vulnerable to soil erosion and deposition in areas with complex topography and variable land cover USLE3D or RUSLE3D E = R.K.C.P.L.S LS = Am.(sin β)n

Where E is soil loss, R is rainfall, K is soil, C is cover and LS is topographic factor, A is normalized upslope are per unit width and β is normalized slope angle Simplified Erosion-Deposition (SED) model T = R.K.C.P.Um.(sin η)n

ED = ∇ · (T s0) = ∂(T cos α)/∂x + ∂(T sin α)/∂y Where T is sediment transport capacity, ED is erosion/deposition, U is contributing area per unit width, η is slope, α is aspect (flow direction)

GIS based models

Complex topography, uniform land cover and soils: USLE3D: Detachment capacity limited erosion, no deposition SED model Transport capacity limited erosion and deposition Represent two extreme cases – actual erosion/deposition dynamically transitions between these two

GIS-based models: variable cover

Variable land cover: C-factor map USLE3D: variable soil loss rate SED: variable soil loss rate and deposition along the land cover change edge

erosion deposition

SED model for installation

Ft. Hood, TX area used in on-line tutorial http://ncsu-osgeorel.github.io/erosion-modeling-tutorial/index.html

http://ncsu-osgeorel.github.io/erosion-modeling-tutorial/index.htm

Input data

10m resolution DEM K-factor from SSURGO converted to 10m resolution raster

Land cover C-factor

Landsat8 bands with field data correlation (r2 ~ 0.4 – 0.8) NLCD with values assigned using USDA tables Landsat and NAIP with high resolution estimates at points sampled at unsupervised classes

NDVI derived from Landsat C-factor derived from correlation between NDVI and field observations

Topographic factor for complex terrain

Topographic factor is based on parameters derived from DEM

Different flow routing techniques: MFD for dispersal flow, least cost path for routing through depressions without filling them Exponents m,n control influence of flow accumulation versus slope

Slope Flow accumulation Topographic factor

LS=Um.(sin η)n

Erosion and deposition map

Sediment flow ~ sediment transport capacity

Change in sediment flow: erosion and deposition

T = R.K.C.P.Um.(sin η)n

ED = ∇ · (T s0) = ∂(T cos α)/∂x + ∂(T sin α)/∂y

Erosion / deposition evaluation

Modeled erosion/deposition classes were compared with visual field estimates at random points generated in each class Results were mixed: Issues with C-factor estimates, model resolution versus local features, quality of DEM, selection of validation points

Yakima, WA

C-factor based on Landsat: installation wide and zoomed-in Points show transects where C-factor was estimated in the field C-factor map was computed from correlation between the field-based C-factor and Landsat8 bands

Yakima

Transport capacity map – installation-wide and zoom in

Yakima

Modeled erosion/deposition: installation-wide and zoom-in with observed points

51 points with observed erosion/deposition ~ 64% classified within 1 class Often neighboring cells have correct class

Yakima

Very complex, dynamic erosion/deposition pattern DEM from USGS NED has potential artifacts (pits). 10-30m resolution overestimates extent of concentrated flow areas

Current research

SUAS and lidar mapping – adaptive, precision conservation based on repeated, high resolution mapping Process-based simulations used to explore, prioritize and optimize conservation measures

Tangible Landscape

Exploring surface runoff and erosion and sediment control design using tangible physical model with near real-time feedback

Ft. Bragg application

Conclusions

Erosion/deposition patterns at military installations are complex and require higher resolution than 10m to avoid overestimation of concentrated flows Reliable and efficient C-factor estimation remains major challenge and would require extensive field work Mapping by sUAS/lidar can provide erosion/deposition data needed to calibrate/validate erosion models at landscape scale – we anticipate possible innovations in theory if the current models cannot consistently reproduce the observed patterns and rates Tutorial for GRASS and ArcGIS available in github, improvements and contributions are welcome

http://ncsu-osgeorel.github.io/erosion-modeling-tutorial/