• Abdoul Oubeidillah University of Texas, Rio Grande Valley
  • Glenn Tootle University of Alabama
  • Venkat Lakshmi University of Virginia
Keywords: Land Cover Change, Hydrological Modeling, Streamflow Impact


A beetle epidemic across the western United States has resulted in the death of millions of acres of forests. This beetle outbreak, referred to as “beetle kill”, has caused many to believe that such dramatic changes in land cover could potentially alter the hydrology of the impacted regions. One of the most important hydrological processes that beetle kill has the potential to impact is streamflow. This research evaluates the hydrologic impacts on streamflow from land cover change due to beetle kill in the North Platte River Basin (NPRB) (Colorado and Wyoming, USA) by utilizing the Variable Infiltration Capacity (VIC) hydrologic model. Utilizing the National Agricultural Imagery Program (NAIP) dataset from 2005 / 2006 (onset of “beetle kill”) to more current conditions (2009), a decrease in tree cover of 16% to 40% was estimated. This decrease in tree cover was applied to VIC modeled streamflow from 1950 to 2000. The VIC model predicted a minimal increase in streamflow of approximately 5% which was not statistically significant.


Download data is not yet available.


Bentz, B. J., & Nordhaus, H. (2009). Bark beetle outbreaks in Western North America: causes and consequences: bark beetle symposium, Snowbird, Utah: University of Utah Press.

Bentz, B. J., Regniere, J., Fettig, C. J., Hansen, M. E., Hayes, J. L., Hicke, J. A., . . . Seybold, S. J. (2010). Climate change and bark beetles of the Western United States and Canada: direct and indirect effects BioScience, 60, 602-613. DOI:

Kenarsari, S. D., & Zheng, Y. (2011). A numerical study of fast pyrolysis of beetle killed pine trees. DOI:

U.S. Forest Service. (2011). 2011 accomplishment report. Golden, CO.

Pugh, E., & Gordon, E. (2012). A conceptual model of water yield effects from beetle-induced tree death in snow-dominated lodgepole pine forests. Hydrological Processes.

Robbins, J. (2008, November 18). Bark beetles kill millions of acres of trees in west, The New York Times

Logan, J. A., & Powell, J. A. (2001). Ghosts forests, global warming, and the mountain pine beetle (Coleoptera: Scolytidae). American Entomologist, 47(3), 160-172. DOI:

Samman, S., & Logan, J. A. (2000). Assessment and response to bark beetle outbreaks in the Rocky Mountain area: report to Congress from Forest Health Protection, Washington Office, Forest Service, USDA. The Bark Beetles, Fuels, and Fire Bibliography, 39.

Flint, C., Qin, H., & Daab, M. c. (2008). Mountain pine beetles and invasive plant species findings from a survey of Colorado community residents. University of Illinois at Urbana-Champaign, Department of Natural Resources and Environmental Sciences.

Grainger, B., & Bates, A. (2010). A semi-quantitive risk analysis for a mountain pine beetle infested watershed in the southern interior of British Columbia. Streamline Watershed Management Bulletin, 13(2), 52-59.

Lukas, J., & Gordon, E. (2010). Impacts of the mountain pine beetle infestation on the hydrologic cycle and water quality: a symposium report and summary of the latest science. Western Water Assessment, 6(4), 1-6.

Maloney, D. (2005). Mid-term impact of mountain pine beetle on watershed hydrology. Association of BC Forest Professionals Forum.

Pendall, E., Ewers, B., Norton, U., Brooks, P., Massman, W. J., Barnard, H., . . . Frank, J. (2010). Impacts of beetle-induced forest mortality on carbon, water and nutrient cycling in the Rocky Mountains. FluxLetter (The Newsletter of FLUXNET), 3(1), 17-21.

Powell, J. A., & Bentz, B. J. (2009). Connecting phenological predictions with population growth rates for mountain pine beetle, an outbreak insect. Landscape Ecology, 24, 657-672. DOI:

Ballantyne, A. P., Fernandez, D., Neff, J. C., Gaston, K., & Scarlata, C. (2009). Beetle-kill wood as biofuel: a Colorado case study.

Regniere, J., & Bentz, B. J. (2009). Mountain pine beetle and climate change. Paper presented at the 2008 USDA Research Forum on Invasive Species.

Regniere, J., & Bentz, B. J. (2007). Modeling cold tolerance in the mountain pine beetle, Dendroctonus Ponderosae. Journal of Insect Physiology, 53, 559-572. DOI:

Liknes, G. C., Perry, C. H., & Meneguzzo, D. M. (2010). Assessing tree cover in agricultural landscapes using high-resolution aerial imagery. Journal of Terrestrial Observation, 2(1), 5.

O'Neill, R., Hunsaker, C., Timmins, S., Jackson, B., Jones, K., Riitters, K., & Wickham, J. (1996). Scale problems in reporting landscape pattern at the regional scale. Landscape Ecology, 11(3), 169- 180. DOI:

Woodcock, C. E., & Strahler, A. H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment, 21(3), 311-332. DOI:

Bales, R. C., Hopmans, J. W., O'Geen, A. T., Meadows, M., Hartsough, P. C., Kirchner, P., . . . Beaudette, D. (2011). Soil moisture response to snowmelt and rainfall in a Sierra Nevada mixedconifer forest. Vadose Zone Journal, 10(3), 786-799. DOI:

Davies, K. W., Petersen, S. L., Johnson, D. D., Davis, D. B., Madsen, M. D., Zvirzdin, D. L., & Bates, J. D. (2010). Estimating juniper cover from National Agriculture Imagery Program (NAIP) imagery and evaluating relationships between potential cover and environmental variables. Rangeland Ecology & Management, 63(6), 630-637. DOI:

Green, K., & Lopez, C. (2007). Using object-oriented classification of ADS40 data to map the benthic babitats of the state of Texas. Photogrammetric Engineering & Remote Sensing, 861.

Cherkauer, K. A., Bowling, L. C., & Lettenmaier, D. P. (2003). Variable infiltration capacity cold land process model updates. Global and Planetary Change, 38(1), 151-159. DOI:

Liang, X., Lettenmaier, D. P., & Wood, E. F. (1996). One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the 2-layer variable infiltration capacity model. Journal of Geophysical Research, 21(403-422). DOI:

Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research, 99(14), 415-428. DOI:

Nijssen, B., Lettenmaier, D. P., Liang, X., Wetzel, S. W., & Wood, E. F. (1997). Streamflow simulation for continental-scale river basins. Water Resources Research, 33(4), 711-724. doi: 10.1029/96wr03517 DOI:

Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., & Nijssen, B. (2002). A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States. Journal of Climate, 15, 3237-3251. DOI:<3237:ALTHBD>2.0.CO;2

Hansen, M., DeFries, R., Townshend, J. R. G., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6-7), 1331-1364. DOI:

Richards, J. A. (2012). Remote sensing digital image analysis: an introduction (5th ed.): Springer.

Hubert-Moy, L., Cotonnec, A., Le Du, L., Chardin, A., & Perez, P. (2001). A comparison of parametric classification procedures of remotely sensed data applied on different landscape units. Remote Sensing of Environment, 75(2), 174-187. DOI:

Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote sensing and image interpretation (5th ed.): John Wiley & Sons Ltd.

Strahler, A. H. (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10, 135-163. DOI:

Gao, H., Tang, Q., Shi, X., Zhu, C., Bohn, T. J., Su, F., . . . Wood, E. F. (2010). Water budget record from Variable Infiltration Capacity (VIC) model. Algorithm Theoretical Basis Document for Terrestrial Water Cycle Data Records.

Abdulla, F. A., Lettenmaier, D. P., Wood, E. F., & Smith, J. A. (1996). Application of a macroscale hydrologic model to estimate the water balance of the Arkansas-Red River Basin. Journal of Geophysical Research, 101(D3), 7449-7459. doi: 10.1029/95jd02416 DOI:

Bowling, L. C., Storck, P., & Lettenmaier, D. P. (2000). Hydrologic effects of logging in western Washington, United States. Water Resources Research, 36(11), 3223-3240. doi: 10.1029/2000wr900138 DOI:

Lohmann, D., Raschke, E., Nijssen, B., & Lettenmaier, D. P. (1998). Regional scale hydrology: II. application of the VIC-2L model to the Weser River, Germany. Hydrological Sciences Journal, 43(1), 143-158. doi: 10.1080/02626669809492108 DOI:

Nijssen, B., O'Donnell, G. M., Lettenmaier, D. P., Lohmann, D., & Wood, E. F. (2001). Predicting the discharge of global rivers. Journal of Climate, 14(15), 3307-3323. doi: 10.1175/1520- 0442(2001)014<3307: ptdogr>;2

Shi, X., Wood, A. W., & Lettenmaier, D. P. (2008). How essential is hydrologic model calibration to seasonal streamflow forecasting? Journal of Hydrometeorology, 9(6), 1350-1363. doi: 10.1175/2008jhm1001.1 DOI:

Su, F., Adam, J. C., Bowling, L. C., & Lettenmaier, D. P. (2005). Streamflow simulations of the terrestrial Arctic domain. Journal of Geophysical Research, 110(D8), D08112. doi: 10.1029/2004jd005518 DOI:

Su, F., Adam, J. C., Trenberth, K. E., & Lettenmaier, D. P. (2006). Evaluation of surface water fluxes of the pan-Arctic land region with a land surface model and ERA-40 reanalysis. Journal of Geophysical Research, 111(D5), D05110. doi: 10.1029/2005jd006387 DOI:

Wood, E. F., Lettenmaier, D. P., Liang, X., Nijssen, B., & Wetzel, S. W. (1997). Hydrological modeling of continental-scale basins. Annual Review of Earth and Planetary Sciences, 25(1), 279- 300. doi: doi: 10.1146/ DOI:

Zhu, C., & Lettenmaier, D. P. (2007). Long-term climate and derived surface hydrology and energy flux data for Mexico: 1925–2004. Journal of Climate, 20(9), 1936-1946. doi: 10.1175/jcli4086.1 DOI:

Acharya, A., Piechota, T. C., Stephen, H., & Tootle, G. (2011). Modeled streamflow response under cloud seeding in the North Platte River watershed. Journal of Hydrology, 409, 305-314. DOI:

Acharya, A., Piechota, T. C., & Tootle, G. (2011). Quantitative assessment of climate change impacts on the hydrology of the North Platte River watershed, Wyoming. Journal of Hydrologic Engineering, 17(10), 1071-1083. DOI:

Hunter, T., Tootle, G.A., and T.C. Piechota, 2006. Oceanic-atmospheric variability and western U.S. snowfall. Geophysical Research Letters, 33(13): L13706. DOI:

Unwin, D. J. (1996). GIS, spatial analysis and spatial statistics. Progress in Human Geography, 20(4), 540-551. DOI:

How to Cite
Oubeidillah, A., Tootle, G., & Lakshmi, V. (2019). IMPACTS OF BEETLE KILL ON MODELED STREAMFLOW RESPONSE IN THE NORTH PLATTE RIVER BASIN . International Journal of Engineering Technologies and Management Research, 6(3), 27-39.