• Sally Rose Anderson Civil and Environmental Engineering, University of Tennessee – Knoxville, USA
  • Amanda Bowen Civil and Environmental Engineering, University of Tennessee – Knoxville, USA
  • Glenn Tootle Civil, Construction and Environmental Engineering, University of Alabama, USA
  • Abdoul Oubeidillah Civil Engineering, University of Texas – Rio Grande Valley, USA



Hydrologic Reconstruction, Streamflow, Snow Water Equivalent, Soil Moisture, Tree Ring Chronologies, North Platte River Basin


Reconstructions of hydrologic variables are commonly created using tree-ring chronologies (TRCs) to generate information about historic climate and potential future variability. This study used TRCs to reconstruct annual streamflow, April 1st Snow Water Equivalent (SWE), and soil moisture in the North Platte River Basin (NPRB). Stepwise linear regression was performed to determine which of the 55 moisture sensitive TRCs were the best predictors of hydrologic variation. The regressions explained 63% of the variability in streamflow, 55% of the variability in SWE, and 66% of the variability in soil moisture. This study then maximized the overlapping period of records which resulted in a decrease in the percent of variability explained but indicated that the regression models were stable for long reconstruction periods. This study successfully reconstructed all three hydrologic variables for NPRB to 1438 or earlier. Temporal wet and dry periods for streamflow and SWE were closely aligned while soil moisture did not follow similar temporal patterns. This was likely due to a natural “lag” between soil moisture and streamflow / SWE given soil moisture tends to retain antecedent signals. The availability of reconstructed hydrologic data in NPRB allows for a better understanding of the long-term hydrologic variability in the region.


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How to Cite

Anderson, S. R., Bowen, A., Tootle, G., & Oubeidillah, A. (2019). RECONSTRUCTIONS OF HYDROLOGIC VARIABLES IN THE NORTH PLATTE RIVER BASIN . International Journal of Engineering Technologies and Management Research, 6(2), 59–71.