Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection
1
Wildland Resources Department, Utah State University, Logan, UT 84322-5230, USA
2
USDA Forest Service, Rocky Mountain Research Station, 507 25th Street, Ogden, UT 84401, USA
3
Key Laboratory of Poyang Lake Basin Agricultural Resources and Ecology of Jiangxi Province, Jiangxi Agricultural University, Nanchang 330045, China
*
Author to whom correspondence should be addressed.
Forests 2018, 9(6), 317; https://doi.org/10.3390/f9060317
Received: 26 April 2018
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Revised: 30 May 2018
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Accepted: 31 May 2018
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Published: 2 June 2018
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
The water balance in a watershed can be disrupted by forest disturbances such as harvests and fires. Techniques to accurately and efficiently map forest cover changes due to disturbance are evolving quickly, and it is of interest to ask how useful maps of different types of disturbances over time can be in the prediction of water yield. We assessed the benefits of using land cover maps produced at annual vs. five-year intervals in the prediction of monthly streamflows across 10 watersheds contained entirely within the US National Forest System. We found that annually updating land cover maps with forest disturbance data significantly improved water yield predictions using the Soil and Water Assessment Tool (SWAT; p < 0.01 improvement for both the Nash–Sutcliffe efficiency measure and the ratio of the root mean square error to the standard deviation of the measured data). Improvement related to using annually updated land cover maps was directly related to the amount of disturbance observed in a watershed. Our results lay a foundation to apply new high-resolution disturbance datasets in the field of hydrologic modeling to monitor ungauged watersheds and to explore potential water yield changes in watersheds if climate conditions or management practices were to change forest disturbance processes.
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Keywords:
forest disturbance; time series; SWAT; water dynamics; land cover
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MDPI and ACS Style
Hernandez, A.J.; Healey, S.P.; Huang, H.; Ramsey, R.D. Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection. Forests 2018, 9, 317. https://doi.org/10.3390/f9060317
AMA Style
Hernandez AJ, Healey SP, Huang H, Ramsey RD. Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection. Forests. 2018; 9(6):317. https://doi.org/10.3390/f9060317
Chicago/Turabian StyleHernandez, Alexander J., Sean P. Healey, Hongsheng Huang, and R. Douglas Ramsey. 2018. "Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection" Forests 9, no. 6: 317. https://doi.org/10.3390/f9060317
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