Abstract
Effects of agricultural activities on temporal variations in streamflow were investigated based on an integrated hydrological model in the Heihe River Basin (HRB). De-trended fluctuation analysis was conducted using the simulated time series of the daily streamflow at 13 observation points along the Heihe River. Both the original and deseasonalized streamflow were obtained and analyzed. We found that temporal variations of the natural watershed streamflow without the interference of agricultural activities are nonstationary, but become relatively stationary when agricultural activities occur. This indicates a weakened damping effect of the hydrological system on hydrological signals. Agricultural activities mainly affect the trends in streamflow, as the trends may have been gradually removed with increasing agricultural activities. Therefore, long-term correlation of the streamflow decreases and eventually converges to the long memory, which remains invariant under the disturbance of agricultural activities. A practical significance of the results from this study for the water resources management in the agricultural regions is that more attention should be given to the trend identification, especially when predicting extreme hydrological events, such as drought or flood.
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Acknowledgements
This study was supported with research grants from the National Natural Science Foundation of China (41807198), the Shenzhen Science and Technology Innovation Commission (JCYJ20160530190547253), the Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control (2017B030301012), the National Students’ Platform for Innovation and Entrepreneurship Training Program (201814325004), and the Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (pdjh0038). The authors thank two anonymous reviewers for their constructive comments that have significantly improved the paper.
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Chen, C., Tian, Y., Zhang, YK. et al. Effects of agricultural activities on the temporal variations of streamflow: trends and long memory. Stoch Environ Res Risk Assess 33, 1553–1564 (2019). https://doi.org/10.1007/s00477-019-01714-x
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DOI: https://doi.org/10.1007/s00477-019-01714-x