A hybrid model to assess the impact of climate variability on streamflow for an ungauged mountainous basin
To quantitatively assess the impact of climate variability on streamflow in an ungauged mountainous basin is a difficult and challenging work. In this study, a hybrid model combing downscaling method based on earth data products, back propagation artificial neural networks (BPANN) and weights connection method was developed to explore an approach for solving this problem. To validate the applicability of the hybrid model, the Kumarik River and Toshkan River, two headwaters of the Aksu River, were employed to assess the impact of climate variability on streamflow by using this hybrid model. The conclusion is that the hybrid model presented a good performance, and the quantitative assessment results for the two headwaters are: (1) the precipitation respectively increased by 48.5 and 41.0 mm in the Kumarik catchment and Toshkan catchment, and the average annual temperature both increased by 0.1 °C in the two catchments during each decade from 1980 to 2012; (2) with the warming and wetting climate, the streamflow respectively increased 1.5 × 108 and 3.3 × 108 m3 per decade in the Kumarik River and the Toshkan River; and (3) the contribution of the temperature and precipitation to the streamflow, which were 64.01 ± 7.34, 35.99 ± 7.34 and 47.72 ± 8.10, 52.26 ± 8.10%, respectively in the Kumarik catchment and Toshkan catchment. Our study introduced a feasible hybrid model for the assessment of the impact of climate variability on streamflow, which can be used in the ungauged mountainous basin of Northwest China.
KeywordsHybrid model Assessment Climate variability Streamflow Ungauged mountainous basin Northwest China
This work was supported by the Open Foundation of the State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (No. G2014-02-07); and the National Natural Science Foundation of China (41630859).
- Kendall MG (1948) Rank correlation methods. Oxford Univ Pr, EnglandGoogle Scholar
- Pachauri RK, Allen MR, Barros VR et al (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC, SwitzerlandGoogle Scholar
- Pettitt AN (1979) A non-parametric approach to the change-point problem. J R Stat Soc 28:126–135Google Scholar
- Pohlert T (2016) Non-Parametric Trend Tests and Change-Point Detection. CC BY-ND 4.0. http://creativecommons.org/licenses/by-nd/4.0/. Accessed 28 May 2017
- Xu Z, Liu P, Liu W (2013c) Automated statistical downscaling in several river basins of the Eastern Monsoon region, China. IAHS AISH Publ 2013:81–85Google Scholar
- Yang YH, Li WH, Wei WS, Hao XM, WAN M, LI H (2009) Discrepancy analysis of the climate changes among mountain, plain, oasis and desert in an inland river basin in the northern slopes of the Tianshan Mountains—a case study in the Sangong river basin. J Glaciol Geocryol 31:1094–1100Google Scholar