首页 » 文章 » 文章详细信息
Advances in Meteorology Volume 2017 ,2017-07-09
ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China
Research Article
Chang Juan 1 Wang Genxu 2 Mao Tianxu 2 Sun Xiangyang 2
Show affiliations
DOI:10.1155/2017/9451802
Received 2017-02-21, accepted for publication 2017-05-04, Published 2017-05-04
PDF
摘要

Precisely quantitative assessments of stream flow response to climatic change and permafrost thawing are highly challenging and urgent in cold regions. However, due to the notably harsh environmental conditions, there is little field monitoring data of runoff in permafrost regions, which has limited the development of physically based models in these regions. To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. The results show that the three-input variable ANN model has a superior real-time prediction capability and performs well in the simulation and forecasting of the runoff variation in the TRHR. Under the different scenarios conditions, the forecasting results of ANN model indicated that climate change has a great effect on the runoff processes in the TRHR. The results of this study are of practical significance for water resources management and the evaluation of the impacts of climatic change on the hydrological regime in long-term considerations.

授权许可

Copyright © 2017 Chang Juan et al. 2017
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

图表

Distribution of meteorological stations, hydrological stations, and subregions in the TRHR.

Comparison of the measured runoff values and those forecasted 6 and 12 months ahead by the ANN model for the YARHR (a), the LARHR (b), and the YERHR (c).

Comparison of the measured runoff values and those forecasted 6 and 12 months ahead by the ANN model for the YARHR (a), the LARHR (b), and the YERHR (c).

Comparison of the measured runoff values and those forecasted 6 and 12 months ahead by the ANN model for the YARHR (a), the LARHR (b), and the YERHR (c).

Comparison of the measured runoff values and those forecasted 24 months ahead by the ANN model for the YARHR (a), the LARHR (b), and the YERHR (c).

Comparison of the measured runoff values and those forecasted 24 months ahead by the ANN model for the YARHR (a), the LARHR (b), and the YERHR (c).

Comparison of the measured runoff values and those forecasted 24 months ahead by the ANN model for the YARHR (a), the LARHR (b), and the YERHR (c).

通讯作者

Chang Juan.Key Laboratory of Western China’s Environmental Systems, Ministry of Education, College of Earth and Environmental Science, Lanzhou University, Lanzhou 730000, China, moe.edu.cn.changjuan@lzu.edu.cn

推荐引用方式

Chang Juan,Wang Genxu,Mao Tianxu,Sun Xiangyang. ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China. Advances in Meteorology ,Vol.2017(2017)

您觉得这篇文章对您有帮助吗?
分享和收藏
0

是否收藏?

参考文献
[1] A. Dahamsheh, H. Aksoy. (2014). Markov Chain-Incorporated Artificial Neural Network Models for Forecasting Monthly Precipitation in Arid Regions. Arabian Journal for Science and Engineering.39(4):2513-2524. DOI: 10.1016/j.geoderma.2008.12.008.
[2] W. L. Quinton, P. Marsh. (1999). A conceptual framework for runoff generation in a permafrost environment. Hydrological Processes.13(16):2563-2581. DOI: 10.1016/j.geoderma.2008.12.008.
[3] S. Ge, J. McKenzie, C. Voss, Q. Wu. et al.(2011). Exchange of groundwater and surface-water mediated by permafrost response to seasonal and long term air temperature variation. Geophysical Research Letters.38(14). DOI: 10.1016/j.geoderma.2008.12.008.
[4] Ö. Kişi. (2007). Streamflow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering.12(5):532-539. DOI: 10.1016/j.geoderma.2008.12.008.
[5] R. Joshi, K. Kumar, V. P. S. Adhikari. (2016). Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier. Hydrological Processes.30(9):1354-1366. DOI: 10.1016/j.geoderma.2008.12.008.
[6] G. Wang, W. Bai, N. Li, H. Hu. et al.(2011). Climate changes and its impact on tundra ecosystem in Qinghai-Tibet Plateau, China. Climatic Change.106(3):463-482. DOI: 10.1016/j.geoderma.2008.12.008.
[7] Y. W. Zhou, D. X. Guo, G. Q. Qiu. (2002). Geocryology in China.33. DOI: 10.1016/j.geoderma.2008.12.008.
[8] Z. Yao, Z. Liu, H. Huang, G. Liu. et al.(2014). Statistical estimation of the impacts of glaciers and climate change on river runoff in the headwaters of the Yangtze River. Quaternary International.336:89-97. DOI: 10.1016/j.geoderma.2008.12.008.
[9] J. W. Pomeroy, D. M. Gray, T. Brown, N. R. Hedstrom. et al.(2007). The cold regions hydrological model: A platform for basing process representation and model structure on physical evidence. Hydrological Processes.21(19):2650-2667. DOI: 10.1016/j.geoderma.2008.12.008.
[10] S. Zhang, D. Hua, X. Meng, Y. Zhang. et al.(2011). Climate change and its driving effect on the runoff in the ‘Three-River Headwaters’ region. Journal of Geographical Sciences.21(6):963-978. DOI: 10.1016/j.geoderma.2008.12.008.
[11] B. Ye, D. Yang, Z. Zhang, D. L. Kane. et al.(2009). Variation of hydrological regime with permafrost coverage over Lena Basin in Siberia. Journal of Geophysical Research Atmospheres.114(7). DOI: 10.1016/j.geoderma.2008.12.008.
[12] Y. Lan, G. Zhao, Y. Zhang, J. Wen. et al.(2010). Response of runoff in the source region of the Yellow River to climate warming. Quaternary International.226(1-2):60-65. DOI: 10.1016/j.geoderma.2008.12.008.
[13] L. C. Smith, T. M. Pavelsky, G. M. MacDonald, A. I. Shiklomanov. et al.(2007). Rising minimum daily flows in northern Eurasian rivers: A growing influence of groundwater in the high-latitude hydrologic cycle. Journal of Geophysical Research: Biogeosciences.112(4). DOI: 10.1016/j.geoderma.2008.12.008.
[14] M. K. Woo. (2012). Permafrost Hydrology. DOI: 10.1016/j.geoderma.2008.12.008.
[15] R. S. Govindaraju. (2000). Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering.5(2):115-123. DOI: 10.1016/j.geoderma.2008.12.008.
[16] D. Magritsky, V. Mikhailov, V. Korotaev, D. Babich. et al.(2013). Changes in hydrological regime and morphology of river deltas in the Russian Arctic. Deltas: Landfoms, Ecosystems and Human Activities.358:67-79. DOI: 10.1016/j.geoderma.2008.12.008.
[17] V. F. Bense, G. Ferguson, H. Kooi. (2009). Evolution of shallow groundwater flow systems in areas of degrading permafrost. Geophysical Research Letters.36(22). DOI: 10.1016/j.geoderma.2008.12.008.
[18] H. Yoon, S. Jun, Y. Hyun, G. Bae. et al.(2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology.396(1-2):128-138. DOI: 10.1016/j.geoderma.2008.12.008.
[19] L. A. Garcia, A. Shigidi. (2006). Using neural networks for parameter estimation in ground water. Journal of Hydrology.318(1-4):215-231. DOI: 10.1016/j.geoderma.2008.12.008.
[20] L. Li, H. Shen, S. Dai, H. Li. et al.(2013). Response of water resources to climate change and its future trend in the source region of the Yangtze River. Journal of Geographical Sciences.23(2):208-218. DOI: 10.1016/j.geoderma.2008.12.008.
[21] T. Mao, G. Wang, T. Zhang. (2016). Impacts of Climatic Change on Hydrological Regime in the Three-River Headwaters Region, China, 1960-2009. Water Resources Management.30(1):115-131. DOI: 10.1016/j.geoderma.2008.12.008.
[22] Z. M. Yaseen, A. El-shafie, O. Jaafar, H. A. Afan. et al.(2015). Artificial intelligence based models for stream-flow forecasting: 2000-2015. Journal of Hydrology.530:829-844. DOI: 10.1016/j.geoderma.2008.12.008.
[23] G. D. Cheng, H. J. Jin. (2013). Groundwater in the permafrost regions on the Qinghai-Tibet Plateau and its changes. Hydrogeology & Engineering Geology.40(1):1-11. DOI: 10.1016/j.geoderma.2008.12.008.
[24] K. Qian, X.-S. Wang, J. Lv, L. Wan. et al.(2014). The wavelet correlative analysis of climatic impacts on runoff in the source region of Yangtze River, in China. International Journal of Climatology.34(6):2019-2032. DOI: 10.1016/j.geoderma.2008.12.008.
[25] J. M. McKenzie, C. I. Voss, D. I. Siegel. (2007). Groundwater flow with energy transport and water-ice phase change: Numerical simulations, benchmarks, and application to freezing in peat bogs. Advances in Water Resources.30(4):966-983. DOI: 10.1016/j.geoderma.2008.12.008.
[26] X. S. Yi, G. S. Li, Y. Y. Yin. (2012). Temperature variation and abrupt change analysis in the Three-River Headwaters Region during 1961–2010. Journal of Geographical Sciences.22(3):451-469. DOI: 10.1016/j.geoderma.2008.12.008.
[27] J. Chang, G. Wang, T. Mao. (2015). Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model. Journal of Hydrology.529:1211-1220. DOI: 10.1016/j.geoderma.2008.12.008.
[28] J. Xu, Y. Chen, W. Li, Q. Nie. et al.(2014). Integrating wavelet analysis and BPANN to simulate the annual runoff with regional climate change: a case study of Yarkand River, Northwest China. Water Resources Management.28(9):2523-2537. DOI: 10.1016/j.geoderma.2008.12.008.
[29] M. T. Sattari, H. Apaydin, F. Ozturk. (2012). Flow estimations for the Sohu Stream using artificial neural networks. Environmental Earth Sciences.66(7):2031-2045. DOI: 10.1016/j.geoderma.2008.12.008.
[30] A. G. Yilmaz, M. A. Imteaz, G. Jenkins. (2011). Catchment flow estimation using Artificial Neural Networks in the mountainous Euphrates Basin. Journal of Hydrology.410(1-2):134-140. DOI: 10.1016/j.geoderma.2008.12.008.
[31] G. K. C. Clarke, E. Berthier, C. G. Schoof, A. H. Jarosch. et al.(2009). Neural networks applied to estimating subglacial topography and glacier volume. Journal of Climate.22(8):2146-2160. DOI: 10.1016/j.geoderma.2008.12.008.
[32] S. Abudu, J. P. King, A. S. Bawazir. (2011). Forecasting monthly streamflow of spring-summer runoff season in Rio Grande headwaters basin using stochastic hybrid modeling approach. Journal of Hydrologic Engineering.16(4):384-390. DOI: 10.1016/j.geoderma.2008.12.008.
[33] V. M. Moya Quiroga, A. Mano, Y. Asaoka, S. Kure. et al.(2013). Snow glacier melt estimation in tropical Andean glaciers using artificial neural networks. Hydrology and Earth System Sciences.17(4):1265-1280. DOI: 10.1016/j.geoderma.2008.12.008.
[34] S. Haykin. (1999). Neural Networks: A Comprehensive Foundation. DOI: 10.1016/j.geoderma.2008.12.008.
[35] A. I. Shiklomanov, R. B. Lammers, D. P. Lettenmaier, Y. M. Polischuk. et al.(2013). Hydrological changes: historical analysis, contemporary status, and future projections. Regional Environmental Changes in Siberia and Their Global Consequences:111-154. DOI: 10.1016/j.geoderma.2008.12.008.
[36] K. P. Sudheer, A. K. Gosain, K. S. Ramasastri. (2002). A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes.16(6):1325-1330. DOI: 10.1016/j.geoderma.2008.12.008.
[37] S. Haykin. (1994). Neural Networks: A Comprehensive Foundation. DOI: 10.1016/j.geoderma.2008.12.008.
[38] A. Frampton, S. L. Painter, G. Destouni. (2013). Permafrost degradation and subsurface-flow changes caused by surface warming trends. Hydrogeology Journal.21(1):271-280. DOI: 10.1016/j.geoderma.2008.12.008.
[39] V. N. Vapnik. (1995). The Nature of Statistical Learning Theory. DOI: 10.1016/j.geoderma.2008.12.008.
[40] W. Genxu, L. Shengnan, H. Hongchang, L. Yuanshou. et al.(2009). Water regime shifts in the active soil layer of the Qinghai-Tibet Plateau permafrost region, under different levels of vegetation. Geoderma.149(3-4):280-289. DOI: 10.1016/j.geoderma.2008.12.008.
[41] G. X. Wang, Y. S. Li, Y. B. Wang, Y. P. Shen. et al.(2007). Impacts of alpine ecosystem and climate changes on surface runoff in the source region of Yangtze River. Journal of Glaciology and Geocryology.29(2):159-168. DOI: 10.1016/j.geoderma.2008.12.008.
[42] L. A. Zadeh. Fuzzy sets. . DOI: 10.1016/j.geoderma.2008.12.008.
[43] H. P. Schwefel. (1981). Numerical Optimization of Computer Models. DOI: 10.1016/j.geoderma.2008.12.008.
[44] A. I. Shiklomanov, R. B. Lammers, M. A. Rawlins, L. C. Smith. et al.(2007). Temporal and spatial variations in maximum river discharge from a new Russian data set. Journal of Geophysical Research: Biogeosciences.112(4). DOI: 10.1016/j.geoderma.2008.12.008.
[45] C. Shu, T. B. M. J. Ouarda. (2008). Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology.349(1-2):31-43. DOI: 10.1016/j.geoderma.2008.12.008.
[46] J. Lin, C. Cheng, K. Chau. (2006). Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal.51(4):599-612. DOI: 10.1016/j.geoderma.2008.12.008.
[47] J. Chang, G. Wang, C. Li, T. Mao. et al.(2015). Seasonal dynamics of suprapermafrost groundwater and its response to the freeing-thawing processes of soil in the permafrost region of Qinghai-Tibet Plateau. Science China Earth Sciences.58(5):727-738. DOI: 10.1016/j.geoderma.2008.12.008.
[48] P. Nilsson, C. B. Uvo, R. Berndtsson. (2006). Monthly runoff simulation: Comparing and combining conceptual and neural network models. Journal of Hydrology.321(1-4):344-363. DOI: 10.1016/j.geoderma.2008.12.008.
[49] K. Chokmani, T. B. M. J. Ouarda, S. Hamilton, M. H. Ghedira. et al.(2008). Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. Journal of Hydrology.349(3-4):383-396. DOI: 10.1016/j.geoderma.2008.12.008.
[50] D. Riseborough, N. Shiklomanov, B. Etzelmüller, S. Gruber. et al.(2008). Recent advances in permafrost modelling. Permafrost and Periglacial Processes.19(2):137-156. DOI: 10.1016/j.geoderma.2008.12.008.
[51] A. Grossmann, J. Morlet. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis.15(4):723-736. DOI: 10.1016/j.geoderma.2008.12.008.
[52] L. Qiao, L. Shiyin, G. Wanqin, N. Yong. et al.(2015). Glacier changes in the Lancang River Basin, China, between 1968-1975 and 2005-2010. Arctic, Antarctic, and Alpine Research.47(2):335-344. DOI: 10.1016/j.geoderma.2008.12.008.
[53] T. M. Carpenter, K. P. Georgakakos. (2006). Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales. Journal of Hydrology.329(1-2):174-185. DOI: 10.1016/j.geoderma.2008.12.008.
[54] O. Kisi. (2008). The potential of different ANN techniques in evapotranspiration modelling. Hydrological Processes.22(14):2449-2460. DOI: 10.1016/j.geoderma.2008.12.008.
[55] Y. F. Shi. (2008). Concise glacier inventory of China. DOI: 10.1016/j.geoderma.2008.12.008.
[56] H. Xie, Y. Lian. (2013). Uncertainty-based evaluation and comparison of SWAT and HSPF applications to the Illinois River Basin. Journal of Hydrology.481:119-131. DOI: 10.1016/j.geoderma.2008.12.008.
[57] Y. Zhao, L. Guo, J. Liang, M. Zhang. et al.(2016). Seasonal artificial neural network model for water quality prediction via a clustering analysis method in a wastewater treatment plant of China. Desalination and Water Treatment.57(8):3452-3465. DOI: 10.1016/j.geoderma.2008.12.008.
[58] L. Xiong, K. Yu, H. Zhang, L. Zhang. et al.(2013). Annual runoff change in the headstream of Yangtze River and its relation to precipitation and air temperature. Hydrology Research.44(5):850-874. DOI: 10.1016/j.geoderma.2008.12.008.
[59] Z. X. Xu, F. F. Zhao, J. Y. Li. (2009). Response of streamflow to climate change in the headwater catchment of the Yellow River basin. Quaternary International.208(1-2):62-75. DOI: 10.1016/j.geoderma.2008.12.008.
[60] J. Yang, Y. Ding, R. Chen. (2007). Climatic causes of ecological and environmental variations in the source regions of the Yangtze and Yellow Rivers of China. Environmental Geology.53(1):113-121. DOI: 10.1016/j.geoderma.2008.12.008.
[61] X. Liu, J. Zhang, X. Zhu, Y. Pan. et al.(2014). Spatiotemporal changes in vegetation coverage and its driving factors in the Three-River Headwaters Region during 2000–2011. Journal of Geographical Sciences.24(2):288-302. DOI: 10.1016/j.geoderma.2008.12.008.
[62] G. Cheng, T. Wu. (2007). Responses of permafrost to climate change and their environmental significance, Qinghai-Tibet Plateau. Journal of Geophysical Research.112(2). DOI: 10.1016/j.geoderma.2008.12.008.
文献评价指标
浏览 40次
下载全文 6次
评分次数 0次
用户评分 0.0分
分享 0次