| 人工神经网络模型预测气候变化对博斯腾湖流域径流影响 |
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| 引用本文： | 陈喜,吴敬禄,王玲.人工神经网络模型预测气候变化对博斯腾湖流域径流影响[J].湖泊科学,2005,17(3):207-212. |
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| 作者姓名： | 陈喜 吴敬禄 王玲 |
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| 作者单位： | 1. 河海大学水文水资源与水利工程科学国家重点实验室,南京,210098
2. 中国科学院南京地理与湖泊研究所,南京,210008 |
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| 基金项目： | 中国科学院南京地理与湖泊所知识创新工程重要方向性项目（CXNIGLAS-A01-2）和国家自然科学基金（40273004）联合资助. |
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| 摘 要： | 温室气体排放量增加造成气候变化，对全球资源环境产生重要影响．本文利用人工神经网络模型建立月降水、气温与径流关系，利用开都河流域降水、气温、径流资料对模型进行训练和验证，通过试算法确定网络模型结构，气温升高和降水量增加对径流影响的敏感程度分析表明，气温升高和降水增加对该区域径流影响较大，且气温升高的影响更为显著，径流增加主要集中在夏季，根据区域气候模型（RCMs）推算的CO2加倍情况下西北地区气候的可能变化，预测位于博斯腾湖流域的开都河大山口站年径流量增加38．6％，其中夏季增加71．8％，冬季增加11．4％。
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| 关 键 词： | 人工神经网络 气候变化 径流 预测 人工神经网络模型 博斯腾湖流域 流域径流 模型预测 温室气体排放量 气温升高 |
| 收稿时间： | 2004-06-17 |
| 修稿时间： | 2004-09-13 |
Prediction of Climate Change Impacts on Streamflow of Lake Bosten Using Artificial Neural Network Model |
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| CHEN Xi,WU Jinglu and WANG Ling.Prediction of Climate Change Impacts on Streamflow of Lake Bosten Using Artificial Neural Network Model[J].Journal of Lake Science,2005,17(3):207-212. |
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| Authors: | CHEN Xi WU Jinglu and WANG Ling |
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| Institution: | Key Laboratory of Water Resources Development, Hohai University, Nanjing 210098, P. R. China,Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, P. R. China and Key Laboratory of Water Resources Development, Hohai University, Nanjing 210098, P. R. China |
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| Abstract: | Climate change takes a great effect on global hydrology and water resources, ecology and environment, and social economic development due to an increasing concentration of greenhouse gases in the atmosphere. Relationship among streamflow and its influences of precipitation and temperature in monthly scale is developed using an artificial neural network model. The model is trained and validated based on inputs of precipitation and temperature data in Bayinbuluke hydrological station within the study catchment and output of streamflow data in the Dashankou hydrological station which controls streamflow of the Kaidu River into the Bosten Lake. The model structure is determined with a trial and error method. Sensitivity analysis of modeling stremflow to temperature rise and precipitation increase demonstrates that influences of temperature rise is more significant than that of precipitation increase , and streamflow increase is primarily concentrated in summer season. Based on input of possible future climate scenarios predicted by regional climate models (RCMs) , the model prediction presents that annual streamflow would increase 38. 6 percent, 71. 8 percent in summer and 11.4 percent in winter. |
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| Keywords: | artificial neural network climate changes streanlflow prediction |
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