| 摘要: |
| 为了预测水文站逐月径流，对该流域水资源变化进行评估，运用小波神经网络建立汉江上游流域气象因子与径流过程模拟预测模型，并依据未来气候变化增量情景，对石泉水文站以上流域径流变化响应过程进行不同时间尺度分析。由已知汉江上游流域的月降水量和月平均温度，经小波神经网络自动“学习”训练获得石泉水文站精度较高的逐月径流数据。模拟计算结果表明：在不同未来气候变化设定情景下，该区域径流变化过程较为明显，年平均径流量最大变化范围为-34.7% ~ 21.4%。在降雨量不变、气温升高的情况下，年平均径流的响应变化范围为-5.1% ~ -13.3%。温度升高引起冬季径流增加较为明显，春季及秋季径流则存在减小趋势，秋季明显减少，而降雨量变化对夏季径流的影响最显著。 |
| 关键词: 气候变化 径流 预估 小波神经网络 汉江上游 |
| DOI： |
| 分类号:P339 |
| 基金项目:国家科技支撑计划项目（2012BAD10B02）；中央高校基本科研业务费专项资金资助项目（22050205）；陕西省水利科技项目（SLKJ-2013-14） |
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| Climate change impact analysis and prediction of runoff characteristics of upper Hanjiang River |
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HE Zi-li1, SHI Liang2, MA Xiao-yi1
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1.College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling;2.Water Resources Department of Shaanxi Province, Xi’an
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| Abstract: |
| In order to estimate the water resources of a river basin under changing conditions by simulating the hydrologic station monthly runoff, a hydrology model was established based on the wavelet neural network using observed meteorological factors to simulate runoff process in the upper Hanjiang River, and according to the future climate change incremental scenarios, runoff response process at the Shiquan hydrologic station was analyzed at different time scales. The wavelet neural network model by automatic learning and training can be used to simulate the reliable accuracy runoff data obtained from the Shiquan hydrologic station at the upper Hanjiang catchment based on the monthly precipitation and average monthly temperature. The simulated results show that, based on the model and different climate change scenarios, the increase in the annual average runoff is significant under the different scenarios, the maximum range of the annual average runoff is from -34.7% to 21.4%. In the case of no changes in rainfall and the rise in temperature, the mean annual runoff variation ranges are from -5.1% to -13.3%. The rise in temperature caused significant increase in the winter runoff, and the spring and autumn runoff also have the decreasing trends, and it is more significant in the autumn, but the rainfall changes have a significant influence on the summer runoff. |
| Key words: climate change runoff prediction wavelet neural network upper Hanjiang River |
