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黄河流域水资源匮乏,径流年内、年际变化大,且地区分布不均匀。受气候变化和人类活动的影响,黄河流域水资源供需关系紧张的形势很可能进一步恶化。由于气候变化影响评价中存在的不确定性,目前对黄河流域未来径流变化趋势的预测并不一致。本研究将气候变化和经济社会发展作为黄河流域水资源系统变化的主要驱动因子,并将气候模式、水文模型、经济社会发展情景列为影响评估结果的主要不确定性来源。通过多模型加权,提高气候和水文模型的模拟精度,通过对经济社会关键发展参数的合理预测提高经济社会发展情景的可信度。评估气候变化和经济社会发展对黄河流域水资源供需平衡的影响,甄别出影响黄河流域水安全的关键因子,并对黄河流域未来的水管理提出适应性对策。
The water resource is insufficient and unevenly distributed in the Yellow River Basin, with great changes in annual and inter-annual runoff. With the impact of climate change and human activities, the strained relation between the supply and demand of water resource in the Yellow River Basin is likely to be further deteriorated. Due to the uncertainties in the climate change impact assessment, current forecasts for the future variation trend of runoff in the Yellow River Basin are inconsistent. This research takes climate change and social-economic developmentas the major driving factors for changes in water resources system of the Yellow River Basin and adopts climate model, hydrological model and scenarios of social-economic development as the main sources of the uncertainties influencing the assessment results. The author improves the simulationaccuracy of climate model and hydrological model through multi-model weighting and improves the reliability of the scenarios of social-economic developmentthrough reasonable forecast of the key development parameters of social-economic development. The author also assesses the impact of climate change and social-economic development on the balance betweenthe supply and demand of water resources in the Yellow River Basin, screens key factors influencing water security in the Yellow River Basin, and proposes applicable countermeasures for future water management in the Yellow River Basin. The main contents and conclusions are as follows:
(1)根据从IPCC数据中心下载的8个GCM模式20C3M输出结果与地面气象站点实测数据的对比,以偏差、均方根误差、简单相关系数和空间相关系数为指标,找出对气温模拟较好的模式是MIROC-m,对降水模拟较好的模式是CSIRO30。总体而言,GCM的输出结果能够较好的模拟黄河流域的气温,但直接利用GCM模式输出结果进行降水模拟的效果则不甚理想。
(1) According to the comparison, which adopts deviations, root mean square errors, simple correlation coefficients and spatial correlation coefficients as the indicators, between the 20C3M outputs of the 8 GCM models downloaded from IPCC Data Center and the measured data from ground meteorological station, MIROC-m is the best model for air temperature simulation and CSIRO30 is a preferable model for the simulation of precipitation. All in all, the outputs of GCM can simulate the air temperature in the Yellow River Basin well, but the outputs are not ideal for the simulation of precipitation.
(2)利用遗传算法对不同GCM在黄河流域的输出结果进行多目标优化加权组合,可以同时提高模拟结果与实测数据之间的时间和空间相关系数,并显著降低模拟值与实测值之间的均方根误差,使降水预测的时、空精度均得到有效提高。
(2) The multi-objective optimization weighted array of the outputs of different GCMs in the Yellow River Basin by genetic algorithm can simultaneously improve the time and spatial correlation coefficients between simulated and measured data and significantly reduce the root-mean-square error between simulated and measured data, which will effectively improve the time and spatial accuracy of precipitation forecast.
(3)基于多模式加权的气候变化情景显示,未来黄河流域的气温将呈明显的上升趋势。2046-2065年期间,A2和B1情景下的年均温分别比基准期上升2.6℃和2.4℃;到2081-2100期间,分别上升4.6℃和3℃。未来黄河流域的降水呈增加趋势,2046-2065年期间,A2和B1情景下的年均降水分别比基准期增加7.3%和4.3%,到2081-2100年,分别增加15.7%和8.1%。
(3) According to the climate change scenarios based on multi-model weighting, the air temperature in the Yellow River Basin will rise significantly in the future. During 2046-2065, the annual average temperatures under scenarios A2 and B1 will be 2.6℃ and 2.4℃ higher than that in the baseline period; during 2081-2100, the figures will be 4.6℃ and 3℃. The precipitation in the Yellow River Basin will also increase in the future. During 2045-2065, the average annual precipitation under scenarios A2 and B1 will be 7.3% and 4.3% higher than that in the baseline period; during 2081-2010, the figures will be 15.7% and 8.1%.
(4)根据黄河流域1961-2000年的降水、蒸发、径流资料对8个水文模型进行率定、检验和多模型加权。选取纳什系数、均方差和水量平衡等指标来评价模型和加权方法的表现。结果发现遗传算法加权在率定期和检验期内均体现出比各模型更高的水文模拟精度和稳定性,可以改善未来水文情景预测的可靠性。
(4) According to the data of precipitation, evaporation and runoff in the Yellow River Basin during 1961-2000, the author calibrates, validates and conducts multi-model weighting on 8 hydrological models. Indicators like Nash-Sutcliffe coefficient, mean square error and water balance are selected to evaluate the performance of the model and the method of weighting. The results shows that genetic algorithm reveals higher hydrological simulation accuracy and stability than various models in both calibration and validation periods, thus it can improve the reliability of hydrological forecasting in the future.
(5)基于多模型加权的水文预测显示,A2情景下,2046-2065年及2081-2100年期间,黄河流域年均径流较基准期分别增加4.1%和16.1%;而B1情景下径流量的变化不明显。不同情景下,径流较大的月份均为8月,而径流较小的月份为1月和2月。
(5) According to hydrological prediction based on multi-model weighting, under scenario A2, the average annual runoff during 2045-2065 and 2081-2100 in the Yellow River Basin is 4.1% and 16.1% higher than that in the baseline period respectively, while the change of the volume of runoff under scenario B1 is not obvious. Under different scenarios, the maximum volume of runoff occurs in August and the minimum occurs in January and February.
(6)经济社会发展情景显示,未来黄河流域水资源需求量相比于基准期的增幅在2.5%~21.4%之间。与气候变化对径流的影响相比,经济社会发展引起水资源需求的增加更为显著。未来黄河流域水资源供需不平衡的矛盾将更为突出。为了有效应对气候变化和经济发展对黄河流域水资源系统带来的影响,应当进一步强化节水能力建设和水量统一调度管理。
(6) The scenario of socio-economic development indicates that,compared to the baseline period,the future demand for water resources in the Yellow River Basin will increase by 2.5%~21.4%. In comparison with the impact of climate change on runoff, socio-economic development has a greater influence on the increase of the demand for water resources. In the future, the contradiction of the imbalance between the supply and demand of water resources in the Yellow River Basin will be more prominent. In order to effectively respond to the impact of climate change and economic development on the water resources system in the Yellow River Basin, we should further strengthen the construction of water-saving capacity and the unified management of water resources.

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