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白山流域春季径流影响因素及作用机理识别

Identification of influencing factors and machanism of spring runoff in Baishan Watershed,China

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【作者】 李文龙次旦央宗王傲李鸿雁田琳

【Author】 LI Wenlong;CI Danyangzong;WANG Ao;LI Hongyan;TIAN Lin;Songhuajiang Hydropower Co., Ltd.,Fengman Power Plant;College of New Energy and Environment, Jilin University;Jilin Provincial Hydrology and Water Resources Changchun Hydrological Branch;

【通讯作者】 李鸿雁;

【机构】 松花江水力发电有限公司吉林丰满发电厂吉林大学新能源与环境学院吉林省水文水资源局长春分局

【摘要】 我国北方流域春汛来水水源组成多元,影响径流过程的因素众多,且各因素的作用过程复杂,给春汛来水预报带来了挑战。春汛是白山流域第二个集中来水期,准确的春汛预报是科学合理制定水库调度方案的前提和基础,既可以保证农业供水需求、航运用水需求,又能为后期防汛预留库容,实现水资源高效利用。依托白山水库1958—2016年春季长系列日入库流量过程资料,通过Eckhardt递归数字滤波法进行基流分割并绘制基流比过程,根据基流比过程演化趋势将春季径流的水源组成划分为融雪产流、冻土条件下融雪、降雨产流和冻土条件降雨产流等3个阶段。多年平均状况下,白山流域融雪产流开始日期为3月28日,冻土条件产流开始日期为4月28日,融雪产流结束日期为5月20日,冻土消融日期为6月15日;多年平均状况下,融雪产流历时为28 d,冻土条件下融雪、降雨产流历时为26 d,冻土条件下降雨产流历时为24 d。以白山流域内3个气象站1960—2016年日降雨、温度、辐射和风速数据为影响因素,以白山水库日入库径流量(径流深)为目标变量,在融雪产流期(3月28日—5月20日)内,采用全局灵敏度分析法识别出日总辐射和平均风速无时滞效应、最低温度具有1 d的时滞效应、平均温度和最高温度具有2 d的滞后效应,继而识别出基于时滞的日总辐射、平均风速、平均温度和降水为融雪产流的关键影响因素;采用遗传算法拟合融雪产流经验公式,1960—2010年校准期内模拟精度较好,在径流系数小于等于1时和大于1时,拟合优度分别为97.1%和77.5%,平均相对误差为7.5%和22.5%,效率系数分别为96.8%和71.2%;在2011—2016验证期内,在径流系数小于等于1时和大于1时,拟合优度分别为99.3%和99.8%,平均相对误差分别为7.4%和16.8%,效率系数分别为97.8%和94.6%。

【Abstract】 There are many formitions of water source in the spring flood of the northern basin of China and factors affecting the runoff process, and the process of each factor is complex, which brings challenges to the spring flood forecasting.Spring flood is the second centralized inflow period in Baishan basin. Spring flood forecasting can scientifically and reasonably formulate reservoir dispatching plan, which can not only guarantee the demand of agricultural water supply and navigation water use, but also reserve reservoir capacity for flood control in the later period, so as to realize efficient utilization of water resources.Based on the long series daily-scale inflow process data of Baishan Reservoir in spring from 1958 to 2016, the base flow is segmented by Eckhardt recursive digital filtering method and basic flow ratio is plotted. According to the evolution trend of the basic flow rate process, the water source composition of spring runoff is divided into three stages, snowmelt runoff, snowmelt-rainfall runoff under frozen soil conditions and rainfall runoff under frozen soil condition.Under the multi-year average condition, the commencing date of snowmelt runoff in Baishan Basin is March 28, the commencing date of runoff under frozen soil conditions is April 28; the end date of snowmelt runoff is May 20, and the date of frozen soil ablation is June 15th.Under the multi-year average condition, the snowmelt runoff duration lasted for 28 days, the snowmelt-rainfall runoff under frozen soil conditions duration lasted for 26 days, the rainfall runoff under frozen soil conditions duration lasted for 24 days.Taking the daily rainfall, temperature, radiation and wind speed data of three meteorological stations in Baishan watershed from 1960 to 2016 as the influencing factors, and taking the daily inflow of Baishan Reservoir(runoff depth) as the objective variable, the global sensitivity analysis method was used to identify the total daily radiation and average wind speed without time lag effect during the snowmelt runoff period(March 28-May 20), and the minimum temperature with time lag effect of 1 day and level effect. Mean temperature and maximum temperature have a two-day lag effect, and then the key influencing factors of snowmelt runoff are identified based on the time lag, such as daily total radiation, average wind speed, average temperature and precipitation.The empirical formula of snowmelt runoff is fitted by genetic algorithm, and the simulation accuracy is better during the calibration period from 1960 to 2010. When the runoff coefficient is less than or equal to 1 and larger than 1, the goodness of fit is 97.1% and 77%. The average relative error is 7.5% and 22.5%, and the efficiency coefficients are 96.8% and 71.2% respectively. During the verification period of 2011-2016, when the runoff coefficient is less than or equal to 1 and greater than 1, the goodness of fit is 99.3% and 99.8%, the average relative error is 7.4% and 16.8%, and the efficiency coefficient is 97.8% and 94.6%, respectively.

【基金】 国网新源控股有限公司科学技术项目(XQJH1885000061);国家自然科学基金委员会与韩国国家研究基金会联合资助合作交流项目“变化环境下区域水资源响应与可持续利用研究”(51711540299)
  • 【文献出处】 水利水电技术 ,Water Resources and Hydropower Engineering , 编辑部邮箱 ,2019年05期
  • 【分类号】P333
  • 【网络出版时间】2019-04-25 15:56
  • 【被引频次】3
  • 【下载频次】159
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