SWAT模型融雪模块的改进与应用研究[J]

融雪是水文过程的重要影响因素,为了对天山北坡气候极端高寒地区进行长期洪水预报预警,该研究以SWAT(土壤和水评估工具)模型为基本框架对其融雪模块进行修改,将基于物理过程的分布式融雪径流模型耦合入SWAT模型,并基于高程带对改进前后的模型进行了对比验证。通过对新疆天山北坡军塘湖河流域进行模拟、参数率定与验证后发现,原SWAT模型在参数率定期间(2000年1月1日至2004年12月31日)的Nash-Stucliffe效率系数(NSE)略低于基于物理过程的模型SWAT-JTH,但在验证期(2006年1月1日至2010年12月31日),SWAT原模型NSE高于改进后的SWAT-JTH模型。两种方法模..

天山北坡中段融雪径流敏感性分析——以军塘湖流域为例/Sensitive Analysis of Snowmelt Runoff on North Slope of Tianshan Mountains——Taking Juntanghu Watershed as an Example[J]

以天山北部中段军塘湖为典型研究区,利用军塘湖河流域径流和气象数据分析了高山融雪径流与温度、降雨的定量关系.基于天山北坡中段10个气象站(乌苏、石河子、沙湾、玛纳斯、呼图壁、昌吉、米泉、小渠子、大西沟、天池)1961-2010年逐月气温、降水数据等实测数据为基础进行Mann-Kendall趋势检验与分析.以模比系数差积曲线在M-K检验基础上分析径流、温度与降水之间的趋势关系.以SWAT模型为框架建立新疆军塘湖河流域水文过程模型,利用红山水库12 a径流数据在SWAT模型参数校准后对新疆天山北坡融雪径流过程进行敏感性分析预测.通过改变SWAT模型输入数据的平均温度、降水量等因素分析了其对融雪径流的敏感性及影响.结果发现,从冬季至初春温度及降水等因素变化仅在融雪期对径流产生较大影响.此外,温度数据的变化对军塘湖天山北坡流域融雪径流的影响较降水量大

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies