在农田管理中,土壤水分是极其重要的因素.为了对黑河中上游土壤水分进行合理评估,利用黑河流域中游自动气象站数据,采用BBH(Bucket with a Bottom Hole)模型,计算得到位于黑河中游盈科灌区农田的表层土壤水分,并与实测数据进行对比研究...在农田管理中,土壤水分是极其重要的因素.为了对黑河中上游土壤水分进行合理评估,利用黑河流域中游自动气象站数据,采用BBH(Bucket with a Bottom Hole)模型,计算得到位于黑河中游盈科灌区农田的表层土壤水分,并与实测数据进行对比研究.结果表明,该模型模拟流域土壤水分有一定的精度,能够满足农田水分预测和灌溉需水分析的要求;而且该模型需要的参数较少,计算过程简单,获取数据容易.另外选择黑河上游阿柔草地站气象数据,对高山草原的土壤水分动态变化进行模拟计算,同样得到很好的模拟结果.认为BBH模型能够满足不同下垫面类型的土壤水分的模拟要求,具有一定的实用意义.采用参数同定法对模型的参数进行了敏感性分析,确定了模型参数的适用范围和敏感程度.在结合模型模拟与实际观测的基础上,探讨了农田和草地土壤水分的变化规律,结果发现:土壤水分冬春两季变化缓慢,夏秋两季变化剧烈.展开更多
The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory si...The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory signals from stellar-mass binary black holes(BBHs),typically targeted by ground-based detectors.We use DECIGO detector as an example.Over 5 years,DECIGO is estimated to detect approximately 2,036 memory signals(SNRs>3)from stellar-mass BBHs.Simulations used frequency-domain memory waveforms for direct SNR estimation.Predictions utilized a GWTC-3 constrained BBH population model(Power law+Peak mass,DEFAULT spin,Madau-Dickinson merger rate).The analysis used conservative lower merger rate limits and considered orbital eccentricity.The high detection rate stems from strong memory signals within DECIGO’s bandwidth and the abundance of stellar-mass BBHs.This substantial and conservative detection count enables statistical use of the memory effect for fundamental physics and astrophysics.DECIGO exemplifies that space interferometers may better detect memory signals from smaller mass binaries than their typical targets.Detectors in lower frequency bands are expected to find strong memory signals from∼10^(4)M⊙binaries.展开更多
文摘在农田管理中,土壤水分是极其重要的因素.为了对黑河中上游土壤水分进行合理评估,利用黑河流域中游自动气象站数据,采用BBH(Bucket with a Bottom Hole)模型,计算得到位于黑河中游盈科灌区农田的表层土壤水分,并与实测数据进行对比研究.结果表明,该模型模拟流域土壤水分有一定的精度,能够满足农田水分预测和灌溉需水分析的要求;而且该模型需要的参数较少,计算过程简单,获取数据容易.另外选择黑河上游阿柔草地站气象数据,对高山草原的土壤水分动态变化进行模拟计算,同样得到很好的模拟结果.认为BBH模型能够满足不同下垫面类型的土壤水分的模拟要求,具有一定的实用意义.采用参数同定法对模型的参数进行了敏感性分析,确定了模型参数的适用范围和敏感程度.在结合模型模拟与实际观测的基础上,探讨了农田和草地土壤水分的变化规律,结果发现:土壤水分冬春两季变化缓慢,夏秋两季变化剧烈.
基金supported by the National Natural Science Foundation of China(Grant Nos.11633001,11920101003,and 12205222 for S.H.)the Key Program of the National Natural Science Foundation of China(Grant No.12433001)+1 种基金the Strate-gic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB23000000)the National Key Research and Development Program of China(Grant No.2021YFC2203001 for Z.C.Z.).
文摘The gravitational memory effect manifests gravitational nonlinearity,degenerate vacua,and asymptotic symmetries;its detection is considered challenging.We propose using a space-borne interferometer to detect memory signals from stellar-mass binary black holes(BBHs),typically targeted by ground-based detectors.We use DECIGO detector as an example.Over 5 years,DECIGO is estimated to detect approximately 2,036 memory signals(SNRs>3)from stellar-mass BBHs.Simulations used frequency-domain memory waveforms for direct SNR estimation.Predictions utilized a GWTC-3 constrained BBH population model(Power law+Peak mass,DEFAULT spin,Madau-Dickinson merger rate).The analysis used conservative lower merger rate limits and considered orbital eccentricity.The high detection rate stems from strong memory signals within DECIGO’s bandwidth and the abundance of stellar-mass BBHs.This substantial and conservative detection count enables statistical use of the memory effect for fundamental physics and astrophysics.DECIGO exemplifies that space interferometers may better detect memory signals from smaller mass binaries than their typical targets.Detectors in lower frequency bands are expected to find strong memory signals from∼10^(4)M⊙binaries.
