The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform
Q C Hu - One of the best experts on this subject based on the ideXlab platform.
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phosphorus Source Coefficient determination for quantifying phosphorus loss risk of various animal manures
Geoderma, 2016Co-Authors: Yi Wang, T Q Zhang, Q C HuAbstract:Abstract Quantification of phosphorus (P) loss risk of animal manures is essential to scientifically sound P risk assessment and environmental friendly nutrient management, but has faced significant challenges due to the shortage of appropriate techniques. This study was conducted to determine P Source Coefficients (PSC) for quantifying differential P loss risk of various manures relative to soluble chemical fertilizer. After 2-d, 2-week, 8-week, and 26-week soil incubations with various manures, P-amended soils were analyzed for Olsen P (Ol), Mehlich-3 P (M3), water extractable P (WEP), and Fe-oxide coated filter paper strip P (FeO), each of which was then used to calculate manure PSC. Manure PSC M3 had the strongest linear relationships ( r 2 = 0.95–0.97) among different incubation durations, compared with PSC WEP ( r 2 = 0.79–0.91), PSC Ol ( r 2 = 0.85–0.94), and PSC FeO ( r 2 = 0.88–0.91). The 2 week incubation yielded PSC M3 which had the strongest linear relationships ( r 2 = 0.87–0.97 with a mean of 0.95) among the tested soils, compared with those from 2-d, 8-week, and 26-week incubations. In addition, laboratory PSC M3 had the strongest linear relationships with those PSC M3 measured under field conditions, relative to PSC Ol , PSC FeO , and PSC WEP . Hence, the 2-week incubation along with Mehlich-3 P yielded the most consistent PSCs for various manures across soil types, incubation durations, and soil conditions, and can be recommended as a common protocol for determining manure PSC. The recommended default PSC values are 110, 65, 46, and 43% for liquid swine, liquid dairy, solid poultry, and solid beef manures, respectively, for the new P index of Ontario.
Gongsheng Li - One of the best experts on this subject based on the ideXlab platform.
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conditional stability for an inverse problem of determining a space dependent Source Coefficient in the advection dispersion equation with robin s boundary condition
Abstract and Applied Analysis, 2014Co-Authors: Shunqin Wang, Gongsheng LiAbstract:This paper deals with an inverse problem of determining the space-dependent Source Coefficient in one-dimensional advection-dispersion equation with Robin’s boundary condition. Data compatibility for the inverse problem is analyzed by which an admissible set for the unknown is set forth. Furthermore, with the help of an integral identity, a conditional Lipschitz stability is established by suitably controlling the solution of an adjoint problem.
Vasily V. Titov - One of the best experts on this subject based on the ideXlab platform.
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Detiding DART^® Buoy Data for Real-Time Extraction of Source Coefficients for Operational Tsunami Forecasting
Pure and Applied Geophysics, 2015Co-Authors: Donald B. Percival, Donald W. Denbo, Marie C. Eblé, Edison Gica, Harold O. Mofjeld, Michael C. Spillane, Vasily V. Titov, Paul Y. Huang, Elena I. TolkovaAbstract:US Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune Source Coefficients of tsunami forecast models. For accurate Coefficients and therefore forecasts, tides and background noise at the buoys must be accounted for through detiding. In this study, five methods for Coefficient estimation are compared, each of which handles detiding differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 preexisting harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 h of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate Source Coefficients after detiding. Method (5) estimates the Coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from 11 DART^® buoys, to which selected artificial tsunami signals are superimposed. These buoys represent a full range of observed tidal conditions and background BP noise in the Pacific and Atlantic, and the artificial signals have a variety of patterns and induce varying signal-to-noise ratios. The root-mean-square errors (RMSEs) of least squares estimates of Source Coefficients using varying amounts of data are used to compare the five detiding methods. The RMSE varies over two orders of magnitude among detiding methods, generally decreasing in the order listed, with method (5) yielding the most accurate estimate of the Source Coefficient. The RMSE is substantially reduced by waiting for the first full wave of the tsunami signal to arrive. As a case study, the five methods are compared using data recorded from the devastating 2011 Japan tsunami.
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Extraction of tsunami Source Coefficients via inversion of DART $$^{\circledR}$$ buoy data
Natural Hazards, 2011Co-Authors: Donald B. Percival, Donald W. Denbo, Marie C. Eblé, Edison Gica, Harold O. Mofjeld, Michael C. Spillane, Liujuan Tang, Vasily V. TitovAbstract:The ability to accurately forecast potential hazards posed to coastal communities by tsunamis generated seismically in both the near and far field requires knowledge of so-called Source Coefficients, from which the strength of a tsunami can be deduced. Seismic information alone can be used to set the Source Coefficients, but the values so derived reflect the dynamics of movement at or below the seabed and hence might not accurately describe how this motion is manifested in the overlaying water column. We describe here a method for refining Source Coefficient estimates based on seismic information by making use of data from Deep-ocean Assessment and Reporting of Tsunamis (DART $$^{\circledR}$$ ) buoys (tsunameters). The method involves using these data to adjust precomputed models via an inversion algorithm so that residuals between the adjusted models and the DART $$^{\circledR}$$ data are as small as possible in a least squares sense. The inversion algorithm is statistically based and hence has the ability to assess uncertainty in the estimated Source Coefficients. We describe this inversion algorithm in detail and apply it to the November 2006 Kuril Islands event as a case study.
Yi Wang - One of the best experts on this subject based on the ideXlab platform.
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phosphorus Source Coefficient determination for quantifying phosphorus loss risk of various animal manures
Geoderma, 2016Co-Authors: Yi Wang, T Q Zhang, Q C HuAbstract:Abstract Quantification of phosphorus (P) loss risk of animal manures is essential to scientifically sound P risk assessment and environmental friendly nutrient management, but has faced significant challenges due to the shortage of appropriate techniques. This study was conducted to determine P Source Coefficients (PSC) for quantifying differential P loss risk of various manures relative to soluble chemical fertilizer. After 2-d, 2-week, 8-week, and 26-week soil incubations with various manures, P-amended soils were analyzed for Olsen P (Ol), Mehlich-3 P (M3), water extractable P (WEP), and Fe-oxide coated filter paper strip P (FeO), each of which was then used to calculate manure PSC. Manure PSC M3 had the strongest linear relationships ( r 2 = 0.95–0.97) among different incubation durations, compared with PSC WEP ( r 2 = 0.79–0.91), PSC Ol ( r 2 = 0.85–0.94), and PSC FeO ( r 2 = 0.88–0.91). The 2 week incubation yielded PSC M3 which had the strongest linear relationships ( r 2 = 0.87–0.97 with a mean of 0.95) among the tested soils, compared with those from 2-d, 8-week, and 26-week incubations. In addition, laboratory PSC M3 had the strongest linear relationships with those PSC M3 measured under field conditions, relative to PSC Ol , PSC FeO , and PSC WEP . Hence, the 2-week incubation along with Mehlich-3 P yielded the most consistent PSCs for various manures across soil types, incubation durations, and soil conditions, and can be recommended as a common protocol for determining manure PSC. The recommended default PSC values are 110, 65, 46, and 43% for liquid swine, liquid dairy, solid poultry, and solid beef manures, respectively, for the new P index of Ontario.
Donald B. Percival - One of the best experts on this subject based on the ideXlab platform.
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Detiding DART^® Buoy Data for Real-Time Extraction of Source Coefficients for Operational Tsunami Forecasting
Pure and Applied Geophysics, 2015Co-Authors: Donald B. Percival, Donald W. Denbo, Marie C. Eblé, Edison Gica, Harold O. Mofjeld, Michael C. Spillane, Vasily V. Titov, Paul Y. Huang, Elena I. TolkovaAbstract:US Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune Source Coefficients of tsunami forecast models. For accurate Coefficients and therefore forecasts, tides and background noise at the buoys must be accounted for through detiding. In this study, five methods for Coefficient estimation are compared, each of which handles detiding differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 preexisting harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 h of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate Source Coefficients after detiding. Method (5) estimates the Coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from 11 DART^® buoys, to which selected artificial tsunami signals are superimposed. These buoys represent a full range of observed tidal conditions and background BP noise in the Pacific and Atlantic, and the artificial signals have a variety of patterns and induce varying signal-to-noise ratios. The root-mean-square errors (RMSEs) of least squares estimates of Source Coefficients using varying amounts of data are used to compare the five detiding methods. The RMSE varies over two orders of magnitude among detiding methods, generally decreasing in the order listed, with method (5) yielding the most accurate estimate of the Source Coefficient. The RMSE is substantially reduced by waiting for the first full wave of the tsunami signal to arrive. As a case study, the five methods are compared using data recorded from the devastating 2011 Japan tsunami.
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Extraction of tsunami Source Coefficients via inversion of DART $$^{\circledR}$$ buoy data
Natural Hazards, 2011Co-Authors: Donald B. Percival, Donald W. Denbo, Marie C. Eblé, Edison Gica, Harold O. Mofjeld, Michael C. Spillane, Liujuan Tang, Vasily V. TitovAbstract:The ability to accurately forecast potential hazards posed to coastal communities by tsunamis generated seismically in both the near and far field requires knowledge of so-called Source Coefficients, from which the strength of a tsunami can be deduced. Seismic information alone can be used to set the Source Coefficients, but the values so derived reflect the dynamics of movement at or below the seabed and hence might not accurately describe how this motion is manifested in the overlaying water column. We describe here a method for refining Source Coefficient estimates based on seismic information by making use of data from Deep-ocean Assessment and Reporting of Tsunamis (DART $$^{\circledR}$$ ) buoys (tsunameters). The method involves using these data to adjust precomputed models via an inversion algorithm so that residuals between the adjusted models and the DART $$^{\circledR}$$ data are as small as possible in a least squares sense. The inversion algorithm is statistically based and hence has the ability to assess uncertainty in the estimated Source Coefficients. We describe this inversion algorithm in detail and apply it to the November 2006 Kuril Islands event as a case study.
