The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta re...The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.展开更多
Seismic data show some important characteristics, such as big volume and strong timeliness. Specific to the time series data of earthquake precursory observations, a piecewise linear representation based on the slidin...Seismic data show some important characteristics, such as big volume and strong timeliness. Specific to the time series data of earthquake precursory observations, a piecewise linear representation based on the sliding window mean value （PLR_MTSW） algorithm is proposed. With this algorithm, the mutation points can be identified accurately according to the rate Of mean value change, while the main features of time series are maintained well. This algorithm can also smooth the noise and improve the compression accuracy with sliding window. Meanwhile the local extreme points can be identified effectively according to the change of mean value trend within window.展开更多
文摘The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.
基金Project supported by the Shanghai Leading Academic Discipline Project(Grant No.J50103)the Natural Science Foundation of Shanghai Municipality(Grant No.08ZR1408400)
文摘Seismic data show some important characteristics, such as big volume and strong timeliness. Specific to the time series data of earthquake precursory observations, a piecewise linear representation based on the sliding window mean value （PLR_MTSW） algorithm is proposed. With this algorithm, the mutation points can be identified accurately according to the rate Of mean value change, while the main features of time series are maintained well. This algorithm can also smooth the noise and improve the compression accuracy with sliding window. Meanwhile the local extreme points can be identified effectively according to the change of mean value trend within window.
