Climate is subject to fluctuations in the majority of the world, mainly caused by rainfall as well as temperature variations. Climate fluctuations in Kenya have resulted in the spread of desert-like conditions in the ...Climate is subject to fluctuations in the majority of the world, mainly caused by rainfall as well as temperature variations. Climate fluctuations in Kenya have resulted in the spread of desert-like conditions in the ASALs region, such as Marigat in Baringo County. As a county, Baringo experiences great variations in climate annually, as well as uncertainty in expected rains, thereby negatively impacting the production of crops such as sorghum. This study applied the rainfall anomaly index (RAI), standardised precipitation evapotranspiration index (SPEI), standard precipitation index (SPI), and Mann-Kendall (MK) statistical test for trends on historical climatic data in analysing both temperature and precipitation data over the period 1990 to 2022 to determine their trend, patterns and how they affect the production of sorghum crops. The machine learning method (R studio) with inputs was used to calculate the SPI, SPEI, RAI and MK trend test. The rainfall varied from below average to above average during the study period with no clear pattern in the RAI, SPEI and SPI values. The years 2020 and 2000 stood out as they had higher and lower rainfall than usual, respectively. The Marigat area generally experienced more rainfall during the high/long rainfall season (AMJJ). The MK trend test on average monthly rainfall, SOND, AMJJ, and annual precipitation confirmed a positive trend in precipitation. However, the short rainy season (SOND) was found to be the most variable period for rainfall, and there was a slight increase in daily average temperatures during this season.展开更多
Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understan...Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index(NDVI) from the Advanced Very High Resolution Radiometer(AVHRR) Global Inventory Modeling and Mapping Studies(GIMMS) time series(1982–2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal(MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends(P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.展开更多
Seasonal and annual rainfall data of the stations: Akluj, Baramati, Bhor and Malsiras stations located in Nira Basin, Central India, were analyzed for studying trend and periodicity using 104 years’ rainfall data. Th...Seasonal and annual rainfall data of the stations: Akluj, Baramati, Bhor and Malsiras stations located in Nira Basin, Central India, were analyzed for studying trend and periodicity using 104 years’ rainfall data. The analysis was carried out by using Mann-Kendall (MK), Modified Mann-Kendall (MMK) and Theil and Sen’s slope estimator tests describing rising trend at all the stations. However, it is statistically significant at Akluj and Bhor stations at 10% significance level. Bhor station showed the maximum increase in percentage change i.e. 0.28% in annual rainfall. Monsoon and post-monsoon seasonal rainfall shows a rising trend while the summer and winter seasonal rainfall shows a falling trend. Wavelet analysis showed prominent annual rainfall periods ranging from 2 to 8 years at all the stations after 1960s resulting in describing more changes in the rainfall patterns after 1960s.展开更多
文摘Climate is subject to fluctuations in the majority of the world, mainly caused by rainfall as well as temperature variations. Climate fluctuations in Kenya have resulted in the spread of desert-like conditions in the ASALs region, such as Marigat in Baringo County. As a county, Baringo experiences great variations in climate annually, as well as uncertainty in expected rains, thereby negatively impacting the production of crops such as sorghum. This study applied the rainfall anomaly index (RAI), standardised precipitation evapotranspiration index (SPEI), standard precipitation index (SPI), and Mann-Kendall (MK) statistical test for trends on historical climatic data in analysing both temperature and precipitation data over the period 1990 to 2022 to determine their trend, patterns and how they affect the production of sorghum crops. The machine learning method (R studio) with inputs was used to calculate the SPI, SPEI, RAI and MK trend test. The rainfall varied from below average to above average during the study period with no clear pattern in the RAI, SPEI and SPI values. The years 2020 and 2000 stood out as they had higher and lower rainfall than usual, respectively. The Marigat area generally experienced more rainfall during the high/long rainfall season (AMJJ). The MK trend test on average monthly rainfall, SOND, AMJJ, and annual precipitation confirmed a positive trend in precipitation. However, the short rainy season (SOND) was found to be the most variable period for rainfall, and there was a slight increase in daily average temperatures during this season.
基金Under the auspices of National Natural Science Foundation of China(No.41771179,41871103,41771138)the National Key Research and Development Project(No.2016YFA0602301)
文摘Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index(NDVI) from the Advanced Very High Resolution Radiometer(AVHRR) Global Inventory Modeling and Mapping Studies(GIMMS) time series(1982–2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal(MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends(P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.
文摘Seasonal and annual rainfall data of the stations: Akluj, Baramati, Bhor and Malsiras stations located in Nira Basin, Central India, were analyzed for studying trend and periodicity using 104 years’ rainfall data. The analysis was carried out by using Mann-Kendall (MK), Modified Mann-Kendall (MMK) and Theil and Sen’s slope estimator tests describing rising trend at all the stations. However, it is statistically significant at Akluj and Bhor stations at 10% significance level. Bhor station showed the maximum increase in percentage change i.e. 0.28% in annual rainfall. Monsoon and post-monsoon seasonal rainfall shows a rising trend while the summer and winter seasonal rainfall shows a falling trend. Wavelet analysis showed prominent annual rainfall periods ranging from 2 to 8 years at all the stations after 1960s resulting in describing more changes in the rainfall patterns after 1960s.
