Remote sensing can provide near real-time and dynamic monitoring of drought. The drought severity index(DSI), based on the normalized difference vegetation index(NDVI) and evapotranspiration/potential evapotranspirati...Remote sensing can provide near real-time and dynamic monitoring of drought. The drought severity index(DSI), based on the normalized difference vegetation index(NDVI) and evapotranspiration/potential evapotranspiration(ET/PET), has been used for drought monitoring. This study examined the relationship between the DSI and winter wheat yield for prefecture-level cities in five provinces of eastern China during 2001–2016. We first analyzed the spatial and temporal distribution of droughts in the study area. Then the correlation coefficient between drought-affected area and detrended yield of winter wheat was quantified and the impact of droughts of different intensities on winter wheat yield during different growth stages was investigated. The results show that incipient drought during the wintering period has no significant impact on the yield of winter wheat, while moderate drought in the same period can reduce yield. Drought affects winter wheat yield significantly during the flowering and filling stages. Droughts of higher intensity have more significant negative effects on the yield of winter wheat. Monitoring of droughts and irrigation is critical during these periods to ensure normal yield of winter wheat. This study has important practical implications for the planning of irrigation and food security.展开更多
Large amounts of data accumulated in ecology and related environmental sciences arouses urgent need to explore useful patterns and information in it.Here we propose coclustering-based methods and a temperatures-photop...Large amounts of data accumulated in ecology and related environmental sciences arouses urgent need to explore useful patterns and information in it.Here we propose coclustering-based methods and a temperatures-photoperiod driven phenological model to explore spatio-temporal differentiation in long-term spring phenology in China.First,we created the first bloom date(FBD)dataset in China from 1979 to 2018 using the extended spring indices and China Meteorological Forcing Dataset.Then we analyzed the dataset using Bregman block average co-clustering algorithm with I-divergence(BBAC_I)and kmeans algorithm.Such analysis delineated the spatially-continuous phenoregions in China for the first time.Results showed three spatial patterns of FBD in China and their temporal dynamics for 40 years(1979–2018).More specifically,overall late spring onsets occur in 1979–1996,in which areas located in Jiangxi,northern Xinjiang and middle Inner Mongolia experienced constant changing spring onsets.Overall increasingly earlier spring onsets occur in 1997–2012,in which areas located in Fujian,Hunan and eastern Heilongjiang experienced the most variable spring onsets.Stable early spring onsets over China occur after 2012.Results also showed 15 temporal patterns of spring phenology over the study period and their spatial delineation in China.More specifically,most areas in China have the same FBD category for 40 years while northern Guizhou,Hunan and southern Hubei have the same category in 1979–1997 and then fluctuate between different categories.Finally,our results have certain directive significance on the design of existing observational sites in Chinese Phenological Network.展开更多
Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global mod...Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location,scale, and shape was used for at-site precipitation forecasting. The results indicate that:(1) winter and spring precipitation processes at most stations are nonstationary,while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the studyregion;(2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation(PDO). The El Nin? o/Southern Oscillation(ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin;(3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and(4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation.展开更多
文摘Remote sensing can provide near real-time and dynamic monitoring of drought. The drought severity index(DSI), based on the normalized difference vegetation index(NDVI) and evapotranspiration/potential evapotranspiration(ET/PET), has been used for drought monitoring. This study examined the relationship between the DSI and winter wheat yield for prefecture-level cities in five provinces of eastern China during 2001–2016. We first analyzed the spatial and temporal distribution of droughts in the study area. Then the correlation coefficient between drought-affected area and detrended yield of winter wheat was quantified and the impact of droughts of different intensities on winter wheat yield during different growth stages was investigated. The results show that incipient drought during the wintering period has no significant impact on the yield of winter wheat, while moderate drought in the same period can reduce yield. Drought affects winter wheat yield significantly during the flowering and filling stages. Droughts of higher intensity have more significant negative effects on the yield of winter wheat. Monitoring of droughts and irrigation is critical during these periods to ensure normal yield of winter wheat. This study has important practical implications for the planning of irrigation and food security.
基金supported by the National Key R&D Program of China(Grant No.2019YFA0606901)the National Natural Science Foundation of China(Grant No.41901317)the China Postdoctoral Science Foundation(Grant No.2018M641246)。
文摘Large amounts of data accumulated in ecology and related environmental sciences arouses urgent need to explore useful patterns and information in it.Here we propose coclustering-based methods and a temperatures-photoperiod driven phenological model to explore spatio-temporal differentiation in long-term spring phenology in China.First,we created the first bloom date(FBD)dataset in China from 1979 to 2018 using the extended spring indices and China Meteorological Forcing Dataset.Then we analyzed the dataset using Bregman block average co-clustering algorithm with I-divergence(BBAC_I)and kmeans algorithm.Such analysis delineated the spatially-continuous phenoregions in China for the first time.Results showed three spatial patterns of FBD in China and their temporal dynamics for 40 years(1979–2018).More specifically,overall late spring onsets occur in 1979–1996,in which areas located in Jiangxi,northern Xinjiang and middle Inner Mongolia experienced constant changing spring onsets.Overall increasingly earlier spring onsets occur in 1997–2012,in which areas located in Fujian,Hunan and eastern Heilongjiang experienced the most variable spring onsets.Stable early spring onsets over China occur after 2012.Results also showed 15 temporal patterns of spring phenology over the study period and their spatial delineation in China.More specifically,most areas in China have the same FBD category for 40 years while northern Guizhou,Hunan and southern Hubei have the same category in 1979–1997 and then fluctuate between different categories.Finally,our results have certain directive significance on the design of existing observational sites in Chinese Phenological Network.
基金financially supported by the Fund for Creative Research Groups of the National Natural Science Foundation of China(Grant No.41621061)the National Science Foundation for Distinguished Young Scholars of China(Grant No.51425903)+1 种基金the National Science Foundation of China(Grant Nos.4160102341401052)
文摘Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location,scale, and shape was used for at-site precipitation forecasting. The results indicate that:(1) winter and spring precipitation processes at most stations are nonstationary,while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the studyregion;(2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation(PDO). The El Nin? o/Southern Oscillation(ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin;(3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and(4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation.
