The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the...The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the direct impact of the environmental conditions. Based on the generalized linear model(GLM) and generalized additive model(GAM), the commercial fishery data from the Chinese squid-jigging fleets during 1995 to 2011 were used to examine the interannual and seasonal variability in the abundance of O. bartramii, and to evaluate the influences of variables on the abundance(catch per unit effort, CPUE). The results from GLM suggested that year, month, latitude, sea surface temperature(SST), mixed layer depth(MLD), and the interaction term(SST×MLD) were significant factors. The optimal model based on GAM included all the six significant variables and could explain 42.43% of the variance in nominal CPUE. The importance of the six variables was ranked by decreasing magnitude: year, month, latitude, SST, MLD and SST×MLD. The squid was mainly distributed in the waters between 40?N and 44?N in the Northwest Pacific Ocean. The optimal ranges of SST and MLD were from 14 to 20℃ and from 10 to 30 m, respectively. The squid abundance greatly fluctuated from 1995 to 2011. The CPUE was low during 1995–2002 and high during 2003–2008. Furthermore, the squid abundance was typically high in August. The interannual and seasonal variabilities in the squid abundance were associated with the variations of marine environmental conditions and the life history characteristics of squid.展开更多
Spring snowmelt peak flow (SSPF) can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by ...Spring snowmelt peak flow (SSPF) can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by shaping snowmelt rate and determining the soil frozen state which partitions snowmelt water into surface runoff and soil infiltration water in spring. It is necessary to identify the important and significant paths of climatic factors influencing the SSPF and provide estimates of the magnitude and significance of hypothesized causal connections between climatic factors and the SSPF. This study used path analysis with a selection of five factors - the antecedent precipitation index (API), spring precipitation (SP), winter precipitation as snowfall (WS), 〈0℃ temperature accumulation in winter ([ATNI), and average 〉0℃temperature accumulation in spring (AT) - to analyze their influences on the SSPF in the Kaidu River in Xinjiang, China. The results show that {ATN}, AT and WS have a significant correlation with the SSPF, while API and SP do not show a significant correlation. AT and WS directly influence the SSPF, while as the influence of[ATN] on SSPF is indirect through WS and AT. The indirect influence of [ATN[ on SSPF through WS accounts for 69% of the total influence of [ATN] on SSPF. Compared to the multiple linear regression method, path analysis provides additional valuable information, including influencing paths from independent variables to the dependent variable as well as direct and indirect impacts of external variables on the internal variable. This information can help improve the description of snow melt and spring runoff in hydrologic models as well as the planning and management of water resources.展开更多
This article contributes to research on how climate change will impact crops in China by moving from ex-post empirical analysis to forecasting. We construct a multiple regression model, using agricultural observations...This article contributes to research on how climate change will impact crops in China by moving from ex-post empirical analysis to forecasting. We construct a multiple regression model, using agricultural observations and meteorological simulations by GCMs, to simulate the possible planting boundaries and suitable planting re- gions of spring wheat under RCP4.5 scenario for the base period 2040s and 2070s. We find that the south bound- ary of possible planting region for spring wheat spreads along the belt: south Shandong-north Jiangsu-north Anhui-central Henan-north Hubei-southeast Sichuan-north Yunnan provinces, and will likely move northward under RCP4.5 scenario in 2040s and 2070s, resulting in the decrease of possible planting area in China. Moreover, the sowing and harvest date of spring wheat in the base period shows a gradually delayed phenomenon from the belt: south Xinjiang - Gansu, to the Tibet Plateau. As a result, the growth period of spring wheat in China will shorten because of the impacts of climate change. These results imply that a variety of adaptations measures should be set up in response to changing climatic conditions, including developing the planting base for spring wheat, restricting the planting area of spring wheat in sub-suitable areas at risk while expanding the planting area of optimal crops.展开更多
基金financially supported by the National HighTech R&D Program(863 Program)of China(2012AA 092303)the Project of Shanghai Science and Technology Innovation(12231203900)+3 种基金the Industrialization Program of National Development and Reform Commission(2159999)the National Key Technologies R&D Program of China(2013BAD13B00)the Shanghai Universities First-Class Disciplines Project(Fisheries A)the Funding Program for Outstanding Dissertations in Shanghai Ocean University
文摘The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the direct impact of the environmental conditions. Based on the generalized linear model(GLM) and generalized additive model(GAM), the commercial fishery data from the Chinese squid-jigging fleets during 1995 to 2011 were used to examine the interannual and seasonal variability in the abundance of O. bartramii, and to evaluate the influences of variables on the abundance(catch per unit effort, CPUE). The results from GLM suggested that year, month, latitude, sea surface temperature(SST), mixed layer depth(MLD), and the interaction term(SST×MLD) were significant factors. The optimal model based on GAM included all the six significant variables and could explain 42.43% of the variance in nominal CPUE. The importance of the six variables was ranked by decreasing magnitude: year, month, latitude, SST, MLD and SST×MLD. The squid was mainly distributed in the waters between 40?N and 44?N in the Northwest Pacific Ocean. The optimal ranges of SST and MLD were from 14 to 20℃ and from 10 to 30 m, respectively. The squid abundance greatly fluctuated from 1995 to 2011. The CPUE was low during 1995–2002 and high during 2003–2008. Furthermore, the squid abundance was typically high in August. The interannual and seasonal variabilities in the squid abundance were associated with the variations of marine environmental conditions and the life history characteristics of squid.
基金financially supported by the Project of State Key Basic R & D Program of China (973 Program, Grant No. 2010CB951002)the key deployment project of Chinese Academy of Sciences (Grant No. KZZD-EW-12-2)Chinese Academy of Sciences Visiting Professorship for Senior International Scientists (Grant No. 2011T2Z40)
文摘Spring snowmelt peak flow (SSPF) can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by shaping snowmelt rate and determining the soil frozen state which partitions snowmelt water into surface runoff and soil infiltration water in spring. It is necessary to identify the important and significant paths of climatic factors influencing the SSPF and provide estimates of the magnitude and significance of hypothesized causal connections between climatic factors and the SSPF. This study used path analysis with a selection of five factors - the antecedent precipitation index (API), spring precipitation (SP), winter precipitation as snowfall (WS), 〈0℃ temperature accumulation in winter ([ATNI), and average 〉0℃temperature accumulation in spring (AT) - to analyze their influences on the SSPF in the Kaidu River in Xinjiang, China. The results show that {ATN}, AT and WS have a significant correlation with the SSPF, while API and SP do not show a significant correlation. AT and WS directly influence the SSPF, while as the influence of[ATN] on SSPF is indirect through WS and AT. The indirect influence of [ATN[ on SSPF through WS accounts for 69% of the total influence of [ATN] on SSPF. Compared to the multiple linear regression method, path analysis provides additional valuable information, including influencing paths from independent variables to the dependent variable as well as direct and indirect impacts of external variables on the internal variable. This information can help improve the description of snow melt and spring runoff in hydrologic models as well as the planning and management of water resources.
基金National Natural Sciences Foundation of China(Study on allocation of water and land resources based on food security at population peaks in ChinaNo.41471463)
文摘This article contributes to research on how climate change will impact crops in China by moving from ex-post empirical analysis to forecasting. We construct a multiple regression model, using agricultural observations and meteorological simulations by GCMs, to simulate the possible planting boundaries and suitable planting re- gions of spring wheat under RCP4.5 scenario for the base period 2040s and 2070s. We find that the south bound- ary of possible planting region for spring wheat spreads along the belt: south Shandong-north Jiangsu-north Anhui-central Henan-north Hubei-southeast Sichuan-north Yunnan provinces, and will likely move northward under RCP4.5 scenario in 2040s and 2070s, resulting in the decrease of possible planting area in China. Moreover, the sowing and harvest date of spring wheat in the base period shows a gradually delayed phenomenon from the belt: south Xinjiang - Gansu, to the Tibet Plateau. As a result, the growth period of spring wheat in China will shorten because of the impacts of climate change. These results imply that a variety of adaptations measures should be set up in response to changing climatic conditions, including developing the planting base for spring wheat, restricting the planting area of spring wheat in sub-suitable areas at risk while expanding the planting area of optimal crops.