The Experts below are selected from a list of 102630 Experts worldwide ranked by ideXlab platform
Fangmin Zhang - One of the best experts on this subject based on the ideXlab platform.
-
shift in potential evapotranspiration and its implications for dryness wetness over southwest china
Journal of Geophysical Research, 2016Co-Authors: Haishan Chen, Guojie Wang, Jinjian Li, Mengyuan Mu, Bei Xu, Jin Huang, Jie Wang, Fangmin ZhangAbstract:During 1961–2012, the regional average annual potential evapotranspiration (PET) of Southwest China (SWC) and the four subregions (named as SR1, SR2, SR3, and SR4) showed different decreases (excluding SR3); while the breakpoint analysis suggested that PET changes (i.e., sign and magnitude) have shifted. Based on a group of sensitivity experiments with Penman-Monteith equation and a new separating method, the contributions of each Climate Factor alone (i.e., net radiation, Rn; mean temperature, Tave; wind speed, Wnd; and vapor pressure deficit, Vpd) to PET changes were calculated. Results showed that declined Wnd in SR1, reduced Rn in SR2, SR4, and SWC, and increased Vpd in SR3 were responsible for the PET changes during 1961–2012. However, the determinant Factor for each subregion and SWC varied in different segmented periods, which were identified using the breakpoint analysis. The impacts of PET shifts on SWC dryness/wetness (reflected by the 3 month Standardized Precipitation-Evapotranspiration index, SPEI-3) during 1961–2012 were then quantified. Briefly, SPEI-3 changes in SR3, SR4, and SWC had the determinant Factor of PET in the first one or two period(s), and precipitation in the last period; while they were attributed to PET (precipitation) in SR1 (SR2) for each segmented period. It is found that PET and precipitation had comparable contributions to the variations in SWC dryness/wetness. Our findings have suggested that more attentions should be paid to the impacts of PET changes and shifts in future studies of dryness/wetness or drought.
Roberto Alvarez - One of the best experts on this subject based on the ideXlab platform.
-
predicting average regional yield and production of wheat in the argentine pampas by an artificial neural network approach
European Journal of Agronomy, 2009Co-Authors: Roberto AlvarezAbstract:Abstract A regional analysis of the effects of soil and Climate Factors on wheat yield was performed in the Argentine Pampas in order to obtain models suitable for yield estimation and regional grain production prediction. Soil data from soil surveys and Climate data from meteorological records were employed. Grain production information from statistics at county level was integrated at a geomorphological level. The Pampas was divided into 10 geographical units and data from 10 growing season were used (1995–2004). Surface regression and artificial neural networks (ANN) methodologies were tested for analyzing the data. Wheat yield was correlated to soil available water holding capacity (SAWHC) in the upper 100 cm of the profiles ( r 2 = 0.39) and soil organic carbon (SOC) content ( r 2 = 0.26). The Climate Factor with stronger effect on yield was the rainfall/crop potential evapotranspiration ratio (R/CPET) during the fallow and vegetative crop growing cycle periods summed ( r 2 = 0.31). The phototermal quotient (PQ) during the pre-anthesis period had also a significant effect on yield ( r 2 = 0.05). A surface regression response model was developed that account for 64% of spatial and interannual yield variance, but this model could not perform a better yield prediction than the blind guess technique. An ANN was fitted to the data that accounted for 76% of yield variability. Comparing predicted versus observed yield a lower RMSE ( P = 0.05) was obtained using the ANN than using the regression or the blind guess methods. Regional production estimations performed by the ANN showed a good agreement with observed data with a RMSE equivalent to 7% of the whole surveyed area production. As variables used for the ANN development may be available around 40–60 days before wheat harvest, the methodology may be used for wheat production forecasting in the Pampas.
Li Zhou - One of the best experts on this subject based on the ideXlab platform.
-
interspecific difference of relationship between radial growth and Climate Factor for larix olgensis and picea jezoensis var komarovii in changbai mountain northeast china
Journal of Applied Ecology, 2019Co-Authors: S Wang, Xiao Yu Wang, Xue Rui Gai, Li Min Dai, Wang Ming Zhou, Li ZhouAbstract:To clarify the responses of radial growth of different tree species to Climate change and its stability, we explored the relationships between radial growth and Climate Factors of larch (Larix olgensis) and spruce (Picea jezoensis var. komarovii) distributed at high altitude (1600-1750 m) on the northern slope of Changbai Mountain, using the chronological method. The results showed that the growth of larch was significantly positively correlated with the maximum temperature in June and negatively correlated with the precipitation in June. The radial growth of spruce was significantly positively correlated with the maximum temperature in May. Results from redundancy analysis showed that larch growth was mainly affected by summer temperature, while spruce growth was significantly restricted by spring temperature. During 1959-2014, the relationship between larch growth and summer temperature was relatively stable. For spruce, the correlation between radial growth and spring temperatures had gradually weakened since 1986, mainly due to the growth slowdown because of decreased maximum air temperature. Our results provide theoretical references for predicting the growth response of conifers at Changbai Mountain region in the context of Climate change.
Santiago C Gonzalezmartinez - One of the best experts on this subject based on the ideXlab platform.
-
environmental effects on fine scale spatial genetic structure in four alpine keystone forest tree species
Molecular Ecology, 2018Co-Authors: Elena Mosca, Erica A Di Pierro, Katharina B Budde, David B Neale, Santiago C GonzalezmartinezAbstract:: Genetic responses to environmental changes take place at different spatial scales. While the effect of environment on the distribution of species' genetic diversity at large geographical scales has been the focus of several recent studies, its potential effects on genetic structure at local scales are understudied. Environmental effects on fine-scale spatial genetic structure (FSGS) were investigated in four Alpine conifer species (five to eight populations per species) from the eastern Italian Alps. Significant FSGS was found for 11 of 25 populations. Interestingly, we found no significant differences in FSGS across species but great variation among populations within species, highlighting the importance of local environmental Factors. Interannual variability in spring temperature had a small but significant effect on FSGS of Larix decidua, probably related to species-specific life history traits. For Abies alba, Picea abies and Pinus cembra, linear models identified spring precipitation as a potentially relevant Climate Factor associated with differences in FSGS across populations; however, models had low explanatory power and were strongly influenced by a P. cembra outlier population from a very dry site. Overall, the direction of the identified effects is according to expectations, with drier and more variable environments increasing FSGS. Underlying mechanisms may include Climate-related changes in the variance of reproductive success and/or environmental selection of specific families. This study provides new insights on potential changes in local genetic structure of four Alpine conifers in the face of environmental changes, suggesting that new Climates, through altering FSGS, may also have relevant impacts on plant microevolution.
Manikam Pillay - One of the best experts on this subject based on the ideXlab platform.
-
developing a safety Climate Factor model in construction research and practice a systematic review identifying future directions for research
Engineering Construction and Architectural Management, 2018Co-Authors: Mohammad Tanvi Newaz, Peter Davis, Marcus Jefferies, Manikam PillayAbstract:Safety Climate and its impact on safety performance is well established; however, researchers in this field suggest that the absence of a common assessment framework is a reflection of the state of development of this concept. The purpose of this paper is to propose a five-Factor model that can be used to diagnose and measure safety Climate in construction safety research and practice.,A systematic review was adopted, and following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 574 articles were selected at the start of the study based on a developed review protocol for investigating safety Climate Factors. While examining the Factor analysis of different studies, data reliability and data validity of the individual research findings were considered and frequency of Factors uploaded was used to determine the significance as a quantitative measure to develop the ranking of safety Climate Factors.,The review identified that, from the established measures of safety Climate in construction, there is little uniformity on Factor importance. However, management commitment safety system role of the supervisor; workers’ involvement and group safety Climate were found to be the most common across the studies reviewed. It is proposed these Factors are used to inform a five-Factor model for investigating safety Climate in the construction industry.,The findings of this study will motivate researchers and practitioners in safety to use the five-Factor safety Climate model presented in this paper and test it to develop a common Factor structure for the construction industry. The fact that the model is comprised of five Factors makes it easier to be used and implemented by small-to medium-sized construction companies, therefore enhancing its potential use.