Sentence examples for maximum water deficit from inspiring English sources
Maximum Water deficit decreased from Africa and Australasia to the Americas and then to Asia.
Our analyses suggest that forests canopy structure is shaped by long-term regional climate, and in particular to the maximum water deficit experienced annually in a region.
Using data collected from 887 tropical forest plots, we show that maximum water deficit, defined across the most arid months of the year, is an important predictor of canopy structure, with all three canopy attributes declining significantly with increasing water deficit.
In particular, the maximum water deficit in a region - which forest stands experience and thus evolve to adapt to (Kergoat et al. 2002) - had the strongest relationship with canopy structure variation across plots and continents, with canopy LAI, fAPAR and FCover declining significantly with increasing long-term averages of maximum water deficits.
We conclude that canopy structure in the tropics is primarily a consequence of forest adaptation to the maximum water deficits historically experienced within a given region.
Our analyses show that forest canopy structure and thus forest functioning may be largely a result of forest adaptation to the maximum water deficits they can experience within a given region.
Here, using a dataset of 531 inventory plots of closed canopy forest distributed across the western Neotropics we investigated how precipitation, evaluated both as mean annual precipitation and as the maximum climatological water deficit, influences the distribution of tropical tree species, genera and families.
6 7 3times { ln }left( {text{DBH}} right), -, 0.0 2 9 9times { ln }left( {text{DBH}} right)^{ 2} } right]where E is a synthetic index of temperature seasonality, maximum climatological water deficit, and precipitation seasonality (E = −0.09162301 at our site), WD is the wood density (g cm−3), and DBH, the diameter at breath height (cm).
Cowpea yields obtained by smallholder farmers in the savannah belt of Nigeria are often less than the maximum obtainable yields because water deficit during critical growth stages is a common occurrence.
Random forest modeling explained up to 63% of the Rim fire burn variance using seven predictors: time since previous fire, actual evapotranspiration (AET), climatic water deficit (Deficit), previous maximum burn severity, burning index, slope position, and solar radiation.
