Relative Importance Of Climatic Variables Research Articles

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014)

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

On the rocks: Biogeography and floristic identity of rocky ecosystems in eastern South America

AbstractThe geodiversity of rocky ecosystems includes diverse plant communities with specific names, but their continental‐scale floristic identity and the knowledge on the role of macroclimate remain patchy. Here, we assessed the identity of plant communities in eastern Brazil across multiple types of rocky landscapes and evaluated the relative importance of climatic variables in constraining floristic differentiation. We provided lists of diagnostic species and an assessment of the conservation status of the identified floristic groups. We compiled a data set of 151 sites (4498 species) from rocky ecosystems, including campos rupestres, campos de altitude, granitic‐gneiss lowland inselbergs, and limestone outcrops. We used unsupervised clustering analysis followed by ANOSIM to assess floristic groups among sites. We performed a random forest variable selection to test whether the identified floristic groups occupy distinct climatic spaces. Six groups (lithobiomes) segregated floristically according to lithology and climate. Alongside campos de altitude and limestone outcrops, inselbergs were divided according to the biome in which they occur (Atlantic Forest or Caatinga), and campos rupestres were largely segregated according to their lithological matrix (ironstone or quartzitic). Plant communities of Caatinga inselbergs were more similar to limestone outcrops, while Atlantic Forest inselbergs communities resembled campos de altitude. The composition of plant communities on outcrops seems to be largely constrained by lithology, but climatic factors are also meaningful for sites with similar lithology. The current network of protected areas does not cover these unique ecosystems and their floristic heterogeneity, with Caatinga inselbergs and limestone outcrops being the least protected.

The effect of climate on the occurrence and abundance of tree recruitment in the province of Quebec, Canada

Abstract Tree recruitment is affected by numerous biotic and abiotic factors, including climate. However, the relative importance of climate variables in empirical models of tree recruitment remains to be evaluated. We fitted models of tree recruitment to 26 species in the province of Quebec, Canada. For a better understanding of the recruitment process, we used a two-part model to distinguish recruitment occurrence from abundance. The relative importance of the different variables was assessed using Akaike weights. Our main hypothesis was that climate is one of the major drivers of tree recruitment. Our results showed that growing degree-days counted among the major drivers of recruitment occurrence but not of recruitment abundance. Stand variables, such as the presence and abundance of adult trees of the species, and broadleaved and coniferous basal areas were found to be relatively more important than all the climate variables for both recruitment occurrence and abundance. Species occupancy within a 10-km radius also had a significant effect on recruitment occurrence for two-thirds of the species, but it was less important than growing degree-days and other stand variables. Climate change is expected to improve the suitability of habitats located at the northern edge of species distributions. However, our model predictions point to a low probability of colonization in newly suitable habitats in the short term.

High resistance of deciduous forests and high recovery rate of evergreen forests under moderate droughts in China

The resistance and recovery rate of forest ecosystems to droughts vary with the severity of the drought. Studies on the impacts of severe droughts on forest ecosystems have suggested that evergreen forests have higher resistance than deciduous forests. However, whether the resistance and recovery rate of forest ecosystems vary under moderate droughts remained largely unknown. Here, we used the Standardized Precipitation Index to identify drought characteristics in China’s forest ecosystems from 2000 to 2018. We quantified the resistance and recovery rate to moderate droughts under different drought timings based on the Enhanced Vegetation Index. We then adopted random forest regression to evaluate the relative importance of climatic variables and species richness as drivers of resistance and recovery rate to moderate droughts. We found that China’s forest ecosystems mainly experienced moderate drought events, resulting in different resistance and recovery rate compared to severe droughts. Deciduous forests had high resistance (30.39 ± 21.32) and evergreen forests had high recovery rate (1.38 ± 1.10) due to their different response strategies under moderate droughts. The resistance and recovery rate of China’s forest ecosystems varied under different drought timings. The differences between deciduous and evergreen forests’ resistance (43.88 – 78.24 %) and recovery rate (-31.04 – −52.20 %) were remarkable in spring, autumn, and winter. In evergreen and deciduous forests, climatic variables were the main influencing factors of resistance and recovery rate to moderate droughts, similar to the leading role of climatic variables under severe droughts. Global radiation (41.06 %) and temperature (30.97 %) were the dominant factors contributing to resistance, whereas the recovery rate was primarily explained by global radiation (40.30 %) and precipitation (27.33 %). Thus, the high resistance in deciduous forests and the high recovery rate in evergreen forests under moderate droughts suggest that evergreen and deciduous forests can be mixed to improve the stability of forest ecosystems under climate change.

Mineral and Climatic Controls Over Soil Organic Matter Stability Across the Tibetan Alpine Permafrost Region

AbstractPermafrost thaw could accelerate microbial decomposition, lead to greenhouse gases emissions into the atmosphere, and thus trigger a positive feedback to climate warming. As a key parameter reflecting the resistance of soil organic matter (SOM) to be decomposed by microorganisms, SOM stability may determine the strength of permafrost carbon (C)‐climate feedback. Adequate understanding of patterns and drivers of SOM stability can thus contribute to predicting permafrost C cycle and its feedback to climate change. However, due to limited observations, it remains unknown whether biochemical selectivity or physico‐chemical protection dominates SOM stability in permafrost regions. By combining large‐scale soil sampling, thermal analysis and random forest model, we quantified SOM stability in the top 10 cm using thermogravimetry and differential scanning calorimetry, and explored its spatial patterns across the Tibetan alpine permafrost region. We then constructed structural equation model to evaluate the relative importance of climatic variables, edaphic properties, substrate quality, and mineral variables in regulating SOM stability over a broad geographic scale. Our results indicated that SOM stability exhibited an increasing tendency from the southeastern to northwestern plateau. The stronger SOM stability was associated with higher mineral‐organic associations and more arid conditions. By contrast, substrate quality had limited effects on SOM stability. Overall, these results provide large‐scale evidence for the physico‐chemical protection hypothesis, highlighting the importance of considering mineral variables in Earth system models to better predict soil C dynamics across permafrost regions.

Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands

Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.

Spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May–September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.

Distribution Pattern of Gymnosperms' Richness in Nepal: Effect of Environmental Constrains along Elevational Gradients.

Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms’ richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern.

Does recent climate warming drive spatiotemporal shifts in functioning of high-elevation hydrological systems?

Reference evapotranspiration (ETo) is the basic component of the hydrological cycle. This study systematically analyzed the spatiotemporal variations of ETo in the Three-River Headwaters Region (TRHR) by utilizing the FAO Penman–Monteith equation during the period of 1961–2016, and studied the impact of climatic variables on ETo fluctuation based on sensitivity analysis and contribution analysis. Over the past 56 years, ETo increased in most months and annually in most regions of the TRHR, and the spatial distribution for annual ETo varied apparently. Sensitivity analysis revealed that ETo responded more to changes in maximum temperature and sunshine duration than relative humidity, wind speed, and minimum temperature. The relative importance of climatic variables to ETo was influenced by the ETo sensitivity and the fluctuation of climatic variables. Contribution analysis indicated that the significant temperature increase was mainly responsible for the observed increase in ETo. Besides, spatially distributed analysis showed that the annual TRHR ETo tended to increase from east to west, north to south, and high to low elevation. The analysis also indicated a dominant altitudinal spatial gradient in the sensitivity of ETo to changes in different climatic variables. The physical mechanisms for ETo variations discussed in this study might provide a reference for water resources management and conservation of high-elevation areas.

Ecological consequences of shrub encroachment in the grasslands of northern China

Shrub encroachment is a critical environmental issue in arid and semi-arid landscapes. However, due to the complicated effects of shrub characteristics and environmental variables, ecological consequences of shrub encroachment significantly differ in different regions. As a result, although the phenomenon of shrub encroachment is widespread in the grasslands of northern China, its ecological consequences and associated drivers have not been well documented at the regional scale. We explored the effects of shrub encroachment on ecosystem structures (herb richness and abundance) and functioning (above- and belowground biomass, and soil carbon and nitrogen content) and evaluated the relative importance of climatic variables (temperature and precipitation) and shrub characteristics (cover, area, and height) as drivers of these effects. Based on field data from 53 sites that stretch across approximately 3000 km in northern China, we conducted the relative interaction intensity (RII) of the response variables between shrub patches and grassy matrix, and then used the structural equation modeling (SEM) to assess the relative importance of chosen drivers on the ecological consequences. Shrub encroachment increased the landscape heterogeneity by decreasing the richness, abundance and aboveground biomass of herbs, while by increasing the soil carbon and nitrogen content. The SEM explained 25–43% of the variation in shrub characteristics and 5–34% of the variation in the effects of shrub encroachment. Shrub height had greater effects on the RII of herb richness, soil carbon and soil nitrogen content, and the standardized total effects were 0.48, 0.25 and 0.18, respectively. Precipitation and temperature positively affected the impacts of shrub encroachment on herb richness, abundance, biomass, and multi-indexes. However, the direct effects of climate could be partially mediated by indirect effects via shrub characteristics. The height, size and cover of shrub patches significantly influenced the community structures and functioning of grassland ecosystems. The increasing aridity could potentially exacerbate the negative consequences of shrub encroachment in arid regions. Our results also demonstrated that the effects of climate variables on grassland ecosystems could be mediated by shrub management.

Incorporating biotic interactions reveals potential climate tolerance of giant pandas

AbstractMany studies have overestimated species’ range shifts under climate change because they treat climate as the only determinant while ignoring biotic factors. To assess the response of giant pandas to climate change, we incorporated spatial effects in modeling bamboo distributions, which in turn was incorporated to represent giant panda–bamboo biotic interactions in predicting giant panda distribution. Our study revealed potential tolerance of giant pandas to climate change. We found significant residual spatial correlation in the bamboo models. The biotic interactions with bamboo understories and anthropogenic activities had large effects on panda distribution, which lowered the relative importance of climatic variables. Our results are fundamentally different from previous studies that used climate‐only and nonspatial approaches, which may have overestimated the effects of climate change on panda and lead to inappropriate conservation recommendations. We strongly advocate that giant panda conservation planning continues to focus on protecting bamboo forest and reducing anthropogenic interferences.

Relative importance of climate changes at different time scales on net primary productivity-a case study of the Karst area of northwest Guangxi, China.

Climate changes are considered to significantly impact net primary productivity (NPP). However, there are few studies on how climate changes at multiple time scales impact NPP. With MODIS NPP product and station-based observations of sunshine duration, annual average temperature and annual precipitation, impacts of climate changes at different time scales on annual NPP, have been studied with EEMD (ensemble empirical mode decomposition) method in the Karst area of northwest Guangxi, China, during 2000-2013. Moreover, with partial least squares regression (PLSR) model, the relative importance of climatic variables for annual NPP has been explored. The results show that (1) only at quasi 3-year time scale do sunshine duration and temperature have significantly positive relations with NPP. (2) Annual precipitation has no significant relation to NPP by direct comparison, but significantly positive relation at 5-year time scale, which is because 5-year time scale is not the dominant scale of precipitation; (3) the changes of NPP may be dominated by inter-annual variabilities. (4) Multiple time scales analysis will greatly improve the performance of PLSR model for estimating NPP. The variable importance in projection (VIP) scores of sunshine duration and temperature at quasi 3-year time scale, and precipitation at quasi 5-year time scale are greater than 0.8, indicating important for NPP during 2000-2013. However, sunshine duration and temperature at quasi 3-year time scale are much more important. Our results underscore the importance of multiple time scales analysis for revealing the relations of NPP to changing climate.

SENSITIVITY ANALYSIS AND REGIONALIZATION OF REFERENCE EVAPOTRANSPIRATION FOR THE AMAZON REGION.

Sensitivity Analysis (SA) is important to understand the relative importance of climate variables in the reference evapotranspiration (ETo) computation. In this study, a sensitivity coefficient was used to predict ETo responses to disturbances of five climatic variables in the Amazonian Hydrographic Region - AHR (Brazilian Amazon). The ETo was estimated using the standardized equation of Penman-Monteith-FAO (PM-FAO). A 15-year meteorological data set of 38 surface meteorological stations were used in the study. An additional analysis was also presented to determine homogeneous regions of ETo by means of Cluster Analysis. The results showed that seven homogeneous sub-regions are sufficient to divide the AHR into different ETo patterns which were separated considering the intensity and the seasonal pattern of ETo. By the SA, the variables that contribute most to the computation of ETo using the PM-FAO method were the balance of radiation (Rn) and wind speed (u2). These results demonstrate that, in general, it should be emphasized to precise measures of insolation, since the precise estimation of Rn is directly associated with the measurement of this variable as well as of u2, which proved to be the second most influential variable in the ETo computation.

Relative Importance of Climate Variables to Population Vital Rates: A Quantitative Synthesis for the Lesser Prairie-Chicken

Climate change is expected to affect temperature and precipitation means and extremes, which can affect population vital rates. With the added complexity of accounting for both means and extremes, it is important to understand whether one aspect is sufficient to predict a particular vital rate or if both are necessary. To compare the predictive ability of climate means and extremes with geographic, individual, and habitat variables, we performed a quantitative synthesis on the vital rates of lesser prairie-chickens (Tympanuchus pallidictinus) across their geographic range. We used an information theoretic approach to rank models predicting vital rates. We were able to rank climate models for three vital rates: clutch size, nest success, and subadult/adult seasonal survival. Of these three vital rates, a climate model was never the best predictor even when accounting for potentially different relationships between climate variables and vital rates between different ecoregions. Clutch size and nest success were both influenced by nesting attempt with larger clutches and greater success for first nesting attempts than second nesting attempts. Clutch size also increased with latitude for first nesting attempts but decreased with latitude for second nesting attempts. This resulted in similar clutch sizes for first and second nest attempts at southern latitudes but larger clutches for first nest attempts than second nest attempts at northern latitudes. Survival was greater for subadults than adults, but there were few estimates of subadult survival for comparison. Our results show that individual characteristics and geographic variables are better for predicting vital rates than climate variables. This may due to low samples sizes, which restricted our statistical power, or lack of precision in climate estimates relative to microclimates actually experienced by individuals. Alternatively, relationships between climate variables and vital rates may be constrained by time lags or local adaptation.

Assessing the relative importance of climate variables to rice yield variation using support vector machines

Climate factors have distinct impacts on crop yields. Understanding the relative importance of these factors to crop yield variation could provide valuable information about crop planting and management under climate change condition for policymakers and farmers. The current study investigated the applicability of support vector machines (SVMs) in determining the relative importance of climate factors (mean temperature, rainfall, relative humidity, sunshine hours, daily temperature range, and rainy days) to yield variation of paddy rice in southwestern China. The SVM models were compared with traditional artificial neural networks and multiple linear regression. The performance accuracy was evaluated by mean absolute error (MAE), mean relative absolute error (MRAE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination (R2). Comparative results showed that SVMs outperformed artificial neural networks and multiple linear regression. The SVM with radial basis function performed best with MAE of 0.06 t ha−1, MRAE of 0.9 %, RMSE of 0.15 t ha−1, RRMSE of 2.23 %, and R2 of 0.94. The results showed that SVMs are suitable for determining the effects of climate on crop yield variability. The relative importance of the studied climate factors to rice yield variation was in order of sunshine hours > daily temperature range > rainfall > relative humidity > mean temperature > rainy days in the current study area.

Beyond annual and seasonal averages: using temporal patterns of precipitation to predict butterfly richness across an elevational gradient

1. Ecologists often make predictions about community richness and diversity using climate variables that include seasonal precipitation totals and mean daily temperatures. While means and totals can be effective predictors to a certain extent, the complexities of faunal–climate relationships might be over‐simplified through the use of coarse‐grained variables.2. The goal of this study was to investigate less commonly studied climate variables, including indices of intra‐annual variation in the timing and intensity of precipitation events that might be used to predict butterfly richness across an elevational gradient. Data from a long‐term, single‐observer dataset at four sites in California were examined with Bayesian model averaging and structural equation modelling. Species‐specific responses to climate were compared with community responses at each site.3. At lower elevations, it was found that the relative importance of climate variables shifted towards temporal patterns of precipitation, including the timing of the first storm event and the annual number of precipitation events. Heterogeneity among sites was apparent in the importance of specific weather variables, and temporal trends (across years) were detected for a small number of variables. Species‐specific results paralleled those obtained from analysis of species richness, thus suggesting a commonality of response to climate across site‐specific assemblages.4. Models were improved by inclusion of the Pacific Decadal Oscillation and El Niño‐Southern Oscillation indices, indicating that regional variables can profitably be included in faunal–climate relationship analyses. These results emphasise the need for researchers to examine climate variables beyond the most readily summarised means and totals.

Climatic effects on breeding grounds are more important drivers of breeding phenology in migrant birds than carry-over effects from wintering grounds

Long-distance migrants may be particularly vulnerable to climate change on both wintering and breeding grounds. However, the relative importance of climatic variables at different stages of the annual cycle is poorly understood, even in well-studied Palaearctic migrant species. Using a national dataset spanning 46 years, we investigate the impact of wintering ground precipitation and breeding ground temperature on breeding phenology and clutch size of 19 UK migrants. Although both spring temperature and arid zone precipitation were significantly correlated with laying date, the former accounted for 3.5 times more inter-annual variation. Neither climate variable strongly affected clutch size. Thus, although carry-over effects had some impact, they were weaker drivers of reproductive traits than conditions on the breeding grounds.

Sensitivity of the reference evapotranspiration to key climatic variables during the growing season in the Ejina oasis northwest China

The standardized FAO56 Penman-Monteith model, which has been the most reasonable method in both humid and arid climatic conditions, provides reference evapotranspiration (ETo) estimates for planning and efficient use of agricultural water resources. And sensitivity analysis is important in understanding the relative importance of climatic variables to the variation of reference evapotranspiration. In this study, a non-dimensional relative sensitivity coefficient was employed to predict responses of ETo to perturbations of four climatic variables in the Ejina oasis northwest China. A 20-year historical dataset of daily air temperature, wind speed, relative humidity and daily sunshine duration in the Ejina oasis was used in the analysis. Results have shown that daily sensitivity coefficients exhibited large fluctuations during the growing season, and shortwave radiation was the most sensitive variable in general for the Ejina oasis, followed by air temperature, wind speed and relative humidity. According to this study, the response of ETo can be preferably predicted under perturbation of air temperature, wind speed, relative humidity and shortwave radiation by their sensitivity coefficients.

Spatiotemporal Characteristics of Reference Evapotranspiration and Its Sensitivity Coefficients to Climate Factors in Huang-Huai-Hai Plain, China

Climate change will have important implications in water shore regions, such as Huang-Huai-Hai (3H) plain, where expected warmer and drier conditions might augment crop water demand. Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation in reference evapotranspiration (ET0). In this study, the 51-yr ET0 during winter wheat and summer maize growing season were calculated from a data set of daily climate variables in 40 meteorological stations. Sensitivity maps for key climate variables were estimated according to Kriging method and the spatial pattern of sensitivity coefficients for these key variables was plotted. In addition, the slopes of the linear regression lines for sensitivity coefficients were obtained. Results showed that ET0 during winter wheat growing season accounted for the largest proportion of annual ET0, due to its long phenological days, while ET0 was detected to decrease significantly with the magnitude of 0.5 mm yr−1 in summer maize growing season. Solar radiation is considered to be the most sensitive and primarily controlling variable for negative trend in ET0 for summer maize season, and higher sensitive coefficient value of ET0 to solar radiation and temperature were detected in east part and southwest part of 3H plain respectively. Relative humidity was demonstrated as the most sensitive factor for ET0 in winter wheat growing season and declining relativity humidity also primarily controlled a negative trend in ET0, furthermore the sensitivity coefficient to relative humidity increased from west to southeast. The eight sensitivity centrals were all found located in Shandong Province. These ET0 along with its sensitivity maps under winter wheat-summer maize rotation system can be applied to predict the agricultural water demand and will assist water resources planning and management for this region.

C:N:P stoichiometry in Australian soils with respect to vegetation and environmental factors

We estimate organic carbon (C): total nitrogen (N): total phosphorus (P) ratios in soils under Australia’s major native vegetation groups. We use digital datasets for climate, soils, and vegetation created for the National Land and Water Resources Audit in 2001. Analysis-of-variance is used to investigate differences in nutrient ratios between ecosystems. Linear discriminant analysis and logistic regression are used to investigate the relative importance of climatic variables and soil nutrients in vegetation patterns. We find that the N:P and C:P ratios have a greater range of values than the C:N ratio, although major vegetation groups tend to show similar trends across all three ratios. Some apparently homeostatic groupings emerge: those with very low, low, medium, or high N:P and C:P. Tussock grasslands have very low soil N, N:P, and C:P, probably due to frequent burning. Eucalypt woodlands have low soil N:P and C:P ratios, although their total P level varies. Rainforests and Melaleuca forests have medium soil N:P and C:P ratios, although their total P level is different. Heathlands, tall open eucalypt forests, and shrublands occur on soils with low levels of total P, and high N:P and C:P ratios that reflect foliar nutrient ratios and recalcitrant litter. Certain plant communities have typical soil nutrient stoichiometries but there is no single Redfield-like ratio. Vegetation patterns largely reflect soil moisture but for several plant communities, eucalypt communities in particular, soil N and P (or N:P) also play a significant role. Soil N:P and the presence of Proteaceae appear indicative of nutrient constraints in ecosystems.