Water-related Variables Research Articles

Predicting multi-annual green roof net ecosystem exchange using machine learning

Green roofs are an urban mitigation strategy to increase CO2 uptake by green infrastructure. Reliable quantification of multi-year net ecosystem exchange (NEE) is essential for evaluating carbon balance, but proves challenging due to lack of long-term data and transferability of models. Machine learning can effectively predict NEE and identify non-linear patterns for spatially and temporally upscaling. For the first time, we developed Random Forest (RF) models using long-term eddy covariance (EC) data from 2015 to 2020 from an extensive green roof at Berlin-Brandenburg Airport (BER) to understand the influence of meteorological predictors on NEE prediction and transferability. A simple (M1), extended (M2) and optimized (M3) model based on different combinations of predictors were built. M3 performed best, deviating by −13 % from the observed uptake of −132.4 gC m−2 a−1 (R2 of 0.74), with M2 (R2 = 0.73) showing similar results. Key predictors were volumetric water content (VWC) and solar radiation flux densities. The models showed robust performance under pronounced environmental conditions. Drought in 2018 introduced significant uncertainties, whereas higher VWC in 2019 and 2020 led to enhanced performance. All models tended to overestimate CO2 assimilation and underestimate respiration of the green roof. M1 deviated by −58 % from observed NEE over the entire period, indicating transferability of M1 to other locations is not feasible, whereas transferability of M2 and M3 showed better potential for broader application with minimal calibration. This study highlights the importance of water-related variables and available energy and demonstrates that RF, incorporating NEE process understanding, can effectively predict NEE.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region

Abstract. Streamflow variability plays a crucial role in shaping the dynamics and sustainability of Earth's ecosystems, which can be simulated and projected by a river routing model coupled with a land surface model. However, the simulation of streamflow at large scales is subject to considerable uncertainties, primarily arising from two related processes: runoff generation (hydrological process) and river routing (hydraulic process). While both processes have impacts on streamflow variability, previous studies only calibrated one of the two processes to reduce biases in the simulated streamflow. Calibration focusing only on one process can result in unrealistic parameter values to compensate for the bias resulting from the other process; thus other water-related variables remain poorly simulated. In this study, we performed several experiments with the land and river components of the Energy Exascale Earth System Model (E3SM) over the Pantanal region to disentangle the hydrological and hydraulic controls on streamflow variability in coupled land–river simulations. Our results show that the generation of subsurface runoff is the most important factor for streamflow variability contributed by the runoff generation process, while floodplain storage effect and main-channel roughness have significant impacts on streamflow variability through the river routing process. We further propose a two-step procedure to robustly calibrate the two processes together. The impacts of runoff generation and river routing on streamflow are appropriately addressed with the two-step calibration, which may be adopted by developers of land surface and earth system models to improve the modeling of streamflow.

Predictive ecological niche modelling of an important bio-control agent: Trichoderma harzianum (Rifai) using the MaxEnt machine learning tools with climatic and non-climatic predictors

ABSTRACT Ecological niche model (ENM) pertains to a class of methodologies that utilise occurrence data alongside environmental data to formulate a correlative model of the environmental circumstances that satisfy a species’ ecological requirements. In the current study, ENM was employed to ascertain the types of habitat for Trichoderma harzianum using machine learning algorithm known as MaxEnt Entropy. Our line of reasoning posits that the efficacy of T. harzianum as a bio-control agent can be enhanced, alongside the advancement of host/crop development and metabolic processes, through its deliberate introduction into geographically appropriate habitats. ENM was performed on 92 spatially thinned presence points of this species across India, considering three bio-climatic time periods (present, 2050, and 2070) and four greenhouse gas scenarios (known as representative concentration pathways RCPs). Non-bioclimatic factors include ecosystem rooting depths (ERD), total plant available water storage capacity (TPAWSC), habitat heterogeneity indices (HHI), land use land cover (LULC) and to soil variables at four depths. Energy-related factors, like Isothermality and minimum temperature of coldest month, were shown to be the most essential for the habitat appropriateness of this species during the current bio-climatic period. Future climate predictions and their associated RCPs revealed that water-related variables, like precipitation of wettest quarter, were the most influential. Non-climatic elements that were shown to have significant impact included soil pH, maximum diversity indices, forest and grassland types, TPAWSC, ERD (95%). Our analysis showed that this species will always find optimal suitability sites in northern eastern India with almost all predictors except root zone variables.

An explainable artificial intelligence approach for mud pumping prediction in railway track based on GIS information and in-service train monitoring data

Automatic and timely identification of mud pumping is important for the reliability and safety of railroads. The current mud pumping prediction model is based on monitoring the dynamic response of railway tracks. The essential geotechnical trigger factors such as the hydrological conditions are not well-considered in these prediction models, as that information is hard to be quantified, which unavoidably reduces the accuracy of the prediction. This paper proposes to utilize the Geographic Information System (GIS) to quantify the hydrological information along railway tracks. Through GIS analysis, the hydrological variables including elevation, near-river distance, rainfall, sink depth, and soil types are estimated and combined with in-service train monitoring data for model development. To deal with multi-attribute data, a dual-channel neural networks model is proposed to separately mine the characteristics in different attributes data for prediction. To further understand the prediction model, Shapely addictive explanations (SHAP) method is applied to estimate the importance of hydrological variables and reveal the possible relationships between the variables and the probability of mud pumping. The proposed approach is applied to a real-life case from the railway tracks in Australia to validate its effectiveness. The prediction results show that the proposed approach can predict mud pumping with balanced accuracy of 90.84%. The results confirm that integrating of GIS information and monitoring data can generate more accurate prediction and reduce the false prediction rate. Based on the explainable results, it is observed that rainfall is the most important hydrological variable that influences mud pumping occurrence. Apart from rainfall, groundwater-related variables show a greater impact on mud pumping occurrence than surface water-related variables. The explainable results also can help infrastructure managers to identify the most vulnerable sections in railway tracks, which facilitates targeted maintenance planning and track substructure design.

Recovery of ecosystem carbon and water fluxes after drought in China

Drought recovery time is a crucial indicator of ecosystem resilience. However, existing drought recovery quantification methods have some limitations in terms of drought identification and selection of recovery factors. In addition, drought recovery time in China and related impacts on different landcover types are largely unknown. Therefore, the present study sought to develop a new drought recovery quantification system that considers recovery of ecosystem carbon- and water-related variables in China across seasons (wet and dry) and landcover types. The results showed that drought conditions worsened, especially in northern and western China, from 1982 to 2016. Drought had a strong effect on soil water storage (SWS), with runoff and SWS decreasing in more than 65% of the study area after drought. Drought led to decreases in gross primary productivity (GPP) and water use efficiency (WUE) over 58.7% and 45.2% of the total area, respectively. The mean drought recovery time of SWS (10.4 months) was much longer than that of GPP (3.2 months), WUE (3.7 months), and runoff (2.5 months), especially in northern China. Drought-reduced carbon and water needed more time to recover from drought in grasslands than in other landcover types due to the lower water availability and high evaporative demand. The mean recovery time after dry season drought was longer than that after wet season drought. In a comparison of recent (2001–2016) and past (1982–2000) periods, increased recovery times for runoff and SWS were observed for all landcover types except for SWS (-0.4 month) in grasslands. However, recent recovery times for GPP and WUE were shorter than that in the past. Highly decreased recovery times for GPP (-2.2 months) and WUE (-2.4 months) were found in grasslands. Runoff recovery time increased by 4.9 months in forest and SWS recovery time increased by 1.4 months in cropland. These results improved our understanding of regional drought recovery mechanisms and future trends in different landcover types.

Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data?

ABSTRACT The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.

COVID-19 and Water Variables: Review and Scientometric Analysis.

COVID-19 has changed the world since 2020, and the field of water specifically, boosting scientific productivity (in terms of published articles). This paper focuses on the influence of COVID-19 on scientific productivity with respect to four water variables: (i) wastewater, (ii) renewable water resources, (iii) freshwater withdrawal, and (iv) access to improved and safe drinking water. The field's literature was firstly reviewed, and then the maps were built, emphasizing the strong connections between COVID-19 and water-related variables. A total of 94 countries with publications that assess COVID-19 vs. water were considered and evaluated for how they clustered. The final step of the research shows that, on average, scientific productivity on the water topic was mostly conducted in countries with lower COVID-19 infection rates but higher development levels as represented by gross domestic product (GDP) per capita and the human development index (HDI). According to the statistical analysis, the water-related variables are highly significant, with positive coefficients. This validates that countries with higher water-related values conducted more research on the relationship with COVID-19. Wastewater and freshwater withdrawal had the highest impact on the scientific productivity with respect to COVID-19. Access to safe drinking water becomes insignificant in the presence of the development parameters.

Latitudinal gradients of α- and β-diversity of aquatic plant communities across eastern China: Helophytes and hydrophytes show inconsistent patterns

The decrease of diversity from the equator to the poles is one of the most well-established patterns in ecology. However, there are exceptions to this general pattern and some groups have not been well studied, especially for the taxonomic and phylogenetic diversity of aquatic plants along latitudinal gradients. Using the field survey data of 152 sampling sites across eastern China, we analyzed the α- and β-diversity-latitude relationships for the total 217 aquatic plant species, as well as for their two main components, 149 helophytes and 68 hydrophytes. We also explored the relationships between diversity measures and two types of climatic variables (energy- and water-related variables). We found that the latitudinal gradient of α-diversity of aquatic plants is a hump-shaped pattern with the highest peak at 45°N. The net relatedness index of hydrophytes is negatively correlated with mean annual temperature and annual precipitation, which is consistent with the prediction of the “tropical niche conservatism” hypothesis. The β-diversity is lowest in the warm temperate zone, which is located in the middle latitudes of our study area. The latitudinal patterns of aquatic plant α- and β-diversity are different between helophytes and hydrophytes. Energy-related climatic variables could more affect the diversity of hydrophytes, while water-related climate variables could more affect the diversity of helophytes. Thus, compared with hydrophytes, helophytes are more similar to terrestrial plants, which are more affected by water-related climatic varibales. This work provided the ideas for further understanding the composition of aquatic plant community and the relationship between its diversity with latitude and climatic variables.

Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods

• We assessed the potential of multi-source data of wheat yield prediction in China. • Large spatial divergences for yield prediction in three wheat planting zones. • Water-related variables outperformed other climatic predictors for yield prediction. • SIF had better predictability for yield than traditional vegetation indices did. • Climate data provided unique predicted information beyond satellite data. Early and reliable crop yield prediction on a large scale is imperative for making in-season crop management decisions as well as for ensuring global food security. The integrated use of climate and remote sensing data for predicting yield at regional and national scales has been previously investigated in various parts of the world. However, such attempts for national scale yield prediction, particularly in different planting zones in China have been rarely reported. For this purpose, this study explored the potential of nine climate variables, three remote sensing-derived metrics, and three machine learning methods (random forest, support vector machine, and least absolute shrinkage and selection operator) for predicting wheat yield based on data acquired during 2002–2010 from 1582 counties across China’s three wheat planting zones. Our results illustrated large spatial divergences for yield prediction. The best performance (R 2 = 0.79 and R 2 = 0.66) was achieved for the northern winter wheat and northern spring wheat planting zones, respectively. Water-related climatic variables outperformed temperature-related variables, with the best individual predictive performance (R 2 = 0.67). Solar-induced chlorophyll fluorescence had better performance (R 2 = 0.60) for predicting the crop yield than NDVI and EVI. Climate data across the whole growing season has provided additional information for yield prediction as compared to remote sensing data. The additional contribution for yield prediction in winter wheat planting zones benefiting from climate data decreased from sowing to maturity, which was the opposite in remote sensing data. Typically, the support vector machine outperformed other models and the prediction in winter wheat planting zones performed better than the spring wheat planting zone. Our study demonstrates the effectiveness of integrating climate and remote sensing data for accurate county-level yield prediction in China. These kinds of simple and scalable machine learning methods could be targeted for further work by agricultural researchers and advisors.

Temporal Patterns of Bacterial and Viral Communities during Algae Blooms of a Reservoir in Macau.

Compositions of microbial communities associated with blooms of algae in a storage reservoir in Macau, China were investigated between 2013 and 2016. Algae were enumerated by visible light microscopy. Profiles of organisms in water were examined by 16S rRNA sequences and viral metagenomics, based on next generation sequencing. Results of 16S rRNA sequencing indicated that majority of the identified organisms were bacteria closely related to Proteobacteria, Cyanobacteria, Verrucomicrobia, Bacteroidetes, and Actinobacteria. Metagenomics sequences demonstrated that the dominant virus was Phycodnavirus, accounting for 70% of the total population. Patterns of relative numbers of bacteria in the microbial community and their temporal changes were determined through alpha diversity indices, principal coordinates analysis (PCoA), relative abundance, and visualized by Venn diagrams. Ways in which the bacterial and viral communities are influenced by various water-related variables were elucidated based on redundancy analysis (RDA). Relationships of the relative numbers of bacteria with trophic status in a reservoir used for drinking water in Macau, provided insight into associations of Phycodnavirus and Proteobacteria with changes in blooms of algae.

Tree recruitment is determined by stand structure and shade tolerance with uncertain role of climate and water relations.

Tree regeneration is a key process for long‐term forest dynamics, determining changes in species composition and shaping successional trajectories. While tree regeneration is a highly stochastic process, tree regeneration studies often cover narrow environmental gradients only, focusing on specific forest types or species in distinct regions. Thus, the larger‐scale effects of temperature, water availability, and stand structure on tree regeneration are poorly understood.We investigated these effects in respect of tree recruitment (in‐growth) along wide environmental gradients using forest inventory data from Flanders (Belgium), northwestern Germany, and Switzerland covering more than 40 tree species. We employed generalized linear mixed models to capture the abundance of tree recruitment in response to basal area, stem density, shade casting ability of a forest stand as well as site‐specific degree‐day sum (temperature), water balance, and plant‐available water holding capacity. We grouped tree species to facilitate comparisons between species with different levels of tolerance to shade and drought.Basal area and shade casting ability of the overstory had generally a negative impact on tree recruitment, but the effects differed between levels of shade tolerance of tree recruitment in all study regions. Recruitment rates of very shade‐tolerant species were positively affected by shade casting ability. Stem density and summer warmth (degree‐day sum) had similar effects on all tree species and successional strategies. Water‐related variables revealed a high degree of uncertainty and did not allow for general conclusions. All variables had similar effects independent of the varying diameter thresholds for tree recruitment in the different data sets.Synthesis: Shade tolerance and stand structure are the main drivers of tree recruitment along wide environmental gradients in temperate forests. Higher temperature generally increases tree recruitment rates, but the role of water relations and drought tolerance remains uncertain for tree recruitment on cross‐regional scales.

Psychological and Situational Variables Associated with Objective Knowledge on Water-Related Issues in a Northern Spanish City.

This study brings together the level of objective knowledge on water-related issues and other variables of psychological and situational nature. A random sample of 459 participants was employed, selected proportionally based on sex and age. In this sample, knowledge on the water-related issues tended to be low, particularly related to the direct source of water in the household, the type of services involved in the management, and consumption itself. In order to understand both the relationship with knowledge on water and the relative importance of all the other factors, a regression model was formulated. The highest standardised effect was for sex, followed by occupation, political leaning, and water-related emotions. The best level of knowledge was attained if the residents were male, if they were actively employed or unemployed, if their political leaning was towards the left, and if they demonstrated greater emotional involvement with the water use. Consequently, the design of programmes would need to consider that the information flow must be greater for citizens as a whole, particularly for certain groups such as women and students. It should contribute to the realistic perception of water as a problem and to seek emotional involvement.

Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration ( R s) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with R 2 of 0.72 and RMSE of 0.96 g·C·m−2·day−1. The performance was also good when predicting across-site ( R 2 = 0.75) and seasonal patterns ( R 2 = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and R s. This study indicates the importance of considering the influence of water-related variables and R s in the estimation of NEE at the global scale.

A diagnostic dashboard to evaluate country water security

Abstract While water security is widely regarded as an issue of global significance and concern, there is not yet a consensus on a methodology for evaluating it. The difficulty in operationalizing the concept comes from its various interpretations and characteristics at different spatial and temporal scales. In this paper, we generate a dashboard comprised of 52 indicators to facilitate a rapid assessment of a country's water security and to focus the first step of a more comprehensive water security diagnostic assessment. We design the dashboard around a conceptualization of water security that builds upon existing framings and metrics. To illustrate its usefulness, we apply the dashboard to a case study of Pakistan and a regional cross-country comparative analysis. The dashboard provides a rapid view of the water security status, trends, strengths, and challenges for Pakistan. The cross-country comparative analysis tentatively identifies relationships between indicators such as water stress and the transboundary dependency ratio, with countries exhibiting high values in both variables being especially vulnerable to transboundary water risk. Overall, this dashboard (1) provides quantitative information on key water-related variables at the country level in a consistent manner and (2) helps to design and focus more in-depth water security diagnostic studies.

DETERMINANTS OF VIRTUAL WATER TRADE OF CEREAL CROPS IN SAUDI ARABIA

In this research, we used a gravity model to investigate whether water scarcity variables influence agricultural trade of cereal crops for Saudi Arabia. We compare the OLS, Fixed effects, Random effects, and Poisson Pseudo-Maximum Likelihood (PPML) estimators to determine the best model. The AIC, and multicollinearity, heteroskedasticity, and autocorrelation tests assist in determining estimation procedures and the final model. We cluster the errors by distance to improve the specific country effect variables, such as economic mass. We find that water-related variables influence virtual water imports of cereals, millet, corn, barely, and sesame.

Investigating the role of urbanisation, wetlands and climatic conditions in nematode parasitism in a large Australian elapid snake.

Tiger snakes (Notechis scutatus) in wetlands of South-West Western Australia (SW WA) are commonly parasitised by the nematode Ophidascaris pyrrhus. Host-parasite interactions are complex and can potentially be impacted by factors such as urbanisation or climate. We assessed whether urbanisation, distance to wetland sites, and climatic factors have influenced parasitism in tiger snakes from specimens collected over the last century. We dissected 91 museum specimens of tiger snakes across SW WA and counted gastrointestinal nematodes. Binomial generalised linear modelling, with presence/absence of nematodes as a response variable, was used to determine which factors were driving infection. Model selection using AICc values showed that proximity to wetlands, rainfall and topographic wetness were most strongly associated with the probability of infection of snakes by nematodes. We also found a slight positive correlation between nematode abundance and annual mean maximum temperature. We found no significant influence of distance to urban centre on nematode burdens; however, our results suggest that water-related variables are a key driver of nematode parasitism in tiger snakes in SW WA. We also suggest that urbanisation is still of interest as its role in wetland and climate modification may increase parasitism in wetland snakes.

Modelling carbon and water balance of Eucalyptus plantations at regional scale: Effect of climate, soil and genotypes

Carbon and water budgets of forest plantations are spatially and temporally variable and hardly empirically predictable. We applied G’DAY, a process-based ecophysiological model, to simulate carbon and water budgets and stem biomass production of Eucalyptus plantations in São Paulo State, Brazil. Our main objective was to assess the drivers of spatial variability in plantation production at regional scale. We followed a multi-site calibration approach: the model was first parameterized using a detailed experimental dataset. Then a subset of the parameters were re-calibrated on two independent experimental datasets. An additional genotype-specific calibration of a subset of parameters was performed. Model predictions of key carbon-related variables (e.g., gross primary production, leaf area index and stem biomass) and key water-related variables (e.g., plant available water and evapotranspiration) agreed closely with measurements. Application of the model across ca. 27,500 ha of forests planted with different genotypes of Eucalyptus indicated that the model was able to capture 89% of stem biomass variability measured at different ages. Several factors controlling Eucalyptus production variability in time and space were grouped in three categories: soil, climate, and the planted genotype. Modelling analysis showed that calibrating the model for genotypic differences was critical for stem biomass prediction at regional scale, but that taking into account climate and soil variability significantly improved the results. We conclude that application of process-based models at regional scale can be used for accurate predictions of Eucalyptus production, provided that an accurate calibration of the model for key genotype-specific parameters is conducted.

Spatial Patterns and Determinants of the Diversity of Hemipteran Insects in the Qinghai-Tibetan Plateau

Large-scale patterns of species richness is an important issue in biogeography and ecology. The Qinghai-Tibetan Plateau (QTP) is a biodiversity hotspot in the world, which has an important status in the zoogeographical realms. Here, we analyzed the diversity patterns of Hemipteran insects in the QTP, and tested whether the patterns can be jointly explained by modern environmental as well as historical factors. A comprehensive geographic distribution dataset consisting of 1166 Hemipteran species, which belong to 510 genera and 53 families, was compiled and used in our analyses. Patterns of richness were mapped into a grid-based map with a spatial resolution of 0.5°x 0.5°. An unbalanced diversity pattern of the Hemipteran insects in the QTP was presented, with more species in the eastern and southern parts of the plateau, while few species in the northern and main surface of the plateau. The northwestern Sichuan, the southern Gansu, the southeastern Tibet, the northwestern Yunnan and the eastern Qinghai were identified as diversity hotspots of species richness. Further analyses based on General linear models and Random Forest indicated that the diversity patterns of Hemipteran insects were influenced by both contemporary environmental factors and historical factors (e.g. habitat heterogeneity, climate stability, energy availability). Specifically, the species richness patterns of all Hemipteran insects in the QTP have been mainly affected by elevation range, temperature annual range, min temperature of coldest month, mean temperature of coldest quarter and the temperature change since the Last Glacial Maximum. In contrast, the water-related variables have relatively small effects on species richness. In addition, although habitat heterogeneity was indicated the most important factor for different suborders of Hemiptera, the climate stability was another dominate factor for Heteroptera and Auchenorrhyncha, while Sternorrhyncha was more affected by historical climate change.

Water-Related Variables for Predicting Yield of Apple under Deficit Irrigation

Predicting apple yield in relation to tree water use is important for irrigation planning and evaluation. The aim of the present study was to identify measurable variables related to tree water use that could predict final fruit yield of apple trees under different strategies of deficit irrigation. Adult ‘Gala’ and ‘Fuji’ apple trees were exposed to conventional irrigation (CI), delivering 100% of crop evapotranspiration; partial root zone drying (PRD), delivering 50% of CI water only on one alternated side of the root-zone; and continuous deficit irrigation (CDI), delivering 50% of CI water on both sides of the root-zone. Integrals of soil (SWDint) and leaf (LWSDint) water deficit along with growth and stomatal conductance (Gsint) were calculated across each season and used to estimate total conductance (GStree) and transpiration (Trtree) per tree, transpiration efficiency on a fruit (GRfruit/Tr) or tree (GRtrunk/Tr) growth basis, and transpiration productivity (Yield/Trtree). ‘Fuji’ trees had higher Yield/Trtree, but had lower GRtrunk/Tr and similar GRfruit/Tr compared to ‘Gala’ trees. In ‘Fuji’, CDI reduced yield, trunk growth, leaf hydration, and gas exchange, while in ‘Gala’, it did not reduce yield and gas exchange. In ‘Fuji’, a linear combination of GRtrunk/Tr, GRfruit/Tr, and Gstree contributed to predicting yield, with GRfruit/Tr explaining nearly 78% of the model variability. In ‘Gala’, a linear combination of LWSDint and Gstree contributed to predicting yield, with Gstree explaining over 79% of the model variability. These results indicate that measuring tree water status or water use may help predict final apple yields only in those cultivars like ‘Gala’ that cannot limit dehydration by closing stomates because of carbon starvation. In more vigorous cultivars like ‘Fuji’, transpiration efficiency based on fruit growth can be a powerful predictor of final yields.