Combination Of Climatic Variables Research Articles

Reference evapotranspiration estimate with missing climatic data and multiple linear regression models.

The reference evapotranspiration (ETo) is considered one of the primary variables for water resource management, irrigation practices, agricultural and hydro-meteorological studies, and modeling different hydrological processes. Therefore, an accurate prediction of ETo is essential. A large number of empirical methods have been developed by numerous scientists and specialists worldwide to estimate ETo from different climatic variables. The FAO56 Penman-Monteith (PM) is the most accepted and accurate model to estimate ETo in various environments and climatic conditions. However, the FAO56-PM method requires radiation, air temperature, air humidity, and wind speed data. In this study in Adana Plain, which has a Mediterranean climate for the summer growing season, using 22-year daily climatic data, the performance of the FAO56-PM method was evaluated with different combinations of climatic variables when climatic data were missing. Additionally, the performances of Hargreaves-Samani (HS) and HS (A&G) equations were assessed, and multiple linear regression models (MLR) were developed using different combinations of climatic variables. The FAO56-PM method could accurately estimate daily ETo when wind speed (U) and relative humidity (RH) data were unavailable, using the procedures suggested by FAO56 Paper (RMSEs were smaller than 0.4 mm d-1, and percent relative errors (REs) were smaller than 9%). Hargreaves-Samani (A&G) and HS equations could not estimate daily ETo accurately according to the statistical indices (RMSEs = 0.772-0.957 mm d-1; REs (%) = 18.2-22.6; R2 = 0.604-0.686, respectively). On the other hand, MLR models' performance varied according to a combination of different climatic variables. According to t-stat and p values of independent variables for MLR models, solar radiation (Rs) and sunshine hours (n) variables had more effect on estimating ETo than other variables. Therefore, the models that used Rs and n data estimated daily ETo more accurately than the others. RMSE values of the models that used Rs were between 0.288 to 0.529 mm d-1; RE(%) values were between 6.2%-11.5% in the validation process. RMSE values of the models that used n were between 0.457 to 0.750 mm d-1; RE(%) values were between 9.9%-16.3% in the validation process. The models based only on air temperature had the worst performance (RMSE = 1.117 mm d-1; RE(%) = 24.2; R2 = 0.423).

Advancing research on compound weather and climate events via large ensemble model simulations

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.

Archetypal classification of vegetation dynamics of a humid subtropical forest region from North-East Argentina

With the aim of studying the primary productivity dynamics of subtropical forests with different degrees of intervention or change due to human intervention, we classify the ecosystems of an area in northeastern Argentina, corresponding to a humid subtropical region, according to their temporal variability. A 22-year time series, ranging from 2000 to 2022, of MODIS EVI was assembled into a spatiotemporal cube, and pixels were classified by an archetypal analysis applied to the frequency components of a Fourier power spectrum. Then the most representative pixels of this classification (archetypoids) were selected. A wavelet decomposition was performed on these archetypoids to identify temporal changes in the frequency composition of the time series, and an ARIMA model to identify changes in the noise pattern of the series. Finally, a distributed-lag model with meteorological variables, was applied to these time series to relate the dynamics of the archetypes to the local climate. A stepwise procedure using Gaussian processes for error and autocorrelation and multiple regressions to determine any univariate relationship between meteorological variables and EVI. The meteorological variables considered were temperature, rainfall, and potential evapotranspiration (PET). The procedure started with a null model with white noise, then Gaussian processes were gradually added to model the errors, and then the explanatory variables, which were filtered moving average time series of the meteorological variables. The interaction between the explanatory variables was assumed to be the minimum EVI of the predicted values of each variable according to Lieibig's law of minimum. The procedure was applied as the BIC decreased to find the optimal model. In this study area, three different archetypes were sufficient to describe most of the variability in the time series matrix. Archetype 1 was characterized by woody plantations of exotic species such as pines, yerba mate and tea, archetype 2 by native forests, and archetype 3 represented a mosaic of forests and agriculture/pasture. The analysis of climate and archetypes indicated that archetype 2 had the highest mean EVI values (i.e., primary productivity), followed by archetype 1 and lastly archetype 3. Also, it showed that all three archetypes responded to the same combination of climate variables (temperature and PET), with varying degrees of sensitivity to each variable. Archetype 2 was the least sensitive to changes in these variables, archetype 1 was more sensitive to PET, and archetype 3 was more sensitive to temperature, while also exhibiting the least response time.

Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

The increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for the scheduling and management of irrigation is thus urgently needed to adapt agricultural activities to the changing climate. The accurate estimation of reference crop evapotranspiration (ET0), a vital hydrological component of the water balance and crop water need, is a tiresome task if all the relevant climatic variables are unavailable. This study investigates the potential of four ensemble techniques for estimating precise values of the daily ET0 at representative stations in 10 agro-climatic zones in the state of Karnataka, India, from 1979 to 2014. The performance of these models was evaluated by using several combinations of climatic variables as inputs by using tenfold cross-validation. The outcomes indicated that predictions of ET0 by all four ensemble models based on all climatic variables were the most accurate in comparison with other input combinations. The random forest regressor was found to deliver the best performance among the four models on all measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, and mean absolute error, 0.011 mm/day). However, it incurred the highest computational cost, whereas the computational cost of the bagging model for linear regression was the lowest. The extreme gradient-boosting model delivered the most stable performance with a modified training dataset. The work here shows that these models can be recommended for daily ET0 estimation based on the users’ interests.

Geospatial and Machine Learning Driven Air Pollution Modelling Using Vehicular Load and Climatic Variables in Agbarho, Delta State, Nigeria

Modeling air pollution concentration is a key aspect of environmental quality assessment and ecological resources management. In-situ measurements of three pollutants VOC, CO, and NO2 mainly determined by a combination of climatic variables and vehicle load were collected during field campaigns in three phases. Also, meteorological data (temperature, wind speed, and precipitation rate) was collected from an online weather forecasting platform. Results from the mean values computed and graphs plotted showed Five junction which has the highest concentration of vehicular movement (50 vehicles every two minutes) and human activities have the highest mean concentration of 980 ppm and a standard error of 40.60 ppm for VOC and 11.04 ppm for CO and a standard error value of 0.41 ppm, NO2 concentration of 0.35 ppm. Express junction which has the next highest vehicular concentration (47 vehicles every 2 minutes) has the next ranked mean concentration of 713.91 ppm and a standard error value of 59.56 ppm, CO value of 8.05 ppm, and NO2 value of 1.88 ppm. Ekwere road along was the next in pollution concentration ranking for VOC with 524.18 ppm and a standard error value of 20.64 ppm. it also had a high CO mean concentration of 9.20 and a standard error value of 0.69 ppm. which was higher than the express junction. Ughughelli road had a VOC mean concentration of 426 ppm, a standard error value of 31.53 ppm, a CO value of 4.14 ppm, and a standard error of 0.32 ppm. CDI and Agofure park which has similar vehicular loads had values of VOC at 323.05 ppm and 327.08 ppm respectively. They also had CO values of 4.03 ppm and 3.29 ppm. Ekwere less residential which has the lowest vehicular and human presence was the least polluted, with a VOC mean concentration of 193.42 ppm, CO mean concentration of 0.25 ppm, and NO2 concentration of 0.14 ppm.

Climate and epiphytic macrolichen communities in the Four Corners region of the U.S.A.

We used data on epiphytic lichen communities in 1215, 0.4-ha plots in the Southwest U.S.A. collected by the Forest Inventory and Analysis (FIA) program to analyze relationships with climate. We sought the climate variables most strongly associated with differences in epiphytic macrolichen communities and described the nature of those relationships, including diversity, community composition, and patterns in individual species. Five lichen community groups were strongly related to temperature and elevation gradients, overall moisture, and summer rain. Lichen abundance was highest in the wettest groups and lowest in the hottest and driest groups. Warm summer monsoonal climates supported the greatest number of species across all plots and within plots. The monsoonal pattern did not occupy a discrete geographic area, but instead formed a gradient, strongest in the southern part of our study area, diminishing to the north and west. In contrast, hot summer monsoonal climates had much lower within-plot richness. Hot, dry climates had the most variation in species composition among plots, but the fewest species within each plot and across all plots. Lichen community gradients had nonlinear relationships with combinations of climate variables rather than strong linear relationships with any single variable, including those derivative climate variables meant to have direct biological relevance. Relationships between air quality and community gradients were weak, potentially overwhelmed by regional climatic variation and complex topographic gradients. Richness of particular functional groups was more strongly related to climate than was overall species richness; functional groups have their own climatic tolerances, owing to the physiological consequences of growth form and photobiont. Presumably species in different functional groups have experienced their own evolutionary tradeoffs, developing peak performance in different climates. On the other hand, overall richness was driven by an even more complex combination of performances relative to climate and was in some functional groups more strongly related to geographic coordinates than to climate variables. Because climatic variables are themselves geographically structured, stronger model fit for geographic coordinates than for climate implies some influence of large-scale historical factors (i.e., factors not clearly expressed in modern climates, such as past climates, vegetation structure, or disturbance regimes).

Estimation of the rice water footprint based on machine learning algorithms

It is essential to investigate the impact of climate change on the water footprint (WF) of rice from both historical simulation and future projections. In this study, four machine learning (ML) models, including random tree (RT), random forest (RF), additive regression (AR) and reduced error pruning tree (REPT) were used to model blue and green water footprint (BWFP and GWFP) for the present and future stages in the Yunnan Province, southwest China. Climate variables of daily precipitation, temperature, solar radiation, sunshine hours, wind speed, relative humidity and vapor pressure deficit data, yield and sown areas of rice were collected form 16 districts from 1990 to 2018. Six different scenarios (Sc1-Sc6) with different combinations of climate variables, crop coefficient and sown areas were used as inputs of the ML models for each of blue and green water footprint. Also, future climate projections of maximum (Tmax) and minimum (Tmin) temperatures, precipitation and sunshine were adopted for two different emission scenarios, RCP 4.5 and 8.5 from 2021to 2050, based on Geophysical Fluid Dynamics Laboratory (GFDL-ESM2M) model. For BWFP, the RT model in Sc1 with inputs of solar radiation, humidity, and vapor pressure deficit, was superior to the other scenarios with root mean square error (RMSE) and mean average percentage error (MAPE) values of 11.82 (m3 ton-1) and 0.5%, respectively. Sc4 (sown area, Tmin, sunshine hours) and Sc5 (sown area, Tmin, Tmax, crop coefficient) were the best two scenarios for all models applied for BWFP, while addition of precipitation to these two scenarios were the best for GWFP. Further, GWFP was highly negative anomaly in 2011 by 46%, 34% and 32% for the districts 5, 1 and 2 respectively, followed by 2010. Predictions of variable trends in the future showed that crop evapotranspiration during the growing season would increase in all the districts, although effective precipitation (Peff) would have both decreasing and increasing trends. Furthermore, BWFP and GWFP will have an upward trend in the future based on RCP4.5 in Sc4. This investigation addresses water footprint prediction which may assist in mitigation plans such as policies for sustainable water-use and development plans for food security.

Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms

Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980–2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.

Which oak provenances for the 22nd century in Western Europe?Dendroclimatology in common gardens.

The current distribution area of the two sympatric oaks Quercus petraea and Q. robur covers most of temperate Western Europe. Depending on their geographic location, populations of these trees are exposed to different climate constraints, to which they are adapted. Comparing the performances of trees from contrasting populations provides the insight into their expected resilience to future climate change required for forest management. In this study, the descendants of 24 Q. petraea and two Q. robur provenances selected from sites throughout Europe were grown for 20 years in three common gardens with contrasting climates. The 2420 sampled trees allowed the assessments of the relationship between radial growth and climate. An analysis of 15-year chronologies of ring widths, with different combinations of climate variables, revealed different response patterns between provenances and between common gardens. As expected, provenances originating from sites with wet summers displayed the strongest responses to summer drought, particularly in the driest common garden. All provenances displayed positive significant relationships between the temperature of the previous winter and radial growth when grown in the common garden experiencing the mildest winter temperatures. Only eastern provenances from continental cold climates also clearly expressed this limitation of growth by cold winter temperatures in the other two common gardens. However, ecological distance, calculated on the basis of differences in climate between the site of origin and the common garden, was not clearly related to the radial growth responses of the provenances. This suggests that the gradient of genetic variability among the selected provenances was not strictly structured according to climate gradients. Based on these results, we provide guidelines for forest managers for the assisted migration of Quercus petraea and Q. robur provenances.

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.

Advanced machine learning model for better prediction accuracy of soil temperature at different depths.

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

Assessing Climate Change Impacts on Human‐Perceived Temperature Extremes and Underlying Uncertainties

AbstractIt has been suggested that global warming impacts on human thermal comfort will cause an increase in the heat stress and a decrease in the cold stress in the future. A recent study has shown elevated increases in human‐perceived equivalent temperature (HPET) by using a single index for summer and winter seasons (Li et al., 2018, https://doi.org/10.1038/s41558‐017‐0036‐2). However, they have not considered multiple indices with combined effects on deriving HPET, which can result in large uncertainties in assessing climate change impacts on HPET and related extremes. Therefore, we develop a new framework with high‐resolution projections and an ensemble of 10 indices to quantify the impacts of climate change on HPET and related perceived extremes as well as to address uncertainties in both empirical indices and emission scenarios over China. Our findings reveal that different combinations of climatic variables can lead to two opposite conclusions for both normal and extreme conditions. For example, by using indices only considering the combined effect of temperature and relative humidity, China is projected to have an elevated increase in the HPET and in the frequency of high‐temperature extremes. By taking into account wind speed, the country expects to have the HPET even lower than the surface air temperature and an increase in the frequency of low‐temperature extremes. In addition, the resulting range of HPET due to uncertainty in indices is greater than the uncertainty range derived from different emission scenarios for the entire country. Therefore, it is necessary to conduct a comprehensive assessment that explicitly addresses uncertainties in the HPET in order to improve the robustness and reliability of assessing climate change impacts on human‐perceived temperature extremes.

Canonical analysis of climatic factors associated with the quality characteristics of drinking water of a city in São Paulo State

Several factors may contribute to changes in monitoring parameters of drinking water quality, especially climatic characteristics. Behavioral studies of these characteristics are relevant to the rational and sustainable use of water resources and better monitoring of the quality of water available for the population. To assess the association between water quality and climate, this research explored physical-chemical and microbiological characteristics considered important for the monitoring of water quality, analyzed in casual samples collected daily from the distribution system of a city, and considered monthly data of climatic characteristics from 2007 to 2011. The associations between these two sets of variables were assessed by the multivariate procedure of the canonical correlation analysis. The results indicate a positive correlation between the canonical variable formed by the linear combination of climatic variables and the canonical variable formed by the linear combination of water quality variables.

Global Aquaculture Productivity, Environmental Sustainability, and Climate Change Adaptability.

To meet the demand for food from a growing global population, aquaculture production is under great pressure to increase as capture fisheries have stagnated. However, aquaculture has raised a range of environmental concerns, and further increases in aquaculture production will face widespread environmental challenges. The effects of climate change will pose a further threat to global aquaculture production. Aquaculture is often at risk from a combination of climatic variables, including cyclone, drought, flood, global warming, ocean acidification, rainfall variation, salinity, and sea level rise. For aquaculture growth to be sustainable its environmental impacts must reduce significantly. Adaptation to climate change is also needed to produce more fish without environmental impacts. Some adaptation strategies including integrated aquaculture, recirculating aquaculture systems (RAS), and the expansion of seafood farming could increase aquaculture productivity, environmental sustainability, and climate change adaptability.

GEP and MLR approaches for the prediction of reference evapotranspiration

In this study, reference evapotranspiration (ETo) is modeled as one of the major items of hydrological applications from different combinations of climatic variables using two different techniques: gene expression programming (GEP) and multiple linear regression (MLR). The data used in modeling were collected from weather stations in Egypt through the CLIMWAT database. The Penman–Monteith FAO-56 equation was considered as a reference target for ETo values depending on the entire climatic variables. The developed ETo models’ performances were compared and evaluated with regard to their predictive abilities using statistical criteria to identify the superiority of one modeling approach over the others and determine climatic variables which have a significant effect on ETo. The results indicated that GEP and MLR models’ contribution toward mean relative humidity and wind speed at 2 m height is greater compared to that of other variables. Meanwhile, when adding temperature data to models, solar radiation has a slight effect on increasing the accuracy of ETo estimate. Moreover, the lower statistical error criteria values of GEP models confirmed their better performance than MLR models and other empirical equations.

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

Accurate prediction of sugarcane yield using a random forest algorithm

Foreknowledge about sugarcane crop size can help industry members make more informed decisions. There exists many different combinations of climate variables, seasonal climate prediction indices, and crop model outputs that could prove useful in explaining sugarcane crop size. A data mining method like random forests can cope with generating a prediction model when the search space of predictor variables is large. Research that has investigated the accuracy of random forests to explain annual variation in sugarcane productivity and the suitability of predictor variables generated from crop models coupled with observed climate and seasonal climate prediction indices is limited. Simulated biomass from the APSIM (Agricultural Production Systems sIMulator) sugarcane crop model, seasonal climate prediction indices and observed rainfall, maximum and minimum temperature, and radiation were supplied as inputs to a random forest classifier and a random forest regression model to explain annual variation in regional sugarcane yields at Tully, in northeastern Australia. Prediction models were generated on 1 September in the year before harvest, and then on 1 January and 1 March in the year of harvest, which typically runs from June to November. Our results indicated that in 86.36 % of years, it was possible to determine as early as September in the year before harvest if production would be above the median. This accuracy improved to 95.45 % by January in the year of harvest. The R-squared of the random forest regression model gradually improved from 66.76 to 79.21 % from September in the year before harvest through to March in the same year of harvest. All three sets of variables—(i) simulated biomass indices, (ii) observed climate, and (iii) seasonal climate prediction indices—were typically featured in the models at various stages. Better crop predictions allows farmers to improve their nitrogen management to meet the demands of the new crop, mill managers could better plan the mill’s labor requirements and maintenance scheduling activities, and marketers can more confidently manage the forward sale and storage of the crop. Hence, accurate yield forecasts can improve industry sustainability by delivering better environmental and economic outcomes.

Modeling daily evapotranspiration in hyper-arid environment using gene expression programming

Accurate estimations of reference evapotranspiration (ETref) are extremely important for maximizing the beneficial use of water and hydrologic applications, particularly in arid and semiarid regions where water sources are so limited. The aim of this study is to develop mathematical models to calculate the daily ETref using a gene expression programming (GEP) technique. Eight GEP models (GEP-MOD1–8) were developed from combinations of climatic variables. The Penman-Monteith equation was considered the reference method, with the reference plant height varying from 5 to 105 cm in 5-cm increments. Daily climatic variables collected from 13 meteorological stations, one station from every region within the Kingdom of Saudi Arabia, covered the 1980 to 2010 period. Of the available climatic data, 65 % was used in the training process for the eight developed GEP models, and 35 % was used in the testing process. The accuracy of the eight developed GEP models to estimate ETref varied in significance depending on the climatic variables that were included. As more climatic parameters were input, the accuracy of the GEP model increased. For the testing process, the coefficient of determination (R 2) ranged from a low of 63.4 % for GEP-MOD1 to a high of 95.4 % for GEP-MOD8, and the root mean square error (RMSE) values ranged from 3.19 mm day−1 for GEP-MOD1 to 1.14 mm day−1 for GEP-MOD8. From the spatial evaluation, the values of RMSE ranged from 3.27 mm day−1 for GEP-MOD1 to 1.21 mm day−1 for GEP-MOD8. In addition, the respective RMSE values resulting from GEP-MOD8 for plant heights of 50 and 12 cm were 0.75 and 0.96 cm. This implies that the developed GEP-MOD8 can be used for any value of the reference plant height ranging from 5 to 105 cm with insignificant errors. Interestingly, solar radiation had an almost insignificant effect on ETref in the hyper-arid conditions. In contrast, wind speed and plant height had a large positive effect on increasing the accuracy of calculating ETref.

Diverse Responses of Winter Wheat Yield and Water Use to Climate Change and Variability on the Semiarid Loess Plateau in China

Crop production and water use in rainfed cropland are vulnerable to climate change. This study was to quantify diverse responses of winter wheat (Triticum aestivum L.) yield and water use to climate change on the Loess Plateau (LP) under different combinations of climatic variables. The crop model APSIM was validated against field experimental data and applied to calculate yield and water use at 18 sites on the LP during 1961 to 2010. The coefficient of variation of yield ranged from 12 to 66%, in which the vulnerability of yield increased from the southeast (12%) to the northwest (66%). This change was attributed to the gradual increase in precipitation variation from the southeast to the northwest. An obvious warming trend during 1961 to 2010 resulted in a significant decrease in the growth duration by 1 to 5 d decade−1. The yield at 12 sites was significantly reduced by 120 to 720 kg ha−1 decade−1. Evapotranspiration was significantly decreased by 1 to 26 mm decade−1; however, water use efficiency at most sites showed no significant trend. Eighteen sites were classified into three climatic zones by cluster analysis: high temperature–high precipitation–low radiation (HHL), medium temperature–medium precipitation–medium radiation (MMM), and low temperature–low precipitation–high radiation (LLH). The trend of decreasing yield was smallest in the HHL cluster because of a minimal reduction in precipitation, while decreasing trends in yield and evapotranspiration were larger in the LLH and MMM because of larger reductions in precipitation. The results imply that among strategies such as breeding for long duration or drought tolerance, modification of the planting date will be necessary to avoid high temperatures associated with climate change.

Spatial dynamic patterns of hand-foot-mouth disease in the People’s Republic of China

Hand-foot-mouth disease (HFMD) is the most common and widespread infectious disease in the People's Republic of China. Although there has been a substantial increase of HFMD in many parts of the country in recent years, its spatial dynamics and determinants remain unclear. When we collected and analysed weekly data on HFMD cases from 1,456 counties and the corresponding meteorological factors from 1 May 2008 to 27 March 2009, it was found that HFMD was spatially dispersed across the country in the summer and winter, while clustered in the spring and autumn. The spatial variation of HFMD was found to be affected by a combination of climate variables, while its spatio-temporal transmission was largely driven by temperature variations with a 7-week lag implying that (i) the dispersal of the disease can be anticipated based on the variation of the temperature and other climate variables; and (ii) the spatial dynamics of HFMD can be robustly predicted 7 weeks ahead of time using temperature data only. The findings reported allow prompt preparation and implementation of appropriate public health interventions to control and prevent disease outbreaks.