GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions

Accurate estimation of reference evapotranspiration (ET₀) becomes imperative for better managing the more and more limited agricultural water resources. This study examined the feasibility of developing generalized artificial neural network (GANN) models for ET₀ estimation using weather data from four locations representing different climatic patterns. Four GANN models with different combinations of meteorological variables as inputs were examined. The developed models were directly tested with climatic data from other four distinct stations. The results showed that the GANN model with five inputs, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed, performed the best, while that considering only maximum temperature and minimum temperature resulted in the lowest accuracy. All the GANN models exhibited high accuracy under both arid and humid conditions. The GANN models were also compared with multivariate linear regression (MLR) models and three conventional methods: Hargreaves, Priestley–Taylor, and Penman equations. All the GANN models showed better performance than the corresponding MLR models. Although Hargreaves and Priestley–Taylor equations performed slightly better than the GANN models considering the same inputs at arid and semiarid stations, they showed worse performance at humid and subhumid stations, and GANN models performed better on average. The results of this study demonstrated the great generalization potential of artificial neural techniques in ET₀ modeling.     

Error assessment of climate variables for FAO-56 reference evapotranspiration

Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ET_o) using the Food and Agriculture Organization Penman?CMonteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ET_o estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ET_o showed small differences [i.e., mean bias error (MBE) varying ?0.22 to 0.25?mm?day^?1 and root-mean-square-error (RMSE) varying 0.06?C0.50?mm?day^?1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ET_o (i.e., MBE of ?0.50?mm?day^?1 and RMSE of 0.60?C0.73?mm?day^?1). Estimated ET_o showed different sensitivity to variations of the meteorological parameters??in order of vapor pressure?>?wind speed?>?radiation. It is clear that the FAO56-PM method is applicable for reasonable ET_o estimation at a daily time scale especially in data-limited regions in Korea.     

Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada

Summary A regression-based methodology was used to downscale hourly and daily station-scale meteorological variables from outputs of large-scale general circulation models (GCMs). Meteorological variables include air temperature, dew point, and west–east and south–north wind velocities at the surface and three upper atmospheric levels (925, 850, and 500 hPa), as well as mean sea-level air pressure and total cloud cover. Different regression methods were used to construct downscaling transfer functions for different weather variables. Multiple stepwise regression analysis was used for all weather variables, except total cloud cover. Cumulative logit regression was employed for analysis of cloud cover, since cloud cover is an ordered categorical data format. For both regression procedures, to avoid multicollinearity between explanatory variables, principal components analysis was used to convert inter-correlated weather variables into uncorrelated principal components that were used as predictors. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response; for example, most hourly downscaling transfer functions could explain over 95% of the total variance for several variables (e.g. surface air temperature, dew point, and air pressure). Downscaling transfer functions were validated using a cross-validation scheme, and it was concluded that the functions for all weather variables used in the study are reliable. Performance of the downscaling method was also evaluated by comparing data distributions and extreme weather characteristics of downscaled GCM historical runs and observations during the period 1961–2000. The results showed that data distributions of downscaled GCM historical runs for all weather variables are significantly similar to those of observations. In addition, extreme characteristics of the downscaled meteorological variables (e.g. temperature, dew point, air pressure, and total cloud cover) were examined. Authors’ addresses: Chad Shouquan Cheng, Guilong Li, Qian Li, Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch-Ontario, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, Canada M3H 5T4; Heather Auld, Adaptation and Impacts Research Division, MSC Branch, Environment Canada, Toronto, Canada.     

Estimation of ground temperatures in permafrost regions of the Qinghai-Tibetan Plateau from climatic variables

Ground temperature is an important factor influencing ground source heat pumps, ground energy storage systems, land-atmosphere processes, and ecosystem dynamics. This paper presents an accurate development model (DM) based on a segment function: it can derive ground temperatures in permafrost regions of the Qinghai-Tibetan Plateau (QTP) from air temperature in case of shallow soil depths and without using air temperature data in case of deep soil depths. Here, we applied this model to simulate the active layer and permafrost ground temperature at the Tanggula observation station. The DM results were compared with those from the artificial neural network (ANN), support vector machine (SVM), and multiple linear regressions (MLR) models, which were based on climatic variables from prior models and on ground temperatures derived from observations at different depths. The results revealed that the effect of air temperature on simulated ground temperatures decreased with increasing depth; moreover, ground temperatures fluctuated greatly within the shallow layers but remained rather stable with deeper layers. The results also indicated that the DM has the best performance for the estimation of soil temperature compared to the MLR, SVM, and ANN models. Finally, we obtained the three average statistics indexes, i.e., mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) at TGL site: they were equal to 0.51 °C, 0.63 °C, and 0.15 °C for the ground temperature of active layer and to 0.08 °C, 0.09 °C, and 0.07 °C for the permafrost temperature.     

Water vapor transfer from a vegetated surface: A numberical study of bulk transfer coefficients and canopy resistances

The semi-analytical model outlined in previous studies (Massman, 1987a, b) to describe momentum and heat exchange between the atmosphere and vegetated surfaces is extended to include water vapor exchange. The methods employed are based on one-dimensional turbulent diffusivities and use numerical solutions to the steady-state diffusion equation. The model formulates stomatal response as a function of vapor pressure deficit and the within-canopy profile of mean photosynthetically-active radiation (PAR). It is then used to assess the influence that foliage structure, density, and sheltering can have upon the bulk transfer coefficient, kB _v ^-1, and the canopy resistance. A general analytical formulation of the canopy resistance based on the mean within-canopy profile of PAR is proposed and found to agree with the model's solutions for canopy resistance to within a few percent.     

Single and dual crop coefficients and crop evapotranspiration for wheat and maize in a semi-arid region

In this study, weighing lysimeters were used to investigate the daily crop coefficient and evapotranspiration of wheat and maize in the Fars province, Iran. The locally calibrated Food and Agriculture Organization (FAO) Penman–Monteith equation was used to calculate the reference crop evapotranspiration (ET_o). Micro-lysimetry was used to measure soil evaporation (E). Transpiration (T) was estimated by the difference between crop evapotranspiration (ET_c) and E. The single crop coefficient (K _c) was calculated by the ratio of ET_c to ET_o. Furthermore, the dual crop coefficient is composed of the soil evaporation coefficient (K _e) and the basal crop coefficients (K _cb) calculated from the ratio of E and T to ET_o, respectively. The maximum measured evapotranspiration rate for wheat was 9.9 mm?day^?1 and for maize was 10 mm?day^?1. The total evaporation from the soil surface was about 30 % of the total wheat ET_c and 29.8 % of total maize ET_c. The single crop coefficient (K _c) values for the initial, mid-, and end-season growth stages of maize were 0.48, 1.40, and 0.31 and those of wheat were 0.77, 1.35, and 0.26, respectively. The measured K _c values for the initial and mid-season stages were different from the FAO recommended values. Therefore, the FAO standard equation for K _c-_mid was calibrated locally for wheat and maize. The K _cb values for the initial, mid-, and end-season growth stages were 0.23, 1.14, and 0.13 for wheat and 0.10, 1.07, and 0.06 for maize, respectively. Furthermore, the FAO procedure for single crop coefficient showed better predictions on a daily basis, although the dual crop coefficient method was more accurate on seasonal scale.     

Evapotranspiration and canopy resistance of grass in a Mediterranean region

Summary A simple method for estimating actual evapotranspiration (ET) could become a suitable tool for irrigation scheduling. Resistance models can be useful if data on canopy resistance to water vapor flow (rc) and on aerodynamic resistance (ra) are available. These parameters are complex and hard to obtain. In this studyrc is analysed for a reference crop (grass meadow). Canopy resistance is dependent on climate, weather (radiation, atmospheric vapor pressure deficit, aerodynamic resistance), agronomic practices (irrigation, grass cutting) and time scale (hour, day). Anrc model, proposed by Katerji and Perrier (KP model), using some meteorological parameters as inputs, is presented. Canopy resistance calculated according to the KP model was used to estimate a referenceET _ref on hourly and daily time scales.TheET _ref estimated using the KP model on a daily time scale was compared with a model proposed by Allen, Jensen, Wright and Burman (AJWB model) — in whichrc depends on leaf area index only — and with direct measurements from a weighing lysimeter. The results show an underestimation of 18% for the AJWB model against an underestimation of 2% for the KP model. Since the hypotheses are the same for both models and aerodynamic resistance plays a secondary role, the better results obtained by the KP model are due torc modelling.With 11 Figures     

北方玉米冠层光合有效辐射垂直分布及影响因子分析

玉米冠层内光合有效辐射(PAR)的大小直接影响冠层内叶片的光合作用,进而影响玉米净第一性生产力或作物产量的准确评估。为弄清玉米冠层内光合有效辐射的分布规律及其影响因子,基于锦州玉米农田生态系统于2006年生育期的光合有效辐射观测数据和叶面积指数动态观测数据,对玉米冠层光合有效辐射的垂直分布特征及其影响因子进行了分析。结果表明:玉米冠层内不同垂直层次叶片的PAR分布随生育期变化显著,与叶面积指数呈显著的负相关(R2=0.89);玉米冠层光合有效辐射的消光系数K值在生育期呈动态变化,约为0.76,且表现为苗期较大、生育后期较小。分析表明,在进行光合有效辐射及与此密切相关的光合作用模拟时,应考虑消光系数的动态变化。     

北方玉米冠层光合有效辐射垂直分布及影响因子分析

玉米冠层内光合有效辐射（PAR）的大小直接影响冠层内叶片的光合作用,进而影响玉米净第一性生产力或作物产量的准确评估。为弄清玉米冠层内光合有效辐射的分布规律及其影响因子,基于锦州玉米农田生态系统于2006年生育期的光合有效辐射观测数据和叶面积指数动态观测数据,对玉米冠层光合有效辐射的垂直分布特征及其影响因子进行了分析。结果表明：玉米冠层内不同垂直层次叶片的PAR分布随生育期变化显著,与叶面积指数呈显著的负相关（R2=0.89）;玉米冠层光合有效辐射的消光系数K值在生育期呈动态变化,约为0.76,且表现为苗期较大、生育后期较小。分析表明,在进行光合有效辐射及与此密切相关的光合作用模拟时,应考虑消光系数的动态变化。     

Water loss and canopy resistance of a young Sitka spruce plantation

Transpiration of a 7 m-high Stika spruce forest was investigated using measurments of net radiation, sensible heat and ground heat fluxes in an energy balance to give latent heat flux, and hence canopy resistance from the Penman-Monteith equation. Sensible heat flux was measured by the eddy-correlation method using a Fluxatron circuit.During six consecutive days of measurement in July/August, canopy resistance typically followed a decreasing trend from high values (150 s m-) at dawn to around 40 s m^–1 at midday and then returning steadily to > 100 s m^–1 at sunset. Transpiration was 3 mm day^–1 on average over the period studied and changes in the rate within the day were significantly correlated with changes in net radiation.Comparisons are drawn with published data from other forest sites and the conclusion is reached that it is imprudent to generalise about transpiration rates and canopy resistances of different species at different sites from results gathered at one or two places.     

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.     

Precise estimation of hourly global solar radiation for micrometeorological analysis by using data classification and hourly sunshine

We have developed a method for estimating hourly global solar radiation (GSR) from hourly sunshine duration data. This procedure requires only hourly sunshine duration as the input data and utilizes hourly precipitation and daily snow cover as auxiliary data to classify time intervals into six cases according to weather conditions. To obtain hourly GSR using a simple algebraic form, a quadratic function of the solar elevation angle and the sunshine duration ratio is used. Daily GSR is given by a sum of hourly GSRs. We evaluated the performance of the newly developed method using data obtained at 67 meteorological stations and found that the estimated GSR is highly consistent with that observed. Hourly and daily root-mean-square misfits are approximately 0.2 MJ/m²/h (~55 W/m²) and 1.4 to 1.5 MJ/m²/day (~16 to 17 W/m²), respectively. Our classification of weather conditions is effective for reducing estimation errors, especially under cloudy skies. Since the sunshine duration is observed at more meteorological stations than GSR, the proposed new method is a powerful tool for obtaining solar radiation with hourly resolution and a dense geographical distribution. One of the proposed methods, GSRgrn, can be applicable to hourly GSR estimations at different observation sites by setting local parameters (the precipitable water, surface albedo, and atmospheric turbidity) suitable to the sites. The hourly GSR can be applied for various micrometeorological studies, such as the heat budget of crop fields.     

Coupled modes of large-scale climatic variables and regional precipitation in the western Mediterranean in autumn

The variability of autumn precipitation in the western Mediterranean and its relationship to the large-scale atmospheric variability during the period 1948 to 1989 is assessed. A singular value decomposition analysis is used to establish modes of coupled variability between regional precipitation and geopotential height (Z300), zonal (U-wind) and meridional (V-wind) wind components at the 300 hPa level. The Z300/precipitation coupling, which accounts for 52% of the total squared covariance, is strong during the autumn. The first Z300 coupled mode, in its positive phase, is characterised by a dipole structure with negative anomalies over Scandinavia and positive anomalies over the Iberian Peninsula in the Z300 and negative precipitation anomalies in the western Mediterranean. In its negative phase, a coupled pattern is found showing a high-over-low block and positive precipitation anomalies over the Mediterranean area. The coupling depicted by the second mode is weaker than that found in the first mode. The second coupled mode (21% of the total squared covariance) is characterised by negative anomalies in the eastern North Atlantic and positive ones over North Africa and the central Mediterranean in the Z300 and negative anomalies in the regional precipitation. Consistent with the results of the two first modes mentioned, the coupled patterns of either U-wind/precipitation or V-wind/precipitation are found to be coherent with those for Z300/precipitation. Composite maps were obtained to give a representation of the average circulation associated with coherent precipitation variability in the western Mediterranean. The regional impacts of both modes are investigated and the large-scale dynamic patterns presented are important modes of variability. Taking into account data for the whole of the twentieth century, results show that the first singular mode is responsible for the decadal trends and long term changes in precipitation. The late 1970s and 1980s is shown as the drier period and the late 1950s and early 1960s as the wettest years of the century.     

五种短期气候预测统计模型对灾害性天气的评估检验

对1996～2004年的灾害实况场与五种统计预测模型的预测结果进行检验,结果表明五种统计模型对灾害性天气的实况场和背景场均有一定的预报能力,尤其是对灾害性天气背景场的预测效果较好,这些结论可适用于常规的短期气候预测业务.     

Potential effects of global climatic change on the phenology and yield of maize in venezuela

Simulated impacts of global and regional climate change, induced by an enhanced greenhouse effect and by Amazonian deforestation, on the phenology and yield of two grain corn cultivars in Venezuela (CENIAP PB-8 and OBREGON) are reported. Three sites were selected:Turén, Barinas andYaritagua, representing two important agricultural regions in the country. The CERES-Maize model, a mechanistic process-based model, in theDecision Support System for Agrotechnology Transfer (DSSAT) was used for the crop simulations. These simulations assume non-limiting nutrients, no pest damage and no damage from excess water; therefore, the results indicate only the difference between baseline and perturbed climatic conditions, when other conditions remain the same. Four greenhouse-induced global climate change scenarios, covering different sensitivity levels, and one deforestation-induced regional climate change scenario were used. The greenhouse scenarios assume increased air temperature, increased rainfall and decreased incoming solar radiation, as derived from atmospheric GCMs for doubled CO₂ conditions. The deforestation scenarios assume increased air temperature, increased incoming solar radiation and decreased rainfall, as predicted by coupled atmosphere-biosphere models for extensive deforestation of a portion of the Amazon basin. Two baseline climate years for each site were selected, one year with average precipitation and another with lower than average rainfall. Scenarios associated with the greenhouse effect cause a decrease in yield of both cultivars at all three sites, while the deforestation scenarios produce small changes. Sensitivity tests revealed the reasons for these responses. Increasing temperatures, especially daily maximum temperatures, reduce yield by reducing the duration of the phenological phases of both cultivars, as expected from CERES-Maize. The reduction of the duration of the kernel filling phase has the largest effect on yield. Increases of precipitation associated with greenhouse warming have no effects on yield, because these sites already have adequate precipitation; however, the crop model used here does not simulate potential negative effects of excess water, which could have important consequences in terms of soil erosion and nutrient leaching. Increases in solar radiation increased yields, according to the non-saturating light response of the photosynthesis rate of a C₄ plant like corn, compensating for reduced yields from increased temperatures in deforestation scenarios. In the greenhouse scenarios, reduced insolation (due to increased cloud cover) and increased temperatures combine to reduce yields; a combination of temperature increase with a reduction in solar radiation produces fewer and lighter kernels.A report of thePAN-EARTH Project, Venezuela Case Study.     

A model for inferring canopy and underlying soil temperatures from multi-directional measurements

Thermal emission is modeled from a canopy/soil surface, where the soil and the leaves are at different temperatures,T _g andT _c respectively. The temperatureT _m corresponding to a radiometer reading is given by $$B_\lambda (T_m ) = \chi B_\lambda (T_g ) + (1 - \chi )B_\lambda (T_c ) ,$$ whereB _λ denotes the Planck blackbody function at wavelength λ, χ specifies the fraction of the field of view occupied by the soil at a given view direction, and an emissivity of 1.0 is assumed for the plants and the soil. The dependence of the soil-fraction χ on the view direction and the structure is expressed by the viewing-geometry parameter, which allows for concise and simple formulation. We observe from our model that at large view zenith angles, only the plants are effectively seen (that is, χ tends to zero), and thereforeT _c can be determined from observations at large zenith angles, to the extent that such observations are practical. Viewing from the zenith, χ = exp(-L _hc), whereL _hc is the projection of the canopy leaf-area (per unit surface area) on a horizontal plane. For off-zenith observations, the soil-fraction χ depends on the distribution in the azimuth of the projected areas of various leaf categories, in addition to the dependence on the sum total of these projections,L _hc.L _hc, rather than the leaf-area index, emerges as the parameter characterizing the optical thickness of the canopy. Inferring bothT _c andT _g from observations from the zenith and from large zenith angles is possible ifL _hc is known from other measurements. Drooping of leaves under water-stress conditions affects the observed temperatureT _m in a complicated way because a leaf-inclination change produces a change inL _hc (for the same leaf area) and also a change in the dependence of χ on the view direction. Water stress can produce an increase of the soil-fraction χ and thus tends to produce an exaggerated increase in the observed temperature compared to the actual increase in canopy temperature. These effects are analyzed for a simulated soybean canopy.     

Assessing the performance of two models on calculating maize actual evapotranspiration in a semi-humid and drought-prone region of China

The two-step and one-step models for calculating evapotranspiration of maize were evaluated in a semi-humid and drought-prone region of northern China. Data were collected in the summers of 2013 and 2014 to determine relative model accuracy in calculating maize evaopotranspiration. The two-step model predicted daily evaoptranspiration with crop coefficients proposed by FAO and crop coefficient calibrated by local field data; the one-step model predicted daily evapotranspiration with coefficients derived by other researcher and coefficients calibrated by local field data. The predicted daily evapotranspiration in 2013 and 2014 growing seasons with the above two different models was both compared with the observed evapotranspiration with eddy covariance method. Furthermore, evapotranspiration in different growth stages of 2013 and 2014 maize growing seasons was predicted using the models with the local calibrated coefficients. The results indicated that calibration of models was necessary before using them to predict daily evapotranspiration. The model with the calibrated coefficients performed better with higher coefficient of determination and index of agreement and lower mean absolute error and root mean square error than before. And the two-step model better predicted daily evapotranspiration than the one-step model in our experimental field. Nevertheless, as to prediction ET of different growth stages, there still had some uncertainty when predicting evapotranspiration in different year. So the comparisons suggested that model prediction of crop evapotranspiration was practical, but requires calibration and validation with more data. Thus, considerable improvement is needed for these two models to be practical in predicting evapotranspiration for maize and other crops, more field data need to be measured, and an in-depth study still needs to be continued.