Penman-Monteith approaches for estimating crop evapotranspiration in screenhouses—a case study with table-grape

In arid and semi-arid regions many crops are grown under screens or in screenhouses to protect them from excessive radiation, strong winds, hailstorms and insects, and to reduce crop water requirements. Screens modify the crop microclimate, which means that it is necessary to accurately estimate crop water use under screens in order to improve the irrigation management and thereby increase water-use efficiency. The goal of the present study was to develop a set of calibrated relationships between inside and outside climatic variables, which would enable growers to predict crop water use under screens, based on standard external meteorological measurements and evapotranspiration (ET) models. Experiments were carried out in the Jordan Valley region of eastern Israel in a table-grape vineyard that was covered with a transparent screen providing 10 % shading. An eddy covariance system was deployed in the middle of the vineyard and meteorological variables were measured inside and outside the screenhouse. Two ET models were evaluated: a classical Penman-Monteith model (PM) and a Penman-Monteith model modified for screenhouse conditions by the inclusion of an additional boundary-layer resistance (PMsc). Energy-balance closure analysis, presented as a linear relation between half-hourly values of available and consumed energy (1,344 data points), yielded the regression Y?=?1.05X–9.93 (W m^?2), in which Y = sum of latent and sensible heat fluxes, and X = net radiation minus soil heat flux, with R ²?=?0.81. To compensate for overestimation of the eddy fluxes, ET was corrected by forcing the energy balance closure. Average daily ET under the screen was 5.4?±?0.54 mm day^?1, in general agreement with the model estimates and the applied irrigation. The results showed that measured ET under the screen was, on average, 34 % lower than that estimated outside, indicating significant potential water saving through screening irrigated vineyards. The PM model was somewhat more accurate than the PMsc for estimating ET under the screen. A model sensitivity analysis illustrates how changes in certain climatic conditions or screen properties would affect evapotranspiration.     

黄河三角洲湿地蒸散量与典型植被的生态需水量

蒸散量（ET）是黄河三角洲湿地水资源的主要消耗项,包括植被蒸腾、水面蒸发以及裸土蒸发等。植被生态需水是为了保证植被生态系统能够健康维持并确保其生态服务功能得到正常发挥而必须消耗的一部分水量。准确地估算湿地蒸散量、研究植被生态需水量对于保护湿地生态环境是十分必要的。应用MODIS的地表反射率、地表温度数据与常规气象数据以及土地利用/覆盖图,利用蒸散量的遥感估算模型SEBS模型估算了晴天条件下的黄河三角洲湿地日蒸散量,采用HANTS算法插补了非晴天条件下的日蒸散量,从而得到2001～2005年的该湿地年蒸散量的时间序列,并对蒸散量进行验证和分析。结合该地区典型植被生态需水量与植被蒸散耗水量,估算了2001～2005年的生态补水量。结果表明：与实测值相比,遥感估算月蒸散量的均方差RMSD为16.4mm,平均绝对百分比误差MAPD是11.9%,两者基本一致。黄河三角洲湿地的蒸散量在空间分布上以水体及周围地区、滨海滩涂、黄河故道以及黄河两岸沼泽湿地等的蒸散量较高,居民地蒸散量较低。蒸散量的年际变化不大,季节变化呈单峰型,以5、6、7月份蒸散量最大,月蒸散量在110～120mm之间。2001～2005年期间,每年至少有40%面积的芦苇沼泽和60%面积的芦苇草甸水分供应不足,植被的正常生长受到影响,尤其2002年较为严重,2004年以后情况有所改善。2002年芦苇的生态补水量最大,在9.9×10^7～3.19×108m^3之间,而2004年的生态补水量最小,在3.0×10^7～2.39×108m^3之间。     

Empirical modelling of lake water-temperature relationships: a comparison of approaches

1.  As a result of the role that temperature plays in many aquatic processes, good predictive models of annual maximum near-surface lake water temperature across large spatial scales are needed, particularly given concerns regarding climate change. Comparisons of suitable modelling approaches are required to determine their relative merit and suitability for providing good predictions of current conditions. We developed models predicting annual maximum near-surface lake water temperatures for lakes across Canada using four statistical approaches: multiple regression, regression tree, artificial neural networks and Bayesian multiple regression.
2.  Annual maximum near-surface (from 0 to 2 m) lake water-temperature data were obtained for more than 13 000 lakes and were matched to geographic, climatic, lake morphology, physical habitat and water chemistry data. We modelled 2348 lakes and three subsets thereof encompassing different spatial scales and predictor variables to identify the relative importance of these variables at predicting lake temperature.
3.  Although artificial neural networks were marginally better for three of the four data sets, multiple regression was considered to provide the best solution based on the combination of model performance and computational complexity. Climatic variables and date of sampling were the most important variables for predicting water temperature in our models.
4.  Lake morphology did not play a substantial role in predicting lake temperature across any of the spatial scales. Maximum near-surface temperatures for Canadian lakes appeared to be dominated by large-scale climatic and geographic patterns, rather than lake-specific variables, such as lake morphology and water chemistry.     

Relationships between plants and climate in France: calibration of 1874 bio-indicators

The influence of climate on plants geography is studied through a probabilistic calibration between a botanical database, containing 12 000 plots, and a meteorological database composed of 574 climatic stations. The calibration measures the climatical optimum (position) and the indicator power (concentration) of 1874 plants for six climatic variables. The validation of these relations is based upon the comparison of the estimation of climate by plants and the values measured by climatic stations near the plots. This validation underlines that plants are accurate (accuracy=88.5%) and stable (stability=96.5%) bio-indicators of climate variables.     

Changes in concentration of Alternaria and Cladosporium spores during summer storms

Fungal spores are known to cause allergic sensitization. Recent studies reported a strong association between asthma symptoms and thunderstorms that could be explained by an increase in airborne fungal spore concentrations. Just before and during thunderstorms the values of meteorological parameters rapidly change. Therefore, the goal of this study was to create a predictive model for hourly concentrations of atmospheric Alternaria and Cladosporium spores on days with summer storms in Szczecin (Poland) based on meteorological conditions. For this study we have chosen all days of June, July and August (2004–2009) with convective thunderstorms. There were statistically significant relationships between spore concentration and meteorological parameters: positive for air temperature and ozone content while negative for relative humidity. In general, before a thunderstorm, air temperature and ozone concentration increased, which was accompanied by a considerable increase in spore concentration. During and after a storm, relative humidity increased while both air temperature ozone concentration along with spore concentrations decreased. Artificial neural networks (ANN) were used to assess forecasting possibilities. Good performance of ANN models in this study suggest that it is possible to predict spore concentrations from meteorological variables 2 h in advance and, thus, warn people with spore-related asthma symptoms about the increasing abundance of airborne fungi on days with storms.     

Agrometeorological parameters for prediction of the maturation period of Arabica coffee cultivars

The objective of this study was to determine the harvest period of coffee fruits based on the relationship between agrometeorological parameters and sucrose accumulation in the seeds. Over the crop years 2004/2005 and 2006/2007, from 150 days after flowering (DAF) onwards, samples of 50 fruits of cultivars Mundo Novo IAC 376-4, Obat? IAC 1669-20 and Catuaí Vermelho IAC 144 were collected from coffee trees located in Campinas, Brazil. The endosperm of the fruits was freeze-dried, ground and analyzed for sucrose content by high-performance liquid chromatography. A weather station provided data to calculate the accumulated growing degree-day (GDD) units, and the reference (ET(o)) and actual (ET(r)) evapotranspiration rates. The results showed that the highest rates of sucrose accumulation occurred at the transition from the cane-green to the cherry phenological stage. Models for the estimation of sucrose content during maturation based on meteorological variables exhibited similar or better performance than the DAF variable, with better results for the variables GDD and ET(o). The Mundo Novo cultivar reached the highest sucrose level in the endosperm after 2,790 GDD, while cultivar Catuaí attained its maximum sucrose concentration after the accumulated evapotranspiration rate has reached a value of 870 mm. As for cultivar Obat?, the maximum sucrose concentration was predicted with the same degree of accuracy using any of the parameters investigated. For the Obat? cultivar, the values of the variables calculated for the maximum sucrose concentration to be reached were 249 DAF, 3,090 GDD, 1,020 ET(o) and 900 ET(r).     

Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques

Different spatial interpolation techniques have been applied to construct objective bioclimatic maps of La Palma, Canary Islands. Interpolation of climatic data on this topographically complex island with strong elevation and climatic gradients represents a challenge. Furthermore, meteorological stations are not evenly distributed over the island, with few stations at high elevations. We carried out spatial interpolations of the compensated thermicity index (Itc) and the annual ombrothermic Index (Io), in order to obtain appropriate bioclimatic maps by using automatic interpolation procedures, and to establish their relation to potential vegetation units for constructing a climatophilous potential natural vegetation map (CPNV). For this purpose, we used five interpolation techniques implemented in a GIS: inverse distance weighting (IDW), ordinary kriging (OK), ordinary cokriging (OCK), multiple linear regression (MLR) and MLR followed by ordinary kriging of the regression residuals. Two topographic variables (elevation and aspect), derived from a high-resolution digital elevation model (DEM), were included in OCK and MLR. The accuracy of the interpolation techniques was examined by the results of the error statistics of test data derived from comparison of the predicted and measured values. Best results for both bioclimatic indices were obtained with the MLR method with interpolation of the residuals showing the highest R ² of the regression between observed and predicted values and lowest values of root mean square errors. MLR with correction of interpolated residuals is an attractive interpolation method for bioclimatic mapping on this oceanic island since it permits one to fully account for easily available geographic information but also takes into account local variation of climatic data.     

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks

Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.     

1965-2018年广西西江流域参考作物蒸散量时空演变及其影响因子

估算参考作物蒸散量(ET0)有助于揭示流域的水热平衡和水循环过程,为合理利用与开发流域水资源提供基础。本研究通过重新拟合研究区的净短波辐射系数,使用改进后的Penman-Monteith模型,计算1965-2018年广西西江流域的ET0,使用Mann-Kendall法对ET0进行趋势分析与突变点检测,用反距离权重法插值后分析ET0时空演变特征,根据气候因子的贡献率判断ET0的影响因子。结果表明:在空间上,ET0呈现随海拔降低而增加的趋势,其高值主要位于流域中部地区,而低值位于西北侧的云贵高原边缘及斜坡带,春季ET0呈现出经度梯度性,夏季ET0与年际的空间格局类似;在时间上,流域年均ET0为637.2mm,增长率为-0.018 mm·a^-1,整体呈微弱的下降趋势。除春季(0.053 mm·a^-1)呈上升趋势外,夏季(-0.053 mm·a^-1)、秋季(-0.011 mm·a^-1)和冬季(-0.007 mm·a^-1)ET0均呈现出下降趋势,ET0的下降主要体现在夏季;影响流域ET0的主导因子是相对湿度(贡献率为39.0%)、平均风速(贡献率为27.2%)、日照时数和平均气温;平均相对湿度对ET0是负贡献(r=-0.673),日照时数、平均风速和平均气温均是正贡献;影响ET0的因子组合和贡献率在流域的不同区域有一定差异。     

基于温度的作物需水量估算方法

科学准确地估算作物需水量是灌溉预报和农业用水管理的基础,本文探索如何充分利用温度这一定量信息估算作物需水量,并对不同时间尺度的估算精度进行评判分析,以更好地服务于灌区的灌溉预报和水土资源管理.利用新乡市1970—2010年的气象数据对Hargreaves公式的3个基本参数和McClound公式的2个参数进行了订正;针对冬小麦生育期,筛选出Hargreaves公式作为参考作物需水量（ET₀）的估算方法,然后结合基于温度的作物系数计算模型建立了基于温度的作物需水量计算模型,根据2011年10月—2012年5月新乡气象和灌溉试验资料对不同时间尺度（1、3、7 d）的ET_c进行了预测和精度评估.结果表明： 1、3、7 d ET_c的预测值与实测值的相关系数分别达到0.883、0.933、0.959,一致性指数分别达到0.94、0.95、0.97,预测误差随时间尺度增大而减小,误差<1 mm·d^-1的预报准确率均>80%,误差<2 mm·d^-1的预报准确率均>90%,各时间尺度的预报精度都较高,可为灌区灌溉预报和农业用水管理提供较为可靠的数据支撑.     

Comparisons of likelihood and machine learning methods of individual classification

Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin ("assignment tests"). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high F(ST)), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0-2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to "learn" and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks.     

Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir,China

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.     

Climate and vegetation in China III water balance and distribution of vegetation

Distributions of 29 vegetation types in China as a function of climatic humidity or aridity were analysed using Thornthwaite's system, by employing meteorological records from 671 stations in China. The annual potential evapotranspiration and the humidity/aridity indices were calculated for every station, and distribution maps of water deficiency, water surplus and moisture index (Im) were constructed. The Im map showed that arid areas (Im<0) occupied about 56% of the country. The effect of the difference in soil water storage capacity on Thornthwaite's indices was examined, and Im values were found to differ little, although some differences were observed in actual annual evapotranspiration, water deficiency and water surplus values. Correlations between Im values and distributions of 29 vegetation types, identified from a vegetation map with a scale of 1/4000000, were investigated. The distributions of desert, steppe, woodland, deciduous forest and evergreen forest corresponded to Im values of below −40, −40–−20, −20-0, 0–60 and over 60, respectively. In addition, climatic factors delimiting the northern distribution of evergreen broadleaf forest were investigated, and it was clarified that the northern limit was restricted by combined hydrothermal conditions, and not by the low temperature in winter.     

Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches

Phytoplankton biomass is an important indicator for water quality, and predicting its dynamics is thus regarded as one of the important issues in the domain of river ecology and management. However, the vast majority of models in river systems have focused mostly on flow prediction and water quality with very few applications to biotic parameters such as chlorophyll a (Chl a). Based on a 1.5-year measured dataset of Chl a and environmental variables, we developed two modeling approaches [artificial neural networks (ANN) and multiple linear regression (MLR)] to simulate the daily Chl a dynamics in a German lowland river. In general, the developed ANN and MLR models achieved satisfactory accuracy in predicting daily dynamics of Chl a concentrations. Although some peaks and lows were not predicted, the predicted and the observed data matched closely by the MLR model with the coefficient of determination (R ²), Nash–Sutcliffe efficiency (NS), and the root mean square error (RMSE) of 0.53, 0.53, and 2.75 for the calibration period and 0.63, 0.62, and 1.94 for the validation period, respectively. Likewise, the results of the ANN model also illustrated a good agreement between observed and predicted data during calibration and validation periods, which was demonstrated by R ², NS, and RMSE values (0.68, 0.68, and 2.27 for the calibration period, 0.55, 0.66 and 2.12 for the validation period, respectively). According to the sensitivity analysis, Chl a concentration was highly sensitive to dissolved inorganic nitrogen, nitrate–nitrogen, autoregressive Chl a, chloride, sulfate, and total phosphorus. We concluded that it was possible to predict the daily Chl a dynamics in the German lowland river based on relevant environmental factors using either ANN or MLR models. The ANN model is well suited for solving non-linear and complex problems, while the MLR model can explicitly explore the coefficients between independent and dependent variables. Further studies are still needed to improve the accuracy of the developed models.     

A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed.     

A neural network model for simulation of water levels at the Sultan Marshes wetland in Turkey

An artificial neural network (ANN) model was developed for simulating water levels at the Sultan Marshes in Turkey. Sultan Marshes is a closed basin wetland located in the semi-arid Central Anatolia region of Turkey. It is one of the thirteen Ramsar sites of Turkey and a national park. Water levels at the Sultan Marshes showed strong fluctuations in recent decades due to the changes in climatic and hydrologic conditions. In this study, monthly average water levels were simulated using a multi-layer perceptron type ANN model. The model inputs consisted of climatic data (precipitation, air temperature, evapotranspiration) and hydrologic data (ground water levels, spring flow rates, and previous month water levels) available from 1993 to 2002. 70 % of the data were used for model training and remaining 30 % were used for model testing. Model training was accomplished by using a scaled conjugate gradient backpropagation algorithm. The performance of the model was evaluated by calculating the root mean square error (RMSE) and the coefficient of determination (R ²) between observed and simulated water levels. The sensitivity of the model to input parameters was determined by evaluating the model performance when a single input variable was excluded. It was found that the ANN model can successfully be used for simulating water levels at the Sultan Marshes. The model developed using all input variables provided the best results with two neurons in the hidden layer. The RMSE and R ² of the simulated water levels were 4.0 cm and 96 %, respectively. The sensitivity analysis showed that the model was most sensitive to previous month water levels and ground water levels.     

一种自优化RBF神经网络的叶绿素a浓度时序预测模型

藻类水华发生过程具有复杂性、非线性、时变性等特点,其准确预测一直是一个国际性难题.以天津市于桥水库为研究对象,根据2000年1月至2003年12月常规监测的水生生态数据(采样周期为10 d),提出了一种结合时序方法的可自优化RBF神经网络智能预测模型,对判断藻类水华的重要指标叶绿素a浓度进行预测.研究了训练样本量及RBF神经网络扩展速度SPREAD值的可自优化性能,以及该模型用于于桥水库叶绿素a浓度的短期变化趋势预测的可行性.结果表明,预测性能指标随SPREAD值及样本量不同发生变化,该预测模型能自动寻到最优SPREAD值,并发现至少需要约两年的训练样本量才能达到较好预测效果.当样本量为105,SPREAD值为10时,预测效果最好,精度较高,预测值与实测值的相关系数R达到0.982.该方法对水库的藻类水华预警有一定的参考价值.     

Establishing climatic constraints shaping the distribution of alien plant species along the elevation gradient in the Alps

In this work, we analyse the role of climatic constraints in shaping the distribution of alien plant species along the elevation gradient in the European Alps. Alien species occurrence was recorded in 278 plots located beside rivers, from 100 to 2,100 m a.s.l. Climate variables were calculated from the data recorded by 145 meteorological stations and interpolated by a multiple regression approach. Both richness and occurrence of aliens were modelled. In particular, relationships between the occurrence of alien plants and (1) elevation or (2) the climatic variables, were tested by applying generalised linear models and generalised linear mixed models; the model parameters obtained were used to estimate upper elevation limits of alien occurrence and their related climate values. Sixty-eight alien species were encountered, the majority (71%) invasive in Italy and worldwide. A steep decrease in alien species richness with elevation was found, with the probability of alien species occurrence decreasing by half for each 100 m increase in elevation. Minimal adequate models based on (1) non-transformed climatic variables and (2) derived PCA values, confirmed that occurrence of alien plant species along the elevation gradient was positively related to the minimum temperature, the mean temperature and the heat sum for the spring season, rather than to the incidence of absolute minimum temperature and frost days, as usually assumed. Although further experimental analyses are needed, these results support the hypothesis that, referring to climate factors, elevation limits along rivers are mainly established by low spring temperatures which operate at the level of population viability rather than plant survival.     

Influence of local climate and climate change on aeroterrestrial phototrophic biofilms

Aeroterrestrial phototrophic biofilms colonize natural and man-made surfaces and may damage the material they settle on. The occurrence of biofilms varies between regions with different climatic conditions. The aim of this study was to evaluate the influence of meteorological factors on the growth of aeroterrestrial phototrophs. Phototrophic biomass was recorded on roof tiles at six sites within Germany five times over a period of five years and compared to climatic parameters from neighboring weather stations. All correlating meteorological factors influenced water availability on the surface of the roof tiles. The results indicate that the frequency of rainy days and not the mean precipitation per season is more important for biofilm proliferation. It is also inferred that the macroclimate is more important than the microclimate. In conclusion, changed (regional) climatic conditions may determine where in central Europe global change will promote or inhibit phototrophic growth in the future.