Comparison of empirical daily surface incoming solar radiation models

Most environmental and agronomic models use climatic inputs such as temperature, solar radiation and rainfall. In Canada, few automatic weather stations can monitor incoming solar radiation. Hence, surface incoming solar radiation must be estimated from other meteorological data. The aim of the present research was to conduct a comparative study of three empirical models for the estimation of surface incoming solar radiation on a horizontal surface. The first two methodologies use the traditional and long-utilized linear approach based on latitude and daily temperature range. A more recent methodology uses neural networks (NN) to build a similar regression based on latitude and temperature range, but providing more flexibility in the formulation as well as a non-linear activation function. Global daily solar radiation data from 11 stations located in northeastern America was used to optimize and test the different models. Even though coefficients of the classical models were locally optimized using a standard unconstrained non-linear scheme, NNs markedly improved the estimation of incoming surface solar radiation.     

A simple cloud-based energy balance model to estimate dew

Two energy balance (EB) analyses, based on two different methods to estimate the downward long wave radiation, were used to model dew duration. Model I utilized cloud cover and cloud altitude to calculate sky temperatures whereas model II was based only on cloud cover to estimate sky apparent emissivity. The models were validated with hourly data from on-site weather station instrumentation for nights with and without occurrence of dew and cloud data from airport weather records, and compared with an empirical model, the CART/SLD model. The accuracy of predicting hourly dew occurrence with the EB models was greater than with the CART/SLD model. EB models I and II correctly predicted dew occurrence in 91 and 88% of hours, respectively, compared with 75% for the CART/SLD model. The majority of CART/SLD errors were due to late estimation of dew onset. It is proposed that an EB model can be used more effectively than the current CART/SLD model without a need for increased environmental observations in most localities.     

福建省降雨侵蚀力R值预测预报方法研究

降雨侵蚀力反映由降雨引起的土壤侵蚀的潜在能力,是建立通用土壤流失方程USLE的最基本因子之一。由于降雨侵蚀力计算过程中所需资料较难收集,给其计算增加了难度。利用福建省46个代表性气象站资料,建立了利用经度、纬度、海拔高度以及月降雨量估算降雨侵蚀力的简易算法模型,结果表明该模型预测预报效果较好,能够用于估算平均降雨侵蚀力。     

Comparison of artificial neural networks and prediction models for reference evapotranspiration estimation in a semi-arid region

Estimation of reference evapotranspiration (ET_o) is essential for determination of crop water requirements. In this research, Penman–FAO (P-FAO) and Penman–Monteith (PM) equations were calibrated and validated by lysimeter-measured ET_o with six and four weather parameters. Furthermore, two input structures (six and four weather parameters) to artificial neural networks (ANNs) were investigated. Results showed that the accuracy of the PM equation is greater than that of the P-FAO equation. An empirical equation was developed to estimate daily ET_o using mean daily temperature and relative humidity, and sunshine hours. The accuracy of the equation to estimate daily ET_o using smooth weather data is greater than that of an equation using original data. Furthermore, ANNs were able to estimate ET_o properly. The accuracy of ANNs with six inputs is higher than that obtained using the P-FAO equation and is similar to that determined using the PM equation. A decrease in number of inputs to ANNs generally decreased the accuracy of estimation, however, ANNs were able to estimate ET_o properly when wind speed and solar radiation were unavailable. Furthermore, the accuracy of ANNs, with four input parameters is greater than that obtained using the PM equation and is similar to that obtained with P–FAO and the developed empirical equations.     

天气发生器MulGETS和k-NN对区域历史气象场特征重现能力的比较

传统单站点天气发生器未考虑不同站点气象变量间的空间相关性,导致其在区域影响评价中的应用受到限制,而多站点天气发生器可以克服单站点天气发生器的缺点,近年来得到迅速发展。评估和验证多站点天气发生器对区域历史气象场特征的重现能力是开展影响评价的前提和基础。为此,本研究选取MulGETS（参数型）和k-NN（非参数型）发生器为代表模型,利用湘江流域12个气象站点1981−2010年日序列降水量、最高气温、最低气温资料,通过均值、标准差、偏度、极值、空间相关系数、空间连接度和自相关系数等指标的对比,评估了MulGETS和k-NN模型的优缺点及适用性。结果表明：MulGETS和k-NN模型均较好地再现了原气象场的均值、标准差和偏度,k-NN表现稍好于MulGETS。同时k-NN相比MulGETS在保持气象要素空间相关性上具有优势,特别是降水量的空间间歇性。由于算法本身的限制,k-NN无法模拟出超出历史数据范围的极值,而MulGETS具备一定的极值模拟能力。此外,MulGETS和k-NN在重现原始日尺度降水量的自相关性上均存在不足。总体来看,两个模型各具优势和不足,MulGETS更适于极端气象事件模拟,而k-NN可以更好地体现原始气象场的空间差异,实际使用时应根据不同的研究目的选择合适的模型。     

基于人工神经网络的数据处理和多目标群方法用土壤传递函数(PTFs)估算保水性

Pedotransfer functions(PTFs) have been developed to estimate soil water retention curves(SWRC) by various techniques.In this study PTFs were developed to estimate the parameters(θ s,θ r,α and λ) of the Brooks and Corey model from a data set of 148 samples.Particle and aggregate size distribution fractal parameters(PSDFPs and ASDFPs,respectively) were computed from three fractal models for either particle or aggregate size distribution.The most effective model in each group was determined by sensitivity analysis.Along with the other variables,the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling(mGMDH) and different topologies of artificial neural networks(ANNs).The architecture of ANNs for parametric PTFs was different regarding the type of ANN,output layer transfer functions and the number of hidden neurons.Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs.The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of θ s.The textural fraction variables in PTF1 for the estimation of α were replaced with PSDFPs in PTF3.The use of ASDFPs as inputs significantly improved α estimates in the model.This result highlights the importance of ASDFPs in developing parametric PTFs.The mGMDH technique performed significantly better than ANNs in most PTFs.     

Estimating uncertainty in the current critical loads exceedance models

The critical loads approach to quantifying areas at risk of damage requires deposition and critical loads data at the same spatial scale to calculate exceedance. While maps of critical loads for soil acidification are available at a 1 km scale no monitoring networks in Europe measure wet and dry inputs at this scale and, further, the models currently used to estimate deposition incorporate a number of assumptions which are not valid at the 1 km scale. Simulations of 1 km deposition from 20 km data show that the uncertainty introduced by using 20 km scale estimates of deposition is small, except in mountain areas where it can give misleading results, but a major problem is the uncertainty in estimates of deposition at the 20 km scale produced by the current models.     

Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City

Suspended particulate matter is significantly related to the degradation of air quality in urban agglomerations, generating adverse health effects. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important in order to improve public awareness and air quality management. This study aims at developing models using multiple regression and neural network (NN) methods that might produce accurate 24-h predictions of daily average (DA) value of PM10 concentration and at comparatively assessing the above mentioned techniques. Pollution and meteorological data were collected in the urban area of Volos, a medium-sized coastal city in central Greece, whose population and industrialization is continuously increasing. Both models utilize five variables as inputs, which incorporate meteorology (difference between daily maximum and minimum hourly value of ground temperature and DA value of wind speed), persistency in PM10 levels and weekly and annual variation of PM10 concentration. The validation of the models revealed that NN model showed slightly better skills in forecasting PM10 concentrations, as the regression and the NN model can forecast 55 and 61% of the variance of the data, respectively. In addition, several statistical indexes were calculated in order to verify the quality and reliability of the developed models. The results showed that their skill scores are satisfying, presenting minor differences. It was also found that both are capable of predicting the exceedances of the limit value of 50 μg/m³ at a satisfactory level.     

Two models for the leaching of a non-reactive solute to a mole drain

Two models of solute leaching to a mole-pipe drainage system are described. The first model is research-oriented. It simplifies two-dimensional water and solute flow to a mole drain by dividing the soil between the mid-mole plane and the mole into notional compartments. Solute movement between compartments is assumed to occur by convection and mechanical dispersion. Within each compartment a mobile and immobile solute phase is defined, with diffusion occurring between them. Rainfall intensity (over approximately hourly intervals) and basic soil hydraulic data are needed as inputs. An explicit finite-difference solution to the water and solute mass-balance and flux equations is used. The second simpler model is management-oriented. It uses daily time steps, and assumes the soil solution behaves as if it were well-mixed system. It requires only daily rainfall and evaporation data, the drainage coefficient of the mole-pipe system, and the soil macroporosity as inputs. In both models a source/sink term accounts for additions of solute in rainfall and fertilizer, and extraction by plant uptake. The models were used to simulate leaching losses of chloride to a mole-pipe drainage system in a silt loam under pasture, following the application of potassium chloride to the soil surface. The first model simulated leaching better immediately after ertilizer application, and during bypass flow induced by heavy rain. However, both models were able to simulate the measured losses over a 2-year period equally well.     

Implications of spatial averaging weather and soil moisture data for broad scale modelling activities

Abstract. Spatial averaging of data before or after modelling has important implications for large area land evaluation studies. Two procedures are evaluated for the spatial averaging of weather and soil moisture data before and after modelling (procedures A and B, respectively). The Thiessen polygon weighting technique is applied to a network of weather stations to derive daily weather values for the period 1955 to 1985 for 12 selected Agroecological Resource Areas (ARAs) on the Canadian prairies. These values are used in the model for procedure A. The components of the soil moisture balance for spring wheat are estimated with a budgeting model, assuming wheat is grown continuously for 30 years on soils with available water-holding capacities (AWCs) of 150 and 250 mm. In procedure B, the data from individual stations are used as input to the model and the same Thiessen polygon weighting coefficients are applied to the output variables. A comparison of the two procedures shows no significant difference for temperature-related variables such as frost dates, harvest date and cumulative potential evapotranspiration. The differences for moisture-related variables (soil moisture content at sowing, cumulative actual evapotranspiration, runoff and deep drainage) are often statistically significant, but the absolute differences are less than 10 mm at probability levels ranging from 10 to 90%. For many practical applications the two procedures give similar results.     

植物致病真菌寄生类型与胞外糖基水解酶家族分析

植物致病真菌侵染寄主植株的过程中,会分泌特异的糖基水解酶至胞外,与寄主植物互作以完成其生活史。本研究利用已公布的基因组数据,注释并比较分析12种植物致病真菌胞外糖基水解酶（GH家族）与寄生类型之间的关系。结果表明,活体与非活体寄生真菌胞外GHs数量与亚家族分布有显著区别。其中参与细胞壁多糖降解的GHs在非活体寄生菌中显著扩增。进一步的系统发育分析显示GH3家族尤其在半活体寄生菌中扩增明显,这意味着植物致病真菌胞外GHs酶谱与病原菌寄生类型密切相关。据分析结果推测,GH53、GH61和GH3亚家族的扩增有助于病原菌非活体寄生;GHs的丰度与多样化可能更有助于致病菌半活体寄生。     

基于5变量局部薄盘光滑样条函数的蒸发空间插值

 高分辨率、栅格化的气候数据作为环境因子是地学模型和气候模型等相关研究的重要参数,国内外的研究多集中于温度、降水等气象要素,对陆面蒸发空间化研究较少。对黄土高原多沙粗沙区及周围共计53个气象站点(多沙粗沙区30个)蒸发皿测量值EE进行空间插值,以5变量局部薄盘样条函数(经纬度为自变量,净辐射、水气压差和风速为协变量),建立具有多元线性子模型的蒸发插值模型,以ANUSPLIN为实现软件,生成连续21年共252个蒸发表面。交叉验证表明:引入蒸发影响因子作为协变量线性子模型进行表面插值能显著提高插值精度,夏季提高幅度更大,拟合表面具有较高的精确度与平滑度。蒸发随协变量的变率显示,在多沙粗沙区,水气压差是夏季蒸发的主要控制因素,风速对蒸发的影响冬季稍强一些,净辐射的影响没有明显的季节性,只在春分和秋分时节有微小提高。     

The effect of temporal aggregation of weather input data on crop growth models’ results

Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather-crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages.Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data.     

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO₂ contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.     

陕北黄土高原月降雨理论分布的初步研究

利用陕北两个典型气象站点多年降雨强度数据,使用数学方法描述月雨强分布的形状,从而满足水文模型、土壤侵蚀模型等对降雨强度数据的需求。通过探讨小时雨强在月时间尺度上应符合的理论分布函数,并采用统计学参数估计方法进行参数拟合,来选择合适的数学方法对月雨强分布进行描述。使用最大似然法对常见的7种理论分布函数进行拟合的结果表明,LOGN、GAMA、WBL等理论分布函数都可以较好地再现月降雨强度分布,90%以上的月份COE值大于0.7,一半以上的月份COE值大于0.9。另外,WBL分布对降雨强度峰值亦有良好的表现能力,因此推荐在陕北黄土高原降雨分布的模拟过程中使用。     

基于IMERG反演降水数据估算广东省降雨侵蚀力

近年来遥感反演降水产品的时空分辨率不断提高，为估算区域尺度上具有空间连续性的降雨侵蚀力提供了新的可能。但以往研究在应用遥感降水产品估算降雨侵蚀力时多忽略了其与站点观测数据间的差异和对其纠偏的可能性。该研究以广东省86个气象站2001—2020年的逐时降水资料估算的降雨侵蚀力为观测值，评估两套IMERG（integrated multi-satellite retrievals for GPM）遥感降水产品-GPM_3IMERGHH（0.1°，逐30-min）和GPM_3IMERGDF（0.1°，逐日）对广东省降雨侵蚀力的估算精度并量化偏差，再结合拟合纠偏确定基于遥感反演降水数据估算广东省降雨侵蚀力的最优方法。结果表明：这两套产品均不适宜直接估算降雨侵蚀力指标，不同时间尺度、不同方法直接应用时精度均较低，克林-古普塔效率系数（Kling-Gupta efficiency, KGE）小于等于0.51。但多年平均和极端次事件降雨侵蚀力与对应观测值间具有强相关性（皮尔逊相关系数大于等于0.78），具备纠偏的潜力。因此，本研究发展线性模型对IMERG估算结果进行纠偏，交叉验证结果表明纠偏后GPM_3IMERGHH估算多年平均降雨侵蚀力（R因子）的KGE可达0.79，10年一遇EI₃₀的KGE可达0.64，优于采用站点日降水估算降雨侵蚀力并插值的精度（KGE分别为0.60和0.59），与采用站点小时降水估算降雨侵蚀力并插值的精度相近（KGE分别为0.77和0.66）。当前研究结果充分展示了遥感反演降水在土壤水蚀领域的应用潜力和前景。     

气象信息化在许昌现代农业的应用与思考

通过系统介绍许昌农业气象信息化的发展历程,着重介绍了农业气象信息化网络的构成、服务方式、建设成果,以及许昌现代农业气象信息化的四种发展模式,即乡级农业气象信息服务站、特色农业气象信息服务协会、手机大喇叭、涉农气象信息服务站,并对未来许昌农业气象信息化发展思路和方向进行展望,提出了气象信息化在许昌现代农业中应用的合理化建议。     

Applying one-dimensional deterministic chemical fate models on a regional scale

A deterministic one-dimensional chemical transport model was applied to soil and weather data from the northeastern United States. The model used a mobile-immobile capacity flow model. Simulations using 20 years of historical rainfall data from each of 13 weather stations applied to a single soil showed that predictions of leaching varied considerably from year to year. The model was then applied to 2270 soil-weather combinations to develop indices for atrazine-leaching potential. Issues relating to quality of soil data and temporal variability of rainfall are discussed. Simulated atrazine leaching was related to the amount and distribution of rainfall, modified by soil organic matter levels.     

Diurnal patterns in dispersal of Monilinia fructigena conidia in an apple orchard in relation to weather factors

The aerial concentration of Monilinia fructigena (causing brown rot in pome fruit) conidia was sampled during 1997 and 1998 in an apple orchard and was related to weather conditions. The highest hourly concentration measured in 1997 was 230 conidia/m³, in 1998 concentrations were lower than in 1997 throughout the season. In both years concentrations were highest in the afternoon hours. Generalised linear models, employing a Poisson distribution for the spore counts and a logarithm link function, were used to study the relationships between spore counts and lagged weather variables. In 1997 the best fitting model had variables temperature lagged at 100 h, an east–west component of wind direction, and wind speed; whereas in 1998 the best model included in addition an effect of relative humidity. Temperature and wind direction had consistent effects on hourly spore counts in both years, but whereas temperature has a biologically relevant effect on spore production and maturation, the effect of wind direction is likely to reflect the purely local effect of orchard layout. Results are compared with observations made in stone fruit orchards where Monilinia laxa and Monilinia fructicola are the predominant species, and differences in epidemiology between these systems are discussed.