Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

Estimation of pan evaporation (E _pan) using black-box models has received a great deal of attention in developing countries where measurements of E _pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E _pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E _pan predictions (r?=?0.97, RMSE?=?0.81?mm?day^?1, MAE?=?0.63?mm?day^?1 and PE?=?0.58?%). The results also showed that the most accurate E _pan predictions with a CANFIS model can be achieved with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E _pan process.     

Simulation of daily energy budget and mean soil temperatures at an arid site

Summary Soil temperature is often inadequately based upon relatively few measurements at widely dispersed locations. Within arid regions, such as the desert southwestern United States, soils, microclimates, and thus soil temperature may be markedly heterogeneous. Because extensive measurement of soil temperature is often not feasible, models are needed that simulate soil temperature based on readily available soil survey and above-ground weather information. This paper describes a simple energy-budget based model for simulating daily mean temperatures within a bare arid land soil. The model requires basic information on soil physical properties, and daily weather data including air temperature, windspeed, rainfall, and solar radiation to calculate daily surface energy budget components and surface temperature. One of two alternative numerical methods is then used to calculated subsurface temperatures. Tests of the model using 1987 daily temperature data from an arid site at Yuma, Arizona resulted in root mean square deviations within 1.4°C between daily modeled and measured temperatures at both 0.05 and 0.10 m depths. Sensitivity analysis showed modeled temperatures at 0.05 m depth to be most sensitive to parameters affecting the surface energy balance such as air temperature and solar radiation. Modeled temperatures at 1.0m depth were relatively more sensitive to initial temperature conditions and to parameters affecting distribution of energy within the profile such as thermal conductivity.With 3 Figures     

近40年中国饱和水汽压差时空变化及影响因素分析

基于全国600多个站点的逐月气象观测资料,利用Mann-Kendall检验、多元线性回归等方法分析了中国地区1980～2018年不同气候区(干旱区、湿润区、半干旱区与半湿润区)饱和水汽压差(Vapor Pressure Deficit,VPD)的时空变化特征,检测了影响中国地区饱和水汽压差变化的主导气象因素.结果 发现...     

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.     

An investigation of near surface wind speed trends in arid and semiarid regions of Iran

Wind plays an important role on the ecosystems and hydrological cycles besides other meteorological parameters such as temperature, precipitation, sunshine, and relative humidity. It strongly affects evapotranspiration, especially in arid and semiarid regions where there are serious problems in regard to water resource management. Evaluating the wind speed trend can provide good information for future agricultural planning. This study was conducted in order to investigate the wind speed trends over 24 synoptic meteorological stations located in arid and semiarid regions of Iran from 1975 to 2005. Near-surface wind speed was trended by nonparametric Mann–Kendall test spatially and temporally in three time scales (annual, seasonal, and monthly). Then, Sen’s slope estimator was used to determine the amount of the changes; furthermore, 10-year moving average low-pass filter was applied to show general trends. Finally, the smoothed time series derived from the mentioned filter were classified in three clusters for each time series and then mapped to show their spatial distribution pattern. Results showed insignificant and significant, increasing and decreasing trends during the surveyed time. Wind speeds in less than 50 % of stations changed statistically in all time scales, and in most cases, the frequency of the upward trends was more than that of downward ones. The spatial distribution of significant wind speed showed that the increase mostly occurred in eastern part. Clustering gave us the turning point around 1990. Clearly, when clusters were mapped, they indicated the same pattern as the Z value maps derived from Mann–Kendall test which meant that the outputs of the mentioned method confirmed the other one. As the wind speed trends in different stations likely to follow the previous evapotranspiration (ET₀) trend results in Iran, it confirms that wind speed was an effective parameter on ET₀, even though other parameters should be considered too.     

基于MODIS白天地温产品的青藏高原海拔依赖型变暖特征分析

基于2001～2018年中分辨率成像光谱仪（MODIS）探测的白天地面温度（简称MODIS 白天地温）资料,与青藏高原（简称高原）122个气象站点观测的最高气温资料,在年尺度上评估了MODIS 白天地温在高原的适用性,研究了高原五个干湿分区下MODIS 白天地温的海拔依赖型变暖特征,得到以下主要结论:（1）MODIS白天地温能够基本再现观测的最高气温的时空以及海拔依赖型变暖特征;（2）高原整体上,MODIS白天地温存在显著的海拔依赖型变暖特征,平均海拔每增加100 m,其趋势增加0.02°C (10a)?1,且受积雪—反照率反馈主导;（3）干湿分区下,海拔依赖型变暖特征在高原表现为偏湿润地区强于偏干旱地区;季风区强于西风区。海拔依赖型特征强弱:半湿润地区＞湿润半湿润地区＞半干旱地区＞湿润地区＞干旱地区。平均海拔每增加100 m,以上区域的地温趋势分别增加0.06,0.03,0.03,0.01,0.01°C (10a)?1。半湿润和湿润半湿润地区年均温在0°C左右,在气候变暖背景下积雪—反照率反馈作用最为强烈,是其海拔依赖型变暖的主导因素;干旱与半干旱地区年均温相对更低,气候变暖程度对积雪影响相对较小,积雪—反照率反馈作用被限制,但仍对上述地区的海拔依赖型变暖起主导作用;而湿润地区的积雪覆盖率的上升可能是由于降雪（固态降水）增加抵消了积雪融化损耗,云辐射、水汽等其他因素主导了其海拔依赖型变暖。     

Projecting impacts of climate change on surface water temperatures of a large subalpine lake: Lake Tahoe, USA

Predicted increases in atmospheric CO₂ concentration are expected to cause increases in air temperatures in many regions around the world, and this will likely lead to increases in the surface water temperatures of aquatic ecosystems in these regions. Using daily air and littoral water temperature data collected from Lake Tahoe, a large sub-alpine lake located in the Sierra Nevada mountains (USA), we developed and tested an empirical approach for constructing models designed to estimate site-specific daily surface water temperatures from daily air temperature projections generated from a regional climate model. We used cluster analysis to identify thermally distinct groups among sampled sites within the lake and then developed and independently validated a set of linked regression models designed to estimate daily water temperatures for each spatially distinct thermal group using daily air temperature data. When daily air temperatures projections, generated for 2080–2099 by a regional climate model, were used as input to these group models, projected increases in summer surface water temperatures of as much as 3 °C were projected. This study demonstrates an empirical approach for generating models capable of using daily air temperature projections from established climate models to project site specific impacts on littoral surface waters within large limnetic ecosystems.     

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R ²) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.     

Modeling daily soil temperature using data-driven models and spatial distribution

The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.     

Climatic trends in Israel 1970–2002: warmer and increasing aridity inland

Climatic trends in Israel during the period 1970–2002 were studied in detail on the basis of three parameters: average annual temperature, annual precipitation and the annual aridity (humidity) index P/PET (P = Precipitation; PET = Potential Evapotranspiration). Significant warming is evident in all 12 evaluated meteorological stations, situated in different parts of Israel. Along the Mediterranean coast, the average annual precipitation and P/PET values remained more or less at the same level. However, more inland, both eastward and southward, precipitation and P/PET trends are declining, indicating increased aridity. Eilat, Beer Sheva and Sedom Pans, situated in the desert, showed the most significant increase in aridity among the 12 meteorological stations we investigated. The relationship between changes in temperature and precipitation showed a negative correlation in all cases except for Eilat, the southernmost and driest part of Israel. The negative correlations for Negba, Kefar Blum, Har Kena’an, Beer Sheva and Sedom Pans are statistically significant. In conclusion, the climate in Israel has become more arid in most regions, except for the coastal plain.     

Statistical downscaling of summer temperature extremes in northern China

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011-35 relative to 1980-99.     

气象观测环境的变化对气温序列的影响分析

本文利用安徽省1971~2000年共30 a逐日平均气温、最高气温、最低气温的气候整编资料,选取气象观测环境完全符合相关规定(A类)和不完全符合相关规定(B类)两类代表站,分别建立了气温序列和两者的气温差值序列,并对气温序列的气候平均值和气温差值序列的气候趋势进行了分析。结果表明:气象观测环境的变化将影响气温序列。与A类台站相比,B类台站平均气温、最高气温和最低气温的气候平均值表现出一致偏高的特征,最低气温偏高最多;B类台站与A类台站平均气温、最高气温和最低气温的差值均有显著增大的趋势,平均气温差值的增大最为显著。     

几种水平面太阳总辐射量计算模型的对比分析

利用中国区域1961-1999年39 a间98个常规气象观测数据,建立6个模型分别以天文辐射、干洁大气总辐射和湿洁大气总辐射为起始数据,进行太阳辐射日总量的模拟,对比分析了6个水平面太阳总辐射量计算模型的性能.结果表明:在三种起始数据中,干洁大气总辐射和湿洁大气总辐射均能较好地体现宏观地势对太阳辐射空间分布的影响,以湿洁大气总辐射为起始数据的计算模型拟合精度相对较高.对6个水平面太阳总辐射量计算模型的对比分析发现:2个以日照百分率为主导因子,气温日较差为修正项的综合模型拟合误差最小,精度最高;经典的日照百分率模型次之,但其模型系数最稳定可靠;3个气温日较差模型拟合效果最差.最终选用经验系数稳定、拟合精度较高的日照百分率模型,制作了2001年中国水平面太阳辐射日总量空间分布图.     

四川盛夏伏旱的MODIS遥感监测方法

本文利用2006～2010年7、8月的MODIS卫星遥感资料、土壤湿度观测资料和Ci综合气象干旱指数,讨论了NDVI-Ts特征空间干、湿边的拟合方法,建立了7～8月各旬的TVDI指数计算模型.根据31个农业气象站点的土壤相对湿度观测资料,获得了5种干旱等级的观测样本,并通过样本类内离散度、类间离散度,以及与土壤相对湿度和Ci指数相关性的比较,对比分析了TVDI和VSWI两种干旱遥感监测方法对四川盛夏伏旱的监测能力,结果表明:对于四川盛夏伏旱的遥感监测来说,用TVDI指数监测比用VSWI指数监测更合适,效果更好,TVDI指数监测到的干旱区比较可靠.     

Patterns of daily maximum and minimum temperature data in Nigeria

Summary ?The knowledge of the dependence pattern of daily weather variables, which tend to persist in time, is important in developing techniques for simulating weather data. The dependence of daily maximum and minimum temperatures is presented for 17 stations in the three main climatic zones (humid, sub-humid and semi-arid) of Nigeria. Daily maximum and minimum temperatures have an average lag-one serial correlation coefficient of 0.833 and 0.802, respectively, depending more on latitude followed by elevation of the site. The correlation between maximum and minimum temperature averaged 0.387 and also varied with season and location. The results of the Student’s T-test show that persistence within maximum and minimum temperatures and interdependence between the two meteorological variables did not differ significantly, 95% of the times, at most of the sites. The implication of these in developing limited area models for weather forecasting and regional climate studies has been stressed. Received July 6, 1999/Revised March 6, 2000     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

北京地区冻土时空分布特征

基于1981—2021年北京地区6个气象站的逐日最大冻土深度、平均气温、平均地表温度及5、10、15、20、40、80 cm地温等资料，分析了近40年北京地区最大冻土深度的时空分布特征及其与气温和地温的关系。结果表明：北京地区最大冻土深度总体呈变浅趋势，气候倾向率为-2.3 cm/10 a，各站点最大冻土深度变浅趋势从西到东呈逐渐减弱趋势。北京地区最大冻土深度与40、80 cm地温相关性最好，与地表温度相关性较差。选取2021至2022年北京地区冻土对比试验数据，评估测温式冻土自动观测仪观测精度，发现仪器安装至少一个冻融周期后与冻土人工观测吻合度更好，测温式冻土自动观测仪的观测精度与仪器安装位置的地下岩层、土质分布密切相关，需要在仪器稳定运行后根据当地实际优化算法和冻融阈值。     

西北干旱区极端高温时空变化特征分析

利用1961—2010年西北干旱区83个气象观测站的日气温资料,通过线性倾向率、百分位法及Mann-Kendall法得出西北干旱区极端高温的具体变化特征。用百分位法对西北干旱区日气温数据进行处理,确定极端高温指标的阈值,得出极端高温强度和极端高温事件的频率。结论如下:自1989年开始,西北干旱区年极端高温呈显著上升趋势,空间上西部大于东部,局部地区盆地南缘大于北缘;极端高温日数呈明显的上升趋势;四季极端高温均有上升趋势,秋季增长率最高、冬季最低,秋季极端高温日数增长速率最大;季极端高温及高温日数高值区分布在西北干旱区西北和东南部的盆地边缘,干旱区沙漠边缘及戈壁区;西北干旱区年、季极端高温日数均与年平均气温相关性突出。     

近46年青藏高原干湿气候区动态变化研究

利用青藏高原62个气象站1961～2006年逐日气象资料, 用世界粮农组织 (FAO) 在1998年推荐的、并唯一承认的Penman-Menteith模式计算潜在蒸散量; 比较了降水量、积温降水比、气温降水比、蒸散降水比和降水蒸散比5种湿润度指标在青藏高原的适用性, 用常规统计方法和墨西哥帽小波变换分析青藏高原各气候区干湿状况及其界线的动态变化。结果表明: 5种指标中, 用降水蒸散比得到的青藏高原湿润、半湿润、半干旱、干旱和极端干旱气候区的分区结果比较合理; 近46年来青藏高原大部分地区湿润度和每个气候区的平均湿润度均呈增加趋势, 半干旱和半湿润气候区的界线呈向西北推进趋势, 气候在向暖湿方向发展。