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1.
Reference evapotranspiration (ETo) is significant for water resources planning and environmental studies. Many equations have been developed for ETo estimation in various geographic and climatic conditions, of which, the Penman–Monteith FAO 56 (PMF-56) equation was accepted as reference method. A major complication in estimating ETo by the PMF-56 model is the requirement for meteorological data that may not be readily available from typical weather stations in many areas of the globe. This restriction necessitates use of simpler models which require less input data. In this study, the original and five modified versions of the Hargreaves equation that require only temperature and rainfall were evaluated in humid, semi-humid, semi-arid and arid climates in Iran. The results showed that the original and modified versions of the Hargreaves equation had the poorest performance in semi-humid climate and the best performance in windy humid environment. Further, the ETo estimations with the Hargreaves equations having rainfall parameter were poor as compared to those from the PMF-56 method under majority of the climatic situations studied.  相似文献   

2.
This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET0) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET0 and that the ANNs with an R 2 of 0.95 and a root mean square error (RMSE) of 0.41 mm day?1 simulated ET0 better than the Hargreaves equation, which had an R 2 of 0.91 and a RMSE of 0.51 mm day?1.  相似文献   

3.

Evapotranspiration estimation is of crucial importance in arid and hyper-arid regions, which suffer from water shortage, increasing dryness and heat. A modeling study is reported here to cross-station assessment between hyper-arid and humid conditions. The derived equations estimate ET0 values based on temperature-, radiation-, and mass transfer-based configurations. Using data from two meteorological stations in a hyper-arid region of Iran and two meteorological stations in a humid region of Spain, different local and cross-station approaches are applied for developing and validating the derived equations. The comparison of the gene expression programming (GEP)-based-derived equations with corresponding empirical-semi empirical ET0 estimation equations reveals the superiority of new formulas in comparison with the corresponding empirical equations. Therefore, the derived models can be successfully applied in these hyper-arid and humid regions as well as similar climatic contexts especially in data-lack situations. The results also show that when relying on proper input configurations, cross-station might be a promising alternative for locally trained models for the stations with data scarcity.

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4.
Estimation of reference evapotranspiration (ET0) is needed to support irrigation design and scheduling, and watershed hydrology studies. There are many available methods to estimate evapotranspiration from a water surface, comprising both direct and indirect methods. In the first part of this study, the generalized regression neural networks model (GRNN) and radial basis function neural network (RBFNN) are developed and compared in order to estimate the reference ET0 for the first time in Algeria. Various daily climatic data, that is, daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature, and wind speed from Dar El Beida, Algiers, Algeria, are used as inputs to the GRNN and RBFNN models to estimate the ET0 obtained using the FAO-56 Penman-Monteith equation (PM56). The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. In the second part of the study, the empirical Hargreaves-Samani (HG) and Priestley-Taylor (PT) equations are also considered for the comparison. Based on the comparisons, the GRNN was found to perform better than the RBFNN, Priestley-Taylor and Hargreaves-Samani models. The RBFNN model is ranked as the second best model.  相似文献   

5.
Global warming has caused unevenly distributed changes in precipitation and evapotranspiration, which has and will certainly impact on the wet-dry variations. Based on daily meteorological data collected at 91 weather stations in Northeast China (NEC), the spatiotemporal characteristics of dry and wet climatic variables (precipitation, crop reference evapotranspiration (ET0), and humid index (HI)) are analyzed, and the probable reasons causing the changes in these variables are discussed during the period of 1961–2014. Precipitation showed non-significant trend over the period of 1961–2014, while ET0 showed a significant decreasing trend, which led to climate wetting in NEC. The period of 2001–2012 exhibited smaller semiarid area and larger humid area compared to the period of 1961–1980, indicating NEC has experienced wetting process at decadal scale. ET0 was most sensitive to relative humidity, and wind speed was the second most sensitive variable. Sunshine hours and temperature were found to be less influential to ET0 in the study area. The changes in wind speed in the recent 54 years have caused the greatest influence on ET0, followed by temperature. For each month, wind speed was the most significant variable causing ET0 reduction in all months except July. Temperature, as a dominant factor, made a positive contribution to ET0 in February and March, as well as sunshine hours in June and July, and relative humidity in August and September. In summary, NEC has experienced noticeable climate wetting due to the significantly decreasing ET0, and the decrease in wind speed was the biggest contributor for the ET0 reduction. Although agricultural drought crisis is expected to be partly alleviated, regional water resources management and planning in Northeast China should consider the potential water shortage and water conflict in the future because of spatiotemporal dry-wet variations in NEC.  相似文献   

6.
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models’ accuracy was also investigated. Including periodicity component in models’ inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.  相似文献   

7.
The monthly rainfall data from 1901 to 2011 and maximum and minimum temperature data from 1901 to 2005 are used along with the reference evapotranspiration (ET0) to analyze the climate trend of 45 stations of Madhya Pradesh. ET0 is calculated by the Hargreaves method from 1901 to 2005 and the computed data is then used for trend analysis. The temporal variation and the spatial distribution of trend are studied for seasonal and annual series with the Mann-Kendall (MK) test and Sen’s estimator of slope. The percentage of change is used to find the rate of change in 111 years (rainfall) and 105 years (temperatures and ET0). Interrelationships among these variables are analyzed to see the dependency of one variable on the other. The results indicate a decreasing rainfall and increasing temperatures and ET0 trend. A similar pattern is noticeable in all seasons except for monsoon season in temperature and ET0 trend analysis. The highest increase of temperature is noticed during post-monsoon and winter. Rainfall shows a notable decrease in the monsoon season. The entire state of Madhya Pradesh is considered as a single unit, and the calculation of overall net change in the amount of the rainfall, temperatures (maximum and minimum) and ET0 is done to estimate the total loss or gain in monthly, seasonal and annual series. The results show net loss or deficit in the amount of rainfall and the net gain or excess in the temperature and ET0 amount.  相似文献   

8.
This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.  相似文献   

9.
The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R 2?>?0.88 and RMSE?<?1.2 mm day?1), but the ANN method gave better estimates than the calibrated Hargreaves method.  相似文献   

10.
This paper focuses on the primary causes of changes in potential evapotranspiration (ETo) in order to comprehensively understand climate change and its impact on hydrological cycle. Based on modified Penman-Monteith model, ETo is simulated, and its changes are attributed by analyzing the sensitivity of ETo to influence meteorological variables together with their changes for 595 meteorological stations across China during the period 1961–2008. Results show the decreasing trends of ETo in the whole country and in most climate regions except the cold temperate humid region in Northeast China. For China as a whole, the decreasing trend of ETo is primarily attributed to wind speed due to its significant decreasing trend and high sensitivity. Relative humidity is the highest sensitive variable; however, it has negligible effect on ETo for its insignificant trend. The positive contribution of temperature rising to ETo is offset by the effect of wind speed and sunshine duration. In addition, primary causes to ETo changes are varied for differing climate regions. ETo changes are attributed to decreased wind speed in most climate regions mainly distributed in West China and North China, to declined sunshine duration in subtropical and tropical humid regions in South China, and to increased maximum temperature in the cold temperate humid region.  相似文献   

11.
Accurate estimation of reference evapotranspiration (ET 0 ) is essential for the computation of crop water requirements, irrigation scheduling, and water resources management. In this context, having a battery of alternative local calibrated ET 0 estimation methods is of great interest for any irrigation advisory service. The development of irrigation advisory services will be a major breakthrough for West African agriculture. In the case of many West African countries, the high number of meteorological inputs required by the Penman-Monteith equation has been indicated as constraining. The present paper investigates for the first time in Ghana, the estimation ability of artificial intelligence-based models (Artificial Neural Networks (ANNs) and Gene Expression Programing (GEPs)), and ancillary/external approaches for modeling reference evapotranspiration (ET 0 ) using limited weather data. According to the results of this study, GEPs have emerged as a very interesting alternative for ET 0 estimation at all the locations of Ghana which have been evaluated in this study under different scenarios of meteorological data availability. The adoption of ancillary/external approaches has been also successful, moreover in the southern locations. The interesting results obtained in this study using GEPs and some ancillary approaches could be a reference for future studies about ET 0 estimation in West Africa.  相似文献   

12.
The FAO Penman–Monteith (F-PM) method is a frequently applied approach for calculating the daily reference evapotranspiration (ET0). This method requires long records of meteorological data, which makes it quite hard to employ in locations with no or limited available data. Evaporation pans are widely used to estimate the reference ET0, but this method requires reliable estimates of the pan coefficient (K p). The objectives of this study were to determine the proper values of monthly and annual K p, as well as the best method among those available for the estimation of K p values in the study area. Measured weather data from 1992 to 2006 were obtained from 18 stations in the North and Northwest of Iran. Daily ET0 calculated using methods by Bernardo et al. and Pereira et al. were compared with those calculated by the F-PM method. The employed methods at all stations, except those located in the north of the study area with high relative humidity, overestimated the ET0 compared to the F-PM method. The constant parameters of these methods were optimized by a trial and error scheme to minimize the root mean square error. The results indicated that modified K p coefficients from Bernardo et al.’s method ranged between 0.41 and 0.87 and the optimal coefficient of Pereira et al.’s method ranged between 0.49 and 0.95. Modified monthly K p from Bernardo et al.’s method ranged between 0.3 and 1.07 and those from Pereira et al.’s method ranged between 0.4 and 1.18. Modified K p of the methods by Bernardo et al. and Pereira et al. showed the higher estimation accuracy of daily ET0 values. In general, the performance of the modified K p of Bernardo et al.’s method was higher than Pereira et al.’s method for all stations. Thus, in the study region and under the same climatic conditions [in areas with only pan evaporation (E p) records], the use of climatic monthly modified K p to calculate ET0 based on class A E p is recommended.  相似文献   

13.
四川省潜在蒸散量估算模型   总被引：3，自引：0，他引：3       下载免费PDF全文
Penman-Monteith法是FAO-56推荐的计算潜在蒸散量的标准方法, 但由于涉及的气象要素较多, 难于在业务中应用。以综合气象干旱指数的业务化应用为目标, 利用1971-2000年四川省156个气象站的观测资料, 以Penman-Monteith法计算结果作为标准,分析了Thornthwaite法和Hargreaves法对川西高原和四川盆地年、月潜在蒸散量的估算精度, 建立了可供业务应用的ET0估算模型, 并应用于2006年四川省特大伏旱监测, 结果表明:Thornthwaite法反映不出ET0的年际变化,在冬季显著偏小, 而Hargreaves法对ET0的年际变化具有较好的反映能力, 与Thornthwaite法相比,其ET0年、月估算值更接近于Penman-Monteith法标准值,且Hargreaves法估算值与Penman-Monteith法标准值之间具有较好的线性关系,引入风速和相对湿度两个订正因子后,Hargreaves订正值的误差可控制在10%以内, 基于该文ET0估算模型计算的综合气象干旱指数对四川干旱具有较强的监测能力。  相似文献   

14.
This paper characterizes droughts in Romania using the approach of both the standardized precipitation index (SPI) and climatic water deficit (WD). The values of the main climatic factors (rainfall, temperature, reference evapotranspiration, etc.) were obtained from 192 weather stations in various regions of Romania. Penman–Monteith reference evapotranspiration (ETo-PM) was used to calculate WD as the difference between precipitation (P) and ETo-PM. SPI was calculated from precipitation values. There is a clear difference between drought and aridity. Drought occurrence determines higher WD values for plains and plateaus and lower climatic excess water (EW) values for high mountains in Romania, depending on the aridity of the specific region considered and drought severity. WD calculated as mean values for both normal conditions and, for all locations studied, various types of drought was correlated with mean annual precipitation and temperature, respectively. The combined approach of WD and SPI was mainly carried out for periods of 1 year, but such studies could also be done for shorter periods like months, quarters, or growing season. The most arid regions did not necessarily coincide with areas of the most severe drought, as there were no correlations between WD and SPI and no altitude-based SPI zones around the Carpathian Mountains, as is the case for other climate characteristics, soils and vegetation. Water resource problems arise where both SPI values characterize extremely droughty periods and WD values are greatly below ?200 mm/year. This combined use of SPI and WD characterizes the dryness of a region better than one factor alone and should be used for better management of water in agriculture in Romania and also other countries with similar climate characteristics.  相似文献   

15.
The accuracy of nine solar radiation (R s ) estimation models and their effects on reference evapotranspiration (ET o ) were evaluated using data from eight meteorological stations in Canada. The R s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R s values were then compared to measured R s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K RS 0.15) estimated R s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET o calculated with estimated R s was reduced by about 50% as compared to deviation in measured versus estimated R s .  相似文献   

16.
Sunshine duration data are desirable for calculating daily solar radiation (R s) and subsequent reference evapotranspiration (ET0) using the Penman–Monteith (PM) method. In the absence of measured R s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R s and ET0 was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R 2), the root mean square error (RMSE) and the mean bias error (MBE). Average R 2, RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R s and ET0 by using Ångström and FAO-PM equations, respectively.  相似文献   

17.
Reference crop evapotranspiration (ET0) is one of the most important climatic parameters which plays a key role in estimating crop water demand and scheduling irrigation. Under global warming and climate change conditions, it is needed to survey the trend of ET0 in Iran. In this study, ET0 values were determined based on FAO-56 Penman-Monteith equation over 32 synoptic meteorological stations during 1960–2005; and analyzed spatially and temporally in monthly, seasonal and annual time scales. After removing the significant lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann–Kendall (MK) test was used to detect the trends. The slope of the changes was determined by Sen’s slope estimator. In order to facilitate in trend analysis, the 10 moving average low pass filter were also applied on the normalized annual ET0 time series. Annual ET0 time series and filtered ones were then classified by hierarchical clustering in three clusters and then mapped in order to show the patterns of different clusters. Results showed that the significant decreasing trends were more considerable than increasing ones. Among surveyed stations, and on an annual time scale, the highest and lowest annual values of Sen’s slope estimator were observed in Tabas with (+) 72.14 mm per decade and Shahrud with (?) 62.22 mm per decade, respectively. Results also indicated that the clustered map based on normalized and filtered annual ET0 time series is in accordance with another map which showed spatial distribution of increasing, decreasing and non-significant trends of ET0 on annually time scale. Exploratory and visual analysis of smoothed time series showed increasing trend in recent years especially after 1980 and 1995. In brief, the upward trend of ET0 in recent years is a crucial issue with regard to the high cost of dam construction for agricultural aims in arid and semi-arid regions e.g. Iran.  相似文献   

18.
Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km2, by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t 47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t 47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.  相似文献   

19.
The present study evaluates firstly the ability of the FAO-56 methodology, based on the two-step approach “reference evapotranspiration (ET0)—crop coefficient (K c),” to accurately determine the actual evapotranspiration (ET) of irrigated crops and proposes, secondly, the alternative approaches for improving this determination. The FAO-56 methodology is supported by two hypotheses: (1) ET0 represents all effects of weather and (2) K c varies predominately with specific crop characteristics and only marginally with climate, which enables the transfer of K c standard values among locations and climates. On the base of the theoretical analysis and experimental observations, a critical examination of the previous hypotheses demonstrates that they are not verified by reality. The first hypothesis is not verified for two reasons: (a) The formulation adapted by the Penman–Monteith equation and proposed in FAO-56 methodology for calculating ET0 uses climatic variables determined at a 24-h average scale. However, in principle it is only valid in permanent regime, in other words at least on an hourly scale. (b) The FAO-56-proposed formulation attributes a constant value to the canopy resistance of the reference surface; but in reality, this resistance is variable in relation to the climatic variables. The second hypothesis, concerning the two-step approach, is also not verified because the values of K c largely vary in relation to climatic variables (radiation, air vapour pressure deficit and wind speed). This fact does not support the possibility of the transferability of K c values into locations where the local conditions deviate from the conditions where the adjusted values of K c were determined. The weakness of the ET estimation, observed on several crops cultivated in the Mediterranean region, through the application of the FAO-56 methodology, is the result of errors accumulation, associated with that affects the determination of either ET0 or K c. The present study underlines the advantage of using a one-step approach in the calculation of ET, since it is based on fewer computation steps and, consequently, on fewer error sources than the two-step model. Two models adopting this approach are proposed and validated, one of which can be considered as operational, i.e. it only needs standard meteorological data as input. The use of these models enables an improvement of the ET estimation. This objective is a key component of any strategy to improve agricultural water management in Mediterranean region.  相似文献   

20.
Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m2 day?1 and 2.25 MJ m2 day?1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.  相似文献   

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