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1.
Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 µg/m3, 11.15 µg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.  相似文献   

2.
Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET0) calculation was investigated. ANNs were trained and tested for arid (west), semi‐arid (middle) and sub‐humid (east) areas of the Inner Mongolia district of China. Three or four climate factors, i.e. air temperature (T), relative humidity (RH), wind speed (U) and duration of sunshine (N) from 135 meteorological stations distributed throughout the study area, were used as the inputs of the ANNs. A comparison was conducted between the estimates provided by the ANNs and by multilinear regression (MLR). The results showed that ANNs using the climatic data successfully estimated ET0 and the ANNs simulated ET0 better than the MLRs. The ANNs with four inputs were more accurate than those with three inputs. The errors of the ANNs with four inputs were lower (with RMSE of 0·130 mm d?1, RE of 2·7% and R2 of 0·986) in the semi‐arid area than in the other two areas, but the errors of the ANNs with three inputs were lower in the sub‐humid area (with RMSE of 0·21 mm d?1, RE of 5·2% and R2 of 0·961. For the different seasons, the results indicated that the highest errors occurred in September and the lowest in April for the ANNs with four inputs. Similarly, the errors were higher in September for the ANNs with three inputs. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

3.
《水文科学杂志》2012,57(15):1824-1842
ABSTRACT

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ETo) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (Tmin, Tmax, RH, Us, Rs) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ETo at study stations.  相似文献   

4.
F. Ashkar 《水文科学杂志》2013,58(6):1092-1106
Abstract

The potential is investigated of the generalized regression neural networks (GRNN) technique in modelling of reference evapotranspiration (ET0) obtained using the FAO Penman-Monteith (PM) equation. Various combinations of daily climatic data, namely solar radiation, air temperature, relative humidity and wind speed, are used as inputs to the ANN so as to evaluate the degree of effect of each of these variables on ET0. In the first part of the study, a comparison is made between the estimates provided by the GRNN and those obtained by the Penman, Hargreaves and Ritchie methods as implemented by the California Irrigation Management System (CIMIS). The empirical models were calibrated using the standard FAO PM ET0 values. The GRNN estimates are also compared with those of the calibrated models. Mean square error, mean absolute error and determination coefficient statistics are used as comparison criteria for the evaluation of the model performances. The GRNN technique (GRNN 1) whose inputs are solar radiation, air temperature, relative humidity and wind speed, gave mean square errors of 0.058 and 0.032 mm2 day?2, mean absolute errors of 0.184 and 0.127 mm day?1, and determination coefficients of 0.985 and 0.986 for the Pomona and Santa Monica stations (Los Angeles, USA), respectively. Based on the comparisons, it was found that the GRNN 1 model could be employed successfully in modelling the ET0 process. The second part of the study investigates the potential of the GRNN and the empirical methods in ET0 estimation using the nearby station data. Among the models, the calibrated Hargreaves was found to perform better than the others.  相似文献   

5.
Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ETo) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (Tmax) required in the ETo models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed Tmax obtained from automatic weather stations. Minimum air temperature (Tmin) was estimated using ASA. Land surface temperature was employed in the ETo models in place of air temperature (Ta) to check the potency of its applicability. The results suggest that the PMT model with Ta as input performed better than the other ETo models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d?1 and ?0.69 for all land cover classes. The ASA yielded better Tmax and Tmin values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).  相似文献   

6.
Ozgur Kisi 《水文研究》2008,22(14):2449-2460
The potential of three different artificial neural network (ANN) techniques, the multi‐layer perceptrons (MLPs), radial basis neural networks (RBNNs) and generalized regression neural networks (GRNNs), in modelling of reference evapotranspiration (ET0) is investigated in this paper. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN techniques so as to estimate ET0 obtained using the FAO‐56 Penman–Monteith (PM) equation. In the first part of the study, a comparison is made between the estimates provided by the MLP, RBNN and GRNN and those of the following empirical models: The California Irrigation Management Information System (CIMIS) Penman (1985), Hargreaves (1985) and Ritchie (1990). In this part of the study, the empirical models are calibrated using the standard FAO‐56 PM ET0 values. The estimates of the ANN techniques are also compared with those of the calibrated empirical models. Mean square errors, mean absolute errors and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the MLP and RBNN techniques could be employed successfully in modelling the ET0 process. In the second part of the study, the potential of ANN techniques and the empirical methods in ET0 estimation using nearby station data is investigated. Among the models, the calibrated Hargreaves model is found to perform better than the others. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

7.
《水文科学杂志》2013,58(5):918-928
Abstract

This study investigates the accuracy of support vector machines (SVM), which are regression procedures, in modelling reference evapotranspiration (ET0). The daily meteorological data, solar radiation, air temperature, relative humidity and wind speed from three stations, Windsor, Oakville and Santa Rosa, in central California, USA, are used as inputs to the support vector machines to reproduce ET0 obtained using the FAO-56 Penman-Monteith equation. A comparison is made between the estimates provided by the SVM and those of the following empirical models: the California Irrigation Management System (CIMIS) Penman, Hargreaves, Ritchie and Turc methods. The SVM results were also compared with an artificial neural networks method. Root mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. The comparison results reveal that the support vector machines could be employed successfully in modelling the ET0 process.  相似文献   

8.
ABSTRACT

In this work, the applicability of 12 solar radiation (RS) estimation models and their impacts on daily reference evapotranspiration (ETo) estimates using the Penman‐Monteith FAO-56 (PMF-56) method were tested under cool arid and semi-arid conditions in Iran. The results indicated that the average increase in accuracy of the ETo estimates by the calibrated RS models, quantified by the decrease in RMSE, was 2.8% and 6.4% for semi-arid and arid climates, respectively. Mean daily deviations in the estimated ETo by the calibrated RS equations in semi-arid climates varied from ?0.283?mm/d-1 for the Glover‐McCulloch model to 0.080?mm/d for the El-Sebaii model, with an average of ?0.109?mm/d-1, and in arid climates, they ranged from ?0.522?mm/d-1 for the Samani model to 0.668?mm/d for the El-Sebaii model, with an average of 0.125?mm/d-1.
Editor D. Koutsyiannis; Associate editor Not assigned  相似文献   

9.
Chaolei Zheng  Quan Wang 《水文研究》2014,28(25):6124-6134
Spatial and temporal variations of reference evapotranspiration (ET0) are useful for regional agricultural and water resources management as well as required in most distributed hydrological modelling. In the current study, the Penman–Monteith estimated ET0 in the arid land of Northwestern China has been explicitly explored using the Mann–Kendall test. Most stations in the study region exhibited significant decreasing trend of ET0 (P < 0.05) with only few occasions showing significant increasing trend (P < 0.05), despite the increase of temperature in the entire region. Analysis results revealed that the overall decreasing wind speed contributed most to the decreasing trend of ET0, whereas the contributions of relative humidity and sunshine duration were limited. Temperature played the second important role on determining ET0 trend, but its effect was opposite to that of wind speed and was largely offset by the decreasing wind speed. Furthermore, sensitivity analysis suggested the impact of temperature to ET0 was much larger than formerly reported if its effect on saturated vapour deficit was taken into account. The results obtained in the current study will help for better understanding of the effects of climate changes to water resource management in the arid land of northwest China. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

10.
Estimation of daily evapotranspiration (ET) over cloudy regions highly desires models which rely on meteorological data only. Notwithstanding, the conventional crop coefficient (Kc) method requires detailed knowledge of geo/biophysical properties of the coupled land-vegetation system, precipitation, and soil moisture. Six Eddy Covariance (EC) towers in Iowa, California and New Hampshire of the USA (covering corn, soybeans, prairie, and deciduous forest) were selected. Investigation on 6 years (2007–2012) 15-min micrometeorological records of these sites revealed that there is an indubitable strong interaction between relative humidity (RH), reference ET (ETo), and actual ET at different timescales. This allowed to bypass the need for the non-meteorological inputs and express Kc as a second-order polynomial function of RH and ETo, the ambient regression evapotranspiration model (AREM). The coefficients of the empirical function are crop-specific and may require calibration over different soil types. The mean absolute percentage error (MAPE) of the regression against daily EC observations was 17% during the growing season, and 32% throughout the year with root mean square error (RMSE) of 0.74 mm day−1 and coefficient of determination of 0.71. The model was fully operational (MAPE of 34% and RMSE of 0.82 mm day−1) over the four Iowan sites based on inputs from local weather stations and NLDAS-2 forcing data of NASA. AREM was capable of capturing the dynamics of ET at 15-min and daily timescales irrespective of varying complexities associated with biophysical, geophysical and climatological states.  相似文献   

11.
Xiaomang Liu  Dan Zhang 《水文研究》2013,27(26):3941-3948
Reference evapotranspiration (ET0) is an important element in the water cycle that integrates atmospheric demands and surface conditions, and analysis of changes in ET0 is of great significance for understanding climate change and its impacts on hydrology. As ET0 is an integrated effect of climate variables, increases in air temperature should lead to increases in ET0. However, this effect could be offset by decreases in vapor pressure deficit, wind speed, and solar radiation which lead to the decrease in ET0. In this study, trends in the Penman–Monteith ET0 at 80 meteorological stations during 1960–2010 in the driest region of China (Northwest China) were examined. The results show that there was a change point for ET0 series around the year 1993 based on the Pettitt's test. For the region average, ET0 decreased from 1960 to 1993 by ?2.34 mm yr?2, while ET0 began to increase since 1994 by 4.80 mm yr?2. A differential equation method based on the Food and Agriculture Organization Penman–Monteith formula was used to attribute the change in ET0. The attribution results show that the significant decrease in wind speed dominated the change in ET0, which offset the effect of increasing air temperature and led to the decrease in ET0 from 1960 to 1993. However, wind speed began to increase, and the amplitude of increase in air temperature also rose significantly since the mid‐1990s. Increases in air temperature and wind speed together reversed the trend in ET0 and led to the increase in ET0 since 1994. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

12.
The temporal trends of reference evapotranspiration (ETref) reflect the combined effects of radiometric and aerodynamic variables, such as global solar radiation (Rs), wind speed, relative humidity and air temperature. The temporal trends of annual ETref during 1961–2006 calculated by Penman‐Monteith method were explored and the underlying causes for these trends were analysed in the Yellow River Basin (YRB). The contributions of key meteorological variables to the temporal trend of ETref were detected using the detrended method and then sensitivity coefficients of ETref to meteorological variables were determined. For ETref, positive trends in the upper, middle and whole of YRB, and significant negative trend (P = 0·05) in the lower basin were obtained by the linear fitted model. Significant increasing trend (P = 0·05) in air temperature and decreasing trend in relative humidity were the main causes for the increasing trends of ETref in the upper, middle and whole basins. For the whole basin, the increasing trend of ETref was mainly caused by the significant increase (P = 0·05) in air temperature and to a lesser extent by a decrease in the relative humidity, decreasing trends of Rs and wind speed reduced ETref. The spatial distribution of sensitivity coefficients addressed that the sensitive regions for ETref response to the changes of the four meteorological variables are different in the YRB. The sensitive region lay in the upper basin for Rs, the northwest portion of the middle basin for wind speed, the south portion of YRB for relative humidity and the west portion of the upper basin and the north portion of the middle basin for air temperature. In general, Rs was the most sensitive variable for ETref, followed by relative humidity, air temperature and wind speed in the basin scale. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

13.
The backward‐averaged iterative two‐source surface temperature and energy balance solution (BAITSSS) model was developed to calculate evapotranspiration (ET) at point to regional scales. The BAITSSS model is driven by micrometeorological data and vegetation indices and simulates the water and energy balance of the soil and canopy sources separately, using the Jarvis model to calculate canopy resistance. The BAITSSS model has undergone limited testing in Idaho, United States. We conducted a blind test of the BAITSSS model without prior calibration for ET against weighing lysimeter measurements, net radiation, and surface temperature of drought‐tolerant corn (Zea mays L. cv. PIO 1151) in a semiarid, advective climate (Bushland, Texas, United States) in 2016. Later in the season (20 days), BAITSSS consistently overestimated ET by up to 3 mm d?1. For the entire growing season (127 days), simulated versus measured ET resulted in a 7% error in cumulative ET, RMSE = 0.13 mm h?1, and 1.70 mm d?1; r2 = 0.66 (daily) and r2 = 0.84 (hourly); MAE = 0.08 mm h?1 and 1.24 mm d?1; and MBE = 0.02 mm h?1 and 0.58 mm d?1. The results were comparable with thermally driven instantaneous ET models that required some calibration. Next, the initial soil water boundary condition was reduced, and model revisions were made to resistance terms related to incomplete cover and assumption of canopy senescence. The revisions reduced discrepancies between measured and modelled ET resulting in <1% error in cumulative ET, RMSE = 0.1 mm h?1, and 1.09 mm d?1; r2 = 0.86 (daily) and r2 = 0.90 (hourly); MAE = 0.06 mm h?1 and 0.79 mm d?1; and MBE = 0.0 mm h?1 and 0.17 mm d?1 and generally mitigated the previous overestimation. The advancement in ET modelling with BAITSSS assists to minimize uncertainties in crop ET modelling in a time series.  相似文献   

14.
Sensitivity analysis is crucial in assessing the impact of climatic variables on reference evapotranspiration estimations. The sensitivity of the standardized ASCE–Penman–Monteith evapotranspiration equation for daily estimations to climatic variables has not yet been studied in Spain. Andalusia is located in southern Spain where almost 1 million ha are irrigated under quite different conditions; it has a high inter‐annual variability in rainfall. In this study, sensitivity analyses for this equation were carried out for temperature, relative humidity, solar radiation and wind speed data from 87 automatic weather stations, including coastal and inland locations, from 1999 to 2006. Topography and Mediterranean climate characterize the heterogeneous landscape and vegetation of this region. Simulated random and systematic errors have been added to meteorological data to obtain ET0 deviations and sensitivity coefficients for different time periods. BIAS and SEE (standard error of estimate) have been used to evaluate the effect of both types of errors. The results showed a large degree of daily and seasonal variability, especially for temperature and relative humidity. In general, the effect on ET0 values of introduced random errors was larger than that of systematic errors. ET0 overestimations were produced using positive errors in temperature, solar radiation and wind speed data, while these errors in relative humidity resulted in ET0 underestimations. The sensitivity of ET0 to the same climatic variables showed significant differences among locations. The geographical distribution of sensitivity coefficients across this region was also studied. As an example, during spring months, ET0 equation was more sensitive to temperature in stations located along the Guadalquivir Valley. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

15.
This study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC-I-AMC-III) by coupling the improved initial abstraction (Ia-λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness-of-fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE-observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process.  相似文献   

16.
Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accur...  相似文献   

17.
Abstract

The Blaney-Criddle (BC) temperature-based equation is used in areas where the complete weather data to estimate reference evapotranspiration (ET0) by the Penman-Monteith FAO-56 (PMF-56) standard model is complex. In this study, the BC equation was first tested and calibrated against the ET0 values computed by the PMF-56 method using data from 17 weather stations in arid regions of Iran. Then, geographical information systems (GIS)-based spatially-distributed maps of ET0 were prepared by means of geographic/topographic factors derived from a digital elevation model (DEM) for all months, separately. The results indicate that the original BC equation overestimated PMF-56 ET0 by 4% at the study sites. The BC equation produced closer ET0 estimates to the PMF-56 method after it was calibrated. The error rate of <3% for the spatial modelling approach suggests that the developed ET0 maps are reliable.

Editor D. Koutsoyiannis; Associate editor D. Yang

Citation Tabari, H., Hosseinzadeh Talaee, P., and Shifteh Some'e, B., 2013. Spatial modelling of reference evapotranspiration using adjusted Blaney-Criddle equation in an arid environment. Hydrological Sciences Journal, 58 (2), 408–420.  相似文献   

18.
The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

19.
The eddy covariance (EC) method was used in a 30‐month study to quantify evapotranspiration (ET) and vegetation coefficient (KCW) for a wetland on a ranch in subtropical south Florida. To evaluate the errors in ET estimates, the EC‐based ET (ETC‐EC) and the Food and Agricultural Organization (FAO) Penman–Monteith (PM) based ET (ETC‐PM) estimates (with literature crop coefficient, KC) were compared with each other. The ETC‐EC and FAO‐PM reference ET were used to develop KCW. Regression models were developed to estimate KCW using climatic and hydrologic variables. Annual and daily ETC‐EC values were 1152 and 3.27 mm, respectively. The FAO‐PM model underestimated ET by 25% with ETC‐EC being statistically higher than ETC‐PM. The KCW varied from 0.79 (December) to 1.06 (November). The mean KCW for the dry (November–April) season (0.95) was much higher than values reported for wetlands in literature; whereas for the wet (May–October) season, KCW (0.97) was closer to literature values. Higher than expected KCW values during the dry season were due to higher temperature, lower humidity and perennial wetland vegetation. Regression analyses showed that factors affecting the KCW were different during the dry (soil moisture, temperature and relative humidity) and wet (net radiation, inundation and wind speed) seasons. Separate regression models for the dry and wet seasons were developed. Evapotranspiration and KCW from this study, one of the first for the agricultural wetlands in subtropical environment, will help improve the ET estimates for similar wetlands. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

20.
Estimation of reference evapotranspiration (ET0) in urban areas is challenging but essential in arid urban climates. To evaluate ET0 in an urban environment and non-urban areas, air temperature and relative humidity were measured at five different sites across the arid city of Isfahan, Iran, over 4 years. Wind speed and sunshine hours were obtained from an urban surrounding weather station over the same period and used to estimate ET0. Calculated ET0 was compared with satellite-based ET0 retrieved from the MOD16A2 PET product. Although MODIS PET was strongly correlated with the Valiantzas equation, it overestimated ET0 and showed average accuracy (r = 0.93–0.94, RMSE = 1.18–1.28 mm/day, MBE = 0.73–0.84 mm/day). The highest ET0 differences between an urban green space and a non-urban area were 1.1 and 0.87 mm/day, which were estimated by ground measurements and MODIS PET, respectively. The sensitivity of ET0 to wind speed and sunshine hours indicated a significant effect on cumulative ET0 at urban sites compared to the non-urban site, which has a considerable impact on the amount of irrigation required in those areas. Although MODIS PET requires improvement to accurately reflect field level microclimate conditions affecting ET0, it is beneficial to hydrological applications and water resource managers especially in areas where data is limited. In addition, our results indicated that using limited data methods or meteorological data from regional weather stations, leads to incorrect estimation of ET0 in urban areas. Therefore, decision-makers and urban planners should consider the importance of precisely estimating ET0 to optimize management of urban green space irrigation, especially in arid and semi-arid climates such as the city of Isfahan.  相似文献   

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