Daily ET Research Articles

Measurement and estimation of evapotranspiration in semi-arid grassland during the summer season in southwest Siberia

This study quantifies actual evapotranspiration (ET a ) for a period from June to September 2016 measured by two weighable gravitation lysimeters in a semi-arid grassland in southwest Siberia. As part of a crop rotation system, the first lysimeter was fallow but covered with ruderal vegetation. The second lysimeter is permanently characterized by pristine steppe vegetation. In addition to ET a measurements, the reference evapotranspiration (ET 0 ) is computed by a Penman-Monteith model. The estimates are related to the ET a records and the model is evaluated with regard to its performance in a semi-arid environment. The results indicated an ET a driven by energy but limited by water. Within 115 days the total amounts of ET a ranged from 205 mm to 374.1 mm, and daily values varied from 0.1 to 6.9 mm day -1 . The large differences are caused by the different vegetation cover of the lysimeters. Due to the high and dense canopy of the pristine steppe vegetation, the transpiration term was considerably higher compared to the ruderal vegetation where soil evaporation took the major part. The daily ET a records differed on average by -91.1% to the ET 0 estimates. The statistical analyses yielded a low correlation between ET a of the ruderal vegetation and ET 0 but an acceptable model performance for the pristine steppe. However, it was observed that ET a occasionally exceeds ET 0 , particularly after precipitation. Due to the high water availability and the subsequent rise of ET a , ET 0 was underestimated, whereas it was overestimated during dry periods. Finally, the quality of the Penman-Monteith model varied substantially with the water supply at the study site.

Evaluation of six equations for daily reference evapotranspiration estimating using public weather forecast message for different climate regions across China

Accurate estimation of daily reference evapotranspiration (ET0) are vital for water resource management and irrigation decision-making. Based on the public weather forecasts, numerous models have been successfully used for daily ET0 estimating, while too many models available for selection, which causes confusion regarding model selection for specific climate regions. In this paper, the estimating performances of six ET0 equations using public weather forecast for a lead time of 1–7 days were compared for four main climatic region across China, and then, the most accurate equation was recommended for each climate region. Meanwhile, the applicability of every equation was assessed in relation to four climates, including subtropical monsoon climate (Cwa), temperate continental climate (Dfc), temperate monsoon climate (Dwa) and mountain plateau climate (HG). The Penman-Monteith Forecast (PMF) equation, which consisting of an adaptation of FAO56-PM equation using temperature and weather type forecast as inputs, provided the best ET0 estimation performance in Cwa and HG climates; and the Temperature Penman-Monteith (PMT) equation using only the temperature data, obtained the most accurate average results for the Dwa and Dfc climates. The best and the second best estimation performance for each climate usually provided by PMF and PMT equations, since they both have the same advantage of following the conceptual approach of the FAO56-PM equation; further the third and the fourth choice would be the Hargreaves-Samani (HS) and the Blaney-Criddle (BC), respectively, while the Thornwaite (TH) and the McCloud (MC) yielded high errors and may not be applicable for ET0 estimation for most climatic regions. As a whole, the PMF and PMT equations were better than the other equations and thus these two equations were recommended for daily ET0 estimation for the near-future at all climate regions across China.

Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001–2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day−1, NRMSE = 0.250 and MAE = 0.508 mm day−1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models.

Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions

Accurate estimation of reference evapotranspiration (ET0) is critical for water resource management and irrigation scheduling. This study evaluated the potential of a new machine learning algorithm using gradient boosting on decision trees with categorical features support (i.e., CatBoost) for accurately estimating daily ET0 with limited meteorological data in humid regions of China. Two other commonly used machine learning algorithms, Random Forests (RF) and Support Vector Machine (SVM), were also assessed for comparison. Eight input combinations of daily meteorological data [including both complete and incomplete combinations of solar radiation (Rs), maximum and minimum temperatures (Tmax and Tmin), relative humidity (Hr) and wind speed (U)] from five weather stations during 2001–2015 in South China were applied for model training and testing. The results showed that all the three algorithms could achieve satisfactory accuracy for ET0 estimation in subtropical China using Rs, Tmax and Tmin, or U, Hr, Tmax and Tmin as inputs, under the circumstances of lacking complete meteorological parameters. The increases in testing RMSE and MAPE over training RMSE and MAPE showed positive correlations with the number of input parameters to the machine learning models. For the local models, among the three algorithms, SVM offered the best prediction accuracy and stability with incomplete combinations of meteorological parameters as inputs, while CatBoost performed best with the complete combination of parameters. Patterns of the generalized models were almost the same as the local models, but the former ones showed less than 10% decreases in RMSE or MAPE in comparison with the latter ones. In addition, the computing time and memory usage for data processing of CatBoost were much less than those of RF and SVM. Overall, as a tree-based algorithm, CatBoost made significant improvements in accuracy, stability and computational cost when compared to RF. Therefore, the CatBoost algorithm has a very high potential for ET0 estimation in humid regions of China, and even possibly in other parts of the world with similar humid climates.

Detecting linear trend of reference evapotranspiration in irrigated farming areas in Brazil’s semiarid region

Northeast Brazil’s semiarid region is characterized by water deficit. However, little is known about the trends in joint water loss by evaporation and transpiration (evapotranspiration) in the region. Thus, this study aimed to analyze indications on the linear trend of reference evapotranspiration (ET0) in the monthly, annual, and interdecadal scales in irrigated agricultural areas in the microregions of Juazeiro, Bahia State (9° 24’ S; 40° 26’ W and 375.5 m), and Petrolina, Pernambuco State (09° 09’ S, 40° 22’ W, and 376 m). For this purpose, we used a set of daily meteorological data, collected from January 1, 1976, to December 31, 2014. Daily ET0 values were estimated by the FAO’s Penman-Monteith equation (ET0PM). The main statistical tests used were the nonparametric Mann-Kendall test, the Sen test to detect the magnitude of the trend, and the Pettitt test to detect the start of the trend. For all tests, statistical significance was 5% and/or 1%. Analysis of variance (ANOVA) was also applied to identify significant differences between the mean interdecadal values for each season. The results showed a significant decreasing trend for ET0 in both Juazeiro (α = 5%) and Petrolina (α = 1%), with declines of 14.5 mm/year (Juazeiro) and 7.7 mm/year (Petrolina) and that this trend started in 1996. Probably, the ET0 decrease was associated with the increase in the irrigated agricultural areas, and the lake formed upstream from the areas by the construction of Sobradinho Dam.

Improvement of Makkink model for reference evapotranspiration estimation using temperature data in Northwest China

Reference evapotranspiration (ET0) is an important parameter for climatological and hydrological studies, as well as for agricultural water resources management. In this study, 7 temperature-based solar radiation models were adopted to improve the Makkink model for ET0 estimation in Northwest China. The temperature-based models only require air temperature as input data, which can be easily monitored in most areas around the world. The applicability of the improved Makkink models (M1-M7), the original Makkink (MK) model, the Jensen-Haise (JH) model as well as the Irmak (IK) model were evaluated on different time scales using meteorological data obtained from 12 weather stations in Northwest China. The results showed that the 7 improved Makkink (MKi) models (R2 ranged 0.71–0.86) were more accurate than the 3 physical models (R2 ranged 0.64–0.76) for daily ET0 estimation at the 4 sub-zones of Northwest China, and the M4, M5, M6 and M7 models were superior to the other models. The M5 model had the best estimation accuracy for daily ET0 on daily scale, followed by M7 and M6, with the R2 median of 0.83, 0.83 and 0.82, the RMSE median of 0.86, 0.88 and 0.89 mm/d, and the GPI median of 1.03, 0.90 and 0.87, respectively. On monthly scale, the 7 MKi models (with relative error almost less than 20%) were also better than the 3 physical models (with relative error usually more than 20%) at the 4 sub-zones. The M5 model also showed the best performance for monthly average daily ET0 estimation, followed by M7 and M6, with the R2 median of 1.00, 1.00 and 1.00, the RMSE median of 0.13, 0.16 and 0.19 mm/d, and the GPI median of 0.25, 0.05 and 0.02, respectively. Overall, the estimation accuracy of the Makkink model was much improved by using the temperature-based solar radiation models, and in Northwest China, M5 model which only requires temperature and relative humidity as input data is highly recommended to estimate ET0 on both daily and monthly scales.

Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China

Accurate estimation of reference evapotranspiration (ET0) is of great importance for the regional water resources planning and irrigation scheduling design. The FAO-56 Penman-Monteith model is recommended as the reference model to predict ET0, but its application is commonly restricted by lack of complete meteorological data at many worldwide locations. This study evaluated the potential of machine learning models, particularly four relatively simple tree-based assemble algorithms (i.e. random forest (RF), M5 model tree (M5Tree), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost)), for estimating daily ET0 with limited meteorological data using a K-fold cross-validation method. For assessment of the tree-based models in terms of prediction accuracy, stability and computational costs, these models were further compared with their corresponding support vector machine (SVM) and extreme learning machine (ELM) models. Four input combinations of daily maximum and maximum temperature (Tmax and Tmin), relative humidity (Hr), wind speed (U2), global and extra-terrestrial solar radiation (Rs and Ra) with Tmax, Tmin and Ra as the base dataset were considered using meteorological data during 1961–2010 from eight representative weather stations in different climates of China. The results showed that, when lack of complete meteorological data, the machine learning models using Tmax, Tmin, Hr, U2 and Ra obtained satisfactory ET0 estimates in the temperate continental, mountain plateau and temperate monsoon zones of China (RMSE < 0.5 mm d−1). However, models with three input parameters of Tmax, Tmin and Rs were superior for daily ET0 prediction in the tropical and subtropical zones. The ELM and SVM models offered the best combination of prediction accuracy and stability. The simple tree-based XGBoost and GBDT models showed comparable accuracy and stability to the SVM and ELM models, but exhibited much less computational costs. Considering the complexity level, prediction accuracy, stability and computational costs of the studied models, the XGBoost and GBDT models have been recommended for daily ET0 estimation in different climatic zones of China and maybe elsewhere with similar climates around the world.

Determination of maize water requirement using remote sensing data and SEBAL algorithm

Efficient agricultural water demand management in arid and semi-arid regions is key in continuing crop production in a changing climate. As such, there’s a need for further investigation on various crops to identify the optimum water requirements to avoid water wasting in regions that are already facing water shortage. The focus of this work is to determine water requirement maize farming Mazandaran Province in Northern Iran, located on the southern side of the Caspian Sea, using Landsat satellite data. In order to use SEBAL algorithm, the images were atmospheric calibrated. Evapotranspiration maps with RMSE values equals to 0.73, 1.38 and 0.74 mm/day were produced and compared to Reference Book (RB), National Water Document (NWD) and FAO56 values. Furthermore, by computing reference evapotranspiration, crop coefficient curve was evaluated. In order to prepare maize water requirement maps, ET0 in 58 weather stations throughout the province were computed and interpolated. By using the daily ET0 maps and resulted Kc values, maize water requirement maps for the cultivated area of the province were 345.16–383.99 mm. The maize water requirement in the observation station of Sari compared to RB and NWD values showed −%20 and +%41 differences and RMSE was 76 and 156 mm, respectively.

Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma

Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO2) and water vapor (H2O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO2 and H2O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO2 exchange (NEE) reached seasonal peaks of −9.4 and −8.8 g C m−2 in 2015 and −6.2 and −7.5 g C m−2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ± 13 and 33 ± 9 g C m−2 year−1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (−128 ± 69 and −119 ± 53 g C m−2 year−1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day−1 in 2015, and 7.2 and 8.2 mm day−1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets.

Application of an energy balance method for estimating evapotranspiration in cropping systems

Accurate quantification of evapotranspiration (ET, consumptive water use) from planting through harvest is critical for managing limited water resources for crop irrigation. Our objective was to develop and apply a land-crop surface residual energy balance (EB) method for quantifying ET and to estimate ET of corn (Zea mays L.) for the first time in the climate of the lower Mississippi Delta region. Actual ET (ETe) was estimated as the residual term of the energy balance equation from measurements of net solar irradiance (Rn) and computed sensible heat (H) and ground heat (Go) fluxes. The H flux was computed from measurements of the air and crop canopy temperature differential and modeling the aerodynamic resistance (ra) to heat and water transport in the turbulent atmospheric boundary layer above the canopy. The Go flux was estimated by measuring heat flux at 8 cm depth and accounting for heat storage in the soil layer above it. The developed EB procedure was tested using simultaneous measurements of EB data and lysimetric ET in a cotton (Gossypium hirsutum L.) field at Bushland, Texas, USA in 2008. The lysimeter measured ET compared well with the computed ETe under cotton (RMSE of daily ET = 1.2 mm, and seasonal ET within 1% error). Further, we quantified irrigated corn ET using EB in a silt loam soil at Stoneville, Mississippi, USA in 2016. The computed seasonal values of ETe were greater than shortgrass reference ET (ETo) by 27 mm and less than alfalfa reference crop ET (ETr) by 80 mm. The instrumentation used in the EB method can be moved, and the estimated ET was comparable with lysimeter measured ET. As such, this method provides a cost-effective, viable alternative for quantifying ET, which should be broadly tested in other locations and cropping systems.

Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two Source Energy Balance Model (TSEB) II—Integration of Remote Sensing Medium and Low Spatial Resolution Satellite Images

Dehesas are highly valuable agro-forestry ecosystems, widely distributed over Mediterranean-type climate areas, which play a key role in rural development, basing their productivity on a sustainable use of multiple resources (crops, livestock, wildlife, etc.). The information derived from remote sensing based models addressing ecosystem water consumption, at different scales, can be used by institutions and private landowners to support management decisions. In this study, the Two-Source Energy Balance (TSEB) model is analyzed over two Spanish dehesa areas integrating multiple satellites (MODIS and Landsat) for estimating water use (ET), vegetation ground cover, leaf area and phenology. Instantaneous latent heat (LE) values are derived on a regional scale and compared with eddy covariance tower (ECT) measurements, yielding accurate results (RMSDMODIS Las Majadas 44 Wm−2, Santa Clotilde RMSDMODIS 47 Wm−2 and RMSDLandsat 64 Wm−2). Daily ET(mm) is estimated using daily return interval of MODIS for both study sites and compared with the flux measurements of the ECTs, with RMSD of 1 mm day−1 over Las Majadas and 0.99 mm day−1 over Santa Clotilde. Distributed ET over Andalusian dehesa (15% of the region) is successfully mapped using MODIS images, as an approach to monitor the ecosystem status and the vegetation water stress on a regular basis.

Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil

ABSTRACT FAO Penman-Monteith (FO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET0) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET0 in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology’s station. Daily ET0 values were randomized, and 70% of these were used to determine the calibration parameters of the ET0 for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on Willmott’s index of agreement, confidence coefficient and root-mean-square error. When one meteorological variable was missing, either solar radiation, relative air humidity or wind speed, or in the simultaneous absence of wind speed and relative air humidity, the FAO-PM method showed the best performances and, therefore, was recommended for Jaíba. The FAO-PM method with two missing variables, one of them being solar radiation, showed intermediate performance. Methods that used only air temperature data are not recommended for the region.

Assessing Accuracy of Vegetation Index Method to Estimate Actual Evapotranspiration

The estimation of actual crop evapotranspiration (ET_a) maps using complex equations and remotely sensed shortwave and thermal infrared imagery can be challenging and may require input data that are not available. There is an opportunity, therefore to create a simpler and faster method to generate ET_a maps using fewer input parameters for situations where limited input data is available or greater uncertainty in the resulting ET estimates are acceptable. We compared the estimates of ET_a produced by a crop coefficient and NDVI-based (K_c-NDVI) method to a full energy balance (EB) method. Clear sky images from Landsat 7 and Landsat 8 were processed and used for the ET_a estimations from maize during two growing seasons in eastern South Dakota, USA. The results showed that the ET_a values from the K_c-NDVI method were lower than the ET_a values from the EB method by 18% for 2015 and 11% for 2016 growing season. During study period the accuracy of ET_a estimation decreased 17% with the K_c-NDVI method. For both years the mean bias error was 0.81 mm day^-1 and the root mean square error (RMSE) was 0.37 mm day^-1. The average daily ET_a of 5.3 mm day^-1. The K_c-NDVI method performed acceptable for ET_a estimations, indicating that this method can be used to estimate ET_a with minimum input parameters at focused regional and field scales for short time periods.

Calibration and spatial modelling of daily ET0 in semiarid areas using Hargreaves equation

Evapotranspiration is difficult to measure and, when measured, its spatial variability is not usually taken into account. The recommended method to estimate evapotranspiration, Penman-Monteith FAO, requires variables not available in most weather stations. Simplified but less accurate methods, as Hargreaves equation, are normally used. Several approaches have been proposed to improve Hargreaves equation accuracy. In this work, 14 calibrations of the Hargreaves equation are compared. Three goodness of fit statistics were used to select the optimal, in terms of simplicity and accuracy. The best option was an annual linear regression. Its parameters were interpolated using regression-kriging combining Random Forest and Ordinary Kriging. Twelve easy to obtain ancillary variables were used as predictors. The same approach was used to interpolate Hargreaves and Penman-Monteith-FAO ET0 on a daily basis; the Hargreaves ET0 layers and the parameter layers were used to obtain calibrated ET0 estimations. To compare the spatial patterns of the three estimations the daily layers were integrated into annual layers. The results of the proposed calibration are much more similar to Penman-Monteith FAO results than those obtained with Hargreaves equation. The research was conducted in south-east Spain with 79 weather stations with data from 01/01/2003 to 31/12/2014.

Real-time irrigation forecasting for ecological water in artificial wetlands in the Dianchi Basin

It is important to understand the real-time irrigation demands of wetland plants in areas experiencing ecological water shortages. The Levenberg-Marquardt algorithm (L-M) in an Artificial Neural Network (ANN) was used to train and forecast daily reference crop evapotranspiration (ET0), using a polynomial fuzzy daily precipitation function, based on short-term meteorological predictions. This method allowed simulation of a real-time irrigation schedule based on field water balance, and was applied to a variety of wetland plants including reeds, Typha orientalis and paddy. The results showed that the determinant coefficient of daily ET0 forecast was 0.945. More than 75% of the sampling error was below 10% and 96% of the sampling error was below 20%. The mean-error (ME) and root mean square error (RMSE) for the daily field water level simulated with reeds, T. orientalis and paddy were less than 6.6 mm, while the value of Index of agreement (IA), and RMSE were greater than 0.986 and 0.946, respectively. The difference in ME, RMSE, IA, and Nash-Sutcliffe efficiency factor (NSE) for reeds and T. orientalis was not significant, but it was twice as high for paddy. For real-time irrigation for paddy in the Dianchi Basin, the values of ME, RMSE, IA, NSE, and R were 0.36–0.91mm, 2.85–4.92mm, 0.986–0.996, 0.946–0.985, and 0.938, respectively. The method can meet the needs of regional water resources allocations and operations.

Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling

Accurate estimation of reference evapotranspiration (ET0) is of importance for regional water resource management. The present study proposed two artificial intelligence models, random forests (RF) and generalized regression neural networks (GRNN), for daily ET0 estimation. Meteorological data including maximum/minimum air temperature, solar radiation, relative humidity, and wind speed during 2009∼2014 from two stations in southwest China were used to train and test the RF and GRNN models by using two input combinations, including complete data and only temperature and extraterrestrial radiation (Ra) data. The k-fold test was adopted to test the performance of models according to temporal and spatial criteria and data set scanning procedures. The results indicated that both local and external RF and GRNN models performed well for estimating daily ET0, and RF was slightly better than GRNN generally. The high fluctuations in the accuracy ranges justify the importance of applying k-fold test for assessing the model performance, which could avoid drawing partially valid conclusions from model assessments based on simple data set assignment. Overall, both temperature-based RF and GRNN models can accurately estimate daily ET0, which is helpful for irrigation scheduling in southwest China.

Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy

For decades, the importance of evapotranspiration processes has been recognized in many disciplines, including hydrologic and drainage studies, irrigation systems design and management.In this research, the suitability of the Prediction Of Worldwide Energy Resource database published by the American National Aeronautics and Space Administration (POWER-NASA), to estimate daily meteorological variables and ET0 was assessed in Sicily, Italy, for the period 2006–2014, based on ground data measured by a network of climate stations belonging to the regional Agro-meteorological Information Service (SIAS). After comparing the climate data (minimum, Tmin, maximum, Tmax, and average, Tavg, air temperature, relative air humidity, RH, global solar radiation, Rs and wind speed, u) available in both databases, a statistical comparison was carried out on ET0 values estimated according to the Penman Monteith equation in the version proposed by the Food and Agriculture Organization (FAO-56 PM).The analysis showed that correlations between air temperature and relative air humidity from both databases are affected by elevation of the weather stations and the distance from the sea. In addition, ET0 values estimated with POWER-NASA database were generally comparable to those obtained by using the SIAS records, with RMSE values ranging between 0.68 and 1.27mmd−1 and MBE varying between −0.39 and 0.73mmd−1. The greatest differences in ET0 values are due to the resolution of POWER-NASA archive (1° latitude by 1° longitude), that cannot detect the actual spatial variability observed on ground, as well as to inaccurate estimations of relative air humidity occurring for the coastal weather stations as well as of the air temperature for those inland stations characterized by high elevations. However, the achieved results support the possibility of obtaining suitable estimates of daily ET0 based on the POWER-NASA agro-climatology archive, even to other Mediterranean countries where most of the climate variables are not measured.

Area-wide evapotranspiration monitoring at the crown level of a tropical mountain rain forest

Ecosystem water regulation couples energy and water balance, depends on the integrity of the ecosystem, and responds to changes in climate. Changes in tree-water relationships in the biodiversity hotspot of the tropical Andes in southern Ecuador might be potentially observed at the level of individual trees, thus providing an efficient ecosystem monitoring method with applications in forest management and conservation at the tree and landscape levels. In this study, we combine area-average measurements from a laser scintillometer above the forest with optical satellite data at high spatial resolution to obtain area-wide evapotranspiration data. The processing of field data includes the calculation of energy storage in forest biomass and the partitioning of evapotranspiration into transpiration and evaporation. Satellite-based estimates are calibrated by using tower flux measurements and meteorological data within periods of humid and less-humid atmosphere. The annual evapotranspiration was 1316mm, of which 1086mm per year corresponds to the forest transpiration at the study site. Average values of 4.7 and 4.1mm d−1 per tree crown are observed under humid and less-humid atmospheric conditions, respectively, when applying high-resolution area-wide evapotranspiration in individual crown analysis. Approximately 24% of the observed crowns show a positive monthly change in ET, and 51% of the crowns show a significant change in the daily ET, which can be considered sensitive individuals concerning water relationships. The limitations in the area-wide evapotranspiration at the crown level can be explained by considering the spectral responses of the crown individuals. The presented method can be robustly deployed in the ecological monitoring of mountain forests.

Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

Accurate estimation of reference evapotranspiration (ET0) is essential to agricultural water management. The present study developed two artificial intelligence models for daily ET0 estimation only with temperature data, including extreme learning machine (ELM) and generalized regression neural network (GRNN) in 6 meteorological stations of Sichuan basin, southwest China, and compared the proposed ELM and GRNN with the corresponding temperature-based Hargreaves (HG) model and its calibrated version considering FAO-56 Penman-Monteith ET0 as benchmark. Two data management scenarios were evaluated for estimation of ET0: (1) the models were trained/calibrated and tested using the local data of each station; and (2) the models were trained/calibrated using the pooled data from all the stations and tested in each station. In the first scenario, the results showed that the temperature-based ELM model provided the better estimation than the GRNN, HG and calibrated HG models, with average relative root mean square error (RRMSE) of 0.198, mean absolute error (MAE) of 0.267mm/d and Nash-Sutcliffe coefficient (NS) of 0.891, respectively. In the second scenario, GRNN model provided the most accurate results among the considered models, with average RRMSE of 0.194, MAE of 0.263mm/d and NS of 0.895, respectively. Both of the temperature-based GRNN and ELM performed much better than the HG and calibrated HG models for the two scenarios, and the temperature-based GRNN and ELM models are appropriate alternatives for accurate estimation of ET0 for Sichuan basin of southwest China, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling.

Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement

Accurate estimation of reference crop evapotranspiration (ET0) is important due to its crucial role in determining crop water requirement in irrigated agriculture. Though a great number of models have been developed, their rigorous evaluation with measurements is still lacking, leading to confusion and arbitrariness in model selection. In this paper daily estimates of 16 ET0 models, including five combination-, six radiation and five temperature-based ones, were compared with weighing lysimeter measurements during crop growing season (April through October) in 2012 at a semiarid site in China. Daily ET0 was measured by two weighing lysimeters (area 1.3m×1.3m, depth 2.3m) located in a fescue grass (Festuca arundinacea Schreb) plot (100m×100m) surrounded by a 167ha crop, winter wheat rotated with summer maize. We found the models were ranked decreasingly as: FAO-ppp-17 Penman>1963 Penman>FAO-24 Blaney-Criddle (BC)>1996 Kimberly Penman>FAO-24 radiation>FAO-56 Penman-Monteith (PM)>FAO-24 Penman>Turc>DeBruin-Keijman>Jensen-Haise>Priestley-Taylor>Hargreaves>Makkink>Hamon>Blaney- Criddle>Mcloud on basis of RMSE (root mean square error). Overall, the combination models performed best with RMSE ranging from 0.93 to 1.32mmd−1, followed by the radiation models with RMSE from 1.28 to 1.79mmd−1, and the temperature models with RMSE from 1.09 to 2.48mmd−1. The best combination model (FAO-ppp-17 Penman) was respectively 29% and 17% more accurate than the best radiation (FAO-24 radiation) and temperature (FAO-24 BC) models. Better performance of the combination and radiation models resulted because they explicitly contain the dominant factors influencing ET0. All models tended to overestimate under low evaporative demand while underestimating the measured values under high demand, but on average the combination and radiation methods underestimated by 0.46mmd−1 and 0.60mmd−1, respectively, whereas the temperature method overestimated by 0.21mmd−1. All combination and radiation models, and the Hargreaves and FAO-24 BC in temperature method showed robust structure. To improve them future efforts should be on local calibration, but for temperature models showing structure failure focus should be on its optimization. The coefficients of commonly used models were calibrated and related to meteorological variables. Particularly, those of the Priestley-Taylor, Makkink, Turc and the Hamon were enhanced, while those of the Hargreaves and BC were decreased. In climate similar to the current site in China we suggest continued use of the older Penman equations for combination method and the FAO-24 radiation or Turc for radiation method. Meanwhile, two questions need to be addressed in future studies: i) adoption of the FAO-56 PM equation as the sole standard for computing ET0 and the proper value for surface resistance; and ii) the effectiveness of the later modifications to the original wind function in the Penman equation.