FAO Penman-Monteith Equation Research Articles

Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data?

This study evaluates the accuracy of daily reference evapotranspiration (ETo), computed according to the FAO Penman-Monteith equation by using a set of input weather variables obtained by blending ERA5-Land (ERA5L) reanalysis data with surface incoming solar radiation (Rs) provided by the instruments on board the Meteosat geostationary satellites, operationally delivered by the Satellite Applications Facility on Climate Monitoring (CM-SAF). Performance assessment was carried out in Sicily (southern Italy) by using data from 38 automatic ground weather stations (AWSs) for years 2003–2020. ERA5L and CM-SAF data were first downscaled and bias-corrected with a calibration dataset; ERA5L air temperature data were also downscaled by lapse-rate correction. ETo estimates obtained with the blended ERA5L and CM-SAF validation dataset (ERA5L+CM-SAF) were compared with two other ETo estimates respectively obtained by using ERA5L and interpolated ground weather data (IGD). Performance indicators of the IGD dataset were evaluated by recursively applying universal kriging or ordinary kriging to the observed weather data, according to a cross-validation procedure. Rs provided by CM-SAF outperformed Rs obtained by ground interpolation, thus confirming the convenience of using bias-corrected CM-SAF data even when ground observations are available in the study area. ETo estimates with ERA5L+CM-SAF showed a normalized RMSE of 12%, outperforming ERA5L ETo estimates while performing comparably to ETo estimates obtained with the IGD dataset. The results suggested that the proposed blended dataset is a good proxy for interpolated ground weather observations in the assessment of ETo at regional scale when weather measurements cannot be easily gathered or in data-sparse regions.

Effectiveness of the new standardized deficit distance index and other meteorological indices in the assessment of agricultural drought impacts in central Italy

In the paper, we tested and compared the potential of some standardized meteorological indices to identify agricultural drought impacts in central Italy. The indices considered are the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Reconnaissance Drought Index (RDI), and the Standardized Deficit Distance Index (SDDI), a new index that is defined and evaluated in this paper. SDDI is a function of the Euclidean distance between the actual (P-ET0) and a reference deficit series (P-ET0 = max). SDDI, unlike other indices, also considers how the deficit is distributed in a certain span and assigns greater severities when the distribution is uneven (e.g., presence of peaks).The comparative analysis refers to 24 provinces of Central Italy and sunflower, a typical non-irrigated crop in the area considered. The sunflower yield time series (1980–2019) were de-trended and standardized for each province, finally obtaining Standardized Yield Residuals (SYRs). The climatic data required for calculating the drought indices (precipitation and temperatures) for each province derive from the E-OBS gridded dataset with 0.25° resolution in the period 1980–2019. From the minimum and maximum daily temperatures, the ET0 was estimated by the FAO Penman-Monteith equation. The drought indices were calculated for different time scales (from 1 to 5 months) and the months corresponding to the sunflower growing season (April–August). The performance of the various indices in the prediction of SYRs was assessed using the Pearson correlation coefficient.For all the indices, the best correlations are found for the 2-month time scale and for July. SPI's performance is only slightly lower than that of the indices that integrate both precipitation and evapotranspiration (SPEI, RDI, SDDI). Among these, SDDI and SPEI provide somewhat better results considering both the percentage of significant correlation (63% and 67%, respectively) and the corresponding mean correlation (0.49 and 0.48, respectively). SDDI demonstrates good potential in assessing agricultural drought impacts while maintaining the advantage of limited data input.

Estimation of Actual Evapotranspiration in Panam Canal Command Using Remote Sensing and Geographical Information System (GIS)

Estimation of reference evapotranspiration (ET0) and actual evapotranspiration (ETc) is a key factor for estimation of crop water requirement, water balance and irrigation scheduling. The FAO-56 Penman–Monteith equation has been accepted universally for estimating of reference evapotranspiration (ET0). Considering the high spatial variation of meteorological phenomenon and limited availability of such dense network for data collection, application of remote sensing and GIS has gained momentum for estimation of ET0 and ETc over the larger area with accurately and efficiently. For estimation of ET0 and ETc, the most widely applied MOD16 remote sensing images as well as Landsat 8 remote sensing images are applied in this study of Panam canal command area which is located in the semi-arid middle Gujarat region. Initially, FAO-56 PM method is used to estimate ET0 and MOD16 data is used to estimate ET0, whereas Landsat 8 is used to estimate land surface temperature and then by using the regression equation to estimate maximum and minimum temperature to find out ET0 for the study area. Based on result obtained, it was found that Landsat 8 remote sensing-based data have better capacity to estimate actual evapotranspiration compared to the MOD16 remote sensing data. The better performance of Landsat 8 data compared to MOD16 data is due to the reason that it has better spatial resolution(30m) compared to MOD16 (1 km) remote sensing image and can represent the actual field conditions of farm fields which are generally smaller.

Estimation of irrigation water requirement of strawberry crop under polyhouse and shadenet house conditions

Strawberry is a commercial crop with high added value, which was extended to new cultivation zones of India.Therefore, it is important to know the suitability of climate condition for growing strawberry in Indian condition. Protected cultivation structures are used to cultivate crops under partial controlled climatic condition to get higher yield and better quality harvest. There are different kinds of protected cultivation structure normally adopted in India such as greenhouse, shadenet house and low tunnels. Exact of amount water and nutrients required to applied to get higher yield through minimizing loss of quality. The objective of the study is to assess the effect of protected cultivation structure on ambient temperature, relative humidity and crop water requirement of strawberry with drip irrigation system grown during winter season (November-February). Reference evapotranspiration was calculated using the FAO-56 Penman-Monteith equation considering the locally recorded weather parameters. Monthly average of daily reference evapotranspiration values are ranging between 1.3 to 3.3 mm day-1, 1.4 to 3.7 mm day-1 and 2.0 to 4.9 mm day-1 for polyhouse, shadenet house and open field, respectively. The total water requirement of drip irrigated straw berry in protected cultivation structure is reduced by about 35.2 % under polyhouse and 25.5 % under shade net house in comparison to open field cultivation.

Performance of the FAO Penman-Monteith equation under limiting conditions and fourteen reference evapotranspiration models in southern Manitoba

Evapotranspiration is a key component of water and energy balance. An accurate estimate of reference evapotranspiration (ETo) is important for determining the water demand of field crops, water management, and hydrological modelling. The FAO-PM ETo equation is the standard equation for estimating ETo. Its use is limited by the requirement for too many observed inputs that are not always available in most weather stations. Empirical models requiring readily available inputs have been developed as an alternative. However, their performance is location-specific. Therefore, this study assesses the performance of the FAO-PM ETo computed with limited data and fourteen empirical ETo models. Meteorological data (2012–2019) was analyzed under the semi-arid climate conditions in southern Manitoba. Model performance was assessed using statistical indices, including R2, RMSE, NSE, MPE, and MAE. Results showed that ETo estimates under missing wind speed, relative humidity, or solar radiation were acceptable, although model performance decreased with increased missing data yielding average to poor performance. Based on R2, RMSE, and NSE values, among the 14 models compared, the best performing models are Valiantzas-1, Valiantzas-3, Irmak, Valiantzas-2, and Priestly-Taylor models. New empirical coefficients were developed, requiring one or two climatic inputs to improve empirical models. Results showed that the calibrated models performed better than the original equation. The sensitivity analysis showed that ETo is most sensitive to maximum temperature (Tmax) and solar radiation (Rs), followed by vapour pressure deficit (VPD), wind speed (U2), and minimum temperature (Tmin). Therefore, it is recommended to ensure accurate measurements of temperature and solar radiation for accurate ETo estimates. This study provides alternative ETo models with reasonable and accurate ETo estimates for southern Manitoba and other areas with similar climate characteristics.

How reliable are the evapotranspiration estimates by Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models for catchment-scale drought assessment and irrigation planning?

Catchment-scale assessment of evapotranspiration (ET) becomes indispensable for evaluating agricultural drought hazard and irrigation water management. As direct estimation of ET is quite intricate under sparse data availability scenarios, the recent indirect ET estimation approaches heavily depend on the water balance-based ET estimates from the popular land surface models, such as, Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC-3L) model or the MOD16A2 satellite product. Along with the land uses, the irrigation application methods of alternate wetting and drying (AWD) could also influence the actual ET (ETact) estimates. This study evaluates the usability of these indirect ET estimation methods with reference to the moisture-stress specific FAO-56 Penman-Monteith (PM) equation for catchment-scale drought and irrigation planning in a real paddy-dominated integrated reservoir-catchment command. The results reveal that the SWAT-derived ET in the non-irrigated paddy fields was consistent with the FAO-56 PM based ET estimates during two crop growing seasons; however, significant overestimation was observed by the VIC-3L, and MOD16A2 products showed a relatively better estimate. The SWAT and VIC-3L derived Standardized Soil Moisture Index (SSI) and Standardized Evapotranspiration Drought Index (SEDI) for the study area revealed that the VIC-3L based drought severity was continuously overestimated or underestimated; whereas the SWAT-derived SEDI indices were almost consistent with the benchmark MOD16A2-ET and ESA-CCI soil moisture derived estimates. Finally, this study endorses to use these indirect catchment-scale drought assessment tools with caution with a need to revise the existing ET subroutines in these hydrological models to deal with the crop dynamics and water management options realistically.

Utilizing homogenized observation records and reconstructed time series data to estimate recent trends in Mid-Atlantic soil moisture scarcity

The Mid-Atlantic region of the USA has experienced increasing annual precipitation amounts in recent decades, along with more frequent extreme events of greater magnitude. Unlike many US regions that have suffered increasing drought conditions from higher evapotranspiration demand, positive trends in the Mid-Atlantic accumulated precipitation are greater than the recent increases in reference evapotranspiration. The temporal correlation between precipitation events and soil moisture capacity is essential for determining how the nature of drought has changed in the region. This analysis has shown that soil moisture scarcity has declined in nine of ten subregions of the Mid-Atlantic that were analyzed from 1985 to 2019. Two algorithms were deployed to draw this conclusion: Climatol enabled the use of the FAO-56 Penman-Monteith equation on daily observation station data for which complete records were unavailable, and the second algorithm calculated soil moisture levels on a daily basis, more accurately capturing drought conditions than common methods using weekly or monthly summaries. Although the declining drought trends were not statistically significant, a result of more extreme events and higher evapotranspiration rates, the inclusion of direct data from an expanded set of locations provides greater clarity from the trends, allowing policymakers and landowners to anticipate changes in future Mid-Atlantic irrigation water demand.

Estimation of Corn Leaf Area Index and Ground Cover With Vegetation Indices as a Result of Irrigation Dose

New technologies have been implemented in the agricultural sector to improve crop production and resource management including irrigation water. A three-year long project was conducted to determine whether vegetation indices (VIs) could be used to estimate the leaf area index (LAI) as well as the soil cover fraction (fc) at different crop growth stages in order to use these parameters in a future study concerning irrigation through drones. A low cost unmanned aerial vehicle (drone) and a multispectral camera were used to calculate different VIs of corn (Zea mays). The irrigation scheduling based on the FAO Penman-Monteith equation and the drip irrigation method were used. Three treatments were organized in three replications and the irrigation doses were equal to 100%, 75%, and 50% of the daily evapotranspiration respectively. The Simple and Multiple Regression analysis were used and different equations were formed where the VIs were the predictor variables and the LAI and fc the predicted ones. According to the two-year period data (2018-2019), during 2018 the average maximum LAI in the full irrigation treatment (100%) was 4.1. In the medium irrigation treatment (75%) the LAI was 4.0. The LAI in the third treatment (50%) was 3.9. In 2019, the LAI was 3.7 (100% treatment). In the second treatment, the LAI was 3.3. The LAI in the third treatment was 3.0. According to the results different VIs and prediction equations could be used to estimate the LAI values with high accuracy with the in situ measurements as well as the soil cover fraction.

New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain

The estimation of Reference Evapotranspiration (ET0) is crucial to estimate crop water requirements, especially in developing countries and areas with scarce water resources. In these regions, the impossibility of collecting all the required data to compute FAO-56 Penman–Monteith equation (FAO56-PM) makes scientists search new methodologies to accurately estimate ET0 with the minimum number of climatic parameters. In this work, several neural network approaches have been evaluated for estimating ET0 using datasets from five weather stations located in Southern Spain (semiarid region of Andalusia). The assessment of statistical performance (Root Mean Square Error -RMSE-, Mean Bias Error -MBE-, coefficient of determination -R2- and Nash-Sutcliffe model efficiency coefficient -NSE-) of models namely Multilayer perceptron (MLP), Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM), Support Vector Machines (SVM), Random Forest (RF) and XGBoost were carried out using different input variables configurations. Only temperature-based data were used as inputs; the calculation of new variables called EnergyT (the integral of the half hourly temperature values of a day) and Hourmin (the difference in hours between time sunset and the time when the maximum temperature occurs) had promising results for the most humid stations. The good results obtained with EnergyT when it is used as an input of the system demonstrated that the information contained on it gives detailed characterization of the daily thermic behavior at each location, resulting in a more efficient model than those using only daily maximum, minimum temperature and extraterrestrial radiation values. In general, the modeling results showed that no model firmly outperformed the others, although MLP and ELM were commonly the models that gave the best performances for all sites: mean values of R2 > 0.89, mean values of NSE > 0.88, mean values of RMSE < 0.67 mm/day and mean values of MBE ranging from −0.17 to 0.30 mm/day. Therefore, EnergyT and Hourmin can be used to estimate ET0 more accurately in stations where data acquisition is limited, like in developing countries or at low-cost weather stations that cannot collect all the required meteorological variables used in FAO56-PM. Overall, the use of ELM is recommended due to its high performance in terms of efficiency (NSE) for all the configurations and for all locations, especially using EnergyT as an input variable.

Development of reference evapotranspiration equations using an artificial intelligence-based function discovery method under the humid climate of Northeast India

The present investigation aims to develop symbolic regression based substitute to the FAO56 Penman-Monteith (FAO56-P-M) equation for calculating reference evapotranspiration (ET0) using meteorological data of thirty sites in northeast India. Further, five different input combination models were developed using a symbolic regression (SR) technique. Extraterrestrial radiation(Ra), altitude (Alt), maximum temperature (Tmax), and minimum temperature (Tmin) were used as default input variables in all the developed models. Temperature (T) based model showed high values of performance indices in all the selected stations and is highly dominated by the Ra and Tmin. Temperature and relative humidity (Rh) based (T-Rh) model had reduced error metrics and improved correlation than the T based model, suggesting that T-Rh models have better prediction capability. Temperature and sunshine hour (n) T-n based model had the lowest error metrics and highest correlation among all combinations, suggesting that T-n based model is the best suitable practical alternative to FAO56-P-M. Temperature, sunshine hour, and Rh (T-n-Rh) based model had almost similar prediction ability as found in T-n based model; hence Rh is a reluctant input variable in ET0 modeling. Temperature, sunshine hour, and wind speed (T-n-Vw) based model had the lowest error metrics and highest correlation among all combinations tested, suggesting that T-n-Vw based model is the best suitable alternative to FAO56-P-M. Sensitivity analysis also showed that the T-n-Vw combination has a cent percent positive contribution in controlling the ET0 process. The performance of the T-n-Vw model on T-n based model was found almost similar accuracy, which could be due to minimal influence of wind speed.

Comparing the use of past and forecast weather data for estimating reference evapotranspiration

The reliability of short-term weather forecast provided by COSMO model in simulating reference evapotranspiration (ET0) was evaluated in 7 study sites distributed in 4 countries (Italy, Norway, Romania and Spain). The main objective of the study was to assess the optimal scenario for calculating ET0, using the FAO-56 Penman-Monteith (PM) equation, by separately considering the accuracy in the use of “past” and “forecast” data input. Firstly, each forecasted variable (air temperature, Tair; relative humidity, RH; wind speed, u2; solar radiation, Rs) and ET0 were compared with in situ observations at hourly and daily scales. Moreover the seasonality effect in the forecast performance was evaluated. Secondly, simulated ET0 were computed every three days with: (i) a “past scenario” that used the observed data input measured in situ during the previous three days, (ii) a “forecast scenario” that used the forecasted input variables for the next three days; and compared with (iii) actual ET0 obtained from the in situ measured data.A general good agreement was found between observed and forecasted agro-meteorological parameters at the different explored time-scales. The best performance was obtained for Tair and Rs, followed by RH and u2. Globally, the comparison between ET0 from the measured and forecasted data input showed high performance, with R2 and RMSE of 0.90 and 0.68 mm d−1. ET0 simulations resulted more accurate using the “forecast scenario” (1.7% overestimation), rather than using the “past scenario” (2.6% underestimation). These results open promising perspectives in the use of forecast for ET0 assessment for different agriculture practices and particularly for irrigation scheduling under water scarcity conditions.

Recent trends in atmospheric evaporative demand in Southwest Iran: implications for change in drought severity

This study evaluates trends in climatic variables, atmospheric evaporative demand (AED) and meteorological drought between 1988 and 2018 over the southwest of Iran, where the Karkheh Basin is located. For this purpose, we used monthly time series of meteorological variables (precipitation, air temperature, wind speed and sunshine duration). The AED was estimated by the FAO-56 Penman-Monteith equation and drought severity by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). There have been significant changes in the climate over the study domain, including a large increase in air temperature and wind speed and a decrease in relative humidity, which have driven a positive trend in the AED. The SPEI for the region has fallen, with the decrease being larger than the magnitude of change observed with the SPI. This suggests an increase in drought severity mostly associated with the rise in AED. The implications for water availability and climate change processes in the region are discussed.

Potential of RT, bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh

Ensemble learning (EL), an alternative approach in the machine-learning field, offers an accurate reference evapotranspiration (ETo) prediction, which is of paramount significance for the hydrological studies and agricultural water practices. Although the FAO-56 Penman-Monteith (PM) equation is regarded as an ideal model for estimating ETo, its applicability is limited due to the absence of required climatic datasets in many regions of the world. Despite its significance, only a few studies use the EL algorithms for the ETo prediction perspective. In this study, we contribute to fill this gap from a two-fold way. First, we present the potential of new EL Random Tree (RT), Bagging and Random Subspace (RS) algorithms which were compared with two commonly used Random Forest (RF), and Support Vector Machine (SVM) algorithms for predicting daily ETo with the climatic data-limited humid region in Bangladesh during 1983–2017 using a five-fold cross-validation scheme. Second, we explore the role of contributing variables influencing ETo change at the regional scale. When a lack of climatic datasets, Tmax (maximum temperature), Tmin (minimum temperature) and Rs (solar radiation) as 3 input combinations obtained the reasonable precision for estimating ETo in all climatic regions except for south-central and south-western regions. Compared to other state-of-arts models, the RT model performed superior to predict ETo in all input combinations followed by the RF, Bagging, RS, and SVM. Considering less difficulty level, high prediction accuracy, more dependability and fewer computation costs of studied models, RT and RF models have been suggested as the promising potentials for daily ETo estimate in subtropical climate regions of Bangladesh and also may be applicable worldwide in the similar climates. The importance analysis from the RF model depicted that the wind speed (U2) and solar radiation (Rs) are the largest influential variables affecting the observed and predicted daily ETo changes in Bangladesh.

Determination of Rice Crop Coefficient Using Modified Microlysimeter

Accurate estimates of crop water requirements are needed in the proper design and management of irrigation systems, and to improve irrigation efficiency. These require knowledge of location-specific crop coefficients (Kc) together with climate-specific reference crop evapotranspiration (ETO). This study aimed to determine Kc values for PSB Rc18 rice variety at each growth stage. A modified microlysimeter setup was used to estimate crop evapotranspiration (ETC) while local climatic data was used in the FAOPenman-Monteith equation to determine ETO. The computed average Kc values during the initial, vegetative, reproductive, and late stages for rice are 1.02, 1.15, 1.14, and 0.78, respectively. These estimated Kc values are very close to locally established and FAO recommended values.

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

The computation of the grass reference evapotranspiration with the FAO56 Penman-Monteith equation (PM-ETo) requires data on maximum and minimum air temperatures (Tmax, Tmin), actual vapour pressure (ea), shortwave solar radiation (Rs), and wind speed at 2 m height (u2). Nonetheless, related datasets are often not available, are incomplete, or have uncertain quality. To overcome these limitations, several alternatives were considered in FAO56, while many other procedures were tested and proposed in very numerous papers. The present study reviews the computational procedures relative to predicting the missing variables from temperature, i.e., the PM temperature approach (PMT), and estimating ETo with the Hargreaves-Samani (HS) equation. For the PMT approach, procedures refer to predicting: (a) the dew point temperature (Tdew) from the minimum or the mean air temperature; (b) shortwave solar radiation (Rs) from the air temperature difference (TD = Tmax-Tmin) combined with a calibrated radiation adjustment coefficient (kRs); and (c) wind speed (u2) using a default value or a regional or local average. The adequateness of computing Tdew from air temperature was reassessed and the preference for using an average u2 has been defined. To ease the estimation of Rs, for the PMT approach and the coefficient of the HS equation, multiple linear regression equations for predicting kRs were developed using local averages of the temperature difference (TD), relative humidity (RH) and wind speed as independent variables. All variables were obtained from the Mediterranean set of CLIMWAT climatic data. Two types of kRs equations were developed: climate-focused equations specific to four climate types - humid, sub-humid, semi-arid, and hyper-arid and arid -, and a global one, applicable to any type of climate. The usability of the kRs equations for the PMT and HS methods was assessed with independent data sets from Bolivia, Inner Mongolia, Iran, Portugal and Spain, covering a variety of climates, from hyper-arid to humid. With this purpose, ETo estimated with PMT and HS (ETo PMT and ETo HS) were compared with PM-ETo computed with full data sets to evaluate the usability of the kRs equations. Adopting the climate-focused kRs equations with ETo PMT, the RMSE averaged 0.59, 0.64, 0.65 and 0.72 mm d−1 for humid, sub-humid, semi-arid, and arid and hyper-arid climates, respectively, while the RMSE values relative to ETo HS when using the respective climate-focused kRs equations averaged 0.58, 0.60, 0.60 and 0.69 mm d−1 for the same climates. These results are similar to those obtained with the kRs global equation. The accuracy of the PMT approach when using the kRs equations was also evaluated when one, two, or all three Tdew, Rs and u2 variables are missing and the resulting goodness-of-fit indicators demonstrated the advantage of the combined use of observed and estimated weather variables. The usability of the kRs equations for an efficient parameterization of both the PMT approach and the HS equation is demonstrated with similar performance of PMT and HS procedures for a variety of climates. Because the ETo HS results depend almost linearly on temperature, the PMT approach, using estimates of the weather variables, is able to mitigate those temperature impacts, which trends may be contrary to those of other variables that determine ETo. The clear advantage of the PMT approach is that it allows using the available weather data in combination with estimates of the missing variables, which provides for more accurate ETo computations.

CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China

Establishing a computational model for accurate prediction of reference crop evapotranspiration (ET0) is critical for regional water resources planning and irrigation scheduling design. FAO Penman-Monteith equation is recommended as the standard model to predict ET0. However, its application is restricted by lack of complete meteorological data in many regions. This study evaluated the performance of CatBoost, an algorithm for gradient boosting on decision trees, for estimating daily ET0 using limited meteorological data in arid and semi-arid regions of Northern China. The CatBoost model was further compared with their corresponding generalized regression neural network (GRNN) and random forests (RF) models. Eight input combinations of daily meteorological data including daily maximum air temperature (Tmax), daily minimum air temperature (Tmin), wind speed at 2 m height (u2), relative humidity (RH) and net radiation (Rn) from 15 weather stations during 1996–2015 were used to train and test the models. Four statistical indicators were used to evaluate the accuracy and performance of the models, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE). The results showed that all the three models using Tmax, Tmin, u2 and Rn could obtain satisfactory ET0 estimates in arid and semi-arid regions of Northern China with incomplete sets of data. For the local models, CatBoost (on average RMSE ranging 0.096–0.821 mm d−1) was superior to GRNN (on average RMSE ranging 0.206–0.847 mm d−1) and RF (on average RMSE ranging 0.169–0.866 mm d−1) under the same meteorological parameters as input. The results of the generalized models were similar to the local models, but the former ones performed worse than the latter ones. Overall, CatBoost is observed to be the best alternative for estimating ET0, which is helpful for irrigation scheduling in arid and semi-arid regions of Northern China and maybe elsewhere with similar climates.

Evapotranspiration and crop coefficients from lysimeter measurements for sprinkler-irrigated canola

Canola is a water-stress tolerant crop, which could be an alternative in areas with limited water resources. However, in arid and semi-arid environments where rainfall events are scarce and increasingly erratic, the use of irrigation is necessary for canola production to reach its maximum yield. The goal of this study was to determine the crop evapotranspiration (ETc) and crop coefficients of sprinkler irrigated canola (Brassica napus L.) under non-limiting soil water content conditions. A 2-year field experiment was conducted in the lysimeter facility located in Albacete (SE Spain). A large weighing lysimeter (2.7 × 2.3 × 1.7 m), with an accuracy of 0.04 mm equivalent water depth, was used to measure the daily crop evapotranspiration (ETc) rate throughout two growing seasons. ETc values were determined using daily mass change in the lysimeter. Cumulative ETc was replaced in the lysimeter through sprinkler irrigation applications, thus crop water stress was avoided. Seasonal lysimeter based (measured) canola ETc was 472 and 602 mm in 2008 and 2012, respectively. The 28 % higher ETc value in 2012 was mainly due to a much higher evaporative demand during the crop growth mid-season period of 2012. The Kc values were determined using grass reference evapotranspiration (ETo) calculated with the FAO56 Penman-Monteith equation and the ETc calculations from the lysimeter data. The dual crop coefficient approach was used to separate crop transpiration (Kcb) from soil evaporation (Ke). For the two canola seasons, mid-season Kc and Kcb values, after FAO56 climate adjustment, were Kc mid (std) = 1.15 and Kcb mid (std) = 1.11. Those values were reached coinciding with maximum fraction of ground cover (fc) values of up 0.95 and 0.97 for 2008 and 2012, respectively. The seasonal evaporation component for sprinkler-irrigated canola was estimated to be about 24 % and 19 % of ETc in 2008 and 2012, respectively. The good linear relationship found between canola Kcb values and fc and the excellent agreement found between remotely sensed vegetation indices (VIs) and different biophysical parameters, such as Kcb and fc, will allow monitoring and estimating the spatially distributed water requirements of canola at field and regional scales using multispectral satellite imagery.

Estimation of evapotranspiration and crop coefficient in Dajiuhu peatland of Shennongjia based on FAO56 Penman-Monteith

We collected evapotranspiration data of Dajiuhu peatland in Shennongjia from 2016 to 2017 with eddy covariance method and estimated the value of crop coefficient (Kc) using FAO56 Penman-Monteith equation and the linear relationship between actual evapotranspiration (ETa) and referenced evapotranspiration (ET0). We analyzed the characteristics of referenced evapotranspiration and its main influencing factors and calculated the crop coefficient of the wetland dominated by Sphagnum. The results showed that the daily averaged ETa were 1.63 and 1.38 mm·d-1 in 2016 and 2017, the daily averaged ET0 were 1.61 and 1.23 mm·d-1 in 2016 and 2017. Environmental factors influencing ET0 included net radiation, air temperature, vapor pressure deficit, wind speed, and relative humidity. The Kc values for the growing seasons of 2016, 2017, and 2016-2017 were 0.95 (R2 of linear regression between ETa and ET0 was 0.96), 1.03 (R2=0.95), and 0.98 (R2=0.95). The Kc values in 2016, 2017, and 2016-2017 were 0.92 (R2=0.94), 0.95 (R2=0.89), and 0.93 (R2=0.92). Kc was effective in the range of 0.92-1.03 for the wetland dominated by Sphagnum. The identified parameters could be widely used in studies on climate change, ecosystem services, and water management in peatlands.

Estimation of evapotranspiration in lesser Himalayas using remote sensing based surface energy balance algorithm

In this study, remote sensing-based algorithm, METRIC is used to estimate evapotranspiration (ET) over a mountainous watershed in the lesser Himalayas in Uttarakhand, India. ET estimates of METRIC are compared with the ET values determined by using FAO Penman-Monteith equation and crop coefficients. In its original form, METRIC underestimated the ET values considerably with mean absolute error (MAE) of 0.95 mm/day. A modification is incorporated in METRIC for better representation of the effect of rainfall on the ET process. The proposed modification estimates ET at the hot pixel using daily rainfall and meteorological data. Using the modification, the MAE is reduced to 0.44 mm/day and underestimation of ET is reduced from 20.9% to 8.4%. This study contributes to the better understanding of ET in the lesser Himalayas of Uttarakhand. It also underlines the importance of accurate assignment of ET to the hot pixel for accurate estimation of ET using METRIC.

Numberical Simulation of Evapotranspiration with Limited Data in the Pearl River Basin

This research compared and analyzed the adaptation of four evapotranspiration equations: the FAO56 Penman-Monteith (PM), Priestley-Taylor, Hargreaves-Samani and Thornthwaite equations by using the day-to-day meteorological data of the 42 observation stations in the Pearl River basin. Numberical simulation of the four equations in different seasons and regions was made in order to find out the most suitable one for the evapotranspiration estimate in the Pearl River basin. Results show that the calculation results of the PM equation seem more stable in different seasons and regions, and is closer to the actual observation, due to the detailed consideration of the effects of aerodynamics and energy supply on evapotranspiration in its calculation procedure. The missing data of relative humidity, sunshine duration, wind speed and air pressure would have influence on the calculation accuracy of the PM equation. Temperature can be used to instead of the above variable in the PM equation, which is called the PMT equation. The applicability and the calculation accuracy is explored in this study. And the results show that the calculation results of the PMT equation is larger than those of the PM equation. By setting a correction coefficient in the PMT equation, the calculation accuracy can be obviously improved. Therefore, in the absence of other data, the PMT equation calculated only using temperature data can be use as an ideal equation for the evapotranspiration calculation and research in the Pearl River basin.