Reference Crop Evapotranspiration Research Articles

A new approach for estimating spatial-temporal phreatic evapotranspiration at a regional scale using NDVI and water table depth measurements

Accurate estimation of the spatial-temporal distribution of phreatic evapotranspiration (ET) is critical for managing water resources and preventing soil salinization in arid and semiarid agricultural areas where substantial water-saving efforts are needed. Traditional phreatic ET estimation approaches either are for small scales or cannot consider spatial and/or temporal variations in phreatic ET. This study developed a new approach for estimating the spatial-temporal phreatic ET based on the normalized difference vegetation index (NDVI) and measured water table depths. The NDVI was used to calculate the actual evapotranspiration (ETc act) by scaling it with the reference crop evapotranspiration (ETo). The water table depths measured during the periods of no other factors (i.e., precipitation, irrigation and groundwater extraction) were used to establish an equation used to estimate the phreatic ET contribution coefficient (defined as the ratio of phreatic ET to the corresponding ETc act). To improve estimation accuracy, a time-related correction factor was considered in the equation for estimating the phreatic ET. The new approach was used to estimate monthly phreatic ET with a spatial resolution of 250 m in the Hetao irrigation district, located in arid Northwest China. The estimated phreatic ET at different spatial and temporal scales matched well with the groundwater balance model results. The results show that the spatial distribution of phreatic ET is affected by both natural factors (e.g., land cover types) and human activities (e.g., groundwater extraction, planted crops). In the Hetao irrigation district, phreatic ET contributes an average of 24.4% to ETc act during the non-freezing-thawing period (from June to November), demonstrating the essential role of phreatic ET in supporting crop growth and the general ecological environment in arid areas with shallow water table depths. The new approach of estimating spatial-temporal phreatic ET may be used for designing effective and efficient water resource management policies at the regional scale.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

Modeling of Reference Crop Evapotranspiration in Wet and Dry Climates Using Data-Mining Methods and Empirical Equations

Modeling of Reference Crop Evapotranspiration in Wet and Dry Climates Using Data-Mining Methods and Empirical Equations

Short-Term Prediction of Reference Crop Evapotranspiration Based on Machine Learning Model with Different Decomposition Methods

Short-Term Prediction of Reference Crop Evapotranspiration Based on Machine Learning Model with Different Decomposition Methods

Spatio-temporal variation of reference crop evapotranspiration and its climatic mechanism in Nenjiang River Basin, China

To understand the temporal and spatial variations of reference crop evapotranspiration (ET0) in Nenjiang River Basin, and clarify the effects of climatic factors on ET0, we calculated the daily ET0 of each station in Nenjiang River Basin from 1970 to 2019 by Penman-Monteith formula, analyzed the temporal variation trend and spatial distribution pattern of ET0. We further quantitatively examined the sensitivity of ET0 to meteorological factors by sensitivity analysis, and explored the contribution of meteorological factors to ET0 changes. The results showed that ET0 generally showed an insignificant decreasing trend during the study period in the Nenjiang River Basin. ET0 decreased in spring, summer, and autumn, but increased in winter, and decreased from southeast to northwest. ET0 had the highest sensitivity to relative humidity at both temporal and spatial scales. The sensitivity coefficients of mean temperature, relative humidity and wind speed increased gradually, while that of sunshine hours decreased gradually. ET0 was sensitive to mean temperature in northern Greater Khingan Mountains and Lesser Khingan Mountains, while to wind speed in southern Greater Khingan Mountains and Songnen Plain. Wind speed was the main factor affecting the change of ET0 in the whole year, spring, autumn, and winter. Sunshine hours was the main affecting factor in summer. The mean temperature and relative humidity had the greatest contribution to ET0 in the north of Greater Khingan Mountains and Lesser Khingan Mountains, and it was the wind speed in Songnen Plain.

Improving reference evapotranspiration (ETo) calculation under limited data conditions in the high Tropical Andes

The computation of the reference crop evapotranspiration (ETo) using the FAO56 Penman-Monteith equation (PM-ETo) requires data on maximum and minimum air temperatures (Tmax, Tmin), relative humidity (RH), solar radiation (Rs), and wind speed (u2). However, the records of meteorological variables are often incomplete or of poor quality. Frequently, in the mountain areas such as those of the Andes, environmental sensors are subject to harsh conditions, due to the diurnal/nocturnal climatic variability causing challenging conditions for meteorological monitoring, which leads to data loss. For high-elevation landscapes like the Andes, the missing variables of vapor pressure deficit and solar radiation cause a high impact on PM-ETo calculation. To assess these limitations, several methods relying on maximum and minimum temperature to estimate the missing variables have been considered in the present investigation. Based on data from three automatic weather stations in the high Tropical Andes (humid páramo, 3298 – 3955 m a.s.l.), we found that the calibration and validation of methods were essential to estimate Rs. Using the (De Jong and Stewart, 1993) (Rs-DS) method we retrieved the highest performance, a RMSE between 2.89 and 3.81 MJ m−2 day−1. Moreover, In the absence of RH observations, replacing the dew point temperature (Tdew) by Tmin was a reliable alternative, when apply the method of (Allen et al., 1998) (VPD-FAO) which showed the highest performance with RMSE between 0.08 and 0.12 kPa. These results yielded highly accurate PM-ETo estimates, with RMSE between 0.29 and 0.34 mm day−1 and RMSE between 0.12 and 0.18 mm day−1, respectively. As expected, when both variables were missing, the ETo calculation increased its error, with an RMSE between 0.32 and 0.42 mm day−1. A proper estimation of ETo in the Andean páramo contributes to improved water productivity for domestic and industrial uses, irrigated agriculture, and hydropower.

Reference crop evapotranspiration in distinct agricultural regions of Southern Brazil: a comparison of improved empirical models

The FAO56 Penman-Monteith model is globally accepted for the accurate determination of reference evapotranspiration (ETo). However, a lack of appropriate data encouraged the improved model’s approach to estimate ETo. This study compared the performance of 10 empirical models of ETo estimation (Penman, Priestley & Taylor, Tanner & Pelton, Makkink, Jensen & Haise, Hargreaves & Samani, Camargo, Benevides & Lopes, Turc, and Linacre) contrasted with the FAO56 model in two regions in Southern Brazil. Data were collected from automatic stations of the Brazilian National Institute of Meteorology (INMET) from December 21, 2019, to February 28, 2021. The determination coefficient (R²), mean square error (nRMSE), mean bias error (MBE), Willmott index (d), and Pearson’s correlation coefficient (r), clustering, and Principal Component Analysis (PCA) were performed. For the different regions, the radiation-based model proposed by Penman was the best alternative for estimating ETo. The model showed the most appropriated values for R2 (0.9015) and r (0.9494). The clustering and PCA analyses indicated the interrelations of the meteorological data and the combination of the models according to the parameters used for the determination of ETo.

Splitting and Length of Years for Improving Tree-Based Models to Predict Reference Crop Evapotranspiration in the Humid Regions of China

To improve the accuracy of estimating reference crop evapotranspiration for the efficient management of water resources and the optimal design of irrigation scheduling, the drawback of the traditional FAO-56 Penman–Monteith method requiring complete meteorological input variables needs to be overcome. This study evaluates the effects of using five data splitting strategies and three different time lengths of input datasets on predicting ET0. The random forest (RF) and extreme gradient boosting (XGB) models coupled with a K-fold cross-validation approach were applied to accomplish this objective. The results showed that the accuracy of the RF (R2 = 0.862, RMSE = 0.528, MAE = 0.383, NSE = 0.854) was overall better than that of XGB (R2 = 0.867, RMSE = 0.517, MAE = 0.377, NSE = 0.860) in different input parameters. Both the RF and XGB models with the combination of Tmax, Tmin, and Rs as inputs provided better accuracy on daily ET0 estimation than the corresponding models with other input combinations. Among all the data splitting strategies, S5 (with a 9:1 proportion) showed the optimal performance. Compared with the length of 30 years, the estimation accuracy of the 50-year length with limited data was reduced, while the length of meteorological data of 10 years improved the accuracy in southern China. Nevertheless, the performance of the 10-year data was the worst among the three time spans when considering the independent test. Therefore, to improve the daily ET0 predicting performance of the tree-based models in humid regions of China, the random forest model with datasets of 30 years and the 9:1 data splitting strategy is recommended.

The Study of Drought in Future Climate Scenarios in the Huang-Huai-Hai Region

In the context of global warming, agricultural production and social and economic development are significantly affected by drought. The future change of climate conditions is uncertain; thus, it is of great importance to clarify the aspects of drought in order to define local and regional drought adaptation strategies. In this study, the meteorological data from 1976 to 2005 was used as a historical reference, and nine Global Climate Models (GCMs), downscaling to meteorological stations from 2039 to 2089, were used as future climate data. Based on Penman–Monteith, the reference crop Evapotranspiration (ET0) and Standardized Precipitation Evapotranspiration Index (SPEI) of the reference crop in three emission scenarios of RCP2.6, RCP4.5, and RCP8.5, under future climate conditions, were calculated. A non-parameter Mann–Kendall trend test was performed on temperature, precipitation, ET0, and SPEI to analyze the drought spatiotemporal distribution traits under upcoming climate scenarios. The results showed that, under future climate conditions, SPEI values in most areas of the Huang-Huai-Hai region would continuously increase year by year, and drought would be alleviated to some extent at the same pace. However, with the increase of greenhouse gas concentration in the emission scenarios, SPEI values continued to decline. In the RCP8.5 scenario, the area of severe drought was large. To sum up, in the future climate scenario, the degree of drought in the Huang-Huai-Hai region will be alleviated to some extent with the increase of rainfall, but with the increase of greenhouse gas concentration, the degree of drought will be further intensified, posing a huge challenge to agricultural water use in the region. This study provides a theoretical foundation for alleviating drought in the Huang-Huai-Hai region in future climate scenarios.

Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes

Over the past decade, there has been an increasing research on the use of machine learning tools for estimating reference crop evapotranspiration (ETo). However, due to the data-hungry nature of the machine learning models, all of these researches are not suitable for regions with limited data supply. This study aims to provide a breakthrough for the bottleneck through coupling of the inter-model ensemble with various data management schemes. The Bayesian modeling approach and a non-linear neural ensemble based inter-model ensemble (BMA-E and NNE-E) were developed locally with data from five different meteorological stations in the Peninsular Malaysia. The NNE-E was found to be highly robust spatially, whereby it can be used to estimate daily ETo accurately at other stations, even though with reduced input meteorological parameters. However, the performances of the locally trained models were found wanting and were fluctuating violently. This was resolved through creating a data pool that include the data from all stations and developing a universal NNE. By following the proposed scheme of things, the daily ETo can be easily estimated across the whole Peninsular Malaysia. This being, without the need for historical data and new models at estimation site.

Trend analysis of weather parameters over Indian Sundarbans

Sundarbans in West Bengal of India by virtue of its strategic location in the Eastern coast on the Bay of Bengal falls in one of the most vulnerable zones of abrupt climate change. Temporal trends of weather parameters of Canning Town (22o18'10.8'' N Latitude, 88o39'58.4'' E Longitude, elevation 3.52 m msl) representing Indian Sundarbans were analysed by non-parametric Mann-Kendall test and Sen's slope approaches. Analysis of long term rainfall data (1966-2015) indicated that Canning receives a mean annual rainfall of 1821 mm (±341.8 mm) with a considerable variation (CV = 18.8%). The results revealed that total annual rainfall trend decreased non-signicantly at the rate of 0.94 mm yr-1. On an average 84.4 rainy days in a year was recorded in the region, whereas during last ten years (2006-2015), the number of rainy days was reduced to 79.7 days yr-1. There was no signicant change in maximum, minimum and mean temperature of the region. Bright sunshine hours declined signicantly at an annual rate of 0.055 hr yr-1. Reference crop evapotranspiration (ET ) calculated using FAO Penman-Monteith method revealed that annual ET signicantly decreased at the rate of 5.98 mm yr-1. There was 2.7 times surplus rainfall than crop evapotranspiration during monsoon months indicating very high scope of water harvesting to tackle water logging during the monsoon season and unavailability of fresh water for irrigation during lean season.

Irrigation water requirement of rice in Long Xuyen Quadrangle area, Vietnam in the context of climate change

Climate variability is deeply affecting all aspects of human life including agricultural sector. In the present study the CROPWAT model was used to estimate reference evapotranspiration (ETo), crop evapotranspiration (ETc), effective rainfall (ER) and crop water requirement (CWR) of summer-autumn rice crop and its yield during baseline period (2002-2017) and also under representative concentration pathways (RCPs) 4.5 scenario for 2020s, 2055s and 2090s-time scales for Long Xuyen Quadrangle (LXQ) area of Vietnam. It was found that the ER significantly increased by 6.2, 16.9 and 15.4 per cent, respectively in 2020, 2055 and 2090; ETo and ETc increased by 2.1 and 2.3 per cent, respectively in 2020s; 4.4 and 5.8 in 2055; and 5.8 and 7.7 per cent in 2090 compared to baseline. The CWR also increased approximately 4.6, 4.4 and 3.5 per cent, respectively in 2020, 2055 and 2090 and consequent decrease in rice yield by 6.5, 7.9 and 10.4 per cent, respectively. Results showed that if the crop planting date is delayed by 20 days, the rice yield would increase approximately 4.9, 7.9 and 9.9 per cent, respectively in three-time scales of RCP 4.5 scenario, compared to base line period.

Implication of climate variable selections on the uncertainty of reference crop evapotranspiration projections propagated from climate variables projections under climate change

The projection of reference crop evapotranspiration (ETo) based on global climate models (GCMs) is an important task in many fields under climate change background. However, ETo projections are typically hindered by the limited data availability or high uncertainty of the GCM-projected climate variable for ETo calculation (e.g., radiation and wind speed). In the current study, we investigate how the selection of climate variables for ETo calculation would influence the reliability and uncertainty of the ETo projections. Based on the Penman-Monteith (P-M) formula and the missing variable estimation approaches provided by FAO, five variable selection schemes (VSs) that select different subsets of the climate variables for ETo calculation are established. Four emission scenarios, five GCMs and four statistical downscaling approaches were adopted, and their uncertainty contributions were quantified by analyses of variation (ANOVA) approach. Results show that, among the climate variables except air temperature, shortwave radiation caused the lowest uncertainty to the projected ETo, and is thus suggested as the most suitable variable to be considered for ETo projections. In most areas, wind speed and relative humidity are determined as marginal variables for their limited influences in ETo projections, yet in some complex-terrain regions, wind speed could propagate considerable uncertainty to the projected ETo. Differences in GCMs is the major source of uncertainty in ETo projections, while the emission scenario generally ranked as second. Statistical downscaling approaches contribute limited uncertainty (below 10%) to ETo projections. Above all, this study shows the applicability of using the missing value estimation approaches for the FAO56 P-M formula to perform future ETo projection, for the practices with limited GCM data availability, and can serve as the reference for selecting suitable variables for ETo forecasting in applications.

Spatio‐temporal variability of reference crop evapotranspiration at different timescales in Sichuan Province, south‐west China*

AbstractAiming to analyse the variation of seasonal reference crop evapotranspiration (ET0) and show the impact of meteorological factors on ET0 across Sichuan Province, this study mainly discussed the variation of ET0 during the periods of 1967–2016 in three subregions of Sichuan Province with the Mann–Kendall test (MK), Morlet wavelet analysis and path analysis. Meanwhile, future ET0 trend variation characteristics at different timescales were evaluated by Hurst exponents based on the rescaled range analysis (R/S) method. The results indicated that over the past 50 years, annual ET0 had a decreasing trend with a rate of −0.64 mm per10 years, confirming the existence of an ‘evaporation paradox’. Through spatial analysis, the distribution of average annual ET0 exhibited a declining trend from south to north over the study region. On the seasonal scale, the highest values of ET0 in summer were distributed in the Sichuan central basin while the highest values in other seasons were in the south‐western mountain area. The Hurst exponents showed that the trends of annual and seasonal ET0 in the future are consistent with past ones in the whole region (H > 0.5). The annual ET0 showed the obvious periodic variation of 28 years in the study regions based on Morlet wavelet analysis. The decreases in ET0 were mostly attributed to the decline in sunshine hours. These findings can help water managers in better water planning and irrigation scheduling for local agricultural production.

On the interchangeability of Landsat and MODIS data in the CMRSET actual evapotranspiration model – Comment on “Monitoring irrigation using Landsat observations and climate data over regional scales in the Murray-Darling Basin” by David Bretreger, In-Young Yeo, Greg Hancock and Garry Willgoose

In a recent paper published in this journal, Bretreger et al. (2020) estimate irrigation water use from satellite remotely sensed estimates of actual evapotranspiration in five irrigated districts of the Murray-Darling Basin (southeast Australia). They used three models that scale crop reference evapotranspiration with vegetation indices acquired by recent Landsat satellites: (i) IrriSAT, (ii) Kamble and (iii) CMRSET. In their paper, irrigation water use computed with CMRSET generally overestimated observed irrigation water use, sometimes fivefold. Based on these results, Bretreger et al. (2020) discouraged the use of CMRSET for irrigation monitoring. In this comment, we reproduce the experiments in Bretreger et al. (2020), and demonstrate that their overestimation was because they used an incorrect Landsat band in their implementation of CMRSET. CMRSET was originally calibrated using both MODIS-derived Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI). To calculate GVMI, a shortwave infrared (SWIR) band with a wavelength of ∼1.6 μm was used, which for MODIS is named SWIR2, and for Landsat is named SWIR1 (i.e., different sensors have different bands and a different number of bands and SWIR1 in MODIS has a wavelength of ∼1.2 μm). In their Landsat CMRSET implementation, Bretreger et al. (2020) computed GVMI using the Landsat SWIR2 band, which has a wavelength of ∼2.1 μm (see their Table 3). We show that CMRSET implemented with the Landsat SWIR1 band (i.e., with a wavelength of ∼1.6 μm, so being the correct Landsat band to calculate GVMI) yields similar results (both for temporal patterns and magnitude) when compared to the other two remote sensing actual evapotranspiration models in Bretreger et al. (2020). For the irrigation districts, these similar results meant that the mean water year (i.e., July to next June from 2010 to 2017) actual evapotranspiration mean absolute relative difference was 15.3% (with a 5.4%–26.5% range) and the water year irrigation water use mean absolute relative difference was 4.1% (with a 9.8%–18.9% range). Conversely, we show that a Landsat CMRSET implementation using the incorrect Landsat band for the calculation of GVMI (i.e., with a wavelength of ∼2.1 μm) led to a large overestimation agreeing with the CMRSET results reported by Bretreger et al. (2020), both for water year actual evapotranspiration (mean absolute relative difference of 64.6%, with a 51.1%–83.6% range) and irrigation water use (mean absolute relative difference of 44.1%, with a 31.4%–65.1% range). The use of the incorrect Landsat band means that the Bretreger et al. (2020) recommendation specific to their Landsat CMRSET implementation is invalid. A demonstration of Bretreger et al.’s (2020) incorrect Landsat CMRSET implementation and the correct one can be accessed at: https://jorgepena.users.earthengine.app/view/comment-on-bretreger-2020-joh-paper.Despite Bretreger et al.’s (2020) shortcomings in their implementation of the Landsat CMRSET actual evapotranspiration model, timely monitoring of irrigation water use via satellite remote sensing is required at Landsat resolutions (i.e., 30 m) or higher due to the heterogeneous nature of irrigation in most agricultural landscapes, and we applaud them for pursuing this line of research.

Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference crop evapotranspiration in different climatic regions of China

Accurate prediction of reference crop evapotranspiration (ET0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R2 = 0.973–0.999) all performed better than the standalone ELM model (R2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting Tmax, Tmin and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only Tmax and Tmin data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET0 with fewer meteorological parameters in different climatic zones of China.

Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation

Reference crop evapotranspiration (ETo) is a determinant factor in agricultural water resource management. Therefore, accurate ETo information is critical to quantify crop water requirements for precision agriculture management. This study coupled bio-inspired optimization algorithms with artificial neural network (ANN), i.e., ANN with bat algorithm (BA-ANN), ANN with cuckoo search algorithm (CSA-ANN), and ANN with whale optimization algorithm (WOA-ANN), and developed three hybrid ANN models for daily ETo modeling with limited inputs. The models were trained and evaluated using a k-fold test approach and long-term daily climatic data from 2001 to 2018 at six climatic stations in the Loess Plateau of north China. Three input scenarios were used, including temperature-based inputs, radiation-based inputs, and mass transfer-based inputs. The statistical comparison showed that the hybrid WOA-ANN offered better estimates than BA-ANN and CSA-ANN in all three input scenarios. In general, the radiation-based WOA-ANN provided the most accurate ETo estimations, with regional average relative root mean square error and Nash-Sutcliffe efficiency coefficient of 13.3% and 0.959, respectively. The temperature-based WOA-ANN offered acceptable and reasonable ETo estimates. Thus, it is a reliable tool for ETo modeling, given that air temperature is available in many regions. Overall, the bio-inspired optimization algorithms are robust tools for enhancing ANN performance in ETo simulation, and thus they are highly recommended to estimate ETo in the study region. Our study proposed powerful models for accurately estimating ETo with limited inputs, offering practical implications for the development of precision agriculture.

Detecting crop water requirement indicators in irrigated agroecosystems from soil water content profiles: An application for a citrus orchard

Most perennial crops sensitive to water scarcity, such as citrus, can benefit from efficient water management, which allows for reduced water consumption while increasing crop production on a long-term basis. However, when implementing water-saving strategies, it is necessary to monitor soil and/or plant water status in order to determine crop water demand. A plethora of devices providing indirect measurements of volumetric soil water content, such as the “drill and drop” multi-sensors probes (Sentek, Inc., Stepney, Australia), have been developed over the last decade.The objective of the paper was to analyse time-series of soil water content profiles and meteorological data collected in an adult citrus orchard over three years of field observations (2017–2020) in order to estimate actual crop evapotranspiration and derive crop coefficients. Simultaneous measurements of sap fluxes also allowed for the estimation of the basal crop coefficient, Kcb, used as a control variable.The temporal dynamics of soil water content profiles following rainfall or irrigation events provided information on soil evaporation, root water uptake, and actual crop transpiration. After soil wetting events, in particular, it was possible to recognize patterns of actual crop evapotranspiration similar to those detected with sap flow sensors. The knowledge of actual crop evapotranspiration at the daily time-step, in conjunction with the corresponding reference crop evapotranspiration, allowed for appropriate estimations of the crop coefficient associated with the various development stages. The proposed method provided interesting insights into the dynamics of root water uptake and crop evapotranspiration of the studied citrus orchard, and it represents a promising tool for precise irrigation scheduling in other agroecosystems.

Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

Accurately estimating reference crop evapotranspiration (ETo) is crucial for water resource management and precise irrigation. The FAO-56 Penman–Monteith model has the highest accuracy in terms of ETo prediction, but its application is restricted by a lack of complete meteorological data. To develop a high accuracy prediction model for ETo with incomplete meteorological data, this study used four principal component analysis methods (i.e. path analysis, stepwise linear regression analysis, logistic regression analysis and factor analysis) to determine few factors that strongly affect ETo as the input to established the ETo model with machine learning (ML). The prediction model for ETo was established using support vector machine (SVM), gradient boosting decision tree (GBDT), particle swarm optimisation (PSO)- SVM, and PSO-GBDT algorithms. This study demonstrated that SVM, GBDT, PSO-SVM and PSO-GBDT models can maintain the highest accuracy by using the five most important affect factors extracted on the basis of path analysis (PA) as input, for various models, the precision ranges of NSE, R2, RMSE, MAE, and GPI were approximately 0.53–0.70, 0.63–0.84, 0.27–0.47, 0.27–0.69, and 0.72–1.01 respectively. The accuracy of SVM and GBDT significantly improved through optimisation using PSO. When inputting the top five factors of importance extracted by PA algorithm in each model, the SVM and GBDT model optimized by PSO algorithm increased the average value of GPI by 17.74% and 46.71% respectively. When inputting the top five factors extracted by the PA algorithm in PSO-GBDT, the average value of NSE, R2, RMSE and MAE is 22.37%, 2.83%, 9.05% and 6.84% higher than that of PSO-SVM, and the GPI is 8.30% higher than that of PSO-SVM. This study shows that the ETo prediction model constructed by the PSO-GBDT algorithm can obtain the highest accuracy when the top five factors extracted by the PA algorithm are used as input. This provides a reference for ETo estimation in Southwest China.

Bias-correcting input variables enhances forecasting of reference crop evapotranspiration

Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts.