Penman-Monteith Model Research Articles

Spatial-Temporal Interpolation of Reference Evapotranspiration for Pakistan

The unequal water distribution in the universe has resulted in more than 2 billion people living in water-stressed areas. Globally, withdrawals of water resources are still below the critical level. Pakistan is also affected by this serious problem. In the context of environmental evaluation, irrigation scheduling, and water resource management, evapotranspiration predictions are crucial. The evapotranspiration estimation methods may need to be evaluated daily or monthly to understand the climate change effects in the local areas. This paper investigates spatial-temporal interpolation of evapotranspiration data from 41 Pakistani meteorological stations. To estimate evapotranspiration, we have used the average time series data between 2006 and 2015 on temperature, relative humidity, wind speed, and solar radiation. We developed a new modified Hargreaves equation to estimate evapotranspiration and evaluate the performance of different models. We found that our modified Hargreaves model could perform better than the original Hargreaves model. To analyze trends in different seasons, such as rainy, dry, and annual, FAO-56 Penman-Monteith, Blaney-Criddle, and Hargreaves-Samani models are used. We used Ordinary Kriging for Functional Data to map and predict evapotranspiration spatially and temporally in various locations.

Urban evapotranspiration estimation based on anthropogenic activities and modified Penman-Monteith model

Urban evapotranspiration (ET) is an essential part of the hydrological cycle. A complete understanding of urban ET is necessary to understand the interaction between the atmosphere and the urban surface. However, estimating urban ET is challenging due to the influence of urban surface heterogeneity and human activities in urban areas. Therefore, this paper proposed an improved multisource parallel model (PM-urban) based on the Penman-Monteith model, which is suitable for the ET estimation for the underlying surface of the city. The PM-urban model takes into account the violent human activities in the city, and its main characteristics are: (1) the anthropogenic heat flux (AHF) was included in the surface energy balance formula; (2) The building water dissipation was considered. In this paper, taking Shenzhen, one of the cities with the fastest urbanization process globally, as an example, seven urban ET maps of Shenzhen were generated using Landsat 8 images. The model was verified using pan evaporation data corrected by complementary relationships. The results showed that: (1) The ET of each land use type has the following relationship: water body > vegetation > bare soil > no building impervious surface, and the value of building water dissipation varies due to the height and type of buildings significant; (2) The PM-urban model that considers AHF is better than the original model that ignores AHF; (3) This model can describe the characteristics of urban ET well and explain the phenomenon of high water consumption in cities. Our research results provide a new perspective for calculating the urban ET, which can be applied to urban management, planning, and reducing the urban heat island effect.

Effect of drip irrigation and plastic mulch on yield and quality of ginger (Zingiber officinale)

The yield enhancement in ginger, an important spice cum medicinal crop, can be obtained by scientific water management strategies. For maximizing the water productivity with reduced losses drip irrigation can be adopted along with plastic mulching. Considering the above, a field study was conducted during 2020–2021 at the Centurion University of Technology and Management (CUTM), Odisha, India under drip irrigation with plastic mulch targeting precise quantification of irrigation water requirement for ginger. There were eight treatments with different irrigation levels and plastic mulch considered in the study and laid out in randomized block design with three replications. Irrigation water requirement of the crop was determined using the FAO-56 Penman-Monteith model. Greater plant height (108.2 No.), number of tillers (13.2 No.), number of leaves (108.2 No.), number of rhizomes (10.5 No.), length of rhizomes (7.9 cm) and weight of rhizomes (433.3 g) and yield of ginger (32 t/ha) were found with the 100% irrigation water requirement along with the plastic mulch. The above results were followed by the results of the treatment irrigating with 80% of crop water requirement using drip irrigation along with plastic mulch. The study revealed that 80% of estimated irrigation water applied (267.4 mm) using drip and plastic mulch could be recommended to obtain higher productivity of ginger.

Medium‐ to long‐term forecast of reference crop evapotranspiration based on the correction of the principal control factor

AbstractThis study selected the temperature Penman–Monteith (PMT) model to solve the difficulty of reference crop evapotranspiration (ET0) prediction caused by incomplete meteorological data. Then, the principal control factor of the PMT model was determined using several indicators. The results show that the annual average correlation and sensitivity coefficients between Tmax and ET0 are the highest. Hence, among the factors, Tmax is the largest contributor in contribution proportion and amount to ET0 and the principal control factor of the PMT model. The root mean square error (RMSE), relative error (RE) and mean absolute error (MAE) of the forecast ET0 increased by 0.7, 0.2, and 0.6 mm/day, respectively, after the 16th day compared with those on the 15th day. Thus, a remarkable drop in ET0 forecast precision is observed and is hardly accurate for the purpose of water distribution management in an irrigation district. The RMSE, RE, and MAE of the Tmax forecast decrease by 2.0–3.0°C, 0.1–0.2, and 2.0–2.8°C, respectively, and those of the corresponding ET0 forecast decrease by 0.7–0.9, 0.2–0.3, and 0.6–0.7 mm/day, respectively, after Tmax correction. Compared with the decaying‐average method, the improved decaying‐average method can correct Tmax better and improve the ET0 prediction accuracy.

Development of a low-cost weather station and real-time monitoring for automated irrigation management

Irrigation is a practice that increases productivity and enables the expansion of agricultural borders. This practice should be well managed to ensure the efficient use of water by plants, and such management requires specialized labor, which increases operating costs. Therefore, the main objective of this study is the implementation of an automated irrigation system that considers factors ranging from water demand to the operation of the system. Because the daily water demand is determined by crop evapotranspiration, a low-cost weather station was developed to acquire data daily. Such data can be used to estimate the reference evapotranspiration (ET0) using the Penman-Monteith model of the Food and Agriculture Organization (FAO). Thus, by combining the amount of water required by a crop with the physical and hydraulic properties of the soil and the hydraulic properties of the irrigation system, it was possible to determine the daily frequency and amount of time required for the system to operate to meet the water requirements of the crop without undergoing stress from a deficit or an excess of water. The management/monitoring system was designed and implemented to allow the user to access data remotely through an online application. This application enables the real-time transmission of irrigation-related data, such as weather station data and system logs, obviating the need for the user to be present at the crop site. This allows the supervision of many areas simultaneously with low cost

Evaluation of the DRAINMOD Model’s Performance Using Different Time Steps in Evapotranspiration Computations

The DRAINMOD model is a superior tool used to predict the changes in farmland water balance under different agricultural drainage layouts, fields, weather conditions, and management practices. In the present study, we assessed the sensitivity of the DRAINMOD predictions in farmland water balance to the time step (hourly or daily) in daily evapotranspiration (ET₀) computations for 12-hectares of farmland located at the lower reaches of the Yangtze River basin. The model was calibrated and validated and then was applied twice under two sets of daily ET₀ values, computed using the standardized ASCE Penman–Monteith model (one using the hourly time step (HTS) and the other using the daily time step (DTS)). Regarding daily computed ET₀ values, results show that abrupt diurnal changes in the weather always result in significant differences between daily ET₀ values when computed based on DTS and HTS. DRAINMOD simulations show that such differences between daily computed ET₀ values affected the model’s predictions of the “water fate” in the study area; e.g., adopting HTS rather than DTS resulted in a 4.8% increase, and a 3.1% and 1% decrease in the models’ cumulative predictions of runoff, drainage, and infiltration, respectively. Therefore, for a particular study area, it is critical to pay attention when deciding the best time step in ET₀ computations to ensure accurate DRAINMOD simulations, thereby ensuring better utilization of agricultural water alongside high agricultural productivity.

Using support vector machine to deal with the missing of solar radiation data in daily reference evapotranspiration estimation in China

Accurate estimation of reference evapotranspiration (ET0) is of great importance for regional water resources planning and irrigation scheduling. The FAO56 recommended Penman-Monteith (P-M) model is widely adopted as the standard method for ET0 estimation, but its application is usually restricted by limited meteorological data worldwide, especially global solar radiation (Rs). This study provided two possible solutions to deal with the missing Rs data in ET0 estimation in China mainland. In the first solution, Rs data were estimated with the Ångström-Prescott (A-P) formula and daily sunshine hours. The values of two A-P formula fundamental coefficients a and b were obtained through three ways: (1) estimated based on limited Rs measurements at 80 solar radiation measurement stations (or site-calibrated); (2) recommended by the FAO-56 manual (or FAO-recommended); and (3) estimated based on the altitude and latitude of each weather station through the support vector machine algorithm (or SVM-estimated). The second solution used the SVM algorithm and available weather variables without Rs. The results showed that the FAO-recommended coefficients a and b were separately overestimated and underestimated in China mainland, which generated the largest simulation errors of Rs. However, the transfer errors from Rs estimations to ET0 estimations were reduced by using the P-M model for all of the three kinds of coefficients. Compared with the Rs-based models, the estimation accuracy of the SVM-ET0 model yielded the highest accuracy both at the training stage (R2 = 0.979; RMSE = 0.273 mm d−1) and the testing stage (R2 = 0.973; RMSE = 0.302 mm d−1). Generally, both the P-M and the machine-learning-based methods could be used for the ET0 estimation, when only Rs data were missing. However, considering the complexity in the programming, the P-M model combining with the A-P formula with the SVM-estimated A-P coefficients is recommended for daily ET0 estimation in China.

Comparison of evaporation estimation methods for water surface under floating coverage in arid areas

The main purpose of this study is to evaluate evaporation estimation methods for water surfaces under floating coverage in arid areas. The natural water surface evaporation methods (namely, the mass transfer method (MT), Bowen ratio energy balance (BREB) method, Penman method, the Penman-Monteith method (PM), and the Priestley-Taylor method (PT)) are corrected to establish the covered water surface evaporation methods, which are based on the micrometeorology near the covered water surface, energy balance components, and Bowen ratio. The radiation term of the natural water surface evaporation model is corrected by introducing the Bowen ratio of the entire surface, which includes the uncovered water surface and the floating cover surface. The mass transfer coefficient and aerodynamic impedance in the aerodynamic terms of the natural water surface evaporation model were corrected. Taking the evaporation of the water surface covered by spheres (coverage rate of spheres is 76.4%) as an example, based on the rose change characteristics of the above parameters, the effects of these modified evaporation models are comprehensively evaluated from three different time scales (even months, odd months and the complete test period). The evaluation methods include the root mean square error (RMSE), index of agreement (D), mean bias error (MBE), and coefficient of determination (R2). The results show that under three different time scales, the Penman-Monteith model is the most promising and superior in estimating water surface evaporation under floating coverage (for the complete test period, RMSE=0.11 mm·d−1, D=0.99, MBE= 0.07 mm·d−1, and R2 =0.99); the Priestley-Taylor model is slightly less effective in estimating evaporation than the Penman-Monteith model, and the mass transfer model and the Penman model are less effective in estimating evaporation. Furthermore, the analysis of the Penman-Monteith model shows that the evaporation of the radiation term (69.5%) is higher than that of the aerodynamic term (30.5%) during the evaporation of the water surface under floating coverage, while the evaporation of the radiation term is dominant. Moreover, during the complete test period, the weights of the radiation evaporation and aerodynamic evaporation changed significantly with time. The proportion of radiation evaporation varies from 54.8% to 81.7%, while that of aerodynamic evaporation varies from 18.3% to 45.2%.

Characteristics of Dry-Wet Climate Change in China during the Past 60 Years and Its Trends Projection

Based on the homogenized daily data of 2255 meteorological stations during the past 60 years from 1961 to 2020, the potential evapotranspiration was calculated using the revised FAO56 Penman–Monteith model, and then the annual AI (aridity index, the ratio of annual potential evapotranspiration to annual precipitation) was employed to analyze the dry-wet climate change in China. The GCM models’ prediction data was used to analyze the possible trends of dry-wet climate in China by the end of this century. The results showed that in the past 60 years, the climate in China was getting wetter, especially in the western regions of China, including Xinjiang, western Qinghai, Gansu, western Inner Mongolia, and northwestern Tibet. In the last 10 years, China’s climate has become more humid. Compared with the 1960s, the total area of aridity has decreased by about 650,000 square kilometers. The changes of different climate zones have regional and periodical characteristics. There was a tendency to get wet periods in all four seasons, especially in summer. Analysis of GCM model projection data shows that by the end of this century, the climate in China would have a general trend of becoming drier. The drier regions are mainly located in the central and eastern parts of China, while the western regions of China continue to maintain the wetting trends. In the case of high emissions, the trends of drying in the central and eastern and wetting in the west are more significant than in the case of medium emission.

Leaf and canopy stomatal resistance, aerodynamic resistance, and evapotranspiration of irrigated continuous no‐till and disk‐till maize

AbstractThe impact of no‐till (NT) management as compared with conventional tillage practices (e.g., disk till [DT]) on plant stomatal behavior in relation to model estimation of crop evapotranspiration (ETc) and plant physiological functions has not been sufficiently evaluated. This research investigated the effects of NT and DT management on maize (Zea mays L.) physiological parameters and ETc losses. We also tested the methodology to estimate ETc using a one‐step Penman–Monteith (PM) model with measured variable resistance from scaled‐up leaf stomatal resistance (RL) to canopy resistance (RC) with Bowen Ratio Energy Balance System (BREBS)‐measured ETc. Photosynthetic photon flux density (PPFD) explained 76 and 79% of the variability in RL for NT and DT maize, respectively, which varied with growth stage. Disk‐till maize RL was 29, 5, 25, and 4% higher than NT maize RL during the vegetative, tasseling and silking, reproductive, and near‐physiological‐maturity stages, respectively. The seasonal average scaled‐up RC values for NT and DT maize were 102.4 and 128.6 s m–1, respectively. The PM‐estimated ETc correlated strongly with the BREBS‐measured ETc, with the PM model estimates being 8 and 6% within the BREBS measurements on an hourly and daily basis, respectively. To the best of our knowledge, this research is the first to develop the response curves (PPFD vs. RL) for maize for different growth stages under NT and DT. The analyses presented here can provide invaluable data and information for plant physiology research, crop modeling, determining crop response to different management, and environmental drivers.

Estimation of maize evapotranspiration in semi-humid regions of northern China using Penman-Monteith model and segmentally optimized Jarvis model

Accurate estimation of maize evapotranspiration (ET) is of great significance for the improvement of crop water use efficiency and precision irrigation. The Penman-Monteith model (P-M) has been widely used to simulate crop ET. In the P-M model, the estimation accuracy of canopy resistance (rc) has a direct impact on ET. In this study, based on the eddy covariance system, large-scale lysimeter and meteorological station data from three sites (Yucheng, Yangling and Shangqiu) in semi-humid regions of northern China, the P-M model was applied to obtain canopy resistance (rc-PM) and correlation significances between rc-PM and different impact factors (Rn net radiation, T temperature, VPD saturated vapor pressure difference, θ soil moisture content, LAI leaf area index) were analysed. The whole growth period of maize was divided according to different LAI thresholds (0.1, 0.5, 1.0, 1.5, 2.0 and 3.0 m2 m−2). The Genetic Algorithms (GA) and Differential Evolution (DE) algorithms were used to optimize the empirical parameters of the Jarvis model, and the P-M model was applied to estimate ET under different LAI thresholds at the three stations. The correlation significances of rc-PM with different influencing factors followed the order Rn > LAI > θ >VPD >T, and it was extremely significant with Rn (P < 0.01) and significant with LAI and θ (P < 0.05). The GA and DE algorithm optimization results showed that the calculation accuracy of rc was highest when LAI=0.5 m2 m−2 at Yucheng station, with R2 of 0.80 and 0.81, respectively, and when LAI=1.0 m2 m−2, and the accuracy of rc was highest at Yangling station, with R2 of 0.87 and 0.89, respectively, and when LAI = 1.0 m2 m−2, and the accuracy of rc was highest at Shangqiu station, with R2 of 0.84 and 0.84, respectively. Combined with P-M model to calculate maize ET under different LAI thresholds, the simulation accuracy of ET was best when LAI = 0.5 m2 m−2 at Yucheng station, with averege R2 of 0.85, the order of simulation ET accuracy was: 0.5 >1.0 >1.5 >2.0 >3.0 >0.1 m2 m−2. When LAI = 1.0 m2 m−2, and the accuracy of maize ET was highest at Yangling and Shangqiu stations, with averege R2 of 0.83 and 0.85, respectively, the order of simulation ET accuracy was: 1.0>0.5>1.5>2.0>3.0>0.1 m2 m−2. ET accuracy calculated by the DE optimization algorithm was better than that of the GA optimization algorithm, with R2 of 0.40–0.84 and 0.58–0.86, respectively. This study suggests that the algorithm is of great importance to optimize the empirical parameters of the Jarvis model, of which DE optimization algorithm is recommended to simulate maize ET in semi-humid regions of northern China.

Monitoring irrigation dynamics in paddy fields using spatiotemporal fusion of Sentinel-2 and MODIS

Rapid and accurate monitoring of irrigation dynamics in paddy fields (the start, end, duration and irrigation peak, etc.) at field scales is crucial to the fine management of agricultural water resources, especially in typical areas with water shortages. However, there is still a lack of sufficient research to depict irrigation dynamics in paddy fields at high temporal and spatial levels. To this end, this study fused Sentinel-2 and MODIS images to map the spatio-temporal dynamics of irrigation events in paddy fields. A popular spatiotemporal fusion algorithm (enhanced spatial and temporal adaptive reflectance fusion model, ESTARFM) was used to generate 25 high-spatial resolution (10 m) remote sensing images based on 9 Sentinel-2 images and 24 MODIS images. Random forest algorithm was used to extract the spatial distribution of irrigated paddy fields. Water body index and vegetation index were employed to identify the start, end and duration of irrigation in paddy fields. Penman-Monteith model was used to estimate water surplus and deficit of irrigation during the critical irrigation period with daily observation data from meteorological stations. This study was carried out in rice-growing areas in the middle and lower reaches of the Yellow River in China. The results indicated that the spatial distribution difference of irrigation events in paddy fields with the shortest 3-day interval could be monitored in collaboration with Sentinel-2 and MODIS images. The start, end, and duration of irrigation in paddy fields presented significant spatial differences at field scales. A large amount of water from groundwater and Yellow River was needed, because the total water shortage of irrigation in paddy fields in the study area was about 80.79% of the total water demand. In addition, paddy fields with higher water demand were more concentrated spatially, as were paddy fields with lower water demand. The feasibility of spatiotemporal fusion of multi-source remote sensing data makes it possible to continuously monitor irrigation dynamics in paddy fields on high spatial resolution scales, which is conducive to the construction of spatiotemporal database and big data platform of agricultural irrigation information. This would not only help in promoting the high-quality development of agricultural water resources management but also alleviating the contradiction of regional water resources.

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 &amp; Taylor, Tanner &amp; Pelton, Makkink, Jensen &amp; Haise, Hargreaves &amp; Samani, Camargo, Benevides &amp; 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.

Simple and Two-Level Hierarchical Bayesian Approaches for Parameter Estimation with One- and Two-Layer Evapotranspiration Models of Crop Fields

The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based ET dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed ET than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the ET estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of ET, with little difference between the SB and HB approaches.

Determining water use and crop coefficients of drip-irrigated cotton in south Xinjiang of China under various irrigation amounts

Crop evapotranspiration (ETc) and crop coefficient (Kc) can vary among various geographical locations and cultivated species. Therefore, determining regional- and species-specific crop coefficients is fundamental for rational irrigation water management. A two-year field study was conducted in south Xinjiang of northwest China to estimate periodic ETc and reference evapotranspiration (ET0), and thus develop the regional crop coefficient (Kc) at various growth stages of fertigated cotton (Gossypium hirstum L. Xinluzhong No 67) under various irrigation amounts. ETc was determined by the soil water balance method, while ET0 was obtained using the FAO (Food and Agriculture Organization of the United Nations) Penman-Monteith model. The results indicated that increasing irrigation amounts resulted in increased seed cotton yields, but reduced crop water productivity (WP). The water consumption at flowering and boll setting stages accounted approximately for 60% of the seasonal water use of cotton under various irrigation amounts. The average locally developed Kc (Kc-local) values over two seasons were 0.38, 1.30 and 1.09 for initial, mid-season, and end-season stages, respectively. The locally developed initial Kc (Kc-local-ini) (0.38) was higher than the optimized initial-stage Kc (Kc-opt-ini) (0.23) and similar to the FAO recommended Kc (Kc-FAO) (0.35). The optimized Kc (Kc-opt) did not significantly differ from Kc-FAO at mid-season and end-season stages. The locally developed middle-stage Kc (Kc-local-mid) was 10% higher than the optimized middle-stage Kc (Kc-opt-mid) and the FAO recommended middle-stage Kc (Kc-FAO-mid), and the locally developed end-stage Kc (Kc-local-end) was approximately 82% higher than the optimized end-stage Kc (Kc-opt-end) and the FAO recommended end-stage Kc (Kc-FAO-end). The difference between Kc-local, Kc-opt and Kc-FAO was attributed to the variations of climate condition, cultivated variety and field management. Kc-local determined by the soil water balance method in the present study fully considered the regional conditions including weather, species and management practices, which can be adopted to guide the irrigation management in this region. This study provided credible cotton periodic Kc in the arid and semiarid regions of northwest China. A second-order polynomial relationship between cotton Kc and t/T (the days after emergence/the days of total cotton growth period) was established, which can provide a reference for the determination of local cotton Kc and irrigation management.

Stochastic Analysis of Hourly to Monthly Potential Evapotranspiration with a Focus on the Long-Range Dependence and Application with Reanalysis and Ground-Station Data

The stochastic structures of potential evaporation and evapotranspiration (PEV and PET or ETo) are analyzed using the ERA5 hourly reanalysis data and the Penman–Monteith model applied to the well-known CIMIS network. The latter includes high-quality ground meteorological samples with long lengths and simultaneous measurements of monthly incoming shortwave radiation, temperature, relative humidity, and wind speed. It is found that both the PEV and PET processes exhibit a moderate long-range dependence structure with a Hurst parameter of 0.64 and 0.69, respectively. Additionally, it is noted that their marginal structures are found to be light-tailed when estimated through the Pareto–Burr–Feller distribution function. Both results are consistent with the global-scale hydrological-cycle path, determined by all the above variables and rainfall, in terms of the marginal and dependence structures. Finally, it is discussed how the existence of, even moderate, long-range dependence can increase the variability and uncertainty of both processes and, thus, limit their predictability.

High-Precision Potential Evapotranspiration Model Using GNSS Observation

Potential evapotranspiration (PET) can reflect the characteristics of drought change in different time scales and is the key parameter for calculating the standardized precipitation evapotranspiration index (SPEI). The Thornthwaite (TH) and Penman–Monteith (PM) models are generally used to calculate PET, but the precision of PET derived from the TH model is poor, and a large number of meteorological parameters are required to evaluate the PM model. To obtain high-precision PET with fewer meteorological parameters, a high-precision PET (HPET) model is proposed to calculate PET by introducing precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) observation. The PET difference (DPET) between TH- and PM-derived PET was calculated first. Then, the relationship between the DPET and GNSS-derived PWV/temperature was analysed, and a piecewise linear regression model was calculated to fit the DPET. Finally, the HPET model was established by adding the fitted DPET to the initial PET derived from the TH model. The Loess Plateau (LP) was selected as the experiment area, and the statistical results show the satisfactory performance of the proposed HPET model. The averaged root mean square (RMS) of the HPET model over the whole LP area is 8.00 mm, whereas the values for the TH and revised TH (RTH) models are 34.25 and 12.55 mm, respectively, when the PM-derived PET is regarded as the reference. Compared with the TH and RTH models, the average improvement rates of the HPET model over the whole LP area are 77.5 and 40.5%, respectively. In addition, the HPET-derived SPEI is better than that of the TH and RTH models at different month scales, with average improvement rates of 49.8 and 23.1%, respectively, over the whole LP area. Such results show the superiority of the proposed HPET model to the existing PET models.

Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China

Accurately estimating the reference evapotranspiration (ET0) is a basic requirement for precision irrigation and the correct planning of regional water resources. This study aimed to investigate the spatiotemporal variations in ET0 in China and to improve the accuracy of ET0 calculations on different spatiotemporal scales. Meteorological data collected at 100 stations in China during 1961 to 2019 were used to calculate ET0 with the Penman–Monteith model, and the temporal and spatial patterns in ET0-PM were analyzed with the Mann–Kendall nonparametric trend test method. Three machine learning models comprising convolutional neural network (CNN), extreme learning machine (ELM), and multiple adaptive regression splines (MARS), and seven empirical models calibrated with mind evolutionary algorithm (MEA) were compared to assess their suitability for calculating ET0 on different spatiotemporal scales in China. The results showed that the annual mean ET0-PM value (413.29–2772.35 mm) in China gradually increased from north to south and from west to east. ET0 exhibited an upward trend in the temperate continental zone (TCZ) and mountain plateau zone (MPZ) but a downward trend in the temperate monsoon zone (TMZ) and subtropical monsoon region (SMZ). By comparing the global performance indicators (GPI), the machine learning models generally performed better than the empirical models at different spatiotemporal scales. And CNN was the best model for calculating ET0 in terms of the model accuracy and stability. On the daily scale, MARS performed well in MPZ, whereas ELM performed well in TMZ and TCZ. On the monthly scale, MARS performed well in TMZ, whereas ELM performed well in SMZ and MPZ. At the annual scale, the accuracy of ELM was higher than that of MARS.

Satellite-Based Water and Energy Balance Model for the Arid Region to Determine Evapotranspiration: Development and Application

Actual evapotranspiration (ETa) plays an important role in irrigation planning and supervision. Traditionally, the estimation of ETa was approximated using different in situ techniques, having high initial and maintenance costs with low spatial resolution. In this context, satellite imagery models play an effective role in water management practices by estimating ETa in small and large-scale areas. All existing models have been widely used for the estimation of ETa around the globe, but there is no definite conclusion on which approach is best for the hot and hyper-arid region of Oman. Our study introduces an innovative approach that uses in situ, meteorological, and satellite imagery (Landsat-OLI/TIRS) datasets to estimate ETa. The satellite-based water and energy balance model for the arid region to determine evapotranspiration (SMARET) was developed under the hot and hyper-arid region conditions of Oman by incorporating soil temperature in the sensible heat flux. The performance of SMARET ran through accuracy assessment against in situ measurements via sap flow sensors and lysimeters. The SMARET was also evaluated against three existing models, including the surface energy balance algorithm for land (SEBAL), mapping evapotranspiration at high-resolution with internalized calibration (METRIC), and the Penman–Monteith (PM) model. The study resulted in a significant correlation between SMARET (R2 = 0.73), as well as the PM model (R2 = 0.72), and the ETa values calculated from Lysimeter. The SMARET model also showed a significant correlation (R2 = 0.66) with the ETa values recorded using the sap flow meter. The strong relationship between SMARET, sap flow measurement, and lysimeter observation suggests that SMARET has application capability in hot and hyper-arid regions.

Modeling evapotranspiration and evaporation in corn/tomato intercropping ecosystem using a modified ERIN model considering plastic film mulching

Intercropping planting pattern under plastic film mulching (PFM) has been widely adopted in the arid regions to reduce soil evaporation (E), improve land-use efficiency, and increase crop yield. However, water competition between intercropping components in the soil-plant-atmosphere continuum remains largely unexplored. The evaporation and radiation interception using the neighboring species model (ERIN) can effectively estimate evapotranspiration (ET) in a different intercropping ecosystem. However, the effects of soil surface resistance in the mulching area on ET are not considered in the ERIN model. Thus, the existing ET models do not accurately estimate ET in the intercropping ecosystem with PFM. In this study, we proposed a modified ERIN model (MERIN). In the MERIN model, soil surface resistance in the mulching area was taken into account, and its performance was compared to ERIN and Penman-Monteith (PM) models. These models were validated against observed ET and E using the water balance method and micro-lysimeters in a corn intercropped tomato experiment under high (HI: 30 mm for corn and 22.5 mm for tomato, a locally recommended irrigation depth), medium (MI: 22.5 mm for corn and 16.9 mm for tomato, 25% of HI), and low irrigation depth (LI: 15 mm for corn and 11.25 mm for tomato, 50% of HI) during 2018-2019, respectively. The outcomes of this study showed that the MERIN model could accurately estimate ET and E variation for a corn-tomato intercropping ecosystem under PFM during the entire crop growth season compared to the other examined models. The most intense water competition between corn and tomato was observed in stage II (the elongation and tasseling stages for corn; the flowering and fruiting stages for tomato). T of corn generally was higher than tomato, but an opposite result was also observed in stage II. Additionally, the variation of water competition under different irrigation levels was similar in the intercropping ecosystem. When irrigation depth decreased to 22.5 and 15 mm from 30 mm, average T for corn decreased by 10.7% and 16.3%, respectively, and by 12.9% and 22.4% for tomato, respectively, in both years.