Penman-Monteith Model Research Articles

Future drought overestimations due to no constraints of CO2 physiological effect and land-atmosphere coupling on potential evapotranspiration

Abstract Various offline drought indices have been widely used to project dryness/wetness and drought changes. However, the results derived from these indices often differ from or even contradict observations and direct projections made by coupled climate models. Therefore, it is crucial to investigate this scientific debate thoroughly and identify the potential causes. This study adopts a water demand-side perspective, focusing on potential evapotranspiration (PET), to address such controversy. Employing the Standardized Precipitation-Evapotranspiration Index (SPEI), three PET models including the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman-Monteith (PM) model, a corrected FAO-56 PM model incorporating CO2 physiological effect (PMCO2), and a land-atmosphere coupled PET model (PET-LAC) are further compared. Despite projected increases in PET across most land areas, the PM shows the most pronounced increases among these PET models. Compared to PMCO2 and PET-LAC, the PM model predicts the most significant drying, with the 3-month SPEI decreasing by 0.50±0.23 /yr. Additionally, it projects the most substantial drought intensification, with increases in areas, intensity, and duration of 28±6.9%, 0.70±0.20/yr, and 2.90±0.83 month/yr, respectively. Meanwhile, these projections correspond to the most extensive area percentages, with 78.5±10% for drying, 94.8±7.2% for drought intensity, and 93.6±7.9% for drought duration. These findings imply that the commonly used PM model overestimates future drought conditions. Differences and contradictions between the drought projections from PM-based offline indices and direct climate model outputs can be partly attributed to the omission of CO2 physiological effect and land-atmosphere coupling constraints in the PM model. This study highlights the importance of improving PET models by incorporating these constraints, thereby providing valuable insights for enhancing the accuracy of future drought assessments.

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Stormwater retention capacity of blue-green roofs with various configurations: Observational data and modelling

Although dynamic changes in stormwater retention capacity (SRC) determine overall stormwater retention performance of blue-green roofs, few previous studies have investigated the dynamic changes of SRC of blue-green roofs with various configurations. In this research, an experiment was designed to observe hydrological processes of blue-green roofs in Beijing. Based on the water balance theory, a conceptual hydrological model was developed by integrating a modified FAO-56 Penman-Monteith model, an AET formula with a water stress coefficient and Dalton’s equation. Then the proposed model was validated using experimental data. Results show that the model can simulate the SRC dynamics well, with the Nash-Sutcliffe and determination coefficients both exceeding 0.6. The consumption and recovery of the SRC in blue-green roofs are mainly driven by rainfall and actual evapotranspiration, respectively. As actual evapotranspiration decreases with seasonal changes, the fluctuation amplitude of SRC in blue-green roofs diminishes, ranging from 32.53 mm to 11.34 mm. Although increasing the depth of substrate and storage layers is able to enhance the SRC of blue-green roofs, there are optimal upper limits for these depth because of regional climate conditions. In Beijing, the optimal upper limits for substrate and storage layer depths are 300 and 30 mm, respectively. This information can assist designers in evaluating the cost-effectiveness of design choices for blue-green roofs. The developed model serves as an effective tool for simulating SRC and is expected to aid in the design of blue-green roofs.

MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

Calibration and evaluation of various reference evapotranspiration estimation methods in a humid subtropical climate: A case study in Samsun Province, Türkiye

Reference evapotranspiration is crucial for estimating plant water needs and managing resources. Simplifying the Penman-Monteith model by reducing input parameters and local calibration can enhance efficiency and reliability. In this study, three empirical formulas—Hargreaves and Samani (HS, 1985), Priestley and Taylor (PT, 1972), and the World Meteorological Organization (WMO, 1966)—representing temperature-, solar radiation-, and mass-transfer-based approaches, respectively, were evaluated. To achieve this objective, meteorological data from five synoptic stations situated in the Samsun province in the Black Sea region of Türkiye, were utilized. Three methods, namely the traditional method, regression analysis, and genetic algorithm, were employed to estimate the local calibration coefficients of empirical equations based on the Penman-Monteith 56 equation. Finally, the outcomes were evaluated based on four criteria: root-mean-square error, coefficient of determination, mean bias error, and percentage error of estimate. The results of empirical formulas both before and after calibration were analyzed. Prior to calibration, HS and PT exhibited greater accuracy for the case study. This accuracy trend was also observed in the calibrated results. Additionally, among the three employed calibration methods, regression analysis and traditional methods demonstrated a higher level of accuracy.

Impact of drip irrigation and plastic mulching on yield and economics of cucumber (Cucumis sativus L.)

Drip irrigation is crucial in the current agricultural scenario for vegetable crops, as it optimizes water use efficiency by delivering precise amounts of water directly to the plant roots, thereby conserving water resources and enhancing crop yields. Yield enhancement in cucumber, a significant horticultural crop, can be achieved by integrating drip irrigation and plastic mulching to maximize water productivity while minimizing losses. In the current consequences, a field study was conducted from November to December in two consecutive years (2021-2022 and 2022-23) at the experimental field of Precision Farming Development Centre, Indian Institute of Technology, Kharagpur, India. The study aimed to quantify the irrigation water requirements for cucumber using drip irrigation and plastic mulch. Eight treatments with varying irrigation levels and the use of plastic mulch were assessed in a randomized block design with three replications. The crop's irrigation water requirement was determined using the FAO-56 Penman-Monteith model. The results showed that the highest plant height (193.5 cm), number of leaves (43.3), fruit length (18.5), fruit weight (279.0 g), and cucumber yield (15.9 t/ha) were achieved with 100% irrigation water requirement along with plastic mulch during the first and second season. These results were closely followed by treatments with 80% of the crop water requirement using drip irrigation and plastic mulch. The study concluded that applying 80% of the estimated irrigation water (142.1 mm) using drip irrigation and plastic mulch could be recommended to obtain higher productivity in cucumber.

Leaf gas exchange, water use, and yield of Marula (Sclerocarya birrea) and Kei Apple (Dovyalis caffra): South African indigenous fruit trees with domestication potential

Conventional crop production is expected to decline in the future in sub-Saharan Africa due to the rising costs of inputs, soil degradation, and increasing water scarcity related to climate variability and change. South Africa has more than 30 species of indigenous fruit trees that are adapted to grow under harsh conditions, yet these species are currently underutilized. Their cultivation is constrained by the lack of detailed ecophysiological information required to develop effective management strategies that optimize tree performance. In this study we investigated how two species with domestication potential namely Marula (Sclerocarya birrea) and Kei apple (Dovyalis caffra) responded to climate and soil factors over a period of two years (2019–2021). Photosynthesis, transpiration, and yield were quantified for mature trees growing in experimental orchards in the Mpumalanga Province of South Africa. Leaf gas exchange data were collected at selected intervals during the growing season using an infrared gas analyser while whole tree transpiration rates were quantified using the heat ratio method of monitoring sap flow. These data were used to parameterize a big leaf Penman-Monteith (PM) model to estimate the transpiration dynamics of individual trees at the daily time step. S. birrea's transpiration responded strongly to both climatic and soil water content variations while the water use of D. caffra was driven mostly by climatic factors. Transpiration rates averaged 0.30 L/m2 of leaf area for S. birrea while that of D. caffra was around 0.45 L/m2. Both species exhibited significant alternate bearing with yield of individual trees fluctuating by more than 50 % between successive years. For the 2019/20 season, water productivity (grams of fruit per litre of water transpired) varied between 6.2 and 17.4 g/L for S. birrea and between 24.3 and 41.7 g/L for D. caffra. Whole tree transpiration by both species were accurately predicted using the PM model albeit with different parameters for each species. This study demonstrates that a simple PM model can accurately estimate the water requirements of these species in other growing regions.

Rain and evapotranspiration using the Penman-Monteih model for the periods 1963-1982. 1983-2001 and 2002-2019 for the amparo de São Francisco – Sergipe, Brazil

Evaluate the performance of the empirical method of estimating evapotranspiration, with the standard Penman-Monteih method, suggested by FAO-56, for Amparo de São Francisco – Sergipe in the periods 1963-1982; 1983-2001 and 2002-2019 generating their respective graphs and information for government decision makers. The Penman-Monteih method makes it possible to estimate (ETo(PM)) in mm/month and, after computing it, it is multiplied by the total number of days in each month of the year, thus obtaining the Penman-Monteith model estimated in mm/month. The standard Penman-Monteih method can help with irrigation planning and management, especially for small irrigators who do not have access to meteorological data. A better adaptation of the Penman-Monteith and Thornthwaite model was observed in the estimation of ETo, where its results showed adaptation for the location studied. It is advisable to evaluate other models so that they can provide more appropriate results. It is stated that the Penman-Monteith and Thornthwaite models are the best alternatives for the use of evapotranspiration in Amparo de São Francisco - Sergipe.

Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators.

Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

Partitioning evapotranspiration of Camellia oleifera during the growing season based on the Penman-Monteith model combined with the micro-lysimeter and stable isotope methods

Frequent seasonal droughts impact on crop growth and yield in the hilly areas of southern China. A good knowledge of its evapotranspiration partitioning of the main economic oil crop Camellia Oleifera is particularly important to increase water productivity and improve water resource management. The isotope-based method was applied to partition ET in the hilly Camellia Oleifera Forest of southern China. The isotopic composition of evapotranspiration vapor (δET), transpiration vapor (δT) and evaporation vapor (δE) were calculated by the Keeling plot method, the Craig-Gordon model (C-G model), and isotopic steady-state assumptions, respectively. The conventional Penman-Monteith model and the micro-lysimeter method (PM-L) were used to compare and assess the ET partitioning results (Ft). It showed that the Ft values calculated by the two methods were in good agreement. The isotopic composition of soil water at the evaporating front (δS) ahead and after DOY151 was sampled at 5 and 10 cm depth, respectively, where the correlation coefficients (R2) of the Ft values calculated by δ18O and δ2H were 0.84 and 0.66. Ft Calculated by δ18O was more accurate than δ2H because of the higher correlation coefficient of δ18O in the keeling plot regression and in the linear fit with PM-L. The isotopic composition of soil evaporation vapor（δE）calculated by dynamic εk (kinetic fractionation effects) values can be more accurate to quantify the ET partitioning results (with the R2 of 0.90). The mean values of Ft were 0.79 and 0.82 by the PM-L method and the isotope-based method respectively over the whole growing season, indicating the isotope method is robust in calculating ET partitioning for Camellia oleifera in the region. We also found that the seasonal variation of Ft is mainly controlled by temperature (T), soil water content (SWC), and leaf area index (LAI), all of which can be effectively expressed as power functions. The results provide potential assistance for water management in Camellia oleifera woodlands.

The Modelling of the Evapotranspiration Portion of the Water Footprint: A Global Sensitivity Analysis in the Brazilian Serra Gaúcha

Water footprints have been widely used to illustrate the consumption of water in many situations, for instance, in products, processes, or regions of interest. In this work, we analyzed—using a sensitivity analysis approach—the effect of some variables in the calculation of the water footprint in the viticulture in the Brazilian Serra Gaúcha (the major producing region of Brazilian wine). The classical Penman–Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering a fraction of the vineyard, maximum temperatures for some months, the altitudes and latitudes of the site). A sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to evapotranspiration is more sensitive to the values of the mulch-covered fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Spatial and Temporal Evolution Patterns of Droughts in China over the Past 61 Years Based on the Standardized Precipitation Evapotranspiration Index

Based on the data of 2254 daily meteorological stations in China from 1961 to 2021, this study calculated the standardized precipitation evapotranspiration index (SPEI) of the national multi-time scale by using the FAO Penman–Monteith model to quantify the changes in dry and wet conditions. The Mann–Kendall mutation test, wavelet analysis, and other methods were used to study the spatial pattern and temporal evolution of drought. The results showed: (1) In the past 61 years, there were obvious spatial and temporal differences in drought in China, and the interannual variation in drought severity in SPEI-1, SPEI-3, and SPEI-12 gradually decreased at a rate of 0.005/10a, 0.021/10a, and 0.092/10a, respectively. (2) The time point of dry and wet mutation was 1989 according to the MK mutagenicity test. (3) Wavelet analysis showed that the drought cycle on the annual scale and the seasonal scale was consistent, and the main period was about 30 years. (4) In the past 61 years, the drought intensity of different degrees in China has shown a weakening trend, and the drought intensity reached the highest value in 61 years in 1978, at 1836.42. In 2020, the drought intensity was the lowest, at 261.55. (5) The proportion of drought stations has shown a decreasing trend. The proportion of drought-free stations has fluctuated greatly, ranging from 42.12% to 89.25%, with 2020 being the highest. This study provides a scientific basis for further research on the causes and coping strategies of drought and is of great significance for strengthening China’s drought monitoring, early warning ,and adaptation capabilities.

Applicability and improvement of different potential evapotranspiration models in different climate zones of China

BackgroundAccurate estimation of potential evapotranspiration (PET) is the key for studying land-air interaction hydrological processes. Several models are used to estimate the PET based on standardized meteorological data. Although combination-based models have the highest level performance estimation of PET, they require more meteorological data and may therefore be difficult to apply in areas lacking meteorological observation data.ResultsThe results showed significant differences in the spatial trends of PET calculated by different models in China, the Doorenbots–Pruitts model revealed the highest PET (1902.6 mm), and the Kuzmin model revealed the lowest PET (349.6 mm), with the largest difference being 5.5 times. The Romanenko and the Rohwer models were the recommended temperature-based and aerodynamic-based models. On the other hand, the Abtew model was more suitable for arid and semi-arid regions, while the Priestley–Taylor model was more suitable for humid regions. Combination-based models revealed ideal calculation accuracies, among which the Penman–Monteith model was the best option for PET calculation.ConclusionsThe accuracy range of Romanenko, Rohwer, Abten, Priestley Taylor, and Penman Monteith models improved in MPZ and TCZ is higher than that improved in TMZ and SMZ. This does not mean that the improved models have higher accuracy in MPZ and TCZ than in TMZ and SMZ. On the contrary, the original model performed poorly in MPZ and TCZ, so the improved accuracy was relatively large. The unimproved model was already more suitable in TMZ and SMZ, so the improved accuracy was relatively small. Therefore, regional calibration of the PET models can improve the accuracy and applicability of PET calculation, providing a reference for studying hydrological processes in different climatic zones.

Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems

Evapotranspiration is a key hydrological process for reducing stormwater runoff in bioretention systems, regardless of their physical configuration. Understanding the volumes of stormwater that can be returned to the atmosphere via evapotranspiration is, therefore, a key consideration in the design of any bioretention system. This study establishes the evapotranspiration dynamics of three common, structurally different, bioretention vegetation treatments (an Amenity Grass mix, and mono-cultures of Deschampsia cespitosa and Iris sibirica) compared with an un-vegetated control using lab-scale column experiments. Via continuous mass and moisture loss data, observed evapotranspiration rates were compared with those predicted by the FAO-56 Penman–Monteith model for five 14-day dry periods during Spring 2021, Summer 2021, and Spring 2022. Soil moisture reductions over the 14-day trials led to reduced rates of evapotranspiration. This necessitated the use of a soil moisture extraction function alongside a crop coefficient to represent actual evapotranspiration from FAO-56 Penman–Monteith reference evapotranspiration estimates. Crop coefficients (Kc) varied between 0.65 and 2.91, with a value of 1.0 identified as a recommended default value in the absence of treatment-specific empirical data. A continuous hydrological model with Kc=1.0 and a loading ratio of 10:1 showed that evapotranspiration could account for between 1 and 12% of the annual water budget for a bioretention system located in the UK and Ireland, increasing to a maximum of 35% when using the highest Kc observed (2.91).

Estimations of potential evapotranspiration from CMIP6 multi-model ensemble over Africa

Potential evapotranspiration (PET) plays a pivotal role in resource management and drought assessment. However, future PET estimates remain underexplored in the African region. This study employs twenty General Circulation Models (GCMs) to estimate past (1979–2014) and future PET changes across near-term (2021–2040), mid-term (2061–2080), and long-term (2081–2100) periods, considering four Shared Socioeconomic Pathways (SSPs) including SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The research assesses the impact of various climatic factors on PET across Africa and its sub-regions using the Penman-Monteith model. The analysis reveals that Penman-Monteith estimates for PET during 1979–2014 exhibit an increase of 0.68 mm per year (mm/a) across Africa. Notably, the Northern region (NAF), Sahara (SAH), Southern region (SAF), and Eastern region (EAF) experience higher PET changes of 1.78 mm/a, 1.75 mm/a, 1.09 mm/a, and 0.12 mm/a, respectively. Conversely, the Western region (WAF) and Central region (CAF) exhibit negative trends of −0.03 mm/a, and − 0.28 mm/a. Future PET in whole Africa is projected to increase by 0.05 mm/a in SSP1–2.6 and SSP2–4.5, and 0.07 mm/a in higher emissions for 2021–2040, by 0.02 mm/an under SSP1–2.6, 0.07 in SSP2–4.5, 0.09 mm/a, and 0.16 mm/a, in SSP3–7.0 and SSP5–8.5 for 2061–2080, and by −0.01 mm/a in SSP1–2.6, 0.05 mm/a SSP2–4.5, 0.10 mm/a SSP3–7.0, and 0.18 mm/a SSP5–8.5 for 2081–2100. Furthermore, higher emissions are anticipated to drive PET increases in various regions during 2081–2100, with NAF, SAH, and SAF projected to rise by 0.17 mm/a, 0.16 mm/a, and 0.23 mm/a, respectively. WAF, CAF, and EAF are expected to experience increases of 0.20 mm/a, 0.19 mm/a, and 0.15 mm/a, respectively. Contribution analysis indicates that solar radiation played a major factor in PET over Africa as well as in WAF, CAF, and EAF. Maximum temperatures were pivotal in NAF, SAH, and SAF. In future periods (2021-2040, 2061-2080, and 2081-2100), maximum temperatures take precedence to Africa's PET, and at varying percentages to different sub-regions. The findings underscore the significance of PET estimation, particularly in the context of drought evaluation locally and regionally.

Modeling transpiration using solar-induced chlorophyll fluorescence and photochemical reflectance index synergistically in a closed-canopy winter wheat ecosystem

The joint use of solar-induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) has been shown to improve gross primary productivity (GPP) estimation across various plant functional types. However, the utility of PRI in combination with SIF for transpiration (T) estimation has not yet been explored. Additionally, current SIF-driven transpiration models including linear models, semi-mechanical models (combination of canopy conductance, gC, derived from a SIF and vapor pressure deficit, VPD, driven linear model with the Penman-Monteith model), and hybrid models (combination of gC derived from a SIF and VPD driven machine learning model with the Penman-Monteith model) have rarely been mutually assessed. Based on concurrent remotely sensed SIF and PRI, and eddy covariance flux measurements during one growing season for a winter wheat ecosystem in northern China, we investigated the mediating effect of PRI on SIF-driven T estimation under different VPD conditions and compared the performance of linear, semi-mechanical, and hybrid models in estimating T. Our results showed that the mediating effect of PRI on T described in the SIF-driven linear, semi-mechanistic, and hybrid models was significant under high VPD conditions rather than under low VPD conditions. Specifically, based on T partitioned using an underlying water use efficiency method as a benchmark, the root mean square error (RMSE) value of the PRI-mediated linear, semi-mechanistic, and hybrid models was 28.01 W/m2, 22.25 W/m2, and 28.71 W/m2 lower, respectively, than those of the corresponding models without PRI when VPD was >1.5 kPa. Based on T partitioned using a transpiration estimation algorithm as a benchmark, these three models also exhibited a significant reduction in RMSE under high VPD conditions after considering PRI. The main rationale behind the PRI improvement is that PRI can track photosynthetic dynamics under high VPD conditions. Based on the simulation results of the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, PRI can serve as an indicator for non-photochemical quenching (NPQ) within this ecosystem. Consequently, PRI can enhance the capability of SIF to characterize the energy dissipation of photosynthetically active radiation and help SIF to yield more accurate information on GPP and gc under high VPD conditions. Finally, the order of model performance in estimating T was generally hybrid model > semi-mechanistic model > linear model. Our findings show the effectiveness of PRI for improving SIF-driven transpiration estimation under high VPD conditions and provide a new hybrid model for estimating T from SIF.

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration.

Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

Analyzing Evapotranspiration in Greenhouses: A Lysimeter-Based Calculation and Evaluation Approach

The absence of accurate measurement or calculation techniques for crop water requirements in greenhouses frequently results in over- or under-irrigation. In order to find a better method, this study analyzed the accuracy, data consistency and practicability of the Penman–Monteith (PM), Hargreaves–Samani (HS), Pan Evaporation (PAN), and Artificial Neural Network (ANN) models. Model-calculated crop evapotranspiration (ETC) was compared with lysimeter-measured crop evapotranspiration (ETC) in the National Precision Agriculture Demonstration Station in Beijing, China. The results showed that the actual ETC over the entire experimental period was 176.67 mm. The ETC calculated with the PM, HS, PAN, and ANN model were 146.07 mm, 189.45 mm, 197.03 mm, and 174.7 mm, respectively, which were different from the actual value by −17.32%, 7.23%, 11.52%, and −1.12%, respectively. The order of the calculation accuracy for the four models is as follows: ANN model > PAN model > PM model > HS model. By comprehensively evaluating the statistical indicators of each model, the ANN model was found to have a significantly higher calculation accuracy compared to the other three models. Therefore, the ANN model is recommended for estimating ETC under greenhouse conditions. The PM and PAN models can also be used after improvement.

Incorporating dynamic schemes of canopy light extinction coefficient improves transpiration model performance for fruit plantations

The light extinction coefficient (C) describes the efficiency of light interception by a plant canopy, and is a key parameter commonly used to partition radiant energy between a vegetation canopy and the soil surface in several important transpiration models. The C has been observed to vary considerably in the field, but is often parametrized as a fixed constant value, resulting in large potential uncertainty in transpiration estimation. We hypothesized that C changes with leaf area index (LAI) and environments, and that accurate characterization of the C variation pattern would improve transpiration modelling. To test these hypotheses, the relationships between measured C and potential influencing factors were explored for three different fruit plantations in different climate zones in China. Based on these relationships, a new method was proposed to capture C dynamics that considered both LAI and environmental factors. Additionally, 16 C schemes (15 dynamic C schemes and one fixed C scheme) were individually embedded in the Penman-Monteith model, and comprehensive comparisons were made to determine the most effective C scheme for simulating transpiration based on a Bayesian framework. Results showed that C displayed clear variations across all sites, and that there were significant relationships of C with LAI and environmental factors. Compared with the model using a fixed C scheme, the model using the best dynamic C scheme significantly improved model performance by increasing estimation accuracy and decreasing model uncertainty. Specifically, the average coefficients of determination (R2) increased from 0.35 to 0.76, 0.82 to 0.85, and 0.90 to 0.93 for the orange orchard, the jujube orchard, and the grape greenhouse, respectively. Additionally, the average mean relative errors (MRE) decreased by 54.99%, 26.62%, and 13.95%, respectively, and the average uncertainty band widths (B) decreased by 46.81%, 21.74%, and 11.43%, respectively. Model performance improvement was more significant at sites with more serious environmental stresses. The results of our study emphasize the necessity for considering dynamic C patterns in water consumption estimation, particularly for regions facing severe abiotic stresses that are likely to occur in many regions under future climate conditions.

Assessing forecasting performance of daily reference evapotranspiration: A comparative analysis of updated temperature penman-monteith and penman-monteith forecast models

Accurate forecast of reference evapotranspiration (ET0) is essential for effective water resource management and efficient irrigation scheduling. The Penman-Monteith model (PM) recommended by FAO56 is widely used as the standard method for ET0 forecasting, but PM model application is often limited by the absence of forecasted meteorological variables, especially solar radiation (Rs). Previous studies have proposed two types of models based on temperature and sunshine duration to estimate Rs. However, there is currently a lack of comprehensive comparative analysis research to evaluate the performance and applicability of these two types of models combined with weather forecast data for ET0 forecasting using the PM model. To address this issue, we selected China as the research area, which has complex climate zones and an uneven distribution of water resources. The forecasting results of Rs from temperature-based models (M1–M8) and a sunshine duration-based model (M9) are input independently into the PM model for ET0 forecasting. The updated PM model is named PMTM1–PMTM8 and PMF, respectively. We conducted a comprehensive and analytical assessment of the updated PMTM1–PMTM8 model, the PMF model and the Hargreaves-Samani (HS) model in different climate zones of China. The results showed that the accuracy of the M1–M8 and the M9 for forecasting Rs decreased as the forecasting period increased. The accuracy of the M9 was higher than that of M1–M8 in the overall accuracy in the subtropical monsoon climate (SMZ). Both the updated PMTM1–PMTM8 and PMF models were utilized to evaluate the ET0 forecasting performance in all five zones. The PMTM3 model exhibited the highest accuracy, with RMSE and MAE ranges of 0.671–1.572 mm d−1 and 0.532–1.365 mm d−1, respectively. In contrast, the PMF model displayed RMSE and MAE ranges of 0.690–1.590 mm d−1 and 0.641–1.437 mm d−1, respectively. Compared to the PMF model, the updated PMTM1–PMTM5 model showed higher accuracy in forecasting ET0 in the plateau mountain climate, temperate continental climate, temperate monsoon climate, and tropical monsoon climatic zones, but was slightly less accurate in the SMZ. Moreover, both models outperformed the Hargreaves-Samani (HS) model in terms of ET0 forecasting accuracy. Specifically, the updated PMTM1–PMTM5 model demonstrated improved accuracy compared to the temperature-based model HS model across various climate zones, with reductions in RMSE and MAE ranging from 0.117 mm d−1 to 0.616 mm d−1and 0.012 mm d−1 to 0.450 mm d−1, respectively. Overall, the updated PMTM3 model was better than the PMF and thus this model was recommended for daily ET0 forecasting for the near-future at all climate regions across China.