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A recommendation on standardized surface resistance for hourly calculation of reference ETO by the FAO56 Penman-Monteith method
Abstract
Continued development of networks of electronic weather stations worldwide has increased the availability of weather data for calculating ETo on an hourly basis. There has been question and debate as well as studies on the appropriate expression and parameterization for the surface resistance (rs) parameter of the Penman-Monteith (PM) equation and the associated coefficient for the reduced form FAO-PM equation when applied hourly. This paper reviews the performance of the FAO-PM method using rs = 70 s m−1 for hourly periods and using a lower rs = 50 s m−1 value during daytime and rs = 200 s m−1 during nighttime. Variability in hour to hour trends in rs among locations and dates makes it difficult, if not impossible, to establish a consistent algorithm for rs. However, the relatively good and consistent accuracy in ETo when using a constant rs = 50 s m−1 during daytime gives good reason to recommend this value as a standardized parameter and coefficient for calculating ETo. Based on a national study in the U.S. and studies by European and American researchers, the authors recommend that the FAO-PM ETo method from FAO56, when applied on an hourly or shorter basis, use rs = 50 s m−1 for daytime and rs = 200 s m−1 for nighttime periods. This use will provide, on average, good agreement with computations made on a 24-h time step basis. No changes are suggested for the FAO-PM method for daily (24-h) time steps, where use of rs = 70 s m−1 should continue.
... To understand this element of ET, remote sensing models are important. Several models are currently available to estimate ET using remote sensing data, ranging from local and regional scales such as Surface Energy Balance System (SEBS) [35,36], Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) [37], and Atmosphere Land Exchange (ALEXI) [38] to continental and global scales such as MOD16 [39], disaggregated Atmosphere-Land Exchange (ALEXI/DisALEXI) [38], Jet Propulsion Laboratory Priestley-Taylor (JPL-PT) [40], and Global Land Evaporation Amsterdam Model (GLEAM), spanning a wide range of temporal scales. MODIS Global Terrestrial evapotranspiration Product is a frequently used technique to calculate ET at both a continental and global scale. ...
... This is a combination of the Penman [51] method and is based on the principle of the Bowen ratio (includes radiation, wind, and humidity factors) and Monteith [52], a method that takes into account resistance factors (including surface drag and aerodynamic drag). The equation was used by Allen [37] on an hourly basis, while the resistance term has a constant value of 70 s/m all day and night and recommended FAO-56 Penman-Monteith equation as the only standard method of determining reference evapotranspiration in all climates, especially if it was available data. The equation is given as follows: ...
... Actual evapotranspiration or evapotranspiration (ET) is calculated using the crop coefficient (Kc) calculated for different crops at different growth stages [37]. The ET is calculated as follows [58]: ...
Maximizing water productivity amid agricultural water scarcity demands accurate crop evapotranspiration (ETc) estimation. While the Penman–Monteith method is standard, its dependence on extensive meteorological data restricts use in data-scarce regions. Eddy covariance offers precise ETc estimation but is resource-intensive. Satellite remote sensing, like MOD16, offers a promising alternative for ET estimation. Several empirical models are also available, out of which suitable alternatives can also be identified for the regions with limited weather data availability, where eddy covariance and remote sensing techniques become limitations. Consequently, a study was undertaken to investigate the performance of eddy covariance method (Eddy Tower based), empirical models, and a remote sensing technique for computing crop evapotranspiration under rice–wheat cropping system at Naraingarh Seed Farm of Punjab Agricultural University, Ludhiana, for the years 2022–2023. The performance evaluation of all the methods was performed using statistical indicators, including mean absolute error, mean bias error, root mean squared error, coefficient of determination, and index of agreement. The eddy covariance method, selected empirical models, and remote sensing technique demonstrated a good correlation with FAO Penman–Monteith ET, with coefficient of determination values greater than 0.85. The eddy covariance tower gives precise ETc estimates, with MOD-16 satellite data closely trailing. When Eddy Tower data is inaccessible, MODIS products provide a reliable alternative on a broader scale. In the absence of MODIS data, such as during cloud cover, empirical models offer effective ETo and hence ETc estimation. Moreover, for regions lacking weather data, models like Hargreaves and Samani (1985) or Priestley and Taylor (1972) stand out as optimal choices for accurate ETo and thereafter ETc estimation.
... The single crop coefficient method is used to calculate potential evapotranspiration. Based on the meteorological data from the weather station near the experimental area, the reference crop evapotranspiration (ET 0 ) is calculated using the Penman-Monteith formula [29]. The potential crop evapotranspiration is then obtained using the single crop coefficient method, and potential evaporation and potential transpiration are separated according to Beer's law. ...
... Potential evapotranspiration (ET p ) refers to the maximum possible evapotranspiration generated by soil and vegetation under ideal conditions with sufficient water, primarily determined by climatic factors, while actual evaporation (E) and actual transpiration (T) refer to the evaporation and transpiration occurring under actual water conditions, which are limited by soil water supply. The calculation methods for potential evapotranspiration (ET p ) and T are shown in Formulas (10)-(12) [29]. ...
The ridge–furrow plastic mulching technique has been widely applied due to its benefits of increasing temperature, conserving moisture, reducing evaporation, and boosting yields. Hydrus-2D is a computer model designed to simulate the two-dimensional movement of water in soil characterized by a low cost and high flexibility compared to field experiments. This study, based on field experiment data from Jianping County, Liaoning Province, China, during 2017–2018, developed Hydrus-2D models for two distinct field management practices: non-mulched flat cultivation (NM-FC) and mulched ridge tillage (M-RT). Furthermore, it simulated the dynamic changes in farmland water variations during the summer maize growth period (2021–2100) under climate change scenarios, specifically medium and high emission pathways (SSP2-4.5 and SSP5-8.5), based on the FGOALS-g3 model, which exhibits the highest similarity to the climate pattern of Jianping County in the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and the Shared Socioeconomic Pathways (SSPs). The results showed that in the future FGOALS-g3 model, net radiation exhibited a significant upward trend under the SSP2-4.5 scenario (Z = 2.38), while the average air temperature showed a highly significant increase under both SSP2-4.5 and SSP5-8.5 scenarios, with Z-values of 6.48 and 8.90, respectively. The Hydrus-2D model demonstrated high simulation accuracy in both NM-FC and M-RT treatments (R2 ranging from 0.86 to 0.96, with RMSE not exceeding 0.011), accurately simulating the dynamic changes in soil water content (SWC) under future climate change. Compared to NM-FC, M-RT reduced evaporation, increased transpiration, and effectively decreased the leakage caused by increased future precipitation, resulting in a 0.04 and 0.01 cm3/cm3 increase in surface and deep soil SWC, respectively, during the summer maize growing season, significantly improving water use efficiency. Moreover, M-RT treatment reduced the impact coefficients of climate change on various water balance parameters, stabilizing changes in these parameters and SWC under future climate conditions. This study demonstrates the significant advantages of M-RT in coping with climate change, providing key scientific evidence for future agricultural water resource management. These findings offer valuable insights for policymakers and farmers, particularly in developing adaptive land management and irrigation strategies, helping to improve water use efficiency and promote sustainable agricultural practices.
... Additionally, CMIP6 models predict similar trends in droughts as the older CMIP5 models (Cook et al., 2020), suggesting a robust finding that droughts are likely to increase in magnitude and frequency. For each GCM, we extracted average monthly air temperature, precipitation, and the variables needed to estimate reference evapotranspiration (Allen et al., 2006), which include maximum air temperature, minimum air temperature, wind speed, relative humidity (used to estimate vapor pressure deficit), and incoming solar radiation (used to estimate net solar radiation). After bi-linearly interpolating each GCM to a common 1.5° ...
... We used the ensemble mean of the monthly mean precipitation along with reference evapotranspiration (Allen et al., 2006) averaged across the growing season months (March-August), to calculate SPEI using the "SPEI" package in R (Beguería et al., 2014;Vicente-Serrano et al., 2010). The parameters of the log-logistic distribution used to estimate SPEI were calculated using the instrumental period . ...
Forests around the world are experiencing changes due to climate variability and human land use. How these changes interact and influence the vulnerability of forests are not well understood. In the eastern United States, well‐documented anthropogenic disturbances and land‐use decisions, such as logging and fire suppression, have influenced forest species assemblages, leading to a demographic shift from forests dominated by xeric species to those dominated by mesic species. Contemporarily, the climate has changed and is expected to continue to warm and produce higher evaporative demand, imposing stronger drought stress on forest communities. Here, we use an extensive network of tree‐ring records from common hardwood species across ~100 sites and ~1300 trees in the eastern United States to examine the magnitude of growth response to both wet and dry climate extremes. We find that growth reductions during drought exceed the positive growth response to pluvials. Mesic species such as Liriodendron tulipifera and Acer saccharum , which are becoming more dominant, are more sensitive to drought than more xeric species, such as oaks ( Quercus ) and hickory ( Carya ), especially at moderate and extreme drought intensities. Although more extreme droughts produce a larger annual growth reduction, mild droughts resulted in the largest cumulative growth decreases due to their higher frequency. When using global climate model projections, all scenarios show drought frequency increasing substantially (3–9 times more likely) by 2100. Thus, the ongoing demographic shift toward more mesic species in the eastern United States combined with drier conditions results in larger drought‐induced growth declines, suggesting that drought will have an even larger impact on aboveground carbon uptake in the future in the eastern United States.
... To address missing data, a method of replacing values was employed, utilizing means between corresponding data points from the preceding and subsequent years. In instances where solar radiation data gaps were present and the correlation exceeded 0.8 at a 95% confidence level, the Angstrom equation (Allen et al. 2006) was applied for gap filling. Initially, the net radiation (Rn, MJ m −2 d −1 ) at the Earth's surface for each day was determined based on latitude, longitude, and the solar constant. ...
... Understanding the limitations in modeling ET ref under extreme climatic conditions is crucial for informing sustainable water management strategies. Additionally, the consistent correlation of all models with the PM-FAO 56 model in humid climates indicates the stabilizing effect of high RH conditions and lower temperature fluctuations, facilitating proper model alignment (Allen et al. 2006). In conclusion, these results underscore the complexity of ET ref estimation, with climatic factors playing a pivotal role. ...
This study examines the effectiveness of various quantile regression (QR) and machine learning (ML) methodologies developed for analyzing the relationship between meteorological parameters and daily reference evapotranspiration (ETref) across diverse climates in Iran spanning from 1987 to 2022. The analyzed models include D-vine copula-based quantile regression (DVQR), multivariate linear quantile regression (MLQR), Bayesian model averaging quantile regression (BMAQR), as well as machine learning algorithms such as extreme learning machine (ELM), random forest (RF), M5 model Tree (M5Tree), least squares support vector regression algorithm (LSSVR), and extreme gradient boosting (XGBoost). Additionally, empirical equations (EEs) such as Baier and Robertson (BARO), Jensen and Haise (JEHA), and Penman (PENM) models were considered. While the EEs demonstrated acceptable performance, the QR and ML models exhibited superior accuracy. Among these, the MLQR model displayed the highest accuracy compared to DVQR and BMAQR models. Moreover, LSSVR, XGBoost, and M5Tree models outperformed ELM and RF models. Notably, LSSVR, XGBoost, and MLQR models exhibited comparable performance (R2 and NSE > 0.92, MBE and RMSE < 0.5, and SI > 0.05) to M5Tree and BMAQR models across all climates. Importantly, these models significantly outperformed EEs, DVQR, ELM, and RF models in all climates. In conclusion, high-dimensional QR and ML models are recommended as promising alternatives for accurately estimating daily ETref in diverse global climate conditions.
... The Penman-Monteith (P-M) model is relatively accurate in calculating reference crop evapotranspiration (ET 0 ) under various climatic conditions [44,45]. It is a formula recommended by the Food and Agriculture Organization (FAO) of the United Nations and widely used domestically and internationally [46]. ...
Drought propagation is a complex process, and understanding the propagation mechanisms of meteorological drought to soil drought is crucial for early warning, disaster prevention, and mitigation. This study focuses on eight tributaries in the upper reaches of the Shiyang River. Based on the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSMI), the Drought Propagation Intensity Index (DIP) and Copula function were applied to quantify the intensity and time of drought propagation from meteorological to soil drought and explored the drought propagation patterns at different temporal and spatial scales in these tributaries. Results showed that, in the 0–10 cm soil layer, the propagation intensity of meteorological drought to soil drought was peer-to-peer, with a propagation time of one month. In the middle (10–40 cm) and deep (40–100 cm) soil layers, propagation characteristics differed between the eastern and western tributaries. The western tributaries experienced stronger drought propagation intensity and shorter propagation times (2–4 months), while the eastern tributaries exhibited peer-to-peer propagation intensity with longer times (4–10 months). The large areas of forests and grasslands in the upper reaches of the Shiyang River contributed to strong land–atmosphere interactions, leading to peer-to-peer drought propagation intensity in the 0–10 cm soil layer. The eastern tributaries had extensive cultivated land, where irrigation during meteorological drought enhanced soil moisture, resulting in peer-to-peer propagation intensity in the middle (10–40 cm) and deep (40–100 cm) soil layers. In contrast, the western tributaries, with larger forest areas and widespread permafrost, experienced high water consumption and limited recharge in the 10–40 cm and 40–100 cm soil layers, leading to strong drought propagation.
... Missing hourly values were gap-filled using linear regression with parallel ETa values from other available lysimeters (same site and same soil) or the potential ET (ET 0 ) for a grass reference vegetation with an albedo of 0.23. The hourly ET 0 is calculated with the FAO Penman-Monteith equation (Allen et al., 2006): ...
Accurate determination of actual evapotranspiration (ETa) is important in various research fields like hydrology,
meteorology, ecology and agriculture. In situ ETa can be determined using weighing lysimeters and eddy
covariance. However, despite being regarded as the most precise in situ method for measuring ETa, the information
content of lysimeter measurements remains poorly understood. Here we examined the spatial correlations
between ETa measured at different locations by lysimeter (ET-LYS) and at different locations by eddy
covariance (ET-EC). This was done for the period 2015 - 2020 and the analysis was made for different spatial
(range: 0 to 500 km) and temporal scales (range: 1 day to 1 year) using 23 lysimeters and 4 eddy covariance
towers. We found that: (a) Same lysimeters at the plot scale show very high correlations of ET-LYS; (b) The
Pearson correlation of daily standardized anomalies of ET-LYS between sites exhibit moderate to high correlations
and were similar to that of ET-EC, indicating that lysimeter is generally as representative as EC regarding
ETa, and can provide certain information at the landscape and larger regional scale. During winter, the spatial
correlations for ET-LYS were smaller; (c) Wavelet analysis indicated that temporal correlations in ETa were
strongest for distances in time around 12 months (yearly cycle) and less than three months. Spatial correlations
were smaller under drought conditions (in the year 2018). Furthermore, combination of multiple ET-LYS from
different sites improved the predictability of ET-LYS for another site, suggesting that ET-LYS can be predicted
well using ET-LYS from different neighboring sites. Overall, lysimeter measurements can provide information at
much larger scales compared to their small measurement area.
... The analysis period of 75 years may not cover the full range of the natural climate variability, especially compared to that suggested by paleoclimate records (7). While the FAO-56 Penman-Monteith (PM) equation that we used is considered the best practice for calculating PET due to its physical basis and reliability validated by numerous studies (54)(55)(56), it assumes timeinvariant surface resistance to avoid the inherent complexity of determining the stomatal and atmospheric conductance and to foster standardization and consistency in climatic research (57,58). Such an assumption may not hold with changes in land surface and vegetation cover in a changing climate; future research could explore the impact of incorporating dynamic surface conductance in PET calculations, as demonstrated by (59). ...
Historically, meteorological drought in the western United States (WUS) has been driven primarily by precipitation deficits. However, our observational analysis shows that, since around 2000, rising surface temperature and the resulting high evaporative demand have contributed more to drought severity (62%) and coverage (66%) over the WUS than precipitation deficit. This increase in evaporative demand during droughts, mostly attributable to anthropogenic warming according to analyses of both observations and climate model simulations, is the main cause of the increased drought severity and coverage. The unprecedented 2020-2022 WUS drought exemplifies this shift in drought drivers, with high evaporative demand accounting for 61% of its severity, compared to 39% from precipitation deficit. Climate model simulations corroborate this shift and project that, under the fossil-fueled development scenario (SSP5-8.5), droughts like the 2020-2022 event will transition from a one-in-more-than-a-thousand-year event in the pre-2022 period to a 1-in-60-year event by the mid-21st century and to a 1-in-6-year event by the late-21st century.
... The URbanTRee model is a hydrological model designed to calculate grass reference evapotranspiration (ET0) on an hourly basis utilizing the Penman-Monteith equation (Allen et al., 2006) via a subroutine from the R package 'Water' (Olmedo et al., 2016) and it incorporates a shading sub-model that reduces solar radiation during shaded hours. ETp, representing potential tree evapotranspiration under sufficient water supply, is derived from ET0 using species-specific crop coefficients (kc). ...
... The station records relative humidity, wind speed, and local volumetric moisture content (VWC). This allows the computation of potential evapotranspiration via the Penman-Monteith method (Allen et al., 2006). As such, consistent rainfall records for the site exist from April 2014 onwards. ...
Geophysical methods have proven to be useful for investigating unstable slopes as they are both non‐invasive and sensitive to the spatial distribution of physical properties in the subsurface. Of particular interest are the links between electrical resistivity and near‐surface moisture content; recent work has demonstrated that it is possible to calibrate hydrological models using geophysical measurements. In this study we explore the use of in‐field electrical resistivity data for calibrating unsaturated soil retention parameters and saturated hydraulic conductivity used for modeling unsaturated fluid flow. We study a synthetic case study, and a well‐characterized site in the northeast of England and develop an approach to calibrate retention parameters for a mudstone and a sandstone formation, the former being an actively failing unit. Petrophysical relationships between electrical resistivity and moisture content (or saturation) are established for both formations. 2D hydrological models are driven by effective rainfall estimations; subsequently these models are coupled with a geophysical forward model via a Markov chain Monte Carlo approach. For the synthetic case, we show that our modeling approach is sensitive to the moisture retention parameters, while less so to saturated hydraulic conductivity. We observe the same characteristics and sensitivities for the field case, albeit with a greater data misfit. Further hydrological simulations suggest that the slope retained high moisture contents in the months preceding a rotational failure. Therefore, we propose that coupled hydrological and geophysical modeling approaches could aid in enhancing landslide monitoring, modeling, and early warning efforts.
... ATS implements the Priestley-Taylor equation (Priestley & Taylor, 1972) to model multiple evapotranspiration (ET) processes, including snow evaporation, canopy evaporation and transpiration, and land surface evaporation. The Priestley-Taylor equation is a simplified version of the Penman equation (Allen et al., 2006) that requires less measurements, ...
Ensemble‐based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process‐based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet‐based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling‐Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics‐informed, seq‐to‐seq streamflow forecasting, which further reduces biases in the pre‐trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.
... For more details about the above derivation, please refer to [22]. To use water from reservoirs for an irrigation purpose, an irrigation pump (IP) is needed. ...
Multi-energy rural microgrids (MERMs) hold both economic potential and multi-energy coordination ability, emerging as a promising energy management paradigm in rural areas. In this paper, an energy scheduling method is investigated for a MERM with renewable energy and biomass resources, aiming to satisfy the rural electrical, thermal, natural gas, and irrigation demands economically. Mathematically, biomass flows are formulated by adopting a differential dynamics model of anaerobic biomass fermentation. The irrigation system is accurately formulated by fully taking into account meteorological information such as ambient temperature and precipitation. To handle the uncertainties in precipitation, reservoir inflows, renewable power generation as well as electrical and thermal load demands, a two-stage stochastic optimization method is employed, and the proposed model is then reformulated into a stochastic mixed integer quadratic programming (SMIQP) problem. To mitigate the computational burden arising from integer variables and enhance the solution efficiency, a scenario decomposition algorithm, progressive hedging (PH), is used to decompose the SMIQP into scenario-wise subproblems, which are then solved in parallel. Finally, the simulation results demonstrate the effectiveness of the proposed MERM scheduling method and the efficiency of the PH algorithm.
... Among these, the FAO56 Penman-Monteith model (FAO56-PM) is widely recognized as the most robust due to its strong physical basis [17,28,29] and high accuracy across diverse climate conditions [28,30]. The FAO56-PM model is now widely used worldwide [31][32][33] and serves as the standard against which other empirical models are evaluated [34][35][36][37][38][39]. ...
The FAO56 Penman–Monteith model (FAO56-PM) is widely used for estimating reference crop evapotranspiration (ET0). However, key variables such as shortwave radiation (Rs) and net longwave radiation (Rln) are often unavailable at most weather stations. While previous studies have focused on calibrating Rs, the influence of large Rln, particularly in high-altitude regions with thin air, remains unexplored. This study investigates this issue by using observed data from Bange in central Tibet to identify the optimal methods for estimating Rs and Rln to accurately calculate ET0. The findings reveal that the average daily Rln was 8.172 MJ m−2 d−1 at Bange, much larger than that at the same latitude. The original FAO56-PM model may produce seemingly accurate ET0 estimates due to compensating errors: underestimated Rln offsetting underestimated net shortwave radiation (Rsn). Merely calibrating Rs does not improve ET0 accuracy but may exacerbate errors. The Liu-S was the empirical model for Rs estimation calibrated by parameterization over the Tibetan Plateau and the Allen-LC was the empirical model for Rln estimation calibrated by local measurements in central Tibet. The combination of the Liu-S and Allen-LC methods showed much-improved performance in ET0 estimation, yielding a high Nash–Sutcliffe Efficiency (NSE) of 0.889 and a low relative error of −5.7%. This strategy is indicated as optimal for ET0 estimation in central Tibet. Trend analysis based on this optimal strategy indicates significant increases in ET0 in central Tibet from 2000 to 2020, with projections suggesting a continued rise through 2100 under climate change scenarios, though with increasing uncertainty over time. However, the rapidly increasing trends in precipitation will lead to decreasing trends in agricultural water use for highland parley production in central Tibet under climate change scenarios. The findings in this study provide critical information for irrigation planning to achieve sustainable agricultural production over the Tibetan Plateau.
... The crop production model methodology is based on the water productivity concept and a yield reduction function based on the ratio of actual evapotranspiration to potential evapotranspiration. The crop water requirement relation was applied, and the cassava crop coefficient (Kc) was obtained from Allen et al. [28], whereas the reference crop evapotranspiration (ETo) was computed using CropWat 8.0 software. The effective rainfall was also computed from CropWat 8.0 software using the fixed percentage (80%) method. ...
In most sub-Saharan African countries, cassava propagation occurs through dry farming techniques that involve cultivation without irrigation, which inhibits water management culture. This study investigated the water productivity of enhanced cassava cultivars in tropical rain-fed locations with dry farming conditions. Soil samples were obtained from three locations in the field using a soil auger and sieve analysis. Four enhanced stem cassava varieties free of disease (TMS 30572, TMS 980505, TMS 920326, and TMS 090581) were analyzed for growth characteristics for 90, 120, and 150 days after planting (DAP), and the number of tubers per plant, tuber length, tuber circumference, root depth, above ground biomass, and tuber yield were measured. Agronomic growth metrics, including height of plant, stem diameter, and number of nodes and stems, were also measured. The cassava crop coefficient was obtained and the reference crop evapotranspiration was computed using CropWat 8.0 software. TMS 090581 had the highest tuber productivity of 1.64 kg/m 3 , 3.77 kg/m 3 and 3.05 kg/m 3 and the highest average tuber yield value of 5.68 t/ha,16.06 t/ha and 18.75 t/ha at 90, 120 and 150 DAP, respectively. TMS 920326 resulted in the highest stem productivity of 1.98 kg/m 3 and 2.72 kg/m 3 at 90 and 150 DAP respectively while TMS 30572 had the highest stem productivity of 2.59 kg/m 3 at 120 DAP. The highest leaf productivity of 1.94 kg/m 3 was attained by TMS 980505 at 90 DAP while TMS 30572 yielded the highest leaf productivity values of 2.32 kg/m 3 and 2.02 kg/m 3 at 120 and 150 DAP, respectively. Despite being susceptible to white flies, TMS 30572 yielded the highest number of nodes, resulting in increased leaf production. TMS 090581 is recommended for tuber water productivity, and TMS 30572 and 980505 for leaf and stem productivity in water-scarce environments.
... The potential evapotranspiration (ET p ) was estimated using the Penman-Monteith equation [27] based on monitored meteorological data. ET p was calculated as K c × ET 0 , where K c represents the T. ramosissima crop coefficient. ...
In an arid climate with minimal rainfall, plant growth is constrained by water scarcity and soil salinity. Ecological Water Conveyance (EWC) can mitigate degradation risks faced by riparian plant communities in these regions. However, its effects on long-term dynamics of root zone soil water content, salt levels, and root water uptake remain unclear. This study examined how groundwater affects salt and water dynamics, in addition to root water uptake, under different scenarios involving Tamarix ramosissima Ledeb. The research was conducted in the lower reaches of the Tarim River in northwestern China. The Hydrus-1D model was used, following the EWC strategy. The results show that the distribution of T. ramosissima roots was significantly influenced by soil water and salt distributions, with 56.8% of roots concentrated in the 60–100 cm soil layer. Under water stress conditions, root water uptake reached 91.0% of the potential maximum when considering water stress alone, and 41.0% when accounting for both water and salt stresses. Root water uptake was highly sensitive to changes in Depth-to-Water Table (DWT), notably decreasing with lower or higher DWT at 40% of the reference level. EWC effectively enhances root water uptake by using water to leach salts from the root zone soil, with optimal results observed at 500–600 mm. This study advocates for sustainable EWC practices to support vegetation and combat desertification in the lower reaches of arid inland rivers.
... The reference crop evapotranspiration (ET 0 ) for each station is calculated using the most widely used Penman-Monteith equation [25], as shown in Equation (1). ...
Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change.
... The majority (two-thirds) of the backward footprint is thus attributed to evaporation from seas and oceans. This is explained by two factors: one is that vegetation brings an additional resistance term (the stomatal resistance) compared to a free water surface and that soil moisture can be a limiting factor; the second is that apart from the aerodynamic resistance, no water limitations are present at the sea surface to limit the upward moisture flux (Allen et al. 2006;Lionello et al. 2003;Seneviratne et al. 2010). In all seasons, the Red Sea plays a dominant role in moisture transport to the AP via the Red Sea Convergence Zone (RSCZ) and the Tokar wind (see the "Discussion" section). ...
Effective irrigation planning is crucial for sustainable agricultural development and ecosystem restoration projects in arid regions. With respect to ambitious greening initiatives, Saudi Arabia is establishing a national strategy toward a more sustainable and eco-friendly future not only for itself but also for the broader Middle East region. Thus, comprehensively understanding the water cycle in the region is essential to identify the most suitable target locations for afforestation and reforestation while considering the potential role of irrigation. Herein, in addition to traditional pedoclimatic factors, we introduce a complementary consideration—“irrigation recycling.” Building on the well-established concept of atmospheric moisture recycling and taking advantage from an atmospheric trajectory dataset, we track the path of evaporated water from current or potential irrigated sites to the location where the evaporated water eventually falls as precipitation. Our analysis offers two key benefits. First, it helps pinpoint the regions in which and the periods during which water recycling is maximum within the country, aiding more precise calculations of the investment return value for irrigation infrastructures. Second, it helps identify the land-use change patterns that contribute to international efforts such as drought mitigation in East Africa as an example. We found that one-third of the actual precipitation in the current Saudi irrigated sites originated from evapotranspiration over land, mainly from Saudi Arabia and surrounding countries. Interestingly, most of the evapotranspiration from these irrigated sites will eventually fall somewhere over land (primarily in Iran). Controlling the seasonality and spatial distribution of the future irrigation expansion will allow controlling the atmospheric moisture recirculation in Saudi Arabia and nearby drought-prone regions such as Eastern Africa. The outcomes of this study will be the subject of future integrated assessments to account for the climatic feedbacks of the land-use change scenarios. At present, they provide crucial insights to support the decision-making process surrounding the Saudi and Middle East Green Initiatives. Further, the presented methodology offers a pragmatic framework that can be applied to similar greening projects for other regions, making it a viable and valuable approach for global sustainability programs.
... Therefore, most users prefer indirect methods based on meteorological data (Kim & Kim, 2008), (Dinpashoh, 2006). The Penman-Monteith FAO 56 (FPM) model is recommended as the sole method for calculation of ETo and it has been reported to be able to provide consistent ETo values in many regions and climates (Allen et al., 2005), (Allen et al., 2006). The main shortcoming of the FPM method is, however, that it needs a large number of climatic data and variables which are unavailable in many regions, especially in developing countries like Libya. ...
This study was conducted with the aim of evaluating the performance of artificial neural networks (ANNs) to estimate the reference evapotranspiration using limited climate data in Shahat region in Libya, compared to using the FAO Penman-Monteith equation (FPM), which requires temperature, wind speed, relative humidity and number of sunshine hours, which are rarely available in most meteorological stations in developing countries. In this study, we used the average temperature (Tmean) and the average relative humidity (RHmean) obtained from Shahat meteorological station for the period from 1963 to 1999, and the extraterrestrial radiation (Ra), which can be calculated given the location and time of the day. These data are divided into two groups, from 1963 to 1988 and from 1989 to 1999 for the training and validation phases of the neural networks, respectively. This study concluded that using (Tmean), (RHmean) and (Ra) gave the best agreement with the results calculated with the FAO Penman-Monteith equation, where the values of R2 and RMSE are equal to 0.98 and 0.26, respectively.
... The Penman-Monteith (PM) model has demonstrated applicability to various surfaces across diverse spatial and temporal scales (Allen et al., 2006;Matejka et al., 2009). In order to exclude the impact of climate change on reference evapotranspiration (ET 0 ), it is necessary to fully consider the impact of different annual rainfall on the evapotranspiration model. ...
Background
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
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.
Conclusion
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.
... The potential evapotranspiration estimation for green infrastructure have been extensively studied [57,58]. The chosen evapotranspiration model is the Penman Monteith FAO-56 physical model [59]. This model evaluates the potential evapotranspiration by considering the presence of grass with an albedo of 0.23, which is representative of vegetated swales. ...
The increase in rainfall intensity caused by climate change, combined with high levels of urban soil sealing and the limited capacity of drainage systems, is significantly increasing flooding risk. Integrated stormwater management is a key factor in dealing with the consequences of climate change by mitigating flood risk based on nature-based solutions. An experimental site was designed to assess the hydrological response of vegetated swales depending on different catchment surfaces, and to evaluate the impact of spatial variability of the hydraulic conductivity (K). A hydrological model was developed on EPA SWMM and calibrated based on measured data in two experimental swales with different active surfaces (N6—50 m2 and N11—100 m2). The model validation was assessed with NSE scores higher than 0.7. The simulations considered various factors, such as the water level in the swales, the actual rainfall, the evapotranspiration, the swale geometry, the catchment area (Sa), and the hydraulic conductivities of the natural silty soil, to assess, for the heaviest rainfall event, the best swale morphological characteristics. The study examined the combined impact of K (8) and Sa (6) on swale storage capacity, designed with a 250 mm depth. The simulations showed that the 250 mm overflow limit was exceeded for N10 (90 m2) when K ≤ 2 × 10−6 m/s, and for N11 (100 m2) when K ≤ 4 × 10−6 m/s. These results provide valuable information on the optimal storage capacity based on the swales’ geometrical and physical properties.
... value finders in many regions and climates (Allen et al., 2006). Many researchers have used it and they suggest that it is better than other methods for determining ET o (Gowda et al., 2016). ...
... This formulation estimates evapotranspiration as water vapor diffusing from the canopy surface based on the aerodynamics and gradient methods (Monteith and Unsworth 2013). A number of studies showed that the Penman-Monteith method is adequate on distinct temporal and spatial scales for different surfaces (Allen et al. 2006;Matejka et al. 2009). Specifically, most of the physically-based rainfall interception models estimate the evaporation rate by using the Penman-Monteith equation assuming a canopy resistance of zero, which provides a good approximation of the evaporation rate from a completely wet canopy. ...
The simulation of rainfall interception by vegetation is essential to water resource management, considering both changing land use and climate change effects. In the rainfall interception models, the evaporation rate is frequently estimated by means of the Penman-Monteith method, but the Priestley-Taylor equation appears as a promising approach with fewer input requirements. In this study these both formulations were evaluated with the sparse Gash model with variable parametrization for estimating rainfall interception by four tree species in a Brazilian dry tropical forest. The Penman-Monteith equation was used with the canopy resistance set to zero, and the momentum method was applied for estimating the aerodynamic resistance. The Priestley-Taylor formulation was tested with the proportional coefficients (α) of 1.26 and 1.34. The results of rainfall predictions were compared with the measurements by statistical indicators, which pointed slightly favorable to Penman-Monteith method. The Priestley-Taylor with α = 1.26 resulted in predictions better than with α = 1.34. Most of the simulations were classified as good (CMRE varying from 5.5 − 9.3%). The Priestley-Taylor method can be used for estimating the evaporation rate in simulations based on the sparse Gash model with variable parametrization in the studied dry tropical forest, under situations with restrictions of micrometeorological measurements or minimal processing time requirement.
... The Food and Agriculture Organization (FAO) of the United Nations (UN) has developed the FAO-56 Penman-Monteith model [10] and recommended it as the standard approach for estimating ET0 in diverse climatic regions worldwide [11][12][13]. Extensive comparisons with other empirical models under various climatic conditions and temporal scales have deemed this model superior, requiring no additional parameter calibration [14][15][16][17][18]. ...
Climate change significantly impacts agriculture and forage production, requiring the implementation of strategies toward increased water and energy use efficiency. So, this study investigated the yield of forage cactus (Opuntia stricta (Haw.) Haw) under different irrigation depths using brackish groundwater (1.7 dS m−1), whose management was based on reference evapotranspiration (ETo) estimated by the Hargreave–Samani (HS) and Penman–Monteith (PM) equations. The research was conducted in Independência, Ceará, Brazil, under the tropical semi-arid climate. A randomized block design in a 2 × 5 factorial scheme was employed, varying the ET0 estimation equations (HS and PM) and irrigation levels (0; 20; 40; 70; and 100% of total required irrigation—TRI). Growth, productivity, and water use efficiency variables were evaluated at 6, 12, and 18 months after treatment initiation. The economic analysis focused on added value, farmer income, and social reproduction level. The results showed no isolated effect of the equations or their interaction with irrigation depths on the analyzed variables, suggesting that irrigation management can be effectively performed using the simpler HS equation. Furthermore, there was no statistical difference between the means of 100% and 70% TRI as well as between 70% and 40% TRI for most variables. This indicates satisfactory crop yield under deficit irrigation. Dry matter productivity and farmer income at 12 months resulting from complementary irrigation with depths between 40% and 70% of TRI were significantly higher than under rainfed conditions. The 70% depth resulted in yields equivalent to those at 100% TRI, with the social reproduction level being achieved on 0.65 hectares in the second year.
... The station continuously recorded the hourly temperature, air relative humidity, wind speed, and global solar radiation. We used these measurements to calculate the ETo using the Penman-Monteith equation (Allen et al. 1998(Allen et al. , 2006, as shown in Fig. 2. Average daily values were used to estimate daily crop water evapotranspiration (ETc) as ETc = ETo × Kc × Kr. The crop coefficient Kc was estimated to be 0.65 in April and October, 0.6 in May and June, 0.55 from July to September, and 0.7 in November and December (Orgaz et al. 2007). ...
The olive tree (Olea europaea L.) is culturally and economically vital in Morocco. However, its sustainability is threatened by aridity and water scarcity. Studying its response to different irrigation strategies is crucial for sustainable cultivation and improved water use efficiency in the face of future drought events. This work aimed to study the responses of sap flow rate, physiological, and agronomic parameters of the Moroccan olive cultivar ‘Menara’ to Regulated Deficit Irrigation (RDI) and Sustained Deficit Irrigation (SDI) strategies. Seven irrigation regimes were studied based on the sensitivity of phenological phases to water stress, distinguished as (SP) ‘Sensitive Period’ and (NP) ‘Normal Period’. SP involves flowering (SP1) and oil synthesis to harvest (SP2), while NP relates to pit hardening. ‘Menara’ olive trees were subjected to four RDI treatments: T1 (SP 100- NP 70% ETc), T2 (SP 100- NP 60% ETc), T3 (SP 80- NP 70% ETc), and T4 (SP 80- NP 60% ETc), and two SDI treatments: T5 (70% ETc) and T6 (60% ETc), compared with control (T0) trees under full irrigation (100% ETc). In comparison to the control T0, the deficit irrigation treatments exhibited lower sap flow rates. Specifically, T1 and T2 experienced reductions of 10% and 19% in sap flow rates, respectively, attributed to a decrease in water application of 11% and 14% compared to T0. Despite this decline, T1 and T2 demonstrated fruit yields comparable to T0. Conversely, T4, which received 28% less irrigation, displayed a yield reduction of approximately 23% compared to T0 in 2022. Moreover, adverse effects were observed in Menara olive trees treated with T4 after two consecutive seasons of deficit irrigation in 2023, indicating that prolonged stress effects could be detrimental in subsequent years. T3, under RDI, showed resilience with a 13% reduction in production despite a 37% decrease in sap flow rate and a 24% water restriction. Conversely, T5 and T6, employing SDI, experienced significant yield declines of 50%, with reductions in water application of 30% and 40% and sap flow rate of 51% and 80%, respectively, in 2022. The alternate bearing pattern significantly impacts Menara olive production, as evidenced by reduced sap flow and yield in the “off” year of 2023, regardless of irrigation strategies. A strong correlation (R² = 0.84) between sap flow and yield indicates that well-irrigated olive trees tend to transpire more, leading to higher yields. Stomatal conductance (gs) notably decreases with increased water deficit, with reductions of 8%, 12%, and 23% observed in T4, T5, and T6, respectively. Furthermore, a significant reduction in FV/FM, indicative of water stress, was observed with a 40% decrease in water supply in the T6 treatment group during both irrigation seasons in 2022 and 2023, with Fv/Fm reaching approximately 0.7. In general, Menara olive trees subjected to deficit irrigation, particularly under the T3 RDI treatment, showed the ability to adapt and cope with low water supply over time. However, the cumulative water shortage effect of the SDI treatment T6 resulted in a decline in both the agronomic and physiological performance of this cultivar.
... Hourly meteorological data, such as air temperature, rainfall, air humidity, solar radiation, atmospheric pressure, and wind speed, were gathered in the experimental region using automated weather stations (Tian Qiong Co., Ltd., Weifang, China). The Penman-Monteith formula [32] was used to calculate crop evapotranspiration (ET0). The potential crop evapotranspiration (ETP) is determined by multiplying ET0 with the crop coefficient (Kc) for sunflowers. ...
The control of irrigation volume is of significant importance in arid regions of northwest China. Particularly, it has a crucial impact on the salinization of shallow groundwater areas. In 2022 and 2023, field experiments were conducted to test three distinct under-membrane irrigation treatments. These treatments were assigned water quotas of HW (27 mm), MW (22.5 mm), and LW (18 mm). The HYDRUS-2D model was integrated with a field experiment to accurately simulate the dynamic fluctuations of soil water and salt in the sunflower root zone. The model’s performance was assessed and verified using real-field data from 2022 and 2023, and the simulation results closely matched the measured values. This research also used stable hydroxide isotopes to assess the water supply from various soil layers at different time intervals in sunflower plants. The results indicated that the three different levels of irrigation applied under the membrane had a significant impact on soil water content. Specifically, there was a significant difference in soil water content at a depth of 0–40 cm (p < 0.05), while there was little effect on the water content at a depth of 40–60 cm (p > 0.05). After irrigation, the average salt content in the top 0–20 cm of soil decreased by 7.0% compared to the medium and low irrigation levels, and by 10.8% compared to the medium irrigation level. Additionally, the medium irrigation level resulted in a 10.8% decrease in salt content compared to the low irrigation level, and a 4.1% decrease compared to the medium irrigation level. During the same period, the soil salinity levels at depths of 0–20 cm, 20–40 cm, 40–60 cm, and 60–100 cm in the area outside the membrane were measured to be 2.7~4.8 g·kg−1, 2.8~4.0 g·kg−1, 2.7~3.4 g·kg−1, and 1.7~2.6 g·kg−1, respectively. These levels decreased by 13.1~55.5%, 0.7~42.8%, −0.4~16.2%, and −72.7~7.5%, respectively. Following irrigation, the HW treatment mostly absorbed water in the 0–40 cm soil layer, while the MW and LW treatments absorbed water in both the 0–40 cm and 60–80 cm soil levels. The results indicated that the most optimal drip irrigation method beneath the membrane in this location was achieved when the amount of water applied was between 25–30 mm. This method demonstrated a combination of water conservation, high crop yield, and effective salt suppression.
... Vicente-Serrano et al. (2010) described the method to calculate the SPEI, which takes as input data the difference between precipitation and PET. For calculating PET, there are various methodologies, such as the Thornthwaite (Thornthwaite 1948), the Hargreaves (Hargreaves 1975), the modified Hargreaves (Ravazzani et al. 2012), and the Penman-Monteith FAO56 methods (Allen et al. 2006). Based on SPI/SPEI indices, a classification of drought into seven categories ranging from an extremely dry to an extremely wet class is given in Table 2. ...
This study examines drought patterns in Ethiopia's 12 major river basins from 1981 to 2018 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Both indices reveal historical drought episodes with slight variations, with significant differences in 1984, 2009, and 2015. Except for the Wabi-Shebelle catchment in southern Ethiopia, all river basins show an increasing trend in SPI12 and SPEI12 indices. The eastern and central regions experience more drought according to SPEI3. Seasonal correlations show that during the March–May rainy season, precipitation is negatively correlated with the Indian Ocean Dipole (IOD) index, while in the June–September season, it negatively correlates with Nino 3.4 and positively with IOD. The study also found that El Niño leads to less rainfall in the Ethiopian highlands, while La Niña results in more rainfall in the central and northern highlands but less in the south.
... The daily meteorological variables including P, minimum and maximum air temperature (T min and T max ), sunshine duration (S d ), relative humidity (RH), and wind speed (U 2 ) for the period 1987-2019 were obtained from IRIMO for 100 synoptic stations (Fig. 1). Daily PET was obtained using the FAO 56 Penman-Monteith (PMF56) method (Allen et al. 2006) and measured meteorological variables for 100 synoptic stations (Table 2). ...
This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and
the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed
using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P,
T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions
to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.
... The ET c rates was calculated weekly using the Penman-Monteith approach on the basis of the daily ET 0 values, calculated using the hourly agrometeorological data provided by the nearest SIAS station, and the crop coefficients for citrus crops [60,61] adjusted by site-specific coefficients (i.e., 0.7), estimated as the ratio between the canopy cover size (m 2 ) and the total area pertaining to each tree (24 m 2 ) [62]. The irrigation season began in early-mid June (DOY 154 and 167, respectively, in 2022 and 2023) and ended on mid-end of September (DOY 259 and 272, respectively, in 2022 and 2023). ...
The quantification of soil carbon dioxide (CO2) flux represents an indicator of the agro-ecosystems sustainability. However, the monitoring of these fluxes is quite challenging due to their high spatially-temporally variability and dependence on environmental variables and soil management practices.
In this study, soil CO2 fluxes were measured using a low-cost accumulation chamber, that was realized ad hoc for the surveys, in an orange orchard managed under different soil management (SM, bare versus mulched soils) and water regime (WR, full irrigation versus regulated deficit irrigation) strategies. In particular, the soil CO2 flux measurements were acquired in discontinuous and continuous modes, together with ancillary agrometeorological and soil-related information, and then compared to the agrosystem scale CO2 fluxes measured by the eddy covariance (EC) technique.
Overall significant differences were obtained for the soil CO2 discontinuous fluxes as function of the WR (0.16 ± 0.01 and 0.14 ± 0.01 mg m⁻² s⁻¹ under full irrigation and regulated deficit irrigation, respectively). For the continuous soil CO2 measurements, the response observed for the SM factor varied from year to year, indicating for the overall reference period 2022-23 higher soil CO2 flux under the mulched soils (0.24 ± 0.01 mg m⁻² s⁻¹) than under bare soil conditions (0.15 ± 0.00 mg m⁻² s⁻¹). Inter-annual variations were also observed as function of the day-of-year (DOY), the SM and their interactions, resulting in higher soil CO2 flux under the mulched soils (0.24 ± 0.02 mg m⁻² s⁻¹) than under bare soil (0.15 ± 0.01 mg m⁻² s⁻¹) in certain periods of the years, according to the environmental conditions.
Results
suggest the importance of integrating soil CO2 flux measurements with ancillary variables that explain the variability of the agrosystem and the need to conduct the measurements using different operational modalities, also providing for night-time monitoring of CO2. In addition, the study underlines that the small-scale chamber measurements can be used to estimate soil CO2 fluxes at orchard scale if fluxes are properly scaled.
... Monteith and others, based on the principles of energy balance and aerodynamics, simplified and modified the Penman formula to derive the FAO Penman-Monteith equation [5]. The incorporation of surface resistance in this equation enhances its calculation accuracy and regional applicability without requiring extensive parameter calibration [6]. Although these semi-empirical and semi-theoretical models can better capture the characteristics of time series data with clear periodicity and gradual changes, they exhibit a high dependency on data correlation and may not effectively capture complex nonlinear relationships. ...
The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m3/hm2, or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development.
... The irrigation depths were applied based on the maximum evapotranspiration of the sorghum crop (ETc), adjusting the conditions of the experiment and the irrigation system (53, 67, 85 and 95% of the total estimated ETc for the crop cycle). ETc was estimated daily from the estimate of daily reference evapotranspiration (ETo), by the Penman-Monteith method according to [27]. ETo was estimated daily from data collected at a meteorological station installed in the experiment area. ...
Sweet sorghum (Sorghum bicolor [L.] Moench) is a plant that can be an alternative for the production of bioethanol in semi-arid regions. The objective of this work was to evaluate sweet sorghum 'BRS 506' under salt and water stress. The experimental design was in randomized blocks, in a factorial scheme (4x4), with the first factor referring to the electrical conductivities of the irrigation water (1.5; 3.0; 4.5; and 6.0 dS m-1) and the second refers to irrigation depths (53, 67, 85 and 95% of crop evapotranspiration). Gas exchange, leaf water status, leaf sugars and plant growth were evaluated. Salt and water stress cause negative effects on the growth of sweet sorghum 'BRS 506'. Salt stress causes disturbances in gas exchange and sugar levels. Sweet sorghum 'BRS 506' is tolerant to combined salt and water stress.
Parameterisation of fully coupled integrated hydrological models is challenging. The state‐of‐the‐art hydrogeology models rely on solutions of coupled surface and subsurface partial differential equations. Calibration of these models with traditional optimisation methods are not yet viable due to the high computational costs. Prior knowledge of the range of the parameters can be helpful as a starting point, however, due to natural variations, abstractions and conceptualizations used in modelling, a systematic exploration of the variable space is needed. In this study, we utilise the natural clustering of the soils based on their saturated and unsaturated hydraulic behaviour derived from soil texture maps in conjunction with two level Latin hypercube sampling to effectively explore model parameter spaces. Soil texture maps are similar to USDA soil classifications; however, the objective is to classify the soil based on their unsaturated behaviour, rather than soil texture. The method has never been utilised in the modelling and the results show that it can be applied to larger watersheds. The area of study is Hubbard Brook Experimental Forest, a northern hardwood forest in the White Mountains of New Hampshire, USA. An average Nash–Sutcliffe value of 0.80 is achieved for hourly discharge for the eight streams in the catchment. The Nash–Sutcliffe measure shows a 7% improvement with the addition of the snow melt and evapotranspiration parameters in the second stage. Exchange flux patterns vary seasonally in the catchment with largest infiltration occurring in spring.
Many phenological models use ambient air temperature to estimate phenological stages during current and projected future climate conditions. However, the difference between ambient air temperature and plant-canopy temperature biases such estimates. Evapotranspiration (ET) has a big impact on the relationship between plant-canopy and air temperature, so awareness of ET facilitates understanding of temperature-based phenological models, their limitations, and possible changes in response to climate change. This chapter presents information on the estimation of reference ET (ETo), applying crop coefficient (Kc) values to determine well-watered crop ET (ETc), and assessing water stress effects on crop ET. It also discusses how to account for water stress effects to determine actual crop ET (ETa) and it presents some of the problems associated with estimating the ET of natural ecosystems. Rising CO2 concentration in the atmosphere is known to increase plant canopy resistance, which reduces transpiration and increases the daytime canopy temperature relative to the air temperature. Consequently, degree-days-based canopy temperature are also projected to rise more than degree-days based on air temperature as CO2 concentrations increase. In this chapter, we show that air temperature degree-day models will probably need periodic updating as the canopy temperature rises relative to air temperature, or canopy-temperature-based degree-days are needed to improve phenological models.
Background
There is an urgent need to save water and decrease fertilizer application rates in agricultural areas of the North China Plain (NCP) and similar regions.
Methods
Field experiments were conducted in 2017 and 2018 in the NCP to investigate the effects of border and furrow irrigation under different fertilizer application rates on the growth, yield, and water and fertilizer use efficiencies of silage corn. The experiment applied two irrigation methods, i.e., border and furrow irrigation, each with four compound fertilizer application rates: 750, 600, 450, and 300 kg/ha.
Results
While the two experiment years were normal hydrological years, variation in precipitation resulted in no irrigation being applied in 2017 and 70 mm of irrigation being applied after sowing in 2018. Plants appeared to grow slightly taller and thicker with larger leaf areas, but with a 9.7% lower fresh weight yield in 2017 relative to 2018. The actual evapotranspiration (ET a ) in 2017 was 37.22 mm more than that in 2018, and the higher fresh weight yield and lower ET a caused higher water use efficiency (WUE) in 2018, which was 32% higher than that in 2017. Furrow irrigation promoted growth compared with border irrigation under the same irrigation amount, but without significant effects on fresh weight yield, ET a , or WUE of silage corn. The fertilizer application amount had a significant effect on fresh weight yield and the partial fertilizer productivity of N, P and K of silage corn, but did not affect ET a or WUE of silage corn. Additionally, the fertilizer rate of 600 kg/ha induced slightly higher growth indexes and fresh weight yields compared with the fertilizer rates of 750, 450, and 300 kg/ha.
Discussion
In the NCP, lower irrigation amounts at the crop development period of silage corn appeared to promote higher yield, WUE, and fertilizer use efficiency, under the condition that there was sufficient water to ensure the emergence of seedlings. The current fertilizer application model, compound fertilizer applied with a seeder at planting, does not match the fertilizer needs of silage corn, and more efficient water and fertilizer application techniques should thus be adopted.
Accurately converting satellite instantaneous evapotranspiration ( λET i ) over time to daily evapotranspiration ( λET d ) is crucial for estimating regional evapotranspiration from remote sensing satellites, which plays an important role in effective water resource management. In this study, four upscaling methods based on the principle of energy balance, including the evaporative fraction method ( Eva-f method), revised evaporative fraction method ( R-Eva-f method), crop coefficient method ( K c -ET 0 method) and direct canopy resistance method ( Direct-r c method), were validated based on the measured data of the Bowen ratio energy balance system ( BREB ) in maize fields in northwestern (NW) and northeastern (NE) China (semi-arid and semi-humid continental climate regions) from 2021 to 2023. Results indicated that Eva-f and R-Eva-f methods were superior to K c -ET 0 and Direct-r c methods in both climatic regions and performed better between 10:00 and 11:00, with mean absolute errors ( MAE ) and coefficient of efficiency ( ɛ ) reaching <10 W/m ² and > 0.91, respectively. Comprehensive evaluation of the optimal upscaling time using global performance indicators ( GPI ) showed that the Eva-f method had the highest GPI of 0.59 at 12:00 for the NW, while the R-Eva-f method had the highest GPI of 1.18 at 11:00 for the NE. As a result, the Eva-f approach is recommended as the best way for upscaling evapotranspiration in NW, with 12:00 being the ideal upscaling time. The R-Eva-f method is the optimum upscaling method for the Northeast area, with an ideal upscaling time of 11:00. The comprehensive results of this study could be useful for converting λET i to λET d .
Application of the two-source energy balance (TSEB) model to satellite data requires the definition of canopy (Tc) and soil (Ts) temperatures for the partitioning of the latent heat flux into transpiration (T) and evaporation (E) components. In this study, we evaluate the possibility of directly separating the satellite land-surface temperature (LST) into soil and canopy components, removing the need to adopt the iterative solution currently used in the TSEB approach. The method exploits contextual information at field scale, under the assumption that the field is homogeneous and characterized by spatially uniform Tc and Ts values. The approach was tested on a set of fields in California with typical woody perennial Mediterranean crops, and it was compared against the outcomes of two other standard versions of TSEB, as well as in-situ flux measurements. Overall, the proposed partitioning approach performs similarly to the standard TSEB models in reproducing instantaneous surface energy fluxes (mean absolute difference, MAD, on the order of 60 W m− 2 for turbulent fluxes), with only a slight tendency to overestimate transpiration. Differences in transpiration are mostly driven by divergences in modelled Tc (MAD between 2 and 3 °C), whereas differences in Ts are more limited (MAD between 1 and 2 °C). The main discrepancies were observed over low canopy coverage conditions (fractional cover lower than 35%), where small changes in Ts resulted in large differences in Tc. Despite this drawback, the results show that the method is a suitable alternative for a more straightforward operational field-scale application of the TSEB under suitable surface conditions.
The propagation of meteorological drought (MD) to agricultural drought (AD) is influenced by various factors, particularly the climate type. This research examined the characteristics of drought propagation, encompassing propagation rates, lag time, and response time, from MD to AD within the context of Iran's diverse climate conditions. This was accomplished using three crucial meteorological and agricultural drought indices, namely the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Soil Moisture Index. The research data included in-situ and ERA5 datasets from 30 basins (catchments) across Iran in the 1979–2021 period. Based on reports from the Global Drought Observatory, the three most prominent MD events were identified in 1999–2002, 2008–2009, and 2017–2019. The correlation coefficients between MD and AD indices across various timescales, in climates ranging from hyper-arid to humid, exhibited a decline from 0.75 to 0.44. The response time, varying between 2.42 to 6.63 months, was determined by the strong correlation between SPI (or SPEI) and SSI1 (or SSI2) within the studied basins. Furthermore, the lag time, which was affected by the onset of MD and AD events, fluctuated between 2 to 6 months in hyper-arid and arid basins, revealing a 1–3 month variation compared to humid basins. The findings on propagation rates highlighted heightened sensitivity or response from meteorological to agricultural drought in humid climates, as opposed to hyper-arid climates. In light of these outcomes, comprehending the transition from MD to AD holds substantial significance for predicting, issuing early warnings, and fortifying preparedness in managing drought risks.
Reference evapotranspiration (ETo) is considered one of the important variables in hydrology and agricultural science and is a determining factor in water resources management. This study investigates a hybrid model of an artificial neural network with an artificial rabbit optimization algorithm (ANN-ARO) for daily modeling of ETo with limited meteorological parameters. It compares it with other hybrid methods, i.e. ANN with a particle optimization algorithm (ANN-PSO). ANN with genetic algorithm (ANN-GA) and five different data mining models. These models were evaluated using long-term daily climate data from 2000 to 2023 in two climates. The investigated stations included Birjand (with a desert climate) and Mashhad (with a cold semi-arid climate). The statistical comparison showed that considering all climatic parameters, the hybrid ANN-ARO model in Mashhad city with R 2 =0.9986 and MSE=0.0001 and in Birjand city with R 2 =0.9986 and MSE=0.0001 gave better estimates than other methods. In addition, the ANN_ARO optimization algorithm has the best estimation with "temperature" and "relative humidity" by considering the minimum meteorological parameter, and also by considering two and three input parameters, it performs better than other methods. In general, nature-inspired optimization algorithms are powerful tools to enhance the performance of ANN in ETo simulation. According to the results, the ANN-ARO model is highly recommended for estimating ETo in similar climate regions with limited climate data. This study proposes powerful models for accurate estimation of ETo with limited inputs in arid and semi-arid climates, which provide practical implications for the development of precision agriculture. Citation: Tosan, M., Maroosi, A., & Khozeymehnezhad, H. (2024). Investigating the performance of a hybrid algorithm based on artificial rabbits optimization (ANN-ARO) in forecasting reference evapotranspiration with limited climatic parameters. Introduction: Water scarcity is a global concern and affects various aspects of human life. Due to the excessive use of water in the agricultural sector, Iran is facing a sharp decrease in the water level, and many experts believe that water management in Iran is not good enough. Accurate estimation of reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith equation (FAO 56) is widely recommended worldwide as the standard ETo estimation method. However, direct measurements of ET are severely limited by enormous costs and technical complexity, thus making it highly meaningful to explore alternative simpler models for acceptable ETo estimation. On the other hand, meteorological data may often be incomplete, which requires models with minimal inputs. With the development of computer technology, the application of the Intelligence Optimization Algorithm (IOA) has been greatly expanded in various fields. The successful use of optimization algorithms in the field of ETo estimation points to the efficiency of IOA methods around the world. Although there is great potential in the field of ETo estimation using the IOA technique, research shows that they show different performances mostly due to unique strategies. Therefore, it is necessary to compare and evaluate different IOA to achieve better results.
The primary goal of the study is to characterize the evapotranspiration of wetlands against the background of changing meteorological conditions. The relatively long measurement period makes it possible to show the dynamics of this process both under conditions of high precipitation and periods of drought. Moreover, the analyzed period also includes measurements of evapotranspiration under conditions of rapid recovery of wetland vegetation after fire.
The accomplishment of the research objectives was based on measurements using the eddy covariance method in the Biebrza National Park in northeastern Poland. The measurement period covers the years 2013–2021.
Latent heat flux Qe is characterized by a distinct annual cycle with the highest values in the summer season. Average daily values of Qe from July to August were in the range of 6–10 MJ m− 2 d− 1, which is on average 60–70% of the value of the radiation balance. The relatively long measurement period showed that the evapotranspiration of the wetland surface is characterized by very high stability. The achieved values of daily as well as monthly totals during periods of drought were very close to those recorded in seasons with high precipitation. The high rate of evapotranspiration led to a decrease in groundwater levels and a significant deterioration in the water resources of the wetland environment.
Extreme weather events and natural hazards are increasingly prevalent with global warming, making it
imperative to focus on the driving factors and dynamic changes associated with drought—a disaster with
widespread impacts and significant losses. This study used a longtime-series of ERA5 reanalysis data to elucidate
the dynamic change characteristics of direct and indirect driving factors across China. Ground observation data
spanning an extended period were used to estimate the performance of the reanalysis data. The selected driving
factors for drought included precipitation (PRE), potential evapotranspiration, soil moisture (SM), bare soil
evapotranspiration (BSET), vegetation canopy evapotranspiration (VCET), subsurface runoff (SUBRO), surface
runoff (SURRO), temperature (TEM), specific humidity, and near-surface wind speed (WS). Meteorological
drought, hydrological drought, and agricultural drought were delineated using the Standardized Precipitation
Evapotranspiration Index, Standardized Runoff Index, and Standardized Soil Moisture Index. The Random Forest
method was used to ascertain the relative importance of driving factors and their main contributions to different
types of droughts. The findings revealed that ERA5 exhibited great simulation performance compared to
observational data. However, the correlation coefficient of SM might be relatively lower than that of other
driving factors across China. Notably, BSET and PRE exhibited higher relative importance degrees (RID) for
meteorological drought in subregions 1, 2, and 3. At the same time, SUBRO demonstrated a more statistically
significant influence in subregions 3, 4, and 5. SURRO played a statistically significant role in subregion 1.
Moreover, BSET and PRE were pivotal for meteorological drought in northern China, whereas VCET assumes
importance for hydrological drought in southern China and the North China Plain. Across different types of
droughts, PRE, SM, and TEM generally exhibited relatively large RID values in China. The dynamic changes in
RID for both direct and indirect driving factors varied for different drought types across different subregions of
China. Notably, the RID displayed contrasting dynamic changes between PRE and BSET for meteorological
drought in most subregions. Additionally, the degree of relative importance of near-surface WS tended to be
lower for hydrological drought across various subregions. This study contributed to a deeper understanding of
drought evolution and enhanced monitoring efforts.
Groundwater flow systems are influenced by the changes in surface waters as well as climatic factors. These teleconnections significantly increase in cases of extreme weather conditions. To prepare and mitigate the effect of such phenomena, the background factors that create and influence natural processes must be recognized. In the present study, 94 shallow groundwater (SGW) wells’ water level time series were analyzed in the inner delta of the River Danube (Europe) the Szigetköz region to explore which factors contribute to the development of diurnal periodicity of SGW and what its drivers are. The relationship between surface meteorological processes and SGW dynamics in the Szigetköz region was investigated using hourly data from monitoring wells. Hourly water temperature data exhibited weak correlations with meteorological parameters. However, daily averaged data revealed stronger correlations, particularly between SGW levels and air temperature and potential evapotranspiration. Diurnal periodicity in SGW fluctuations correlated strongly with potential evapotranspiration. The study also demonstrated the role of capillary fringe dynamics in linking surface evapotranspiration with SGW fluctuations. Changes in groundwater levels, even small, can significantly affect soil moisture, vegetation, and ecosystem functioning, highlighting the sensitivity of the unsaturated zone to SGW fluctuations driven by surface processes.
Estimating reference evapotranspiration (ETo) at 24 h timesteps has been considered sufficiently accurate for a long time. However, recent advances in weather data acquisition have made it feasible to apply hourly procedures in ETo computation. Hourly timesteps can improve the accuracy of ETo estimates, as data averaged daily may misrepresent evaporative power during parts of the day. This study investigates the differences between daily ETo computations performed at 24 h (ETo,d) and sum of hourly (ETo,h) for rice–wheat cropping systems in the Ganga Basin, India. The meteorological data were collected from an automatic weather station located in an experimental plot at IIT Kanpur, India. Daily and sum-of-hourly ETo computations were performed according to the FAO-PM equation for rice and wheat cropping seasons. Diurnal variations of meteorological variables resulted in an underestimation of ETo when the daily timestep was considered. No significant difference was observed during wet periods. The sum-of-hourly estimates of ETo were able to capture the abrupt changes in climate variables, while the daily ETo failed to represent it as it considered the average values only. As a result, the sums of hourly ETo estimates are more reliable in the Ganga Plains.
To evaluate the WaPOR model across the entire country of Iran, initially, 16 provinces located in four different climatic regions were selected for calculating and comparing the evapotranspiration using both the FAO-56 method and the WaPOR approach. The comparison of 10-day evapotranspiration values obtained from the FAO56 method and WaPOR demonstrates that the WaPOR model exhibits the highest correlation with FAO-56 values in semi-arid regions, with an R2 = 0.95 and an RMSE = 0.43. The analysis of evapotranspiration variations indicates that the evapotranspiration in the Caspian Sea and Zagros foothill regions has experienced more significant changes from 2015 to 2022. The annual analysis of net blue water productivity demonstrates that the net productivity in rainfed lands strongly depends on the precipitation received. Also, considering the importance of investigating the accuracy of biomass estimation, the correlation between the accuracy of biomass estimation and actual evapotranspiration was examined in four Iran climatic regions. Using the WaPOR model provides acceptable results for water consumption management and assessment in different regions and climates of Iran, particularly in agricultural lands. The WaPOR model can serve as a guide for determining reliable values of evapotranspiration and planning related to water resources in the agricultural sector in Iran.
In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman-Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial-and-error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy-neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input-output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass-covered lysimeters and estimations with the FAO Penman-Monteith equation. The model-estimated ET vs. lysimeter-measured ET gave a coefficient of determination (r2) value of 0.88 and a standard error of the estimate (Syx) of 0.48 mm d-1. For the same set of independent data, the FAO Penman-Monteith-estimated ET vs. lysimeter-measured ET gave an r2 value of 0.85 and an Syx value of 0.56 mm d-1.. These results show that the optimized fuzzy-neural-model is reasonably accurate, and is comparable to the FAO Penman-Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models.
A new approach to modeling canopy resistance is presented as an alternative to the Food and Agricultural Organization of the United Nations Penman-Monteith method with the constant canopy resistance. The evapotranspiration (ET) model is based on the ''big-leaf'' approach and a variable canopy resistance. The model's input requires standard meteorological data as in the Penman-Monteith combination approach. The model was validated using weather and grass lysimeter data measured on an hourly basis at Davis, Calif., and on a daily basis at Policoro, Southern Italy. ET estimates from the model were compared with the results of ET values obtained by the Food and Agricultural Organization of the United Nations Penman-Monteith approach using the constant canopy resistance r c = 70 s m 1 . The results showed a very convincing performance of the model for estimating reference ET on both an hourly and daily basis. This work confirms that the canopy resistance depends on climate, and that a variable r c is recommended for ET models. The proposed model does not introduce any empirical parameter, does not require calibration for the two sites tested or for different time scales, and it is simple enough for direct practical application.
The Bolivian Altiplano (high plateau between 3600 and 4000m a.s.l.) is one of the highest agricultural areas in the world. Due to low levels of rainfall, high evapotranspiration rate and soils with low water retention capacity, water stress is a major constraint to crop production. Under these conditions, irrigation would be an asset to reduce the increased risk for agriculture. For that purpose, reliable reference evapotranspiration (E0) estimates for the design and management of irrigation systems are necessary but not available. In this study, E0 calculated by means of the Thornthwaite, Hargreaves–Samani and FAO Penman–Monteith equations is compared with measured (lysimeters) grass crop evapotranspiration (Egrass) during the growing period (October–April) at the Bolivian Highlands. The E0 estimated by means of the FAO Penman–Monteith method agrees well with Egrass at the four locations. The temperature-based Hargreaves–Samani formula is able to estimate the reference evapotranspiration at the northern locations of the Altiplano but not at south due to the exclusion of aerodynamic factors affecting evapotranspiration. The temperature (mean temperature)-based Thornthwaite formula strongly underestimates E0 at all locations. The mean bias error (Merror) for estimates of grass crop evapotranspiration applying the Penman–Monteith, Hargreaves–Samani and Thornthwaite and compared to the lysimetric measurements, were (on average for the four locations) of −0.2, −0.4 and 2.2mm per day, respectively, when the average lysimetric grass crop evapotranspiration was 4.3mm per day for the growing season, demonstrating the suitability of the application of the FAO Penman–Monteith equation in the Altiplano. To overcome the problem of the availability of climatic parameters for the application of this equation, applications of procedures for estimating E0 by means of the FAO Penman–Monteith method with a limited data set of climatic data revealed that the difference between E0 obtained with a full and limited data set both applying this equation, is smaller than deviations resulting from the use of another method. Both the Merror and the root mean square error (Rerror) of the comparison of full and limited sets of data are less than 0.4mm per day leading to small errors in the E0 estimates. The higher deviations occur when the only available information is minimum and maximum temperature.
Procedures and guidelines are recommended for assessing integrity, quality, and reasonableness of measured weather data and equipment calibration for automated and electronic agricultural weather stations. The procedures include calculation of hourly and 24-h clear sky envelopes for solar radiation, validation of net radiation measurements using calculation equations, and evaluation of expected trends and relationships between air vapor content and air temperature. The procedures for creating clear sky solar radiation envelopes include equations to account for the effects of atmospheric water vapor content and sun angle. Procedures for adjusting air temperature and air vapor content data are introduced to compensate for the aridity of the weather station environment. All of the guidelines are simple and straightforward, and can serve as preliminary 'filters' by which to scrutinize weather measurements and as near real-time data flagging procedures for agricultural weather networks.
Comparison among commonly used reference evapotranspiration (ET) equations in the United States and the recently recommended ASCE standardized reference ET equation was made as part of the ASCE standardization effort. Analyses used hourly and daily weather data from 49 geographically diverse sites in the United States. Calculations were performed for both grass and alfalfa reference crops in a consistent manner, using weather data that passed integrity and quality assessment checks. Comparisons were made between reference ET computed by the various methods and the ASCE Penman-Monteith (PM) equation used for a daily calculation time step. In addition, calculations using hourly time steps and summed daily were compared with daily calculations for the same method as well as against the ASCE-PM method. Results showed that the ASCE standardized equation agreed best with the full form of ASCE-PM. The results provide a basis for objectively assessing the relative performance of reference ET equations in a variety of climates and support adoption of a standardized equation as recommended by the ASCE Task Committee.
Effective use of water supplies via irrigation requires good methods for determining crop water needs. To implement irrigation scheduling programs requires an accurate estimate of water use by the crop. This study was undertaken to compare different forms of the evapotranspiration (ET) equations that include Priestley-Taylor and Penman-Monteith for reference ET. To estimate actual ET, the Priestley-Taylor with an adjusted coefficient for available soil water and the Penman-Monteith with a variable surface resistance were compared to water use for grain sorghum, cotton, and grass forage at three locations: Davis, Calif.; Lubbock, Tex.; and Logan, Utah. Both models provided acceptable results; however, the Penman-Monteith model with daily meteorological data input provided more consistent results over the growing season. The Priestley-Taylor overestimated actual ET when crops were limited in soil water because the adjustment for available soil water was not sensitive to soil-water depletion. Irrigation scheduling using the Penman-Monteith model requires daily meteorological data, an estimate of the available soil water depletion, and a measurement of crop leaf area. This method would be useful for irrigation scheduling programs.
Past research on evapotranspiration has provided sound theoretical knowledge and practical applications that have been validated through field measurements. Many different approaches have been used; however, when primary concepts and standard definitions are accepted, it is possible to find reasonable agreement among methods. This paper reviews such approaches, from Penman to Penman-Monteith. The standard concepts of potential evaporation (EP) and equilibrium evaporation (E-e), and the introduction of the climatic resistance (r(e)), provide a better understanding of the role of the climate together with surface and aerodynamic resistances (r(s) and r(a)). Therefore, the concept of reference evapotranspiration (ETa), particularly the new one adopted by the Food and Agricultural Organization of the United Nations, can be better understood, as well as its limitations. Crop evapotranspiration (ETc) is related to both ETa and E-e. Crop coefficients (K-c) can be shown to have two components, alpha(o) and alpha(c), with K-c = alpha(o)alpha(c). The alpha(o) is a function of the climatic resistance and of the aerodynamic resistances of the crop and of the reference crop. The alpha(c) is a function of both surface and aerodynamic resistances of the crop and of the reference crop. From this analysis some ideas on future developments result that are directed toward providing compatibility between the one- and two-step calculation of ETc.
Hourly evapotranspiration ET o rates were measured during the irrigation season in a highly advective area in Southern Spain, using a precision weighing lysimeter. Close to the lysimeter, an automatic weather station was located to register hourly values of the most relevant climatic variables. Several methods to estimate ET o were evaluated for hourly and daily estimates. Accuracy was assessed from ordinary regression and from error analysis of the comparisons against measured values. FAO-24 methods showed a strong tendency to overestimate throughout the whole range of evaporation. Ritchie-type and Hargreaves equations had a similar behavior, underpredicting for ET o 5.3 mm day −1 and vice versa. Penman locally adjusted and ASCE-PM performed the best, followed by FAO-PM. Penman-63 also gave excellent daily estimates. The poor behavior of the Priestley–Taylor equation improved after applying the Jury and Tanner correction, but still remains at a very high noise level. With the exception of ASCE-PM and FAO-PM, the rest of the methods showed a tendency to underpredict more with increasing advective intensities. However, that tendency is very mild in the case of Penman locally adjusted.
A comparison is made between the Pruitt and Doorenbos version of an hourly Penman-type equation, the Food and Agriculture
Organization (FAO) hourly Penman-Monteith equation, and an independent measure of reference evapotranspiration (ET0) from lysimeter data. Reducing the canopy resistance improved the hourly FAO Penman-Monteith estimates. Daytime soil heat
flux density is estimated as 10% of net radiation in the FAO hourly Penman-Monteith equation; however, the measured soil heat
flux density under grass that was never shorter than 0.10 m in this study was between 3% and 5% of net radiation. The daytime
totals of hourly ET0 from the hourly Penman-Monteith and Pruitt-Doorenbos equations and ET0 from the 24-h FAO Penman-Monteith equation were computed using data from five Italian and five Californian stations. A comparison
showed that all of the equations gave acceptable results. The Pruitt-Doorenbos equation may slightly over-estimate ET0 in conditions of summertime cold air advection.
A simple method for estimating actual evapotranspiration (ET) could become a suitable tool for irrigation scheduling. Resistance models can be useful if data on canopy resistance to water vapor flow (rc) and on aerodynamic resistance (ra) are available. These parameters are complex and hard to obtain. In this studyrc is analysed for a reference crop (grass meadow). Canopy resistance is dependent on climate, weather (radiation, atmospheric vapor pressure deficit, aerodynamic resistance), agronomic practices (irrigation, grass cutting) and time scale (hour, day). Anrc model, proposed by Katerji and Perrier (KP model), using some meteorological parameters as inputs, is presented. Canopy resistance calculated according to the KP model was used to estimate a referenceET
ref on hourly and daily time scales.TheET
ref estimated using the KP model on a daily time scale was compared with a model proposed by Allen, Jensen, Wright and Burman (AJWB model) — in whichrc depends on leaf area index only — and with direct measurements from a weighing lysimeter. The results show an underestimation of 18% for the AJWB model against an underestimation of 2% for the KP model. Since the hypotheses are the same for both models and aerodynamic resistance plays a secondary role, the better results obtained by the KP model are due torc modelling.
This paper presents a study of the sensibility of the Penman-Monteith evapotranspiration model to climatic (available energy
and vapour pressure deficit) and parametric (aerodynamic and canopy resistances, r
a
and r
c
respectively) factors in a semi-arid climate, for crops in contrasting water status (well irrigated and under water stress)
and of different heights. Three experiments were carried out in southern Italy on reference grass (≈ 0.1 m), grain sorghum
(≈ 1 m) and sweet sorghum (≈ 3 m). For this analysis the sensitivity coefficients, taken as hourly means, were evaluated during
the growth season when the crops completely covered the soil. The relative errors on evapotranspiration were also evaluated
for r
a
and r
c
. The results showed that, for reference grass, available energy and aerodynamic resistance play a major role. For crops under
water stress the most important term to evaluate is canopy resistance. For a tall crop, as sweet sorghum, the role of the
vapour pressure deficit is fundamental, both when the crop is in good water status and under water stress.
¶The performance of the Penman-Monteith (PM) equation to estimate daily reference evapotranspiration (ETO) was investigated by attributing three distinct features to the canopy resistance (r
c): (i) r
c constant at 70 s m−1 (Allen et al., 1998; FAO Irrigation and Drainage Paper n. 56), (ii) r
c variable as linear function of a critical resistance r
c, depending on weather variables and empirical parameters relating r
c to r
* (Katerji and Perrier, 1983; Agronomie, 3[6]: 513–521) and (iii) r
c variable as a mechanistic function of weather variables only (Todorovic, 1999; J. Irrig. Drainage Eng., ASCE, 125[5]: 235–245). Daily weather and grass lysimeter data, measured for a period of seven years at Policoro (Southern
Italy), were used. The results confirmed the relative robustness of the PM method with constant r
c while better estimates were obtained only when variable r
c was used. The mechanistic approach of Todorovic (1999) provided the best estimates, while the approach of Katerji and Perrier
(1983), with empirically derived parameters, has shown to be not conservative enough to be extended to different locations
without calibration.
Ratios of reference evapotranspiration for alfalfa (ET r) and for grass (ET 0) are evaluated using measured ET r and ET,, from lysimeter systems at Kimberly, Idaho and Bushland, Texas. In addition, ratios are evaluated using estimates by the Kimberly Penman and ASCE Penman- Monteith evapotranspiration methods. An ET reference conversion equation from FAO-56 is also compared.The ASCE-PM and Kimberly Penman methods predict differently for both ET, and ET0 so that ratios of ET r / ET° computed from both methods behave differently Ratios of ETr / ET0 from lysimeter measurements averaged 1.15 at both locations.
The standardized ASCE Penman-Monteith (ASCE-PM) model was used to estimate grass-reference evapotranspiration (ETo) over a range of climates at seven locations based on hourly and 24 h weather data. Hourly ETo computations were summed over 24 h periods and reported as sum-of-hourly (SOH). The SOH ASCE-PM ETo values (ETo,h,ASCE) were compared with the 24 h timestep ASCE-PM ETo values (ETo,d) and SOH ETo values using the FAO Paper 56 Penman-Monteith (FAO56-PM) method (ETo,h,FAO). The ETo,h,ASCE values were used as the basis for comparison. The ETo,d estimated higher than ETo,h,ASCE at all locations except one, and agreement between the computational timesteps was best in humid regions. The greatest differences between ETo,d and ETo,h,ASCE were in locations where strong, dry, hot winds cause advective increases in ETo. Three locations showed considerable signs of advection. Some of the differences between the timesteps was attributed to uncertainties in predicting soil heat flux and to the difficulty of ET o,d to effectively account for abrupt diurnal changes in wind speed, air temperature, and vapor pressure deficit. The ETo,h,FAO values correlated well with ETo,h,ASCE values (r2 ≥ 0.997), but estimated lower than ETo,h,ASCE at all locations by 5% to 8%. This was due to the impact of higher surface resistance during daytime periods. Summing the ETo values over a weekly, monthly, or annual basis generally reduced the differences between ETo,d and ET o,h,ASCE. Summing the ETo,d values over multiple days and longer periods for peak ETo months resulted in inconsistent differences between the two timesteps. The results suggest a potential improvement in accuracy when using the standardized ASCE-PM procedure applied hourly rather than daily. The hourly application helps to account for abrupt changes in atmospheric conditions on ETo estimation in advective and other environments when hourly climate data are available.
The performance of a feedback-linearizing control for excitation
control of a synchronous generator is investigated with respect to
unmodeled dynamics of both the turbine generator unit and the
transmission network. It is found that certain types of dynamics that
were not modeled during the design of the control enter in a manner that
does affect the performance of the control, but that preserves the
linearity of the closed-loop system. Moreover, the control acts to
decouple the dynamics associated with the machine from the dynamics of
the transmission grid, thus preventing subsynchronous resonance between
the two subsystems when a series capacitor is used to compensate the
transmission line. The stability robustness of the feedback-linearizing
control is investigated with respect to a structured uncertainty. The
uncertainty considered corresponds to the spring modes of the generator
shaft and enters in such a way that analysis by Kharitonov's theorem is
feasible. It is shown that the control remains stable over a wide range
of values of the shaft parameters. A sliding control is designed and
compared to the feedback-linearizing control with respect to performance
degradation for this type of uncertainty, and it is found that, because
of the tight saturation limits on the control signal, the sliding
control offers no discernable performance advantage for this type of
structured uncertainty
Almost as soon as mankind began to wear, or at least wash, clothes, he realized that evaporation was strongly related to weather; that they dried best on sunny, windy days, particularly if the air was “fresh” and dry, with low humidity. When hydrologists first began to model natural evaporation, now more than 40 years ago, this is where they started. Weather variables measured near the ground often exhibit some statistical intercorrelation; sunny days tend to be warmer, for instance. As soon as evaporation rate could be measured, it was therefore easy to observe and formulate empirical relationships with meteorological variables, either individually or in groups. Hydrologists created a whole range of evaporation “models” as a result.
The purpose of this paper is to document the design and installation of lysimeters at Davis California during the late 1950's and early 1960's and to highlight results of some of the studies from 1957 to the present time. The studies briefly highlighted are those where prior publication of the results was restricted to in-house reports, Contract or Grant reports or remote publications. In order to document the rather wide spectrum of lysimeter-involved research at Davis, brief reference is made to results of other studies which were documented in society journals or books.
The Penman-Monteith equation is based on the assumption that the canopy can be reduced to a 'big leaf'. Given the most commonly used formulation of aerodynamic resistance (r(a)), this 'big leaf' is considered to be, implicitly, at the d + Z(oH) level (where d is zero plane displacement height and Z(oH) is roughness length for heat transfer). This can lead to negative values of surface resistance (r(s)) when the leaves of the top of the canopy (between d + z(oH) and crop height h(c)) are the ones that most contribute to total water loss to the atmosphere. To avoid this, r(a) should be computed from the top of the canopy to the reference height in the atmosphere. Also, one concludes that r(s) for complete cover crops cannot be computed by simply averaging stomatal resistance since the main condition, the driving force being the same in all of the elements of the 'circuit', is violated.
Evapotranspiration research lysimeters with 1 m2 area and 1.2 m depth were designed and constructed using commercially available cantilever load cells. These commercially available cantilever load cells. These lysimeters were operated continuously for a three-year period at the Drainage Research Farm of Utah State University. Resolution of daily measurements of evapotranspiration (Et) from the fescue/forage grass mix was generally better than 0.05 mm or 1%. Accuracy of hourly measurements of Et was influenced by numerical resolution of the data logger and slight thermal instabilities within the load cells. Lysimeter measurements agreed well with Et estimated with the Penman-Monteith method. The total cost for a two-lysimeter installation including labor was $11,000. Maintenance and operation requirements were low.
Three analytic and two emperic methods are described to calculate the surface resistance rs of crops transpiring at a measured rate. The profile method is applicable when detailed temperature, humidity, and wind profiles are measured; for the residual method, measurements of surface temperature, wind, and humidity are enough; for the heat balance method, the calculation is based on the ratio of potential to actual evaporation. For rough estimates, an empiric equation of Monteith or a relation between leaf area and surface resistance can be used. In southern England and in California all three analytic methods agree closely. Hourly values of rs in California demonstrate the effect of water stress on an irrigated grass canopy by midday, whereas in England the rs of a barley crop is maintained constant for almost the whole day. From Aslyng's measurements of evaporation, the relation of rs to soil-water potential is calculated and used to show how relative rates of transpiration and photosynthesis may change in response to water stress. For an equatorial rain forest in Kenya, mean monthly rs varied systematically with soil moisture deficit, ranging between 0.3 and 1.1, sec cm−1.
A more complete understanding of the evaporative processes and a fund of
experimental data have spurred the development of numerical modeling of
the energy transfer processes in the lower atmosphere. Numerical
simulation for weather forecasts and the prediction of the temperature
and moisture fields require accurate prediction of each of the
constituents of the energy budget. Models, though still primitive,
suggest the relative importance of the input components to demonstrate
the major role of the evaporation field in the development of the urban
heat island [Myrup, 1969] and to emphasize the major role of positive
advection in the evapotranspiration in arid and semi arid zones [Luther,
1970].
Surface energy fluxes can be evaluated from remotely sensed data using models describing the transfers of energy, mass and radiation between soil, vegetation and atmosphere.To be applied over partial canopies where the soil evaporation is comparable with that from foliage, these models need three key soil parameters: thermal inertia P, hydraulic diffusivity Dw, and limit evaporation Elim, which depend on the soil type and on its moisture and three key vegetation parameters; height of vegetation h, leaf area index L, and minimum leaf resistance to transpiration rs,min which depend on the type and on the physiology of the vegetation.In this paper, we propose a methodology to estimate these parameters. The soil parameters are fitted during a period of bare soil. The vegetation parameters, h and L may be estimated using remote sensing in the optical bands and mean biophysical relationships. The single parameter which remains to be estimated is rs,min. Assuming that rs,min is constant during the growing period, it is adjusted during a period of dense canopy where the contribution of soil to the total evaporation is negligible. This method is tested using the Special Observing Period (SOP) (7 May–15 July 1986) of the international HAPEX-MOBILHY experiment where the sites ranged from bare soils to dense covered areas.It is shown that the model is able to reproduce the surface energy flux components over both dense and partial canopies, and that the partition between soil evaporation and foliage transpiration can be made, leading to the monitoring of global vegetation stomatal resistance through the growing season.
Concurrent observations of leaf water potential, stomatal diffusion resistance and canopy temperature were made on two plots of wheat (Triticum aestivum L. cv. Anza) growing at Phoenix, Arizona under two different soil water conditions. These data were further complemented by weather observations and lysimeter measurements of total evaporative water loss from the plots. Transpiration fluxes for each plot were estimated by an aerodynamic-energy balance approach and compared with the lysimeter data. Plant resistances were computed from the transpiration flux results and the leaf water potential measurements using van den Honert's equation, while canopy resistances were also computed from the transpiration flux using Monteith's equation. The calculated plant resistance decreased by a factor of almost two from morning to mid-afternoon whereas the ratio of canopy and stomatal resistances was constant during most of the day.
Many forms of the Penman combination equation have been proffered for estimating daily evapotranspiration (ET) by the agricultural reference crops grass and alfalfa (Medicago sativa L.). This study was conducted to evaluate popular forms of the Penman equation, and to develop and evaluate general relationships for estimating daily average values of canopy and aerodynamic resistance parameters required by the Penman-Monteith equation. For simplicity and ease of use, resistance relationships were expressed as linear and logarithmic functions of mean plant height. The Penman-Monteith and other forms of the Penman equation were compared at 11 international lysimeter sites, with the Penman-Monteith method and a Penman equation with variable wind function developed at Kimberly, ID providing the best estimates of reference ET across the sites. Ratios of computed alfalfa to grass reference ET during peak months at various locations averaged 1.32, and ranged from 1.12 to 1.43. Values of computed ratios were related to local wind and humidity conditions. The development of relationships for canopy and aerodynamic resistances as functions of reference crop height allowed use of the Penman-Monteith equation in an operational mode, and improved transferability of this resistance form of the Penman equation to a wide variety of climates.
This investigation was supported by the Utah Agric. Exp. Stn.
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Analytical expressions which specify non-dimensionalized wind speed and
potential temperature gradients as functions of stability are
integrated. The integrated equations are tested against Swinhank's wind
and temperature profiles measured at Kerang, Australia. It is found that
a representation suggested independently by Businger and by Dyer gives
the best fit to temperature profiles and describes the wind profiles
equally as well as a relation suggested by Panofsky et al.
The diabatic mean profile forms in the surface layer are studied, by applying analysis methods having high resolving power to data from O'Neill, U.S.A. (heights up to 6.4 m) and from Kerang and Hay, Australia (heights mostly up to 16 m).
It is found, concordantly from the O'Neill and Australian data, that the log‐linear law is valid for z/L values between — 0.03 and + 1, which includes a small range of unstable and a surprisingly wide range of stable conditions. For all quantities studied (wind, potential temperature, and specific humidity), it is concluded that the Monin‐Obukhov coefficient α is near 4.5 in unstable and 5.2 in stable conditions, within a standard error of about 10 per cent. The ratios K H /K M and K W /K M evidently remain constant, equal to unity, over the whole of the log‐linear range (and somewhat beyond).
In stable conditions, the log‐linear law implies that Ri approache