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Reference evapotranspiration (ETo) is often calculated using the Penman-Monteith (FAO 56 PM; Allen et al 1998) method, which requires data on temperature, relative humidity, wind speed, and solar radiation. But in high-mountain environments, such as the Andean páramo, meteorological monitoring is limited and high-quality data are scarce. Therefore,...
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... meteorological data for this study came from 2 automatic weather stations, both located in the high- elevation páramo of Ecuador: 1 in the Zhurucay river basin (79.24uW; 3.06uS; 3780 masl) on the Pacific side of the Andes, from which we obtained 2 years of data (March 2011- February 2013, and 1 near Toreadora Lake (79.22uW; 2.78uS; 3979 masl) on the Atlantic side, from which we obtained 1 year of data (2013) (Figure 2). At each site, temperature, relative humidity, wind speed, and solar radiation were recorded every 5 minutes. ...
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... evaluation of the Hargreaves method showed that it overestimated ET o , as it usually does under humid conditions (Gelcer et al 2010). For the case when only temperature data were available, it performed slightly better than the FAO 56 procedure (Table 3) but still yielded a poor result. ...
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O método de Penman-Monteith (PM-FAO 56) é recomendado como padrão para a estimativa da evapotranspiração de referência (ETo). Porém, este método requer um grande número de dados de entrada como radiação solar, temperatura do ar, velocidade do vento e umidade relativa, o que tem limitado sua aplicação em muitos locais. Baseado nisto, com o presente...
Reference evapotranspiration (ET0) explains the climatic effects on crop water demand. The Food and Agriculture Organization (FAO) recommends the Penman Monteith equation as a standard method for estimating ET0. However, because this equation requires a large amount of meteorological data, it has limited application. An alternative is the Hargreave...
Estimation of reference evapotranspiration (ETo) is very relevant for water resource management. The Penman-Monteith (PM) equation was proposed by the Food and Agriculture Organization (FAO) as the standard method for estimation of ETo. However, this method requires various weather data, such as air temperature, wind speed, solar radiation and rela...
Evapotranspiration (ET) is an indispensable component of surface energy and water balance. Accurate ET estimations play significant roles in the study of global climate change, environmental evolution, and water resource utilization. The FAO Penman-Monteith equation is the most common method for estimating reference crop evapotranspiration (ET). Ho...
The aim of this study was to investigate climatological aspects (characterization) in seasonal potential evapotranspiration east of Acre, with the unit of study is the city of Rio Branco, capital of the State, considering a period of 34 years from 1980 to 2013, from monthly data from a weather station in a conventional surface. The methodology was...
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... Moreover, climate projections indicate an increase in the heaviest events caused by global warming that vastly exceeds the increase inferred from the Clausius-Clapeyron relationship (Allan et al. 2010), and that the higher amount Responsible Editor: Clemens Simmer, Ph.D. of rain falling as extreme precipitation will be dominated by changes in frequency rather than changes in intensity (Myhre et al. 2019). Within the tropics, a region that historically has been poorly monitored (as mountain regions worldwide) (Córdova et al. 2015) so that the understanding of extreme precipitation events is limited are the tropical Andes. In this context, a better understanding of the dynamics of extreme precipitation events in the tropical Andes is urgently needed to improve the accuracy of forecasting (Villalobos-Puma et al. 2022) and the development of climate change adaptation measures for this vulnerable region. ...
Extreme precipitation events are a global threat to human wellbeing, they are the main cause of natural disasters such as flooding and landslides. Floods are particularly harmful because they can trigger infectious disease outbreaks, cause damage to essential infrastructure, impact food and water security, and even affect the mental health of the population. Climate change has caused an intensification of extreme precipitation events globally in the last decades, including the tropics, which receive more than half of the global precipitation. In this context, the understanding of the climate dynamics related to extreme precipitation events is vital for climate change adaptation. Such understanding has been particularly limited in the tropical Andes, primarily because of the paucity of data in the region. In the present study, we used data from a weather radar installed at 4450 m a.s.l. in the southern Ecuadorian Andes and ERA5 reanalysis to examine the large-scale dynamics associated with extreme precipitation events in the tropical Andes during the 2015–2022 period. We found that extreme rainfall in the Interandean valley is connected to local positive near surface temperature and CAPE anomalies that extend to the eastern slopes of the Andes, which cause strong thermal convection and intense afternoon events. On the other hand, extreme rainfall over the western slopes and the coastal plains is associated with El Niño-like conditions that produce Mesoscale Convective Systems that survive well after sunset and cause extreme nighttime precipitation. The present study allowed us to reveal the connections between large-scale dynamics and extreme precipitation for the first time in the southern Ecuadorian Andes. The outcome of the present study could be useful in future research to improve forecasting of extreme precipitation events and early warning systems in the region.
... In this study, SR was highly influential (approximately 90.5%) for estimating PMC, which agrees with [8], who point out that SRg in Sinaloa is decisive for the estimation of PMC. According to results of PMC vs PMO (Figure 5b), Equations 1-10 were reliable and sensitive for estimating PMC, even when the series presented missing data [9,48,49]. The results of Figure 5c and according to [19,22,26,27], the models of this study are also reliable and sensitive for predicting PMR. ...
The goal of this study is to create regression models estimating the daily Penman–Monteith reference evapotranspiration (PMR) using latitude–temperature data for the state of Sinaloa, Mexico. Daily series of minimum–maximum temperature (1979–2017) were obtained from seven weather stations in Sinaloa. The reference evapotranspiration was calculated by the methods of Penman–Monteith using empirical equations (PMC), Hargreaves (HAC), and PMR. Prior to calculating PMC, the incident solar radiation (SR) was calculated. From the Acaponeta station (2005–2008, 2011–2013 and 2015–2017), all complete observed variables were obtained: mean temperature, incident solar radiation (SRg), average relative humidity and average wind speed at a height of 10 m. The data from the eight weather stations were provided by the National Meteorological Service and the National Water Commission. The daily observed Penman–Monteith reference evapotranspiration (PMO) was calculated. For validation, three simple linear regressions (SLR) were applied: SR vs SRg, PMC vs PMO and PMR vs PMO. hypothesis tests were applied to each SLR: Pearson correlation (Pr) vs critical Pearson correlation (Pcr). All rP were significantly different from zero (> |0.576|): SRg vs SR (Pr = 0.951), PMC vs PMO (Pr = 0.592), and PMR vs PMO (Pr = 0.625). This study provides new models that can motivate and support the design and implementation of intelligent irrigation in the state considered “the breadbasket of Mexico.”
... The PET data was calculated using the Penman-Monteith method as detailed in Moeletsi et al. (2022). The Penman-Monteith method assumes a well-watered reference crop with fixed characteristics (Córdova et al., 2015;Mashabatu et al., 2023). These fixed characteristics assume a reference crop, which Allen et al. (1998) defined as a hypothetical short grass with a height of 0.12 m, a surface resistance of 70 s/m and an albedo of 0.23. ...
The impact of climate variability and extreme weather events on agricultural productivity in arid environments has become a focal point in contemporary research. Monitoring crop water productivity (CWP) is critical and urgently required especially in the arid regions where agriculture consumes an above-average portion of the available fresh water resources. In this context, this study aimed to demonstrate the utility of remotely sensed data in assessing CWP and water use dynamics across diverse crop types in South Africa during the El Niño (2018/19) and non-El Niño (2021/22) events. In addressing the objective, the study also assessed the intra-and inter-annual variations in crop water productivity for diverse crop types including, grains, grapes, citrus fruits, teas, planted pastures, and oil seeds. The study used potential evapotranspiration and biomass derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to estimate CWP from 2017 to 2021. This period included El Niño (2018/19) and non-El Niño (2021/22) years. The results showed that potential evapotranspiration (PET) derived from MODIS was related to the PET estimated from weather stations (R 2 > 0.6; RMSE < 21.90; p-value < 0.001). In terms of water use, planted pastures had the highest water use 114 mm/ month), while teas and citrus fruits had the lowest water use (6 mm/month). Citrus fruits, grapes and teas consistently had the lowest annual mean crop water productivity (<0.02 kg/m 3 /annually), while oil seeds had the highest annual mean crop water productivity (>0.1 kg/m 3 /annually). Lastly, there were no significant differences (p-value > 0.05) between the CWP for all the crops observed between El Niño (2018/19) and non-El Niño (2021/22) periods, suggesting the effectiveness of adaptation measures and interventions during this period. These results provide a simple, spatially explicit framework, relevant to understanding crop-water use, laying the groundwork for informed decision-making and sustainable agricultural practices. Integrating these findings into policy frameworks and agricultural strategies is paramount for ensuring food security and resilience in a changing climate.
... Yang et al. [15] conducted a comparison between the HS equation and RPM (T), concluding that the HS equation outperformed RPM in subtropical regions. Similarly, Córdova et al. [45] reported that the HS equation performed slightly better than RPM (T), but still yielded an unsatisfactory result. ...
... The results in Table 5 indicate that accurate ET 0 estimates can still be achieved even without u 2 . Therefore, as mentioned by Córdova et al. [45], the absence of u 2 was not a major source of error in humid climates. The role of u 2 in ET 0 calculation is subject to two main perspectives: some argue that it is a decisive factor due to the potential for measurement errors, while others contend that u 2 does not have much impact on ET 0 [17]. ...
... Even though the RPM approach estimates the absent R s information through Equation (11), its performance remains unsatisfactory. Córdova et al. [45] observed that estimating R s based on Equation (11) yielded poor results in humid condi-tions for the RPM approach. Sentelhas et al. [12] discovered that when the actual R s falls below 20 MJ/m 2 /day, Equation (11) tends to systematically overestimate radiation. ...
The reference evapotranspiration (ET0) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET0 calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years of meteorological data from 18 stations in four geographic regions of Taiwan to evaluate the effectiveness of an artificial intelligence (AI) model for estimating PM-calculated ET0 using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET0 forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET0 estimation. The artificial neural network using temperature, solar radiation, and relative humidity as input variables performed best, with the correlation coefficient (r) ranging from 0.992 to 0.998, mean absolute error (MAE) ranging from 0.07 to 0.16 mm/day, and root mean square error (RMSE) ranging from 0.12 to 0.25 mm/day. For short-term ET0 forecasting, the long short-term memory model using temperature, solar radiation, and relative humidity as input variables was the best structure to forecast four-day-ahead ET0, with the r ranging from 0.608 to 0.756, MAE ranging from 1.05 to 1.28 mm/day, and RMSE ranging from 1.35 to 1.62 mm/day. The percentage error of this structure was within ±5% for most meteorological stations over the one-year test period, underscoring the potential of the proposed models to deliver daily ET0 forecasts with acceptable accuracy. Finally, the proposed estimating and forecasting models were developed in regional and variable-limited scenarios, making them highly advantageous for practical applications.
... The mean air temperature is 6°C at 3780 m.a.s.l. (Cordova et al., 2015) and primarily constant throughout the year (Crespo et al., 2011). The annual precipitation is 1345 mm (Mosquera et al., 2016a), but fog and drizzle interception account for an additional 15% (Padrón et al., 2015). ...
... Reported runoff coefficient values of 0.68 (Mosquera et al., 2015) and 0.8 (Correa et al., 2016) suggest the catchment is highly responsive to rainfall events. Annual reference evapotranspiration is 732 mm (Cordova et al., 2015), representing about 40% of the yearly precipitation volume (Mosquera et al., 2015). ...
Highly conductive topsoils in neotropical high-elevation grassland-dominated ecosystems, or so-called paramos in the Andean region, influence the local rainfall-runoff processes predominated by saturation-excess overland flow as the primary source of freshwater. The Soil and Water Assessment Tool (SWAT) model has shown limitations when applied to mountainous catchments with highly conductive soils that generate surface runoff as saturation-excess overland flow. In this study, we enhanced SWAT to simulate runoff as saturation-excess overland flow and examined the hydrological responses of an intensively monitored paramo catchment in Ecuador. The model setup considered a detailed representation of the hydro-physical properties of the soils at different depths, including high infiltration and lateral flow rates in the hillslopes and restricted groundwater interactions, a characteristic of the páramo catchments. SWAT reasonably reproduced the daily discharge during dry and wet periods and the cumulative occurrence of high and low flows. The performance metrics NSE, RSR, and PBIAS values during calibration/validation period were 0.86/0.84, 0.31/0.4, and −11.2/-7.58, respectively. The runoff ratio and partitioning of the total runoff into the lateral flow and surface runoff were physically meaningful. More significantly, SWAT was able to simulate saturation-excess overland flow, which is dominant compared to infiltration excess, and it is a distinctive characteristic of páramo catchments. Nevertheless, the model showed limitations in simulating low flows.
... However, since the PM-FAO56 method requires several input variables, which are not available in all CWS and/or GCD and reanalysis datasets (Oudin et al. 2005;Bormann 2011;Trajkovic et al. 2019;Monteiro et al. 2021), it is necessary to choose a PET method that is both accurate and requires widely available input data, e.g., air temperature (Oudin et al. 2005;Almorox et al. 2015;Aschonitis et al. 2022;Martins et al. 2022). In this sense, many studies have compared PET or ET o estimation methods worldwide for different locations and climatic conditions, given the importance of ET for several purposes, e.g., water use management and climate studies (Pereira et al. 2015;Lacerda and Turco 2015;Córdova et al. 2015;Venancio et al. 2019;Trajkovic et al. 2019;Monteiro et al. 2021;Hu et al. 2021;Hebbalaguppae Krishnashetty et al. 2021;Li et al. 2022b). Since Brazil has a great territorial extension, the contrasting landscape in climatic and topographical terms, and presents limited spatial weather station density (Gotardo et al. 2016;Gurski et al. 2018aGurski et al. , 2018bMonteiro et al. 2021), to estimate PET or ET o through simple and accurate methods is fundamental. ...
We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series.
... Gudmundsson et al. (2016) demonstrated, using probability and statistical analysis, that the sensitivity of water availability to the FAO AI is especially strongest for humid regions. The PET component is usually estimated by the FAO recommended Penman-Monteith equation (FAO 56 PM), computed from four meteorological variables: temperature, relative humidity, wind speed, and solar radiation, found to yield good estimations for a wide range of ecosystems (Córdova et al., 2015;Garcia et al., 2004;López-Urrea et al., 2006;Xing et al., 2008). A major bottleneck, however, is the substantial data requirements which are often unmet due to data scarcity, thus poses application challenge (Stöckle et al., 2004;Trajkovic and Kolakovic, 2009;Li et al., 2012;Rahimikhoob et al., 2012). ...
... Accurate and consistent estimates of reference evapotranspiration (ETo) in irrigated agriculture are important for planning, design, operation, management and efficient utilization of irrigation and water resources to optimize crop production. Determination of ETo is function of availability of climate input data [1]. In many places in general and for greenhouses in particular determination of (ETo) is constrained by lack of daily data of maximum (Tmax), minimum (Tmin) air temperature, solar radiation (Rs), average relative humidity (RH avg), and wind speed (U2). ...
Estimation of reference evapotranspiration (ETo) using FAO-56 Penman-Monteith method (PM) in greenhouses is critically needed for irrigation scheduling, water management, and design of their irrigation system. Such estimation is confronted, in many cases, by the unavailability of adequate and precise climatic input data. Hence, this study analyzes the sensitivity of estimating PM-reference evapotranspiration (ETo) to climatic variables in greenhouses with the objective of minimizing the effort in their precise collection without significant loss of information. Therefore, it is assumed that assessing the FAO guidelines to compute ETo when meteorological data are missing could lead to a better understanding of which variables are critically important for reliable estimates of ETo. The needed meteorological inputs in greenhouses on daily basis are the maximum (Tmax), minimum (Tmin) air temperature, solar radiation (Rs), average relative humidity (RH avg), and wind speed (U2 at 2 m height). However, Rs may be predicted from temperature data. Sensitivity analysis in this study is performed by changing (increasing and decreasing) each one of these climate variables by one unit of (10% increment and decrement) for ten cases. Inside house climate data was measured from nine greenhouses in three areas with three houses per areas around Khartoum-Sudan (El Alafoon, Halfaya, and Shambat) for a period of three months in each house. Data where taken at ten day per month (every other three days) at three times per day (morning, mid-noon and evening). Sensitivity of each climate variable to predict ETo was assessed using descriptive statistics (standard deviation-std, coefficient-CV%, and t-test), regression coefficient-r2, slope and Christiansen uniformity of distribution (Ed). The results showed that the change in (ETo) is linearly related to change in all climate variables (r2 = 0.94) except wind speed (U) (r2 = 0.46 to 0.68) at all years. Further, According to relative dispersion (CV %), Std. and t-test ETo is significantly sensitive to (Tmax), (Tmin) followed by (RH) and least sensitive to (U) at all years. This result imply that determination of ETo inside greenhouse require more accuracy in determining Tmax, Tmin, RH than Rs and U2 which can be estimated with reduced precision.
... Monthly evapotranspiration (ET o ) is calculated using the FAO Penman-Monteith equation as shown in Eq. (1) [20]: ...
In South Sulawesi, the development of irrigation may be hindered by the continuing limitations and inadequacies of hydrological data. It is well known that the rainfall monitoring station is more extensive than the river flow monitoring station. Therefore, the Malino Catchment Area was selected to illustrate the theory of four tank components. The 1st tank (tank A) has two horizontal outlets ( Q a 1 and Q a 2 ) and one vertical outlet ( I a ); the 2nd tank (tank B) has one horizontal outlet ( Q b ) and one vertical outlet ( I b ); the 3rd tank (tank C) has the same conceptual structure as tanks A and B; and the 4th tank (tank D) has only one horizontal outlet ( Q d ). To ensure that the tank model represents vertical and horizontal flows in a watershed region, the flows ( Q a 1 , Q a 2 , Q b , Q c , and Q d ) are predicted to accumulate in one flow, more or less, and must equal the measured discharge ( Q o ) at the specified time. Rainfall and evapotranspiration data are required to calculate this model. The 264.55 km ² (25902 ha) research area has an elevation range of 400–2400 masl. The findings for land use are dominated by plantations (41.01%), forests (40.79%), rice fields (15.44%), and residential areas (0.96%). In the calibration of the tank model, R ² is evaluated at 0.560% (good) and Nash–Sutcliffe efficiency is evaluated at 0.526% (good) to ensure that the model can represent the distribution of water flow components. Additionally, the measurements for the total water flow ( Q total ) were 13702 m ³ /y with a total rainfall of 3996 mm/y. Furthermore, surface flow accounts for 77.26% of the total runoff water, while intermediate flow accounts for 20.25%.
... Based on these calculations, multiannual averages were estimated. The multiannual average monthly effective evapotranspiration during the rainy period (ET) was calculated using the Penman-Monteith equation [31] for several meteorological stations. Evapotranspiration values at the Karney Shomron station located at 325 m ASL for the Te'enim catchment and at the Harasha station located in the Dolev region near Talmon at 770 m ASL for the Natuf catchment were used for groundwater recharge estimation. ...
Climate change and anthropogenic development considerably influence groundwater resource distribution and conditions. Catchment basin groundwater recharge—discharge computation reliability is needed for effective groundwater management policy formulation and implementation and also for resolving environmental challenges in such a watershed. This paper compares groundwater recharge patterns between urbanized and nearly natural small catchment basins of Israel’s Western Mountain Aquifer (WMA). The correlation between precipitation volumes and surface runoff shows that surface runoff volume constitutes 3–4% of the precipitation volume in the Natuf catchment and 1–2% in the Te’enim catchment. These assessments reflect the differences in the land use, outcrop lithology, topography and hydrodynamic properties of the WMA within the model basins. A groundwater recharge assessment based on water balance and water table fluctuation methods was performed for the mountainous karstic Te’enim and Natuf catchment basins for all the available data from 2000 to 2020. The water balance method provided reliable estimates. The groundwater recharge assessment considered land use classification and climate changes during this period. The average multiannual groundwater recharge values for the 2000–2021 period varied from 17.6 × 106–24.8 × 106 m3 to 24.5–29.2 × 106 m3 for the Te’enim and Natuf catchment basins, respectively. For the relatively dry period of the 2013/2014–2017/2018 hydrological years when detailed measurements of the surface runoff were available, the corresponding groundwater recharge volumes were 17.6 × 106 m3 and 24.5 × 106 m3. The corresponding local groundwater recharge coefficients constitute 0.46–0.57 for the mostly agricultural Te’enim basin and 0.29–0.32 for the urbanized Natuf basin. A significant difference in the groundwater recharge coefficients between the studied catchments is caused mostly by the differences in land use. It is suggested that applying such a groundwater recharge estimation for small hydrological sub-basins can improve one’s understanding of the groundwater recharge distribution within a major basin, enabling the application of an accurate regional hydrogeological model that may be extrapolated to other similar regions.
