Potential Evapotranspiration Model Research Articles

Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis

Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R2 = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Potential evapotranspiration (PET) is a crucial component of the hydrological cycle and energy balance. Although the Penman-Monteith (PM) model is the most widely used method to estimate daily PET, it requires temperature, relative humidity, solar radiation, and wind speed. In Thailand, the number of potential weather stations to provide the required data is limited, which resulted in the absence of some input variables in many locations. The objective of this study is to develop the revised potential evapotranspiration (RPET) model to estimate daily PET using Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and temperature data. The multiple linear regression analysis was used to develop and validate the RPET model. The performance of the RPET model along with the Global Land Evaporation Amsterdam Model (GLEAM v3.2 b) and the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5-Land) products was investigated using the PM model. The results revealed that the RPET model showed a strong correlation with the PM model (r = 0.85, RMSE = 0.97 mm day−1, RSR = 0.53, NSE = 0.72) under limited meteorological inputs. The RPET model performance was superior when compared to GLEAM and ERA5-Land (r = 0.80, RMSE = 1.06 mm day−1). Therefore, the proposed model is greatly suitable for daily PET estimation with only required GNSS-PWV and temperature data, and this can be implemented for drought assessment and water resources management.

A physically-based potential evapotranspiration model for global water availability projections

When the projections from the hydrological models conflict with those from the fully-coupled climate model, the vegetation appears to be responsible for the divergence. In the presence of an elevated concentration of atmospheric CO2 (Ca), the leaf-level stomatal conductance (gsl) tends to decrease thereby reducing water demand, and such effects can be partly offset by the increased leaf area (generally represented by leaf area index, LAI) simultaneously. Nonetheless, the impacts of vegetation are rarely explicitly quantified in existing potential evapotranspiration (EP) models. In this study, we proposed a new EP equation that combines a well-established gsl model with a simple bulk upscale model. The new model allows us to investigate the effects of elevated Ca-induced physiological and structural changes in vegetation on EP estimations. The results show that, compared to conventional EP models (e.g., the Penman-Monteith reference crop model), the new EP equation performs better in capturing long-term EP changes. Furthermore, we embedded the new EP equation in a simple hydrologic model, the Budyko framework, to estimate the changes in long-term runoff. By accounting for both physiological and structural responses of vegetation, estimated runoff by the Budyko framework shows a significant increase of ∼ 30 mm/year in 2071–2100 compared to 2006–2035, consistent with projections from fully-coupled climate models. The increase in LAI offset ∼ 30% of the water-saving caused by stomatal closure on a global average. The newly developed EP model provides a fully transparent and easy-to-use way for describing the impacts of climate change-induced vegetation responses on EP and can be effectively employed to detect long-term hydrological changes.

Overestimated global dryland expansion with substantial increases in vegetation productivity under climate warming

Drylands are serviced as an essential component of the earth’s ecosystem. The potential changes in dryland areas are of great importance to the environment, but various debates remain as to whether and to what extent drylands are expected to expand. Here we employ a physically-based potential evapotranspiration (E P) model accounting for vegetation response to climate change to quantify potential changes in dryland areas, on the basis of a commonly used indicator, aridity index (multiyear mean E P over precipitation). Results show that by the end of this century, drylands will expand slightly by ∼5%, while vegetation productivity will increase by ∼50%. Elevated CO2 slows down the increase rate of E P that impedes the expansion of drylands, but greatly promotes vegetation growth with increases in both leaf assimilation and canopy foliage. These findings improve our understanding of the potential changes in dryland and their ecological impacts in a warmer climate.

Assessment of the Priestley-Taylor coefficient and a modified potential evapotranspiration model

The Priestley–Taylor α coefficient was adjusted to the surrounding environment in a Mediterranean greenhouse. The estimated values of α were 0.86 and 0.72 in greenhouse compartments equipped with a forced air ventilation system or wetted evaporative-pad system, respectively. The proposed α coefficient values observed were not equivalent to typical values recommended elsewhere. Furthermore, hourly sensitivity of coefficient α shows that potential evapotranspiration model computations should be applied with caution over time-intervals of a day. The validation of the proposed potential evapotranspiration model indicated a high correlation coefficient (r2=0.99) between forecast and observed cucumber evapotranspiration rates. Considering the paucity climatological data in typical plastic Mediterranean greenhouses, the proposed modified potential evapotranspiration model based on leaf temperature could be used as a practical method to optimize the irrigation scheduling.

Estimation of Potential Evapotranspiration across Sri Lanka Using a Distributed Dual-Source Evapotranspiration Model under Data Scarcity

Evapotranspiration estimations are not common in developing countries though most of them have water scarcities for agricultural purposes. Therefore, it is essential to estimate the rates of evapotranspiration based on the available climatic parameters. Proper estimations of evapotranspiration are unavailable to Sri Lanka, even though the country has a significant agricultural contribution to its economy. Therefore, the Shuttleworth–Wallace (S-W) model, a process-based two-source potential evapotranspiration (PET) model, is implemented to simulate the spatiotemporal distribution of PET, evaporation from soil (ETs), and transpiration from vegetation canopy (ETc) across the total landmass of Sri Lanka. The country was divided into a grid with 6 k m × 6 k m cells. The meteorological data, including rainfall, temperature, relative humidity, wind speed, net solar radiation, and pan evaporation, for 14 meteorological stations were used in this analysis. They were interpolated using Inverse Distance Weighting (IDW), Universal kriging, and Thiessen polygon methods as appropriate so that the generated thematic layers were fairly closer to reality. Normalized Difference Vegetation Index (NDVI) and soil moisture data were retrieved from publicly available online domains, while the threshold values of vegetation parameters were taken from the literature. Notwithstanding many approximations and uncertainties associated with the input data, the implemented model displayed an adequate ability to capture the spatiotemporal distribution of PET and its components. A comparison between predicted PET and recorded pan evaporations resulted in a root mean square error (RMSE) of 0.75 mm/day. The model showed high sensitivity to Leaf Area Index (LAI). The model revealed that both spatial and temporal distribution of PET is highly correlated with the incoming solar radiation fluxes and affected by the rainfall seasons and cultivation patterns. The model predicted PET values accounted for 80–90% and 40–60% loss of annual mean rainfall, respectively, in the drier and wetter parts of the country. The model predicted a 0.65 ratio of annual transpiration to annual evapotranspiration.

Development of random forest model as decision support tool in water resources management of Ogun headwater catchments

Water resources cannot be effectively managed unless potential evapotranspiration is determined with high accuracy at headwater catchments. The study presents the most suitable feature combinations for building a reliable potential evapotranspiration (PET) model in the headwater catchments of Ogun River Basin, Southwest Nigeria. Using rainfall (R), wind speed (U2), sunshine hour (S), relative humidity (Rh), minimum temperature (Tmin) and maximum temperature (Tmax) as input features, a Random Forest (RF) model was developed to predict PET. Although the model yielded satisfactory results, it was subjected to the minimal depth and percentage increase in mean square error (%IncMSE). This was done to reduce the input features and to increase model accuracy. Thereafter various combinations of important input features were examined in order to establish the best combinations required to yield optimum results. The study revealed that although Tmax (%IncMSE of 652.09, p value < 0.05) and Rh (%IncMSE of 254.36, p value < 0.05) were the most important predictors of PET, a more reliable RF model was achieved when S and U2 were combined with them. Consequently, this study presents RF with a combination of four parameters (Tmax, Rh, S and U2) as an excellent computational technique for the prediction of PET in headwater catchments.

Estimating future daily pan evaporation for Qatar using the Hargreaves model and statistically downscaled global climate model projections under RCP climate change scenarios

Evaporation plays a significant role in the hydrologic cycle, and thus the estimation of evaporative demand is crucial for water resources management and irrigation scheduling. Evaporation is subjected to the effects of global climate change, which can be quantified by the representative concentration pathway (RCP) scenarios. However, forecasts of future daily pan evaporation under RCP climate change scenarios using a temperature-based pan evaporation model and downscaled temperature forecasts for an arid region (e.g., Qatar) have rarely been reported in the literature. The present study provides estimations of future daily pan evaporation in Qatar under four RCP climate change scenarios for the period January 1, 2018 to December 31, 2050. Modelled maximum and minimum daily temperature data from a general circulation model (GCM) within the Coupled Model Intercomparison Project Phase 5 (CMIP5) experimental framework are extracted and downscaled using the Quantiles-matching approach. A pan evaporation model coupling the Hargreaves potential evapotranspiration model and a Class A pan coefficient model is calibrated against observed pan evaporation data and utilized to estimate future daily pan evaporation in the study area using downscaled daily temperature. Various measures of fit are employed to evaluate the performance of the downscaling approach and pan evaporation model, and the results show that the performance of the downscaling approach and pan evaporation method are both satisfactory. The study also finds that the pan evaporation increased fastest under the RCP60 scenario, and the average increase in pan evaporation was roughly 0.01095 mm/day per year. The study provides datasets for future pan evaporation for the period between 2018 and 2050 that can be useful for the development of numerical hydrologic models, management of water resources, and irrigation scheduling.

Surface Energy Budgets of Arctic Tundra During Growing Season

AbstractThis study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.

An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China

A modified form of the distributed Grid-Xinanjiang model (GXAJ) characterizing the infiltration excess and saturation excess runoff mechanisms coupled to a two-source potential evapotranspiration model (TSPE) was proposed to simulate the hydrological process and study the spatiotemporal pattern of the precipitation, evapotranspiration, and soil moisture in the Jinshajiang River basin. In the flow routing module, the flow is routed by the physically nonlinear Muskingum–Cunge method. The TSPE model can calculate the spatiotemporal variation of the potential canopy transpiration (CT), interception evaporation (IE), and potential soil evaporation (SE). Subsequently, the calculated potential evapotranspiration (PE) is coupled to the GXAJ model to calculate the water budget in each grid. An a priori parameter estimation was developed to obtain the spatially varied parameters from geographical data, including digital elevation model (DEM) data, soil data, vegetation data, and routing data. Hydrometeorological data were interpolated to 4750 grids with cell sizes of 10 km × 10 km by the Thiessen Polygon method. The DEM data was used to extract the flow direction, river length, hillslope, and channel slopes and to adjust the altitude-related meteorological variables. The reprocessed Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) from the Beijing Normal University (BNU) dataset, which has a spatial resolution of 1 km × 1 km, was used to obtain the spatiotemporal variation in the LAI. The developed GXAJ model was applied to three sub-basins in the Jinshajiang River basin and was compared to the traditional GXAJ model. The developed GXAJ model satisfactorily reproduced the streamflow at each catchment outlet and matched the peak discharges better than the traditional GXAJ model for both the dry and wet seasons. The uneven distribution of the simulated mean annual evapotranspiration in the whole watershed was closely related to the vegetation types, ranging from 189.81 to 585.45 mm. Forest and woodland, shrubland, grassland, and cropland were shown to have mean annual evapotranspiration values of 485.6, 289.4, 275.9, and 392.3 mm, respectively. The ratios of the annual evapotranspiration to precipitation (E/P) of the forest, woodland, shrubland, grassland, and cropland were 54, 83, 53, and 48%, respectively.

SENSITIVITY ANALYSIS OF A FAO PENMAN MONTEITH FOR POTENTIAL EVAPOTRANSPIRATION TO CLIMATE CHANGE

Sensitivity of the FAO Penman-Monteith (FPM) potential evapotranspiration (PET) model under tropical climates has been studied in the present study. A total of 17 meteorological stations covering Peninsular Malaysia starting from 1987-2003 were used as model inputs. A sensitivity analysis (SA) was carried out using the graphical method for temperature, wind speed and solar radiation within the possible range of ±20% with increments of 5%. From the comparison done on the sensitivity of PET to climatic change, the Kuala Krai station gave the highest percentage change in terms of temperature (±6%). The highest percentage change for wind speed (±2%) and solar radiation (±17%) were shown at the Alor Setar and Kuala Krai stations, respectively. The Alor Setar station had the lowest percentage change for temperature (±0.3%) and solar radiation (±9.9). The lowest percentage change of wind speed (± 0.2%) was observed at the Kuala Krai station. PET percentage changes have a positive correlation to the percentage change of all climatic variables except for the Cameron Highlands station. Results revealed that solar radiation has the most significant effect on PET (±14%), followed by temperature (±4%) and wind speed (±1%). Taken together, these results suggest that solar radiation plays an important role in estimating PET in Peninsular Malaysia.

An Evaluation of the Performance and the Contribution of Different Modified Water Demand Estimates in Drought Modeling Over Water‐stressed Regions

AbstractThe lack of reliable estimation of water demand in drought study has been an important obstacle in efforts for characterizing the variability of water consumption and its effects on drought monitoring and prediction, particularly over water‐stressed regions. This study evaluated the performance and the contribution of three modified water demand estimates in drought modeling, including Penman–Monteith (PM) method, dual‐source potential evapotranspiration model (2S PET model), and climatically appropriate for existing conditions precipitation ( ). The results show that Standardized Moisture Anomaly Index based on shows the highest correlations with different types of drought evidence, demonstrating that the as a water demand metric performs the best in drought modeling over water‐stressed regions. Standardized Precipitation Evapotranspiration Index based on 2S PET model performs better than that based on PM equation. Although the 2S PET model is physically superior to PM equation over agricultural or water‐stress regions where the plant canopy is not “closed,” the limitations of using PET as water demand estimates in drought study still exist. Theoretically speaking, the actual water demand in non‐humid regions should be highly associated with the climatically averaged water supply capability (precipitation, P), and the contributions of water demand and supply to drought indices are generally balanced in any region, which is realistically reflected by the relationships between P and . On the contrary, the magnitude and contribution of PET2S and PETPM were always higher than P over water‐stressed regions, which could result in a water imbalance and generate more systemic errors in drought identification. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A Sensitivity Study of Applying a Two‐Source Potential Evapotranspiration Model in the Standardized Precipitation Evapotranspiration Index for Drought Monitoring

AbstractThe Standardized Precipitation Evapotranspiration Index (SPEI) based on three different potential evapotranspiration (PET) estimation methods, including the Thornthwaite (TW) equation, the Penman–Monteith (PM) equation, and the Two‐Source (2S) PET model were compared and evaluated by observed evidences over the Loess Plateau, which includes large area of sparse vegetation cover. The results show that the Loess Plateau has experienced substantial climate warming over the past four decades, which intensively raised the atmospheric water demand (PET), thus the drought condition. The upward trends of PETTW were the most extensive, while the upward trends of PET2S and PETPM were similar to each other. The differences between SPEIPM and SPEITW were larger than the differences between SPEIPM and SPEI2S. The variability of SPEI2S and SPEIPM were more consistent with the observed streamflow and the normal difference vegetation index than that of SPEITW, and the SPEI2S even showed higher agreements than the SPEIPM. The 2S PET model considers radiation balances at the canopy level and soil surface separately, which helps this model more accurately estimate the PET in regions with sparse vegetation. The PM equation is a more physically based PET estimation method than the TW equation, which takes the other variables that effect atmospheric water demand into account. Therefore, the 2S PET model (first) and the PM equation (second) are recommended in calculating the SPEI over regions with large area of sparse vegetation cover, as a result of better physical mechanism and higher correlations with different types of observations. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Potential Evapotranspiration Estimation in the Upper Huaihe River Basin, China

The atmospheric evaporative demand, one of principal components in water cycle, its accurate estimation is essential for hydrological simulation and forecasting. The purpose of this paper was to propose a plant-growth based dual-source potential evapotranspiration distributed model and investigate its feasibility with the upper Huaihe River basin (China) above the Xixian station as a case study site. Based on the topography, land-use, hydrological, and meteorological data in the Xixian basin, daily gridded potential evapotranspiration was calculated by the newly-build dual-source potential evapotranspiration model during 2000-2010. The relationship between potential evapotranspiration and pan evaporation at different time scales was investigated thereafter. The result revealed that the newly-built model could capture the temporal and spatial variation patterns of potential evapotranspiration moderately well in the upper Huaihe River basin. The analysis of this study also showed that the correlation between potential evapotranspiration and pan evaporation observations at daily, monthly and seasonal scales was affected together by underlying conditions that include, elevation, seasonality and precipitation.

Inter-annual and spatial variability of Hamon potential evapotranspiration model coefficients

Summary Monthly calibrated values of the Hamon PET coefficient (C) are determined for 109,951 hydrologic response units (HRUs) across the conterminous United States (U.S.). The calibrated coefficient values are determined by matching calculated mean monthly Hamon PET to mean monthly free-water surface evaporation. For most locations and months the calibrated coefficients are larger than the standard value reported by Hamon. The largest changes in the coefficients were for the late winter/early spring and fall months, whereas the smallest changes were for the summer months. Comparisons of PET computed using the standard value of C and computed using calibrated values of C indicate that for most of the conterminous U.S. PET is underestimated using the standard Hamon PET coefficient, except for the southeastern U.S.

Drought Tolerance in Wild Plant Populations: The Case of Common Beans (Phaseolus vulgaris L.)

Reliable estimations of drought tolerance in wild plant populations have proved to be challenging and more accessible alternatives are desirable. With that in mind, an ecological diversity study was conducted based on the geographical origin of 104 wild common bean accessions to estimate drought tolerance in their natural habitats. Our wild population sample covered a range of mesic to very dry habitats from Mexico to Argentina. Two potential evapotranspiration models that considered the effects of temperature and radiation were coupled with the precipitation regimes of the last fifty years for each collection site based on geographical information system analysis. We found that wild accessions were distributed among different precipitation regimes following a latitudinal gradient and that habitat ecological diversity of the collection sites was associated with natural sub-populations. We also detected a broader geographic distribution of wild beans across ecologies compared to cultivated common beans in a reference collection of 297 cultivars. Habitat drought stress index based on the Thornthwaite potential evapotranspiration model was equivalent to the Hamon estimator. Both ecological drought stress indexes would be useful together with population structure for the genealogical analysis of gene families in common bean, for genome-wide genetic-environmental associations, and for postulating the evolutionary history and diversification processes that have occurred for the species. Finally, we propose that wild common bean should be taken into account to exploit variation for drought tolerance in cultivated common bean which is generally considered susceptible as a crop to drought stress.

Impacts of land use and land cover changes on evapotranspiration and runoff at Shalamulun River watershed, China

The study assesses the effect of land use and land cover changes (LUCC) on evapotranspiration and runoff in the Shalamulun River watershed of 2,453 km2 located in Inner Mongolia Autonomic Region of China. First, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1987, 2001 and 2007 are used to quantify LUCC in the watershed. A knowledge-based decision tree (K-DT) classification technique is used to detect LUCC. By comparison of post-classification change among 1987, 2001 and 2007, the results showed significant modification and conversion of land use and cover of the watershed over the 20-year period 1987–2007. The results show that the forest area underwent the greatest change, decreasing by 159.2 km2 in the study period. At the same time, the area of farmland, barren land and residential land increased by 89.5, 46.4 and 25.3 km2, respectively. Subsequently, a two-source potential evapotranspiration (PET) model is used to estimate the potential evapotranspiration response to LUCC. Finally, the influence of LUCC on annual runoff is evaluated using a statistical method. LUCC potentially caused a decrease in annual PET and runoff. Meanwhile, the land use changes resulted in spatio-temporal variations of monthly PET in the growing season (April–September).

Evaluation of some empirical methods of estimating potential evapo-transpiration for determination of length of growing season in a tropical wet and dry climate

A comparison of 4 empirical methods of calculating potential evapo-transpiration (PE) using climatic data for the estimation of length of growing season from rainfall – potential evapo-transpiration model was carried out using 15 years of estimates recorded in Shaki, Southwestern Nigeria. The length of the growing season estimated from the 4 empirical methods of PE were related to the moisture requirements of some selected tropical crops. Data were analyzed by one – way ANOVA taking each yearly mean as the average of the 15 years (1991 - 2005) for the station. Results show that the methods varied greatly in their ability to define the magnitude and variability of the length of growing season. The original Penman and the modified Penman of McCulloch models showed no significant difference in their use for the determination of length of growing season. However, the Thornthwaite, the Priestly and Taylor showed differences in the estimates of length of growing season. Priestly and Taylor gave the least estimate of length of growing season. The relative reliability of each method in terms of accuracy of both measured and extrapolated meteorological data utilized was also discussed. Key words: Potential evapo-transpiration, length of growing season, moisture requirement, wet and dry climate.

Computation of Potential Evapotranspiration Using a Two-Source Method for the Xin’anjiang Hydrological Model

In this study, a physically based two-source potential evapotranspiration (PE) model was developed to calculate the spatio-temporal variation of PE over the Hanzhong watershed in China. The calculated PE was then integrated with the Xin’anjiang hydrological model to perform streamflow simulation. A formula similar to the Penman–Monteith equation was used in the PE model to explicitly calculate the PE components of potential canopy transpiration, interception evaporation, and potential soil evaporation. Based on the 1 km global land cover data, the related vegetation parameters were derived from the Land Data Assimilation System. The monthly maximum composite of NOAA-AVHRR NDVI data with a 1 km resolution was used to retrieve the spatio-temporal variation of leaf area index. Meteorological data from the China Meteorological Administration were employed for calculations. A single grid cell test shows that land cover exercises a significant impact on the PE amount, its temporal variation, and associated comp...

Potential Evapotranspiration Model for Muda Irrigation Project, Malaysia

Methods to estimate free-surface evaporation Ep and potential evapotranspiration ETp using well known models, namely, Penman–Monteith model, modified Penman method, Food and Agriculture Organization Penman–Monteith (FAO-PM) method, Blaney–Criddle method, and Christiansen method, without calibrating any model calibration parameters, for monthly time series are presented. The long-term monthly averaged daily models results are calculated using recorded average historic meteorological data (1980–1997) and compared with the USBR Class-A black pan evaporation data obtained from the Muda Agricultural Development Authority, Malaysia. The comparison results of the long-term monthly averaged daily estimates of Ep using these models show that the Ep values for the Penman–Monteith model and FAO-PM method for different months are found to be compared satisfactorily with the recorded pan evaporation data. The results of the estimated Ep values for different months, the variations of relative errors in different months, the values of mean monthly differences between recorded and estimated Ep, and the comparison between the models annual Ep with the recorded annual Ep, using these models suggest that the Penman–Monteith model can be selected as the best model in Ep estimation in the Muda Irrigation Project, Malaysia. The FAO-PM method can be considered as the second best model, successively followed by the Blaney–Criddle method, modified Penman method, and Christiansen method. Thus, the results of the Penman–Monteith model can be interpreted as the validation of the Ep model and can safely be used in ETp estimation in the Muda Irrigation Project, Malaysia.