Potential Evapotranspiration Models Research Articles

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

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

Analyzing the uncertainty of potential evapotranspiration models in drought projections derived for a semi-arid watershed

Analyzing the uncertainty of potential evapotranspiration models in drought projections derived for a semi-arid watershed

Performance of potential evapotranspiration models across different climatic stations in New South Wales, Australia

Study regionNew South Wales, southeast Australia Study focusEstimating potential evapotranspiration (ETp) rates, detecting its temporal trends and analysing its interannual oscillation are critical for long-term assessment of water availability and regional drought. This study aimed to evaluate the comprehensive performance of 12 simplified models in characterising ETp against the benchmark Penman model across different climate sites in southeast Australia. This study used Taylor skill score (S), normalised root mean square error (nRMSE) and relative mean bias error (rMBE) to estimate models’ capability in estimating ETp rates. Then, this study adopted Mann-Kendall test and continuous wavelet transform (CWT) to test temporal trends and periodicity of ETp estimated by all models. New hydrology insights for the regionJensen-Haise model was capable to produce fair (nRMSE ≤ 30%) estimates of daily ETp across all stations. Models except Mak1 were generally able to produce reasonable estimates of ETp at larger time scale. In addition, we found that the 12 alternative ETp models generally agreed with the Penman model on the primary (9.6–12.4-year) and quasi (2.6–3.9-year) periods of ETp, but they did not show matchable ability in detecting ETp temporal trends. The comprehensive investigation on models’ performance will shed light on models’ selection in estimation of drought and hydrological cycle.

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.

On the Sensitivity of Standardized-Precipitation-Evapotranspiration and Aridity Indexes Using Alternative Potential Evapotranspiration Models

This paper examines the impacts of three different potential evapotranspiration (PET) models on drought severity and frequencies indicated by the standardized precipitation index (SPEI). The standardized precipitation-evapotranspiration index is a recent approach to operational monitoring and analysis of drought severity. The standardized precipitation-evapotranspiration index combines precipitation and temperature data, quantifying the severity of a drought as the difference in a timestep as the difference between precipitation and PET. The standardized precipitation-evapotranspiration index thus represents the hydrological processes that drive drought events more realistically than the standardized precipitation index at the expense of additional computational complexity and increased data demands. The additional computational complexity is principally due to the need to estimate PET within each time step. The standardized precipitation-evapotranspiration index was originally defined using the Thornthwaite PET model. However, numerous researchers have demonstrated the standardized precipitation-evapotranspiration index is sensitive to the PET model adopted. PET models requiring sparse meteorological inputs, such as the Thornthwaite model, have particular utility for drought monitoring in data scarce environments. The aridity index (AI) investigates the spatiotemporal changes in the hydroclimatic system. It is defined as the ratio between potential evapotranspiration and precipitation. It is used to characterize wet (humid) and dry (arid) regions. In this study, a sensitivity analysis for the standardized precipitation-evapotranspiration and aridity indexes was carried out using three different PET models; namely, the Penman–Monteith model, a temperature-based parametric model and the Thornthwaite model. The analysis was undertaken in six gauge stations in California region where long-term drought events have occurred. Having used the Penman–Monteith model as the PET model for estimating the standardized precipitation-evapotranspiration index, our findings highlight the presence of uncertainty in defining the severity of drought, especially for large timescales (12 months to 48 months), and that the PET parametric model is a preferable model to the Thornthwaite model for both the standardized precipitation-evapotranspiration index and the aridity indexes. The latter outcome is worth further consideration for when climatic studies are under development in data scarce areas where full required meteorological variables for Penman–Monteith assessment are not available.

Projection of future dry‐wet evolution in Northwest China and its uncertainty attribution analysis

AbstractProjection of future dry‐wet evolution is essential for making long‐term regional climate adaptation strategies. In this study, the projection of regional dry‐wet evolution is conducted with a careful consideration on uncertainty attribution. The Evaporative Stress Index (ESI) is adopted due to its physical mechanisms for taking evaporative demand into account. A three‐dimensional framework is constructed for quantifying the range of uncertainty of the ESI in which six Global Climate Models (GCMs) in Coupled Model Intercomparison Project 6 (CMIP6), three latest Shared Socioeconomic Pathway (SSP) scenarios, and six Potential Evapotranspiration Models (PETMs) are used. The framework provides 108 different ESI simulations for two future periods: 2041–2070 (mid‐future) and 2071–2100 (far‐future). An agglomerative‐hierarchical clustering method and the Analysis of Variance methodology are employed to evaluate the relative contribution of each uncertainty source. The region of Northwest China is used as a case to illustrate the effectiveness of the proposed framework. The results indicate that most of the parts in Northwest China would experience dry mitigation in both mid‐future and far‐future periods. Projected ESI by PM[CO2] model and ACCESS‐ESM1‐5 suggestes a higher tendency for dry mitigation. Hierarchical clustering analysis of the 108 sets of ESI predictions indicate that most clusters are dominated by GCM forcing, and one cluster is dominated by the SSP1‐2.6 scenario. Furthermore, GCM‐related uncertainty′s relative contribution to the total projection uncertainty is the greatest, with an average value of 49.98% in the far future (i.e., 2071–2100 s). Although the contribution of SSP uncertainty is smaller (21.68%−28.43%), it increases in far‐future over mid‐future. The case study indicates that the large scale ensemble prediction of ESI and its uncertainty analysis provide a more comprehensive data set on climate change and help water managers to gain in‐depth understanding of future trends of drought projections.

Evaluation of different methods to estimate monthly reference evapotranspiration at Raichur region, India

The potential evapotranspiration (PET) is an important parameter to address water needs for growing crops. Estimation of potential evapotranspiration (PET) requires knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although potential evapotranspiration is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. Numerous potential evapotranspiration models have been developed and are being used, depending upon the availability of weather data. For the proposed study, the daily data of all weather parameters viz., maximum and minimum air temperature, maximum and minimum relative humidity, wind speed, actual sunshine hours and rainfall for estimating potential evapotranspiration for the duration of 31 years (1991-2021) were collected from the weather station at Main Agricultural Research Station (MARS), Raichur. A number of empirical formulae or approaches have been used for the determination of potential evapotranspiration (PET) from meteorological data. Availability of climatic data and accurately converting them in terms of water requirement are of great constraints. Judging the accuracy of different PET estimation models is a difficult task. The FAO 56-Penman Monteith model was considered as a standard reference for PET estimation in this investigation over the study location. The objective was to compare various PET models with standard reference model (FAO 56 Penman-Monteith model) and choose the best suitable model for estimating PET for the study area.

A case study of the RothC soil carbon model with potential evapotranspiration and remote sensing model inputs

Soil carbon modeling is an important tool to inform scientists, land managers, and policy makers about the impacts of land management decisions on soil health and climate change mitigation. Models can require a wide range of data inputs to make predictions, not all of which are readily available but can often be substituted with estimates. Understanding the impact that inputs have on the model output is critical to understand the uncertainties of their predictions. This study evaluated the output of the RothC soil carbon model using three potential evapotranspiration (PET) models (Tegos et al., 2017; Droogers and Allan, 2002; and Thornthwaite and Mather, 1955; Te, DA, and TM, respectively) to estimate open pan evaporation, and three vegetation indices (NDVI, SAVI, and MSAVI) from satellite imagery to estimate corn (Zea mays L.), soybean (Glycine max (L.) Merr.), and wheat (Triticum aestivum L.) yield to convert to C input for the RothC model to predict soil organic carbon over 11 years at a research farm in Michigan. The objectives of this study were to evaluate the correlation with observed data for each estimated input as well as the variability in RothC output they produced. The TM, Te, and DA PET models resulted in 13%, 38%, and 40% wet bias, respectively, resulting in increases of 1.6, 5.5 and 7.4 Mg C ha−1, respectively, over the baseline model output. All three vegetation indices estimated corn yields poorly (R2 = 0.25, 0.26, and 0.26 for NDVI, SAVI, and MSAVI, respectively), but soybean yields exceptionally well (all R2 = 0.91). However, despite NDVI producing significantly higher values (p < 0.001), there were no significant differences between each index's estimated yield used to calculate C input for RothC, and the resulting model runs are almost indistinguishable.

Modeling tree radial growth in a warming climate: where, when, and how much do potential evapotranspiration models matter?

Process-based models of tree-ring width are used both for reconstructing past climates and for projecting changes in growth due to climate change. Since soil moisture observations are unavailable at appropriate spatial and temporal scales, these models generally rely on simple water budgets driven in part by temperature-based potential evapotranspiration (PET) estimates, but the choice of PET model could have large effects on simulated soil moisture, moisture stress, and radial growth. Here, I use four different PET models to drive the VS-Lite model and evaluate the extent to which they differ in both their ability to replicate observed growth variability and their simulated responses to projected 21st century warming. Across more than 1200 tree-ring width chronologies in the conterminous United States, there were no significant differences among the four PET models in their ability to replicate observed radial growth, but the models differed in their responses to 21st century warming. The temperature-driven empirical PET models (Thornthwaite and Hargreaves) simulated much larger warming-induced increases in PET and decreases in soil moisture than the more physically realistic PET models (Priestley–Taylor and Penman–Monteith). In cooler and more mesic regions with relatively minimal moisture constraints to growth, the models simulated similarly small reductions in growth with increased warming. However, in dry regions, the Thornthwaite- and Hargreaves-driven VS-Lite models simulated an increase in moisture stress roughly double that of the Priestley–Taylor and Penman–Monteith models, which also translated to larger simulated declines in radial growth under warming. While the lack of difference in the models’ ability to replicate observed radial growth variability is an encouraging sign for some applications (e.g. attributing changes in growth to specific climatic drivers), the large differences in model responses to warming suggest that caution is needed when applying the temperature-driven PET models to climatic conditions with large trends in temperature.

Performance of potential evapotranspiration models in Peninsular Malaysia

Abstract Potential evapotranspiration (PET) is an important parameter for the operation of irrigation projects and water resources management. The globally recognized PET estimation model, the FAO-56 Penman–Monteith (FAO-56 PM) model, had been criticized for its requirement of many detailed meteorological variables, but nevertheless has been accepted as the baseline model in many worldwide studies. The performances of different PET models can be found to be excellent for a specific location but may not be representative in other regions. The aim of this study is to select the most suitable PET model to estimate PET in Malaysia. Three radiation-based models and four temperature-based models were compared with the FAO-56 PM model at seven selected meteorological stations in Peninsular Malaysia. The mean bias error, relative error (Re) and normalized root-mean-square error (NRMSE) and coefficient of determination (R2) were used to evaluate the performances of the PET models. The Re values of Turc models were below 0.2 at all stations, while Priestly–Taylor, Thornthwaite, Thornthwaite-corrected and Blaney–Criddle models were above 0.2. The Makkink and Hargreaves–Samani models were below 0.2 at most of the stations. Thus, the Turc model was recommended as the best model to estimate PET in Peninsular Malaysia.

Characterization of drought using four drought indices under climate change in the Sahel region of Nigeria: 1981–2015

Drought is a natural hazard that has affected agriculture which is the main livelihood of the people in the Sahel region of Nigeria (SRN) in the last few decades. Continental and regional drought monitoring is very essential for the development of an early warning system especially in the context of global warming. The severity of drought was simulated to evaluate climate change impacts on drought conditions in the SRN using the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), the original and self-calibrated Palmer drought severity indices (PDSI and scPDSI). The difference between the Hargreaves (Har) and Penman-Monteith (PM) potential evapotranspiration (PET) models used for the computation of the SPEI were also studied. The Mann-Kendall test was used to analyze the trends and significance of the climatic data. The time series of four drought indices were compared at six synoptic stations within the SRN. The influences of climate change on drought conditions were examined with a hypothetical gradual precipitation changes (+ 10%) and 2 °C increase in temperature. The findings showed that there was a significant correlation between Har and PM models, and between the SPEIs estimated from the two PET models. However, a major drought episode (1982–1983) indicated by SPEI-PM was not captured under SPEI-Har. Considering climate change conditions, the severity and intensity of drought increases as the twenty-first century progresses under both the scPDSI and SPEI mainly due to more demand for moisture based on a temperature rise of 2 °C. Either a slight (10%) increase or decrease in the monthly accumulation of rainfall depth will not have a significant impact on drought, if there is a slight increase in temperature as it is being currently observed in the SRN. Thus, it is pertinent for stakeholders to critically consider establishing policies that can ameliorate this phenomenon as the twenty-first century progresses.

Quantifying future drought change and associated uncertainty in southeastern Australia with multiple potential evapotranspiration models

Projection of drought under a changing climate is important for drought risk assessment. Changes in precipitation (P) and potential evapotranspiration (ETp) are expected to influence future drought occurrence. Thus, it is important to include both factors to accurately quantify change in drought frequency under future climate scenarios. Standardized precipitation evapotranspiration index (SPEI) is a widely used index in drought assessment because it considers the influence of both P and ETp on drought. Thus, in this study we used SPEI to quantify change in drought frequency under two different emission scenarios (RCP4.5 and RCP8.5) in the wheat belt of southeastern Australia with climatic data downscaled from 34 global climate models (GCMs). We also investigated whether differences ETp models would make a difference on drought projection. Therefore, we employed five different traditional ETp models (Penman, Jensen-Haise, Makkink, Abtew, Hargreaves) and three random forest (RF)-based models to calculate SPEI in this study. Results showed that drought, especially moderate and severe drought, would occur more frequently under future climate scenarios and the increased frequency was generally greater in spring and winter than in summer and autumn. Severe drought occurring in spring would increase by 3.1%–21.7% under RCP4.5 and 5.2%–41.0% under RCP8.5. In autumn, the likely mean increase of severe drought frequency was 0.7%–13.0% under RCP4.5 and 2.7%–27.9% under RCP8.5. Differences in the projected increase of drought frequency were found among the different ETp models. In general, RF-based ETp models, which projected larger increases in ETp, generally also projected larger increases in drought occurrence. A multilinear regression relationship was built between changes in drought frequency and changes in ETp and P. The regression showed that the increased drought frequency was a combined result of the increasing ETp and decreasing P, and that the increasing ETp might be the more dominant factor. The contribution of GCMs, RCPs, different ETp models, and their interaction to the uncertainty in drought projection was quantified with the use of analysis of variance. Results showed that GCMs and their interaction with RCPs were the dominant factors influencing uncertainty in drought projection.

Potential evapotranspiration models evaluation, modelling, and projection under climate scenarios, Kesem sub-basin, Awash River basin, Ethiopia

Evapotranspiration is a complex process in the hydrological cycle that influences the quantity of surface and groundwater. This study strives to evaluate potential evapotranspiration (PET) estimating models, modeling, and project potential evapotranspiration under climate scenarios. For models evaluation, the modified Hargreaves???Samani, Thornthwait, and Blaney???Criddle were evaluated against the FAO 56 Penman???Monteith method using the relative error (Re), normalized root-mean-squared error, and Pearson correlation (r). Multiple linear regression technique has been used to develop a PET estimation model using six climate parameters (minimum and maximum temperatures, relative humidity, wind speed, solar radiation, and sun hours). Outputs of 17 global climate models ensemble were used for RCP 4.5 and 8.5 emission scenarios to predict future PET. The correlation of climate parameters with PET reveals solar radiation, and Tmax had strong correlation (r) (0.73 and 0.8) at Shola Gebeya and (0.65 and 0.8) at Aleltu Agriculture, respectively, than other considered parameters. The models evaluation shows the modified Hargreaves???Samani equation performed better than others at both Shola Gebeya and Aleltu Agriculture stations. The result of multiple linear regression model shows the input variables used in the modeling were sufficient, implying that this model can be successfully used in estimating PET. Overall, the estimated PET using the multiple linear regression models under RCP 4.5 and 8.5 emission scenarios shows an increasing trend???this is pillar information for the water users in the study catchment.

Assessing the sensitivity of standardized precipitation evapotranspiration index to three potential evapotranspiration models in Nigeria

Drought indices are very essential in monitoring and planning for drought occurrences. Though many indices have been established over the years, it is expected that proper investigation should be done using these indices to know the ones that can better characterize the drought condition of a region or country. The current study evaluated the performance of three potential evapotranspiration models (Hargreaves, Penman-Monteith and Thornthwaite) as inputs into Standardized Precipitation Evapotranspiration Index (SPEI) in regards to identified historic drought events for five agroecological zones in Nigeria. The computation of the indices was done using either one or more of the monthly time series of rainfall, minimum, maximum and mean temperature, sunshine hours and wind speed from 1981 – 2015 at five timescales (1, 3, 6, 12 and 24 months). Although all the models correlated significantly with one another, SPEI-H (Standardized Precipitation Evapotranspiration Hargreaves) vs SPEI-P (Standardized Precipitation Evapotranspiration Penman-Monteith) show higher correlation for all timesteps than SPEI-H vs SPEI-T (Standardized Precipitation Evapotranspiration Thornthwaite) and SPEI-P vs SPEI-T. The efficiency of the indices in identifying historic drought episodes: 1983 – 1985, 2007 and 2011 was examined using information obtained from Emergency Events Database (EM-DAT) and previous literature on drought studies in Nigeria. Drought onsets, severity and duration determined by the indices were compared with that of the EM-DAT. All the three indices show earlier onset for both 3- and 12- month timesteps than that of EM-DAT, but indicated a significant difference in severity and duration during the 1983 – 1985 drought episode for 12-month timestep. Though SPEI-P has a higher comparative predictive ability as indicated by EM-DAT, it was discovered that SPEI-H is a good alternative for regions where there is a paucity of data.

Accuracy of Potential Evapotranspiration Models in Different Time Scales

Abstract Know the precision of potential evapotranspiration models in different agronomic and climatic conditions is useful for irrigated agriculture. Therefore, we aimed to compare 18 methods of estimation of ETP with the Penman-Monteith (FAO-56) method, at different time scales for the State of Mato Grosso do Sul. Time series of climatic data were used on a daily scale between 1983 and 2018 from 22 locations in the state of Mato Grosso do Sul. ETP estimation models tested were: Benevidez-Lopez, Blaney-Criddle, Camargo, Hamon, Hargreaves, Hargreaves-Samani, Jensen-Haise, Jobson, Kharrufa, Linacre, Makkink, Penman, Priestley-Taylor, Radiation, Romanenko, Tanner-Pelton, Thornthwaite, and Turc. These models were compared with Penman-Monteith in daily, weekly, and monthly scales. The comparison between the ETP estimation models and the Penman-Monteith model was performed by the statistical indices: accuracy (MAPE) and precision (R2aj). Estimation methods showed differences in efficiency over time scales. The best performances of the models were on the daily scale. For daily scale, methods of Priestley-Taylor, Hargreaves, Hamon, and Makkink present the best values of accuracy and precision for the State of Mato Grosso do Sul. In the weekly scale, the most accurate methods are Hamon and Makkink, while for monthly scale the best methods are Makkink and Priestley-Taylor.

Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.

Assessing the ability of potential evapotranspiration models in capturing dynamics of evaporative demand across various biomes and climatic regimes with ChinaFLUX measurements

Estimates of atmospheric evaporative demand have been widely required for a variety of hydrological analyses, with potential evapotranspiration (PET) being an important measure representing evaporative demand of actual vegetated surfaces under given metrological conditions. In this study, we assessed the ability of various PET models in capturing long-term (typically 2003–2011) dynamics of evaporative demand at eight ecosystems across various biomes and climatic regimes in China. Prior to assessing PET dynamics, we first examined the reasonability of fourteen PET models in representing the magnitudes of evaporative demand using eddy-covariance actual evapotranspiration (AET) as an indicator. Results showed that the robustness of the fourteen PET models differed somewhat across the sites, and only three PET models could produce reasonable magnitudes of evaporative demand (i.e., PET≥AET on average) for all eight sites, including the: (i) Penman; (ii) Priestly-Taylor and (iii) Linacre models. Then, we assessed the ability of these three PET models in capturing dynamics of evaporative demand by comparing the annual and seasonal trends in PET against the equivalent trends in AET and precipitation (P) for particular sites. Results indicated that nearly all the three PET models could faithfully reproduce the dynamics in evaporative demand for the energy-limited conditions at both annual and seasonal scales, while only the Penman and Linacre models could represent dynamics in evaporative demand for the water-limited conditions. However, the Linacre model was unable to reproduce the seasonal switches between water- and energy-limited states for some sites. Our findings demonstrated that the choice of PET models would be essential for the evaporative demand analyses and other related hydrological analyses at different temporal and spatial scales.

Evaluation of six potential evapotranspiration models for estimating crop potential and actual evapotranspiration in arid regions

Using potential evapotranspiration (PET) to estimate crop actual evapotranspiration (AET) is a critical approach in hydrological models. However, which PET model performs best and can be used to predict crop AET over the entire growth season in arid regions still remains unclear. The six frequently-used PET models, i.e. Blaney-Criddle (BC), Hargreaves (HA), Priestley-Taylor (PT), Dalton (DA), Penman (PE) and Shuttleworth (SW) models were considered and evaluated in the study. Five-year eddy covariance data over the maize field and vineyard in arid northwest China were used to examine the accuracy of PET models in estimating daily crop AET.Results indicate that the PE, SW and PT models underestimated daily ET by less than 6% with RMSE lower than 35Wm−2 during the four years, while the BC, HA and DA models under-predicted daily ET approximately by 10% with RMSE higher than 40Wm−2. Compared to BC, HA and DA models, PE, SW and PT models were more reliable and accurate for estimating crop PET and AET in arid regions. Thus the PE, SW and PT models were recommended for predicting crop evapotranspiration in hydrological models in arid regions.

A COMBINED SENSITIVITY ANALYSIS OF SEVEN POTENTIAL EVAPOTRANSPIRATION MODELS

Graphical and partial derivatives approaches were used to analyse the sensitivity of variables for the seven potential evapotranspiration models (PET). The models, which have different data requirements and structures, are Hamon, Hargreaves-Samani, Jensen-Haise, Makkink, Turc, Priestley-Taylor, and Penman. Julian date based mean imputation was used to fill the missing data. Tukey's outlier detection method was employed before estimating the PET. Partial derivative approach was conducted by combining the absolute values of the error term through a root mean square and changing to the finite difference form. According to partial derivatives analysis, Hamon is the most sensitive model followed by Penman, Priestley-Taylor, Hargreaves-Samani, Jensen-Haise, Turc, and Makkink models. Temperature is more sensitive meteorological input in Jensen-Haise and Makkink models while solar radiation is more sensitive ones in Turc and Priestley-Taylor models. Wind speed and relative humidity are the most and less sensitive ones in Penman model. Graphical analysis showed that Hamon was the most sensitive PET model with respect to the temperature while Priestley-Taylor was the one with respect to the solar radiation. Turc is the less sensitive PET model with respect to temperature and solar radiation. Overall, graphical method gives clearly comparison for sensitivity of PET. However, it does not indicate its sensitivity values compared to partial derivative approach.

Evaluation of common evapotranspiration models based on measurements from two extensive green roofs in New York City

Although the evapotranspiration (ET) process has historically received limited attention, it is an important factor for assessing the health and behavior of urban green spaces, including green roofs. In this study, common potential evapotranspiration (PET) models, which assume non-water-limited substrate moisture conditions, and actual evapotranspiration (AET) models, which account for water-limited substrate moisture conditions, are used to predict ET from local climate conditions at two extensive Sedum green roof sites in New York City (NYC); one a vegetated mat system (termed W118) and the other a built in place system (termed USPS). Results from the predictions are compared to 12,000h of on-site ET measurements obtained using a dynamic chamber system. Among the Hargreaves, Priestley–Taylor, Penman, and American Society of Civil Engineers Penman–Monteith PET models, results from the Priestley–Taylor model, which was developed to predict ET from a wet vegetated surface with minimal advection, best correlate with the dynamic chamber measurements (r-squared=0.96 for W118, 0.82 for USPS). Nonetheless, a systematic error is seen whereby the Priestley–Taylor model overestimates the low ET fluxes observed during the winter months and underestimates the high ET fluxes observed during the summer months. This error is only exaggerated by the inclusion of an advective ET term. To estimate green roof ET under water-limited conditions, a storage model, antecedent precipitation index (API), and advection-aridity model are applied to the Priestley–Taylor formulation to calculate AET. Results indicate that only the API model, which is based on precipitation history alone, can improve upon the Priestley–Taylor PET predictions (r-squared=0.96 on W118, 0.85 on USPS). Use of a more-physically based, substrate moisture storage greatly over-estimates ET reduction during dry periods. The work provides insight into which common ET models best capture the behavior of full-scale, extensive green roofs, and points to the need for better estimates of green roof ET under climate conditions typical of NYC's winter and summer months.