Potential Evapotranspiration Methods Research Articles

Impact Assessment of the Potential Evapotranspiration Method on Results of Hydrological Modeling

Impact Assessment of the Potential Evapotranspiration Method on Results of Hydrological Modeling

Drought evaluation using various evapotranspiration models over semi-arid river basins

ABSTRACT Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Sensitivity of global hydrological models to potential evapotranspiration estimation methods in the Senegal River Basin (West Africa)

Study regionSenegal River Basin in West Africa Study focusThis paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984–1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the regionGood consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.

Comparison of potential evapotranspiration methods in Ethiopia

The Food and Agricultural Organization method for estimating reference crop evapotranspiration (ETo), FAO56, is the standard method across the globe. Daily ETo estimated using seventeen potential evapotranspiration (PET) methods that has less data requirments were compared with FAO56 at five sites in Ethiopia (Bahir Dar, Bale Robe, Hawassa, Metehara, and Nazareth). The average rank of five model validation metrics namely standard deviation, RMSE, MAPE, NSE, and correlation were used to compare the methods. By taking the areal average ETo and PET, ten methods, Wen, Ant, Pen, Mak, PT, Ha5, Ha4, Tho, Rad, and Enk, having correlation > 0.57 and MAPE < 28% were ranked 1st to 10th, respectively. Also, at each site, the above ten PET methods were compared. By taking the average ranks of the five sites, Wen, Pen, Ant, PT, and Mak & Ha5 ranked 1st to 5th, respectively. Wen & Ant methods which are applied in Ethiopia for the first time had shown the best performance.

Unravelling the Drought Variance Using Machine Learning Methods in Six Capital Cities of Australia

Understanding and projecting drought, especially in the face of climate change, is crucial for assessing its impending risks. However, the causes of drought are multifaceted. As the environmental research paradigm pivots towards machine learning (ML) for predictions, our investigation contrasted multiple ML techniques to simulate the Standardized Precipitation Evapotranspiration Index (SPEI) from 2009 to 2022, utilizing various potential evapotranspiration (PET) methods. Our primary focus was Australia, the world’s driest inhabited continent. Given the challenges with ML model interpretation, SHAP (SHapley Additive exPlanations) values were employed to decipher SPEI variations and to gauge the relative importance of precipitation (Prec) and PET in six key Australian cities. Our findings revealed that while different PET methods resulted in distinct mean values, their trends remained consistent. Post the Millennium Drought, Australia experienced several drought events. SPEI discrepancies based on PET methods were minimal in humid regions like Brisbane and Darwin. However, for arid cities, the Priestley–Taylor equation-driven SPEI differed notably from other methods. Ridge regression was the most adept at mirroring SPEI changes among the assessed ML models. Furthermore, the SHAP explainer discerned that PET-related climate variables had a greater impact on SPEI in drier cities, whereas in humid cities, Prec was more influential. Notably, the research emphasised CO2′s role in influencing drought dynamics in humid cities. These insights are invaluable for enhancing drought mitigation strategies and refining predictive models. Such revelations are crucial for stakeholders aiming to improve drought prediction and management, especially in drought-prone regions like Australia.

Assessing Potential Evapotranspiration Methods in Future Drought Projections across Canada

abstract Recently, concerns have arisen as to whether temperature-based proxy methods used to estimate potential evapotranspiration (PET) are reliable when examining future drought severity, especially in the context of a warmer climate. The objective of this study was to assess the effect of different PET approaches, focusing on proxies for radiation and humidity, on future Standardized Precipitation Evapotranspiration Index (SPEI) calculations across Canada. Using output from 22 CMIP6 global climate models (GCMs), seasonal and annual SPEI comparisons were carried out between the physically-based Penman-Monteith (PM) method and two approaches that incorporate temperature proxies to calculate radiation and/or humidity. These included the temperature-based Hargreaves (HG) approach and a PM method with derived humidity (PM-m). Results revealed that although the general patterns of SPEI projections across Canada were consistent among the methods, notable spatial and temporal differences were apparent. Specifically, both median and extreme SPEI projections based on the two temperature proxy methods revealed less annual and summer drying in much of central, eastern, and northern regions of Canada when compared to the physically based SPEI-PM. In extreme western regions (British Columbia, Yukon) these two methods, particularly HG, projected drier conditions. Differences of using temperature derived radiation and humidity were also most apparent in spring (and to a lesser degree, autumn), where the HG approach overestimated spring drying (and autumn wetting) over large regions of the country. Overall, differences tended to be more pronounced for the fully temperature-based HG approach during all periods considered. Results from this study strongly suggest that when possible, a physically-based approach be used when estimating PET to assess future drought projections. If a temperature proxy is used, the differences to a physically-based method should be understood and resultant implications be evaluated.

Comparison of process-based and statistical approaches for simulation and projections of rainfed crop yields

Accurate and comprehensive modelling aimed at investigating the impact of climate change on rainfed crop yields is of great importance due to the interconnected issues of water scarcity and food security. Because the process-based and statistical approaches to simulating crop yields are different in nature, a comparison between them is needed. This study investigates the accuracy of crop yield simulations in the historical period as well as future projections using two modelling approaches: 1) a process-based approach employing the Soil and Water Assessment Tool+ (SWAT+) model, and 2) a statistical approach employing a data-driven model, Feed Forward Back Propagation Neural Network (FFBPNN) over a medium-sized catchment in north-western Poland. The application of two potential evapotranspiration methods (Penman-Monteith and Hargreaves) in SWAT+ permitted calibration (2004–2011) and validation (2012–2019) of runoff and yields of winter wheat and spring barley. Different combinations of climatic parameters with a drought index based on Joint Deficit Index were applied to simulate and project rainfed crop yields (winter wheat, barley, potato, rye, rapeseed, sugar beets, cereals, maize for grain, maize for green forage, pulses) with FFBPNN. The results reveal that adding the new drought index helped increase the FFBPNN performance. This approach showed that future yields of the studied crops would slightly increase under RCP8.5 by 2060. Winter wheat and spring barley projections from SWAT+ showed very small changes using both the Penman-Monteith and Hargreaves method. Policy-wise, the results should be of interest to climate change adaptation practitioners and food security experts. Future studies should aim at more thorough investigation of the role of the downscaling technique and extreme events, as well as the effect of elevated CO2 on future crop yields.

Water Requirement of Naked Oats under Subsurface Drip Irrigation in the North Foot of Yinshan Mountain Based on Single Crop Coefficient Approach

In order to accurately and rapidly calculate the water requirement of naked oats under drip irrigation in the north foot of Yinshan Mountain, the study adopted the K c and calculation method for potential evapotranspiration recommended in document FAO56 and applied SCCA to calculate the water requirement of crops based on the deficient irrigation experiments. The results indicated that the water requirements of naked oats without drought influence was 383.8 mm, and the maximum daily average water requirement intensity was heading flowering, which was the critical period of crop water requirement, and the mean value of E T 0 in the whole growth period was 3.89 mm/d. After correction, K C of naked oats in the initial growth stage, crop development growth stage, mid-season growth stage, and late season growth stage was 0.34, 0.94, 1.05, and 0.36. The average K C of naked oats in the whole growth stage was 0.71. SCCA does not consider the influence of wetting depth in the initial growth period of crops and the correction of local irrigation in the mid-season and late season stage of crops. Therefore, when the irrigation quota of naked oats is 315 mm, the crop water requirement calculated by SCCA is 363.84 mm, which is about 10% different from the measured value. The results show that SCCA can be used to calculate the water requirement of naked oats under drip irrigation at the northern foot of Yinshan Mountain in Inner Mongolia, and the calculation error is within the allowable range. However, it is also necessary to consider the influence of different irrigation forms and plant height of crops on calculation value.

Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985–2014). The changes and uncertainty associated with the changes in PET in the near (2031–2060) and far (2071–2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2–4.5 and 5–8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

Evaluation of Evapotranspiration in Brazilian Cerrado Biome Simulated with the SWAT Model

Evapotranspiration represents a significant part on the water balance and, thus, the correct evaluation of this hydrological parcel is relevant when modeling a watershed. The objective of this work is to evaluate the Soil and Water Assessment Tool (SWAT) model’s capability in adequately simulating evapotranspiration in a watershed with predominance of the Brazilian Cerrado biome. Hydrological modeling of the Gama watershed located in the Federal District, which has 57.5% of its total area covered by pristine Cerrado, was conducted. Hydrometeorological and turbulent flow variables have been monitored in weather station and Eddy Covariance (EC) tower, respectively. SWAT simulations were performed for potential evapotranspiration methods: Hargreaves (H), Priestley–Taylor (PT) and Penman–Monteith (PM). Modified versions of SWAT for estimating actual (ET) by Strauch and Volk (2013) (SV) and Arroio Junior (2016) (AR) were also tested. The calibration and verification of the SWAT model, in terms of daily flow, were carried out using a Particle Swarm Optimization algorithm, and fair results were obtained with all the methods evaluated. The actual evapotranspiration (ET) simulated with SWAT (ETsim) using the PM, PT, H, SV and AR methods for a Cerrado hydrological response unit (HRU) were evaluated and compared with the ET obtained using the turbulent flow (Eddy Covariance) method (ETobs). Comparing ETobs and ETsim results, the PM method showed the best fitness and the H and PT methods showed better fit for the dry and the rainy periods, respectively. Although representing an advance on ET modeling, the SV and AR modifications did not improve the response in terms of simulation of the studied area.

Future projection of low flows in the Chungju basin, Korea and their uncertainty decomposition

AbstractLow flow projections in response to global warming and the uncertainty decomposition into different components are performed with the focus on the Chungju basin, South Korea. Four downscaled climate simulations, two bias correction methods, and two hydrological models with two different potential evapotranspiration computation methods are applied to constitute the 32 ensemble members (4 × 2 × 4) of low flows projections. An analysis of the variance is used to decompose the total variance in the low flow projections into the contribution from three different sources. The result shows that hydrological models, particularly their structures, are the key factor that affects the low flow projections. They contribute more than 85% of the total uncertainty, and the projected change of low flow using different hydrological models can be of opposite signs, even under the same climate input data. On the other hand, the uncertainties related to climate simulation and bias correction are much smaller, comprising around 10 and 2%, respectively. The possible mechanisms behind the large uncertainty of projected low flow are investigated by comparing the relevant hydrological variables obtained from different hydrological simulations. The different representations in the hydrological models for the freezing of the soil layer during winter, which will undergo large changes as temperature increases, are likely to affect the projected change in low flow greatly. The difference in potential evapotranspiration methods also affects the low flow projections, but its contribution is relatively weaker than the effects of the hydrological model structure.

A note on some uncertainties associated with Thornthwaite's aridity index introduced by using different potential evapotranspiration methods

Potential evapotranspiration (PET) is a key parameter for climate classification and aridity assessment. The widely used UNEP (1992) classification system is based on the estimation of the aridity index AI, which requires annual average values of precipitation P and PET. For the calculation of PET the Thornthwaite's (1948) formula is, in principle, suggested. Recent studies use more advanced and accurate methods for PET estimation but apply to AI the same thresholds proposed by UNEP for aridity classification. This work deals with the uncertainties introduced by the use of different PET methods in the estimation of the aridity index AI. Specifically, the Hargreaves-Samani (HS) method and four of its modifications, three modifications of Thornthwaite's formula and the equation of Hamon, are evaluated against the widely used Thornthwaite's original method, by assessing their impact on the AI. Climatic data as monthly average values of at least 30 years of measurements from 122 stations in the Greek peninsula are used. Results show that AI is highly affected by the PET method adopted, resulting thus to changes in climatic classification of a region. Further, results imply the need for an adjustment of the threshold values that determine aridity classes according to the method each time adopted. Therefore, new threshold values for the aridity classes are developed and presented in this work. The proposed threshold values cover a range of sites that belong to semi-arid SA, sub-humid SH and humid H aridity classes of the Greek peninsula.

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

Effect of projected climate change on potential evapotranspiration in the semiarid region of central India

Abstract The performance of potential evapotranspiration (PET) methods such as pan evaporation (physical measurement), empirical formulas (Penman–Monteith (PM), Hargreaves and Thornthwaite) and satellite-derived PET (MOD16) were assessed in a semiarid region of central India. The satellite-based PET was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons between different methods were made with observed pan evaporation (ETpan) data to identify the best method for the semiarid region. Further, the future projection of PET was carried out using RCP4.5 emission scenarios of seven CMIP5 models. Two approaches were applied for the projection of PET. In the first approach, RCP4.5 scenario data are directly used in the PM method, and in the second approach, these variables are used as a predictor in the calibrated and validated least square support vector machine (LS-SVM) model. The projection of PET was made using the best-identified model among PM and LS-SVM from the years 2006–2100. The results show that MOD16 and Hargreaves overestimate the PET; however, PM and Thornwaite underestimate the PET. PM based PET is closely related with ETpan and is a good indicator of ETpan in a semiarid region. GFDL-ESM2M is identified as the most skillful CMIP5 model, and results show that PET is projected to increase in future using the LS-SVM model. HIGHLIGHTS The PET estimation methods, namely empirical and satellite-based PET, were evaluated and compared with ground-based observed ET. Satellite-based PET is closely matching with ground-based PET in wet season. Satellite-based PET can be used after applying a suitable correction factor in the study area. The PM method was found suitable for the semiarid region. PET is projected to increase in future using CMIP5 models.

A water balance model to estimate climate change impact on groundwater recharge in Yucatan Peninsula, Mexico

ABSTRACTThe aim of this paper is to estimate the effect that climate change will have on groundwater recharge at the Yucatan Peninsula, Mexico. The groundwater recharge is calculated from a monthly water balance model considering eight methods of potential and actual evapotranspiration. Historical data from 1961–2000 and climate model outputs from five downscaled general circulation models in the near horizon (2015–2039), with representative concentration pathway (RCP) 4.5 and 8.5 are used. The results estimate a recharge of 118 ± 33 mm·year–1 (around 10% of precipitation) in the historical period. Considering the uncertainty from GCMs under different RCP and evapotranspiration scenarios, our monthly water balance model estimates a groundwater recharge of 92 ± 40 mm·year–1 (RCP4.5) and 94 ± 38 mm·year–1 (RCP8.5) which represent a reduction of 23% and 20%, respectively, a result that threatens the socio-ecological balance of the region.

Impact of Potential Evapotranspiration Method on Sensitivity and Uncertainty in Streamflow Analysis for Kelantan River Basin

Impact of Potential Evapotranspiration Method on Sensitivity and Uncertainty in Streamflow Analysis for Kelantan River Basin

Comparing Different Methods of Potential Evapotranspiration and Studying Temporal and Spatial Changes in the Mahalou Watershed using GIS

Comparing Different Methods of Potential Evapotranspiration and Studying Temporal and Spatial Changes in the Mahalou Watershed using GIS

Climate Friendly Urban Green Areas: Roadside Green Spaces in Sakarya/Turkey

The role of urban green areas to adapt climate change impacts has been emphasized globally as urbanization is a very common fact around the world. This issue has been adressed by the sustainable development goals, several studies also underlined that sustainable urban planning is a cruical issue to enable more livable cities. The most natural and sustainable solution is to benefit green spaces with specific landscape design approaches including xeriscape. In this study we focus on roadside green spaces in an industrilized city. Traffic islands have been assessed based on the share of hardscape and green parts together with plant species and their irrigation specifications. The irrigation requirements have been analyzed by using a potential evapotranspiration method which is Blaney Criddle equation. The need for irrigation and the influence of the roadside green spaces as an adaptation tool emerge as a contraversal situation in case of low water resources. We analysed water needs of current plantation and irrigation systems and came out with a conclusion that xeriscape practices like mulching, use of xeric plants and grey water has a good potential to optimise water usage, mitigate urban heat island effect and support adaptation to the climate change.

Linacre Derived Potential Evapotranspiration Method and Effect on Supplementary Irrigation Water Needs of Tomato/Cabbage/Carrot

The study examined the dynamic nature of water balance parameters over Kano town, a semi-arid environment and impact of Linacre derived potential evapotranspiration method on the supplementary irrigation water needs of selected crops. Monthly Rainfall and Temperature data were collected from the Nigerian Meteorological Agency, Lagos for the period 1953-2012. The study observed that there is a steady decline in annual precipitation over Kano from the first decade (1953-1962) to the fifth decade (1993-2002), after which there was a sign of weak recovery in the last decade (2003-2012). For water loss through potential evapotranspiration, there was a steady rise from the first decade (1953-1962) to the fifth decade (1993-2002), and then followed by a sudden decline in the last decade (2003-2012). The total average of water storage on the other hand, first experienced a rise between the first two decades (1953-1962) and (1963-1972), followed by a steady decline, up until the fifth decade (1993-2002) and finally a rise in the last decade (20032012). The total average of soil water deficit experienced a steady rise between the first and the fifth decades (1953-1962) to (1993-2002), this was followed by a decline in the last decade (20032012). Finally, the total average of water surplus experienced a steady decline between the first and the fifth decades. The observed decline in precipitation, storage, and water surplus, and the rise in water loss from potential evapotranspiration and soil water deficit, suggests that there have been changes in the climatic pattern over Kano and this could be seen in the supplementary irrigation water needs of Tomato/Cabbage/Carrot.