Evapotranspiration Methods Research Articles

Applicability and improvement of different evapotranspiration methods of reference crops in Jiangxi Province

To achieve accurate evaluation of evapotranspiration of reference crops (ET0) in Jiangxi, China, in the absence of systematic climatological data, with reference to the FAO-56 Penman–Monteith (P-M) equation, the Priestley-Taylor (P–T) method, the Makkink method, the Hargreaves-Samani (H–S) method, the Irmak-Allen (I-A) method, the Penman1948 (48PM) method, the Penman-Van Bavel (PVB) method, the Baier-Robertson (B-R) method, the improved Baier-Robertson (M-B-R) method, the Schendel (Sch) method, the Turc method, the Jensen-Haise (J-H) method, and the Brutsaert-Stricker (B-S) method were used to evaluate the daily climatological data collected by 26 weather stations in Jiangxi, China, and 17 weather stations in adjacent provinces. The results were compared with each other and parameter rate determination was conducted. The results indicated that the Turc method exhibited optimized applicability before parameter rate determination and the average root mean square error (RMSE) and the average normalized root mean square error (NRMSE) by this method were 0.39 mm/d and 0.157 mm, respectively. However, parameter rate determination led to negligible improvement in accuracy for this method. The Turc method could be directly applied in Jiangxi (except Nanchang). For special distribution of error after parameter rate determination, all methods exhibited significant errors in Northern Jiangxi. Herein, the 48PM method and the B-S method showed good applicability after parameter rate determination and RMSE and NRMSE of data by these methods ranged in 0.06 ~ 0.34 mm/d and 0.08 ~ 0.27, 8 ~ 27%, respectively, and their d-indices were close to 1. The annual over-estimations in weather stations in Jiangxi were below 30 mm. In the absence of data about relative humidity and wind speed, the P–T method was an appropriate simplified method for Jiangxi. In this case, α was slightly lower than the default value (1.05 ~ 1.18), RMSE was within 0.21 ~ 0.66 mm/d, and NRMSE was within 0.08 ~ 0.308 ~ 30%. Accuracy of RMSE, d-index, and NRMSE of data by the P–T method, the I-A method, and the PVB method was consistent with all stations, while that by the Mak method was slightly lower, which could be attributed to severe over-estimation in July and August. RMSE of the H–S method, the B-R method, the M-B-R method, the J-H method, and the Sch method were above 0.75 mm/d and these methods were not suitable for accurate evaluation of ET0 in Jiangxi, China. The annual ET0 was calculated by various methods (except the 48PM method and the B-S method) exhibited significant variation around 2003. This may be attributed to significant changes in certain meteorological factors over recent years.

Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

Reference evapotranspiration estimation by different methods for the sucroenergy sector of Colombia

ABSTRACT In the Colombian Sugarcane Research Center CENICAÑA, efficient use of water is always performed based on estimating reference evapotranspiration. In this method, class A pans have been used to estimate the reference evapotranspiration, which provides a more precise estimate of this important variable for water resource management. The objective of this study was to evaluate different reference evapotranspiration methods for the region of influence of the climatological station of the CENICAÑA experimental station. The class A pan method traditionally used in CENICAÑA was compared with and the Penman-Monteith FAO 56 standard method. The historical series used was from January 1, 1994, to December 31, 2014. The climatic variables were the maximum, mean, and minimum temperatures, the mean relative air humidity, solar radiation, and wind speed at 10 m. Willmott’s coefficients, the confidence index (c), and the root mean squared error were used in the performance evaluation. In the comparison with the Penman-Monteith FAO 56 method, all methods presented performance below the minimum requirement of (c) = 0.400. When all methods were compared with the method used in CENICAÑA (Class A pan), only the Penman-Monteith FAO 56 method showed performance classified as good (c = 0.689).

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.

Water and Energy Balance Model GOES-PRWEB: Development and Validation

In 2009, the University of Alabama-Huntsville configured their GOES satellited-based solar radiation product to include Puerto Rico, the US Virgin Islands (USVI), Dominican Republic, Haiti, Jamaica, and Cuba. The half-hourly and daily integrated data are available at 1 km resolution for Puerto Rico and the USVI and 2 km for Hispaniola, Jamaica, and Cuba. These data made it possible to implement estimates of satellite radiation-based evapotranspiration methods on all of the islands. The use of the solar radiation data in combination with estimates of other climate parameters facilitated the development of a water and energy balance algorithm for Puerto Rico. The purpose of this paper is to describe the theoretical background and technical approach for estimating the components of the daily water and energy balance. The operational water and energy balance model is the first of its kind in Puerto Rico. Model validation results are presented for reference and actual evapotranspiration, soil moisture, and streamflow. Mean errors for all analyses were less than 7%. The water and energy balance model results can benefit such diverse fields as agriculture, ecology, coastal water management, human health, renewable energy development, water resources, drought monitoring, and disaster and emergency management. This research represents a preliminary step in developing a suite of gridded hydro-climate products for the Caribbean Region.

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.

Evaluations of MODIS and microwave based satellite evapotranspiration products under varied cloud conditions over East Asia forests

Satellite remote sensing is an important tool to retrieve terrestrial evapotranspiration (ET). Widely-used MOD16 ET product (MOD-ET) is a representative of Penman-Monteith method coupled with MODerate Resolution Imaging Spectroradiometer (MODIS) optic observations. Although MOD-ET has been extensively evaluated over the world, its accuracy under various cloud conditions remains unevaluated. Combining MODIS-observed cloud cover (Frc) and in-situ measurements at sixteen forests sites in East Asia, we evaluated 8-day MOD-ET and its primary MODIS inputs (i.e. LAI, FPAR and albedo) from clear to cloudy sky. A new satellite microwave ET method based on microwave Emissivity Difference Vegetation Index (EDVI-ET) was also compared with MOD-ET. Results showed that the accuracy of MOD-ET was highly variable under the changing Frc over the forests. The largest bias (>30%) in MOD-ET was found under Frc < 10%, while slight increase of Frc (10%–30%) reduced the bias (10%–20%). Further increase of Frc (>30%) deteriorated the bias in MOD-ET (20%–30%). In contrast, EDVI-ET performed stably with lower bias (<13%) under similar sky conditions. A higher Taylor score (0.84) for EDVI-ET was found under Frc of 4% than MOD-ET (0.66), while two ET had close scores (>0.81) under other sky conditions. Further investigation found that MOD-ET over four tropical coastal forests contributed most to the bias, especially under least cloudy sky. A case study at a tropical forest showed that MODIS LAI/FPAR and surface albedo products were overestimated, which could directly cause the overestimation of canopy-scale conductance and the underestimation of net solar radiation in MOD-ET method, respectively. Analysis showed that the bias in MOD-ET was significantly related to the bias in MODIS LAI under various Frc, but it was weakly related to that in MODIS albedo, suggesting that LAI-based conductance might dominate the overestimation of MOD-ET. During a consecutive cloud cover period when fewer reliable MODIS pixels are available, slight increase of clouds partly reduced MODIS-observed signals of LAI/FPAR and increased those of albedo over the tropical forest, resulting in the lower bias in MOD-ET. More clouds reduced surface MODIS albedo and might increase the uncertainty in all-sky shortwave radiation from reanalysis data, which deteriorated MOD-ET accuracy under overcast sky. Our study highlighted the importance of cloud impacts on the satellite ET estimation.

Remote Sensing of Ecosystem Water Use Efficiency: A Review of Direct and Indirect Estimation Methods

Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon–water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon–water coupling mechanisms and vegetation functions–climate feedbacks to serve for the future global carbon neutrality.

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.

Correcting Thornthwaite potential evapotranspiration using a global grid of local coefficients to support temperature-based estimations of reference evapotranspiration and aridity indices

Abstract. Thornthwaite’s formula is globally an optimum candidate for large scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has the lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (former FAO-56), which is the most data demanding method and is commonly used as benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature and Er data of the period 1950–2000 at 30 arc-sec resolution (~1 km at equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er / Ep ratios by setting the ratios’ weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep can be assessed using the following link: https://doi.pangaea.de/10.1594/PANGAEA.932638 (Aschonitis et al., 2021).

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.

Reconstruction of remotely sensed daily evapotranspiration data in cloudy-sky conditions

The unavailability of thermal infrared satellite observations in cloudy conditions has limited the spatial distribution and temporal coverage of remotely sensed evapotranspiration (ET) data. As a result, a number of approaches have been developed to reconstruct remotely sensed daily ET data in cloudy-sky conditions. Despite the wide application of these approaches, no work has been conducted to compare their performance over a wide variety of climatic and vegetative conditions. In this study, three commonly used ET reconstruction approaches namely, reference ET fraction (EToF), land surface temperature reconstruction (LSTR), and variational data assimilation (VDA) are used to obtain daily ET data under cloudy conditions. The abovementioned three approaches are applied to the Heihe River Basin (HRB) in the northwest of China during the growing season in 2015. The HRB covers an area of approximately 1,432,000 km2 and contains various land covers. The large aperture scintillometer (LAS) and eddy covariance (EC) measurements are used to evaluate performance of the abovementioned three methods over the grassland, cropland, and riparian forest sites in the HRB. The results show that the EToF approach underestimates ET at the grassland and riparian sites in cloudy days because of the negatively biased ET/ETo (where ETo is the reference evapotranspiration) in clear-sky days. The VDA approach overestimates ET in cloudy-sky conditions because of the overpredicted evaporative fraction values. The LSTR approach overestimates ET due to the under-reconstructed LST in cloudy day. The mean absolute percentage difference (MAPD) and root mean square error (RMSE) statistical metrics are used to compare performance of the three approaches The LSTR (MAPD = 43.1% and RMSE = 1.3 mm/day) and VDA (MAPD = 46.7% and RMSE = 1.4 mm/day) approaches slightly outperform EToF (MAPD = 46.6% and RMSE = 1.5 mm/day). The RMSEs of reconstructed ET values by EToF, VDA, and LSTR increase respectively from 1.6, 1.2, and 1.5 (mm/day) to 2.2, 1.9, and 2.0 (mm/day) as the number of consecutive cloudy days increases from 1 to 3. These outcomes suggest that synergistic use of space-borne microwave and thermal infrared LST observations into remote sensing-based ET methods can improve the reconstruction of ET data under cloudy conditions.

Application of the Hargreaves method for calculating the potential evapotranspiration in the hydrological modelling

To improve the results of the Slovenian Environment Agency’s hydrological forecasting system, especially in the river basins with lower specific runoff (Pomurje) and during high water events following a long dry period, we decided to find a good method for calculating daily values of the potential evapotranspiration (PET). We were deciding between several temperature-based methods for the daily reference evapotranspiration (ET0) values calculation. For selected meteorological stations we calculated ET0 using three different methods and then compared them to the ET0 values calculated using the much more complex Penman-Monteith method. Among the tested temperature methods the results given by the Hargreaves method fitted best to the results of the Penman-Monteith method. The reason for this may lie in the fact that the Hargreaves method, besides the mean daily air temperature as other temperature-based methods, considers the daily temperature range as well. Afterwards, considering the ground cover factor, we calculated the PET values from the ET0 values and then applied them in the hydrological modelling. The model setups for the Sava, Soča, and Mura Rivers were reanalysed twice, considering firstly the climatologic monthly PET values that were already used in the hydrological forecasting system of the Slovenian Environment Agency for many years, and, secondly the daily PET values calculated according to the Hargreaves method and using hourly air temperature 2 m above the ground, originating from the short-term weather forecasting model ALADIN or the INCA/AT meteorological system. At all selected calculation points, the model setups using daily PET values showed better performance over the model setups using climatological monthly values.

Reference Evapotranspiration (ETo) Methods Implemented as ArcMap Models with Remote-Sensed and Ground-Based Inputs, Examined along with MODIS ET, for Peloponnese, Greece

The present study develops ArcMap models to implement the following three methods: FAO-56 Penman–Monteith (FAO PM), Hargreaves–Samani (HS) and Hansen, with the former used as a reference. Moreover, three models implementing statistical indices (RMSD, MB, NMB) are also created. The purpose is threefold, as follows: to investigate the variability in the daily mean reference evapotranspiration (ETo) for the Decembers and Augusts during 2016–2019, over Peloponnese, Greece. Furthermore, to investigate the agreement between the methods’ ETo estimates, and examine the former along with MODIS ET (daily) averaged products. The study area is a complex Mediterranean area. Meteorological data from sixty-two stations under the National Observatory of Athens (NOA), and MODIS Terra LST products, have been employed. FAO PM is found sensitive to wind speed and depicts interactions among climate parameters (T, evaporative demand and water availability) in the frame of climate change. The years 2016–2019 are four of the warmest since the preindustrial era. Hargreaves–Samani’s estimations for the Decembers of 2016–2019 were almost identical to MODIS ET, despite their different physical meaning. However, for the Augusts there are considerable discrepancies between the methods’ and MODIS’s estimates, attributed to the higher evaporative demand in the summertime. The GIS models are accurate, reliable, time-saving, and adjustable to any study area.

Forecasting evapotranspiration equation based on Hargreaves Evapotranspiration Method for Semangat Village, Merdeka Sub-district, Karo Regency, North Sumatera Province, Indonesia

Providing water according to plant water requirement is a problem faced by many farmers because it’s related to cost of production and water scarcity. Some methods of calculating evapotranspiration are very difficult for farmers to use because they require some meteorological data. Simplifying the use of calculation equation is needed to make it easier for farmers to determine plant water requirements. The aims of study were to forecast evapotranspiration equation based on Hargreaves Evapotranspiration Method for Semangat Village. The methods divided two steps, they were observing potential evapotranspiration using Hargreaves Evapotranspiration method, and forecasting evapotranspiration equation. The results show potential evapotranspiration (ETo) in Semangat Village were 2.9 – 7.1 mm/days during January until July, 2020. Forecasted evapotranspiration equation for Semangat Village y = 0.444x – 4.0964 with R2 = 0,435 for daily average temperature, y = 0.2265x – 1.9029 with R2 = 0.8518 for daily maximum temperature, and y = -0.2137x + 8.6148 with R2 = 0.1361 for minimum temperature. The conclusion shows that the formula y = 0.2265x – 1.9029 where x is daily maximum temperature is the best formula which can be used to forecast evapotranspiration in Semangat Village, Merdeka Sub-district, Karo Regency, North Sumatera Province, Indonesia.

Spatial‐temporal evaluation of different reference evapotranspiration methods based on the climate forecast system reanalysis data

AbstractEvapotranspiration is a major component of the interaction between land‐surface processes and the atmosphere. Climate Forecast System Reanalysis (CFSR) data offer a promising database for overcoming the limitations in availability and reliability of climatological data and, hence, for understanding the evapotranspiration process. Using these data on grid‐by‐grid daily, seasonal and yearly scales, the present study attempts to advance the spatio‐temporal evaluation of two radiation‐based and three temperature‐based methods for estimating potential evapotranspiration (PET) against estimates of grass reference evapotranspiration (ETo) by FAO Penman–Monteith method (FAO‐PM). The analysis was performed for the period 1979–2013, considering the second largest (79 000 km2) river system in Ethiopia, that is, Omo‐Gibe basin, which accommodates national parks and vast hydropower, cultivation and afforestation developments and discharges its flow to Lake Turkana in Kenya. Despite the large regional variations in climate and elevation, the results in overall emphasize the outperformance of the simple temperature method, viz. Hargreaves–Samani method, in capturing both the annual and seasonal FAO‐PM estimates. Calibration of the Hargreaves–Samani equation is, however, a requisite for spectacular improvement of its performance. Accordingly, new coefficients of the equation are proposed. The annual trends in the basin's ETo increased with rising temperature and decreasing relative humidity, wind speed, and solar radiation, but with decreasing (increasing) rainfall in the upper region (the middle and lower regions). It is deduced that trends in simple methods do not necessarily reflect the true trends in ETo. Annual ETo decreases with increasing elevation and annual rainfall. The present findings are discussed in the context of a worldwide literature, thereby improving the understanding of the best performing PET methods in similar data‐scarce national or transboundary rivers basin in Ethiopia, the region or worldwide. The wider implications regarding water loss from reservoirs and the rain‐fed food and sugar production in the basin under study are also highlighted.

A method for partitioning ecosystem evapotranspiration based on fluxnet data

A method for partitioning ecosystem evapotranspiration based on fluxnet data

Evapotranspiration partitioning for multiple ecosystems within a dryland watershed: Seasonal variations and controlling factors

Partitioning of evapotranspiration (ET) into evaporation (E) and transpiration (T) is challenging but important to better understand the mechanisms of water loss from the ground surface to the atmosphere, especially in semiarid and arid regions. In this study, the underlying water use efficiency (uWUE) method is compared with hydrometric and remote sensing-based ET partitioning methods. The uWUE method is considered to be a promising method for partitioning ET, and can also be used to obtain temporal continuous data with fine spatial representation. Thus, the uWUE method was used to partition ET in six typical ecosystems in the Heihe River basin (HRB), which is the second largest endorheic river basin in western China. During 2008–2016, the daily average contributions of T to ET (T/ET) were 0.53, 0.52, 0.59, 0.37, 0.56, and 0.59 in the alpine meadow, Qinghai spruce, maize, desert, Tamarix, and Populus euphratica–Tamarix ecosystems, respectively. The T/ET ratio exhibited obvious seasonal variations in alpine meadow, maize, Tamarix, and Populus euphratica–Tamarix ecosystems. The ET partitioning results were related to air temperature in all ecosystems, especially in areas with sufficient precipitation or irrigation water supply. The vapor pressure deficit was also a main controlling factor in the upper- and middle-reach ecosystems, especially in the Qinghai spruce ecosystem. Groundwater and/or soil moisture made high relative contributions (RCs, %) to the T/ET ratio of the riparian forest in the lower reaches. Additionally, the leaf area index also made a high RC to the T/ET ratio of deciduous vegetation. Determining the quantitative contribution of T to ET in the HRB is beneficial for water resource management to guide the rational allocation and efficient use of water resources.

Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon

In recent years, the degradation of the Amazon forest has been the target of studies at the global level to identify elements and factors influencing this process. Thus, this study had as objective to study the vegetation degradation during the years of El Niño Southern Oscillation (ENSO) phenomenon in the State of Amazonas. We used remote sensing data, forest typologies, and weather elements from 14 conventional stations located in Amazonas. Evapotranspiration images obtained from the MODIS Global Evapotranspiration Project (MOD16A2), and data from fire sources from the Terra and Aqua collection (MOD146) platforms were used. The 6 different forms of forest typologies found in the region were also used, in addition to the climatic elements maximum, minimum temperature, relative humidity, sunshine and wind speed of 14 conventional stations in the state of Amazonas. The weather station data were used to estimate the reference evapotranspiration, using the empirical methods Val-1, Val-2, Val-3, Val-4, Irmak-1, Irmak-2 and Alex. The results show differences in the correlations between the evapotranspiration methods used. The highest evapotranspiration was found in the Open Ombrophilous Forest. Fire foci were concentrated in Dense Ombrophilous Forest areas. The Standardised Precipitation-Evapotranspiration Index (SPEI) showed to be an efficient index to characterize the drought in El Niño events, besides showing the agricultural expansion in the region. The largest trends of the weather elements and the SPEI were during the El Niño phenomenon. A better understanding of the ENSO effects on vegetation could prevent uncontrolled forest loss. Governance interventions involving national and financial actors, as well as local populations, also play a role in this.

Partitioning evapotranspiration by measuring soil water evaporation with heat-pulse sensors and plant transpiration with sap flow gauges

Understanding the contributions of soil water evaporation (E) and crop transpiration (T) to evapotranspiration (ET) is essential for irrigation management and improving crop water use efficiency. Current methods for partitioning ET are subject to uncertainties, due in part to the scale differences in determining E, T, and ET. In this study, ET of a lysimeter planted with maize (Zea mays L.) was partitioned into E and T using sensors that had compatible measurement scales: E was measured with heat pulse sensors (following the sensible heat balance (SHB) and modified sensible heat balance (MSHB) approaches), and T was measured with sap flow gauges (following the heat-balance sap-flow (HBSF) method). The accuracy of the measurements was evaluated by comparing the sum of E and T values (E + T) to weighing lysimeter ET data. During the study period, E, T, and ET had average values of 0.9, 4.0, and 4.8 mm d−1, respectively, and the fractions of E and T were 19% and 81% of E + T, respectively. In general, the E + T values agreed well with the lysimeter ET data, but slight overestimations and underestimations were observed at relatively small and relatively large ET rates, respectively. By combining the SHB and MSHB approaches and the HBSF method, it is possible to partition ET into E and T with satisfactory accuracy.