Priestley-Taylor Equation Research Articles

Evaluation of ERA5, ERA5-Land, GLDAS-2.1, and GLEAM potential evapotranspiration data over mainland China

Study regionChina. Study focusAccurate estimation of potential evapotranspiration (PET) is essential for understanding climate change. Using ground-based pan evaporation measurements over continental China, the monthly scale PET data during 2000–2017 of ERA5, ERA5-Land, GLDAS-2.1/Noah, and GLEAM V3.8a are evaluated, from the perspectives of their consistency in spatiotemporal variation, and performance measures. Factors controlling the data quality of the four datasets are investigated from the perspective of their PET calculation models and meteorologically input data. New hydrological insights for the regionPETERA5 performs the best in mainland China among four gridded PET datasets with higher correlation coefficients (r) and smaller biases, which can well capture the temporal variation of Epan. The outstanding performance of PETERA5 in China mainly results from the utilization of the Penman-Monteith (P-M) equation which performs the best among several competing formulas for PET computation, as well as its better meteorological inputs for computing PET than other datasets. Although the PETERA5-Land is a replay of the land component of the ERA5 climate reanalysis, it exhibits substantial overestimation of PET values and temporal trends, particularly in coastal areas of Southern China and the eastern side of Northeastern China, mainly caused by the overestimation of its net radiation. The PETGLDAS shows significant overestimation, partly due to its overestimation of wind speed, but mostly due to its modified P-M equation with its parameterization of land surface conditions for computing PETGLDAS. The PETGLEAM underestimated PET generally mainly due to the joint effect of the use of the Priestley-Taylor equation with small P-T parameter α, and the underestimation of the net radiation input from ERA-Interim, especially in Northwest and Qinghai Tibet.

Combining the Generalized Complementary Relationship and the Modified Priestley-Taylor Equation to estimate and partition the evapotranspiration of typical plantations and grasslands in the Loess Plateau of China

Combining the Generalized Complementary Relationship and the Modified Priestley-Taylor Equation to estimate and partition the evapotranspiration of typical plantations and grasslands in the Loess Plateau of China

Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial–temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76–0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30–0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling.

Accuracy of methods for simulating daily water surface evaporation evaluated by the eddy covariance measurement at boreal flux sites

Evaporation from the surface of water is a vital component of the hydrological cycle. Accurately simulating evaporation is crucial for modeling hydrological processes, ensuring ecosystem water efficiency, and managing water resources. Previous methods for evaluating evaporation have generally been limited to a single site and on a regional scale. Based on eddy covariance measurements of 14 boreal flux sites, the accuracy of 26 methods for simulating daily water surface evaporation was comprehensively evaluated in this study. Most methods accurately simulated daily evaporation at most sites with a KGE >0.60. The combination methods with simultaneous calibration of the energy and aerodynamic terms and the double-parameter aerodynamic methods based on the linear and exponential functions showed the best performance, with the median KGE of the evaluated sites ranging from 0.72 to 0.76. The Bowen ratio energy balance (BREB) method embedded with Tw, Rs-based two-variable empirical methods, double-parameter aerodynamic methods, and the combination method of the energy term based on net radiation (Rn) and the aerodynamic term based on the exponential function method performed better than the other methods of the same type. The relative error (RE) simulated by most methods was generally within ± 30 %, with the median RE of all sites within ± 10 % for each method. The combination methods tended to overestimate the level of evaporation, whereas the BREB-type and aerodynamic methods tended to underestimate the extent of evaporation. The simulation accuracy of the daily evaporation showed significant variance among the sites. It showed the best model performance at two FLUXNET and two sites taken from the literature, in which 75 % of the methods were able to accurately simulate daily evaporation with a Kling–Gupta efficiency (KGE) >0.80. All methods performed poorly at the three sites from the literature and were mainly due to the lack of measured variables with a strong correlation with the latent heat flux (LE). The FLUXNET sites generally showed better performance. The observed LE of the sites with the best model performance generally showed a unimodal distribution throughout the year. The simulation accuracy of non-unimodal distribution sites mainly depended on the correlation between the LE and related variables. The combination of the energy term based on Rn and the aerodynamic term based on the power function method showed the best model stability. This finding indicated that the calibrated method was robust and showed high stability in simulating daily evaporation, and it was recommended for application in other sites or regions. The optimization of parameters calibrated in this study may improve the accuracy of simulating the daily evaporation for the application of the Penman and Priestley–Taylor equations when the water heat storage measurement is missing.

Effects of the new Priestly-Taylor equation on determining the boundary of LST/FVC space for soil moisture monitoring

Land Surface Temperature and Fractional Vegetation Coverage (LST/FVC) space is a classical model in remote sensing of soil moisture (SM). Its most vital issue is the determination of the boundary i.e. dry and wet edges. Visual interpretation and automatic fitting methods are very demanding for the research area. In contrast, theoretical calculation of the boundary has great application potential, where the traditional Priestley-Taylor (PT) equation has been introduced to derive a Sun2016 method. Recently, a new Priestly-Taylor equation was suggested. In order to furtherly improve the optical & thermal remote sensing of SM, we evaluated the new PT equation for boundary determination through deriving a Sun 2021 method. The evaluation was conducted using data from three aircraft experiments for SM observation i.e. SMAPVEX12, SMAPVEX16 in Iowa, and SMAPVEX16 in Manitoba. Simulated data with the Simsphere model was also used. Results demonstrated that the effects of the new PT equation are related to air temperature (Ta). For a certain range of Ta such as from 290 K to 310 K, the Sun 2021 method is close to the Sun2016 method, which implies limited influence of the new PT equation within that range. For Ta out of that range, the Sun 2021 method presented better performance than the Sun2016, which implies stronger suitability of the new PT equation. Sensitivity analysis indicated that the new PT equation increases the sensitivity of calculated wet edge to Ta while decreases its sensitivity to the other input variables. For future extensive application, we also explored convenient ways for determining some essential parameters in the Sun 2021 method. The new PT equation has potential to promote the optical & thermal remote sensing of SM, evapotranspiration, drought, etc.

Seasonal variation of evapotranspiration, Priestley-Taylor coefficient and crop coefficient in diverse landscapes

Priestley-Taylor equation (PT) and the Penman-Monteith equation (PM) are commonly used methods for regional evapotranspiration monitoring, using the PT coefficient (αa) and PM crop/ vegetation coefficient (Kc). This paper investigates the seasonal changes in αa and Kc at five sites in Australia and China, to understand the relationship between environmental conditions and evapotranspiration when applying different evaporation estimation methods. The research shows that higher actual evapotranspiration does not lead to higher αa and Kc values. αa and Kc perform similarly in cropland and forest environments in both China and Australia. Both αa and Kc continuously increase to a peak during the growing season and then decrease to their lowest values during the winter season. Considering Kc’s similar performance to αa and its greater data processing requirements, Kc has few advantages for estimating regional evapotranspiration. Applying the Priestley-Taylor equation with a regional α indicator will enhance the accuracy and reduce the workload when estimating regional evapotranspiration for similar landcover types based on remote sensing.

Application of SEBAL and Ts/VI Trapezoid Models for Estimating Actual Evapotranspiration in the Algerian Semi-Arid Environment to Improve Agricultural Water Management

Abstract Accurate spatio-temporal estimation of evapotranspiration (ET) and surface energy fluxes is crucial for many agro-environmental applications, including the determination of water balance, irrigation scheduling, agro-ecological zoning, simulation of global changes in land use and forecasting crop yields. Remote sensing based energy balance models are presently most suitable for estimating ET at both temporal and spatial scales. This study presents an intercomparison of ET maps over the Habra plain in western Algeria obtained with two different models: Ts/VI trapezoid (Surface temperature/Vegetation Index Trapezoid Model) and SEBAL (Surface Energy Balance Algorithm for Land). Ts/VI trapezoid is the most used model, due to its simplicity, ease of use, few data input requirements and relatively high accuracy. It allows estimating ET directly by using the Priestley-Taylor equation. Whereas SEBAL allows estimating ET as the residual term of the energy balance equation, by using a rather complex hot and cold pixel based contextual approach to internally calibrate sensible heat flux through an iterative approach. The data set consists of four Landsat-8 OLI/TIRS images acquired on 2018-2019 and some ground measurements. In conclusion, the results show that SEBAL and Ts/VI trapezoid models provide comparable outputs and suggest that both the two models are suitable approaches for ET mapping over agricultural areas where ground measurements are scarce or difficult to collect.

Utilizing digital image processing and two-source energy balance model for the estimation of evapotranspiration of dry edible beans in western Nebraska

Having an accurate yet simple method to estimate crop evapotranspiration (ETC) is a vital component of reliable irrigation scheduling. In this study, two versions of the two-source energy balance (TSEB) model: the TSEB model with the Priestley–Taylor equation (TSEB-PT) and the Penman–Monteith equation (TSEB-PM), were used to estimate ETC of dry edible beans in western Nebraska. Compared with previous studies, this study is unique in that a Visual Basic software—Crop Canopy Image Analyzer (CCIA) was developed to process digitally captured RGB canopy images to obtain necessary canopy cover (CC) parameters for the TSEB models such as CC percentage and leaf shape factor (leaf area divided by its perimeter). Software-estimated CC percentage was closely correlated with commercial sensor-derived CC percentage with an R2 of 0.96. Additionally, estimated leaf shape factor was closely correlated with measured leaf shape factor with R2 of 0.99. Both TSEB-PT and TSEB-PM models estimated ETC well for fully irrigated dry edible beans with a root-mean-square error (RMSE) that ranged from 0.95 to 1.63 mm day−1 in 2018, and 0.75 to 1.35 mm day−1 in 2019, as compared to ETC estimated from FAO56. Furthermore, ETC from TSEB-PT and TSEB-PM were compared with a soil water balance-derived ETC and the RMSE ranged from 2.03 to 9.65 mm in an approximate 1-week period under four irrigation treatments ranging from dry land to fully irrigated. The proposed methods in this study, by integrating digital image processing with TSEB models, have great potential to be automated and used in field-scale operations for various irrigation management scenarios of many crops.

Connotation analysis of parameters in the generalized nonlinear advection aridity model

The generalized nonlinear advection aridity model (GNAA) for evaporation (E) estimation can be expressed in a basic form with a single parameter αe-0, or an extended form using two parameters, αe-c and c. The implications of these model parameters in the model and the accurate estimation of E are receiving increasing attention. Our study shows that αe-0 and αe-c are affected by precipitation (P), the aridity index (Epa/P) and the climate seasonality and asynchrony index (SAI), etc, with which αe-0 has stronger correlations. This demonstrated that annual αe-0 and αe-c could cover the α parameter in the Priestley–Taylor formula, as well as other factors, particularly the aridity index. For the basic GNAA form, annual αe-0 is smaller than 1.00 in most catchments, but the GNAA model with the developed empirical formula between αe-0 and Epa/P can accurately estimate E at an annual scale. For the extended GNAA form, most annual αe-c were larger than 1.00, with a mean value of 1.08 for the Loess Plateau; αe-c tended to restore the original α values in the Priestley-Taylor equation. Functional differences exist between the basic and extended GNAA forms in estimating E and explaining the complementary relationship. Our results bridge some gaps in understanding the GNAA model from previous studies, and provide useful information to extend the application of the GNAA model.

A novel approach to the use of earth observation to estimate daily evaporation in a sugarcane plantation in Xinavane, Mozambique

Efficient irrigation water management for an 18,000 ha sugarcane plantation in Xinavane in southern Mozambique is a challenge. Sugarcane is an irrigation intensive crop and its productivity is sensitive to water stress. Options to adopt field water management best practices and proper irrigation scheduling are limited due to the lack of plot-level information on the actual crop water use and stress levels throughout the growing season. Due to heterogeneity in cropping calendar within the sugarcane plantation, at a certain point of time, different plots are at different growth stages. This makes scheme level irrigation scheduling complex and calls for frequent crop water use information. To fill this gap, this study presents a novel approach where a combination of satellite imagery with local weather data is used to provide daily evaporation rates. The Priestley–Taylor equation is applied to quantify evaporation (soil evaporation + transpiration) using radiation and temperature data from a meteorological station and spatial albedo estimates derived from the Sentinel-2 satellites. The results show 20 meter resolution maximum crop evaporation estimates can be derived with the proposed methodology. Additionally, the results show NDVI in the last two crop stages is able to distinguish between poor and good performing fields. Therefore, NDVI can be a useful index to estimate actual evaporation. First, the evaporation estimates were corrected for the crop stage using NDVI proxies and an additional stress indicator was used to calculate the actual evaporation flux spatially. The spatial evaporation estimates provide the water manager with information on actual crop water use and biomass development, which is relevant to both crop monitoring and irrigation management water management when drought-related stress is filtered.

Water balance of several subarctic catchment areas

The scientific article by American hydrologists (Bolton et al. in Water balance dynamics of three small catchments in a Sub-Arctic boreal forest, 2004. https://pdfs.semanticscholar.org/c06b/7bad86cc15c5625fef0d847748bd9468f989.pdf) which is devoted to the water balance of three subarctic catchment areas, evoked great interest and encouraged to continue research in this field. The article deals with water balance elements of three small subarctic catchment areas near Fairbanks, Alaska, USA, published on the Internet (Bolton et al. 2004). The catchment area under study is Cariboo-Pocker Creek (CACPC). It is 48 km to the north of Fairbanks, Alaska. CACPC is situated in the tundra, its territory is 101.5 km2 and the soil below the surface of the ground is always frozen. Three catchment areas within CACPC are: C-2 (5.2 km2), C-3 (5.7 km2) and C-4 (11.4 km2). The catchment areas consist of 3%, 53% and 19% of permafrost, respectively. Taking into consideration the information from the American research, it turned out to be rather interesting to compare some water balance parameters presented in the article with those which are calculated with the help of a new method (Iofin in Proceedings of VI all-Russia hydrology congress, vol 1, pp 62–67, 2004). This new method is based on the linear-correlation theory and linear-correlation equation of water balance. This method has an effect in the catchment areas where hydrological observations are not carried out. Apart from this comparison, it is also of great interest to calculate the value of infiltration capacity in permafrost areas. It is also compelling to learn how accurately evapotranspiration in permafrost areas with lack of observations can be calculated with the help of rather popular Priestley–Taylor equation. The accuracy of calculation of evapotranspiration in a number of regions in the temperate zone made by Russian scientists with the help of different existing empirical formulae was under consideration. It is proved to be unsatisfactory. We had a rare opportunity to use basic data from the permafrost area and to calculate infiltration capacity and evapotranspiration. The comparison of the values of evapotranspiration shows that the calculations made with the help of the linear-correlation model (LCM) and Priestley–Taylor equation differ considerably.

Seasonal and Diurnal Variations in the Priestley–Taylor Coefficient for a Large Ephemeral Lake

The Priestley–Taylor equation (PTE) is widely used with its sole parameter (α) set as 1.26 for estimating the evapotranspiration (ET) of water bodies. However, variations in α may be large for ephemeral lakes. Poyang Lake, which is the largest freshwater lake in China, is water-covered and wetland-covered during its high-water and low-water periods, respectively, over a year. This paper examines the seasonal and diurnal variations in α using eddy covariance observation data for Poyang Lake. The results show that α = 1.26 is overall feasible for both periods at daily and subdaily scales. No obvious seasonal trend was observed, although the standard deviation in α for the wetland was larger than that for the water surface. The mean bias in evaporation estimations using the PTE was less than 5 W·m−2 during both periods, and the root mean square errors were much smaller than the average evaporation measurements at daily scale. U-shaped diurnal patterns of α were found during both periods, due partly to the negative correlation between α and the available energy (A). Compared to the vapor pressure deficit (VPD), wind speed (u) exerts a larger contribution to these variations. In addition, u is positively correlated with α during both periods, however, VPD was positively and negatively correlated with α during the high-water and low-water periods, respectively. Subdaily α exhibited contrasting clusters in the (u, VPD) plane under the same available energy ranges. Our study highlights the seasonal and diurnal course of α and suggests the careful use of PTE at subdaily scales.

Numberical Simulation of Evapotranspiration with Limited Data in the Pearl River Basin

This research compared and analyzed the adaptation of four evapotranspiration equations: the FAO56 Penman-Monteith (PM), Priestley-Taylor, Hargreaves-Samani and Thornthwaite equations by using the day-to-day meteorological data of the 42 observation stations in the Pearl River basin. Numberical simulation of the four equations in different seasons and regions was made in order to find out the most suitable one for the evapotranspiration estimate in the Pearl River basin. Results show that the calculation results of the PM equation seem more stable in different seasons and regions, and is closer to the actual observation, due to the detailed consideration of the effects of aerodynamics and energy supply on evapotranspiration in its calculation procedure. The missing data of relative humidity, sunshine duration, wind speed and air pressure would have influence on the calculation accuracy of the PM equation. Temperature can be used to instead of the above variable in the PM equation, which is called the PMT equation. The applicability and the calculation accuracy is explored in this study. And the results show that the calculation results of the PMT equation is larger than those of the PM equation. By setting a correction coefficient in the PMT equation, the calculation accuracy can be obviously improved. Therefore, in the absence of other data, the PMT equation calculated only using temperature data can be use as an ideal equation for the evapotranspiration calculation and research in the Pearl River basin.

Inferring transpiration from evapotranspiration: A transpiration indicator using the Priestley-Taylor coefficient of wet environment

Abstract Transpiration (T) from plant tissues is an important variable in ecological and agricultural studies. T is usually estimated using the Priestley-Taylor (PT) equation by an analog to the wet environment evaporation (ETw), because vegetation can use water from deep soils. However, the relation between the PT coefficients of T and ETw (αc and αe) was still unclear. In this paper, we tested whether αe can be used as a reasonable indicator of αc at intra-annual and annual scales. We obtained αe by inverting a complementary relationship model with measured evapotranspiration (ET) at the flux sites and obtained αc after ET was partitioned into evaporation (E) and T. We found that αc tends to converge to αe at annual scales, and a linear equation can adequately fit the αc = f(αe) functions at annual scales. With the αc = f(αe) functions determined, satisfactory accuracy (R2 = 0.89) was achieved in estimating T from ET following the “ET → αe → αc → T” calculating process at annual scales. In comparison, the αc ~ αe relationship at 8-day scales are modulated by leaf area index and vapor pressure deficit. However, similar αc = f(αe) functions are identified in the region with the same climate type at 8-day scales, indicating that a single αc = f(αe) function is applicable in the same region. The accuracy in T predictions at 8 day scales deteriorated compared to those at annual scales. However, R2 were still larger than 0.58 at most of the sites. Because great progress in ET estimation has been achieved with the help of thermal remote sensing technique in the last two decades, our results here can provide a new method to estimate T from remotely sensed ET.

Modeling Actual Evapotranspiration using the Advection Aridity Model

Evapotranspiration is a major process in the hydrological cycle. Between 40 to 60% of precipitation is returned back to the atmosphere in the form of evapotranspiration. Therefore, estimation of Actual Evapotranspiration (AET) is important in studies related to hydrology, agriculture and climate. However, due to the fact that calculation of AET using conventional methods require data pertaining to climate, vegetation/crop and soil moisture levels, estimation of spatial AET becomes a very tedious task. The Complementary Relationship Areal Evapotranspiration (CRAE) hypothesis has been proposed as a simpler alternative to estimate AET from large spatial domains using only routinely observed meteorological data thereby circumventing the need for data on soil-vegetation. The objective of the present work was to apply the CRAE hypothesis in Karnataka State, India and evaluate the accuracy of estimates using MODIS satellite-derived AET estimates. The Advection Aridity (AA) model which is based on the CRAE hypothesis was chosen to estimate AET using historical climate records of the Afzalpur and Hungund stations. The AA model uses the Penman equation to estimate the Potential ET and the Priestley-Taylor equation to estimate Wet Environment ET which are needed to estimate AET. Accuracy of AET values obtained from the AA model were validated using the MODIS MOD16A2 evapotranspiration product, which provides an 8-day composite AET estimate at 500 m resolution. AET estimated by the AA model compared favorably with MOD16A2 with the average Coefficient of determination (R2)for the two stations being 0.8095. While the results of this study have shown that the AA model provides reasonably accurate AET estimates with minimum data, further studies need to be carried out to improve the accuracy through local calibration of parameters.

Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran

Improving the accuracy of single crop coefficient (Kc) for major crops at regional scales would significantly result in optimizing the consumption of agriculture water. In this study, a new approach is applied to generate Kc curves from satellite images based on the Priestley-Taylor equation. The equation mainly consists of shortwave radiation and thermal terms. Therefore in order to meet the equation’s requirements, the Landsat optical data was considered as the essential satellite-based input of the approach, yet thermal data was obtained from the MODIS imagery which was primarily downscaled using the TsHARP algorithm to fit the 30 m spatial resolution of Landsat; therefore, in this study the method is referred to as the MultiSensor Data Fusion for potential EvapoTranspiration calculation (MSDF-ET). The Kc trends were first evaluated for annual crops (forage maize, sugar beet, wheat, and barley) in two different regions (the Qazvin and Tehran-Karaj plains) using field observation data of Leaf Area Index (LAI). The method was then applied to the major crops in 31 selected plains with three different climatic conditions in Iran. Climatic conditions were categorized into cold (North West region), arid (Central region), and warm and humid (South West region). Eventually, crop water requirements (CWR) in different regions were compared considering long-term air temperature, wind, and relative humidity (RH) variations. Evaluation of the method in the Qazvin and Tehran-Karaj plains showed promising results. The Kc trend varied according to the LAI and temperature variability. Crop growth duration and especially Kc curves were successfully extracted using remotely sensed data in the studied areas. CWR in the zone with higher values of air temperature, notably in warm seasons, was higher in comparison with other climatic zones. Also lengths of the growing seasons were shorter. In conclusion, surface and groundwater resources management would be improved as the result of taking satellite data into account for Kc and subsequently CWR calculations.

Evapotranspiration estimation using SMAP soil moisture products and bouchet complementary evapotranspiration over Southern Great Plains

Evapotranspiration (ET) is an important water withdrawal in the water balance, particularly in arid and semiarid regions. In this study, a formulation to derive the relative evapotranspiration (β) and ET from in situ and passive microwave data, is presented. The original Komatsu's β equation was modified by introducing a calibration parameter to eliminate the wind speed effects. Then, a simple formulation to estimate mean daily ET was obtained by combining the β parameter, Bouchet's complementary relationship, and Priestley-Taylor's equation. Estimates of β and ET were compared with ground observations in the study area, Southern Great Plains (SGP). These comparisons suggest that β characterizes the relative evapotranspiration behavior in the region. Besides, ET results show a good agreement with ground data, yielding a root mean square error and bias of 28.5 and −6 W/m2, respectively. The proposed approach took advantages of passive microwave images, combining them with ground observations in a simple and robust equation.

Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates

Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. Approaches for accurate decomposition of the component temperature and E/T partitioning from ET based on TSEB requires careful investigation. In this study, three TSEB models are used: (i) the TSEB model with the Priestley-Taylor equation, i.e., TSEB-PT; (ii) the TSEB model using the Penman-Monteith equation, i.e., TSEB-PM, and (iii) the TSEB using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space, i.e., TSEB-TC-TS. These models are employed to investigate the impact of component temperature decomposition on E/T partitioning accuracy. Validation was conducted in the large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research-Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE) in the northwest of China, and results showed that root mean square errors (RMSEs) of latent and sensible heat fluxes were respectively lower than 76 W/m2 and 50 W/m2 for all three approaches. Based on the measurements from the stable oxygen and hydrogen isotopes system at the Daman superstation, it was found that all three models slightly overestimated the ratio of E/ET. In addition, discrepancies in E/T partitioning among the three models were observed in the kernel experimental area of MUSOEXE. Further intercomparison indicated that different temperature decomposition methods were responsible for the observed discrepancies in E/T partitioning. The iterative procedure adopted by TSEB-PT and TSEB-PM produced higher LEC and lower TC when compared to TSEB-TC-TS. Overall, this work provides valuable insights into understanding the performances of TSEB models with different temperature decomposition mechanisms over semiarid regions.

Intercomparison of three methods to estimate evapotranspiration over temperate meadow in Inner Mongolia: Penman–Monteith, Makkink and Priestley–Taylor equation

AbstractThree methods of calculating evapotranspiration (ET), Penman–Monteith (PM), Makkink (MK) and Priestley–Taylor (PT) equation, were applied to a temperate meadow in Inner Mongolia, China. We compared these methods with eddy covariance (EC) method. The results indicated that a reduction in mean absolute error (MAE) and root mean squared error (RMSE) was achieved for MK and PT methods (45.6–83.0%) when using site‐specific parameters compared with referenced ones, but an increase of 75.9–93.9% for PM method. Intercomparison of three methods through comparison with EC measurements indicated that PT method with site‐specific parameters performed the best to estimate ET, followed by PM method with referenced parameters, and then MK equation with site‐specific parameters (overestimate ET by 8.2, 12.1, 16.5% and 3.4, 12.8, 21.0% respectively for half‐hourly and daily values).

Projecting the potential evapotranspiration by coupling different formulations and input data reliabilities: The possible uncertainty source for climate change impacts on hydrological regime

Representing atmospheric evaporating capability for a hypothetical reference surface, potential evapotranspiration (PET) determines the upper limit of actual evapotranspiration and is an important input to hydrological models. Due that present climate models do not give direct estimates of PET when simulating the hydrological response to future climate change, the PET must be estimated first and is subject to the uncertainty on account of many existing formulae and different input data reliabilities. Using four different PET estimation approaches, i.e., the more physically Penman (PN) equation with less reliable input variables, more empirical radiation-based Priestley-Taylor (PT) equation with relatively dependable downscaled data, the most simply temperature-based Hamon (HM) equation with the most reliable downscaled variable, and downscaling PET directly by the statistical downscaling model, this paper investigated the differences of runoff projection caused by the alternative PET methods by a well calibrated abcd monthly hydrological model. Three catchments, i.e., the Luanhe River Basin, the Source Region of the Yellow River and the Ganjiang River Basin, representing a large climatic diversity were chosen as examples to illustrate this issue. The results indicated that although similar monthly patterns of PET over the period 2021–2050 for each catchment were provided by the four methods, the magnitudes of PET were still slightly different, especially for spring and summer months in the Luanhe River Basin and the Source Region of the Yellow River with relatively dry climate feature. The apparent discrepancy in magnitude of change in future runoff and even the diverse change direction for summer months in the Luanhe River Basin and spring months in the Source Region of the Yellow River indicated that the PET method related uncertainty occurred, especially in the Luanhe River Basin and the Source Region of the Yellow River with smaller aridity index. Moreover, the possible reason of discrepancies in uncertainty between three catchments was quantitatively discussed by the contribution analysis based on climatic elasticity method. This study can provide beneficial reference to comprehensively understand the impacts of climate change on hydrological regime and thus improve the regional strategy for future water resource management.