Parameterization of Four Models to Estimate Crop Evapotranspiration in a Solar Greenhouse.

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman-Monteith, Priestley-Taylor, and Shuttleworth-Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016-2017 and 2019-2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse.

Analysis of factors affecting evapotranspiration zoning.

Reference evapotranspiration (ETo) has a significant role in water resource planning and management as well as analysis of crop production and other agricultural tasks. Methods for estimating ETo may require diurnal/monthly assessments to perceive the consequences of climatic changes on local regions. The spatial and temporal patterns of ETo were analyzed in the current work using data from 340 weather stations in Iran. The entropy theory was used to assess the uncertainty of the utilized variables and the modified Kendall test was applied for temporal trend analysis. The interpolation (e.g., kriging) and ordinary least squares (OLS) methods were used for spatio-temporal ETo classification/modeling. The spatial analysis demonstrated that the OLS method with a good fit measure (R2 = 0.985) successfully simulated the spatial relationships of ETo with climatic parameters. After examining error indices, the cokriging method with an exponential variogram was introduced as the best method of seasonal and annual ETo classification in Iran. Spatially and temporally calculated ETo patterns using modified Hargreaves (MHGR) and MODIS methods closely resembled the standard FAO Penman-Monteith (FPM-56) method, all indicating a gradual increase in ETo. MHGR and MODIS methods serve as suitable alternatives for estimating ETo in various climatic regions of Iran, provided data availability.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

Spatiotemporal variation of drought in the Western Sichuan Plateau based on standardized precipitation evapotranspiration index.

Drought is a destructive natural disaster in the Western Sichuan Plateau. Understanding its spatiotemporal variations has important practical significance for drought prevention, ensuring agricultural production safety, and maintaining ecosystem health in the region. Based on the daily meteorological data from 48 meteorological stations in the Western Sichuan Plateau from 1980 to 2020, we used the Penman-Monteith model to calculate potential evapotranspiration and standardized precipitation evapotranspiration index (SPEI). The temporal and spatial variations of drought in the Western Sichuan Plateau were analyzed using linear trend analysis and drought characteristics analysis methods. The results showed that the annual and spring SPEI of the Western Sichuan Plateau showed a weak wetting trend from 1980 to 2020, while summer, autumn, and winter showed a drought trend. The southwest mountains and northeast grasslands in the study region were prone to drought. The range of interannual drought impact in the study area was weakly increasing, with a decreasing trend in spring and an increasing trend in summer, autumn, and winter. The overall drought frequency in the whole region was relatively high. The areas drought of low-frequency were mainly located in parts of west and northeast of the Western Sichuan Plateau, while the rest were high frequency areas.

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor.

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

Reference evapotranspiration estimate with missing climatic data and multiple linear regression models.

The reference evapotranspiration (ETo) is considered one of the primary variables for water resource management, irrigation practices, agricultural and hydro-meteorological studies, and modeling different hydrological processes. Therefore, an accurate prediction of ETo is essential. A large number of empirical methods have been developed by numerous scientists and specialists worldwide to estimate ETo from different climatic variables. The FAO56 Penman-Monteith (PM) is the most accepted and accurate model to estimate ETo in various environments and climatic conditions. However, the FAO56-PM method requires radiation, air temperature, air humidity, and wind speed data. In this study in Adana Plain, which has a Mediterranean climate for the summer growing season, using 22-year daily climatic data, the performance of the FAO56-PM method was evaluated with different combinations of climatic variables when climatic data were missing. Additionally, the performances of Hargreaves-Samani (HS) and HS (A&G) equations were assessed, and multiple linear regression models (MLR) were developed using different combinations of climatic variables. The FAO56-PM method could accurately estimate daily ETo when wind speed (U) and relative humidity (RH) data were unavailable, using the procedures suggested by FAO56 Paper (RMSEs were smaller than 0.4 mm d-1, and percent relative errors (REs) were smaller than 9%). Hargreaves-Samani (A&G) and HS equations could not estimate daily ETo accurately according to the statistical indices (RMSEs = 0.772-0.957 mm d-1; REs (%) = 18.2-22.6; R2 = 0.604-0.686, respectively). On the other hand, MLR models' performance varied according to a combination of different climatic variables. According to t-stat and p values of independent variables for MLR models, solar radiation (Rs) and sunshine hours (n) variables had more effect on estimating ETo than other variables. Therefore, the models that used Rs and n data estimated daily ETo more accurately than the others. RMSE values of the models that used Rs were between 0.288 to 0.529 mm d-1; RE(%) values were between 6.2%-11.5% in the validation process. RMSE values of the models that used n were between 0.457 to 0.750 mm d-1; RE(%) values were between 9.9%-16.3% in the validation process. The models based only on air temperature had the worst performance (RMSE = 1.117 mm d-1; RE(%) = 24.2; R2 = 0.423).

Implications of climate change and drought on water requirements in a semi-mountainous region of the Vietnamese Mekong Delta.

As the backbone of Vietnam's economy, the country has recently established a number of policies for promoting and investing in smart agriculture in the Mekong Delta, the country's largest agricultural hub, to foster overall socio-economic development. However, water remains a critical constraint for crop production, with progress being hindered by water scarcity and quality issues, and compounded by socio-economic transformation and climate change. Considering these challenges, this study used the CROPWAT model and a wide spectrum of climate change scenarios to investigate future total water demands in the 2030s and 2050s as well as drought levels in two underdeveloped semi-mountainous reservoir catchments, i.e., O Ta Soc and O Tuk Sa in An Giang province. The results suggest that the usable storage capacity of the O Ta Soc reservoir will increase to 650,000 m3 to meet water supply demands under all climate change scenarios and the medium-term, moderate drought conditions. The useable storage capacity of the O Tuk Sa reservoir will also be increased to 880,000 m3 and the irrigation area would see a marked 70% reduction compared to its design irrigation. Under these circumstances, the O Tuk Sa reservoir will continue to supply water under all climate change scenarios and medium-term droughts. As a core element for strategic planning and to ensure efficient management of water resources, the results highlight the importance of estimating potential runoff and rainfall in semi-mountainous reservoir catchments under various drought conditions in order to propose the suitable expansion of the useable water storage capacities.

Assessment of pan coefficient models for the estimation of the reference evapotranspiration in a Mediterranean environment in Turkey.

Reference evapotranspiration (ETo) is essential for irrigation practices and the management of water resources and plays a vital role in agricultural and hydro-meteorological studies. The FAO-56 Penman-Monteith (PM) equation, recommended as the sole standard method of calculating ETo by the Food and Agriculture Organization of the United Nations (FAO), is the most commonly used and accurate model to determine the ETo and evaluate ETo equations. However, it requires many meteorological variables, often restricting its applicability in regions with poor or missing meteorological observations. Many empirical and semi-empirical equations have been developed to predict the ET0 from numerous meteorological data. The FAO-24 Pan method is commonly used worldwide to estimate ETo because it is simple and requires only pan coefficients. However, pan coefficients (Kpan) should be determined accurately to estimate ET0 using the FAO-24 Pan method. As the accuracy and reliability of the Kpan models can be different from one location to another, they should be tested or calibrated for different climates and surrounding conditions. In this study, the performance of the eight Kpan models was evaluated using 22-year daily climate data for the summer growing season in Adana, which has a Mediterranean climate in Turkey. The results showed that the mean seasonal pan coefficients estimated by all Kpan models differed significantly at a 1% significance level from those observed by FAO-56 PM according to the two-tail z test. In the study, ETo values estimated by Kpan models were compared against those obtained by the FAO-56 PM equation. The seasonal and monthly performance of Kpan models was varied, and the Wahed & Snyder model presented the best performance for ETo estimates at the seasonal scale. (RMSE = 0.550 mm d-1; MAE = 0.425 mm d-1; MBE = -0.378 mm d-1; RE = 0.134). In addition, it showed a good performance in estimating ETo on a monthly scale. The Orang model showed the lowest performance in estimating ETo among all models, with a very high relative error on the seasonal scale. (RMSE = 1.867 mm d-1; MAE = 1.806 mm d-1; MBE = -1.806 mm d-1; RE = 0.455). In addition, it showed the poorest performance on a monthly scale. Hence, the Wahed & Snyder model can be considered to estimate ETo under Adana region conditions after doing the necessary calibration.

Spatial-temporal variations of reference evapotranspiration and its driving factors in cold regions, northeast China.

Reference evapotranspiration ([Formula: see text]) is an important indicator for hydrometeorological change, which integrates atmospheric and surface conditions, and its downward trends have been reported in many regions over the past several decades. Cold regions constitute an important ecological barrier in China; however, few studies focus on change in [Formula: see text] in cold regions. Especially in the cold region of northeast China (CRNEC), as one of the national strategic grain bases, understanding spatial-temporal variations of [Formula: see text] is important for agriculture water management and ecological protection. This study selected the observations at 113 national meteorological stations located in CRNEC and evaluated the trends of [Formula: see text] and their driving factors from 1961 to 2017. Results indicate that annual [Formula: see text] increases from the northeast to the southwest of CRNEC and has an insignificant decreasing trend in the whole study period, in which 33 stations (29.2%) show significant decreasing trends and only 19 stations (16.8%) show significant increasing trends at the 95% confidence level. An abrupt change in [Formula: see text] data is detected from 1994. Reasons for this abrupt change in [Formula: see text] vary largely over the study areas. Analysis shows that wind speed and minimum air temperature are the two major factors that control the change of [Formula: see text] before 1994. It also shows that wind speed and actual vapor pressure are the two major controlling factors after 1994. We also found that [Formula: see text] shows a certain correlation with Pacific Decadal Oscillation and Western Pacific Index, but there is a significant correlation between meteorological factors and teleconnection factors related to [Formula: see text]. These findings will promote agricultural water management and improve water ecological protection in the CRNEC. We investigated changes in reference evapotranspiration relationships with atmospheric circulation and its attributions over the cold regions in northeast China during 1961 ~ 2017. The results indicate that the wind speed and minimum air temperature are the two major factors that control the change of ET0 before 1994, and wind speed and actual vapor pressure are the two major controlling factors after 1994. We also found that ET0 shows a certain correlation with Western Pacific Index in the whole period.

Assessment of spatiotemporal variability and trend analysis of reference crop evapotranspiration for the southern region of Peninsular India.

Accurate estimation of reference evapotranspiration (ET0) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET0 across diverse climate regimes over the past decades. In this study, the Python implementation for estimation of daily and monthly ET0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 to 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET0 values was higher than temporal variation, indicating major difference in ET0 values was with respect to the stations rather than years under study. The nonparametric Mann-Kendall test conducted at 1% significance level on the annual ET0 values revealed a statistically significant increasing trend for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity, and solar radiation signifies their influence on the annual ET0 values. The magnitude changes in the trends detected by the Theil Sen's slope indicated that increasing values of mean temperature, solar radiation, and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET0 values for the 10 stations. A trivial impact of wind speed on annual ET0 values was observed for the stations. Kalburgi and Udupi stations exhibited a positive ET0 trend with the highest and lowest annual values among ten stations.

Spatio-temporal variations of aridity index and its response to climate factors in Northwest China during 1989-2019.

Based on the meteorological data of 143 meteorological site, we calculated aridity index (AI) with the potential evaporation formulated by FAO-56 Penman-Monteith and precipitation in Northwest China during 1989-2019. Mann-Kendall trend analysis, wavelet analysis and partial differential equation were used to examine the AI change trend, variation cycle, and contribution rate of main climate impact factors to AI. The results showed that there was a non-significant decreasing trend of AI in Northwest China on the whole, a significant decreasing trend of AI in Qinghai, and a non-significant increasing trend of AI in Xinjiang during 1989-2019. There was an abrupt change of AI in the study area in 2010. There was a primary 17-year periodicity in the change of AI in Northwest China. The spatial distribution of AI was shown as a larger AI in the middle of Northwest China and a smaller AI in the Southeast and Northwest in Northwest China. The tendency rates of AI were -1.27, -1.17·(10 a)-1, -0.41, -0.49, -1.77 and -2.73·(10 a)-1 in Northwest China, Gansu, Ningxia, Shanxi, Qinghai, and Xinjiang, respectively. The possibility of drought risk was higher in Xiaozaohuo, Korla, Aksu, and Turpan region. Precipitation and actual water vapor pressure were the dominant factors of AI changes in Gansu, Ningxia, Qinghai, and Shaanxi. But the potential evapotranspiration, solar radiation, and average temperature were the main climate factors for AI changes in Xinjiang.

Comparação entre métodos de estimativa de evapotranspiração de referência no município de São José dos Ausentes (RS), Brasil / Comparison between methods of estimation of reference evapotranspiration in the municipality of São José dos Ausentes (RS), Brazil

RESUMO A evapotranspiração de referência é uma componente muito importante do balanço hídrico e sua estimativa é essencial para execução de projetos agrícolas e ambientais, estudos de balanço hídrico, projetos e manejo de irrigação, modelagem de processos climatológicos e planejamento do gerenciamento dos recursos hídricos. O método de Penman-Monteith é considerado pela Food and Agriculture Organization of the United Nations como padrão para estimar a evapotranspiração de referência, contudo, dada a dificuldade de se obter um número grande de variáveis meteorológicas que são empregadas nesse método, tem-se utilizado vários outros métodos para estimar a evapotranspiração de referência. O presente trabalho teve como objetivo comparar, por meio de valores diários e mensais estimados pelo método de Penman-Monteith, o desempenho dos métodos de Thornthwaite, Hargraves-Samani, Makkink, Blaney-Criddle, Camargo e Jensen-Haise para o município de São José dos Ausentes, no Rio Grande do Sul. Os dados utilizados para estimar a evapotranspiração de referência foram obtidos pelo sistema Agritempo, que armazena e disponibiliza os dados da estação meteorológica automática do Instituto Nacional de Meteorologia. Os resultados apontam que o método de Blaney-Criddle foi o que apresentou os melhores resultados nas escalas tanto diária quanto mensal, seguido pelo método de Jensen-Haise, na escala mensal. Já os métodos que apresentaram os piores desempenhos foram o de Thorntwaite e Camargo, sendo classificados com desempenho "sofrível" na escala mensal e como "péssimo" e "mau", respectivamente, na escala diária.

ABSTRACT Reference evapotranspiration is a very important component of the water balance and its estimation is essential for the execution of agricultural and environmental projects, for studies of water balance, irrigation projects and management, modeling of climatological processes and planning of water resources management. The Penman-Monteith method is considered by the Food and Agriculture Organization of the United Nations as a standard for estimating reference evapotranspiration; however, due to the difficulty of obtaining a large number of meteorological variables that are employed in this method, several other methods have been used to estimate the reference evapotranspiration. The present coursework had the objective to compare the performance of the Thornthwaite, Hargraves-Samani, Makkink, Blaney-Criddle, Camargo, and Jensen-Haise methods for the municipality of São José dos Ausentes, using daily and monthly values estimated by the Penman-Monteith method. The data used to estimate the reference evapotranspiration were obtained through the Agritempo system, which stores and makes available the data of the automatic meteorological station of the National Meteorological Institute. The results show that the Blaney-Criddle method presented the best results, both daily and monthly, followed by the Jensen-Haise method in the monthly scale. On the other hand, the methods that presented the worst performances were those of Thorntwaite and Camargo, classified as "poor" on the monthly scale, and as "terrible" and "bad", respectively, on the daily one.

Performance evaluation of FAO Penman-Monteith and best alternative models for estimating reference evapotranspiration in Bangladesh.

Proper assessment of reference evapotranspiration (ET 0 ) is necessary for pastoral activity and water management. The Penman-Monteith FAO56 (ET pmf ) method has been recommended as the identical ET 0 estimation model; nonetheless, it belongs to a vast climatic data requirement. There is an urgent need to discover an ideal alternate model for evaluating ET 0 in particular places where all climatic data is insufficient. The performances of 15 empirical models were assessed to get the best alternative model by comparing it with the PMF-56 model. These 15 models were evaluated by employing a daily scatter plot and three well known numerical approaches: relative root-mean-square error, mean absolute error and Nash-Sutcliffe coefficient in this study. Furthermore, a linear regression model was implemented to calibrate and validate the empirical models' performances throughout the 1981-2005 and 2006-2018 time intervals, separately. The outcomes displayed that the ET pmf rose primarily and declined later on a monthly period with the topmost amount in April and the lowermost amount in January. Overall, the Abtew model was the best alternate method showing the highest determination coefficient values more than 0.85 from January to December. In contrast, the Penman, WMO, Trabert, Valiantzas1, Valiantzas2, Valiantzas3 and Jensen-Haise models presented moderate performances with fewer inaccuracies. Afterwards, modification, the version of the above-described models every month has been upgraded deliberately related to actual. The Abtew model had simplicity in the computation process, only used maximum temperature and solar radiation data and linearly well connected to the PMF-56 model.

An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm.

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman-Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

The impact of livestock grazing on the evapotranspiration-vegetation biomass relationship in a Southern Hemisphere salt marsh, Buenos Aires (Argentina).

Among the ecosystem services provided by salt marshes is the use of their natural vegetation as pastures for livestock production. As a result, the prediction of biomass productivity can be of great interest for the sustainable management of these environments. Evapotranspiration is one of the variables most used to estimate the yield of green biomass in pastures and crops, which to date has not been examined for natural environments such as salt marshes. We studied the aboveground biomass and species cover variability for two categories (erect and sward plants) in three plots affected by low, moderate, and high cattle grazing. Erect biomass was associated only with Spartina densiflora while for sward plants it gathered a diverse set of prostrate and stoloniferous species with high seasonal turnover. The evapotranspiration was estimated with a coupled surface resistance-Penman-Monteith model developed for these environments. The biomass of the plant categories shows different growth response according to livestock impact. S. densiflora has a slow-growing response after cattle consumption, even with high evapotranspiration. On the other hand, sward plants respond with biomass overproduction to livestock consumption, and a significantly positive relationship to evapotranspiration rate.

A process-based coupled model of stomatal conductance-photosynthesis-transpiration during leaf ontogeny for water-saving irrigated rice.

Process-based coupled model of stomatal conductance-photosynthesis-transpiration was developed to estimate simultaneously stomatal conductance gsw, photosynthetic rate Pn, and transpiration rate Tr during leaf ontogeny. The modified Jarvis model was constructed by superposing the influence of leaf age LA on gsw in traditional Jarvis model. And the modified Farquhar model was constructed by incorporating the relationships of the LA with parameters in Farquhar model into traditional Farquhar model. The average and leaf-age-based coupled models were constructed, respectively, by combining traditional Farquhar and Penman-Monteith models with traditional Jarvis, and combining modified Farquhar and Penman-Monteith models with modified Jarvis. The results showed that the gsw, the maximum rate of carboxylation, maximum rate of electron transport, rate of triose phosphates utilization, and mitochondrial respiration rate varied in a positive skew pattern, while the mesophyll diffusion conductance decreased linearly with increase in LA. The average coupled model underestimated gsw, Pn, and Tr for young leaves and overestimated gsw, Pn, and Tr for old leaves. And the leaf-age-based coupled model generally perfected well in estimating gsw, Pn, and Tr for all leaves during leaf ontogeny. The study will provide basic information for either modeling leaf gsw, Pn, and Tr continuously, or upscaling them from leaf to canopy scale by considering the variation of LA within canopy.

Assessing the physical and empirical reference evapotranspiration (ETo) models and time series analyses of the influencing weather variables on ETo in a semi-arid area.

Accurate estimation of irrigation requirement is necessary for conserving the quantity and quality of water resources. Generally, irrigation requirement is estimated by calculating reference evapotranspiration (ETo). In this study, radiation-based, temperature-based, and combination-based ETo models were assessed based on the monthly averaged weather data between 1987 and 2017. The combination-based Standardized ASCE Penman-Monteith (ASCE PM Std.) was selected as the benchmark model due to its global acceptance and accuracy. Results showed that the combination-based Penman models were ranked as the top models among the other ETo models. However, if some weather variables are missing, the Priestly-Taylor model followed by the Makkink and Turc models (all as radiation-based models) were the next recommended ETo models.The performance of the temperature-based models and some other radiation-based models (FAO24 Radiation and Jensen-Haise) were not satisfactory. Trend and change point detection analyses on air temperature, relative humidity, and wind speed showed that the study area is getting warmer and drier, which indicate that ETo would increase in the study area. Therefore, it is recommended to use the ETo models that consider the majority of the weather variables that influence ETo. The results of this study could serve as a reliable guide for selection of appropriate ETo models to protect water resources in arid and semi-arid areas. .

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We collected evapotranspiration data of Dajiuhu peatland in Shennongjia from 2016 to 2017 with eddy covariance method and estimated the value of crop coefficient (Kc) using FAO56 Penman-Monteith equation and the linear relationship between actual evapotranspiration (ETa) and referenced evapotranspiration (ET0). We analyzed the characteristics of referenced evapotranspiration and its main influencing factors and calculated the crop coefficient of the wetland dominated by Sphagnum. The results showed that the daily averaged ETa were 1.63 and 1.38 mm·d-1 in 2016 and 2017, the daily averaged ET0 were 1.61 and 1.23 mm·d-1 in 2016 and 2017. Environmental factors influencing ET0 included net radiation, air temperature, vapor pressure deficit, wind speed, and relative humidity. The Kc values for the growing seasons of 2016, 2017, and 2016-2017 were 0.95 (R2 of linear regression between ETa and ET0 was 0.96), 1.03 (R2=0.95), and 0.98 (R2=0.95). The Kc values in 2016, 2017, and 2016-2017 were 0.92 (R2=0.94), 0.95 (R2=0.89), and 0.93 (R2=0.92). Kc was effective in the range of 0.92-1.03 for the wetland dominated by Sphagnum. The identified parameters could be widely used in studies on climate change, ecosystem services, and water management in peatlands.

Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts.

Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014-2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

Analysis of alternative climate datasets and evapotranspiration methods for the Upper Mississippi River Basin using SWAT within HAWQS.

This study reports the application of Soil and Water Assessment Tool (SWAT) within the Hydrologic and Water Quality System (HAWQS) on-line platform, for the Upper Mississippi River Basin (UMRB). The UMRB is an important ecosystem located in the north central U.S. that is experiencing a range of ecological stresses. Specifically, testing of SWAT was performed for: (1) Hargreaves (HG) and Penman-Monteith (PM) PET methods, and (2) Livneh, National Climatic Data Center (NCDC) and Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate datasets. The Livneh-PM combination resulted in the highest average annual water yield of 380.6 mm versus the lowest estimated water yield of 193.9 mm for the Livneh-HG combination, in response to 23-year uncalibrated simulations. Higher annual ET and PET values were predicted with HG method versus the PM method for all three weather datasets in response to the uncalibrated simulations, due primarily to higher HG-based estimates during the growing season. Based on these results, it was found that the HG method is the preferred PET option for the UMRB. Initial calibration of SWAT was performed using the Livneh data and HG method for three Mississippi River main stem gauge sites, which was followed by spatial validation at 10 other gauge sites located within the UMRB stream network. Overall satisfactory results were found for the calibration and validation gauge sites, with the majority of R2 values ranging between 0.61 and 0.82, Nash-Sutcliffe modeling efficiency (NSE) values ranging between 0.50 and 0.79, and Kling-Gupta efficiency (KGE) values ranging between 0.61 and 0.84. The results of an additional experimental suite of six scenarios, which represented different combinations of climate data sets and calibrated parameters, revealed that suggested statistical criteria were again satisfied by the different scenario combinations. Overall, the PRISM data exhibited the strongest reliability for the UMRB.