Prediction Of Evapotranspiration Research Articles

Modeling nitrogen leaching from Andosols amended with different composted manures using LEACHM

Andosols, distributed widely around the Pacific basin, have unique soil–water and solute transport properties because of their stepwise water retention curves and high anion-adsorption capacity. The model modification and verification for these properties are crucial for evaluating the potential for improved agricultural management (e.g., using organic matter instead of inorganic fertilizer) to reduce N loss from the soils. Here, we improved an existing biogeochemical model, LEACHM, to predict long-term N leaching from Andosols amended with composted manure, without optimization to fit measured field data. The modified model was verified by observations from a 5.6-year lysimeter experiment with different rates of inorganic N fertilizer plus composted manure (100 + 0, 75 + 25, or 25 + 75%) of two different types (cattle, swine) on lettuce, sorghum (as a catch crop), and Chinese cabbage in rotation. Incorporation of Durner’s bimodal model dramatically improved predictions of drainage water volume and evapotranspiration. The non-linear Langmuir adsorption isotherms for soil NH4 + and NO3 − improved model performance in simulating crop N uptake and N leaching loss. The RMSE, R2, and index of Agreement were evaluated as satisfactory in all lysimeters. Our model explained reasonably well that improved agricultural management decreased in current available N addition rates by 8.82–35.6% and reduced in the yearly averaged NO3 leaching by 8.70–41.8%. A modified model relating soil hydraulic properties and N adsorption properties could thus accurately predict N leaching under different long-term N application rates/types, and could be useful for supporting agricultural management decisions in cropped Andosols.

Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

This study applied a time series evapotranspiration (ET) data derived from the remote sensing to evaluate Soil and Water Assessment Tool (SWAT) model calibration, which is a unique method. The SWAT hydrologic model utilized monthly stream flow data from two US Geological Survey (USGS) stations within the Big Sunflower River Watershed (BSRW) in Northwestern, Mississippi. Surface energy balance algorithm for land (SEBAL), which utilized MODerate Resolution Imaging Spectro-radiometer (MODIS) to generate monthly ET time series data images were evaluated with the SWAT model. The SWAT hydrological model was calibrated and validated using monthly stream flow data with the default, flow only, ET only, and flow-ET modeling scenarios. The flow only and ET only modeling scenarios showed equally good model performances with the coefficient of determination (R2) and Nash Sutcliffe Efficiency (NSE) from 0.71 to 0.86 followed by flow-ET only scenario with the R2 and NSE from 0.66 to 0.83, and default scenario with R2 and NSE from 0.39 to 0.78 during model calibration and validation at Merigold and Sunflower gage stations within the watershed. The SWAT model over-predicted ET when compared with the Modis-based ET. The ET-based ET had the closest ET prediction (~8% over-prediction) as followed by flow-ET-based ET (~16%), default-based ET (~27%) and flow-based ET (~47%). The ET-based modeling scenario demonstrated consistently good model performance on streamflow and ET simulation in this study. The results of this study demonstrated use of Modis-based remote sensing data to evaluate the SWAT model streamflow and ET calibration and validation, which can be applied in watersheds with the lack of meteorological data.

Estimation of air temperature and reference evapotranspiration using MODIS land surface temperature over Greece

ABSTRACTModerate Resolution Imaging Spectroradiometer (MODIS), land surface temperature data, during daytime (LSTday) or night-time (LSTnight), were employed for predicting maximum (Tmax) or minimum (Tmin) air temperature measured at ground stations, respectively, in order to be used as alternative inputs in minimum data-based reference evapotranspiration (ET) models in 28 stations in Greece during the growing season (May–October). The deviations between daily LSTnight and Tmin were found to be small, but they were greater between LSTday and Tmax. Furthermore, the temperature vegetation index (TVX) method was employed for achieving more accurate Tmax values from LSTday, after determining the normalized difference vegetation index of a full canopy (NDVImax). The TVX method was validated on ‘temporal’ basis, but when the method was tested spatially, the improvement on the Tmax estimates from LSTday was not encouraging, for being used operationally over Greece. Thus, LSTday or LSTnight MODIS data were used as inputs in three ET models [Hargreaves–Samani, Droogers–Allen, and Reference Evapotranspiration Model for Complex Terrains (REMCT)] and their estimations, as compared with ground-based Penman–Monteith estimates, indicated that the REMCT model achieved the most accurate ET predictions (r = 0.93, mean bias error = 0.44 mm day–1 and root mean square error = 0.74 mm day–1), which can allow the spatial analysis of ET at higher spatial resolutions in areas with lack of ground temperature data.

Predicting Forest Evapotranspiration by Coupling Carbon and Water Cycling Based on a Critical Stomatal Conductance Model

Quantifying forest evapotranspiration (ET) is essential for understanding of climatic response of forest carbon and water cycling. However, there are still large uncertainties in forest ET predictions, especially in plant transpiration (PT). The poor estimations of forest ET and PT are largely attributed to the neglect of wet canopy evaporation and uncertainties in the stomatal conductance. Thus, by coupling a revised Ball—Woodrow–Berry (BWB) model, a precipitation intercepted algorithm and the gross primary production (GPP) model to Shuttleworth–Wallace (SW) model, this study introduced a modified SW ${\rm{(SW}}_{{\rm{m}}})$ model. The performances of this model were subsequently tested in three different forest sites with long-term observed records. Compared with previous models, ${\rm{SW}}_{{\rm{m}}}$ had a canopy stomatal scheme with stronger ecological significance and simpler GPP estimation scheme. Our analyses reveal the following. 1) ${\rm{SW}}_{{\rm{m}}}$ evidently improves the agreements between estimated and measured ET compared to original SW ( R 2 increasing by 0.19–0.68). ${\rm{SW}}_{{\rm{m}}}$ could more accurately partition PT and evaporation, when compared with an earlier BWB-based SW ( R 2 increasing by ∼0.03). This finding also supports the use of Lohammer function in semiempirical model of stomatal conductance. 2) Accurate predictions of GPP are helpful for improving ET estimations in ${\rm{SW}}_{{\rm{m}}}\,(r\,= \,{\text{0.73,\,p}}\, , suggesting that carbon and water fluxes are inherently linked. 3) In addition to GPP, leaf area index evidently affects the performances of estimated ET in ${\rm{SW}}_{{\rm{m}}}$ . These results suggest that critically coupling carbon and water cycling are very important for improving forest ET prediction.

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

The importance of radiation for semiempirical water-use efficiency models

Abstract. Water-use efficiency (WUE) is a fundamental property for the coupling of carbon and water cycles in plants and ecosystems. Existing model formulations predicting this variable differ in the type of response of WUE to the atmospheric vapor pressure deficit of water (VPD). We tested a representative WUE model on the ecosystem scale at 110 eddy covariance sites of the FLUXNET initiative by predicting evapotranspiration (ET) based on gross primary productivity (GPP) and VPD. We found that introducing an intercept term in the formulation increases model performance considerably, indicating that an additional factor needs to be considered. We demonstrate that this intercept term varies seasonally and we subsequently associate it with radiation. Replacing the constant intercept term with a linear function of global radiation was found to further improve model predictions of ET. Our new semiempirical ecosystem WUE formulation indicates that, averaged over all sites, this radiation term accounts for up to half (39–47 %) of transpiration. These empirical findings challenge the current understanding of water-use efficiency on the ecosystem scale.

Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration

Continuous monitoring of actual evapotranspiration (ET) is critical for water resources management at both regional and local scales. Although the MODIS ET product (MOD16A2) provides viable sources for ET monitoring at 8-day intervals, the spatial resolution (1km) is too coarse for local scale applications. In this study, we propose a machine learning and spatial temporal fusion (STF)-integrated approach in order to generate 8-day 30m ET based on both MOD16A2 and Landsat 8 data with three schemes. Random forest machine learning was used to downscale MODIS 1km ET to 30m resolution based on nine Landsat-derived indicators including vegetation indices (VIs) and land surface temperature (LST). STF-based models including Spatial and Temporal Adaptive Reflectance Fusion Model and Spatio-Temporal Image Fusion Model were used to derive synthetic Landsat surface reflectance (scheme 1)/VIs (scheme 2)/ET (scheme 3) on Landsat-unavailable dates. The approach was tested over two study sites in the United States. The results showed that fusion of Landsat VIs produced the best accuracy of predicted ET (R2=0.52–0.97, RMSE=0.47–3.0mm/8days and rRMSE=6.4–37%). High density of cloud-clear Landsat image acquisitions and low spatial heterogeneity of Landsat VIs benefit the ET prediction. The downscaled 30m ET had good agreement with MODIS ET (RMSE=0.42–3.4mm/8days, rRMSE=3.2–26%). Comparison with the in situ ET measurements showed that the downscaled ET had higher accuracy than MODIS ET.

Scale dependent prediction of reference evapotranspiration based on Multi-Variate Empirical mode decomposition

Abstract This study proposes a novel method for estimation of reference evapotranspiration (ETo) by accounting the time scale of variability using the Multivariate Empirical Mode Decomposition (MEMD). First the ETo and the four predictor variables such as solar radiation, air temperature, relative humidity and wind velocity are decomposed into different intrinsic mode functions (IMFs) and a residue using MEMD. To model ETo, first the modes are modeled separately using the Stepwise Linear Regression (SLR) after identifying the significant predictors at different time scales based on the p-value statistics. Subsequently, the predicted modes are recombined to obtain ETo at the observation scale. The method is demonstrated by predicting the monthly ETo from Stratford station in United States. The results of the study clearly exhibited the superior performance of the proposed MEMD-SLR model when compared with that by M5 model tree, SLR and the EMD-SLR hybrid model.

Assessing the performance of two models on calculating maize actual evapotranspiration in a semi-humid and drought-prone region of China

The two-step and one-step models for calculating evapotranspiration of maize were evaluated in a semi-humid and drought-prone region of northern China. Data were collected in the summers of 2013 and 2014 to determine relative model accuracy in calculating maize evaopotranspiration. The two-step model predicted daily evaoptranspiration with crop coefficients proposed by FAO and crop coefficient calibrated by local field data; the one-step model predicted daily evapotranspiration with coefficients derived by other researcher and coefficients calibrated by local field data. The predicted daily evapotranspiration in 2013 and 2014 growing seasons with the above two different models was both compared with the observed evapotranspiration with eddy covariance method. Furthermore, evapotranspiration in different growth stages of 2013 and 2014 maize growing seasons was predicted using the models with the local calibrated coefficients. The results indicated that calibration of models was necessary before using them to predict daily evapotranspiration. The model with the calibrated coefficients performed better with higher coefficient of determination and index of agreement and lower mean absolute error and root mean square error than before. And the two-step model better predicted daily evapotranspiration than the one-step model in our experimental field. Nevertheless, as to prediction ET of different growth stages, there still had some uncertainty when predicting evapotranspiration in different year. So the comparisons suggested that model prediction of crop evapotranspiration was practical, but requires calibration and validation with more data. Thus, considerable improvement is needed for these two models to be practical in predicting evapotranspiration for maize and other crops, more field data need to be measured, and an in-depth study still needs to be continued.

Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model

The modeling and partitioning of regional evapotranspiration (ET) are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP) model into the Shuttleworth–Wallace model to develop a simplified carbon-water coupling model, SWH, for estimating ET using meteorological and remote sensing data. To enable regional application of the SWH model, we optimized key parameters with measurements from global eddy covariance (EC) tower sites. In addition, we estimated soil water content with the principle of the bucket system. The model prediction of ET agreed well with the estimates obtained with the EC measurements, with an average R2 of 0.77 and a root mean square error of 0.72 mm·day−1. The model performance was generally better for woody ecosystems than herbaceous ecosystems. Finally, the spatial patterns of ET and relevant model outputs (i.e., GPP, water-use efficiency and the ratio of soil water evaporation to ET) in China with the model simulations were assessed.

Using the artificial neural network to estimate leaf area

Artificial neural network (ANN) is applied for many subjects in agricultural science such as: crop yield and evapotranspiration prediction, soil parameters estimation, water demand forecasting, hydrological forecasting. Leaf area is one of parameters that is used to assess the plant vegetative growth. In this study, leaf areas of 61 plant species with different leaf shapes were estimated by ANNs and the effect of input data and pre-processing methods on ANNs performance was assessed. Results showed that the ANNs could provide good estimation of leaf area. ANNs input variable combination affected the ANNs performance to estimate the leaf area. With increase in number of hidden layers the epochs decreased and accuracy of the leaf area prediction and running speed increased. Results of test data set showed that MinMax pre-processing method resulted in more accurate prediction in comparison with the no pre-processed method and Norm STD method. The most conclusive result of this study is the application of ANNs for all of plant species, whereas, in application of other methods: specific equation should be prepared for each plant.

Prediction of evapotranspiration using Fuzzy logic

In this paper, evapotranspiration prediction is done using Fuzzy Inference System (FIS) of Fuzzy Logic.For the prediction of evapotranspiration, mean temperature, relative humidity, wind speed and net radiation is taken as inputs to the fuzzy inference system. To check the efficiency of the FIS model, the results were compared with the FAO-56 Penman Monteith (FPM-56) method. FIS model has given the coefficient of determination (R2) 0.979. Results indicated that, FIS model has better efficiency for prediction of evapotranspiration.

Rainfall as proxy for evapotranspiration predictions

Abstract. In this work, we evaluated the relationship between evapotranspiration and precipitation, based on the data recently made available by the Brazilian Meteorological Institute. ETP tend to be lower in rainy periods and vice-versa. This relationship was assessed both in physical and statistical ways, identifying the contribution of each explaining variable of ETP. We derived regression equations between monthly rainfall and ETP, which can be useful in studies where ETP time series are not available, such as reservoir design, irrigation management and flow forecast.

Effects of agricultural management on measurements, prediction, and partitioning of evapotranspiration in irrigated grasslands

Irrigation is an important component of the hydrologic cycle in agricultural ecosystems, affecting both quantity and quality of surface and ground water. Well-managed irrigation involves balancing irrigation with water consumption by evaporation and transpiration (collectively evapotranspiration), maximizing ecosystem water-use efficiency and minimizing drainage. Here we compare rates of actual crop evapotranspiration (ETC) measured by eddy covariance with reference evapotranspiration (ET0) calculated from meteorological variables for two irrigated ryegrass systems in central South Island, New Zealand between June 2011 and March 2013. The sites were similar in climate, but contrasted in management: one grazed by dairy cattle and the other harvested annually for seed. Over the first year of measurements, cumulative ETC was very similar at the two sites, totalling 791 and 819mm for the dairy pasture and seed crop respectively, although temporal patterns of partitioning of ETC amongst evaporation and transpiration differed as a result of management activities. Responses of ETC to global radiation, temperature and vapour pressure deficit were all similar during active growing season periods. Differences between the two sites were observed at the end of the second measurement season, when irrigation was ceased in the seed crop prior to final harvest and ETC was reduced compared to ET0. As a result, cumulative ETC was 13% greater for the dairy pasture at the end of the study period.

Technical note: Improving the AWAT filter with interpolation schemes for advanced processing of high resolution data

Abstract. Weighing lysimeters with appropriate data filtering yield the most precise and unbiased information for precipitation (P) and evapotranspiration (ET). A recently introduced filter scheme for such data is the AWAT (Adaptive Window and Adaptive Threshold) filter (Peters et al., 2014). The filter applies an adaptive threshold to separate significant from insignificant mass changes, guaranteeing that P and ET are not overestimated, and uses a step interpolation between the significant mass changes. In this contribution we show that the step interpolation scheme, which reflects the resolution of the measuring system, can lead to unrealistic prediction of P and ET, especially if they are required in high temporal resolution. We introduce linear and spline interpolation schemes to overcome these problems. To guarantee that medium to strong precipitation events abruptly following low or zero fluxes are not smoothed in an unfavourable way, a simple heuristic selection criterion is used, which attributes such precipitations to the step interpolation. The three interpolation schemes (step, linear and spline) are tested and compared using a data set from a grass-reference lysimeter with 1 min resolution, ranging from 1 January to 5 August 2014. The selected output resolutions for P and ET prediction are 1 day, 1 h and 10 min. As expected, the step scheme yielded reasonable flux rates only for a resolution of 1 day, whereas the other two schemes are well able to yield reasonable results for any resolution. The spline scheme returned slightly better results than the linear scheme concerning the differences between filtered values and raw data. Moreover, this scheme allows continuous differentiability of filtered data so that any output resolution for the fluxes is sound. Since computational burden is not problematic for any of the interpolation schemes, we suggest always using the spline scheme.

Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper

ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. The used data encompass the second crop cycle, from September 2013 to February 2014, constituting 135 days of daily meteorological data, referring to the following variables: temperature and relative air humidity, wind speed and solar radiation (input variables), as well as evapotranspiration (output variable), determined using data obtained by load-cell weighing lysimeter. The recorded data were divided into three sets for training, testing and validation. The ANN learning model recognized the evapotranspiration patterns with acceptable accuracy, with mean square error of 0.005, in comparison to the data recorded in the lysimeter, with coefficient of determination of 0.87, demonstrating the best approximation for the 4-21-1 network architecture, with multilayers, error back-propagation learning algorithm and learning rate of 0.01.

Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging

Using high-quality dataset from 12 flux towers in north China, the performance of four evapotranspiration (ET) models and the multi-model ensemble approaches including the simple averaging (SA) and Bayesian model average (BMA) were systematically evaluated in this study. The four models were the single-layer Penman–Monteith (P–M) model, the two-layer Shuttleworthe–Wallace (S–W) model, the advection–aridity (A–A) model, and a modified Priestley–Taylor (PT-JPL). Based on the mean value of Taylor skill (S) and the regression slope between measured and simulated ET values across all sites, the order of overall performance of the individual models from the best to the worst were: S–W (0.88, 0.87), PT-JPL (0.80, 1.17), P–M (0.63, 1.73) and A–A (0.60, 1.68) [statistics stated as (Taylor skill, regression slope)]. Here, all models used the same values of parameters, LAI and fractional vegetation cover as well as the forcing meteorological data. Thus, the differences in model performance were mainly attributed to errors in model structure. To the ensemble approach, the BMA method has the advantage of generating more skillful and reliable predictions than the SA scheme. However, successful implementation of BMA requires accurate estimates of its parameters, and some degradation in performance were observed when the BMA parameters generated from the training period were used for the validation period. Thus, it is necessary to explore the seasonal variations of the BMA parameters according the different growth stages. Finally, the optimal conditional density function of half-hourly ET approximated well by the double-exponential distribution. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison of Evapotranspiration Computation by FAO-56 and Hargreaves Methods

AbstractIn an urban environment, vegetated roofs are sustainable systems that provide a variety of valuable benefits, such as the reduction of stormwater volumes and the mitigation of urban heat islands strongly linked to their evaporative processes. The evapotranspiration (ET) is one of the most important processes of the hydrological cycle, but it also represents one of the most difficult hydrological phenomena to quantify because of the complex interaction between the ground surface, vegetation, and atmosphere. However, an accurate computation of ET from vegetated roofs is essential to properly predict the benefits of such systems. The aim of this paper is to evaluate and compare three ET models for the prediction of ET from a green roof: a combination model and two temperature-based models under Mediterranean climate conditions. The study was carried out on an experimental green roof, located at University of Calabria, Italy, by using the onsite data with a time step of 5 min and a monitoring period r...

Model development in DNDC for the prediction of evapotranspiration and water use in temperate field cropping systems

This paper integrates recent research to improve, test, and verify the process based DeNtrification DeComposition (DNDC) model to estimate evapotranspiration (ET) through a revised FAO Penman–Monteith approach. ET estimates along with biomass and soil water content for spring wheat and corn were improved and found to be in good agreement with the observations at three field sites in eastern Canada (Ottawa). The statistical evaluations (RMSE (mm day−1) and R2) of ET for the newly revised model (0.88 and 0.76 for corn and 0.93 and 0.78 for wheat) showed improvements over DNDC93 (1.95 and 0.22 for corn and 1.62 and 0.09 for wheat) which used the Thornthwaite equation for estimating potential evapotranspiration. In addition, evaluations of water use efficiency for corn and wheat showed good agreement between observations and the modified model. The study demonstrates the inter-dependencies of ET with biomass production and soil/atmosphere water balance.

Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

In the last two decades, a number of single-source surface energy balance (SEB) models have been proposed for mapping evapotranspiration (ET); however, there is no clear guidance on which models are preferable under different conditions. In this paper, we tested five models-Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), Surface Energy Balance System (SEBS), and operational Simplified Surface Energy Balance (SSEBop)—to identify the single-source SEB models most appropriate for use in the humid southeastern United States. ET predictions from these models were compared with measured ET at four sites (marsh, grass, and citrus surfaces) for 149 cloud-free Landsat image acquisition days between 2000 and 2010. The overall model evaluation statistics showed that SEBS generally outperformed the other models in terms of estimating daily ET from different land covers (e.g., the root mean squared error (RMSE) was 0.74mmday−1). SSEBop was consistently the worst performing model and overestimated ET at all sites (RMSE=1.67mmday−1), while the other models typically fell in between SSEBop and SEBS. However, for short grass conditions, SEBAL, METRIC, and S-SEBI appear to work much better than SEBS. Overall, our study suggests that SEBS may be the best SEB model in humid regions, although it may require modifications to work better over short vegetation.