Open access peer-reviewed chapter

Modeling Accumulated Evapotranspiration Over Time

Written By

Omar Cléo Neves Pereira and Altair Bertonha

Submitted: February 6th, 2019 Reviewed: May 17th, 2019 Published: October 23rd, 2019

DOI: 10.5772/intechopen.86913

From the Edited Volume

Water Chemistry

Edited by Murat Eyvaz and Ebubekir Yüksel

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The knowledge of accumulated evapotranspiration by seasonal vegetation crops throughout their life cycle can be an important tool in decision-making when considering the economic viability of the crop. This knowledge can help understand how much the plants, subject to specific management, can evapotranspirate at the end of their cycle. This information assists in estimating the quantity of a production variable, for example, the mass of shoot fresh matter, besides indicating a more interesting period for its harvest. The objective of this chapter is, from the daily evapotranspiration estimative throughout the cycle, to model the accumulated evapotranspiration over the entire growth period of the crop. In order to do so, we must understand that the behavior of the response variable, i.e., the accumulated evapotranspiration, over time is not linear and keep in mind that the several observations performed in the same experimental unit have correlations and these correlations are more intense the closer temporally the measurements are. This understanding leads us to the analysis of longitudinal data from the nonlinear mixed effect models perspective.


  • longitudinal data
  • nonlinear mixed effect model
  • growth curve
  • correlation structure
  • irrigation

1. Introduction

With available water in the soil, the water flow through the plants depends only on atmospheric demand. Therefore, physical variables as temperature, relative air humidity, and wind and solar radiation affect directly the evapotranspiration (ET) of a vegetated surface [1]. Besides that, the plants development state may also affect the ET.

Seasonal vegetation crops present a small demand for water, while their root system is small, reaching maximum rates in full growths and decreasing in the final stages of development. For these species, the accumulated ETover the plant cycle is directly related to its productivity. In other words, the greater the accumulated amount of evapotranspirated water, the greater the quantity of the production variables as shoot fresh and dry matter masses, number of leaves, and leaf area [2, 3].

The objective of this chapter is to illustrate the use of nonlinear mixed effect models to fit accumulated ETover time in seasonal vegetation crops.


2. Response profile

Figure 1 presents the accumulated ETfrom a single lettuce plant over 23 consecutive days, being the first day equivalent to the 35thday after seeding. Throughout this period, the plant’s daily ETwas measured. Hence, the first day in the graph presents the ETvalue from the last 24 h, the second day refers to the last 48 h, and so on. The daily ETmust be understood as the rate at which the accumulated EToccurs over time. In mathematical terms, the daily ETcan be understood as an approximation of the derivative (rate) of the accumulated ETwith respect to time.

Figure 1.

Response profile of the accumulatedETfor a single lettuce plant over 23 consecutive days.

We wish to describe the behavior of our variable of interest or response variable, the accumulated ETover time for seasonal vegetation crops. As the amount of evapotranspirated water in 1 day is added to the accumulated ETfrom previous days, its values over time are equal (in case ETfrom a whole day is null) or greater than the immediately preceding value.

Observations from the response variable in more than one moment in the same experimental unity constitute what we call as response profile. Therefore, Figure 1 presents the response profile of a lettuce plant over time. This profile, apparently, presents an S-shaped format.

At the first days of observations (Figure 1), the accumulated ETis small because the plant is at the beginning of its growth. As the days go by, the daily ETvalues increase successively approximately until the 20thday. After the 20thday, the daily ET, i.e., the rate of change of the accumulated ET, begins to decrease. This causes the accumulated ET, which has been growing exponentially, to have a less vigorous growth and, therefore, tending slowly to a maximum value.


3. A model for growth data

In order to describe the behavior of the accumulated ETover time, we also need an S-shaped function. In addition, it is expected that the chosen model will be interpretable and parsimonious in the parameters. An empirical option is the polynomial model, which has linear parameters. This type of model can promote good statistical adjustments and be computationally simpler but does not add any theoretical consideration to the physical and/or biological mechanisms that generated the data. On the other hand, a nonlinear model is associated with some theoretical knowledge regarding the studied phenomenon. Besides the interpretability, these models use few parameters when compared to linear models, thus configuring a more parsimonious description of the data [4].

Regarding the accumulated ETover time, we have some physical and biological aspects that we can use to choose a proper model. A function that describes this response variable needs parameters that delimit it between a minimum and a maximum value. In other words, the minimum value should be really close to zero and indicate the beginning of the plant’s growth, and the maximum should be a value to which the accumulated ETtends asymptotically as the end of the life cycle approaches. Another aspect to highlight is that the model should present an inflection point, which indicates the day that the accumulated ETrate (daily ET) reaches its maximum value.

There are several functions capable of characterizing the accumulated ETover time in the sense we just described. For example, we will use the four-parameter logistic function (4PL) to describe our response variable. This function is widely used to fit growth or decay data. There are some parametrizations for this function in the literature, but we are using the one given by [4]


with ETtbeing the accumulated ETover time and ϕ14the model parameters (Figure 2). ϕ1is the inferior horizontal asymptote which gives the accumulated ETvalue when t. Biologically, this parameter does not have a consistent interpretation, but it is important in the fitting because it ensures the accumulated ET, in times close to zero, to be very small [2, 3]. ϕ2is the superior horizontal asymptote and gives the accumulated ETvalue when t. This parameter can be interpreted as the maximum accumulated ETestimative that the plant can reach in its final life cycle. ϕ3is the curve’s inflection point and indicates the day (time) in which the daily ETreaches a maximum value. The corresponding time to ϕ3results in an accumulated ETbetween ϕ1and ϕ2. More precisely, the accumulated ETuntil time ϕ3is the mean value of both asymptotes, i.e., ϕ1+ϕ2/2. ϕ4is the scale parameter. The day (time) corresponding to ϕ3+ϕ4gives 0.75ϕ2ϕ1of the accumulated ET. Therefore, ϕ4indicates how quickly the accumulated ETleaves the proximity of ϕ1until it reaches values close to ϕ2. The greater the ϕ4, the slower this occurs.

Figure 2.

Graphical representation of the4PLparameters (Eq. (1)). Figure adapted from [4].

To better understand what we have said, let’s observe Figure 3. It brings the observed data shown in Figure 1 represented by the black dots and two other curves representing two different fits made from this data. The first is a fifth-degree polynomial model, and the second is given by Eq. (1). The graph in this figure was extended until the 40thday in order to present the behavior of these fittings over time.

Figure 3.

AccumulatedETresponse profile for a single lettuce plant over 23 consecutive days. The graph was extended until the40thday to present the behavior of two fittings made for this data, a fifth-degree polynomial and a nonlinear model given byEq. (1).

Both the fifth-degree polynomial and the nonlinear 4PLmodels have fitted well with the data. However, the polynomial model goes to zero after the 30thday, which physically is impossible. Besides that, this model presents five parameters without any physical and/or biological explanation for the phenomenon in this study. The 4PLnonlinear model is a strictly increasing function, being compatible with a variable which is accumulated over time. Nevertheless, this fitting does not allow the infinite growth of the accumulated ETas time grows larger and larger. It limits the accumulated ETto a value which can be understood as a maximum amount in which this plant can evapotranspirate throughout its life cycle.

We also see that, from approximately the 20thday, the accumulation of evapotranspiration water is decreasing. This indicates that this day is the inflection point of the nonlinear 4PLmodel. For a practical example of the model’s inflection point regarding seasonal vegetation crops, suppose that the commercial product of a given crop plant is its leaves. It is known that the production variables present a positive correlation with the amount of total evapotranspired water throughout the plants’ life cycle. That is, the larger the total evapotranspiration water, the higher the values of the production variables [2, 3]. Hence, the inflection point can indicate the proximity of the harvest day. From this point, the ETrates decrease, and therefore, the plants start to accumulate less quantities of shoot fresh matter over time. Thus, this point can be considered when thinking in terms of the economic viability of the crop.

Another important aspect, besides the parameter interpretability, is that the nonlinear 4PLmodel is more parsimonious than the linear model. In general, nonlinear models use a small number of parameters than the linear ones, which grant them more parsimony. Besides that, as can be observed in Figure 3, in regions outside the data interval, the nonlinear model gives a more trustworthy prediction for the response variable [4].


4. Longitudinal data

Clearly, in order to make sense, an experiment must provide data from more than one experimental unit. In our case, more than one plant should be observed over time, i.e., we must obtain more than one response profile. Studies in which the response variable is observed repeatedly throughout time in the experimental units are called longitudinal studies. This kind of work is common in agriculture when analyzing the increase or decrease of the response variable over time [2, 3, 5, 6, 7, 8].

Measurements performed in the same experimental unit are most likely to be correlated. Suppose two plants which its ETis registered daily. If all covariables (fertilization, cultivate, planting season, soil water, and so on) were kept constant over time, plants with high rates of ETin a given day will most likely also have high rates of ETin the next day, the same for plants with smaller ETrates. If in a day the rate is small, probably in the next day it will also be small. In other words, measurements performed in the same experimental unit tend to be similar over time. It’s the individual expression of each plant.

Besides the correlation between the observations within the same experimental unit, we must consider that, most likely, these correlations are greater for observations performed between neighboring times than those performed between more distant times.


5. Mixed effect model

In a longitudinal study, the monitoring of the experimental units over time generates correlated dataset. As mentioned, these correlations within the same experimental unit are stronger among neighboring observations. The greater the time distance between two measures, the weaker the correlation between them. Besides that, when we observe experimental units which received the same conditions regarding growth over time and are part of the same treatment, we have a variability among them that we attribute to chance. The treatment effects, the correlations, and the variabilities in a longitudinal study indicate that we need a tool that, in addition to being flexible in specifying a mathematical model, also emphasizes each experimental unit.

In mixed effect models, we select an ordinary function to describe the response variable regarding the studied covariables, that is, the responses of the experimental units in a population. Besides that, specific coefficients of this function can be unique for each experimental unit. In a mixed effect model, we assume that the experimental units of a population have the same functional form, but the function parameters may vary among the units.

The name mixed modelcomes from the fact that this model combines fixed effectsand random effects. A mixed effect model is a parametric model which describes the relations between the response variable and the covariables (fixed effects) and takes into account the individual responses of each experimental unit (random effects). In other words, the fixed effects parameters describe the relations of the response variable and the covariables in an entire population, and the random effects specify the contribution of each individual within the population [4, 9, 10, 11, 12].

To illustrate how to write a 4PLnonlinear model with mixed effect, let’s assume that we are studying the accumulated ETregarding four levels of water in the soil, W1, W2, W3, and W4(there could be more levels of water in the soil or less, but to exemplify, let’s assume four treatments). We will consider that the response variable, the accumulated ET, has a normal probability distribution. Consider ETijas the accumulated ETat the jsituation, for plant i, with j=1,2,,nie i=1,2,,N, where niis the number of observations for the i-th plant and Nis the total number of plants. The nonlinear 4PLmixed effect model can be expressed by [2, 3]




with the parameters β, γ, δ, ζrepresenting the fixed effects and bithe random effects in the model. xkiwith k=1,2,3are indicative covariables of treatments or groups and may have values zero or one. The parameter βis the reference level in the study. When x1i=x2i=x3i=0, thus, ϕi=β+biand the i-th plant belongs to the treatment W1. When x1i=1and x2i=x3i=0, ϕi=β+γ+biand the i-th plant belongs to the treatment W2. If x2i=1and x1i=x3i=0, ϕi=β+δ+biand the i-th plant belongs to the treatment W3. And lastly, if x3i=1and x1i=x2i=0, ϕi=β+ζ+biand the i-th plant belong to the treatment W4. The random effects biare considered independent among the plants and are normally distributed with mean zero and covariance matrix Ψ(biN0Ψ). In this case, the covariance matrix is given by


with σ12, σ22, σ32, and σ42being the variances of random effects b1i, b2i, b3i, and b4i, respectively, and σ12, σ13, σ14, σ23, σ24, and σ34are the covariances between them. The variances of the random effects indicate how a model parameter varies between the experimental units. Frequently, we suppose that the errors within the groups εijare independent between the observations of the same experimental unit and that they are distributed the same way among the experimental units. For a given sample unit i, we can describe the error εijas the time jindexed vector, i.e.,


where each vector εihas niobservations throughout time and we assume that they follow a normal multivariate distribution with mean zero and covariance matrix Λi, i.e.,


Most of the times, we consider Λi=σ2I, being σ2a constant variance for all jtimes.

By using nonlinear mixed effect models, we must consider the technical difficulty in the parameter estimation. In a mixed effects linear model, the derivative of the logarithmic of the likelihood function allows, in a simple way, the algorithm implementation, like Newton-Raphson, to obtain the estimative of the models parameters. Nonlinear models can, however, present nonlinear random coefficients which make it impossible to directly explain the parameters from the likelihood function. Methods that depend on linear approximations such as the first-order Taylor approximation can be used to estimate the model.

Nonlinear mixed effect model analysis can be, preferably, made by the R software [13] with the package name [14], also, at the SAS software using PROC NLMIXED. An excellent text to learn how to use these skills is given in [4].


6. Covariance structure of Λi

Mixed effect model allows the dependence between the observations to be specified in the model parameters through random effects. In other words, the experimental unit responses from a population tend to follow a nonlinear growth path; however, each experimental unit has its own growth path, and the mixed effect model allows the inclusion of specific coefficients to obtain fitted growth curves that align better with the individual responses of these experimental units.

Thus, mixed models allow relevant flexibility for the specification of the random effects correlation structure. However, the dependence structure of the observations within the experimental units Λiuntil now has been considered independent, identically distributed with mean zero and constant variance. Depending on the chosen model, the growth responses can be explained just by including specific coefficients for the experimental units. However, this may not be enough, and, in this case, modeling the residual dependence of the data becomes important.

There are cases where dependence on observations not accommodated by the growth function is not well understood or, sometimes, additional covariables that could explain this dependences are absent from the model. Thus, an important resource to model this dependence is to identify the covariance structure that allows correlation between the residuals in different occasions. Then, let us relax on the assumption that the errors are independent and allow them to have heteroscedasticity and/or are correlated within the experimental units.

There are several covariance structures for the residues available in the software to help model longitudinal data. However, in our text, we will highlight only two that we consider more important for these studies, the covariance structure with heterogeneous variance and the first-order auto-regressive.

6.1 Heterogeneous variance

The first covariance structure we will consider for Λiis the one that admits heterogeneity of the niresidual variances. In this structure, which has niparameters, we assume that the residuals associated with the observed values at the nioccasions for the i-th experimental unit are independent:


Other variables, besides time, can also be considered with heterogeneous variance in the model. For example, there are cases in which it is important to model the heterogeneity of the treatments, and we can do it by using mixed models.

6.2 First-order auto-regressive

Another covariance structure for Λiwhich is widely used for longitudinal data is the first-order auto-regressive, also called AR1:


This structure has only two parameters, the variance parameter σ2, always positive, and the covariance parameter ρ, which may vary between 1and 1. This kind of structure allows the residues associated with the observations in neighboring occasions to be more correlated than those whose observations are further apart. The AR1is preferred for datasets in which the longitudinal observations are equally spaced.


7. Real data example

To exemplify what we have done so far, let’s work with some real data of the ETfrom lettuce plants grown in pots. A total of N=12were completely randomized into three levels of water in the soil. At the first treatment, W1, the water level for the plant were kept between 50.0 and 75.0% of the substrate’s retention capacity. In the other two treatments, W2and W3, the water level in the substrate was kept between 50.0 and 87.5% and between 50.0 and 100.0%, respectively. When the retention capacity of the substrate reached 50.0%, the pots were irrigated until their maximum level regarding each treatment.

The profile graphs from the accumulated ETfor all pots in each treatment are shown in Figure 4. Note that the inferior asymptote, when t, is apparently the same for all plants, i.e., the individual and treatment effects do not seem to be important for this parameter.

Figure 4.

Response profile for the accumulated ET of the lettuce plants over 23 consecutive days for the soil water levelsW1,W2, andW3.

We model this data using Eq. (2) and considering treatment W1as baseline. The random effects were added to the parameters ϕ2i, ϕ3i, and ϕ4i, and the treatments were important to explain the parameters ϕ2ie ϕ3i. Besides that, we consider the heterogeneity in the treatments and an AR(1) correlation structure for Λi. The parameter estimative of the model is presented in Table 1.


Table 1.

Estimative for the models fixed effects parameters.

The only nonsignificant parameter NSwas ϕ3ifor the treatment W2with p-value >0.34. The other parameters presented p-value <0.001.

The parameter ϕ2iseems to be influenced by the other treatments, and its value was estimated in 2.44kgfor W1, 2.44+0.94=3.38kgfor W2, and 2.44+0.27=2.17kgfor W3. The inflection point, i.e., the day the accumulated ETrate was maximum, also seems to be influenced by the treatments. W1and W2were not statistically different for the parameter ϕ3i, but W3, with estimative of 14days, appears to be statistically different from W1(17days).

The first graph presented in Figure 5 brings the accumulated ETmean in each day of the four plants in each treatment. The solid lines are fitting for the treatments made by Eq. (2). The other graphs present all values observed for the four plants in each treatment, and the solid lines indicate the individual model for each plant.

Figure 5.

Response profile of the accumulated ET by the lettuce plants over 23 consecutive days for the water soil levelsW1,W2, andW3.


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Written By

Omar Cléo Neves Pereira and Altair Bertonha

Submitted: February 6th, 2019 Reviewed: May 17th, 2019 Published: October 23rd, 2019