Rainfall induced Geohydraulic and Evapotranspiration Characteristics: An

Indian Case Study

1 North Eastern Regional Institute of Science and Technology, Arunachal Pradesh, INDIA

2Elitte College of Engineering, Aff. MAKA University of Technology, Kolkata, INDIA

Formerly, Graduate Student, Assam Kaziranga University, Jorhat, Assam, INDIA

Abstract: - Evaporation demand or potential evaporation is projected to increase almost everywhere in the world in

future climate scenarios. Estimation of evapotranspiration (ET) is important for determining the agro-climatic

potential of a particular region, water requirements of field crops, irrigation scheduling, and suitability of crops or

varieties, which can be grown successfully with the best economic returns therefore numerous models have been

developed for determining evapotranspiration. The present study was taken for Jorhat, Assam with the main

objective of the present study is to highlight the governing equations for infiltration and compare three temperature-

based methods for determining reference evapotranspiration namely Thornthwaite, Blaney-Criddle, and Ivanov

method. The results obtained from the methods were compared with the evapotranspiration data measured using the

class A pan. The interrelationship between the class A pan data and the other reference evapotranspiration method is

also determined in the study. Moreover, the result obtained shows that the average monthly ET estimated by Blaney-

Criddle, Thornthwaite, and Ivanov methods are 1.57, 3.05, and 2.62 mm/month. The correlation coefficient result

shows that all the three methods compared well with the observed pan evaporation. the results of this investigation

suggest that the Blaney-Criddle is the better method as compared to the Thornthwaite and Ivanov methods under

climatic conditions of Jorhat. Furthermore, the engineering properties of soil collected from the study area were

determined and presented in this study.

Key-Words: - Blaney-Criddle; Class A Pan; Ivanov; Potential evapotranspiration, Soil Properties; Thornthaite.

Received: March 30, 2021. Revised: February 18, 2022. Accepted: March 21, 2022. Published: April 27, 2022.

Introduction

Water foot printing is a valuable method for estimating

future usage for agricultural production and consumer

goods. In arid places, irrigation can help to mitigate

the hazards associated with rain-fed agriculture's

unpredictability. [1,2] Efficient water use can increase

crop diversity and produce higher yields, enhance

employment, and lower food prices. [3,4] PET

(potential evapotranspiration) is a notion that is mostly

independent of soil and plant conditions but has been

demonstrated to be influenced by climatic factors.

PET's temporal fluctuations and measurement of its

trend may be used in hydrological modeling,

agricultural water management, irrigation planning,

and water resource management.[3, 5]

The PET needs to be estimated to determine the crop

water requirements using crop-specific coefficients.

There are numerous different formulae available in the

literature for the calculation of PET. [5-10] Several

limitations are there in data availability for the Indian

conditions. Water managers need to have a thorough

understanding of the evapotranspiration process. They

should also study the engineering properties of the soil

to understand the geohydraulic properties. A study of

the geohydrologic balance for an area generally

includes an analysis of the total water loss from all

sources. The determination of potential

evapotranspiration is of interest to agriculturists and

hydrologists. In this study, the relation of evaporation

to climatic factors, geographic location, and the

vegetative cover has been investigated. Most of the

methods are based on empirical formulae. After an

extensive literature review, this study uses three

temperature-based methods namely Blaney-Criddle,

Thornthwaite, and Ivanov.

Jong and Tugwood [11] investigated long-term

climatic data from selected regions in Canada They

found the Priestly-Taylor model appropriate for a

station with vapor pressure deficits and high wind

speeds. The Empirical Robertson model appeared to

require regional calibration to improve potential

evapotranspiration estimates. Lu et al., [12] compared

three temperatures based and three radiation-based

potential evapotranspiration models. They found that

PET values calculated from the six methods were

highly correlated. Based on the criteria of availability

of input data and correlations with AET values, the

Priestly-Taylor, Turc, and Hamon methods are

English Language Faculty of Engineering, Technical University of Sofia, Sofia, BULGARIA

3GHRITARTHA GOSWAMI, 2SUDIP BASACK, 1KHAIRUZ ZAMAN, 4NIKOS E. MASTORAKIS

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recommended for regional applications. Trajkovic and

Gocic [13] found that the Penman-Monteith can be

used as the standard method of estimating reference

evapotranspiration. They further mentioned that It

cannot be widely used due to its requirement of

numerous weather parameters. Shahidian et al., [14]

have recommended the turc method for humid or

semi-humid areas, they found the Thornthwaite

equations tend to underestimate ET. They further

recommended the priestly-Taylor and Makkinik

equations should not be used in winter months in

locations with high latitudes such as North Europe.

Stanford and Selnick [15] found that a regression

equation can predict evapotranspiration at any given

site based solely on climate or climate and land cover

variables with an R2 value of 0.87 or greater.

Racz et al., [16] in their paper stated that Makkink

and Shuttleworth-Wallace, Blaney-Criddle, and

Makkink models were found to be the closest to the

Penman-Monteith –FAO-56 method as a reference

value. Based on the correlation between the models'

results, Pereira and FAO-56 models agreed most with

the Pan Evaporation measurement. Tomar [17]

analyzed the Impact of different meteorological

parameters and their inter-relationship with observed

values of pan evaporation at Udham Sing Nagar

district situated in the Tarai region of Uttarakhand.

Evaporation is maximum during the summer season

(March, April, and May) and minimum during the

monsoon season (June, July, and August). Manikumari

and Vinodhini [18] have developed a model using the

reference evapotranspiration by three different

regression models. Analysis was carried out based on

the data collected in the command area of the

Veeranam tank system during the period 1987-2008.

The SVR models proposed by them showed a

marginal improvement over MLR models. Mashru and

Dwivedi [19] evaluated eight commonly used

evapotranspiration estimation models for Junagadh

city of Gujarat. Daily records of meteorological

parameters i.e. maximum temperature, minimum

temperature, relative humidity morning, wind speed,

bright sunshine hours, and pan evaporation record for

10 years were collected for the study.

Study Areas

Jorhat is a prominent city in the Indian state of Assam.

Majuli is the world's biggest riverine island, formed by

the Brahmaputra River. According to the 2011 census,

the district spans 2,851 square kilometers and has a

population of 1,091,295 people. evaporation data

acquired using a Class A-Pan were gathered from the

Meteorological Observatory of Assam Agriculture

University in Jorhat, Assam, from 2007 to 2016.

Figure 1 depicts the Kakodonga Watershed

Fig. 1: Study Area

Methodology

This section contains the systematic procedures that

have been followed throughout the research study's

progress. The flow chart in Fig. 2 depicts the Research

Methodology's methodical progression. The steps are

outlined below.

i) Integration of previous research: Journals and

articles about the issue are gathered, reviewed,

and discussed. The research work on

Kakodonga is completed using the articles as a

guide.

ii) Data collection: Assam Agriculture

University, Jorhat, provided data on

temperature, relative humidity, wind speed,

and bright sunlight hours.

iii) Analytical Model: Three evapotranspiration

models are chosen after a review of the journal

and the availability of data.

iv) Construct and Interpretation: The data

collected is utilized to design the framework

of the research project, and interpretation is

carried out to determine the outcomes.

INDIA

Assam

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Fig. 2: Flowchart of the Research Methodology

Geohydraulic Characteristics

The undisturbed soil sample was taken from nearby

excavation peat on the Kaziranga University campus

in Assam, India, at a depth of 1 m. The collected soil

mass has a natural moisture level of 17%. Clay content

is 32.2 percent, silt is 25.8%, and fine sand is 42

percent, Cu = 20 and Cc = 2.25 have been determined

as the uniformity coefficient and the coefficient of

curvature, respectively. As a result, the soil is classed

as a well-graded low-plasticity c-soil [20, 21].

Standard laboratory experiments were used to

determine the engineering parameters of the soil, as

shown in Table 1

Table 1: Engineering properties of soil [21]

Parameter

Value

The specific gravity of solids, G

2.56

Atterberg Limits

Liquid limit

24.8

Plastic Limit

16.5

Shrinkage Limit

6.5%

Standard Proctor

Compaction Test

Maximum dry

density

16.9 kN/m3

Optimum

moisture content

7.2%

Shear Strength

(by Direct Shear

Test)

Unit cohesion, c

20 kPa

Friction angle, ϕ

17o

4.1. Governing Equation for Infiltration

Analysis

The 1D Richard's equation may be calculated using

Buckingham Darcy's -law and the mass conservation

law for water flow. as [22-25]

󰇛󰇜

 

 󰇡󰇛󰇜

󰇢 

 󰇛󰇜 (1)

Eagleson [26] and Raudkivi [27] have shown that the

Horton formula, Eq. (2) can be derived from Richard’s

equation if 󰇛󰇜 and 󰇛󰇜 are assumed as constants and

independent of the moisture content of the soil:

󰇛󰇜 󰇛󰇜 (2)

The Horton formula is often considered a purely

empirical formula. Philip [28-30] converted Richard’s

equation into an ordinary differential equation and

yielded an infinite series of solutions. The leading

term at the surface boundary became the infiltration

formula

󰇛󰇜

󰇛󰇜

 (3)

An exact solution of Richard’s equation was obtained

by Green and Ampt [31] for a simplified wetting front

movement approximation, in which a sharp boundary

dividing soil of constant initial moisture content lies

below the saturated soil with a moisture content of us



 

󰇛󰇜

 (4)

For the beginning period of infiltration ( f.2ks), and by

Taylor’s expansion technique, the Green-Ampt

formula can be simplified by taking only two terms of

the expansion. After integrating, one obtains the same

form of expression as the Philip formula

󰇛󰇜

 (5)

This might suggest that the Green-Ampt formula can

describe the infiltration processes for a longer period

than Philip’s formula. Since all three formulas are

related to Richard’s equation, it motivates to compare

their behaviors to the numerical solution of Richard’s

equation.

Analytical Model for

Evapotranspiration

This study employed three temperature-based

methods: Blaney-Criddle, Thornthwaite, and Ivanov.

Latitude, as well as the meteorological parameters of

mean monthly temperature, mean monthly wind

speed, mean monthly relative humidity, and mean

monthly bright sunlight hours, were necessary for the

usage of these approaches. Each approach needed one

or a different combination of two or more of the

parameters, but none of the methods required all of

the aforementioned parameters. Each of the three

approaches for calculating potential

evapotranspiration using climatological data is briefly

detailed in this section of the paper.

5.1 Blaney-Criddle Method

Blaney and Criddle (1950) observed that the amount

of water consumptively used by crops during their

growing seasons was closely correlated with mean

monthly temperatures and daylight hours and the

length of the growing seasons. The correlation

coefficients are then applied to determine the ET for

other areas where only climate data are available. The

Blaney-Criddle formula is one of the best-known

procedures for estimating Potential Evapotranspiration

(PET) and is widely used. The popularity of the

procedure is due to its simplicity and its use of readily

available data. It requires the use of only two factors,

namely, the temperature which is readily available

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from the weather stations, and information on daylight

hours which is a factor-based purely on the latitude of

the place.

Using the Blaney-Criddle approach, PET can be

expressed as follows,

  󰇛󰇜 (6)

5.2. Thornthwaite Method

This formula is based mainly on temperature with an

adjustment being made for the number of daylight

hours. An estimate of the potential evapotranspiration

is calculated every month.

The Thornthwaite equation given by

  󰇡

󰇢󰇡

󰇢󰇡

󰇢 (7)

Here,

  󰇛

󰇜



 (8)

  󰇛  󰇜

(9)

The main advantage of this method is that only the

temperature information is needed besides the

sunshine hours. Generally, it is known that the

Thornthwaite method gives the underestimate in the

arid area while the overestimate in the humid area,

respectively

5.3. Ivanov method

Concerning the relationship between evaporation rate,

temperature, and relative humidity, the monthly

evapotranspiration rate (mm) is obtained as follows:

  󰇛  󰇜󰇛󰇜 (10)

Data Collection

The description of the instruments used to measure

all the meteorological variables required for the study

is explained briefly.

6.1 Wind speed measurement

The wind speed was recorded with a Cup Counter

Anemometer (Fig. 3) at the meteorological station.

The Cup Counter Anemometer recorded the amount

of wind that passed through the device over time.

Three semi-conical copper cups, each 127mm in

diameter, with beaded borders are attached to a central

spider by three brass rods. The cup assembly is

friction-coupled to a vertical spindle, which has a tiny

ball bearing at the top and another at the bottom. The

stainless-steel cup wheel spindle is coupled to a

revolution counter installed in a watertight enclosure

through worm gearing. The average wind speed

during the interval may be estimated by examining the

counter reading at the beginning and conclusion of

any time of interest.

Fig. 3: Photographic view of cup counter anemometer

(Photograph taken by: Khairuz Zaman)

6.2. Temperature and Humidity Measurement

The Stevenson Screen or thermometer screen, which is

typical protection for meteorological equipment (as

shown in Fig.4), notably wet and dry bulb

thermometers used to record humidity and air

temperature, was used to measure temperature and

humidity at the station. To avoid high ground

temperatures, the Stevenson screen is positioned 4 feet

above the ground surface. To calculate relative

humidity, it has a wet bulb and a dry bulb

thermometer. A maximum thermometer and a

minimum thermometer are also included.

Fig. 4: Stevenson Screen (Photograph: Khairuz Zaman)

6.3. Bright Sunshine hours

A sunshine recorder, as illustrated in Fig. 5, is a

device that records the amount of sunlight in a certain

area or region at any particular moment. The result

includes information about the weather, climate, and

temperature of a certain location. At a height of 10

feet above ground level, a sunlight recorder is

attached.

Fig. 5. Sunshine recorder used at the station (Photograph:

Khairuz Zaman)

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6.4. Pan evaporation

There are a variety of standardized pans for measuring

evaporation, with the United States Class-A pan being

the most used as shown in Fig. 6 With a diameter of

1.21m and a depth of 225mm, the pan has a capacity

of about 0.3 m3. Because the basin is surrounded by

air, it is mounted on a 150mm high wooden frame.

Due to percolation and the necessity for water, the

water level is kept around 50 mm below the rim.

Every day, the water level is measured, or the

difference between the current and the original water

level is measured. alternatively, you measured the

amount of water you put into the pan if you wanted to

get the water level in the pan. As a result of the sun

hitting the edges of the pan, the temperature rises,

causing the evaporation to exceed the real

evaporation. To adjust this result, multiply the

evaporation value from the pan by a pan coefficient,

which value varies according to the climatic location

where your lest was taken.

Fig. 6: USWB Class A Pan (Photograph: Khairuz Zaman)

Fig. 7: Wind vane used for showing wind direction

(Photograph: Khairuz Zaman)

Analysis

Since the present paper aimed to evaluate the three

methods, the measured evaporation was compared

with the rates estimated by each method to determine

the relationships between the three methods.

At first, using Eq. (6) of the Blaney-Criddle Method

we get ET values as shown in Table 1. The below

process goes on for the remaining nine months

Table 1: ET Values using the Blaney-Criddle Method

Months

ET (mm/month)

January

1.149

February

1.50

March

1.57

For the Thornthwaite method, using Eq. (7) we get ET

values as shown in Table 2. The below process goes

on for the remaining nine months.

Table 2: ET Values using the Thornthwaite method

Months

ET (mm/month)

January

2.12

February

2.30

March

2.41

Similarly, for Ivanov Method, Eq. (10) was used to

compute the ET as shown in Table 3. This process

goes on for the remaining nine months.

Table 3: ET Values using the Thornthwaite method

Months

ET (mm/month)

January

1.79

February

2.29

March

2.49

7.1 Design and Interpretation

This chapter discusses the results of the present study

in the form of tables and graphs for clarity of

understanding. The evaporation rate estimates made

by the selected methods and their comparison with the

pan evaporation data are given. Also, the correlation

between all the estimated rates and the rates measured

is given. The meteorological data recorded at the

Meteorological Observatory of Assam Agriculture

University, Jorhat Assam during the period 2007-2016

were collected. Also, the evaporation data measured

using a Class-A pan was collected for the same

period. Fig. 8 to Fig. 10 shows the monthly and yearly

average of pan evaporation, maximum and minimum

temperature, relative humidity, wind speed, and

rainfall data.

Table 4: The mean monthly evaporation rates

Month

Pan Evaporation (mm)

January

1.09

February

1.64

March

2.37

April

2.74

May

3.31

June

2.72

July

2.93

August

2.57

September

2.12

October

1.75

November

1.82

December

1.04

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Fig 8: Monthly average graph of Pan Evaporation

Fig 9: Yearly average graph of Pan Evaporation

The values of all the required parameters needed for

estimating the potential evapotranspiration are given

in the table below.

Table 5: Required climatological parameters for the

calculation of potential evapotranspiration

Month

Avg.

Temp.

(oC)

Relative

Humidity

Avg.

Wind

Speed

(m/s)

January

15.8

91.5

0.46

February

19.4

0.54

March

0.79

April

23.5

May

27.4

88.4

0.88

June

28.3

88.3

0.69

July

29.0

93.0

0.92

August

28.7

0.88

September

27.8

92.3

0.65

October

25.5

94.7

0.43

November

20.9

92.7

0.38

December

17.0

95.6

0.25

Fig 10: Monthly average graph of maximum and

minimum temperature

The mean daily ET values for different months

estimated by the three reference evapotranspiration

estimation methods as discussed in the section above

for the study area are given in Table 6. The data is

represented graphically in Figure 11-13.

Table 6: The measurement of potential evapotranspiration

by the mentioned methods

Month

Class-

A Pan

(mm)

Blaney-

Criddle

(mm)

Thornthwaite

(mm)

Ivanov

(mm)

January

1.09

1.15

2.12

1.79

February

1.64

1.50

2.30

2.29

March

2.37

1.57

2.41

2.49

April

2.74

1.45

2.97

2.79

May

3.31

1.98

3.18

4.14

June

2.72

1.29

3.96

3.87

July

2.93

1.63

3.99

2.34

August

2.57

1.64

3.82

2.74

September

2.12

1.23

3.67

3.21

October

1.75

1.94

3.03

2.39

November

1.82

2.18

2.37

2.29

December

1.04

1.36

2.20

1.15

Fig 11: Comparison of different methods for estimating

potential evapotranspiration through the measured

evaporation in Class A Pan

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1 2 3 4 5 6 7 8 9 10 11 12

Evaporation (mm)

Month

2007 2008

2009 2010

2011 2012

2013 2014

0.0

1.0

2.0

3.0

4.0

5.0

2007 2009 2011 2013 2015

Evaporation (mm)

Year

Jan Feb Mar

Apr May Jun

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Evapotranspiration (mm)

Month

Class A Pan Blaney-Criddle Thornthwaite Ivanov

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Fig 12: Percentage variation with Class A Pan

Table 7: Comparison of annual estimated with Class A pan

evapotranspiration

Method

Blaney-

Criddle

Thornthwaite

Ivanov

Evaporation

Annual

Average

(mm)

18.92

36.65

31.49

26.10

Fig 13: Comparison of the annual estimated with Class A

pan evapotranspiration

The mean daily ET values for different months

estimated by the three reference evapotranspiration

estimation methods as discussed in the section above

for the study area are given in Table 6. The data is

represented graphically in Fig. 16.

From Fig 18, we can see that the estimated

evapotranspiration by the Blaney-Criddle method is

closer to the one measured by Class A Pan while the

Thornthwaite and Ivanov methods did not show

satisfying results. The peak values are found from

May to June as the temperature is high during this

period. On the other hand, the least ETo values are

observed in December. The monthly pattern produced

by different methods is not similar. The Ivanov

method showed a different pattern than the other

methods and also, the percentage variation of three

evapotranspiration methods against Class A Pan data

is shown in Fig. 19.

From Fig 19, it can be seen that the Blaney-

Criddle method shows the closest relation with the

pan evaporation as compared to the other two. The

Ivanov method is the second appropriate method after

the Blaney-Criddle method. The Thornthwaite

method did not show a satisfactory result in most of

the cases. Therefore, Blaney-Criddle is the most

appropriate temperature-based method to estimate the

potential evapotranspiration under the climatic

condition of the Jorhat region.

Conclusions

This study provides information on the

evapotranspiration (ET) estimates obtained from

indirect methods by using meteorological variables

for the climatic conditions of Jorhat. These

evapotranspiration estimates were also compared

with the observed pan evaporation values obtained

from the meteorological station to obtain the

correlation coefficient. The main conclusion is, that

the average monthly evapotranspiration values

obtained from Blaney-Criddle, Thornthwaite, and

Ivanov methods are 1.57, 3.05, and 2.62 mm/month

respectively. The result of this study suggests that all

the models compared well with observed pan

evaporation. It was observed that the performance of

the Blaney-Criddle method was the best as compared

to the other two methods

The soil sample collected from the study area is

found to be a well-graded low-plasticity c-soil

Reviewing kinds of the literature suggested five

governing equations to compute the infiltrations.

Acknowledgments

The authors thankfully acknowledge the research

data collected from Assam Agriculture University

for carrying out the investigation. The authors also

acknowledge the support and guidance received by

Assistant Professor Upasana Kashyap, and Rupantar

Senapaty Laboratory in Incharge The Assam

Kaziranga University for their support during this

research.

References:

[1] Allen R. G., (1993). Evaluation of a temperature

difference method for computing grass reference

evapotranspiration. Report submitted to the

Water Resource Department and Man. Serv.,

Land and Water development. Div., FAO,

Rome.49pp.

[2] Allen, R. G. (1996). Assessing integrity of

Weather Data for Reference Evapotranspiration

Estimation, Journal of Irrigation and Drainage

Engineering, Vol. 122, No.2.

[3] Jensen, M. E., Burman R. D., and Allen. R. G.

(1990). Evaporation and irrigation water

requirements, ASCE Manuals and Reports on

Eng. PracticesNo.70, Am.Soc.Civil Eng., New

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Percentage variation with

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Blaney-Criddle Thornthwaite Ivanov

1 2 3 4

Annual average (mm)

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Legends: 1. Pan evapouration. 2. Blaney-Criddle.

3. Thorntwaite. 4. Ivanov

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Khairuz Zaman, Nikos E. Mastorakis

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Notations

ET = Evapotranspiration in mm/m.

P = Percentage of daylight in hours in a year.

T = Temperature in degree Celsius

Ti = Mean monthly temperature [°C],

N = Mean monthly sunshine hour

E = Monthly evapotranspiration rate (mm)

R = Relative humidity

T = Monthly temperature average (oC)

F = Cumulative infiltration.

󰇛󰇜

= Specific moisture capacity.

󰇛󰇜󰇛󰇜= hydraulic conductivity (function of , )

= Infiltration-rate capacity when abundant rainfall

supply (cm/h)

= Final infiltration rate in Horton formula (cm/h)

 = Maximum infiltration rate in Horton formula

(cm/h)

 = Exponential decay rate in Horton formula (1/h)

ks =

Saturated hydraulic conductivity (cm/h)

= Hydraulic Conductivity (cm/h)

= Soil sorptivity (cm/h)

= Time (h)

t p

Ponding time (h)

 = Deviation between saturated and initial

pressure head (cm)

 = Soil water content (cm3/cm3)

Conflict of Interest Statement

The authors declare that there is no conflict of interest

in this paper. No financial support was received to

carry out this study.

Authors’ Contributions

Ghritartha Goswami has done the data analysis,

interpretations and writing; Sudip Basack is

responsible for overall supervision; Khairuz Zaman

has collected the data; Dubai Professor did the

revisions.

Creative Commons Attribution License

4.0 (Attribution 4.0 International, CC BY

4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en_

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2022.18.44

Ghritartha Goswami, Sudip Basack,

Khairuz Zaman, Nikos E. Mastorakis

E-ISSN: 2224-3496

460

Volume 18, 2022