International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-2, Mar -Apr- 2017 http://dx.doi.org/10.22161/ijeab/2.2.56 ISSN: 2456-1878 The Relationship between Surface Soil Moisture with Real Evaporation and Potential Evaporation in Dr. Osama T. Al-Taai1, Safa H. Hadi2

1, 2 Department of Atmospheric Sciences, College of Science, Al-Mustansiriyah University, Ministry of Higher Education and Scientific Research, - Iraq

Abstract— The aim of this research is to determine the state. There are three main reasons for evaporation from relationship between surface Soil Moisture (SSM) of both the surface of the soil. First, sufficient energy must be Real Evaporation (E) and surface Potential Evaporation available to convert the water from the liquid phase to the (SPE) for thirty years during the period of (1985-2014) for gas phase. Second, there is a vapor pressure gradient in the the eight stations (Sulaymaniya, , , Baghdad, atmosphere sufficient to transform the water from Liquid Rutba, , Nukhayib, Basrah) in Iraq, from (NOAA) and to vapor. Third, there is sufficient water level in the taking advantage of some statistics such as the Simple surface of the soil, which affects the moisture content [1]. Linear Regression (SLR) and the Spearman Rho test. Potential Evaporation (PE) can be defined as the amount Calculated the monthly average for Soil Moisture, Real of evaporation that can be obtained if sufficient water Evaporation and Potential Evaporation, and found to source is available. Real Evaporation (E) is a sum of both increase the values of SPE in hot months and decreased in Potential Evaporation (PE) and Actual Evaporation (AE) cold months while opposite to SM There was a strong in the soil. Several factors affect the potential evaporation inverse relationship between them, where the correlation (solar radiation as the most important atmospheric coefficient was in Sulaymaniya -0.91, in Mosul -0.89, in influences, temperature, relative humidity, and wind the Rutba -0.92, in Tikrit -0.89, in Baghdad -0.89, in speed), other factors indirectly affecting (water level, Nukhayib -0.89, in Kut -0.87, and in Basrah -0.83, and atmospheric stability, soil temperature, latent heat and there is a high correlation in stations (Basrah, Kut, sensible heat emissions). Nukhayib, and Rutba), while there is an average The task in the biological processes as it contributes to the correlation in the stations (Baghdad and Tikrit), and there process of formation of clouds and then precipitation, is low correlation in the stations (Sulaymaniya, Mosul), we which is one of the most important determinants of the also note an inverse correlation between RE and PE, cycle of water in nature and therefore measure the amount where there is a low correlation in Sulaymaniya and of potential evaporation to assess the water requirements medium correlation in the Mosul and Rutba stations, and as well as the need to calculate when planning irrigation there is a high correlation in the stations (Tikrit, Baghdad, projects president widely in many environmental studies, Nukhayib, Kut, and Basrah). including meteorology, hydrology, agriculture, climate Keywords— Soil moisture, Potential evaporation, Real change and affect the surface of the soil, especially at a evaporation, Spearman rho test, Iraq. depth of one to two meters, a key interaction between the earth and the atmosphere is one of the key variables that I. INTRODUCTION control the exchange of and the thermal energy between The evaporation of the main processes in the water cycle is the surface of the Earth and the atmosphere through a link between energy and water budget balance as the evaporation and plant transpiration [2]. This variable has water cycle include the release of energy and water vapor multiple links with other anaerobic variables, which makes transmission and for leading to condensation for it very effective predictively although it constitutes a very precipitation on the surface of the globe. Water flow on the small layer compared to the global total water but is very surface of the earth is through surface runoff, groundwater, important in many of the basic processes of many eyes and others. Then the water cycle ends to the starting hydrologists, chemists and biologists are important point, which is the water surfaces such as lakes, rivers, variables used in many applications (numerical weather oceans, seas and the surface of the soil. Unfortunately, this predictions, global climate change monitoring, flow process is one of the most incomprehensible processes in forecasting and evaporation modeling) [3]. Spatial and the water cycle. Water is from the liquid to the gaseous temporal differences of soil water content [4]. www.ijeab.com Page | 991 International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-2, Mar -Apr- 2017 http://dx.doi.org/10.22161/ijeab/2.2.56 ISSN: 2456-1878 Agriculture is the sector most economically affected by 6 ∑푛 푑2 푟 = 1 − 푖=1 푖 (3) extreme weather events such as drought. Many other 푠 푛(푛2˗1) economic sectors of society rely on agro-ecosystems, which are a specific form of human-adapted ecosystems If the value of n large can choose the value of rs to their for food production. Can lead to many negative economic importance by calculating the value of ts which is given by and social impacts such as loss of income in agriculture equation: and food industries and high costs for water and 푛 − 2 production technologies such as irrigation systems [5] [6]. 푡 = 푟 √ (4) 푠 푠 1 − 푟 2 푠 II. METHODOLOGY If the value of ts false calculated within the trusted A. Methods of Analysis boundary for the selection of a dual party from this we There were several statistical tests available. Spearman rho conclude that there is no trend in the data series and was selected. The regression analysis was also selected, through the “Table 1”. Can determine the value of the particularly the simple linear regression and the use of the degree of correlation and interpretation of test transactions. P-value for the relationship [7]: 1. Simple Linear Regression (SLR) Table.1: The degree of correlation and interpretation of Is the study of the relationship between two variables only test transactions [8]. accessible to a linear relationship (i.e. a straight line Value Correlation Interpretation of relation equation) between these two variables, a parametric test as it is assumed that the data are distributed normally Less 0.2 Few No relation distributed and to find out the value of the regression slope 0.2-0.4 Low Small relation of the regression is calculated by the following linear 0.4-0.7 Medium Acceptable relation equation: 0.7-0.9 High Special relation 0.9-1 Very high Strong relation 푌̅ = 푎 + 푏푋̅ (1) B. The Data and Study Stations ∑푛 (푋 − 푋̅) − (푌 − 푌̅) Was used the monthly average surface soil moisture, 푏 = 푖=1 푖 푖 (2) 푛 ̅ 2 surface potential evaporation and real evaporation data for ∑푖=1(푋푖 − 푋) eight different stations in Iraq (Mosul, Sulaymaniya, Where b: slope of the regression and found a mile straight Tikrit, Baghdad, Rutba, Kut Nukhayib, and Basrah) were line equation (1), a: a constant gradient and demonstrate used for thirty years (1985-2014) from The National the value of the lump of axis Ῡ for Straight equation (1). Oceanic and Atmospheric Administration (NOAA) [9], 2. Probability-Value (see “Fig. 1” and “Table 2”). It is purely a statistical term, which is a number or the number of measurements used to evaluate the statistical Table.2: The latitude, longitude and altitude of the study value of a show that was a contrast factor it is an stations in Iraq [10]. Latitude Longitude Altitude influential factor or not really? If the P-Value is less than Stations 0.05, the contrast factor it is an influential factor in the (oN) (oE) (meter) variable that we are trying to study the change may Mosul 36.19 43.09 223 consider factor affecting even the value of P-Value equal Sulaymaniya 35.33 45.27 853 to 0.1, but that exceeds 0.1, this factor should be removed Tikrit 34.56 43.70 103 from the form it is ineffective. Baghdad 33.14 44.14 34 3. Spearman Rho Test Rutba 33.02 40.17 615 It is a test of a set of observed data (xi = 1,2,……,n) is Kut 32.48 45.73 91 based on the null hypothesis that is, all xi values are Nukhayib 32.02 42.15 305 independent and have the same distribution and to Basrah 30.34 47.47 2 calculate the Spearman Rho coefficient statistical ranks (rs) must convert the original model to the ranks mediated arranged in descending order in terms of amount and then the value of the account through di (di = ki -i) where the (i

= 1,2,…..,n) and rs is given by the following [8]:

www.ijeab.com Page | 992 International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-2, Mar -Apr- 2017 http://dx.doi.org/10.22161/ijeab/2.2.56 ISSN: 2456-1878 III. RESULTS AND DISCUSSION 1. The Relationship between SPE and SSM The “Fig. 2”, shows the relationship between the surface PE with the surface SM, where there is strong inverse relationship where we note there is a high correlation in the eight stations where the highest value of the correlation coefficient was in Sulaimaniya and Rutba stations and the lowest value of the correlation coefficient in the station and the reason for this relationship reverse that connects SPE and SSM, potential evaporative occurs only in the presence of moisture and when evaporation reaches the latent limit less moisture in the atmosphere where the high temperature and solar radiation directly affects the SPE and SSM, note that the southern stations characterized by high temperatures and this leads to the high in potential evaporation values and low values in SSM values, the opposite is happening in the northern stations (see “Table 3”). Fig.1: Iraq map, explaining the study stations [10].

MOSUL SULAIMANIYA TIKRIT

0.30 0.30 0.30 SSM = 0.213 - (0.0000745 * SPE) 0.28 SSM= 0.255 - (0.000135 * SPE) 0.28 SSM = 0.262 - (0.000115 * SPE) 0.28 0.26 0.26

)

) 0.24 ) 0.24 0.26

-3

-3

-3

m

m 0.22 m 0.22

3

3 3 0.24 0.20 0.20 0.18 0.18 0.22 0.16 0.16 0.20 0.14 0.14 0.12 0.12 0.18 0.10 0.10 0.16 0.08 0.08

Surface (m Soil Moisture 0.06 Surface (m Soil Moisture 0.06

Surface (m Soil Moisture 0.14 0.04 0.04 0.02 0.02 0.12 0.00 0.00 0.10 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 Surface Potential Evaporation (W/m2) 2 Surface Potential Evaporation (W/m ) 2 Surface Potential Evaporation (W/m )

BAGHDAD RUTBA KUT

0.30 0.25 0.30 0.28 SSM = 0.197 - (0.0000496 * SPE) 0.28 SSM = 0.198 - (0.0000392 * SPE) SSM = 0.202 - (0.0000473 * SPE) 0.24 0.26 0.26

) ) 0.24 ) 0.23 0.24

-3 -3

-3

m m

0.22 m 0.22

3 3 3 0.22 0.20 0.20 0.18 0.21 0.18 0.16 0.16 0.20 0.14 0.14 0.12 0.19 0.12 0.10 0.10 0.18 0.08 0.08

Surface (m Soil Moisture Surface (m Soil Moisture 0.06 Surface (m Soil Moisture 0.17 0.06 0.04 0.04 0.16 0.02 0.02 0.00 0.15 0.00 0 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 800 900 1000 Surfacs Potential Evaporation (W/m2) Surface Potential Evaporation (W/m2) Surface Potential Evaporation (W/m2) NUKHAYIB BASRAH

0.25 0.30

0.24 SSM = 0.190 - (0.0000419 * SPE) 0.28 SSM = 0.194 - (0.0000433 * SPE)

) 0.23 ) 0.26

-3

-3

m

m

3 0.22 3 0.24

0.21 0.22

0.20 0.20

0.19 0.18

0.18 0.16

Surface (m Soil Moisture 0.17 Surface (m Soil Moisture 0.14

0.16 0.12

0.15 0.10 0 100 200 300 400 500 600 100 200 300 400 500 600 700 800 Surface Potential Evaporation (W/m2) Surface Potential Evaporation (W/m2) Fig.2: The relationship between the surface potential evaporation (SPE) and surface soil moisture (SSM) of eight different stations in Iraq for thirty years (1985-2014) www.ijeab.com Page | 993 International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-2, Mar -Apr- 2017 http://dx.doi.org/10.22161/ijeab/2.2.56 ISSN: 2456-1878 Table.3: Spearman rho test results and Simple Linear Regression (SLR) to find the strength of the relationship between the SPE and SSM. Simple Linear Regression Spearman Rho Test Station P-value Interpretation rs Correlation Mosul 0.0001 Linear relation -0.89 High-inverse correlation Sulaymaniya 0.0001 Linear relation -0.91 Very high-inverse correlation Tikrit 0.0001 Linear relation -0.89 High-inverse correlation Baghdad 0.0001 Linear relation -0.89 High-inverse correlation Rutba 0.0001 Linear relation -0.92 Very high-inverse correlation Kut 0.0002 Linear relation -0.87 High-inverse correlation Nukhayib 0.0001 Linear relation -0.89 High-inverse correlation Basrah 0.0009 Linear relation -0.83 High-inverse correlation

2. The Relationship between E and SSM The “Fig. 3”, shows that there is a strong positive Nukhayib, Kut, and Basrah) for reasons the following is relationship between E and SSM, where there is a high because evaporation occurs on the surface of the earth correlation in the stations (Basrah, Kut, Nukhayib, and when the water moves into an atmosphere shaped like Rutba), while there is an medium correlation in stations water vapor from the various confiscations as well as the (Baghdad and Tikrit), There is a low correlation in the evaporation plays a significant role in the occurrence of stations (Sulaymaniya and Mosul), and also note through moisture and consequent upon the occurrence of the P-Value, there is a nonlinear relationship in stations condensation or fog or include rain dew, as well as the (Sulaymaniya and Mosul), while there is a linear evaporation occurs only the existence and availability of relationship in the stations (Rutba, Tikrit, Baghdad, sources of moisture (see “Table 4”). MOSUL SULAIMANIYA TIKRIT

0.30 0.30 0.30 SSM = 0.178 + (0.0370 * E) SSM = 0.158 + (0.0564 * E) SSM = 0.176 + (0.0388 * E) 0.28 0.28 0.28

)

) )

-3

-3 -3

m

m m

3 0.26 3 0.26 3 0.26

0.24 0.24 0.24

0.22 0.22 0.22

0.20 0.20 0.20

Surface Soil Moisture (m Moisture Soil Surface

Surface Soil Moisture (m Moisture Soil Surface (m Moisture Soil Surface 0.18 0.18 0.18

0.16 0.16 0.16 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Evaporation (mm/month) Evaporation (mm/month) Evaporation (mm/month) BAGHDAD RUTBA KUT

0.30 0.30 0.30 SSM = 0.158 + (0.0527 * E) SSM = 0.167 + (0.0481 * E) SSM = 0.162 + (0.0487 * E)

0.28 0.28 0.28

) )

)

-3 -3

-3

m m

m

3 3 0.26 3 0.26 0.26

0.24 0.24 0.24

0.22 0.22 0.22

0.20 0.20 0.20

Surface Soil Moisture (m Moisture Soil Surface (m Moisture Soil Surface

Surface Soil Moisture (m Surface Soil Moisture 0.18 0.18 0.18

0.16 0.16 0.16 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.0 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Evaporation (mm/month) Evaporation (mm/month) Evaporation (mm/month) NUKHAYIB BASRAH

0.30 0.30 SSM = 0.163 + (0.0595 * E) SSM = 0.161 + (0.0509 * E) 0.28 0.28

)

)

-3

-3 0.26 0.26

m

m

3

3

0.24 0.24

0.22 0.22

0.20 0.20

0.18 0.18

Surface Soil Moisture (m Surface Soil Moisture

Surface Soil Moisture (m Surface Soil Moisture

0.16 0.16

0.14 0.14 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Evaporation (mm/month) Evaporation (mm/month) Fig.3: The relationship between the real evaporation (E) and SSM of eight different stations in Iraq for thirty years (1985-2014) www.ijeab.com Page | 994 International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-2, Mar -Apr- 2017 http://dx.doi.org/10.22161/ijeab/2.2.56 ISSN: 2456-1878 Table.4: Spearman rho test results and Simple Linear Regression (SLR) to find the strength of the relationship between the E and SSM. Simple Linear Regression Spearman Rho Test Stations P-value Interpretation rs Correlation Mosul 0.2528 Non-Linear relation 63.0 Low-positive correlation Sulaymaniya 0.2364 Non-Linear relation 6337 Low-positive correlation Tikrit 0.0121 Linear relation 6301 Medium-positive correlation Baghdad 0.0139 Linear relation 6301 Medium-positive correlation Rutba 0.0039 Linear relation 63.0 High-positive correlation Kut 0.0013 Linear relation 63.9 High-positive correlation Nukhayib 0.0001 Linear relation 0.89 High-positive correlation Basrah 0.0001 Linear relation 63.. High-positive correlation

3. The Relationship between E and SPE we note there is a low correlation in the Sulaimaniya The “Fig. 4”, shows the relationship between surface real station and the medium correlation in the (Mosul and evaporation (E) and potential evaporation (SPE) where Rutba) stations and high correlation in the stations (Tikrit, there is a strong inverse relationship between them, where Baghdad, Nukhayib, Kut and Basrah) (see “Table 5”).

MOSUL SULAIMANIY TIKRIT

1000 1000 1000

900 900 SPE = 714.438 - (295.521 * E) 900 SPE = 746.441 - (760.857 * E) ) SPE = 605.068 - (319.365 * E) ) )

2 2 2

800 800 800

700 700 700

600 600 600

500 500 500

400 400 400

300 300 300

200 200 200

Surface Potential Evaporation (W/m Potential Evaporation Surface 100 (W/m Potential Evaporation Surface 100 (W/m Evaporation Potential Surface 100

0 0 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Evaporation (mm/month) Evaporation (mm/month) Evaporation(mm/month) BAGHDAD RUTBA KUT

1000 1000 1000 SPE = 853.698 - (1039.201 * E) 900 SPE = 936.924 - (1128.300 * E) SPE = 587.213 - (846.153 * E) ) 900 900

) )

2

2 2

800 800 800

700 700 700

600 600 600

500 500 500

400 400 400

300 300 300

200 200 200

Surface Potential Evaporation (W/m Evaporation Potential Surface 100 (W/m Evaporation Potential Surface 100 (W/m Potential Evaporation Surface 100

0 0 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.0 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Evaporation (mm/month) Evaporation (mm/month) Evaporation (mm/month) NUKHAYIB BASRAH

1000 1000

900 SPE = 583.835 - (1156.003 * E) 900 SPE = 673.351 - (861.832 * E)

) )

2 2

800 800

700 700

600 600

500 500

400 400

300 300

200 200

Surface Potential Evaporation (W/m Evaporation Potential Surface 100 (W/m Evaporation Potential Surface 100

0 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Evaporation (mm/month) Evaporation (mm/month) Fig.4: The relationship between the E and SPE of eight different stations in Iraq for thirty years (1985-2014) www.ijeab.com Page | 995 International Journal of Environment, Agriculture and Biotechnology (IJEAB) Vol-2, Issue-2, Mar -Apr- 2017 http://dx.doi.org/10.22161/ijeab/2.2.56 ISSN: 2456-1878 Table 5: Spearman rho test results and Simple Linear Regression (SLR) to find the strength of the relationship between the E and SPE. Simple Linear Regression Spearman Rho Test Stations P-value Interpretation rs Correlation Mosul 0.1239 Non-Linear relation -0.47 Medium-inverse correlation Sulaymaniya 0.2527 Non-Linear relation -6336 Low-inverse correlation Tikrit 0.0024 Linear relation -0.79 High-inverse correlation Baghdad 0.0033 Linear relation -0.77 High-inverse correlation Rutba 0.0074 Linear relation -0.73 High-inverse correlation Kut 0.0026 Linear relation -0.78 High-inverse correlation Nukhayib 0.0016 Linear relation -0.80 High-inverse correlation Basrah 0.0026 Linear relation -0.78 High-inverse correlation

IV. CONCLUSIONS http://www.ghcc.msfc.nasa.gov/landprocess/lp_home The results showed a strong inverse relationship between .html. SPE and surface SSM. SPE was found to increase in the [7] S. Mohammed, ''Data Analysis,'' Site Management southern stations and increase in hot months, in contrast and Industrial Engineering, 2009. to SSM. It also showed an inverse relationship between E http://samehar.wordpress.com/2009/08/13/0120809/ and SPE. Results there is a strong direct relationship [8] Mathematics in Education and Industry (MEI), between E and SSM where E occurs only with the “Spearman’s rank correlation,'' December 2007. presence of water (Soil Moisture). The results show that [9] A. Seidenglanz, and U. Bremen, ''Panoply a Tool for SPE is an evaporative energy in the atmosphere E of Visualizing Net CDF-Formatted Model Output,'' surface soil moisture because real evaporation is a process NASA, February 2016. of transformation from the liquid phase to the gas phase. http://www.noaa.gov/ E occurs only with soil moisture. E is the sum of SPE and [10] General Authority for The Rough Waters of The Iraqi Actual Evaporation (AE) in the soil. Air and Seismic Monitoring, ''Atlas Climate of Iraq for The Period (1961-1990),'' Baghdad, Iraq, pp. 8-7, ACKNOWLEDGEMENTS 1994. An acknowledgment to The National Oceanic and Atmospheric Administration (NOAA) on the data used in this research.

REFERENCES [1] E. M. Shaw, ''Hydrology in practice 3rd ed.''. Stanly Thomas pub. Ltd, U. K, pp. 569, 1999. [2] C. A. Appelo, and D. Postma,''Geochemistry ground water and Pollution Rotterdam,'' Balkama Ltd, pp. 536, 1999. [3] J. Walker, ''Estimating Soil Moisture Profile Dynamics from Near-Surface Soil Moisture Measurements and Standard Meteorological Data,'' Ph.D. dissertation, The University of Newcastle, Australia, 1999. [4] W. Lingli, and J. Q. John, ''Satellite remote sensing applications for surface soil Moisture monitoring a review,'' Journal of Front, Earth Sci. Chine, vol. 3, issue2, pp. 237- 247, 2009. [5] J. D. Fast, and M. D. McCorcle, ''The Effect of Heterogonous Soil Moisture on a Summer Bar-clinic Circulation in the Central ,'' Journal of Mon. Wee. Rev., vol.119, pp. 2140-2167, 1991. [6] E. A. James, "Soil moisture," Ltd, U. K, 1999. www.ijeab.com Page | 996