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INDIAN INSTITUTE OF TECHNOLOGY ROORKEE
Deep Percolation from Surface Irrigated
Water Intensive Crop Fields
By
K.S., Hari Prasad (CED, IIT Roorkee, India)
Hatiye, Samuel D. (WRIE, Ethiopia)
C.S.P. Ojha (CED, IIT Roorkee, India)
Presentation Outline
Introduction
The problem/concern
Objective of the study
Literature
Methods and materials
Experimental Works
Mathematical modelling
Results and Discussions
Conclusion
Scope for future work
2
Introduction
• Fresh water is mainly consumed for the
purposes of agricultural, domestic and
industrial water needs.
• Agriculture is by far the largest consumer of
fresh water of the globe; that is, water put to
irrigate a cropland to produce crops.
• Mainly in developing countries, More than
80% of fresh water withdrawals goes for
agricultural water input (FAO, 2004).
3
Introduction ....
• Due to limitation in resources, surface methods
of irrigation are usually practiced in
developing countries.
• In particular, flooding way of water application
is implemented in water intensive crops such
as paddy(rice) and berseem fodder crops.
• Large areas are in paddy and berseem
cultivation in many parts of the world, on the
other hand.
4
Introduction...
• Rice is a staple food grain
for nearly half of the world
population.
• Paddy field is a
major/largest consumer
of water in the irrigated
agriculture.
5
Introduction...
• There is also an increasing
demand for berseem fodder
production due to increasing
demand for dairy products.
• Berseem also needs frequent
irrigation due to its shallow root
depth.
• But the resource base (water) is
limited.
• Less supply ????? More
demand.
6
Introduction
• In agricultural water use, processes such as Deep
percolation, seepage, evaporation and runoff are
taken as unproductive water losses.
• Deep percolation refers to the water that flows
beyond the crop root zone of a given crop (Wang et
al., 2012; Bethune et al., 2008; Ma et al., 2013; Huang
et al, 2003).
Problem Statement
• Deep percolation phenomena from frequently
irrigated fields such as paddy and berseem fields
seriously diminishes irrigation efficiency,
jeopardise proper water management and
minimize water productivity.
• Further it can cause environmental havoc by
carrying agricultural based residues and chemicals
(surface and/or groundwater pollution).
• Groundwater level rise and hence water logging
and secondary salinization.
Problem Statement ….
• Specifically, deep percolation from water
intensive crops in relatively permeable soils needs
more attention.
• Most available studies deal with deep percolation
under puddled root zone conditions and ignoring
the un-puddled field situations where most
farmers practice irrigating their paddy and
berseem.
So far ( The Gap)
• Very few studies were conducted on deep
percolation from paddy and berseem fields
covering different regimes of water application
and employing drainage type lysimeters.
• Only little understanding about deep
percolation under unpuddled field conditions
and different seasons exits.
10
Problem Statement ....
Objective of the Study
• The main objective of the present study
is to estimate deep percolation from
surface irrigated water intensive crops
such as paddy and berseem fodder
fields using the water balance and
physically based models while
employing drainage type lysimeters.
11
Materials and Methods
• Experimental Program
 Laboratory Experiments (Soil physical and hydraulic)
Field Experiments(Soil, crop, irrigation monitoring)
• Deep Percolation Estimation Models
 Water balance Model
 Physically based Model
12
Simple Water Balance Model
 Spatially lumped (root zone) and temporally
distributed has been used (Allen et al., 1998; Ochoa
et al. 2007; Abrahaoa et. al, 2011).
 where P is precipitation, I is Irrigation, SPin and SPot are
seepage/lateral inflow and outflow respectively from the root
zone, GW is the capillary rise from groundwater, RO is
surface runoff, DP is deep percolation, ET is
evapotranspiration and ∆S is change in soil water storage.
SSPETDPROGWSPIP otni ∆=+++−+++ )()(
13
Physically Based Model
 The one dimensional Richards (1931) Equation (Liu
et al., 2014; Tan et al., 2014) as used in HYDRUS-
1D package is (Simunek et al. 1998):
where θ is the moisture content, 𝛹𝛹 is the pressure head, z is
the vertical coordinate usually taken positive upwards, t is the
time coordinate, K is the hydraulic conductivity of the soil and
S(z,t) is the sink term representing root water uptake.
14
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
=
𝜕𝜕
𝜕𝜕𝜕𝜕
𝐾𝐾(𝛹𝛹)
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
+
𝜕𝜕𝜕𝜕(𝛹𝛹)
𝜕𝜕𝜕𝜕
− 𝑆𝑆(𝑧𝑧, 𝑡𝑡)
Constitutive Relationships
15
𝜽𝜽 − 𝜳𝜳 Relationship:-
𝛩𝛩 = �
1
1+ 𝛼𝛼v 𝛹𝛹 𝑛𝑛v
𝑚𝑚
for 𝛹𝛹 ≤ 0
1 for 𝛹𝛹 > 0
where 𝛼𝛼v and 𝑛𝑛v are unsaturated soil
parameters with m = 1 − (1/𝑛𝑛v ) for
𝑛𝑛v > 1 ; and 𝛩𝛩 is the effective
saturation defined as
𝛩𝛩 =
𝜃𝜃−𝜃𝜃r
𝜃𝜃s−𝜃𝜃r
where 𝜃𝜃s = Saturated moisture
content; and 𝜃𝜃r = Residual moisture
content of the soil.
K -𝜃𝜃 Relationship:-
𝐾𝐾 𝜃𝜃 = 𝐾𝐾sa𝑡𝑡 𝛩𝛩
1
2 1 − 1 − 𝛩𝛩
1
𝑚𝑚
𝑚𝑚 2
for 𝛩𝛩 < 1
= 𝐾𝐾sat for 𝛩𝛩 = 1
where 𝐾𝐾sat is saturated hydraulic conductivity
Experimental Program
Laboratory Experiments
• Laboratory experiments consisting of
determination of soil, crop and soil hydraulic
parameters were conducted.
• These include: Soil bulk and particle density, soil
texture and soil hydraulic characteristics.
• These are presented in the following slides.
16
Experimental Program... Laboratory
Experiments
s
s
d
V
M
=ρ
d
b
n
ρ
ρ
−=1
t
s
b
V
M
=ρ
17
Experimental Program... Laboratory
Experiments
18
Experimental Program... Laboratory
Experiments
0
10
20
30
40
50
60
70
80
90
100
0.0001 0.0010 0.0100 0.1000 1.0000 10.0000
Grain size (mm)
Grain Size Distirbution Curve
Sample 1(0-30cm)
sample 2 (30-60cm)
sample 3(60-80cm)
sample 4(80-100 cm)
sample 5(100-140cm)
Average
PercentFiner,%
0.001 0.005 0.075 4.75
Grain Size Boundary According To ASTM
colloids clay Silt Sand
19
Sample
No
Depth
(Below
GL),cm
Percent
Sand (%)
Percent
silt (%)
Percent
Clay (%)
Soil Class
(USDA)
1 0-30 69.10 26.95 3.83 Sandy Loam
2 30-60 69.20 25.52 5.30 Sandy Loam
3 60-80 68.10 26.18 5.77 Sandy Loam
4 80-100 73.60 22.15 4.23 Sandy Loam
5 100-140 65.40 26.87 7.71 Sandy Loam
Average 0-140cm 69.10 25.53 5.37 Sandy Loam
Experimental Program... Laboratory
Experiments
20
Experimental Program... Laboratory
Experiments
Depth below
ground level
(cm)
Bulk
density
(g/cm3)
Particle
density
(g/cm3)
Sand
(%)
Silt
(%)
Clay
(%)
Soil Class
(USDA)
Porosity
0-30 1.58 2.55 73.40 22.70 3.90 Sandy Loam 0.38
30-60 1.55 2.57 66.89 28.39 4.72 Sandy Loam 0.40
60-80 1.54 2.56 68.57 26.54 4.89 Sandy Loam 0.40
80-100 1.54 2.58 69.10 26.54 4.36 Sandy Loam 0.40
100-140 1.59 2.62 68.01 27.38 4.61 Sandy Loam 0.39
21
Experimental Program... Laboratory
Experiments
Pressure Plate Experiment
The picture can't be displayed.
22
Experimental Program... Laboratory Experiments
Pressure Plate Experiment
Suction
Pressure
(cm)
Water Content (%)
Sample 1 Sample
2
Sample
3
Sample
4
Sample
5
0 35.70 39.97 38.13 38.97 39.10
300 17.90 24.43 19.20 19.94 19.41
375 17.50 21.71 18.87 18.84 19.35
850 13.80 16.72 13.56 14.78 14.00
1900 7.90 8.56 7.55 8.92 10.68
5000 6.80 6.70 6.12 6.86 7.71
7000 7.00 7.13 6.07 7.11 7.84
9000 6.90 5.86 5.61 5.97 6.49
12000 6.90 7.15 6.56 6.58 7.95
23
0
0.1
0.2
0.3
0.4
0.5
0 5000 10000 15000moisturecontent(-)
Suction pressure head(-ψ, cm)
Observed (0-30 cm) van Genuchten (0-30 cm)
Observed (30-60) cm van Genuchten (30-60)
Observed(60-80) cm van Genuchten(60-80 cm)
Observed(80-100) cm van Genuchten (80-100 cm)
Observed (100-140 cm) van Genuchten100-140 cm
Experimental Program... Laboratory
Experiments
Pressure Plate Experiment
Depth (cm) θr θs α (1/cm) n R2
0-30 0.046 0.357 0.016 1.493 0.9751
30-60 0.056 0.399 0.006 1.741 0.9827
60-80 0.041 0.381 0.013 1.545 0.9732
80-100 0.018 0.390 0.022 1.366 0.9750
100-140 0.053 0.391 0.018 1.473 0.9855
Average 0.043 0.384 0.015 1.523 0.9783
Standard deviation 0.015 0.016 0.006 0.138 0.00544
Soil Hydraulic Parameters
24
Study site and
Field experimental set up
25
Study site and
experimental set up
26
Field preparation and Lysimeters
27
Transplanting paddy and sowing
Berseem crops
28
Crop Growth stages and Irrigation
29
Crop Growth stages and Harvest
30
Crop Parameters
• Crop Parameters including Root depth, crop
height and leaf area index (LAI) were
monitored in each of the crop seasons.
• Root depth and crop Height were monitored
using simple tape measurement for randomly
selected crops and locations and the
measurement values were averaged.
• LAI was monitored using L-80 ceptometer
(leaf area monitoring device in field).
31
Root Depth
0
10
20
30
40
50
60
0 20 40 60 80 100 120 140 160
paddy rice (season-1)
paddy rice (season-2)
berseem fodder(season-1)
berseem fodder(season- 2)
Number of days after transplanting/sowing)
RootDepth(cm)
32
Crop Height
0
0.4
0.8
1.2
1.6
0 20 40 60 80 100
Cropheight(m)
Number of days after transplanting
Paddy Rice (Season-1)
Paddy rice (sesaon-2)
0
0.2
0.4
0.6
0 20 40 60 80 100 120 140
Cropheight(m)
Number of days after sowing
Berseem fodder(season-1)
Berseem fodder (sesaon-2)
33
Leaf Area Index
0
1
2
3
4
5
6
7
8
0 20 40 60 80 100
LAI(m2/m2)
Number of days after transplanting
Paddy Rice (Season-1) Paddy rice (sesaon-2)
0
1
2
3
4
5
6
0 50 100 150
LAI(m2/m2)
Number of days after sowing
Berseem fodder(season-1)
Berseem fodder (sesaon-2)
34
Deep percolation Monitoring
Using Field Lysimeters
35
Soil Moisture Monitoring
Using Profile Probe (PR2/6)
36
Saturated soil Hydraulic Conductivity
Using Guelph Permeameter
37
Crop Yield
38
Climatic Data
• The climatic data needed for the current study
has been obtained from the nearby stations
(800 m distance from the experimental
station).
• The climatic variables are: Rainfall, Maximum
and Minimum Temperature, Wind velocity,
Relative Humidity and Sunshine hours all for
daily time step.
• NIH and Department of Hydrology.
39
Results
• The Water Balance Model has a bit modified and
used
where DP [L]= Deep percolation of water moving
below the root zone; θ= is the volumetric soil moisture
content (%); P[L] = rainfall; I [L]= applied irrigation;
ETa [L]= actual evapotranspiration; R [L]= surface
runoff, i and i-1 are, respectively, the current and
previous time steps; j is an index for root zone layer and
nl is the number layers.
40
iaiii
nl
j
jiii RETIPDP −−++−= ∑=
−
1
1 )( θθ
41
-28
-18
-8
2
12
22
32
42
52
07/12/2013 06/01/2014 05/02/2014 07/03/2014 06/04/2014 06/05/2014
Deeppercolation(mm)
Growing dates
Measured DP
Computed DP
(a)
-16
-6
4
14
24
34
44
54
12/11/2014 12/12/2014 11/01/2015 10/02/2015 12/03/2015 11/04/2015
Deeppercolation(mm)
Growing dates
Measured DP
Computed DP
(b)
Fig. 6. Computed and measured deep percolation on daily time step in lysimetre 1 in
berseem season 1 (a) and 1 in berseem season 2 (b)
42
0
20
40
60
80
100
120
27/12/2013 26/01/2014 25/02/2014 27/03/2014 26/04/2014
Deeppercolation(mm)
Growing dates
Measured DP
Computed DP
(a)
-5
5
15
25
35
45
55
65
25/11/2014 25/12/2014 24/01/2015 23/02/2015 25/03/2015
Deeppercolation(mm)
Growing dates
Measured DP
Computed DP
(b)
Fig. Computed and measured deep percolation with lumped time
steps in lysimetre 1 in berseem season 1 (a) and in berseem season 2
(b)
In general:
• The performance of the simple water
balance model is poor for daily time step
while it performs well on the longer time
step (lumped time step)as depicted in the
above figures.
43
The Physically based model results: Calibration
44
0
20
40
60
80
100
120
140
20/07/2013 09/08/2013 29/08/2013 18/09/2013 08/10/2013 28/10/2013
Deeppercolation(mm)
Growing dates
Measured DP
Computed DP
(a)
0
10
20
30
40
50
10/12/2013 14/01/2014 18/02/2014 25/03/2014 29/04/2014
Deeppercolation(mm)
Growing dates
Measured DP
Computed DP
(b)
Measured and model predicted deep percolation in lysimetre 1 for rice season 1 (a)
berseem season 1(b)
• The physically based model performs well on
daily as well as lumped time steps.
• Although, both models perform well on
lumped time steps (weekly bases in this
study), the physically based model performs
superior than the simple water balance
model. However, the benefit is compensated
for large input data requirement in the case of
physically based model.
45
Soil moisture content
• To verify the efficacy of the physically based
model, computed soil moisture contents
(obtained after calibration of the model using
deep percolation data) and observed soil
moisture contents were compared.
• The comparison yielded good results,
although some discrepancy in estimating SWC
by the model has been observed.
46
Water Productivity
• The water productivity (water use efficiency) of the crop is
determined to evaluate the effect of water saving on crop
yield.
• It can be expressed by following equations (Li et al. 2014;
Sudhir-Yadav et al. 2011; Michael 2005):
𝑊𝑊𝑊𝑊𝐸𝐸𝐸𝐸𝑎𝑎
=
𝑌𝑌
𝐸𝐸𝐸𝐸𝑎𝑎
𝑊𝑊𝑊𝑊𝐼𝐼 =
𝑌𝑌
𝐼𝐼
𝑊𝑊𝑊𝑊𝐼𝐼+𝑃𝑃 =
𝑌𝑌
𝐼𝐼 + 𝑃𝑃
where,
WPETa = water productivity based on evapotranspiration (Kg.m-3)
Y = actual crop yield (Kg)
ETa = actual evapotranspiration (m3)
WPI = water productivity based on irrigation input (Kg.m-3)
I = irrigation input (m3)
WPI+P = water productivity based on total water input (Kg.m-3)
48
Paddy season 1
Plot ID A11 A12 A13 A14 L2 L1
Average Yield (kg/ha) 4140.0 4140.0 4030.0 4860.0 3250.0 3540.0
ETa (mm) 408.64 411.72 411.72 411.72 410.9 410.99
I (mm) 2418.8 2418.8 2418.8 2418.8 2418.8 2418.80
I+P(mm) 3087.1 3087.1 3087.1 3087.1 3087.1 3087.10
WPETa (Kg/m3) 1.01 1.01 0.98 1.18 0.79 0.86
WPI (Kg/m3) 0.17 0.17 0.17 0.20 0.13 0.15
WP(I+P) (Kg/m3) 0.13 0.13 0.13 0.16 0.11 0.11
Paddy season 2
Yield (kg/ha) 3036.44 2666.67 2603.00 4700.0 2695.0 2688.17
ETa (mm) 430.87 430.91 431.05 430.62 414.04 431.05
I (mm) 643.1 639 855 644 851.00 630.00
I+P(mm) 1176 1171.9 1387.9 1176.9 1383.9 1162.9
WPETa (Kg/m3) 0.70 0.62 0.60 1.09 0.65 0.62
WPI (Kg/m3) 0.47 0.42 0.30 0.73 0.32 0.43
WP(I+P) (Kg/m3) 0.26 0.23 0.19 0.40 0.19 0.23
Yield decrease (%) 26.66 35.59 35.41 3.29 17.08 24.06
Crop yield and water productivity indices for paddy (grain yield)
49
Crop yield and water productivity indices for berseem (green forage)
Berseem season 1
Plot ID/Lysimeter A11 A12 A13 A14 L2 L1
Average Yield (kg/ha) 48900 53800 55500 51700 41200 37200
ETa (mm) 341.38 342.36 342.36 342.36 341.38 342.36
I (mm) 520.00 520.00 520.00 520.00 520.00 520.00
I+P(mm) 745.8 745.8 745.8 745.8 745.8 745.8
WPETa (Kg/m3) 14.32 15.71 16.21 15.10 12.07 10.87
WPI (Kg/m3) 9.40 10.35 10.67 9.94 7.92 7.15
WP(I+P) (Kg/m3) 6.56 7.21 7.44 6.93 5.52 4.99
Berseem season 2
Yield (kg/ha) 40641.2 49660.2 35944.2 40904.0 27333.0 27893.0
ETa (mm) 162.81 162.81 162.81 162.81 162.81 162.81
I (mm) 175.10 164.50 127.00 127.00 91.90 63.10
I+P(mm) 395.90 385.30 347.80 347.8 312.70 283.90
WPETa (Kg/m3) 24.96 30.50 22.08 25.12 16.79 17.13
WPI (Kg/m3) 23.21 30.19 28.30 32.21 29.74 44.20
3
50
Conclusions
• Deep percolation computed using the water balance
model on daily time step do not agree with field
observed deep percolation for both crop seasons and
lysimeters. However, the model predicts deep
percolation very well on lumped time steps.
Therefore, accurate estimation of deep percolation
can be made on lumped time steps using simple
water balance model.
• Physically based model, unlike the water balance
model, predicts the deep percolation very well on
daily time step.
• Both models predict DP very well on lumped time
steps.
51
Conclusions
• The amount of deep percolation in both crop
seasons is large. Deep percolation values
ranging from 82 to 87% of input water has
been lost through deep percolation in paddy
season 1. In paddy season 2, deep percolation
was 77-80% of the overall input water. In
berseem season 1, the field observed deep
percolation was 62-67% of input water while it
has been reduced to 42-52% of input water in
the berseem season 2.
• Increasing input water increases DP.
52
Conclusions
• Locally constructed drainage type lysimeters
are demonstrated to be robust enough in
capturing deep percolation from the bottom of
crop root zones. The lysimeters were
responding well to the imposed irrigation and
rainfall events in the growing seasons of paddy
and berseem crops subjected to varying
regimes of water application.
• The lysimeters were also depicted the
phenomena of preferential flow transport,
distinguished the difference between daily and
nocturnal deep percolation values.
53
Conclusions
• Simulations using physically based model
also showed a visible association between
the observed and model simulated soil
moisture content in the soil profile.
• The performance of the physically based
model shows that the model performs
comparatively better for wet season than
the dry season.
54
Conclusions
• The values of saturated hydraulic
conductivity near soil surface is large. This
would be attributed to root profile, the
activities of soil fauna and soil cracking near
the soil surface.
• It is possible to reduce deep percolation
without the implementation of puddling
practice in particularly in paddy fields and
achieve large saving in input water by
employing alternative irrigation scheduling
strategy.
55
Conclusions
• Large saving in input water has been achieved
with nominal yield decrease by employing
alternative irrigation scheduling strategy
during both crop seasons.
• Irrigation water saving on the other hand
ranges from approximately 65% to 74% of the
typical existing irrigation application for the
rice crop in the region. On the other hand, in
the berseem season input water saving ranging
from 47% to 62% has been achieved.
Irrigation water saving in the order of 66% to
88% of the conventional approach in berseem
has been attained.
56
Conclusions
• There was yield reduction due to the
reduced application of irrigation. However,
the water productivity has been increased.
• Therefore, it can also be concluded that
increased water productivity in a given field
can be realized by altering an irrigation
scheduling strategy.
57
Scope of Future Work
• The current study was limited to few number
of experimental trials. Large trial experiments
may be needed to asses an optimum irrigation
scheduling strategy which would reduce DP
and provide optimum water productivity for a
given cropping condition.
• The current work may be extended to other
soil types for the rice, berseem and other
cropping conditions to quantify and investigate
deep percolation characteristics.
58
Scope of Future Work
• In the current study due consideration is given for
single porosity model, assuming matrix flow
conditions. Future works may also consider
macropore flow conditions.
• The effect of other variables (crop variety,
agronomic conditions, climatic conditions etc...)
on crop yield may be investigated in future.
• The locally constructed drainage type lysimeters
play an important role in monitoring deep
percolation. These types of lysimeters may be
constructed elsewhere to study groundwater
recharge, solute transport etc.
Thank You
ध�वाद

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Prasad H - UEI Day 1 - Kochi Jan18

  • 1. INDIAN INSTITUTE OF TECHNOLOGY ROORKEE Deep Percolation from Surface Irrigated Water Intensive Crop Fields By K.S., Hari Prasad (CED, IIT Roorkee, India) Hatiye, Samuel D. (WRIE, Ethiopia) C.S.P. Ojha (CED, IIT Roorkee, India)
  • 2. Presentation Outline Introduction The problem/concern Objective of the study Literature Methods and materials Experimental Works Mathematical modelling Results and Discussions Conclusion Scope for future work 2
  • 3. Introduction • Fresh water is mainly consumed for the purposes of agricultural, domestic and industrial water needs. • Agriculture is by far the largest consumer of fresh water of the globe; that is, water put to irrigate a cropland to produce crops. • Mainly in developing countries, More than 80% of fresh water withdrawals goes for agricultural water input (FAO, 2004). 3
  • 4. Introduction .... • Due to limitation in resources, surface methods of irrigation are usually practiced in developing countries. • In particular, flooding way of water application is implemented in water intensive crops such as paddy(rice) and berseem fodder crops. • Large areas are in paddy and berseem cultivation in many parts of the world, on the other hand. 4
  • 5. Introduction... • Rice is a staple food grain for nearly half of the world population. • Paddy field is a major/largest consumer of water in the irrigated agriculture. 5
  • 6. Introduction... • There is also an increasing demand for berseem fodder production due to increasing demand for dairy products. • Berseem also needs frequent irrigation due to its shallow root depth. • But the resource base (water) is limited. • Less supply ????? More demand. 6
  • 7. Introduction • In agricultural water use, processes such as Deep percolation, seepage, evaporation and runoff are taken as unproductive water losses. • Deep percolation refers to the water that flows beyond the crop root zone of a given crop (Wang et al., 2012; Bethune et al., 2008; Ma et al., 2013; Huang et al, 2003).
  • 8. Problem Statement • Deep percolation phenomena from frequently irrigated fields such as paddy and berseem fields seriously diminishes irrigation efficiency, jeopardise proper water management and minimize water productivity. • Further it can cause environmental havoc by carrying agricultural based residues and chemicals (surface and/or groundwater pollution). • Groundwater level rise and hence water logging and secondary salinization.
  • 9. Problem Statement …. • Specifically, deep percolation from water intensive crops in relatively permeable soils needs more attention. • Most available studies deal with deep percolation under puddled root zone conditions and ignoring the un-puddled field situations where most farmers practice irrigating their paddy and berseem.
  • 10. So far ( The Gap) • Very few studies were conducted on deep percolation from paddy and berseem fields covering different regimes of water application and employing drainage type lysimeters. • Only little understanding about deep percolation under unpuddled field conditions and different seasons exits. 10 Problem Statement ....
  • 11. Objective of the Study • The main objective of the present study is to estimate deep percolation from surface irrigated water intensive crops such as paddy and berseem fodder fields using the water balance and physically based models while employing drainage type lysimeters. 11
  • 12. Materials and Methods • Experimental Program  Laboratory Experiments (Soil physical and hydraulic) Field Experiments(Soil, crop, irrigation monitoring) • Deep Percolation Estimation Models  Water balance Model  Physically based Model 12
  • 13. Simple Water Balance Model  Spatially lumped (root zone) and temporally distributed has been used (Allen et al., 1998; Ochoa et al. 2007; Abrahaoa et. al, 2011).  where P is precipitation, I is Irrigation, SPin and SPot are seepage/lateral inflow and outflow respectively from the root zone, GW is the capillary rise from groundwater, RO is surface runoff, DP is deep percolation, ET is evapotranspiration and ∆S is change in soil water storage. SSPETDPROGWSPIP otni ∆=+++−+++ )()( 13
  • 14. Physically Based Model  The one dimensional Richards (1931) Equation (Liu et al., 2014; Tan et al., 2014) as used in HYDRUS- 1D package is (Simunek et al. 1998): where θ is the moisture content, 𝛹𝛹 is the pressure head, z is the vertical coordinate usually taken positive upwards, t is the time coordinate, K is the hydraulic conductivity of the soil and S(z,t) is the sink term representing root water uptake. 14 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 = 𝜕𝜕 𝜕𝜕𝜕𝜕 𝐾𝐾(𝛹𝛹) 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 + 𝜕𝜕𝜕𝜕(𝛹𝛹) 𝜕𝜕𝜕𝜕 − 𝑆𝑆(𝑧𝑧, 𝑡𝑡)
  • 15. Constitutive Relationships 15 𝜽𝜽 − 𝜳𝜳 Relationship:- 𝛩𝛩 = � 1 1+ 𝛼𝛼v 𝛹𝛹 𝑛𝑛v 𝑚𝑚 for 𝛹𝛹 ≤ 0 1 for 𝛹𝛹 > 0 where 𝛼𝛼v and 𝑛𝑛v are unsaturated soil parameters with m = 1 − (1/𝑛𝑛v ) for 𝑛𝑛v > 1 ; and 𝛩𝛩 is the effective saturation defined as 𝛩𝛩 = 𝜃𝜃−𝜃𝜃r 𝜃𝜃s−𝜃𝜃r where 𝜃𝜃s = Saturated moisture content; and 𝜃𝜃r = Residual moisture content of the soil. K -𝜃𝜃 Relationship:- 𝐾𝐾 𝜃𝜃 = 𝐾𝐾sa𝑡𝑡 𝛩𝛩 1 2 1 − 1 − 𝛩𝛩 1 𝑚𝑚 𝑚𝑚 2 for 𝛩𝛩 < 1 = 𝐾𝐾sat for 𝛩𝛩 = 1 where 𝐾𝐾sat is saturated hydraulic conductivity
  • 16. Experimental Program Laboratory Experiments • Laboratory experiments consisting of determination of soil, crop and soil hydraulic parameters were conducted. • These include: Soil bulk and particle density, soil texture and soil hydraulic characteristics. • These are presented in the following slides. 16
  • 19. Experimental Program... Laboratory Experiments 0 10 20 30 40 50 60 70 80 90 100 0.0001 0.0010 0.0100 0.1000 1.0000 10.0000 Grain size (mm) Grain Size Distirbution Curve Sample 1(0-30cm) sample 2 (30-60cm) sample 3(60-80cm) sample 4(80-100 cm) sample 5(100-140cm) Average PercentFiner,% 0.001 0.005 0.075 4.75 Grain Size Boundary According To ASTM colloids clay Silt Sand 19
  • 20. Sample No Depth (Below GL),cm Percent Sand (%) Percent silt (%) Percent Clay (%) Soil Class (USDA) 1 0-30 69.10 26.95 3.83 Sandy Loam 2 30-60 69.20 25.52 5.30 Sandy Loam 3 60-80 68.10 26.18 5.77 Sandy Loam 4 80-100 73.60 22.15 4.23 Sandy Loam 5 100-140 65.40 26.87 7.71 Sandy Loam Average 0-140cm 69.10 25.53 5.37 Sandy Loam Experimental Program... Laboratory Experiments 20
  • 21. Experimental Program... Laboratory Experiments Depth below ground level (cm) Bulk density (g/cm3) Particle density (g/cm3) Sand (%) Silt (%) Clay (%) Soil Class (USDA) Porosity 0-30 1.58 2.55 73.40 22.70 3.90 Sandy Loam 0.38 30-60 1.55 2.57 66.89 28.39 4.72 Sandy Loam 0.40 60-80 1.54 2.56 68.57 26.54 4.89 Sandy Loam 0.40 80-100 1.54 2.58 69.10 26.54 4.36 Sandy Loam 0.40 100-140 1.59 2.62 68.01 27.38 4.61 Sandy Loam 0.39 21
  • 22. Experimental Program... Laboratory Experiments Pressure Plate Experiment The picture can't be displayed. 22
  • 23. Experimental Program... Laboratory Experiments Pressure Plate Experiment Suction Pressure (cm) Water Content (%) Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 0 35.70 39.97 38.13 38.97 39.10 300 17.90 24.43 19.20 19.94 19.41 375 17.50 21.71 18.87 18.84 19.35 850 13.80 16.72 13.56 14.78 14.00 1900 7.90 8.56 7.55 8.92 10.68 5000 6.80 6.70 6.12 6.86 7.71 7000 7.00 7.13 6.07 7.11 7.84 9000 6.90 5.86 5.61 5.97 6.49 12000 6.90 7.15 6.56 6.58 7.95 23 0 0.1 0.2 0.3 0.4 0.5 0 5000 10000 15000moisturecontent(-) Suction pressure head(-ψ, cm) Observed (0-30 cm) van Genuchten (0-30 cm) Observed (30-60) cm van Genuchten (30-60) Observed(60-80) cm van Genuchten(60-80 cm) Observed(80-100) cm van Genuchten (80-100 cm) Observed (100-140 cm) van Genuchten100-140 cm
  • 24. Experimental Program... Laboratory Experiments Pressure Plate Experiment Depth (cm) θr θs α (1/cm) n R2 0-30 0.046 0.357 0.016 1.493 0.9751 30-60 0.056 0.399 0.006 1.741 0.9827 60-80 0.041 0.381 0.013 1.545 0.9732 80-100 0.018 0.390 0.022 1.366 0.9750 100-140 0.053 0.391 0.018 1.473 0.9855 Average 0.043 0.384 0.015 1.523 0.9783 Standard deviation 0.015 0.016 0.006 0.138 0.00544 Soil Hydraulic Parameters 24
  • 25. Study site and Field experimental set up 25
  • 27. Field preparation and Lysimeters 27
  • 28. Transplanting paddy and sowing Berseem crops 28
  • 29. Crop Growth stages and Irrigation 29
  • 30. Crop Growth stages and Harvest 30
  • 31. Crop Parameters • Crop Parameters including Root depth, crop height and leaf area index (LAI) were monitored in each of the crop seasons. • Root depth and crop Height were monitored using simple tape measurement for randomly selected crops and locations and the measurement values were averaged. • LAI was monitored using L-80 ceptometer (leaf area monitoring device in field). 31
  • 32. Root Depth 0 10 20 30 40 50 60 0 20 40 60 80 100 120 140 160 paddy rice (season-1) paddy rice (season-2) berseem fodder(season-1) berseem fodder(season- 2) Number of days after transplanting/sowing) RootDepth(cm) 32
  • 33. Crop Height 0 0.4 0.8 1.2 1.6 0 20 40 60 80 100 Cropheight(m) Number of days after transplanting Paddy Rice (Season-1) Paddy rice (sesaon-2) 0 0.2 0.4 0.6 0 20 40 60 80 100 120 140 Cropheight(m) Number of days after sowing Berseem fodder(season-1) Berseem fodder (sesaon-2) 33
  • 34. Leaf Area Index 0 1 2 3 4 5 6 7 8 0 20 40 60 80 100 LAI(m2/m2) Number of days after transplanting Paddy Rice (Season-1) Paddy rice (sesaon-2) 0 1 2 3 4 5 6 0 50 100 150 LAI(m2/m2) Number of days after sowing Berseem fodder(season-1) Berseem fodder (sesaon-2) 34
  • 35. Deep percolation Monitoring Using Field Lysimeters 35
  • 36. Soil Moisture Monitoring Using Profile Probe (PR2/6) 36
  • 37. Saturated soil Hydraulic Conductivity Using Guelph Permeameter 37
  • 39. Climatic Data • The climatic data needed for the current study has been obtained from the nearby stations (800 m distance from the experimental station). • The climatic variables are: Rainfall, Maximum and Minimum Temperature, Wind velocity, Relative Humidity and Sunshine hours all for daily time step. • NIH and Department of Hydrology. 39
  • 40. Results • The Water Balance Model has a bit modified and used where DP [L]= Deep percolation of water moving below the root zone; θ= is the volumetric soil moisture content (%); P[L] = rainfall; I [L]= applied irrigation; ETa [L]= actual evapotranspiration; R [L]= surface runoff, i and i-1 are, respectively, the current and previous time steps; j is an index for root zone layer and nl is the number layers. 40 iaiii nl j jiii RETIPDP −−++−= ∑= − 1 1 )( θθ
  • 41. 41 -28 -18 -8 2 12 22 32 42 52 07/12/2013 06/01/2014 05/02/2014 07/03/2014 06/04/2014 06/05/2014 Deeppercolation(mm) Growing dates Measured DP Computed DP (a) -16 -6 4 14 24 34 44 54 12/11/2014 12/12/2014 11/01/2015 10/02/2015 12/03/2015 11/04/2015 Deeppercolation(mm) Growing dates Measured DP Computed DP (b) Fig. 6. Computed and measured deep percolation on daily time step in lysimetre 1 in berseem season 1 (a) and 1 in berseem season 2 (b)
  • 42. 42 0 20 40 60 80 100 120 27/12/2013 26/01/2014 25/02/2014 27/03/2014 26/04/2014 Deeppercolation(mm) Growing dates Measured DP Computed DP (a) -5 5 15 25 35 45 55 65 25/11/2014 25/12/2014 24/01/2015 23/02/2015 25/03/2015 Deeppercolation(mm) Growing dates Measured DP Computed DP (b) Fig. Computed and measured deep percolation with lumped time steps in lysimetre 1 in berseem season 1 (a) and in berseem season 2 (b)
  • 43. In general: • The performance of the simple water balance model is poor for daily time step while it performs well on the longer time step (lumped time step)as depicted in the above figures. 43
  • 44. The Physically based model results: Calibration 44 0 20 40 60 80 100 120 140 20/07/2013 09/08/2013 29/08/2013 18/09/2013 08/10/2013 28/10/2013 Deeppercolation(mm) Growing dates Measured DP Computed DP (a) 0 10 20 30 40 50 10/12/2013 14/01/2014 18/02/2014 25/03/2014 29/04/2014 Deeppercolation(mm) Growing dates Measured DP Computed DP (b) Measured and model predicted deep percolation in lysimetre 1 for rice season 1 (a) berseem season 1(b)
  • 45. • The physically based model performs well on daily as well as lumped time steps. • Although, both models perform well on lumped time steps (weekly bases in this study), the physically based model performs superior than the simple water balance model. However, the benefit is compensated for large input data requirement in the case of physically based model. 45
  • 46. Soil moisture content • To verify the efficacy of the physically based model, computed soil moisture contents (obtained after calibration of the model using deep percolation data) and observed soil moisture contents were compared. • The comparison yielded good results, although some discrepancy in estimating SWC by the model has been observed. 46
  • 47. Water Productivity • The water productivity (water use efficiency) of the crop is determined to evaluate the effect of water saving on crop yield. • It can be expressed by following equations (Li et al. 2014; Sudhir-Yadav et al. 2011; Michael 2005): 𝑊𝑊𝑊𝑊𝐸𝐸𝐸𝐸𝑎𝑎 = 𝑌𝑌 𝐸𝐸𝐸𝐸𝑎𝑎 𝑊𝑊𝑊𝑊𝐼𝐼 = 𝑌𝑌 𝐼𝐼 𝑊𝑊𝑊𝑊𝐼𝐼+𝑃𝑃 = 𝑌𝑌 𝐼𝐼 + 𝑃𝑃 where, WPETa = water productivity based on evapotranspiration (Kg.m-3) Y = actual crop yield (Kg) ETa = actual evapotranspiration (m3) WPI = water productivity based on irrigation input (Kg.m-3) I = irrigation input (m3) WPI+P = water productivity based on total water input (Kg.m-3)
  • 48. 48 Paddy season 1 Plot ID A11 A12 A13 A14 L2 L1 Average Yield (kg/ha) 4140.0 4140.0 4030.0 4860.0 3250.0 3540.0 ETa (mm) 408.64 411.72 411.72 411.72 410.9 410.99 I (mm) 2418.8 2418.8 2418.8 2418.8 2418.8 2418.80 I+P(mm) 3087.1 3087.1 3087.1 3087.1 3087.1 3087.10 WPETa (Kg/m3) 1.01 1.01 0.98 1.18 0.79 0.86 WPI (Kg/m3) 0.17 0.17 0.17 0.20 0.13 0.15 WP(I+P) (Kg/m3) 0.13 0.13 0.13 0.16 0.11 0.11 Paddy season 2 Yield (kg/ha) 3036.44 2666.67 2603.00 4700.0 2695.0 2688.17 ETa (mm) 430.87 430.91 431.05 430.62 414.04 431.05 I (mm) 643.1 639 855 644 851.00 630.00 I+P(mm) 1176 1171.9 1387.9 1176.9 1383.9 1162.9 WPETa (Kg/m3) 0.70 0.62 0.60 1.09 0.65 0.62 WPI (Kg/m3) 0.47 0.42 0.30 0.73 0.32 0.43 WP(I+P) (Kg/m3) 0.26 0.23 0.19 0.40 0.19 0.23 Yield decrease (%) 26.66 35.59 35.41 3.29 17.08 24.06 Crop yield and water productivity indices for paddy (grain yield)
  • 49. 49 Crop yield and water productivity indices for berseem (green forage) Berseem season 1 Plot ID/Lysimeter A11 A12 A13 A14 L2 L1 Average Yield (kg/ha) 48900 53800 55500 51700 41200 37200 ETa (mm) 341.38 342.36 342.36 342.36 341.38 342.36 I (mm) 520.00 520.00 520.00 520.00 520.00 520.00 I+P(mm) 745.8 745.8 745.8 745.8 745.8 745.8 WPETa (Kg/m3) 14.32 15.71 16.21 15.10 12.07 10.87 WPI (Kg/m3) 9.40 10.35 10.67 9.94 7.92 7.15 WP(I+P) (Kg/m3) 6.56 7.21 7.44 6.93 5.52 4.99 Berseem season 2 Yield (kg/ha) 40641.2 49660.2 35944.2 40904.0 27333.0 27893.0 ETa (mm) 162.81 162.81 162.81 162.81 162.81 162.81 I (mm) 175.10 164.50 127.00 127.00 91.90 63.10 I+P(mm) 395.90 385.30 347.80 347.8 312.70 283.90 WPETa (Kg/m3) 24.96 30.50 22.08 25.12 16.79 17.13 WPI (Kg/m3) 23.21 30.19 28.30 32.21 29.74 44.20 3
  • 50. 50 Conclusions • Deep percolation computed using the water balance model on daily time step do not agree with field observed deep percolation for both crop seasons and lysimeters. However, the model predicts deep percolation very well on lumped time steps. Therefore, accurate estimation of deep percolation can be made on lumped time steps using simple water balance model. • Physically based model, unlike the water balance model, predicts the deep percolation very well on daily time step. • Both models predict DP very well on lumped time steps.
  • 51. 51 Conclusions • The amount of deep percolation in both crop seasons is large. Deep percolation values ranging from 82 to 87% of input water has been lost through deep percolation in paddy season 1. In paddy season 2, deep percolation was 77-80% of the overall input water. In berseem season 1, the field observed deep percolation was 62-67% of input water while it has been reduced to 42-52% of input water in the berseem season 2. • Increasing input water increases DP.
  • 52. 52 Conclusions • Locally constructed drainage type lysimeters are demonstrated to be robust enough in capturing deep percolation from the bottom of crop root zones. The lysimeters were responding well to the imposed irrigation and rainfall events in the growing seasons of paddy and berseem crops subjected to varying regimes of water application. • The lysimeters were also depicted the phenomena of preferential flow transport, distinguished the difference between daily and nocturnal deep percolation values.
  • 53. 53 Conclusions • Simulations using physically based model also showed a visible association between the observed and model simulated soil moisture content in the soil profile. • The performance of the physically based model shows that the model performs comparatively better for wet season than the dry season.
  • 54. 54 Conclusions • The values of saturated hydraulic conductivity near soil surface is large. This would be attributed to root profile, the activities of soil fauna and soil cracking near the soil surface. • It is possible to reduce deep percolation without the implementation of puddling practice in particularly in paddy fields and achieve large saving in input water by employing alternative irrigation scheduling strategy.
  • 55. 55 Conclusions • Large saving in input water has been achieved with nominal yield decrease by employing alternative irrigation scheduling strategy during both crop seasons. • Irrigation water saving on the other hand ranges from approximately 65% to 74% of the typical existing irrigation application for the rice crop in the region. On the other hand, in the berseem season input water saving ranging from 47% to 62% has been achieved. Irrigation water saving in the order of 66% to 88% of the conventional approach in berseem has been attained.
  • 56. 56 Conclusions • There was yield reduction due to the reduced application of irrigation. However, the water productivity has been increased. • Therefore, it can also be concluded that increased water productivity in a given field can be realized by altering an irrigation scheduling strategy.
  • 57. 57 Scope of Future Work • The current study was limited to few number of experimental trials. Large trial experiments may be needed to asses an optimum irrigation scheduling strategy which would reduce DP and provide optimum water productivity for a given cropping condition. • The current work may be extended to other soil types for the rice, berseem and other cropping conditions to quantify and investigate deep percolation characteristics.
  • 58. 58 Scope of Future Work • In the current study due consideration is given for single porosity model, assuming matrix flow conditions. Future works may also consider macropore flow conditions. • The effect of other variables (crop variety, agronomic conditions, climatic conditions etc...) on crop yield may be investigated in future. • The locally constructed drainage type lysimeters play an important role in monitoring deep percolation. These types of lysimeters may be constructed elsewhere to study groundwater recharge, solute transport etc.