The document discusses a study on deep percolation from surface irrigated water intensive crop fields like paddy and berseem crops. It outlines the objectives, which are to estimate deep percolation using water balance and physically based models employing drainage lysimeters. It describes the experimental methods including soil property tests, field instrumentation, crop growth monitoring, soil moisture and percolation measurement. Preliminary results comparing measured and computed deep percolation using a modified water balance model are also presented.
Practical Research 1 Lesson 9 Scope and delimitation.pptx
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)
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
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
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)
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.