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PUMPMOD: A simulation model for multipump rice irrigation systems
Agricultural Water Management, 15 (1989) 333-346 Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands
333
P U M P M O D : A Simulation Model for Multipump Rice Irrigation S y s t e m s A.L.A. GALANG and S.I. BHUIYAN
Water Management Department, International Rice Research Institute, P.O. Box 933, Manila (The Philippines) (Accepted 28 March 1988)
ABSTRACT Galang, A.L.A. and Bhuiyan, S.I., 1989. PUMPMOD: a simulation model for multipump rice irrigation systems. Agric. Water Manage., 15: 333-346. A microcomputer model called PUMPMOD was developed for simulating the operation of multipump rice-irrigation systems. The model works on a daily basis; the principal inputs required to run it are the number of pumps operating and duration of their operation in relation to rainfall, demand for water in the field, and the irrigation distribution schedule followed in the system. The model captures the temporal and spatial changes in the water demand-and-supply relationship within the system as land-soaking, land-preparation, transplanting and crop-irrigation activities progress within a season. The principal outputs of the model are the extent of area that can be land-prepared, transplanted to rice and supported with required irrigation water until the crop reaches maturity. PUMPMOD has been adapted and evaluated for a rice irrigation system in The Philippines and uses four pumps of about 6.0 m3/s total discharge capacity, using field data collected during eight consecutive seasons and taking into account its existing major physical and socio-economic constraints which influence the operation of the system. The results are satisfactory. PUMPMOD is designed to be used as a tool for making decisions to optimize the supply of irrigation water from the source in relation to rainfall and field-water demands. It can also be used for various investigative purposes, including the evaluation of the efficacy of alternative designs to allocate and distribute water for maximizing irrigation benefits.
INTRODUCTION S t u d y o f rice i r r i g a t i o n s y s t e m s a i m e d at i m p r o v i n g t h e i r o p e r a t i o n a l effic i e n c y a n d m a x i m i z i n g b e n e f i t s f r o m t h e u s e o f a v a i l a b l e w a t e r r e s o u r c e is v i t a l l y i m p o r t a n t f o r f u t u r e f o o d - p r o d u c t i o n i n c r e a s e s in m o s t d e v e l o p i n g countries. Different types of irrigation systems need different procedures for e f f i c i e n t o p e r a t i o n . A p u m p i r r i g a t i o n s y s t e m , a l t h o u g h p e r h a p s s i m i l a r t o diversion-type systems with respect to water conveyance and delivery to the crop field, h a s t h e u n i q u e a d a p t a b i l i t y o f i n t e r m i t t e n t o p e r a t i o n a i m e d a t m a x i m i z -
0378-3774/89/$03.50
© 1989 Elsevier Science Publishers B.V.
334
ing use of rainfall and minimizing costs. High investment and operation costs are major concerns to the development of pump irrigation systems, and high operational efficiency is essential to make them economically viable. In studying irrigation systems, it is often difficult, inconvenient and timeconsuming to experiment on the actual system. Sometimes such experimentation may not even be possible due to some political or social constraints, or because some irreversible consequences may result from the experiment. A logical alternative is to simulate the system to be studied. This would involve the development of a simulation model that will describe the system in logical or mathematical terms. Although model development is in itself time-consuming and would still require actual data for evaluation, it can be an effective tool for studying the dynamic properties of irrigation systems and to gain insights into how they would work under varying management rules. This paper describes a computer simulation model developed for multipump irrigation systems designed to support rice production. The model is constructed to respond to varying operating conditions and physical limitations that are often encountered in pump-based irrigation systems. REVIEW OF LITERATURE
Different types of simulation models have been used in recent years for studying various aspects of irrigation-system behavior. However, it is somewhat difficult to use and apply these models to special types of irrigation systems such as a rice-based pump-irrigation system having unique characteristics such as a high degree of control over water supply and the presence of physical limitations in power supply and canal capacities. O'Mara and Dulay (1984) studied the joint effects of various canal water and associated private tubewell tax or subsidy policies on overall irrigation efficiency using a simulation model. Their models linked the hydrology of a conjunctive stream aquifer system to an economic model of agricultural production for 53 regions of the Indus Basin in Pakistan with a network model of the flows in river reaches, link canals and irrigation canals. The scope of these models is large since they cover an entire basin. Moreover, the main objective here was the simulation of response of the policy receivers to the actions of policy makers. Some researchers incorporated crop-growth models in their simulation studies to evaluate alternative irrigation technologies. These studies, however, had greater emphasis on farm-level irrigation-water management and, although an important objective is increased pumping and application efficiency, physical limitations and other constraints concerning pumps and distribution schemes were not considered. Mapp (1983) included a dynamic grain-sorghum plant model into a farm-level simulation model to derive the effects of alternative irrigation schedules on crop yield, water use and producer's net returns, while Swaney et al. (1983) developed an algorithm incorporating a dynamic crop-
335 simulation model (SOYGRO) to evaluate the expected yield benefits and profits from irrigating the crop on any day of the season. The algorithm used current-season weather prior to the decision date and several years of historical weather data for the rest of the season. A Bayesian decision-theory optimization model was developed by Udek and Busch ( 1982 ) for optimal irrigation management strategy. The model was used to select the optimum land area to be irrigated as controlled by hydrologic and irrigation-efficiency input parameters and the irrigator's risk-response function under specified conditions. The model did recognize the stochastic nature of water supply as a whole but did not consider the different factors affecting water supply. Furthermore, this model was applied on a seasonal basis and did not allow for changes in input parameters on a more frequent interval as would normally be done in simulation models. The simulation model used by Johnson (1982) to evaluate alternative water-management systems in the Punjab, looking into the effect of controlling water supply showed that the prevailing canal-closure schedules restricted economic returns from different cropping systems. Closure during February or April after wheat harvest increased returns by $20 to $30 per hectare. In the same manner, a computer simulation model for rice production developed by Escalante et al. (1982) considered a rotational scheme of water distribution. The model's input included maximal irrigable area, variety of rice, soil type, and irrigation-system efficiency. Each of the models reviewed had its own emphasis and strong points depending upon its objectives. In the development of the P U M P M O D simulation model described in this paper, the strong points of these modeling concepts were considered in order to make P U M P M O D practically useful in the operation and evaluation of irrigation systems using multiple pumps. PUMPMOD MODEL The P U M P M O D is a microcomputer-based model to simulate the dynamic behavior of multi-pump irrigation systems used for rice production. It is designed to capture the continual changes in the hydrologic and agronomic status of the service area as the irrigation system supports activities in land soaking, land preparation, transplanting, crop irrigation, etc., for rice production. The system basically operates for providing irrigation water to supplement rainfall characterized by high temporal variability. The conceptual framework of P U M P M O D is shown in Figs. 1 and 2 in the form of a relational diagram. Figure 1 gives the system-level part, showing the different factors affecting daily pump operation. The amount of irrigation water that could be delivered to the system's service area within a day depends on several factors, such as: (a) number of pumps operated during the day; (b) duration of each pump operation; (c) carrying capacity of the canal network; (d) distribution efficiency of the canal network; (e) hydraulic response time
336 TPump House?
River
,,.-- Other Laterals
No.of pumps~.~ Main canal duration of I [ discharge operation [
b'a:c%ge]
@
A --I
] I I
4
lI I l
--i ~
decision
)
@J Other tertiaries Fig. 1. PUMPMOD relational diagram of the system level.
(i.e. the time required for developing a working hydraulic head at various points in the canal network to deliver water to their respective service areas for different numbers of pumps operated simultaneously); and (f) the water distribution scheme used for the various service areas within the system. The number of pumps to be operated simultaneously and the duration of their operation, which are decision inputs of the system manager, are determined using the available information on electric power supply schedule/limitation, condition of pump (s), water stage at the source, canal conditions, and rainfall. Figure 2 shows the service-area level components of the relational diagram. In this section, the total water supply, which consists of irrigation supply and rainfall, is shown distributed to areas under the different stages of farming activities, namely, land soaking (AULS), land preparation (AULP), crop irrigation practice (AUCIP), terminal drainage (AUTD) and harvesting (AH), taking into account their water requirements and prevailing farm water-status. In the figure, the area under irrigation (AUI) is used as sum total of AULS, AULP and AUCIP.The principal output of PUMPMOD is the extent of area that could
337
~0TAL WATER SUPPLY [ I
-
[
t
r
I
i i I
I _ _ I.-q
]-
I
'7
r-----
l
J l
I
I
__~--~ AUTD ~-~ AH I
AULP
I 1 I
I I
I J
. . . .
r
ATLS ___J
Fig. 2. PUMPMODrelationaldiagramof the servicearea level. be land-prepared, transplanted and fully irrigated to rice, based primarily on the amount of rainfall and the pumped water delivered to the service area. The computer program is written in CBASIC and would require at least 1024K of memory on TRS-80 Model-16B under CP/M-68K operating system. The program is available in 8-inch diskettes using C P / M format. Figure 3 shows the main program flow chart. It has two major paths. The first (vertical) path uses actual data to obtain the output, while the other path uses generated data. The first path is specifically useful in evaluating the model, while the path that uses generated data would be used for prediction and optimization purposes. Probability distributions of rainfall and of electric power supply interruptions and the water stage at the source would be needed for generation
338
-co o
°
5~
~0.~
~
~" O 0
oo>"
~
~-zOZm_
z
339
of water inflow data. A program control routine is also incorporated in the program to facilitate runs for specific outputs.
General functional relationships In the response computation subroutine, a number of parameters to be used in the model's functional relationships need to be specified. These include the total number of pumps t h a t are operational, the capacity of each pump, the distribution scheme being implemented, water requirements in each stage of farming activities, distribution efficiency, farm plot spillway heights and maximum rate of land preparation possible. The important functional relationships needed are as follows: QT = f(NP) Q ( i ) = f ( q w ..... q ( j ) , . . . )
i , j = l , 2,...,n;
i#j
satisfying the condition: ~ Q ( i ) _< QT
where QT is the total discharge at main canal in a given day (l/s), Q(i) the discharge in lateral (canal) i (l/s), and n number of laterals. The extent of the area that could be irrigated in the service area of a lateral, or in a section of it in case some form of rotational irrigation distribution scheme is being implemented, is determined by the a m o u n t of water that the lateral or section receives. Hence, the total volume of water received by a lateral or section of a lateral in a given day can be computed as follows:
V(i, s) = 3.6 RTC(i, S) X sc(i, s) X PH X Q(i) + 10 RN X AS (i, S) where V(i, s) is volume of water received in section s of lateral i during day d (m3), RTC(i, s) reduction factor due to hydraulic response time (in the equation, this is multiplied by 3.6 for converting it into a consistent unit), sc (i, s ) irrigation schedule determinant, PH pump-hours spent, Q(i) canal discharge in lateral i (1/s ), RN daily rainfall ( m m ) , and AS ( i, s ) service area ( ha ). The reduction factor due to hydraulic response time (RTC) for a section was computed from the following: RTC (8) = EC (S) [PH/NP -- RT (S) ] / [PH/NP ]
where EC (s) is distribution efficiency of the canal (in fraction) and RT (S) : NDH(S)-HRTe(S) (h), in which NDH is the period between start of pump operation and buildup of working head at the section's delivery point. The term HRTe denotes the time taken for the working head to start receding, counting from the time pump operation has been stopped.
340
The irrigation schedule determinant ( s c ) is a factor which assumes a value of either 0 or 1 depending upon whether the section is scheduled to receive water or not on a particular day. The factor may have a value between 0 and 1 if two or more sections receive water simultaneously. In such a case, sc for a section will assume a value between 0 and 1 but proportional to its service area with respect to the total service area of the sections. Service area computation P U M P M O D captures changes in the area under the different stages of farming activities which are computed using an area-computation subroutine developed for this purpose. In this subroutine, the potential area that could be landsoaked (PAL) is determined from the residual volume of water after subtracting from the total volume available for the day the requirements of those areas which are under land preparation (AULP (S) ) and crop irrigation practice (AUCIP (S)). Together, AULP (8) and AUCIP (S) comprise the area under irrigation (AVI(S)). If PAL turns out to be a negative value on a particular day, it means that water stress occurred and hence no area is landsoaked on that day. Area to be landsoaked (ATBLS(S)) is the area remaining after the AVI(s) is subtracted from the section's c o m m a n d area (AS (s)). Any area not landsoaked within a reasonable period (70 days is used in the model) after the start of the season is considered to remain fallow for the season. Area that was landsoaked during the previous day will go to the land-preparation stage and its transplanting completed in 30 days counting from the first day of landsoaking. The area under land preparation may be reduced depending upon the availability of water because the area under normal irrigation practice (AUCIP (s) ) is given priority when water supply is limited. A field is considered to require no water for the last 15 days before harvesting. PUMPMOD ADAPTATION FOR A MULTIPUMP RICE IRRIGATION SYSTEM
The system and its parameters P U M P M O D was adapted for the Libmanan-Cabusao Pump Irrigation System (LCPIS). Constructed in 1981 in the Camarines Sur province, The Philippines, LCPIS has four electrically driven turbine pumps, each with a designed discharge capacity of 1.5 m3/s. Water is lifted from the Libmanan river for distribution to a potential service area of about 3350 ha in each of the two rice-growing seasons, wet and dry. The actual irrigated area is, however, much less, mostly because of the presence of multiple operational constraints which are typical of rice irrigation systems in m a n y developing countries. These constraints are discussed later. The average farm size individually cultivated by farmers of LCPIS is about
341 1.3 ha. About 27% of the farmers are tenants, 53% leasing and amortizing owners, and 16% owners (Moya et al., 1984). The model works on a daily basis for each season. The wet season starts on 1 May and continues for 190 days. The dry season is of equal duration, starting on 1 November. The seasons overlap when the whole system is taken into consideration but not for individual farms. At the rice-field level, 100 mm of water is considered necessary to soak the land for start of plowing. For land preparation, including puddling of the soil needed to get it ready transplanting, a water delivery rate of 13 mm per day, or 1.5 1/s per ha of land, is considered necessary for a period of 30 days from start of land preparation. Transplanting is done immediately after completion of land preparation with 15-day-old seedlings. The field water requirement to meet the evapotranspiration plus seepage and percolation needs is 8 ram/day. The field is terminally drained 15 days before harvest date. It is assumed that all rice grown is of the 110-day maturity period, seed-to-seed. In the management of field water, the height of the spillway (i.e. the lowest level in the bund surrounding the field plot) maintained by the farmer is an important factor that determines how much water can possibly be stored in a plot. It is assumed that all farmers maintain a spillway height of 75 mm. Therefore any water supplies from rainfall, pump source, or both that would raise the water level in a field beyond the 75 mm height are considered drained out of the system. The distribution efficiency of all canals is considered to be 85%. In LCPIS, all canals are earthen-excavated in clay soil and hence water distribution losses were found to be within 10-15%. The values assumed above are mostly the average values obtained from field data gathered in LCPIS during 1981-85. These inputs to the model can be changed to suit local conditions when the model is to be applied to other irrigation systems. The model operates for LCPIS under a number of specific constraints: Only three pumps can operate simultaneously because the main canal could not accomodate the flow from more than three pumps operating at the same time. This is a technical constraint of the irrigation system. Electric power supply to operate the pumps is available only during 17 h in a day, from midnight to 5 pm. This is another technical constraint under which the system has to operate. A third major constraint is the capacity of the farmers to prepare (plow and puddle) lands in one day when water supply is not limiting. This socio-economic constraint is related to the availability of draft power, farm labor, and farm operational credit within the community. Based on available data for LCPIS, we have assumed that farmers can complete land preparation and transplanting at a maximum daily rate of 2.5% of the system's service area.
342
LCPIS response computation LCPIS functional relationships Most of the functional relationships used in the model are based on field data gathered from LCPIS during 1981-85. The average discharge from each pump was measured to be 1.38 cms; hence the total discharge would be the number of operating pumps multiplied by the average discharge per pump. In order to estimate the total discharge in the main canal used for the 97% of LCPIS service area, for which field data on other items were available, the following regression equation was used: QT=288.42+1024.28 NP
(r2:0.95;
n=10)
The above equation is applicable only for NP :> 1. The model considered the area served by four lateral canals, namely, lateral B, C, D and main canal tail (MC tail) within LCPIS (Fig. 4). Relationships between main canal and the lateral canal daily discharges were determined based on the average water discharge for eight consecutive rice-growing seasons (1981-85) when water was distributed was according to a predetermined weekly schedule of sectionwise rotation in each lateral. These relationships were applied throughout each season. Each lateral canal was composed of a number of predetermined sections (or stretches), each of which was responsible for irrigating a corresponding part of the lateral service area. The following regression equations were made use of for this purpose: QB=
--
117.13+0.71
(r2=0.79;
QT--0.60
QMC-0.66 QC
n----233)
QC =254.02+0.57 QT--0.51 QB (r2=0.74; n=233) QMC = --
96.32 + 0.60
(r2=0.60;
Q T - - 0 . 5 3 QC - -
QB
n--233)
Main Canal PumpHouse ( LCPIS)
0.61
QB
c
QMC ~1 M c Tail n QD
Fig. 4. S c h e m a t i c d i a g r a m o f L C P I S c a n a l s y s t e m .
343 Time
(h)
16 pump (extrapolated)
I
\
2 pumps ( r2-= .72 , n = '0 ) ~ 3 pumps ( r2= .
7
6
Y
l
I
~
~
~
NDH Lines
10 8 -6 -4 -2 -00
k
I 2
J A
I
4
I 6
I
!
I
8
Distance from pumphouse
I
I
10 (kin)
I 12
I
I 14
I 16
Fig. 5. Relationship between distance of service area and time required to develop sufficient working head in canal with different number of pumps operating simultaneously (NDH lines) in LCPIS. The HaTe line shows the relationship between the elapsed time from the stoppage of pump operation to the time when the water head starts to recede and the distance of service area.
Solutions of the above set of simultaneous equations led to the following equations which were used by the model for computation of lateral discharges throughout the season: QMCT---- - - 1 2 6 . 0 + 0 . 1 9
QT
QB ----- - - 4 2 7 . 2 + 0 . 5 0 Q T - - 0 . 9 0 Q M C T QC--254.0+0.57
QT--0.51
QB
QD -----QT - - QB - - QC - - QMCT
where QMCT, QB, QC and QD are discharges (l/s) in lateral MC tail, lateral B, lateral c, and lateral D, respectively. The model equates the discharge to zero in case the resulting value from any of the above equations becomes negative. The NDH and HaTe relationships with distance from the pumphouse, which are necessary in the determination of reduction factor (RTC) due to hydraulic response time, were adapted from the work of Sumayao (1984) (Fig. 5). The curves are for different numbers of pumps operating simultaneously in LCPIS.
344 MODEL EVALUATION AND FUTURE OPPORTUNITIES
P U M P M O D underwent several stages of adjustment and fine-tuning to take into account some factors which influence the status of farming within riceirrigation systems. For example, when an earlier version of the model was applied for LCPIS, it overestimated the area where rice could be grown. Close scrutiny of the model revealed that all areas landsoaked as computed by the model based on rainfall amounts and irrigation supply, were assumed to be landprepared and eventually transplanted. However, this is not true in reality since the total area that can be planted in a season depends on the amount of water that can be supplied on a sustained basis and the rate of land preparation that can be sustained by the farmers. Area landsoaked at the start of the season can be very large due to high short-duration rainfall. But during subsequent dry days, large portions of the area may not receive enough canal water required for land preparation, transplanting or crop irrigation and would therefore not be able to grow a crop. W h e n the model was adjusted for this phenomenon, its outputs were in good agreement with the actual data. P U M P M O D has been adapted and evaluated for L C P I S using six seasons of field data, starting with the 1982 wet season. Typical final outputs of P U M P M O D are shown in Figs. 6 and 7, which are for the 1984 wet season and 1985 dry season, respectively. It can be seen from the figures that the model approximates well the actual (he,) 4000
.....
Simulated
Actua
5000
CAP - AUC IP
Cumulative area Area under crop
planted
CAH
Cumulative area
harvested
.]
irrigation practice or crop mointenonce
2000
CAP
CAH
zF~,~-.
ALICIP
1000
0 ie
210
2'5
~0
:55
,~0451 Week
50'
~
I 5
~0
I 15
no
Fig. 6. Actual and PUMPMOD-simulated progress of farming activities for Libmanan-Cabusao Pump Irrigation System, The Philippines, 1984 wet season.
345 (ha) 4000
.....
S}mulated Actual
CAP AUCIP - 3000 CAH
Cumulative area planted Area under crop irrigation practice or crop maintenance C u m u l a t i v e area harvested
2000
CAH
/'
1000
0 45
I 52
I
I
iI
~--.%
I 5 Week number
ff
\, /
f
r
l
I0
15
20
Fig. 7. Actual and PUMPMOD-simulated progress of farming activities for Libmanan-Cabusao Pump Irrigation System, The Philippines, 1985 dry season. dynamic changes in the status of farming within the irrigation system service area as the season progresses. Result of regression analysis of actual vs. simulated progress of farming activities are as follows: CAP CAH
AUCIP
Y-- 1.01X Y=O.99X Y=l.23X
(n=26; (n=22; (n=40;
r2--0.90) r2=0.76) r2=0.89)
where Y and X represent actual and simulated values, respectively. The coefficients of regression for CAP and CAH were very close to 1, which means that Y and X are almost equal. As for AUCIP, the coefficient is 1.23 which means that the model underestimates AUCIP by about 23%. This may be attributed to the following: (a) P U M P M O D assumes that all rice varieties grown by farmers have the same l l 0 - d a y maturity period while in actuality the varieties grown have maturity periods of 90-130 days; and (b) in each season some farmers had actually started farming activities either earlier or later than the seasonal schedule used in the model. However, the difference is of minor significance with respect to the model's ability to simulate the system, especially in predicting the total area that can be planted and irrigated. P U M P M O D can be used for various investigative purposes. It can assess relationships of irrigated area to such variables as maximum sustainable rate of land preparation, farm-plot spillway management practices to store more water on-farm, and the degree of water-stress allowed within the system. Like-
346
wise, the model can be applied to assess the effects of alternative designs of water allocation in the canal network to maximize irrigation benefits. PUMPMOD can also be used to investigate the impact of removing the identified technical and socio-economic constraints which have been taken into account in the model.
REFERENCES Escalante, M.C., Johnson, H.P. and Anderson, C., 1982. Irrigation water management simulation model for lowland rice production. Ann. Trop. Res., 4: 136-150. Johnson, S.H., III, 1982. A simulation approach to economic evaluation of modifications in Pakistan's irrigation systems. Am. J. Agric. Econ., 64(5): 1087. Mapp, H.P., Jr., 1983. Analysis of irrigation pumping and application efficiency in the Central Ogalla Formation. Complet. Rep. 114, Water Resources Research Institute, Oklahoma State University, OK. Moya, P.F., Lantican, M.A., Mandac, A.M. and Flinn, J.C., 1984. Farm level benefit of irrigation in the Libmanan-Cabusao irrrigation system. Paper presented in the Workshop to review selected researches in the Bicol River Basin area, BRBDPO, San Jos6 Pili, Camarines Sur, The Philippines, 18 June 1984. O'Mara, G.T. and Dulay, J.H., 1984. Modelling efficient water allocation in a conjunctive use regime: the Indus Basin of Pakistan. Water Resour. Res., 20: 1489-1498. Sumayao, A., 1984. Development of pump operation and suspension criteria for optimum rainfall utilization. M.S. thesis, University of the Philippines, Los Bafios, The Philippines. Swaney, D.P., Jones, J.W., Boggess, G.G., Wilkenson, G.G. and Mishoe, J.W., 1983. Real-time irrigation decision analysis using simulation. ASAE Trans., 26: 562-568. Udek, C.N. and Busch, J.R., 1982. Optimal irrigation management using probabilistic hydrologic and irrigation efficiency parameters. ASAE Trans., 25: 954-960.
333
P U M P M O D : A Simulation Model for Multipump Rice Irrigation S y s t e m s A.L.A. GALANG and S.I. BHUIYAN
Water Management Department, International Rice Research Institute, P.O. Box 933, Manila (The Philippines) (Accepted 28 March 1988)
ABSTRACT Galang, A.L.A. and Bhuiyan, S.I., 1989. PUMPMOD: a simulation model for multipump rice irrigation systems. Agric. Water Manage., 15: 333-346. A microcomputer model called PUMPMOD was developed for simulating the operation of multipump rice-irrigation systems. The model works on a daily basis; the principal inputs required to run it are the number of pumps operating and duration of their operation in relation to rainfall, demand for water in the field, and the irrigation distribution schedule followed in the system. The model captures the temporal and spatial changes in the water demand-and-supply relationship within the system as land-soaking, land-preparation, transplanting and crop-irrigation activities progress within a season. The principal outputs of the model are the extent of area that can be land-prepared, transplanted to rice and supported with required irrigation water until the crop reaches maturity. PUMPMOD has been adapted and evaluated for a rice irrigation system in The Philippines and uses four pumps of about 6.0 m3/s total discharge capacity, using field data collected during eight consecutive seasons and taking into account its existing major physical and socio-economic constraints which influence the operation of the system. The results are satisfactory. PUMPMOD is designed to be used as a tool for making decisions to optimize the supply of irrigation water from the source in relation to rainfall and field-water demands. It can also be used for various investigative purposes, including the evaluation of the efficacy of alternative designs to allocate and distribute water for maximizing irrigation benefits.
INTRODUCTION S t u d y o f rice i r r i g a t i o n s y s t e m s a i m e d at i m p r o v i n g t h e i r o p e r a t i o n a l effic i e n c y a n d m a x i m i z i n g b e n e f i t s f r o m t h e u s e o f a v a i l a b l e w a t e r r e s o u r c e is v i t a l l y i m p o r t a n t f o r f u t u r e f o o d - p r o d u c t i o n i n c r e a s e s in m o s t d e v e l o p i n g countries. Different types of irrigation systems need different procedures for e f f i c i e n t o p e r a t i o n . A p u m p i r r i g a t i o n s y s t e m , a l t h o u g h p e r h a p s s i m i l a r t o diversion-type systems with respect to water conveyance and delivery to the crop field, h a s t h e u n i q u e a d a p t a b i l i t y o f i n t e r m i t t e n t o p e r a t i o n a i m e d a t m a x i m i z -
0378-3774/89/$03.50
© 1989 Elsevier Science Publishers B.V.
334
ing use of rainfall and minimizing costs. High investment and operation costs are major concerns to the development of pump irrigation systems, and high operational efficiency is essential to make them economically viable. In studying irrigation systems, it is often difficult, inconvenient and timeconsuming to experiment on the actual system. Sometimes such experimentation may not even be possible due to some political or social constraints, or because some irreversible consequences may result from the experiment. A logical alternative is to simulate the system to be studied. This would involve the development of a simulation model that will describe the system in logical or mathematical terms. Although model development is in itself time-consuming and would still require actual data for evaluation, it can be an effective tool for studying the dynamic properties of irrigation systems and to gain insights into how they would work under varying management rules. This paper describes a computer simulation model developed for multipump irrigation systems designed to support rice production. The model is constructed to respond to varying operating conditions and physical limitations that are often encountered in pump-based irrigation systems. REVIEW OF LITERATURE
Different types of simulation models have been used in recent years for studying various aspects of irrigation-system behavior. However, it is somewhat difficult to use and apply these models to special types of irrigation systems such as a rice-based pump-irrigation system having unique characteristics such as a high degree of control over water supply and the presence of physical limitations in power supply and canal capacities. O'Mara and Dulay (1984) studied the joint effects of various canal water and associated private tubewell tax or subsidy policies on overall irrigation efficiency using a simulation model. Their models linked the hydrology of a conjunctive stream aquifer system to an economic model of agricultural production for 53 regions of the Indus Basin in Pakistan with a network model of the flows in river reaches, link canals and irrigation canals. The scope of these models is large since they cover an entire basin. Moreover, the main objective here was the simulation of response of the policy receivers to the actions of policy makers. Some researchers incorporated crop-growth models in their simulation studies to evaluate alternative irrigation technologies. These studies, however, had greater emphasis on farm-level irrigation-water management and, although an important objective is increased pumping and application efficiency, physical limitations and other constraints concerning pumps and distribution schemes were not considered. Mapp (1983) included a dynamic grain-sorghum plant model into a farm-level simulation model to derive the effects of alternative irrigation schedules on crop yield, water use and producer's net returns, while Swaney et al. (1983) developed an algorithm incorporating a dynamic crop-
335 simulation model (SOYGRO) to evaluate the expected yield benefits and profits from irrigating the crop on any day of the season. The algorithm used current-season weather prior to the decision date and several years of historical weather data for the rest of the season. A Bayesian decision-theory optimization model was developed by Udek and Busch ( 1982 ) for optimal irrigation management strategy. The model was used to select the optimum land area to be irrigated as controlled by hydrologic and irrigation-efficiency input parameters and the irrigator's risk-response function under specified conditions. The model did recognize the stochastic nature of water supply as a whole but did not consider the different factors affecting water supply. Furthermore, this model was applied on a seasonal basis and did not allow for changes in input parameters on a more frequent interval as would normally be done in simulation models. The simulation model used by Johnson (1982) to evaluate alternative water-management systems in the Punjab, looking into the effect of controlling water supply showed that the prevailing canal-closure schedules restricted economic returns from different cropping systems. Closure during February or April after wheat harvest increased returns by $20 to $30 per hectare. In the same manner, a computer simulation model for rice production developed by Escalante et al. (1982) considered a rotational scheme of water distribution. The model's input included maximal irrigable area, variety of rice, soil type, and irrigation-system efficiency. Each of the models reviewed had its own emphasis and strong points depending upon its objectives. In the development of the P U M P M O D simulation model described in this paper, the strong points of these modeling concepts were considered in order to make P U M P M O D practically useful in the operation and evaluation of irrigation systems using multiple pumps. PUMPMOD MODEL The P U M P M O D is a microcomputer-based model to simulate the dynamic behavior of multi-pump irrigation systems used for rice production. It is designed to capture the continual changes in the hydrologic and agronomic status of the service area as the irrigation system supports activities in land soaking, land preparation, transplanting, crop irrigation, etc., for rice production. The system basically operates for providing irrigation water to supplement rainfall characterized by high temporal variability. The conceptual framework of P U M P M O D is shown in Figs. 1 and 2 in the form of a relational diagram. Figure 1 gives the system-level part, showing the different factors affecting daily pump operation. The amount of irrigation water that could be delivered to the system's service area within a day depends on several factors, such as: (a) number of pumps operated during the day; (b) duration of each pump operation; (c) carrying capacity of the canal network; (d) distribution efficiency of the canal network; (e) hydraulic response time
336 TPump House?
River
,,.-- Other Laterals
No.of pumps~.~ Main canal duration of I [ discharge operation [
b'a:c%ge]
@
A --I
] I I
4
lI I l
--i ~
decision
)
@J Other tertiaries Fig. 1. PUMPMOD relational diagram of the system level.
(i.e. the time required for developing a working hydraulic head at various points in the canal network to deliver water to their respective service areas for different numbers of pumps operated simultaneously); and (f) the water distribution scheme used for the various service areas within the system. The number of pumps to be operated simultaneously and the duration of their operation, which are decision inputs of the system manager, are determined using the available information on electric power supply schedule/limitation, condition of pump (s), water stage at the source, canal conditions, and rainfall. Figure 2 shows the service-area level components of the relational diagram. In this section, the total water supply, which consists of irrigation supply and rainfall, is shown distributed to areas under the different stages of farming activities, namely, land soaking (AULS), land preparation (AULP), crop irrigation practice (AUCIP), terminal drainage (AUTD) and harvesting (AH), taking into account their water requirements and prevailing farm water-status. In the figure, the area under irrigation (AUI) is used as sum total of AULS, AULP and AUCIP.The principal output of PUMPMOD is the extent of area that could
337
~0TAL WATER SUPPLY [ I
-
[
t
r
I
i i I
I _ _ I.-q
]-
I
'7
r-----
l
J l
I
I
__~--~ AUTD ~-~ AH I
AULP
I 1 I
I I
I J
. . . .
r
ATLS ___J
Fig. 2. PUMPMODrelationaldiagramof the servicearea level. be land-prepared, transplanted and fully irrigated to rice, based primarily on the amount of rainfall and the pumped water delivered to the service area. The computer program is written in CBASIC and would require at least 1024K of memory on TRS-80 Model-16B under CP/M-68K operating system. The program is available in 8-inch diskettes using C P / M format. Figure 3 shows the main program flow chart. It has two major paths. The first (vertical) path uses actual data to obtain the output, while the other path uses generated data. The first path is specifically useful in evaluating the model, while the path that uses generated data would be used for prediction and optimization purposes. Probability distributions of rainfall and of electric power supply interruptions and the water stage at the source would be needed for generation
338
-co o
°
5~
~0.~
~
~" O 0
oo>"
~
~-zOZm_
z
339
of water inflow data. A program control routine is also incorporated in the program to facilitate runs for specific outputs.
General functional relationships In the response computation subroutine, a number of parameters to be used in the model's functional relationships need to be specified. These include the total number of pumps t h a t are operational, the capacity of each pump, the distribution scheme being implemented, water requirements in each stage of farming activities, distribution efficiency, farm plot spillway heights and maximum rate of land preparation possible. The important functional relationships needed are as follows: QT = f(NP) Q ( i ) = f ( q w ..... q ( j ) , . . . )
i , j = l , 2,...,n;
i#j
satisfying the condition: ~ Q ( i ) _< QT
where QT is the total discharge at main canal in a given day (l/s), Q(i) the discharge in lateral (canal) i (l/s), and n number of laterals. The extent of the area that could be irrigated in the service area of a lateral, or in a section of it in case some form of rotational irrigation distribution scheme is being implemented, is determined by the a m o u n t of water that the lateral or section receives. Hence, the total volume of water received by a lateral or section of a lateral in a given day can be computed as follows:
V(i, s) = 3.6 RTC(i, S) X sc(i, s) X PH X Q(i) + 10 RN X AS (i, S) where V(i, s) is volume of water received in section s of lateral i during day d (m3), RTC(i, s) reduction factor due to hydraulic response time (in the equation, this is multiplied by 3.6 for converting it into a consistent unit), sc (i, s ) irrigation schedule determinant, PH pump-hours spent, Q(i) canal discharge in lateral i (1/s ), RN daily rainfall ( m m ) , and AS ( i, s ) service area ( ha ). The reduction factor due to hydraulic response time (RTC) for a section was computed from the following: RTC (8) = EC (S) [PH/NP -- RT (S) ] / [PH/NP ]
where EC (s) is distribution efficiency of the canal (in fraction) and RT (S) : NDH(S)-HRTe(S) (h), in which NDH is the period between start of pump operation and buildup of working head at the section's delivery point. The term HRTe denotes the time taken for the working head to start receding, counting from the time pump operation has been stopped.
340
The irrigation schedule determinant ( s c ) is a factor which assumes a value of either 0 or 1 depending upon whether the section is scheduled to receive water or not on a particular day. The factor may have a value between 0 and 1 if two or more sections receive water simultaneously. In such a case, sc for a section will assume a value between 0 and 1 but proportional to its service area with respect to the total service area of the sections. Service area computation P U M P M O D captures changes in the area under the different stages of farming activities which are computed using an area-computation subroutine developed for this purpose. In this subroutine, the potential area that could be landsoaked (PAL) is determined from the residual volume of water after subtracting from the total volume available for the day the requirements of those areas which are under land preparation (AULP (S) ) and crop irrigation practice (AUCIP (S)). Together, AULP (8) and AUCIP (S) comprise the area under irrigation (AVI(S)). If PAL turns out to be a negative value on a particular day, it means that water stress occurred and hence no area is landsoaked on that day. Area to be landsoaked (ATBLS(S)) is the area remaining after the AVI(s) is subtracted from the section's c o m m a n d area (AS (s)). Any area not landsoaked within a reasonable period (70 days is used in the model) after the start of the season is considered to remain fallow for the season. Area that was landsoaked during the previous day will go to the land-preparation stage and its transplanting completed in 30 days counting from the first day of landsoaking. The area under land preparation may be reduced depending upon the availability of water because the area under normal irrigation practice (AUCIP (s) ) is given priority when water supply is limited. A field is considered to require no water for the last 15 days before harvesting. PUMPMOD ADAPTATION FOR A MULTIPUMP RICE IRRIGATION SYSTEM
The system and its parameters P U M P M O D was adapted for the Libmanan-Cabusao Pump Irrigation System (LCPIS). Constructed in 1981 in the Camarines Sur province, The Philippines, LCPIS has four electrically driven turbine pumps, each with a designed discharge capacity of 1.5 m3/s. Water is lifted from the Libmanan river for distribution to a potential service area of about 3350 ha in each of the two rice-growing seasons, wet and dry. The actual irrigated area is, however, much less, mostly because of the presence of multiple operational constraints which are typical of rice irrigation systems in m a n y developing countries. These constraints are discussed later. The average farm size individually cultivated by farmers of LCPIS is about
341 1.3 ha. About 27% of the farmers are tenants, 53% leasing and amortizing owners, and 16% owners (Moya et al., 1984). The model works on a daily basis for each season. The wet season starts on 1 May and continues for 190 days. The dry season is of equal duration, starting on 1 November. The seasons overlap when the whole system is taken into consideration but not for individual farms. At the rice-field level, 100 mm of water is considered necessary to soak the land for start of plowing. For land preparation, including puddling of the soil needed to get it ready transplanting, a water delivery rate of 13 mm per day, or 1.5 1/s per ha of land, is considered necessary for a period of 30 days from start of land preparation. Transplanting is done immediately after completion of land preparation with 15-day-old seedlings. The field water requirement to meet the evapotranspiration plus seepage and percolation needs is 8 ram/day. The field is terminally drained 15 days before harvest date. It is assumed that all rice grown is of the 110-day maturity period, seed-to-seed. In the management of field water, the height of the spillway (i.e. the lowest level in the bund surrounding the field plot) maintained by the farmer is an important factor that determines how much water can possibly be stored in a plot. It is assumed that all farmers maintain a spillway height of 75 mm. Therefore any water supplies from rainfall, pump source, or both that would raise the water level in a field beyond the 75 mm height are considered drained out of the system. The distribution efficiency of all canals is considered to be 85%. In LCPIS, all canals are earthen-excavated in clay soil and hence water distribution losses were found to be within 10-15%. The values assumed above are mostly the average values obtained from field data gathered in LCPIS during 1981-85. These inputs to the model can be changed to suit local conditions when the model is to be applied to other irrigation systems. The model operates for LCPIS under a number of specific constraints: Only three pumps can operate simultaneously because the main canal could not accomodate the flow from more than three pumps operating at the same time. This is a technical constraint of the irrigation system. Electric power supply to operate the pumps is available only during 17 h in a day, from midnight to 5 pm. This is another technical constraint under which the system has to operate. A third major constraint is the capacity of the farmers to prepare (plow and puddle) lands in one day when water supply is not limiting. This socio-economic constraint is related to the availability of draft power, farm labor, and farm operational credit within the community. Based on available data for LCPIS, we have assumed that farmers can complete land preparation and transplanting at a maximum daily rate of 2.5% of the system's service area.
342
LCPIS response computation LCPIS functional relationships Most of the functional relationships used in the model are based on field data gathered from LCPIS during 1981-85. The average discharge from each pump was measured to be 1.38 cms; hence the total discharge would be the number of operating pumps multiplied by the average discharge per pump. In order to estimate the total discharge in the main canal used for the 97% of LCPIS service area, for which field data on other items were available, the following regression equation was used: QT=288.42+1024.28 NP
(r2:0.95;
n=10)
The above equation is applicable only for NP :> 1. The model considered the area served by four lateral canals, namely, lateral B, C, D and main canal tail (MC tail) within LCPIS (Fig. 4). Relationships between main canal and the lateral canal daily discharges were determined based on the average water discharge for eight consecutive rice-growing seasons (1981-85) when water was distributed was according to a predetermined weekly schedule of sectionwise rotation in each lateral. These relationships were applied throughout each season. Each lateral canal was composed of a number of predetermined sections (or stretches), each of which was responsible for irrigating a corresponding part of the lateral service area. The following regression equations were made use of for this purpose: QB=
--
117.13+0.71
(r2=0.79;
QT--0.60
QMC-0.66 QC
n----233)
QC =254.02+0.57 QT--0.51 QB (r2=0.74; n=233) QMC = --
96.32 + 0.60
(r2=0.60;
Q T - - 0 . 5 3 QC - -
QB
n--233)
Main Canal PumpHouse ( LCPIS)
0.61
QB
c
QMC ~1 M c Tail n QD
Fig. 4. S c h e m a t i c d i a g r a m o f L C P I S c a n a l s y s t e m .
343 Time
(h)
16 pump (extrapolated)
I
\
2 pumps ( r2-= .72 , n = '0 ) ~ 3 pumps ( r2= .
7
6
Y
l
I
~
~
~
NDH Lines
10 8 -6 -4 -2 -00
k
I 2
J A
I
4
I 6
I
!
I
8
Distance from pumphouse
I
I
10 (kin)
I 12
I
I 14
I 16
Fig. 5. Relationship between distance of service area and time required to develop sufficient working head in canal with different number of pumps operating simultaneously (NDH lines) in LCPIS. The HaTe line shows the relationship between the elapsed time from the stoppage of pump operation to the time when the water head starts to recede and the distance of service area.
Solutions of the above set of simultaneous equations led to the following equations which were used by the model for computation of lateral discharges throughout the season: QMCT---- - - 1 2 6 . 0 + 0 . 1 9
QT
QB ----- - - 4 2 7 . 2 + 0 . 5 0 Q T - - 0 . 9 0 Q M C T QC--254.0+0.57
QT--0.51
QB
QD -----QT - - QB - - QC - - QMCT
where QMCT, QB, QC and QD are discharges (l/s) in lateral MC tail, lateral B, lateral c, and lateral D, respectively. The model equates the discharge to zero in case the resulting value from any of the above equations becomes negative. The NDH and HaTe relationships with distance from the pumphouse, which are necessary in the determination of reduction factor (RTC) due to hydraulic response time, were adapted from the work of Sumayao (1984) (Fig. 5). The curves are for different numbers of pumps operating simultaneously in LCPIS.
344 MODEL EVALUATION AND FUTURE OPPORTUNITIES
P U M P M O D underwent several stages of adjustment and fine-tuning to take into account some factors which influence the status of farming within riceirrigation systems. For example, when an earlier version of the model was applied for LCPIS, it overestimated the area where rice could be grown. Close scrutiny of the model revealed that all areas landsoaked as computed by the model based on rainfall amounts and irrigation supply, were assumed to be landprepared and eventually transplanted. However, this is not true in reality since the total area that can be planted in a season depends on the amount of water that can be supplied on a sustained basis and the rate of land preparation that can be sustained by the farmers. Area landsoaked at the start of the season can be very large due to high short-duration rainfall. But during subsequent dry days, large portions of the area may not receive enough canal water required for land preparation, transplanting or crop irrigation and would therefore not be able to grow a crop. W h e n the model was adjusted for this phenomenon, its outputs were in good agreement with the actual data. P U M P M O D has been adapted and evaluated for L C P I S using six seasons of field data, starting with the 1982 wet season. Typical final outputs of P U M P M O D are shown in Figs. 6 and 7, which are for the 1984 wet season and 1985 dry season, respectively. It can be seen from the figures that the model approximates well the actual (he,) 4000
.....
Simulated
Actua
5000
CAP - AUC IP
Cumulative area Area under crop
planted
CAH
Cumulative area
harvested
.]
irrigation practice or crop mointenonce
2000
CAP
CAH
zF~,~-.
ALICIP
1000
0 ie
210
2'5
~0
:55
,~0451 Week
50'
~
I 5
~0
I 15
no
Fig. 6. Actual and PUMPMOD-simulated progress of farming activities for Libmanan-Cabusao Pump Irrigation System, The Philippines, 1984 wet season.
345 (ha) 4000
.....
S}mulated Actual
CAP AUCIP - 3000 CAH
Cumulative area planted Area under crop irrigation practice or crop maintenance C u m u l a t i v e area harvested
2000
CAH
/'
1000
0 45
I 52
I
I
iI
~--.%
I 5 Week number
ff
\, /
f
r
l
I0
15
20
Fig. 7. Actual and PUMPMOD-simulated progress of farming activities for Libmanan-Cabusao Pump Irrigation System, The Philippines, 1985 dry season. dynamic changes in the status of farming within the irrigation system service area as the season progresses. Result of regression analysis of actual vs. simulated progress of farming activities are as follows: CAP CAH
AUCIP
Y-- 1.01X Y=O.99X Y=l.23X
(n=26; (n=22; (n=40;
r2--0.90) r2=0.76) r2=0.89)
where Y and X represent actual and simulated values, respectively. The coefficients of regression for CAP and CAH were very close to 1, which means that Y and X are almost equal. As for AUCIP, the coefficient is 1.23 which means that the model underestimates AUCIP by about 23%. This may be attributed to the following: (a) P U M P M O D assumes that all rice varieties grown by farmers have the same l l 0 - d a y maturity period while in actuality the varieties grown have maturity periods of 90-130 days; and (b) in each season some farmers had actually started farming activities either earlier or later than the seasonal schedule used in the model. However, the difference is of minor significance with respect to the model's ability to simulate the system, especially in predicting the total area that can be planted and irrigated. P U M P M O D can be used for various investigative purposes. It can assess relationships of irrigated area to such variables as maximum sustainable rate of land preparation, farm-plot spillway management practices to store more water on-farm, and the degree of water-stress allowed within the system. Like-
346
wise, the model can be applied to assess the effects of alternative designs of water allocation in the canal network to maximize irrigation benefits. PUMPMOD can also be used to investigate the impact of removing the identified technical and socio-economic constraints which have been taken into account in the model.
REFERENCES Escalante, M.C., Johnson, H.P. and Anderson, C., 1982. Irrigation water management simulation model for lowland rice production. Ann. Trop. Res., 4: 136-150. Johnson, S.H., III, 1982. A simulation approach to economic evaluation of modifications in Pakistan's irrigation systems. Am. J. Agric. Econ., 64(5): 1087. Mapp, H.P., Jr., 1983. Analysis of irrigation pumping and application efficiency in the Central Ogalla Formation. Complet. Rep. 114, Water Resources Research Institute, Oklahoma State University, OK. Moya, P.F., Lantican, M.A., Mandac, A.M. and Flinn, J.C., 1984. Farm level benefit of irrigation in the Libmanan-Cabusao irrrigation system. Paper presented in the Workshop to review selected researches in the Bicol River Basin area, BRBDPO, San Jos6 Pili, Camarines Sur, The Philippines, 18 June 1984. O'Mara, G.T. and Dulay, J.H., 1984. Modelling efficient water allocation in a conjunctive use regime: the Indus Basin of Pakistan. Water Resour. Res., 20: 1489-1498. Sumayao, A., 1984. Development of pump operation and suspension criteria for optimum rainfall utilization. M.S. thesis, University of the Philippines, Los Bafios, The Philippines. Swaney, D.P., Jones, J.W., Boggess, G.G., Wilkenson, G.G. and Mishoe, J.W., 1983. Real-time irrigation decision analysis using simulation. ASAE Trans., 26: 562-568. Udek, C.N. and Busch, J.R., 1982. Optimal irrigation management using probabilistic hydrologic and irrigation efficiency parameters. ASAE Trans., 25: 954-960.