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J. Indian Water Resour. Soc., Vol. 34, No.4, October, 2014

2

stream of the river Lemhi. DHI, Inc. (2008) developed model for the river Mckenzie. Jha and Gupta (2003) described the application of MIKE BASIN simulation model to the Mun river basin located in northeastern Thailand. Various operating models and decision support system (DSS) have been developed and applied by researchers to address the issues of water supply from reservoirs for irrigation planning and reservoir operation (Koch & Allen, 1986; Arumugam & Mohan, 1997; Prajamwong et al., 1997; Oliveira & Loucks, 1997; Manoli et al., 2001; Nicco et al., 2013; Wang & Liu, 2013 etc.). Most of the studies carried out in the past, mainly concentrated on getting an optimum solution under certain fixed constraints rather than generating scenarios which may be helpful for water resource managers to take appropriate decision under changing situations.

In this study, GIS based MIKE BASIN software is used for irrigation management, reservoir operation, computation of irrigation demand and deficit by generating twelve different scenarios for changing rainfall, reservoir storage, losses and climatic conditions. The MIKE BASIN is a network model in

which rivers and their main tributaries are represented by a network of branches and nodes. Branches represent individual stream sections while the nodes represent confluences, diversions, locations where certain water activities may occur (municipal, industrial, reservoir, and hydropower water uses), or important locations where model results are required.

STUDY AREA AND DATA USED Rangawan dam project is a major inter-state irrigation project of Madhya Pradesh (M.P.) and Uttar Pradesh (U.P.) operate under an agreement of water sharing which states that M.P. can utilize 2000 M.cft (56.63 Mm3) water for kharif crops up to 31st October each year and the balance as on 1st November will be divided between M.P. and U.P. in the ratio of 15:36. The Rangawan dam is an earthen dam located near village Rangawan in Rajnagar Tahsil of Chhatarpur district of M.P. (India). The length of earthen dam is 1.829 km with catchment area is about 731.70 km2. The gross and live storage capacity of Rangawan reservoir up are 163.574 Mm3 and 156.075 Mm3 respectively. The location map of Rangawan reservoir and its command in M.P. have been presented in Fig. 1.

Fig. 1: Location map of Rangawan reservoir in M.P.

 

Rangawan Dam 

Main canalAkona distributory

J. Indian Water Resour. Soc., Vol. 34, No.4, October, 2014

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The rainfall data of Rajnagar block from 1988 to 2008 have been analyzed and two representative years as 1997 having 1075 mm annual rainfall as wet/average rainfall year and 2005 having annual rainfall 798.70 mm as dry year have been used for estimation of crop water requirement. Reservoir details such as river bad level, dam crest level, top of dead storage, flood control level etc. have been used for defining reservoir properties. The soil testing in the commands of Rangawan reservoir has been conducted to determine field capacity, wilting point, initial water content, depth of evaporable layer and porosity. The design cropping pattern for Rangawan command consists of 5600 ha wheat and 878 ha gram in rabi season, 10500 ha soybean and 107 ha paddy in kharif season. As whole cropping area of major crops cannot be grown simultaneously in a command, the wheat was divided in three part as wheat-1 (1400 ha sown in third 10-daily period of October each year), wheat-2 (2800 ha sown on first 10-daily period of November each year) and wheat-3 (1400 ha sown on second 10-daily period of November each year) while soybean in two parts as soybean-1 (5250 ha sown on third 10-daily period of June each year ) and soybean-2 (5250 ha sown on first 10-daily period of July each year ). The crop calendar for design cropping pattern in M.P. commands of Rangawan project has been presented in Table 1.

METHODOLOGY MIKE BASIN works on digitized river network that can be generated directly on the computer screen in Arc Map 9.3 (a GIS software package) with the help of digital elevation model (DEM) or schematic river network and reservoir nodes which can be connected to command, hydro power and water user nodes through channels. The reservoir general properties, reservoir operation properties are needed for reservoir operation. The reservoir general properties, includes level-area-volume time series, characteristic levels time series and losses and gain time series. The characteristic levels time series requires bottom level, top of dead storage, dam crest level and losses and gain time series with bottom infiltration, potential ET and precipitation. In MIKE BASIN, reservoirs can be operated as rule curve reservoir, allocation pool reservoir, and lake. Various sub-model, nodes, users required to set up an irrigation management model from reservoir supplies in MIKE BASIN are described below.

Climate Sub-model Presently two types of climate sub-models are available which include rainfall only and FAO 56 climate model. In rainfall only model, the rainfall is used as an input in the form of time series. The FAO 56 model requires the climatic input required for the computation of evapotranspiration as per FAO 56 method. Relative humidity, air temperature (min and max), wind speed, sunshine hour and rainfall are required in this model.

Reference ET Sub-model The evapotranspiration rate may either be computed based on climate sub-model, or provided directly as time series. The FAO 56 uses the standardized Penman-Monteith equation for calculation of reference evapotranspiration.

Soil Water Sub-model Presently, FAO56 Soil Water Model, which is a simple water balance model, is available in MIKE BASIN software. It requires field capacity, wilting point, initial water content, depth of evaporable layer, porosity.

Irrigation Methods The irrigation sub-model is used to specify how and when a

given field is irrigated. Presently, the FAO 56 irrigation model is available in MIKE BASIN. It requires wetting fraction, irrigation method, trigger option, application option.

Crop Sub-model The crop sub-model is used to compute crop evapotranspiration and soil evaporation for crops using soil moisture content and reference evapotranspiration. Dual crop Coefficient model (FAO56) is currently available in MIKE BASIN software. It requires stage length, basal crop coefficient, root depth, max vegetation height, depletion fraction. The root depth determines the maximum depth from which the crop can extract water and the minimum and maximum depth has to be specified. It is assumed that the maximum depth is obtained at the beginning of the middle stage, and that the variation between the initial depth and the maximum depth is determined using equation 1. K= (Rmax-Rmin)+Rmin (1)

Table 1: The crop calendar of Rangawan project for design cropping pattern

(Kcb-Kcb,ini) (Kcb,mid-Kcb,in)

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where, Kcb,ini is the initial Basal coefficient, Kcb,mid is the Basal crop coefficient in the middle stage, Rmax is the maximum root depth and Rmin is the minimum root depth. The influence of the surface roughness on the evapotranspiration is taken into account through a climatic factor applied to the basal crop coefficient. If Hmax is the maximum height of crop, the vegetation height (H) is assumed to scale with the Basal crop coefficients and is calculated by equation 2.

,

, , (2)

Crop Sequence Sub-model A crop sequence is really not a sub-model but it is just a convenient way of specifying how a field is managed. It requires crop type, sowing date, irrigation method.

Irrigation Node An irrigation node represents an irrigation area comprising one or more irrigation fields, which are drawing water from the same source(s). The irrigation node property dialog contains three options, such as scheme, surface water (optional) and groundwater (optional). Deficit distribution methods are used when the irrigation demand exceeds the available water at the sources. In such cases, the deficit distribution methods describe how the available water should be distributed among the fields represented by node. Three options are available equal shortage, by yield stress, by priority.

Water Users A water user node could have multiple sources and multiple return flow points. For operating the water user in MIKE BASIN, its general, ground water and water quality (optional) properties are need to be specified. In general property of water user, water use time series has been defined for model.

Simulation After setting up all sub models, reservoirs, channel details and priority setting, the model can be run for the simulation. The most general output items of the irrigation node are written to the MIKE BASIN output files and imported into Arc GIS just like the output from the remaining MIKE BASIN model

building blocks. The output files contain evapotranspiration, total irrigation demand, net flow, demand deficit, stored volume and water levels in reservoirs, channel flows at given time span assigned during simulation. A water user node could have multiple sources and multiple return flow points. It also means that the allocations can be controlled from the resource perspective with groundwater, surface water (run of river) and reservoirs all having rules in which the water use can be prioritized.

RESULTS AND DISCUSSION MIKE BASIN models for operation of Rangawan reservoir for management of irrigation in the command of M.P. have been developed and twelve different scenarios (DCP-1 to DCP-12) have been generated for designed cropping pattern under changing climatic variables, storages, groundwater uses, probable storage and losses during conveyance and application of water. Various scenarios generated in MIKE MASIN have been presented in Table 2. For allocation of total storage owned and inflows between the states/parties, it was necessary to use Rangawan reservoir as allocation pool reservoir, but the allocation pool operating rule cannot compute irrigation demand directly in MIKE BASIN. To overcome this shortcoming, the irrigation demand have been computed considering the reservoir as rule curve reservoir in the first model and then this demand has been given as demand of user from allocation pool reservoir, where, there is fixed percentage of water available for M.P. part in second model. To make model capable of consumptive use, 20% requirement of total demand was considered to be met from groundwater resource in few scenarios. Similarly, probability analysis of reservoir storage was carried out to determine 75% probable storage of reservoir. The digitized network models for computation of crop water requirement and water sharing between the states have been presented in Fig. 2.

Various sub-models required to run the simulation have been created and necessary changes have been made in sub-models, reservoir and channel properties for generation of scenarios. The soil test in the study area confirmed that the soils can be divided into two major groups (Soil-1 and Soil-2) and the

Table 2: Different scenarios generated for irrigation management

S.N. Scenario Climatic variability Conveyance efficiency

Application efficiency

Groundwater use

1. DCP-1 Average/wet rainfall year 60% 70% - 2. DCP-2 Average/wet rainfall year 70% 80% - 3. DCP-3 Average/wet rainfall year 60% 70% 20% 4. DCP-4 Average/wet rainfall year 70% 80% 20% 5. DCP-5 Dry rainfall year 60% 70% - 6. DCP-6 Dry rainfall year 70% 80% - 7. DCP-7 Dry rainfall year 60% 70% 20% 8. DCP-8 Dry rainfall year 70% 80% 20% 9. DCP-9 75 % probable storage 60% 70% -

10. DCP-10 75 % probable storage 70% 80% - 11. DCP-11 75 % probable storage 60% 70% 20% 12. DCP-12 75 % probable storage 70% 80% 20%

J. Indian Water Resour. Soc., Vol. 34, No.4, October, 2014

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details of soil and irrigation methods have been presented in Table 3. As the whole area of command cannot be sown on a single date and hence the wheat has been subdivided into three parts and soybean into two parts. The showing dates of crops under design cropping pattern and their areas have been presented in Table 4. For planning of irrigation management and efficient operation of Rangawan project, probability analysis was done, with annual capacity records of 18 years for determination of probability of occurrence of reservoir storage at 75% probability level. The dependable capacity of reservoir at 75% of probability analysis was estimated 48.63 Mm3.

After simulation run, the model computes used water, deficit and flow at different nodes, reservoir volume, reservoir level and ground water abstraction in different periods. The graphical representation of water used and deficit for the

Rangawan command under different scenarios have been presented in Fig. 3. The irrigation demand, used water and demand deficit under different scenarios have been presented in Table 5. From the analysis of simulation results in MIKE BASIN for design cropping pattern in average/wet rainfall year with 60% conveyance efficiency and 70% application efficiency, the irrigation demand and demand deficit have been estimated as 37.81 Mm3 and 14.39 Mm3 respectively (DCP-1). The demand deficit can be reduced to 1.5 Mm3 by increasing conveyance efficiency and application efficiency to 70% and 80% respectively and using 20% groundwater (DCP-4)

In case of dry rainfall year, when the rainfall is not sufficient to fill the reservoir completely, considering conveyance

Fig. 2: Representation of MIKE BASIN models for Rangawan Project

S.N.

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J. Indian Water Resour. Soc., Vol. 34, No.4, October, 2014

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efficiency 60% and application efficiency 70% respectively demand deficit has been estimated as 34.48 Mm3 (DCP-5), but in case of using 20% ground water, it was observed that the demand deficit may be reduced to 25.58 Mm3 (DCP-6). If conveyance efficiency increased to 70% and application efficiency to 80 %, the demand deficit may be reduced to 31.00 Mm3 (DCP-7) and using ground water, the deficit may further be reduced to 22.17 Mm3 with efficient water management, reduction of losses and proper releases especially in dry years (DCP-8). The results of probability analysis indicated that at 75% probability, the reservoir capacity reached to is 48.63 Mm3. From the simulation run on 75% probable storage, the demand deficit has been computed as 22.13 Mm3 considering conveyance efficiency as 60% and application efficiency as 70% respectively (DCP-9). If conveyance efficiency increased to 70% and application efficiency to 80 %, the demand deficit reduced to 17.15 Mm3 and with 20% ground water, the deficit can be reduced to 8.98 Mm3 (DCP-12).

CONCLUSION In the present study, Arc GIS based MIKE BASIN river basin simulation package, designed for analyzing water sharing problems and environmental issues at international, national and project scale has been used for irrigation management in a command of Rangawan reservoir has been developed. In the first model, the rule curve reservoir has been used for computation of irrigation demand while the other model used to run the simulation as allocation pool reservoir to make model capable of addressing the issue of water sharing between M.P and U.P. From the simulation run of irrigation model the total irrigation demand was computed and taken as input for determination of demand deficit, supply to Rangawan command in the second model. For conducting irrigation management and reservoir operation studies, 12 different scenarios were generated on the basis of different rainfall years, conveyance and application losses, probable storage and ground water use.

For design cropping pattern in average/wet year, considering conveyance efficiency as 60% and application efficiency as

70%, irrigation demand and demand deficit were estimated as 37.81 Mm3 and 14.39 Mm3 respectively. If the conveyance efficiency and application efficiency were increased to 70% and 80% respectively with using ground water 20%, the demand deficit may be reduced to minimum which can be met through other resources. In dry rainfall years, the reservoir could not reach to its FRL and in these year demand deficit of design cropping pattern may rose to 34.48 Mm3. The improved conveyance and reservoir operation through as per model results can reduce this deficit to 22.17 Mm3. It is recommended that in case of low rainfall in June and early July, the farmers should be advised for alternative crops that require less water. From the analysis, it may be concluded that the MIKE BASIN model can be used development of decision support system and real time reservoir operation of Rangawan reservoir using real time field data of crops, reservoir stages, meteorological and other data.

REFERENCES 1. Arumugam, N. and Mohan, S., 1997. Integrated

decision support system for tank Irrigation system operation. J. of Water Resources Planning and Management, 123 (5): 266-273.

2. Bhadra, A., Bandyopadhyay, A., Singh, R. and Raghuwanshi, N. S., 2009. An integrated reservoir-based canal irrigation model (IRCIM). J. of Irrigation and Drainage Engineering, 135 (2): 158-168.

3. Delavar, M., Moghadasi, M., and Morid, S., 2012. “Real-time model for optimal water allocation in irrigation system during droughts”, J. of Irrigation and Drainage Engineering, 138(6): 517–524.

4. DHI Inc., 2006. A tool for evaluating stream flows, diversion operations and surface water-ground water relationships in the Lemhi river basin, Idaho: An unpublished report, Danish Hydraulic Institute, Copenhagen, Denmark, 102 p.

5. DHI Inc., 2008. McKenzie River MIKE BASIN Model: An unpublished report, Danish Hydraulic Institute, Copenhagen, Denmark, 42 p.

Table 5: Irrigation demand, water used and deficit from MIKE BASIN results in DCP

Scenarios Demand (Mm3)

Water used (Mm3)

Deficit (Mm3)

Reservoir Groundwater Total water

DCP-1 37.81 23.42 0.00 23.42 14.39 DCP-2 37.81 22.95 7.35 30.3 7.51 DCP-3 37.81 30.14 0.00 30.14 7.67 DCP-4 37.81 28.95 7.35 36.3 1.51 DCP-5 45.16 10.68 0.00 10.68 34.48 DCP-6 45.16 10.78 8.8 19.58 25.58 DCP-7 45.16 14.16 0.00 14.16 31.00 DCP-8 45.16 14.19 8.8 22.99 22.17 DCP-9 37.81 15.68 0.00 15.68 22.13

DCP-10 37.81 15.82 7.35 23.17 14.64 DCP-11 37.81 20.65 0.00 20.65 17.16 DCP-12 37.81 21.48 7.35 28.83 8.98

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6. Jha, M. K., and Gupta, A. D., 2003. Application of MIKE BASIN for water management strategies in a watershed, Water International, 28: 27-35.

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8. Lerson, A., Makropoulos, C. and Maksimolc, C., 2006. Water resources modeling under data scarcity coupling MIKE BASIN model and ASM Groundwater model. Water Resources Management, 4 (20): 567-590.

9. Manoli, E., Arampatzis, G., Pissias, E., Xenos, D. and Assimacopoulos, D., 2001. Water demand and supply analysis using a spatial decision support system, Global Nest: The International Journal, 3(3): 199-209.  

10. Martin, L. D. W., Darrell, G. and Gilley J. R., 1984. Model and production function for irrigation management, J. of Irrigation and Drainage Engineering, 110(2): 149–164.

11. Nicco, M. R. , Karimi, A., Kerachian, R., 2013. Optimal long-term operation reservoir-river systems under hydrologic uncertainties: Application of interval programming, Water Resources Management, 27(11): 3865-3883.

12. Oliveira, R. and Loucks, D. P., 1997. Operating rules for multi-reservoir systems, Water Resources Research, 33(4): 839-852.

13. Mujumdar, P.P. and Ramesh, T.S.V., 1997. Real-time reservoir operation for irrigation, Water Resources Research 3, Doi: 10.1029/96WR03907.

14. Prajamwong, S., Merkley, G. P. and Allen, R. G., 1997. Decision support model for irrigation water management, J. of Irrigation and Drainage Engg., 123(2): 106-113.

15. Wang H. and Liu, J., 2013. Reservoir operation incorporating hedging rules and operational inflow forecast, Water Resources Management, Doi: 10.1007/s11269-012-0246-3