International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 6, November-December 2016, pp. 364–370, Article ID: IJCIET_07_06_039 Available online at http://iaeme.com/Home/issue/IJCIET?Volume=7&Issue=6 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 © IAEME Publication

SIMULATION OF RAINFALL RUNOFF OF SHIPRA RIVER BASIN

H.L. Tiwari Faculty, Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal,

Ankit Balvanshi Research Scholar, Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India

Deepak Chouhan Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India

ABSTRACT Runoff Modeling of any catchment assumes a vital part for building legitimate planning of the water r esources. T he ch ange o f p recipitation i nto o verflow o ver a ca tchment is known exceptionally f or t he co mplex h ydrological o ccurrence, n onlinear, t ime-shifting and spatially distributed. Different methodologies for overflow estimation are accessible going from lumped to physically b ased d istributed m odels. T his p aper d epicts t h e u tilization o f MIKE 11 N AM Rainfall runoff m odel t o exp lore i t s e xecution, p roficiency a nd a ppropriateness i n S hipra r iver b asin in , India. The MIKE 11 Nedbor Afrstromnings Model is a lumped theoretical model for simulating the runoff from the precipitation. The input data utilized by the model was weighted precipitation, Potential evapotranspiration and observed runoff. The total time series of 11 years duration from 1996 to 2006 was u sed in t his study. The model calibration was done for the time frame from 1996 t o 2001 a nd va lidated for the years 2 002 t o 2 006. T he d ependability and performance o f t he N AM m odel w as a ssessed b ased o n A ccuracy cr iteria Coefficient of determination (R2). The coefficient of determination R2 value for calibration and validation period was observed 0.720 and 0.502 respectively. The Nash–Sutcliffe Efficiency (EI) for calibration and validation is 76% and 85% respectively. The NAM model was found appropriate for simulation and prediction of daily runoff with good degree of accuracy. Key words: Evapotranspiration, MIKE11 NAM, Rainfall runoff modeling, Shipra basin Cite this Article: H.L. Tiwari, Ankit Balvanshi and Deepak Chouhan, Simulation of Rainfall Runoff of Shipra River Basin. I nternational Journal of Civil Engineering and Technology, 7(6), 2016, pp.364–370. http://iaeme.com/Home/issue/IJCIET?Volume=7&Issue=6

1. INTRODUCTION Water is t h e g reatest resource of humanity. Life on earth can not be p ersistent without water, whether of humans, animals, flora or fauna. Water is the natural important resource which needs preservation, control and management. Design of all water resources planning and management project require long term runoff

http://iaeme.com/Home/journal/IJCIET 364 [email protected] Simulation of Rainfall Runoff of Shipra River Basin data which is generally not available at the project sites. Hence runoff has to be predicted with the help of rainfall data. Rainfall data, watershed area, and other parameters like soil type, land use pattern, vegetation and m oisture co ntent o f s oil are t he m ajor i nputs r equired f or an y k ind o f m odels. Presently the hydrological models are very important tool to calculate the runoff, sediment yield and soil erosion (Prasad and Tiwari, 2016). A rainfall runoff model is helpful in computation of discharge from a basin. A Rainfall- Runoff m odel i s a m athematical m odel w hich d escribes t h e r elation b etween r ainfall an d r unoff o f a watershed o r catch ment area. A R ainfall-Runoff m odel p roduces t h e s urface r unoff h ydrograph when precipitation is given as an input. Horton was probably the first person who gave a model which was based on i n filtration cap acity f o r t h e g eneration o f t h e r u noff ( H orton, 1 933). T he r ai nfall-runoff m odels are classified as Conceptual, Theoretical, Deterministic, Stochastic, Black box, Continuous, Complete, Event, Routing an d S implified ( Linsley, 1 982). B ecause o f m ulti-dimensional an d n on-linear n ature, rainfall- runoff modeling is ex tremely complicated (Lipiwattanakarn et al ., 2004). Various hydrologic models are available depending upon nature, complexity and purpose of the water resource project (Shoemaker et al., 1997). T he w idely k nown r ai nfall-runoff m odels i d entified are t h e r ational m ethod, S oil Conservation Services Curve Number method, and Green-Ampt method (Balvanshi and Tiwari, 2014).

2. HISTORICAL REVIEW The M IKE11 N AM i s an i n tegrated an d co nceptual m odel o f r ai nfall-runoff w hich i s ab le t o simulate surface f l ow, s ubsurface an d b ase f low, t h is m odel h as b een d eveloped b y D anish H ydraulic Institute (DHI, 1972, DHI, 1999). Many studies have been conducted using MIKE11 NAM model. The reliability of the MIKE 11 NAM model was evaluated by introducing the Root Mean Square error method (Fleming, 1975). A NAM model was developed for predicting the runoff rate in Liang River located in northern part of Malaysia. The results showed that the predicted amounts by the NAM model were in accordance with the historical data appropriately and in general the results were satisfactory (Shamsdin and Hashim, 2002). MIKE 11 N AM model w as also ap plied at Y uvacik Dam b asin, Turkey t o s i mulate t h e r u noff b y using rainfall as well as snowmelt as inputs for the model (Keskin et al., 2007). The MIKE 11 NAM model was also employed for simulation on Rahatgarh site of Bina basin, Madhya Pradesh. The model was developed, calibrated and validated using stream flow data at the Rahatgarh site. The coefficient of determinations for calibration an d v alidation w ere 0 .796 an d 0 .609 r espectively. T he m odel w as f ound efficient with Efficiency Index is 81% and found suitable for extended time period in Bina basin (Galkate et al., 2011). MIKE 11 was also employed for Basin part of Shipra Basin, Madhya Pradesh for the availability of water in the Ujjain city mainly for the Khumb Mela using MIKE 11 software (Galkate et al., 2011).

3. STUDY AREA This paper focuses on the Shipra river basin of Madhya Pradesh which is the tributary of . It is one of the sacred rivers in . Shipra river basin has been extended between 760 06ˈ 20 ˈˈand 750 55ˈ 6 0ˈˈ North Latitude and 220 97ˈ 0 0ˈˈ and 230 76ˈ 20ˈˈ East Longitude and covers an area of 5679 sq. km. The river traverses total course of about 190 km through four districts namely , , Ujjain, and Ratlam b efore j o ining C hambal R iver n ear K alu-Kher v illage. T he Shipra, al so k nown as the Kshipra, originates from Kakribardi hills in north of and flows north across the plateau to join the Chambal River. The average annual rainfall of area is about 931.87 mm. The rainfall in the area is due to the southwest monsoon which starts from the middle of June and ends in last week of September. The t o pography i s g enerally r o lling t o u ndulating. T he h oly ci ty o f Ujjain is s i tuated o n i t s r ight bank. After every 12 years, the (also called Simhastha) takes place at Ujjain on the city's elaborate riverside Ghats, b esides y early celeb rations o f t h e r i ver g oddess K shipra. T here are h undreds o f Hindu shrines along the banks of the river Shipra. The index map of Shipra river basin is shown in Figure 1.

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Figure 1 Index map of Shipra river basin

4. METHODOLOGY

4.1. About MIKE 11 NAM Danish H ydraulic I n stitute ( D HI), D enmark f o rmulated t h e M IKE 1 1 s oftware. I t i s a o ne dimensional modeling tool which was formulated in 1972 particularly for the water resource planning and management applications. The MIKE 11 software is specifically meant for imitation of river flows, irrigation systems and ch annels. T he q uality an alysis o f r i vers & ch annels, an d s ed iment t r ansport i s al so i m itated b y the MIKE 11 software. The MIKE 11 NAM model i s a rainfall runoff m odel ( R R model). The f ull form of NAM is Nedbor Afrstromnings Model. This NAM model is a lumped conceptual model for imitating the runoff from the rainfall. It is a deterministic, and conceptual rainfall runoff model which breaks the flow into overland flow ( surface flow), i n terflow ( subsurface flow), an d base flow. It has a set of linked mathematical s tatements d escribing t he b ehavior o f t he l and p hase o f t he h ydrological cycle. The simulation o f t h e N AM m odel i s d one b y four d issimilar and i n terconnected s t orages w hich are surface storage, ground water storage, root zone storage and snow storage as shown in Figure 2. Therefore NAM model can be prepared for number of model parameters but accounting the surface zone storage, root zone storage an d g round w ater s torage, m odel au tomatically acco unts 9 p arameters as d efault. N AM model requires t he v arious i nput d ata w hich i ncludes t he m odel p arameters, i nitial conditions, hydro- meteorological data and stream flow data for calibration and validation and produces the runoff time series, the input data like rainfall and evapotranspiration which is required by the model must be in the time series format.

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Figure 2 Structure of NAM model

4.2. Model Setup The MIKE 11 NAM model was setup to carry out rainfall-runoff modeling in Shipra river basin at Ujjain G/d site having catchment area 2102 km2 and average annual rainfall 931.87 mm. The input information of daily rainfall, runoff and evapotranspiration for the period of 11 years from 1996 to 2006 was converted to dfso format and used for model development.

4.3. Model Calibration Calibration i s a p rocess t o s tandardize es timated o r s i mulated v alues b y u sing d eviations f rom observed values f o r a p articular b asin. I t t h us h elps i n d eriving co rrection f acto rs t h at can b e ap plied t o generate predicted v alues. T hese s imulated v alues are co nsistent w ith t h e o bserved v alues. W hen t h e M IKE 11 NAM model was set up, model was calibrated from 1st Jan 1996 to 31st Dec 2001. The model was first run in auto-calibration mode. The model parameters were optimized manually to obtain best set of model parameter simulating runoff with high accuracy.

4.4. Model Validation Model validation means for judging the performance of the calibrated model over the portion of historical records w hich h ave n ot b een u sed f o r t h e calib ration. M IKE 1 1 N AM m odel t h us calib rated w as then validated f o r t h e r em aining period o f f i ve y ears f r om 2 002 t o 2 006. D uring v alidation t h e s et o f model parameters obtained during the calibration was used. The statistics of the simulated results were analyzed and o utputs o f t h e m odel w ere ch ecked to co mpare t he simulated an d observed runoff t o verify the capability of calibrated model to simulate the runoff.

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4.5. Accuracy Criteria The use o f t h e co efficient of determination is to test the goodness of f i t o f the model and to as sess how well a m odel ex plains an d p redicts f u ture o utcomes. T he co efficient o f d etermination (R2) o f t h e model was calculated by using the following equation:

  ∑  (1)  =     [∑     ][∑  ] Where, qo= observed flow, q͞ o= mean value of observed flow, qs= simulated flow and n = number of data points. The reliability of t h e model w as ev aluated o n t h e b asis o f E fficiency I ndex ( E I) as d escribed by the Nash and Sutcliffe. E I depends upon the error present in the model like missing data or inconsistency in the data and it is directly proportional to errors present in the input information of the model. The value of efficiency index l ies b etween 0-1. T he efficiency i n dex eq ual t o 1 i n dicates t h e b est p erformance o f the model. The efficiency index was calculated by using the following relationship:

    ∑    ∑    (2)  =   ∑  Where, qo= observed flow, q͞ o= mean value of observed flow, q s= simulated flow and n = number of data points.

5. RESULTS AND DISCUSSIONS The N AM model w as calibrated for s ix years p eriod from 1996 t o 2 001 and t hen validated f or the remaining period of five years from 2002 to 2006. The graph presenting comparison between observed and simulated discharge during model calibration is shown in Figure 3 which gives the idea and view of best match obtained during the model calibration. Similarly Figure 5 shows the comparison between observed discharge an d s imulated d ischarge d uring t h e v alidation o f N AM m odel. T he o bserved an d simulated hydrographs were found matching well for peak and low flows as well reasonably.

Figure 3 Comparison between observed and simulated discharge for calibration

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Figure 4 Comparison between observed and simulated discharge for validation The P erformance o f M IKE 1 1 N AM m odel w as ev aluated b ased o n A ccuracy criteria s uch as Coefficient of determination (R2 ). The coefficient of determination R2 value for calibration and validation period is 0 .720 an d 0 .502 r espectively w hich i ndicating g ood ag reement b etween t he observed and simulated runoff.

Table 1 Accuracy Parameter value during calibration, validation and total period

Accuracy criteria Calibration Validation Total period Coefficient of determination (R2) 0.720 0.502 0.678

Nash Sutcliff efficiency (EI) % 76 85 80.60

6. CONCLUSION In this study, MIKE 11 NAM model has been developed and tested for the performance and suitability in Shipra river basin o f M adhya Pradesh, I n dia. T he NAM model was calib rated an d v alidated using daily weighted precipitation, daily Potential evapotranspiration and daily observed runoff time series of 11 years period f rom 1 996 t o 2 006. T he N AM m odel w as t ested an d ev aluated b ased o n Accuracy criteria Coefficient of determination (R2 ). The coefficient of determination R2 value for calibration and validation period is 0 .720 an d 0 .502 r espectively w hich i ndicating g ood ag reement b etween t he observed and simulated runoff. The Nash–Sutcliffe Efficiency Index (EI) for calibration and validation is 76% and 85% respectively. T he v alue o f t hese a ccuracy p arameters i ndicates t hat t he M IKE 1 1 N AM model is performing well i n p redicting r unoff. T he m odel w as f ound suitable f or Shipra basin i n simulating hydrological response of the basin to the rainfall and predicting daily runoff with good degree of accuracy. REFERENCE [1] Prasad, B . a nd T iwari, H .L., (2016). GIS b ased S oil E rosion Modeling. I nternational Journal of Civil Engineering and Technology (IJCIET).7(6). pp. 166–171. [2] Horton, R .E. ( 1933).The r ole o f i nfiltration i n t he h ydrological c ycle, Transactions American Geophysical Union. 446-460.

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