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Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia

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Abstract

This paper presents the application of a long-term streamflow forecasting model developed using artificial neural networks at a stream gauging station in the Awash River Basin, Ethiopia. The gauging station is located above the headworks of a large irrigation scheme called the Middle Awash Agricultural Development Enterprise (MAADE). Based on the forecasted streamflow time series and water requirements for irrigation and environmental purposes, appropriate agricultural water management strategies have been proposed for the irrigation scheme (MAADE). The water management strategies which were evaluated in this study are based on different scenarios of abstraction demands. These demands were formulated based on a range of options for agricultural development and change in MAADE. The scenarios evaluated were based on such factors as the existing planting patterns, changing planting dates, changing crop varieties and reducing the area under cultivation. An appropriate scenario of agricultural development was decided on the basis of the modified flows in the river vis-à-vis the trigger/threshold value established at the Melka Sedi stream gauging station. Considering all the scenarios, it is suggested that a 1–24% reduction in the area currently irrigated in the scheme will ensure a reliable supply of water to the scheme throughout the growing season and will provide sustainable environmental flow in the river.

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References

  • Altunkaynak A (2007) Forecasting surface water level fluctuations of lake van by artificial neural networks. Water Resour Manage 21:399–408

    Article  Google Scholar 

  • Brouwer C, Hoevenaars JPM, van Bosch BE, Hatcho N, Heibloem M (1992) Irrigation water management: training manual No. 6—scheme irrigation water needs and supply. FAO, Rome, Italy

  • Carson DJ (1998) Seasonal forecasting. J R Meteorol Soc 124:1–26

    Article  Google Scholar 

  • Chen C-S, Chou FN-F, Chen BP-T (2010) Spatial information-based back-propagation neural network modeling for outflow estimation of ungauged catchment. Water Resour Manage. doi:10.1007/s11269-010-9652-6

    Google Scholar 

  • Danon Y (1997) Scientific software. URL address http://www.nuceng.com/WinNN.htm. Access date: 15/01/2004

  • Desalegn CE, Babel MS, Gupta AD, Seleshi BA, Merrey D (2006) Farmers’ perception of water management under drought conditions in the Upper Awash Basin, Ethiopia. Int J Water Resour Dev 22(4):589–602

    Article  Google Scholar 

  • Desalegn CE, Babel MS, Gupta AD (2010) Drought analysis in the Awash River Basin, Ethiopia. Water Resour Manage 24(7):1441–1460

    Article  Google Scholar 

  • Dong H, Li W, Tang W, Li Z, Zhang D, Niu Y (2006) Yield, quality and leaf senescence of cotton grown at varying planting dates and plant densities in the Yellow River Valley of China. Field Crops Res 98(2–3):106–115

    Article  Google Scholar 

  • Doorenbos J, Pruitt WO (1977) Crop water requirements. FAO Irrigation and Drainage Paper No. 24, Rome, Italy

  • Dracup JA, Lee KS, Paulson EG Jr (1980) On the definition of droughts. Water Resour Res 16(2):297–302

    Article  Google Scholar 

  • El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manage 23:2289–2315

    Article  Google Scholar 

  • English M (1990) Deficit irrigation. I: analytical framework. J Irrig Drain Eng 116(3):399–412

    Article  Google Scholar 

  • English M, Nuss GS (1982) Designing for deficit irrigation. J Irrig Drain Eng 108(2):91–106

    Google Scholar 

  • English M, James L, Chen C (1990) Deficit irrigation. II: observations in Columbia basin. J Irrig Drain Eng 116(3):413–427

    Article  Google Scholar 

  • Ethiopian Valleys Development Studies Authority (1979) Amibara irrigation project II: inception report for technical support services. Halcrow-ULG Ltd

  • European Environment Agency (2001) Sustainable water use in Europe, Part 3: Extreme Hydrological Events: Floods and Droughts. Environmental Issue Report No. 21, 84pp

  • Goddard L, Mason SJ, Zebiak SE, Ropelewski CF, Basher R, Cane MA (2001) Current approaches to seasonal-to-interannual climate predictions. Int J Climatol 21:1111–1152

    Article  Google Scholar 

  • Gopakumar R, Takara K, James EJ (2007) Hydrologic data exploration and river flow forecasting of a humid tropical river basin using artificial neural networks. Water Resour Manage 21:1915–1940

    Article  Google Scholar 

  • Gorantiwar SG, Smout IK (2003) Allocation of scarce water resources using deficit irrigation in rotational systems. J Irrig Drain Eng 129(3):155–163

    Article  Google Scholar 

  • Hargreaves GH, Samani ZA (1984) Economic considerations of deficit irrigation. J Irrig Drain Eng 110(4):343–358

    Article  Google Scholar 

  • Howell T (1990) Relationship between crop production and transpiration, evaporation and irrigatio. In: Steward BA, Neilson DR (eds) Irrigation of agricultural crops, agronomy monograph No. 30 ASA, CSSA and SSSA, Madison, WI, pp 391–434

  • Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7(2):585–592

    Article  Google Scholar 

  • Keller J, Sivanappan RK, Varadan KM (1992) Design logic for deficit drip irrigation of coconut tree. Irrig Drain Syst 6:1–7

    Article  Google Scholar 

  • Lee KT, Hung W-C, Meng C-C (2008) Deterministic insight into ANN model performance for storm runoff simulation. Water Resour Manage 22:67–82

    Article  Google Scholar 

  • Lyle WM, Bordovsky JP (1995) LEPA corn with limited water supplies. Trans ASAE 38:2455–2462

    Google Scholar 

  • Mohanty S, Jha MK, Kumar A, Sudheer KP (2009) Artificial neural network modeling for groundwater level forecasting in a River Island of Eastern India. Water Resour Manage. doi:10.1007/s11269-009-9527-x

    Google Scholar 

  • Nagesh Kumar D, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural networks. Water Resour Manage 18:143–161

    Article  Google Scholar 

  • Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manage 20:77–90

    Article  Google Scholar 

  • Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour Manage 23:2877–2894

    Article  Google Scholar 

  • Ochoa-Rivera JC, Garcia-Bartual R, Andreu J (2002) Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks. J Hydrol Earth Sci 6:641–654

    Article  Google Scholar 

  • Oktem A, Simsek M, Oktem AG (2003) Deficit irrigation effects on sweet corn (Zea mays saccharata Sturt) with drip irrigation system in a semi-arid region. I: water–yield relationship. Agric Water Manag 61:63–74

    Article  Google Scholar 

  • Pandey RK, Marancilla JW, Chetima MM (2000) Deficit irrigation and nitrogen effects on maize in a Sahelian environment. Part II. Shoot-growth, nitrogen uptake and water extraction. Agric Water Manag 46:15–27

    Article  Google Scholar 

  • Pulido-Calvo I, Portela MM (2007) Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J Hydrol 332(1):1–15

    Article  Google Scholar 

  • Richard GA, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements. FAO Irrigation and drainage paper No. 56, Rome, Italy

  • Rodrigues P, Machado T, Pereira L, Teixeira J, El Amami H, Zairi A (2003) Feasibility of deficit irrigation with center-pivot to cope limited water supplies in Alentejo, Portugal. In: Rossi G, Cancelliere A, Pereira L, Oweis T, Shatanawi M, Zairi A (eds) Tools for drought mitigation in Mediterranean regions. Kluwer, The Netherlands, pp 203–222

    Google Scholar 

  • Smith M, Clarke D, El-Askari K (1998) CropWat 4 Windows Version 4.3. FAO, IIDS and NWRC

  • Sohail A, Watanabe K, Takeuchi S (2008) Runoff analysis for a small watershed of Tono area Japan by back propagation artificial neural network with seasonal data. Water Resour Manage 22:1–22

    Article  Google Scholar 

  • Sudar RA, Saxton KE, Spomer RG (1981) A predictive model of water stress in corn and soybeans. Trans ASAE 24(1):97–102

    Google Scholar 

  • Vaux HJ, Pruit WO (1983) Crop–water production functions. In: Hillel D (ed) Advances in irrigation, vol 2. Academic, New York, pp 61–97

    Google Scholar 

  • Vogt JV, Somma F (eds) (2000) Drought and drought mitigation in Europe. Kluwer Academic Publishers, The Netherlands, p 325

    Google Scholar 

  • Wilhite DA, Dinar A (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10(3):111–120

    Article  Google Scholar 

  • Zhang H, Oweis T (1999) Water–yield relations and optimal irrigation scheduling of wheat in the Mediterranean region. Agric Water Manag 38:195–211

    Article  Google Scholar 

Download references

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Correspondence to Desalegn Chemeda Edossa.

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Edossa, D.C., Babel, M.S. Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia. Water Resour Manage 25, 1759–1773 (2011). https://doi.org/10.1007/s11269-010-9773-y

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