AIMS Geosciences, 2018, 4(3): 166-179. doi: 10.3934/geosci.2018.3.166

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Assessment of irrigation shortfall using WATHNET in the Otago region of New Zealand

National Institute of Water and Atmospheric Research, Christchurch, New Zealand

Knowledge of the likely rainfall and river flow for a coming season can improve management of an overall water resources system without unduly compromising either the environmental or productive behaviour of the system. The objective of this study has been to assess the probability of irrigation demand shortfalls, i.e. soil moisture deficits, for a typical “run of the river” irrigation scheme so as to identify the duration and severity of potential shortfalls. In this study a multi-objective linear programming tool WATHNET has been used to build and run a model of the irrigation scheme. The focus of this study has been on how to use predictions of 3-monthly rainfall and temperature to estimate potential daily water available for irrigation. The method uses Monte-Carlo simulations, to produce multiple replicates of equally likely sequences of river flows, rainfall and potential evaporation values. A sub-set of the equally likely sequences is then selected using prediction information of the likely seasonal climate outlook from NIWA’s Climate Update. The selected sequences, which are biased towards the seasonal climate prediction, are then used as inputs to multiple model runs. By using the output from all the “biased” model runs a probability distribution can be made of water availability for irrigation. The methodology has been demonstrated using the Shag River Irrigation Scheme located in the Otago region of New Zealand. The results compare the predicted soil moisture variation over two three-month periods with retrospective simulations based on the observed rainfalls, river flows and potential evaporation values. Results suggest that WATHNET can simulate 3-month soil moisture dynamics. In order to develop WATHNET as a tool to assess probabilities of irrigation shortfall it needs to be validated using measurements of soil moisture variations over an irrigation season at sites with different soil types.
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1. Davids Engineering I (2015) Solano Irrigation District Water Supply Shortage Risk Assessment, Available from:

2. Zotarelli L, Dukes M, Morgan K (2010) Interpretation of soil moisture content to determine soil field capacity and avoid over-irrigating sandy soils using soil moisture sensors. University of Florida Cooperation Extension Services, AE460.

3. Oweis T, Hachum A (2006) Water harvesting and supplemental irrigation for improved water productivity of dry farming systems in West Asia and North Africa. Agr Water Manage 80: 57–73.    

4. Singh SK (2016) Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand. Water Resour Manage 30: 2295–2309.    

5. Kuczera G, Cui L, Gilmore R, et al. (2009) Addressing the shortcomings of water resource simulation models based on network linear programming. 32nd Hydrology and Water Resources Symposium (H2009). Newcastle, Australia Engineers Australia/Causal Productions.

6. Singh SK, Williams G, Ibbitt R (2016) Opeartional and strategic planning for dynamic water supply system. Water New Zealand's Annual Conference & Expo, 19–21 Oct Rotorua, New Zealand.

7. Singh S, Ibbitt R, Mullan B (2016) Sustainable Yield Model Upgrade 2016: SYM Upgrade 2016. Wellington Water Ltd (Working on behalf of the Greater Wellington Regional Council), 85.

8. Ibbitt RP (2004) KARAKA model: A seasonal water availability model based on the SYM. WRC05501. Christchurch: NIWA, 57: 57 figs, 51 table.

9. Kuczera G, Cui L, Gilmore R, et al. (2010) Enhancing the robustness of water resource simulation models based on network linear programming. 9th International conference on Hydroinformatics. Tianjin, China: Chemical Industry Press (CIP), 2245–2252.

10. Cui L, Ravalico J, Kuczera G, et al. (2011) Multi-onjective Optimisation Methodology for the Canberra Water Supply System. Canberra, Australia: eWater Cooperative Research Centre.

11. Mortazavi M, Kuczera G, Cui L (2012) Multiobjective optimization of urban water resources: Moving toward more practical solutions. Water Resour Res 48.

12. Mortazavi-Naeini M, Kuczera G, Kiem AS, et al. (2013) Robust optimisation of urban drought security for an uncertain climate. National Climate Change Adaptation Research Facility Gold Coast, 74.

13. Fahey B, Ekanayake J, Jackson R, et al. (2010) Unisg the WATYIELD water balance model to predict catchmnet water yields and low flows. J Hydrol 49: 35–58.

14. Ibbitt RP (2005) Otago Strategic Water Study: 1. Natural flow series and water availability for eight catchments. ORC05503. Christchurch: NIWA, 96: 146 figs, 144 tables, 145 refs.

15. UKWIR (2002) An Improved Methodology for Assessing Headroom. ReportWR-13, UK Water Industry Research Ltd, London.

16. Ibbitt RP, Woolley K (2008) Seasonal prediction of supply and demand for a dynamic water supply system. 16–18 October Bangkok.

17. Ibbitt RP (1997) Documentation for the Reliable Yield Model for the bulk water supply system for the Wellington Regional Council. WRC70501. Christchurch: NIWA, 3 v.: ill. (some missing) tables, refs.

18. Varley I (2016) WATHNET Water Supply System Model Independent Review 2016-Final report, WaterNSW, WR2016-002J, Available from:

19. SKM, Water Supply System Model and Yield Review 2009/2010, Volume 1: Main Report, 2011. Available from:

20. Bandaragoda C, Tarboton DG, Woods R (2004) Application of TOPNET in the distributed model intercomparison project. J Hydrol 298: 178–201.    

21. Ibbitt RP, Woods RA, McKerchar AI (2004) Otago Strategic Water Study: 1. Natural flow series and water availability for eight catchments-Draft. Christchurch: NIWA, 72 leaves: 152 figs, 153 tables, 153 refs.

22. ORC, Flow at Shag River, 2018. Available from:, Accesed on 01 Feb 2018.

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