Water Productivity Journal (WPJ) Quarterly Publication

Document Type : Original Research Paper


1 Ph.D. School of Engineering, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT Australia and Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC Australia

2 Ph.D., Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC Australia.

3 Assistant Professor, Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC Australia.

4 Professor, Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC Australia


Introduction: Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving the water productivity by at least 10%. A key aspect of irrigation scheduling is accurate estimation of crop water use and soil water status, which often require modelling with good information on soil, crop, climate and field management. However, this input information is often highly uncertain. Our study aims to obtain a comprehensive understanding of uncertainties in irrigation scheduling that arise from individual model inputs, from which identifying the key contributor of uncertainty. Our study aims to understand the uncertainty in model-based irrigation scheduling and the key model inputs that contribute to this uncertainty. To achieve this, we first performed a comprehensive literature review to identify the key sources and the expected ranges of uncertainty in individual model inputs. Secondly, a global sensitivity analysis was conducted to quantify the influence of each model input on the total uncertainty of the modelled irrigation scheduling decision, across 14 climatically different locations in Australia.
Materials and Methods: To achieve this, we used a global sensitivity analysis to assess the relative importance of the uncertainty in each model input to the total uncertainty in output. This analysis focused on the modelled irrigation scheduling (summarized with irrigation amount per day during an irrigation cycle) with a single-bucket soil water balance model following the Food and Agriculture Organisation (FAO). The key input variables required by the model include weather data, crop parameters (i.e., crop coefficient and root depth), soil parameters (plant available water capacity) and management factors (depletion factor).
Results: To define the uncertainty in each model input, we first performed a comprehensive literature review to summarize the key sources of uncertainty in estimating each of these model inputs, and the expected range of uncertainty in the data of each input. Based on these uncertainty ranges, we ran the global sensitivity analysis with the soil water scheduling model. In this analysis, a large number of random samples were drawn for each input variable within its expected range of uncertainty, to produce ensemble simulations of soil water status and thus irrigation scheduling decisions. The total uncertainty in these scheduling decisions were then analysed with respect to that of each input variable, to establish the relative importance of the uncertainty in individual input variables. The sensitivity analysis was performed at 14 climatically different locations in main irrigation districts across Australia to provide a comprehensive understanding of sensitivity.
Conclusions: Our results highlight the crop coefficient as the most important contributor to the total uncertainty in irrigation scheduling simulation, across different climate zones in Australia. The uncertainty in crop coefficient can be potentially reduced by better representation of its spatial and temporal variation, as well as considering alternative approaches such as remote sensing estimates. Our findings are useful to inform the future direction of research to improve irrigation scheduling in Australia. Further, our modelling approach is transferable to other irrigation regions to better understand the uncertainties associated with irrigation scheduling and the key data sources that lead to these uncertainties.


Main Subjects

Acharya, S.C., Nathan, R., Wang, Q.J., Su, C.-H. & Eizenberg, N. (2019). An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia. Hydrology and Earth System Sciences, 23: 3387-3403.
Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, Italy, 300, D05109.
Beck, H.E., Zimmermann, N.E., Mcvicar, T.R., Vergopolan, N., Berg, A. & Wood, E.F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific data, 5: 1-12.
BOM. http://www.bom.gov.au/research/projects/reanalysis/ [Online]. [Accessed].
CAai, X., Hejazi, M.I. & Wang, D. (2011). Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling. Journal of Water Resources Planning and Management, 137: 391-403.
Canadell, J., Jackson, R.B., Ehleringer, J.B., Mooney, H.A., Sala, O.E. & Schulze, E.D. (1996). Maximum rooting depth of vegetation types at the global scale. Oecologia, 108: 583-595.
Charlesworth, P. (2005). Soil water monitoring. Irrigation Insights. No. 1, Second Edition. CSIRO/CRC. Irrigation Futures.
Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G. & Bauer, D.P. (20110. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137: 553-597.
Elliott, R.L., Harp, S.L., Grosz, G.D. & Kizer, M.A. (1988). Crop coefficients for peanut evapotranspiration. Agricultural Water Management, 15: 155-164.
Gelaro, R., Mccarty, W., Suarez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G. & Reichle, R. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30: 5419-5454.
George, B.A., Shende, S.A. & Raghuwanshi, N.S. (2000). Development and testing of an irrigation scheduling model. Agricultural Water Management, 46: 121-136.
Grayson, R.B. & Western, A.W. (1998). Towards areal estimation of soil water content from point measurements: time and space stability of mean response. Journal of Hydrology, 207: 68-82.
Gu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X. & Xu, J. (2020). Irrigation Scheduling Approaches and Applications: A Review. Journal of Irrigation and Drainage Engineering, 146: 04020007.
Guerra, E., Ventura, F. & Snyder, R. (2016). Crop coefficients: a literature review. Journal of Irrigation and Drainage Engineering, 142: 06015006.
Guerra, E., Ventura, F., Spano, D. & Snyder, R.L. (2015). Correcting Midseason Crop Coefficients for Climate. Journal of Irrigation and Drainage Engineering, 141: 04014071.
Jakob, D., Su, C.-H., Eizenberg, N., Kociuba, G., Steinle, P., Fox-hughes, P. & Bettio, L. (2017). An atmospheric high-resolution regional reanalysis for Australia. Bulletin of the Australian Meteorological and Oceanographic Society, 30.
Jones, D.A., Wang, W. & Fawcett, R. (2009). High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal, 58.4: 233.
Ladson, A.R., Lander, J.R., Western, A.W., Grayson, R.B. & Zhang, L. (2006). Estimating extractable soil moisture content for Australian soils from field measurements. Soil Research, 44: 531-541.
Montgomery, J., Hornbuckle, J., Hume, I. & Vleeshouwer, J. (2015). IrriSAT—Weather based scheduling and benchmarking technology. Proceedings of the 17th ASA Conference, Hobart, Australia: 20-24.
Paraskevopoulos, A.L. & Singels, A. (2014). Integrating soil water monitoring technology and weather-based crop modelling to provide improved decision support for sugarcane irrigation management. Computers and Electronics in Agriculture, 105: 44-53.
Peddinti, S.R. & Kambhammettu, B.P. (2019). Dynamics of crop coefficients for citrus orchards of central India using water balance and eddy covariance flux partition techniques. Agricultural Water Management, 212: 68-77.
Pereira, L.S., Allen, R.G., Smith, M. & Raes, D. (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management, 147:
Pôças, I., Paço, T.A., Paredes, P., Cunha, M. & Pereira, L.S. (2015). Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data. Remote Sensing, 7: 2373-2400.
Rab, M., Chandra, S., Fisher, P., Robinson, N., Kitching, M., Aumann, C. & Imhof, M. (2011). Modelling and prediction of soil water contents at field capacity and permanent wilting point of dryland cropping soils. Soil Research, 49: 389-407.
Ratliff, L.F., Ritchie, J.T. & Cassel, D.K. (1983). Field‐measured limits of soil water availability as related to laboratory‐measured properties. Soil Science Society of America Journal, 47: 770-775.
Rawls, W.J., Brakensiek, D.L. & Saxtonn, K. (1982). Estimation of soil water properties. Transactions of the ASAE, 25: 1316-1320.
Richards, L. & Weaver, L. (1944). Moisture retention by some irrigated soils as related to soil moisture tension. Journal of Agricultural Research, 69: 215-235.
Sharma, V. & Irmak, S. (2017). Soil-Water Dynamics, Evapotranspiration, and Crop Coefficients of Cover-Crop Mixtures in Seed Maize Cover-Crop Rotation Fields. II: Grass-Reference and Alfalfa-Reference Single (Normal) and Basal Crop Coefficients. Journal of Irrigation and Drainage Engineering, 143: 04017033.
Sobol, I.M. (1993). Sensitivity Estimates for Nonlinear Mathematical Models. Mathematical Modelling and Computational Experiments, 4: 407-414.
Sobol, I.M. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55:
Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C.J., Rennie, S., Franklin, C., Dharssi, I. & Zhu, H. (2019). BARRA v1. 0: the Bureau of Meteorology atmospheric high-resolution regional reanalysis for Australia. Geoscientific Model Development, 12: 2049-2068.
The world bank (2017). World Development Indicators: Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal). In: Food and Agriculture Organization, Genova, Switzerland, A.D. (ed.).
Veihmeyer, F. & Hendrickson, A. (1928). Soil moisture at permanent wilting of plants. Plant Physiology, 3: 355.
Wang, S., Zhu, G., Xia, D., Ma, J., Han, T., Ma, T., Zhang, K. & Shang, S. (2019). The characteristics of evapotranspiration and crop coefficients of an irrigated vineyard in arid Northwest China. Agricultural Water Management, 212: 388-398.
Webb, C. (2010). Bureau of meteorology reference evapotranspiration calculations: Climate Services Centre. Queensland Regional Office, Bureau of Meteorology, Australia.
WMO (2018). Guide to Instruments and Methods of Observation. World Meteorological Organization Geneva, Switzerland.
Wmo, G. (19960. Guide to meteorological instruments and methods of observation, Geneva, Switzerland.
Yang, X., Leys, J., Gray, J. & Zhang, M. (2022). Hillslope erosion improvement targets: Towards sustainable land management across New South Wales, Australia. Catena, 211: 105956.
Yang, X., Zhang, M., Oliveira, L., Ollivier, Q.R., Faulkner, S. & Roff, A. (2020). Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment. Remote Sensing, 12: 3805.
Zeng, C., Wang, Q., Zhang, F. & Zhanng, J. (2013). Temporal changes in soil hydraulic conductivity with different soil types and irrigation methods. Geoderma, 193: 290-299.