An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Abstract

Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.

System management of irrigation under climate change conditions for determining the crop water demand and solutions for the water-saving

“…The estimates of the increase in crop water demand in Ukraine, given in paragraphs 3.1 and (Kovalchuk P. et al 2021), proved the need to develop system management of irrigation as a system approach, including an algorithm for irrigation operational control based on the instrumental-calculative method and water-saving irrigation regimes. This approach, compared with works using short-term weather forecasting (Guo et al 2023;Perera et al 2014;Perera et al 2016), quantitatively reduces the risks of over-and under-irrigation and ensures water savings when administering irrigation. Watering, assigned on a sliding interval by the instrumental method, based on the measurement of the initial state of moisture in the soil, reduces the risk of insufficient moisture.…”

“…The calculation method daily determines the evapotranspiration for 5-7 days in advance and sets the timing of irrigation according to short-term forecasts (Perera et al 2016;Kovalchuk et al 2018) of precipitation, temperature and air humidity deficit. This gives the farmer the opportunity, as in work (Guo et al 2023;Cao et al 2019) to prepare for watering and effectively use precipitation.…”

“…The use of water-saving irrigation regimes with a reduced rate not only saves up to 30% of irrigation water compared to biologically optimal regimes (Gadzalo et al 2019;Zhovtonog et al 2015) , but also reduces water infiltration into deep soil layers and reduces the risks of overflow under conditions of uncertainty (Guo et al 2023).…”

Irrigation challenges in the COVID-19 scenario

“…7. An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts (Guo et al 2022).…”

“…However, irrigation scheduling is typically conducted based on simple water requirement calculations without accounting for the strong link between water movement in the root zone, crop yields, and irrigation expenses. Guo et al (2022) addresses the knowledge gap in providing decision support information and provides an alternative view compared with previous work on irrigation scheduling optimization. An uncertainty-based analysis framework for evaluating irrigation scheduling decisions is developed with focus on the uncertainty arising from short-term rainfall forecasts.…”