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General circulation models (GCMs) are the main tools for estimating changes in the climate for the future. The imperfect representation of climate models introduces biases in the simulations that need to be corrected prior to their use for impact assessments. Bias correction methods generally assume that the bias calculated over the historical period does not change and can be applied to the future. This study investigates this assumption by considering the extent and nature of bias nonstationarity using 20 th century precipitation and temperature simulations from six CMIP5 GCMs across Australia. Four statistics (mean, standard deviation, 10 th and 90 th quantiles) in monthly and seasonal biases are obtained for three different time window lengths (10, 25 and 33 years) to examine the properties of bias over time. This approach is repeated for two different phases of the Interdecadal Pacific Oscillation (IPO), which is known to have strong influences on the Australian climate. It is found that bias non-stationarity at decadal timescales is indeed an issue over some of Australia for some GCMs. When considering interdecadal variability there are significant difference in the bias between positive and negative phases of the IPO. Regional analyses confirmed these findings with the largest differences seen on the east coast of Australia, where IPO impacts tend to be the strongest. The nature of the bias non-stationarity found in this study suggests that it will be difficult to modify existing bias correction approaches to account for non-stationary biases. A more practical approach for impact assessments that use bias correction maybe to use a selection of GCMs where the assumption of bias non-stationarity holds.

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In agriculture irrigation management, irrigation scheduling is typically performed in an open-loop fashion and is done only once at the beginning of a growing season. In this work, we study whether closed-loop scheduling with closedloop control can lead to improved performance in terms of crop yield and water conservation in agriculture irrigation. The interaction between soil, water, crop (maize in this work), and atmosphere is described by an agro-hydrological model, which is a partial differential equation. In the proposed scheduling and control scheme, both the scheduler and the controller are designed using model predictive control (MPC). The scheduler uses a long horizon (with a sampling period of 1 day) that covers the entire crop growth season, and the horizon shrinks as time moves. The primary objective of the scheduler is to maximize the crop yield. The controller uses a much shorter prediction horizon and a much finer sampling period. The primary objective of the controller is to track the soil moisture reference calculated by the scheduler. To alleviate the computational complexity of the scheduler and the controller, a linear parameter varying (LPV) model is identified for the scheduler and controller, respectively. The performance of the closed-loop scheduling scheme is evaluated against the traditional open-loop scheduling scheme under different scenarios.

Conventional bias correction is usually applied on a grid‐by‐grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two‐step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back‐transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA‐based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.