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• Decades with more drought years have a potential 25% reduction in average annual profit. • Greater risks to irrigation enterprises may come from large increases in variability of returns and large loss-making years. • Decadal average returns compared across climate change scenarios, consider long run viability under a changing climate. The Interactive Land use Strategic Assessment (ILSA) tool allows irrigators to examine and compare the likely effects of a range uncertain future climates on their individual enterprise along multiple time frames. The scientific basis of the tool is predicated on the influence of the prevailing climate conditions on expected levels of water allocations for irrigation activities under multiple climate scenarios. The model reports both annual returns and ten year average annual expected returns, given probability weighted allocations for a selected decade and climate scenario. Irrigators are able to adjust default farm operational parameters to suit their particular circumstances and examine the expected returns across multiple possible future water availability and market price years. A case study in the Loxton irrigation district of Southern Australia demonstrates the models capability examining the effects climate change, climatic variability, and water trade have on irrigation operations in a manner that is understandable to irrigators. [ABSTRACT FROM AUTHOR]

Distributed catchment models such as SWAT (Soil and Water Assessment Tool) are widely used to assess catchment characteristics and facilitate informed decisions for safeguarding water quantity and quality. This study applied SWAT to simulate monthly stream flow and loadings of total suspended sediment (TSS), total nitrogen (TN) and total phosphorus (TP) for five monitoring stations within the Onkaparinga catchment, and tested the models’ performance based on single-site or multi-site calibration. The results showed that multi-site calibration did not improve simulations of flow and sediments compared to single-site calibration. However, simulation results for TN and TP loads improved in both rural and urban sub-catchments of this catchment. Uncertainty analysis revealed that there is high uncertainty in model simulation of TSS by both strategies. The study has demonstrated: (1) the capability of SWAT to simulate realistically the extreme flow conditions of the semi-arid Onkaparinga catchment; (2) the benefit of local monitoring data for more realistic simulations of nutrient loads by means of the multi-site calibration of SWAT as pre-requisite for scenario analysis of spatially-explicit management options. [ABSTRACT FROM AUTHOR]

Abstract: Environmental targets are often used in planning for sustainable agricultural landscapes but their impacts are rarely known. In this paper we introduce landscape futures analysis as a method which combines linear programming optimisation with scenario analysis in quantifying the environmental, economic, and social impacts associated with achieving environmental targets, on a landscape scale. We applied the technique in the Lower Murray in southern Australia. Landscape futures models were used to identify specific geographic locations in the landscape for six natural resource management (NRM) actions such that regional environmental targets are achieved. The six potential NRM actions that may be undertaken to achieve environmental targets include remnant vegetation management, ecological restoration, conservation farming, deep-rooted perennials, and the production of biomass and biofuels feedstock for renewable energy generation. We developed landscape futures under four alternative spatial prioritisation policy options and four future climate and commodity price scenarios. The impacts of each landscape future were calculated across a range of environmental, economic, and social indicators. The external drivers, climate change and commodity prices, and internal decisions such as policy options for spatially prioritising NRM actions, both have a strong influence on the costs and benefits of achieving environmental targets. Illustrative results for the cleared agricultural areas in the Mallee region indicate that whilst achieving targets can have substantial environmental benefits, it requires large areas of land use and land management change, and is likely to be costly (up to $348.5 M per year) with flow-on impacts on the regional economy and communities. Environmental targets can be achieved more cost-effectively through spatial planning. Costs can be further reduced if markets are established for carbon, biomass, and biofuels to enable landholders to generate income from undertaking NRM. Landscape futures analysis is an effective tool for supporting the strategic regional NRM policy and planning decisions of how best to set and achieve environmental targets. [Copyright &y& Elsevier]

Aerodynamic resistance (hereafter r a) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of r a plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links r a with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates. The present study investigates the influence of r a and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE , the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST , LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019. Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in r a retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low r a (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of r a on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on r a for modelling LE (VIP < 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of r a. • Modelled latent heat flux is uncertain under drought due to aerodynamic resistance. • Overestimation of aerodynamic resistance at water-limited sites for low wind speed. • Stability links aerodynamic resistance with surface temperature and sensible heat. • Novel formulations for aerodynamic resistance are needed to model latent heat flux. [ABSTRACT FROM AUTHOR]