Search

Monsoonal rainfall in northern Australia (AUMR) varies substantially on interannual, decadal, and longer time scales, profoundly impacting natural systems and agricultural communities. Some of this variability arises in response to sea surface temperature (SST) variability in the Indo-Pacific linked to both El Niño–Southern Oscillation (ENSO) and the interdecadal Pacific oscillation (IPO). Here we use observations to investigate unresolved issues regarding the influence of the IPO and ENSO on AUMR. Specifically, we show that during negative IPO phases, central Pacific (CP) El Niño events are associated with below-average rainfall over northeast Australia, an anomalous anticyclonic pattern to the northwest of Australia, and eastward moisture advection toward the date line. In contrast, CP La Niña events (distinct from eastern Pacific La Niña events) during negative IPO phases drive significantly wet conditions over much of northern Australia, a strengthened Walker circulation, and large-scale moisture flux convergence. During positive IPO phases, the impact of CP El Niño and CP La Niña events on AUMR is weaker. The influence of central Pacific SSTs on AUMR has been stronger during the recent (post-1999) negative IPO phase. The extent to which this strengthening is associated with climate change or merely natural internal variability is not known.

The Northern Australia Climate Program (NACP) is a fully integrated research, development and extension (RDandE) program operating across extensive pastoral regions of northern Australia. The NACP aims to improve existing climate models and forecast tools, develop new products to meet user needs and build the capacity of rangeland producers to manage the challenges posed by droughts (or failed wet seasons) and climate variability. Climate information gaps identified through earlier surveys of graziers and communities in rural and remote Australia informed the design of the research component of the NACP, which aims to address the low and variable accuracy of seasonal climate forecasts in many regions, the need for proof of value of forecasts and relevance of existing forecast systems and technologies, and perceived lack of effective support from climate experts for the use of climate resources and technologies in agricultural decision making. The development and extension components of the program aim to improve climate literacy and the use of climate information. Building on the research program, they deliver a climate service that provides local extension and technical support, with a focus on building trust in climate information through locally sourced, industry connected NACP trained and supported extension advisers called Climate Mates. Two-way information flow between decision makers and researchers, facilitated by the Climate Mates, ensures that forecasts and decision- and discussion-support tools developed through the program are regionally relevant and targeted to the needs of end users. Monitoring and evaluation of the program indicates that this approach is contributing to positive outcomes in terms of awareness and knowledge of climate forecasting and products, and their adoption and use in decision making (i.e. practice change). In the longer term, the Climate Mates have potential for enduring impact beyond the program, leaving a knowledgeable and trusted climate resource across regional northern Australia.

The El Niño-Southern Oscillation (ENSO) produces large shifts in ocean and atmospheric conditions in the Pacific and Indian Ocean basins, including major changes in sea level in equatorial regions across the Pacific basin, including the Pacific Rim1-6, 18. The Pacific Ocean rim is also well known as a region prone to a high frequency of earthquakes16,17, which has a profound influence on lives, economies, and ecosystems in the region. An ability to better estimate earthquake risk frequency would be of enormous value in preparedness and response planning16. Here we show that ENSO-driven sea-level variability, measured in the equatorial, northern or overall Pacific Ocean is coherent with subsequent variability in earthquake frequency in the western Pacific Ocean Basin. While the statistical link is evident for M4.7 to M5.5 and ≥M6.5 earthquakes, it is strongest for M4.7 to M5.5 earthquakes. We hypothesize that the relationship is due to a systematic influence of the climate-ocean system on the lithospheric stress field and plate coupling along subduction zones. We suggest these results would underpin an ability to better assess earthquake frequency risk in and around the western Pacific several months in advance.

Climate change will significantly impact the future viability and security of food production systems, with increased frequency and intensity of droughts, floods, storms and other extreme climatic events predicted in many regions. In order for food production systems to remain viable and resilient under a changing climate, novel approaches, which integrate risk management (i.e. adaptation) and risk transfer strategies, such as insurance, are required. We argue that the coordinated integration of risk management and risk transfer approaches will support greater resilience of food production systems under climate change. Conversely, if risk management and risk transfer strategies are not carefully integrated, there is potential to undermine adaptive capacity (e.g. insurance subsidies may dissuade farmers from investing in climate adaptation) and ultimately reduce the capacity of food production systems to cope with and recover from the adverse impacts of climate change. Here we propose a resilience-based conceptual framework for integrating risk management and risk transfer strategies along with four key principles, which we believe could underlie their successful integration and thus enhance food production system resilience under climate change. These are as follows: (1) pro-active investments in farmer climate adaptation rather than re-active disaster relief, (2) structuring of government subsidies around insurance and climate disaster relief to incentivise farmer climate adaptation, (3) rewarding farmer efforts towards climate adaptation with cheaper insurance premiums for those farmers that invest resources into climate adaptation and (4) recognising investments in the integration of farm climate adaptation and risk transfer schemes within the broader context of future climate disaster risk management and global food security. Such an integrated investment approach could substantially reduce future economic losses for farmers while also enhancing food security under climate change.

Climate change, population growth and extreme events can trigger social crises and instability. The processes that dominate a society’s emergence, resilience and collapse, and the complex interactions among such processes, operating within a small region, at a multicentury or even larger time scale, remain to be identified. The causes or driving forces responsible for societal changes must be identified for a plausible explanation. Historical records provide unique examples of societies that have failed to develop buffers and strategic resilience against climate change and natural variability. Using a wide range of observations from China’s Hexi Corridor, the complex interactive processes linking climate change with human society over the past two millennia were investigated. This paper proposes a domino effect resulting from a society’s failure to respond to climate change in which individual small problems create a greater challenge over long time spans. Building resilience against the impacts of climate change requires a deep understanding of social and environmental feedbacks to create a reliable buffer against future changes. This study offers lessons learned from the past 2,000 years that remain relevant today, given the projected changes in climate and extreme events. Social responses to changes can trigger further crises. This study uses historical data and finds society’s failures to respond to climate change in China’s Hexi Corridor amplified problems.

Seasonal climate forecasts (SCFs) have potential to improve productivity and profitability in the sugar industry. However, they are often underutilised due to insufficient evidence of the economic value of the forecasts, especially when there is a level of uncertainty associated with SCFs. Here, we demonstrate the value of integrating SCFs at various forecast quality (skill) levels into seasonal irrigation planning for sugarcane farming. A seasonal forecast system based on ENSO (El Nino Southern Oscillation) phases was parameterised by forecast quality to predict seasonal precipitation tercile (i.e. wet, neutral and dry) categories. A bio-economic model was developed to determine water-yield-profit relationships. Sugarcane production under different climatic conditions and irrigation scheduling scenarios was simulated using the Agricultural Production Systems sIMulator (APSIM)-Sugar, calibrated using case study information from one of Australia’s major irrigated sugarcane growing regions. We then employed an expected profit approach to achieve an optimal profit, rather than the more conventional optimal yield, for plant and ratoon crops to quantify the potential value of using SCFs in sugarcane irrigation decision making. The results show that using skilled SCF systems in sugarcane irrigation decision making can help growers improve their gross margin compared to that achieved in the absence of climate information (economic value). With a perfect forecast of moderate climatic conditions, an average economic value of up to AUD 27 ha−1 per annum was achieved, while forecasts of moderate wet or dry conditions indicated gains of up to AUD 40 and 43 ha−1 per annum, respectively, and forecasts of extreme wet or dry conditions delivered economic gains of up to AUD 150 and 260 ha−1 per annum, respectively. With the current seasonal climate forecast skill of 60% (based on the ENSO phases forecasting system) in the case study region, an average gain of up to AUD 4.5 ha−1 per annum was realised, with up to AUD 6.2 and 7.1 ha−1 per annum, respectively, for moderate wet and dry forecasting and up to AUD 92 and 43 ha−1 per annum, respectively, for extreme wet and dry forecasting. Improvements in the skill and reliability of SCFs will be important for achieving greater productivity and/or profitability and the wider uptake of climate forecasts in agricultural decision making.

Wheat is key global food crop that is heavily influenced by climatic variability. There has been extensive research on improving forecasts and management practices to minimise climate related yield losses, but less on how to handle yield losses caused by climate variability. We investigated whether index insurance could be used to manage climate related losses, specifically from winter rainfall drought for wheat crops in Australia. We utilised 31 years of yield data from 15 of Australia’s key wheat producing regions. The winter rainfall index was developed and tested using generalised additive regression models, allowing for non-linear effects. Models with the winter rainfall index explained significant variation in wheat yields in each of the regions assessed. Wheat yield models had cross-validated R2 values > 0.5 for two-thirds of the 15 regions modelled and best explained wheat yields in the Mallee, Western Australia (cross-validated R2 of 0.70). Calculated fair premiums ranged from $8.62 to $77.1 AUD/ha, while maximum liability was $59.25 to $212.12 AUD/ha. Throughout the eastern most wheat growing regions the winter rainfall index was consistently inefficient (i.e. not beneficial). In contrast, the winter rainfall index was financially efficient (i.e. beneficial) in the western wheat regions of eastern Australia and parts of Western Australia, with benefits of up to $97 AUD/ha and loss reductions of $9 AUD/ha. The spatial variability in insurance efficiency was explained by rainfall variance. As rainfall variance increased the efficiency of the winter rainfall index insurance for wheat decreased. Our findings have two important policy implications; (1) in areas where climate change is anticipated to increase rainfall variability risk-transfer options, such as index insurance, may become less viable and as such policies that support the development of index insurance without acknowledging or adjusting for variability in its benefit could lead to inefficient outcomes for both government and agricultural producers; and (2) where index rainfall insurance is not efficient then greater emphasise may need to be placed on developing alternate types of index insurance (e.g. using satellites) and/or on risk-management and climate adaptation strategies that minimise losses. Keywords: Climate variability, Drought, Wheat, Risk transfer, Climate adaptation

Managing the risks of climate variability on crop production is central to ensuring financially viable farming systems and sustainable food production. Insurance provides a mechanism to manage and transfer climate risks. However, traditional multi-peril crop insurance (MCPI) is often too expensive and so other methods, such as index insurance, are being explored as a cheaper way to insure farmers against climate induced crop losses. Here we investigate the potential financial benefits of index insurance (protecting against excessive rainfall) for agricultural producers, namely sugar cane farmers in Tully, northern Australia. We used 80 years of historical climate and yield data to develop an excessive rainfall index. The index was developed and tested using generalized additive regression models (allowing for non-linear effects) and quantile regression, which allows relationships with lower quantiles (i.e. low yield events) to be assessed. From the regression models we derived relationships between the excessive rainfall index and sugar cane yield losses that were converted to insurance fair premiums (i.e. premiums that cover expected losses). Finally, we used efficiency analysis, based on Conditional Tail Expectation (CTE), Certainty Equivalence of Revenue (CER) and Mean Root Square Loss (MRSL), to quantify financial benefits to farmers if they purchased excessive rainfall index insurance. The regression model predicted sugar cane yields well (cross-validated R2 of 0.65). The efficiency analysis indicated there could be financial benefit to sugar cane farmers if they were to use excessive rainfall index insurance. Index insurance (based on the assumption of a fair premium) could make farmers better off by $269.85 AUD/ha on average in years with excessive rainfall (i.e. years with rainfall over the 95th percentile). Index insurance could offer a viable method for managing the financial risks posed by excessive rainfall for sugar cane producers in northern Australia. We are not aware of any other study demonstrating the potential benefits of excessive rainfall index insurance in the literature, but our results suggest this type of insurance may be viable for sugar cane producers, and other crops, in parts of the world where extreme rainfall poses a risk to the financial sustainability of production. Keywords: Weather index, Flooding, Extreme precipitation, Climate, Crop loss, Crop variability

This research analyses the observed relationship between eastern and central Pacific El Niño Southern Oscillation (ENSO) events and Australian monsoon rainfall (AUMR) on a decadal timescale during the December to March monsoon months. To assess the decadal influence of the different flavours of ENSO on the AUMR, we focus on the phases of the Interdecadal Pacific Oscillation (IPO) over the period 1920 to 2020. The AUMR is characterized by substantial decadal variability, which appears to be linked to the positive and negative phases of the IPO. During the past two historical negative IPO phases, significant correlations have been observed between central Pacific sea surface temperature (SST) anomalies and AUMR over both the northeast and northwest of Australia. This central Pacific SST-AUMR relationship has strengthened from the first negative IPO phase (mid-1940s to the mid-1970s) to the second (late 1990s to mid-2010s), while the eastern Pacific SST-AUMR influence has weakened. Composite rainfall anomalies over Australia reveal a different response of AUMR to central Pacific El Niño/La Niña and eastern Pacific La Niña events during positive IPO and negative IPO phases. This research clearly shows that ENSO's influence on AUMR is modulated by Pacific decadal variability, however this teleconnection, in itself, can change between similar decadal Pacific states. Going forward, as decadal prediction systems improve and become more mainstream, the IPO phase could be used as a potential source for decadal predictability of the tendency of AUMR.

International audience; Highlights:• A dynamic, process-based model is proposed for simulating robusta coffee growth.• The model quantifies interactions between solar radiation, temperature and water at a daily time step.• Independent climate and historical yields datasets are used for model evaluation.• Good agreements between predicted and reported yields at the provincial scale were found.• The model can be used readily within an integrated climate-robusta production forecasting system.Abstract: Reliable and timely prediction of robusta coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting robusta coffee yield at the regional scale in Vietnam. The model includes the key processes of coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese robusta coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial coffee yield data and climate station data for the 2001-2014 period, and field data collected during a 10-year (2008-2017) survey. Overall, good agreements were found between the observed and predicted coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t ha(-1), and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were

In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding flood event preparedness plans and mitigation measures. Introducing an initial framework of spatially probabilistic analysis of flood research, this study highlights an integrated statistical copula and satellite data-based approach to modelling the complex dependence structures between flood event characteristics, i.e., duration (D), volume (V) and peak (Q). The study uses Global daily satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (spatial resolution of ∼5 km) during 1981–2019 to derive a Standardized Antecedence Precipitation Index (SAPI) and its characteristics through a time-dependent reduction function for Myanmar. An advanced vine copula model was applied to model joint distributions between flood characteristics for each grid cell. The southwest (Rakhine, Bago, Yangon, and Ayeyarwady) and south (Kayin, Mon, and Tanintharyi) regions are found to be at high risk, with a probability of up to 40% of flood occurrence in August and September in the south (Kayin, Mon, and Tanintharyi) and southwest regions (Rakhine, Bago, Yangon, and Ayeyarwady). The results indicate a strong correlation among flood characteristics; however, their mean and standard deviation are spatially different. The findings reveal significant differences in the spatial patterns of the joint exceedance probability of flood event characteristics in different combined scenarios. The probability that duration, volume, and peak concurrently exceed 50th-quantile (median) values are about 60–70% in the regions along the administrative borders of Chin, Sagaing, Mandalay, Shan, Nay Pyi Taw, and Keyan. In the worst case and highest risk areas, the probability that duration, volume, and peak exceed the extreme values, i.e., the 90th-quantile, about 10–15% in the southwest of Sagaing, southeast of Chin, Nay Pyi Taw, Mon and areas around these states and up to 30% in the southeast of Dekkhinathiri township (Nay Pyi Taw). The proposed approach could improve the evaluation of exceedance probabilities used for flood early warning and risk assessment and management. The proposed framework is also applicable at larger scales (e.g., regions, continents and globally) and in different hydrological design events and for risk assessments (e.g., insurance).

The development of the Australian Community Climate and Earth-System Simulator-Seasonal prediction system version 1 (ACCESS-S1) signifies a major step towards addressing predictive limitations in multi-week to seasonal forecasting throughout Australia. It is anticipated that moving to ACCESS-S1 will provide improved skill in rainfall prediction during the dry to wet season transition period across tropical northern Australia. This is an important time for northern Australian livestock producers in terms of the decisions they make around pasture and livestock management. This study quantifies the hindcast skill of ACCESS-S1 for the northern rainfall onset (NRO), defined as the date when 50 mm of precipitation has accumulated at a given location from the 1st of September, heralding the shift towards greener pastures. We evaluate the raw model hindcasts, and compare them to hindcasts corrected for mean biases and those calibrated against observations. It is found that the raw ACCESS-S1 hindcasts broadly replicate the observed median NRO over the period 1990–2012, despite a ten- dency for earlier than observed onsets. In terms of forecasting the interannual variability of the NRO, the ca- librated hindcasts show the greatest skill, with the largest improvements over a climatological forecast in their probabilistic forecasts of an earlier or later than usual onset, with a large portion of northern Australian showing more than 10% improvement. With real-time NRO forecasts now generated by ACCESS-S1, it is expected that the calibrated predictions will help northern Australian graziers make better informed decisions around livestock management prior to the wet season.

The development of the Australian Community Climate Earth-System Simulator-Seasonal version 1 (ACCESS-S1) prediction system signifies a major step in addressing predictive limitations in multi-week to seasonal forecasting. It is anticipated that ACCESS-S1 will provide greater skill in its prediction of the wet season onset and intensity, which are crucial to the viability of cattle grazing across northern Australia. We evaluate the hindcast skill of the ACCESS-S1 for the northern rainfall onset, defined as the date when 50 mm of precipitation has accumulated at a given location from the 1st of September, heralding the start of the seasonal dry-to-wet transition over northern Australia. We show that the raw ACCESS-S1 hindcasts, regridded to a 5 km observed grid, capture the broad-scale features of the median onset, including an early October onset over the western Top End and southeast Queensland. However, the hindcasts fail to capture the later December onsets over central Australia. The greatest improvement in onset skill comes from first calibrating the hindcasts using observations, which outperform the raw model and bias corrected hindcasts over central Australia and the far west in the Pilbara-Gascoyne basin. Based on its simulation of realistic northern rainfall onset dates and variability alone, ACCESS-S1’s prediction performance can be considered an improvement over the older predictive system. As the real-time onset forecasts have were issued using ACCESS-S1 in July 2019, it is expected that the calibrated predictions will help improve the resilience of cattle producers and graziers to drought across northern Australia.

International audience; AbstractClimate strongly influences agricultural profitability. Climate risks on agriculture can be managed by moving production to other areas, but this may be costly and reduce profitability if climatic conditions are dynamic. Here, we test whether integrating spatial diversification and climate information could minimize climate risk, while not sacrificing profitability. We use 27 years of farm business profit and climate data (1991–2018) from four of Australia’s most climatically diverse regions where grazing underpins socioeconomic activity. We show that spatial diversification coupled with seasonal climate information from El Niño-Southern Oscillation (ENSO) provides better estimates of optimized risk-profit tradeoffs in different ENSO years compared to estimates without climate information. Conditional Value-at-Risk (CVaR) (a measure of financial risk) is lower when climate information is used in El Niño (1.23 $/ha), La Niña (1.19 $/ha), and Neutral years (1.22 $/ha), compared to when no climate information (1.57 $/ha) is used. When targeting high profits, CVaR is reduced by 15, 86, and 22% in El Niño, La Niña, and Neutral years, respectively, compared to a 5% reduction when no climate information is used. When aiming to minimize CVaR in drought (El Niño), profit is higher using climate-informed spatial diversification (expected profits of 2.71 $/ha), relative to when it is not (expected profits of 2.30 $/ha). Climate-informed spatial diversification also provides options to graziers to achieve much higher gains (expected profits of up to ~ 4.13 $/ha) under La Niña versus 2.62 $/ha when no climate information is used. Here, we show for the first time that seasonal climate information coupled with spatial diversification can provide strategies to help graziers reduce risk and increase profitability. Our approach is applicable to other parts of the world and could be used to decrease climate risk and increase profitability for other agricultural sectors exposed to variable climatic conditions.

Frost, during reproductive developmental stages, especially post head emergence frost (PHEF), can result in catastrophic yield loss for wheat producers. Breeding for improved PHEF tolerance may allow greater yield to be achieved, by (i) reducing direct frost damage and (ii) facilitating earlier crop sowing to reduce the risk of late-season drought and/or heat stress. This paper provides an economic feasibility analysis of breeding options for PHEF tolerant wheat varieties. It compares the economic benefit to growers with the cost of a wheat breeding program aimed at developing PHEF tolerant varieties. The APSIM wheat model, with a frost-impact and a phenology gene-based module, was employed to simulate direct and indirect yield benefits for various levels of improved frost tolerance. The economic model considers optimal profit, based on sowing date and nitrogen use, rather than achieving maximum yield. The total estimated fixed cost of breeding program was AUD 1293 million, including large scale seed production to meet seed demand, with AUD 1.2 million year −1 to run breeding program after advanced development and large scale field experiments. The results reveal that PHEF tolerant varieties would lead to a significant increase in economic benefits through reduction in direct damage and an increase in yield through early sowing. The economic benefits to growers of up to AUD 4841 million could be realised from growing PHEF tolerant lines if useful genetic variation can be found. Sensitivity analyses indicated that the benefits are particularly sensitive to increases in fixed costs, seed replacement, discount rate, and to delays in variety release. However, the investment still remains viable for most tested scenarios. Based on comparative economic benefits, if breeders were able to develop PHEF tolerant varieties that could withstand cold temperatures −4 °C below the current damage threshold, there is very little further economic value of breeding total frost tolerant varieties.

Central America is one of the regions most exposed to climate change (Giorgi 2006). A narrow isthmus between the Pacific Ocean and the Caribbean Sea, it is strongly affected by droughts, hurricanes and the El Niño-southern oscillation (ENSO) phenomena (CEPAL 2011). As a result, three countries in the region rank in the top 10 of the Global Climate Risk Index (Kreft and Eckstien 2013) based on the impacts of extreme weather events between 1993 and 2012.Much of the regional economy is based on agriculture. In Guatemala, Honduras and Nicaragua, more than two thirds of the population depends on agriculture. This agricultural base is often intimately tied to ecosystems, especially in diverse farming systems of smallholders. But it is increasingly threatened by climate variability and change (Bouroncle et al. 2016; Baca et al. 2014), which are inducing changes in areas suitable for crops and leading to high yield variability. Storms, floods and droughts have had the greatest impacts on agriculture in Central America over the last century (Guha-Sapir et al. 2014). This special issue addresses the adaptation challenges facing smallholders, ecosystems and ecosystem services in the region. In this introduction, we review the literature on regional climate and its drivers, climate change projections, impacts on agriculture and ecosystems, and information management for adaptation in the region. Short descriptions of the special issue contributions are provided throughout the text. Universidad de Costa Rica/[805-B6-143]/UCR/Costa Rica Universidad de Costa Rica/[805-A9-532]/UCR/Costa Rica Universidad de Costa Rica/[805-B4-227]/UCR/Costa Rica Universidad de Costa Rica/[805-B0-065]/UCR/Costa Rica Universidad de Costa Rica/[805-B0-810]/UCR/Costa Rica Universidad de Costa Rica/[805-A4-906]/UCR/Costa Rica Universidad de Costa Rica/[805-B6-143]/UCR/Costa Rica Universidad de Costa Rica/[805-A9-532]/UCR/Costa Rica Universidad de Costa Rica/[805-B4-227]/UCR/Costa Rica Universidad de Costa Rica/[805-B0-065]/UCR/Costa Rica Universidad de Costa Rica/[805-B0-810]/UCR/Costa Rica Universidad de Costa Rica/[805-A4-906]/UCR/Costa Rica UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI) UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Física UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencias del Mar y Limnología (CIMAR)

Robusta coffee is critically important for the economy and farmers of Vietnam, but also requires substantial irrigation leading to dwindling water resources. Developing clear recommendations for improved irrigation water management, while maintaining or increasing yield is therefore a key knowledge need for the coffee industry. We analyse 10-cropping-year data (2008/2009–2017/2018) of 558 farms across four major coffee-producing provinces in Vietnam’s Central Highlands using CROPWAT and hierarchical Bayesian modelling to (1) identify irrigation requirements under different climatic conditions, and (2) investigate the potential for improved irrigation management strategies. In average rainfall years the majority of farmers in Dak Nong and Lam Dong supplied an equivalent of 455–909 L tree−1 (assuming 1100 plants ha−1) with corresponding average yields ranging from 2149 to 3177 kg ha−1. In Dak Lak and Gia Lai the predominant range was equivalent to 1364–1818 L tree-1 (corresponding average yields: 2190 to 3203 kg ha−1). In dry years more water was supplied through irrigation at various levels depending on the province: varying between 1364–1818 L tree−1 in Dak Lak and Gia Lai, and 909–1364 L tree−1 in Dak Nong and Lam Dong. Our study also shows that irrigation water can be reduced by 273–536 L tree−1 (300–590 m3 ha−1) annually from the current levels in average rainfall years while still achieving average yield levels greater than 3000 kg ha−1. In dry years reductions of 27–218 L tree−1 (30–240 m3 ha−1) are possible. With adequate management of the key crop practices affecting coffee yields, substantial water savings at the provincial scale could be achieved. Thus, our findings could serve as a basis for province-specific irrigation water management in robusta coffee farms that will not only reduce overall water use, but also potentially maintain satisfactory yield levels.

Purpose: This paper documents and evaluates collaborative learning processes aimed at developing farmer’s knowledge, skills and aspirations to use seasonal climate forecasting (SCF). Methodology: Thirteen workshops conducted in 2012 engaged over 200 stakeholders across Australian sugar production regions. Workshop design promoted participant interaction, stimulated discussion, collected farmer experience of regional climate variability, improved understanding of climate drivers and increased participant skill in interpreting SCF linked to management decisions. Post-workshop surveys collected quantitative and qualitative data for statistical analysis and manual thematic coding. Findings: Over 68% of participants identified improved decision-making and risk reduction as the main benefits of using SCF products. High median self-evaluation ratings for gains in skills, knowledge and understanding of climate forecasts and perceived benefits in using climate forecasts in on-farm decision-making were foun...

To enable society to better manage the risks and opportunities arising from changes in climate, engagement between the users and the providers of climate information needs to be much more effective and should better link climate information with decision-making.

Recent years have seen a growing recognition of and attention to strengthening community flood resilience as a key leverage for achieving sustainable flood risk management. The literature on the success of focusing on ex-post responses to flood events shows poor outcomes in mitigating flood risks across the board. Here, we employ systems thinking to conceptualise the dynamics underlying interactions among endogenous characteristics of the flood-prone community, and apply this, as a case study example, to the regional township of Roma in inland Queensland, Australia—a location that has experienced significant flooding in recent decades. Our results show that current flood mitigation policies are likely to be maladaptive due to unintended consequences that ultimately undermine the effectiveness of the interventions in the longer term. Based on our analysis, we conclude that integrated flood risk management, effected through well-targeted proactive investments—such as investment in infrastructure; technology advances; capacity improvements; shifts in systems, management practices and behaviour—and risk transfer (insurance) options, provide opportunities for reducing future relief and recovery costs and increasing fiscal stability and the long term well-being of communities in the face of increasing climate variability and flood risk under climate change. Conceptualisation and visualisation of the flood risk system, as exemplified in this paper, provides insights which might help to guide more cost-effective and sustainable flood risk mitigation investment in flood-prone communities.

Australia has the world׳s highest levels of year to year climatic variability with drought a naturally occurring component of this variability, but which may also occasionally persist for many years. Over the past 40 years climate science has provided a major contribution to improved understanding of the reasons for this high level of climatic variability with advances in seasonal forecasting research providing incentives for primary producers to adopt a more self-reliant approach to their farming operations, including drought preparedness. Over 20 major scientific publications pointed to aspects of the key climatic mechanisms – mainly associated with the El Niño phenomenon – that were now known to be responsible for drought events in Australia. Some of these publications also pointed to the means of forecasting such extreme climate events which implied the potential to prepare for drought events. Coincident enhancement of farming technologies over this period further enabled producers to create more drought resilient systems. Australian Governments have also provided many incentives to improve self-reliance and farm management and so assist preparedness for the poorer (drought) seasons and years when they occur. Government policy development over this period has been conducted with an awareness of all these factors and has therefore been able to provide changes in drought assistance through new policy endeavours that are probably unequalled in the world. This paper presents insights to the scientific, technological, and policy aspects of managing drought in Australia.

Flash drought is a term and concept that has gained increasing attention in the research literature and media since it was first coined in the United States in the early 2000s to describe a drought that has undergone rapid intensification. In Australia the term has recently been used in reference to the 2017/18 drought in eastern Australia. Due to its rapid intensification, the impacts of flash droughts will likely occur too quickly for many of the usual drought-coping mechanisms to be deployed. This study proposes the use of the evaporative stress index (ESI), the standardized anomaly of the actual evapotranspiration to potential evapotranspiration ratio, to identify flash droughts in Australia computed using daily outputs from the Bureau of Meteorology’s land surface water balance model AWRA-L. The case study of the January 2018 flash drought in eastern Australia is used to assess and demonstrate the suitability of the ESI. Results show that the ESI accurately highlighted the event and offered potential for flash drought pre-warning by a few weeks. In addition, the availability of long term high-resolution outputs from AWRA-L offers the ability to investigate multiple flash drought events in detail for greater understanding and to inform stakeholders.

Seasonal climate forecasts (SCFs) have the potential to improve productivity and profitability in agricultural industries, but are often underutilised due to insufficient evidence of the economic value of forecasts and uncertainty about their reliability. In this study we developed a bio-economic model of forecast use, explicitly incorporating forecast uncertainty. Using agricultural systems (ag-systems) production simulation software calibrated with case study information, we simulated pasture growth, herd dynamics and annual economic returns under different climatic conditions. We then employed a regret and value function approach to quantify the potential economic value of using SCFs (at both current and improved accuracy levels) in decision making for a grazing enterprise in north-eastern Queensland, Australia – a region subject to significant seasonal and intra-decadal climate variability. Applying an expected utility economic modelling approach, we show that skilled SCF systems can contribute considerable value to farm level decision making. At the current SCF skill of 62% (derived by correlating the El Niño Southern Oscillation (ENSO) signal and historical climate data) at Charters Towers, an average annual forecast value of AU$4420 (4.25%) was realised for the case study average annual net profit of AU$104000, while a perfect (no regret) forecast system could result in an increased return of AU$13475 per annum (13% of the case study average annual net profit). Continued improvements in the skill and reliability of SCFs is likely to both increase the value of SCFs to agriculture and drive wider uptake of climate forecasts in on-farm decision making. We also anticipate that an integrated framework, such as that developed in this study, may provide a pathway for better communication with end users to support improved understanding and use of forecasts in agricultural decision making and enhanced sustainability of agricultural enterprises.

Isolating the causes of extreme variations or changes in the hydroclimate is difficult due to the complexities of the driving mechanisms, but it is crucial for effective natural resource management. In Australia's Murray-Darling Basin (MDB), ocean-atmosphere processes causing hydroclimatic variations occur on time scales from days to centuries, all are important, and none are likely to act in isolation. Instead, interactions between all hydroclimatic drivers, on multiple time scales, are likely to have caused the variations observed in MDB instrumental records. A simplified framework is presented to assist natural resource managers in identifying the potential causes of hydroclimatic anomalies. The framework condenses an event into its fundamental elements, including its spatial and temporal signal and small-scale evolution. The climatic processes that are potentially responsible are then examined to determine possible causes. The framework was applied to a period of prolonged and severe dry conditions occurring in the southern MDB from 1997–2010, providing insights into possible causal mechanisms that are consistent with recent studies. The framework also assists in identifying uncertainties and gaps in our understanding that need to be addressed.

An innovative approach to aiding farmer decisions that involve seasonal climate risk has been trialed with farmers in Andhra Pradesh, India. A key concept has been to copy concepts of 'discussion-support' approaches, rather than only relying on computerized decision-support approaches. Use of 2d Life/eLearning distance education methods have been trialed with these farmers and advisers with some success. However, it is suggested that vital aspects not necessarily related to core climate and crop science issues, such as farmers' dress and informal interactions within a discussion environment must be appropriately captured in any synthetic video discussion-support if this approach is to gain widespread uptake.

As a response to extreme water shortages in southeast Queensland, Australia, brought about by reduced rainfall and increasing population, the Queensland government decided to explore the potential for cloud seeding to enhance rainfall. The Queensland Cloud Seeding Research Program (QCSRP) was conducted in the southeast Queensland region near Brisbane during the 2008/09 wet seasons. In addition to conducting an initial exploratory, randomized (statistical) cloud seeding study, multiparameter radar measurements and in situ aircraft microphysical data were collected. This comprehensive set of observational platforms was designed to improve the physical understanding of the effects of both ambient aerosols and seeding material on precipitation formation in southeast Queensland clouds. This focus on gaining physical understanding, along with the unique combination of modern observational platforms utilized in the program, set it apart from previous cloud seeding research programs. The overarching goals of the ...