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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).

Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.

This article employs a global computable general equilibrium economic model (GTAP-E-PowerS) to examine the impact on the world economy if households in every country self-supply power to meet 30–100% of residential demand, with subsequent monetary savings diverted to consuming more food. Results show the power generation sector reduces output levels by 14%–42% across various countries if households 100% self-supply. Coal mining sectors are adversely affected in numerous countries with contractions of 9%–28% ($6,086-$18,935 million) in the United States and 4%–13% ($2,505–$8,143 million) in Australia. Improved outcomes for the world environment are found with reductions of CO2e emission levels of 2.24%–7.38% (or 924–3,042 MtCO2 equivalent). The agriculture and food-processing sectors expand significantly in many countries but also cause major increases in land prices, particularly in land-scarce countries in Middle East, Europe, Japan, and Taiwan. Results also show the security of food and energy supply are improved along with environmental gains from lower emission levels. However, the energy sector is adversely affected and those countries with a heavy reliance on fossil fuel extraction and mining activities experience significant reductions in real GDP.

Agricultural climate index-based-insurance (IBI) compensates farmers for losses from adverse climatic conditions. Using a systemic review, we show that research related to agricultural climate index-based-insurance efficacy and application is lacking in many climate and food security vulnerable countries. We concluded that there are countries with high climate and food insecurity risk based on several climate and food security indicators that lack agricultural climate index-based-insurance research that could help farmers in these countries. Research to date has also largely focused on cereal crops and drought, which only represent a fraction of the crops and climate risks that agricultural climate index-based-insurance could be beneficial in managing. Our paper provides evidence-based recommendations for countries that should be focused on to redress the current disparities in agricultural climate index-based-insurance research.

Vine copulas are employed to explore the influence of multi-synoptic-scale climate drivers – El Nino Southern Oscillation (ENSO) and Inter-decadal Pacific Oscillation (IPO) Tripole Index (TPI) – on spring precipitation forecasting at Agro-ecological Zones (AEZs) of the Australia’s wheat belt. To forecast spring precipitation, significant seasonal lagged correlation of ENSO and TPI with precipitation anomalies in AEZs using data from Australian Water Availability Project (1900–2013) was established. Most of the AEZs exhibit statistically significant dependence of precipitation and climate indices, except for the western AEZs. Bivariate and trivariate copula models were applied to capture single (ENSO) and dual predictor (ENSO & TPI) influence, respectively, on seasonal forecasting. To perform a comprehensive evaluation of the developed copula-statistical models, a total of ten one- and two-parameter bivariate copulas ranging from elliptical to Archimedean families were examined. Stronger upper tail dependence is visible in the bivariate model, suggesting that the influence of ENSO on precipitation forecasting during a La Nina event is more evident than during an El Nino event. In general, while the inclusion of TPI as a synoptic-scale driver into the models leads to a notable reduction in the mean simulated precipitation, it depicts a general improvement in the median values. The forecasting results showed that the trivariate forecasting model can yield a better accuracy than the bivariate model for the east and southeast AEZs. The trivariate forecasting model was found to improve the forecasting during the La Nina and negative TPI. This study ascertains the success of copula-statistical models for investigating the joint behaviour of seasonal precipitation modelled with multiple climate indices. The forecasting information and respective models have significant implications for water resources and crop health management including better ways to adapt and implement viable agricultural solutions in the face of climatic challenges in major agricultural hubs, such as Australia’s wheat belt.