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The upstream construction of hydropower dams may drastically intensify climate change impacts due to changing the natural river flood-drought cycle and reducing the amount of water that flows into the lower Mekong Delta river, leading to hydrological and environmental health impacts. However, until now the influence of drought on residents’ health in the lower MDR, where river drought is highly sensitive to recently built hydropower plants, has not been examined. The objectives of this study are, for the first time, to detect the health impacts of river drought on residents and to evaluate the contribution of hydropower dams to the impacts of drought on health in the lower Mekong Delta Region (MDR). We applied the multi-step approaches of a Detection and Attribution study. First, we detected the effects of the river drought on the risk of hospitalization using a Multivariable Fractional Polynomials algorithm (MFP). Second, we linked the long-term changes of the river water level (RWL) to the operation of the first hydropower dam in the upper MDR using the interrupted time-series model (ITS). Finally, we quantified the hospitalizations and related economic loss attributed to the river drought. The results show that the percentage changes in risk of all-cause, respiratory, and renal hospitalizations attributed to the river drought were 2%, 2%, and 7%. There were significant reductions in average level and trend of the RWL during the post-1995 period, when the first hydropower dam began operation in the upper MDR, even though the cumulative rainfall in the MDR had not changed. The all-cause hospitalizations attributed to the river drought were 1134 cases during the period 1995–2014, which resulted in total additional cost at two provincial hospitals of US $360,385. This current study demonstrates the link between hydropower dams, river drought, and health impacts. As the MDR is highly vulnerable to climate change, these findings about the devastating impacts of hydropower dams and environmental change have important implications for the lives of downstream residents.

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.

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.

Skillful probabilistic seasonal rainfall forecasts play a vital role in supporting water resource users, developing agricultural risk-management plans, and improving decision-making processes. This chapter applies a novel statistical copula-based approach to develop a probabilistic seasonal rainfall forecast model using multiple large-scale oceanic and atmospheric climate indices. Here, a d -vine copula is used to forecast the seasonal cumulative rainfall in 16 weather stations across the Australia's Wheatbelt. These stations span different climate conditions recording historical data for the period 1889–2012. The seasonal rainfalls are forecast in different quantile levels using different climate predictor data sets. The corrected Akaike information criterion (AIC)–conditional log-likelihood is then used to screen the most influential covariates to be additively incorporated into the multivariate probabilistic forecast model, resulting in a parsimonious predictive model. The mutually inclusive correlations between El Nino–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) indices and seasonal rainfall are found to be statistically significant. Therefore, using the climate information, skillful rainfall forecasts can be made three to 6 months ahead. The d -vine copula model is found to outperform the traditional quantile regression methods in forecasting rainfall in the median and the upper levels. The information from lagged, concurrent, and combined climate indices is therefore demonstrated to be a potentially useful predictor for forecasting seasonal rainfall in Australia's Wheatbelt region.

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.

Coffea canephora (robusta coffee) is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers.

Rice is one of the world’s most dominant staple foods, and hence rice farming plays a vital role in a nation’s economy and food security. To examine the applicability of synthetic aperture radar (SAR) data for large areas, we propose an approach to determine rice age, date of planting (dop), and date of harvest (doh) using a time series of Sentinel-1 C-band in the entire Mekong Delta, Vietnam. The effect of the incidence angle of Sentinel-1 data on the backscatter pattern of paddy fields was reduced using the incidence angle normalization approach with an empirical model developed in this study. The time series was processed further to reduce noise with fast Fourier transform and smoothing filter. To evaluate and improve the accuracy of SAR data processing results, the classification outcomes were verified with field survey data through statistical metrics. The findings indicate that the Sentinel-1 images are particularly appropriate for rice age monitoring with R2 = 0.92 and root-mean-square error (RMSE) = 7.3 days (n = 241) in comparison to in situ data. The proposed algorithm for estimating dop and doh also shows promising results with R2 = 0.92 and RMSE = 6.2 days (n = 153) and R2 = 0.70 and RMSE = 5.7 days (n = 88), respectively. The results have indicated the ability of using Sentinel-1 data to extract growth parameters involving rice age, planting and harvest dates. Information about rice age corresponding to the growth stages of rice fields is important for agricultural management and support the procurement and management of agricultural markets, limiting the negative effects on food security. The results showed that multitemporal Sentinel-1 data can be used to monitor the status of rice growth. Such monitoring system can assist many countries, especially in Asia, for managing agricultural land to ensure productivity.