Background: Globally, rice systems are a major source of atmospheric CH₄ and for major rice‐producing countries, such as Vietnam, CH₄ as well as N₂O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national‐scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.Aims: Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH₄ and N₂O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N₂O to the total non‐CO₂ (CH₄+N₂O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management .Methods: The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH₄ and N₂O emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH₄ and N₂O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions.Results: Our study shows that CH₄ and N₂O fluxes from rice systems in Vietnam are highly seasonal, with national CH₄ and N₂O emissions totaling to about 2600 Gg CH₄ year^–1 and 42 Gg N₂O year^–1, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield‐scaled emissions varied largely in a range of 300–3000 kg CO₂‐eq Mg^–1 year^–1, with CH₄ emissions during the rice season(s) dominating (>82%) the total annual non‐CO₂ GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH₄ and N₂O emission inventory.Conclusions: Our study shows that Tier 3 approaches, that is, process‐oriented model approaches combined with GIS databases, for estimating national‐scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows to identify regions with elevated emissions, thereby accounting not only for CH₄, but as well for N₂O emissions. However, our study also shows that specifically better information on land management is required to narrowing uncertainties.

The Lower Mekong Basin (LMB) is a subsidiary region of the Mekong River, with approximately 50 million people directly dependent on the river for livelihood and economic activities. However, communities in the region are increasingly exposed to multiple hazards that have significant direct and indirect impacts on their livelihoods. To implement efficient risk management strategies, it is important to understand the interlinkages between the different dimensions and factors that influence livelihood security and resilience in such communities. Through a literature review and expert workshop, this paper studies the multi-hazard scenario and impacts in the LMB region and the interlinkages between livelihoods and resilience in the LMB communities. The paper consolidates these findings and proposes a localized assessment framework that can be used by stakeholders in decision-making process. Floods and droughts were identified as primary natural hazards, while a multi-hazard assessment highlighted a wide spatial variation in the hazard levels across the region. The primary impacts of such hazards are on the agricultural communities dependent on the basin’s ecosystem and natural resources for their livelihoods. A holistic framework has been proposed to measure the multi-hazard livelihood security and resilience in LMB communities that can be used by government authorities and development partners in planning and implementing mitigation and preparedness activities to manage and reducing the risk of hazards.

The sustainability of social–ecological systems within river deltas globally is in question as rapid development and environmental change trigger “negative” or “positive” tipping points depending on actors’ perspectives, e.g. regime shift from abundant sediment deposition to sediment shortage, agricultural sustainability to agricultural collapse or shift from rural to urban land use. Using a systematic review of the literature, we show how cascading effects across anthropogenic, ecological, and geophysical processes have triggered numerous tipping points in the governance, hydrological, and land-use management of the world’s river deltas. Crossing tipping points had both positive and negative effects that generally enhanced economic development to the detriment of the environment. Assessment of deltas that featured prominently in the review revealed how outcomes of tipping points can inform the long-term trajectory of deltas towards sustainability or collapse. Management of key drivers at the delta scale can trigger positive tipping points to place social–ecological systems on a pathway towards sustainable development.

Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model to investigate the responses of soybeans to the climate. We conducted two experiments on crop growth in the Summer–Autumn season of 2020, and Winter–Spring 2021 in the Hoa Binh Commune, in the Mekong Delta, Vietnam, which is an area that is vulnerable to climate change impacts, to obtain data for our model input and assessment. The assessment was concerned with the effects of climate variables (temperature and CO₂) on soybean biomass and yield. The results indicated that the SIMPLE model performed well in simulating soybean yields, with an RRMSE of 9–10% overall. The drought stress results showed a negative impact on the growth and development of soybeans, although drought stress due to less rainfall seemed more serious in Spring–Winter 2021 than in Summer–Autumn 2020. This study figured out the trend that higher temperatures can shorten biomass development and lead to yield reduction. In addition, soybeans grown under high CO₂ concentrations of 600 ppm gave a higher biomass and a greater yield than in the case with 350 ppm. In conclusion, climate variance can affect the soybean yield, which can be well investigated using the SIMPLE model.