Abstract: Flood-attributed damages to infrastructure and public safety are expected to escalate in the future due to climate change, land use change, and associated hydrologic changes. In recent years, the reliability of flood forecasts has increased due to the availability of meteorological and hydrological data and advancements in flood prediction science. However, there is limited effort to apply emerging advanced hydrological models for flood prediction in poorly gauged watersheds.

The current global road network expansion scenario poses a conflict of interest between Sustainable Development Goals of human well-being and biosphere, which could be mitigated through strengthening of the Environmental Impact Assessment (EIA) process. Here, we propose the integration in EIAs of a method focusing on landscape-level connectivity for wildlife, based on easily accessible satellite imagery and basic species data that need not be site-specific.

The Amazon Basin features a vast network of healthy, free-flowing rivers, which provides habitat for the most biodiverse freshwater fauna of any basin globally. However, existing and future infrastructure developments, including dams, threaten its integrity by diminishing river connectivity, altering flows, or changing sediment regimes, which can impact freshwater species.

The field monitoring activities within the partner network play a crucial role in understanding the territory, production systems, and socio-economic conditions in communities. This knowledge enables effective support for partners, facilitating better interventions. The close relationship between technicians and producers, backed by a supportive team, fosters shared interventions, considering both local knowledge and technical expertise. Identifying production issues and leveraging community experiences with crops, yields, pests, and diseases further enhances collaboration.

Sea-level rise and resulting salinity inundation are making many coastal areas increasingly unfavorable for rice production. This paper examines intensive and extensive adaptations to rice production in salinity-prone areas of the Mekong River Delta (MKD) of Vietnam using a two-year panel dataset of 788 rice-growing households.