Drainage basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The problem is compounded by the impacts of human-induced ch... Les bassins versants de drainage de nombreuses régions du monde sont peu ou pas du tout jaugés, et dans certains cas les réseaux de mesures existants sont en déclin. Le problème est compliqué par l...

Risk management has reduced vulnerability to floods and droughts globally^1,2, yet their impacts are still increasing³. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data^4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change³.

A comprehensive assessment of water security can incorporate several water‐related concepts, while accounting for Blue and Green Water (BW and GW) types defined in accordance with the hydrological processes involved. Here we demonstrate how a quantitative analysis of provision probability and use of BW and GW can be conducted, so as to provide indicators of water scarcity and vulnerability at the basin level. To illustrate the approach, we use the Soil and Water Assessment Tool (SWAT) to model the hydrology of an agricultural basin (291 km²) within the Cantareira Water Supply System in Brazil. To provide a more comprehensive basis for decision making, we analyze the BW and GW‐Footprint components against probabilistic levels (50th and 30th percentile) of freshwater availability for human activities, during a 23 year period. Several contrasting situations of BW provision are distinguished, using different hydrological‐based methodologies for specifying monthly Environmental Flow Requirements (EFRs), and the risk of natural EFR violation is evaluated by use of a freshwater provision index. Our results reveal clear spatial and temporal patterns of water scarcity and vulnerability levels within the basin. Taking into account conservation targets for the basin, it appears that the more restrictive EFR methods are more appropriate than the method currently employed at the study basin. The blue/green water‐based accounting framework developed here provides a useful integration of hydrologic, ecosystem and human needs information on a monthly basis, thereby improving our understanding of how and where water‐related threats to human and aquatic ecosystem security can arise.

With the emergence of big data and new data sources, a challenge posed to today's organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN+, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organization's decision-making. An extended model-based framework connects decision-making to big data sources.A better understanding of decision-making is achieved with the framework.A modeling process is outlined for systematically using the framework in practice.Decision-making can be improved through the use of standard models and notations.Lessons were learned from a case study on a Brazilian disaster early-warning center.