A Bibliometric Analysis of Drought Indices, Risk, and Forecast as Components of Drought Early Warning Systems

In this study, we apply a bibliometric analysis to characterize publication data on droughts, mainly focusing on drought indices (DIs), drought risk (DR), and drought forecast (DF). Data on publications on these selected topics were obtained through the Scopus database, covering the period from 1963 to June 2021. The DI-related publications, based on meteorological, soil moisture, hydrological, remote sensing, and composite/modeled Dis, accounted for 57%, 8%, 4%, 29%, and 2% of the scientific sources, respectively. DI-related studies showed a notable increase since the 1990s, due perhaps to a higher number of major droughts during the last three decades. It was found that USA and China were the two leading countries in terms of publication count and academic influence on the DI, DR, and DF studies. A network analysis of the country of residence of co-authors on DR and DF research highlighted the top three countries, which were the USA, China, and the United Kingdom. The most productive journal for the DI studies was found to be the International Journal of Climatology, whereas Natural Hazards was identified as the first-ranked journal for the DR and DF studies. In relation to individual researchers, Singh VP from the USA was found to be the most prolific author, having the greatest academic influence on DF study, whereas Zhang Q from China was identified as the most productive author on DR study. This bibliometric analysis reveals that further research is needed on droughts in the areas of risk management, water management, and drought management. This review maps trends of previous research in drought science, covering several important aspects, such as drought indices, geographic regions, authors and their collaboration paths, and sub-topics of interest. This article is expected to serve as an index of the current state of knowledge on drought warning systems and as guidance for future research needs.

Torrent Monitoring and Early Warning Systems Development

The level of sustainability of a modern society is associated with the ability to manage unwanted stressors from the environment, regardless of origin. Torrential floods represent a hydrological hazard whose frequency and intensity have increased in recent years, mainly due to climate changes. In order to effectively manage the risks of torrents, it is necessary to apply early warning systems, since torrential floods are formed very quickly, especially on the watercourses of a small catchment area. The early warning system is part of a comprehensive torrential flood risk management system, seen as a technical entity for the collection, transformation, and rapid distribution of data. Modern early warning systems are the successors of rudimentary methods used in the past, and they are based on ICT and mobile applications developed in relation to the requirements of end users. The chapter presents an analysis of characteristic examples of the use. The main conclusion of the chapter indicates the need to implement early warning systems in national emergency management structures.

Brief communication: The role of geophysical imaging in local landslide early warning systems

We summarise the contribution of geophysical imaging to local landslide early warning systems (LoLEWS), highlighting how the design and monitoring components of LoLEWS benefit from the enhanced spatial and temporal resolutions of time-lapse geophysical imaging. In addition, we discuss how with appropriate laboratory-based petrophysical transforms, geophysical data can be crucial for future slope failure forecasting and modelling, linking other methods of remote sensing and intrusive monitoring across different scales. We conclude that in light of ever-increasing spatiotemporal resolutions of data acquisition, geophysical monitoring should be a more widely considered technology in the toolbox of methods available to stakeholders operating LoLEWS.

Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

The role of anticipatory humanitarian action to reduce disaster displacement

Displacement due to weather and climate-related events (disaster displacement), including the adverse effects of climate change, is one of the greatest humanitarian challenges of the 21st century. Even though the forecasting of extreme events and early warning systems has improved globally, less attention has been given to translating anticipatory humanitarian action into the disaster displacement context with the aim to minimise forced displacement from extreme weather events through pre-allocated funds for the readiness, pre-positioning and activation costs. In this analysis, we assess the opportunities and challenges associated with utilising forecast-based financing (FbF) to expand anticipatory and early humanitarian action, based on the structured judgements of experts. These multi-disciplinary experts agree that FbF can reduce displacement risks and address the humanitarian impacts of disaster displacement early, or before a hazard materialises. We propose four action steps along the stages of disaster displacement to provide practical intervention points for researchers and practitioners. Finally, we discuss the implications of our findings and outline next steps. By integrating cross-disciplinary expert judgement, this paper provides a much-needed pathway to transform humanitarian action to be more anticipatory and adaptable to change, and help minimize disaster displacement in climate change vulnerable regions.

Tipping Points in the Earth System

There is mounting evidence that some parts of the Earth system may be at risk of abrupt and potentially irreversible changes, driven by the cumulative impact of incremental global warming. Such a non-linear transition could be triggered if a critical threshold in global temperature – a “tipping point” – is crossed, when a small change could push a system into a completely new state, with potentially catastrophic impacts. In this technical briefing, we will first define tipping points and tipping elements, then explore several tipping elements in more detail and discuss the questions of abruptness, irreversibility, timescales and uncertainties for each of them. We also investigate the possibility of developing early warning systems for tipping points, and the risk of cascades of interacting tipping points, where one tipping point could trigger another.