Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities.
Flood forecasting in a transboundary river basin is challenging due to insufficient data sharing between countries in the upper and lower reaches of a basin. A solution is the use of satellite-observed rainfall and numerical weather prediction (NWP) for hydrological forecasting. We applied this method to the transboundary sparsely gauged Chenab River basin in Pakistan and India to reproduce the exceptionally high flood in 2014. We employed global NWPs by three weather centers to consider forecast uncertainty and downscaled them using the Weather Research and Forecasting (WRF) Model to prepare precipitation inputs. For hydrological simulations, we used a kinematic wave model, the Integrated Flood Analysis System (IFAS), for the upper-reach basin with high mountains and steep slopes, and we used a diffusive-wave rainfall–runoff–inundation (RRI) model for low altitudes and mild slopes. In our forecasting experiment, the precipitation by the global NWP was not able to predict flood peaks consistently. However, the downscaled rainfall by regional NWP showed good performance in predicting flood waves quantitatively, and a multimodel approach provided added value in issuing reliable warning as early as 6 days in advance. A confident streamflow forecasting near the border of the countries also led to reliable inundation forecasting by the RRI model in the lower-reach basin.
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