Abstract:Flooding is one of the most frequent natural disasters that have significant impact on communities in terms of loss of life, direct and indirect economic losses, and disruption of daily life. Decision makers often consult flood data inventories to make more informed decisions on the development of flood mitigation plans to protect flood prone communities. A comprehensive inventory that covers multiple aspects of a flood event is critical to identify vulnerable regions, historical trends, and mitigate possible … Show more
“…The mentioned activities and reasons cause to be under the pressure of high-water consumption in the basin, and reveal the basin’s importance. In the basin, flood frequency is high; fortunately, mortality rates are low (SYGM 2018 ; Haltas et al 2021 ). Fig.…”
The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979–2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The standard triangular diagram, quite new in the literature, was also modified and used for graphical evaluation. The results of the study revealed that near grids were more effective on precipitation than single or far grids, and 1.50° × 1.50° resolution showed similar performance to 0.25° × 0.25° resolution. When the polynomial multivariate adaptive regression splines model, which performed slightly higher than ANN, tended to capture skewness and standard deviation values of precipitations and to hit wet/dry occurrence than the other models, all models were quite well able to predict the mean value of precipitations. Therefore, RBSDMs can be used in different basins instead of black-box models. RBSDMs can also be established for mean precipitation values without dry/wet classification in the basin. A certain success was observed in the models; however, it was justified that bias correction was required to capture extreme values in the basin.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00477-022-02345-5.
“…The mentioned activities and reasons cause to be under the pressure of high-water consumption in the basin, and reveal the basin’s importance. In the basin, flood frequency is high; fortunately, mortality rates are low (SYGM 2018 ; Haltas et al 2021 ). Fig.…”
The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979–2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The standard triangular diagram, quite new in the literature, was also modified and used for graphical evaluation. The results of the study revealed that near grids were more effective on precipitation than single or far grids, and 1.50° × 1.50° resolution showed similar performance to 0.25° × 0.25° resolution. When the polynomial multivariate adaptive regression splines model, which performed slightly higher than ANN, tended to capture skewness and standard deviation values of precipitations and to hit wet/dry occurrence than the other models, all models were quite well able to predict the mean value of precipitations. Therefore, RBSDMs can be used in different basins instead of black-box models. RBSDMs can also be established for mean precipitation values without dry/wet classification in the basin. A certain success was observed in the models; however, it was justified that bias correction was required to capture extreme values in the basin.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00477-022-02345-5.
“…While some flash flood specific datasets exist (such as HYDRATE and HANZE in Europe, FLASH in USA), spatial and temporal coverage is limited [45,46]. In databases with records of floods, it is uncommon for criteria on event inclusion to require a declaration of flood subtype [47][48][49]. Even in databases that include subtypes, protocols are unlikely to exist for disaggregating, tagging [50,51] or for attributing magnitude of direct and indirect impacts [52].…”
Section: Flash Flood As a Disaster Subtypementioning
The analysis of historical disaster events is a critical step towards understanding current risk levels and changes in disaster risk over time. Disaster databases are potentially useful tools for exploring trends, however, criteria for inclusion of events and for associated descriptive characteristics is not standardized. For example, some databases include only primary disaster types, such as ‘flood’, while others include subtypes, such as ‘coastal flood’ and ‘flash flood’. Here we outline a method to identify candidate events for assignment of a specific disaster subtype—namely, ‘flash floods’—from the corresponding primary disaster type—namely, ‘flood’. Geophysical data, including variables derived from remote sensing, are integrated to develop an enhanced flash flood confidence index, consisting of both a flash flood confidence index based on text mining of disaster reports and a flash flood susceptibility index from remote sensing derived geophysical data. This method was applied to a historical flood event dataset covering Ecuador. Results indicate the potential value of disaggregating events labeled as a primary disaster type into events of a particular subtype. The outputs are potentially useful for disaster risk reduction and vulnerability assessment if appropriately evaluated for fitness of use.
“…Due to global warming and urbanization, torrential rains and floods events have become more frequent [1]. Among the recorded natural hazards globally, the frequency of flooding events accounts for nearly half of all major events [2].…”
Frequent natural hazards such as flooding and the devastating consequences of severe events make the humanitarian supply chain particularly important in alleviating the suffering of those communities impacted by such events. However, the ambiguity of information and the different goals of stakeholders demand that the humanitarian supply chain must be resilient. This research adopts the use of literature review and expert opinions to identify the indicators that affect the resilience of the humanitarian supply chain using the flood event in Hechuan District, China in 2020 as an example. Based on the combination of fuzzy Decision-making Trial and Evaluation Laboratory and Analytic Network Process (fuzzy-DEMATEL-ANP), the interrelationships between the indicators and the weights of each indicator are calculated. The research results indicate that decision-makers in the humanitarian supply chain should vigorously coordinate the cooperation among stakeholders, ensure the effective transmission of information, and formulate forward-looking strategic plans. At the same time, these key decision makers should also be aware of the need to adjust their strategies at different stages of the flooding event in order to achieve a flexible humanitarian supply chain that responds to the varying demands over the course of a flooding event. The results of this study will help professionals involved in humanitarian supply chains to develop strategies and plans to become more resilient thus helping to reduce losses from natural hazards such as floods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
