Local impacts on road networks and access to critical locations during extreme floods.
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ABSTRACT:
SUBMITTER: Loreti S
PROVIDER: S-EPMC8799679 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods.
| S-EPMC6520386 | biostudies-literature
Project description:Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
| S-EPMC10954541 | biostudies-literature
Project description:BackgroundThe ability of disaster response, preparedness, and mitigation efforts to assess the loss of physical accessibility to health facilities and to identify impacted populations is key in reducing the humanitarian consequences of disasters. Recent studies use either network- or raster-based approaches to measure accessibility in respect to travel time. Our analysis compares a raster- and a network- based approach that both build on open data with respect to their ability to assess the loss of accessibility due to a severe flood event. As our analysis uses open access data, the approach should be transferable to other flood-prone sites to support decision-makers in the preparation of disaster mitigation and preparedness plans.MethodsOur study is based on the flood events following Cyclone Idai in Mozambique in 2019 and uses both raster- and network-based approaches to compare accessibility to health sites under normal conditions to the aftermath of the cyclone to assess the loss of accessibility. Part of the assessment is a modified centrality indicator, which identifies the specific use of the road network for the population to reach health facilities.ResultsResults for the raster- and the network-based approaches differed by about 300,000 inhabitants (~ 800,000 to ~ 500,000) losing accessibility to healthcare sites. The discrepancy was related to the incomplete mapping of road networks and affected the network-based approach to a higher degree. The modified centrality indicator allowed us to identify road segments that were most likely to suffer from flooding and to highlight potential backup roads in disaster settings.ConclusionsThe different results obtained between the raster- and network-based methods indicate the importance of data quality assessments in addition to accessibility assessments as well as the importance of fostering mapping campaigns in large parts of the Global South. Data quality is therefore a key parameter when deciding which method is best suited for local conditions. Another important aspect is the required spatial resolution of the results. Identification of critical segments of the road network provides essential information to prepare for potential disasters.
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Project description:Here we assess the quality and in-season development of European wheat (Triticum spp.) yield forecasts during low, medium, and high-yielding years. 440 forecasts were evaluated for 75 wheat forecast years from 1993-2013 for 25 European Union (EU) Member States. By July, years with median yields were accurately forecast with errors below ~2%. Yield forecasts in years with low yields were overestimated by ~10%, while yield forecasts in high-yielding years were underestimated by ~8%. Four-fifths of the lowest yields had a drought or hot driver, a third a wet driver, while a quarter had both. Forecast accuracy of high-yielding years improved gradually during the season, and drought-driven yield reductions were anticipated with lead times of ~2 months. Single, contrasting successive in-season, as well as spatially distant dry and wet extreme synoptic weather systems affected multiple-countries in 2003, '06, '07, '11 and 12', leading to wheat losses up to 8.1 Mt (>40% of total EU loss). In these years, June forecasts (~ 1-month lead-time) underestimated these impacts by 10.4 to 78.4%. To cope with increasingly unprecedented impacts, near-real-time information fusion needs to underpin operational crop yield forecasting to benefit from improved crop modelling, more detailed and frequent earth observations, and faster computation.
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Project description:The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. To supplement this analysis, we have developed an interactive website with detailed information for every flood of record. We identify four primary categories of circulation patterns: tropical moisture exports, tropical cyclones, atmospheric lows or troughs, and melting snow. We find that large flood events are generally caused by tropical moisture exports (tropical cyclones) in the western and central (eastern) United States. We identify regions where extreme floods regularly occur outside the normal flood season (e.g., the Sierra Nevada Mountains due to tropical moisture exports) and regions where multiple extreme flood events can occur within a single year (e.g., the Atlantic seaboard due to tropical cyclones and atmospheric lows or troughs). These results provide the first machine-learning based near-continental scale identification of atmospheric circulation patterns associated with extreme floods with valuable insights for flood risk management.
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Project description:A new perspective on the severity of aquatic toxicity impact of road salt was gained by a focused research effort directed at winter runoff periods. Dramatic impacts were observed on local, regional, and national scales. Locally, samples from 7 of 13 Milwaukee, Wisconsin area streams exhibited toxicity in Ceriodaphnia dubia and Pimephales promelas bioassays during road-salt runoff. Another Milwaukee stream was sampled from 1996 to 2008 with 72% of 37 samples exhibiting toxicity in chronic bioassays and 43% in acute bioassays. The maximum chloride concentration was 7730 mg/L. Regionally, in southeast Wisconsin, continuous specific conductance was monitored as a chloride surrogate in 11 watersheds with urban land use from 6.0 to 100%. Elevated specific conductance was observed between November and April at all sites, with continuing effects between May and October at sites with the highest specific conductance. Specific conductance was measured as high as 30,800 μS/cm (Cl = 11,200 mg/L). Chloride concentrations exceeded U.S. Environmental Protection Agency (USEPA) acute (860 mg/L) and chronic (230 mg/L) water-quality criteria at 55 and 100% of monitored sites, respectively. Nationally, U.S. Geological Survey historical data were examined for 13 northern and 4 southern metropolitan areas. Chloride concentrations exceeded USEPA water-quality criteria at 55% (chronic) and 25% (acute) of the 168 monitoring locations in northern metropolitan areas from November to April. Only 16% (chronic) and 1% (acute) of sites exceeded criteria from May to October. At southern sites, very few samples exceeded chronic water-quality criteria, and no samples exceeded acute criteria.
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Project description:The Intergovernmental Panel on Climate Change Fifth Assessment Report reveals vast evidence of increasing climate variability and a higher frequency and intensity of extreme events. Vulnerable regions to such disturbances have been widely studied in some areas, but considerably less is known about other vulnerable regions that are key to global climatic regulation, such as Amazonia. In terms of the human dimensions of climate impacts, rural and indigenous communities in developing regions are among the most vulnerable due to their limited economic capital and direct reliance on natural resources. The purpose of this research was to reveal local perceptions about the impacts of, and adaptations to, recent climatic and associated hydrological changes caused by extreme events in the Brazilian Amazon. We worked in severely impacted rural Amazonian riverine communities utilizing a qualitative case study approach that included interviews, observation while living in the community, and participatory mapping. Our results indicate that participants perceived that there has been an increased occurrence and intensity of hydroclimatic events in the last decade, especially extreme floods. Findings also show that the repeated occurrence of extreme floods resulted in severe impacts, including some that had never been experienced by the local communities, such as the complete loss of perennials. We found that a wide range of locally devised responses was implemented, despite incipient governmental support. Data also showed that responses have evolved significantly over time due to local experience with repeated extreme events. A variety of factors also affected participants' abilities to respond to hydroclimatic changes, notably information exchange among farmers and access to technological advancements.Supplementary informationThe online version contains supplementary material available at 10.1007/s10113-021-01857-0.
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Project description:Road networks are a classical stage for applications in network science and graph theory. Meanwhile, many combinatorial problems that arise in road networks are computationally intractable. Thus, an attractive way of tackling them is through efficient heuristics with provable performance guarantees, better known as approximation algorithms. This motivates the intersection of algorithm design with the aforementioned fields. Specifically, identifying measures that characterize graphs and exploiting them in the design of algorithms may yield practical heuristics with rigorous mathematical justification. Herein, we propose a new graph measure, namely the asymmetry factor ΔG of a directed graph G, with immediate algorithmic results via a symmetrization procedure and the black box use of approximation algorithms for symmetric graphs. Crucially, we analyze the asymmetry factors of the road networks from a diverse set of twelve cities, providing empirical evidence that road networks exhibit low bounded asymmetry and thereby justifying the practical use of algorithms for symmetric graphs.
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