The Egyptian Red Sea coast is periodically exposed to flash floods that cause severe human and economic losses. That is due to its hydro-geomorphological characteristics. Therefore, identifying flash ...flood hazards in these areas is critically important. This research uses an integrated approach of remote sensing data and GIS techniques to assess flash flood hazards based on morphometric measurements. There are 12 drainage basins in the study area. These basins differ in their morphometric characteristics, and their main streams range between the 4th and 7th order. The morphometric parameter analysis indicates that three wadis are highly prone to flooding, five wadis are classified as moderate hazard, and four wadis are rated under low probability of flooding. The study area has a probability offlooding, which could cause serious environmental hazards. To protect the region from flash flood hazards and the great benefit of rainwater, the study recommended detention, crossing, diversion, and/or storage of the accumulated rainwater by building a number of dams or culverts along the main streams of wadis to minimize the flooding flow.
This study examines the hydrologic and climatic conditions that precede major flood events on Lake Ontario, with the purpose of understanding the potential for seasonal forecasts to inform lake level ...management. Seven late spring/early summer flood events are identified since 1949, including the record‐breaking flood of 2017. The surface climate, atmospheric circulation, and antecedent lake levels for the preceding winter and spring seasons are examined. Results suggest that flood events are caused by different combinations of high, initial wintertime water levels across all of the Great Lakes, anomalously wet winters across the entire Great Lakes basin, and wet spring conditions, particularly in the eastern part of the basin. Wet winters that precede flood events are often associated with La Niña conditions, while wet springs are often associated with a westward shift of the North Atlantic Subtropical High. As the critical drawdown period for Lake Ontario occurs in the fall, before the onset of anomalous winter or spring/summer inputs, a generalized additive model was used to predict April–August maximum monthly average Lake Ontario water levels using November levels for all Great Lakes, a nonlinear response to the wintertime Niño 3.4 index, and scenarios of April–May overbasin precipitation. The Niño 3.4 index significantly improves lake level predictions, suggesting that an El Niño‐Southern Oscillation signal may be useful for lake level management. Future work needed to verify the use of El Niño‐Southern Oscillation for Lake Ontario flood forecasting and to link the North Atlantic Subtropical High to predictions of springtime Great Lakes climate is discussed.
Key Points
Lake Ontario floods are caused by high antecedent water levels in all of the Great Lakes and anomalous winter and spring precipitation
ENSO and the North Atlantic Subtropical High are drivers of winter/spring hydroclimatological extremes impacting Lake Ontario
ENSO can inform hydroclimatic forecasts at critical time scales for lake level management
Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as ...well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood‐prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data‐driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.
Key Points
The present study proposes a data‐driven methodology for extreme weather forecasts
The present methodology improves the probability of detection of extremes from 0.2 to 0.9 as compared to GFS
The method provides quantile‐based forecasts to address uncertainty in precipitation process resulting from a synoptic circulation
Process Controls on Flood Seasonality in Brazil Chagas, Vinícius B. P.; Chaffe, Pedro L. B.; Blöschl, Günter
Geophysical research letters,
16 March 2022, Letnik:
49, Številka:
5
Journal Article
Recenzirano
Odprti dostop
A coincidence in the timing of floods and their drivers can be used as a proxy for the causality of flood generation. Here, we investigate the relationship between the seasonality of floods, maximum ...annual rainfall, and maximum annual soil moisture data of 886 basins in Brazil for 1980–2015 to shed light on process controls of flood generation. Floods tend to occur at the same time of year as soil moisture peaks and lag behind rainfall peaks by 3 weeks. In Amazonia, central and northern Brazil, flood timing is more correlated with the timing of soil moisture peaks than with that of rainfall peaks, which is interpreted as resulting from high subsurface water storage capacities. In southern and southeastern Brazil, on the other hand, flood timing is highly correlated with both soil moisture and rainfall because of low subsurface water storage capacities. These findings can support flood forecasting and climate impact studies.
Plain Language Summary
In warm regions, floods are usually generated by a combination of intense rainfall and wet soils. In this paper, we analyze the average timing within the year of floods, extreme rainfall, and soil moisture to elucidate how floods come about in the main Brazilian rivers. We find that in some regions, such as Amazonia and central Brazil, floods tend to occur when soils are wet. In other regions, such as southern Brazil, floods tend to occur when rainfall is most extreme. We believe that these differences are related to differences in the soil water storage capacity. The understanding of the regional importance of each of these components helps increase the efficiency of flood prevention measures and climate change adaptation.
Key Points
Flood peaks tend to occur at the same time of year as annual soil moisture peaks and lag behind annual rainfall peaks by 3 weeks
Flood seasonality is linked mainly with soil moisture peaks in Amazonia and central Brazil, where soil storage capacity is high
Flood timing is highly correlated with rainfall and soil moisture peaks in the south and southeast, where soil storage capacity is low
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•First-hand multi-hazard susceptibility mapping framework based on Convolutional Neural Network.•Evaluation of flash floods, landslides, and debris flows.•62.57% of the study area is ...prone to multi-hazards, of which 0.18% are prone to three main hazards.
Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.
The landscape of Pakistan is vulnerable to flood and periodically affected by floods of different magnitudes. The aim of this study was aimed to assess the flash flood susceptibility of district ...Jhelum, Punjab, Pakistan using geospatial model and Frequency Ratio and Analytical Hierarchy Process. Also, the study considered eight most influential flood-causing parameters are Digital Elevation Model, slop, distance from the river, drainage density, Land use/Land cover, geology, soil resistivity (soil consisting of different rocks and soil formation) and rainfall deviation. The rainfall data was collected from weather stations in the vicinity of the study area. Estimated weight was allotted to each flood-inducing factors with the help of AHP and FR. Through the use of the overlay analysis, each of the factors were brought together, and the value of drainage density was awarded the maximum possible score. According to the study several areas of the region based on the parameters have been classified in flood zones viz, very high risk, high risk, moderate risk, low risk, and very low risk. In the light of the results obtained, 4% of the study area that accounts for 86.25 km 2 is at high risk of flood. The areas like Bagham, Sohawa, Domeli, Turkai, Jogi Tillas, Chang Wala, Dandot Khewra were located at the very high elevation. Whereas Potha, Samothi, Chaklana, Bagrian, Tilla Jogian, Nandna, Rawal high-risk zones and have been damaged badly in the flood history of the area. This study is the first of its kind conducted on the Jhelum District and provides guidelines for disaster management authorities and response agencies, infrastructure planners, watershed management, and climatologists.
Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring ...and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.
The retreating behavior of glaciers observed in most of the Hindu Kush–Karakoram–Himalaya (HKH) region has given rise to the formation and expansion of numerous glacial lakes in the region. The lakes ...expansion under changing climate usually poses high risk of glacial lake outburst flood (GLOF) hazard for the downstream communities. In the present study, the risk of glacial lake outburst flood was investigated in the HKH region of Pakistan using LANDSAT-8 OLI (Operational Land Imager) image data of 2013 period coupled with ground information. The results of present study revealed 3044 lakes (surface area about 134.8 km
2
) in the three HKH ranges, maximum in the Karakoram (1325) and minimum in the Hindu Kush range (722) during 2013. The lakes exhibited an overall increase of about 26% in number and 7% in area in the region during 2001–2013 period. The increase in lake number was 91% within 2500–3500 m, 20% within 3500–4500 m and 31% within 4500–5500 m elevation range. Among total identified lakes during 2013, 36 were characterized as potentially dangerous glacial lakes (PDGLs) that can pose GLOF hazard in the HKH region. A regular monitoring of cryosphere changes and critical glacial lakes is essential to develop sustainable risk management strategies for this region in future.
Cataclysmic eruption of Mount St. Helens (USA) in 1980 reset 30 km of upper North Fork Toutle River (NFTR) valley to a zero‐state fluvial condition. Consequently, a new channel system evolved. ...Initially, a range of streamflows eroded channels (tens of meters incision, hundreds of meters widening) and transported immense sediment loads. Now, single, large‐magnitude, or multiple moderate‐magnitude events are needed to accomplish substantial channel modification. Three large floods (two ≥100‐year events; one ∼10–25‐year event along lower Toutle River) from 1996 to 2015 indicate flood effectiveness in this environment is affected by both geomorphic and environmental factors. The largest and smallest of these floods (February 1996, November 2006) transported the most sediment by single floods since 1982; erosion and sediment transport by an ∼100‐year flood in December 2015 was not exceptional. Strong coupling between NFTR and its tall corridor banks, local geologic and hydraulic conditions promoting threshold erosion, event sequencing, and possibly a longitudinal gradient in stream power are important factors affecting event effectiveness on channel modification. In addition, environmental factors have also been influential, as variations in snowpack, storm trajectories and rainfall distributions, and episodic mobilization of debris flows have also influenced geomorphic response. Other factors such as vegetation anchoring, strong channel–hillside coupling, disparities between flood frequencies and perturbation relaxation times, and large variations in flood duration do not appear to be critical influences. Climate forecasts for warmer temperatures and a shift from snowfall to rainfall at high elevations may promote further acute geomorphic responses.
Key Points
Three large floods affected an evolving, cataclysmically disturbed river system over a 20‐year span and produced varied geomorphic responses
Event responses were influenced by storm character, snowpack cover, geomorphic process, and local hydraulic and geologic conditions
Single, large‐magnitude, or multiple moderate‐magnitude events are currently needed to accomplish substantial channel modification
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