Recent Severe Flooding Research Articles

High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.

Review on socio-economic impacts of ‘Triple Threats’ of COVID-19, desert locusts, and floods in East Africa: Evidence from Ethiopia

East Africa region is facing an unprecedented triple socio-economic threat caused by the combined effects of the ongoing COVID-19 pandemic, the upsurge of desert locusts, and recent severe floods. The socio-economic impacts of the COVID-19 pandemic with the massive economic impacts of desert locust, and flooding resulted in devastating implications on livelihood and food security situation in East Africa. The overall objective of this review is to assess and compile the widespread socio-economic impacts of the three threats in East Africa evidence from Ethiopia. To achieve this objective, in this review, all relevant information were collected through different search engines of PubMed/PMC/Medline, Web of Science, Google Scholar, Scopus, Google, and Science Direct databases. In addition, local media news, reports relevant to the topic from local and national governments, international organizations, and NGOs were used. The review found that the threats do not just have short-term impacts on socio-economic conditions of the people in the region in general and in Ethiopia in particular, they exacerbate prevailing food insecurity and undermine livelihoods and development gains that took years to build. Restriction measures introduced by governments to contain the spread of COVID-19 pandemic inside their boundaries would have adverse socio-economic-impacts on the people of their countries. Prolonged cessation in economic activities due to the pandemic with the overwhelming effects of flooding and desert locust will result in a permanent loss in the economy of Ethiopia and its GDP is expected to fall by 3% in 2019/2021. The findings of this review can help and inform that the observed impacts of COVID-19 pandemic, desert locust, and floods call for policy measures to curb future occurrences. Countries’ preparedness for these threats is crucial to prevent the adverse socio-economic impacts and loss of human lives. Therefore, to ensure socio-economic recovery within a reasonable period the governments need to make credible commitments in each country of the region. Particularly, the government of Ethiopia should put in place plans and resources to improve the risk resilience capacity of the people to cope up and minimize the adverse effects of such disasters in the short-term basis and, also to build a more resilient, inclusive and sustainable future in the long-term basis.

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.

Xylem pledges US$1mn for hurricane and flooding relief efforts

Xylem Inc is committing US$1 million in contributions and volunteer time to the recovery and rebuild efforts following Hurricanes Harvey and Irma, and the recent severe flooding in South Asia.

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

Abstract. Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

“Climate change and recent severe flooding in Uyo, Akwa Ibom State, Nigeria”

This paper considered the phenomenon of climate change in relation to frequent and intensified flood problems in Uyo, the Capital City of Akwa Ibom State, Nigeria. Empirical evidence on the onset of climate change in Nigeria was presented. Flood problems worldwide were highlighted, together with their impacts in terms of deaths, economic losses and associated human sufferings. To this end, a detailed rainfall analysis was conducted for the study area, Uyo, using statistical distributions commonly deployed to describe climate states, such as the mean, the running mean, the standard deviation, coefficient of variability, skewness, and kurtosis, as well as, parametric statistics such as Kruskal-Wallis test and ANOVA. Results showed that recent rainfall amounts and patterns in Uyo were out of step with long-term mean conditions. For instance the 2005-2014 mean rainfall was 22% above the 30-year mean from 1985 to 2014. The rainfall Skewness and Kurtosis showed significant swings in the central tendency and confirmed the establishment of a new mean and a new standard deviation in the 2005-2014 decade. These trends were further confirmed to be statistically significant by Post Hoc Test at the 0.05 level. The implication of these findings is that rainfall is adding more water to surface runoff, leading to incessant, severe flood conditions. Although some effort has been made to reduce the incidence of flooding in Uyo through the provision of drains and storm sewers (pipe-jacking), a lot more needs to be done in terms of provision of infiltration surfaces (green areas) through sustainable urban renewal programme. Climate change mitigation options such as reducing gas flaring, bush burning, and fossil fuel combustion are recommended.Keywords: Climate Change; Flooding; Pipe-Jacking; Green Areas.

A hydrogeomorphic assessment of twenty‐first century floods in the UK

AbstractThe occurrence of devastating floods in the British uplands during the first two decades of the twenty‐first century poses two key questions: (1) are recent events unprecedented in terms of their frequency and magnitude; and (2) is climate and/or land‐use change driving the apparent upturn in flooding? Conventional methods of analysing instrumental flow records cannot answer these questions because upland catchments are usually ungauged, and where records do exist they rarely provide more than 30–40 years of data. In this paper we analyse all lichen‐dated upland flood records in the United Kingdom (UK) to establish the longer‐term context and causes of recent severe flooding. Our new analysis of torrential sedimentary deposits shows that twenty‐first century floods are not unprecedented in terms of both their frequency (they were more frequent before 1960) and magnitude (the biggest events occurred during the seventeenth–nineteenth centuries). However, in some areas recent floods have either equalled or exceeded the largest historical events. The majority of recent floods have been triggered by torrential summer downpours related to a marked negative phase of the summer North Atlantic Oscillation (NAO) between 2007 and 2012. It is of concern that historical data suggests there is far more capacity in the North Atlantic climate system to produce wetter and more prolonged flood‐rich periods than hitherto experienced in the twenty‐first century. Looking forwards, an increased likelihood of weather extremes due to climate change means that geomorphological based flood series extensions must be placed at the centre of flood risk assessment in the UK uplands and in similar areas worldwide. Copyright © 2015 John Wiley & Sons, Ltd.

A simple model to quantify the potential trade-off between water level management for ecological benefit and flood risk

Throughout the world, historic drainage of wetlands has resulted in a reduction in the area of wet habitat and corresponding loss of wetland plant and animal species. In an attempt to reverse this trend, water level management in some drained areas is trying to replicate a more natural ‘undrained’ state. The resulting hydrological regime is likely to be more suitable to native wetland species; however the raised water levels also represent a potential reduction in flood water storage capacity. Quantifying this reduction is critical if the arguments for and against wetland restoration are to be discussed in a meaningful way. We present a simple model to quantify the hydrological storage capacity of a drainage ditch network under different water level management scenarios. The model was applied to the Somerset Levels and Moors, UK, comparing areas with and without raised water level management. The raised water level areas occupy 11% of the maximum theoretical storage but when put in the context of the recent severe flooding of winter 2013/2014 occupy only 0.6% of the total flood volume and represent an average increase in flood level of 7mm. These results indicate that although the raised water level scheme does occupy an appreciable volume of the maximum possible ditch storage, in relation to a large flood event the volume is very small. It therefore seems unlikely that the severity of such large flood events would be significantly reduced if the current water level management for ecological benefit ceased.

Crop adaptation processes to extreme floods in Bangladesh: a case study

This paper investigates farmers’ crop adaptation processes in response to three recent devastating floods in Islampur, a case-study area in rural Bangladesh. The paper reports a multi-method research project which comprised a questionnaire survey, focus-group discussions and interviews with agricultural block supervisors. The author analyses three recent severe floods in Bangladesh, occurring in 1988, 1995 and 1998, and reviews the adaptation techniques and strategies embraced by the same group of farmers in order to survive the more devastating inundations that occur from time to time. The study concluded that vulnerable farmers are highly resilient and, with appropriate support, their adjustments can be sustainable. This enquiry showed that in the face of climate change both the inclusion of autonomous adaptations into planning and policy-making and the enhancement and support of community-based adaptation can be effective in ensuring the survival of riverine farming systems. This case study can be considered as a key reference case in regard to vulnerable locations in the Ganges–Brahmaputra–Meghna mega-delta basin, particularly in respect to Bangladesh.

A probabilistic approach for estimating return period of extreme annual rainfall in different cities of Punjab

Multi-day rainfall events are an important cause of recent severe flooding in Pakistan, and any change in the magnitude of such events may have severe impact upon urban structures such as dams, urban drainage systems, and flood. This article uses statistical distributions to define extremes of annual rainfall of different cities of Punjab (Lahore, Murree, Sialkot, and Jhelum) with given return periods. Our calculations suggest that general extreme value is the best-fitted distribution for the extreme annual rainfall of different cities of Punjab. Our calculations show that different cities of Punjab have 5 years return period for receiving more than 100 mm daily rainfall. While they have 30 years return period for receiving more than 200 mm daily rainfall. This asks for construction of new dams in Pakistan.

INVESTMENT IN FLOOD PROTECTION MEASURES UNDER CLIMATE CHANGE UNCERTAINTY

Recent severe river flooding in Europe has triggered debates among scientists and policy-makers on future projections of flood frequency and the need for adaptive investments, such as flood protection measures. Because there exists uncertainty about the impact of climate change on flood risk, such investments require a careful analysis of expected benefits and costs. The objective of this paper is to show how climate change uncertainty affects the decision to invest in flood protection measures. We develop a model that incorporates flexible timing of investment decisions and scientific uncertainty on the extent of climate change impact. This model allows decision-makers to cope with the uncertain impact of climate change on the frequency and damage of river flood events and minimizes the risk of under- or over-investment. One of the innovative elements of our paper is that we explicitly distinguish between structural and non-structural flood protection measures. Our results show that the effects of uncertainty on the optimal initial investment depends on the cost structure of these measures which has several important implications for flood management policy.

Using remotely sensed data to support flood modelling

Recent severe flooding in the UK has highlighted the need for better information on flood risk, increasing the pressure on engineers to enhance the capabilities of computer models for flood prediction. This paper evaluates the benefits to be gained from the use of remotely sensed data to support flood modelling. The remotely sensed data available can be used either to produce high-resolution digital terrain models (DTMs) (light detection and ranging (Lidar) data), or to generate accurate inundation mapping of past flood events (airborne synthetic aperture radar (SAR) data and aerial photography). The paper reports on the modelling of real flood events that occurred at two UK sites on the rivers Severn and Ouse. At these sites a combination of remotely sensed data and recorded hydrographs was available. It is concluded first that light detection and ranging Lidar generated DTMs support the generation of considerably better models and enhance the visualisation of model results and second that flood outlines obtained from airborne SAR or aerial images help develop an appreciation of the hydraulic behaviour of important model components, and facilitate model validation. The need for further research is highlighted by a number of limitations, namely: the difficulties in obtaining an adequate representation of hydraulically important features such as embankment crests and walls; uncertainties in the validation data; and difficulties in extracting flood outlines from airborne SAR images in urban areas.

Impact of Recent Severe Floods on Rice Production in Bangladesh

The long-term variations of rice production in Bangladesh are examined in the latter half of the 20th century and the impact of recent severe floods is discussed. Unprecedented severe floods, which submerged nearly two thirds of the country, occurred recently in Bangladesh in 1988 and 1998, and the effects of these severe floods to rice cropping are revealed. In these severe flood years, the rainy season crop aman drastically declined in production due to the decrease of the cultivated area and yield. On the other hand, the dry season crop boro, which is planted after the flood withdrawal, increased its production much higher than that of the previous year, mainly because of utilization of the residual flood water. In terms of total annual rice production, it turns out that severe floods have even a positive effect on rice production in Bangladesh. Furthermore, rice production after these severe flood years is higher than that of the previous years, suggesting that severe floods may act as a trigger for increasing rice production level through the change of the hydrological environment and farmers' reaction to it.

A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000

AbstractMulti‐day rainfall events are an important cause of recent severe flooding in the UK, and any change in the magnitude of such events may have severe impacts upon urban structures such as dams, urban drainage systems and flood defences and cause failures to occur. Regional pooling of 1‐, 2‐, 5‐ and 10‐day annual maxima for 1961 to 2000 from 204 sites across the UK is used in a standard regional frequency analysis to produce generalized extreme value growth curves for long return‐period rainfall events for each of nine defined climatological regions. Temporal changes in 1‐, 2‐, 5‐ and 10‐day annual maxima are examined with L‐moments using both a 10 year moving window and the fixed decades of 1961–70, 1971–80, 1981–90 and 1991–2000. A bootstrap technique is then used to assess uncertainty in the fitted decadal growth curves and to identify significant trends in both distribution parameters and quantile estimates.There has been a two‐part change in extreme rainfall event occurrence across the UK from 1961 to 2000. Little change is observed at 1 and 2 days duration, but significant decadal‐level changes are seen in 5‐ and 10‐day events in many regions. In the south of the UK, growth curves have flattened and 5‐ and 10‐day annual maxima have decreased during the 1990s. However, in the north, the 10‐day growth curve has steepened and annual maxima have risen during the 1990s. This is particularly evident in Scotland. The 50 year event in Scotland during 1961–90 has become an 8‐year, 11‐year and 25‐year event in the East, South and North Scotland pooling regions respectively during the 1990s. In northern England the average recurrence interval has also halved. This may have severe implications for design and planning practices in flood control. Copyright © 2003 Royal Meteorological Society