A new method to compile global multi-hazard event sets

This study presents a new method, the MYRIAD-Hazard Event Sets Algorithm (MYRIAD-HESA), that compiles historically-based multi-hazard event sets. MYRIAD-HESA is a fully open-access method that can create multi-hazard event sets from any hazard events that occur on varying time, space, and intensity scales. In the past, multi-hazards have predominately been studied on a local or continental scale, or have been limited to specific hazard combinations, such as the combination between droughts and heatwaves. Therefore, we exemplify our approach by compiling a global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards from varying hazard classes (e.g. meteorological, geophysical, hydrological and climatological). This global database provides new scientific insights on the frequency of different multi-hazard events and their hotspots. Additionally, we explicitly incorporate a temporal dimension in MYRIAD-HESA, the time-lag. The time-lag, or time between the occurrence of hazards, is used to determine potentially impactful events that occurred in close succession. Varying time-lags have been tested in MYRIAD-HESA, and are analysed using North America as a case study. Alongside the MYRIAD-HESA, the multi-hazard event sets, MYRIAD-HES, is openly available to further increase the understanding of multi-hazard events in the disaster risk community. The open-source nature of MYRIAD-HESA provides flexibility to conduct multi-risk assessments by, for example, incorporating higher resolution data for an area of interest.

Examinations on global changes in the total and spatial extent of tropical cyclone precipitation relating to rapid intensification

Moderate tropical cyclone precipitation (TCP) is of great significance to regional water resource supply, while extreme TCP could bring significant adverse impacts to ecosystems and society, especially when tropical cyclones intensify rapidly, leaving no time to take prevention actions. Whether rapid intensification (RI) of tropical cyclones (TCs) affect TCP in both land and ocean remains unknown. Here we classified TCs which have undergone increases in the maximum sustained wind speed (MSW) by at least 30 knots within 24-h into RI category. We analyzed TCP totals provided by daily precipitation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and spatial extent from 1983 to 2019 in the four categories based on regions (land and ocean) and RI-experiencing characteristics (with- and without-RI). TCP totals and spatial extent was identified by the restricted moving neighborhood method and semi-variogram framework. The results show that TCP totals on the ocean are larger than those on the land, since RI-experiencing TCP are higher than TCP without RI-experiencing, although RI processes tend to increase TCP totals in the extremely high percentiles more significantly on land than ocean. The effects of RI processes on global TCP spatial extent are not statistically significant, and there are no definite relations between MSW and TCP spatial extent. The four regions of the Northeast Pacific Ocean (EP), South Pacific Ocean (SP), Northwest Pacific Ocean (WP), and North Atlantic Ocean (NA) show increases in regional mean and extreme TCP totals. The highest increase in the extreme TCP totals (0.37 mm day^-1 year^-1) over the NA region occurs in the RI_ocean category, which is 2.6 times the average positive enhancement trend across all basins. The increasing rate of the extreme TCP totals over the WP region is higher in track points with RI-experiencing than without RI-experiencing. The category of RI_land over the regions of NA, EP and SP shows a significant increase in the regional mean TCP spatial extent.

US tropical cyclone flood risk: Storm surge versus freshwater

Despite persistent record-breaking flood losses from tropical cyclones (TCs), the United States continues to be inadequately prepared for TC flood events, with the deficiency in residential flood insurance being a prime representation of this. One way to address this is through a better quantification of TC flood risk including variations associated with freshwater versus storm surge flood hazard and damage. We analyze actual residential flood claim data from the National Flood Insurance Program (NFIP) for the full set of all 28 significant US landfalling TC-related flood events from 2001 to 2014 which we split by storm surge and freshwater. We illustrate key differences between the numbers of claims, paid claim amounts, and damage for freshwater and surge claims, as well as evaluate differences associated with flood zone, state, TC event, and flood depth. Despite the typical focus on surge TC flooding, freshwater flooding accounts for over 60% of TC paid claim and damage amounts. Surge flooding often occurs outside of high-velocity flood zones, which is not reflected in the NFIP premiums. Statistical analysis indicates that depth-damage ratios vary significantly by surge versus freshwater and by geography. State-level analysis shows that land-use policies and building codes likely affect differences in damage along with storm characteristics and geography. The findings highlight the need to mitigate and manage both freshwater and surge TC flood risk and for more individualized flood insurance premiums less tied to flood zone. It appears that the latter need may be addressed by the Federal Emergency Management Agency (FEMA)'s Risk Rating 2.0.

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction

A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.

The role of cyclonic activity in tropical temperature-rainfall scaling

The attribution of changing intensity of rainfall extremes to global warming is a key challenge of climate research. From a thermodynamic perspective, via the Clausius-Clapeyron relationship, rainfall events are expected to become stronger due to the increased water-holding capacity of a warmer atmosphere. Here, we employ global, 1-hourly temperature and 3-hourly rainfall data to investigate the scaling between temperature and extreme rainfall. Although the Clausius-Clapeyron scaling of +7% rainfall intensity increase per degree warming roughly holds on a global average, we find very heterogeneous spatial patterns. Over tropical oceans, we reveal areas with consistently strong negative scaling (below -40%∘C-1). We show that the negative scaling is due to a robust linear correlation between pre-rainfall cooling of near-surface air temperature and extreme rainfall intensity. We explain this correlation by atmospheric and oceanic dynamics associated with cyclonic activity. Our results emphasize that thermodynamic arguments alone are not enough to attribute changing rainfall extremes to global warming. Circulation dynamics must also be thoroughly considered.

Episodic deluges in simulated hothouse climates

Earth's distant past and potentially its future include extremely warm 'hothouse'¹ climate states, but little is known about how the atmosphere behaves in such states. One distinguishing characteristic of hothouse climates is that they feature lower-tropospheric radiative heating, rather than cooling, due to the closing of the water vapour infrared window regions². Previous work has suggested that this could lead to temperature inversions and substantial changes in cloud cover^3-6, but no previous modelling of the hothouse regime has resolved convective-scale turbulent air motions and cloud cover directly, thus leaving many questions about hothouse radiative heating unanswered. Here we conduct simulations that explicitly resolve convection and find that lower-tropospheric radiative heating in hothouse climates causes the hydrologic cycle to shift from a quasi-steady regime to a 'relaxation oscillator' regime, in which precipitation occurs in short and intense outbursts separated by multi-day dry spells. The transition to the oscillatory regime is accompanied by strongly enhanced local precipitation fluxes, a substantial increase in cloud cover, and a transiently positive (unstable) climate feedback parameter. Our results indicate that hothouse climates may feature a novel form of 'temporal' convective self-organization, with implications for both cloud coverage and erosion processes.

Time Series Analysis of Monthly and Annual Precipitation in The State of Texas Using High-Resolution Radar Products

Precipitation is the main source for replenishing groundwater stored in aquifers for a myriad of beneficial purposes, especially in arid and semi-arid regions. A significant portion of the municipal and agricultural water demand is satisfied through groundwater withdrawals in Texas. These withdrawals have to be monitored and regulated to be in balance with the recharge amount from precipitation in order to ensure water security. The main goal of this study is to understand the spatio-temporal variability of precipitation in the 21st century using high spatial resolution stage-IV radar data over the state of Texas and examine some climatic controls behind this variability. The results will shed light on the trends of precipitation and hence will contribute to improving water resources management strategies and policies. Pettit’s test and Standard Normal Homogeneity Test (SNHT), tools for detecting change-point in the monthly precipitation, suggested change-points have occurred across the state around the years 2013 and 2014. The test for the homogeneity of the data before and after 2013 revealed that, in over 64% of the state, the precipitation means were significantly different. The Panhandle region (northern part) is the only part of the state that did not show a significant difference in the mean precipitation before and after 2013. Theil-Sen’s slope test, Correlated Seasonal Mann-Kendall Test, and Cox and Stuart Trend Test all indicated that there were no significant trends in the monthly precipitation after 2013 in over 98% of the area of the state. Texas precipitation was found to be influenced significantly by the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). A significant correlation in more than 82% and 60% of the state was found with ENSO at two-month and with PDO at four-month lag, respectively.

Spatio-Temporal Analysis of Precipitation Frequency in Texas Using High-Resolution Radar Products

Understanding the frequency and intensity of precipitation is needed for many vital applications including water supply for agricultural, municipal, industrial, and power generation uses, design of hydraulic structures, and analysis and forecasting of hazards such as flood, drought, and landslide. This study examines, in detail, the spatial and temporal variability of precipitation frequency over the State of Texas and its trends from 2002 to 2019. The results indicate that Texas receives around 325 wet hours on average annually (3.7% of the time). The northern part of the Gulf Coast region witnesses the highest average precipitation frequency reaching 876 wet hours annually. The year 2015 was found to have the highest precipitation frequency across the state with an average frequency of 6% (525 wet hours) and 2011 was the driest, with an average frequency of 1.9% (170 wet hours). In terms of seasonality, the highest precipitation frequency was observed in the summer with a frequency of 4.1%. The areal average time-series of the precipitation frequency indicates that the 2011–2012 drought to be a change point. The Mann–Kendall trend analysis shows that 16.2% of the state experienced a significant positive trend in precipitation frequency including the dry western region and major cities. The results can provide useful information about storm characteristics and recent change and variability of precipitation at high spatial resolutions and can be used in a multitude of practical applications.

A Research Framework to Integrate Cross-Ecosystem Responses to Tropical Cyclones

Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. We present a research framework designed to compare tropical cyclone effects within and across ecosystems that: a) uses a disaggregating approach that measures the responses of individual ecosystem components, b) links the response of ecosystem components at fine temporal scales to meteorology and antecedent conditions, and c) examines responses of ecosystem using a resistance–resilience perspective by quantifying the magnitude of change and recovery time. We demonstrate the utility of the framework using three examples of ecosystem response: gross primary productivity, stream biogeochemical export, and organismal abundances. Finally, we present the case for a network of sentinel sites with consistent monitoring to measure and compare ecosystem responses to cyclones across the United States, which could help improve coastal ecosystem resilience.

Response of Extreme Rainfall for Landfalling Tropical Cyclones Undergoing Extratropical Transition to Projected Climate Change: Hurricane Irene (2011)

Extreme rainfall and flooding associated with landfalling tropical cyclones (TCs) have large societal impacts, both in fatalities and economic losses. This study examines the response of TC rainfall to climate change projected under future anthropogenic greenhouse emissions, focusing on Hurricane Irene, which produced severe flooding across the Northeastern United States in August 2011. Numerical simulations are made with the Weather Research and Forecasting model, placing Irene in the present-day climate and one projected for the end of 21st century climate represented by Phase 5 of the Coupled Model Intercomparison Project Representative Concentration Pathway 8.5 scenario. Projected future changes to surface and atmospheric temperature lead to a storm rainfall increase of 32% relative to the control run, exceeding the rate expected by the Clausius-Clapeyron relation given a ~3-K lower atmospheric warming. Analyses of the atmospheric water balance highlight contributions to the increase in rainfall rate from both increased circulation strength and atmospheric moisture. Storm rainfall rate shows contrasting response to global warming during TC and extratropical transition periods. During the TC phase, Irene shows a significant increase of storm rainfall rate in inner core regions. This increase shifts to outer rainbands as Irene undergoes extratropical transition, collocated with the maximum tangential wind increase and the change of secondary circulation strength. Changes of storm track from the control run to global warming projections play a role in the change of spatial rainfall pattern. Distinct roles of surface and atmospheric warming in storm rainfall and structure changes are also examined.

Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors

Impacts of floods on human society have been drawing increasing human concerns in recent years. In this study, flood observations from EM-DAT (Emergency Events Database) and DFO (Dartmouth Flood Observatory) datasets were analyzed to investigate frequency and intensity of floods, and flood-induced mortality, flood-affected population as well during 1975-2016 across the globe. Results indicated that: (1) occurrence rate of floods, flood-induced mortality and flood-affected population were generally increasing globally. However, flood-induced mortality and flood-affected people per flood event were in slight decrease, indicating that flood-induced mortality and flood-affected people due to increased floods exceeded those by individual flood event; (2) annual variation of mortality per flood event is highly related to floods with higher intensity. Specifically, the flood frequency and flood-induced mortality are the largest in Asia, specifically in China, India, Indonesia and Philippine; while significantly increased flood-affected population and mean annual mortality was detected in China, USA and Australia; (3) tropical cyclones (TC) are closely related to flood-induced mortality in parts of the countries along the western coast of the oceans. The frequency of channel floods in these regions is the largest and large proportion of flood-induced deaths and the highest flood-induced mortality can be attributed to TC-induced flash floods; (4) Population density and GDP per unit area are in significantly positive correlation with the number of flood-related victims per unit area, number of deaths and economic losses with exception of low-income countries. However, the flood-affected population and flood-induced mortality increase with decrease of per capita GDP; while the per capita economic loss increases with the increase of per capita GDP, indicating that the higher the population density and GDP per unit for a region, the higher sensitivity of this area to flood hazards.