Integrating susceptibility maps of multiple hazards and building exposure distribution: a case study of wildfires and floods for the province of Quang Nam, Vietnam DOI Creative Commons
Chinh Luu, Giuseppe Forino,

Lynda Yorke

et al.

Natural hazards and earth system sciences, Journal Year: 2024, Volume and Issue: 24(12), P. 4385 - 4408

Published: Dec. 5, 2024

Abstract. Natural hazards have serious impacts worldwide on society, economy, and environment. In Vietnam, throughout the years, natural caused significant loss of lives as well severe devastation to houses, crops, transportation. This research presents a new approach multi-hazard (floods wildfires) exposure estimates using machine learning models, Google Earth Engine, spatial analysis tools for typical case study in province Quang Nam Central Vietnam. A geospatial database is built multiple-hazard modeling, including an inventory climate-related wildfires), topography, geology, hydrology, climate features (temperature, rainfall, wind), land use, building data assessment. The susceptibility each hazard first modeled then integrated into matrix demonstrate profiling risk results are explicitly illustrated flood wildfire buildings. Susceptibility models random forest provide model accuracy AUC (area under receiver operating characteristic curve) = 0.882 0.884 floods wildfires, respectively. combined within semi-quantitative assess different hazards. Digital maps wildfires aid identification areas exposed potential can be used inform communities regulatory authorities where develop implement long-term adaptation solutions.

Language: Английский

The perfect storm? Co-occurring climate extremes in East Africa DOI Creative Commons
Derrick Muheki, Axel Deijns, Emanuele Bevacqua

et al.

Earth System Dynamics, Journal Year: 2024, Volume and Issue: 15(2), P. 429 - 466

Published: April 24, 2024

Abstract. Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and environment relative to extremes occurring in isolation. While changes frequency of individual have been researched extensively, their interactions, dependence, joint occurrence received far less attention, particularly East African region. Here, we analyse pairs following within same location calendar year over Africa: river floods, droughts, heatwaves, crop failures, wildfires tropical cyclones. We co-occurrence a yearly timescale because some consider play out timescales up several months. use bias-adjusted impact simulations under past future conditions from Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). find an increase area affected by these events, with strongest increases for heatwaves (+940 % end century RCP6.0 present day), followed floods (+900 %) (+250 %). The projected occurrences typically outweighs historical even aggressive mitigation scenario (RCP2.6). illustrate that are often driven probability one pairs, instance heatwaves. most locations Africa region co-occurring areas close River Nile parts Congo basin. Our results overall highlight will become norm rather than exception Africa, low-end warming scenarios.

Language: Английский

Citations

4
Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure DOI Creative Commons
Gabriel Pérez, Ethan T. Coon, Saubhagya Singh Rathore

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 639, P. 131620 - 131620

Published: July 6, 2024

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved be a robust strategy estimate peak flows specific annual exceedance probability (e.g., 100-year flow) that can reflect natural and anthropogenic disturbances, including changes in land meteorological patterns. With objective advancing FFA-SST this study presents for first time spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) FFA-SST. allows us extend analysis from flow responses flood extent, enabling unique view hazard population exposure at basin scale. As proof-of-concept, we used ISSHM, Amanzi–ATS, SST model, RainyDay, by simulating response 5000 synthetic storm events ∼2000km2 Southeast Texas watershed. We demonstrate ATS, without site-specific calibration, provides representation flows, streamflow, evapotranspiration, soil moisture content, water storage changes. Our results analyses, covering frequency curves up 500-year return period inundation fractions, number people exposed flooding, offer perspective analyze impacts across spatial scales. Overall, critical insights risk management extending framework include both analyses Such an approach will empower stakeholders disaster emergency agencies more comprehensive understanding entire domain, facilitating informed decision-making assessment management.

Language: Английский

Citations

4
Wildfire‐Induced Enhancement in Downstream Flood Discharge in Watersheds of California DOI Creative Commons
Wasitha Dilshan, Yusuke Hiraga, So Kazama

et al.

Journal of Flood Risk Management, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 24, 2025

ABSTRACT Global climate change is increasingly associated with the prevalence of extreme precipitation and large wildfires. The influence wildfires on downstream flood discharge concerning, particularly from a risk management perspective, where understanding impact at watershed scale still fairly limited. This study investigates impacts in 30 Californian watersheds. We employed Soil Water Assessment Tool (SWAT) to simulate daily over 20 years, achieving robust model performance R 2 values 0.67–0.86 Nash–Sutcliffe efficiency (NSE) 0.65–0.86. differences between observed volume simulated unburned scenario, including errors (i.e., enhancement), during post‐fire years were assessed. Substantial increases, an average 17.1% increment 83.3% watersheds, found first year. Statistically significant positive correlations ( p < 0.01) enhancement percentage burned area. quantified wildfire by adjusting curve number (CN) SWAT model, CN increasing increments ranging 16.5% 30% their original values, depending burn severity land use type. A novel relationship area could be described equation %CN = 0.39 × % + β, which highlights proportional increase due burned. also showed that incorporating historical activity significantly raised probable maximum flood, increases 3.74% 25.9%. These wildfire‐induced are par California's projections (10%–50%), underscoring need factor effects assessments water strategies this type location.

Language: Английский

Citations

0
Survey of Wildfire Effects on the Peak Flow Characteristics DOI

Farshad Jalili Pirani,

Paulin Coulibaly

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Language: Английский

Citations

0
Quantifying the compounding effects of natural hazard events: a case study on wildfires and floods in California DOI Creative Commons
Sam Dulin, Madison Smith,

Beth Ellinport

et al.

npj natural hazards., Journal Year: 2025, Volume and Issue: 2(1)

Published: May 7, 2025

Language: Английский

Citations

0
A Streamlined Model-Based Strategy for Screening Wildfire Impact Scenarios Related to Peak Flood Flows: Hazard Prevention in Data-Limited Regions DOI
Jonathan Romero-Cuéllar, James R. Craig, Bryan A. Tolson

et al.

Journal of Hydrologic Engineering, Journal Year: 2024, Volume and Issue: 30(1)

Published: Nov. 26, 2024

Language: Английский

Citations

1
Integrating susceptibility maps of multiple hazards and building exposure distribution: a case study of wildfires and floods for the province of Quang Nam, Vietnam DOI Creative Commons
Chinh Luu, Giuseppe Forino,

Lynda Yorke

et al.

Natural hazards and earth system sciences, Journal Year: 2024, Volume and Issue: 24(12), P. 4385 - 4408

Published: Dec. 5, 2024

Abstract. Natural hazards have serious impacts worldwide on society, economy, and environment. In Vietnam, throughout the years, natural caused significant loss of lives as well severe devastation to houses, crops, transportation. This research presents a new approach multi-hazard (floods wildfires) exposure estimates using machine learning models, Google Earth Engine, spatial analysis tools for typical case study in province Quang Nam Central Vietnam. A geospatial database is built multiple-hazard modeling, including an inventory climate-related wildfires), topography, geology, hydrology, climate features (temperature, rainfall, wind), land use, building data assessment. The susceptibility each hazard first modeled then integrated into matrix demonstrate profiling risk results are explicitly illustrated flood wildfire buildings. Susceptibility models random forest provide model accuracy AUC (area under receiver operating characteristic curve) = 0.882 0.884 floods wildfires, respectively. combined within semi-quantitative assess different hazards. Digital maps wildfires aid identification areas exposed potential can be used inform communities regulatory authorities where develop implement long-term adaptation solutions.

Language: Английский

Citations

0