scholarly journals Blasts from the past: Reimagining historical storms with model simulations to modernize dam safety and flood risk assessment

2021 ◽  
pp. 1-35
Author(s):  
Kelly Mahoney ◽  
Chesley McColl ◽  
Douglas M. Hultstrand ◽  
William D. Kappel ◽  
Bill McCormick ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Water Resources ◽  
Flood Risk ◽  
Extreme Precipitation ◽  
Historical Event ◽  
Accurate Estimation ◽  
Dam Safety ◽  
Model Simulations ◽  
Upper Limit ◽  
Safety Rules

AbstractAccurate estimation of the potential “upper limit” for extreme precipitation is critical for dam safety and water resources management, as dam failures pose significant risks to life and property. Methods used to estimate the theoretical “upper limit” of precipitation are often outdated and in need of updating. The rarity of extreme events means that old storms with limited observational data are often used to define the upper bound of precipitation. Observations of many important old storms are limited in spatial and temporal coverage, and sometimes of dubious quality. This reduces confidence in flood hazard assessments used in dam safety evaluations and leads to unknown or uncertain societal risk.This paper describes a method for generating and applying ensembles of high-resolution, state-of-the-art numerical model simulations of historical past extreme precipitation events to meet contemporary stakeholder needs. The method was designed as part of a research-to-application-focused partnership project to update state dam safety rules in Colorado and New Mexico. The results demonstrated multiple stakeholder and user benefits which were applied directly into storm analyses utilized for extreme rainfall estimation, and diagnostics were developed and ultimately used to update Colorado state dam safety rules, officially passed in January 2020. We discuss how what started as a prototype research foray to meet a specific user need may ultimately inform wider adoption of numerical simulations for water resources risk assessment, and how the historical event downscaling method performed offers near-term, implementable improvements to current dam safety flood risk estimates that can better serve society today.

Modelling extreme precipitation fields for large scale flood insurance

2021 ◽  
Author(s):  
David Cross ◽  
John Paul Gosling
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Extreme Precipitation ◽  
Weather Prediction ◽  
Dependence Structure ◽  
Flood Insurance ◽  
Precipitation Field ◽  
Marginal Distributions ◽  
Continental Scale ◽  
Extreme Precipitation Events

<p>Assessment of both localised and widespread flooding is vital for flood insurance to ensure adequate financial protection for businesses and property owners alike. But modelling precipitation and catchment response on very large spatial scales remains a challenge because of the availability of data and the high dimensionality of the problem. Modelling flood risk for insurance requires spatially coherent estimation of extremes which go beyond the historical record. At the national and continental scale, it can be difficult to apply models which maintain both the dependence structure of the precipitation field and the marginal distributions which determine local impacts. Recent research into spatiotemporal random fields modelling is highly promising. Numerical weather prediction is also an attractive prospect because correlations are implicitly captured in physical processes, but the computational demand and the uncertainty of perturbed physics ensembles can limit its usefulness.  </p><p>We introduce a data driven approach for widescale flood risk assessment based on modelling extreme precipitation fields. Using gridded reanalysis precipitation data, we identify extreme precipitation events in space and time using a measure of correlation in the tails of the marginal distributions. The simulation of extreme precipitation follows two main processes. First, the timing and extent of events are modelled using a Poisson distribution for event triggers, and a spatial Poisson process perturbs event footprints for observed events in the neighbourhood of the trigger location. The second stage is to model the extreme precipitation field within the event footprint. A Copula process is used to estimate extreme precipitation quantiles for all simulation points within the event ensuring internal spatial coherence. Our method has the flexibility to model extreme precipitation with any underlying physical conditions using computationally efficient models which facilitate widescale risk assessment.</p>

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

10.1596/28574 ◽  
2017 ◽  
Author(s):  
Satya Priya ◽  
William Young ◽  
Thomas Hopson ◽  
Ankit Avasthi
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
River Basins ◽  
Flood Risk Assessment ◽  
The Ganges

scholarly journals Method for fluvial and pluvial flood risk assessment in rural settlements

MethodsX ◽  
2021 ◽  
pp. 101463
Author(s):  
Maurizio Tiepolo ◽  
Elena Belcore ◽  
Sarah Braccio ◽  
Souradji Issa ◽  
Giovanni Massazza ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Flood Risk Assessment ◽  
Rural Settlements

scholarly journals Risk Assessment of Future Climate and Land Use/Land Cover Change Impacts on Water Resources

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 38
Author(s):  
Nick Martin
Keyword(s):  
Risk Assessment ◽  
Land Use ◽  
Water Resources ◽  
Land Cover ◽  
Water Balance ◽  
Water Availability ◽  
Reference Conditions ◽  
Weather Generator ◽  
Future Climate ◽  
Lulc Change

Climate and land use and land cover (LULC) changes will impact watershed-scale water resources. These systemic alterations will have interacting influences on water availability. A probabilistic risk assessment (PRA) framework for water resource impact analysis from future systemic change is described and implemented to examine combined climate and LULC change impacts from 2011–2100 for a study site in west-central Texas. Internally, the PRA framework provides probabilistic simulation of reference and future conditions using weather generator and water balance models in series—one weather generator and water balance model for reference and one of each for future conditions. To quantify future conditions uncertainty, framework results are the magnitude of change in water availability, from the comparison of simulated reference and future conditions, and likelihoods for each change. Inherent advantages of the framework formulation for analyzing future risk are the explicit incorporation of reference conditions to avoid additional scenario-based analysis of reference conditions and climate change emissions scenarios. In the case study application, an increase in impervious area from economic development is the LULC change; it generates a 1.1 times increase in average water availability, relative to future climate trends, from increased runoff and decreased transpiration.

scholarly journals An Integrated Flood Risk Assessment Model for Cities Located in the Transitional Zone between Taihang Mountains and North China Plain: A Case Study in Shijiazhuang, Hebei, China

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 104 ◽  
Author(s):  
Qiang Liu ◽  
Hongmao Yang ◽  
Min Liu ◽  
Rui Sun ◽  
Junhai Zhang
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
North China Plain ◽  
North China ◽  
Transitional Zone ◽  
Assessment Model ◽  
Analysis Tool ◽  
Risk Assessment Model ◽  
Flood Risk Assessment ◽  
Risk Map

Cities located in the transitional zone between Taihang Mountains and North China plain run high flood risk in recent years, especially urban waterlogging risk. In this paper, we take Shijiazhuang, which is located in this transitional zone, as the study area and proposed a new flood risk assessment model for this specific geographical environment. Flood risk assessment indicator factors are established by using the digital elevation model (DEM), along with land cover, economic, population, and precipitation data. A min-max normalization method is used to normalize the indices. An analytic hierarchy process (AHP) method is used to determine the weight of each normalized index and the geographic information system (GIS) spatial analysis tool is adopted for calculating the risk map of flood disaster in Shijiazhuang. This risk map is consistent with the reports released by Hebei Provincial Water Conservancy Bureau and can provide reference for flood risk management.

Sign in / Sign up

Export Citation Format

Share Document