Can we estimate flood frequency with point-process spatial-temporal rainfall models?

Chen, Yuting; Paschalis, Alec; Wang, Li-Pen; Onof, Christian

doi:10.1016/j.jhydrol.2021.126667

Cited by 11 publications

(6 citation statements)

References 95 publications

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“…Moreover, OST50 has been validated to meet the positional requirements for key features such as waterbodies and to capture topography (Ordnance Survey, 2022b). Although Yunus et al (2016) showed it likely led to a small overprediction of flood impacts relative to lidar, it has been used successfully for hydrological modelling (Chen et al, 2021). The combination of relative accuracy and positional validation against key features of the DEM explains the performance of the hydrodynamic model.…”

Section: Discussionmentioning

confidence: 99%

Intercomparison of global reanalysis precipitation for flood risk modelling

McClean

Dawson

Kilsby

2023

Hydrol. Earth Syst. Sci.

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Abstract. Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the implications of four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in five basins in England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). The benchmark demonstrated better accuracy than reanalysis products when compared with observations of water depth and flood extent. All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14 %–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically, and no product performed best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data are available, but they should be used with caution due to the underestimation of impacts shown here. Until a more systematic international strategy for the collection of rainfall and flood impact data ensures more complete global coverage for validation, multiple reanalysis products should be used concurrently to capture the range of uncertainties.

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Section: Discussionmentioning

confidence: 99%

Intercomparison of global reanalysis precipitation for flood risk modelling

McClean

Dawson

Kilsby

2023

Hydrol. Earth Syst. Sci.

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“…Cluster-based models conceptualize the precipitation system as a hierarchical organization in which groups of small rain "cells" (~10-50 km 2 ) are embedded within large rainband areas (~10 3 -10 4 km 2 , e.g., Waymire et al, 1984). These models simulate rainband centers using Poisson processes, with elliptical rain cells randomly generated around each rainband following a clustered point process (e.g., Chen et al, 2021). Properties of these rain cells, including intensity, lifetime, area, and orientation are modeled via distributions fitted to observed data (Northrop, 1998).…”

Section: Manuscript Submitted To Water Resources Researchmentioning

confidence: 99%

A climate model-informed nonstationary stochastic rainfall generator for design flood analyses in continental-scale river basins

Liu,

Wright,

Lorenz

2023

Preprint

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Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6-hour and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by strong water vapor transport from the Gulf of Mexico—the key flood-generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large-scale atmospheric fields from a climate model, reflecting both spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine-scale precipitation arising from a specific large-scale atmospheric environment. Model parameters were fitted for each month from December-May, based on storms identified from 1979-2021 ERA5 reanalysis data and AORC precipitation. Validation showed good consistency in key storm characteristics between StormLab simulations and AORC data. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agreed well with AORC data and displayed bounded tail behavior. To our knowledge, this is the first SRG simulating nonstationary, anisotropic high-resolution precipitation over continental-scale river basins, demonstrating the value of conditioning such stochastic models on large-scale atmospheric variables. The simulated events provide a wide range of extreme precipitation scenarios that can be further used for design floods in the MRB.

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“…This modeling approach attempts to mimic the physical organization of precipitation systems and enables analytical derivation for key precipitation properties like moments and spatial correlation (Cowpertwait, 1995; Leonard et al., 2008). However, cluster‐based models struggle to fully describe the precipitation statistical structure across scales (Foufoula‐Georgiou & Krajewski, 1995) and typically lack dependence modeling between precipitation properties, for example, rain cell duration and intensity, with implications for simulating extremes (Chen et al., 2021; Paschalis et al., 2013). The multi‐level model structure also introduces many parameters, increasing model calibration difficulty (Jothityangkoon et al., 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Cluster‐based models conceptualize the precipitation system as a hierarchical organization in which groups of small rain “cells” (∼10–50 km 2 ) are embedded within large rainband areas (∼10 3 –10 4 km 2 , e.g., Waymire et al., 1984). These models simulate rainband centers using Poisson processes, with elliptical rain cells randomly generated around each rainband following a clustered point process (e.g., Chen et al., 2021). Properties of these rain cells, including intensity, lifetime, area, and orientation are modeled via distributions fitted to observed data (Northrop, 1998).…”

Section: Introductionmentioning

confidence: 99%

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

Liu,

Wright,

Lorenz

2024

Water Resources Research

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Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

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Can we estimate flood frequency with point-process spatial-temporal rainfall models?

Cited by 11 publications

References 95 publications

Intercomparison of global reanalysis precipitation for flood risk modelling

Intercomparison of global reanalysis precipitation for flood risk modelling

A climate model-informed nonstationary stochastic rainfall generator for design flood analyses in continental-scale river basins

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

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