Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

doi:10.5194/hess-2016-620

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…An alternative to removing the channel is to estimate its dimensions empirically given observations from surveyed rivers using downstream hydraulic geometry theory, as demonstrated in the GFM of Yamazaki et al. (2013) and many regional models (Fleischmann et al., 2019; Grimaldi et al., 2019; Mateo et al., 2017; Neal et al., 2012; Paiva et al., 2011; Sayama et al., 2017; Schumann et al., 2013). Downstream hydraulic geometry theory aims to estimate how the width and depth of the channel are related to bank full discharge by a series of power laws (Leopold & Maddock, 1953).…”

Section: Methods For Defining River Channels In Data Sparse Flood Inu...mentioning

confidence: 99%

Estimating River Channel Bathymetry in Large Scale Flood Inundation Models

Neal

Hawker

Savage

et al. 2021

Water Resources Research

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Flood inundation modeling across large data sparse areas has been increasing in recent years, driven by a desire to provide hazard information for a wider range of locations. The sophistication of these models has steadily advanced over the past decade due to improvements in remote sensing and modeling capability. There are now several global flood models (GFMs) that seek to simulate water surface dynamics across all rivers and floodplains regardless of data scarcity. However, flood models in data sparse areas lack river bathymetry because this cannot be observed remotely, meaning that a variety of methods for approximating river bathymetry have been developed from uniform flow or downstream hydraulic geometry theory. We argue that bathymetry estimation in these models should follow gradually varying flow theory to account for both uniform and nonuniform flows. We demonstrate that existing methods for bathymetry estimation in GFMs are only accurate for kinematic water surface profiles and are unable to simulate unbiased water surface profiles for reaches with diffusive or shallow water wave properties. The use of gradually varied flow theory to estimate bathymetry in a GFM reduced model error compared to a target water surface profile by 66% and eliminated bias due to backwater effects. For a large-scale test case in Mozambique this reduced flood extents by 40% and floodplain storage by 79% at the 5 years return period. The wet bias associated with uniform flow derived channels could have significant implications for modeling the role floodplains play in attenuating river discharges, potentially overstating their role. NEAL ET AL.

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Section: Methods For Defining River Channels In Data Sparse Flood Inu...mentioning

confidence: 99%

Estimating River Channel Bathymetry in Large Scale Flood Inundation Models

Neal

Hawker

Savage

et al. 2021

Water Resources Research

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“…Several studies have demonstrated the benefit of increasing the spatial resolution in macroscale RRMs. For example, Mateo et al (2017) showed that the river connectivity is better described at high spatial resolution, which improves the representation of the river flow dynamics within the river network. Nguyen-Quang et al (2018) concluded that high streamflow simulation performance requires a precise river catchment description, along with accurate forcing data (namely precipitation).…”

Section: Introductionmentioning

confidence: 99%

River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies

Munier

Decharme

2022

Earth Syst. Sci. Data

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Abstract. Global-scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large-scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and increasing computing capacities have allowed better performance by RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides the flow directions of all grid cells. River networks may be derived at various spatial resolutions by upscaling high-resolution hydrography data. This paper presents a new global-scale river network at 1/12∘ derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the hierarchical dominant river tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is provided here with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high-resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global-scale simulations with the CTRIP model, one with the current spatial resolution (1/2∘) and the other with the new spatial resolution (1/12∘). It is shown that, overall, CTRIP at 1/12∘ outperforms CTRIP at 1/2∘, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters, freely available for download from Zenodo (https://doi.org/10.5281/zenodo.6482906, Munier and Decharme, 2022), may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.

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“…Moreover, GHMs usually performed worse in simulating hydrological extremes than normal values in some river basins. Possible reasons include the inadequate mathematical representation of hydrological systems (despite including some human impact parameterizations), limited availability and coarse spatial‐temporal resolution of global forcing data, and insufficient calibrations conducted in a few basins (Bierkens, 2015; W. B. Liu, Lim et al., 2018; Mateo et al., 2017; Veldkamp et al., 2018; Yang et al., 2019; Zaherpour et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming

Liu

Tao

Sun

et al. 2021

Water Resources Research

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River flooding is among the costliest natural disasters with severe economic, societal, and environmental consequences. However, substantial uncertainties remain in global and regional projections of future flood conditions simulated by global climate models (GCMs) and/or global hydrological models (GHMs). Using physical models coupled with machine learning (ML), for the first time, we project changes in flood magnitudes of 2062 global river basins by constraining physical‐based streamflow simulations with observations under 1.5°C and 2°C warming scenarios identified for the Representative Concentration Pathway 8.5. We found that, during the validation period, the GHMs‐simulated flood magnitudes would improve with reduced uncertainty over the selected river basins after ML with a Long Short‐Term Memory network. Our estimation suggested that flood magnitudes would increase in many Northern Hemisphere mid‐ and high‐latitude rivers (e.g., Lena River, Amur River and Volga River) but decrease in some river basins in southern Finland and Eastern Europe in future periods (i.e., 1.5°C and 2°C warming levels). In 1.5°C and 2°C warmer worlds, the decreasing flood magnitudes in most South American rivers are associated with decreased soil moisture and increased evapotranspiration induced by warmer temperatures. Although the geographical pattern of changes in flood magnitudes for the +2°C experiment is close to that of the +1.5°C experiment, a 1.5°C warming target is more likely to reduce flood magnitudes of many river basins worldwide (e.g., in central and eastern Siberia, Alaska/Northwest Canada and South America).

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Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

Cited by 5 publications

References 29 publications

Estimating River Channel Bathymetry in Large Scale Flood Inundation Models

Estimating River Channel Bathymetry in Large Scale Flood Inundation Models

River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies

Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming

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