Human alterations of the global floodplains 1992–2019

Rajib, Adnan; Zheng, Qianjin; Lane, Charles R.; Golden, Heather E.; Christensen, Jay R.; Isibor, Itohaosa I.; Johnson, Kris

doi:10.1038/s41597-023-02382-x

Cited by 21 publications

(6 citation statements)

References 78 publications

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“…Natural disasters have caused severe damage to the environment and society globally, in places such as Australia [1], Western Europe [2], Asia [3], and the United States (US) [4]. Reducing the impact of natural disasters is connected with the United Nations Sustainable Development Goals 4, 9, and 11 [5,6].…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Li,

Woodley,

Chappell

2024

Sustainability

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Throughout history, natural disasters have caused severe damage to people and properties worldwide. Flooding is one of the most disastrous types of natural disasters. A key feature of flood assessment has been making use of the information derived from remote-sensing imagery from optical sensors on satellites using spectral indices. Here, a study was conducted about a recent spectral index, the Normalised Difference Inundation Index, and a new ensemble spectral index, the Concatenated Normalised Difference Water Index, and two mature spectral indices: Normalised Difference Water Index and the differential Normalised Difference Water Index with four different machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbours applied to the PlanetScope satellite imagery about the Brisbane February 2022 flood which is in urban environment. Statistical analysis was applied to evaluate the results. Overall, the four algorithms provided no significant difference in terms of accuracy and F1 score. However, there were significant differences when some variations in the indices and the algorithms were combined. This research provides a validation of existing measures to identify floods in an urban environment that can help to improve sustainable development.

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Section: Introductionmentioning

confidence: 99%

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Li,

Woodley,

Chappell

2024

Sustainability

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“…Thus, accurate 50 delineation of floodplain boundaries has attracted wide attention among ecologists, flood practitioners/engineers, and geomorphologists (Wohl, 2021). Among various mapping efforts across different scales and resolutions (Dhote et al, 2023), global-scale floodplain maps are particularly valuable as they require a consistent and spatially continuous framework, which can be leveraged to offer insights into the changing global floodplain characteristics and flood risks 55 (Du et al, 2018;Lindersson et al, 2020;Rajib et al, 2021Rajib et al, , 2023Rentschler et al, 2022Rentschler et al, , 2023.…”

Section: Introductionmentioning

confidence: 99%

“…Second, thresholding schemes are applied to these attributes to delineate the floodplain boundary (Dhote et al, 2023). For example, the GFPlain algorithm, a widely applied method for terrain-based floodplain delineation (Knox et al, 2022;Manfreda et al, 2014;Nardi et al, 2006;Rajib et al, 2023), adopts such an approach to create the 95 GFPlain250m dataset (Nardi et al, 2019). In a recent comparative study, GFPlain250m was proved to show the highest consistency with several existing floodplain maps, highlighting the potential of geomorphic floodplain delineation in reducing model uncertainties (Lindersson et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains

Zheng,

Lin,

Yin

2024

Preprint

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Abstract. Floodplain is a vital part of the global riverine system. Among all the global floodplain delineation strategies empowered by remote sensing, DEM-based delineation is considered computationally efficient with relatively low uncertainties, but the parsimonious model struggles with incorporating spatial heterogeneity into the floodplain map. In this study, we propose a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. Specifically, we develop a stepwise approach to estimate the Floodplain Hydraulic Geometry (FHG) scaling parameters for 269 river basins worldwide to best respect the scaling law while approximating the spatial extent of two publicly available global flood maps derived from hydrodynamic modeling. Based on the spatially-varying FHG parameters, a ~90-m resolution global floodplain map named Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT) is delineated, which takes the hydrologically corrected MERIT-Hydro dataset as the DEM inputs and the Height Above Nearest Drainage (HAND) as the terrain attribute. Our results demonstrate that SHIFT validates well with reference maps with the overall accuracy exceeding 0.85. At the same time, it shows superior consistencies with several other datasets sourced from independent hydrodynamic modeling and DEM-based approaches. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data. The estimated FHG exponent exhibits a significant positive relation with the basins’ climatic aridity conditions, particularly for 34 world’s major river basins, suggesting the ability of the scaling exponents in capturing more spatial heterogeneity. SHIFT estimates global floodplain area to be 8.2 million km2, representing 5.5 % of the world's total land area, and we anticipate SHIFT available at https://zenodo.org/records/10440609 (Zheng et al., 2023) to be used to support a range of applications requiring boundary delineations of the global geomorphic floodplains.

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“…However, the dependence of these ecosystems on groundwater or floodwater makes them highly vulnerable to changes in the hydrological regimes of their rivers (Capon et al, 2013;Poff & Zimmerman, 2010). Because of climate change (Moradkhani et al, 2010), land use change (Rajib et al, 2023), and dam construction (de Resende et al, 2019), natural floodplains are one of the most threatened ecosystems (Dynesius & Nilsson, 1994;Greet et al, 2012;Morrison et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Inundation dynamics in seasonally dry floodplain forests in southeastern Brazil

Meyer Oliveira,

van Meerveld,

Gianasi

et al. 2024

Hydrological Processes

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Floodplains are one of the most threatened ecosystems. Even though the vegetation composition in floodplain forests is expected to reflect the variation in groundwater levels and flood duration and frequency, there is little field data on the inundation dynamics (e.g., the variability in flood duration and flood frequency), especially for the understudied seasonally dry tropics. This limits our understanding of these ecosystems and the mechanisms that cause the flooding. We, therefore, investigated six floodplain forests in the state of Minas Gerais in Brazil for 1.5 years (two wet seasons): Capivari, Jacaré, and Aiuruoca in the Rio Grande basin, and Jequitaí, Verde Grande, and Carinhanha in the São Francisco basin. These locations span a range of climates (humid subtropical to seasonal tropical) and biomes (Atlantic forest to Caatinga). At each location, we continuously measured water levels in five geomorphologically distinct eco‐units: marginal levee, lower terrace, higher terrace, lower plain, and higher plain, providing a unique hydrological dataset for these understudied regions. The levees and terraces were flooded for longer periods than the plains. Inundation of the terraces lasted around 40 days per year. The levees in the Rio Grande basin were flooded for shorter durations. In the São Francisco basin, the flooding of the levees lasted longer and the water level regime of the levees was more similar to that of the terraces. In the Rio Grande basin, flooding was most likely caused by rising groundwater levels (i.e., “flow pulse”) and flood pulses that caused overbank flooding. In the São Francisco basin, inundation was most likely caused by overbank flooding (i.e., “flood pulse”). These findings highlight the large variation in inundation dynamics across floodplain forests and are relevant to predict the impacts of changes in the flood regime due to climate change and other anthropogenic changes on floodplain forest functioning.

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Human alterations of the global floodplains 1992–2019

Cited by 21 publications

References 78 publications

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains

Inundation dynamics in seasonally dry floodplain forests in southeastern Brazil

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