Spatial dependence of floods shaped by extreme rainfall under the influence of urbanization

Liu, Miao; Yu, Zhigang; Hua, Jian; Congxuan, Kang; Lin, Zhibin

doi:10.1016/j.scitotenv.2022.159134

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…Thus, the water reaches the outlet faster in urbanized areas, bringing forward the peak flow. The results indicate that urbanization has sped up the flood arrival time, as already demonstrated in other studies (e.g., Lu et al, 2023;Nardi et al, 2018).…”

Section: Resultssupporting

confidence: 86%

Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

Brandão,

de Menezes Filho,

Oliveira

et al. 2024

Proc. IAHS

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Abstract. Flow simulation using artificial neural networks (ANNs) in the modelling has been widely applied and has gained prominence in regions lacking data. The hydrological variables are subject to the influence of morphological characteristics and urbanization in the watershed. Statistical models, such as ANNs, need to be able to identify the relationship between the hydrological inputs and outputs of the model, without explicitly considering the other relationships involved in physical processes. This work aimed to apply a Multilayer Perceptron (MLP) neural network for predicting flows in an urban basin subject to recurrent floods, using precipitation and flow data from previous periods as inputs. After model calibration and validation for the current state of the basin (2018–2019), its responses were analysed using input data before the basin urbanization (1985–1986) to identify the error behaviour at the output as a proxy for the basin changes effect. Its efficiency was evaluated using hydrographs, showing satisfactory results in both periods. In the urbanization period, there is more dispersion for maximum flows. For the day 4 steps back in the current forecast, NSE = 0.59 was observed, whereas in the other period, NSE = 0.70. The evaluation of the models for the current period of basin urbanization showed that the model could capture the basin's physical dynamics within the established static relationship. Also, the result found in the statistical relationships for the inputs showed once again the impact of urbanization on the basin.

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Section: Resultssupporting

confidence: 86%

Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

Brandão,

de Menezes Filho,

Oliveira

et al. 2024

Proc. IAHS

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“…This is likely caused by direct water diversion from rivers or groundwater pumping that reduces water storage and thus baseflow. Urbanization, conversely, is found to increase Q max and Q min that can be primarily caused by reduced surface infiltration rate, implying an increasing annual Q trend and a higher risk of flooding (Lu et al., 2023). Lastly, reservoir operations are associated with a reduction in Q max and an increase in Q min , signifying decreased intra‐annual Q variability and consequently reduced vulnerabilities and impacts of floods and droughts in catchments with reservoir management practices.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Stationarity of High‐ and Low‐Flows Under Climate Change and Human Interventions Across Global Catchments

Wang,

Yang

2024

Earth and Space Science

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The assumption of stationarity is fundamental for predicting future hydrologic changes based on historical data. Here we present the first global‐scale, observation‐based assessment of long‐term stationarity in annual streamflow extremes (i.e., maximum and minimum monthly streamflow, or Qmax and Qmin). Observational evidence from 11,069 catchments worldwide reveal that Qmax and Qmin series remain stationary in approximately 93% and 67% of catchments exclusively influenced by climate change, respectively, indicating that climate change alone has not disrupted stationarity in annual streamflow extremes. In contrast, these proportions decrease to 76% and 44% for catchments subjected to direct human interventions, with the prevalence of non‐stationary Qmax and Qmin series generally increasing in tandem with the degree of human interventions. These findings provide valuable quantitative insights into the extent of hydrologic extreme alterations caused by human activities and emphasize the need for adaptive measures to mitigate direct human impacts on the hydrological system.

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“…This advantage unfortunately, cannot be inherited by in-situ stream gauging observations. The spatial dependence of floods are related to large-scale weather systems (Villarini et al, 2011), land-surface processes (Brunner et al, 2020b;Lu et al, 2023) and hydraulic structures (Turner-Gillespie et al, 2003;Brunner, 2021). Brunner et al (2020b) show that the spatial dependence of floods varies with season and region, with winter and spring showing the largest spatial dependence and thus the highest widespread flooding potential over US.…”

Section: Introductionmentioning

confidence: 99%

Processes and controls of regional floods over eastern China

Yang,

Zhang

et al. 2024

Preprint

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Abstract. Mounting evidence points to elevated regional flood hazards under a changing climate, but existing knowledge about their processes and controls is albeit limited. This is partially attributed to inadequate characterizations of the spatial extent and potential drivers of these floods. Here we develop a machine-learning based framework (mainly including the density-based clustering algorithm DBSCAN and conditional random forest model) to examine the processes and controls of regional floods over eastern China. Our empirical analyses are based on a dense network of stream gauging stations with continuous observations of annual maximum flood peak (i.e., magnitude and timing) during the period 1980–2017. A comprehensive catalog of 318 regional floods is developed. We reveal a pronounced clustering of regional floods in both space and time over eastern China. This is dictated by cyclonic precipitating systems and/or their interactions with topography. We highlight contrasting behaviors of regional floods, in terms of their spatial extents and intensities. These contrasts are determined by fine-scale structures of flood-producing storms and anomalous soil moisture. While land surface properties might play a role in basin-scale flood processes, it is more critical to capture spatial-temporal rainfall variabilities and soil moisture anomalies for reliable large-scale flood hazard modeling and impact assessments. Our analyses contribute to flood science by better characterizing the spatial dimension of flood hazards and can serve as basis for collaborative flood risk management under a changing climate.

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Spatial dependence of floods shaped by extreme rainfall under the influence of urbanization

Cited by 16 publications

References 41 publications

Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

Stationarity of High‐ and Low‐Flows Under Climate Change and Human Interventions Across Global Catchments

Processes and controls of regional floods over eastern China

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