Novel Landslide Susceptibility Mapping Based on Multi-criteria Decision-Making in Ouro Preto, Brazil

Mantovani, José Roberto Amaro; Bueno, Guilherme Taitson; Alcântara, Enner; Park, Edward; Cunha, Ana Paula; Londe, Luciana de Resende; Massi, Klécia Gili; Marengo, José A.

doi:10.1007/s41651-023-00138-0

Cited by 16 publications

(6 citation statements)

References 31 publications

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“…Also, the point-wise CHF and other metric diagnoses with various advantages can be directly referenced for local convection initiation decisions, while the ensemble statistic relations and nighttime LoCo diagnoses should be further studied. In addition, with the development of finer-scale observations of rainfall-related matters [52][53][54], not only finer-scale land-atmosphere coupling assimilation research but also high-resolution radar [55,56] and satellite [57][58][59][60][61][62][63][64][65] data assimilation studies within PBL layers but not completely observed could also benefit from the LoCo metrics for comprehensive data or method evaluation.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China

Guo,

Shao,

2023

Atmosphere

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In this study, a rainstorm of the type experienced on 20 July 2021 over central East China was simulated using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather research and forecasting model. Based on this, the gridded planetary boundary layer (PBL) profiles and ensemble states within soil perturbations were collected to investigate the typical land–atmosphere coupling chain during this modeled rainstorm by using various local coupling metrics and introduced ensemble statistical metrics. The results show that (1) except for the stratospheric thermodynamics and the surface temperature over mountain areas, the main characteristics of the mid-low atmospheric layers and the surface have been well captured in this modeled rainstorm; (2) the typical coupling intensity is characterized by the dominant morning moistening, an early afternoon weak PBL warming factor of around 2, a noontime buoyant mixing temperature deficit around 274 K, daytime PBL and surface latent flux contributions of around 100 and 280 W/m2, respectively, and significant afternoon soil-surface latent flux coupling; and (3) an overall negative soil–rainfall relationship can be identified from the ensemble metrics in which the moist static energy is more significant than PBL height, and this is consistent with the significance of daytime surface moistening indicated by local coupling metrics. Taking the multi-process chain in chronological order, the wet soil contributes greatly to daytime moisture evaporation, which then increases the early noon PBL warming and enhances the noon period buoyant mixing within weak moist heating; however, this is suppressed by large-scale forcing such as the upper southwestern inflows of rainstorms, which further significantly shapes the spatial distribution of the statistical metrics. These quantitatively described local daytime couplings highlight the potential local application of promoting public weather forecasting efforts, while the high spatial differences in the coupling indicate the more applicable threshold diagnoses within finer-scale spatial investigations.

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Section: Discussionmentioning

confidence: 99%

Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China

Guo,

Shao,

2023

Atmosphere

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“…This advanced modeling approach is particularly valuable when dealing with areas prone to flooding, offering a more comprehensive understanding of flood behavior and aiding in the development of effective flood mitigation strategies. HEC-RAS 2D is a widely used hydrodynamic modeling tool that has been employed in various regions worldwide [63][64][65] . For the model calibration, daily rainfall data from 1981 to September 2023, sourced from CHIRPS, were analyzed using a box and whisker plot.…”

Section: Flooding Inundation Modelingmentioning

confidence: 99%

Assessing flood risks in the Taquari-Antas Basin (Southeast Brazil) during the September 2023 extreme rainfall surge

Mantovani,

Alcântara,

Pampuch

et al. 2024

npj Nat. Hazards

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This analysis delves into precipitation dynamics in the Bacia Taquari Antas region, with a focus on September 2023. Employing a multi-scale approach encompassing monthly, daily, and subdaily analyses, the study unveils a consistent precipitation distribution throughout the year. September 2023’s anomaly, the second-highest in the dataset, prompts investigation into potential climatic variability. Notably, the daily analysis highlights September 4th, 2023, as significant, emphasizing the importance of historical context in evaluating weather event severity. Subdaily scrutiny of September 4th reveals intense, localized precipitation, raising concerns about hydrological impacts such as flash floods. Positive trends in Rx5day (maximum consecutive 5-day precipitation amount) and R25 (number of days in a year when precipitation exceeds 25 mm) indices indicate an increase in heavy precipitation events, aligning with broader climate change concerns. Shifting focus to flood extent and impact assessment in the Taquari-Antas Basin, a simulation model depicts the temporal evolution of the flood, reaching its peak on September 4th. Examination of affected areas, rainfall volumes, and impacts on census sectors, cities, and buildings furnishes critical data for disaster management. This study contributes to localized precipitation comprehension and broader issues of climate trends, flood risk evaluation, and urban vulnerability, providing a basis for informed decision-making and resilient planning strategies.

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“…The mechanical properties of loose rock mass will change under the action of water [43,44]. The fragile surface materials of slopes exacerbate their instability during intense rainfall, seismic events, and drastic fluctuations in river water levels [45][46][47]. As depicted in Figures 6 and 7, in Region A, high susceptibility areas for landslides predominantly align along both sides of watercourses, while in Region B, these areas primarily cluster along the Yangtze River, Qing River, and their tributaries.…”

Section: Region B Serves As An Additional Study Area To Independently...mentioning

confidence: 99%

A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets

Zhao,

Ma,

Dong

et al. 2023

Remote Sensing

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Landslide susceptibility mapping is typically based on binary prediction probabilities. However, non-landslide samples in modeling datasets are often unlabeled data, and the phenomenon of class-priori shift, that is, the proportion of landslide samples frequently deviates from real-world scenarios and is spatially heterogeneous. By comparing the classification performance and predicted probability distributions across multiple unbalanced datasets with known and unknown sample proportions, this study assesses the landslide susceptibility model’s generalization ability in the context of class-prior shifts. The study investigates the potential of Bagging PU Learning, a semi-supervised learning approach, in improving the generalization performance of landslide susceptibility models and proposes the Bagging PU-GDBT algorithm. Our findings highlight the effectiveness of Bagging PU Learning in enhancing the recall of landslides and the generalization capabilities of models on unbalanced datasets. This method reduces prediction uncertainties, especially in high and very high susceptibility zones. Furthermore, results emphasize the superiority of models trained on balanced datasets with 1:1 sample ratio for landslide susceptibility mapping over those trained on unbalanced datasets.

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Novel Landslide Susceptibility Mapping Based on Multi-criteria Decision-Making in Ouro Preto, Brazil

Cited by 16 publications

References 31 publications

Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China

Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China

Assessing flood risks in the Taquari-Antas Basin (Southeast Brazil) during the September 2023 extreme rainfall surge

A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets

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