A physics-based model to derive rainfall intensity-duration threshold for debris flow

Zhang, S.J.; Xu, Cundong; Wei, Fangqiang; Hu, Kaiheng; Xu, Hui; Zhao, L.Q.; Zhang, G.P.

doi:10.1016/j.geomorph.2019.106930

Cited by 39 publications

(37 citation statements)

References 47 publications

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“…Oppositely, the advantage of boosting algorithm could capture the pattern of complex rainfall-runoff relations in semi-arid area to enhance the modeling more significantly. In addition, because rainfall-induced runoff is a crucial parameter for debris-flow early warning (Zhang et al, 2020), ensemble learning for runoff modeling may further promote related researches.…”

Section: Discussionmentioning

confidence: 99%

Ensemble learning of daily river discharge modeling for two watersheds with different climates

Zhang²,

Liu

et al. 2020

Atmospheric Science Letters

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In order to reduce the uncertainties and improve the river discharge modeling accuracy, several topography-based hydrological models (TOPMODEL), generated by different combinations of parameters, were incorporated into an ensemble learning framework with the boosting method. Both the Baohe River Basin (BRB) with humid climate, and the Linyi River Basin (LRB) with semiarid climate were chosen for model testing. Observed daily precipitation, pan evaporation and stream flow data were used for model development and testing. Different Nash-Sutcliffe efficiency coefficients, the coefficient of determination and the Root Mean Square Error were adopted to implement a comprehensive assessment on model performances. Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. During the validation periods, the boosting method could increase the modeling accuracy by 9 and 16% for BRB and LRB, respectively. The ensemble method significantly narrowed the gap of model performances over watersheds with different climatic conditions. Hence, using the ensemble learning to enhance the feasibility of hydrological models for different climatic regions is promising.

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Section: Discussionmentioning

confidence: 99%

Ensemble learning of daily river discharge modeling for two watersheds with different climates

Zhang²,

Liu

et al. 2020

Atmospheric Science Letters

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“…The Jiangjia Gully area is located in the northeastern part of Kunming, Yunnan, China (103 • 6 -103 • 13 E and 26 • 13 -26 • 17 N) ( Figure 1a). This watershed is well-known as an area prone to shallow landslides and debris flows [5,23]. The region extends over an area of 47.1 km 2 and is 12.1 km in length.…”

Section: Shallow Landslides Of the Jiangjia Gully Areamentioning

confidence: 99%

“…Shallow and deep-seated landslides have different geomorphological features in nature. Shallow landslides occur at a smaller scale, with sliding masses <2-3 m thick [17][18][19][20][21][22][23]. In the field, multiple shallow landslides are often found at different locations on a slope with a homogenous gradient and aspect.…”

Section: Introductionmentioning

confidence: 99%

Identification and Extraction of Geomorphological Features of Landslides Using Slope Units for Landslide Analysis

Wang

Zhang

et al. 2020

IJGI

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A slope unit is commonly used as calculation unit for regional landslide analysis. However, the capacity of the slope unit to reflect the geomorphological features of actual landslides still needs to be verified. This is because such accurate representation is critical to ensure the physical meaning of results from subsequent landslide stability analysis. This paper presents work conducted on landslides and slope extraction in two areas in China: The Jiangjia Gully area (Yunnan Province) and Fengjie County (Chongqing Municipality). Ground-based light detection and ranging (LiDAR) data are combined with field landslide terrace measurements to allow for the comparison of slope unit extraction methods (conventional vs. MIA-HSU) in terms of their ability to reflect the geomorphological features of shallow and deep-seated landslides. The results indicate that slope unit boundaries extracted by the conventional method do not match the geomorphological variations of actual landslides, and the method is therefore deficient in meaningfully extracting slope units for further landslide analysis. By contrast, slope units obtained using the MIA-HSU method accurately reflects the geomorphological features of both shallow and deep-seated landslides, and thus provides clearer geomorphological meaning and more reasonable calculation units for regional landslide assessment and prediction.

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“…We will also stress in which cases it is necessary to put care in the estimate of the input parameters, why it is advisable to apply the BDA to other study areas (namely to test this approach with different data), how it is possible to implement the developed uncertainty analysis to other parametersdependent thresholds (e.g. the threshold proposed by Zhang et al, 2020) and finally, why it is important to perform a suitable validation analysis (not addressed in this manuscript) to assess the threshold forecast capability.…”

Section: Major Issuesmentioning

confidence: 99%

Replies to Referee 1

Martinengo¹

2020

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A physics-based model to derive rainfall intensity-duration threshold for debris flow

Cited by 39 publications

References 47 publications

Ensemble learning of daily river discharge modeling for two watersheds with different climates

Ensemble learning of daily river discharge modeling for two watersheds with different climates

Identification and Extraction of Geomorphological Features of Landslides Using Slope Units for Landslide Analysis

Replies to Referee 1

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