A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

Orland, Elijah; Kirschbaum, Dalia; Stanley, Thomas

doi:10.1029/2022gl099850

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…The formation of debris flow and the severity of disasters mainly have the following reasons: topographic factors, soil factors, water factors, and human factors [8,9].…”

Section: Hazard Source Identificationmentioning

confidence: 99%

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

Xiao,

Wang,

et al. 2024

Water

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With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the potential sudden hazards of debris flows based on deep neural networks. According to hazard identification, ten risk indicators of potential sudden debris flows are determined. The risk indicators of a potential sudden debris flow in each region were used as the input factors of a deep trust network (DBN) composed of a back propagation (BP) neural network and a restricted Boltzmann machine (RBM). The DBN is pre-trained using the contrast divergence method to obtain the optimal value of the parameter set of the DBN model, and a BP network is set at the last layer of the DBN for fine-tuning to make the network optimal. Using the DBN model with the best parameters, the risk probability of debris flows corresponding to each region is taken as an output. The risk grade is divided, the risk degree of potential sudden debris flow in each region is analyzed, and the potential sudden debris flow risk in each region is divided individually. The results show that this method can effectively complete the risk zoning of sudden debris flow. Moreover, the cumulative contribution of the indicators selected by this method is significant, and the correlation of indicators is not significant, which can play a role in the risk assessment of potential sudden debris flow. This study not only provides new ideas and methods for risk assessment of sudden debris flow disasters, but also fills a gap in the field of geological hazard susceptibility mapping.

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“…The formation of debris flow and the severity of disasters mainly have the following reasons: topographic factors, soil factors, water factors, and human factors [8,9].…”

Section: Hazard Source Identificationmentioning

confidence: 99%

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

Xiao,

Wang,

et al. 2024

Water

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“…This product integrates data from multiple passive microwave (PMW) and infrared (IR) sensors, ensuring consistency and accuracy through intercalibration with state‐of‐the‐art precipitation measurement instruments onboard the GPM Core Observatory, and is ultimately adjusted by the monthly gauge analysis. IMERG is recognized as one of the most accurate high‐resolution satellite precipitation data sets available (Guilloteau et al., 2021; Pradhan et al., 2022; Tang et al., 2020), and has been widely employed in various applications (Nie & Sun, 2022; Orland et al., 2022; Zhang et al., 2023).…”

Section: Datamentioning

confidence: 99%

A New Event‐Based Error Decomposition Scheme for Satellite Precipitation Products

Li,

Guilloteau,

Kirstetter

et al. 2023

Geophysical Research Letters

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Understanding the nature and origin of errors in satellite precipitation products is important for applications and product improvement. Here we propose a new error decomposition scheme incorporating precipitation event (continuous rainy periods) information to characterize satellite errors. Under this framework, the errors are attributed to the inaccuracies in event occurrence, timing (event start/end time), and intensity. The Integrated MultisatellitE Retrieval for Global Precipitation Measurement (IMERG) is used as our test product to apply the method over CONUS. The above‐listed factors contribute approximately 30%, 20%, and 50% to the total bias, respectively. Significant asymmetry exists in the temporal distribution of biases throughout events: early event endings cause threefold more precipitation amount bias than late event beginnings, while early event beginnings cause fourfold more bias than late event endings. Dominant contributors vary across seasons and regions. The proposed error decomposition provides insight into sources of error for improved retrievals.

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Opportunities and challenges for precipitation forcing data in post‐wildfire hydrologic modeling applications

Partridge,

Johnson,

Sleeter

et al. 2024

WIREs Water

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The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post‐wildfire hydrologic hazards including debris flows, flash floods, water‐quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data‐sparse, headwater catchments in complex terrain, and post‐wildfire hydrologic effects are particularly sensitive to high‐intensity, short‐duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire‐induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade‐offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post‐wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire‐prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Science of Water > Water and Environmental Change

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A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

Cited by 6 publications

References 51 publications

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

A New Event‐Based Error Decomposition Scheme for Satellite Precipitation Products

Opportunities and challenges for precipitation forcing data in post‐wildfire hydrologic modeling applications

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