Desensitised flood risk perception to extensive disasters in a marginalised urban kampong in Indonesia

Kegel, Jimmy Franklin; MacAfee, Elizabeth; de Jong, Edwin

doi:10.1080/17477891.2024.2343404

Cited by 2 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Floods are major natural disasters faced by humans, particularly in mountainous areas, often causing severe geological disasters such as landslides and mudslides. Such disasters pose serious threats to life, property safety, local economic development, and the ecological environment [1][2][3]. Obtaining accurate rainfall data is of great importance to flood forecasting, early warning systems, and flood management.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

Lyu,

Li,

2024

Remote Sensing

View full text Add to dashboard Cite

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas.

show abstract

Section: Introductionmentioning

confidence: 99%