Share

Export Citation

APA
MLA
Chicago
Harvard
Vancouver
BIBTEX
RIS
Universitas Hasanuddin
Research output:Contribution to journalArticlepeer-review

Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia

Mustamin M.R.

Earth Switzerland

Q1
Published: 2024Citations: 4

Abstract

A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers.

Access to Document

10.3390/earth5030015

Other files and links

Fingerprint

Flood mythSciences
HEC-HMSSciences
WatershedSciences
Warning systemSciences
Flood forecastingSciences
Hydrology (agriculture)Sciences
Environmental scienceSciences
Water resource managementSciences
Flood warningSciences
GeographySciences
Computer scienceSciences
GeologySciences
ArchaeologySciences
Geotechnical engineeringSciences
TelecommunicationsSciences
Machine learningSciences