A Framework of Dam-Break Hazard Risk Mapping for a Data-Sparse Region in Indonesia

Abstract

This paper introduces a new simple approach for dam-break hazard mapping in a data-sparse region. A hypothetical breaching case of an earthen dam, i.e., the Ketro Dam in Central Java, (Indonesia) was considered. Open-access hydrological databases, i.e., TRMM and CHIRPS, were collected and compared with the rainfall ground station data to ensure data quality. Additionally, the 3-h rainfall distribution of the TRMM database was employed and validated with the measured data to establish the 24-h rainfall distribution of the probable maximum precipitation. The probable maximum flood discharge was computed with the SCS method, and the reservoir routing computation was conducted to determine the possible breaching mechanisms. The result shows that the Ketro Dam proves safe against overtopping, and thus only the piping mechanism has been taken into consideration. Using the breaching hydrograph, the open-access Digital Elevation Model MERIT Hydro, and the high-performance shallow water model NUFSAW2D, the flood propagation to the downstream part of the dam was simulated, enabling fast computations for different scenarios. The quantification of the susceptibility rate of urban areas was eased with overlay analysis utilizing InaSAFE, a plugin for the QGIS model. This study shows that even for a data-sparse region, the recent open-access databases in terms of hydrological and hydraulic aspects may be used to generate a dam-break hazard map. This will benefit the related stakeholders to take proper action to reduce the loss of life.

Review on model development techniques for dam break flood wave propagation

“…Multi‐Error‐Removed Improved‐Terrain (MERIT) DEM (Yamazaki et al, 2017; Yamazaki et al, 2019) is another global dataset developed based on SRTM DEM, AW3D30 DEM. It has been corrected and adjusted according to hydrologic and hydraulic features with removal of vegetation canopy having the highest vertical accuracy out of the global datasets (Latrubesse et al, 2020; Yudianto et al, 2021). Latrubesse et al (2020) have utilized MERIT DEM to identify the shorelines of the reservoir for the dam breach model, while Yudianto et al (2021) have utilized it to create dam breach hazard maps in data sparse regions.…”

“…The use of globally available topographic data becomes a viable alternative with necessary corrections and accuracy assessments in the absence of other topographic data, such as in the dam breach studies of Álvarez et al (2017) and Yudianto et al (2021). Overall, most of the global datasets (Table 1) have World Geodetic System‐ 1984(WGS84) as the horizontal coordinate system.…”

“…In addition, data might not be available in past dam breach events as catastrophic dam events may not be frequent, and available data might not be complete or entirely accurate (Aureli et al, 2021) or virtually absent when the measuring instruments and equipment are not prepared for extreme events (Farooq et al, 2019; Jančíková & Unucka, 2015). Thus, to eliminate the parametric uncertainty, the results can be evaluated using experimental data from physical models (Haltas, Elçi, & Tayfur, 2016; Pilotti et al, 2020) or by calibration and validation of parameters for historical flood events in the same study area (Mhmood et al, 2022; Yudianto et al, 2021). In the latter, the model is first calibrated for a flood model and then used for the dam breach study.…”

Establishing a Simple-yet-effective Approach of Early Warning System for Storm-Induced Earth-Filled Dam-Break Cases in Data-sparse Region

“…Li et al (2018) and Ge et al (2019) stated that other methods include adding indicators such as understanding the failure mode, building vulnerability, and type of downstream land use. A simple and precise method of determining the risk of a dam-break event in the form of quantitative parameters called Flood Risk Level (FRL) was proposed by Yudianto et al (2021). FRL determines the level of risk into four index values by evaluating several factors such as flood depth, flow velocity, distance to shelter, people demography, and building types in the areas with minimal data availability, such as Indonesia.…”

Quantitative Assessment of Uncertainties and Sensitivities in the Estimation of Life Loss Due to the Instantaneous Break of a Hypothetical Dam in Switzerland

“…In recent years, fuzzy set theory has been applied to introduce information such as characteristics of the dam failure, flood severity, cause of the failure, population distribution, etc. [8][9][10]. However, as with regression-based equations, these studies come with a series of limitations, for example, the lack of physical interactions between receptors (e.g., people and flood wave).…”