Flood Estimation for SMART Control Operation Using Integrated Radar Rainfall Input with the HEC-HMS Model

Ramly, Salwa; Tahir, Wardah; Abdullah, Jazuri; Jani, Janmaizatulriah; Asmat, Arnis

doi:10.1007/s11269-020-02595-4

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…We use the physical and hydrological parameters for the sub-basins obtained from HEC-GeoHMS and available in previous literature (Ramly and Tahir, 2016;Ramly et al, 2020;Mourato et al, 2021). As mentioned in Section 3.1, rainfall data are collected from three rain gauge stations (see panel c in Figure 1).…”

Section: Hec-hms Model Implementationmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Preprint

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Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge on physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to discriminate in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.

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Section: Hec-hms Model Implementationmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…(1) SA Density-based measure We use the physical and hydrological parameters for the sub-basins obtained from HEC-GeoHMS and available in previous literature (Ramly and Tahir 2016;Ramly et al 2020;Mourato et al 2021). As mentioned in Sect.…”

Section: Hec-hms Model Implementationmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge of physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to distinguish in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.

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“…This comprehensive and detailed rainfall representative will enhance the reliability of the hydrological model. Other areas of application of radar rainfall in operational hydrology include storm hazard assessment, flood forecasting, warning and monitoring [3,4] of land surface hydrological processes, and verification of sub-grid rainfall parameterizations for satellite-based, atmospheric and global circulation models [5,6].…”

Section: Introductionmentioning

confidence: 99%

Subang Radar Cappi Data Processing and Z-R Optimization for Quantitative Precipitation Estimates (Qpe) Over Langat River Basin

et al. 2022

Self Cite

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The main advantage of radar data over point gauge rainfall is its ability to provide continuous spatial and temporal resolution of rainfall details over a large area. Weather radar transmits electromagnetic wave, interacts with raindrops and reflects some of the intercepted power (backscattering) that is subsequently converted into rainfall intensity. Despite its advantages, indirect rainfall estimation using radar reflectivity factor suffers from various sources of error such as ground clutter, partial beam occultation, beam blockage and attenuation effects. Literatures on the use of radar quantitative precipitation estimates (QPE) in Malaysia have been increasing since the past 15 years ago. However, none of the previous work have detailed out the sources of radar data used and its processing for rainfall rate conversion. This paper will discuss the fundamentals in radar data acquisition and processing for rainfall input to a case study of Langat river basin, Malaysia. The methodology in raw radar data processing is decribed in details and the use of CAPPI data for rainfall estimation over Langat river basin is discussed. The findings indicate a good performance of the radar CAPPI data as an alternative source to the rainfall measurement for Langat river basin with correlation coefficient between radar rainfall and gauged rainfall ranging from 0.69 to 0.75. Improvement on radar rainfall estimates is also recommended by newly derived optimized Z-R equations based on monsoon season. The results presented in this study are encouraging, especially for the application of water resources management for the river basin.

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Flood Estimation for SMART Control Operation Using Integrated Radar Rainfall Input with the HEC-HMS Model

Cited by 17 publications

References 33 publications

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Subang Radar Cappi Data Processing and Z-R Optimization for Quantitative Precipitation Estimates (Qpe) Over Langat River Basin

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