Rainfall-runoff modeling using the HEC-HMS flow modeling framework for the Halda River catchment, Bangladesh

Haque, Marjena Binte; Karmakar, Shyamal; Hossain, Mohammad Mozaffar

doi:10.21203/rs.3.rs-3824469/v1

Cited by 1 publication

(2 citation statements)

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“…The letter "S" (mm) represents the probable maximum retention once surface runoff starts. The probable maximum retention for the Indian condition is represented by the letter "S" (iv which is The most flourishing method for estimating the watershed's direct surface runoff with simplicity, versatility and adaptability is the SCS-curve number runoff equation (Haque et al, 2024). This method takes into account the soil's capacity to absorb water in conjunction with the land cover, land use, and antecedent moisture condition (AMC) (Amutha & Porchelvan, 2009).…”

Section: (𝑖𝑖𝑖)mentioning

confidence: 99%

“…Numerous studies also investigated rainfall-runoff simulation in several watersheds in Bangladesh (Aziz et al, 2023;Chakraborty & Biswas, 2021;Haque, Karmakar, & Hossain, 2024). However, the Dhaka River basin, which has the most industrial areas and is the capital city of Bangladesh, has no study conducted focusing on historical and future rainfall-runoff estimation by a semi-distributed, physically based model like HEC-HMS.…”

Section: Introductionmentioning

confidence: 99%

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HEC-HMS-based future streamflow simulation in the Dhaka River Basin under CMIP6 climatologic projections

Islam,

Aziz,

Imran

et al. 2024

Preprint

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This study aims at developing a physically based semi-distributed rainfall-runoff model in the HEC-HMS platform to predict the historical and future stream flow of the Dhaka River basin. This model adopted and integrated several physio-hydrographic parameters as input data, such as LULC, HSG, DEM, observed stream flow, historical and projected future precipitation of the ACCESS-CM2 ensemble of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The stream flow predictability of the HEC-HMS is observed subject to the model functions and simulation controls. After developing the model architecture, during calibration, a Nash-Sutcliffe efficiency (NSE) of 0.78 and a coefficient of determination (R2) of 0.81 were found, which indicates the simulation efficacy of the model setup. Furthermore, in the validation phase, the model demonstrated its robust performance, with R2 = 0.80 and NSE = 0.78. The study showed the predicted yearly peak discharge of about 341685.8 m3/s, 330017.4 m3/s, and 315588.9 m3/s under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Here, the Mann-Kendall and Sen's slope tests were conducted to analyze the daily, monthly, and yearly stream flow trends and they substantiate a significant increase in predicted daily streamflow under both the SSP1-2.6 and SSP2-4.5 scenarios and a gradual increase in monthly discharge in May under the SSP5-8.5, as well as in July and August under the SSP1-2.6. Outcome of this study underscores the model’s robustness and contributes vital perceptions for flood control and mitigation strategies.

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Section: (𝑖𝑖𝑖)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%