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<p>Accurate streamflow estimation in urban catchments remains challenging under accelerating hydroclimatic variability and urbanisation. Conventional models such as Runoff-Routing-Burroughs (RORB), widely adopted in Australian practice, often struggle to represent the non-linear rainfall-runoff dynamics, governing urban flood responses. This study develops and evaluates a Deep-Learning-Neural-Network (DLNN) framework for streamflow estimation in the highly urbanised Gardiners Creek catchment, situated in southeastern Melbourne, using a dual-simulation-approach, integrating event-based and continuous-modelling. Historical rainfall and streamflow data (1989–2021) were utilised for calibration and validation against the RORB model, while future rainfall inputs were derived from dynamically downscaled ACCESSS1-0-CCAM projections under RCP4.5 and RCP8.5 scenarios from the Victorian-Climate-Projections dataset. Results demonstrate the DLNN’s improved overall predictive accuracy and adaptability (R²: 79.2–95.3%, NSE/KGE: 0.83–0.95, VE &lt; 10%) relative to RORB (R²: 72.3–91.8%, NSE/KGE: 0.68–0.89, VE: −17.13% to − 13.20%). The DLNN-model effectively captured short-term flood peaks and long-term runoff variability, maintaining stability across diverse hydroclimatic regimes. Future projections indicate increased short-duration peak flows under RCP8.5, highlighting heightened flash-flood risks and limitations of current design frameworks. These findings establish DLNN as a robust, parsimonious, and climate-adaptive alternative for supporting flood prediction and resilient water resource management.</p>

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Evaluation of Deep Learning and Physically Based Models for Urban Streamflow Estimation Under Historical and Future Climate Conditions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Harshanth Balacumaresan,
  • Monzur Alam Imteaz,
  • Iqbal Hossain,
  • Md Abdul Aziz,
  • Tanveer Choudhury

摘要

Accurate streamflow estimation in urban catchments remains challenging under accelerating hydroclimatic variability and urbanisation. Conventional models such as Runoff-Routing-Burroughs (RORB), widely adopted in Australian practice, often struggle to represent the non-linear rainfall-runoff dynamics, governing urban flood responses. This study develops and evaluates a Deep-Learning-Neural-Network (DLNN) framework for streamflow estimation in the highly urbanised Gardiners Creek catchment, situated in southeastern Melbourne, using a dual-simulation-approach, integrating event-based and continuous-modelling. Historical rainfall and streamflow data (1989–2021) were utilised for calibration and validation against the RORB model, while future rainfall inputs were derived from dynamically downscaled ACCESSS1-0-CCAM projections under RCP4.5 and RCP8.5 scenarios from the Victorian-Climate-Projections dataset. Results demonstrate the DLNN’s improved overall predictive accuracy and adaptability (R²: 79.2–95.3%, NSE/KGE: 0.83–0.95, VE < 10%) relative to RORB (R²: 72.3–91.8%, NSE/KGE: 0.68–0.89, VE: −17.13% to − 13.20%). The DLNN-model effectively captured short-term flood peaks and long-term runoff variability, maintaining stability across diverse hydroclimatic regimes. Future projections indicate increased short-duration peak flows under RCP8.5, highlighting heightened flash-flood risks and limitations of current design frameworks. These findings establish DLNN as a robust, parsimonious, and climate-adaptive alternative for supporting flood prediction and resilient water resource management.