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<p>To effectively retrofit urban water systems and improve urban climate resilience under threats of increased pluvial flooding induced by climate change, it is critical to scale existing Intensity-Duration-Frequency (IDF) curves to quantify the projected burden on urban stormwater infrastructure. To attain this, one needs to understand how well future climate projections understand the local climate patterns, in an efficient manner. This study utilizes the daily-step Quantile Delta Mapping (QDM) method in the Hyderabad Metropolitan Region (HMR) via a split-sample validation using a 12-year retrospective period (1991–2002) and a 12-year prospective period (2003–2014). The Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated on the basis of three metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Percentual bias (PBIAS), and the Modified Willmott Index (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{d}_{\varvec{1}}\)</EquationSource> </InlineEquation>). While the QDM bias correction significantly increased the RMSE, it moderately stabilized the MAE and the PBIAS, while improving the Modified Willmott Index (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{d}_{\varvec{1}}\)</EquationSource> </InlineEquation>) across a majority of the models. The models were then ranked based on their MAE, percentual bias and Modified Willmott Index (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{d}_{\varvec{1}}\)</EquationSource> </InlineEquation>), and were utilized to extract the climate change multipliers to scale the IDF curves for the years 2031, 2036, and 2046. Projections indicate that the HMR will face increased hydro-climatic volatility in the near-future, with short-duration intense storm events increasing significantly and an increase in the average annual precipitation, indicating a more varied temporal precipitation distribution. These findings underscore the need for well-designed flood mitigation strategies using nature-based solutions, which are critical in balancing water scarcity and high-intensity pluvial flooding, due to their ability to increase natural soil moisture capacity and flexibility in water storage.</p>

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Bias correction of CMIP6 models using quantile delta mapping for projecting future IDF curves: case study of the hyderabad metropolitan region

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Sudarshan Saravanan,
  • Shiva Ji

摘要

To effectively retrofit urban water systems and improve urban climate resilience under threats of increased pluvial flooding induced by climate change, it is critical to scale existing Intensity-Duration-Frequency (IDF) curves to quantify the projected burden on urban stormwater infrastructure. To attain this, one needs to understand how well future climate projections understand the local climate patterns, in an efficient manner. This study utilizes the daily-step Quantile Delta Mapping (QDM) method in the Hyderabad Metropolitan Region (HMR) via a split-sample validation using a 12-year retrospective period (1991–2002) and a 12-year prospective period (2003–2014). The Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated on the basis of three metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Percentual bias (PBIAS), and the Modified Willmott Index ( \(\varvec{d}_{\varvec{1}}\) ). While the QDM bias correction significantly increased the RMSE, it moderately stabilized the MAE and the PBIAS, while improving the Modified Willmott Index ( \(\varvec{d}_{\varvec{1}}\) ) across a majority of the models. The models were then ranked based on their MAE, percentual bias and Modified Willmott Index ( \(\varvec{d}_{\varvec{1}}\) ), and were utilized to extract the climate change multipliers to scale the IDF curves for the years 2031, 2036, and 2046. Projections indicate that the HMR will face increased hydro-climatic volatility in the near-future, with short-duration intense storm events increasing significantly and an increase in the average annual precipitation, indicating a more varied temporal precipitation distribution. These findings underscore the need for well-designed flood mitigation strategies using nature-based solutions, which are critical in balancing water scarcity and high-intensity pluvial flooding, due to their ability to increase natural soil moisture capacity and flexibility in water storage.