Impact of Climate Change on Non-stationary IDF Curves for Urban Areas
- 1Department of Civil Engineering, Indian Institute of Science, Bengaluru, India (firstname.lastname@example.org)
- 2Interdisciplinary Centre for Water Research, Indian Institute of Science, Bengaluru, India
High-intensity rainfall in a short duration has become the primary reason for the flooding of urban areas, and quantifying this may help to reduce the destruction caused by the floods. Continuous human interventions, change in land use land-cover and urbanization have significantly altered the climate patterns in many places of the world. Urban infrastructure, economic activity, and social well-being are greatly affected by the increase in rainfall intensity resulting in more runoff, drainage system overflow, and subsequent flooding disasters. Water infrastructure planners and designers have traditionally used Intensity-Duration-Frequency (IDF) curves as tools for urban flood assessment and management. However, IDF curves created based on the stationarity hypothesis are inaccurate and may underestimate the present or future results due to continuous changes in climatic conditions. This study investigates the non-stationary behavior of IDF curves due to climate change. It is assumed that the likelihood of quantile occurrence changes with time. An optimal solution is determined by comparing Generalized Extreme Value (GEV) parameters with a stationary GEV incorporating time, space, location, and shape as covariates. These covariates are associated with the most significant physical processes, such as urbanization, local temperature changes, and global warming, that make the time series non-stationary. In addition, for downscaling the climate change model data to station-level data, a modified K-Nearest Neighbour (KNN) approach is used, incorporating non-stationarity wherever appropriate. The method is applied to 100 Telemetric Rain Gauge (TRGs) stations that are spatially dispersed throughout the urban catchment of Bangalore city, India. According to the findings, the spatial plots for IDFs can capture the current patterns and translate them into predictions of future rainfall intensities. The return period can be shortened by more than one-tenth of its length in the estimations of future rainfall intensities. These analyses along with a comparison study with the existing and future IDFs will help raise awareness and provide potential warnings to the existing water infrastructure systems.
How to cite: Kishan Rastogi, N., Wadhwa, A., and P. Mujumdar, P.: Impact of Climate Change on Non-stationary IDF Curves for Urban Areas, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2673, https://doi.org/10.5194/egusphere-egu23-2673, 2023.