Multi-Temporal Downscaling of Streamflow for Ungauged Stations/ Sub-Basins from Daily to Sub-Daily Interval Using Hybrid Framework – A Case Study on Flash Flood Watershed

Abstract

Unprecedented flash floods (FF) in urban regions are increasing due to heavy rainfall intensity and magnitude as a result of human-induced climate and land-use changes. The changes in weather patterns and various anthropogenic activities increase the complexity of modelling the FF at different spatiotemporal scales: which indicates the importance of multi-resolution forcing information. Towards this, developing new methods for processing coarser resolution spatio-temporal datasets are essential for the efficient modelling of FF. While a wide range of methods is available for spatial and temporal downscaling of the climate data, the multi-temporal downscaling strategy has not been investigated for ungauged stations of streamflow. The current study proposed a multi-temporal downscaling (MTD) methodology for gauged and ungauged stations using Adaptive Emulator Modelling concepts for daily to sub-daily streamflows. The proposed MTD framework for ungauged stations comprise a hybrid framework with conceptual and machine learning-based approaches to analyze the catchment behavior and downscale the model outputs from daily to sub-daily scales. The study area, Peachtree Creek watershed (USA), frequently experiences flash floods; hence, selected to validate the proposed framework. Further, the study addresses the critical issues of model development, seasonality, and diurnal variation of MTD data. The study obtained MTD data with minimal uncertainty on capturing the hydrological signatures and nearly 95% of accuracy in predicting the flow attributes over ungauged stations. The proposed framework can be highly useful for short- and long-range planning, management, and mitigation measurements, where the absence of fine resolution data prohibits flash flood modeling.