Ungauged Tributary Discharge Reconstruction for Monsoon Flood Inundation Modelling: A Geomorphic-Recession Approach Applied to Peninsular India

Accurate flood inundation modelling in monsoon-dominated regions remains fundamentally limited by the scarcity of discharge observations, particularly along small tributaries that contribute substantially to flood peaks. Operational hydraulic models such as LISFLOOD-FP typically use boundary conditions derived from a small number of main-stem gauging stations. Consequently, floodplain dynamics in headwater and fringe zones are systematically underestimated, especially in peninsular India where ungauged tributaries and side valleys can supply 20–40% of peak flow during extreme rainfall events. This omission introduces major errors in hazard assessment and reduces the usefulness of model outputs for early warning and risk preparedness. This study presents a data-efficient geomorphic–recession-based method for reconstructing discharge hydrographs for ungauged tributaries without requiring additional gauge infrastructure. The approach integrates three components: (1) Recession constant estimation from CAMELS-IND catchments using baseflow separation and multi-event recession analysis; (2) Geomorphic prediction of tributary-specific recession behaviour based on drainage area, basin slope, and land-cover characteristics, enabling flow recession prediction where gauge data are unavailable; (3) Event-based hydrograph disaggregation and scaling, where synthetic hydrograph shapes are generated and apportioned across tributaries according to drainage area ratios and their predicted recession behaviour. Reconstructed tributary hydrographs are automatically introduced into LISFLOOD-FP as distributed lateral boundary conditions at tributary–floodplain junction nodes, enabling both main-stem and tributary-driven flood dynamics to be simulated simultaneously. The framework is tested on multiple monsoon flood events across peninsular India. LISFLOOD-FP simulations are conducted under two forcing scenarios: (i) conventional configuration using only main-stem discharge data, and (ii) the proposed distributed tributary inflow scheme. Model outputs are evaluated against Sentinel-1 SAR flood extent maps, which provide an independent, satellite-based benchmark of observed inundation patterns. Key performance metrics include spatial correspondence (F1-score, precision, recall), headwater and fringe-zone inundation extent, and the model's ability to capture compound flooding behaviours (e.g., tributary–main-stem surge interactions). This work provides the first operational framework for generating tributary-scale discharge inputs for flood inundation models in data-scarce monsoon basins using only regional hydrological signatures and topographic data. The method is computationally efficient, scalable across complex tributary networks, and requires no additional hydrometric infrastructure. By explicitly representing ungauged tributary forcing, the approach aims to substantially improve flood hazard mapping and forecasting in regions that are often highly exposed to tributary driven flooding yet poorly resolved in existing operational models. The framework offers a practical and transferable pathway for enhancing flood early warning systems in peninsular India and comparable monsoon-affected regions globally.

Keywords: CAMELS-IND; LISFLOOD-FP; recession analysis; Sentinel-1; ungauged tributaries.