First-Flush Driven Sediment Graph Modeling with Soil Moisture Accounting

The first-flush is the initial surge of highly concentrated mass of pollutants in storm runoff, mobilizing mass accumulated on land surface after dry periods. Like initial abstraction (Ia) in hydrology—which is water lost through interception, infiltration, evaporation, and surface depression storage and does not contribute to immediate surface runoff—the first-flush also appears at the watershed outlet but isn't a true sediment loss. It is crucial for designing on-site treatment facilities, allowing efficient isolation, storage, and treatment of stormwater in small catchments. Neglecting the first-flush may underestimate early-stage erosion. Developing sediment yield and graph models (SYMs and SGMs) is challenging due to nonlinearity of rainfall-runoff-sediment transport, unrealistic inputs, parameter sensitivities, oversimplified processes, landscape heterogeneity, human impacts, and data limitations. The SYMs predict total sediment load, while SGMs capture its temporal variation. Integrating the first flush with conventional SGM improves its accuracy and reliability by considering loose surface debris, and prior land use. The proposed improved first-flush driven sediment graph model with soil moisture accounting (IFF-SMA-SGM) couples the first-flush concept with the soil conservation service-curve number (SCS-CN) method (now the natural resource conservation service (NRCS) curve number method), power law, and Nash’s instantaneous unit sediment graph (IUSG) approach. It considers initial or existing soil moisture (V0) and Ia to work out the sediment yield. The model was calibrated and validated using a total of 16 sediment graphs (in an 8:8 ratio) recorded at Sub-watershed 6 (W6), Sub-watershed 7 (W7), and Sub-watershed 14 (W14), in the Goodwin Creek (GC) experimental watersheds in Oxford, Mississippi, USA, and the Mansara watershed in Uttar Pradesh, India. The model closely replicated the observed sediment graphs during both calibration and validation, demonstrating strong agreement in peak sediment load (QPS), total sediment load (QS), and time to peak sediment load (tPS). The model demonstrated high efficiency across most calibration and validation events, reflecting strong alignment between simulated and observed sediment yields. This study also highlights the IFF-SMA-SGM model's strong potential for accurate sediment yield prediction in hydro-meteorologically similar watersheds.