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<p>This study developed a rainfall-runoff-sediment yield integrated modelling using KINEROS2 (K2) and the inverse problem (IP) method to evaluate sediment yield in a small Amazonian catchment. This contribution aims to explore the challenges associated with quantifying sediment yield in small catchments, where data availability is limited and uncertainties in both measurements and parameters are prevalent. Additionally, the region is experiencing significant deforestation, agricultural expansion, and forest fires, which contribute to erosion, degradation of productive land, and increased sediment yield. The inverse problem (IP) method was considered for modelling-prediction of hydrosedimentological data in watershed, as it offers an analytical approach that integrates measured data with mathematical models simultaneously. This method is particularly valuable in contexts of data scarcity and parameter calibration, enabling simulations to align with observed data. This process is challenging due to the complexity and variability of hydrosedimentological data. The IP approach uses KINEROS2, incorporating all available information on sediment yield and model parameters to improve prediction accuracy. However, the model showed good agreement with the correlation coefficient (R<sup>2</sup>) equal to 0.75 in calibration and 0.78 in validation, respectively. In this case, Nash–Sutcliffe coefficients were above 0.70 and RMSE values between 0.27 and 1.99, indicating a 95% reliability in sediment yield simulations.</p>

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Inverse problem models for assessment of rainfall-runoff-sediment processes applied to a small river basin in the Amazon

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • Cindy Falcón,
  • Claudio Blanco,
  • Diego Estumano,
  • Ana Julia Barbosa

摘要

This study developed a rainfall-runoff-sediment yield integrated modelling using KINEROS2 (K2) and the inverse problem (IP) method to evaluate sediment yield in a small Amazonian catchment. This contribution aims to explore the challenges associated with quantifying sediment yield in small catchments, where data availability is limited and uncertainties in both measurements and parameters are prevalent. Additionally, the region is experiencing significant deforestation, agricultural expansion, and forest fires, which contribute to erosion, degradation of productive land, and increased sediment yield. The inverse problem (IP) method was considered for modelling-prediction of hydrosedimentological data in watershed, as it offers an analytical approach that integrates measured data with mathematical models simultaneously. This method is particularly valuable in contexts of data scarcity and parameter calibration, enabling simulations to align with observed data. This process is challenging due to the complexity and variability of hydrosedimentological data. The IP approach uses KINEROS2, incorporating all available information on sediment yield and model parameters to improve prediction accuracy. However, the model showed good agreement with the correlation coefficient (R2) equal to 0.75 in calibration and 0.78 in validation, respectively. In this case, Nash–Sutcliffe coefficients were above 0.70 and RMSE values between 0.27 and 1.99, indicating a 95% reliability in sediment yield simulations.