Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: experience from the Adyar catchment, India

Abstract

Process-based hydrologic models can provide necessary information for water resources management. However, the reliability of hydrological models depends on the availability of appropriate input data and proper model calibration. In this study, we demonstrate that common calibration procedures that assume stationarity of hydrological processes can lead to unsatisfactory model performance in areas that experience a strong seasonal climate. Moreover, we develop a more robust calibration procedure for the Soil and Water Assessment Tool (SWAT) in the Adyar catchment of Chennai, India. Calibration was carried out based on seasonal decomposition and by successively shifting the calibration period. Daily and monthly streamflow records were used to investigate how these different calibration procedures influence model parameterization. Results show that SWAT model performance improved when calibrated after separating the streamflow into wet and dry seasons. The wet season calibration increased the Kling Gupta Efficiency coefficient and Nash–Sutcliffe Efficiency coefficient values from 0.56 to 0.68 and 0.19 to 0.51, respectively, compared to calibration based on wet and dry seasons together. In addition, when calibration time periods were shifted, resultant sets of model parameter values and performance metrics differed. Calibration based on the 2004–2009 period resulted in an overestimation of streamflow by 8.2%, whereas the overestimation was 12.1%, 18.3%, and 20.0% for the 2004–2010, 2004–2011, and 2004–2012 periods, respectively. This study underlines that both the availability of observed streamflow data and the way these data are applied to calibration have a strong impact on model parameterization and performance.

Enhancing hydrologic modelling through the representation of traditional rainwater harvesting systems: A case study of water tanks in South India

“…Elevations range from 24 to 94 m above MSL. Alluvium soil with a sandy‐loam and loamy sand textures are the dominant soils which is classified into soil hydrologic groups C and D with low infiltration rates and a high runoff potential (Tigabu et al, 2023; Venugopal et al, 2009). The percentages of grassland, agricultural land, water bodies and herbaceous wetland, and urban areas were 35.5%, 20%, 16.4%, and 8.3% in 2015/16, respectively (Steinhausen et al, 2018, Figure 1).…”

“…The model with the implemented water tanks was calibrated with the available streamflow data. The hydrologic parameters used for model calibration were based on a previous study (Tigabu et al, 2023) and manual sensitivity analysis (Table 2). A set of 5000 parameter combinations were generated by applying Latin Hypercube Sampling (LHS) from the R package FME and the model was run 5000 times with these different parameter sets.…”