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Understanding natural processes, particularly the water cycle, is inherently challenging due to their unpredictable and complex nature. This complexity is especially pronounced when employing hydrological models, where simplifications introduce various uncertainties. Failing to acknowledge and address these uncertainties can introduce biases into the model outcomes, potentially influencing subsequent decision-making processes. This issue is particularly pertinent in the Indian Himalayan Regions, where significant contributions come from melting of snow and glaciers. The uncertainties in both model inputs and structures are exacerbated in this region, which is further compounded by the scarcity of reliable data. Consequently, there is a critical need to systematically quantify the diverse sources of uncertainty to ensure accurate and reliable hydrological predictions. This study focuses on the snow-dominant Alaknanda basin within the Indian Himalayan Region (IHR), encompassing three gauging stations. The SWAT+ hydrological model and Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) framework (Trotter et al., 2022) are employed to assess parameter and model structure uncertainties, respectively. The SWAT+ model, calibrated with the Latin Hypercube Sampling (LHS) algorithm, achieved Nash-Sutcliffe Efficiency (NSE) values of 0.56, 0.79, and 0.61. Parameter uncertainty is further examined using diverse parameter sets generated through the LHS algorithm. Furthermore, with the application of 47 lumped conceptual models within MARRMoT framework, assessment of model structure uncertainty underscores the varying importance of processes, particularly snow storage, soil moisture, and routing storage in the study region. The findings reveal that the inclusion of additional storage components in the model leads to a decline in performance, accompanied by an increase in complexity and uncertainties. Notably, the study concludes that, for the investigated region, the contribution of parameter uncertainty surpasses that of model structure uncertainty. These insights emphasize the need for a nuanced understanding of both parameter and structural uncertainties to enhance the reliability of hydrological predictions in data-scarce and complex regions like the IHR. References: Trotter, L., Knoben, W. J. M., Fowler, K. J. A., Saft, M., Peel, M. C. (2022). Modular Assessment of RainfallRunoff Models Toolbox (MARRMoT) v2. 1: an object-oriented implementation of 47 established hydrological models for improved speed and readability. Geoscientific Model Development, 15(16), 63596369.
Gupta et al. (Sat,) studied this question.