| 15 Oct 2025
Harnessing multi-source hydro-meteorological data for flood modelling in a partially glacierized Himalayan basin
Abstract. The southern rim of the Indian Himalayas is highly susceptible to floods during the summer monsoon, making accurate streamflow modelling critical yet difficult due to complex terrain, climate variability, and sparse ground observations. This study uses a conceptual, semi-distributed hydrological model – enhanced with both static and dynamic glacier modules – to reproduce streamflow into the Alaknanda River at Rudraprayag gauge (~8600 km²), a representative basin in northern India. The model was calibrated using multi-variable data, including satellite-based glacier water loss and actual evapotranspiration, also to address bias in the precipitation input. Despite inherent data uncertainties and simplified process conceptualization, the tailored hydrological modelling captured key features of observed streamflow and produced internally consistent water balance estimates. Multi-variable calibration improved the simulation of hydrological fluxes and highlighted the value of using complementary satellite-based information in data-poor mountain regions. Parsimonious precipitation adjustment approaches are proven effective for hydrological applications. However, input data errors such as unaccounted-for heavy precipitation events limited short-term streamflow prediction accuracy. The study demonstrates that a viable, parsimonious modelling strategy can still be developed for data-scarce, monsoon-dominated Himalayan basins, offering insights into the spatiotemporal dynamics of streamflow generating processes, the inter-seasonal redistribution of precipitation, the role of cryosphere contributions, and flood simulation. The approach is transferable to other monsoon-dominated, glacier-influenced, and data-limited mountain catchments facing increasing hydroclimatic risks.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
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Hydrological modelling in complex-terrain areas is highly challenging due to uncertainties in model structure, parameters, and input data, further complicated by the issue of equifinality, which often obstructs accurate interpretation of hydrological processes. This study utilizes multi-source data to calibrate a semi-distributed hydrological model, thereby providing better constraints on key hydrological processes. The findings offer valuable insights and contribute to advancing the understanding of water cycle in complex terrains. Main concerns are listed as follows.
1) Regarding the hydrological and calibration strategy, more details are needed. Key information, such as the inputs and outputs of the model, model integration time step, temporal resolution of reference data used for model calibration, and the number of iterations for each calibration, needs to be clearly specified.
2) In terms of validation, the comparison of water balance components across different scenarios should be strengthened, particularly with respect to AET and glacier meltwater loss.
3) Given that runoff observations are rare in complex terrain areas, while satellite-observed evapotranspiration and glacier changes are more readily available, it is recommended to consider another scenario that explores the model performance when calibrated without using observed runoff (i.e., utilizing only AET and glacier meltwater loss), which may have broader applicability in complex-terrain areas.
Some minor points:
4) The title focuses on flood modelling, which is inconsistent with the paper's limited coverage of this topic. It is suggested that the title be revised to more accurately reflect the actual content of the paper.
5) Line 15, “the model … precipitation input”, observed streamflow was also used for model calibration.
6) Line 18, “Multi-variable calibration improved…”, Multi-variable calibration not always improved the simulation of hydrological fluxes, as evidenced by the poorer streamflow simulation in Scenario 1 compared to Scenario 4. However, multi-source data calibration can provide a more plausible representation of hydrological processes.
7) Line 184, the method for rainfall-snowfall partitioning should be provided.
8) Does DDF in Equation 3 represent the degree-day factor for snow, ice, or both?
9) Line 227-229, it would be appreciated to provide a figure or table related to these results.
10) Why are glacier mass losses not simulated in Scenarios 2-4?
11) Line 289, the basis for determining these weights requires clarification.
12) Line 423-424, it is suggested to provide the root mean square errors for calibration and validation periods, respectively.
13) Figure 5, it is recommended that the mean annual AET for both the calibration and validation periods be presented separately.
14) Figure 6, it is recommended to incorporate reference values for glacier mass loss into this figure.
15) It is recommended to compare the mean annual water balance across different scenarios, following the format of Figure 8.