Multi-station calibration strategy for evaluation and sensitivity analysis of the snowmelt runoff model using MODIS satellite images

In this study, the snowmelt runoff model (SRM) was employed to estimate the effect of snow on the surface flow of Aji-Chay basin, northwest Iran. Two calibration techniques were adopted to enhance the calibration. The multi-station calibration (MSC) and single-station calibration (SSC) strategies applied to investigate their effects on the modeling accuracy. The runoff coefficients ( c (s) and c (r) ) were selected as calibration parameters because of their uncertainty in such an extended basin. To determine the most substantial input of the model which is the snow-covered area (SCA) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor imagery, MOD10A2 images were collected with spatial and temporal resolutions of 500 meters and 8 days, respectively. The results show an average of 15% improvement in the model performance in the MSC strategy from the data period of 2008-2012. Also, an appropriate agreement with physical characteristics of the study area could be seen for the calibration parameters. The contribution of snowmelt in the river flow reaches its peak in April and May, then with increasing temperature, the contribution decreased gradually. Furthermore, analysis of parameters indicates that the SRM is sensitive to recession coefficient and runoff coefficients.

Automated summary

This study utilized the snowmelt runoff model to estimate the impact of snow on surface flow in the Aji-Chay basin of northwest Iran, employing two calibration techniques to enhance accuracy. Results showed a 15% improvement in model performance with the multi-station calibration strategy, and significant variations in snowmelt contributions to river flow across different months.