Hybrid global gridded snow products and conceptual simulations of distributed snow budget:evaluation of different scenarios in a mountainous watershed

Considering snowmelt in mountainous areas as the important source of streamflow,the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes.The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models.To tackle these challenges,Global Gridded Snow Products(GGSPs)are introduced,which effectively simplify the identification of the spatial characteristics of snow hydrological variables.This research aims to investigate the performance of multisource GGSPs using multi-stage calibration strategies in hydrological modeling.The used GGSPs were Snow-Covered Area(SCA)and Snow Water Equivalent(SWE),implemented individually or jointly to calibrate an appropriate water balance model.The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow.The results showed that using GGSPs as complementary information in the calibration process,besides streamflow time series,could improve the modeling accuracy compared to the conventional calibration,which is only based on streamflow data.The SCA with NSE,KGE,and RMSE values varying within the ranges of 0.47–0.57,0.54–0.65,and 4–6.88,respectively,outperformed the SWE with the corresponding metrics of 0.36–0.59,0.47–0.60,and 5.22–7.46,respectively,in simulating the total streamflow of the watershed.In addition to the superiority of the SCA over SWE,the twostage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy.On the other hand,the consistent contribution of snowmelt to the total generated streamflow(ranging from 0.9 to 1.47)and the ratio of snow melting to snowfall(ranging from 0.925 to 1.041)in different calibration strategies and models resulted in a reliable simulation of the model.

An improvement of snow/cloud discrimination from machine learning using geostationary satellite data

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover.To address the error,satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask,but the error still remains.Machine Learning(ML)has recently been applied to remote sensing to calculate satellite-based meteorological data,and its utility has been demonstrated.In this study,snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover,and ML models(Random Forest and Deep Neural Network)were applied to accurately distinguish snow and clouds.The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing.We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data,which are satellite-based snow cover and ground truth data,as validation data to evaluate whether the snow/cloud discrimination is improved.The ML-based integrated snow cover detected 33–53%more snow compared to the GK-2A/AMI snow cover.In terms of performance,the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06%and 89.99%,respectively,and those of the integrated snow cover were 76.78–78.28%and 90.93–91.26%,respectively.