Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
https://doi.org/10.24057/2071-9388-2023-2899
Abstract
Water storage is one of the key components of terrestrial water balance, therefore its accurate assessment is necessary for a sufficient description of hydrological processes within river basins. Here we compare terrestrial water storage using calibrated hydrological model ECOMAG forced by gauge observations, uncalibrated INM RAS–MSU land surface model forced by reanalysis and GRACE satellite-based data over Northern Dvina and Pechora River basins. To clearly identify differences between the datasets long-term, seasonal and residual components were derived. Results show a predominance of the seasonal component variability over the region (~64% of the total) by all datasets but INM RAS–MSU shows a substantial percentage of long-term component variability as well (~31%), while GRACE and ECOMAG demonstrate the magnitude around 18%. Moreover, INM RAS–MSU shows lowest magnitude of annual range. ECOMAG and INM RAS–MSU is distinguished by earliest begin of TWS decline in spring, while GRACE demonstrates latest dates. Overall, ECOMAG has shown the lowest magnitude of random error from 9 mm for Northern Dvina basin to 10 mm for Pechora basin, while INM RAS–MSU has shown largest one.
About the Authors
V. Yu. GrigorevRussian Federation
Faculty of Geography MSU
Leninskie Gory, Moscow, 119991
Gubkina str., Moscow, 119234
I. N. Krylenko
Russian Federation
Faculty of Geography MSU
Leninskie Gory, Moscow, 119991
Gubkina str., Moscow, 119234
A. I. Medvedev
Russian Federation
Research Computing Center MSU
Leninskie Gory, Moscow, 119991
123242, Moscow
V. M. Stepanenko
Russian Federation
Faculty of Geography MSU
Research Computing Center MSU
Leninskie Gory, Moscow, 119991
123242, Moscow
Leninskie Gory, Moscow, 119991
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Review
For citations:
Grigorev V.Yu., Krylenko I.N., Medvedev A.I., Stepanenko V.M. Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2023;16(4):6-13. https://doi.org/10.24057/2071-9388-2023-2899