Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia

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 obs...

Full description

Bibliographic Details
Main Authors: V. Yu. Grigorev, I. N. Krylenko, A. I. Medvedev, V. M. Stepanenko
Other Authors: The study was funded by the Russian Science Foundation, grant No. 21-47-00008 (terrestrial water storage change analysis), the statistical analysis was carried out under the Development Program of the Interdisciplinary Scientific–Educational School of Moscow State University “Cosmos”. Part of data were collected and processed under Russian Science Foundation Project 21-17-00181
Format: Article in Journal/Newspaper
Language:English
Published: Russian Geographical Society 2024
Subjects:
three-cornered hat method
GRACE
LSM
hydrological model
cold climate
Arctic
dvina
Pechora
Online Access:https://ges.rgo.ru/jour/article/view/3184
https://doi.org/10.24057/2071-9388-2023-2899
id ftjges:oai:oai.gesj.elpub.ru:article/3184
record_format openpolar
institution Open Polar
collection Geography, Environment, Sustainability (E-Journal)
op_collection_id ftjges
language English
topic three-cornered hat method
GRACE
LSM
hydrological model
cold climate
spellingShingle three-cornered hat method
GRACE
LSM
hydrological model
cold climate
V. Yu. Grigorev
I. N. Krylenko
A. I. Medvedev
V. M. Stepanenko
Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
topic_facet three-cornered hat method
GRACE
LSM
hydrological model
cold climate
description 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.
author2 The study was funded by the Russian Science Foundation, grant No. 21-47-00008 (terrestrial water storage change analysis), the statistical analysis was carried out under the Development Program of the Interdisciplinary Scientific–Educational School of Moscow State University “Cosmos”. Part of data were collected and processed under Russian Science Foundation Project 21-17-00181
format Article in Journal/Newspaper
author V. Yu. Grigorev
I. N. Krylenko
A. I. Medvedev
V. M. Stepanenko
author_facet V. Yu. Grigorev
I. N. Krylenko
A. I. Medvedev
V. M. Stepanenko
author_sort V. Yu. Grigorev
title Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
title_short Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
title_full Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
title_fullStr Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
title_full_unstemmed Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia
title_sort evaluation of terrestrial water storage products from remote sensing, land surface model and regional hydrological model over northern european russia
publisher Russian Geographical Society
publishDate 2024
url https://ges.rgo.ru/jour/article/view/3184
https://doi.org/10.24057/2071-9388-2023-2899
genre Arctic
dvina
Pechora
genre_facet Arctic
dvina
Pechora
op_source GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY; Vol 16, No 4 (2023); 6-13
2542-1565
2071-9388
op_relation https://ges.rgo.ru/jour/article/view/3184/736
Chen J., Tapley B., Tamisiea M.E., Save H., Wilson C., Bettadpur S. and Seo K.W. (2021). Error Assessment of GRACE and GRACE Follow-On Mass Change. Journal of Geophysical Research: Solid Earth, 126(9), e2021JB022124, DOI:10.1029/2021JB022124
Cleveland R.B., Cleveland W.S., McRae J.E. and Terpenning I. (1990). STL: A Seasonal-Trend Decomposition Procedure Based on Loess (with Discussion). Journal of Official Statistics, 6, 3–73.
Eicker A., Jensen L., Wöhnke V., Dobslaw H., Kvas A., Mayer-Gürr T. and Dill R. (2020). Daily GRACE satellite data evaluate short-term hydrometeorological fluxes from global atmospheric reanalyses. Scientific Reports 2020 10, 4504, DOI:10.1038/s41598-020-61166-0
Ferreira V.G., Montecino H.D.C., Yakubu C.I. and Heck B. (2016). Uncertainties of the Gravity Recovery and Climate Experiment timevariable gravity-field solutions based on three-cornered hat method. Journal of Applied Remote Sensing, 10(1), 015015, DOI:10.1117/1. JRS.10.015015
Frolova N.L., Grigorev V.Y., Krylenko I.N. and Zakharova E.A. (2021). State-of-the-art potential of the GRACE satellite mission for solving modern hydrological problems. Vestnik of Saint Petersburg University. Earth Sciences, 66(1), 107–122 (in Russian with English summary), DOI:10.21638/SPBU07.2021.107
Georgiadi A.G. and Groisman P.Y. (2022). Long-term changes of water flow, water temperature and heat flux of two largest arctic rivers of European Russia, Northern Dvina and Pechora. Environmental Research Letters, 17(8), 085002, DOI:10.1088/1748-9326/AC82C1
Gupta D. and Dhanya C.T. (2021). Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows. Journal of Hydrologic Engineering, 26(4), 04021007, DOI:10.1061/(ASCE)HE.1943-5584.0002072
Han J., Yang Y., Roderick M.L., McVicar T.R., Yang D., Zhang S. and Beck H.E. (2020). Assessing the Steady-State Assumption in Water Balance Calculation Across Global Catchments. Water Resources Research, 56(7), e2020WR027392. DOI:10.1029/2020WR027392
Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., Nicolas J., Peubey C., Radu R., Schepers D., Simmons A., Soci C., Abdalla S., Abellan X., Balsamo G., Bechtold P., Biavati G., Bidlot J., Bonavita M., … Thépaut J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049, DOI:10.1002/qj.3803
Humphrey V., Gudmundsson L. and Seneviratne S.I. (2016). Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes. Surveys in Geophysics, 37(2), 357–395, DOI:10.1007/S10712-016-9367-1
Kalugin A.S. and Motovilov Y.G. (2018). Runoff Formation Model for the Amur River Basin. Water Resources, 45(2), 149–159, DOI:10.1134/S0097807818020082
Kim H., Yeh P.J.F., Oki T. and Kanae S. (2009). Role of rivers in the seasonal variations of terrestrial water storage over global basins. Geophysical Research Letters, 36(17), DOI:10.1029/2009GL039006
Kvas A., Behzadpour S., Ellmer M., Klinger B., Strasser S., Zehentner N. and Mayer-Gürr T. (2019). ITSG-Grace2018: Overview and Evaluation of a New GRACE-Only Gravity Field Time Series. Journal of Geophysical Research: Solid Earth, 124(8), 9332–9344, DOI:10.1029/2019JB017415
Landerer F.W., Flechtner F.M., Save H., Webb F.H., Bandikova T., Bertiger W.I., Bettadpur S. V., Byun S.H., Dahle C., Dobslaw H., Fahnestock E., Harvey N., Kang Z., Kruizinga G.L.H., Loomis B.D., McCullough C., Murböck M., Nagel P., Paik M., … Yuan D.N. (2020). Extending the Global Mass
Change Data Record: GRACE Follow-On Instrument and Science Data Performance. Geophysical Research Letters, 47(12), e2020GL088306, DOI:10.1029/2020GL088306
Loveland T.R., Reed B.C., Ohlen D.O., Brown J.F., Zhu Z., Yang L. and Merchant J.W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330, DOI:10.1080/014311600210191
Machul’skaya E.E. and Lykosov V.N. (2009). Mathematical modeling of the atmosphere-cryolitic zone interaction. Izv. Atmos. Ocean. Phys., 45, 687–703, DOI:10.1134/S0001433809060024
Massoud E.C., Bloom A.A., Longo M., Reager J.T., Levine P.A. and Worden J.R. (2022). Information content of soil hydrology in a west Amazon watershed as informed by GRACE. Hydrol. Earth Syst. Sci, 26, 1407–1423, DOI:10.5194/hess-26-1407-2022
Motovilov, Yu., Gottschalk, L., Engeland, K. and Belokurov, A. (1998). ECOMAG — regional model of hydrological cycle. Application to the NOPEX region. Department of Geophysics, University of Oslo.
Nasonova O.N., Gusev Y.M. and Kovalev E. (2022). Climate change impact on water balance components in Arctic river basins. Geography, Environment, Sustainability, 15(4),148–157, DOI:10.24057/2071-9388-2021-144
Popova V.V., Turkov D.V. and Nasonova O.N. (2021). Estimates of recent changes in snow storage in the river Northern Dvina basin from observations and modeling. Ice and Snow, 61(2), 206–221 (in Russian with English summary), DOI:10.31857/S2076673421020082
Premoli A. and Tavella P. (1993). A Revisited Three-Cornered Hat Method for Estimating Frequency Standard Instability. IEEE Transactions on Instrumentation and Measurement, 42(1), 7–13, DOI:10.1109/19.206671
Scanlon B.R., Zhang Z., Rateb A., Sun A., Wiese D., Save H., Beaudoing H., Lo M.H., Müller-Schmied H., Döll P., van Beek R., Swenson S., Lawrence D., Croteau M. and Reedy R.C. (2019). Tracking Seasonal Fluctuations in Land Water Storage Using Global Models and GRACE Satellites. Geophysical Research Letters, 46(10), 5254–5264, DOI:10.1029/2018GL081836
Scanlon Bridget R., Zhang Z., Save H., Sun A.Y., Schmied H.M., Van Beek L.P.H., Wiese D.N., Wada Y., Long D., Reedy R.C., Longuevergne L., Döll P. and Bierkens M.F.P. (2018). Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proceedings of the National Academy of Sciences of the United States of America, 115(6), E1080–E1089, DOI:10.1073/PNAS.1704665115
Tapley B.D., Watkins M.M., Flechtner F., Reigber C., Bettadpur S., Rodell M., Sasgen I., Famiglietti J.S., Landerer F.W., Chambers D.P., Reager J.T., Gardner A.S., Save H., Ivins E.R., Swenson S.C., Boening C., Dahle C., Wiese D.N., Dobslaw H., … Velicogna I. (2019). Contributions of GRACE to understanding climate change. Nature Climate Change, 9(5), 358–369, DOI:10.1038/s41558-019-0456-2
Volodin E.M. and Lykosov V.N. (1998). Parametrization of heat and moisture transfer in the soil-vegetation system for use in atmospheric general circulation models: 1. Formulation and simulations based on local observational data. Izvestiya, Atmospheric and Oceanic Physics, 37(4), 405–416.
Wilson M.F. and Henderson-Sellers A. (1985). A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, 5(2), 119–143, DOI:10.1002/JOC.3370050202
Wu R.J., Lo M.H. and Scanlon B.R. (2021). The Annual Cycle of Terrestrial Water Storage Anomalies in CMIP6 Models Evaluated against GRACE Data. Journal of Climate, 34(20), 8205–8217, DOI:10.1175/JCLI-D-21-0021.1
Xu T., Guo Z.X., Xia Y.L., Ferreira V.G., Liu S.M., Wang K.C., Yao Y., Zhang X. and Zhao C. (2019). Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. Journal of Hydrology, 578, 124105. DOI:10.1016//J.JHYDROL.2019.124105
Zhang Y., He B., Guo L., Liu J. and Xie X. (2019). The relative contributions of precipitation, evapotranspiration, and runoff to terrestrial water storage changes across 168 river basins. Journal of Hydrology, 579, 124194. DOI:10.1016/J.JHYDROL.2019.124194
https://ges.rgo.ru/jour/article/view/3184
doi:10.24057/2071-9388-2023-2899
op_rights Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).The information and opinions presented in the Journal reflect the views of the authors and not of the Journal or its Editorial Board or the Publisher. The GES Journal has used its best endeavors to ensure that the information is correct and current at the time of publication but takes no responsibility for any error, omission, or defect therein.
Авторы, публикующие в данном журнале, соглашаются со следующим:Авторы сохраняют за собой авторские права на работу и предоставляют журналу право первой публикации работы на условиях лицензии Creative Commons Attribution License, которая позволяет другим распространять данную работу с обязательным сохранением ссылок на авторов оригинальной работы и оригинальную публикацию в этом журнале.Авторы сохраняют право заключать отдельные контрактные договорённости, касающиеся не-эксклюзивного распространения версии работы в опубликованном здесь виде (например, размещение ее в институтском хранилище, публикацию в книге), со ссылкой на ее оригинальную публикацию в этом журнале.Авторы имеют право размещать их работу
op_doi https://doi.org/10.24057/2071-9388-2023-289910.1029/2021JB02212410.1038/s41598-020-61166-010.1117/110.21638/SPBU07.2021.10710.1088/1748-9326/AC82C110.1061/(ASCE)HE.1943-5584.000207210.1029/2020WR02739210.1002/qj.380310.1007/S10712-016-9367-110.1134/S00978
_version_ 1790595529575497728
spelling ftjges:oai:oai.gesj.elpub.ru:article/3184 2024-02-11T09:59:46+01:00 Evaluation Of Terrestrial Water Storage Products From Remote Sensing, Land Surface Model And Regional Hydrological Model Over Northern European Russia V. Yu. Grigorev I. N. Krylenko A. I. Medvedev V. M. Stepanenko The study was funded by the Russian Science Foundation, grant No. 21-47-00008 (terrestrial water storage change analysis), the statistical analysis was carried out under the Development Program of the Interdisciplinary Scientific–Educational School of Moscow State University “Cosmos”. Part of data were collected and processed under Russian Science Foundation Project 21-17-00181 2024-01-12 application/pdf https://ges.rgo.ru/jour/article/view/3184 https://doi.org/10.24057/2071-9388-2023-2899 eng eng Russian Geographical Society https://ges.rgo.ru/jour/article/view/3184/736 Chen J., Tapley B., Tamisiea M.E., Save H., Wilson C., Bettadpur S. and Seo K.W. (2021). Error Assessment of GRACE and GRACE Follow-On Mass Change. Journal of Geophysical Research: Solid Earth, 126(9), e2021JB022124, DOI:10.1029/2021JB022124 Cleveland R.B., Cleveland W.S., McRae J.E. and Terpenning I. (1990). STL: A Seasonal-Trend Decomposition Procedure Based on Loess (with Discussion). Journal of Official Statistics, 6, 3–73. Eicker A., Jensen L., Wöhnke V., Dobslaw H., Kvas A., Mayer-Gürr T. and Dill R. (2020). Daily GRACE satellite data evaluate short-term hydrometeorological fluxes from global atmospheric reanalyses. Scientific Reports 2020 10, 4504, DOI:10.1038/s41598-020-61166-0 Ferreira V.G., Montecino H.D.C., Yakubu C.I. and Heck B. (2016). Uncertainties of the Gravity Recovery and Climate Experiment timevariable gravity-field solutions based on three-cornered hat method. Journal of Applied Remote Sensing, 10(1), 015015, DOI:10.1117/1. JRS.10.015015 Frolova N.L., Grigorev V.Y., Krylenko I.N. and Zakharova E.A. (2021). State-of-the-art potential of the GRACE satellite mission for solving modern hydrological problems. Vestnik of Saint Petersburg University. Earth Sciences, 66(1), 107–122 (in Russian with English summary), DOI:10.21638/SPBU07.2021.107 Georgiadi A.G. and Groisman P.Y. (2022). Long-term changes of water flow, water temperature and heat flux of two largest arctic rivers of European Russia, Northern Dvina and Pechora. Environmental Research Letters, 17(8), 085002, DOI:10.1088/1748-9326/AC82C1 Gupta D. and Dhanya C.T. (2021). Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows. Journal of Hydrologic Engineering, 26(4), 04021007, DOI:10.1061/(ASCE)HE.1943-5584.0002072 Han J., Yang Y., Roderick M.L., McVicar T.R., Yang D., Zhang S. and Beck H.E. (2020). Assessing the Steady-State Assumption in Water Balance Calculation Across Global Catchments. Water Resources Research, 56(7), e2020WR027392. DOI:10.1029/2020WR027392 Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., Nicolas J., Peubey C., Radu R., Schepers D., Simmons A., Soci C., Abdalla S., Abellan X., Balsamo G., Bechtold P., Biavati G., Bidlot J., Bonavita M., … Thépaut J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049, DOI:10.1002/qj.3803 Humphrey V., Gudmundsson L. and Seneviratne S.I. (2016). Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes. Surveys in Geophysics, 37(2), 357–395, DOI:10.1007/S10712-016-9367-1 Kalugin A.S. and Motovilov Y.G. (2018). Runoff Formation Model for the Amur River Basin. Water Resources, 45(2), 149–159, DOI:10.1134/S0097807818020082 Kim H., Yeh P.J.F., Oki T. and Kanae S. (2009). Role of rivers in the seasonal variations of terrestrial water storage over global basins. Geophysical Research Letters, 36(17), DOI:10.1029/2009GL039006 Kvas A., Behzadpour S., Ellmer M., Klinger B., Strasser S., Zehentner N. and Mayer-Gürr T. (2019). ITSG-Grace2018: Overview and Evaluation of a New GRACE-Only Gravity Field Time Series. Journal of Geophysical Research: Solid Earth, 124(8), 9332–9344, DOI:10.1029/2019JB017415 Landerer F.W., Flechtner F.M., Save H., Webb F.H., Bandikova T., Bertiger W.I., Bettadpur S. V., Byun S.H., Dahle C., Dobslaw H., Fahnestock E., Harvey N., Kang Z., Kruizinga G.L.H., Loomis B.D., McCullough C., Murböck M., Nagel P., Paik M., … Yuan D.N. (2020). Extending the Global Mass Change Data Record: GRACE Follow-On Instrument and Science Data Performance. Geophysical Research Letters, 47(12), e2020GL088306, DOI:10.1029/2020GL088306 Loveland T.R., Reed B.C., Ohlen D.O., Brown J.F., Zhu Z., Yang L. and Merchant J.W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330, DOI:10.1080/014311600210191 Machul’skaya E.E. and Lykosov V.N. (2009). Mathematical modeling of the atmosphere-cryolitic zone interaction. Izv. Atmos. Ocean. Phys., 45, 687–703, DOI:10.1134/S0001433809060024 Massoud E.C., Bloom A.A., Longo M., Reager J.T., Levine P.A. and Worden J.R. (2022). Information content of soil hydrology in a west Amazon watershed as informed by GRACE. Hydrol. Earth Syst. Sci, 26, 1407–1423, DOI:10.5194/hess-26-1407-2022 Motovilov, Yu., Gottschalk, L., Engeland, K. and Belokurov, A. (1998). ECOMAG — regional model of hydrological cycle. Application to the NOPEX region. Department of Geophysics, University of Oslo. Nasonova O.N., Gusev Y.M. and Kovalev E. (2022). Climate change impact on water balance components in Arctic river basins. Geography, Environment, Sustainability, 15(4),148–157, DOI:10.24057/2071-9388-2021-144 Popova V.V., Turkov D.V. and Nasonova O.N. (2021). Estimates of recent changes in snow storage in the river Northern Dvina basin from observations and modeling. Ice and Snow, 61(2), 206–221 (in Russian with English summary), DOI:10.31857/S2076673421020082 Premoli A. and Tavella P. (1993). A Revisited Three-Cornered Hat Method for Estimating Frequency Standard Instability. IEEE Transactions on Instrumentation and Measurement, 42(1), 7–13, DOI:10.1109/19.206671 Scanlon B.R., Zhang Z., Rateb A., Sun A., Wiese D., Save H., Beaudoing H., Lo M.H., Müller-Schmied H., Döll P., van Beek R., Swenson S., Lawrence D., Croteau M. and Reedy R.C. (2019). Tracking Seasonal Fluctuations in Land Water Storage Using Global Models and GRACE Satellites. Geophysical Research Letters, 46(10), 5254–5264, DOI:10.1029/2018GL081836 Scanlon Bridget R., Zhang Z., Save H., Sun A.Y., Schmied H.M., Van Beek L.P.H., Wiese D.N., Wada Y., Long D., Reedy R.C., Longuevergne L., Döll P. and Bierkens M.F.P. (2018). Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proceedings of the National Academy of Sciences of the United States of America, 115(6), E1080–E1089, DOI:10.1073/PNAS.1704665115 Tapley B.D., Watkins M.M., Flechtner F., Reigber C., Bettadpur S., Rodell M., Sasgen I., Famiglietti J.S., Landerer F.W., Chambers D.P., Reager J.T., Gardner A.S., Save H., Ivins E.R., Swenson S.C., Boening C., Dahle C., Wiese D.N., Dobslaw H., … Velicogna I. (2019). Contributions of GRACE to understanding climate change. Nature Climate Change, 9(5), 358–369, DOI:10.1038/s41558-019-0456-2 Volodin E.M. and Lykosov V.N. (1998). Parametrization of heat and moisture transfer in the soil-vegetation system for use in atmospheric general circulation models: 1. Formulation and simulations based on local observational data. Izvestiya, Atmospheric and Oceanic Physics, 37(4), 405–416. Wilson M.F. and Henderson-Sellers A. (1985). A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, 5(2), 119–143, DOI:10.1002/JOC.3370050202 Wu R.J., Lo M.H. and Scanlon B.R. (2021). The Annual Cycle of Terrestrial Water Storage Anomalies in CMIP6 Models Evaluated against GRACE Data. Journal of Climate, 34(20), 8205–8217, DOI:10.1175/JCLI-D-21-0021.1 Xu T., Guo Z.X., Xia Y.L., Ferreira V.G., Liu S.M., Wang K.C., Yao Y., Zhang X. and Zhao C. (2019). Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. Journal of Hydrology, 578, 124105. DOI:10.1016//J.JHYDROL.2019.124105 Zhang Y., He B., Guo L., Liu J. and Xie X. (2019). The relative contributions of precipitation, evapotranspiration, and runoff to terrestrial water storage changes across 168 river basins. Journal of Hydrology, 579, 124194. DOI:10.1016/J.JHYDROL.2019.124194 https://ges.rgo.ru/jour/article/view/3184 doi:10.24057/2071-9388-2023-2899 Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).The information and opinions presented in the Journal reflect the views of the authors and not of the Journal or its Editorial Board or the Publisher. The GES Journal has used its best endeavors to ensure that the information is correct and current at the time of publication but takes no responsibility for any error, omission, or defect therein. Авторы, публикующие в данном журнале, соглашаются со следующим:Авторы сохраняют за собой авторские права на работу и предоставляют журналу право первой публикации работы на условиях лицензии Creative Commons Attribution License, которая позволяет другим распространять данную работу с обязательным сохранением ссылок на авторов оригинальной работы и оригинальную публикацию в этом журнале.Авторы сохраняют право заключать отдельные контрактные договорённости, касающиеся не-эксклюзивного распространения версии работы в опубликованном здесь виде (например, размещение ее в институтском хранилище, публикацию в книге), со ссылкой на ее оригинальную публикацию в этом журнале.Авторы имеют право размещать их работу GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY; Vol 16, No 4 (2023); 6-13 2542-1565 2071-9388 three-cornered hat method GRACE LSM hydrological model cold climate info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2024 ftjges https://doi.org/10.24057/2071-9388-2023-289910.1029/2021JB02212410.1038/s41598-020-61166-010.1117/110.21638/SPBU07.2021.10710.1088/1748-9326/AC82C110.1061/(ASCE)HE.1943-5584.000207210.1029/2020WR02739210.1002/qj.380310.1007/S10712-016-9367-110.1134/S00978 2024-01-16T18:00:09Z 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. Article in Journal/Newspaper Arctic dvina Pechora Geography, Environment, Sustainability (E-Journal)

Similar Items