Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Zeitschriftenartikel

Potential future changes of terrestrial water storage based on climate projections by ensemble model simulations

Urheber*innen

Jia,  Binghao
External Organizations;

Cai,  Ximing
External Organizations;

/persons/resource/fangzhao

Zhao,  Fang
Potsdam Institute for Climate Impact Research;

Liu,  Jianguo
External Organizations;

Chen,  Si
External Organizations;

Luo,  Xin
External Organizations;

Xie,  Zhenghui
External Organizations;

Xu,  Jianhui
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PIKpublic verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Jia, B., Cai, X., Zhao, F., Liu, J., Chen, S., Luo, X., Xie, Z., Xu, J. (2020): Potential future changes of terrestrial water storage based on climate projections by ensemble model simulations. - Advances in Water Resources, 142, 103635.
https://doi.org/10.1016/j.advwatres.2020.103635


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_24437
Zusammenfassung
An accurate estimation of terrestrial water storage (TWS) is crucial for water resource management and drought monitoring. However, the uncertainties in model physics, surface parameters and meteorological data often limit the accuracy of land surface hydrological models in estimating TWS. In this study, a multi-model-based framework was developed to predict TWS in China by 2050 using a Bayesian model averaging (BMA) method and GRACE satellite observations. Compared to GRACE observations, our BMA-based TWS anomaly (TWSA) estimations reduce root mean square errors by 10–16% and increase correlation coefficients by 26–46% over semi-humid and semi-arid basins than simple arithmetical averaging for the validation period (2008–2016). At the same time, BMA shows decreasing root mean square differences (10–12%) over humid basins. The calibrated BMA weights were then applied to future projections of TWSA under two Representative Concentration Pathways (RCP): RCP 2.6 and RCP 6.0. The overall rate of TWSA for the future period (2021–2050) was detected with the same direction as that from past decades (2003–2016), but with larger decreasing values. Especially for the Haihe basin in North China, BMA-based TWSA would decrease faster by about 19% for RCP 2.6 and 26% for RCP 6.0. These results suggest a decreasing trend in future TWS over most of the basins in China due to combined effects of global warming and human activities, which suggests likely aggravated risk of water shortage and a growing need for adaptive water resources management.