## 5 Conclusions  
In this study, a combination of multi-satellite data and modeled variables was employed to examine the TWS and the dynamics of its components in the LLRB. The main conclusions are listed as follows:  
1. The spatial and temporal patterns of the TWSA in the LLRB vary between 2002 to 2009 and 2010 to 2019. The fluctuations in precipitation and the construction of reservoirs are the primary drivers of changes in the TWSA in the region. Precipitation decreased significantly from 2010 to 2019 (\(-\)34.68 cm/yr), but the TWSA showed a significant increase (8.96 cm/yr) due to enhanced water storage capacity in the Xiaowan and Nuozhadu reservoirs. 2. SWS exhibited an overall increasing trend at a rate of 0.51 cm/yr from 2002 to 2019. However, SWS showed a decrease (\(-\)5.48 cm/yr) from 2010 to 2019 due to declining precipitation (\(-\)34.68 cm/yr) and increasing evaporation, causing a reduction in reservoir volume after a stabilized operation. 3. The linear trends of the GWSA and TWSA in the study region follow a similar pattern after adjusting for regulation of the Xiaowan and Nuozhadu reservoirs. From the initiation of water storage in 2010 to the stabilized operation of the reservoirs, GWS exhibited a steady rise (9.73 cm/yr) due to the maintenance of groundwater levels by the reservoirs. This study serves as a benchmark for evaluating the impacts of climate change and reservoir regulation on changes in TWS and its associated water components in the LLRB. It will provide critical support for water resource management and cooperation in the Lancang-Mekong River basin. Furthermore, the utilization of multi-satellite EO datasets demonstrates their potential to support regional-scale monitoring, particularly in regions with limited data and transboundary river basins. The precision of these EO datasets is expected to improve with the launch of new and upgraded missions, such as Sentinel-6, Sentinel-3C/-3D, and SWOT missions. The research was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grants No. XDA2006020202), the National Natural Science Foundation of China (Grants No. 42201037), and the project was funded by the China Postdoctoral Science Foundation (Grants No. 2022M713122). [PERSON] was financially supported by the Chinese Scholarship Council, the Special Research Assistant Program of the Chinese Academy of Sciences (Grant No. E2S20001Y5), which is gratefully acknowledged. River discharge data were provided by the Hydrology and Water Resources Bureau of Yunnan Province in China (accessed on 11 July 2022), which is acknowledged here. GRACE data are available at [[https://grace.jpl.nasa.gov/data/get-data/jpl_global_masons/](https://grace.jpl.nasa.gov/data/get-data/jpl_global_masons/)]([https://grace.jpl.nasa.gov/data/get-data/jpl_global_masons/](https://grace.jpl.nasa.gov/data/get-data/jpl_global_masons/)) (accessed on 11 May 2022). Jason-2 data are from the CNES AVISO+ program (Centre National D'Etudes Spatiales, Archiving, Validation and Interpretation of Satellite Oceanographic data, [[ftp://avisofptpc.es.fr/AVISO/pub/](ftp://avisofptpc.es.fr/AVISO/pub/)]([ftp://avisofptpc.es.fr/AVISO/pub/](ftp://avisofptpc.es.fr/AVISO/pub/))) (accessed on 11 May 2022). Cryosat-2 data are publicly accessible at [[https://earth.esa.int/eogateway/missions/cryosat/data](https://earth.esa.int/eogateway/missions/cryosat/data)]([https://earth.esa.int/eogateway/missions/cryosat/data](https://earth.esa.int/eogateway/missions/cryosat/data)) (accessed on 11 May 2022). Sentinel-3 data are publicly accessible at [[https://scihub.copernicus.eu/dhus/](https://scihub.copernicus.eu/dhus/)]([https://scihub.copernicus.eu/dhus/](https://scihub.copernicus.eu/dhus/)) (accessed on 11 May 2022). JRC can be processed using the Google Earth Engine. GRAS was provided by the DHI Company of Denmark (accessed on 11 January 2022).