## 5 Conclusions  
Land use and land cover assessment and change detection between 2014 and 2019 were successfully conducted using visual interpretation and a post-classification comparison change detection algorithm. The overall accuracy and Kappa hat coefficient of the LULC map in 2019 were more than 95%. Then, time-series LULC data between 2020 and 2029 were simulated using the CLUE-S model. These simulated LULC data provide realistic results that reflect expected trends. After that, to evaluate the water balance, the interpreted and simulated LULC data between 2019 and 2029 were used as the primary input to estimate water supply and demand. For the water supply assessment, annual and monthly water yields were successfully estimated using the SWAT model. In the calibration and validation periods under the dry and wet year conditions, the SWAT model performance ranged from good to very good according to statistical measurements, namely, the RMSE-observations standard deviation ratio, Nash-Sutcliffe efficiency, and percent bias. Furthermore, for the water demand assessment, three primary consumption activities--residential, tourist, and agriculture and forest use--were calculated under normal and new normal conditions  
Figure 16: Monthly water balance with consideration of ecological water requirements under two scenarios in normal and new normal conditions: (**a**) dry year scenario; (**b**) wet year scenario. using the water footprint basis. Finally, the annual and monthly water balance in terms of water surplus and deficit between 2020 and 2029 under dry and wet year scenarios in normal and new normal conditions was successfully evaluated without and with the consideration of ecological water requirements for water resource management. The annual water balance evaluation with the consideration of ecological water requirements revealed a water deficit every year under the dry year scenario in normal and new normal conditions. In addition, the monthly water balance indicated a water deficit in the summer every year, both without and with the consideration of ecological water requirements. Consequently, it can be concluded that integrating remote sensing data (very high spatial resolution) with advanced geospatial models (CLUE-S model, SWAT model, and water footprint) can provide essential information about water supply, demand, and balance for water resources management, particular water scarcity, in Phuket Island in the future. This study's conceptual framework and research workflows can assist government agencies in examining the water deficit in other areas, particularly the Northeast Region of Thailand, where agricultural drought frequently occurs. Conceptualization, S.O. and N.P. ; methodology, S.O. and N.P. ; software, N.P. ; validation, S.O. and N.P. ; formal analysis, S.O. and N.P. ; investigation, S.O. and N.P. ; data curation, N.P. ; writing--original draft preparation, N.P. ; writing--review and editing, S.O. ; visualization, N.P. ; supervision, S.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors would like to thank the Suranaree University of Technology and Prince of Songkla University for supporting the facilities to undertake this research. The special thanks from the authors go to anonymous reviewers for their valuable comments and suggestions that improve our manuscript from various perspectives. The authors declare no conflict of interest. The evapotranspiration coefficient (Kc) of each agriculture and forest type. \begin{tabular}{c c} \hline \hline
**No.** & **Agriculture and Forest Type** & **Evapotranspiration** & **Coefficient (Kc)** \\ \hline
1 & Field crop & 0.6 \\
2 & Paddy field & 0.6 \\
3 & Para rubber trees & 1.0 \\
4 & Evergreen forest & 1.0 \\
5 & Mangrove forest & 1.0 \\
6 & Scrub forest & 0.5 \\ \hline \hline \end{tabular}  Note: The Kc values were obtained from [PERSON] et al. [72]. The reference evapotranspiration under the Penman-Monteith method.