62 examine the relationship between driving factors and land use change in study
area. Calibration result hydrology model gave value or R
2
achieve 0.524, while the values of Nash-Sutcliffe Efficient NSE is 0.67 and 42.9 of relative volume
errors RV
E
. By using three tests it can be stated that the model is satisfactory accepted.
The values of water yield from 4 scenarios for Year 2030 scenario 1, scenario 2, scenario 3 and scenario 4 are not significantly different due to limited sized
of research area and low dynamic of land use driving factor. However, based on the comparison about values of water yield between scenario-based
simulation for Year 2030 and existing condition of Year 2010 data shows increasing of water yield from Year 2010 to Year 2030.
The increasing values of water yield influenced by forest rehabilitation activity by the government inside forest area and the development of community forest
outside the forest area. Increasing values of water yield is quite high for scenario 2 and scenario 4, where government policy about restriction of land
use inside forest area applied. That means government policy to prohibit land use conversion inside forest is appropriate to apply.
4.2. Further Research Directions
By considering some limitations that are discussed before and possible improvement of the model approach to support forecasting land use change and
analysis on impact to water yield, further development of this research would be interesting to consider some factors:
Further research could be implemented in cooperation with planners and decision makers. By implementing the methodology in practice, some benefits
can be obtained, including the results of this research could be effectively communicated with local managers, improvement of the applicability of the
model and the methods, could involve more data in the analysis and more relevant policy could be put into practice.
63 This research was completely done; however further research could apply
scenario-based approach in the larger scale by using higher spatial resolution data. CLUE-S model framework gives possibility to accommodate more
detailed scale data and level of analysis. The advantage is that the research could explore more information from the specific locations and improve the
understanding about a specific problem on upstream watershed zone. Improvement of the model could be made in the driving factors selection before
they are incorporated in the logistic regression. All of influencing variables could be examined by using expert knowledge approach to improve the
selection process and reduce subjective preference. Moreover, the dynamic social-economic might need to include on the analysis for future research, to
determine the best watershed management that can be implemented in Upstream Cisadane Watershed.
64
