58 Figure 18.
Simulated hydrograph using 2010 rainfall data for different land use scenarios.
The simulated hydrograph obtain the peak flow and water yield information for four different land use scenarios. The values of each scenario peak flow are
81.00 m3s, 81.10 m3s, 81.00 and 81.10 m3s for scenario 1, scenario 2, scenario 3 and scenario 4 respectively. While the value of water yield are 276,085.00 m3,
278,038.90 m3, 275,143.20 m3 and 279,178.20 m3 all values for water yield are multiplied by 1000, for scenario 1, scenario 2, scenario 3 and scenario 4
respectively.
Table 19. Water Yield Result for Existing and Each Scenario
Condition Peak Flow
m
3
s Water Yield
1000 m3
Existing 2010 81
273,973.60 Scenario 1 2030
81 276,085.00
Scenario 2 2030 81.1
278,038.90 Scenario 3 2030
81 275,143.20
Scenario 4 2030 81.1
279,178.20
59 The table shows that values of water yield from those 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 area is
appropriate to apply.
3.5. Conclusion
Integration between ArcHydro, HEC-GeoHMS and HEC-HMS applied to assess the impact of land use changes on water yield in Upstream Cisadane
Watershed. For HEC-HMS itself applied of several model, i.e. loss model, transform model, base flow model and routing model. Four scenarios was develop
base on difference demand of land use and spatial restrictionpolicy. The model calibration done by adjusting the curve number and impervious
values, until the results matched the field data. However, the performance measures used in this study are the Nash-Sutcliffe Efficiency NSE and Relative Volume
Error RV
E
. Calibration result gave value or R
2
achieve 0.524, while the values of 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 success of forest rehabilitation inside andor outside forest area in Upper Cisadane Watershed increases the values of water yields during period 2010
until 2030. Nevertheless, to maintain water yield sustainability, implementation of a good government policy is certainly needed.
60
61
CHAPTER IV CONCLUSIONS
4.1. General Conclusion
This study is intended to integrate remote sensing and GIS for forecasting land use change in Upstream Cisadane Watershed and also assessing the impact of
land use changes to water yield at Upstream Cisadane Watershed. By integrating the use of GIS and remote sensed data in both land use and hydrological model are
very useful. Based on the results from land use and hydrological modeling processes and its analysis, some conclusions can be drawn as follows:
Land use changes in Upstream Cisadane Watershed during 1990-2009 periods were mainly dominated by expansion of settlement area with the annual rate of
change 13 during 1991-2009, followed by forest 5.05 and grassland 4.72 in the same period. Estate is decrease in period 1991-2009 with annual
rate of change -6.21 . The trend of land use change in 1991-2009 showed this area faces the
expansion of settlement, forest and grassland area and the decreasing of estate. The selection of driving factors that has significant effect to land use change
was conducted by using logistic regression analysis. The most significant driving factor for grassland and settlement was distance to public facility, while
distance to education facility was significant driving factor for estate and forest. Meanwhile water remains constant. The relationship between driving factors
and land use change showed positive value which means that the higher the value of these factors, the higher the probability of land use to change. In
contrast, negative value that implies that the higher the value of these factors, the decrease the probability of land uses to change.
The goodness of the statistical measurement revealed that ROC values for urban water area, grassland area, estate area, settlement area and forest area
were 0.903, 0.701, 0.780, 0.813 and 0.994, which indicated that the probability of land uses built from these models were capable to represent land use changes
and empirical analysis by using logistic regression method was satisfactory to
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.
