Areal Rainfall Estimation Model Parameter and Input .1 Schematic Watershed Model – Watershed Delineation

41 Table 4.3 – 4.4 demonstrate the land cover changes by providing information “initial state” into a “new state” to the respective period. Mostly, the forest cover decreased due to the new agricultural land development. During the period of 1990 to 2009, is about 250 km 2 of forest cover on Palu catchment has been converted into agricultural and other land-use type such as urban area or settlement. Based on Table 4.3, the statistics report shows that between 1990 and 2001, about 134 km 2 of forest cover has been converted into other land use type such as agricultural land, shifting cultivation, and also use as settlement. During this period, agricultural land has increased by 132 km 2 from previous state with only 327 km 2 in 1990 become 466 km 2 on 2001. In addition of forest cover, shrub land also decreased during this period. About 127 km 2 of shrub land has been converted into agricultural land and about 11.5 km 2 of shrub land has been converted into other land use. Between 2001 and 2009 the statistics report shows that 250 km 2 of forest cover has been converted; 169 km 2 into agricultural land and 82 km 2 into shrub land. On the other hand, shrub land had also change. About 182 km 2 have changed into agricultural land, 3.5 km 2 into build up area, and 1.4 km 2 into water or marshy land. Figure 4.6 Land use on the Palu catchment 42

4.2 Hydrological Modeling

4.2.1 Model Input and Parameters 4.2.1.1 Lag Time and Peaking Coefficient Lag time for each sub-basin can be derived from the elevation map. Lag time is time differences between mean effective rainfall and peak discharges. This is most important parameter to assess river response to the land cover changes. The lag time for each sub-basin has been calculated using NRCS method to be used for NRCS transform parameters as shows in Table 4.5. All needed parameters to calculate lag time Tp are derived from digital elevation model DEM. All of those parameters are: Table 4.5 Lag time parameters for each sub-basin in Palu catchment Name Ct L mile Lca mile S m Sub‐Basin 1 1.8 32.3481 14.449 203.68 0.39 Sub‐Basin 2 1.8 30.942 17.364 152.899 0.39 Sub‐Basin 3 1.7 21.701 9.366 213.609 0.39 Sub‐Basin 4 1.6 24.716 13.315 239.495 0.39 Sub‐Basin 5 1.7 24.92 9.555 204.567 0.39 Sub‐Basin 6 1.6 20.749 10.996 281.269 0.39 Sub‐Basin 7 1.7 26.053 8.33 216.4 0.39 Table 4.6 Lag time and peaking coefficient values for each sub-basin in Palu catchment. Parameter Lag Time Hr Peak Coefficient Sub-basin 1 7.0 0.32 Sub-basin 2 7.8 0.32 Sub-basin 3 4.7 0.35 Sub-basin 4 5.3 0.38 Sub-basin 5 5.1 0.35 Sub-basin 6 4.4 0.38 Sub-basin 7 4.8 0.35 Based on calculation, the longest lag time is on sub-basin 2 with 7.8 hour and the shortest is on sub-basin 6 with only 4.42 Hour. Its mean the sub-basin 2 has longest peaking time compared with the other sub-basin.

4.2.1.2 Curve Number for Surface Runoff Estimation

This research is using NRCS curve number method to estimate to number of runoff occurred on the watershed. There are six-land cover classes was used to estimate the NRCS curve number value to each sub-basin on the study area.