Model Calibration and Verification Responses of River System to Land Cover Changes on the Catchment

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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. 43 • Agricultural land including all type of farming method; dry and wet paddy field, cultivated land, and plantation area. • Barren land including grass wide area of grass land • Buildup area including high and low density urban area, settlement, and scattered settlement. • Forest including protected and non-protected forest. • Shrub land including bush land with small wood. • Water body including river, lake, and marshy land. NRCS curve is a function of land-use, soil type, and slope. The determine of the curve number have been done by applying these three step; classifying the land-cover and land use into respective classes based on the NRCS Table references see appendix 1, classifying the hydrological soil classes, and overlaying it all with the slope map. The area with high slope, less vegetation, and less infiltration resulting higher curve number value. Where the flat area with high vegetation density and high soil infiltration value are produce low curve number value. In this research, averages curve number value were used as input to the model. It have been done by multiplying the curve number of each land cover classes with its area to get the total results of each land cover value the summarizing all of its products and divide it by total sub-basin area. When look at the curve number maps as shown on the Figure 4.7, the higher values of curve number area are increasing over the years with averages value is ranging from 76 to 92. Generally these areas are agriculture land and buildup area. On the upper side of the catchment also has high curve number value ranging from 76 to 84 due to the hydrological soil type in which has been grouped into class D, event thought its mostly forest area. As HEC-HMS model need uniform curve number for each sub-basin, the weighting averages method was employed to calculate the sub-basin average curve number. These methods works by calculate the curve areas of each land cover classes, and then divide it with the total sub-basin area. Based on the calculation, the curve number are varying on each sub-basin thich is the sub-basin 1 has lowest curve number with 57 and sub-basin 7 has highest curve number with 44 67 on 1990. Its mean, the sub-basin 1 will produce lower surface runoff compared with the sub-basin 7. As shown in Table 4.8, the curve number values are increased in each sub-basin over the land cover period due to reduce of forest cover and increased of agricultural land areas. Since here we believe that forest with high density of vegetation can reduce the surface runoff significantly during the rain event. Table 4.7 Average curve number for each sub-basin were used during simulation Sub-Basin Years 1990 2001 2009 Sub-Basin 1 57 59 62 Sub-Basin 2 66 66 68 Sub-Basin 3 59 60 65 Sub-Basin 4 59 60 65 Sub-Basin 5 59 63 69 Sub-Basin 6 54 54 59 Sub-Basin 7 67 70 75 The curve number maps are served on the following figures. The figure 4.7 indicates the curve number on the study area that is represented by graduated color; greenest mean has lower CN values, while more red area indicates the area has higher CN values that are means has higher surface runoff. 45 Figure 4.7 Curve number map of existing land cover on 1990, 2001, and 2009.