Conclusion FORECASTING LAND USE CHANGE IN UPSTREAM CISADANE

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3.3.1. Data Preparation

Data source that is used for this research are primary and secondary data. Primary data is collected by observation of field condition to gain understanding on physical condition of Upstream Cisadane Watershed. Primary data collected e.g. land use, water discharge and soil characteristic in Upstream Cisadane Watershed. Data requirement for hydrologic model is shown in Table 12. Table 12. Data Requirement for Hydrological Model. No Data Type Source of Data Spatial Resolution 1 Soil map Puslitanak Derived from semi-detailed soil map 1: 100.000 2 Stream network USGS Derived from Digital Elevation Model DEM SRTM 30 m 3 Rainfall data BMKG Derived from daily rainfall - 4 Water Discharge Public Work Department - 5 Land use map Derived from forecasted land use -

3.3.2. Hydrologic Model Development

The HEC-HMS rainfall-runoff hydrologic model was developed using HEC- GeoHMS 5.0, an ArcGIS extension program and preprocessor to HEC-HMS. The program takes advantage of terrain and geographic data publicly available over the Internet. GIS algorithms incorporated within HEC-GeoHMS were used to delineate a study area based on the stream gage locations. The stream flow gage data provides pertinent information regarding inputoutput stream discharge volumes through the study area Olivera, 2001. HEC-GeoHMS was further used to delineate drainage networks and sub- basin boundaries. The SCS runoff and SCS lag equations were both implemented to estimate runoff volumes and the transformation of these volumes toward the channel network. Parameters for the runoff and transform model components SCS runoff model, SCS lag equation, respectively were initially based on soil and land- use conditions, determined through the pre-processing of forecasted land use model. Lumped curve number CN values were derived and assigned to each delineated sub-basin using HEC-GeoHMS. 49 HMS project setup was completed with creation of the HMS project file. All required project files were copied to the HEC-HMS project folder. The completion of the HEC-GeoHMS application was difficult, problem prone, and time consuming. A well-documented application of HEC-GeoHMS was not found during the review of pertinent literature. Because of lack of documentation of its application, the HEC-GeoHMS procedure used herein was discovered, in part, by trial and error. In determining the correct procedure, numerous incorrect steps were easily taken which required much time and additional effort to correct. In the deliberate process of discovering the correct steps to take, many steps in the process were repeated to remove problematic issues that were faced.

3.3.2.1. Creation of the Curve Number Grid

The 2009 land use and scenario-base land use for 2030 dataset and the Soils dataset were used to create the curve number grid. The 2001 Land use was converted to vector data so it could be combined with the soils. The soils dataset, when downloaded, has a soils database, which includes the hydrologic soil group classification, among other information. A table of curve numbers for different land use types and hydrologic soil groups was created and used in the creation of the curve number grid. The curve numbers used in this analysis, listed in Table 14 below. The land use and soils layers were used to create a curve number grid with a 50-meter cell size. The initial resolution of the Land Cover dataset was approximately 30 meter, which means that the raster grid was created with square grid cells 30 meters long on each side. The 30-meter DEM was re-sampled to increase the cell size to 50-meters. In reducing the cell density, computational time for the GIS procedures was reduced to a manageable amount of time.

3.3.3. Hydrologic Model Calibration

Replication of a perfect hydrograph through calibration was not the primary goal of this work. Effort was focused primarily on establishing a repeatable procedure for linking the hydrologic model with a land-use model. Simulated runoff 50 estimates need only to generate a reasonable level of accuracy to be used for a comparative analysis of land-use alternatives reflective of specific land-use policies. Calibration results presented here were deemed acceptable for this purpose. Following calibration done graphically visual comparison or numerically comparison of observed and predicted values using performance measure. The performance measures used in this study are the Nash-Sutcliffe Efficiency NS and Relative Volume Error RV E . The Coefficient of Efficiency E Nash and Sutcliffe, 1970 was calculated as a measure of goodness-of-fit to assess model performance within the watershed and at the outlet. The Coefficient of Efficiency E has often been applied to assess the performance of the hydrologic model and is described as the ratio of the Mean Square Error MAE to the variance in the observed data, subtracted from unity Legates and McCabe, 1999. E values can range from minus infinity poor model to 1.0 perfect model and is given by: � = . − ∑ � − � 2 ∑ � − ̅ 2 Where O represents observed values and P represents predicted values, and Ō denotes the mean for the entire evaluation time period. The Nash-Sutcliffe efficiency measure goes to 1 as the fit improves. A value between 0.6 and 0.8 indicates that the model performs reasonably. Values between 0.8 and 0.9 indicate that the model performs very well and values between 0.90 and 1.0 indicate that the model performs extremely well Nash and Sutcliffe, 1970. The second performance measure, the RV E is used for quantifying the volume errors. This RV E can vary between ∞ and - ∞ but performs best when a value of 0 is generated since no difference between simulated and observed discharge occurs Janssen and Heuberger 1995. A relative volume error less than +5 or -5 indicates that a model performs well while relative volume errors between +5 and +10 and -5 and -10 indicate a model with reasonable performance. �� � = [ ∑ � − � ∑ � ] ×