1. Assessment of Runoff, Sediment Yield and Nutrient Load on Watershed Using Watershed Modeling Mohammad Sholichin Mohammad Sholichin 1) Faridah Othman 2) Shatirah Akib 3) 1) Lecturer, Water Resources Department, Brawijaya University, Indonesia PhD Student, Department of Civil Engineering, Universiti Malaya, Malaysia; 2) Lecturer, Department of Civil Engineering, Universiti Malaya, Malaysia 3) PhD Student, Department of Civil Engineering, Universiti Malaya, Malaysia

2. Background Increasing population people, wastewater from industry and fertilizer use for farmer become water quality problem in stream. Increasing population people, wastewater from industry and fertilizer use for farmer become water quality problem in stream. Main problem in Upstream Brantas River Basin are land erosion, sedimentation and pollutant load from both agricultural area and residential Main problem in Upstream Brantas River Basin are land erosion, sedimentation and pollutant load from both agricultural area and residential Propose of watershed modeling (AVSWAT) to predict water quality by difference management practice Propose of watershed modeling (AVSWAT) to predict water quality by difference management practice

3. The main objective of this research are : to assessment of runoff, sediment yield and nutrient load at Watershed to assessment of runoff, sediment yield and nutrient load at Watershed To identification of potentially source of sediment and nutrient load on watershed To identification of potentially source of sediment and nutrient load on watershed to propose the relationship relating sediment and nutrient loads to physiographic and hydrologic properties of the each sub watershed to propose the relationship relating sediment and nutrient loads to physiographic and hydrologic properties of the each sub watershed

4. The Case Study

5. Upstream Brantas River Upstream Brantas Banggo River Basin Amprong River Basin Upper Lesti River Basin Genteng River Basin Metro River Basin Manten River Basin Lower Lesti River Basin Kedung Banteng River Basin

6. Soil Water Assessment Tool (SWAT) Uses physically-based input such as soil, weather, land use, and topographic data to predict the impact of land management practice on water, sediment, and agricultural chemical yields Uses physically-based input such as soil, weather, land use, and topographic data to predict the impact of land management practice on water, sediment, and agricultural chemical yields Continuous Long-term Simulation on daily step Continuous Long-term Simulation on daily step Assesses impacts of climate and management on yields of water, sediment, and agricultural chemicals Assesses impacts of climate and management on yields of water, sediment, and agricultural chemicals

7. Processing and Display of AVSWAT Model

8. Schematic of input and outputs of the AVSWAT model

9. AVSWAT Routing Phase Flow routing Flow routing - Inputs (runoff, rain, point-source discharge) - Outputs (evaporation, transmission, extraction for human use) human use) Sediment routing: deposition, resuspension, erosion Sediment routing: deposition, resuspension, erosion Nutrient Routing Nutrient Routing - Model dissolved and adsorbed nutrients. - In stream kinetics adapted from QUAL2E

10. 1. Land Phase

12. Phosphorous Cycle

13. 2. Routing Phase

14. Period of Calibration: January 1, 1992 – Dec 2002 Period of Validation : Jan 1 2003 – Dec 31 2006 Rainfall/Runoff/Routing: Daily Rain/Curve Number/Daily Rainfall Distribution: Normal Skewed Potential ET Method: Priestley-Taylor method Channel Water Routing: Muskingum Channel Degradation: Not Active Stream Water Quality Processes: Active Lake Water Quality Processes: Not Active Printout Frequency: Monthly and Annually SWAT Setup Preparation

15. Slope Classification

16. Slope Down Area

17. Land use Map

18. Soil Type Map

19. Calibration Period

20. Correlation Value

21. Results 1. Land Area Phase

22. Sediment Yield (ton/ha/yr)

23. > 1.74 1.74 – 4.53 4.53 – 7.88 7.88 – 27.33 27.33 – 85.95 Upper Lesti River Basin Amprong River Basin

24. Organic N (kg N/ha)

25. Upstream Brantas Bango River Basin

26. Organic P (kg P/ha)

27. Bango River Basin

28. Annual Average Estimates of Sediment Yield and Nutrient Load for Each Basin Based on Period 1995 to 2006 Simulated

29. 2. In Stream

30. Concentration of Organic N & P

31. Annual Average Estimates of Nutrient Concentration in outlet stream for Each Basin Based on Period 1995 to 2006 Simulated

32. 3. In Reservoir

34. Conclusion Banggo river basin is both highest of rainfall 2.043 mm and surface runoff 876.67 mm, respectively Banggo river basin is both highest of rainfall 2.043 mm and surface runoff 876.67 mm, respectively The highest sediment yield is 85,94 ton/ha/year in Upper Lesti river basin due to maximum erosion rate The highest sediment yield is 85,94 ton/ha/year in Upper Lesti river basin due to maximum erosion rate The concentration of organic P (range 0,36 – 0,99 mg/l) in all sub basin is over of the WQ standard regulation in C class. The concentration of organic P (range 0,36 – 0,99 mg/l) in all sub basin is over of the WQ standard regulation in C class. The concentration of organic N (range 9,34 – 20,80 mg/l) in all sub basins is lower of the WQ standard regulation in C class. The concentration of organic N (range 9,34 – 20,80 mg/l) in all sub basins is lower of the WQ standard regulation in C class. The AVSWAT model was successes for assessment of runoff, sediment yield and nutrient load in Upper Brantas River Basin The AVSWAT model was successes for assessment of runoff, sediment yield and nutrient load in Upper Brantas River Basin

35. Suggestion The AVSWAT model would be testing to another watershed The AVSWAT model would be testing to another watershed In the next simulation, modification of difference fertilizer used and management tillage in agriculture would be done. In the next simulation, modification of difference fertilizer used and management tillage in agriculture would be done. Simulation of SWAT model can also done which difference of divided of each sub basin which smaller than 200 km2. Simulation of SWAT model can also done which difference of divided of each sub basin which smaller than 200 km2.

36. Acknowledgement We would like to thank you the following for making this study possible:  Brawijaya University, Indonesia  University of Malaya, Malaysia  Perum Jasa Tirta I, Malang, Indonesia  Department of Agriculture, Indonesia  Department of Surveying, Indonesia

37. Thank you