Land Cover Changes and Potential Hydrological Responses in Palu Catchment, Central Sulawesi Province.
LAND COVER CHANGES AND POTENTIAL HYDROLOGICAL
RESPONSES IN PALU CATCHMENT,
CENTRAL SULAWESI
I MADE ANOMBAWA
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
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STATEMENT OF THESIS AND INFORMATION SOURCES
Hereby I declare the thesis entitled Land Cover Changes and Potential
Hydrological Responses in Palu Catchment Central Sulawesi Province, is result
of my own work under supervision of supervisory committee and it has not been submitted yet in any form in any university to obtain a degree. The researcher has full responsibility for all contents of this thesis. Sources of information derived or quoted from other researchers, whether it is published or not are mentioned in the text and listed in the bibliography at the end of this thesis.
Bogor, August 2012
I Made Anombawa G051080071
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ABSTRACT
I MADE ANOMBAWA. Land Cover Changes and Potential Hydrological
Responses in Palu Catchment, Central Sulawesi Province. Supervised by PROF.
DR. IR. HIDAYAT PAWITAN, M.SC. as chairman and DR. ANTONIUS B. WIJANARTO as co-supervisor.
In past 20 years the land cover in Palu catchment with total area 3,050 km2
has changed due to the pressure of population activities. This mainly is reducing the amount of forest cover area and increased of urban and agricultural area. It gives impact to the water balance in the catchment, such as the changes of runoff and stream discharges. This study was focused to assess the hydrological response on the stream channel due to the changes of the land use and land cover on the watershed by utilizing remote sensed data, GIS, and hydrological model. Through remote sensing image interpretation, the forest cover has been decreased by 130.6
km2 or equal to 4.3% of total watershed area during period 1990-2001 and 250.9
km2 or equal to 8.2% of total watershed area during period 2001-2009. The conversion of the forest cover is might due to expansion of the agricultural land by 139.2 km2 or 4.6 % during period of 1990 to 2001 and 339.9 km2 or 11.1% during period of 2001 to 2001. From the simulation, it indicated that the total annual discharges volume was increased about 152,390,000 m3 or 4.5% during period 1990 to 2001 and 292,448,000 m3 or 8.2% during period 2001 to 2009. This might due to the changes of land cover and decreased of forest area in Palu Catchment.
Keywords: Stream flow, surface runoff, hydrological response, peak flow, HEC-HMS.
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ABSTRAK
I MADE ANOMBAWA. Perubahan Penutupan Lahan dan Potensi Respon
Hydrologinya di DAS Palu, Sulawesi Tengah. Di bawah bimbingan dari PROF.
DR. IR. HIDAYAT PAWITAN, M.SC. sebagai pembimbing I and DR. ANTONIUS B. WIJANARTO sebagai pembimbing II.
Dalam kurun waktu 20 tahun terakhir, penutupan lahan di DAS Palu dengan luas wilayah 3050 km2 telah mengalami perubahan akibat tingginya tekanan akibat aktifitas penduduk yang tinggal disekitar DAS Palu tersebut. Perubahan ini pada umumnya berkurangnya luah hutan akibat meningkatnya luas lahan pertanian. Hal ini memberikan dampak terhadap neraca air di DAS Palu, seperti meningkatnya aliran permukaan dan debit air. Penelitian ini difokuskan untuk mempelajari perubahan respon hidrologi pada sungai akibat terjadinya perubahan penutupan lahan dengan menggunakan data-data penginderaaan jauh, SIG, dan Model Hidrologi. Berdasarkan hasil interpretasi data citra satelit, luas hutan telah
berkurang sebesar 130.6 km2 atau sekitar 4.3% dari luas DAS pada periode
1990-2001, dan 250,9 km2 atau sekitar 8.2% dari keseluruhan luas DAS pada periode 2001-2009. Berkurangnya luas hutan ini diakibatkan oleh bertambahnya luas lahan pertanian sebesar 4.6% dan 11.1% pada periode yang sama. Dari hasil simulasi menunjukan peningkatan jumlah total volume aliran tahunan di DAS
Palu sebesar 152.390.000 m3 atau sekitar 4.5% pada tahun 2001 dan 292.448.000
m3 atau sekitar 8.2% pada tahun 2009. Kenaikan total volume aliran tahunan ini
kemungkinan besar diakibatkan oleh terjadinya perubahan penutupan lahan dan berkurangnya luas hutan di DAS Palu.
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SUMMARY
I MADE ANOMBAWA. Land Cover Changes and Potential Hydrological
Responses in Palu Catchment, Central Sulawesi Province. Supervised by PROF.
DR. IR. HIDAYAT PAWITAN, M.SC. as chairman and DR. ANTONIUS BAMBANG WIJANARTO as co-supervisor.
The increasing of population activities in Palu Catchment is shows by the increasing of the agricultural land and the decreased of forest cover. In a watershed system, forest cover plays important role to maintain the water balances. Forest including the trees and its litters are work to absorb the water when the rain falls down from the sky and store it as ground water. Reduced of forest cover will affected to the forest capacity to absorb the amount of rainfall that will turn the surface runoff increased along with the decreased of forest cover area. If continues, it will lead to deforestation that might to disturb the hydrological system in the watershed. The disruption of the hydrological system is indicated by the increased of peak flow and flood intensity.
Palu City located at the downstream of the Palu Cacthment is mostly flooded in every year. Due to high discharges of Palu River and poor watershed management system makes its occurred frequently and even tended increased by years. It is need serious attention from all stakeholders to makes better watershed management system that can reduce the increased surface runoff. This might be able to be done by conducting reforestation or makes better agricultural system that is can increase the water absorption especially on the upper side of the catchment. The objective of this study is to assess the hydrological response on the stream channel due to the changes of the land use and land covers on its watershed by utilize remote sensed data, GIS, and hydrological model.
In this research, the hydrological responses of Palu watershed due to land cover changes have been simulated by using HEC-HMS hydrological model that using daily time step of rainfall data. The NRCS Curve Number method was employed to assess the surface runoff in Palu Catchment, and the curve number was obtained from slope map and land cover map of 1990, 2001, and 2009 that derived from Landsat TM images trough visual classification and soil map. An calibration and verification of the model was employed to assess model’s performances by using three years different paired observed rainfall-discharge data. one year rainfall-discharge data of 2002 was used to calibrate the model while two years rainfall-discharges data of 2006-2007 were used during verification process. To assess the model’s performances Nash-Sutcliffe coefficient and relative volume error were used. Besides both Nash-Sutcliffe coefficient and relative volume error, the model performances is also evaluate by using correlation coefficient of simulated and observed discharges. To assess the
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impact of land cover changes to hydrological response, three land cover maps of 1990, 2001, and 2009 were used. Besides of those three land cover maps, two different scenarios were employed to find the best scenario’s that is can reduce the surface runoff, the scenarios are; (1) using the Provincial Development Plan (RTRW), and (2) using projected land cover maps on 2020 and 2030.
Trough calibration process 0.81 and 42.9 % of Nash-Sutcliffe coefficient and relative volume error (RVE) was achieved respectively, and 0.758 of R2 of
correlation between observed and simulated discharges. During verification process, it gives values 0.9 of Nash-Sutcliffe efficiency and 12.1 % of relative volume errors, with 0.58 of R2 correlation between observed and simulated discharges. This indicated that between observed and simulated discharges using HEC-HMS hydrological model in Palu Catchment have good correlations.
From remote sensing process, can be state that large forest areas have been converted into other land cover classes during period of 1990 to 2009. On land cover period of 1990, 71.3% of total watershed area was covered by forest, 10.7% is used as agricultural land, and 13.6% is shrub land. In 2001 forest cover and shrub land has decreased become 67% and 12.9%, while the agricultural land areas were increased become 15.3%. On 2009 the forest cover and shrub land were reduced significantly become 58.8% and 9.7% respectively, and agricultural land increased to 26.4% of total watershed area. From the hydrological simulations by using rainfall data of 2007 obtained peak discharges about 257.63 m3/s, 268.92 m3/s, and 290.58 m3/s with total discharges 3,422,287 m3, 3,574,677 m3, and 3,867,125 m3 on 1990, 2001, and 2009 respectively.
The total discharges volume of each simulation using different land covers maps was produce 3,422,287,000 m3 on 1990, 3,574,677,000 m3 on 2001, and
3,867,125,000 m3 on 2009. From its simulation found that the discharge volumes
were increased 152,390,000 m3 during period 1990 to 2001 and 292,448,000 m3 during period of 2001 to 2009. The contributions of each sub-basin to generate river discharge are difference among sub-basin. Generally, sub-basin 2 gave highest contribution with 31.1% of total discharges was generated, followed by sub-basin 1 with 30.4%, then sub-basin 3 with 13.1%, sub-basin 7 generate about 10% of total discharges, basin 5 with 7.2%, basin 4 with 5.2% and sub-basin 6 with 3.2% respectively.
Simulated hydrograph using two different scenarios; projected land cover on 2020 and 2030, and RTRW are obtain different peak flow which is the lowest
peak flow was obtained by using RTRW scenario’s with value 257.82 m3/s, while
the others scenario produce higher peak flow; 292.29 m3/s for projected land
cover on 2020, and 301.76 m3/s for projected land cover on 2030. From those two
different scenarios, can be state that the first scenario that is using Provincial Development Plan is the best scenario that is can reduce the peak flow in Palu watershed. The simulated hydrographs from those three scenarios prove that the RTRW has developed with a good approach in terms of hydrological perspective,
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although the actual condition shows that the catchment has been disturbed by the human activities in the catchment. This is indicated in the simulated hydrograph that by using existing condition has produced much higher discharges and peak flows. In addition, physically the water flow in Palu River shows high sedimentation that indicates the large volume of erosion may have occurred in upper side of the catchment.
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Copyright © 2012, Bogor Agriculture University Copyright are protected by law,
1. It is prohibited to cite in part or the whole contents of this thesis without referring to and mentioning the sources:
a. Citation only permitted for education purpose, research, scientific writing,
report writing, critical writing or reviewing scientific problem.
b. Citation does not inflict the name and honor of Bogor Agricultural
University.
2. It is prohibited to republish and reproduced in part or the whole of this thesis without written permission from Bogor Agricultural University.
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LAND COVER CHANGES AND POTENTIAL
HYDROLOGICAL RESPONSES IN PALU CATCHMENT,
CENTRAL SULAWESI PROVINCE
I MADE ANOMBAWA
Thesis is a prerequisite to obtain degree Master of Science in Information Technology for Natural Resources Management Program Study
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
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Research Tittle : Land Cover Changes and Potential Hydrological Responses in Palu Catchment, Central Sulawesi Province.
Student Name : I Made Anombawa
Student ID : G051080071
Study Program : Master of Science in Information Technology for Natural
Resources Management
Approved by, Advisory Board
Prof. Dr. Ir. Hidayat Pawitan, M.Sc Supervisor
Dr. Antonius B. Wijanarto Co-Supervisor
Endorsed by,
Program Coordinator
Dr. Ir. Hartrisari Hardjomidjojo, DEA
Dean of Graduate School
Dr. Ir. Dahrul Syah, M.Sc.Agr
Date of Examination: Date of Graduation:
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ACKNOWLEDGEMENT
Thanks to The Almighty God for all of the bless that gave to me, at last this final project was finished successfully. Sure this research will never complete without support from various parties: family, colleagues, classmate, MIT secretariat, and all instance who not able to be mentioned one by one. And through this opportunity, I would like to express my gratitude to:
1. My beloved families thank you for all of your support, patience, and prayer during my study.
2. Prof. Dr. Ir. Hidayat Pawitan, M.Sc and Dr. Antonius Bambang Wijanarto, as
supervisor and co-supervisor for all their input, idea, comments, advise, and all constructive critics during completing my research. I have learned many things from them.
3. Dr. Surya Darma Tarigan, M.Sc. as my external examiner for his suggestion
to improve my thesis.
4. BMKG, Ministry of Public Work for Water Resources, Bappeda of Central
Sulawesi Province, and BPDAS Palu Poso, for their assistance during field work and data collection.
5. MIT coordinator, lecturers, and MIT secretariat for their assistance.
6. My classmate of 2008 and all MIT students, for your support and motivation.
7. All others instance that might not able to mention one by one. Thank you for
your support.
I wish that this thesis will give positive contribution to all peoples who read it. Thank you.
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CURRICULUM VITAE
I Made Anombawa was born in Balinggi, Central Sulawesi, Indonesia on March 14th, 1983, child couple of I Made Subagia and Ni Nengah Derti. He is second of three brothers. He completed his undergraduate degree in physics at Tadulako University in 2006. He continued his study in Master of Science in Information Technology for Nature Resources Management at Bogor Agricultural University enrolled in 2008, and completed his master degree in 2012. Since 2010, he was working as GIS Coordinator for humanitarian support on disaster reduction program in one of United Nation Agency that is based in Bandung, Indonesia.
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TABLE OF CONTENTS
Page LIST OF FIGURES ... iii
LIST OF TABLES ... v
LIST OF APPENDICCES ... vii
I. INTRODUCTION ... 1
1.1 Background ... 1
1.2 Problems Statement ... 2
1.3 Objectives ... 2
II. LITERATURE REVIEW ... 3
2.1 Land Cover Change Detection ... 3
2.1.1 Remote Sensing Application ... 3
2.1.2 Image Classification ... 5
2.1.3 Change Detection ... 6
2.2 Hydrological Characteristic in a Watershed ... 7
2.2.1 Hydrological Cycle ... 7
2.2.2 Watershed Systems ... 8
2.3 Modeling Hydrological Response in a Watershed ... 9
2.3.1 Surface Runoff Estimation ... 10
2.3.2 Unit Hydrograph of Watershed ... 12
III. METHODOLOGY ... 15
3.1 Study Area ... 15
3.1.1 Topography Condition ... 15
3.1.2 Climate Condition ... 16
3.1.3 Land Cover Condition ... 17
3.1.4 Geological Condition ... 17
3.1.5 Demography Condition ... 17
3.2 Data Availability ... 18
3.2.1 Raster and Vector Datasets ... 18
3.2.2 Meteorology and Climate Datasets ... 19
3.2.3 Soil Dataset ... 21
3.2.4 Hydrological Dataset ... 22
3.3 Research Method ... 22
3.3.1 Image Processing ... 24
3.3.1.1 Image Classification ... 24
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3.4 Hydrological Modeling ... 26
3.4.1 General Description of HEC-HMS ... 26
3.4.2 Model Parameter and Input ... 28
3.4.2.1 Schematic Watershed Model – Watershed Delineation 28
3.4.2.2 Sub-Basin Lag Time and Peaking Coefficient ... 29
3.4.2.3 Curve Number Determination ... 30
3.4.2.4 Areal Rainfall Estimation ... 31
3.5 Model Calibration and Verification ... 32
3.6 Responses of River System to Land Cover Changes on the Catchment
Area ... 32
IV. RESULTS AND DISCUSSION ... 34
4.1 Land Cover Changes Assessment ... 35
4.1.1 Land Cover Maps from Landsat Data ... 36
4.1.2 Summary of Land Cover Maps ... 39
4.1.3 Change Detection ... 40
4.2 Hydrological Modeling ... 42
4.2.1 Model Input and Parameters ... 42
4.2.1.1 Lag Time and Peaking Coefficient ... 42
4.2.1.2 Curve Number for Surface Runoff Estimation ... 42
4.2.2 Model Calibration and Verification ... 46
4.2.3 Changes in Seasonal Stream Flow ... 48
4.2.4 Response Hydrology to the Land Cover Changes. ... 50
4.2.5 Hydrological Response for some Land Cover Changes
Scenarios. ... 53
4.2.6 Impact of Land Cover Changes Scenarios on Stream Flows .... 55
V. CONCLUSION AND RECOMMENDATIONS ... 59
5.1 Conclusion ... 59
5.2 Recommendation ... 59
REFERENCES ... 61
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LIST OF FIGURES
Page Figure 2.1 Hydrological cycles on the earth surface. ... 8
Figure 2.2 Volume of direct runoff as a function rainfall and curve number . 11
Figure 3.1 Palu Catchment; located in Central Sulawesi. ... 15
Figure 3.2 Digital Elevation Model of Palu Catchment Area ... 16
Figure 3.3 Selected meteorological and hydrological stations ... 20
Figure 3.4 Annual averages rainfall of each rain gauge ... 20
Figure 3.5 Soil map on Palu catchment. ... 21
Figure 3.7 Study workflow. ... 23
Figure 3.8 Image classification processes; four multi temporal images was used to produce land use and land cover classes. ... 25 Figure 3.9. Change detection procedure ... 26
Figure 3.10 Conceptual schematic of the continuous soil moisture model .... 27
Figure 4.1 Satellite imagery of Landsat TM and calculated NDVI. ... 35
Figure 4.2 Land cover map of 1990 and coverage of each land covers class. 37
Figure 4.3 Land cover map of 2001 and Coverage of each land covers class 38
Figure 4.4 Land cover map of 2009 and Coverage of each land covers class.39
Figure 4.5 Increased and decreased of each land cover class ... 39
Figure 4.6 Land use on the Palu catchment ... 41
Figure 4.7 Curve number map of existing land cover on 1990, 2001, and 2009. ... 45
Figure 4.8 Simulation versus observation hydrograph, and correlation
between simulation and observed discharges during calibration process.47
Figure 4.9 Simulation versus observation hydrograph, and correlation between simulation and observed discharges during verification
process. ... 48
Figure 4.10 Twenty years simulated hydrograph in Palu River. ... 49
Figure 4.11. Variations between high wet seasons flow, low dry season flow and peak to low ratio. ... 49
Figure 4.12 Simulated hydrograph from three different lands cover period. . 51
Figure 4.13 Peak stream flow from three different land cover period and Base flow from filtering process for each land cover period. ... 52
Figure 4.14 Simulated hydrograph using 2007 rainfall data for different land cover scenarios. ... 56
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v
LIST OF TABLES
Page Table 2.1 Soil group classification ... 12
Table 3.1 Population on sub-district level across the sub-basin ... 18
Table 3.2 List of used datasets ... 19
Table 3.3 Geographical coordinate of selected hydrology and meteorology observation stations. ... 20
Table 3.4 Metrological station weight calculated using theissen polygon method ... 31
Table 4.1 Summary of land cover maps in Palu Catchment on 1990, 2001, and 2009 ... 39
Table 4.2 Summary of land cover changes in Palu Catchment between period of 1990-2001 and 2001-2009. ... 40
Table 4.3 Change detection statistics report on 1990 – 2001 ... 40
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vii
LIST OF APPENDICES
Page Appendix 1 Runoff curve numbers for urban areas ... 65
Appendix 2 Runoff curve numbers for cultivated agricultural lands ... 66
Appendix 3 Runoff curve numbers for other agricultural lands ... 67
Appendix 4 Runoff curve numbers for arid and semiarid rangelands ... 68
Appendix 5 Observed Discharges ... 69
Appendix 6 Spatial Development Plan of Central Sulawesi Province. ... 72
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I.
INTRODUCTION
1.1 Background
Watershed is the most important factors to maintain the water balance on the basin area. Watershed with its vegetation including the trees and its litters are work to absorb the water when the rain falls down from the sky, and store it as ground water. The increasing of human population is affected to the human need to land and space either to open new agricultural area or settlement. In developing countries as well as Indonesia, the land cover changes are mostly issued by the development of agriculture, residential, and industrial area. Population growth and their activities to meet the needs are the main driving factors, which lead to the resources degradation. Land cover changes from forest into agricultural and urban areas on the watershed system will reduce the capacity of the forest in terms of to maintain the water supply.
Due to human activities in Palu catchment, large area of forest cover has been converted into agricultural land in past twenty years. Trough remotely sensed data; the forest cover has been reduced from 71% in 1990 to 58% of total watershed area in 2009. It makes the surface runoff increased when the rain falling down into the catchment. Study of hydrological responses to the land cover changes enable to assess the sustainability of land use system on the stream river basin system; because stream flows reflects on the hydrological state of the entire watershed. Hydrological impact of land cover changes is referencing issues and must research necessary (Calder, 2002).
Study of hydrological responses to the land cover changes enables us to assess the sustainability of land use system on the stream river basin system; because stream flows reflect on the hydrological state of the entire watershed. The changes of hydrological response due to the land cover changes can be assessed by integrating appropriately remote sensing data, geographical information system (GIS), and hydrological models. The results of its integrating method can be applied to forecast the likely effect of any potential changes in land cover and water resources system.
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In this study, some analyses were conducted; such as multi temporal analysis using remote sensing data, hydrological modeling by using hydrological models tools (HEC-HMS), and GIS for representing the results.
1.2 Problems Statement
Palu catchment area has high density of population. This caused many effects to the nature resources such as deforestation, changes of forest management system, overgrazing of the rangeland, and expansion of residential area and agriculture area. Besides that, the traditional people also do shifting agriculture system. This shifting agriculture system has significant effect to the deforestation. Based on last field visit, in last few years some nature hazards such as flood and landslide happen in this area. Even the last flood hazard is causing causalities. Besides that, the Palu River also carries out big number of sediment to the sea, causing pollutant on the sea.
1.3 Objective
This study is aimed to assess the hydrological response on the stream channel due to the changes of the land use and land cover on its watershed by utilizing remote sensing, GIS, and hydrological model.
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II.
LITERATURE REVIEW
2.1 Land Cover Change Detection
2.1.1 Remote Sensing Application
Remote sensing (RS) usually refers to the technology of acquiring information about the earth’s surface (land and ocean) and atmosphere, using sensors onboard airborne (aircraft, balloons) or space-borne (satellites, space shuttles) platforms (Ranganath et al, 2007). The electromagnetic radiation is normally used as an information carrier in RS. Remote sensing employs passive and/or active sensors. Passive sensors are those which sense of natural radiations, either reflected or emitted from the earth. On the other hand, the sensors are produce their own electromagnetic radiations are called active sensors. Remote sensing can also be broadly classified as optical and microwave. In optical remote sensing, sensors detect solar radiation in the visible, near, middle, and thermal-infrared wavelength regions, reflected/scattered or emitted from the earth, forming images resembling photographs taken by a camera/sensor located high up in space.
Different land cover features, such as water, soil, vegetation, cloud and snow reflect visible and infrared light in different ways. Interpretation of optical images requires the knowledge of the spectral reflectance patterns of various materials (natural or man-made) covering the surface of the earth. It is essential to understand the effects of atmosphere on the electromagnetic radiation travelling from the Sun to the Earth and back to the sensor through the atmosphere.
The atmospheric constituents cause wavelength-dependent absorption and scattering of radiation. These effects degrade the quality of images. Some of the atmospheric effects can be corrected before the images are subjected to further analysis and interpretation. A consequence of atmospheric absorption is that certain wavelength bands in the electromagnetic spectrum are strongly absorbed and effectively blocked by the atmosphere. The wavelength regions in electromagnetic spectrum, weather usable for remote sensing, are determined by their ability to penetrate the atmosphere. These regions are known as atmospheric
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transmission windows. Atmospheric windows used for remote sensing are 0.4-1.3, 1.5-1.8, 2.2-2.6, 3.0-3.6, 4.2-5.0, 7.0-15.0 µm and 10 mm to 10 cm wavelength regions of the electromagnetic spectrum.
There are also infrared sensors, which measure the thermal infrared radiation emitted from the earth, from which, the land or sea surface temperatures and thermal inertia properties can be derived. It is observed that all bodies at temperatures above zero degrees absolute emit electromagnetic radiation at different wavelengths, as per Planck’s law, which relates the spectral radiant emittance E (λ, T) with the temperature, T of the object.
! !,! = 2ℎ!!
!!
1
!!! !"#
−1… … … ….(1)
Where h = 6.625 x 10–27 erg s (Planck’s constant); k = 1.38 x 1016 erg/K (Boltzmann constant); c = 3 x 108 cm/s (speed of light).
In remote sensing application, the emitted of spectral radiant is recorded by the remote sensing sensors to an image. The values of spectral radiant are stored as digital number in the image bands. Remote sensing instrument storing the digital number of spectral radiant reflectance of an object in earth surface from different range of wavelength; where each object have different wavelength characteristic.
Remote sensing application to land cover changes monitoring is related to detection process the changes of image in different time period. The land cover changes from rural to urban condition and the mapping process to land cover change establishes the baseline to predict to plan water resources, to monitor adjacent environmentally sensitive areas, and to evaluate development, resource management, industrial activity, and/or reclamation efforts. The vital component of mapping is to show the land cover changes in the watershed area and to divide land use in the various classes of land use. At this stage, remotely sensed imagery is of great help for obtaining information on temporal trends and spatial distribution of watershed areas and possible changes over the time dimension for projecting land cover changes but also to support changes impact assessment. Furthermore, multi-temporal remotely sensed images are widely considered effective data sources that can be used to monitor the rapid changes of land cover,
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5 to classify types of land cover, and to obtain a timely regional overview of land cover information in a practical and economical manner over large areas.
2.1.2 Image Classification
The overall objective of image classification procedures is to automatically categorize all pixels in an image into land cover classes or themes (Thomas et al., 2004). Normally, multispectral image are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. That is, different feature type manifest different combinations of Digital Numbers (DN) based on their inherent spectral reflectance and emittance properties. Rather, the terms pattern refers to set of radiance measurements obtained in the various wavelength bands for each pixel.
Spatial pattern recognition involves the categorization of image pixel on the basis of their spatial relationship with the pixel surrounding them. Spatial classifier might consider such aspects as image texture, pixel proximity, feature size, shape, directionally, repetition, and context. These types of classifiers attempt to replicate the kind of spatial synthesis done by human analyst during the visual interpretation processes.
A large number of classification methods are exists which are generally grouped into two major classification methods; supervised and unsupervised classification. The classification may either by supervised or unsupervised classification method. In supervised classification method the pixel categorized process by specifying, to the computer algorithm, numerical descriptors of the various land cover types present in an image scene. To do this, representative sample site of known cover type are used to compile a numerical interpretation key that describes the spectral attributes of each feature type of interest. Each pixel in the dataset is then compared numerically to each category in the interpretation key and labeled with the name of category looks most like. In unsupervised classification method, the images are first classifying by aggregating them into the natural spectral groupings present in the image scene. Then the image analyst determines the land cover identity of these spectral groups by comparing the classified image data to the ground reference data.
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To assess the accuracy, the knowledge required to manage territory is increasingly based on information and maps created from remote sensing images (Campbell, J.B. 2007; Jensen, J.R. 2005). The accuracy assessment of classification results is an important feature of land cover and mapping that helps to determining the quality and reliability of information derived from remote sensed data. Many factors can be used to assess the accuracy of image classification results such as classification error matrix and sampling consideration. Classification error matrices compare, on a category bay category basis, the relationship between known reference data and the corresponding results of an automated classification. Sampling consideration is area of representative, uniform land cover that is different from and considerably more extensive than training area.
2.1.3 Change Detection
Multi temporal image analysis usually deals with the changes of the appearance of the object on the earth surface over the time. The differences between the past and current condition is called the “changes”. These changes could be the changes of land cover, temperature, land use, rainfall, etc. In terms of land cover changes, it’s related with to the changes of usage of land in one form to another form.
Change detection is a process to identifying difference in the state of a feature by observing it at different moment of time (Singh, 1989). There are large numbers of change detection algorithm or technique developed and used over the years to estimate the changes using remote sensing data. The techniques are based on various mathematical or statistical relationship, principles, and assumption (Singh, A. 1989). Change detection include image overlay, image digitizing, image differencing, image regression, image rationing, vegetation index differencing, principal component analysis, spectral/temporal classification, post classification comparison, change vector analysis, and background subtraction (Singh, A 1989; Coppin & Bauer, 1996; Sunar, 1998). Although these methods have been successful applied in monitoring changes for several applications, there is no consensus as to a “best” change detection approach. The types of change
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To determinate the land cover changes in past 20 years in the study area, post classification and matrix analysis have been employed to detect amount of the changes of each land cover classes. Post classification comparison method gives us advantages, since this method bypasses the difficulties associated with the analysis of the multi temporal images or image that came from different sensors (Alphan, 2003 in Kebede, 2009). This perhaps the most common approach to assess change detection, and the method comparison uses separate classification of multi temporal images to produce different maps which is contain “from-to” information can be generated (Jensen, 2004).
Matrix analysis also called transitional matrix is comparing the area of each classes from one image to another image. This produces thematic layer which is contain of information of number of pixel of each classes from two different images.
2.2 Hydrological Characteristic in a Watershed
2.2.1 Hydrological Cycle
Hydrological cycles are related to the movement of the water from above, below, and on the earth surfaces. The water on the Earth’s surface; surface water occurs as streams, lakes, and wetlands, as well as bays and oceans. Surface water also includes the solid forms of water; snow and ice. The water below the surface of the Earth primarily is ground water, but it also includes soil water (Winter, T.C. et al. 1998). The hydrologic cycle commonly is portrayed by a very simplified diagram that shows only major transfers of water between continents and oceans.
The hydrological cycles consist of precipitation, evaporation, transpiration, infiltration, percolation, and runoff. Precipitation, which is the source of virtually all freshwater in the hydrologic cycle, falls nearly everywhere, but the water distribution, is highly variable. Similarly, evaporation and transpiration return water to the atmosphere nearly everywhere, but evaporation and transpiration rates vary considerably according to climatic conditions. As a result, much of the precipitation never reaches the oceans as surface and subsurface runoff before the
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water is returned to the atmosphere. The relative magnitudes of the individual components of the hydrologic cycle, such as evapotranspiration, may differ significantly even at small scales, as between an agricultural field and a nearby woodland (Winter, T.C. et al. 1998).
Figure 2.1. Hydrological cycles on the earth surface.
Water is evaporated from water bodies such as lakes, ponds, reservoirs, oceans and rivers, as well as wet land surfaces or transpired trough the plants as vapor to the atmosphere and is transported in the atmosphere to a location where the vapor water are condensed and falls as precipitation on the surface of the earth (Singh, V.J, 1992). The evaporation water from the oceans, lakes, and other free water surfaces throughout the world occurs due to the energy from the sun, thereby providing a supply of vapor to the atmosphere. The water vapor is transported by the atmosphere to various part of the world, where it is eventually condensed and precipitated.
2.2.2 Watershed Systems
A watershed can be defined as the area of land that drains to a particular point along a stream. Each stream has its own watershed. Topography is the key element affecting this area of land. The boundary of a watershed is defined by the highest elevations surrounding the stream. Geographically catchment area of watershed system is the extent of land where water from rain and melting snow or
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9 ice drains downhill into a body of water, such as a river, lake, reservoir, estuary, wetland, sea or ocean. The drainage basin includes both the streams and rivers that convey the water as well as the land surfaces from which water drains into those channels, and is separated from adjacent basins by a drainage divide. The characteristics of watershed pertain to the land and channel elements of the watershed. The element of watershed consists of size, shape, slope, elevation, vegetation, land use, soil type, hydrogeology, lakes, swamps, density of channel, and artificial drainage (Singh, V.J. 1992).
2.3 Modeling Hydrological Response in a Watershed
Models are considered to simplified representation of real world where each model has their own conceptual approach, parameters, and related to mathematical expression. Hydrological models are attempts to represent the hydrological system from precipitation to stream flow in mathematical form. The complexity of a hydrological model is varies with the user requirements and the data availability. Models vary from simple statistical techniques that use graphical methods for their solution to physically based simulations of the complex three-dimensional nature of a watershed.
An important issue in modeling the hydrological response of a catchment is the level of detail at which land cover properties are represented, both where land cover patterns are stable and where they are changing over time. Nowadays, various approaches are available to assess the impacts of land cover changes in different parts of the world. Based on the assessment, most of the hydrological models belong to the categories of distributed physically based and semi distributed conceptual hydrological models. In this terminology physically based stand for the physiographic information of the catchment and climatic factors in a simplified manner while conceptual stands for the hydrologic state of a catchment, flows process at any time or instant.
Conceptual rainfall runoff models are normally run with area and average values of precipitation and evaporation as primary input data and, subject to the selected approach, produces catchment values of soil-moisture, runoff volumes, peak flows etc. The conceptual rainfall runoff model is common approach to represent a catchment area into a model. On this study, the conceptual rainfall
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runoff model was applied to assess the hydrological responses to land cover changes.
2.3.1 Surface Runoff Estimation
Rain that falls into earth surface will experience several processes during the rainfall process until the water flow to the river. The processes are soil percolation, and surface runoff. Percolation is process the water infiltrated into the soil, and surface runoff is the water flow that occurs when the soil is infiltrated full capacity. The surface runoff happen when the soil is fully infiltrated by the water. Besides that, surface runoff is very influenced by the soil texture and slope factors.
Runoff is general term used to indicate the accumulation of precipitation excess. The volume of runoff is total volume of runoff water occurring over a period of time. The runoff volume is expressed by integrals of discharge at time period.
!!= !(!)
!
!
!"… … … …(2)
Where VQ is runoff volume and Q(t) is the discharge at time t.
Estimation of runoff volume from a drainage basin involves precipitation, infiltration, evaporation, transpiration, interception, and depression storage, each of which is complex and can interact with the other variables to either enhance or reduce runoff (Singh, V.J. 1992). These variables are variously distributed within a drainage basin. Actually, to estimate the volume of runoff on drainage basin can be done by using NRCS Curve Number method.
NRCS Curve Number surface runoff method was introduced by the Soil Conservation Services United States Department of Agriculture. NRCS curve number method is an empirical approach parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess. The runoff curve number is based on the area's hydrologic soil group, land use, treatment and hydrologic condition.
The basic concept of this method is the ratio of actual soil retention after runoff begins to potential maximum retention is equal to the ratio of direct runoff
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11 to available rainfall (Dean Snider, 1972). General form of total runoff volume estimation is expressed by:
!! =(!− 0.2!)!
!+0.8! … … … …(3)
Where VQ is the runoff volume uniformly distributed over the drainage basin, P is
the mean precipitation, and S is the retention of the water. The retention parameter (S) is depending on characteristic of soil, vegetation, land use, and soil moisture condition in a watershed, where condition of those parameters are expressed by the curve number. The relation between retention and curve number is expressing as:
!=
1000
!"−10… … … …(4)
Where CN is curve number. The value of curve number is range from 20 to 100. The lower numbers indicate low runoff potential, while larger numbers are for increasing runoff potential. The curve number values are determined also by the soil types, where each soil type has their own of infiltration characteristics (presented in Table 2.1). The relationship between runoff and curve number are shows in Figure 2.2, while the value of curve number for different land use and land cover type are shows in list of appendix of this proposal.
Figure 2.2 Volume of direct runoff as a function rainfall and curve number (Singh, V.J. 1992)
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Table 2.1 Hydrological soil group classifications
Group Soil Characteristics Minimum Infiltration
Rate (in/h)
A Deep sand, deep loess, and aggregate silts 0.3 - 0.45
B Shallow loess and sandy loam 0.15 - 0.30
C Clay loams, shallow sandy loam, soils in organic
content, and soil usually high in clay 0.05 - 0.15
D Soil that swell upon wetting, heavy plastic clay, and
certain saline soils 0 - 0.05
2.3.2 Unit Hydrograph of Watershed
Unit hydrograph of watershed is the direct runoff hydrograph resulting from one unit of effective rainfall occurring uniformly over the watershed at a uniform rate during a unit period of time (Singh, V.J. 1992). Actually, the unit hydrograph is representing the effect of rainfall in particular basin. It is a hypothetical unit response of the watershed to a unit input of rainfall. The unit hydrograph firstly developed by Sherman in 1932. Unit hydrograph will use to determining the surface or direct runoff hydrograph from the effective rainfall hyetograph (ERH).
The fundamental assumptions implicit in the use of unit hydrographs for modeling hydrologic systems are:
1. Watersheds respond as linear systems. On the one hand, this implies that the
proportionality principle applies so that effective rainfall intensities (volumes) of different magnitude produce watershed responses that are scaled accordingly. On the other hand, it implies that the superposition principle applies so that responses of several different storms can be superimposed to obtain the composite response of the catchment.
2. The effective rainfall intensity is uniformly distributed over the entire river basin.
3. The rainfall excess is of constant intensity throughout the rainfall duration. 4. The duration of the direct runoff hydrograph, that is, it’s time base, is
independent of the effective rainfall intensity and depends only on the effective rainfall duration.
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13 Hydrologic system is said to be a linear system if the relationship between storage, inflow, and outflow is such that it leads to a linear differential equation. The hydrologic response of such systems can be expressed in terms of an impulse response function through a so-called Convolution Equation. Linear systems possess the properties of additively and proportionality, which are implicit in the convolution equation (Ramírez, 2000).
The impulse response function of a linear system represents the response of the system to an instantaneous impulse of unit volume applied at the origin in time (t=0). The response of continuous linear systems can be expressed, in the time domain, in terms of the impulse response function via the convolution integral as follows,
! ! = !! ! ! !−! !", ℎ ! =0 !"# ! <0 … … … ….(5) !
!
Where u(t) represents the instantaneous unit hydrograph, and Q(t) and Ie(t) represent direct runoff and excess or effective precipitation, respectively.
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III.
METHODOLOGY
3.1 Study Area
The study area is situated in Palu catchment, Central Sulawesi Province. Geographically, it is located between 0o52’50” to 1o35’12” S and 119’45’24” to
120’10’50” E.Palu River is the main river that crosses the Palu City and ended in
Palu Bay, study site is show in Figure 3.1. Palu catchment has unique topographic condition; where in the west and east sides are the mountainous area, while the north side is hilly and some parts in the southern side is coverage of National Reserve of Lore Lindu.
Figure 3.1 Palu Catchment; located in Central Sulawesi. 3.1.1 Topography Condition
Palu catchment has unique of topographic characteristic. The elevation of Palu River Catchment is varying from 0 to 2500 meters above sea level. On the eastern and western side of Palu River Catchment are hilly areas. As well as the upstream area of Palu River is a mountainous region that is located in Lore Lindu National Park.
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Figure 3.2 Digital Elevation Model of Palu Catchment Area (Sources: SRTM)
3.1.2 Climate Condition
Rain falls throughout the year, the heaviest period occurrs from April to July and October to December. The spatial variation of rainfall amount in the area indicated a decreasing trend from north to south part of the catchment area, where the north side is urban with high population and south part is mountain with high vegetation. The averages rainfall on the study area is varying depend on the location with range from 1,000 mm/year – 3,500 mm/year; generally the rainfall becomes heavier on the southern part of the catchment. According to Palu-Poso Watershed Management Agency (BPDAS Palu-Poso), generally February is the driest month while May and November are the wettest months during wet season.
Temperatures vary only a few degrees over the course of the year. Daytime temperatures in lowland areas of the catchment area range from 26° C – 32° C. Highland areas are significantly decreased, as air temperature drops about 6° C with every 1,000 m rise in altitude.
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17 3.1.3 Land Cover Condition
Based on the topographic map which is issued by the National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL) now is Geospatial Information Agency (BIG), in 1991 the land cover composition of Palu Catchment is dominated by the forest, which is covered more than 70% of total watershed area, followed by the shrub land, and agricultural land. The forests are mainly found on the southern part of the catchment, since this area is part of the Lore-Lindu National Reserves. The main populations were concentrated on the northern part of the catchment, which is the capital of Central Sulawesi namely Palu City.
3.1.4 Geological Condition
The geology condition of Palu River Watershed is almost the same for the overall area. Generally, alluvial soil, innocuous intrusive rocks, metamorphosed rocks, and sediment compose the geology structures.
The mountainous areas generally consist of acid rock such as gneisses, schist and granite possessing sensitive to the erosion. The other rock formation,
lacustrine formation can be found on the east side of the study area. On the west
side, alluvium rock that derived from metamorphosed rocks and granite can be found.
3.1.5 Demography Condition
Demography is important aspect that leads to land cover change in a catchment area. Most of the changes of the land use are influences by population. Increasing of the human population means that the needs to the space are increasing also. Palu Catchment with wit area about 3,050 km2 consists of 13 sub-districts that intersect on the whole catchment area. Where mostly of the population works as a farmer, which is are utilize a space to farming. Based on the statistical bureau, the total population that settled in those 13 sub-districts is about 187,535 or 88,763 of households. The demography conditions of study area are shows in Table 3.1.
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Table 3.1 Population on sub-district level across the sub-basin
CODE Subdistrict Male Female Householder
7210110 Dolo 10,101 10,116 5,046
7210090 Dolo barat 6,145 7,184 3,423
7210080 Dolo selatan 8,641 7,976 4,070
7210030 Kulawi 7,468 6,991 3,691
7210020 Kulawi selatan 4,576 4,529 2,233
7210040 Lindu 2,398 2,359 1,174
7210130 Marawola 10,207 9,815 4,188
7210140 Marawola barat 5,423 5,461 2,883
7210060 Palolo 13,838 12,840 7,112
7210120 Sigi biromaru 19,577 19,163 10,270
7271010 Palu barat 44,179 44,194 19,782
7271020 Palu selatan 50,041 51,766 22,658
7271040 Lore utara 4,941 4,321 2,233
Jumlah 187,535 186,715 88,763
Source: National Statistical Bureau, 2009
3.2 Data Availability
3.2.1 Raster and Vector Datasets
Time series of satellite images of Landsat were used to estimate the land cover changes. Three different acquiring times of Landsat imagery were used to analyze the land cover changes. The imageries were acquired in 1990, 2001, 2007 and 2009. The imageries data are available to download freely at U.S. Department of the Interiors U.S. Geological survey (USGS) at the global visualization viewer website (http://glovis.usgs.gov/). The images consist of eight electromagnetic channels including visible, near infrared, middle infrared, thermal, middle thermal 2 and panchromatic, except the images on 1990 that only have five electromagnetic channels.
Besides of Landsat images, other raster data were used for analysis. The elevation data of SRTM (Shuttle Radar Terrain Mission) were obtained from USGS. It has approximately 90 meters of spatial accuracy. The elevation data were used to delineate the watershed on the study site.
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19 Table 3.2 List of used datasets
Landsat ETM (WRS: P/R) Acquisition Date Data Format
116/061 16/12/1990 GeoTiff
116/061 24/08/2001 GeoTiff
116/061 17/10/2009 GeoTiff
DEM Ground Resolution (m) Data Format
SRTM 92 ESRI Grid
Others Data Sources Data Format
Reconnaissance soil map Scale 1:50,000
PUSDIKTANAK Shape Files
Topographic Map Geospatial Information Agency Vector Provincial Spatial
Development Plan
National Land Agency
Shapefiles and Hardcopy
Rainfall BMKG and PU SDA Tabular hard copy
River Discharge PU SDA Tabular hard copy
Meteorological data BMKG and PU SDA Tabular hard copy
Population Statistics Bureau Hard Copy
3.2.2 Meteorology and Climate Datasets
Meteorological data were collected during field data recording process. The collected meteorological data came from two observation agencies; Meteorological, Geophysical, and Climatology agency (BMKG) and Ministry of Public Work for water resources management. Both of these government agencies have rainfall observation stations that spreading around the Palu Catchment. The number of rain gauge data were collected during field campaign is 10 rain gages station with 20 years of rainfall data recording period. According to these agencies, the rainfall observation devices divided into two types; manual and automatic data logger. The automatic observation device is recording the rainfall data every 1 hour and for manual devices, the officers were recording the data once per day. In summary, the locations of the rain gauge stations on the study area as shown in Figure 3.3.
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Figure 3.3 Selected meteorological and hydrological stations for Palu Catchment
Table 3.3 Geographical coordinate of selected hydrology and meteorology observation stations.
Name LATTITUDE LONGITUDE
Bora ‐1.027500 119.931389
Kalawara ‐1.166667 119.925278
Palolo ‐1.068611 120.078889
Wuasa ‐1.425000 120.322778
Bangga Atas ‐1.287222 119.900278
Bangga Bawah ‐1.243056 119.909722
Tuwa ‐1.321667 119.977222
Mutiara ‐0.915817 119.905517
mantikole ‐1.076600 119.868800
The daily rainfall data have been collected from time period of 1990 – 2009. Besides the rainfall data, temperature, humidity, sunshine, wind speed and relative humidity data have been collected from two stations with time period of 1990-2009.
Figure 3.4 Annual averages rainfall of each rain gauge
1439 1174 725 1513 2129 1261 687 1074 1893 2309 1354 0 500 1000 1500 2000 2500
Bangga Atas Bora Kulawi Mu\ara Tanamea Wuasa
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21 3.2.3 Soil Dataset
The soil types are obtained from Reconnaissance soil map (PUSLITANAK, 1994). There are 9 kind of soil type found on the study area; brown alluvial (tropofluvents), grey alluvial (udifluvents), greik alluvial
(hydraquents), cambisol dystrik (distropepts), cambisol eutrik (eutripepts),
litosol-latosol block, podsoic-litosol block, brown litosol-latosol (tropodalfts), and brown padsolik (trofodulfts). All of these soil types can be categorized into two main order; inceptisols and ultisols. Inceptisols group can be identified by their texture; sandy clay, sandy loam clay, clay loam, and loam. This group can be found on each sub-basin on the study area.
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3.2.4 Hydrological Dataset
Daily series of discharge data of Palu River were collected from observation station of Ministry of Water Resources (PU-SDA) during the field-work. There are two of river discharge stations available; one is located near to the estuary precisely located under Palu II Bridge, and one is located on the upper side of Palu River. Both of these discharge stations are recording the river discharge once per day. The collected discharge data were used to validate the model.
Figure 3.6 Daily discharges recorded at Palu River observation station. Palu Catchment consist of more than 50 river which is can be grouped into five major group; detail information about its stream network served in figure 3.7 as below:
Figure 3.7. List of existing river in Palu Catchment (Draft of disaster management plan (RPB) of Palu City, 2009).
0 100 200 300 400 500
2002 2003 2004 2006 2007
Q
(m
3 /s
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3.3 Research Method
There are two main processes of this research; first is image processing to produce land cover maps and the second is hydrological modeling to evaluate the hydrological response to the land cover changes. Image processing procedure contain for some analysis executed during the research such as image classification, change detection, and land cover change prediction in the future. While the hydrological modeling procedure was apply some scenarios to simulate hydrograph response. Data Collection Secondary Data Field Data Hydrological Dataset Meteorological dataset Topographic Data Remote Sensed Data Land Cover Information for Ground truth Image Classification Change Detection Analysis of Statistic Report of Change Detection DEM Hydro Processing / Watershed Deleniation Watershed Parameters Hydrological Model Land Cover Map Analysis of Hydrological Model Results
Figure 3.8. Study workflow.
Remote sensing processes were started by collecting the land cover information through secondary data and field work. Secondary data obtained from Geospatial Information Bureau, while filed work is mostly a ground check process to take the coordinate and land cover information as well by using GPS. All of these data was use for accuracy assessment to the classified images.
Hydrological modeling process was started from deriving the watershed, calculating curve number grid, and executing hydrological scenarios. The main concern of this hydrological modeling was to evaluate the river discharge by adjusting the curve number based on the land cover changes on each sub-basin.
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3.3.1 Image Processing
Three different time series images of Landsat were used to identifying the land cover changes. Those images are Landsat ETM 5 image on 1990, Landsat ETM+ on 2001, and 2009 of years respectively. The provided images covered completely all of Palu Catchment area. To ensure that all the images conform to each other, co-referencing process was done while the 2001 Landsat of year image was used as baseline image. Co-referencing process is image-to-image geo-referencing of the images, where one of the images is used as base to geo-correct other images. Using image geo-referencing method, the maximum allowed of root mean square error (RMSE) is 0.5 of pixel resolution was achieved, and all of the images was projected into UTM zone 51S with WGS 1984 datum.
The Landsat images used in this study was acquired from different season, the atmosphere condition have highly effect to the quality of the images. To prevent the bad effect of the atmospheric condition, radiometric correction was done to the all images. The radiometric correction has been done by subtract each band on the image by its minimum digital number value.
Beside both image co registration and radiometric correction, image enhancement process was done in this research. Image enhancement is the improvement process of digital quality on an image. Image enhancement process was done to get the good image to make easier to identify the object on the Landsat satellite imageries.
3.3.1.1 Image Classification
Classification is a process to grouping all pixels in an image into certain classes. Thus, every class can represent an entity with specific properties. Four time series of Landsat images were used to get the information about land cover information on the study area. To obtain good accuracy of the land cover classes, the image was classified through visual interpretation; the land cover classification flowchart for each Landsat images is shown in Figure 7.
Visual interpretation is utilizing several band combinations to obtain clear images. Images with Red, Green, and Blue (RGB) combination 542 and 741 are commonly used to classifying Landsat imagery; these combinations produce good
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25 RGB images. Visual interpretation procedure is semi-automatic method using on screen digitizing.
Landsat 1990 Landsat 2001Landsat 2001 Landsat 2009
Images Correction (Geometric and
Atmospheric)
Images Classification
Accuracy Assessment
Land Cover Maps
Change Detection
Change Detection Analysis
Ground Truth Data No
Figure 3.9 Image classification processes; four multi temporal images was used to produce land use and land cover classes.
Visual interpretation was done by observing the pattern of visible object on the imagery; the object such as river, settlement, and road network are very helpful to assist us to map the vegetation or land cover. The vegetation mapping is performed by delineating the outer boundary of pixels that have same pattern, then it was classified by using an support maps such as land cover maps, topographic, concessions, and vegetation as a reference maps.
Based on the existing condition of land cover type in study area, the Landsat images were classed into 6 major classes. The classes are:
1. Forest Land: Area with high density of trees which include primary dry land
forest, secondary dry land forest, swamp forest, mangrove, and plantation forest.
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2. Agriculture: Area used for both annual and perennial crop cultivation, and the
scattered rural settlements are closely associated with the large sized cultivated field.
3. Shrubs Land: Area covered with shrubs, bushes and small trees, with little wood mixed with some grasses.
4. Water Body: Area which remains water logged and swampy throughout the
year, the man made dam, the rivers with its main tributaries, and the lake. 5. Build up: Area with high density of settlement that including high density
township residences, and urban area.
6. Barren land: Area dominated by grass and small number of small trees.
3.3.1.2 Change Detection
To identify the differences between two or more land cover maps, post classification and matrix analysis was performed during image processing stage. The matrix analysis is comparing the area of each class in each land cover map, and consists of with two kinds of values; the diagonal matrix contains unchanged value while the other cell contain with a value that have been changed. Second step is generating the probability of changes between classes.
Figure 3.10. Change detection procedure (Wijanarto, 2006)
3.4 Hydrological Modeling
3.4.1 General Description of HEC-HMS
HEC-HMS model was designed to simulate the precipitation-runoff processes of dendrites watershed systems (Fleming, 2009). It’s designed to be applicable in a wide range of geographic areas for solving a broad range of
Image 1 Image 2 Registration and Calibration Interpretation ‐ Land cover ‐ NDVI Classification Registration and Calibration Interpretation - Land cover - NDVI Classification Transition Matrix Trend Analysis / Prediction
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27 problems. This includes large river basin water supply and flood hydrology to small urban or natural watershed runoff. Hydrographs produced by the program can be used directly or in conjunction with other software for studies of water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain regulation, wetlands hydrology, and systems operation (Fleming, 2009). HEC-HMS model is a mathematical model and was designed originally to apply for runoff simulation and hydrological forecasting.
The main concept of HEC-HMS hydrological model is the use of NRCS Curve Number process, the model that can be used to assess the availability of water on a watershed. The NRCS-CN model’s itself describing how the precipitation entrance to the watershed system through canopy interception, soil infiltration, percolation, and evapotranspiration. These models also represent the watershed with a series of storage layer such canopy interception storage, surface depression storage, upper ground storage, and groundwater storage.
Figure 3.11 Conceptual schematic of the continuous soil moisture model (HEC-HMS, 2000)
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3.4.2 Model Parameter and Input
3.4.2.1 Schematic Watershed Model – Watershed Delineation
First step in hydrological modeling is delineating watershed boundaries and discretize to hydrology response unit. The aim of the watershed delineation process is to determine the boundary of the watershed and also to break it into smaller management unit (sub-basin) if necessary. The watershed boundary was derived from Suttle Radar Terrain Mission (SRTM) data with 90 by 90 meters of spatial resolution. It divided into seven sub-basins as shows in Figure 3.12. In additional to determining the catchment boundary and its sub-basins area, the watershed delineation process is also determined the stream network and its related parameters such as basin slope, river slope, basin distance, river length, etc. The watershed delineation process was done by using HEC-GeoHMS tool that is can be integrated as plug-in on ArcMap.
Connector Reach Junction
Outlet/Sink
Sub-Basin
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Appendix 2
Runoff curve numbers for cultivated agricultural lands (Handbook
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Appendix 3
Runoff curve numbers for other agricultural lands (Handbook of
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Appendix 4
Runoff curve numbers for arid and semiarid rangelands (Handbook
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Appendix 5
Observed Discharges (Ministry of Public Work for Water
Resources Management)
Observed discharges on 2002
Tanggal Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 44.08 16.72 29.25 76.2 259.16 95.53 62.95 34.69 26.52 38.77 25.6 54.62 2 39.25 14.2 29.53 77.37 309.63 93.22 60.9 34.18 26.08 39.6 25.55 63.92 3 38.45 10.11 28.83 84.59 229.65 83.97 61.14 31.45 25.62 39.85 23.69 64.32 4 36.17 9.38 29.2 90.11 175.05 72.92 61.47 31.54 24.54 38.38 23.61 65.41 5 35.55 13.84 21.16 101.88 177.36 62.63 59.61 31.76 24.68 38.34 23.61 57.75 6 35.74 12.89 22.22 94.28 197.08 61.44 59.85 38.09 23.62 38.34 23.61 60.76 7 45.22 11.29 23.08 94.34 177.54 57.33 55.95 34.65 23.76 36.62 23.61 59.81 8 48.75 9.81 25.16 74.06 319.82 56.47 50.26 33.24 23.94 36.55 22.99 57.52 9 50.13 9.19 29.37 73.57 335.45 63.25 48.37 31.94 22.34 36.55 22.96 54.52 10 52.4 23.05 31.22 80.06 324.95 67.13 44.88 32.1 22.4 36.55 22.96 55.37 11 45.18 42.26 30.77 87.2 280.88 69.89 43.28 31.59 22.56 35.82 22.96 57.35 12 42.57 48.73 30.96 89.24 225.17 62.6 42.62 31.04 22.12 35.79 22.96 52.59 13 44.41 42.92 22.64 89.7 168.29 59.27 41.14 30.5 21.25 35.06 23.59 54.32 14 42.2 47.33 22.47 88.75 142.4 71.79 39.63 29.23 21.82 35.03 23.61 61.73 15 47.66 51.68 24.52 93.18 124.9 66.87 38.97 30.12 22.01 35.03 23.61 83.24 16 63.48 54.07 27.89 91.04 114.95 103.54 36.95 29.64 22.2 35.03 24.85 95.08 17 27.17 35.83 28.88 83.22 103 98.12 36.04 29.82 22.38 33.56 24.9 77.23 18 19.34 25.52 32.89 83.25 88.98 65.56 36.19 29.31 21.95 33.5 24.9 69.18 19 16.64 23.44 37.87 77.5 78.49 95.9 36.41 29.5 20.89 33.5 25.53 67.79 20 16.68 26.65 42.55 75.16 73.53 89.46 37.45 28.74 21.26 32.77 26.17 67.74 21 20.46 26.35 42.98 71.75 69.98 73.01 35.43 27.14 21.84 32.74 26.82 67.74 22 15.12 25.89 44.93 87.08 65.77 118.76 35.55 27.24 24.51 32.01 26.85 67.74 23 12.49 26.05 50 112.56 63.73 126.1 40.76 27.43 22.31 31.98 31.04 67.74 24 21.36 27.48 40.62 147.89 91.14 142.26 37.83 26.37 21.72 31.25 39.75 74.89 25 58.7 29.13 45.56 127.26 87.23 148.82 35.54 26.51 21.93 31.22 40.11 78.86 26 46.01 30.87 44.33 130.1 82.13 160.81 34.93 26.69 22.12 31.22 40.11 88.08 27 24.51 29.69 54.96 177.48 87.6 72.03 34.39 26.26 22.31 30.48 58.97 87.09 28 19.71 27.71 59.9 260.26 88.23 69.71 33.85 26.42 22.49 29.72 61.95 79.34
29 18.15 63.68 411.54 90 68.99 34.04 26.61 22.68 29.69 60.95 96.24
30 14.31 56.58 172.45 93.18 67.09 34.26 26.79 24.73 28.2 59.81 94.24
31 11.75 56.56 95.07 34.48 26.36 26.9 94.12
Rata‐rata 33.9884 26.86 36.4697 113.436 155.495 84.8157 43.391 29.9016 22.9527 34.1952 30.921 70.2042 aliran/km2 (l/det) 11.106 8.772 11.91 37.046 50.782 27.699 14.171 9.7654 7.496 11.168 10.098 22.928 Tinggi aliran (mm) 29.747 21.221 31.901 96.024 136.01 71.797 37.955 26.156 19.43 29.911 26.175 61.409 meter kubik (106) 91.086 64.98 97.68 294.03 416.48 219.84 116.22 80.088 59.493 91.588 80.147 188.03
Observed Discharges on 2003
Tanggal Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 66.4 94.66 94.17 164.18 162.71 63.09 91.71 67.89 60.91 75.68 74.34 79.72 2 68.61 94.8 94.2 154.64 155.71 52.76 83.9 59.2 62.89 65.26 74.01 82.35 3 65.44 113.93 94.23 115.13 159.35 51.38 83.58 58.86 79.42 63.75 74.04 82.48 4 74.65 98.38 96.98 112.03 139.49 44.78 82.38 77.27 104.9 74.1 82.64 94.66 5 81.19 95 97.12 104.5 130.15 69.86 82.36 69.53 99.89 69.66 79.35 97.91 6 81.48 97.64 97.15 114.72 129.83 67.91 82.38 60.44 95.58 60.72 79.35 102.48 7 79.05 93.7 97.18 151.07 133.16 67.79 89.11 59 66.58 71.59 67.28 102.69 8 83.88 72.99 98.57 118.21 134.97 68.91 128.7 45.37 62.12 73.31 69 107.23 9 84.11 64.22 112.13 107.81 126.85 68.98 96.52 44.82 60.91 96.81 65.83 105.95 10 85.57 61.73 112.73 102.97 145.56 69 89.7 53.4 72.16 144.66 62.43 119.78 11 78.09 59.47 97.91 113.31 204.59 69.02 96.31 54.74 93.28 183.48 78.65 125.33 12 77.8 60.5 97.32 121.8 220.63 67.95 92.54 54.8 75.99 146.43 73.23 133.79 13 79.06 55.33 97.35 127.11 232.97 67.93 89.68 63.3 71.58 127.61 62.79 139.51 14 79.13 54.17 97.38 185.46 199.4 67.95 76.75 61.49 69.06 191.22 61.29 146.93 15 77.93 71.59 104.77 214.09 163.48 67.98 68.98 64.71 79.96 227.34 61.27 194.7 16 75.46 68.76 103.61 250.79 154.37 65.81 64.32 53.35 68.37 201.68 72.61 203.03 17 77.83 60.98 99.07 254.75 134.46 66.84 60.88 60.03 67.89 149.03 73.1 142.42 18 86.91 60.98 106.42 242.78 123.81 68 62.96 82.58 69.01 194.29 73.13 126.35 19 87.31 60.71 109.77 192.37 115.31 75.26 61.98 88.9 144.53 190.01 78.06 132.39 20 85.98 60.73 69.89 256.41 109 80.48 68.52 86.53 217.55 150.5 73.39 134.35 21 91.39 60.75 67.15 271.19 99.74 83.16 66.63 65.84 138.11 124.75 83.01 150.56 22 91.65 60.78 74.21 199.96 93.95 83.29 65.47 80.82 113.63 112.62 90.25 128.58 23 87.6 60.8 89.92 172.28 88.31 87.39 63.26 66.77 102.29 103.19 81.3 116.18 24 87.46 92.7 96.03 173.24 79.08 83.52 56.7 82.97 88.01 94.33 79.72 108.24 25 87.49 94.06 106.53 183.57 73.83 86.09 55.48 86.45 86.1 88.56 80.92 115.45 26 86.16 94.08 148.5 212.18 67.76 83.51 55.46 64.87 83.37 81.76 79.77 127.3 27 86.13 94.11 183.5 171.15 66.44 90.23 60.76 66.17 87.35 77.84 78.52 107.31 28 86.16 94.14 193.33 167.49 64.23 87.82 66.47 61.91 73.68 74.04 76.05 96.29 29 86.19 173.1 161.61 65.25 95.9 61.25 53.48 74.35 72.68 75.97 82.44 30 86.21 178.12 144.8 64.23 84.01 67.62 55.1 74.43 93.29 76 73.34 31 91.68 162.84 63.11 66.82 65.91 83.34 69.36 Rata‐rata 82.06452 76.84607 111.3284 168.72 125.8623 72.88667 75.45742 65.04839 88.13 114.9526 74.57667 117.0677 aliran/km2 (l/det) 26.801 25.093 36.358 55.199 41.105 23.804 24.643 21.24 28.782 37.542 24.319 38.232 Tinggi aliran (mm) 71.784 60.705 97.381 143.08 110.09 61.699 66.004 56.891 74.603 100.55 63.036 102.4
(5)
70
Observed Discharges on 2004
Tanggal Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 77.11 103.32 86.03 67 115.6 65.57 43.69 65.57 97.1 104.25 82.82 40.7
2 73.43 93.61 86.06 66.8 109.67 65.57 42.88 65.57 97.1 103.23 119.26 39.31
3 72.05 91.9 67.97 67.9 112.42 64.48 42 66.67 97.1 98.7 120.77 38.47
4 81.79 86.39 75.55 109.36 112.54 66.62 41.96 67.81 97.1 94.53 120.77 38.43
5 79.75 78.54 102.33 111.08 93.54 89.08 41.96 68.95 98.46 94.26 120.77 37.58
6 75.97 77 103.44 106.58 105.75 ‐ 41.96 68.99 98.51 91.54 108.37 84.4
7 72.13 76.94 ‐ 133.45 133.35 ‐ 41.96 71.39 98.51 90.07 70.55 40.97
8 66.25 75.71 ‐ 154.29 154.18 ‐ 41.12 71.49 98.51 91.37 65.71 39.38
9 59.42 68.46 ‐ 162.89 162.78 ‐ 40.23 71.49 98.51 83.31 62.29 38.47
10 59.16 64.87 ‐ 150.04 149.95 110.98 43.59 75.16 98.51 93.81 78.46 42.67
11 56.99 64.73 ‐ 123.64 123.56 123.67 44.58 77.77 98.51 92.9 74.24 41.15
12 79.93 69.1 ‐ 119.27 103.94 96.82 44.61 77.87 98.51 91.49 62.63 41.08
13 66.59 71.71 ‐ 102.37 94.62 104.37 44.61 77.87 98.51 91.43 58.87 39.38
14 59.42 60.62 ‐ 101.66 88.82 86.52 54.11 79.09 98.51 88.71 54.67 38.47
15 65.71 63.44 ‐ 98.68 88.6 92.56 78.16 79.14 72.92 79.52 72.03 36.55
16 69.26 67.95 65.73 109.02 73.4 96.93 68.31 79.14 79.14 79.14 72.76 42.63
17 95.11 69.22 64.63 122.01 66.96 79.86 61.29 81.59 89.14 80.37 72.76 38.61
18 142.51 59.41 66.77 106.97 72.52 74.24 54.76 81.7 81.59 80.42 77.66 38.43
19 140.85 56.85 85.18 92.05 63.67 72.81 53.54 73.12 86.96 81.64 72.97 37.58
20 176.78 56.75 73.44 117.98 61.11 72.76 53.49 66.96 87.18 81.7 82.56 37.55
21 136.77 63.28 68.19 102.32 56.7 72.76 54.46 71.3 85.82 81.7 89.73 85.19
22 103.44 57.02 64.71 98.65 56.52 71.54 54.5 77.61 83.08 76.8 80.8 42.92
23 86.86 86.26 55.02 108.99 55.55 71.49 74.48 93.61 82.97 71.69 79.19 39.38
24 81.05 87.48 78.3 121.97 65.17 71.49 75.32 94.26 81.75 72.71 80.37 38.47
25 83.28 87.48 79.28 106.94 65.57 71.49 59.4 94.26 81.7 73.99 79.19 39.28
26 91.54 87.47 79.28 92.02 64.48 69.09 60.92 95.62 86.96 76.49 75.47 38.47
27 90.51 86.11 79.27 94.15 63.34 64.62 63.2 97.04 93.98 71.69 75.32 38.43
28 93.17 86.05 79.27 114.81 63.29 41.17 63.29 97.1 95.62 65.81 75.32 44.37
29 86.48 86.04 78.04 105.17 63.29 40.2 63.29 97.1 92.96 71.25 74.09 43.76
30 136.66 74.31 113.73 71.16 42.74 71.16 97.1 92.85 76.39 74.09 42.88
31 133.5 71.7 65.81 65.81 97.1 79.04 42
Rata‐rata 90.11194 75.30034 76.56818 109.393 89.60839 76.13192 54.34323 79.98194 91.60233 84.19194 81.14967 42.80516
aliran/km2 (l/det) ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Tinggi aliran (mm) ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
meter kubik (106) ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Observed Discharges on 2006
Tanggal Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 30.45 26.22 34.43 34.7 30.04 69.15 32.86 36.1 34.6 33.36 12.93 14.46
2 31.19 25.58 47.79 42.45 34.81 79.88 30.55 44.17 31.34 23.98 12.48 12.11
3 31.22 24.93 34.1 28.1 23.45 102.1 27.61 43.68 27.64 18.53 12.46 16.51
4 32.68 23.66 26.49 23.15 22.34 121.54 26.87 41.95 33.26 16.25 12.46 14.73
5 39.81 22.99 24.34 24.21 24.8 77.07 24.98 40.18 31.31 14.28 12.46 19.72
6 72.56 20.05 23.02 30.21 27.39 65.85 30.23 3.23 34.87 13.79 12.46 27.19
7 75.14 18.9 20.05 26.37 31.07 57.85 26.37 39.06 27.79 12.93 12.46 28.11
8 52.33 20.92 18.9 27.44 26.4 57.75 36.14 34.46 25.01 12.48 13.72 22.55
9 40.56 24.75 17.82 43.84 24.96 52.59 30.7 31.34 24.28 12.46 12.93 27.29
10 45.2 20.65 15.19 45.37 24.28 53.35 25.13 27.64 32.02 12.46 12.48 23.15
11 51.14 25.35 14.66 40.32 24.26 42.34 23.04 26.25 23.59 12.88 12.88 22.96
12 43.95 24.31 13.8 49.96 26.74 44.42 22.34 24.96 23.61 12.48 12.48 19.02
13 46.18 21.14 13.35 91.01 26.85 54.01 20.54 23.66 50.27 12.46 12.46 16.27
14 50.2 19.98 13.33 65.46 32.5 67.21 19.95 22.37 80.36 12.04 12.46 14.7
15 49.4 19.93 13.33 108.96 34.2 91.71 20.45 22.32 46.85 12.02 12.46 20.24
16 44.72 24.71 16.05 89.35 28.38 69.83 30.79 22.32 36.63 12.02 11.15 20.99
17 43.68 19.1 13.45 63.1 26.28 65.6 25.16 24.8 27.93 12.44 12.42 21.01
18 44.49 18.34 12.91 51.8 24.96 115.01 24.91 27.39 25.01 12.46 12.46 18.94
19 50.13 17.8 14.99 44.8 30.23 76.86 27.39 28.11 23.04 12.46 12.88 16.78
20 43.06 16.22 14.24 43.68 29.72 52.33 40.45 24.41 20.57 12.04 12.06 29.17
21 40.21 15.12 13.79 41.1 27.58 43.95 48.05 20.62 24.73 11.6 12.02 31.89
22 41.8 15.08 12.93 38.45 27.49 41.95 38.74 20.99 28.01 11.58 16.51 31.98
23 36.76 16.12 12.9 41.73 29.6 41.88 33.7 30.08 35.48 11.58 19.8 43.17
24 34.36 16.16 12.9 60.14 53.41 51.96 31.31 31.19 35.06 11.58 19.93 78.83
25 35.73 14.7 26.29 46.87 38.99 48.51 29.75 31.22 32.1 12 17.34 139.29
26 33.59 14.22 44.66 40.36 47.95 56.1 28.2 26.4 36.37 12.02 13.07 89.23
27 31.31 27.58 31.78 35.23 39.59 49.64 27.52 48.43 29.97 11.6 16.03 66.32
28 28.26 37.93 25.16 32.83 44.31 41.33 35.46 34.87 25.1 12 16.68 78.47
29 27.52 33.16 34.2 50.13 39.3 49.78 59.83 45.43 12.44 17.73 132.1
30 26.87 41.54 38.18 60.48 42.62 52.3 75.84 48.27 13.72 20.36 146.82
31 26.85 27.45 48.85 36.07 43.99 44.11 13.77 232.12
Rata‐rata 41.3339 21.1586 22.0903 46.1123 32.969 61.6052 31.1374 32.6445 33.35 13.7971 14.0007 47.6168
aliran/km2
(l/det) 13.499 6.91 7.2143 15.06 10.767 20.16 10.169 10.998 10.815 4.5059 4.5724 15.551
Tinggi aliran (mm) 36.156 16.717 19.323 36.034 28.839 52.255 27.237 29.458 28.033 12.069 11.852 41.651
meter kubik (106
(6)