Soil erosion modeling using geographical information system research study in binh gia district lang son province

RUSLE is an erosion model designed average soil losses from sheet and rill erosion under specified condition and was developed by Wischmeier and... LIST OF FIGURES Figure 3.1 Diagrams of

THAI NGUYEN UNIVERSITY

UNIVERSITY OF AGRICULTURE AND FORESTRY

JOSE ALBERTO UMALI DUNCA

SOIL EROSION MODELING USING GEOGRAPHICAL INFORMATION SYSTEM: RESEARCH STUDY IN BINH GIA DISTRICT, LANG SON PROVINCE

BACHELOR THESIS

Study Mode: Full-time

Major: Environmental Science and Management

Batch: 2012-2016

Thai Nguyen, 2016

Thai Nguyen University of Agriculture and Forestry

Degree program Bachelor of Environmental Science and Management

Full name Jose Alberto Umali Dunca

Student ID DTN1353110554

Thesis title

Soil Erosion Modeling Using Geographical Information System: Research Study In Binh Gia District, Lang Son Province

Supervisor MSC NGUYEN VAN HIEU

Supervisor Signature

Abstract: Land area is one major component in the progress of the

world’s biophysical resources Nowadays, soil erosion is an emerging topic

regarding in the land’s degradation Erosion whether by the subjects’ water,

wind, or tillage; involves three (3) diverse actions – soil detachment,

movement and deposition Soil erosion is not just an ecological issue in

Vietnam in general; additionally flash flooding is a significant danger to

human life and property

Binh Gia District is located75 kilometers far from the capital city of

the province which is Lang Son City It is located in the tropical monsoon

climate, influenced by the general climate of the north; the climate is humid

tropical monsoon Average annual rainfall is 1,540 mm, and during the rainy

season is 212 mm per month

RUSLE is an erosion model designed average soil losses from sheet and rill erosion under specified condition and was developed by Wischmeier and

Smith in 1978 However, there are significant limitations due the model only estimates rill and inter-rill erosion, this means that no wind erosion was taken

in consideration for the simulation.By the used of GIS technology, this method was adapted by the researcher in conducting a case study in Binh Gia District

to model soil erosion The result of the analysis showed that the amount of soil loss in the research area ranges from 0 to 5893.09t/ha/year Furthermore the total soil loss in the area was about 80169 ton per year from 11.1 thousand hectare

Acknowledgements

First of all I want to express my sincerest gratitude to my Research Adviser MSc Nguyen Van Hieu to this support to my Bachelor’s Thesis, as well as for his patience, motivation, and great knowledge His guidance has helped me from the beginning, from learning at first, and all throughout my research and for the writing of this thesis as well My special thanks also to his assistants for their support to the completion of my paper

Deepest thanks to Laguna State Polytechnic University Siniloan Campus, Siniloan, Laguna to their recommendation to us to study abroad, and also to Thai Nguyen University of Agriculture and Forestry to their acceptance to study full-time in their University with a 100% scholarship

Sincere thanks also to Nguyen Vu Tuan Anh, Jimlea Nadezhda Mendoza, Keraia Vince Geronimo, and Paul Ezekiel Losaria for always around to help, and share their knowledge for me to finish my study

Last but not the least, I want to thank God for everything he gave to us; for my family, my aunt and uncle, grandma and grandpa for their love, supports and their challenges for me to study hard and be a better student than before And for my Dad, this is for you

Thai Nguyen, 2016

Student

Jose Alberto Umali Dunca

Table of Contents

Table of Contents v

LIST OF FIGURES 1

LIST OF TABLES 2

LIST OF ABBREVIATION 3

PART I INTRODUCTION 4

1.1 Background and rationale 4

1.2 Research objective 5

1.3 The requirement 5

1.4 The significance 5

PART II.LITERATURE REVIEW 6

2.1 Theoretical basis 6

2.1.1 Soil Erosion 6

2.1.2 Geographical Information System (GIS) 13

2.2 Practical Basis 14

PART III.METHODS 26

3.1 Materials 26

3.2 The content 26

3.3 Methods 26

3.3.1 Collecting and selecting data 26

3.3.2 Inherited method 26

3.4 The Revised Universal Soil Loss Equation (RUSLE) 27

3.4.1 R factor (rainfall erosivity) 29

3.4.2 K Factor (soil erodibility) 29

3.4.3 LS Factor (Slope Steepness and Slope Length) 32

3.4.4 Factor C (Crop Management) 34

3.4.5 P Factor (Management Practice) 35

PART IV.RESULTS 37

4.1 The natural conditions and socioeconomic in research area (Binh Gia District) 37

4.1.1 Area’s Climate and weather 38

4.1.2 Digital Elevation Model Map of Binh Gia District 38

4.1.3 Water Resources 39

3.1.4 Forest resources 39

4.1.5 Mineral Resources 40

4.1.6 Human 41

4.2 Result of soil erosion map 42

4.2.1 Rainfall Erosivity Factor (R) 42

4.2.2 Soil Erodibility Factor (K) 43

4.2.3 Slope length and Slope steepness factor (LS) 44

4.2.4 Crop Management (C) 48

4.2.5 Erosion Management Practice Factor (P) 50

4.2.6 Map editor (In ArcGIS 10.2 Software) 54

PART V DISCUSSION AND CONCLUSION 57

PART VI.REFERENCES 59

LIST OF FIGURES

Figure 3.1 Diagrams of RUSLE Method

Figure 3.2 Diagrams of Calculating LS Factor

Figure 4.1 Binh Gia District Map

Figure 4.2 DEM of Binh Gia District

Figure 4.3 Rainfall Erosivity Map (Factor R)

Figure 4.4 Soil Erodibility Map (Factor K)

Figure 4.5 Slope Map of Binh Gia District

Figure 4.6 Slope Steepness (Factor S)

Figure 4.7 Flow Directions and Accumulation

Figure 4.8 Factor M and F

Figure 4.9 Slope Lengths (Factor L)

Figure 4.10 Topographic Map (Factor LS)

Figure 4.11 Normalized Difference Vegetation Index Map

Figure 4.12 Crop Management Map (Factor C)

Figure 4.13 Soil Loss Map of Binh Gia

Figure 4.14 Soil erosion chart

Figure 4.15 Date Frame Tool

Figure 4.16 Map Locator

Figure 4.17 Other map elements

Figure 4.18 Edited Soil Erosion Map of Binh Gia District

LIST OF TABLES

Table 3.1 K Factor Value in Northern Part of Vietnam

Table 3.2 Coefficient of Vegetation in Vietnam

Table 4.1 Soil Erosion Value in every commune

LIST OF ABBREVIATION

ADB: Asian Development Bank

DBMS: Database Management System

DEM: Digital Elevation Model

ESRI: Environmental Systems Research Institute

ETM: Enhanced Thematic Mapper

FAO: Food And Agriculture Organization

GIS: Geographical Information System

IDW: Inverse Distance Weighted

MNF: Minimum Noise Fraction

MSEC: Management Of Soil Erosion Consortium

NDVI: Normalized Difference Vegetation Index

PLER: Predict And Localize Erosion And Runoff

RUSLE: Revised Universal Soil Loss Equation

SLR: Soil Loss Ratio

SMA: Spectral Mixture Analysis

SWAT: Soil And Water Assessment Tools

TIN: Triangulated Irregular Network

USLE: Universal Soil Loss Equation

USPED: Unit Stream Power Erosion/Deposition

WCP: World Climate Programme

PART I INTRODUCTION

1.1 Background and rationale

Land area is to be deliberated as the one important geographic sector in the progress advancement of the world's biophysical assets (Bakimchandra, 2011) Impacts of soil erosion picking up the danger of lessening area accessibility and crisp water accessible per capita, in this way, nourishment security and manageable advancement are vital issues in the low accessible area per capita nations (Dercon et al., 2012), for example, in Vietnam The essential reason of soil erosion are ecological debasement, for example, deforestation, heightened land use, and the expanding scene populace (Ahmed et al., 2010), atmosphere and morphological conditions, for occurrence high concentrated precipitation, steep hill slopes Sometime ago in tropical locales, the top soil layer was regularly ensured by thick vegetation spread, root frameworks (Kefi et al., 2011)

Soil erosion is not just an ecological issue in Vietnam in general; additionally flash flooding is a significant danger to human life and property Flash floods are characterized as remarkable floods delivered by extreme precipitation, over rapidly reacting of catchments and happen inside six hours of the causal precipitation occurrences

Binh Gia District is located in the hilly and mountainous part of Lang Son Province Binh Gia’s population is 53 214 and covering land area of 1,091 km2 Binh Gia district is fragmented by rocky hills that have a slope of 25-300 or more The valley is narrow that annual crops are not much, leading to low revenue It is located in the tropical monsoon climate, influenced by the general climate of the north; the

climate is humid tropical monsoon The districts have cold winter; and dry, hot, humid, and rainy summer The average temperature is 20°c and the temperature ranges from -1°c to 37°c Surface water in Binh Gia is abundant Bac Giang River is an important source of water irrigating the crops and water for the people in daily use

1.2 Research objective

The researcher aim to calculate the rate of soil loss, and to model the soil erosion map of Binh Gia District by the use of ArcGIS software and RUSLE Method

1.3 The requirement

- To classify and process spatial data

- To know the rate of erosion in the research area

- To be familiarized in GIS software in mapping and analyzing data

1.4 The significance

For learning and researching purpose: to apply the researching methods, ways

to model the soil loss of the research area, to increase the knowledge about Geographic Information System as well as the ArcGIS software

The practical significance: applying the ability on reality combine with collecting and analyzing data, assessing the loss of soil

PART II.LITERATURE REVIEW

2.1 Theoretical basis

2.1.1 Soil Erosion

Soil erosion is a naturally occurring process that affects all landforms Erosion, whether it is by water, wind or tillage, involves three distinct actions – soil detachment, movement and deposition Topsoil, which is high in organic matter, fertility and soil life, is relocated elsewhere "on-site" where it builds up over time

or is carried "off-site" where it fills in drainage channels Soil erosion reduces cropland productivity and contributes to the pollution of adjacent watercourses, wetlands and lakes (Ritter, 2015)

Soil erosion can be a slow process that continues relatively unnoticed or can occur

at an alarming rate, causing serious loss of topsoil Soil compaction, low organic matter, loss of soil structure, poor internal drainage, salinization and soil acidity problems are other serious soil degradation conditions that can accelerate the soil erosion process Soil erosion is a normally happening process on all area Soil erosion might be a moderate procedure that proceeds generally unnoticed, or it might happen at an disturbing rate creating genuine loss of topsoil The loss of soil from farmland might be reflected in diminished yield generation potential, lower surface water quality and harmed waste systems

Causes of Soil erosion is controlled by the following factors:

Rainfall Intensity and Runoff

Both rainfall and runoff factors must be considered in assessing a water erosion problem The impact of raindrops on the soil surface can break down soil aggregates and disperse the aggregate material Lighter aggregate materials such

as very fine sand, silt, clay and organic matter can be easily removed by the raindrop splash and runoff water; greater raindrop energy or runoff amounts might

be required to move the larger sand and gravel particles Soil movement by rainfall (raindrop splash) is usually greatest and most noticeable during short duration, high-intensity thunderstorms Although the erosion caused by long-lasting and less intense storms is not as spectacular or noticeable as that produced during thunderstorms, the amount of soil loss can be significant, especially when compounded over time Runoff can occur whenever there is excess water on a slope that cannot be absorbed into the soil or trapped on the surface The amount

of runoff can be increased if infiltration is reduced due to soil compaction, crusting

or freezing Runoff from the agricultural land may be greatest during spring months when the soils are usually saturated, snow is melting and vegetative cover

is minimal

Soil Erodibility

Soil erodibility is an estimate of the ability of soils to resist erosion, based on the physical characteristics of each soil Generally, soils with faster infiltration rates, higher levels of organic matter and improved soil structure have a greater

resistance to erosion Sand, sandy loam and 2 loam textured soils tend to be less erodible than silt, very fine sand, and certain clay textured soils Tillage and cropping practices which lower soil organic matter levels, cause poor soil structure, and result of compacted contribute to increases in soil erodibility Decreased infiltration and increased runoff can be a result of compacted subsurface soil layers A decrease in infiltration can also be caused by a formation

of a soil crust, which tends to "seal" the surface On some sites, a soil crust might decrease the amount of soil loss from sheet or rain splash erosion, however, a corresponding increase in the amount of runoff water can contribute to greater rill erosion problems Past erosion has an effect on a soils' erodibility for a number of reasons Many exposed subsurface soils on eroded sites tend to be more erodible than the original soils were, because of their poorer structure and lower organic matter The lower nutrient levels often associated with subsoil contribute to lower crop yields and generally poorer crop cover, which in turn provides less crop protection for the soil

Slope Gradient and Length

Naturally the steeper the slope of a field, the greater the amount of soil loss from erosion by water, soil erosion by water also increases as the slope length increases due to the greater accumulation of runoff Consolidation of small fields into larger ones often results in longer slope lengths with increased erosion potential, due to increased velocity of water which permits a greater degree of scouring (carrying capacity for sediment)

Vegetation

Soil erosion potential is increased if the soil has no or very little vegetative cover

of plants and/or crop residues Plant and residue cover protects the soil from raindrop impact and splash, tends to slow down the movement of surface runoff and allows excess surface water to infiltrate The erosion-reducing effectiveness of plant and/or residue covers depends on the type, extent and quantity of cover Vegetation and residue combinations that completely cover the soil, and which intercept all falling raindrops at and close to the surface and the most efficient in controlling soil (e.g forests, permanent grasses) Partially incorporated residues and residual roots are also important as these provide channels that allow surface water to move into the soil The effectiveness of any crop, management system or protective cover also depends on how much protection is available at various periods during the year, relative to the amount of erosive rainfall that falls during these periods In this respect, crops which provide a food, protective cover for a major portion of the year (for example, alfalfa or winter cover crops) can reduce erosion much more than can crops which leave the soil bare for a longer period of time (e.g row crops) and particularly during periods of high erosive rainfall (spring and summer) However, most of the erosion on annual row crop land can

be reduced by leaving a residue cover greater than 30% after harvest and over the winter months, or by inter-seeding a forage crop (e.g red clover) 3 Soil erosion potential is affected by tillage operations, depending on the depth, direction and timing of plowing, the type of tillage equipment and the number of passes

Generally, the less the disturbance of vegetation or residue cover at or near the surface, the more effective the tillage practice is in reducing erosion

Conservation Measures

Certain conservation measures can reduce soil erosion by both water and wind Tillage and cropping practices, as well a land management practices, directly affect the overall soil erosion problem and solutions on a farm When crop rotations or changing tillage practices are not enough to control erosion on a field, a combination of approaches or more extreme measures might be necessary For example, contour plowing, strip cropping, or terracing may be considered

Effects

Sheet and Rill Erosion

Sheet erosion is soil movement from raindrop splash resulting in the breakdown of soil surface structure and surface runoff; it occurs rather uniformly over the slope and may go unnoticed until most of the productive topsoil has been lost Rill erosion results when surface runoff concentrates forming small yet well-defined channels These channels are called rills when they are small enough to not interfere with field machinery operations The same eroded channels are known as gullies when they become a nuisance factor in normal tillage

Surface runoff, causing gull formation or the enlarging of existing gullies, is usually the result of improper outlet design for local surface and subsurface drainage systems The soil instability of fully banks, usually associated with

seepage of ground water, leads to sloughing and slumping (caving-in) of bank slopes Such failures usually occur during spring months when the soil water conditions are most conducive to the problem

Gully formations can be difficult to control if remedial measures are not designed and properly constructed Control measures have to consider the cause of the increased flow of water across the landscape, and a multitude of conservation measures come into play Operations with farm machinery adjacent to gullies can

be quite hazardous when cropping or attempting to reclaim lost land

Stream and Ditch Bank Erosion

Poor construction, or inadequate maintenance, of surface drainage systems, uncontrolled livestock access, and cropping too close to both stream banks has led

to bank erosion problems

The direct damages from bank erosion include:

1 The loss of productive farmland

2 The undermining of structures such as bridges

3 The washing out of lanes, roads and fence rows

Poorly constructed tile outlets may also contribute to stream and ditch bank erosion Some do not function properly because they have no rigid outlet pipe, or

have outlet pipes that have been damaged by erosion, machinery, inadequate or no splash pads, and bank cave-ins

On-Site Effects: The implications of soil erosion extend beyond the removal of valuable topsoil Crop emergence, growth and yield are directly affected through the loss of natural nutrients and applied fertilizers with the soil Seeds and plants can be disturbed or completely removed from the eroded site Organic matter from the soil, residues and any applied manure is relatively lightweight and can be readily transported off the field, particularly during spring thaw conditions Pesticides may also be carried off the site with the eroded soil

Soil quality, structure, stability and texture can be affected by the loss of soil The breakdown of aggregates and the removal of smaller particles or entire layers of soil or organic matter can weaken the structure and even change the texture Textural changes can in turn affect the waterholding capacity of the soil, making it more susceptible to extreme condition such a drought

Off-Site Effects: Off-site impacts of soil erosion are not always as apparent as the on-site effects Eroded soil, deposited down slope can inhibit or delay the emergence of seeds, bury small seedling and necessitate replanting in the affected areas Sediment can be deposited on down slope properties and can contribute to road damage

Sediment which reaches streams or watercourses can accelerate ban erosion, clog drainage ditches and stream channels, silt in reservoirs, cover fish spawning

grounds and reduce downstream water quality Pesticides and fertilizers, frequently transported along with the eroding soil can contaminate or pollute downstream water sources and recreational areas

2.1.2 Geographical Information System (GIS)

Geographical Information System, it is frequently applied to geographically oriented computer technology, integrated systems used in substantive applications, and more The GIS field is further characterized by a great diversity of applications GIS are integrating systems which bring together ideas developed in many areas including the fields of agriculture, botany, computing, economics, mathematics, photogrammetry, surveying, zoology and geography The various ideas about GIS can be synthesized and presented in the form of three distinct but overlapping views These can be termed the map, database, and spatial analysis views Other views of GIS have been suggested, the most notable being the application view in which the idea of GIS as the technology to deal with global scientific problems are prominent The map views focuses on cartographic aspects of GIS The database view of GIS emphasizes the importance of a well designed and implemented database Last the third view of GIS emphasizes the importance of spatial analysis, this view focuses on analysis and modeling in which GIS is seen more as a spatial information science than a technology

A case study of Pham Huu Ty in Huong Tra District, Thua Thie Hue Province: Soil Erosion Risk Modeling within Upland Landscapes using Remotely Sensed Data and the RUSLE Model

The researcher stated that Soil erosion is one form of soil degradation that has been recognized as a serious hazard not only for agricultural lands, but also areas used

for forestry, transport, and recreation Soil erosion can result in both on- site and site impacts On-site impacts are particularly important on agricultural land where the redistribution of soil within a field, the loss of topsoil from a field, the breakdown of soil structure and the decline in organic matter and nutrient levels result in reduction of cultivatable soil depth and a decline in soil fertility Stream systems in Huong Tra district either flow into the Huong or the Bo River, and eventually transport sediments

off-to low paddy field areas surrounding, and within Tam Giang Lagoon Annual crops and forestry are the two main land use types, in which forest land accounts for 58 % of the total area Additionally, unused land makes up 23 % of the total area These types

of land, forest and unused land have been prone to water erosion due to poor vegetation cover and steep slopes Ho Kiet in 1999 measured soil losses on seven cropping systems from 1996 to 1998 and indicated that annual soil loss rates varied from 18.28 t ha-1 (dry crops applying soil conservation practices) to 204.56 t ha-1

(agro-forestry systems) illustrating soil loss rates are very severe

An Open Source Gis Approach For Soil Erosion Modeling In Danang City, Vietnam By: Bien Le Van, Minh Truong Phuoc, An Tran Thi, and Venkatesh Raghavan

The authors presented that Da Nang is the international gateway to the sea of Central Region, Central Highlands of Vietnam, and is also an important part of the strategy built East-West Economic corridor, which is end in the seaport system of the city In this developed city, hilly terrain occupies an area of mostly 75%, the average annual rainfall is large (1500 to 2000 mm), and therefore the risk of erosion as well as natural disaster is very high Presently, the production and exploitation of territory in

western mountains have a negative impact on the erosion situation in Danang city Research on mapping the erosion situation, potential erosion of Danang city is very important in urban planning and natural resources utilities for the objective of

sustainable development of the city

Open source GIS software (GRASS GIS and QGIS) is a new approach as well

as method in soil erosion research in Vietnam This study has also added new methods

in modeling soil erosion using USLE equation - methods with the use of Landsat 8 satellite image to interpolate C factor Through the use of model USLE and GIS technology, this research has built potential erosion and erosion situation maps for Danang area, which was analyzed clearly in term of spatial distribution

The Authors; Do Duy Phai, D Orange, J.-B Migraine, Tran Duc Toan and Nguyen Cong Vinh applied GIS-Assisted Modelling to Predict Soil Erosion for a Small Agricultural Watershed within Sloping Lands in Northern Vietnam

GIS-assisted distributed modeling is particularly useful for supplying information to decision-makers regarding land-use, water management and environmental protection This study deals with the prediction of soil losses by a simple distributed and GIS-assisted model within a small experimental agricultural watershed on sloping lands in northern Vietnam The Predict and Localize Erosion and Runoff (PLER) model predicts the spatial and temporal distribution of soil erosion rates; thus it can be used to identify erosion hot spots in a watershed The model has been built specifically to take into account steep slopes It is a conceptual erosion model on a physical base Indeed, the model imitates soil erosion as a dynamic process which includes three phases: i) detachment, ii) transport and iii) deposition In this

study the PLER model was used for two complete years, 2003 and 2004 The disparity for the soil erosion quantity between the experiment and the run model was 5.1 % in

2003 and 4.9% in 2004, even though these two years had a very different annual amount of rain Indeed, 40% of the rainfall events were of a strong intensity (>75 mm

hr-l) in 2003 as opposed to only 4% in 2004 The amount of rainfall in 2003 and 2004 was 1,583 mm and 1,353 mm, respectively The PLER model took into account this discrepancy in the rainfall characteristics between the two years Between April to September, the disparity fluctuates between just 4.7%-5.3% The maps drawn by the PLER model underline that the erosion process occurs mainly at the top of the landscape and highlights a different behavior for detachability and soil erosion between the western and the eastern parts of the studied watershed

“Application of Usle and Gis Tool to Predict Soil Erosion Potential and Proposal Land Cover Solutions to Reduce Soil Loss in Tay Nguyen” research study by MSc Nguyen Manh Ha in the year 2011

The soil loss potential map can be achieved by integrating the erosion factors (R, K and LS) The partitions of the soil loss are created by the difference of the rainfall, runoff and topological conditions GIS is a power-full tool for building the coefficient maps and integrating them to establish the soil loss map The soil loss in Tay Nguyen was much depends on the steep slope and rainfall The level strong and very strong soil loss were distributed almost fix with in the areas that had the high of rainfall and steep slope The soil type wasn’t strong impact to the soil loss here All of the experience modules and soil profiles in field were also showed the close relationship between amount of soil loss with the steep slope and rainfall The cover

are include type of vegetation, % of the LAI, grow time period, and cultivate way, as the control method to find out the solutions to reduce the soil loss based on the soil potential map The average soil erosion potential for each of soil unit can get from the soil erosion potential So that, Tay Nguyen can be divided into three zones, that are the North, the Center and the South zones The average soil erosion potential was from 200-500 ton/ha/yr in the North zone, from 0-100 ton/ha/yr in the Center zone and from 200-400 ton/ha/yr in the South zone The average soil erosion potential for each soil unit in each zone is more effect for solutions to reduce soil erosion in Tay Nguyen thought collect suitable crops, seasonal, and cultivate method

A Modeled Soil Erosion within Small Moutainous Watershed in Central Vietnam Using GIS and SWAT by Tran Thi Phuong, Chau Vo Trung Thong, Nguyen Bich Ngoc, Huynh Van Chuong of University of Agriculture and Forestry, Hue University, Hue City, Vietnam

Soil erosion has been considered the primary cause of soil degradation because soil erosion leads to the loss of topsoil and soil organic matter, which are essential for the growing of plants The purpose of this study is to integrate Geographic Information System (GIS) and Soil and Water Assessment Tools (SWAT) for simulating soil erosion within small mountainous watershed that is an upstream of Bo River watershed in Central Vietnam The results of this study found that the largest amount

of soil erosion was 92.33 t ha-1 in 2007, followed by 2010 (85.41 t ha-1) and 2005 (76.79 t ha-1) The average soil loss in the whole period from 2000 to 2010 was 62.50t/ha-1 Additionally, this study indicated that high soil erosion level still occupies high percentage in 2000 and 2010 with more than 30 %, and this trend tends to

increase mainly in the Southwest and North of the watershed Soil loss occurred mainly in Dry agriculture land area with slope above 250in Ferralic Acrisols (Fa) while there is very low amount of soil loss in slope from 80 to 150 with land use type

of forest mixed in Ferralic Acrisols (Fs) The case study also provides an example quantitative indication of how well GIS and SWAT may perform under limited availability of input meteorological data These results will be useful for water and soil conservation management and the planning of mitigation measures

M.L Gecolea authored a case study about “Weighing Decision Factors In A Assisted Dss: Application in Upland Development Planning and Allocation in the Philippines”

Gis-The author stated that using GIS, it was established that the extent and location

of the selected sites for upland development using computed factor weights were also affected by the differences in the preferences rating Which preference is "right" cannot be easily ascertained, but by integration one can easily get a consensus Moreover, the consultation process that would emerge may generate greater participation that would lead to the achievement of the project's goals and objectives as well as the attainment of more socially acceptable and meaningful project aspirations Indeed, GIS can be a very powerful decision support tool for analyzing complex information It offers an advantage when integration of complex decision factors is necessary The beauty of GIS-based system lies in its easy retrieval, storage and analyses of information in aiding the making of objective and systematically based decisions It is capable of integrating various development goals and objectives of as many stakeholder groups as possible, whether at individual group level or at group consensus level (not completed under the study) to enable an objective planning and

allocation process to occur It can help development actors and providers better allocate their programs and program funds

A potential weakness on the use the procedure is the cost associated with specialized nature and form of data being used (i.e., satellite data, digital data), the initial cost of system installation (hardware, software, skills and political support), the cost of processing and analyzing the data, and the cost of maintaining and upgrading the system The cost may prove prohibitive to many development users as compared to manual and conventional way of doing the same procedure of selecting development sites in the past While this is an ideal procedure, many may resort to shortcuts and biased decision process

In year 2006 Ariel C Blanco, and Kazuo Nadaoka from Tokyo Institute of Technology conducted a study in the Philippines titled “A Comparative Assessment And Estimation Of Potential Soil Erosion Rates And Patterns In Laguna Lake Watershed Using Three Models: Towards Development Of An Erosion Index System For Integrated Watershed-Lake Management”

Soil erosion remains an environmental concern in the Laguna Lake watershed where land use changes have caused increased sediment delivery into the lake causing continued to shallow of the lake, underscoring the importance of an integrated watershed-lake management However, soil erosion in the watershed has not been comprehensively assessed and approaches to doing this have not been explored fully There is also a need to develop an assessment system which local government units within the watershed can use in dealing with erosion problems In this study, three spatially distributed-type models - Universal Soil Loss Equation (USLE), Unit Stream

Power Erosion/Deposition (USPED), and CASC2D - implemented in GIS were used

to assess changes in the relative magnitude and pattern of soil erosion as a result of land use/land cover changes determined from Landsat images (1993-2002) and to examine their utility in identifying “hot spots”, where soil conservation measures are most needed USPED results for 1993 and 2002 indicate that around 65% of the watershed is experiencing net erosion Total estimated soil loss for the entire watershed is overestimated by as much as 50% if USLE is also applied for net depositional areas, a common mistake by users Marikina, Tanay and Angono sub watersheds have the highest mean soil loss rate with Marikina and Tanay experiencing rate reduction of 16% and 22%, respectively, compared to 10% for the watershed GIS analysis is used to discover relationship between watershed characteristics, erosion estimates and lake sedimentation pattern The spatial pattern of erosion generated by USLE and USPED are compared to CASC2D results to determine whether the models are applicable for tropical environments This will also provide insights on how to develop a soil erosion index system more appropriate to local area characteristics

Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin by Ganasri, B.P., Ramesh, H

The authors stated that: Soil erosion is a serious problem arising from agricultural intensification, land degradation and other anthropogenic activities Assessment of soil erosion is useful in planning and conservation works in a watershed

or basin Modeling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions In the present study, the

soil loss model, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS has been used to estimate soil loss in the Nethravathi Basin located in the southwestern part of India The Nethravathi Basin is a tropical coastal humid area having a drainage area of 3128 km2 up to the gauging station The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were deter-mined using GIS The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 2948.16 to 4711.4 MJ/mm$ha 1hr 1/year, 0.10 to 0.44 t ha

GIS-based RUSLE methodology was used to identify the spatial distribution of different erosion prone areas in the Nethravathi Basin The outcome would help to take suitable erosion control measures in the severely affected areas The results obtained from the study can assist in developing management scenarios and provide options to policy makers for managing soil erosion hazards in the most efficient manner for prioritization of different regions of the basin for treatment

Daniel Fonseca de Carvalho, Valdemir Lucio Durigon, Mauro Antonio Homem Antunes, Wilk Sampaio de Almeida, and Paulo Tarso Sanches de Oliveira authored

a study about “Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5” in Sao Paolo, Brazil

The authors’ objective of the work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (RUSLE), in order to estimate watershed soil losses in a temporal scale Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use

and management factor (C factor) A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard RUSLE method Estimated soil losses were grouped into classes and ranged from 0.13

Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season) In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively Mean annual soil loss in the watershed was 109.5 Mg ha-1, but the central area, with a loss of nearly 300.0 Mg ha-1, was characterized as a site of high water-erosion risk The use of C factor obtained from remote sensing data, associated

to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the RUSLE in different seasons, unlike of other studies which keep these factors constant throughout time The use of C factor from remote sensing data, associated to corresponding R factor, is fundamental to evaluate soil erosion estimates by the revised universal soil loss equation (RUSLE) in different seasons It is possible to monitor land cover changes, and to evaluate the effects of seasonality on soil erosion estimates through time series of remotely sensed vegetation index Over the rainy season, rainfall becomes more intense and, thus, more erosive, but soil cover is gradually recomposed with an increase of canopy density, thereby reducing erosion

Soil Erosion Calculation using Remote Sensing and GIS in Río Grande de Arecibo Watershed, Puerto Rico by Alejandra M Rojas Gonzales

As sediment production estimates from watersheds are very important, because these sediments decrease the lake capacity, to impact the ecosystems in the bays and the water quality So is necessary locate the areas potentials in the watershed where exist major erosion and to establishment a program to watershed management Soil

erosion assessment is a capital-intensive and time-consuming exercise A number of parametric models have been developed to predict soil erosion at drainage basins, yet Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) is most widely used empirical equation for estimating annual soil loss from agricultural basins While conventional methods yield point-based information, Remote Sensing (RS) technique makes it possible to measure hydrologic parameters on spatial scales while GIS integrates the spatial analytical functionality for spatially distributed data

The work used the USLE equation to calculate and evaluate these zones in Puerto Rico, basically in Río Grande de Arecibo basin, some of the inputs of the model such as cover factor and conservation practice factor can also be successfully derived from remotely sensed data The LS factor map was generated from the slope and aspect map derived from the DEM The K factor map was prepared from the soil map, which was obtained from SURGO data and K factor values from a Soil Survey of United States and Virgin Islands Maps covering each parameter (R, K, LS, C and P) were integrated to generate a composite map of erosion intensity based on the advanced GIS functionality The statistics of the soil loss map have a mean of 1.903 ton/year and a standard deviation of 3.249 ton/year The values obtained are annual average, so it’s important calculate a map the soil erosion with the maximum precipitation, because in the 2004 year found a precipitation 3 times biggest than the mean annual precipitation So the soil erosion could be increased considerably

This methodology can be applied to the island and small basins The application of Remote Sensing Images is useful to approximate the land cover of a basin

Atesmachew Bizuwerk, Girma Taddese, and Yasin Getahun authored a case study in Ethiopa titled “Application of GIS for Modeling Soil loss rate in Awash River Basin, Ethiopia”

The authors explained that soil erosion is one of the major factors affecting sustainability of agricultural production In most developing countries, like Ethiopia, anthropological or accelerated erosion, which is mainly favored by human activities,

is the major trigger factor for the loss of soil and water resources To facilitate the urgent policy intervention that targeted soil degradation, study the amount of soil loss

is inevitable In this paper, a GIS simulating model using a universal soil loss equation (USLE) was applied to analyze the amount of soil loss in Awash basin of Ethiopia The result of the analysis depicted that the amount of soil loss in the Awash basin ranges from 0 to 330414.5 t/ha/year Moreover the total soil movement in the basin was 37684000 ton per year from 11.2 million hectare GIS provides a great advantage

to analyze multi-layer of data spatially and quantitatively within the basin The estimation of soil loss in the basin using GIS is also in the ranges of other studies GIS not only provides accurate results but also provides cost and time effective ways of analysis

3.3.1 Collecting and selecting data

Collected data that were related to research area, research object (natural conditions, socio-economic conditions) from website of Binh Gia district

Collected the information, data that were needed to evaluate RUSLE method, and to model soil erosion map of the research area

3.3.2 Inherited method

Inherited the researches that were related to Soil Erosion Modeling inside and outside of Viet Nam and collecting the documents were summarized and served for researching pu*-rpose such as modeling soil erosion map by using satellite images, situation and developing tendency of GIS, RS etc

3.4 The Revised Universal Soil Loss Equation (RUSLE)

The Revised Universal Soil Loss Equation (RUSLE) enables planners to predict the average rate of soil erosion for each feasible alternative combination of crop system and management practices in association with a specified soil type, rainfall pattern, and topography When these predicted losses are compared with given loss tolerances, they provide specific guidelines for effecting erosion control within specified limits The equation groups the numerous interrelated physical and management parameters that influence erosion rate under six major factors whose site-specific values can be expressed numerically RUSLE is an erosion model designed to compute longtime average soil losses from sheet and rill erosion under specified condition It is also useful for construction sites and other agricultural conditions, but it does not predict deposition and not compute sediments yield from gully, stream bank, and streambed erosion RUSLE method was the chosen process of the researcher because it was the most appropriate one to use in the research area This method was a widely used by researchers when conducting soil erosion modeling

The RUSLE model was generally expressed as follows:

A = R * K * LS * C * P

Where:

A = the annual average soil loss (t ha-1),

R = the rainfall and runoff factor (MJ mm ha-1 hr-1), which is is the number of rainfall erosion index units, plus a factor for runoff from snowmelt or applied water where such runoff is significant,