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Krishna Prasad Bhandari Western Region Campus Institute of Engineering, Tribhuvan University Nepal. Application of Participatory GIS on Soil Erosion Mapping and Financial Analysis Tools in Soil Conservation Planning: Case Study of Phewa Watershed, Nepal.
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Krishna Prasad BhandariWestern Region Campus Institute of Engineering, Tribhuvan University Nepal Application of Participatory GIS on Soil Erosion Mapping and Financial Analysis Tools in Soil Conservation Planning: Case Study of Phewa Watershed, Nepal United Nations/ Belarus International workshop on Space Technology Applications for socio-Economic Benefits 11-15November Minsk
Background of the study • Soil erosion threatens environment and agriculture which adverse economic and environmental impacts • A serious problem hilly country like Nepal geologically young, ‘sloppy’ fragile mountains and built with rugged surface topography • 34% of agricultural land from water erosion( UNEP 1997) • Economic importance of the watershed due to agriculture and tourism. • Annual siltation rate range of about 175,000-225,000 m3
A time series map analysis indicate a decrease in area from 10 km2 (1956/57), to 5.5 (’76) and 4.4 km2 (’98). • Lack of empower the community people participation in conservation of soil erosion activities • PGIS is methodological tool to decision making, help to better understanding of environmental issues and challenges related to climate change for better analyze and process • PGIS are considered to have superior effects in terms of relevance, usefulness, sustainability, empowerment, and meeting good governance objectives • This tool is based on the integration of farmers’ knowledge, perceptions and scientific knowledge of soil degradation and use for soil erosion management.
Research objectives • To identify major socio-economic, biophysical drivers and climatic factors and their trends that influence soil erosion based on stakeholders’ perception. • To estimate a spatially explicit of soil erosion risk in the study area by using Revised Universal soil Loss Equation (RUSLE) and PGIS for soil erosion management. • To compare the stakeholders perception on soil erosion risk with the estimated soil erosion explicit. • Design plan for the PGIS based system for Soil erosion management system for the reduction of soil erosion in the watershed
Scope of the study • Contribute for the integration of PGIS and the local knowledge from the stakeholders • Contribution relates soil erosion risk linking participatory methodologies with quantitative analysis to understand spatial and social differentiation of soil erosion management. • Relates ongoing debate about triangulation of qualitative, quantitative and methodologies. • PGIS could relate the GIS and related technologies more effectively within their social and public context. • to understand the PGIS and stakeholders perception for soil erosion management in the study area This study can solve the gap between expert and grass root stakeholders problem.
Limitation of the study • Study focus on data base and exposure to the grass root stakeholders • Reduce the gap between the exports and the stakeholders for understanding and management to reduces the soil erosion • The research does not aim to forecast tool for the soil erosion and sedimentation but It is data base that enable for the more supportive participatory GIS approach to find the rule and root cause for the reduction of soil erosion
Study area Location the six Village Development Committees (VDC) Sarangkot, askikot, Dhikurpokhari, BhadaureTamangi, Chapakot and PumdiBhumdi and 7 wards of Pokhara valley.
Socioeconomic Characteristics Demographic Characteristics Land Use activities Livelihood Activities
Soil erosion problem is socially related to physical and social processes defines the spatial location of the soil erosion problem. • The geography of the watershed, environmental degradation, social economic, political, and migration of the watershed population for business, service and foreign labor responsible for soil erosion prone area. • Deforestation, internal and external migration made the soil erosion increases in the watershed. • Lack of manpower on soil conservation. Construction of the road without conservation, agricultural activity without conservation, increase in settlement brought land degradation and accelerates erosion from upstream to downstream.
Data Used The materials used in the study • Topographic map of sheet at 1:25,000 scales • Soil map from soil conservation department • Land sat TM Image resolution 30x30m of 1995 and April 2010 • Equipment used in the field included:GPS, Tape, Topographic map
Land Use Land Cover • Supervised classification with 12 classes • Training sample verified by the GPS point • Accuracy assessment for validation
RUSLE FACTORS Rainfall factor(R-factor)s • Rain fall map – from DEM and elevation • ion rainfall • relationship between elevation and annual rainfall is (R 2 = 0.821) Where P is annual rainfall • The average annual R factor value varies from 785 to 6845 MJ mmha−1 h−1year−1 . • High rainfall erosivity observed in the northwest, west and south west of the watershed that coincides with higher elevation and ridge of study area with green color • The decreasing R factor has a strong relationship with the decreasing trend of elevation and rainfall from the north, northwest, south west and west ridges to the east and southeast of watershed.
Soil erodibility factor (K-factor): • 10 different soil samples were Used to verify the soil map from FRA soil map • The average K value varied from 0.13 to 0.38 t h MJ-1 mm -1 • It can be seen that higher amounts of K values coincides Ancient lake and River Terraces formation that have the greatest sensitivity to erosion as shown with green yellow color. Topographic factors (LS) • A slope raster derived from the DEM model • flow accumulation raster created • two raster were used in the Map Algebra equation • The LS factor was calculated by • LS factor varies from 0 to 200.5. • decreasing elevation values from north, northwest and west ridges to the southeast (outlet) and south
The Cover Factor (C) • LULC layer was generated by supervised classification and using the visual Interpretation (VI). The C-factor value varied from 0.0 to 1as in table The conservation factor (P) • P is calculated by • for agricultural land only and for all other lands were assumed as 1 because there were no any control practices measures. • Where, Pc= Contouring factor based on slope • Ps = Strip cropping factor for crop strip width s • Pt =Terrace sedimentation factor
Soil Erosion Estimation RUSLE Model A=R*K*LS*C*P Average annual soil erosion rate estimated by RUSLE for study area ranged from 0 to 206.7 t/ha/year Average annual soil loss of the whole watershed was 14.7 t/ha/year. Low risk of erosion is 50.10% of total area of watershed (<10 t/ha) Moderate (10-15t/ha) 9.83 % and 36.26 % high to very severe erosion risk levels
Assessment of Current Erosion • A field base approach result provided realistic and rapid way to assess erosion which can be compared in wide range of environment. • Qualitative assessment of soil erosion was converted into assessment of soil erosion feature in the field. • Qualitatively Soil loss erosion feature asses the visible product of the water erosion process which include depth of root exposure, flow channel, surface litter translocation, depth of the rills, depth of stem wash and depth of soil movement.
Total 30 plots were observed for assessment of current erosion damaged • 10 plots-maize, 20plots- LULC (open forest, grass land,dense forest, paddy and barren land • Erosion features -surface litter translocation (Slt), flow channel (Fc), depth of rills (Rill), depth of root exposure (Rex), depth of stem wash (Sw), depth of soil movement (Sm) • Three observations (replications) for each plot were observed for the six types of erosion features during ACED survey • mean values of measured erosion features were assigned severity class according to the table. Severity classes slight, moderate, severe and very severe were express as codes 0,1,2,3 respectively. Maximum and mode function of all six types of measured features were assigned by occurrence of severity class.
Analysis of Variance (ANOVA) Where # stand for value greater than 2.31 and * stands for values less than 2.31
Selection of Assessment factors Stakeholders’ perception and professional expert judgment from field survey; questionnaires, focus group discussion information analysis found factors The factors of soil erosion was socioeconomic factors such as size of farm land, migrated population, road construction without conservation, settlement and population density. Thematic layers of factors The information were used to prepare map of size of farm land, migrated population, road construction without conservation, settlement and population density
Weight Criteria • The questionnaire and focus group discussion and the key informants’ interview • 5 factors influenced for the soil erosion process out of 21 factors. • five criteria arranged in hierarchy for the AHP calculation by the pair wise comparison method • The weight, maximum Eigen value and CI value were calculated in the open software CGI. • All factors were calculated in arc GIS software in raster calculator. The criteria defined on the basis of combination of expert opinion, stakeholder’s opinion and literature review.
Degrees of risk of thematic layers on soil erosion for different dominating factors The expert judgment combining with stakeholders and researcher view, literature review the risk degree of ith factor Si can be decided with number from 0 to1. Si is set as 0 for little effects on soil erosion and 1 for great effect.
Determination of factor weights by AHP technique • The consistency test can be performed by examine total ratio CRtotal:
Result and Discussion • Some sub watershed showed similar risk with natural risk map but some sub watershed were not near to the natural risk map it means that socio economic data has not represent reality of the causes of the soil erosion due to the socio economic data • It should be revivified and direct for the analysis of the causes of soil erosion concept and measurement of society for the change in their perception. PGIS map, socioeconomic map, natural risk map of soil erosion help to find out the conservation of soil erosion in the watershed.
The density of population and migrated population could not be precisely represented spatially and size of the land survey could not be done in plot level only percentage of the land size was used due to the time frame which also could not relate the problem precisely • This overall analysis showed that Mure /Birim, Tora, Betani and Orlang have some different soil erosion status but in other sub watershed nearly same to both model. The region of the different level in social and natural risk is due to the pixel based calculation in RUSLE but socio economic model in sub watershed level. The fusion of these two models by participatory GIS can represent the sustainable management of soil erosion by natural and socioeconomic factors.
Conclusion • RUSLE model for soil erosion and PGIS mapping of soil erosion in study area showed similar results. • PGIS mapping of soil erosion assist stakeholders to understand the causes of soil erosion and implement appropriate conservation technique to reduce soil erosion.
Remaining work (Pilot project in one subwatershd) • The financial tool will be applied based on assumption of costs and benefits of SWC measures among farmers and across different physical and socio-economic situations. • Stakeholders will be involved in identifying and costing the various SWC options for their current status of soil erosion based on the final step of participatory GIS soil erosion mapping tool.
