ESTIMATION OF SOIL USING GIS:A CASE STUDY OF TAITA HILLS

1. ESTIMATION OF SOIL USING GIS:A CASE STUDY OF TAITA HILLS PRESENTER:EVANS ARABU UNIVERSITY OF NAIROBI.

2. OBJECTIVES 1) Erosion modeling with GIS to focus on description of spatial distributions of soil erosion by water. 2) To predict the pattern of erosion and to identify the location of high risk areas for various land use alternatives. 3) To produce an erosion susceptibility map

3. SOIL EROSION • A natural geomorphic process • Soil erosion mechanisms by water vary over time and space • These mechanisms are: • Sheet erosion- un concentrated slope wash • Rill erosion- concentrated wash leads into formation of a gully • A mixed process

4. SOIL EROSION MODELS AND GIS MODELING • Modeling of soil erosion provides sophisticated tool for soil conservation • The models are categorized as: • Empirical models are generally the simplest of the three model types • Conceptual models- aim at reflecting the physical processes governing the system but describe them with empirical relationships. • Physically based models-understanding of the physics of the erosion and sediment transport processes and describe the sediment system using equations governing the transfer of mass, momentum and energy

5. Current Models • Modeling can be used for dynamic simulation and dramatic visualizations. • Current Models • Universal Soil Loss Equation (USLE) ., Agricultural Non Point Source Pollution (AGNPS), ANSWERS, the Erosion Productivity Impact Calculator (EPIC) and SWAT ,., the Kinematics and Runoff Erosion model (KINEROS2) and the European Soil Erosion Model (EUROSEM), CREAMS .

6. SOIL EROSION AND GIS MODELING The advantages of linking soil erosion models with a GIS include the following: • The possibility of rapidly processing input data to simulate different scenarios. A GIS provides an important spatial and analytical function, performing the time consuming georeferencing and spatial overlays to develop the model input data at various spatial scales. • The ability to look at spatial variation; thus areas can be simulated at a user-defined resolution. • The facility of displaying the model outputs (i.e., visualization).

7. Materials • Soil map • Rainfall data • Land use and cover map • Digital elevation Model at reasonable resolution • Landsat images for land cover

8. STUDY AREA. • The Taita Hills (Latitude 3°25´,longitude 38°20´) cover an area of 1000 km2 and are surrounded by both western and eastern sections of Tsavo National Park • The average height of Taita Hills is 1500m the highest peak, Vuria, being at 2208m • The hills were once covered with cloud forest, but after 1960s the forests have suffered substantial loss and degradation.

9. CONT. • . Land use in the Taita Hills is dominated by intensive agriculture • The scarcity of arable land has forced the local communities to take more land under agriculture, which has caused dynamic changes in land use patterns and has led to serious land degradation (deforestation & soil erosion) • Due to poor agricultural management, erodible soils and the large relative height differences of the hills, the foothills especially are subject to land degradation and accelerated soil erosion

10. MAP OF STUDY AREA • Taita Hills

11. METHODOLOGY: • Collection of the above materials from either remotely sensed data and other collecting agencies • Developing a database of the materials/datasets. • Using either the analytical or descriptive methods of soil erosion modeling so as to develop a spatial distribution of soil erosion map using RUSLE

12. Cont. Methodology • The project involved : RUSLE (Revised Universal Soil Loss Equation) A =KRLSCP A= Soil loss amount [ton ha-1 yr-1] K=soil erodibility factor [(ton ha-1)(MJ mm ha-1 hr-1)-1] R=is the rainfall-runoff or erosivityfactor. [MJ mm ha-1 hr-1] LS=ratio of soil loss from the fieldslope length gradient to the soil loss from a 9% slope C= vegetation cover and crop management factor (ratio) P= erosion control practice or support practice factor

13. Land Cover Map

14. Results- K factor • K factor. • Soil Erodibility generated from soil structure, soil texture • P factor was taken as 0.9 due inadequate data on conservation practices

15. C factor • C factor developed from land cover

16. Erosivity (R )factor • Erosivity R=P *0.5 • Where P= annual rainfall

17. LS factor • LS factor generated from the DEM • LS = (Flow accumulation * Cell Size/22.13)0.4 *(sin slope/0.0896)1.3.

18. Erosion Potential • A = R*LS *K *C*P

19. Discussion of Results • The area of highest erosion values in the RUSLE model had the following attributes: • It is lying the region of medium rainfall values i.e. 800mm to 12000mm • The height above mean sea level was between 1000 – 1200m • The vegetation cover was open (general shrubs with 65% - 15% field density • The soil in the region was mostly the MU3P (well drained, moderately deep to deep, friable sandy clay loam to sandy clay.

20. Conclusion • GIS tools have enhanced exponentially the possibilities of handling spatial information such as topography, soil and land use, thus simplifying the implementation of spatially distributed models. • Finally throughout the study it is clear that GIS and other modern geospatial technologies provide the needed cartographical and graphical visualizations of numerical and model outputs

21. Recommendation • The soil erosion modeling using GIS should be carried in all environmentally pressured regions in the country • The project should be carried to another level, this time using the cartographic modeling of the RUSLE embedded in the latest ESRI's Arc GIS 9.3 to improve the accuracy and time taken in the study • The rainfall dataset to be used in future in the modeling should be in point form as opposed to the polygon formats

22. Recommendation • Heavy erosion in the mountain scarp soils (MUIP) and in the open shrubs and open trees points to either overgrazing done in the regions or deforestation .The effects of these practices should be mitigated through adoption of proper farming and conservation alternatives such as involving locals in finding alternative economic activities, educating them on the importance of forests and improving the agricultural extension services

23. Thank You