Soil erosion modeling and risk analysis for a mountainous watershed

Soil erosion modeling and risk analysis for a mountainous watershed

Ping Zhou

Nordic GIS Conference 2 Oct.2006

University of Helsinki

Viikki Tropical Resources Institute (VITRI), University of Helsinki

Project: Trees for the Yangtze River: Watershed management and ecosystem rehabilitation in Sichuan Province, China (WAMEC) 2004-2006

Project collaborating partners and advisors:

University of HelsinkiDept. of Forest Ecology / VITRI, project leader (Prof. Olavi Luukkanen) Dept. of Forest Resources Management (Prof. Timo Tokola) Dept. of Applied Chemistry and Microbiology (Dr Kristina Lindström)

China Chinese Academy of Sciences/Chengdu Institute of Biology (Director,

Prof. Chunyang Li) Sichuan Agricultural University, Yaan (Prof. Xiaoping Zhang) Chinese Academy of Forestry (CAF)/Institute of Forest Ecology,

Environment and Protection, Beijing (RIFEEP; Institute Director Prof. Shirong Liu)

Beijing Forestry University (Prof. Jijue Li)

Introduction

The Upper Minjiang River Watershed (UMRW) is an ecologically and environmentally fragile area

Deforestation (The forest cover decreased from 50% to 10%~15% in 1980’s, even to 5%~7% along the river, after “returning farmland to forest”, now is about 21%)Soil degraded (Degraded soils are found on 44% of the area)

Studies have already been done: the runoff and sediment loads (Hayashi et al. 2004 ) soil moisture distribution (Ma et al. 2004) vegetation type on hydrological process (Jiang et al. 2004) soil erosion (Ma et al. 2003) Little research has been directly addressed towards soil

erosion and risk analysis at landscape scale in this degraded watershed.

Problems

Soil erosion is a serious problem in the watershed, which can

-degrade soil productivity

-decrease water quality

-cause sedimentation

-increase the probability of flood.

Very hard to measure erosion in montanious watershed.

Large scale problem, needs large scale analysis to provide solution and recommendations.

Method 1_conceptual modelRainfall data

Digital Elevation Model

Satellite images

Inventory data

Elevation map

Digitized streams

Soil map

Soil erodibility

Rainfall raster

Landcoverraster

Slope & contributing area rasters

Soil raster

R

C

LS

K

P

Soil loss

k-NN method

Revised Universal Soil Loss Equation(RUSLE)

Geographic Information System (GIS) As a tool to integrate, analyse, evaluate data

Remote Sensing (RS) Bands information

k-NN method For C factor estimation

Techniques

PCLSKRA

pole pole

pole pole

TREEPLOT20M

PLOT10M

Data

Soil map drainage network map Elevation map (contour lines) Satellite Images Inventory data on vegetation cover

Rainfall data from meteorological stations Soil erodibility data

Result 1_R factor

Average annual precipitation (APP) showed a significant (p < 0.01) correlation r = 0.74 with the station elevation.

A multivariate cokriging method was used to make the interpolation

2006661.00334.0 aa PPR

Result 2_C factor

Vegetation coverage (625 points) (surface coverage and canopy coverage)

k-NN method

C factor (0.015-0.892)

WRS2 130/037 & 130/038, 10th July 2002

k=8

D=55km

Bands = (1,2,3,4,5,7)

Result 3_LS factor

Result 4_K factor

1317.0))3(025.0)2(0325.0)12(10)(1.2( 614.12 stpeomsavfvfK

K factor (0.0363 - 0.0427)

Numeric range

(Mg ha-1yr-1)

Erosionpotential

Area (ha)

Proportion(%)

0 - 1000 low 297007.7 40.2

1000 - 3000 moderate 306909.9 41.6

3000 - 10000 high 120109 16.3

>10000 extreme 9202.3 1.2

no data 4896.8 0.7

Result 5_soil loss

(Zhou et al. 2006)

Suggested species for restoration

Table 4 Potential species for restoration on different soil types and at different elevations

Soil

Brown Soil Cinnamon Soil Alpine Meadow Soil Elevation

Species Predict

% Species Predict % Species Predict %

1 300 - 2 200 Juniperus formosana 1.24 Tilia chinensis 1.00

Subtropical Acer tetramerum 1.24 Acer mono 1.63

Koelreuteria bipinnata 1.29 Cephalotaxus fortunei 1.64 ………….. …………..

Cephalotaxus sinensis 10.64 Quercus dentata 7.95

19 20

2 200 - 2 600 Cupressus chengiana 1.14 Cephalotaxus fortunei 1.01 Ailanthus altissima 1.13

Temperate Cerasus pleiocerasus 1.25 Aralia chiensis 1.31 Cephalotaxus fortunei 1.38

………….. ………….. …………..

Ilex franchetiana 5.02 Quercus dentata 19.61 Rhus potaninii 6.67

23 18 21 2 600 - 3 200 Salix fargesii 1.12 Picea brachytyla 1.28 Pinus densata 1.19

Subalpine Acer erianthum 1.14 Salix tetrasperma 1.54 Hippophae rhamnoides 1.29

………….. ………….. …………..

Abies fabri 5.13 Picea asperata 27.75 Populus davidiana 7.54

17 13 16 3 200 - 3 600 Euptelea pleiosperma 1.00 Populus davidiana 1.00 Betula albo-sinensis 1.08

Boreal Picea brachytyla 1.33 Abies ernestii 1.08 Picea prupurea 1.67

………….. ………….. …………..

Picea asperata 17.93 Abies fabri 12.12 Picea asperata 27.50

12 14 12 3 600 - 5 700 Hippophae rhamnoides 2.16

Arctic Salix tetrasperma 2.45

…………..

Abies fabri 13.03

0 0 12