1. The study models erosion in the Mizewa watershed in the Upper Blue Nile Basin of Ethiopia using the SWAT model. Erosion rates were high, with average annual soil loss of 40.91 tons/ha. 2. Calibration and validation of the SWAT model showed good performance with R2 and NSE values over 0.5 and fair agreement between observed and simulated results. 3. The highest erosion occurred in July and August due to high rainfall. Surface runoff accounted for 24% of rainfall and was the main driver of erosion in the watershed.

1. Erosion modeling in the Upper Blue Nile Basin: the
Case for Mizewa Watershed
Maru Alem Assegahegn
Birhanu Zemadim(PhD)
Nile Basin Development Challenge (NBDC) Science Workshop
Addis Ababa, Ethiopia, 9–10 July 2013

2. Contents
1. Introduction
2. Materials and Methods
3. Result and Discussion
4. Conclusion and Recommendation

3. 1. Introduction
1.1. Background and Problem statement
 Erosion is a universally accepted environmental
problem that threat man’s dev’t (Hudson, 1981)
Ethiopia is described as the most soil erosion
affected country in the world (Beyene, 2011)
Annual soil loss ranges from16 to 300 t/ha
Lake Tana Basin-experiences severe erosion-soil
fertility & water quality degr’n, siltation, flooding
Characterization of erosion rates in a catchment
scale especially in the study area is rarely done

4. 1.2. Objectives
The overall objective of the research is studying soil
erosion and its impact on water productivity in the
study area.
Specific Objectives
• To investigate hydrological processes and r/nships
between water budget and soil loss in Mizewa
watershed
• To estimate magnitude of erosion spatially and
temporally
• To identify erosion sensitive areas in the study area

5. 2. Material and Methods
2.1. Study Area Description

6. 3.2. Data collection and Analysis
Flow data- was collected from IWMI
SS data-sampled from July 1 to Aug 31/2012 at
the outlet and upstream on site 2 (2 times/day)
o Simple grabbing technique was used
o Suspended sediment conc.(g/L) was analyzed
o Sediment rating curve
 Climatic Data-collected from NMSA and IWMI
 Landuse/land cover data - collected in the field
DEM, Soil Map Data

7. 3.3 Modeling- the SWAT Model
SWAT- WBE
Surface Runoff Volume-
 Peak Runoff rate or
PET- Penman Monteith
 Soil Water and Groundwater were simulated by SWAT
 Sediment- MUSLE-
 Sensitivity Analysis, Calibration & Validation- were done
 Model Performance test-

8. 3.4 SWAT Model Setup- Watershed delineation,
Landuse map, soil map, slope class, HRU

9. 4. RESULT AND DISCUSSION
4.1. Sediment Rating Curve
,

10. Calibration Result
10 flow & 5 sediment sensitive parameters - calibrated
• Flow calibration • Sediment calibration

11. Model Validation Result
Flow & sediment Validation

12. Simulated Results
 Flow simulation- the model captured both dry &
wet periods flow - the dry period slightly deviates
negatively and wet period slightly deviates
positively with 15% annual deviation
 Simulated rate of average annual soil loss of the
watershed is 40.91 ton/ha (high)
 Simulated average annual sediment yield at the
outlet is 12.78 t/ha & measured 11.45 ton/ha
 The highest measured and simulated soil loss
was recorded in July followed by August

13. Subbasin erosion simulation-temporal/spatial

14. Water budget and erosion r/nship
Relatively high average annual rainfall (1385mm
measured & 1374 mm simulated) responsible for
high erosion in the watershed
From SWAT water balance simulation, surface
runoff component, 335.70 mm (24%) of Rainfall is
responsible for soil loss in the catchment (high
erosion potential)
High soil water storage characterizes high runoff in
turn high erosion

15. 5. Conclusion and Recommendation
 Modeling was conducted with one year primary data
 Model calibration & validation were done with data of the
same time series but with varied spatial location
 The R2
and NSE
values (>0.5) (good model performance) and
with fair agreement of observed Vs simulated result
 High SSC(g/L) and soil loss recorded in July and Soil loss in
subbasin 3> subbasin 2 > subbasin 1
 Long duration time series primary data of the watershed is
vital to minimize uncertainty in modeling
 Catchment scale controlled modeling should be encouraged
 Continuous studies should be conducted to recommend
appropriate erosion control remedies at the watershed