swat based runoff and sediment yield.pdf

SWAT based runoff and sediment yield modelling: a case study of the Gumera

watershed in the Blue Nile basin

Meqaunint Tenaw Asres1, Seleshi B. Awulachew2

1 Ministry of Water Resource, P.O. Box 15497, Addis Ababa, e-mail: [email protected]

2 International Water Management Institute, P.O. Box 5689, Addis Ababa, Ethiopia, e-mail: [email protected]

Abstract

Land degradation caused by soil erosion (sheet and rill erosion) and soil fertility decline is a serious threat in the Ethiopian highlands, especially in the Gumara watershed. In this study the SWAT (Soil and Water Assessment Tool) model was applied to the Gumara watershed to predict sediment yield and runoff, to establish the spatial distribution of sediment yield and to test the potential of watershed management measures to reduce

contributing to a mean annual sediment load ranging from 11 to 22 t ha-1 yr-1. The model

Key words: land degradation, soil erosion, sediment yield, SWAT, critical sub water-

manifested primarily by soil erosion and loss of soil fertility (Teketay 2004).

Sheet and rill erosion are, by far, the most widespread types of accelerated water erosion, constituting a principal cause of land degradation

(Constable 1984). A rapidly increasing population,

1. IntroductionEthiopia possesses huge amount of potential

natural resources, which include an annual 3 of surface water and

2.6 billion m3 of ground water as well as 3.7 million ha of potentially irrigable land, that could be used

(Awulachew et al.despite this potential resource base, agricultural production is low in some parts of the country, as

DOI: 10.2478/v10104-011-0020-9

Vol. 10 No. 2-4, 191-2002010 Ecohydrology for water ecosystems and society in Ethiopia

ECOHYDROLOGYHYDROBIOLOGY

192 M.T. Asres, S.B. Awulachew

use rights (grazing and woody biomass resource

country in general and within the Gumara watershed in particular (BCEOM 1998; MoARD 2004).

Despite the recognition of the problem, based on estimates of gross soil loss and sediment measure-

conducted to determine the spatial and temporal

problems, such as soil erosion and non-point source pollution, will require changes in management on the landscape scale (Wilson et al. 2000).

Measurement of sediment transport at a water-

not indicate which part of the watershed is susceptible to erosion and contributing more sediment to the out-let. Furthermore, lack of procedures for transferring

-

at the location of interest (Admasu 2006). One of the possible solutions to the problem

of land degradation due to soil erosion is therefore, to understand the processes causing erosion at the

-ning requires information on runoff and erosion rates at the plot, hill slope, and small watershed

the landscape. In addition, there is

the potential for high erosion, so

to reduce sediment production from these areas.

-

estimating soil erosion and enhanc-ing understanding of the spatial and temporal complexities of catchment response. Such models facilitate as-

large areas, in order to identify and target priority management areas.

study were to determine the spa-

identify critical micro watersheds -

tion scenarios for reducing sediment yield, based on the simulation results

spatially distributed SWAT model.

2. Materials and methods

2.1. Description of the study area The Gumara watershed is located in the North

West part of Ethiopia in Amhara Regional State; south Gondar zone (Fig. 1). It is situated to the

area of about 1464 km2.The major landforms of the watershed include

mountains. The upper and middle parts of the catchment are characterized by mountainous, highly rugged and dissected topography with steep slopes

and teff (Eragrostis tef), maize, barley, and wheat are the major crops. Bush or shrub land, grazing land, forest/wood land and wetland/swamp are

(WWDSE 2007).

-

-mon soil types in the watershed (BCEOM 1998;

watershed is unimodal and most of the rainfall is concentrated in the season extending from June

months contribute 85 percent of the total annual

Fig. 1.

SWAT based runoff and sediment yield modeling 193

rainfall. The dry season (October to May) has a total rainfall of about 15% of the mean annual rainfall (WWDSE 2007).

2.2. Methods The SWAT 2005 model integrated with geo-

graphic information system (GIS) techniques was used to simulate runoff and sediment yield in this study. SWAT is a physically-based and computa-

the impact of land management practices on water, sediment and agricultural chemical yields in large

time (Neitsch et al. 2005).

of weather stations were used as basic input to the model. Other inputs include daily rainfall, mini-

solar radiation, and wind speed. We delineated the watershed using a 90 m × 90 m resolution DEM and digitized stream networks for the study area. After watershed delineation, it was partitioned

unique soil and land use combinations within the watershed to be modeled. Accordingly, multiple HRU with a 20% land use threshold and a 10% soil threshold were adopted.

For modelling surface runoff and sediment

analysis was carried out using a built-in SWAT -

cube One-factor-At-a-Time (LH-OAT) procedure

obtained. Daily precipitation, maximum and minimum

wind speed data collected from 9 weather stations were used as an input for the model. The missing metrological data from these weather stations were

SWAT. Daily discharge data were collected and the gap of daily suspended sediment was estimated

-

Before calibration proceeds, the performance -

from literature. The results of the model from the

calibration statics was obtained. Therefore, the manual calibration procedure was used in this study. For each calibration run and parameter change, the corresponding model performance statistics (R² and Ens) were calculated. This procedure continued until

2) and the Nash and Sutcliffe simulation efficiency index (Ensthe model’s performance during calibration and

-

sediment calibration. The model with calibrated

were not used during model calibration. We used

to assess the potential reduction in sediment loads from critical sub-watersheds.

3. Results

3.1. Sensitivity analysis

The threshold water depth for flow in the

or management factor, the USLE support practice


channel sediment routing and the exponential factor

3.2. Calibration and validation Flow calibration resulted in a Nash–Suttcliffe

ns) of 0.76, a correla-2

3.29% showing a good agreement between measured

-

2) of 0.83, a Nash–Sutcliffe ns) of 0.68 and a mean

of -5.4% (Table II).In calibrating the sediment response, a good

agreement between simulated and measured monthly sediment yield was demonstrated by a correlation coefficient (R2) of 0.85, a Nash-Sutcliffe model

ns

Fig. 2.

Fig. 3.

Table I.

Parameter Calibrated (1998-2002) Validated (2003-2005)R2 0.87 0.83Ens 0.76 0.68

3.29 -5.4

Table II.

Period3 s-1)

Measured SimulatedCalibration (1998-2002) 31.63 32.69

33.98 32.15


the model for sediment, a good match between simulated and measured sediment was demonstrated by

2) of 0.79, a Nash–Sutcliffe ns

3.3. The spatial pattern of sediment source areas

run for a period of 10 years from 1996 to 2005. From

the model simulation output, the main sediment

measured mean annual suspended sediment yield for the 10 year period generated from the sediment

-1 year-1 and the mean annual suspended sediment yield simulated by the SWAT model was 16.2 t ha-1 year-1.

The spatial distribution of sediment yield within the Gumara watershed is presented in Figure 6. The spatial distribution of sediment yields indicates that, out of the total 30 SWAT sub-basins, 18 sub-basins produce mean annual sediment yields ranging from

Table III.

Parameter Calibrated (1998-2002) Validated (2003-2005)R2 0.85 0.79Ens 0.74 0.62

-14.2 -16.9

Table IV. Comparison of simulated and measured mean monthly and annual sediment yields for the calibration

Simulation periods Mean measured sediment yield Mean simulated sediment yield (t ha-1 m-1) (t ha-1 yr-1) (t ha-1 m-1) (t ha-1 yr-1)

Calibration (1998-2002) 1.80 21.6 1.55 18.61.91 22.92 1.60 19.2

Fig. 5.

Fig. 4. Calibration results for monthly mean measured and simulated sediment yield.


11-22 t ha-1 year-1, while most of the lowland and wetland areas are characterized by sediment yields in the range of 0-10 t ha-1 year-1.

3.4. Scenario analysis

results considered acceptable, the model could be parameterized to explore the scenarios of interest

width to reduce sediment production from critical

strips, three management scenarios were considered and simulated:

sub-watersheds; and

sub-watersheds.-

duce the mean annual sediment yield by 58% to 62% with 5 m buffer strip and 74.2 to 74.4% with

high reductions in sediment yield occurred when 5 -

4. Discussion

for decision making. In this study, an attempt was made to characterize the Gumera watershed in

-

and offsite effects of soil erosion in the watershed.

statistics. A good agreement between simulated -

2 = 0.87), ns = 0.76)

2 = 0.83, Ens = 0.8

flows by 5.4% for the calibration (1998-2002)

range of ±15%. In general, the time series trend of

In simulating sediment yield, good agreement between simulated and measured flows for the calibration period was again obtained, as demon-

2 = 0.85), ns = 0.74.)

Fig. 6. The spatial distribution SWAT simulated annual sediment yield classes by sub-basin (t ha-1 yr-1). Numbers 1-30 indicate a sub-basin number.


Table V. selected critical sub-watersheds.

Selected critical

sub-watersheds

Mean annual sediment yield (t ha-1 yr-1) (1996-2005) Reduction in sediment yield (%)Base case Filter strip width (m) Filter strip width (m)

5 m 10 m 5 m 10 m 11 11.800 4.5 3.03 -62 -74.3516 18.200 7.6 4.68 -58 -74.3017 12.100 4.6 3.11 -62 -74.2922 17.600 6.8 4.54 -61 -74.2324 21.300 8.2 5.48 -62 -74.2928 12.700 4.9 3.28 -61 -74.1929 19.200 7.4 4.95 -61 -74.23

Fig. 7. Reduction in sediment yield (t ha-1 yr-1

compared to the base case.

Fig. 8. Per cent of reduction in simulated mean annual suspended sediment yield (t ha-1 yr-1) due to


R2 = 0.79, Ens = 0.62 and D = -16.9%. For both tsediment yield were underestimated by 14.2% and

series trend of the measured sediment yield is well matched by the simulated sediment yield for both

Considering the acceptable limits of the statisti-

a good match between measured and simulated sediment yield. A good performance of the model

parameters for the calibration period can be taken

the Gumara watershed, and further simulation and

carried out for other periods using the SWAT model.

After calibration, it was possible to successfully

-pact of broadly adoptable watershed management

scenario analyses were tested to reduce sediment loads from critical sub-watersheds. The simulation

-

yield by 58% to 74%. The simulation results are consistent with results from soil and water conser-

indicated that soil loss was reduced by 55% to 84%

grass strips (SCRP 1996).

Table VI.

Parameter description Parameter code Range

Initial (default)

valueAdjusted

parameter value

CN2 ±25% * +13%±25% ** -20 %

-1) SLOPE ±25% 12%-1) ±25% ** +25

GWQMN 0-5000 0.0 0.0Maximum canopy storage (mm) canmx® 0-10 0.0 0.1

0-1 0.048 0.048Deep aquifer percolation fraction 0-1 0.05 0.03

ESCO 0-1 0.95 0.90Soil depth (mm) ±25 ** -12%

Table VII.

Parameter Description Range (g) Initial value

Adjusted parameter value

0.003-0.5 0.25(a) 0.350.001-0.5 0.15(b) 0.250.001-0.5 0.10(c) 0.180.001-0.5 0.02(d) 0.09

USLE (c factor) (forest) 0.001-0.5 0.003(e) 0.0040.001-0.5 0.01(f) 0.050.001-0.5 0.10 0.180.001 -0.5 0.15 0.25

±25% ** +15%-1): SLOPE ±25% ** +12%

Linear factor for channel sediment routing (SPCON) 0.00001-0.01 0.0001 0.0035Exponential factor for channel sediment routing (SPEXP) 1.0-2.0 1.0 1.1

0-1 1 0.85


The SWAT model predictions demonstrated that about 72% of the Gumara watershed is erosion prone and contributes high sediment yields, exceeding the tolerance limit (soil formation rate) in the study

runoff and sediment yield on monthly basis at the watershed scale and thus can be used as a planning tool for watershed management. The study can be further extended to similar watersheds in the Abay

address the lack of information on processes op-erating at scales between the micro watershed and

by targeting critical areas of the Gumara watershed.

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