Evaluation of SWAT’s surface runoff estimation using field observed data in the Upper Blue Nile Basin – Ethiopia

Yihun Dilea,b, Raghavan Srinivasanc, Louise Karlbergb, and Johan Rockströma

aStockholm Resilience Center, Stockholm University, Stockholm, Sweden

bStockholm Environment Institute, Stockholm, Sweden cTexas A&M University, Texas, USA

estimates surface runoff from single precipitation events in agricultural watersheds developed in 1954 by the SCS of the USDA based on thousands of infiltrometer tests carried out from 1930s and 1940s it is adopted for urbanized and forested watersheds

Advantages simplicity, practicality, and predictability reliance on single parameter responsiveness to watershed properties

Disadvantages marked sensitivity to CN fixing the initial abstraction ratio at 0.2 (i.e. Ia=0.2S) equating the ratio of actual retention (F) to potential retentionto (S) to the ratio of actual runoff to potential runoff temprate vs tropical watershed conditions

Curve Number Method

The Curve Number – A theoretical Background

0 < S < ∞

100 < CN < 0

,

Assuming

After F=P-Q and including initial abstraction Ia

Mico-watershed Area (ha)

Elevation(m.a.s.l

Land use type Distance from Met-

station (meters) Max. Min. Gigudeguad 4.63 1920 1879 crops 726

Aletu 6.71 1892 1866 crops 416 Shimbaraye 1.84 1925 1881 grazing 1140

Micro-watersheds in the Lake Tana Basin, Upper Blue Nile Basin - Ethiopia

Research Area

Field Data Vantage Davis pro2 weather station

Rainfall Max & Min Temp. Solar Radiation Wind Speed Relative Humidity

@5minutes

Micro-divers

2.5 H-Flume & SDR (Q =0 .001499 -0.01992H.4 + 0.727294H1.4 + 1.698273H2.5)

Manual flow measurments

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Secondary input data Land cover types

Area (% of basin)

Dominantly cultivated 51.35 C2: Moderatly cultivated 22.34

Woodland open; Shrubland; Afro alpine; Forest 2.91 Grassland 2.83 Water body 20.19

FAO soil name Area (% of basin) Texture

Eutric Leptosols 12.38 LOAM Haplic Nitisols 1.29 CLAY_LOAM Chromic Luvisols 16.00 CLAY_LOAM Eutric Vertisols 11.74 CLAY Eutric Cambisols 0.01 LOAM Eutric Fluvisols 9.79 LOAM Haplic Luvisols 20.62 LOAM Eutric Regosols 0.28 SANDY_LOAM Lithic Leptosols 2.86 CLAY_LOAM Haplic Alisols 4.77 CLAY

Climate data rainfall, Max & Min - 1990-2011

Model setup threshold area of 0.5ha is used for watershed delineation zero percent threshold area is used to define HRUs teff was the dominent agricultural crop in the micro-watersheds

Operation Tillage Planting Fertilizer Harvest & kill UREA* DAP

Timing Apr 1-July 22

July 22 July 22nd & August 22nd

July 22nd Dec 5th

Amount/ frequency

4 times/year 18.75kg/ha & 18.75kg/ha

37.5kg/ha

Tillage operations depth of till of 15cm, and mixing efficiency of 0.3 tillage frequency of 4

Management

Pescticide application 2.4.D amine weed killer 1 liter/ha ~ 0.379kg/ha

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Model Simulation Performance test Measurments were event based (2011 & 2012) SWAT simulations were daily (1990-2012)

The applicability of 5 days antecedent rainfall for adjusting the CN is also studied

Climate data rainfall, Max & Min - 1990-2012

Surface runoff estimation Curve number method Retention parameter was updated based soil water content

Evapotranspiration Hargreaves’s method Global weather data – weather genrator

Stream routing Variable storage method

Results, and Discussion

Micro-watershed Mean (mm) Standard Deviation (mm)

Maximum (mm) NSE PBIAS

Observed simulated Observed simulated Observed simulated

Gegudeguad (31) 3.15 2.48 4.37 5.17 19.45 25.90 0.72 21

Aletu (9) 2.61 1.79 3.94 2.94 10.55 7.16 0.85 32

Shimbraye (13) 1.79 1.26 2.82 2.51 8.70 7.14 0.87 28

Based on Event Rainfall-Runoff data

CNs range from 83 to 99

Hjelmfelt, 1991 median CN is CN2= 95

CN1=88 and CN3=98

and

Five days Antecedent Rainfall Dry: 20.47-49.43 mm Average: 42.17-65.23mm Wet: 49.37-89.43mm

slope=0.44 (R2 =0.91) slope=0.44 (R2 =0.71) slope=0.41(R2 =0.65)

High 5-days antecedent rainfall + Low rainfall intensity

Low 5-days antecedent rainfall + High rainfall intensity

slope=0.1 (R2 =0.14) slope=0.46 (R2 =0.97)

Conclusion

CN method performed reasonably well in simulating surface runoff in the Upper Blue Nile Basin Calibration can further improve the agreement between simulated and observed flows Five days antecedent runoff is not a good indictor to adjust the curve number Rather rainfall intensity or soil moisture as implemented in continues models is a better approach to adjust the curve number

Thank you