1. MODELLING SURFACE RUNOFF TO MITIGATE HARMFUL IMPACT OF SOIL EROSION Pavel KOVAR, Darina VASSOVA Faculty of Environmental Sciences Czech University of Life Sciences Prague Research Grant NAZV QH 72085 (2010) Simulation of Rain Erosivity and Soil Erodibility Prague, January 2011

2. INTRODUCTION Problems Caused by Water Erosion –Loss of soil is an important issue worldwide, due to: Increased frequency of hydrological extremes Inexistent or insufficient erosion control measures Improper land use Improper agricultural/forest management –First steps for solving problems related to water erosion : Empirical models: –USLE/MUSLE (Modified/Universal Soil Loss Equation, Delivery Ratio) Simulation models: –CN-based models (EPIC, CREAMS, AGNPS,...) –Surface Runoff and Erosion Processes (SMODERP, EROSION 2D,...) Advanced simulation models: –EUROSEM (European Erosion Model, http://www.cranfield.ac.uk/eurosem/Eurosem.htm) http://www.cranfield.ac.uk/eurosem/Eurosem.htm –WEPP (Water Erosion Prediction Project, http://milford.nserl.purdue.edu/weppdocs/) http://milford.nserl.purdue.edu/weppdocs/

3. INTRODUCTION CAN WATER EROSION BE PREDICTED USING A MODIFIED HYDROLOGIC MODEL? In this presentation we will try to determine the common principles of surface runoff and soil erosion analyses: –Physically-based models –Natural rainfall-runoff events data –Simulated rainfall-runoff data (using rain simulator) –Design rainfall data –Observed and computed rain erosivity data assessment –Soil loss analysis based on soil erodibility (rill and interrill erosion assessment)

4. EXPERIMENTAL RUNOFF PLOTS Area: TŘEBSÍN

5. EXPERIMENTAL SITES DESCRIPTION Soil characteristics: –Brown soil “Eutric Cambisol” on weathered eluvials and deluvials –Field capacity (average): 33.5% –Porosity (average): 48.3% Plot No.Length (m) Wide (m) Slope (%) Area (m 2 ) Crop 2007 Crop 2008 Crop 2009 Crop 2010 937.76.611.2248.8sunflowermaize 637.86.712.8253.3sunflowermaize 437.46.814.3254.3sunflowermaize Average37.66.712.8250.0 Plot parameters and crops Soil hydraulic parameters Plot No.Satur. hydraulic conductivity K s (mm · min -1 ) Sorptivity at FC So (mm · min -0.5 ) Storage suction factor SF (mm) 90.2141.062.63 60.1771.204.07 44.3604.642.47

6. RAIN SIMULATOR

8. SHEET FLOW

9. DISCHARGE/LOAD MEASUREMENT DEVICE

10. 0.001 0.01 0.1 1 10 GRANULARITY CURVE Plot No. Grain <0.002 Grain <0.01 Grain 0.01- 0.05 Grain 0.05- 0.25 Grain 0.25- 2.0 111.427.861.580.7100.0 210.727.760.883.0100.0 39.127.666.781.2100.0 49.930.871.285.1100.0 511.933.276.487.8100.0 613.133.775.388.5100.0 716.636.180.691.3100.0 817.235.279.392.1100.0 917.635.279.592.1100.0

11. MODEL KINFIL – PRINCIPLES EINFIL Part –Infiltration computation: Green Ampt (and Morel- Seytoux) –Storage suction factor: –Ponding time: KINFIL Part –Computation of flow on slopes using kinematic wave computation: (Lax-Wendroff numerical scheme)

12. THE KINFIL PARAMETERS ROOTdepth of root zone (m) KSsaturated hydraulic conductivity (m·s -1 ) SOsorptivity at field capacity (m·s -0.5 ) PORporosity (–) FCfield capacity (–) SMC(or API) soil moisture content (mm) JJnumber of planes in cascade (–) SLOslope of plane (–) LENlength of plane (m) WIDwidth of plane (m) NMManning roughness DSmean soil particle diameter (mm) D(i)soil particle category diameters (mm) ROsoil particle density (kg · m -3 ) – cascade of planes – cascade of segments

13. IMPACT OF PHYSIOGRAPHIC CHARACTERISTICS ON SURFACE RUNOFF Length of slopeAngle of slope Hydraulic roughness

14. NATURAL RAINFALL-RUNOFF OBSERVATION DT = 30 min, area 250 m 2 (36.0 × 7.0 m), 10 August 2007 Rainfall-runoff events Depths, velocity and shear velocity Soil loss: 5330 kg · ha -1 Soil loss: 281 kg · ha -1

15. SIMULATED RAINFALL-RUNOFF EVENTS TŘEBSÍN 9, DT = 1 min, area 30 m 2 (3.0 × 10.0 m) 26 Aug. 2009 (DRY, SMC o =23.4%, Maize) 26 Aug. 2009 (DRY)26 Aug. 2009 (WET) 26 Aug. 2009 (WET, SMC o =39.3%, Maize) Depths and Velocities

16. DESIGN RAINFALLS Rain gauge Benešov:P t,N =P 1d,N · a · t 1-c i t,N =P 1d,N · a · t -c Design rainfall depths P t,N (mm): years

17. DESIGN RAIN INTENSITIES Design rain intensities i t,N (mm · min -1 ): years

18. SURFACE RUNOFF FROM DESIGN RAINFALL Locality: TŘEBSÍN 9, area 30 m 2, Maize

19. DESIGN RUNOFF: DEPTH, VELOCITIES AND SHEAR STRESS VALUES AT DIFFERENT TIME Locality: TŘEBSÍN 9, area 30m 2, N = 2 years, TD = 10 min

20. DESIGN RUNOFF: POTENTIAL SOIL LOSS Locality: TŘEBSÍN 9, N = 2 years, TD = 10 min Grain size categories and their critical shear stress: Effective medium grain size D s = 0.030 mm,  c = 0.5 Pa Experimental runoff area: Category (mm)< 0.010.01–0.050.05–0.250.25–2.00  c (Pa) 0.00760.03800.19001.6700 Potential soil loss (for D s ) at 10’ 0.450.690.881.061.19 Potential soil loss (for D s ) at 20’ 0.911.391.762.122.38Pa Potential soil loss (for D s ) at 30’ 0.020.060.100.150.21

21. CONCLUSIONS ADVANTAGES OF THE KINFIL MODEL –provides results from the physically-based scheme. –provides possibilities to calibrate model parameters for natural rainfall-runoff event reconstructions. –simulates surface runoff discharges, depths, velocities and shear stress accurately enough to be compared with measured discharges and soil losses measured by rain simulator equipment. –simulates also the change of land use and farming management. MODELLERS AIM –to extend research in soil losses caused by rill erosion (  0 vers.  K for various granulometric spectra). –to compare the KINFIL and WEPP models results.

22. Thank you for your attention