SWAT application to simulate the impact of soil conservation measurements on soil and nutrient losses in a small basin with mechanised vineyards

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1 Soil loss prediction using SWAT in a small ungaged catchment Soil loss prediction using SWAT in a small ungaged catchment with Mediterranean climate and vines as the main land use Ramos MC & Martínez-Casasnovas JA SWAT application to simulate the impact of soil conservation measurements on soil and nutrient losses in a small basin with mechanised vineyards Benito C, Ramos MC & Martínez-Casasnovas JA Department of Environment and Soil Science University of Lleida (Spain)

2 Mediterranean areas Climate, topography, soil characteristics, land use change, intensive agricultural practices major cause of soil erosion

3 Objective To analyse the suitability of SWAT to predict soil and water losses by erosion for a small ungaged agricultural basin in the Mediterranean area, with vines as the main land use. To analyse the effects of the implementation of drainage terraces and filter-strips in the reduction of soil and nutrient losses

4 Study area: Material and methods Basin: 46 ha ArcSWAT daily time scale

content, steady infiltration rate, available water capacity

5 Input data Soil data Soil map (1:25,) -Instituto Geológico de Cataluña Soil survey: 4 additional points texture, bulk density, organic carbon content, steady infiltration rate, available water capacity K-erodibility factor Soil loss prediction using SWAT in a small ungaged catchment

7 alfalfa 8.47 winter barley 9.45 winter pasture 1.

6 Land Use Visual interpretation of very detail aereal photogrpahs Land Use % vineyards olive trees 4.7 alfalfa 8.47 winter barley 9.45 winter pasture 1.49 ranges 3.5 Management practices Farmer information

7 Topography A 1m-resolution digital elevation model: altitude photogrammetric aerial survey (21) slope Soil loss prediction using SWAT in a small ungaged catchment Sub-basins 34 HRU 1182

8 Climatic data Els Hostalest de Pierola (1.89 E; N, 316 m.a.s.l.) (Instituto Meteorológico de Cataluña) 16-year series ( ) daily data: temperatures (maximum and minimum), precipitation, solar radiation, relative humidity wind velocity T m = 15 C (9-25ºC) Mediterranean Maritime influence Tmax = 2 ºC ( ºC) Tmin = 9º C ( ºC) Pm = 52 mm

9 Sensitivity analysis Parameter Description 1- CN2 _SCS runoff curve number for moisture condition II 2- ESCO: Soil evaporation compensation factor 3- EPCO Plant evaporation compensation factor 4- GW_REVAP Groundwater revap coefficient 5- GW_DELAY Groundwater delay (days) 6- REVAPMIN Threshold depth of water in the shallow aquifer required for revap to occur (mm) 7- Sol_K Soil conductivity (mm h 1 ) 8-.Ch_K2 Effective hydraulic conductivity in main channel alluvium (mm h 1 ) 9- CH_N Manning coefficient for channel 1-GWQMN Threshold depth of water in shallow aquifer required for return flow to occur (mm) 11- Alpha_Bf Baseflow Alpha factor (days) value agric urban

SWAT application Soil loss prediction using SWAT in a small ungaged catchment Model calibration and validation Soil moisture: TFR probes (Decagon) at four depths: 1-3, 3-5, 5-7 and 7-9 cm Sediment

10 SWAT application Soil loss prediction using SWAT in a small ungaged catchment Model calibration and validation Soil moisture: TFR probes (Decagon) at four depths: 1-3, 3-5, 5-7 and 7-9 cm Sediment and nutrient concentration in runoff in 2 HRUs (total N and P) calibration validation Model run Years with different climatic characteristics HRUs Calibration & validation SB1

11 Soil conservation measures Drainage terraces rases 1 terrace 24m (8 wine rows) (Ramos and Porta, 1997) Operations: terraces P factor:.5 CN- 5% red c d b a Filter strips Separated 3 m (between alternated vine rows) Operations: filter strips 3 m width

12 Effects of Calibration soil conservation period measures Validation period Soil water RSR PBIAS % NSE Soil water (mm) Results SB P (mm) SB1 SB1 cal May-1 Aug-1 Nov-1 Feb-11 May-11 Aug-11 Nov-11 Feb-12 May-12 val May-1 Jul-1 Sep-1 Nov-1 Jan-11 Mar-11 May-11 Jul-11 Sep-11 P SW-model SW-measured Nov-11 Jan-12 Mar-12 day May-12 2 Calibration period Validation period Comparisons between simulated and measured soil water during calibration and validation periods Soil water (mm) P (mm) P SW-model SW-measured

13 calibration validation Soil loss prediction using SWAT in a small ungaged catchment SB1 Runoff Runoff (mm) May-1 Jul-1 Sep-1 Nov-1 Jan-11 Mar-11 May-11 Sim Runoff (mm) Jul-11 Sep-11 Nov-11 Est. Runoff (mm) Jan-12 Mar-12 May-12 day SB1 cal SB1 val RSR PBIAS % NSE Comparisons between simulated and measured runoff during calibration and validation periods May-1 Jul-1 Sep-1 Nov-1 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Runoff (mm) calibration validation Sim Runoff (mm) Est. Runoff (mm) day

14 Soil loss (Mg/ha) May-1 Jul-1 calibration Sep-1 Nov-1 Jan-11 Mar-11 May-11 Sim. Soil loss validation Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 Est. Soil loss May-12 SB1 SB1 cal SB1 val RSR Soil losses PBIAS % NSE Comparisons between simulated and measured soil losses during calibration and validation periods May-1 Jul-1 Sep-1 Nov-1 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Sedimentos (t/ha) calibration validation Sim. Soil loss Est. Soil loss

15 N and P losses N _org (kg/ha) 8 6 SB1 4 cal SB1 val /4/11 3/6/11 RSR /7/ PBIAS % N SB N_med N_sim NSE RSR PBIAS % P _org (kg/ha) /4/11 3/6/11 15/7/11 P NSE SB P_med P_sim 25 7 N _org (kg/ha) P _org (kg/ha) /1/1 1/11/1 29/4/11 3/6/11 22/6/11 26/1/11 24/3/212 16/4/12 8/5/12 N_med N_sim 1/1/1 1/11/1 29/4/11 3/6/11 22/6/11 26/1/11 24/3/212 16/4/12 8/5/12 P_med P_sim Comparisons between simulated and measured N and P losses in some events during calibration and validation periods

16 Results of simulations for the period Year P (mm) SurQ (mm) LatQ (mm) GwQ (mm) Soil loss prediction using SWAT in a small ungaged catchment Percol (mm) ET (mm) Water yield (mm) Sed yield (Mg ha -1 ) aver

17 Soil losses for: 28 (very wet year) and 26 (dry year) Average annual soil losses (22-211)

18 Year Sediment Yield Mg ha -1 SurfN_NO 3 kg ha -1 Soil loss prediction using SWAT in a small ungaged catchment Annual soil and nutrient losses in the basin for the analysed period Org N kg ha -1 P sol kg ha -1 P Org kg ha

19 Effects of terraces and filter strips application on sediment yield 14 Sediment Yield (Mg/ha) SY_wSCM SY_T SY_FS SY_T&FS SYred: T:3-2.9% FS:1-42% T+FS:1-52%

20 Effects of terraces and filter strips application on sediment yield

21 Effects of terraces and filter strips on N and P losses N_NO3 (kg/ha) P sol(kg/ha) wscm T FS T&FS wscm T FS T&FS N_org(kg/ha) P org (kg/ha) wscm SY_T FS T&FS wscm T FS T&FS

22 Conclusions The use of soil moisture data and runoff and samples collected at different points within the basin allowed to adjust and calibrate the model to the study conditions. The detail information of soil allowed a better understanding of the processes occurring in the basin.

Conclusions Soil loss prediction using SWAT in a small ungaged catchment At the analysis scale, SWAT showed an agreement between measured and simulated soil water content.

23 Conclusions Soil loss prediction using SWAT in a small ungaged catchment At the analysis scale, SWAT showed an agreement between measured and simulated soil water content. Runoff rates and soil losses predicted by the model were in agreement with the soil loss estimated by combining runoff rates and sediment concentration in runoff on average conditions (PBIAS 16-23% ) Nutrient losses: N was relatively well fitted, while for total P the statistics showed a poorer fit. Nevertheless, the simulated results were in agreement with data observed in the area measured at plot scale.

Conclusions High variability was observed in the annual runoff and soil depending on climate characteristics. However, the average soil losses of about 5.5 Mg/ha (ranging between <1 and about 13.

24 Conclusions High variability was observed in the annual runoff and soil depending on climate characteristics. However, the average soil losses of about 5.5 Mg/ha (ranging between <1 and about 13.9 Mg/ha) overpasses the soil loss tolerance, which point out the need of establishing soil conservation measures in the basin. High variability is observed within the basin, with the highest values near the outlet.

The implementation of filter strips 3m width could also produce a soil loss reduction estimated in about 33%.

25 Conclusions Soil loss prediction using SWAT in a small ungaged catchment The implementation of drainage terraces, with a separation of 24 m, equivalent to eight vine rows would allows a soil loss reduction estimated in about 17%, on average. The implementation of filter strips 3m width could also produce a soil loss reduction estimated in about 33%. If both measures were combined the reduction was estimated in about 4% on average. These SCM could produce an average reduction of nutrient losses, particularly org- N and P, up to 6% for N and up to 7% for P.

impact of soil conservation measurements on soil and nutrient losses in a small basin with mechanised

26 Soil loss prediction using SWAT in a small ungaged catchment Soil loss prediction using SWAT in a small ungaged catchment with Mediterranean climate and vines as the main land use SWAT application to simulate the impact of soil conservation measurements on soil and nutrient losses in a small basin with mechanised vineyards Benito C, Ramos MC & Martínez-Casasnovas JA Department of Environment and Soil Science University of Lleida (Spain)

soil losses in a small rainfed catchment with Mediterranean

Using SWAT to predict climate change effects on runoff and soil losses in a small rainfed catchment with Mediterranean climate of NE Spain Ramos MC, Martínez-Casasnovas JA Department of Environment and