Sediment load responses to simulated conservation management practices

Transcription

1 Sediment load responses to simulated conservation management practices - towards water and soil protection in a Central Brazilian catchment Michael Strauch, Jorge E.W. Lima, Martin Volk, Carsten Lorz, Franz Makeschin 2013 International SWAT Conference & Workshops - Toulouse, France Paul Sabatier University, July 17-19, 2013

2 1 Introduction Distrito Federal Brasília Inhabitants: 2.6 Mio (2009) Area: 5789 km 2 Population density: ~450/km 2 Page 2

3 1 Introduction Journal de Brasília Page 3

4 1 Introduction Population in thousands in the DF Increasing risk of water scarcity due to population growth & land use change in the Federal District, Brasilia (DF) IWAS/Água-DF: Research project to support IWRM for the DF Program Produtor de Água : Support of sustainable management by Payments for Environmental Services (PES) Effect of Best Management Practices (BMPs) on sediment loads and water quantity? Land use change in the DF Other Urban Agriculture Cerrado Mata Page 4

5 2 Study area Pipiripau River Basin (PRB), 188km² Source: ANA (2010) 5

6 3 Precipitation uncertainty Storm event in Brasília,

7 3 Precipitation uncertainty Ensemble of precipitation input data TAQ TAQM THIE TRMM Gauge Taquara Moving average of TAQ Thiessen polygons Satellite data uniform uniform spatially distributed spatially distributed Sensitive Model parameters? (LH-OAT; van Griensven et al., 2006) Best fit parameters? Flow & sediments? (SUFI-2; Abbaspour et al., 2004) 7

8 Initial range of parameter values 3 Precipitation uncertainty and its influence on parameter uncertainty Best solution (auto-calibration) for rain input model: Calibrated range (Ø 37% of initial range!) A B C D E Model parameters 8

9 4 Model calibration Streamflow (daily) Stream gauge Montante Captação R²: 0.68 R²: 0.79 NSE: 0.67 NSE:

10 4 Model calibration Sediment load (daily) Sampling gauge: Montante Captação R²: 0.37 R²: 0.52 NSE: 0.37 NSE:

11 4 Model calibration Sediment load (monthly) R²: 0.73 R²: 0.64 NSE: 0.71 NSE: Sampling gauge: Montante Captação 11

12 5 BMP scenarios Terraces (TER) Barraginhas (BAR) Multi-diverse crop rotation (ROT) 1 st 3 crops per C year 2 nd Source: BRASIL (2010) Source: BRASIL (2010) on pasture and cropland using the lup-file USLE P-Factor: 0.5 => 0.12 Curve Number: calibrated value -5 simulated as ponds pond parameters derived by GIS and expert knowledge SWAT code modification: only surface runoff is routed through ponds all scenarios were run in different quantities of implementation 12 3 rd on cropland (using lup-file) crops change each year: soybean/corn/cotton corn/beans/sorghum/ /sunflower/canola beans/wheat/bell pepper/ sweet corn/potato

13 5 BMP scenarios Results Cumulative distribution of daily model predictions (extreme scenarios) for period

14 5 BMP scenarios Cost-benefit analysis Implementation costs (ANA, 2010) : Terraces: USD 150/ha (implementation) USD 100/ha (re-establishement) Barraginhas: USD 120/unit 14

15 5 Conclusions Recommendations for the Pipiripau basin: Erosion control constructions (terraces, Barraginhas ) are promising (up to 40 % less sediment load) Crop rotation with irrigation during dry season is no option! Model results plausible, however problems regarding Sediment loads (reference data, model performance) Rain input (ensemble is advantageous but has limitations) Process representation in general (semi-humid tropics!) and for BMPs Pilot program Produtor de Água as a chance to study the effects of BMPs (monitoring!) and thus to evaluate BMP representation in SWAT 15

16 Thank you. Comments / questions?

17 X Appendix Case studies for PhD within IWAS project, Central Brazil: Precipitation uncertainty, Strauch et al. (2012), J. Hydrol. Model setup for status quo Modelling workflow Input data, GIS preprocessing, Initial parameters Plant growth, Strauch & Volk (subm.), Ecol. Mod. Calibration, Plausibility check, Validation Source code adaptation Reference data for streamflow, turbidity, LAI, ET BMP scenarios, Strauch et al. (2013), J. Environ. Manage. Scenario simulation, Impact analysis, Optimization-based trade-offs Scenario data / design Page 17

18 X Appendix Parameter uncertainty due to rain input ensemble 18

19 X Appendix Rating curve to derive daily turbidity Correlation between TU and CSS: CSS = TU [R² = 0.958] 19

20 X Appendix Source: BRASIL (2010) Terraces implemented to 25, 50, 75, and 100 % on pasture and cropland using the land-use-update file (TER25, TER50, TER75, TER100) reduced USLE P-Factor from 0.5 (terraces in poor condition) to 0.12 reduced SCS Curve Number II by 5 from calibrated value Status quo area = X1 area = X2 area = X3 area = X4 area = X5 area = 0 area = 0 area = 0 Land-use update using the lup-file Scenario area = X1*0.75 area = X2*0.75 area = X3*0.75 area = X4 area = X5 area = X1*0.25 area = X2*0.25 area = X3*

21 X Appendix Sediment basins along roads ( Barraginhas ) Source: BRASIL (2010) simulated as ponds in different quantities (BAR25, BAR50, BAR75, BAR100) pond parameters (subbasin-level) derived by GIS and expert knowledge SWAT code modification: only surface runoff is routed through ponds 21

22 X Appendix Multi-diverse crop rotation 8-year crop rotation with 3 crops per year suggested by EMBRAPA implemented on cropland (soybean and corn monocultures, or soybean-corn rotation) to different percentages using the lup-file (5, 10, 25, 50 %) and 8 placeholder HRUs per subbasin, in which the rotation is shifted 22

23 X Appendix Combined simulations of TER and BAR 23

24 X Appendix Average percentage change of streamflow and sediment load in BMP scenarios 24