2013 International SWAT Conference & Workshops - Toulouse, France

Paul Sabatier University,

July 17-19, 2013

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

Page 2

Distrito Federal Brasília • Inhabitants: 2.6 Mio (2009)

• Area: 5789 km2

• Population density: ~450/km2

Introduction 1

Page 3

Introduction 1

Journal de Brasília 22.3.2011

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Population in thousands in the DF

Land use change in the DF

Other Urban

Agri-culture

Cerrado

Mata

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?

Introduction 1

Source: ANA (2010)

5

Study area 2 Pipiripau River Basin (PRB), 188km²

Storm event in Brasília, 27.03.2011

6

Precipitation uncertainty 3

7

Ensemble of precipitation input data

TAQ Gauge Taquara

TAQM Moving average of TAQ

THIE Thiessen polygons

TRMM 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)

Precipitation uncertainty 3

Init

ial r

ange

of

par

amet

er

valu

es

Model parameters

A B C D E

…

…

Best solution (auto-calibration)

for rain input model: 1 2 3 4

Calibrated range (Ø 37% of initial range!)

8

…and its influence on parameter uncertainty

Precipitation uncertainty 3

9

Streamflow (daily)

Model calibration 4

NSE: 0.67 NSE: 0.57 R²: 0.68 R²: 0.79

Stream gauge Montante Captação

Sampling gauge:

Montante Captação

10

Sediment load (daily)

Model calibration 4

R²: 0.37 R²: 0.52 NSE: 0.37 NSE: -2.10

R²: 0.73 R²: 0.64 NSE: 0.71 NSE: -2.08

Sampling gauge:

Montante Captação

11

Sediment load (monthly)

Model calibration 4

Terraces (TER)

on pasture and cropland using the lup-file USLE P-Factor: 0.5 => 0.12 Curve Number: calibrated value -5

Source: BRASIL (2010) Source: BRASIL (2010)

Barraginhas (BAR)

simulated as ponds pond parameters derived by GIS and expert knowledge SWAT code modification: only surface runoff is routed through ponds

Multi-diverse crop rotation (ROT)

crops change each year:

all scenarios were run in different quantities of implementation

C

1st

2nd

3rd

3 crops per year

soybean/corn/cotton

corn/beans/sorghum/ /sunflower/canola beans/wheat/bell pepper/ sweet corn/potato

12

BMP scenarios 5

on cropland (using lup-file)

Cumulative distribution of daily model predictions (extreme scenarios) for period 2004-2009…

Results

13

BMP scenarios 5

Cost-benefit analysis

Implementation costs (ANA, 2010) :

Terraces: USD 150/ha (implementation) USD 100/ha (re-establishement) Barraginhas: USD 120/unit

14

BMP scenarios 5

15

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

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!

Conclusions 5

Thank you. Comments / questions?

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Modelling workflow

Model setup for status quo

Calibration, Plausibility check,

Validation

Scenario simulation,

Impact analysis,

Optimization-based trade-offs

Source code adaptation

Input data,

GIS preprocessing,

Initial parameters

Reference data for

streamflow,

turbidity, LAI, ET

Scenario data / design

Appendix X

Plant growth,

Strauch & Volk (subm.),

Ecol. Mod.

Precipitation uncertainty, Strauch et al. (2012), J. Hydrol.

BMP scenarios,

Strauch et al. (2013),

J. Environ. Manage.

Case studies for PhD within IWAS project, Central Brazil:

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Appendix X Parameter uncertainty due to rain input ensemble

19

Rating curve to derive daily turbidity

Correlation between TU and CSS: CSS = 1.114 TU + 1.4731 [R² = 0.958]

Appendix X

20

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

area = X1 area = X2 area = X3 area = X4 area = X5 area = 0 area = 0 area = 0

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*0.25

Status quo

Scenario

Land-use update using the lup-file

Source: BRASIL (2010)

Appendix X

21

Sediment basins along roads (‘Barraginhas’)

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

Source: BRASIL (2010)

Appendix X

22

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

Appendix X

23

Combined simulations of TER and BAR

Appendix X

24

Appendix X

Average percentage change of streamflow and sediment load in BMP scenarios