Vinícius Marques Louzada

Celso Bandeira de Melo Ribeiro

Energy Demmand

Necessity ofmore energy

Belo Monte

EconomicGrowth

Introduction

HidroeletricsEnvironmental

ImpactsStudies

Necessity

Necessity of a System to support the decisions in order to achieve

an adequate Water Resource Management.

Modeling

Advantages

Simulatedifferentsscenarios

Economy oftime andmoney

Support todecisionmakers

Desadvantages

It is not 100% accurate

Necessity ofdata in

quantity andquality

Modelling

Developed by:

USDA Agricultural Research Service

Texas A&M AgriLife Research

Global application:

SWAT Model

•Digital Elevation Model - DEM

•Weather data

• Soils maps

• Soil use and occupation

Input

•Hydrology

• Sediments Transportation

• Plant grownth

•Nitrogen Cicle

• Phosphorus Cicle

Output

SWAT Model

Software to Calibrate and Validate a model

Time Optimizer: Identification of most sensitives parameters

Identification of best values for each parameter

SWAT - CUP

Calibrate a SWAT model for Xingu River subbasin using SWAT and SWAT-CUP and identifythe influence of soil use changes on the flow ofthe main river.

Objective

Metodology

Study Area:

Xingu River Basin

Area = 509 000 km2

Database for the Xingu basin preveously

prepared

Soil use

Soil type

Topography

(DEM)

Weather data

MetodologyBase de Dados - SWAT

Land Use

MODIS – MCD12Q1

Types of Soil

ISRIC – World Soil

Information

http://www.isric.org/

Digital Elevation Model - DEM

http://hydrosheds.cr.usgs.gov/dataavail.php

Weather Data

Data from monitoring

satations:

INMET

ANA

Use of monthly flows to calibrate the model

Inventary of monitoring stations

from National Water Agency -

ANA

Definition of weather stations according to the

period of available data.

Weather Stations Used

Boa Sorte Altamira

Code 18460000 Code 18850002

Period: January1976 to

Februrary 2009

Período: January 1971 to

January 2013

Calibration withAltamira station

Calibration with Boa Sorte station

SWAT - CUP

•Coefficiente of determination

• 0 a 1R²

•Nash-Sutcliffe efficiency

•Relatice magnitude data variance compared tothe measured data variance

NSE

NSE = 1- 𝑖=1

𝑛(𝑌𝑜𝑏𝑠−𝑌𝑠𝑖𝑚)2

𝑖=1

𝑛(𝑌𝑜𝑏𝑠−𝑌𝑚𝑒𝑎𝑛)2

SWAT-CUP

• Tendency of simulated data to belarger or smaller than theobserved values

PBIAS

PBIAS = 𝑖=1

𝑛(𝑌𝑜𝑏𝑠−𝑌𝑠𝑖𝑚)∗100

𝑖=1

𝑛(𝑌𝑜𝑏𝑠)

The response of a model is related to the quality ofinput database. It is necessary to adjust parametersto improve model response

The most sensitive parameters must be determined, trhough sensibility analysis.

Calibration

Parameter Description Range of parameter Best ValueCN2 Surface runoff 35 to 98 75.163ESCO Compensation of soil

evaporation0 to 1 0.2958

ALPHA_BF Base flow 0 to 1 0.40416RCHRG_DP Deep aquifer percolation 0 to 1 0.5458

SLSUBBSN Average length of lateralramp

10 to 150 32.75

EPCO Compensation for plantgrown

0 to 1 0.85416

SURLAG Surface runoff retardationcoefficient

0.05 to 24 20.3078

CH_W2 Average width of mainchannel at top of bank

0 to 1000 287.5

CH_L2 Length of main channel -0.05 to 500 160.383CH_N2 Manning’s roughness

coefficient value for themain channel

-0.01 to 0.3 0.200

Most Sensitives Parameters

R² NSE PBIAS

0.63 0.59 17.3

Results

NSE Evaluation

Model ValuePerformance

ratingModeling Phase Reference

SWAT >0.65 Very GoodCalibration and Validation Saleh et al. (2000)

SWAT0.54 to 0.65 Adequate

Calibration and Validation Saleh et al. (2000)

SWAT >0.50 SatisfactoryCalibration and Validation

Santhi et al. (2001); adapted by Bracmort et al. (2006)

PBIAS Evaluation

Model Value

Performance rating

Modeling Phase Reference

SWAT <10% Very GoodCalibration and Validation Van Liew et al. (2007)

SWAT <10% to <15% GoodCalibration and Validation Van Liew et al. (2007)

SWAT <15% to <25% SatisfactoryCalibration and Validation Van Liew et al. (2007)

SWAT >25%Unsatisfactory

Calibration and Validation Van Liew et al. (2007)

Land use Changes

Flow Simulation

Satisfatory results for calibration

Streamflow results shows tiny variation from 2002 to2012

When property calibrated and validated SWAT model is a very efficient tool to plan interventions and changes in the basins

Conclusion

SALLES, L. A. Calibração e Validação do Modelo SWAT Para a Predição de Vazões na Bacia do Ribeirão Pipiripau.

Dissertação de Mestrado, Universidade de Brasília, Brasília, DF. 2012.

NETO, A. R. et al. Simulação na Bacia Amazônica com Dados Limitados: Rio Madeira. Revista brasileira de recursos hídricos. Volume 13, n. 3. Jul/Set 2008, 47-58.

MATA, R. A. Estudo da influência do escoamento superficial no comportamento hidráulico em um corpo hídrico urbanizado. In: XII simpósio ítalo-brasileiro de engenharia sanitária e ambiental. 2014, Natal-RN. PEN DRIVE DO XII SIMPÓSIO... Natal-RN, Abes, 19/05/14.

BRASIL, Lei n° 9433, de 8 de Janeiro de 1997. Institui a Política Nacional de Recursos Hídricos, cria o Sistema Nacional de Gerenciamento de recursos Hídricos, regulamenta o inciso XIX do art. 21 da Constituição Federal, e altera o art. 1° da Lei n° 7.990, de 28 de dezembro de 1989. Diário oficial da República Federativa do Brasil, Brasília, DF, 1997. Disponível em: <http://www.planalto.gov.br/ccivil_03/Leis/L9433.htm#art38vi> Acessado em: 11/06/2014.

INPA, CARACTERÍSTICAS DA BACIA HIDROGRÁFICA DO RIO XINGU. Figura 7.2.4-1 Folha 2 de 2, 2014. Escala: 1:2.500.000. Disponível em: <http://philip.inpa.gov.br/publ_livres/Dossie/BM/DocsOf/EIA-09/Vol%2005/AAR%20MEIO%20BIOTICO/FIGURAS/figura_7_2_4_1_caract_bacia_xingu_folha_2.pdf> Acessado em: 11/06/2014.

RIBEIRO, C. B. M. et al. Parametrization of physical and climatic characteristics in the Amazon basin for hydrological simulation with SWAT model. 2014 Internations SWAT Conference

Referências Bibliográficas