Vinícius Marques Louzada Celso Bandeira de Melo Ribeiro
Introduction Economic Growth Energy Demmand Necessity of more energy Belo Monte
Necessity of a System to support the decisions in order to achieve an adequate Water Resource Management. Hidroeletrics Modeling Environmental Impacts Studies Necessity
Modelling Advantages Desadvantages Simulate differents scenarios Economy of time and money Support to decision makers It is not 100% accurate Necessity of data in quantity and quality
SWAT Model Developed by: USDA Agricultural Research Service Texas A&M AgriLife Research Global application:
SWAT Model Input Digital Elevation Model - DEM Weather data Soils maps Soil use and occupation Output Hydrology Sediments Transportation Plant grownth Nitrogen Cicle Phosphorus Cicle
SWAT - CUP Software to Calibrate and Validate a model Time Optimizer: Identification of most sensitives parameters Identification of best values for each parameter
Objective Calibrate a SWAT model for Xingu River sub basin using SWAT and SWAT-CUP and identify the influence of soil use changes on the flow of the main river.
Metodology Study Area: Xingu River Basin Area = 509 000 km 2
Metodology Base de Dados - SWAT Soil use Database for the Xingu basin preveously prepared Soil type Topography (DEM) Weather data
MODIS MCD12Q1 Land Use
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: January 1976 to Februrary 2009 Período: January 1971 to January 2013
Calibration with Altamira station Calibration with Boa Sorte station
SWAT - CUP R² Coefficiente of determination 0 a 1 NSE Nash-Sutcliffe efficiency Relatice magnitude data variance compared to the measured data variance NSE = 1- n i=1 (Yobs Ysim) 2 n (Yobs Ymean) 2 i=1
SWAT-CUP PBIAS Tendency of simulated data to be larger or smaller than the observed values PBIAS = n i=1 (Yobs Ysim) 100 n i=1 (Yobs)
Calibration The response of a model is related to the quality of input database. It is necessary to adjust parameters to improve model response The most sensitive parameters must be determined, trhough sensibility analysis.
Most Sensitives Parameters Parameter Description Range of parameter Best Value CN2 Surface runoff 35 to 98 75.163 ESCO Compensation of soil 0 to 1 0.2958 evaporation ALPHA_BF Base flow 0 to 1 0.40416 RCHRG_DP Deep aquifer percolation 0 to 1 0.5458 SLSUBBSN Average length of lateral 10 to 150 32.75 ramp EPCO Compensation for plant 0 to 1 0.85416 grown SURLAG Surface runoff retardation 0.05 to 24 20.3078 coefficient CH_W2 Average width of main 0 to 1000 287.5 channel at top of bank CH_L2 Length of main channel -0.05 to 500 160.383 CH_N2 Manning s roughness -0.01 to 0.3 0.200 coefficient value for the main channel
Results PBIAS Evaluation NSE Evaluation Performance Modeling Phase Reference Performance rating Model Model Value Value rating R² Modeling NSE Phase PBIASReference Calibration and SWAT <10% Very Good Validation Van Liew et al. (2007) 0.63 Calibration 0.59 and 17.3 SWAT >0.65 Very Good Validation Calibration and Saleh et al. (2000) SWAT <10% to <15% Good Validation Van Liew et al. (2007) 0.54 to Calibration and SWAT 0.65 Adequate Validation Calibration and Saleh et al. (2000) SWAT <15% to <25% Satisfactory Validation Van Liew et al. (2007) Calibration and Santhi et al. (2001); adapted by SWAT >0.50 Satisfactory Unsatisfactor Validation Calibration and Bracmort et al. (2006) SWAT >25% y Validation Van Liew et al. (2007)
Land use Changes
Flow Simulation
Conclusion Satisfatory results for calibration Streamflow results shows tiny variation from 2002 to 2012 When property calibrated and validated SWAT model is a very efficient tool to plan interventions and changes in the basins
Referências Bibliográficas 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