Impacts of deforestation on water balance components of a watershed on the Brazilian East Coast

The Brazilian East coast was intensely affected by deforestation, which drastically cut back the original biome. The possible impacts of this process on water resources are still unknown. The purpose of this study was an evaluation of the impacts of deforestation on the main water balance components of the Galo creek watershed, in the State of Espirito Santo, on the East coast of Brazil. Considering the real conditions of the watershed, the SWAT model was calibrated with data from 1997 to 2000 and validated for the period between 2001 and 2003. The calibration and validation processes were evaluated by the Nash-Sutcliffe efficiency coefficient and by the statistical parameters (determination coefficient, slope coefficient and F test) of the regression model adjusted for estimated and measured flow data. After calibration and validation of the model, new simulations were carried out for three different land use scenarios: a scenario in compliance with the law (C1), assuming the preservation of PPAs (permanent preservation areas); an optimistic scenario (C2), which considers the watershed to be almost entirely covered by native vegetation; and a pessimistic scenario (C3), in which the watershed would be almost entirely covered by pasture. The scenarios C1, C2 and C3 represent a soil cover of native forest of 76, 97 and 0 %, respectively. The results were compared with the simulation, considering the real scenario (C0) with 54 % forest cover. The Nash-Sutcliffe coefficients were 0.65 and 0.70 for calibration and validation, respectively, indicating satisfactory results in the flow simulation. A mean reduction of 10 % of the native forest cover would cause a mean annual increase of approximately 11.5 mm in total runoff at the watershed outlet. Reforestation would ensure minimum flows in the dry period and regulate the maximum flow of the main watercourse of the watershed.

[1]  G. O. Garcia,et al.  Qualidade da água em microbacias hidrográficas com diferentes coberturas do solo no sul do Espírito Santo , 2013 .

[2]  J. Huba,et al.  Simulation of the seeding of equatorial spread F by circular gravity waves , 2013 .

[3]  L. H. C. Anjos,et al.  Sistema Brasileiro de Classificação de Solos. , 2006 .

[4]  Youpeng Xu,et al.  Hydrological Simulation by SWAT Model with Fixed and Varied Parameterization Approaches Under Land Use Change , 2013, Water Resources Management.

[5]  H. Liniger,et al.  Impacts of environmental change on water resources in the Mt. Kenya region , 2007 .

[6]  C. Mello,et al.  Development and application of a simple hydrologic model simulation for a Brazilian headwater basin , 2008 .

[7]  Mauro Naghettini,et al.  Simulação Hidrológica Mensal em Bacias Hidrográficas sem Monitoramento Fluviométrico , 2011 .

[8]  Jeffrey G. Arnold,et al.  Soil and Water Assessment Tool Theoretical Documentation Version 2009 , 2011 .

[9]  Walter Collischonn Simulação Hidrológica de Grandes Bacias , 2001 .

[10]  N. Fohrer,et al.  Using a simple model as a tool to parameterise the SWAT model of the Xiangxi river in China , 2009 .

[11]  M. H. Costa,et al.  Deforestation causes different subregional effects on the Amazon bioclimatic equilibrium , 2013 .

[12]  Raghavan Srinivasan,et al.  Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a Northeastern watershed. , 2010 .

[13]  R. W. Skaggs,et al.  Simulation of the Hydrologic Effects of Afforestation in the Tacuarembó River Basin, Uruguay , 2007 .

[14]  J. Arnold,et al.  Suitability of SWAT for the Conservation Effects Assessment Project: Comparison on USDA Agricultural Research Service Watersheds , 2007 .

[16]  Carlos Rogério de Mello,et al.  Simulação hidrológica em uma bacia hidrográfica representativa dos Latossolos na região Alto Rio Grande, MG , 2013 .

[17]  J. Monteith Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[18]  J. V. Soares,et al.  Comparação entre uso de água em plantações de Eucalyptus grandis e floresta ombrófila densa (Mata Atlântica) na costa leste do Brasil , 2003 .

[19]  P. Jones,et al.  REPRESENTING TWENTIETH CENTURY SPACE-TIME CLIMATE VARIABILITY. , 1998 .

[20]  Luiz Carlos Eduardo Milde,et al.  Propriedades Físicas dos Solos na Parametrização de um Modelo Hidrológico , 2003 .

[21]  Performance of a distributed semi-conceptual hydrological model under tropical watershed conditions , 2011 .

[22]  C. Mello,et al.  Hydrologic modeling in the Aiuruoca river basin, Minas Gerais State , 2009 .

[23]  A. van Griensven,et al.  Autocalibration in hydrologic modeling: Using SWAT2005 in small-scale watersheds , 2008, Environ. Model. Softw..

[24]  Lu Zhang,et al.  Response of mean annual evapotranspiration to vegetation changes at catchment scale , 2001 .

[25]  Mauro Naghettini,et al.  Applicability of the SWAT model for hydrologic simulation in Paraopeba river basin, MG. , 2011 .

[26]  Adequação dos parâmetros do modelo de Green-Ampt-Mein-Larson em condições de campo , 2010 .

[27]  C. Mello,et al.  EVAPOTRANSPIRATION AND ESTIMATION OF AERODYNAMIC AND STOMATAL CONDUCTANCE IN A FRAGMENT OF ATLANTIC FOREST IN MANTIQUEIRA RANGE REGION, MG* , 2010 .

[29]  F. Mutua,et al.  Climate change impact on SWAT simulated streamflow in western Kenya , 2009 .

[30]  Yongping Yuan,et al.  Assessment of subsurface drainage management practices to reduce nitrogen loadings using AnnAGNPS. , 2011 .