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The predominant soil class is Cambisol and most of the land is covered by Atlantic forest Menezes et al.

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These runs are described in Chou et al. After verification of the simulated present climate, we obtained the future projection of the simulated streamflow for the , and periods. As the observed data are from regions close to the region of this study, the correction of simulated temperature and precipitation data correction bias was not feasible, however, in the work of Chou et al. The model solves the water and energy balance for each grid cell, at each time step.

The DHSVM uses a two-layer canopy model overstory and understory for calculating evapotranspiration using the Penman-Monteith equation. The soil surface can receive water from total precipitation, throughfall or surface flow from adjacent cells. The maximum infiltration rate determines the maximum amount of water that can infiltrate at every time step.

The water movement in the unsaturated soil is simulated using a model of several layers and each vegetation layer can extract water from one or more soil layers.

The percolation is calculated by Darcy's law, using the Brooks-Corey equation to estimate the hydraulic conductivity. The surface flow is generated through saturation and excess infiltration mechanisms. The climatic data of the central points of the cells to feed the hydrologic model were extracted.

Vegetation parameters were not changed in the calibration. There are three types of soils and based on the sensitivity analysis of DHSVM, lateral and vertical soil hydraulic conductivity and exponential decrease rate of lateral soil hydraulic conductivity were used to calibrate the model. All soil and vegetation parameters were based on literature e. An hourly time step was used and the period of January 1, to September 30, was selected as the stabilization period warm up of the model.

For calibration, we selected the period from October 1, to September 30, Finally for validation we selected the period from October 1, to September 30, The streamflow in the LW was monitored by means of an automatic water level gauge that recorded the water depth in the control section of the basin. Thus, by means of the stage-discharge rating curve obtained in field campaigns, it was possible to generate the observed streamflow data.

The model was calibrated and validated manually searching for a fit between the simulated and observed streamflow daily and monthly averages in LW. Thus, the performance of the model was verified based on assessments of the coefficient of determination R 2 and Nash-Sutcliffe efficiency E. Van Liew, Arnold and Garbrecht, suggest that R 2 values higher than 0.

Regional Hydrologic Response to Climate Change: An Ecological Perspective | SpringerLink

The criteria proposed by Moriasi et al. Safeeq and Fares also reported that E values greater than 0. The version of the model Eta used in this study was developed for studies of climate changes in the South America Pesquero et al. The study area was covered with six points corresponding to the horizontal resolution of 5 km.

The atmosphere is represented in the vertical until the pressure level of 25 hPa, with 38 levels. Until the end of the century, the radiative forcing in the RCPs 2. Thus, the RCP 8. Emissions of greenhouse gases in the RCP 8. The simulated and observed values showed that the rainy season presents maximum quarterly total precipitation and temperature values during the summer months of December, January and February DJF. The dry season has quarterly minimum temperature and total precipitation values during the winter months of June, July and August JJA. Due to the complex and non-linear nature of the system there are many uncertainties involved in this study.

Chou et al.

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And total precipitation Precip. Variations of temperature and precipitation simulated for the , and periods future scenario-RCP 8. These authors also suggest that the large reduction in precipitation in the region, particularly during December, January and February, may be associated with a decrease of both the occurrence frequency, as well as the intensity of the episodes, for the South Atlantic Convergence Zone SACZ in summer. The reduction of the occurrence frequency and activity of SACZ is also in line with the intensification of the subtropical high pressure that can block the passage of cold fronts in the region.

In Figures 2 a and b the daily and monthly streamflow simulated by DHSVM are reasonable according to the streamflow observed These figures also expressed the results of the precision statistics coefficient of determination-R 2 and Nash-Sutcliffe efficiency-E to evaluate the performance of DHSVM in the simulation of daily and monthly streamflow of the LW in the calibration and validation period.

The statistical indices values were considered appropriate according to the classification of Van Liew, Arnold and Garbrecht, , Moriasi et al. Analysis in monthly time steps is used in most studies of climate changes impacts Leung; Wigmosta, ; Viola et al. Thus, DHSVM was calibrated in the daily and monthly time step in this study because if the model is calibrated for daily time step with good results, the monthly time step is good enough as well.

The observed average annual streamflow in the LW was 0. Therefore, although the average annual streamflow comparisons are in different periods, the underestimation of the simulated streamflow shows that there are errors and uncertainties in these simulations.

Nevertheless, the observed and simulated values are in order of comparable magnitude. In Table 2 a , the underestimation of the simulated precipitation during the summer DJF corroborates these statements. During the period the average annual runoff was mm. For the RCP 8.

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These reductions can produce very severe impacts on water availability in the region as a whole, and consequently in the generation of electricity from the Upper Grande River region. Precipitation interpolated precipitation data and monthly average streamflows simulated by DHSVM in the LW for present climate , , and future climates are shown in Figures 3 a and b and Figures 4 a and b.

The seasonality of the average monthly streamflow for all periods remained consistent with the seasonality of the average monthly precipitation. In general, the maximum simulated streamflows were between October and March and the minimum between the months of April and September Figures 3 a and b and Figures 4 a and b. In the future scenario-RCP 8.

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For the other months of the year the monthly precipitation and streamflow showed the same reduction trend, on average, for the present climate Figure 3 b and Figures 4 a and b. Therefore, the greatest impacts on the LW hydrology could occur at the end of the XXI century Figure 3 b and Figures 4 a and b. The results also showed that the LW hydrology is strongly influenced by the wet season. Thus, the positive effects regarding the increase in precipitation in the dry season were less pronounced than in the rainy season Figure 3 b and Figures 4 a and b.

The authors simulated the hydrologic response for 43 years, taking into account future scenarios of the AR4. For the climate scenario A1FI high emission of greenhouse gases they verified that the increase of 6. This watershed has an area of km 2.

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It was found that from to the future climate projections indicated an increase of the average monthly temperature in summer of up to 5. Viola et al. This study covered a larger area watershed with drainage area of , , and km 2. Although the study cited was done in the same study area as this present work, the results of both surveys indicated different hydrologic trends. Between and , the results of Viola et al. However after the projections indicated that with the increasing precipitation over the XXI century there may occur an increase in the mean annual streamflow Marengo et al.

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It is valid to emphasize that the future climate scenario A1B AR4 can be considered as an intermediate scenario between the A2 scenario high greenhouse gas emissions and B2 scenario low greenhouse gas emissions. In this present study the future climate scenario-RCP 8. Finally, in the present study the reduction in the monthly and annual average streamflows is due to the increase and decrease in the monthly average temperature and precipitation in the region of this study.

These changes in the streamflow on a micro-scale that depend on future temperature and precipitation have a high degree of uncertainty. However, despite the uncertainties of the simulated projections, the results presented provide an indication of how the hydrological conditions of the LW may change in a possible future scenario of high greenhouse gas emissions.

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  • Moreover, it is also important to emphasize that this is a pioneering study of one of the most important headwater regions of the Brazil Mantiqueira Range. Sreekesh , P. Jansi Rani , S. Babel , Sarawut Ninsawat , Shiro Ochi. References Publications referenced by this paper. Large area hydrologic modeling and assessment part I: model development Jeffrey G. Arnold , Raghavan Srinivasan , Ranjan S.

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