Does forest replacement increase water suply in watersheds? Analysis through hydrological simulation

The forest plays an important role in a watershed hydrology, regulating the transfer of water within the system. The forest role in maintaining watersheds hydrological regime is still a controversial issue. Consequently, we use the Soil and Water Assessment Tool (SWAT) model to simulate scenarios of land use in a watershed. In one of these scenarios we identified, through GIS techniques, Environmentally Sensitive Areas (ESAs) which have watershed been degraded and we considered these areas protected by forest cover. This scenario was then compared to current usage scenario regarding watershed sediment yield and hydrological regime. The results showed a reduction in sediment yield of 54% among different scenarios, at the same time that the watershed water yield was reduced by 19.3%.


INTRODUCTION
Knowledge on how forests affect the various aspects of water is essential to assess the role of forest cover on watershed's hydrological regime (LIMA, 2010). The forest is often regarded as effective to stabilize and maintain the river flow rates and this is one of the reasons why revegetation is repeatedly recommended to recover watersheds (BACELLAR, 2005). Some of the hydrological functions usually ascribed to forests, however, such as to increase rivers water availability are disputable and lack a technical and scientific basis. We observe, however, that this is still a worldwide controversy, especially regarding the establishment of water conservation and sustainable use of natural resources policies.
In this line of research we find a large collection of data in scientific literature, resulting from watersheds systematic monitoring all over the world, which use three methodologies, 4 The Pinhal River is important for being the source of water for Limeira, state of São Paulo. The watershed has suffered in the past few decades from environmental degradation.
The current situation may compromise this water source, if the process of degradation continues.

The SWAT model and input data
SWAT is a distributed parameter model which simulates different physical processes in watersheds and which aims at analyzing land-use changes impacts on surface and subsurface runoff, sediment yield and water quality in agricultural watersheds that were not instrumented (SRINIVASAN & ARNOLD, 1994). The model operates on a daily basis and can simulate periods of 100 years or longer to determine the effects of management

Model evaluation
During analysis period (2012 to 2014) calibration of model is not possible due to inconsistency in observed data (the measuring station was constantly drowned during the operating period of a reservoir associated with a power station).
Despite the impossibility of calibrating the model for the Pinhal hydrographic basin, we used the hydrological regionalization methodology to validate the behavior of the model (Vandewiele, 1995;Bardossy, 2007). A hydrological regionalization is a technique that allows to transfer information between watersheds with similar characteristics in order to calculate, in sites where there are no data on the hydrological variables on interest (Emam et al., 2016). This technique becomes a useful tool for water resource management, especially when applied to most important instruments of Brazilian water resource policy that are the concession of water resource use rights and charging for the use of water resources (Fukunaga et al., 2015).
According to Tucci (2005), the hydrological information that can be regionalized can be in the form of variable, parameter or function. The hydrological function represents the relationship between a hydrological variable and one or more explanatory or statistical variables, such as flow-duration curve or relationship between impermeable areas and housing density (Tucci, 2002). The flow-duration curve relates the flow or level of a river and the probability of flowing greater than or equal to the ordinate value, thus being a simple, but concise and widely used method to illustrate the pattern of flow variation over time (Naghettini and Pinto, 2007).
For the construction of the flow-duration curve in this work, the series of simulated flows in the period from 2012 to 2014 was initially ordered decreasingly. This series was statistically divided into 10 equal intervals. For each interval, the number of flows was counted and the respective cumulative frequencies of the interval from highest to lowest are calculated. For the purposes of comparison, in the same graph, we plot the regionalised flows, according to the State Department of Water and Electric Energy (DAEE -state entity responsible for granting concessions of water resources in the State of São Paulo) and simulated, allowing The verification of sub or overestimation by the simulated curve. The Nash-Sutcliffe model efficiency coefficient (Nash and Sutcliffe, 1970) was used to validate the simulation results, besides the visual analysis of simulated flow-duration curve regionalized (NSE). The NSE (equation 2) was used to compare the regionalized and simulated flows in intervals of 5 in 5% probability of occurrence of the flow-duration curve.
NSE can range from -∞ to 1, where 1 is the optimal value and values above 0.75 can be considered very good (Moriasi et al, 2007). NSE is calculated as eq. 2: The PBIAS (Eq. 3) of the simulated discharge in relation to that regionalised were too utilized (Gupta et al., 1999).
Where, and corresponds to the observed and simulated discharge, respectively, on day i (m 3 /s), and corresponds to the average observed discharge, in (m 3 /s), and n corresponds to the number of events.

Identification of Environmentally Sensitive Areas (ESAs)
The concept of "Environmentally Sensitive Areas" was created in industrialized countries approximately 30 years ago due to increased soil and water degradation and the degree of severity of degradation (RUBIO, 1995). Degradation has been caused by uncontrolled forest destruction, water pollution, wind and water erosion, salinization and inappropriate management of cultivated and uncultivated soil (GOURLAY, 1998).
Environmentally Sensitive Areas (ESAs) are areas that contain natural or cultural features important for an ecosystem functioning. They may be negatively impacted by human activities and are vital to long-term maintenance of biological diversity, soil, water, or other natural resource, in the local or regional context (NDUBISI et al., 1995). An environmentally sensitive area may also be considered, in general, a specific and delimited entity with unbalanced environmental and socioeconomic factors, or not sustainable for that particular environment (GOURLAY, 1998). As an example, high sensitivity may be related to land use, which in certain cases causes soil degradation. Annual crops in areas where the relief is hilly, with declivity and shallow soils, have a high risk of degradation. To

Scenario simulation
We made two scenario simulations using SWAT model interfaced with GIS ArcGis, aiming to verify the effect of land-use change on sediment yield (sediment transported from sub-watersheds to the main channel over time, ton/ha) and the watershed hydrological Central Vietnam. The hydrological regionalization (i.e., ratio method) approach was used to predict the river discharge at the outlet of the basin. The model was calibrated with Nash-Sutcliff and R 2 coefficients greater than 0.7 in time scales daily by river discharge.

Environmentally Sensitive Areas (ESAs)
ESAs identified in the Pinhal River watershed are shown in Figure 4 and Table 2. 16% of the watershed area are degraded due to improper land use, which is a threat to the surrounding environment. These areas are severely eroded and have high rates of surface runoff and soil loss. In this case, there may be higher peak streamflow and water bodies sedimentation (critical ESAs).

Land-use change between scenarios
Figure 5 presents the land use map for the two scenarios and Table 3   We present in Figure 6 the variation of land-use change in sub-watersheds scale between the two scenarios. The decrease in pasture and sugarcane areas, where soils are exposed to erosion during soil management and the increase of native vegetation area explain lower sediment yield and water yield. The decrease of pasture and increase of forest area in the Northwest region (Sub-watershed 12) also contributed to lower sediment and water yield in this region.

Sediment Yield
The results of sediment yield presented in Figure  trafficability. These soils occupy 19% of the watershed total area. In current use scenario, 22.4% of this soil area is being occupied with native vegetation. In ESAs scenario the percentage increased to 68.3% (Table 4). Lithosols occupy approximately 4% of the watershed total area and are located in areas of greater declivity. They are in a geomorphologically unstable zone and erosion affects soil development and they are constantly renewed through superficial erosion (TERAMOTO, 1995). Extensive areas are occupied with sugarcane, pasture and orange (62.3%) cultivation on these soils. In the current scenario, 24.3% of lithosol is covered with native vegetation. In ESAs scenario the percentage is 95.7% (Table 4). Increased native vegetation on both soils explains 54% reduction (PBIAS) in sediment yield in the watershed, when we compare the two scenarios.
Spatial location of agricultural areas in relation to relief, soil and climate is important to control erosion in watersheds.  (Oliveira, 1999).  (1977) is 7.9 ton/ha for podzol and 4.2 tons/ha for lithosol. According to Figure 7, the lowest rates of sediment yield occurred in sub-watersheds with greater forest cover. As SWAT model simulates many processes in the watershed, some parameters may affect several processes (ARNOLD et al., 2012). With reduction of surface runoff in -45.8% (PBIAS) among scenarios (Table 5) due to greater soil protection, sediment yield has also been directly affected. Sediment yield difference between the two scenarios is presented in Figure   10. Analyzing Figure 10, this difference is greater in upstream sub-watersheds and in those with greater forest cover (sub-watersheds 11, 14, 15 and 16), according to Figure 5b.

Hydrological regime
It is widely reported that land-use and land cover changes can affect quantity and quality of water resources of a watershed. We analyzed discharge (m 3 /s), surface runoff (mm), water yield (mm), evapotranspiration (mm) and soil water content (mm) (Figures 11-15 Cui et al. (2012) showed that increased forest cover in watersheds decreased water yield.
As both surface runoff and baseflow are the main components that contribute to water yield, we expected greater infiltration rate in ESAs scenario, for infiltration rate in forest areas is greater than in other land covers, e.g., sugarcane and pasture (Liu et al., 2012). Higher infiltration rate will increase baseflow, because in this scenario areas previously occupied with other land uses were now occupied with native vegetation. On the other hand, forest evapotranspiration will consume more water (PBIAS of evapotranspiration equal to +3.5%, Figure 14), because it is known that the forest is the surface with higher rates of evapotranspiration, higher than all the other vegetation types and also higher than a liquid's surface (Birkinshaw et al., 2011). Roots, especially of larger trees, increase water absorption from baseflow and, consequently, decrease water yield in the watershed, which may be seen in Figure 15, as the water content in the soil decreased in the studied period (-14.1%).
Differently, with scenario change, this type of land cover provides greater resistance to runoff and, consequently, this component had a lower contribution to water yield in the watershed (-45.8%). The influence of forest recovery in the hydrological regime can also be analyzed separately in two different periods. Comparing evapotranspiration demand independently in the wet period (October to March, Figure 14a) and dry period (April to September, Figure   14b), the difference between the two scenarios is even greater. In the wet period the difference is +1.3%, whereas in the dry period this difference is +8.2%. In the wet period available water in the soil ( Figure 15a) compensates increased evapotranspiration demand of vegetation, even with increased forest cover (ESAs scenario), which contributes to lower water losses through evapotranspiration in the watershed (Figure 14a). In the dry period when SW is lower (Figure 15b), large forest vegetation access more easily underground water than a small vegetation, having, therefore, greater evapotranspiration demand and reducing water yield in the watershed. Based on results obtained from more than 90 experimental micro watersheds in different parts of the world, Bosch & Hewlett (1982) asserted that deforestation decreases evapotranspiration, which results in more water available in the soil and in streamflow. On the other hand, reforestation decreases streamflow at the watershed scale. It is worth mentioning, however, that these results vary from place to place and are often unpredictable (BROWN et al., 2005).      Figure 16 shows spatial distribution of the hydrological regime variation (surface runoff, evapotranspiration, soil water content and water yields) at sub-watersheds scale between scenarios. The influence of land-use change on the hydrological regime is more visible in some of the sub-watersheds than others at the watershed scale. These variations were smaller in upstream sub-watersheds and as with sediment yield, major variations occurred in sub-watersheds with greater forest cover when we compare current scenario with ESAs scenario. Watersheds hydrological regime is the result of complex interactions between climate (wet versus dry years), plant physiological properties (e.g., leaf area and successional stages) and soil type (ANDREASSIAN, 2004). According to Singh & Mishra (2012), these and other factors together make hydrological effects of forests a markedly different scenario.

CONCLUSION
The role of forests in watersheds hydrological cycle and water yield is controversial.
Although reducing sediment yield as the results obtained from simulation of different scenarios show (PBIAS = -54%), for it offers the soil greater protection, its influence on increasing and maintaining streamflow is questionable, because the results obtained from this study also showed that increased forest cover decreased water yield in the watershed in -19.3% (PBIAS) due mostly to its greater evapotranspiration capacity (+3.5%), this demand being even greater during dry season (+8.2%). Simulation results lead us to conclude that the impacts of land-use change on hydrological processes are complex and their consequences are not equal in all situations and with the same intensity.