Anthropogenic activities alter the landscape, vegetation, climate, and atmospheric composition, resulting in changes in the terrestrial water balance, water resources, and ecohydrological patterns (Van der Velde et al., 2014; Wang et al., 2016). The effects of changes in hydrology show strong spatial and temporal variations due to variability in climatic factors such as precipitation and watershed heterogeneity. An rising trend in air temperatures and a falling trend in potential evaporation have been reported in many parts of the world (Golubev et al., 2001; Hobbins et al., 2004; Roderick and Farquhar, 2004; Souza-Filho et al., 2016).

Research on the hydrological role of vegetation has been conducted over several decades (Horton, 1919; Wicht, 1941; Penman, 1963; Bosch and Hewlett, 1982; Turner, 1991). Experimental results show that reductions in forested areas produce increases in streamflow, although the magnitudes of these increases may be different (Bosch and Hewlett, 1982; Bruijnzeel, 1990). Large watersheds, with an area greater than 10,000 km², tend to show a mosaic of uses and practices, with heterogeneities in geology, topography, and soil. This factor, added to the spatial and temporal variability in climate, causes the hydrological responses in large and small watersheds to be different (Wilk et al., 2001).

Forests and cerrado vegetation have given way to pasture, agricultural cultivation, economic development, expansion of cities and mining enterprises, etc. In Brazil, forest has been widely replaced by pasture, which covers large-scale watersheds, but there are still few studies on the impacts of land-use changes in these watersheds, and there is a lack of a common method for such investigations (Bayer, 2014; Lucas-Borja et al., 2020). In the Amazon basin, one of the most important watersheds in the world, territorial planning and water management are critical, as nearly 370,000 km2 of the native vegetation has been cleared since 1990 (Pailler, 2018).

Characterizing the water balance behavior at various time scales (especially in the Amazon region) remains an important but challenging research task because the factors controlling precipitation partitioning vary with the time scale, such as rainfall intensity, vegetation cover type, and topography, and are fundamental to all types of runoff (Dunne and Black, 1970; Beven and Kirkby, 1979; Rizzo et al., 2020; Moniruzzaman et al., 2021). The regional water yield at a mesoscale can be estimated as the difference between input (precipitation) and output (evapotranspiration). Evapotranspiration (evaporation + transpiration) is an important flow term in the water cycle and a key variable for understanding the complex interactions between climate, vegetation, and soil (Dooge, 1992; Zhang et al., 2001; Donohue et al., 2007; Huo et al., 2013; Wang et al., 2016). Evapotranspiration is also used to diagnose changes in hydrological processes at the watershed scale (Liu and Yang, 2010).

Budyko (1958, 1974) created a widely used system to investigate energy and water flows in watersheds. It considers that the available energy (expressed as potential evapotranspiration, ETo) and water supply (expressed as precipitation, P) control the current long-term mean evapotranspiration and runoff rates on a regional or watershed scale. The Budyko model results from the coevolution of vegetation, soil, and geomorphology with climate and explains hydrology as a system without focusing on physical processes in isolation (Gentine et al., 2012; Troch et al., 2013; Wang and Wu, 2013; Harman and Troch, 2014; Wang and Tang, 2014; Wang et al., 2016).

Therefore, understanding and quantifying the impacts of land use and land cover variation on hydrological responses is important for devising strategies to manage water resources and land use and resolve conflicts over the use of water resources, especially in areas where the water supply is limited during the dry season (Kumar et al., 2019). This assertion establishes a hydrological paradox associated with the paradigm that in the Amazonian environment, one of the largest water sources on the planet, despite the high precipitation rates, many rivers and water bodies, such as the Itacaiúnas River Watershed (IRW), have intermittent regimes, reduced water storage capacity, and lack of watershed regulation (Iriondo and Latrubesse, 1994; Alves and Beserra Neta, 2018; Salomão et al., 2019).

The IRW is located in the Amazonian deforestation arc, where 50% of the area was deforested between the 1970s and 2010s (Pontes et al., 2019). Changes in land use in the IRW (Souza-Filho et al., 2015, 2016, 2018; Silva Júnior et al., 2017a) associated with the development of agriculture, which has been replacing forest with pastures, together with the increase in agricultural area, have caused an imbalance in the hydrological regime, leading to increased soil and water degradation in recent decades. To investigate this further, this study quantified the response of long-term (annual) and seasonal (monthly) water balance components to changes in land use and vegetation cover from 1980 to 2013 in the IRW, Eastern Amazon.

Although the curve number (CN) theoretically varies from 0 to 100, in practice the validated values (Mishra and Singh, 2003) are in the 40–98 range. The higher the CN value is, the greater the runoff potential in the watershed. The CN values calculated for the IRW are always above 70 (Table 2). The results obtained for storage indicate an 8.1% decrease in the water retention capacity of the watershed soil. The CN values represent the potential water losses of a watershed. As the CN value increases, the initial storage of a watershed decreases, increasing the likelihood of runoff (Tyagi et al., 2008).

Cavalcante et al. (2019) studied the effects of climate variability and human activity on the annual water balance in the IRW and found that the mean annual runoff coefficient underwent major changes between the 1973–1984 period and the 1985–1994 period. Even with the continuous reduction in the forested area in the last two decades (1994–2004 and 2003–2016), the changes in the mean annual runoff coefficient were relatively small, probably due to the intensity of the cutting and burning activities and vegetation regeneration. Figure 2 shows the evolution of the conversion of forest to pasture over the decades along with the soil textures identified in the IRW.

In the 1980s (1980–1989), vegetation cover consisted of forest (89.85%) and pasture (10.07%) (Silva Júnior et al., 2017a). In the following decade (1990–1999), the conversion process increased, forest cover decreased (71.73%), and pasture increased (28.07%) (Souza-Filho et al., 2016). In the last analyzed period (2000–2013), this trend intensified, resulting in the predominance of pasture (50.47%) over primary forest cover (48.91%). Variables such as albedo, temperature, and net radiation (R_n) are directly affected by these changes, especially when they occur on a large scale (Pavão et al., 2017).

Removal of the forest results in less absorption of global solar radiation by the surface and in greater re-emission of longwave radiation from the surface; consequently, the R_n is lower over the deforested area (Andrade et al., 2014). The reduction in the roughness length in deforested area can affect the turbulent transport of heat and water vapor near the surface (Eltahir and Bras, 1994). The higher albedo and surface temperature and lower R_n in deforested areas result in lower evapotranspiration in these locations, thus reducing the available energy for convection and precipitation (Santos et al., 2014).

The annual and seasonal variability in ET_o in the IRW and at the Marabá weather station is shown in Figures 3a,b in terms of the mean values calculated by the TC and PM methods. Between 1980 and 1992, the ET shows a trend toward maintenance of the mean values. From 1992 to 2004, there was an increasing trend in the mean annual rates of ET. In this period, the forest cover (53%) is still higher than the pasture cover (46%), and the expected behavior of ET in this period would be a reduction in the mean evapotranspiration values (Wohl et al., 2012). However, there is an increase, which may be associated with the intensification of land use and occupation and the decrease in vegetation cover (Silva Júnior et al., 2017a).

Recent simulations of hydroclimate-related models (Khanna et al., 2017) have shown that horizontal variations in surface roughness between aerodynamically smooth pasture and rugged forests may represent a spatial redistribution of precipitation not explained only by thermal processes. The results indicate a transition from the thermal convective regime to a dynamically dominated one associated with increasing deforestation scales. The direct influence of this transition is a substantial humidification of the downwind sectors of the deforested areas and a similar dryness of the downwind sectors. This contrasts with a thermally dominated convective regime, which resulted in most of the non-precipitating cloudiness and, therefore, may be consequential to ecosystem adaptation in regions deforested downwind. This general behavior may perhaps explain the maintenance of high ET_o rates in the IRW, especially between 1990 and 2006, as shown in Figure 3.

A greater pasture area causes greater surface exposure of water bodies (Figures 3c,d), higher albedo, and consequently a higher evaporation rate. The analysis of evapotranspiration (transpiration + evaporation) together with the sum of the surface area of the water bodies and the forest-vs.-pasture relationship suggests that the increase in evapotranspiration is more closely related to the increase in evaporation as a function of the increase in pasture area than to the reduction in transpiration due to forest loss, reinforcing the hypothesis that evapotranspiration increases with pasture area.

Starting in 2004, there is a reversal in the increasing trend in the annual and seasonal mean ET_o values, whose decrease is in accordance with the expected behavior (Von Randow et al., 2004; Da Rocha et al., 2009) since the decrease in the area of native vegetation reduces local ET due to the decrease in water demand by plants.

Table 3 and Figure 4 show the temporal variability in precipitation, E, and ET calculated by the methods of Schreiber (1904), Pike (1964), and Budyko (1974), in addition to the annual aridity index (AI).

ET_o shows an upward trend throughout the period, and the annual mean values for 1980–1989 and 1990–1999 were 1,326.8–1,357.0 mm, whose increase represents a rate of 2.0 mm·y⁻¹ in the first period and 5.3 mm·y⁻¹ in the second (Figure 4). However, there was a decrease of 2.7 mm·y⁻¹ in ET_o for 2000–2013. Overall, ET shows a decreasing trend by the three methods throughout the study period, though different behaviors are observed: between 1980 and 1986, there is an upward trend; between 1987 and 1997, there is a decreasing trend; between 1998 and 2004, there is an increasing trend; and last, after 2005, there is a decreasing trend in the annual mean values. The highest values were estimated by the Pike equation, with an annual mean of 815.0 mm·y⁻¹, and the lowest by the Schreiber equation (737.0 mm·y⁻¹), while the Budyko equation resulted in an intermediate value of 798.1 mm·y⁻¹.

Table 4 shows the estimated monthly seasonal values of the water balance components. The seasonality of rainfall and the seasonal dynamics of soil water storage are directly incorporated into the seasonal model derived, revealing the dominant control factors in the water balance. For each component, for example, P_u or P_s, and so for the others, the value in the table represents the cumulative number of months identified as wet (P_w) or dry (P_d) within each year. The values of ΔS_u and ΔS_s for the years 2009 and 2012 showed outlier behavior, without any known explanation, so they were excluded from the analysis.

The Budyko model is based on the water “supply–demand” concept and addresses two extreme conditions: very dry and very wet. The results for the IRW fall within the relatively wet condition. On the annual scale (Figures 5a–c), there is an increasing trend in all three methods, both for EI (ET/P) and AI (ET_o/P), whose data set meets the criterion ET/P <1 and has available energy as the factor controlling ET. However, when considering the amount of plant-available water (w), all points are below the 0.5 line, and more than half of them are below the 0.1 line, i.e., for the IRW, these points are located near the lower water limit. Cavalcante et al. (2019) concluded that the IRW shows a greater variation in the AI than in the EI, suggesting that it has a high elasticity related to climatic changes.

Figures 5a₁-c₂ shows, on the seasonal scale, the behavior of the new parameter (y₀) introduced by Greve et al. (2016), which explicitly represents the additional water available for E, in addition to instantaneous precipitation. The separation between the wet and dry seasons is clear. In the wet season, under all three models, the set of points does not exceed the limits (demand and energy). In the Pike and Budyko methods (Figures 5a₁,b₁), the behavior is very similar, and most of the points are between y₀ = 0.6 and 0.8, unlike the Schreiber method (Figure 5c₁), whose points concentrate between y₀ = 0.4 and 0.6. For the most recent period (2007–2013), a different behavior is observed under all models (Figures 5a₁,b1,c₁). Table 4 shows AI > 0 for all the dry seasons analyzed. In Figures 5a₂,b2,c₂, the exceedance of the water supply limit in the IRW increases with the increase in y₀, suggesting that this additional parameter can represent the conditions in which E monthly or annually exceeds the monthly or annual precipitation, respectively. With the Pike and Budyko methods (Figures 5a₂,b₂), the behavior is very similar, and most points exceed the curve y₀ = 0.4 to 0.2, unlike with the Schreiber method (Figure 5c₂), in which the points concentrate below y₀ = 0.4 to y₀ > 0. This result suggests that water storage plays a significant role in maintaining evaporation, especially during years with aridity indices >1.

The mean monthly variations in soil water storage estimated for the IRW as residuals of the water balance closure, using the available data for P, ET, and Q (Figure 6), were compared with the El Niño/South Oscillation (ENSO) extremes analyzed by Kayano et al. (2016) for the last three decades (1986–2015), whose types and intensities were determined by the sea-surface temperature anomaly index. The most intense El Niño (EN) events ever recorded (1997–1998 and 2015–2016) occurred in this period, along with two strong La Niña (LN) events (1988–1989 and 1998–2001) of long duration. The EN of 1997–1998 was the one with the most marked climatic impacts around the globe, and it developed rapidly and decayed abruptly (McPhaden, 1999). The 2015–2016 EN presented sea-surface temperature indices much higher than those of previous events, with strong influences on the Amazon, especially in the eastern part (Costa and Blanco, 2018; Tavares et al., 2018). The two LN events overall had point impacts throughout the Amazon region (Aguiar et al., 2019; Coutinho et al., 2019).

The mean annual variation in soil water storage shows different behavior in the analyzed periods. The first period (1980–1990) has higher amplitudes in the variation in storage between the wet and dry seasons than the following periods. This behavior probably reflects the occurrence of the strong LN event (1988–1989), followed by the very strong EN event (1997–1998), in addition to the occurrences of moderate EN and LN events throughout the analyzed period, considering that in the Amazon, EN causes a decrease in rainfall and LN does the inverse (Moura et al., 2019).

In a study by Moura et al. (2019), it was observed that the studied variables, among them evapotranspiration, was severely affected in the rainiest months, the La Niña phenomenon, and in the less rainy months, El Niño. The variation in evapotranspiration showed that the lowest values were associated with LN. The months with the lowest rainfall were those with the highest average evapotranspiration, showing the influence of EN on the increase in values. The region that has lower anthropogenic pressures and has denser vegetation cover, therefore, has a higher incidence of solar radiation, increasing vegetative transpiration and evaporation of water bodies, consequently, conditioning an increase in evapotranspiration. This is different from what happens in the rainiest periods, where the presence of greater cloud cover inhibits the incidence of solar radiation, decreasing the temperature and consequently reducing evapotranspiration (Flantua et al., 2016).

Likewise, Jiménez-Muñoz et al. (2016), calculated the self-calibrating Palmer Drought Severity Index (scPDSI)19 during the course of the El Niño 2015–2016 event in the Amazon rainforest. Among the parameters used for its calculation, potential evapotranspiration, corresponding to moisture demand, was abnormally high during the period, with record warming at the end of 2015. The authors concluded that this scenario suggested that warming is responsible for about a fifth of the abnormally high area under extreme drought severity observed during 2015/2016.

At the beginning of the first period, the forest cover represented 70%, and the pasture cover represented ~30%; that is, the starting decade was marked by a strong LN event, resulting in higher rainfall over the region, which still had a vegetation cover that allowed higher levels of soil water storage capacity. In the second (1990–1999) and third (2000–2013) periods, there was occurrence of a very strong EN event associated with the marked advance of the conversion of forest into pasture, whose direct consequence was a reduction in water infiltration and retention capacity. This finding corroborates the study by Coutinho et al. (2019), who concluded that there is a positive trend toward evaporation in the Amazon, confirmed by the deforestation in the region, and this impacts storage. In addition, in view of the more frequent occurrence of climatic phenomena, it is necessary to pay greater attention to the IRW because deforestation in the watershed intensifies water flows, and the consequences can lead to increased flooding in flat areas (Pontes et al., 2019).

Its consideration of subannual or interannual changes in water storage in a watershed as insignificant is a major limitation of the original Budyko structure. Additional water can also be introduced by changes in the landscape (Jaramillo and Destouni, 2015) and human interventions (Milly et al., 2008), for example, increased pasture area causing greater surface exposure of water bodies (Figures 3c,d), increasing the albedo, and thereby increasing the evaporation rate or causing phase changes in the water in the system or the water provided by precipitation (Berghuijs et al., 2014; Jaramillo and Destouni, 2015). Long-term changes in soil moisture can also result from transient climatic changes (Wang et al., 2005; Orlowsky and Seneviratne, 2013). In summary and corroborating the study by Cavalcante et al. (2019), further research in the IRW needs to analyze water storage and the dependence of the rainfall–runoff relationship on climate and its main drivers, looking more closely at the relationship between the transpiration and evaporation components, in order to better manage the area and to avoid the impacts of extreme events, like droughts or floods (Kumar et al., 2019).

From 1980 to 2013, there were marked changes in the vegetation cover of the IRW, mainly due to the conversion of the forest into pasture. Before, it was a trend; in the last decade, it has become a fact: the forest remnant covers only 48.91% of the area, whereas pasturelands cover 50.47%. As a consequence, the water storage capacity of the soil has decrease (8.1%) continuously. Although, the IRW does not have low water availability, its pedological characteristics indicate an increasing trend in runoff rates (CN > 72).

The behavior of the water balance components (P, ET_o, E, and ΔS) was evaluated on annual and seasonal (monthly) scales. ET_o and E showed an upward trend over the decades. The analysis of evapotranspiration together with the sum of the surface area of the water bodies and the forest-vs.-pasture relationship suggests that the increase in evapotranspiration is more closely related to the increase in evaporation as a function of the increasing pasture area than to the reduction in transpiration due to forest loss, reinforcing the hypothesis that evapotranspiration increases with pasture area. Precipitation in 1990–1999 shows a reduction, possibly associated with the very strong La Niña (1988–1989) and El Niño (1997–1998) events, whose rainfall indices were below normal over much of the region.

Therefore, subannual or interannual changes in soil water storage in a watershed need to be considered in the analysis of water balance. Additional water may be introduced due to changes in the landscape or human interventions, such as increases in pasture area, causing greater surface exposure of water bodies and elevation of the local albedo, the consequence of which is an increase in the evaporation rate or water phase changes in the system. Last, further research is needed in the IRW to analyze water storage and its dependence on the rainfall–runoff relationship and climate drivers, looking more closely at the relationship between the transpiration and evaporation components in the local and regional hydrological cycle.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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