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Climatic and Physical Controls on the Hydrological Regimes of Southern Brazil: An Analysis of the 1975-2010 Period

V. B. P. Chagas1 and P. L. B. Chaffe2

1Undergraduate Course of Geography, Federal University of Santa Catarina, Florianópolis-SC, Brazil 2Department of Sanitary and Environmental Engineering, Federal University of Santa Catarina,

Florianópolis-SC, Brazil – E-mail: pedro.chaffe@ufsc.br

1. INTRODUCTION

Climate change has affected precipitation and hydrological regimes in many regions of the world (Milly et al, 2008; Arnell & Gosling, 2013). Under changing environmental conditions, our understanding of the variability and trends in hydrologic variables becomes crucial for a sustainable water resources management (Wagener et al, 2010). Brazilian economy is highly dependent on water resources, thus climate change might play a major role in the country’s future (MME, 2014).

Since the seventies, rapid industrialization lead to the intensification of water resources use in Brazil. More than 60% of its energy is drawn from hydropower generation (MME, 2014). Agriculture stands as a major economic activity in several regions. Several climatic changes have been observed and are expected to affect the country in the twenty first century. Many areas in the North and Southwest present decreasing trends for precipitation rates since the seventies (Rao et al, 2015). It is expected that the precipitation rates in the Southern Amazon and Northeast region will decrease by 20% by the year 2100 (Llopard et al, 2014).

Differently from other regions, precipitation rates had a step increase in the Southern part of Brazil in the early seventies (Carvalho et al, 2011). River regimes have also changed consequently; forcing an adaptation in water resources management and uses (Pasquini & Depetris, 2007; Doyle & Barros, 2011). Berbery & Barros (2002) observed an increase of 35% in the mean streamflow of the La Plata River Basin for the 1980-1999 period relative to 1951-1970. Many natural disasters started to catch our attention, such as inundations, soil susceptibility to erosion, and desertification (Herrmann, 2005; Suertegaray, 2011, Santos et al, 2013b).

In order to anticipate the possible outcomes of climate change in the hydrological regime, it is important to understand the role played also by the physical characteristics of the basin (e.g. topography, soil characteristics and geology). That way we might understand what deems a catchment more or less sensitive to climate change (Tague et al, 2008; Sawicz et al, 2014).

The objective of this study was to analyze climatic and physical controls on the hydrological regimes of Southern Brazil through empirical analysis of 138 catchments. We used precipitation and streamflow data from the 1975-2010 period to calculate hydrological signatures that represent intra and inter annual variability. By trying to relate the hydrological variability to physical characteristics, we were able to assess major controls on the hydrological regime. Therefore, we try to answer the question: where would a possible climate change have the highest impacts on the hydrological regime?

2. STUDY AREA

Our study area is Southern Brazil (Figure 1). The total area is about 576,410 km² and has high demand for water resources use. To the north is an intensive agricultural use, which includes soy bean and maze as major productions. The south and southwest is mostly used for pasture and irrigated rice production. Varied agricultural use mixed with secondary Subtropical Atlantic forest are present to the central-north areas. The coast is heavily occupied by urban areas, pasture, and native broadleaf moist forest.

Southern Brazil climate is in a transition from the tropics to the subtropics. The average precipitation rates ranges from 1,900 mm annually in the central-west region, gradually decreasing to 1,500 mm to the north; and gradually decreasing to 1,200 mm in the extreme south. The central-northern region has a summer monsoon regime, with higher precipitation rates between November and February. The southern region has uniform precipitation rates through the year. The major source of humidity that forms the precipitation in Southern Brazil comes from the tropical areas to the north and northwest (Grimm, 2003).

Higher topographic areas are found in the central-northern areas. The majority of the areas above 400 m altitude are composed by volcanic rocks, mainly the basalt formed in the middle Mesozoic Era period (Diniz et al, 2014). Its formation has an average thickness of 500 m. The soil in these areas are composed of at least 40% of clay content. The largest aquifer production is contained below the basalt formation (i.e. far from the surface).

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Figure 1 Study area, Southern Brazil. (*) HPG stands for Hydroelectric Power Generation.

There are two major sedimentary formations: one area that goes from north to central-west region, composed of both shale and sandstone, with thickness that ranges from 100 to 500 m; another area that goes from southwest to southeast and is also composed of shale and sandstone, but with thickness below 100 m. The coast is composed of granite rocks, with very low aquifer production and a presence of complex disconformities between aquifers. The soil texture consists of below 35% of clay and a higher sand content. Throughout the study area the regolith has a thickness above 10 m and very commonly reaching 30 m, mainly in the most humid areas. The exception are the highest elevation areas, with the cooler temperatures leading to a regolith commonly below 2 m thickness (EMBRAPA, 2011).

3. DATA AND METHODS

We obtained the precipitation and streamflow data from the Brazilian Water Agency (ANA, 2001). It consists of daily mean data averaged from two daily measurements. The precipitation gauges chosen have at least 30 years of data, since it is the recommendation by the World Meteorological Organization (WMO, 2008). We chose 138 river gauges according to data reliability; and 745 rain gauges. To estimate daily rainfall we applied ordinary Kriging for each catchment. The topographic variables were obtained by the data from the SRTM (USGS, 2006), which consists of 30 meters resolution Digital Elevation Model. The soil classification used are from the Brazilian Agricultural Research Corporation (Santos et al, 2013a), which uses a classification based on the U.S. Soil Taxonomy (Soil Survey Staff, 2014). We used soil texture data provided by the World Soil Information (ISRIC, 2013). Hydrogeology information used are from the Brazilian Geological Survey (Diniz et al, 2014).

By using precipitation and streamflow data it is possible to calculate hydrological signatures that enable us to analyze hydrological behavior and catchment characteristics. In this study we use signatures to analyze intra and inter annual variability of precipitation and hydrological regime. It allows us to describe the relations between the signatures and physical characteristics like soil, geology, and topography. A list of the signatures used are presented in the Table 1. The flow duration curve (FDC) is used as a major hydrological signature. It represents the relationship between magnitude and frequency of daily streamflow, providing the percentage of time which a given streamflow is equaled or exceeded over a time period. The 33rd and 66th percentiles are considered to be a relatively linear part of the FDC, therefore it was used to calculate the slope of the curve (Yadav et al, 2007). A steep slope in the FDC indicates high variability in the intra annual flow regime, indicating flashiness response to the precipitation input. A flatter curve shows a lower variability of streamflow, therefore indicating a higher storage capacity that results from the catchment physical characteristics.

The intensity of the precipitation and number of dry days are calculated for each rain gauge for the full analysis period. We interpolated its values and extracted the signatures for each catchment. Missing rainfall data are recalculated using the mean observed values from the three closest gauges with available data. A robust measurement of the streamflow sensitivity to changes in rainfall is the elasticity signature provided by Sankarasubramanian et al (2001). As an example, a value of 2.91 indicates that for every 1% increase in mean annual rainfall is expected a 2.91% increase in mean annual streamflow. Through this signature it is possible to quantify the impact of an eventual climate change on mean annual streamflow. We calculated it using the water year and the following non-parametric estimator:

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Q

P

PP

QQ medianEQP (1)

where Q is yearly streamflow, P is yearly precipitation, Q and P are long-term sample means. The median

value is used since it filters outliers.

Table 1 Climatic and physical signatures investigated in this study. Signature Name Description [units] Min. value Max. value QMEAN Mean flow Mean flow for the analysis ]year [mm -1 443.45 2546.6

,Q,Q 51 Q,Q 9995

Flow percentiles of the normalized FDC

High and low-flow exceedance of the FDC normalized by mean streamflow [-]

2.72; 1.79; 0.01; 0.01

13.45; 4.61; 0.52; 0.42

,CVQ,CVQ 51 CVQ,CVQ 9995

CV of flow percentiles

Coefficient of variation of inter annual values of flow percentiles [%]

15.0; 12.6; 8.99; 9.56

71.73; 32.32; 24.21; 140.9

SFDC Slope of the normalized FDC

Slope between the 33 and 66% exceedance values of the FDC normalized by mean annual streamflow (Yadav et al, 2007) [-]

0.95 4.62

CVQM CV of mean monthly flow

Coefficient of variation of mean monthly flow for the analysis period [%]

7.82 48.3

PMEAN Mean precipitation Mean precipitation for the analysis period ]year [mm -1

1418.2 2025.8

PINT Precipitation intensity of wet days

Mean precipitation intensity of days when rainfall rate ≥ 1 mm ]year mm[ -1

13.4 22.16

NDD Number of dry days Average number of days per year when rainfall rate < 1 mm ]year days[ -1

206.23 293.38

CVPM CV of mean monthly precipitation

Coefficient of variation of mean monthly precipitation for the analysis period [%]

10.13 38.59

RQP Total runoff ratio Total runoff divided by total precipitation[-] 0.31 1.37

EQP Precipitation elasticity of streamflow

Sensitivity of mean streamflow to changes in precipitation at the annual time scale [-]

0.77 2.91

Ar Area Catchment area [km²] 123.3 63901.8 AMED Median altitude Median value of altitude for all catchment's

grid cells [m] 105 1392

SMED Median slope Median value of slope for all catchment's grid cells [%]

5 29

R Relief Elevation difference between catchment's and outlet and highest elevation [m]

176 1688

4. RESULTS AND DISCUSSION

Figure 2 shows the relationship between the signatures found with the highest correlations. The correlation coefficient was calculated using the Spearman non-parametric test. Values in bold indicate correlation coefficient that are statistically significant (p<0.001). At first sight it is noticed a strong topographic influence on precipitation characteristics, such as the number of dry days and precipitation intensity.

Figure 2 Scatter plots and Spearman correlation coefficient between signatures. In bold are statistically significant values (p<0.001). In the diagonal are histograms of the values from the signatures.

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The correlation between topographic features and hydrological signatures is not as high as the observed for precipitation signatures. Strong correlation is found between the slope of the FDC and high flows, low flows, and inter annual variability of low flows. It agrees with Sawicz et al (2014) that found the same flow relations for catchments in the U.S. Since flatter FDC suggests higher storage in the catchment, it represents catchments with a more damped response to precipitation inputs. Catchments with lower intra annual variability presents lower peak flows and higher flow during dry periods. To illustrate this, Figure 3 shows a typical example of two catchments with distinct values of the slope of the normalized FDC. Both catchments have similar areas and are not close to each other. The middle graph is a catchment with higher slope of the FDC. It has higher peak flows, lower dry period flows, and high variability of inter annual low flows. On the other hand, the left side shows a lower slope of the FDC, with lower peak flows, higher flows during dry periods, and a more stable inter annual variability. Therefore, the FDC is shown as a strong indicator of flow variability both intra and inter annually.

Figure 3 Comparison of two catchments with distinct flow duration curves. Qm is mean daily flow.

Yaeger et al (2012) found, for catchments in the U.S., that lower values of the slope of the FDC are related

to highly uniform intra annually precipitation regime. It is expected a higher seasonal streamflow variability as a result of a higher seasonal precipitation variability. Our study found both signatures (CV of mean monthly precipitation and the slope of the FDC) are inversely correlated by a coefficient of -0.52. It is opposed to what was expected from other studies and somewhat a counter intuitive relation. Therefore, it suggests a strong catchment physical control rather than a seasonal climatic control on the hydrological regime. A spatial distribution of some of the signatures are presented in Figure 4. It is clear that climate characteristics varies smoothly in space. Other signatures, such as hydrological characteristics, varies abruptly in space. It is also an indicator of physical controls on the hydrological regime, such as topography, geology, soils, and land use.

We investigated further relationships observed between geology, soil classification, soil texture, and hydrological signatures. Sayama et al (2011) hypothesized that, for permeable bedrock that stores and releases precipitation, there is a positive relation between median slope gradient of a catchment and total water storage. We did not find the same relation in our study area. For catchments with sedimentary bedrock, median slope gradient have no relation to lower values of the slope of the FDC. For catchments with greater bedrock hydraulic conductivity, we did not find increased storage capacity with a higher median slope gradient.

Figure 4 Spatial distribution of selected signatures for study catchments.

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Assessing the soil influence on the hydrological regimes it is evident that there is an importance from the soil texture composition of the catchments. Such is the case since clays have lower specific yield and higher specific retention (Fetter, 2000). Considering soils with high infiltration capacity and therefore not dominated by overland flow, a catchment with higher clay content is expected to have greater moisture holding capacity. Merz & Blöschl (2009) analyzed physical characteristics for catchments in Austria and found antecedent soil moisture is dominant on event runoff coefficients. Similarly, Sankarasubramanian & Vogel (2003) found soil moisture holding capacity are related to higher buffer of streamflow variability in the U.S.

Figure 5 presents data from the three of the most common soil types found in Southern Brazil. Catchments with at least 70% of its area composed by the same soil group are selected. According to the data used by EMBRAPA (2011) but under the denomination used by FAO (IUSS, 2015), ferralsols are characterized as well drained soils. Alisols, acrisols, and lixisol range from badly to well drained. Cambissols are an intermediate soil group. Our results suggest precipitation elasticity of streamflow is greater where the soil presents lower sand content and higher percentage well drained soils. Such is the case for the slope of the FDC as well. Soils with higher clay content and permeability have higher flow variability and therefore lower storage capacity. It might seem controversial that well drained soils are those with the higher clay content. Even though, Diniz et al (2014) state that these well drained soils have stable aggregated particles. It is a common feature of soils from the tropics. These aggregates enable water to percolate through larger pores, giving it high permeability even though its high clay content. Another characteristic that could change the permeability is land use, that is not considered in this study. Therefore, our results are different from Sawicz et al (2011), that analyzed 280 catchments in the U.S. and found higher elasticity values for highest percentage poorly drained soils.

Figure 5 Values of the slope of the FDC, precipitation elasticity of streamflow, clay and sand soil content, for three of the most common soil types of Southern Brazil.

5. CONCLUSIONS

We used data from 138 catchments in Southern Brazil to analyze climatic and physical controls on the hydrological regime. Better understanding of climatic and physical controls help us understand hydrological change and its mechanisms. Topography, geology, and soils interacts with changes occurred in climate, defining the resilience of a catchment to changes. The FDC gives us good understanding of the streamflow variability. Controversially, where the precipitation has a highly intra annually uniform regime, the streamflow has a higher seasonal variability. Geologic characteristics did not show direct relation to the hydrological regimes. Higher precipitation elasticity of streamflow and higher slope of the FDC is observed where the soil clay content and permeability are higher. The precipitation changes will interact with the soil properties and will not impact streamflow regimes equally.

REFERENCES

ANA (2001), HidroWeb: Hydrological Information System (in Portuguese), online at http://hidroweb.ana.gov.br . Arnell, N.W. and Gosling, S.N. (2013), The impacts of climate change on river flow regimes at the global scale, J.

Hydrol., 486, 351-364. Berbery, E.H. and Barros, V.R. (2002), The Hydrologic Cycle of the La Plata Basin in South America, J.

Hydrometeor., 3, 630-645. Carvalho, L.M.V., Jones, C., Silva, A.E., Liebmann, B. and Dias, P.L.S. (2011), The South Monsoon System and

the 1970s climate transition. Int. J. Climatol., 31, 1248-1256. Diniz , J.A.O., Monteiro, A.B., Silva, R.C. and Paula, T.L.F. (2014), Hydrogeologic Map of Brazil, 1:1 Million scale: Technical Note (in Portuguese), CPRM, Recife.

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Doyle, M.E. and Barros, V.R. (2011), Attribution of the river flow growth in the Plata Basin, Int. J. Climatol., 31, 2234-2248.

EMBRAPA (2011), Soil Map of Brazil (in Portuguese), EMBRAPA, Rio de Janeiro. Fetter, C.W. (2000), Applied Hydrogeology, Prentice Hall, New Jersey. Grimm, A.M. (2003), The El Niño Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences, J. Climate, 16, 263-280.

Herrmann M.L.P. (Ed) (2005), Natural Desaster Atlas of Santa Catarina State, IOESC, Florianópolis. ISRIC (2013). SoilGrids: an automated system for global soil mapping. Online at http://soilgrids1km.isric.org . IUSS (2015), World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. FAO, Rome.

Llopard. M., Coppola, E., Giorgi, F., Rocha, R.P. and Cuadra, S.V. (2014), Climate change impact on precipitation for the Amazon and La Plata basins, Clim. Change, 125, 111-125.

Merz, R. and Blöshl, G. (2009), A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria, Water Resources Res., 45, W01405, doi:10.1029/2008WR007163.

Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lattenmaier, D.P. and Stouffer, R.J. (2008), Stationarity Is Dead: Whither Water Management?, Science, 319, 573-574.

MME (2014), Review on Brazilian Energy, MME, Brasília. Pasquini, A.I. and Depetris, P.J. (2007), Discharge trends and flow dynamics of South American rivers draining the southern Atlantic seabord: An overview. J. Hydrol., 333, 385-399.

Rao V.B., Franchito, S.H., Santo, C.M.E. and Gan, M.A. (2016), An update on the rainfall characteristics of Brazil: seasonal variations and trends in 1979-2011. Int. J. Climatol., 291-302.

Sankarasubramanian, A and Vogel, R.M. (2003), Hydroclimatology of the continental United States, Geophys. Res. Lett., 30 (7), 1363-1366.

Sankarasubramanian, A., Vogel, R.M. and Limbrunner, J.F. (2001), Climate elasticity of streamflow in the United States, Water Resources Res., 37 (6), 1771-1881.

Santos, H.G., Jacomine, P.K.T., Anjos, L.H.C., Oliveira, V.A., Lumbreras, J.F., Coelho, M.R., Almeida, J.A., Cunha, T.J.F. and Oliveira, J.B. (2013a), Brazilian System of Soil Classification (in Portuguese), EMBRAPA, Brasília.

Santos M.L., Morais, E.S., Silveira, H. and Alves, F.C. (2013b), Study of overland flow in the Ivaí river basin, Paraná, Brazil (in Portuguese). Revista Brasileira de Geomorfologia, 14 (3), 259-267.

Sawicz, K.A., Kelleher, C., Wagener, T., Troch, P., Sivapalan, M. and Carrillo, G. (2014), Characterizing hydrologic change through catchment classification, Hydrol. Earth Syst. Sci., 18, 274-285.

Sawicz, K., Wagener, T., Sivapalan, M., Troch, P.A. and Carrillo, G. (2011), Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Hydrol. Earth Syst. Sci., 15, 2895-2911.

Sayama T., McDonnel, J.J., Dhakal, A. and Sullivan, K. (2011), How much water can a watershed store? Hydrol. Process., 25, 3899-3908.

Soil Survey Staff (2014), Keys to Soil Taxonomy, USDA-Natural Resources Conservation Service, Washington. Suertegaray, D.M.A. (2011), Desertification: Recovery and Sustainable Development (in Portuguese), in GUERRA, A.J.T. and CUNHA, S.B. (Eds) Geomorfologia e Meio Ambiente, Bertrand Brasil, Rio de Janeiro, 2011, pp. 249-290.

Tague, C., Grant, G.E., Farrel, M., Choate, J. and Jefferson, A. (2008), Deep groundwater mediates streamflow response to climate warming in the Oregon Cascades, Clim. Change, 86, 189-210.

USGS (2006), Shuttle Radar Topography Mission 1 Arc-Second scene. Online at http://earthexplorer.usgs.gov. WMO (2008), Guide to Hydrological Practices: Volume 1 Hydrology - From Measurement to Hydrological Information, WMO, Geneva.

Yadav, M., Wagener, T. and Gupta, H. (2007), Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Adv. Water Res., 30, 1756-1774.

Yaeger, M., Coopersmith, E., Ye, S., Cheng, L., Viglione, A. and Sivapalan, M. (2012), Exploring the physical controls of regional patterns of flow duration curves - Part 4: A synthesis of empirical analysis, process modeling and catchment classification. Hydrol. Earth Syst. Sci., 16, 4483-4498.

Wagener, T., Sivapalan M., Troch P.A., McGlynn B.L., Harman C.J., Gupta H.V., Kumar P., Rao P.S.C., Basu N.B., and Wilson J.S. (2010), The future of hydrology: An evolving science for a changing world, Water Resour. Res., 46, W05301, doi:10.1029/2009WR008906.

(Submitted on 3/31/2016)