HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-20-4673-2016Reservoir storage and hydrologic responses to droughts in the Paraná River basin, south-eastern BrazilMeloDavi de C. D.melo.dcd@gmail.comhttps://orcid.org/0000-0002-0098-5095ScanlonBridget R.ZhangZizhanWendlandEdsonYinLeiDepartment of Hydraulic and Sanitary Engineering, University of São Paulo, Avenida Trabalhador São-carlense, 400,
Parque Arnold Schimidt, São Carlos, SP, 13566-590, BrazilBureau of Economic Geology, University of Texas at Austin, 10100 Burnet Rd, Austin, TX 78758, USADepartment of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin,
23 San Jacinto Blvd & E 23rd St, Austin, TX 78712, USADavi de C. D. Melo (melo.dcd@gmail.com)24November201620114673468824May20162June201614October20163November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/20/4673/2016/hess-20-4673-2016.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/20/4673/2016/hess-20-4673-2016.pdf
Droughts are particularly critical for Brazil because of impacts
on water supply and because most (70 %) of its electricity is derived from
hydroelectric generation. The Paraná basin (PB), a major hydroelectric
producing region with 32 % (60 million people) of Brazil's population,
recently experienced the most severe drought since the 1960s, compromising
the water supply for 11 million people in São Paulo. The objective of
this study is to quantify linkages between meteorological and hydrological
droughts based on remote sensing, modelling, and monitoring data using the
Paraná River basin in south-eastern Brazil as a case study. Two major
meteorological droughts were
identified in the early 2000s and 2014, with precipitation 20–50 % below the long-term mean. Total water storage
change estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites declined by 150 km3 between
April 2011 and April 2015. Simulated soil moisture storage declined during the droughts, resulting in decreased runoff
into reservoirs. As a result, reservoir storage decreased by 30 % relative
to the system's maximum capacity, with negative trends ranging from 17
(May 1997–April 2001) to 25 km3 yr-1 (May 2011–April 2015).
Storage in upstream reservoirs is mostly controlled by natural climate
forcing, whereas storage in downstream reservoirs also
reflects dam operations. This study emphasizes the importance of integrating remote sensing, modelling, and monitoring
data to evaluate droughts and to establish a preliminary understanding of the linkages between a meteorological and
hydrological drought for future management.
Introduction
Droughts have large-scale socio-economic impacts, responsible for 35 % of
disaster-related deaths and 200 billion US dollars (USD, adjusted to 2012 by
WMO) in losses globally between 1970 and 2012 . In South
America, 48 droughts were responsible for 23 % (USD 16.5 billion) of
losses caused by disasters (1970–2012), including the 1978 Brazilian
drought, responsible for a loss of USD 8 billion .
There are a variety of different types of droughts, including meteorological,
agricultural, hydrological, and socio-economic .
Investigating individual types of drought limits understanding of how they
are connected, i.e. how meteorological drought (precipitation deficit)
propagates through the hydrological system, resulting in socio-economic
drought, for example. Socio-economic drought is characterized by the failure
to supply economic goods (water, hydroelectric power, etc.) as a result of
water deficits . Because these drought types are usually
related to one another, societal impacts of droughts are often conveyed
through linkages between them .
Establishing linkages between meteorological and hydrologic droughts is
challenging due to the large spatio-temporal variability in water
distribution. Increasing availability of remotely sensed terrestrial total water storage anomalies (TWSA) data from the Gravity Recovery and
Climate Experiment (GRACE) satellites, precipitation estimates from the
Tropical Rainfall Measuring Mission (TRMM), and evapotranspiration (ET)
estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS)
greatly enhances our ability to assess linkages between the different types
of droughts . In addition to remote
sensing data, Global Land Data Assimilation Systems (GLDAS) land surface
models (LSMs) provide valuable data on water budgets related to droughts
.
Meteorological drought indicators, such as the Standardized Precipitation
Index (SPI), have been used to forecast hydrologic droughts based on a
Streamflow Drought Index .
Major hydrological regimes have been characterized using satellite data
(GRACE, TRMM) and GLDAS LSMs . GRACE
satellite data have been used to assess impacts of droughts on TWSA in large
basins globally .
In Brazil, drought-related studies have focused mostly on the Amazon basin
or semi-arid north-eastern Brazil
. However, south-eastern Brazil (80 million people),
accounting for 55 % of national GDP in 2012 , has
been subjected to two major droughts since 2000. The early 2000s drought was
responsible for a major energy crisis in Brazil, leading to energy-rationing
programmes and even blackouts, attributed in part to limited transmission and
interconnection . The more recent drought (2014) compromised
the water supply for 11 million people in Brazil's largest metropolis:
São Paulo.
Reservoir levels in São Paulo's main water supply system (Cantareira
system) dropped below 15 % capacity. The 2014 drought jeopardized potable
water supplies of 130 cities (28 million people) in the south-eastern region
, where there are ≈ 50 reservoirs with individual
areas exceeding 1000 ha, mostly in the Paraná basin. The 2014 water year
(September 2013–August 2014) was the driest on record in the São Paulo
city area since 1962 , with simulated
reservoir dynamics changing in response to drought
. Analysis of GRACE TWSA data indicates
that between February 2012 and January 2015, total water storage declined by
6 cm yr-1 (56 km3 yr-1, totalling 160 km3) in
south-eastern Brazil as a result of reduced rainfall
.
In this context, it is reasonable to ask whether the meteorological forcing
is primarily responsible for the socio-economic droughts in the region. Would
an improved electric distribution system avoid the blackouts that occurred in
the early 2000s? Is the water crisis in São Paulo solely linked to
meteorological factors? Was 2014 also the driest water year in the entire
south-eastern region in decades? Were these two droughts similar and, if so,
did they result in similar impacts? Finding the linkages between different
types of droughts is important to answer these questions. Hence, the
objective of this study is to address the following questions related to
linking meteorological and hydrological droughts in the Paraná River basin
in south-eastern Brazil.
What are the intensity, extent, and duration of the recent droughts?
What are the drought impacts on terrestrial total water storage and reservoir storages?
How do the droughts propagate through the hydrologic system?
How are different reservoirs operated under drought conditions?
The Paraná basin (PB) was selected as a case study because of the
severity of recent droughts and widespread impacts on water supply and
hydroelectricity generation. To answer these questions, we used remotely
sensed total water storage anomalies from GRACE (Sects.
and S3.4 in the Supplement), remotely sensed and ground-based gridded rainfall data sets
(Sects. and S3.3), remotely sensed ET
(Sects. and S3.3), simulated soil moisture storage and
runoff from four LSMs (Sects. and S3.2), and monitoring
data from 37 reservoirs (Sects. and S3.1). We used
(i) statistical indices to characterize meteorological and hydrologic
droughts (Sects. and S4.3) and (ii) tests statistics to
evaluate the impacts on reservoir storage (Sects. , 4.1
and 4.2), and (iii) studied differences and similarities between individual
reservoirs (Sects. and S4.4).
Unique aspects of this study include the preliminary assessment of droughts
using a variety of remote sensing, modelling, and monitoring approaches and
indicators, comparison of multiple droughts and related hydrologic impacts,
and a variety of scales of analyses from regional evaluation using GRACE
satellites to local reservoir responses. This study builds on previous
studies, such as the evaluation of drought in south-eastern Brazil based on
GRACE satellite data by by expanding remote
sensing, modelling, and monitoring data.
(a) The Paraná River basin in the national context.
(b) The analysed reservoirs are highlighted in the digital elevation
map (1” horizontal resolution; ) and in
(c) the 2012 land use map . States include Distrito
Federal (DF), Goiás (GO), Minas Gerais (MG), São Paulo (SP), Paraná
(PR), Santa Catarina (SC), and Mato Grosso do Sul (MS).
The Paraná basin is one of the
most studied areas in Brazil, given its relevance in the national context. Previous hydrologic studies in this area include assessment of climate change
impacts on water resources , energy and
hydrologic modelling
, assessment of
remotely sensed evapotranspiration , and
energy-based estimation of evapotranspiration . In terms of
drought-related studies, the area of the Paraná River basin is much larger
than evaluated in some previous analyses that were restricted to São Paulo
.
Another recent study brought some insights regarding drought propagation by
quantifying the time lag responses of the hydrological system to
meteorological shifts; they found a lag of ≈ 6 months between
significant change in SPI and reservoir storage, and ≈1 month
between SPI and river discharge . The large areal extent
allows surface reservoir impacts to be assessed at local to system scales,
considering upstream–downstream drought impacts based on observed reservoir
storage (RESS) data. The results of this study should enhance our
understanding of linkages between meteorological and hydrologic droughts to
better manage water resources in this region and other similar regions.
Study area, data, and methods
The study area (830 000km2) comprises the contributing
basins to 37 reservoirs: 35 within the Paraná basin (PB) and two other
nearby reservoirs (Três Marias and Paraibuna) selected because they are in
areas affected by the 2014 drought (Fig. and Table S2 in
the Supplement). The Paraná basin was originally covered by Cerrado and
Mata Atlantica biomes which have been replaced by pasture (44 %), annual
crops (24 %), sugarcane (9 %) with original Cerrado, and forests only
occupying 7–9 % each of the land area . Most of the
reservoirs are located near the centre of the basin, where the land use
consists, basically, of annual crops and sugar cane. Centre pivots in the
region are mainly located in the northern and south-eastern parts of the PB
(Fig. S2f in the Supplement). Mean rainfall is 1500 mmyr-1
and temperature is 23 ∘C (1980–2014; ).
The topography in the PB consists, basically, of high plains with maximum
altitudes higher than 2000 m a.m.s.l. (Fig. ). Most of
the PB is under temperate highland tropical climate with dry winters (Cwb)
and humid subtropical climate with hot summer (Cfa) or with dry winter (Cwa;
Fig. S3b). This basin covers parts of seven Brazilian states (SP, MG, DF, GO,
MS, PR, and SC; Fig. ). The population in the basin (60
million in 2010) represents 32 % of the Brazilian population (Sect. S2.2),
including the most populated city in Brazil (São Paulo), with 11 million
people in 2015 .
The Cantareira system, São Paulo's main water supply system, has an overall
storage capacity of 1.45 km3, including the following reservoirs and
respective storage capacities: Jaguari (0.14 km3), Jacareí
(0.89 km3), Cachoeira (0.11 km3), and Atibainha (0.3 km3).
Extended dry periods can be critical for the Cantareira and other surface
systems. Since the 1960s, seven droughts (1977, 1984, 1990, 1992, 2001, 2012,
and 2014) reduced
reservoir storage supplies for São Paulo .
The Cantareira system contribute 47 % (33 m3 s-1) of the total
water supply to São Paulo's metropolitan region that encompasses 39
municipalities (19.6 million people in 2007; ). Before
the water crises caused by the 2014 drought, 8.8 million people were supplied
by the Cantareira system, with ≈ 164 L per inhabitant per day
.
Data sources and processing
This section provides a general overview of the data sets used in this study.
Additional details are provided in Sect. S3.0. Ground-based rainfall
data (Pobs) from ≈1270 gauges (Fig. S3) for the period
1995–2013 were interpolated to a 0.25∘× 0.25∘ grid by . Because Pobs is not
available throughout the whole analysed period, remotely sensed rainfall
estimates (PSat) were derived from the Tropical Rainfall Measuring
Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B43 version 7
product, for the period 2013–2015.
The GRACE-based monthly gravity solutions in spherical harmonic format from
April 2002 through to April 2015 were obtained from the University of Texas
Center of Space Research (CSR; ). To reduce noise
while minimizing signal loss, we applied standard post-processing, including
truncation to degree and order 60, de-striping , and
application of a 250 km fan filter . Then the filtered
monthly gravity fields, after removing the mean, were converted to total
water storage anomalies (TWSAs) in gridded
1∘× 1∘ degree solutions to match outputs
from land surface models spatially.
The analysis of soil moisture storage (SMS) and runoff (Roff) is
based on outputs from four land surface models (LSMs) from GLDAS 1.0: NOAH,
Mosaic, VIC, and CLM . The number of vertical
layers (VL) and respective depths (D) varies among LSMs: CLM (10 VL,
0 ≤D≤ 3.43 m), Mosaic (3 VL, 0 ≤D≤ 3.5 m), NOAH (4
VL, 0 ≤D≤ 2.0 m), and VIC (3 VL, 0 ≤D≤ 2.0 m). SMS
is the average layer soil moisture (ALSM) from individual LSMs. ALSM was
obtained by depth-averaging the water amounts in specific soil layers.
Descriptions of the LSMs and GLDAS are provided in Sect. S3.2. The ET data
sets used were derived from the global ET algorithm (ETGlob) developed by
and from the MOD16 global evapotranspiration
product (; Sect. S3.3).
There are a large number of reservoirs in the PB, several of which with
negligible volumes in the context of this study. Considering the effort to
compile and process the data from individual reservoirs, only reservoirs with
individual areas exceeding 1000 ha were selected for analysis (criterion I).
The volume of a reservoir with an area less than 1000 ha ranges around
0.25 km2, accounting for less than 0.1 % of the average storage
capacity analysed in this study. Approximately 50 reservoirs remained after
the application of criterion I. Most of those reservoirs have the primary
purpose of generating hydroelectricity. A second criterion was applied,
removing cases whose time series contained gaps accounting for more than
50 % of their records. Due to data limitations, only 37 of the 50
reservoirs were considered in this study. The maximum storage capacity of the
37 reservoirs is ≈ 250 km3. Daily data on inflow, outflow,
water level and storage for 37 reservoirs were downloaded from the Brazilian
Water Agency (ANA, Agência Nacional de Águas) website for the period
January 1995–June 2015.
Data analyses
The Standardized Precipitation Index (SPI) was selected as the meteorological
drought index because it is probabilistic, its implementation is relatively
simple, and its interpretation is spatially invariant
. SPI uses historical rainfall data to
determine, at different timescales, the periods of positive and negative
anomalies in rainfall based on the cumulative probability of rainfall
occurrence over an area or point . We used the 12-month SPI
based on historical monthly rainfall data relative to a 35-year time span
(1980–2015; Sect. 4.3).
The Streamflow Drought Index (SDI; ) was
selected as the hydrologic drought index because it is analogous to SPI in
that it is computationally inexpensive, easy to implement, and reduces the
drought characterization to a simple severity vs. frequency relationship
. For each water year, SDI is obtained for
overlapping periods of 3, 6, 9, and 12 months based on cumulative streamflow
data. In addition, it is not data demanding as it requires only streamflow
data (Sect. S4.3). For practical purposes, drought onsets were classified
when SPI or SDI were <-1 for at least 6 months. Further details related
to calculating SDI are provided in Sect. 4.3.
The statistical significance of reservoir depletion and trends in monthly
reservoir storage were investigated by applying the non-parametric
Mann–Whitney U test (MW U) and a modified version of the rank-based
non-parametric Mann–Kendall test (MK), respectively
. The MW U test is a common alternative
to the parametric Student's t test for testing whether two samples come
from the same population (Sect. 4.1). The MK method is used to avoid making
assumptions regarding the distribution of the data and reducing sensitivity
to outliers . To overcome possible issues due to
positive correlation in the analysed time series (Sect. S4.2), we adopted a
modified MK trend test for seasonal data with serial correlation
.
Time series of (a) rainfall and SPI, (b) runoff,
(c) GRACE total water storage anomaly (TWSA), (d) soil
moisture, (e) evapotranspiration, and (f) reservoir storage
in the equivalent system (ES). (a) Standardized Precipitation Index
(SPI) categories include extremely wet (SPI > 2), severely wet
(1.5≤SPI<2), moderately wet (1≤SPI<1.5), wet
(0.5≤SPI<1), normal (-0.5≤SPI<0.5), moderately
dry (-1<SPI≤-0.5), dry (-1.5<SPI≤-1), severely
dry (-2<SPI≤-1.5), and extremely dry (SPI <-2).
(b) Runoff, (c) GRACE total water storage anomaly (TWSA),
and (d) soil moisture are expressed in equivalent water thickness
(EWT).
Hierarchical clustering (HC) was used to group the reservoirs and is a
commonly adopted approach to identify similar groups among hydrological time
series . The similarities among elements and groups of
elements are measured by a distance function which, along
with the maximum cluster distance, compose the main parameters to be defined
in a HC method. In this study, we used the Euclidean distance (see Sect. S4.4
for equations) as a distance function because it has been shown to produce
good results in past studies and is available in
the Matlab toolbox used here. The maximum cluster distance (MCD) defines the
distance below which the objects are considered as part of a single group
(Fig. S12). In this study, we adopted an interactive process to define MCD in
which various values were tested, the resulting clusters were observed, and a
final option was chosen based on its capability to represent the variability
existing in the sample. The elements used to generate the clusters are time
series of normalized monthly reservoir storage (Sect. S4.4); that is, we seek
to group the reservoirs with similar responses at a monthly scale. Hence, the
clustering analysis performed here does not consider other reservoir
characteristics such as storage capacity, location, and shape.
Rainfall anomaly relative to the 1982–2015 mean for 20 analysed water years (September–August).
ResultsMeteorological droughts
Two distinct droughts were identified in the Paraná basin between 1995 and
2015 based on SPI (Fig. ). The first drought began in
October 1999 and extended through to August 2000, during which SPI was
≤-1.25, characterizing a moderate to severe drought (-2 ≤ SPI
≤-1). This drought was followed by a moderately dry year as the
average SPI was ≈-0.6 during the rainy season of 2001
(December–February). The second driest period occurred between February 2014
and November 2014, with SPI ≤-1.20 (Fig. ).
The first drought is hereafter referred to as the early 2000s drought and the
second drought as the 2014 drought. The 2014 rainfall deficit was previously
identified as part of a prolonged drought (2012–2015) by
, who applied break tests to TWSA time series
and found a change occurring in February 2012. Although our analysis of
GRACE-based TWSA also indicates an abrupt change between 2011 and 2012, this
change in TWSA reflects a hydrological drought.
The intensity and duration of the drought are spatially variable. Rainfall
anomalies in water year (WY) 2001 (September 2000–August 2001) were more
negative over the eastern and northern parts of the Paraná basin, whereas
the spatial extent of the 2014 drought was greater as most of the PB
experienced a reduction of 20–40 % in annual rainfall
(Fig. ). Most of the reservoirs are in areas where rainfall
deficits ranged from 20 to 50 % of the long-term average (1982–2015). The
negative rainfall anomalies decreased towards the south-western portion of
the basin, which experienced a positive anomaly of up to 20 %. Between 2002
and 2009, two periods of average rainfall with different inter-annual ranges
were found, followed by an extremely wet year (WY 2010), mainly over the
south-eastern part of the PB (Fig. ), after which rainfall
systematically decreased.
Spatial trends of TWSA between April 2011 and April 2015.
GRACE total water storage anomaly and component storages
The GRACE satellite data provide valuable information on the regional extent
of drought impacts on total water storage anomaly (TWSA), despite its coarse
spatial resolution (≈ 100–200 km2;
Fig. ). TWSA data from GRACE do not include the 2001
drought as its monitoring period is from 2002 to the present. Analysis of
GRACE data indicates greater depletion in TWSA (≈-60 to
≈-90 mm yr-1 between April 2011 and April 2015) in
south-eastern Brazil, which corresponds to the north-eastern part of the PB.
This range encompasses the results reported for the period between February
2012 and January 2015 by , whose findings
indicate a water depletion rate of -61 mm yr-1 in south-eastern
Brazil (≈ 920 km2), corresponding to ≈ 160 km3
over 3 years. The spatial extent of the negative TWSA
(Fig. ) is generally consistent with the spatial
distribution in the negative rainfall anomaly in WY 2014
(Fig. ).
GRACE TWSA shows large seasonal variability as a result of
seasonal fluctuations in soil moisture storage (SMS) from LSMs and monitored
reservoir storage RESS (Fig. ). Inter-annual
variability in GRACE TWSA shows anomalously wet years in 2007 and 2010,
related to elevated rainfall. SMS and RESS were also above average in those
years. The peak TWSA in January 2007 shows the rapid response of the system
to the peak in SPI during the same period (Fig. ). Note
that SPI was low or close to 1 between 1999 and 2006; therefore, the peak
TWSA was not preceded by high rainfall in 2006. There is a long-term decline
in TWSA from April 2011 to April 2015 (37 km3 yr-1,
42 mm yr-1), totalling 148 km3. Depletion in TWS
(42 mm yr-1) is greater than that in SMS and RESS combined
(24 mm yr-1) by ≈ 40 %. The discrepancy is most likely
related to depletion in deep SMS or groundwater storage (GWS; Fig. S11). Simulated SMS
from LSMs is restricted to the upper 2 m of the soil profile.
Water storage anomalies from GRACE TWSA, soil moisture storage
(SMS), and reservoir storage (RESS), all expressed as equivalent water
thickness.
Analysis of combined reservoirs as an equivalent system
This section presents the results relative to the analysis of the total
monthly storage of all 37 reservoirs considered as one equivalent system.
According to the MW U test, there is strong evidence (probability
≥ 95 %) that the early 2000s (p value = 0.027) and 2014
(p value = 0.01) droughts resulted in significant depletion of the total
reservoir storage. This depletion corresponds to a reduction of 40 km3
(17 %) in WY 2001 and 34 km3 (15 %) in WY 2014 of the average
storage volume and of 90 km3 (-33 %) and 86 km3
(-31 %) below the equivalent system maximum capacity.
Comparing the negative trends in RESS, the recent drought was more intense
than the earlier drought: between 1997 and 2001, the equivalent RESS
decreased by 17.1 relative to 25.3 km3 yr-1 between 2011 and 2015
(Fig. S10). The reservoir system responded rapidly to the meteorological
shifts. RESS was lowest at the beginning of the water year 2001; SPI values
indicate the meteorological drought began in October 1999, when the SPI was
at -1.3. During the wet period of 2002, the reservoir systems began to
recover and by early 2003 the reservoirs were operating at normal capacity,
even though the SPI indicated a normal to moderately dry condition.
Additional information about the recovery/depletion of reservoirs in a
spatial context is presented in Sect. S5.7.
(a) Changes between the mean annual ET from 2007 to 2014 and 2000 to 2006;
(b) short-term trends of ET in the Paraná basin.
Drought propagation through the system
Variations in precipitation translate to changes in soil moisture storage
(SMS) that affect runoff (Roff) and ultimately impact RESS. SMS and
Roff were similarly affected by the early 2000s drought
(Fig. ). After 2001, the almost 1 decade of relatively
normal rainfall was insufficient for SMS and Roff to recover from
the drought. Not even the extreme wet period in 2010/2011 resulted in SMS and
Roff recovery. Given that rainfall continued to decrease in the
following years, the negative trend in SMS and Roff persisted.
The average temperature in the Paraná basin decreased by
0.04 ∘C yr-1 within the past 20 years (Fig. S9).
However, the analysis of both temperature and ET were inconclusive regarding
their impacts on reservoir storage change. Comparisons between ET estimates
from the global algorithm (ETGlob) by
and from the MOD16 algorithm (ETMOD) by
indicate a larger inter-annual variation of the
latter relative to the former (Fig. ). Given the
large uncertainty in remotely sensed ET , no attempt was
made to identify whether the minimums are overestimated by ETGlob
or underestimated by ETMOD; rather, we analyse the changes in ET
signal. Although no significant trend of ET in response to the analysed
droughts was observed with a confidence level ≥ 95 %
(α= 0.05), ET decreased by -2.8 cm yr-1 between January 1998
and January 2001, and by -0.3 cm yr-1 between February 2010 and
February 2014 (Fig. ). From January 2003 to January 2010,
a positive trend, significant at α= 0.05, show that ET increased by
3 cm yr-1. Such an increase reflects the recovery of the hydrologic
system as the moisture, absent due to the drought, becomes available again to
be consumed by the vegetation.
In terms of annual ET, the ETMOD signal is practically invariant
from 2000 through to 2006, but a discrete increase in the moving average
suggests that ET rates were higher in the following years (2007–2014;
Fig. ). An increase in ET (70 to 200 mm) was observed in
most of the Paraná basin, especially over the contributing areas of most of
the analysed reservoirs (Fig. ).
showed that replacing pasture by sugar cane in the
Cerrado bioma increases ET, and São Paulo state (30 % of the PB) has been
reported as the largest producer of sugar cane . However,
the comparison between Figs. and
shows a higher increase in ET (≥ 120 mm) in the PB occurring mostly in
areas with annual crops and pasture, whereas the increase in ET in areas
preponderantly occupied by sugar cane ranged from 0 to 200 mm. Further
investigation would be necessary to, precisely, identify the causes of that
increase.
Spatial variation of the Standardized Precipitation Index (SPI) and
Streamflow Drought Index (SDI) in the period of two droughts. SPI and SDI are
shown for the water years of 2000 (September 2000 to August 2001) and 2014
(September 2013 to August 2014).
The analysis of Roff, SMS, and TWSA provides insights into the
mechanisms that may explain the reservoir responses to droughts. According to
the SPI, the rainfall regimes during both droughts are similar; however, the
greater impacts on reservoir storage in 2014 are likely explained by
different antecedent soil moisture conditions. The fact that SMS did not
recover after the early 2000s drought implies that higher rainfall amounts
would be required for recovery to overcome the cumulative SMS deficit. The
extremely wet conditions in 2010/2011 were only sufficient to partially
replenish the reduced SMS. Complementary graphs are presented in Sect. S4.2.
Runoff can be classified as infiltration excess (when rainfall exceeds the
infiltration rate of the soils) or saturation excess (when soils are close to
saturation) and differs from river discharge. Therefore, Roff is
highly sensitive to SMS conditions. If rainfall is insufficient to recover
SMS, then Roff cannot recover either. After 2010/2011, SMS,
Roff, and TWSA continued to decline; hence, the main inflow to the
reservoirs (river discharge), which depends on runoff and baseflow
(groundwater discharge to streams), also decreased. The years preceding the
early 2000s drought were wetter than those preceding the 2014 drought: SPI
exceeded 1.5 (severely wet) throughout most of the 1997 through 1999 period,
and SMS and Roff were more than 20 % higher than the following years.
Therefore, SMS links meteorological drought to Roff, which affects
the primary input to RESS: streamflow.
Streamflow data were used to calculate the Streamflow Drought Index and
provide insights into linkages between meteorological and hydrologic droughts
(Fig. ) for the water years of 2001 (WY 2001) and 2014 (WY
2014). In general, meteorological droughts resulted in hydrologic droughts,
as indicated by the extremely low values of the SDI, where the SPI was
negative (Fig. ). However, some upstream reservoirs
(highlighted with arrows) seem to have buffered the effects of the 2014
drought in the downstream reservoirs. Although the SPI indicates a severe to
extremely dry situation (SPI <-2) over those reservoirs, the SDI
increased from upstream (SDI <-2.50) to downstream (-2.5< SDI <-2.0). This means that the river discharge deficit (hydrologic
drought) caused by the meteorological drought was (modestly) attenuated by
the upstream reservoirs.
Dendrogram plot showing the hierarchical cluster tree. The distance between individual
clusters is given by the height of the links. The red horizontal line indicates the maximum cluster
distance (MCD) adopted to determine the clusters.
Comparison between WY 2001 and 2014 shows a larger extent of the most recent
drought within the Paraná basin, which agrees with the rainfall anomaly in
Fig. . Except for the south and central south of the PB,
the extent of the hydrologic drought was more critical in WY 2014 than that
observed for WY 2001. For instance, the same sub-basin in the centre of the
PB had, in WY 2001, -1< SDI ≤ 0, whereas, in WY 2014, -2.7< SDI ≤-2.00.
Cluster analysis applied to reservoir storage
Changes in RESS reflect the impacts of climate extremes through SMS and
Roff and also reservoir management for hydroelectricity and water
supply. Cluster analysis suggested that the reservoirs could be subdivided
into six groups (G1, G2, …, G6) based on the time-series signal of
monthly storage (Figs. and ). The
main features intended to be highlighted by creating those clusters in
Fig. are seasonality and changes in time, which will be
discussed below. The hierarchical tree of the groups and linkages between
them shown in a dendrogram were obtained by setting the maximum cluster
distance (MCD) = 0.6 (Fig. ). Although the dendrogram
in Fig. may suggest higher link consistency for MCD
≈ 0.5, similar characteristics of the seasonal signal would be
present in the new groups formed from G1 (Sect. 5.6). Hence, the
configuration in Fig. was kept. Further details and
discussion about such a choice are provided in Sect. S5.4. Dam operations are
constrained by non-human-controlled variables (e.g. natural inflows) and
legal obligations to maintain outflows exceeding a minimum value
(Qmin_out) at all times. The compliance with
Qmin_out aims to guarantee multiple uses of water resources
and is defined by the Electric System National Operator (ONS – Operador
Nacional do Sistema Elétrico) for each hydroelectric power plant (HEP).
Hence, even though the released outflow from a given reservoir may be reduced
to control the decline in storage during a drought, the reservoir will,
eventually, experience some depletion given the need to observe
Qmin_out. To manage hydroelectric generation, ONS uses
rainfall–runoff models forced with rainfall forecast from the ETA model
; then discharge forecasts are used in stochastic models
to generate scenarios of projected natural discharges at different
timescales. Here, we sought to identify how human control and natural forcing
dictate the responses in each reservoir.
Time series of monthly reservoir storage of the six reservoir
groups. Individual reservoirs are in light grey. Black lines show the group
average.
Natural controls
The reservoirs in group 1 (G1, 15 out of 37) are characterized by
well-defined seasonal variations, with good correspondence between storage
change and natural input to the contributing basins
(Figs. and ). In general, their storage
through time is similar to that described by the equivalent system of
reservoirs in terms of depletion during the early 2000s and 2014 droughts.
Within G1 reservoirs, the inflows compare well with SPI, indicating a major
role of natural forcing in reservoir responses.
Similarly, comparison between SPI, SDI, and RESS in G3 reservoirs also suggests their responses are
strongly affected by natural variability (Figs. A1–A15 in the Supplement).
Different responses between G1 and G3 reservoirs can be explained by
climatological variations (Fig. ). The main climatic
difference between G1 and G3 reservoirs is the pronounced dry season that
occurs in the climate sub-types Cwa (humid subtropical with dry winter), Cwb
(temperate highland tropical), and Aw (tropical wet and dry) in G1
reservoirs, whereas rainfall is more evenly distributed throughout the year
in sub-type Cfa (humid subtropical) in G3 reservoirs. The occurrence or
absence of dry winters affects the seasonal distribution of inflows to
reservoirs, hence impacting the seasonal signal in reservoir storage. Good
correspondence between reservoir response and precipitation regime is not
restricted to reservoirs in the upper part of the basin, i.e. reservoirs with
no upstream reservoir affecting their inflow. What happens in the other cases
is that the natural inflow (from undisturbed basins) contributes to the total
inflow that explains the reservoir storage change as much as the regulated
discharge delivered by the reservoir(s) upstream or that the outflow from
upstream mimics natural discharge variations (Sect. S5.7).
(a) The 37 analysed reservoirs in the context of the
Paraná basin clustered in six groups and the number of elements per
group. (b) Example of a typical reservoir from group 1 (16
reservoirs): Furnas hydroelectric power plant (HEP). Time series of monthly
rainfall relative to the contributing area of Furnas HEP and inflow to Furnas
reservoir were used to derive the Standardized Precipitation Index (SPI;
c) and Streamflow Drought Index (SDI; d). Furnas monthly
storage is shown in km3(e). Hydrologic dry conditions are
defined by the following states: SDI ≥0: non-drought;
-1≤ SDI < 0: mild drought; -1.5 ≤ SDI <-1: moderate
drought; -2 ≤ SDI <-1.5: severe drought; and SDI <-2:
extreme drought.
Although G6 reservoirs are similar to G1 reservoirs in terms of having
well-defined seasonal variations with good correspondence between
precipitation variability and reservoir storage change, G6 reservoirs seem to
deplete/recover more slowly than those in G1. The reservoirs of the
Cantareira system are included within G6 reservoirs (Fig. S53). This system
experienced major depletion as a result of natural water stress imposed by
the recent drought (2014) combined with high demand from the São Paulo
metropolitan area. The total rainfall in the 2014 water year was 1150 mm,
25 % lower than the average since 1995, resulting in SPI ≤-2
(extremely dry). The lowest reservoir levels registered in the storage of the
system (early 2015) reached 10 % of the total capacity, making the impacts
of the 2014 drought unique.
Anthropogenic controls
Reservoirs in G2 and G5 do not show distinct seasonal variations, indicating
that their responses are mainly governed by how they are operated and how the
upstream dam is operated, given that all reservoirs in these groups are
downstream of other hydroelectric power plants. In addition, the natural
component of the total inflow is minimal because the upper undisturbed basin
accounts for a small fraction of the total contributing area (Figs. A16–A20
and A31–S34). As a result, SPI fluctuations are not always reflected in
reservoir storage. In such cases, analysis of SDI is inconclusive as it
cannot provide information on natural discharge variability unless the
human-controlled component of Q is removed.
For example, storage doubled in the Jaguará reservoir (G2) between 2001 and
2005 (0.04 to 0.08 km3) even though SPI and SDI indicate the onset of a
meteorological and hydrological drought (Fig. S34). That period was followed
by an extremely wet year (2007/2008), but the rainfall increase was not
reflected in the inflow (SDI ≈ 0) or in increased reservoir
storage. Finally, no significant depletion was found during the extremely dry
period in 2014. The main difference between G2 and G5 reservoirs is the
change in average reservoir level (mainly after 2002), positive for G2 and
negative for G5, displayed by most of those reservoirs
(Fig. ).
Natural and anthropogenic controls
Responses in G4 indicate that these reservoirs are equally controlled by
natural and operational forcing. The natural component is reflected in the
seasonality of storage variation. Their location in the PB, downstream to
large reservoirs (Figs. A25–A30), makes them vulnerable to anthropogenic
controls. Similar to G2 and G5 reservoirs, storage changes in G4 reservoirs
are highly affected by dam operations, which implies that a precipitation
deficit can be compensated by reducing outflow and benefiting from regulated
discharge from upstream. However, persistence of low inflow may require
operation that drastically reduces reservoir storage to maintain
Qmin_out. That is precisely what happened at the M. M. Moraes
Hydroelectric Power Plant (Fig. S43) in 2014 as the Electric System National
Operator (ONS – Operador do Sistema Elétrico) decided to reduce the
reservoir level by 8 m.
Future research
The findings presented here and in previous studies related to drought
impacts in the Paraná basin are the baseline for future analysis. There are
a number of gaps that need to be addressed; here we name some. A first
prospect to be considered in the future is to quantify drought impacts on the
regional water budget. Because remote sensing data sets, especially ET
estimates, are not sufficiently reliable to close the budget
, future studies should incorporate
more ground-based data, such as groundwater level data.
Further analysis of drought propagation features is necessary to better
characterize such extreme events in the PB.
compiled a number of studies on that topic and identified the following
features: pooling (combined meteorological droughts causes a prolonged
hydrologic drought); attenuation (terrestrial stores attenuate meteorological
drought); lag (between meteorological drought, soil moisture, and
hydrological drought); and lengthening (longer droughts moving through soil
moisture to hydrological droughts). The lag feature was partially addressed
by as lag times between changes in SPI, reservoir storage,
and river discharge were estimated. Future researches can profit from more
detailed information regarding the decision processes considered for dam
operations. Such information is not usually publicly available in Brazil.
Hence, this can create a good opportunity to promote more bilateral
collaboration, especially between hydrology researchers and engineers.
Summary
Regional intense droughts in south-eastern Brazil have caused major depletion
of water resources. We analysed remote sensing, monitoring, and modelling
data to identify linkages between meteorological and hydrological droughts.
Based on SPI, two major meteorological droughts occurred in the Paraná
basin between 1995 and 2015. A moderate to severe drought
(-2 ≤ SPI ≤-1) occurred in the early 2000s, with
SPI ≤-1.25 between October 1999 and August 2000. The second driest
period occurred between February and November 2014, with
SPI ≤-1.20. Drought intensity and duration are spatially variable.
The 2014 drought was more critical over the north-eastern part of the study
area, with rainfall anomalies ranging between -20 and -60 %, resulting
in SPI values ≤-2.0 for 6–12 months in some cases (e.g. Furnas
reservoir, Fig. ).
The recent drought monitored by GRACE satellites shows depletion of TWSA of
37 km3 yr-1 (42 mm yr-1) over 4 years from 2011 to 2015 in
the Paraná basin, totalling 150 km3. Simulated SMS and monitored RESS
together decreased by 24 mm yr-1, accounting for 60 % of TWSA
depletion. This recent drought was preceded by an earlier drought (early
2000s) that occurred prior to GRACE monitoring. Reduced rainfall and negative
SPI during this drought translated to low SMS and reduced runoff (SDI
anomalies), decreasing RESS by 30 km3 in 2001 relative to the average
storage volume. Depletion of reservoir storage caused by the early 2000s and
2014 droughts corresponds to a 31 % reduction relative to the reservoir
equivalent system maximum capacity. Two negative short-term trends in RESS
were found during the studied period: -17.1 (1997–2001) and
25.3 km3 yr-1 (2011–2015), totalling 68 and 101.2 km3,
respectively.
The period between these two droughts is characterized by slightly below
average to near normal rainfall; however, rainfall levels were insufficient
to overcome the cumulative water deficit that built up during the early
drought. Low SMS compromised recovery even after the severely wet year in
2010. As a result, the system storage reserves were low going into the recent
drought and were rapidly depleted during 2014.
While GRACE satellites provide data on regional water storage depletion and
recovery related to drought, SMS and Roff from LSMs link
meteorological drought to hydrologic drought, as shown by streamflow
anomalies (SDI) that are reflected in inflow anomalies to the reservoirs.
However, detailed assessment of drought impacts on reservoir storage requires
more thorough analysis of reservoirs at the local scale. Clustering analyses
in this study revealed three groups of reservoirs (23 reservoirs) with
storage controlled mainly by natural climatic forcing, two groups (9
reservoirs) controlled mainly by reservoir operations, and one group (6
reservoirs) controlled by a combination of natural and anthropogenic forcing
(dam operations). The analysis highlights the importance of reservoir
location within the system (upstream vs. downstream) in determining the
dominant controls on drought impacts on reservoir storage. For most
reservoirs, including the Cantareira system, meteorological droughts were
reflected in the hydrologic system through reduced inflow to the reservoirs.
The vulnerability to recent droughts in São Paulo not only underscores the need for
reservoir storage expansion, but also reinforces the urgency for diversifying
the water sources to enhance drought resilience. In other cases, the upstream
reservoirs performed an important role in regulating river discharge and,
hence, reducing meteorological drought impacts on inflow to downstream
reservoirs.
A preliminary understanding of drought propagation, i.e. how the
meteorological drought culminate in hydrological drought, was presented here.
Our analysis indicate that socio-economic droughts (failure to supply water,
electricity, etc.) in the PB are subject to a natural cascade effect
(rainfall deficits > soils moisture reduction > run-off reduction >
reservoir depletion) that is related to antecedent soil moisture conditions
and dam operation.
An important practical measure is to continuously monitor meteorological
indices such as SPI. Based on such indices, it may be possible to anticipate
and reduce drought impacts by means of public campaigns to alert the
population about the potential drought and to encourage reduction in water
and electricity consumption. The lag time between meteorological droughts and
hydrologic responses results in time for some actions to be taken to reduce
drought impacts, such as modifying dam operations. Given the spatial
variability of droughts and the interconnected electric grid in Brazil,
another possible measure is to reduce hydroelectric generation in a region
potentially affected by an imminent drought and, temporarily, increase
electricity generation in other regions.
Given the uncertainties in the modelling process adopted by ONS to manage
hydroelectric generation, dam operators can profit from radar-based real-time
rainfall measurements or remotely sensed near-real-time rainfall estimates.
The difficulty in gathering station data for short timescales emphasizes the
importance of remote sensing rainfall for reservoir operations. Finally, land
surface models can be used in addition to the rainfall–runoff models
currently used by ONS, to project hydrologic responses by inputting weather
forecast data.
This study emphasizes the importance of integrating remote sensing,
modelling, and monitoring data to quantify the duration, extent, and severity
of regional droughts and their impacts on water resources, specifically
reservoir storage, system evaluation and detailed analysis of individual
reservoirs to determine controls on reservoir response to drought (e.g.
natural climate forcing vs. dam operations), and the importance of this
comprehensive understanding of the linkages between the meteorological and
hydrologic droughts for future management.
Data availability
All the data used in this study are hosted by the Laboratory of Computational
Hydraulics of the University of São Paulo and are available at
http://www.lhc.shs.eesc.usp.br/dados_de_pesquisa/Drought-impacts/dataset_hess-2016-258.zip.
The Supplement related to this article is available online at doi:10.5194/hess-20-4673-2016-supplement.
The first author collected and processed the data from GLDAS, ANA, and ONS. Zizhan Zhang processed and analysed the
data from GRACE. Lei Yin processed the rainfall data. Edson Wendland and Bridget R. Scanlon analysed the data and commented on the paper,
which was written by Davi C. D. Melo and Bridget R. Scanlon.
Acknowledgements
The first author would like to thank the National Council for Scientific and
Technological Development (CNPq – Conselho Nacional de Desenvolvimento
Científico e Tecnológico) for the financial support (grant agreement
numbers 206857/2014-4 and 142252/2013-1).
Edited by: A. Weerts Reviewed by: F. Mainardi Fan and T.
Conradt
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