Drought analysis portugal

João Filipe Fragoso dos Santos
2012.03.081
Drought analysis in mainland Portugal: spatial
distribution, frequency and hindcasting
João Filipe Santos
1. Introduction
2. Models applied, results and conclusions
2.1 Spatial and temporal variability of droughts
2.2 Regional frequency analysis of droughts
2.3 Dimensionality reduction in drought modeling
2.4 Spring drought prediction based on winter NAO and global SST
2.5 Precipitation thresholds for drought recognition
3. General conclusions and future developments

2012.03.082
üDrought is one of the most complex and least understood of
all natural hazards, affecting more people than any other one. The
marked natural climate variability in the Mediterranean region
makes drought events appear as a recurrent phenomenon causing
important damages in the economy, the environment and the
society.
ü However, until recently, few attempts have been made to fully
characterize drought and even to test its potential
predictability for Portugal. This thesis is the first one to
embrace the drought throughout mainland Portugal in different
and complementary aspects.
1. Introduction. General framework

2012.03.083
ü Study supported by 94-years (from
October 1910 to September 2004) of
monthly precipitation records in
144 rain gauges regularly distributed
throughout the country.
üMonthly precipitation samples with a
few gaps were filled by applying linear
regression… common reconstruction
technique of hydrologic time series.
(mean annual precipitation … more than 2800 mm, in the
north-western region, to less than 400 mm, in the southern
region, following a complex spatial pattern, in close
connection with the relief, far beyond the most
determinant factor of the precipitation pattern).
1. Introduction. Study area and data

2012.03.084
ü Another data... North Atlantic Oscillation (NAO) and sea surface
temperature (SST) were considered as potential external predictors
since it is proved that they affect Europe’s climate (several authors).
ü NAO index... defined as the
diference between the normalized
sea level pressure over Gibraltar
and Southwest Iceland
(http://www.cru.uea.ac.uk/ftpdata/nao.dat)
ü SST data... NCDC Extended Reconstructed
SST v3b dataset. Three SST fields:
Mediterranean Ocean – SST1 (37oE-7oW;
31oN-47oN); Brazil in the south-western part
of Atlantic Ocean – SST2 (35oW-25oW; 15oS-
10oS); and the North Atlantic – SST3 (45oW-
25oW; 38oN-42oN).
1. Introduction. Study area and data

2012.03.085
üIn the research… it was important the choice of the indicator used to
assess drought: the standardized precipitation index (SPI)
designed to quantify the precipitation deficit at multiple time
1. Introduction. Drought Index
Non-exceedance
probability
SPI Drought category
0.05 >1.65 Extremely wet
0.10 >1.28 Severely wet
0.20 >0.84 Moderately wet
0.60 >-0.84 and <0.84 Normal
0.20 <-0.84 Moderate Drought
0.10 <-1.28 Severe Drought
0.05 <-1.65 Extreme Drought
Drought Classification
scales, according to the
impact of droughts on the
different components of the
hydrological cycle.
üThree modifications in the
SPI calculation were adopted:
(i) the use of the Pearson Type
III distribution (instead of the
Gamma); (ii) the consideration of
the L-moment method for the
calculation of the Pearson Type III
parameters; (iii) the adoption of
a different classification for drought intensity (instead of the
original one).
Standardized Precipitation index (SPI)

2012.03.086
üSPI, advantages… (a) great flexibility, as it can be applied
at different time scales; (b) less complexity, comparatively to
other indexes, as it requires simple and well set calculations; (c)
adaptability to hydro-climatologic variables besides
precipitation; and (d) suitability to spatial representation,
allowing comparison between areas within the same region.
üSPI, timescales … at 1 month is mainly a meteorological
drought index … between 3 and 6 months it can be
considered an agricultural drought index … and between 6
and 12 months it is considered a hydrological drought
index, becoming useful for monitoring the surface water
resources.
1. Introduction. Drought Index

2012.03.087
ü Objectives (addressed in the different chapters and published in journals
with very high IF)
• Characterization of the temporal and spatial variability of droughts regarding the
regions identified by the spatial classification… Also the identification of cycles
of dry and wet events in those temporal patterns.
• Assessment of regional frequency analysis based on the Hosking and Wallis
methodology aiming at identifying the regional probability distribution functions in order to
produce drought hazard maps.
• Identification of the minimum number of rain gauges that need to be considered
in a given drought monitoring network in order to improve the monitoring and,
consequently, the predictability of drought-related events.
• Hindcasting* the drought phenomena (*a way of testing or validating a model, by running it for
a past time period and comparing results with observations).
• Establishment of precipitation surfaces that provide thresholds for expeditious
drought recognition on a monthly basis and across different time scales.
1. Introduction. Objectives

2012.03.088
2.1 Spatial and temporal variability of
droughts in Portugal*
ü … Characterization of the temporal and spatial
variability of droughts … Also identification of cycles
of dry and wet events in the temporal patterns.
üSPI time scales… at 1 (SPI1), 6 (SPI6), and 12
(SPI12); at 6 months of the dry season (April to
September, SPI6Apr-Sep); and at 12 months of the
hydrologic year (October to September, SPI12Oct-Sep).
*Santos J.F., Pulido-Calvo I., Portela M.M., 2010. “Spatial and Temporal Variability of Droughts in Portugal”.
Journal of Water Resources Research, 46, W03503. ISSN 0043–1397 (IF: 2.597)

2012.03.089
üPrincipal components analysis (PCA)
(… SPI data…) identification of patterns and expressing of the
data in such a way as to highlight their similarities and
differences.
üNon-hierarchical cluster analysis (KMC)
(… SPI data…) grouping of rain gauges so that similar SPI
variations are assigned to the same group, while different
variations are grouped separately.
üSpectral analysis
(… SPI data…) recognition of cyclical patterns by decomposing
complex time series into a few underlying functions of particular
wavelengths.
2.1. Spatial and temporal variability of droughts. Models

2012.03.0810
üThe three main
patterns (F1, F2 and F3)
explain more than 70%
of the total variance.
üThe first component
(F1) explains between 30
and 37% of total variance.
PCA – Principal Components Analysis: variance explained by
each component
ü The second component (F2) explains 30% of total variance for
the lower time scale (1 and 6 months) being less important than
the third one (F3) for highest time scale (12 months).
2.1. Spatial and temporal variability of droughts. Results – PCA

2012.03.0811
… representation of the spatial extent of the first three components (F1,
F2 and F3) by mapping the values of the factorial matrix (correlations
between each component and the original field of SPI series)
ü For all the SPI time scales, the first
component (F1) highlights an area
located in the south of Portugal

2012.03.0812
ü… F2 is mainly
representative of the
north-western region.
üF1 and F2 do not overlap,
being clearly spatially
disjunctive.
ü… the third component F3 highlights a central
region which confines with the regions identified by F1
and F2.

2012.03.0813
(…similar results for SPI1, SPI6, SPI12 and SPI12Oct-Sep)SPI6Apr-Sep
KMC - K-means clustering: classification groups
… spatial
distribution
of the clusters
for 2, 3 and 4
classification
groups
2.1. Spatial and temporal variability of droughts. Results – KMC

2012.03.0814
… From the cluster analysis, three
different well-defined regions, a number
similar to the one shown by the PCA
method, were adopted
… suggesting that both methods can
be used to identify spatial areas of SPI
with different temporal patterns
2.1. Spatial and temporal variability of droughts. Results – KMC

2012.03.0815
Southern
Portugal
(SPI6Apr-Sep)
PCA – F1 Cluster 3
RPearson= 0.964
North-western
Portugal
(SPI6Apr-Sep)
PCA - F2 Cluster 1
RPearson= 0.938
Temporal patterns: … the results from PCA and KMC
showed once more to be similar
2.1. Spatial and temporal variability of droughts. Results

2012.03.0816
üTemporal evolution of the
area affected by moderate,
severe and extreme drought in
the regions (highlighted in the figure)
with correlations according to the
PCA greater than 0.7
Areal evolution of droughts: … according to PCA and
for SPI6Apr-Sep …

2012.03.0817
üSouthern region (F1) is more affected by drought events than the
north-western part (F2). In fact, the number of events affecting more than
50% of the area was 17 in south and 12 in the north.
üThe most widespread extreme droughts occurred in the southern
region with 69% and 73% of area affected in 1944 and 1954, respectively,
and in the northern region with 43% in 1954 and 1989.

2012.03.0818
üThe fast Fourier transform technique, or FFT, was used to analyze the
cyclical behavior of the SPI patterns (based on PCA and cluster analysis).
Spectral Analysis
Southern region
One clear peak in the periodogram
plot, representing a period of 3.6
years.
Northern region
Two clear peaks in the
periodogram plot, representing, one,
a period of 2.4 years and, the other,
a period of 13.4 years.
F1 CL3
F2
CL1

2012.03.0819
ü… The characterization achieved can provide a basis for drought risk
assessment, by highlighting regions more prone to droughts,
where additional storage capacity is required for drought protection.
üFor all times scales, the PCA and the KMC analysis resulted in
an equivalent areal zoning, with three regions with different
behaviors: the north, the center and the south of Portugal
üThe similarity of the results achieved by PCA and the KMC analysis
were an indicative that both methods can be used for drought classification
purposes, regarding spatial and temporal pattern analysis
nevertheless… the components produced by PCAs provide a more
clear and simple method to recognize distinct temporal patterns
2.1. Spatial and temporal variability of droughts. Conclusions

2012.03.0820
2.2 Regional Frequency Analysis of Droughts:
Portuguese Case*
üDrought hazard maps were produced by
statistical tools with the aim of drought risk
analysis.
ü Long series of drought characteristics
and suitable statistical models are required
to address any statistical characterization.
ü … study based on the three well defined
sub regions with different drought patterns
previously identified.
*Santos J.F., Portela M.M., Pulido-Calvo I., 2011. “Regional Frequency Analysis of Droughts in Portugal”.
Journal of Water Resources Management, Springer. ISSN 0920-4741. DOI10.1007/s11269-011-9869-z (IF: 2.054)
Cluster 1:
North
Cluster 2:
Center
Cluster 3:
South

2012.03.0821
ü … the concept of drought
comprehends a beginning, an
ending, a drought intensity
and a drought magnitude.
Threshold adopted: -0.84
2.2. Regional frequency analysis of droughts. Models
ü … for each one of the 144 rain gages and for the different times scales
of SPI, the time series of the drought magnitude were established and
analyzed following two approaches:
üAMS (annual maximum series) … series built upon the severest
event in each year: one drought per year, the one with the highest
magnitude, given that such drought event is larger than a given threshold.
üPDS (partial duration series) … series built upon several events
per year: the first, second, third, etc. largest drought events, provided
those events are mutually independents and larger than a given threshold.

2012.03.0822
ü The regional frequency analysis
was applied separately in each one of
the three regions (… number of rain gages
in each of 56, 53 and 35, cluster 1, 2 and 3,
respectively).
ü …according to Hosking and Wallis methodology, two
steps were applied: (1) screening of the data to identify the
rain gauges discordant within a group of sites in order to test
the assumption of regional homogeneity; (2) once the
homogeneity of a given region is confirmed, identiﬁcation
(L-moments diagrams and Z-statistic) of the regional
probability distribution functions.
2.2. Regional frequency analysis of droughts. Models

2012.03.0823
ü…discordancy measure (Si) resulted in
the selection of 131 and 133 rain gauges for
the AMS and PDS approaches,
respectively.
ü…heterogeneity measure (H1)… AMS
led to better results in all clusters,
comparatively, to the PDS approach.
(1) screening of the data and testing for regional homogeneity
2.2. Regional frequency analysis of droughts. Results
.
(2) identiﬁcation of the regional
probability distribution function

2012.03.0824
• PEIII: Pearson III
• GP: generalized
Pareto
• GEV: generalized
extreme value
• GL: generalized
logistic;
• LN: lognormal-3.
Legend:
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)
CL1
PDS
Legend:
AMS
CL1
CL2 CL2
CL3 CL3
Identiﬁcation of the regional probability distribution function (L-moments diagrams):

2012.03.0825
ü For the AMS approach, the better choices were the PEIII and
the GP distributions, depending on the time scales.
ü For the PDS approach a wider distribution of the average
points was obtained. There was a close match between the SPI1
and the GP distribution while the PEIII distribution provides
the better model for the remaining. However, when the Z-statistics
> 1.64, the kappa distribution was adopted.
ü To compare between the drought magnitudes provided by the
AMS and PDS by the chosen regional distribution models, an
error measure, RMSE, was applied to the differences between
observed and estimated magnitudes.
ü The results achieved suggested the better performance of
the AMS model.

2012.03.0826
üAMS .. maps of drought magnitude for the return periods of
50 and 100 years
50
years
SPI1 SPI3
100
years
50
years
100
years

2012.03.0827
üAMS .. maps of drought magnitude for the return periods of
50 and 100 years
50
years
SPI6 SPI12
100
years
50
years
100
years

2012.03.0828
ü Several authors state the PDS approach as being more
efficient than the AMS which was not confirmed by the
results obtained.
ü However, those results also reflect the threshold adopted (-0.84).
Some authors found that the quantile estimates may vary
signiﬁcantly when small changes are made in the threshold.
ü When drought evaluation studies are envisaged the values of the
threshold are fixed which compromise the discussion about the
PDS approach.
ü So it was not clear the advantage of using the PDS approach
when modeling the spatial distribution of drought
magnitude.
2.2. Regional frequency analysis of droughts. Conclusions

2012.03.0829
2.3 Dimensionality reduction in drought modeling*
*Santos J.F., Portela M.M., Pulido-Calvo I., 2012. “Dimensionality reduction in drought modeling”. Hydrological
Processes. DOI:10.1002/hyp.9300 (IF: 2.488)
ü The monitoring of hydro-climatological events
characterized by high spatial and temporal variability, such
as droughts, requires the selection of the optimal number of
gauge stations able to describe the phenomenon within an
area.
üIdentification of the minimum number of rain gauges in
each one of the three regions previously identified in order to
improve the monitoring and, consequently, the
predictability of drought-related phenomena.

2012.03.0830
2.3 Dimensionality reduction in drought modeling. Models
Three different reduction techniques were applied:
(i) a method that relies on extracting knowledge contained within
artificial neural networks (ANNs) by incorporating sensitivity
analyses (ANN-SA);
(ii) the mutual information (MI) criterion, which is a measure of
the strength of dependence between two variables;
(iii) a K-means clustering technique (Kmeans-ED) that, using
the Euclidean distance measure, selects the most important rain gauges
from each of the clusters.
THE PERFORMANCE OF THE DIMENSIONALITY REDUCTION TECHNIQUES
WAS TESTED BY COMPARING THE TEMPORAL AND SPATIAL DROUGHT
PATTERNS PROVIDED BY THE SPI INDEX

2012.03.0831
2.3 Dimensionality reduction in drought modeling. Models
Assessing the performance of the dimensionality reduction techniques
Cluster 1:
North
Cluster 2:
Center
Cluster 3:
South
ü… two essential requisites: the
set of selected rain gauges in
each region by any of the
reduction techniques should be
able to reproduce the temporal
and spatial patterns of the
droughts in that region.
(Four accuracy measures were used to evaluate
each reduction technique: RMSE, R, E1, E2 and PI).

2012.03.0832
2.3 Dimensionality reduction in drought modeling. Results
ü … the ANN-SA resulted in the maximum reduction in the
number of stations and the Kmeans-ED method in the
minimum one…
ü … for each reduction technique were compared patterns of
the droughts produced by the original and by the reduced
sets of rain gauges.
ü …in general terms, all the reduction techniques identified a
set of rain gauges able to reproduce the original drought
temporal patterns.
Performance of the dimensionality reduction techniques: temporal pattern

2012.03.0833
Performance of the dimensionality reduction techniques: spatial pattern
üMaps (… based on the IDW spatial
interpolation technique
that outperformed the
kigging technique … ) of
the dimensionless error
obtained by comparing the
maps of drought events
using the original and
the reduced sets of rain
gauges.
SPI1
SPI3

2012.03.0834
ü The error increases as the SPI timescale increases…
ü The performance of the dimensionality reduction
methods showed to be dependent of the SPI time scale and region.
… drought temporal patterns…
…. the accurate measures obtained
with the best methods in southern
were worse then those obtained for
the northern and central regions.
Nevertheless … from a statistical
point of view all the results were
fairly good…
Performance of the dimensionality reduction techniques: spatial pattern
SPI6
SPI12

2012.03.0835
*Santos J.F., Portela M.M., Pulido-Calvo I., 2012. “Spring drought prediction based on winter NAO and global SST
in Portugal”. Hydrological Processes. DOI: 10.1002/hyp.9641 (IF: 2.488)
üObjective… hindcast drought phenomena (a way of testing and validating a
model by running it for a past time period and comparing results with observations).
ü Based… (1) consideration of the impact of large-scale predictors
(SST – sea surface temperature; Royal Netherlands Meteorological
Institute database) and of atmospheric circulation patterns (NAO –
North Atlantic Oscillation; CRU, University of East Anglia database) on the
spatio-temporal variability of drought; (2) assessment of the capability of
the models to hindcast based on a new concept of probability
maps for droughts.
2.4 Spring drought prediction based on winter NAO and
global SST in Portugal*

2012.03.0836
üANNs with several predictors to hindcast droughts in the
North (CL1), Center (CL2) and South (CL3)…
2.4 Spring drought prediction based on winter NAO and global SST. Models
The drought data to be hindcast was the SPI6 for the spring
months: SPI6April, SPI6May and SPI6June, respectively.
(Why? Because the 6-month time scale is a useful monitoring tool for agricultural
drought assessment)
ANNs
Internal predictor: normalized cumulative
precipitation until March
External predictor: normalized winter SST
and the winter NAO index
I
N
P
U
T
S
SPI6April
SPI6May
SPI6June
ü Individual ANN calibration and validation (error measures were applied to
identify the best model) for each of the rain gauge dataset (144 ANN models)
ANN
Modeling approach:
Model applied:
O
U
T
P
U
T
S

2012.03.0837
2.4 Spring drought prediction based on winter NAO and global SST. Models
The residuals should be a random variable that is approximately
normally distributed with a null mean.
ü Were obtained maps of spatial linear correlation between
SPI6April, SPI6May and SPI6June and SST1, SST2, SST3 and NAO…
providing the basis to select the input variables among the
considered ones.
üUncertainty of the predictions were assessed by probability
maps based on the distribution of the residuals of the best ANN
model (in each rain gauge and SPI6 spring month).
Modeling approach:

2012.03.0838
SPI6April-NAO
SPI6April-SST1 SPI6April-SST2 SPI6April-SST3
SPI6April–NAOand
SPI6April–SST3
SPI6April–SST1and
SPI6April–SST2
Selection of the external predictors, SST and
NAO, based on the linear correlation between
SPI6 and each predictor … period from October 1910 to
September 2004.
2.4 Spring drought prediction based on winter NAO and global SST. Results
üSimilar patterns for SPI6April, SPI6May or SPI6June
üCorrelations between SST2 and any of
the SPI6 always very weak and not
statistically significant SST2
was excluded as external predictor.
üResults indicating that stable indices
such as the NAO, SST1 and SST3 can be
adopted as external predictors for
the hindcasting of spring SPI6.

2012.03.0839
Selection internal predictors, precipitation
üPrecipitation is the most important variable for drought
monitoring and prediction, and the only one used to calculate SPI6
its reliability as a internal predictor was also assessed.
ü … at each rain gauge correlation between normalized cumulative
precipitations (from November to March, P(NDJFM), from December to
March, P(DJFM), and from January to March, P(JFM), and the SPI6April, SPI6May and
SPI6June, respectively).
difference terms, is reflected in E2. The measure E1 (here
called the modified coefficient of efficiency) reduces the
effect of the squared terms. A value of zero for E2
indicates that the observed mean is as good predictor as
the hindcast SPI6, whereas negative values indicate that
the observed mean is a better predictor than the hindcast
pattern (Legates and McCabe, 1999). For a good match,
the values of R2
and of Ej should be close to one.
Lastly, the PI used to evaluate the performance of the
models is given by (Kitanidis and Bras, 1980):
PI ¼ 1
XN
i¼1
SPI6m À S^PI6m
À Á2
XN
i¼1
SPI6m À SPI6m-Lð Þ2
(3)
where SPI6m-L is the observed SPI value at the time
step m-L and L is the lag time. In our calculations, L
one reflects a perfect fit between observed and h
temporal patterns, and a value of zero is equiv
saying that the model is no better than a ‘naïve’
Uncertainty of neural models
To address the likelihood of the predictions g
each of the best ANNs models, for each of the rain
and spring SPI6 hindcast month, probability ma
generated based on the distribution of the residual
best ANN model. The residuals are defined as th
ANN hindcast estimates minus the SPI6 indices
from the observations. At each rain gauge and f
time series, the residuals should be a random varia
is approximately normally distributed (i.e. the sk
coefficient is not significantly different from zero
null mean (Demyanov et al., 1998). To che
hypothesis of normality of the residuals, the
Snedecor and Cochran (Snedecor and Cochran
Figure 4. Correlation plot between spring SPI6 months and the normalized cumulative precipitation from November to March – Prec(NDJ
Stronger correlations
between P(NDJFM) and
SPI6April ; followed by
P(DJFM) and SPI6May
and by P(JFM) and
SPI6June

2012.03.0840
ü … in each rain gauge, based on the accuracy measures (RMSE, r, E1,
E2 and PI) applied to the observed and hindcasted droughts given by the
different ANNs models.
ü Results quite good for SPI6April and less good as the lag time
increased (April May June); but even for SPI6June the values of
the error measures were still statistically good (RMSE<0.55; r>0.78; E1>0.37;
E2>0.60; PI>0.77).
ü The average of the mean of the residuals for the ANNs in each
region nearly zero consistent with the normality assumption
(based on the Snedecor and Cochran test for normality).
ü Based on the best ANN in each rain gauge maps comparing the
hindcast and the observed droughts in the spring months of 2002
and 2004 when severe drought events were registered.
Selection of the best ANNs models

2012.03.0841
Historical droughts versus hincasted droughts in two years
2002 2004
ü The ANNs underestimate the spatial extent of the drought areas.
ü The performance of the ANNs is better in April 2002.
ü Especially for 2004, the hindcast capabilities of the ANNs progressively decreases as the
lag time increases.
April May June April May June
April May June April May June
Historical
Hindcasted

2012.03.0842
such as the NAO, SST1 and SST3 can be potential external
predictors for the hindcasting of SPI6 for April, May and
June. In line with where the highest correlation coefﬁcients
normalized cumulative precipitation from Nove
March (NDJFM), from December to March
and from January to March (JFM) for SPI6April, SPI6
Figure 7. Probability maps for different drought thresholds, Tr, based on the predicted values of SPI6 for the spring months of: a) April, S
üProbability maps for the spring
months of 2002 and 2004
… probability of having a drought of
intensity equal to or worse than a
given threshold -0.84
(moderate), -1.28 (severe) and -1.65
(extreme).
Historical
droughts
April May June
ay, SPI6May; and c) June, SPI6June of 2004 (see Table I for
Hydrol. Process. (2012)
2002
Probability maps
April May June
April May June
April May June
-1.28
-1.65
Despite the decreasing predictive capacity as
the lag time increases, the regions that
experienced drought are always highlighted.
-0.84

2012.03.0843
precipitation (NDJFM), followed by SPI6May (DJFM) and
by SPI6June (JFM).
while the measures E1, E2 and PI are the closes
(0.81, 0.95 and 0.98, respectively).
üProbability maps for the spring
months of 2002 and 2004
… probability of having a drought of
intensity equal to or worse than a
given threshold -0.84
(moderate), -1.28 (severe) and -1.65
(extreme).
Historical
droughts
ay, SPI6May; and c) June, SPI6June of 2004 (see Table I for
Hydrol. Process. (2012)
2004
April May June
April May June
April May June
-1.28
-1.65
April May June
Despite the decreasing predictive capacity as
the lag time increases, the regions that
experienced drought are always highlighted.
Probability maps
-0.84

2012.03.0844
üDrought forecasting was achieved with a probabilistic approach
based on climatological indicators (SPI), teleconnection patterns
(NAO) and SST.
ü The concept of drought probability maps was developed.
ü Such maps can act as a fundamental support tool in drought
forecasting and, consequently, in the integrated planning and
management of water resources.
ü Any decision making approach under water stress scenarios
would benefit from the predictions several months in
advance obtained by the type of models developed.
2.4 Spring drought prediction based on winter NAO and global SST. Conclusions

2012.03.0845
*Santos J.F., Portela M.M., Naghettini M., Matos J.P. e A.T. Silva, 2013. “Precipitation thresholds for drought recognition: a
complementary use of the Standardized Precipitation Index, SPI”. River Basin Management 2013, Wessex Institute, UK.
üPrecipitation is the most widely used operational variable in
many water resources issues due to the close correlation between
its temporal and spatial variability and the water resources
excess or shortage (…. floods and drought episodes).
ü An additional calculation was developed that gives the SPI
values that represent drought thresholds back to the
precipitation field, thus facilitating an adequate interpretation
of the meaning of such index and quite easily identifying the
drought episodes.
2.5 Precipitation thresholds for drought recognition*

2012.03.0846
ü… by inversion of the SPI maps with
the spatial distribution of the precipitation
in 1, 3, 6 and 12 months for different
droughts thresholds: if in a certain location
the precipitation registered in any of the
considered time intervals falls below the
value given by the map, then a drought is
occurring (… severe or extreme according
to the threshold to which the map relates).
2.5 Precipitation thresholds for drought recognition. Models
Figure 7.5 Inversion of SPI3=-1.28. Precipitation in 3 consecu
drought threshold.
September to November October to December Novem
May to July June to August July t
Chapter 7 – Precipitation thresholds for dr
January to March February to April
May to July June to August
!!Rain!Gauges!
Surfaces of monthly and cumulative
precipitation thresholds for drought recognition
SPI3=-1.28 (severe drought)

2012.03.0847
2.5 Precipitation thresholds for drought recognition. ResultsChapter 7 – Precipitation thresholds for drought recognition: A complementary use of
the standardized precipitation index, SPI
August to
October
January to March February to April March to May April to June
September to November October to December November to January December to February
May to July June to August July to September
!!Rain!Gauges!
ü… equivalent results for
the other time scales …
üFrom severe droughts to
extreme droughts the
precipitation thresholds
decrease.
Surfaces of the precipitation in
the successive periods of 3
consecutive months (beginning
in the successive months of the civil
year) for recognition of severe
droughts (SPI3=-1.28)

2012.03.0848
2.5 Precipitation thresholds for drought recognition. Results
ü… very useful, practical and
perceptive tool to monitor the
progressive development
of drought events.
Surfaces of the precipitation in
the successive periods of 6
consecutive months (beginning
in the successive months of the civil
year) for recognition of extreme
droughts (SPI3=-1.68)
January to June February to July March to August April to September
September to February October to March November to April
December
to May
!!!!!!!!Rain!Gauges!
May to October June to November July to December August to January

2012.03.0849
Main achievements (for mainland Portugal)
1. Three coherent regions with different drought behavior were
identified based on principal components analysis, PCA, and non-
hierarchical cluster analysis, KMC: the North, the Centre and the
South.

2012.03.0850
Main achievements (for mainland Portugal):
1. Three coherent regions with different drought behavior were
identified based on principal components analysis, PCA, and non-
hierarchical cluster analysis, KMC: the North, the Centre and the
South.
2. By applying spectral analysis, the periodic signals in the SPI
time series were recognized, revealing more frequent cycles of
the SPI in the South than in the North.

2012.03.0851
3. The comparative assessment between annual maximum duration
series, AMS, and partial duration series, PDS, based on Hosking
and Wallis methodology was also addressed. Especially for the
PDS, the estimates proved to be deeply dependent on the
threshold and less reliable than those of the AMS approach.
Maps of drought magnitudes were obtained.
50 years 100 years
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5 0.6
L-Skewness (t3)
L-Kurtosis(t4)3. General conclusions and future developments

2012.03.0852
3. The comparative assessment between annual maximum duration
series, AMS, and partial duration series, PDS, based on Hosking
and Wallis methodology was also addressed. Especially for the
PDS, the estimates proved to be deeply dependent on the
threshold and less reliable than those of the AMS approach.
Maps of drought magnitudes were obtained.
4. By applying three reduction techniques (artificial neural
networks combined with sensitivity analyses, ANN-SA, mutual
information criterion, MI, and K-means clustering technique,
Kmeans-ED), it was shown that is possible to reduce the
network of rain gauges allocated to the drought monitoring thus
resulting in a more sustainable and equally efficient
monitoring system with less cost.

2012.03.0853
SST1 presents positive correlation in the northern region
(cluster 1), the highest values being around 0.5. The
correlations between SST2 and any of the SPI6 time
series (SPI6April, SPI6May and SPI6June) were always very
weak and not statistically significant. Accordingly, the
external predictor SST2 was excluded from further
analysis. For the SST3, significant negative correlations
were obtained mainly in the coastal southern region, but
with acceptable values for the entire southern region.
The results indicate that significant and stable indices
such as the NAO, SST1 and SST3 can be potential external
predictors for the hindcasting of SPI6 for April, May and
June. In line with where the highest correlation coefficients
were obtained, NAO was considered a potential predictor
for the northern, central and southern region; while SST1
was only considered for the north and SST3 for the south.
Based on these assumptions, multiple ANN multilayer
perceptron hindcast models have been developed.
Since the precipitation is the most important trigger
for drought monitoring and prediction, and is the only
variable used to calculate SPI6, its reliability as a
predictor was also assessed. The predictive variables
related to the precipitation at each rain gauge were the
normalized cumulative precipitation from November to
March (NDJFM), from December to March (DJFM)
and from January to March (JFM) for SPI6April, SPI6May and
J. F. SANTOS, M. M. PORTELA AND I. PULIDO-CALVO
5. It was shown that it is possible to have a seasonal hindcasting
structure to hindcast droughts based on a new concept of
probability maps and using oceanic variables (sea surface
temperature, SST) and atmospheric circulation patterns (North
Atlantic Oscillation, NAO).

2012.03.0854
Chapter 7 – Precipitation thresholds for drought recognition: A complementary use of
the standardized precipitation index, SPI
Figure 7.5 Inversion of SPI3=-1.28. Precipitation in 3 consecutive months corresponding to the severe
August to
October
January to March February to April March to May April to June
September to November October to December November to January December to February
May to July June to August July to September
!!Rain!Gauges!
5. It was shown that it is possible to have a seasonal hindcasting
structure to hindcast droughts based on a new concept of
probability maps and using oceanic variables (sea surface
temperature, SST) and atmospheric circulation patterns (North
Atlantic Oscillation; NAO).
6. With more a practical objective, a new application of the SPI was
proposed for expeditious drought recognition based on surfaces
of monthly and cumulative precipitation thresholds.

2012.03.0855
Some future developments
ü The periodicity of the SPI of more than 13 years in northern
region and around 4 years in the southern region, remains a
challenging task that should include the study of the interaction
with other regional phenomenon’s such as the temperature or the
soil moisture variation.
ü Bivariate frequency analysis methods need to be developed to
improve the coupled analysis of drought duration and
magnitude aiming at estimating probabilities for fixed drought
duration with different magnitudes or for fixed drought
magnitude with different durations.

2012.03.0856
Some future developments
ü Further research focused on the optimal location of the rain
gauges for drought monitoring purposes should be developed.
üA better understanding of the historical relations and
patterns among climatic or oceanographic variables and
atmospheric circulation modes is needed.
ü The analysis of the behavior of the droughts in connection to the
climate change phenomenon should be addressed.

2012.03.0857
Drought analysis in mainland Portugal: spatial
distribution, frequency and hindcasting
João Filipe Santos
(Supervisor: Doctor Maria Manuela Portela, IST, UTL, Portugal
Co-Supervisor: Doctor Inmaculada Pulido-Calvo, EU, Spain)

Drought analysis portugal

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