Séminaire de Statistique, Départment de Mathématiques et...
Transcript of Séminaire de Statistique, Départment de Mathématiques et...
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APPLICATION OF GEOSTATISTICS AND GIS TECHNIQUES IN HYDROLOGY AND SOIL SCIENCES
Silvio Simoes
Séminaire de Statistique, Départment de Mathématiques et Statistique, Université Laval, 25/03/2010
(1) Associate Professor, Civil Engineering Department,
Guaratingueta Campus, UNESP
(2) Coordinator of the Laboratory of GeoSpatial Analysis (LAGE)
(3) Fellow, Center for Geographic Analysis, Harvard University
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Under constructionconstruction
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PeopleFaculty:
Prof. Silvio Simões (GeoSpatial Analysis, Hydrology)
Prof. George de Paula Bernardes (Geotechnical Enginerring)
Prof. Isabel Cristina Trannin (Soil pollution)
PhD Students:
Leila Youssef (Hydropower and land use change)
Master Students:Master Students:
Pedro Camarinha (Geostatistics)
Talita Oliveira (GeoSpatial Analysis)
Mariana Ferreira (Fluvial Recovery)
Danilo Farias (Water and Soil Quality)
Undergraduate students
About 30 undergraduate students were involved in our researches last 5
years
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Presentation Topics
• Introduction
• Application of Geostatistics in the Geosciences
• Characteristics of the Study Area
• Example 1: Spatial Variability Pattern of • Example 1: Spatial Variability Pattern of
Precipitation
• Example 2: Spatial Variability Pattern of Soil
Properties
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Main Objective of this presentation:
Show practical way of analyzing hydrological
and soils data using Geostatistics and GIS-and soils data using Geostatistics and GIS-
based approach at the different scales.
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APLICATION OF GEOSTATISTICS IN THE GEOSCIENCES
• Gold and Diamond Industries - The genesis of geostatistics (South
Africa) – Danie Kringe, 1951/1952;
• Hydrogeology - “Elements pour une théorie des milieux poreux”
(Matheron, 1967). Foundation of the relatioship between
geostatistics and fluid mechanics. Macroscopic stochastic
hydrogeology: work at the pore scale, about Darcy´s law and hydrogeology: work at the pore scale, about Darcy´s law and
hydrodynamic dispersion;
• Petroleum Geostatistics (over thirty years) – Geology (meadering
channelized reservoirs similarity with meadering river, models in
3D); Geophysics (seismic data); Reservoirs (dynamic model of the
reservoir including available production history);
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• Environmental Geostatistics - Pollution problems in the different
environments (soils, water or air) analysed using mining concepts .
Beginning of the research – 1973/1974
Other applications: hydrology, climate, soil
Environmental studies presents some common characteristics:
-Variable properties
- Space-time variability
- Stationarity issues
- Important question of exceeding a threshold
Environment and its component attributes, such as soil, result form many physical and
biological processes that interact some in highly non-linear and chaotic ways.
We need to estimates of the errors; estimates errors are lacking in the classical
methods of interpolation
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Context of study area• Surface area is about 55,000 km2
• Paraiba do Sul valley links the two most important cities in Brazil (Sao Paulo and Rio de Janeiro)
•Population inside the basin of 5,6 million inhabitants
• About 8.7 million of people in Rio de Janeiro Metropolitan Region depends Janeiro Metropolitan Region depends on its water resources via a diverted system
• Preliminary studies suggest the possibility of Paraiba do Sul Basin will have to supply water to the Sao Paulo Metropolitan Region in the future
• The region concentrates one of the largest industrial park in Brazil
Paraiba do Sul basin
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São Jose dos Campos:One of the most important industrial and research centers in Latin America.São Jose is considered the main Latin America center main Latin America center for aeronautics (EMBRAER, INPE, CTA)
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In the study area, mean river discharge is 217m3/s; the largest withdrawals of water are made for agricultural irrigation 10.4 m3/s, followed by industrial use, 6.5 m3/s and domestic use, 3.4 m3/s (Sao Paulo State Government, 2002).
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Sistema de Reservatórios -
Paraíba do Sul
GU
AR
AR
EM
AILH
r.P
arai
buna
AN
TA
JAC
AR
EÍ
S.J
OS
É
TA
UB
AT
É
PIN
DA
GU
AR
Á
C.P
AU
LIS
TA
LOR
EN
A
SCI
r.P
iaba
nha
r.Paraíbado Sul
BA
RR
A D
O P
IRA
Í
r.P
iraí
TR
ÊS
RIO
S
B.M
AN
SA
V.R
ED
ON
DA
RE
SE
ND
E
GU
AR
AR
EM
AILHA
POMBOS
r.P
arai
buna
AN
TA
JAC
AR
EÍ
S.J
OS
É
TA
UB
AT
É
PIN
DA
GU
AR
Á
C.P
AU
LIS
TA
LOR
EN
A
SCI
r.P
iaba
nha
r.Paraíbado Sul
BA
RR
A D
O P
IRA
Í
r.P
iraí
TR
ÊS
RIO
S
B.M
AN
SA
V.R
ED
ON
DA
RE
SE
ND
E
TOCOSTOCOS
LAJESLAJES
SANTANASANTANA
VIGÁRIOVIGÁRIO
PEREIRAPEREIRAPASSOSPASSOS
PPS
NLP
CalhaCEDAE
FTN/FTV
VIG
r.Piraí
r.da Vargem
Rosário
Us. de RecalqueUs. Geradora
Us. Reversível
Reservatório
Túnel
Canal
Fluxo de águaCidades
TOCOSTOCOS
LAJESLAJES
SANTANASANTANA
VIGÁRIOVIGÁRIO
PEREIRAPEREIRAPASSOSPASSOS
PPS
NILO PEÇANHA
CalhaCEDAE
FONTES
VIGÁRIO
r.Piraí
r.da Vargem
Rosário
Us. de RecalqueUs. Geradora
Us. Reversível
Reservatório
Túnel
Canal
Fluxo de águaCidades
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PARAIBUNA SANTA BRANCA
JAGUARI FUNIL
SANTA CECÍLIA
VIGÁRIO
SANTANA
TOCOS
LAJES
PEREIRA PASSOSPONTE COBERTA
ILHA DOS POMBOS
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Usina Hidrelétrica
Jaguari
Usina Hidrelétrica Paraibuna
Usina Hidrelétrica
Santa BrancaUsina
Hidrelétrica Funil
Reservatório de
Santa Cecília
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Why is it important to understand the spatial pattern of precipitation?
1. Agricultural Irrigation
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Rainfall
2. Hydroclimatology….
Large Interannual Variability (2100 – 800 mm)
TimeSON DJF MAM JJA
Spring
Summer
Fall “Winter”
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0
10
20
30
40
50
60
70
80
90
100
19311
9331
935193
7193
9194119431
9451947194
9195119531
9551
957195
9196
119631
9651
9671969197
1197319751
9771979198
1198319851
9871
9891991199
319951
9971
9992001
Vaz
ão M
éd
ia A
flu
ente
(m
3/s)
ANO
méd ia
Vazão Média Anual Afluente ao Reservatório de Paraibuna
QMLT = 69 m3/s
19311
9331
935193
7193
9194119431
9451947194
9195119531
9551
957195
9196
119631
9651
9671969197
1197319751
9771979198
1198319851
9871
9891991199
319951
9971
9992001
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“DROUGHTS” IN THE SOUTHEASTERN BRAZIL
1943/19441953-1957
19631968198419941997
2001/2003
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May 13, 2001
The beginning of Rationing Plan: 20% savings across the board
The energy crisis: What went wrong
Photo-Op with the the president of the ANEEL
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1996
2003
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Sistema de Reservatórios -
Paraíba do Sul
GU
AR
AR
EM
AILH
r.P
arai
buna
AN
TA
JAC
AR
EÍ
S.J
OS
É
TA
UB
AT
É
PIN
DA
GU
AR
Á
C.P
AU
LIS
TA
LOR
EN
A
SCI
r.P
iaba
nha
r.Paraíbado Sul
BA
RR
A D
O P
IRA
Í
r.P
iraí
TR
ÊS
RIO
S
B.M
AN
SA
V.R
ED
ON
DA
RE
SE
ND
E
GU
AR
AR
EM
AILHA
POMBOS
r.P
arai
buna
AN
TA
JAC
AR
EÍ
S.J
OS
É
TA
UB
AT
É
PIN
DA
GU
AR
Á
C.P
AU
LIS
TA
LOR
EN
A
SCI
r.P
iaba
nha
r.Paraíbado Sul
BA
RR
A D
O P
IRA
Í
r.P
iraí
TR
ÊS
RIO
S
B.M
AN
SA
V.R
ED
ON
DA
RE
SE
ND
E
TOCOSTOCOS
LAJESLAJES
SANTANASANTANA
VIGÁRIOVIGÁRIO
PEREIRAPEREIRAPASSOSPASSOS
PPS
NLP
CalhaCEDAE
FTN/FTV
VIG
r.Piraí
r.da Vargem
Rosário
Us. de RecalqueUs. Geradora
Us. Reversível
Reservatório
Túnel
Canal
Fluxo de águaCidades
TOCOSTOCOS
LAJESLAJES
SANTANASANTANA
VIGÁRIOVIGÁRIO
PEREIRAPEREIRAPASSOSPASSOS
PPS
NILO PEÇANHA
CalhaCEDAE
FONTES
VIGÁRIO
r.Piraí
r.da Vargem
Rosário
Us. de RecalqueUs. Geradora
Us. Reversível
Reservatório
Túnel
Canal
Fluxo de águaCidades
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“Normal” hydrological year
109 m3/s
160 m3/s
51 m3/s
Dry hydrological year2003
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São Luiz do Paraitinga, 12/31/2009
Six years later...
São Luiz do Paraitinga, 12/31/2009
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3. Orographic Effect PrecipitationGeologic Map
Geological complex evolution:
large Cenozoic rift valley
bordered by two mountain
chains
Consequently,
the region presents large
diversity of rocks, relief and soils
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GEOMORPHOLOGIC MAP
(Poncano et al., 1981)
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4. Land Cover Diversity
A - Spring CompartmentB – Reservoir CompartmentC – Urban-Industry CompartmentD – Urban-Agriculture-Industry Compartment
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Precipitation data
Example 1: Spatial Variability Pattern of Precipitation
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Methodology
1. We have selected 42 rainfall station based on the following
criteria: 1) a minimum 30-years period of time series; 2) the
total length of the gaps should not exceed 5% of the length of total length of the gaps should not exceed 5% of the length of
the series;
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Estação Nome do posto Número anos / período Altitude
(m)
Dados interpolados (%)
D1-022 Faz. Sta. Isabel 30 (70-99) 650 1,1
D1-001 Bananal 47 (53-99) 460 0
D1-021 Usina Bocaina 30 (70-99) 560 1,1
D2-013 Cachoeira Paulista 40 (56-99) 520 0,9
D2-084 Cachoeira 30 (70-99) 520 0,6
D1-005 Campos da Cunha 39 (61-99) 1020 1,9
E1-001 Faz. do Cume 42 (58-99) 900 0,6
E1-005 B. Paraibuna 30 (70-99) 1120 1,7
E1-006 Sertão do Rio Manso 30 (70-99) 1460 3,7
E3-054 Parateí 42 (58-99) 600 3,0
D2-009 Brumado 43 (57-99) 640 2,5
D2-031 Faz. Palmeira 42 (58-99) 530 0,0
D2-077 Monte Verde 42 (58-99) 900 1,6
E2-031 Jacareí 57 (43-99) 570 2,7
E2-007 Faxinal 40 (60-99) 840 0,2
E2-049 Lagoinha 51 (49-99) 910 1,6
D2-035 Lorena 40 (58-99) 540 0,6
D2-020 Monteiro Lobato 59 (41-99) 680 2,5
D2-026 São Benedito 55 (40-99) 780 2,6
E2-024 Bairro Alto 60 (40-99) 710 3,5
E2-116 Laranjal 30 (70-99) 800 1,7
Selected rain gauge location
E2-116 Laranjal 30 (70-99) 800 1,7
E2-039 Comercio 57 (43-99) 670 1,8
E2-130 Alegre 30 (70-99) 970 2,8
D2-041 Sta. Rita do Massain 30 (70-99) 540 2,4
D2-070 Pindamonhangaba 42 (58-99) 530 3,5
D2-072 Faz. Sapucaia 40 (60-99) 540 3,8
D2-075 Faz. Vera Cruz 42 (58-99) 700 1,4
E2-028 Redenção da Serra 46 (54-99) 740 0,2
E2-106 Faz. Ipiranga 38 (62-99) 710 2,6
D2-060 São João 42 (58-99) 560 1,6
E2-055 Catucaba 52 (48-99) 830 1,0
D1-020 Silveiras 30 (70-99) 620 0,0
D1-003 S. José do Barreiro 43 (57-99) 530 0,0
D2-021 S. Francisco 50 (50-99) 730 0,7
E2-036 S. José dos Campos 57 (43-99) 560 2,6
E2-057 Capuava 40 (60-99) 620 1,3
E2-099 Pararangaba 40 (60-99) 570 1,5
E2-102 Bairro do Cajuru 40 (60-99) 590 2,5
E3-055 Represa 40 (60-99) 630 2,5
E2-022 Taubaté 36 (64-99) 610 3,7
E2-092 Faz. São João 46 (54-99) 660 1,6
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2. The analysis was performed on the entire time series as well
as by seasonal time scales. Austral summer (DJF) corresponds
to rainy season and austral winter (JJA) is the dry season.
MAM and SON are transition seasons;
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Rainfall
2. The analysis was performed on the entire time series as well
as by seasonal time scales. Austral summer (DJF) corresponds
to rainy season and austral winter (JJA) is the dry season.
MAM and SON are transition seasons;
Rainfall
Time
SON DJF MAM JJA
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3. GIS and Geostatistics
Recently , standard statistical packages have been linked to GIS
for both exploratory data analysis and statistical analysis
GIS can serves geostatistics:
• aiding georegistration of data;
• facilitating spatial exploratory data;
• providing a spatial context for interpolation and conditional • providing a spatial context for interpolation and conditional
simulation;
In spite of differences in hystory and approach, the linkage of
GIS, statistics and geostatistics provides a powerful and
complementary suite of tools for spatial analysis in the
agricultural, earth sciences and environmental sciences
Burroughs, 2004
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We have used Geostatistical Module of the SPRING (Brazil's National Institute for Space Research (INPE/DPI)
A free software GIS and remote sensing image remote sensing image processing system, which provides for the integration of raster and vector data representations in a single environment. SPRING is a product of
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Geostatistical analysis consists of some steps:
a)Exploratory data analysisb)Spatial distributionc)Spatial analysis: the variogramd)Modeling the variogramad)Modeling the variogramae)Spatial estimation or prediction: kriging
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A correct assumption?
* Assuming time series are
stationary...
Vazão Média Anual Afluente ao Reservatório de Paraibuna
Data Analysis
0
10
20
30
40
50
60
70
80
90
100
19311
9331
935193
7193
9194119431
9451947194
9195119531
9551
957195
9196
119631
9651
9671969197
1197319751
9771979198
1198319851
9871
9891991199
319951
9971
9992001
Vaz
ão M
éd
ia A
flu
ente
(m
3/s)
ANO
méd ia
QMLT = 69 m3/s
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Experimental variogramAnisotropy
Modeling the variogramModeling the variogram
Fit statistically , view afterwards
The reverse: fit visually, statiscally afterwards
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A J U S T E D O S E M I V A R I O G R A M A
**********************************************************
Sumário:
Arquivo: c:\SPRINGDB\SPRINGDB\Vale_do_paraiba\Vale_do_paraiba/GeoStatistic\Pontos_0.var
No. de variáveis: 3
No. de Lags: 12
No. de Lags usados: 12
Parâmetros iniciais: Parâmetros iniciais:
Efeito Pepita (Co): 782.424
Para modelo transitivo: Exponencial
Contribuição (C1): 6106.767
Alcance (a): 52897.365
Modelo de Semivariograma Exponencial
No. Akaike Efeito Pepita Contribuição Alcance
----- ------------------- ------------------- ------------------- -------------------
1 -48.416 665.162 5813.723 34971.655
2 -48.865 541.626 5994.085 36624.192
3 -48.865 544.066 5993.587 36692.624
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Spatial estimation: Ordinary kringing
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Results
The spatial precipitation pattern for the summer, fall, winter and spring. The darkest patterns represent highest precipitation values and the brightest pattern represent
lowest precipitation values (Silva, Simoes, in preparation)
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Example 2: Spatial Variability Pattern of
Soil Properties
Partnership with
The area of study corresponds with a planting of eucalyptus area (Santa Edwirges Farm) comprising 1200 ha, situated in Paraíba do Sul basin, southeast Brazil.
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Land use and land cover
Preservation areas 43,0%
Silviculture 53,0%
Rural roads 2%
Others 2%
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Different levels of erosional and
landslides processes
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Paraiba do Sul River: Oversedimentation
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Methodology
1. We have produced and compiled different thematic maps
Pedological map
Geological Map
Hypsometric map
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2. In the current stage of the research the property analyzed was
the porosity of the soil from 38 sampling points considering three
different soil layers (surface, 50 cm and 100 cm in depth).
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3. We have used the ArcGIS/Geostatistics Extension®
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Semivariograms obtained from (a) surface, (b) 50 cm, (c) 100cm
Fitting experimental semivariograms
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(a) Surface; (b) 100 cm in depth
Cross Validation Analysis
(a) Surface; (b) 100 cm in depth
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Error Distribution Map – surface (left) and 100 cm in depth (right)
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Prediction maps: Surface (left) and 100 cm in depth (right)
“A blind application of kringing, which does not take into account the properties of the study variables can obviously lead to absurd results” (Bruno & Fouquet, 2005)
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Geostatistical Analysis Contribution
Spatial precipitation data:
• Contribute to develop a hydro-climatological model for thestudy area;
• Identify the areas that need more irrigation;
• Contribute to the regional water planning (what regionswould be more affected in case of a drought years?)
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Spatial soil data:
• Contribute to select the more reliable regions for silviculture activity;
• Contribute to identify soil erosion potential areas;
• Contribute to produce an integrated infiltration map in a GIS environment;
• Improve and optimize soil sample in mountainous region.
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Merci de votre attention!