Seminario
The Detection and Monitoring of Droughts: Approximations from
Climatological and Hydrological parameters
Nicolas A. Mari
IG - 27 / 12 / 2012
Organization of the
Seminary
• Introduction
• Principal definitions of drought
• Classification of droughts
• Drought indices
• Remote Sensing Applications
• Conclusions
Principal definitions of
drought
The conceptual definition:
The conceptual definitions are those stated in relative terms, such as the description of a
drought as “a long dry period”
The operational definition:
The operational definition relies on the identification of the quantitative characteristics of a
drought for a given period of time, which can help to detect the onset, severity and
termination. The operational definition uses the concepts of frequency, severity and duration,
commonly used to describe the regime of a certain disturbance
Principal definitions of
drought
•
Drought is a naturally occurring phenomenon that exists when precipitation has been
significantly below normal recorded levels, causing serious hydrological imbalances that
adversely affect land resource production systems (UN Secretariat General, 1994).
•
FAO defines a drought hazard as the percentage of years when crops fail from the lack of
moisture (FAO, 1983).
•
(Shneider, 1996) defines a drought as an extended period – a season, a year, or several years
of deficient rainfall relative to the statistical multiyear mean for a region.
•
Gumbel (1963) defined a drought as the smallest annual value of daily streamflow (caudal).
•
Palmer (1965) defined a drought as a significant deviation from the normal hydrologic
conditions of an area.
•
Linseley et al. (1959) defined drought as a sustained period of time without significant
rainfall
“Most of the above mentioned definitions are mainly focused on the registered
deficits of rainfall over a period of time for a certain region”.
Classification of droughts
Meteorological
drought
Hydrological
drought
Socio –economic
drought
Agricultural
drought
Classification of droughts
Meteorological drought
Meteorological drought is related to the amount of lacking rainfall for
a period of time. Precipitation is the main variable used for
meteorological drought analysis. Monthly precipitation data is usually
compared with average values (Gibs, 1975). Other analyses are focused
on determining drought duration and intensity in relation to
cumulative precipitation shortages (Chang and Kleopa, 1991; Estrela et
al., 2000).
Classification of droughts
Hydrological drought
Hydrological drought is defined
when a given water resources
management system is affected by a period of insufficient surface and
subsurface water supply. Streamflow drought is proven to be related to
the catchment properties, being geology an important factor in
hydrological droughts.
Classification of droughts
Agricultural drought
Agricultural drought is specifically related to the insufficiency of soil
moisture for a period of time, independent of the availability of surface
water resources, which affects crops. Actual and
potential
evapotranspiration plays a key role on the decline of soil moisture,
which is related to the plant water demand, prevailing weather
conditions, the physiological characteristics of the plants and the
physical and biological properties of the soil itself. The combination of
meteorological variables with soil moisture has been useful to produce
several drought indices related to study agricultural droughts.
Classification of droughts
Socio –economic drought
Socio –economic drought is referred to
the failure of water supply from water
resources system.
It could be originated by an increasing demand that exceeds the
capacity of water supply, or simply by the lack of water resources
originated by weather related anomalies. In all cases, the economic
losses are implicated.
Drought Indices
Drought indices are designed to define the prime
parameters that are involved in drought processes
1. Intensity
2. Duration
3. Severity
4. Spatial extent
Drought Indices
Meteorological
Hydrological
Drought indices can be designed from a combination
of such variables, enhancing their capacity of
discrimination.
Drought Indices
Biota
Clima
coupled systems
Suelo
Sub-suelo
Drought Indices
Water Cycle
Pp,
Drought Indices
T
Long time series of data are essential to evaluate the effect of drought
at different time scales.
One year of data is useful to abstract information on the
regional behavior of droughts
and the monthly time scale of data is useful for monitoring
drought in agricultural practices, water supply and
groundwater data analysis
Drought Indices
Drought Index
Author
Year of
Publication
Palmer drought severity index (PDSI)
Palmer
1965
Rainfall anomaly index (RAI)
Deciles
Crop moisture index (CMI)
Bhalme and Mooly drought index
(BMDI)
Van Roy
Gibbs and Maher
Palmer
1965
1967
1968
Bhalme and Mooly
1980
Surface water suply index (SWSI)
Shafer and Dezman
1982
National rainfall index (NRI)
Gommes and Petrassi
1994
Standardized precipitation index (SPI)
Mckee et al.
1995
Reclamation drought index (RDI)
Soil moisture drought index (SMDI)
Weghorst
Hollinger et al.
1996
1993
Crop-specific drought index (CSDI)
Meyer and Hubbard
1995
Corn drought index (CDI)
Meyer and Pulliman
1992
Soy-bean drought index (CDI)
Meyer and Hubbard
1995
Vegetation condition index (VCI)
Liu and Kogan
1996
Drought Indices
4.1 Standardized precipitation index (SPI)
Standardized precipitation index (SPI)
The SPI is computed by fitting a probability density function to the frequency distribution of
precipitation summed over the time scale of interest. This is performed separately for each
month (or whatever the temporal basis is of the raw precipitation time series) and for each
location in space.
Each probability density function is then transformed into the standardized normal
distribution.
Once standardized, the strength of the anomaly is classified as set out in Table II. This table also
contains the corresponding probabilities of occurrence of each severity, these arising naturally
from the normal probability density function. Thus, at a given location for an individual month,
moderate droughts (SPI −1) have an occurrence probability of 15.9%, whereas extreme
droughts (SPI −2) have an event probability of 2.3%. Extreme values in the SPI will, by
definition, occur with the same frequency at all locations.
Drought Indices
Drought Indices
Drought Indices
Weather Stations
Drought Indices
SPI
NOV-3 meses
Drought Indices
SPI
NOV-3 meses
Agua total en el perfil
Drought Indices
SPI
NOV-3 meses
Drought Indices
Palmer drought severity index (PDSI)
The index is a sum of the current moisture anomaly and a fraction of the previous index value.
The moisture anomaly is defined as
d = P − Pˆ
where P is the total monthly precipitation, and ˆ P is the precipitation value ‘climatologically
appropriate for existing conditions’ (Palmer 1965). ˆ P represents the water balance equation
defined as
ˆ P = ET + R + RO −L (2)
where ET is the evapotranspiration, R is the soil water recharge, RO is the run off, and L is the
water loss from the soil. The overbars signify that these are average values for the given month
taken over some calibration period. ˆ P is a hydrological factor and needs be parameterized
locally.The Palmer moisture anomaly index (Z index) is then defined as
Z = Kd (3)
and the PDSI for month i is defined as
PDSIi = 0.897PDSIi−1 + Zi/3 (4)
Drought Indices
Drought Indices
Drought Indices
And the hydrological parameters?
Drought Indices
georeferenced water meters
http://napas.iyda.net/
Drought Indices
Dinámica media del nivel freático
durante el mes de mayo en los
últimos ocho años.
http://napas.iyda.net/
Accumulated rain 2010, 2011
Drought
Indices
Remote Sensing Applications
Reflexión
Bandas de absorción
De agua
Absorción
Asner, G.P., 1998, Biophysical and Biochemical
Sources of Variability in Canopy Reflectance,
Remote Sensing of Environment, 64:234-253.
Drought
Indices
Remote Sensing Applications
Drought
Indices
Remote Sensing Applications
Drought
Indices
Remote Sensing Applications
Remote Sensing Applications
Drought
Indices
Remote Sensing Applications
NDVI anomaly in Africa for March 2000, based
off data collected over the 1981-2000 time
frame
Remote Sensing Applications
Two variables related
to general vegetation
conditions – the
Percent Average
Seasonal Greenness
(PASG) and Start of
Season Anomaly
(SOSA)
http://vegdri.unl.e
du/FAQ.aspx
Remote Sensing Applications
And what happens with soil moisture?
Remember?
Biota
Climate
coupled systems
Soil types
Soil moisture
Optical sensors can´t penetrate the surface, but Microwave radiometers do.
Microwave Imaging Radiometers?
Soil Moisture Ocean Salinity (SMOS)
Remote Sensing Applications
SMOSS Mission Overview
For optimum results, SMOS will measure microwave radiation emitted
from Earth's surface within the L-band (1.4 GHz) using an
interferometric radiometer.
Remote Sensing Applications
Measurement principles
Moisture and salinity decrease the emissivity of soil and seawater
respectively, and thereby affect microwave radiation emitted from the
surface of the Earth. Interferometry measures the phase difference
between electromagnetic waves at two or more receivers, which are a
known distance apart – the baseline.
A two-dimensional 'measurement image' is taken every 1.2 seconds. As
the satellite moves along its orbital path each observed area is seen
under various viewing angles.
Remote Sensing Applications
Remote Sensing Applications
https://earth.esa.int/c/document_l
ibrary/get_file?folderId=127856&n
Conclusions
Conclusions
•
Meteorological approximations are usefull to derive the occurence of
dry and wet periods for regional scale applications (eg. SPI).
•
The quality of these estimations will depend on the density of
weather stations and the long data record.
•
For agricultural purposes, it is recommended to use the accumulated
rainfall over the past 3 months.
•
The PDSI is usefull to estimate the total moisture status of a region
in combination with SPI.
Conclusions
• The applications developed for optical sensors are usefull for
vegetation monitoring, while is not cappable to retrieve soil
characteristics.
• Temperature estimations in combination with vegetations indices are
good indicators of vegetation stress.
• The new era of microwave radiometers is the future of soil moisture
estimations
Muchas Gracias por su atención!