Seminario - aulavirtual.ig.conae.gov.araulavirtual.ig.conae.gov.ar/moodle/pluginfile.php/513/mod_page/... · cumulative precipitation shortages (Chang and Kleopa, 1991; Estrela et

The Detection and Monitoring of Droughts: Approximations from Climatological and Hydrological parameters

Nicolas A. Mari

IG - 27 / 12 / 2012

Seminario

• Introduction

• Principal definitions of drought

• Classification of droughts

• Drought indices

• Remote Sensing Applications

• Conclusions

Organization of the Seminary

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

Principal definitions of drought

“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

Agricultural drought

Socio –economic 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).

Meteorological 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.

Hydrological 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.

Agricultural drought


Socio –economic drought

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.

Socio –economic drought is referred to

the failure of water supply from water

resources system.

Drought Indices

1. Intensity 2. Duration 3. Severity 4. Spatial extent

Drought indices are designed to define the prime parameters that are involved in drought processes

Drought Indices

Meteorological

Hydrological

Drought indices can be designed from a combination of such variables, enhancing their capacity of discrimination.

Drought Indices

Suelo Sub-suelo

Biota Clima

coupled systems

Drought Indices

Water Cycle

Drought Indices

Long time series of data are essential to evaluate the effect of drought at different time scales.

Pp

,

T

and the monthly time scale of data is useful for monitoring drought in agricultural practices, water supply and groundwater data analysis

One year of data is useful to abstract information on the regional behavior of droughts

Drought Indices

Drought Index Author Year of

Publication

Palmer drought severity index (PDSI) Palmer 1965

Rainfall anomaly index (RAI) Van Roy 1965

Deciles Gibbs and Maher 1967

Crop moisture index (CMI) Palmer 1968

Bhalme and Mooly drought index

(BMDI) 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) Weghorst 1996

Soil moisture drought index (SMDI) Hollinger et al. 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

4.1 Standardized precipitation index (SPI)

Drought Indices

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



Weather Stations


SPI NOV-3 meses


SPI NOV-3 meses

Agua total en el perfil


SPI NOV-3 meses

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)




And the hydrological parameters?


georeferenced water meters

http://napas.iyda.net/

http://napas.iyda.net/

Drought Indices Drought Indices Drought Indices

Dinámica media del nivel freático durante el mes de mayo en los últimos ocho años.

Accumulated rain 2010, 2011

Asner, G.P., 1998, Biophysical and Biochemical Sources of Variability in Canopy Reflectance, Remote Sensing of Environment, 64:234-253.

Absorción

Reflexión

Bandas de absorción De agua

Drought Indices Remote Sensing Applications



Remote Sensing Applications Drought Indices Remote Sensing Applications

Remote Sensing Applications


NDVI anomaly in Africa for March 2000, based off data collected over the 1981-2000 time frame


Two variables related to general vegetation conditions – the Percent Average Seasonal Greenness (PASG) and Start of Season Anomaly (SOSA)

http://vegdri.unl.edu/FAQ.aspx


Microwave Imaging Radiometers?

Soil Moisture Ocean Salinity (SMOS)

And what happens with soil moisture?

Optical sensors can´t penetrate the surface, but Microwave radiometers do.

Soil types Soil moisture

Biota Climate

coupled systems

Remember?


For optimum results, SMOS will measure microwave radiation emitted

from Earth's surface within the L-band (1.4 GHz) using an

interferometric radiometer.

SMOSS Mission Overview

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.



https://earth.esa.int/c/document_library/get_file?folderId=127856&name=DLFE-2302.pdf

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!

Documents

Seminario - aulavirtual.ig.conae.gov.araulavirtual.ig.conae.gov.ar/moodle/pluginfile.php/513/mod_page/... · cumulative precipitation shortages (Chang and Kleopa, 1991; Estrela et