1

Master in Emergency Early Warning and Response Space

Applications

The Detection and Monitoring of Droughts: Approximations from

Climatological and Hydrological parameters

Seminar final report

Author: Nicolas A. Mari

nmari@cnia.inta.gov.ar

Keywords: Rainfall, Climate monitoring, Hydrologic patterns, Soil Moisture, Vegetation

Dynamics, Water supply, Soil evaporation, Vegetation indices, Remote Sensing, Optical

sensors, SAR.

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Contents Introduction ........................................................................................................................................ 4

2. Principal definitions of drought................................................................................................... 4

3. Classification of droughts ............................................................................................................ 5

3.1 Meteorological drought ...................................................................................................... 5

3.2 Hydrological drought ........................................................................................................... 5

3.3 Agricultural drought ............................................................................................................ 6

3.4 Socio –economic drought .................................................................................................... 6

4. Drought Indices ........................................................................................................................... 6

4.1 Standardized precipitation index (SPI) ................................................................................ 7

4.2 Palmer drought severity index (PDSI) ................................................................................. 7

4.3 Crop moisture index (CMI) .................................................................................................. 9

4.4 Surface water index (SWI) ................................................................................................... 9

4.5 Vegetation condition index (VCI) ...................................................................................... 10

4.6 Effective precipitation (EP) ................................................................................................ 10

4.7 Soil moisture deficit index (SMDI) ..................................................................................... 10

4.8 Standardized runoff index (SRI) ........................................................................................ 11

4.9 Normalized difference water index (NDWI) ...................................................................... 11

4.10 Drought Monitor (DM) ...................................................................................................... 11

5. Remote sensing applications for the monitoring of droughts .................................................. 12

5.1 Optical sensors & Synthetic Aperture Radar –SAR- systems. ........................................... 12

5.2 Operational Systems ......................................................................................................... 15

5.2.1 National Integrated Drought Information System (NDIS) ......................................... 15

5.2.2 Centro de Relevamiento y Evaluación de Recursos Agrícolas y Naturales (CREAN) . 16

6. Available information and state of applications in Argentina. ................................................. 18

7. Conclusions ............................................................................................................................... 19

8. References ................................................................................................................................. 20

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Tables and Figures

Table 1: List of drought indices with the correspondent author and year of publication. ................. 7

Table 2: Summary of spectral measurements for soil moisture estimations ................................... 12

Table 3: Summary of the current applications on drought estimations in Argentina * ................... 18

Figure 1: Drought conditions from California for the 18th of December 2012. ............................... 16

Figure 2: SPI index as calculated from CREAN for Argentina (November 2012) ............................... 17

Figure 3: Palmer index for Argentina produced for November 2012 by CREAN. ............................. 18

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Introduction

The Monitoring of drought occurrence is one of the principal environmental concerns of

governmental and scientific institutions which need to estimate and predict the impacts and

severity of drought periods over natural and productive regions. Droughts are widely

recognized as a mayor environmental disaster (Mishra, 2010), affecting many regions of the

world, with enormous ecological, social and economic consequences. Between 1900-1999, the

87% of the world disasters corresponded to famine and droughts, which it can be interpreted as

a correlative effect, being drought the trigger of food crisis.

Many efforts have been done to study the fundamental parameters that control the

occurrence of droughts, principally the ones related to meteorological conditions and

hydrological regimes. The occurrence of droughts can be defined from the concepts of a certain

disturbance regime, which includes frequency, severity and duration. For example, the

combination of high temperatures, low relative humidity, the intensity and periods of

occurrence of rains have important significance in the occurrence of droughts. These conditions

impacts both surface and ground water resources, implicating the reduction of water supply

from a certain region, poor water quality, crop failure, reduce range productivity, diminished

power generation, riparian habitat loss, among other activities related to the use of water

resources.

The implications of droughts are of great importance in the planning of water resources,

in particular on the area of freshwater planning and management. Future scenarios of drought

occurrence are increasingly advertising the need of a better understanding of the impacts of

droughts, as the importance to minimize future water supply failures, which are more and more

evident in semi-arid and deserted regions of the world. On the other hand, there is a very

important concern about food production and the need of hydrological ground water resources

to supply areas of poor precipitation regimes. The use of remote sensing techniques, are very

prominent for the management of productive areas, since provides a good approach to reach

systematically huge regions and monitor the properties of vegetation water conditions with the

possibility of predicting drought adverse scenarios.

2. Principal definitions of drought

The importance of defining drought resides in the fact that many uses and definitions of the

term arise as a function of the differences in hydro meteorological variables and socioeconomic

factors around the world. There are conceptual and operational definitions that must be

considered (Wilhite and Glanz, 1987). The conceptual definitions are those stated in relative

terms, such as the description of a drought as “a long dry period”. Qualitative expressions are

not useful for operational purposes. The operational definition relies on the identification of the

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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. The

World Meteorological Organization (World Meteorological Organization 1986) defines

drought as a sustained, extended deficiency in precipitation. The UN Convention to Combat

Drought and Desertification (UN Secretariat General, 1994) defines drought as 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. The Food and Agricultural Organization (FAO, 1983) of the United

Nations defines a drought hazard as the percentage of years when crops fail from the lack of

moisture. The encyclopedia of climate and weather (Schneider, 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. 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. A better understanding of these definitions

will be carried out in the next classification schemes of droughts types.

3. Classification of droughts

3.1 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).

3.2 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.

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3.3 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

3.4 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.

4. Drought Indices

There are several indices that have been developed to detect and assess the effect of droughts.

In most cases, drought indices are designed to define the prime parameters that are involved in

drought processes, which include the dimensionality of the intensity, duration, severity and

spatial extent. These are the main characteristics which can be generalized under the context of

“Drought regimes”. Even if droughts are produced from meteorological or hydrological

variables, drought indices can be designed from a combination of such variables, enhancing

their capacity of discrimination. 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. Both timescales

are the most used ones for accounting drought parameters of interest. Table 1 presents a list of

the most important drought indices available from the last 50 years.

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Table 1: List of drought indices with the correspondent author and year of publication.

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)

The SPI is calculated as the “difference of precipitation from the mean divided by

the standard deviation”. The SPI is simply the transformation of the precipitation time

series into a standardized normal distribution (z-distribution). The SPI is calculated based

on a long-term precipitation record of data for a desired period. The precipitation record is

fitted to a probability curve, which is then transformed to a Gaussian distribution, so that

the mean SPI for a particular location and period is zero (Mishra and Singh 2010). This

means that the long-term data sets used to determine the SPI at any time scale must be

normalized. The normalization procedure using a probability distribution is a very

important feature of the SPI and makes it unique. SPI allows monitoring the occurrence and

duration of dry and wet periods. For agricultural purposes, it is recommended to use the

accumulated rainfall over the past 3 months.

4.2 Palmer drought severity index (PDSI)

The PDSI is based on precipitation and temperature data for the estimation of

moisture supply and demand within a two-layer soil model (Palmer 1965). This index was

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the first approximation to estimate the total moisture status of a region. Since its creation,

the PDSI has been used to characterize the spatial and temporal drought characteristics and

severity, including the exploration of the periodic behavior of droughts. Its implementation

has been focused on the monitoring of hydrological trends, crop forecasts, as well as a for

potential fire severity. The PDSI is based upon a set of empirical relationships derived by

Palmer (1965) to express regional moisture supply standardized in relation to local

climatological norms. The index is a sum of the current moisture anomaly and a fraction

of the previous index value. The moisture anomaly is defined as

ppd ˆ

where P is the total monthly precipitation, and p̂ is the precipitation value

‘climatologically appropriate for existing conditions’ (Palmer 1965). p̂ represents the water

balance equation defined as

LRORETp ˆ

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. p̂ is a hydrological factor and needs be

parameterized locally. The Palmer moisture anomaly index (Z index) is then defined as

KdZ

and the PDSI for month i is defined as

3/897.0 1 iii ZPDSIPDSI

K acts as a climate weighting factor and is applied to yield indices with comparable local

significance in space and time. The resultant PDSI values are broken down into 11

categories, ranging from extremely dry to extremely wet. The PDSI is usually calculated

over a monthly period.

During the life of PDSI, some limitations were pointed: 1) More suitable for agricultural

impacts than for hydrological droughts. 2) Rain values are questionable for winter months

and at high elevations since precipitation is all considered as rain. It is also considered that

runoff only occurs after all soil layers have become saturated, which can underestimate the

total runoff, and 3) PDSI can be slow to respond to developing and diminishing droughts.

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After describing its limitations it is worth to describe what the positive aspects of PDSI are.

The principal positive fact is that it has been in use for a long time, and has been tested and

verified in many cases. In addition it accounts for temperature and soil characteristics and is

standardized, which means that it can be compared for different climatic zones. PDSI is

sensitive to temperature and precipitation. It has been observed that precipitation anomalies

tend to dominate the change of PDSI in cold seasons when evaporation is minimal; the

effect of temperature on PDSI becomes more important in warm seasons, however the

response of PDSI often lags anomalies of temperature and precipitation by a few months.

PDSI can be equally affected by temperature and precipitation when both have similar

magnitudes of anomalies.

4.3 Crop moisture index (CMI)

The CMI was also developed by Palmer in 1968. It was thought as an index for the

evaluation of short-term moisture conditions for major crop productive regions. The idea

was to have an approximation to the weekly moisture budget of crops, based on weekly

values of temperature and precipitation. These are compared to long-term average values

and are modified by empirical relations to arrive at final CMI values. Limitations of the

CMI are related to an increase of its values with an increase in potential evapotranspiration.

Higher CMI values correspond to wetter conditions, which are opposite to higher potential

evapotranspiration values. This unnatural behavior is related to changes in temperature

which is due to the dependence of the abnormal evapotranspiration term on the magnitude

of potential evapotranspiration. On the other hand, CMI may provide misleading

information about long term conditions, as it is designed for short-term monitoring.

4.4 Surface water index (SWI)

The SWI was designed by Shafer and Dezman in 1982. It was developed as a

hydrological drought index, primarily designed for the monitoring of abnormalities in

surface water supply sources. It is calculated based on monthly non-exceedance probability

from available historical records of reservoir storage. Snowpack, streamflow, precipitation

and reservoir storage are the main four inputs to compute SWI. Since there is a seasonal

dependence, during summer months, streamflow replaces snowpack as a component within

the SWI equation. Limitations of SWI are related to differences in hidroclimatic variability

along different watersheds and time periods. The definition of surface water supply and the

factor weights are variable, which makes SWIs with different statistical properties.

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4.5 Vegetation condition index (VCI)

The VCI was designed by Kogan in 1995 for the monitoring of agricultural drought. It is one of

the principal drought indices based on satellite data, which provides a synoptic view of the land

surface and a spatial context for measuring drought impacts in a convenient spatio-temporal

approximation. The VCI is represented by the following equation:

)()(*100 minmaxmin NDVINDVINDVINDVIVCI

Where maxNDVI and minNDVI are the multi year maximum and minimum NDVI in a given

area and a period of the growing season. The VCI index is based on the concept of ecological

potential of an area given by geographical resources such as climate, soil variation, vegetation

type and quantity , and topography of the area. The method is usefull to separate the short term

weather signal in the NDVI data from the long term ecological signal. The VCI changes from

zero for extremely unfavorable conditions to 100 for optimal conditions.

4.6 Effective precipitation (EP)

It is the summed value of daily precipitation with a time-dependent reduction function which

can more precisely determine drought duration, monitor an ongoing drought, and define the

variety of ways in which drought characteristics can be described (Byun and Wilhite, 1999).

Three additional indices complement EP. The first index is each day’s mean of EP (MEP). This

index shows climatological characteristics of precipitation as a water resource for a station or

area. The second index is the deviation of EP (DEP) from MEP. The third index is the

standardized value of DEP (SEP). Using these three indices, consecutive days of negative SEP,

which can show the onset, the end date, and the duration of a water deficit period, are

categorized

4.7 Soil moisture deficit index (SMDI)

Narasimhan and Srinivasan (2005) developed soil moisture deficit index (SMDI) and

evapotranspiration deficit index (ETDI) based on weekly soil moisture and evapotranspiration

simulated by a calibrated hydrologic model, respectively. The drought indices were derived

from soil moisture deficit and evapotranspiration deficit and scaled between -4 and +4 for

spatial comparison of droughts, irrespective of climatic conditions. Recently soil moisture

index (SMI; Hunt et al., 2009) was developed based on the actual water content and known

field capacity and wilting point.

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4.8 Standardized runoff index (SRI)

This index is based on the concept of standardized precipitation index (SPI), discussed earlier.

Shukla and Wood (2008) derived standardized runoff index (SRI) which incorporates

hydrologic processes that determine the seasonal loss in streamflow due to the influence of

climate. As a result, on month to seasonal time scales SRI is a useful complement to SPI for

depicting hydrological aspects of droughts.

4.9 Normalized difference water index (NDWI)

The normalized difference water index (NDWI) is a more recent satellite-derived index

from the NIR and short wave infrared (SWIR) channels that reflect changes in both the

water content and spongy mesophyll in vegetation canopies. NDWI calculated from the

500-m SWIR band of MODIS has recently been used to detect and monitor the moisture

condition of vegetation canopies over large areas (Xiao et al., 2002; Jackson et al., 2004;

Maki et al., 2004; Chen et al., 2005; Delbart et al., 2005). Because NDWI is influenced by

both desiccation and wilting of vegetative canopy, it may be a more sensitive indicator than

normalized difference vegetation index (NDVI) for drought monitoring.

4.10 Drought Monitor (DM)

NOAA, USDA and national drought mitigation derived a weekly drought monitor (DM)

product that incorporates climatic data and professional input from all levels (Svoboda, 2000).

It blends numeric measures of drought and experts' best judgment into a single map every

week. The key parameters are objectively scaled to five DM drought categories. The

classification scheme includes categories D0 (abnormally dry area) to D4 (exceptional drought

event, likened to a drought of record) and labels indicating the sectors being impacted by

droughts (A for agricultural impacts, W for hydrological impacts, and F to indicate the high risk

of wildfires). The Monitor is produced by a rotating group of authors from the U.S. Department

of Agriculture, the National Oceanic and Atmospheric Administration, and the National

Drought Mitigation Center. It incorporates review from a group of 250 climatologists,

extension agents, and others across the nation. A limitation of DM lies in its attempt to show

droughts at several temporal scales (from short term drought to long-term drought) on one map

product (Heim, 2002).

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5. Remote sensing applications for the monitoring of

droughts

Remote sensing has become the most efficient way to systematically retrieve

information from the land surface, atmosphere and water resources. The collection of data from

huge regions and long periods of time represents a great advantage for the monitoring of natural

resources and the study of dynamic processes. There are a number of advantages related to the

properties of remote sensing. For example, since the collection of rainfall data by means of

meteorological stations is not very well distributed over remote regions, the use of remote

sensing is one of the most effective ways to approximate the estimations of rainfall, directly

observed by meteorological infrared sensors (e.g GOES) or by the use of vegetation indices

related to the vegetation water content, or stress, which is useful as a surrogate of the effective

precipitation absorbed by the different vegetation types. On the other hand, there is an

increasing research towards the study of soil moisture content by means of active sensors, as

the Synthetic Aperture Radar (SAR) which has important advantages related to their

penetration capability in the first layers of the ground, insensitive to cloud cover and night time

observation. The monitoring of surface water bodies is also an important feature of remote

sensing for hydrological monitoring.

5.1 Optical sensors & Synthetic Aperture Radar –SAR- systems.

There are multiple studies that refers to the use of optical sensors and SAR systems or the

use of coupled methodologies for estimating the land properties related to soil moisture

( sm ) and vegetation water content. Introductory material on spectral measurements for sm

estimation is summarized in Table 2 (Moran et al. 2004).

Table 2: Summary of spectral measurements for soil moisture estimations

Physics Advantages Limitations

Visible, NIR, SWIR reflectance Fine spatial resolution Weak relation to ms

Spectral information in visible, NIR,

and Broad coverage Minimal surface penetration (~1 mm)

SWIR wavelengths is related to ms as a Multiple satellite sensors

available Limited ability to penetrate clouds and

function of spectral absorption features;

for

Hyperspectral sensors show

promise vegetation; attenuated by earth’s

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bare soils, increase in ms generally leads

to

atmosphere

a decrease in soil reflectance

Infrequent repeat coverage

Strongly perturbed by vegetation

biomass.

TIR emittance

Soil moisture directly influences soil Fine spatial resolution Minimal surface penetration (~1 mm)

temperatures by increasing both specific Broad coverage Limited ability to penetrate clouds and

heat and thermal conductivity, thus

thermal

Multiple satellite sensors

available vegetation; attenuated by earth’s

inertia of soils; for bare soil, variations

in

Strong relation to ms TR/VI

approaches atmosphere

surface TR primarily due to varying ms show promise Infrequent repeat coverage

Strongly perturbed by vegetation

biomass.

Microwave TB

Intensity of microwave emission (at σ0

= Broad coverage

Perturbed primarily by surface

roughness

1–30 cm) from soil is related to ms

because Satellite sensor recently available and vegetation biomass

of large differences in dielectric

constant of Strong relation to ms Coarse spatial resolution (~30 km).

dry soil (~3.5) and water (~80); for bare Surface penetration up to ~5 cm

soils, increase in ms generally leads

to Insensitive to clouds and earth’s

atmosphere

increase in TB

Radar σ0

As with passive microwave sensing, Fine spatial resolution Infrequent repeat coverage

magnitude of σ0 is related to ms

through Multiple satellite sensors

available

Perturbed primarily by surface

roughness

contrast of dielectric constants of bare

soil Strong relation to ms and vegetation biomass.

and water; for bare soils, increase in ms Surface penetration up to ~5 cm

generally leads to increase in σ0

Insensitive to clouds and earth’s

atmosphere

Despite the multitude of optical sensors currently in orbit (Kustas et al., 2003), a limited body

of literature exists on the use of visible, near-infrared (NIR), shortwave infrared (SWIR) wide-

band and (or) hyperspectral sensors for soil moisture assessment (Muller and Décamps, 2000).

This is due partly to the fact that optical remote sensing measures the reflectance or emittance

from only the top millimetre(s) of the surface. Furthermore, unlike the longer microwave

wavelengths, the optical signal has limited ability to penetrate clouds and vegetation canopy,

and is highly attenuated by the earth’s atmosphere. In addition to moisture content, soil

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reflectance measurements are also strongly affected by the soil composition, physical structure,

and observation conditions, resulting in poor predictors of soil moisture on combined soil- type

samples (e.g., Musick and Pelletier, 1988). Because of these controls, efforts to directly relate

soil reflectance to moisture have achieved success only when models are fit for specific soil

types in the absence of vegetation cover (e.g., Muller and Décamps, 2000).

With respect to hyperspectral sensors in the visible, NIR, and SWIR spectrum, analysis

performed by Liu et al. (2002) showed that while at low moisture levels, increasing moisture

content led to a decrease in soil reflectance, the opposite was true at higher moisture levels.

That is, increasing moisture content led to an increase in soil reflectance, determined albeit by

much poorer regression results. Ben-Dor et al. (2002) performed a field study of mapping

multiple soil properties (including soil moisture) using DAIS-7915 hyperspectral scanner data.

The hyperspectral premise is that narrow-band spectral information in the visible, NIR, and

SWIR wavelengths allows material identification as a function of their spectral absorption

features. Their results were mixed. In all, the use of optical reflectance as a direct measure of

watershed-level soil moisture is greatly constrained, though reflectance information has an

important indirect role in soil moisture estimation through data fusion and assimilation in soil

vegetation atmosphere transfer (SVAT) models. Far better success in direct measurement of

surface soil moisture is achievable when thermal and microwave measurements are employed.

The estimation of ms using remotely sensed thermal wavebands is primarily related to the use

of radiative temperature (TR) measurements, either singularly or in combination with

vegetation indexes derived from visible and NIR wavebands. Variations in TR of bare soils

have been found to be highly correlated with variations in ms (Friedl and Davis, 1994;

Schmugge, 1978). Recent studies have explored the added value of view angle variation on TR

measurements to estimate ms. Chehbouni et al. (2001) found that for a semiarid grassland site

with static vegetation conditions, multidirectional TR data from field infrared thermometers

could 808 be used to estimate ms. In a study of coupled SVAT-infrared thermal radiative

transfer models, François (2002) could not determine a universal relationship between surface

wetness and soil temperature, even when using differences between directional TR, because of

the influence of rapidly varying factors (wind speed, soil texture, incoming solar radiation,

vegetation condition, leaf area index). However, he did report that directional TR

measurements dramatically improved soil moisture detection. Although the dual view design of

the along track scanning radiometer (ATSR) aboard the European Remote Sensing (ERS)

satellites provides multidirectional TR measurements, few studies have been published using

such data to estimate surface water fluxes over heterogeneous surfaces (Chehbouni et al.,

2001). Advanced applications of the dual use of thermal imagery and spectral vegetation

indices employ thermodynamic principles embodied in surface energy balance models to

estimate surface evapotranspiration rates, and thus improve soil moisture estimation (Kustas et

al., 2003). Such approaches have the potential to estimate mp by using the transpiration of

vegetation as a surrogate measure ofmp. Many such approaches are based on the consistent

negative correlation between TR and spectral vegetation indices, such as the normalized

difference vegetation index (NDVI). Numerous labels have been given to variations of this

technique including the triangle method (Carlson et al., 1995), temperature-vegetation

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contextual approach (TVX) (Prihodko and Goward, 1997; Czajkowski et al., 2000), surface

temperature-vegetation index (Ts/NDVI) space (Lambin and Ehrlich, 1996), temperature-

vegetation dryness index (TVDI) (Sandholt et al., 2002), moisture index (Dupigny-Giroux and

Lewis, 1999), and the VI/Trad relation (Kustas et al., 2003). Gilles et al. (1997) used the

triangle method on airborne multispectral radiometer data and achieved standard error estimates

of 0.16 for ms relative to field measurements for sites in Kansas and Arizona. A simpler

approach was employed by Bosworth et al. (1998) in which linear and equally spaced isopleths

of soil moisture were computed under the assumption that ms varies within the triangle from

completely dry to completely saturated. Sandholt et al. (2002) defined TVDI, where corners

and moisture isolines were completely image- derived under the assumption that an entire range

of surface moisture contents and vegetation cover was included in the scene. They reported

regression coefficients of 0.70 when comparing TVDI results for a study site in Senegal to

those from a distributed hydrological model. Goward et al. (2002) found in a simulation study

that while the slope of the TR/NDVI line was only weakly correlated to ms, the relation

endpoints (closed canopy temperature and bare ground temperature) along with incident

radiation measurements could predict ms with a residual standard error of 0.04. At continental

scales, the slope of the TR/NDVI relation was strongly correlated with considerable scatter

(regression coefficient of 0.83, standard error of 0.06) to crop-moisture-index values, and thus

by implication, to surface moisture conditions (Nemani et al., 1993).

Approaches based on either the directional TR or the complimentary TR-vegetation index are

powerful but have limitations in addition to those common to all optical techniques (shallow

soil penetration, cloud contamination, infrequent coverage at spatial resolutions suitable for

watershed management). They are often empirical and thus vary across time and land cover

types (Smith and Choudhury, 1991; Czajkowski et al., 2000) and are a function of local

meteorological conditions such as wind speed, air temperature, and humidity (Nemani et al.,

1993), and local relief (Gillies and Carlson, 1995).

5.2 Operational Systems

5.2.1 National Integrated Drought Information System (NDIS)

The National Oceanic and Atmospheric Administration (NOAA) is taking the lead in

implementing the National Integrated Drought Information System (NIDIS). The NIDIS

Program Office was established at NOAA's Earth System Research Laboratory in Boulder,

Colorado, in 2007. Since its creation, the NDIS is focused on providing tools to access and

interact with drought and climate related data, including maps and graphing capabilities, to help

understand drought and how it changes over time. For example, the area drought information

tool provides a look at drought at the state level. Find local drought information, state plans for

drought, and contacts for more information, within each state (figure 1).

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Figure 1: Drought conditions from California for the 18th of December 2012.

Each week the author revises the previous map based on rain, snow and other

events, observers' reports of how drought is affecting crops, wildlife and other indicators. Authors

balance conflicting data and reports to come up with a new map every Wednesday afternoon. It is

released the following Thursday morning.

5.2.2 Centro de Relevamiento y Evaluación de Recursos Agrícolas y

Naturales (CREAN)

CREAN is an applied research unit belonging to the Faculty of Agricultural Sciences at the National

University of Córdoba ,Argentina. Is composed of researchers, professionals and technicians of the

National Scientific and Technical Research (CONICET) and by faculty and staff of the National

University of Córdoba.

The work that the CREAN is developing in the area of drought monitoring can keep informed the

public sector and producers about drought conditions in the Central part of the country. This task

is performed in conjunction with professionals from the Ministry of Environment and Sustainable

Development and the National Weather Service, with the National Weather Service data.

One of the indices used in assessing drought is the SPI. It has proven to be well suited to

determine the occurrence and monitoring of droughts in Argentina pampas. The standardized

precipitation index (PSI) was developed by McKee (1993) and classified into different categories

wet and dry periods (figure 2).

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Figure 2: SPI index as calculated from CREAN for Argentina (November 2012)

Another useful index that CREAN runs operationally is the PDI (Palmer, 1965). It was

developed as an index "to measure the moisture deficiency." It is based on the concept of

demand-supply of water, taking into account the gap between the actual rainfall and

precipitation required to maintain climatic conditions or normal moisture. The calculation

requires as input, potential evapotranspiration, monthly rainfall and the useful water content of

the soil (Figure 3).

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Figure 3: Palmer index for Argentina produced for November 2012 by CREAN.

6. Available information and state of applications in

Argentina.

The availability of diverse products from different organizations at the national level is

deeply marked by a superposition of activities and incomunication. Nevertheless, there are

several products that meet specific needs for specific regions. A summary of the available

drought products in Argentina are presented in table 3.

Table 3: Summary of the current applications on drought estimations in Argentina *

Institution Drought Type Variable Methodology Time scale Spatial extent

CREAN-

UNC1 Meteorological Precipitation (P)

Standardized

precipitation

index (SPI)

Monthly Pampean Region

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CREAN-UNC Meteorological

Precipitation and

Potential

Evapotranspiration

(PET)

Palmer drought

severity index

(PDSI)

Monthly Pampean Region

SMN2 Meteorological Precipitation Days without P Monthly Country level

SMN Meteorological P & PET P-PET Every 10

days Country level

SMN Agricultural Soil Moisture Water balance Every 10

days Country level

MAGyP3 Agricultural Soil Moisture Qualified

observations Weekly Pampean Region

MAGyP3 Agricultural Crop conditions Qualified

observations Weekly

Pampean Region

countie's

INTA4 Agricultural Crop conditions Qualified

observations Weekly

Wheat

Subregions of the

Pampean Region

INA5 Hydrological Depth of rivers Observations and

registered data Weekly La Plata basin

1Centro de Relevamiento y Evaluación de Recursos Agrícolas y Naturales (CREAN) de la Facultad de Ciencias

Agropecuarias de la Universidad Nacional de Córdoba (Argentina). 2Servicio Meteorológico Nacional. 3Ministerio de Agricultura, Ganadería y Pesca. Oficina de Estimaciones Agrícolas 4Instituto Nacional de Tecnología Agropecuaria. Instituto de Clima y Agua, Estaciones Experimentales y Agencias de

Extensión Rural de la región pampeana. 5Instituto Nacional del Agua. Oficina de Alerta Hidrológica.

*(Data Provided by Dr. Raúl Díaz from Instituto de Clima y Agua - (CIRN-INTA) Hurlingham, Buenos Aires, Argentina.

7. Conclusions

It is reasonable to think, that there are many work to do for the monitoring of droughts, especially

in countries like Argentina, one of the main crop producers in the world. As it is exposed in this

work, many applications are developed with different instruments and methodologies. For each

case, there are advantages and disadvantages, all related to the area of interest, or in

consideration of the field of work. In any case, the most effective way of predicting and setting the

severity of a drought will depend on the amount of knowledge of a certain region, and the quality

of the human and technological resources.

In Argentina, it seems to be a great task all the work to be done in relation to agricultural

droughts, since there is such a pressure to increase the total production of crops, and in

consideration of the economic loss of each drought implies huge impacts to the global economy of

20

the country. The use of SAR systems seems to be the future for the estimation of the properties of

soil moisture, a principal component of drought analysis. Nevertheless, the literature shows great

difficulties to have precise estimations from SAR data itself, which it is a very big deal to consider

for operational systems. In any case, the uses of coupled systems are the most recommendable

applications.

On the other hand, optical sensors through the use of spectral indices, are actually the most

common field of research, through the use of spectral features related to the plant water

absorption and photosynthetic properties. These are the most commonly fields of work, since

there is a great facility through the use of free optical low moderate resolution sensors (eg.

MODIS). Despite de limitations that optical sensors has in relation to the discrimination of deep

soil layers, it is very important to consider its use on the monitoring of the vegetation parameters

related to water stress. A continuum monitoring of the different stages of vegetation, is

particularly important for drought monitoring, especially for agricultural purposes. As the new era

of earth observation satellites will be on orbit, the non/stop monitoring will permit to extend the

record of data, and in this way, develop long term data useful for vegetation monitoring. New

indices must be created and validated in situ or by the use of simulations. Much work must be

done to make the current available literature into operational products.

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