DROUGHT MONITORING FROM SPACE USING EMPIRICAL INDICES AND PHYSICAL INDICATORS Jürgen V. VOGT, Alain A. VIAU, Isabelle BEAUDIN, Stefan NIEMEYER, Francesca SOMMA Space Applications Institute Joint Research Centre of the European Commission, TP 441, 21020 Ispra (Va), Italy email:
[email protected]
ABSTRACT - This paper presents results from a project for the development of drought monitoring tools at regional to sub-continental scales. The methodology is based on the analysis of multi-year sets of daily AVHRR data, daily meteorological data and various surface characteristics held as coverages in a GIS (i.e. digital elevation model, soil types, and land cover types). Results will be presented for the use of meteorological indices, satellite based indices and process based indicators such as the evaporative fraction. Examples are shown for the regions of Andalusia in southern Spain and Sicily in southern Italy.
1 - INTRODUCTION Drought is a major natural hazard affecting large areas and millions of people every year. The recent occurrence of severe and prolonged drought conditions in various regions of Europe, such as Spain (1992-1995) and Italy (1988-1989) has emphasised the need for adequate monitoring tools as well as further research on the causes and the environmental and socio-economic impacts of drought events. Based on current climate change scenarios, the frequency and impact of drought conditions are, in addition, likely to increase in many parts of the world, including parts of Europe (Watson et al. 1997). This situation asks for improved preparedness, including the set-up of adequate monitoring and mitigation strategies. The main objective of this work is the development of spatially and temporally consistent indices to be used for the detection, assessment and monitoring of drought conditions at different scales. These indices should give information on the spatial extent, the intensity, and the duration of a drought period. Together with additional data they should further allow for an improved evaluation of the impact of a drought on natural ecosystems and agriculture. Examples of preliminary results will be given for two study areas in the Mediterranean: Andalusia in southern Spain and Sicily in southern Italy.
Proceedings International Symposium on ‘Satellite-Based Observation: A Tool for the Study of the Mediterranean Basin’ 23 – 27 November 1998, Tunis, Tunisia.
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2 - METHODOLOGY 2.1 - Data Long-term daily minimum and maximum temperature and precipitation measurements were available at 60 and 100 meteorological stations in Andalusia and Sicily, respectively. These data were compared to data derived from satellite images from the afternoon overpasses of NOAA-11 from 1989 to 1994. In addition, digital terrain models of 300 m grid-cell resolution and digital landuse/landcover maps at a nominal scale of 1:100,000 were analysed for the same areas. The image data have been acquired by the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA series of satellites. The data have been preprocessed by the SPACE software developed at the Space Applications Institute (SAI), Ispra. This program package allows for the automatic processing of AVHRR scenes into a daily AVHRR mosaic for the European Community with a grid cell size of 1.1×1.1 km. SPACE performs tasks such as data calibration and atmospheric and geometric correction of images (Vowles, 1992; Millot, 1995). The geometric registration error of the images is less than 1 km. Image data are stored as reflectances for the visible and near-infrared channels and as brightness temperatures for the thermal infrared channels. Vogt (1995) has discussed the main characteristics of the resulting mosaics. From the database of European AVHRR mosaics, two windows covering the regions of Andalusia and Sicily, respectively, have been extracted for the years 1989 to 1994. From these images the normalised difference vegetation index (NDVI) and the surface skin temperature (Ts) have been calculated. Ts has been retrieved using the split window algorithm of Coll et al. (1994). Finally, the data have been aggregated to 10 day (decadal) maximum value composites for the calculation of satellite based indices. 2.2 - Drought Indicators For the purpose of our work, drought indicators are classified as meteorological, satellite based and process based indicators. Meteorological indicators are based on meteorological parameters as recorded at meteorological stations. An example is the Standardised Precipitation Index (SPI) (McKee et al. 1993, 1995). The SPI is a statistical indicator evaluating the lack or surplus of precipitation during a given period of time as a function of the long-term “normal” precipitation to be expected during that period. It is calculated using a continuos, long-term (more than 30 years) series of historic monthly precipitation records. A moving window is selected (1, 3, 6, 12, 24 months, depending on the purpose of the analysis) and a new series generated. Because rainfall is not normally distributed for aggregation periods of less than 12 months a gamma distribution is fitted to the frequency distribution. The SPI for a given rainfall amount is then given by the precipitation deviation from the mean of an equivalent normally distributed probability distribution function with a zero mean and a standard deviation of one (McKee et al. 1993, Hayes 1996). Results are given in units of standard deviation. This
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is an advantage since the SPI is normalised such that wetter and drier climates can be represented in the same way. In addition, wet periods can be monitored as well. Satellite based indicators are calculated from satellite derived surface parameters. Examples are various vegetation indices such as the Normalised Difference Vegetation Index (NDVI), the Global Environmental Monitoring Index (GEMI, Pinty and Verstraete 1992), the Vegetation Condition Index (VCI, Kogan 1997) and the Temperature Condition Index (TCI, Kogan 1997). The VCI and the TCI are defined according to equations 1 and 2, where NDVImin (Tsmin) and NDVImax (Tsmax) refer to the absolute minimum and maximum NDVI (Ts) measured for a given decade (or month) over a multi-year series of image data. NDVI (T) refers to the current year NDVI (T) for the same decade.
VCI =
NDVI − NDVI min NDVI max − NDVI min
[--]
(1)
Tmax − T Tmax − Tmin
[--]
(2)
TCI =
Vegetation indices can be efficient indicators of water stress in relatively homogeneous terrain. However, in more heterogeneous regions their interpretation becomes more difficult (Kogan 1990). The VCI is an indicator of the status of the vegetation cover as a function of the NDVI minima and maxima encountered for a given ecosystem over many years. It normalises the NDVI (or any other vegetation index) and allows for a comparison of different ecosystems. It is an attempt to separate the short-term climatic signal from the long-term ecological signal and in this sense it is a better indicator of water stress conditions than the NDVI (Kogan & Sullivan 1993). The significance of the VCI is strongly related to the relation between the vegetation index and the vitality of the vegetation cover under investigation. In addition, it depends on the number and quality of images available for the calculation of the absolute minimum and maximum. The Temperature Condition Index (TCI) is an equivalent indicator based on the surface skin temperature derived from NOAA AVHRR data. Both, the VCI and the TCI, are dimensionless and vary between the values of zero and one. Zero indicates the worst condition ever encountered over the period of available images, one indicates the best condition encountered during the same period of time. If the period covered includes dry and wet years and under the assumption that the vegetation condition is mainly related to the water availability, these indicators have a high potential for monitoring water stress. A process-based indicator is the result of the modelling of the energy and matter transfer between the atmosphere and the surface. An example is the evaporative fraction, EF. EF is defined as the part of the available energy used for evapotranspiration, i.e., the latent heat flux. This quantity is regarded as indicative of the moisture status of the surface cover.
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The estimation of the EF is based on the general energy balance equation as given in equation 3, where Rn refers to the net radiation flux density, λE to the latent heat flux density, H to the sensible heat flux density, and G to the ground heat flux density. Rn = λE + H + G
[W/m2]
(3)
The latent heat flux density may be derived from equation 3 by calculating the sensible heat flux density (H) from the difference between the daily maximum air temperature (Tx) and the satellite derived surface skin temperature (Ts) multiplied by a surface roughness term (ra) (eq. 4). λE = ( Rn − G ) − c p * ρ air * 1 * (Ts − Tx) ra
[W/m2]
(4)
Rn can be obtained from direct measurements or derived from standard meteorological measurements and G is estimated as a function of the percentage vegetation cover and of Rn. In this equation Ts refers to the surface skin temperature around solar noon, cp is the specific heat of air, and ρair corresponds to the density of air. Finally, EF is defined as follows: EF =
λE Rn − G
[--]
(5)
As long as moisture is available, energy will be used for its evapotranspiration and EF will be close to one (no water stress). With little or no moisture left, all available energy will be directed into warming up the surface and the ambient air and EF will approach zero (serious water stress). 3 - RESULTS 3.1 - Meteorological and Satellite Based Indices NDVI, VCI and TCI have been calculated for Andalusia on a decade basis for the years 1989-1994. The SPI was calculated for the 60 meteorological stations of the Andalusia network. The NDVI, VCI and TCI were then extracted from the images as the mean value for 3×3 pixel windows, centred at each station and compared to the SPI. For the purpose of this paper three stations have been selected to present the results on basis of their geographical characteristics and the related precipitation regime. In figure 1 the results for the three stations in Andalusia are presented. Aldea de Cuenca (4258) with a mean rainfall of 564 mm, Galarosa (4515) with a mean rainfall of 1020 mm, and Lanjaron (6258) with a mean rainfall of 503 mm. The selected period includes the relatively wet winter of 1989-1990 and the first years of a 5 years drought starting in winter 1991-1992. The monthly precipitation amounts for these three stations and during the same period are presented in figure 2.
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Aldea de Cuenca (4258) SPI 3
NDVI, VCI, TCI
SPI
1
NDVI VCI
2
TCI
1 0.5
0 -1 -2
0
-3 Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep89 90 90 90 91 91 91 92 92 92 93 93 93
A
Galaroza (4515) NDVI, VCI, TCI
SPI 3
SPI
1.0
NDVI VCI
2
TCI
1 0.5
0 -1 -2
0.0
-3
B
Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep89 90 90 90 91 91 91 92 92 92 93 93 93
Lanjaron (6258) SPI 3
SPI
2
VCI
NDVI
NDVI, VCI, TCI 1.0
TCI
1 0.5
0 -1 -2
0.0
-3
C
Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep89 90 90 90 91 91 91 92 92 92 93 93 93
Figure 1: 3-monthly running mean of the decadal NDVI, VCI, and TCI and the 3-months SPI for the stations of Aldea de Cuenca (A), Galaroza (B) and Lanjaron (C) from Sept. 1989 to Sept. 1993
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MONTHLY PRECIPITATION 500 400
4258 4515 6258
300 200 100 0 Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep- Jan- Mai- Sep89 90 90 90 91 91 91 92 92 92 93 93 93
Figure 2: Monthly precipitation for the stations of Aldea de Cuenca (4258), Galaroza (4515) and Lanjaron (6258) from Sept. 1989 to Sept. 1993 The landuse in the 3×3 km window centred at the Aldea de Cuenca station is mostly dry-farming (71%) and figure 1a demonstrates that the curve of the NDVI follows the vegetation dynamics throughout the growing season. Values range from 0.15 for bare soil to 0.50 for the period of maximum vegetation cover. However, the index does not show a significant relation to the increasingly unfavourable growing conditions in the years 1992 and 1993 as shown by the SPI and the rainfall distribution in figure 2. The VCI, to the contrary, shows significant changes in the vegetation conditions over the four years period, which are strongly related to the SPI and the total rainfall over the growing period. Figure 1b shows the situation for the station of Galaroza. The main landuse types in the 3×3 km window centred at this station are forest (63%), tree crops (13%) and dry farming (20%). This gives rise to a smoother NDVI curve, although some clear reduction of the NDVI is seen during the spring time of 1992 and 1993. However, the VCI and the TCI reveal a much clearer and significant relationship with the drought conditions as shown by the SPI. The apparent contradiction between the SPI and the VCI in the summer months of 1990, when the SPI falls to a value of minus three while the VCI remains at a normal level, is indicative of an advantage of the VCI. Since the mean rainfall over the summer months is very low in this area (less than 20 mm), a standard deviation (SPI) of minus three is actually not significant in terms of drought conditions. The SPI needs a careful interpretation in this sense. The VCI, to the contrary, reflects the effects of the rainfall on the vegetation cover and, therefore, indicates no significant departure from the expected conditions for this period of the year. Figure 1c, finally, presents the case of the station of Lanjaron (6258) with a mixed landuse of irrigated agriculture (25%), grasslands (25%), tree crops (19%) and forest (10%). Also here the VCI and TCI seem to be clearly related to the SPI, although the relation to the TCI is less pronounced.
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In conclusion, our results show that for the case of Andalusia the TCI and VCI well reproduce the vegetation changes according to water stress and in this sense support the findings of Kogan (1993, 1997) for the United States, Argentina and southern Africa. Figure 3 presents an image of the VCI for the seventh decade of 1992. Values range from very bad conditions in red to favourable conditions in dark green. Clouds appear in white. The image demonstrates the degree of spatial resolution to be obtained with this methodology.
Figure 3: VCI for Andalusia. Decade 7 of 1992.
3.2 - Evaporative Fraction The EF has been calculated for Sicily using daily meteorological data from surface stations, daily remote sensing data from NOAA AVHRR and landuse/landcover data. Preliminary results show a realistic evolution of the EF during the summer season. Sufficiently detailed spatial resolution is obtained, even if the original resolution of the AVHRR sensor of 1.1×1.1 km has been smoothed by a 5×5 pixel moving-window filter (see figure 4). The approach allows for the combination of heterogeneous data with different spatial features, such as point, raster and vector data, different temporal resolution, and different data qualities like measured data and thematic data. Their integrated analysis within a Geographical Information System results in the derivation of a physically meaningful parameter to describe the daily development of the moisture status of the land surface on the regional scale.
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Figure 4: Evaporative fraction (EF) as derived for Sicily on two dates in 1991. The maps presented in figure 4 demonstrate that in June 1991 the mountainous regions in the Northeast remain relatively moist (high values of EF), while the values of EF approach zero in the southern lowlands, signifying severe water stress. In August, large parts of the island are dry with almost no evapotranspiration occurring and consequently very low values of EF. This situation must be put into relation to the general climatology of the region, in order to determine whether the mapped low EF’s are related to actual drought events or if they correspond to the normal annual evolution of the surface moisture status in the given Mediterranean climate. Since equivalent information is not available from other sources, this can only be achieved through the analysis of time series of images covering several years. These years should, if possible, represent the natural variability of the climate in the area analysed. More detailed results of this approach are given in the paper of Niemeyer and Vogt (1998), this volume. 4 - CONCLUSIONS The combination of meteorological data, satellite data and various environmental layers in a GIS gives promising results for the mapping and monitoring of drought conditions in the Mediterranean basin. Both approaches, using either a combination of meteorological and satellite based indices or a process based model, have proven their capability to detect and monitor water stress conditions of the vegetation canopy. The results will have to be further validated and tested at different scales. The major advantage of the methodology is seen in the high spatial information content of the satellite data, which allows for an accurate mapping of the spatial extent of water stress conditions. A combination of this information with landcover information from databases such as CORINE will lead to a more detailed assessment of the possible impact of drought events on the various landcover types.
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