Context of drought in Chile
- The last years Chile has been affected for different level of drought.
- In early 2015 in Chile unusual drought conditions occurred, the central-south part of Chile was affected for drought a situation almost unprecedented.
- On March 2015 in Chile were 194 administrative units (county) declarated under drought conditions (56% of whole country)
- According with the fifth report of the IPCC
- there is an increase in global temperatures.
- a decrease of precipitation is expected in central-south Chile.
- a increase of droughts frequency
- Main problems in Chile:
- low density of weather stations
- there is a lack of knowledge of the impact of rainfall deficits in agriculture
Hypothesis
It is possible to develop a merging remote sensing products of precipitation and evapotranspiration locally using in-situ data and so improve the spatial and temporal characterization of agricultural drought in Chile
Objectives
Develop a merging remote sensing product of precipitation and evapotranspiration with monthly frequency from 2000 to 2015 for the watersheds of rivers Limari, Itata y Bueno.
Derive drought indices based on vegetation, land surface temperature, precipitation and evapotranspiration and assess the combined effect.
Perform the characterization of agricultural drought
Remote sensing data
- MODIS (Vegetation)
-
MCD12Q1 (IGBP) : Landcover Type, 1km, yearly, 2001 - 2013.
-
MOD13A3 : NDVI, 1km, monthly, 2000 - 2015.
-
MOD11A2 : LST, 1km, 8 days, 2000 - 2015
-
MOD16 : \(ET_0\), 1km, monthly, 2000 - 2015
- Precipitation
-
TRMM 3B43 v7: Precipitation, 0.25° , monthly, 1998 - 2015
-
Persiann-CDR : Precipitation, 0.25° , daily, 1983 - 2015
-
CHIRPS 2.0 : Precipitation, 0.05° , monthly, 1981 - 2015
Meteorological stations
- From the data base of Center for Science for Climate and Resilience (CR2) from the University of Chile (http://www.cr2.cl).
- Precipitation : 781 station with daily and monthly data
- Extreme temperatures : 281 stations
Merging
To improve the accuracy of the precipitation and evapotranspiration (ET) remote sensing products merging techniques will be used.
- Regression-Kriging (RK) : Spatial prediction of the in-situ measurements using the remote sensing product as predictor and additional information.
- Bayesian combination.
Derivation of drought indices
2000 - 2015
-
Standardized vegetation index (SVI) : From NDVI (MOD13A3) the standardized anomaly was calculated as:
\[SVI = \frac{NDVI -\overline{NDVI}}{NDVI_\sigma}\]
-
Vegetation condition index (VCI) : The time series of NDVI images was smoothed using a locally-weighted polynomial regression (LOWESS), then:
\[VCI = \frac{NDVI - NDVI_{min}}{NDVI_{max}-NDVI_{min}}\]
Derivation of drought indices
2000 - 2015
-
Temperature condition index : The Land Surface Temperature (LST) images of 8 days was converted to monthly and then:
\[TCI = \frac{LST_{max} -LST}{LST_{max}-LST_{min}}\]
-
Vegetation health index : Combines the response of vegetation and land surface temperature
\[VHI = 0.5\cdot VCI + 0.5 \cdot TCI\]
Derivation of drought indices
1983/1998 - 2015
-
Precipitation Condition Index (PCI): this index was/will calculated for monthly accumulated precipitation of 1, 3 and 6 months using the remote sensing precipitation products.
\[PCI = \frac{PP_{rs}-PP_{rs}_{min}}{PP_{rs}_{max}-PP_{rs}_{min}}\]
-
Scaled Drought Condition Index (SDCI) : this index was calculated using the three time scales of PCI (1, 3 and 6)
\[SDCI = 0.5\cdot PCI + 0.25 \cdot TCI + 0.25 \cdot VCI\]
Derivation of drought indices
1983/1998 - 2015
Standardized Precipitation Index (\(SPI_{rs}\)) : with the resulting precipitation from the mergin procedure it would be possible to derive an a spatial SPI.
Standardized Precipitation Evapotrasnpiration Index (\(SPEI_{rs}\)) : with the resulting precipitation and evapotranspiration from the mergin procedure it would be possible to derive an a spatial SPEI.
Data analysis
- Exploratory data analsis od the derived indices
- Different statistics to compare merging products.
- Point to point correlation between vegetation/temperture indices and precipitation.
- Maximum Covariance Analysis (MCA) between pair of indices vegetation/temperature, vegetation/precipitation and precipitation/temperature
Persiann-CDR/TRMM 3B43 v7 vs in-situ
Correlation between indices in the Itata river basin
Comparison between indices in Chile on March 2015
PCI-1, PCI-3 and PCI-6
Comparison between indices in Chile on March 2015
SVI, TCI, VCI and VHI
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