Drought Projection and Modeling Causal Feedbacks with Earth Observations in East Africa
Abstract
Understanding time lags between climatic and surface properties that impact water uptake and loss by vegetation is essential for early drought detection in dryland ecosystems. The Normalized Difference Vegetation Index (NDVI) is a common metric used to identify vegetation condition. Here we employ empirical dynamic modeling (EDM) to forecast NDVI change in East Africa at a dekadal (10-day) time scale using satellite-derived environmental forcing variables. The datasets incorporated into the model are routinely used by the Famine Early Warning Systems Network (FEWS NET) to quantify food insecurity and drought risk. Inputs include: USGS EROS Moderate Resolution Imaging Spectroradiometer (eMODIS) NDVI, Climate Hazards Center InfraRed Precipitation with Station Data (CHIRPS), MODIS Land Surface Temperature (LST), Hobbins Reference Evapotranspiration (Hobbins ETp), soil moisture from the FEWSNET Land Data Assimilation System (FLDAS), and the new Climate Hazards Center Infrared Temperature with Stations data set (CHIRTSmax). The model relies on state space reconstruction with lagged coordinate embedding of multiple time series observations to recover the dynamic environmental system. Additionally, we demonstrate how to apply convergent cross mapping based on Takens' Theorem (1981) to detect directional causal interactions and time delays between driving (e.g. LST, rainfall) and response variables (NDVI). This study focuses on NDVI projection in the Oromia region of southeast Ethiopia, an area dominated by agro-pastoralism facing critical livelihood and food security risk due to frequent drought and high population density. Preliminary results indicate CHIRPS is a strong causal predictor of NDVI with about one dekad lead time while LST and ETp have longer delays of about five dekads. Understanding historical seasonal NDVI patterns as well as producing rapid and reliable projections are essential for monitoring crop failure and poor pasture conditions. Additionally, short term forecasts can assist relief organizations in advising drought management, declaring food security classifications and providing early emergency response to famine.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMGC53B..06G
- Keywords:
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- 1616 Climate variability;
- GLOBAL CHANGE;
- 1632 Land cover change;
- GLOBAL CHANGE;
- 1640 Remote sensing;
- GLOBAL CHANGE;
- 1655 Water cycles;
- GLOBAL CHANGE