NHESSD Natural Hazards and Earth System Sciences Discussions NHESSD Nat. Hazards Earth Syst. Sci. Discuss. 2195-9269 Göttingen, Germany 10.5194/nhess-2018-133 Early warning and drought risk assessment for the Bolivian Altiplano agriculture using high resolution satellite imagery data Canedo Rosso Claudia https://orcid.org/0000-0003-3766-249X 1 2 Hochrainer-Stigler Stefan 3 Pflug Georg 3 4 Condori Bruno 5 Berndtsson Ronny 1 6 Division of Water Re sources Engineering, Lund University, P.O. Box 118, SE-22100 Lund, Sweden Institute of Hydraulics and Hydrology, Mayor University of San Andres, Cotacota 30, La Paz, Bolivia International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria Institute of Statistics and Operations Research, Faculty of Economics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Wien, Austria National Institute of Agriculture and Forestry Innovation (INIAF), Batallón Colorados 24, La Paz, Bolivia Center for Middle Eastern Studies, Lund University, P.O. Box 201, SE-22100 Lund, Sweden 17 05 2018 2018 1 23 Copyright: © 2018 Claudia Canedo Rosso et al. 2018 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://nhess.copernicus.org/preprints/nhess-2018-133/ The full text article is available as a PDF file from https://nhess.copernicus.org/preprints/nhess-2018-133/nhess-2018-133.pdf

Implementation of agriculturally related early warning systems is fundamental for the management of droughts. Additionally, risk-based approaches are superior in tackling future drought hazards. Due to data-scarcity in many regions, high resolution satellite imagery data are becoming widely used. Focusing on ENSO warm and cold phases, we employ a risk-based approach for drought assessment in the Bolivian Altiplano using satellite imagery data and application of an early warning system. We use a newly established high resolution satellite dataset and test its accuracy as well as performance to similar (but with less resolution) datasets available for the Bolivian Altiplano. It is shown that during the El Niño years (warm ENSO phase), the result is great difference in risk and crop yield. Furthermore, the Normalized Difference Vegetation Index (NDVI) can be used to target specific hot spots on a very local scale. As a consequence, ENSO early warning forecasts as well as possible magnitudes of crop deficits could be established by the government, including an identification of possible hotspots during the growing season. Our approach therefore should not only help in determining the magnitude of assistance needed for farmers on the local scale but also enable a pro-active approach to disaster risk management against droughts that can include economic-related instruments such as insurance as well as risk reduction instruments such as irrigation.