Floods in Agricultural Plains Distant From Rivers

CAPPELLETTI, LUCÍA M.; SCHRAPFFER, A.; SORENSSON, A.

Simposio; International Symposium of Sorbonne Sustainable Development; 2022

According to the Fundación Agropecuaria para el Desarrollo de Argentina (FADA, Agricultural Foundation for the Development of Argentina), in 2021 the agro-industrial sector generated a total of USD 54,895 million in terms of exports. Thus, 7 out of every 10 dollars per export come from agro-sectors. Argentinean Pampas is the core of Argentina agro-industrial production and it is one of the main global grain belts. The Pampas is the leading argentinean region of soybean production, consolidating its position in 2021 as the first global exporter of soy oil and soy meal and, the third largest exporter of soybeans. Floods and waterlogging in Pampas directly affect the country's economy, since the region provides work and livelihoods for hundreds of thousands of people, and these hydrological extreme events are a direct threat to their well-being. Knowledge about water content status and trends is essential for sustainable land management and crop production. Argentina’s public institutions and private agro-industrial agencies have reported floods and waterlogging in Central Pampas areas distant from rivers are triggered by local processes, such as rainfall and rising water tables. Addressing the study of these hydrological extremes allows understanding the mechanisms linked to their triggering and persistence, which is relevant for risk assessments, decision making, and also to participate in the effort of flood and waterlogging mitigation in a context of climate and land use changes. The objective of this study is to quantify and analyze the influence of local hydrological processes on the flooded area in a pilot site located in the Central Pampas in which shallow water table, low slopes and flood proneness coexist. The flooded areas are estimated with data from MODIS and Landsat satellite images and using different categories: Open Water and Mixed-Water (pixels composed by soil/vegetation with a given percentage of water). This is done by applying an unsupervised classification method to two spectral indices: the Normalized Difference Water Index (NDWI) and its modified version (mNDWI). The hydrological dynamic of floods and waterlogging in agricultural plains is analyzed through the estimated surface water from spectral information, along the water content from different water land surface-ground compartments and water fluxes involved: in situ measurements (water table height; rainfall) and remote sensing estimations (surface soil moisture; ground water storage).Our study underlines the importance of using multiple sources of information to interpret the hydrological status, including data from in situ measurements and remote sensing estimations, as well as locally collected information such as reports from national, sub- national and private agro-industrial agencies.