Assessment of Flood-Damaged Cropland Trends Under Future Climate Scenarios Using Convolutional Neural Network

Every year flood causes severe damages in the cropland area leading to global food insecurity. As climate change continues, floods are predicted to be more frequent in the future. To cope with the future climate impacts, mitigate damages, and ensure food security, it is now imperative to study the future flood damage trends in the cropland area. In this study, we use a convolutional neural network (CNN) to estimate the damages (in acre) in the corn and soybean lands across the mid-western USA with projections from climate models. Here, we extend the application of the CNN model developed by Lazin et. al, (2021) that shows ~25% mean relative error for county-level flood-damaged crop loss estimation. The meteorological variables are derived from the reference gridMet datasets as predictors to train the model from 2008-2020. We then use downscaled climate projections from Multivariate Adaptive Constructed Analogs (MACA) dataset in the trained CNN model to assess future flood damage patterns in the cropland in the early (2011-2040), mid (2041-2070), and late (2071-2100) century, relative to the baseline historical period (1981-2010). Results derived from this study will help understand the crop loss trends due to floods under climate change scenarios and plan necessary arrangements to mitigate damages in the future.

 

Reference:

[1] Lazin, R., Shen, X., & Anagnostou, E. (2021). Estimation of flood-damaged cropland area using a convolutional neural network. Environmental Research Letters, 16(5), 054011.