Predicting spatial distribution of Palmer Drought Severity Index

TL;DR

This paper introduces a spatio-temporal neural network model for predicting the Palmer Drought Severity Index, improving accuracy over baseline methods and considering climate change impacts for better drought management.

Contribution

The paper presents a novel end-to-end neural network approach for PDSI prediction that outperforms traditional gradient boosting models and evaluates its applicability across diverse global regions.

Findings

Model achieves $R^2$ score of 0.90, outperforming baseline.

The approach effectively captures complex spatial-temporal drought patterns.

Analysis of climate change scenarios shows impact on future PDSI predictions.

Abstract

The probability of a drought for a particular region is crucial when making decisions related to agriculture. Forecasting this probability is critical for management and challenging at the same time. The prediction model should consider multiple factors with complex relationships across the region of interest and neighbouring regions. We approach this problem by presenting an end-to-end solution based on a spatio-temporal neural network. The model predicts the Palmer Drought Severity Index (PDSI) for subregions of interest. Predictions by climate models provide an additional source of knowledge of the model leading to more accurate drought predictions. Our model has better accuracy than baseline Gradient boosting solutions, as the $R^2$ score for it is $0.90$ compared to $0.85$ for Gradient boosting. Specific attention is on the range of applicability of the model. We examine various regions across the globe to validate them under different conditions. We complement the results with an analysis of how future climate changes for different scenarios affect the PDSI and how our model can help to make better decisions and more sustainable economics.