Global Land Surface Dry/Wet Conditions Mining Based on Spatial‐Temporal Association Rules

Severe drought and wetness would have serious impacts on human society and natural environment. Discovering the implicit relationships among severely dry/wet conditions is of great significance for meteorological disaster early warning and risk management strategy formulation. In this study, we propose an improved spatial‐temporal association rule mining algorithm to mine the global dry/wet associations. A modified time‐lagged association rule mining algorithm is first developed to mine unit‐to‐unit dry/wet association rules. Then, a density‐based clustering algorithm is employed to identify associated dry/wet zones. The gridded Standardized Precipitation Evapotranspiration Index (SPEI) data sets at 12‐ and 3‐month time scales were used to characterize annual and seasonal dry/wet conditions. Analysis results show that there are strong dry/wet associations between many regions. Some of the discovered association rules reflect similar associations with known phenomena, indicating the effectiveness and feasibility of the method. Other rules were previously unknown and can provide new knowledge for this research field. Several predictions can be made according to the discovered rules that western Oman and southeastern Saudi Arabia would suffer severe or extreme drought in 2021 and northwestern China would suffer severe or extreme drought in 2025 with probabilities exceeding 77%; northwestern China, northern Argentina, and western Australia would experience severe or extreme wetness in 2021, 2022, and 2023, respectively, with probabilities exceeding 85%. The algorithm provides a new perspective for analyzing dry/wet associations and the discovered associations can be used as theoretical bases for local governments to take precautions to mitigate the potential impacts of severe drought or wetness.