  Under climate change, the increasing frequency, intensity, and spatial extent
of drought events lead to higher socio-economic costs. However, the
relationships between the hydro-meteorological indicators and drought impacts
are not identified well yet because of the complexity and data scarcity. In
this paper, we proposed a framework based on the extreme gradient model
(XGBoost) for Texas to predict multi-category drought impacts and connected a
typical drought indicator, Standardized Precipitation Index (SPI), to the
text-based impacts from the Drought Impact Reporter (DIR). The preliminary
results of this study showed an outstanding performance of the well-trained
models to assess drought impacts on agriculture, fire, society & public health,
plants & wildlife, as well as relief, response & restrictions in Texas. It also
provided a possibility to appraise drought impacts using hydro-meteorological
indicators with the proposed framework in the United States, which could help
drought risk management by giving additional information and improving the
updating frequency of drought impacts. Our interpretation results using the
Shapley additive explanation (SHAP) interpretability technique revealed that
the rules guiding the predictions of XGBoost comply with domain expertise
knowledge around the role that SPI indicators play around drought impacts.
