Date of this Version
Zhang, B. (2019). Investigation of GRACE-derived Information on Forest Drought Stress Across the Contiguous US. MS Thesis, School of Natural Resources, University of Nebraska-Lincoln.
This research derives z-score monthly groundwater storage (GWS) anomalies and z-score monthly root zone soil moisture (RZSM) anomalies from products of Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA). Z-score monthly GWS and RZSM anomalies are compared to two drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) to investigate the usefulness of GRACE-DA information to detect drought conditions at tree-ring sites. This study also compares z-score monthly GWS and RZSM anomalies with the Tree Ring Standardized Growth Index (TRSGI) that is resampled by bootstrapping to investigate the capability of monitoring forest drought stress. Finally, this research uses multiple linear regression to develop a model for predicting tree-ring widths at selected study sites.
The results of the comparisons of z-score monthly GWS and RZSM anomalies and commonly-used drought indices (SPI and SPEI) indicate that GWS anomalies have strong correlations (> 0.4) with long-term droughts (> 9 months) and RZSM anomalies have strong correlations (> 0.5) with short-term droughts (< 3 months). The results of comparisons of TRSGI suggest that z-score monthly GWS and RZSM anomalies are significantly related to tree-ring widths with a significant level of 0.05. This research suggests that the relationships between GWS anomalies and drought indices (SPI and SPEI) and TRSGI highly depend on the geological formations, such as the types of the aquifers, and geographical environments such as the soil texture. The multiple linear regression in this paper quantifies the impacts of z-score monthly GWS and RZSM anomalies on tree-ring widths, which suggests GRACE-DA products can provide useful information to detect and predict the growth of trees. The results also suggest the predictor, monthly RZSM anomalies, is one of the most important parameters in the regression model. Overall, the study suggests that GRACE-DA information can be used to help detect and monitor the stress from drought impacts on trees at a large spatial scale.
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