Open Access. Powered by Scholars. Published by Universities.®
University of Nebraska - Lincoln
United States National Aeronautics and Space Administration: Publications
- Keyword
Articles 1 - 2 of 2
Full-Text Articles in Instrumentation
Groundwater Withdrawals Under Drought: Reconciling Grace And Land Surface Models In The United States High Plains Aquifer, Wanshu Nie, Benjamin Zaitchik, Matthew Rodell, Sujay V. Kumar, Martha C. Anderson, Christopher Hain
Groundwater Withdrawals Under Drought: Reconciling Grace And Land Surface Models In The United States High Plains Aquifer, Wanshu Nie, Benjamin Zaitchik, Matthew Rodell, Sujay V. Kumar, Martha C. Anderson, Christopher Hain
United States National Aeronautics and Space Administration: Publications
Advanced Land Surface Models (LSM) offer a powerful tool for studying hydrological variability. Highly managed systems, however, present a challenge for these models, which typically have simplified or incomplete representations of human water use. Here we examine recent groundwater declines in the US High Plains Aquifer (HPA), a region that is heavily utilized for irrigation and that is also affected by episodic drought. To understand observed decline in groundwater and terrestrial water storage during a recent multiyear drought, we modify the Noah-MP LSM to include a groundwater irrigation scheme. To account for seasonal and interannual variability in active irrigated area, …
Joint Hierarchical Models For Sparsely Sampled High-Dimensional Lidar And Forest Variables, Andrew O. Finley, Sudipto Banerjee, Yuzhen Zhou, Bruce D. Cook, Chad Babcock
Joint Hierarchical Models For Sparsely Sampled High-Dimensional Lidar And Forest Variables, Andrew O. Finley, Sudipto Banerjee, Yuzhen Zhou, Bruce D. Cook, Chad Babcock
United States National Aeronautics and Space Administration: Publications
Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatially misaligned LiDAR signals and forest variables. We propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals. The processbased framework offers richness in inferential capabilities, e.g., inference on the entire underlying processes instead of estimates only at pre-specified points. Key challenges we obviate include misalignment between the …