Geology topics

Jia Hu

Publications and source records attributed to Jia Hu.

3 recordsLinked to original sources

USGS2026

Creating usable science: A Southwest Climate Adaptation Science Center study

The southwestern United States consists of diverse ecosystems that are experiencing increasing pressures from rising temperatures, increasing aridity, and sea level rise. To prepare this region for future uncertainty, there is a need for strong partnerships among researchers and societal partners. The Southwest Climate Adaptation Science Center (SW CASC) acts to foster engaged scholarship from diverse perspectives to produce science relevant for management and policy decisions. Here, we synthesized a subset of SW CASC-funded projects and published manuscripts to illustrate how the combination of new scientific knowledge and the research and practice of engaged scholarship produce actionable science with direct societal impacts. In the SW CASC Contributions to Regional Science section, we touch on new research produced from funded projects on the most common topics: (1) Forest Ecosystems, (2) Coastal Ecosystems, (3) Hydrometeorology, and (4) Research on Engaged Scholarship. In the SW CASC Contributions to Regional Impacts section, we outline engaged scholarship activities to demonstrate how they created societal impacts: (1) the Keepers of the Flame course and workshops, (2) the Southwest Fire Climate Adaptation Partnership and the Southern California Montane Forests Project, (3) the Coastal Managers Workshop, (4) the Colorado River Basin Wiki, and (5) the Natural Resource Workforce Development Program. In the SW CASC Publications Cited in Policy section, we used a bibliometric analysis to assess the impact of the SW CASC-funded research on policy. We analyzed how often publications were used in policy documents, finding a citation rate of 27.6%. This is high compared to similar analyses that found peer-reviewed research articles cited at a rate of 3.9% in policy documents. We conclude that coproduced science makes unique and important contributions to the resilience of the Southwest, not only by generating new knowledge but by building capacity, creating lasting relationships, and informing policy and conservation and management decisions.

Arizona, California, Nevada, Utah↗

USGS2026

Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States

In arid and semi-arid regions, groundwater sustains vegetation through subsurface water access, yet the responses of groundwater-dependent ecosystems (GDEs) to changing hydroclimate and groundwater availability are relatively understudied. This study investigates seasonal and spatial patterns in vegetation greenness using Landsat Enhanced Vegetation Index (EVI) values across riparian and upland zones in the semi-arid Upper San Pedro (USP) watershed, southern Arizona, which experiences a bimodal precipitation regime. We paired 25 years (2000–2024) of EVI and depth to groundwater (DTG) data from 89 wells and climate metrics (precipitation and vapour pressure deficit) to quantify the sensitivity of vegetation to subsurface moisture as well as atmospheric moisture supply and demand. Vegetation at wells near the USP riparian area showed strong associations between EVI and DTG anomalies during the monsoon season, indicating sustained groundwater use even during this wet period when summer precipitation is abundant. In contrast, upland vegetation that lacked access to groundwater showed minimal sensitivity in EVI to DTG and was generally less responsive to vapour pressure deficit. Interestingly, the riparian GDEs were not decoupled from precipitation and climate variability. These results underscore the importance of groundwater for maintaining riparian productivity and highlight the utility of remote sensing in identifying vegetation-climate-groundwater linkages across heterogeneous dryland landscapes.

Arizona↗

USGS2012

Exploring similarities among many species distributions

Collecting species presence data and then building models to predict species distribution has been long practiced in the field of ecology for the purpose of improving our understanding of species relationships with each other and with the environment. Due to limitations of computing power as well as limited means of using modeling software on HPC facilities, past species distribution studies have been unable to fully explore diverse data sets. We build a system that can, for the first time to our knowledge, leverage HPC to support effective exploration of species similarities in distribution as well as their dependencies on common environmental conditions. Our system can also compute and reveal uncertainties in the modeling results enabling domain experts to make informed judgments about the data. Our work was motivated by and centered around data collection efforts within the Great Smoky Mountains National Park that date back to the 1940s. Our findings present new research opportunities in ecology and produce actionable field-work items for biodiversity management personnel to include in their planning of daily management activities.

Conference Paper↗