Convergence in Water Use Efficiency Within Plant Functional Types across contrasting climates

Water use efficiency (WUE) provides a direct measure of the inextricable link between plant carbon uptake and water loss, and it can be used to study how ecosystem function varies with climate. We analysed WUE data from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), leveraging the high spatial resolution of ECOSTRESS to study the distribution of WUE values both within and among regions with different plant functional types. Our results indicate that despite wide local variability of WUE estimates, WUE tended to converge to common global optima (peaked distributions with variance \u3c0.5 g C per kg H2O, kurtosis \u3e3.0) for five of nine plant functional types (grassland, permanent wetland, savannah, deciduous broadleaf and deciduous needleleaf forest), and this convergence occurred in functional types that spanned distinct geographic regions and climates

Paths to research-driven decision making in the realms of environment and water

Now more than ever it is critical for researchers and decision makers to work together to improve how we manage and preserve the planet\u27s natural resources. Water managers in the western U.S., as in many regions of the world, are facing unprecedented challenges including increasing water demands and diminishing or unpredictable supplies. The transfer of knowledge (KT) and technology (TT) between researchers and entities that manage natural resources can help address these issues. However, numerous barriers impede the advancement of such transfer, particularly between organizations that do not operate in a profit-oriented context and for which best practices for university-industry collaborative engagement may not be sufficient. Frameworks designed around environmental KT – such as the recently-developed Research-Integration-Utilization (RIU) model – can be leveraged to address these barriers. Here, we examine two examples in which NASA Earth science satellite data and remote-sensing technology are used to improve the management of water availability and quality. Despite differences in scope and outcomes, both of these case studies adopt KT and TT best practices and can be further understood through the lens of the RIU model. We show how these insights could be adopted by NASA through a conceptual framework that charts individual- and organizational-level integration milestones alongside technical milestones. Environmental organizations can learn from this approach and adapt it to fit their own institutional needs, integrating KT/TT models and best practices while recognizing and leveraging existing institutional logics that suit their organization\u27s unique history, technical capability and priorities

SBG Applications: Water Resources and Agriculture

No abstract availabl

Earth observations into action: the systemic integration of earth observation applications into national risk reduction decision structures

Purpose - As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements. Design/methodology/approach - Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures. Findings - The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR. Originality/value - The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners

Assessing the Impact of Drought in Guanacaste, Costa Rica and Evaluating Potential Contributions of ECOSTRESS Evapotranspiration Data to Improve Drought Estimation

First, this study quantifies the annual change in precipitation and analyzes historical rainfall patterns using the Standard Precipitation Index (SPI) with combined precipitation datasets from the Global Precipitation Climatology Centre and the Costa Rica Instituto Meteorologico Nacional. The research then assesses plant stress at a regional scale by examining annual and seasonal anomalies from 2000-2015 in normalized difference vegetation index (NDVI) data at 30m spatial resolution and comparing these results to the coarser 1km ET, potential evapotranspiration (PET) and evaporative stress index (ESI) anomalies in Guanacaste at 1km spatial resolution from PT-JPL. The third phase of analysis investigates the differences in plant stress and drought resilience observed across different categories of landscapes in Guanacaste, including forest, grassland and agriculture. Results from the SPI analysis suggest that Guanacaste experienced a long term negative trend in precipitation over the past 15 years relative to the historical rainfall data, with 2015 having the most severe drought conditions (annual SPI = -4.07). The NDVI, ET, PET and ESI analyses reveal that the greatest amount of plant stress experienced in the region during the drought occurred in 2014, particularly during the wet season. Furthermore, differences in drought response across land use categories varied significantly (p\u3c0.001). The findings of this research illustrate how ECOSTRESS ET information can help improve drought estimation through complementing the current suite of precipitation-based drought indicators

May the forest be with you: leveraging GEDI’s spaceborne lidar data for tropical ecosystem applications

Here, we provide an overview of the use of light detection and ranging (lidar) for tropical ecosystem applications, with a particular focus on the Global Ecosystem Dynamics Investigation (GEDI). We summarize how data from GEDI measures vegetation vertical structure and give a step-by-step description of how to obtain spatially-subset GEDI Level 2A data from the NASA EarthData Search web portal. We then provide an example of how to characterize the structure of various vegetation classes in Ucayali, Peru. These vegetation classes include: (1) old-growth lowland forest, (2) young lowland vegetation regrowth (‘Purma’)”, (3) secondary lowland forest, (4) mature oil palm plantations, and (5) cacao plantations (monocrop and agroforestry). We interpret the structural height metrics from GEDI among each of these vegetation classes, identifying edge effects as a possible influence on our results. To address this issue, we conducted a final analysis of the data with an area of 35m diameter footprint (25m of the original diameter area of the beam, and 10m as a conservative additional buffer) and excluded any observations that did not completely overlap with each land cover polygon. When we removed edge effects, no observations remained in the cacao data set and fewer observations remained in the forest stage data set. Nonetheless, the overall structural patterns shown in the relative heights of each forest stage remained very similar. We recommend that future projects utilizing spaceborne lidar for tropical ecosystems consider adopting the techniques and best practices we describe here, including refined noise filtering and explicit consideration of edge effects

Spatial Discovery Expert Meeting, Final Report

This report summarizes a two-day expert meeting on “Spatial Discovery,” organized jointly by the Library and the Center for Spatial Studies of the University of California, Santa Barbara (UCSB), and held on June 16–17, 2015 at the Upham Hotel, in Santa Barbara. The 24 participants contributed expertise in Library Science, as well as knowledge pertaining to spatial information and relevant research on data-seeking behavior. Five keynote addresses as well as several plenary and break-out discussions explored the challenges, best practices, and potential strategies associated with the cross-platform discovery of spatial data in the context of modern libraries

Spatial Discovery and the Research Library

Academic libraries have always supported research across disciplines by integrating access to diverse contents and resources. They now have the opportunity to reinvent their role in facilitating interdisciplinary work by offering researchers new ways of sharing, curating, discovering, and linking research data. Spatial data and metadata support this process because location often integrates disciplinary perspectives, enabling researchers to make their own research data more discoverable, to discover data of other researchers, and to integrate data from multiple sources. The Center for Spatial Studies at the University of California, Santa Barbara (UCSB) and the UCSB Library are undertaking joint research to better enable the discovery of research data and publications. The research addresses the question of how to spatially enable data discovery in a setting that allows for mapping and analysis in a GIS while connecting the data to publications about them. It suggests a framework for an integrated data discovery mechanism and shows how publications may be linked to associated data sets exposed either directly or through metadata on Esri’s Open Data platform. The results demonstrate a simple form of linking data to publications through spatially referenced metadata and persistent identifiers. This linking adds value to research products and increases their discoverability across disciplinary boundaries

Spatial Discovery and the Research Library

Academic libraries have always supported research across disciplines by integrating access to diverse contents and resources. They now have the opportunity to reinvent their role in facilitating interdisciplinary work by offering researchers new ways of sharing, curating, discovering, and linking research data. Spatial data and metadata support this process because location often integrates disciplinary perspectives, enabling researchers to make their own research data more discoverable, to discover data of other researchers, and to integrate data from multiple sources. The Center for Spatial Studies at the University of California, Santa Barbara (UCSB) and the UCSB Library are undertaking joint research to better enable the discovery of research data and publications. The research addresses the question of how to spatially enable data discovery in a setting that allows for mapping and analysis in a GIS while connecting the data to publications about them. It suggests a framework for an integrated data discovery mechanism and shows how publications may be linked to associated data sets exposed either directly or through metadata on Esri’s Open Data platform. The results demonstrate a simple form of linking data to publications through spatially referenced metadata and persistent identifiers. This linking adds value to research products and increases their discoverability across disciplinary boundaries