ARSET - Application of NASA SPoRT-Land Information System (SPoRT-LIS) Soil Moisture Data for Drought

Start Date:
May 17, 2023
End Date:
May 31, 2023
Description:
This training is focused on the introduction of the NASA Land Information System (LIS) output of soil moisture at various depths for drought analysis and monitoring. Traditional drought applications and indices focus on sensible weather (temperature, wind, etc.) and precipitation trends and their impacts on the hydrologic system. As a result of the NASA Short-term Prediction Research and Transition Center (SPoRT) program research-to-operations (R2O) work, this training will demonstrate how LIS output of soil moisture from a land surface model (LSM) can be included in traditional drought monitoring. The NASA SPoRT Program has implemented a version of NASA LIS (SPoRT LIS) that utilizes near real-time Green Vegetation Fraction (GVF) from S-NPP/VIIRS retrievals, as opposed to static, climatology values of GVF. Additionally, near real-time forcing of the LSM by the Multi-Radar Multi-Sensor (MRMS) precipitation and the Global Data Assimilation Suite (GDAS) sensible weather data provide low latency. Each of these allow for a more accurate depiction of vegetation and soil moisture impacts related to both long-term and flash drought events. Research-to-Operations work has led to derived percentile products for soil moisture, and low percentile values from SPoRT LIS have been shown to correspond well to areas experiencing short-term drought within the U.S. Drought Monitor (USDM). Via operations-to-research (O2R) activities with end-users, soil moisture changes on various timescales pertinent to drought applications have been added to the SPoRT-LIS output data suite. This training will improve the user’s understanding of how water infiltration and retention occur within LIS in order to improve the interpretation and application of this output for drought monitoring. Conducted by experts in LIS and operational drought monitoring, the overall course will enable users to implement LIS soil moisture percentile products for drought applications. Examples from operational applications as well as practice exercises (for using LIS data for drought monitoring) will be included in this course. Access to the LIS products via online viewer, in GIS formats, and GIS-based display tools, will also be included. Moreover, self-paced microlessons will be available to allow users to confirm their understanding and improve their skill via homework lessons between live sessions.
Targeted Audience:
This training series is intended for local, state, regional, federal, and international governmental and non-governmental organizations interested in using NASA data for drought monitoring and forecasting. Appropriate for operational end-users, decision-makers, and researchers with interests/needs in assessing drought, water resources, agriculture, and wildland fire management.
Expected Outcomes:
A user will be able to: - Apply LIS output to efficiently analyze drought over large spatial areas in conjunction with current practices and to integrate this capability with existing data. - Identify the NASA/LIS basics regarding the framework, input forcing, static fields, LSM structure, and output most relevant to drought - Summarize the derived soil moisture percentile products and how these are created - Apply SPoRT-LIS output and/or derived products to both complement existing data and overcome limitations to monitoring drought over large areas - Recognize ‘best practices’ for LIS impact related to drought - Configure LIS output file for viewing within a GIS-based display tool and for tailored output products and graphics
Host:
NASA, ARSET
Organizer:
NASA, ARSET
Format/Training Type:
Online Course, Workshop
Language:
English
Attendance:
Open
Application Deadline:
May 31, 2023
Contact Name:
Sarah Cutshall
Contact Email:
sarah.cutshall@nasa.gov
Link:
https://appliedsciences.nasa.gov/join-mission/training/english/arset-application-nasa-sport-land-information-system-sport-lis-soil
Thematic Area:
Water Resources
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