A Composite Moisture Index for the Missouri River Basin
A holistic framework for assessing regional moisture conditions on a monthly basis prior to the agricultural growing season
Introduction
NASA DEVELOP , hosted by the NASA Earth Applied Sciences’ Capacity Building Program, offers participants the opportunity to work on 10-week-long feasibility projects that utilize NASA satellite-derived data products to address environmental and public policy decision-making issues. This project, focusing on the Water Resources application area, integrates climatic variables from modeled products as well as satellite-derived datasets to create a composite moisture index. Through its project work, the NC Fall 2020 DEVELOP team hopes to streamline the communication of drought and flooding risks to local stakeholder groups across the Missouri River Basin.
Four recent graduates comprise the NASA DEVELOP Fall 2020 Montana Water Resources Team: Chloe Schneider (Project Lead), Dean Berkowitz, Egla Ochoa-Madrid, and Julie Sorfleet. The team’s collective educational background spans across the fields of Environmental Science, Geography, Physics, and Spanish.
The Missouri River Basin reservoir system is the largest in the United States, containing about 73.1 million acre-feet of water storage capacity. Covering approximately one-sixth of the United States’ landmass, the basin is a major global breadbasket that encompasses some of the most agriculturally productive lands in the country.
Over the last decade, climatic extremes such as severe drought and flood events have prompted state governments throughout the region to invest in drought mitigation efforts.
While the Missouri River Basin experienced record high flooding in 2011, the following year it faced a severe drought that brought record low flows across the region.
Current drought and flood mitigation efforts include the Upper Missouri River Basin (UMRB) drought indicators dashboard , the National Integrated Drought Information System (NIDIS) Drought Early Warning System (DEWS) for the Missouri River Basin region , and The State of the Climate reports produced by the National Oceanographic and Atmospheric Administration (NOAA).
Community Concerns
Winter drought conditions degrade moisture availability during the start of the agricultural growing season. Therefore, local agencies such as the Montana Climate Office and the NOAA National Weather Service (NWS) Missouri Basin River Forecast Center are interested in investigating the regionally specific environmental variables that could inform drought mitigation efforts across the Missouri River Basin.
A composite moisture index (CMI) that can be referenced in the context of historic wet and dry events can give the public a stronger understanding of current and antecedent drought and flooding conditions.
A composite index is a useful tool for streamlining the communication of drought conditions and risks to stakeholder groups, including agricultural producers, ranchers, fisheries, tribal entities, and other natural resource managers.
Problem
In order to effectively communicate soil moisture availability to stakeholders, multiple climate and hydrology variables were combined as indicators and optimized into a composite moisture index to give a single holistic assessment of a region’s moisture availability.
Existing platforms such as the United States Drought Monitor (USDM) provide information on drought monitoring on a weekly basis based on physical observations and author expertise. Climatologists and hydrologists are always looking for additional tools that can indicate future drought and flood potential. For example, hydrology forecasters at the NWS want to provide outlooks months in advance. To that extent, our team developed a CMI which programmatically integrates recent satellite data and modeled measurements of climatic variables into a data pipeline that outputs drought conditions mapped across the Missouri River Basin on a monthly basis.
Solution
Our team used NASA Earth observations data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to retrieve snow cover and soil moisture data from the Soil Moisture Active Passive (SMAP) satellite while drawing upon ancillary modeled snowpack data to create the Composite Moisture Index.
Below are a series of interactive maps illustrating the different data products that comprise the inputs to our CMI. Each map showcases the month of March across 2016 (a dry year) and 2019 (a wet year) to demonstrate how regional moisture conditions differ over time in the Missouri River Basin. Swipe each map left and right to toggle between data layers!
MODIS Normalized Difference Snow Index (NDSI)
Left: March 2016, Right: March 2019 Normalized Difference Snow Index (NDSI) monthly mean data from the Terra MODIS satellite gridded at a 500m spatial resolution. This data product reveals how much of the land area in a given pixel is covered by snow. Deeper blues indicate a higher percentage of snow cover detected on average, while lighter blues indicate lower percentage of snow cover.
SMAP Surface Soil Moisture Anomalies (SSMA)
Left: March 2016, Right: March 2019 Monthly mean surface soil moisture anomalies (SSMA) acquired via the NASA-USDA SMAP Global Soil Moisture Data product . This dataset is at approximately 26km spatial resolution (0.25 arc-degrees), which is why the pixel sizes are much larger than the other data products. Shades of brown indicate drier than normal soil moisture conditions, while shades of green indicate wetter than normal conditions; white indicates normal conditions.
SNODAS Snow Water Equivalent (SWE)
Left: March 2016, Right: March 2019 Gridded modeled estimates of snow water equivalent in meters at 1km spatial resolution from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) Snow Data Assimilation System (SNODAS) . Darker blues indicate more water contained in snowpack, while lighter shades indicate less.
SNODAS Snow Depth
Left: March 2016, Right: March 2019 Gridded modeled estimates of snowpack depth in meters at 1km spatial resolution from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) Snow Data Assimilation System (SNODAS) . Lighter magenta colors indicate deepest snowpack, purples indicate moderate snowpack depth, and light blues indicate shallower snowpack.
Drought or flood events can be characterized as exceptional moisture states that deviate from the long-term average conditions of an area. To detect above or below normal moisture conditions, climate normals were established for the four datasets. Monthly average observations were reclassified into bins based on relationship to ‘normal’ as defined by historic trends for each dataset. These standardized bin units were then summed to calculate CMI values for a common area.
Composite Moisture Index
Left: March 2016, Right: March 2019 Composite Moisture Index outputs at 26km resolution. Shades of blue represent moister than normal conditions, while shades of red indicate drier than normal conditions; white indicates normal conditions.
Conclusion
The team developed a framework for a CMI in the Missouri River Basin which the Montana Climate Office and the NOAA NWS Missouri Basin River Forecast Center can build upon to improve communication of drought conditions to local communities that rely on timely, accurate information for decision-making.
The preliminary CMI outputs indicate interannual and regional variability in moisture state across the study area that correlate with known historic wetness/dryness for the years of interest. For instance, the following CMI maps illustrate that the Missouri River Basin experienced below normal moisture conditions in 2016 and above normal moisture conditions in 2019.
This feasibility project successfully demonstrated that a CMI based on NASA Earth observations and ancillary modeled data for snowpack and soil moisture is capable of capturing the hydrologic status of the Missouri River Basin. Future work could be conducted to more rigorously validate the model and automate all analyses. Ultimately, this CMI tool could be operationalized and deployed to inform drought management decision making.
