University of Arizona College of Agriculture and Life Sciences

Monsoon precipitation is difficult to forecast and analyze. Daily and seasonal precipitation are commonly used, but other sources of data, such as citizen science monitoring, could be integrated into a higher resolution and more accurate monsoon assessment framework. Tucson has dozens of observations collected by these networks, along with datasets based on radar and weather models. A central monsoon data repository would form a dense network of observations, facilitate innovative visualizations, and offer an unparalleled high-resolution view of regional precipitation patterns. This project is testing a process to combine southwestern data networks into an integrated monsoon assessment database and data visualization platform.

The Monsoon Viewer shows current monsoon precipitation patterns, based on specific requests from National Weather Service as well as other emergency management stakeholders. They want next day decision support tools for post-event management – researchers designed the viewer with this application in mind. We are exploring a real-time use for the dataset, but the focus is on short term post-event recovery and management. http://monsoon.environment.arizona.edu/

The Southwest U.S. Summer Monsoon Season Precipitation Mapping Tool. This mapping tool is a near real-time monsoon season precipitation mapping system developed in May 2019. https://cals.arizona.edu/climate/misc/SWMonsoonMaps/current/swus_monsoo….

Monsoon Season Station Summaries can be found at this link: https://cals.arizona.edu/climate/misc/stations/monsoon/current.html

Both of these tools were recently added to the National Weather Service’s Tracking the Monsoon webpage: https://www.wrh.noaa.gov/twc/monsoon/monsoon_tracker.php

Drought is a complex phenomenon that can vary widely over space to due to precipitation patterns and in time due to lagging impacts in slowly varying systems. These factors are magnified in the semiarid Southwest U.S., where extreme interannual climate variability, topography, and highly localized precipitation patterns (e.g., monsoon season thunderstorms) create highly varying hydroclimatic patterns in both space and time and subsequent drought impacts. Current climate monitoring networks across Arizona and New Mexico struggle to capture this variability and accurately portray potential drought conditions.

Complementary datasets, like remote sensing greenness, used in conjunction with existing climate data, offer the potential to monitor drought conditions across large landscapes with sparse monitoring networks. Several efforts, including online geovisualization tools to access raw normalized-difference vegetation index (NDVI or greenness) data and more formalized remote sensing-based drought monitoring tools like VegDRI, have been developed over the past decade. A new effort supported by a recent NOAA-SARP/NIDIS grant spurred the development of tool called DroughtView, which takes a slightly different approach in combining cutting-edge online geovisualization tools with derived remote sensing products targeted at detecting drought conditions. DroughtView builds on the success of its precursor, RangeView, which was developed with guidance from agriculturists and resource managers who need environmental monitoring data. The tools in DroughtView are used to monitor biweekly changes in land surface greenness conditions as a proxy for drought impacts at very fine spatial scales across the Southwest U.S. More information can be found at http://droughtview.arizona.edu

DroughtView combines geovisualization tools with remote sensing products to detect drought conditions. The DroughtView tool served as a key piece of information to help the National Resources Conservation Service, US Bureau of Land Management, and US Forest Service range managers determine drought conditions and Farm Services Agency drought disaster assistance eligibility. DroughtView is a web-based decision-support tool for computers and mobile devices that combines satellite-derived measures of surface greenness with additional geospatial data so that users can visualize and evaluate vegetation dynamics across space and over time.