TEMPE -- When a hurricane is bearing down on the Florida coast, any one of countless, well-established models can forecast the projected path of the storm. But when a summer monsoon is brewing over the deserts of the Southwest, no reliable models exist to help predict this phenomenon.
The key, it turns out, lies in the first two inches of desert soil.
Two Arizona State University hydrosystems engineering professors, Giuseppe Mascaro and Enrique R. Vivoni, are filling this forecasting void. They are creating the first hydrologic modeling system that uses satellite data from NASA and other space agencies to increase the spatial resolution of soil moisture estimates generated from orbit — a process known as “downscaling.” The model accounts for the water cycle on land and its interaction with the atmosphere. Their results were detailed in the paper “Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations,” published in the Nature Publishing Group journal, Scientific Reports.
Water content in soil evaporating into the atmosphere is an important source of fuel for monsoon storms in Phoenix, and the ability to track what’s happening on the ground gives a clearer forecast of monsoon systems in the atmosphere.
This model, which has been 10 years in the making, represents a major shift in how society can understand and make decisions about water consumption, land-use planning and natural disasters such as flooding.
Mascaro and Vivoni combined interdisciplinary work in water science, engineering and sustainability through their respective positions at ASU to create the new model.
“The model is that link between satellite observations everywhere and the ability to make decisions at local sites,” Vivoni said. “It makes satellite products actionable information.”
A cross-border phenomenon requires cross-border collaboration
The yearly monsoon storms that occur in the Phoenix area between mid-June and mid-September, known as the North American Monsoon, spans the United States and Mexican borders.
Until recently, each country has individually observed different parts of the North American monsoon weather system, operating without coordinated efforts nor a cohesive model of the phenomenon. Government funding in either country has skipped over this issue until recently.
In 2004, Vivoni established the first attempt to create a network of measurement stations in the Rio Sonora basin in northern Mexico, chosen due to its variability in soil moisture conditions.
Making big satellite data small
For decades, NASA and other space agencies have been deploying satellites that use sensors to infer soil moisture conditions for nearly all the land on Earth.
Typically, satellite imagery represents large areas measuring 50 to 100 square kilometers (about 20 to 40 square miles) per data point. However, using this scale of information about soil moisture isn’t very helpful when you’re trying to see conditions for a small town in northern Mexico or a family farm in southern Arizona where soil moisture can vary every 100 feet, for example.
Mascaro is using mathematical concepts from fractal theory to create an algorithm that takes the low-resolution satellite data and “downscales” it to be high resolution. Fractals are repeating structures that appear commonly in nature. Think of a tree trunk that splits into branches. That same pattern repeats as those branches “branch” off into smaller ones, continuing to branch off even as far down as the veins of leaves.
Fractal geometry can explain these patterns at different scales,” Mascaro says, such as from a satellite image taken hundreds of miles above the Earth’s surface and a picture taken by a plane flying just hundreds of feet off the ground. This concept is also known as scale invariance.
Mascaro combines his statistical algorithm with additional data about terrain, soil conditions and vegetation cover to create the hydrologic model. With Mascaro’s scale-invariance calculations, Vivoni’s model can account for both the satellite data (the large branches) and the output of the model at a much higher resolution of 100 meters, or about 330 feet (the veins of the leaves).
The field data collected over the years by Vivoni’s team from the stations in the Rio Sonora basin helped Mascaro and Vivoni confirm the effectiveness of the model, which used only satellite, terrain and hydrologic data.
“We’ve created a method that others can replicate and use to create a model with satellite data in their region,” Vivoni said.
Monique Clement is a writer with the Arizona State University Fulton Schools of Engineering.