On the Northern Colorado Plateau, many park managers have access to data on weather and long-term vegetation change. What they don’t always have is information on how climate affects vegetation from month to month and year to year. Traditional monitoring—which typically involves visiting vegetation plots just once each year—can help answer questions about long-term change and spatial patterns in composition and diversity, but is generally too infrequent for understanding dynamic, within-year response to wet and dry periods. If managers had access to information that could help them to see even a short distance into the future, then short-term decision making could be made easier and more effective.
A recently completed grassland study demonstrates how monthly satellite measurements of large landscapes, together with climate data and predictive modeling, can help fill in some of these information gaps. An interagency group of scientists, including staff from the Northern Colorado Plateau Network and U.S. Geological Survey, has developed a way to forecast vegetation response to recent weather events at landscape scales on the Colorado Plateau.
The study connects three elements: annual, plot-level species data collected on the ground; high-frequency measurements of landscape greenness from NASA satellites; and associated weather data. The result is a quantitative model that is able to forecast short-term vegetation condition—that is, it has been shown to reliably predict what vegetation condition will be a few months into the future.
Many studies have shown that there is a strong vegetation response to precipitation on an annual or seasonal scale in semi-arid environments. Fewer studies have investigated these relationships at more frequent intervals, such as monthly. Even fewer have included multiple climate and environmental factors that influence vegetation at different times. This study analyzed multiple variables to determine which are most important at different months throughout the year—and within that context, which are the best predictors of vegetation condition later in the growing season.
The model is based in the understanding that vegetation response lags weather, so plant response to precipitation might come a day, a week, or even months after the precipitation falls. Also, precipitation is not the only environmental factor that affects the availability of water to plants. Measures such as soil moisture and evapotranspiration, called water balance metrics, can be more useful than precipitation alone for examining plant response. In semi-arid environments, high annual, seasonal, and monthly variations in plant productivity can be largely explained by relating climate and water balance factors across time. For instance, on one grassland, soil moisture conditions beginning in February were found to be useful for predicting vegetation abundance in October.
The model was tested using two large grassland sites (1 grazed, 1 ungrazed) at Capitol Reef National Park. In both grasslands, green-up (as observed through the satellite-derived Normalized Difference Vegetation Index, or NDVI), occurred in March through May, in response to soil moisture that had accumulated during the winter. In years without summer monsoon rains, NDVI typically peaked in June. In years with a summer monsoon, NDVI peaked later, generally between August and October.