Semi‐arid vegetation response to antecedent climate and water balance windows

Abstract

AbstractQuestionsCan we improve understanding of vegetation response to water availability on monthly time scales in semi‐arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near‐term forecasts from climate data that can be used to prepare for future climate change effects on vegetation?LocationSemi‐arid grasslands in Capitol Reef National Park, Utah, USA.MethodsWe built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi‐model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years.ResultsWater balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi‐model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology.ConclusionsWe determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high‐frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.