资源环境科技发展态势分析平台

Vegetation acclimation resulting from elevated atmospheric CO2 concentration, along with response to increased temperature and altered rainfall pattern, is expected to result in emergent behavior in ecologic and hydrologic functions. We hypothesize that microtopographic variability, which are landscape features typically of the length scale of the order of meters, such as topographic depressions, will play an important role in determining this dynamics by altering the persistence and variability of moisture. To investigate these emergent ecohydrologic dynamics, we develop a modeling framework, Dhara, which explicitly incorporates the control of microtopographic variability on vegetation, moisture, and energy dynamics. The intensive computational demand from such a modeling framework that allows coupling of multilayer modeling of the soil-vegetation continuum with 3-D surface-subsurface flow processes is addressed using hybrid CPU-GPU parallel computing framework. The study is performed for different climate change scenarios for an intensively managed agricultural landscape in central Illinois, USA, which is dominated by row-crop agriculture, primarily soybean (Glycine max) and maize (Zea mays). We show that rising CO2 concentration will decrease evapotranspiration, thus increasing soil moisture and surface water ponding in topographic depressions. However, increased atmospheric demand from higher air temperature overcomes this conservative behavior resulting in a net increase of evapotranspiration, leading to reduction in both soil moisture storage and persistence of ponding. These results shed light on the linkage between vegetation acclimation under climate change and microtopography variability controls on ecohydrologic processes.

Plain Language Summary Changes in climate, which include elevated atmospheric CO2 concentration and temperature, are altering ecophysiological responses of vegetation. Higher atmospheric CO2 has a water conservative effect on certain types of vegetation while increase in temperature drives higher evapotranspiration demand. We, therefore, expect that this interplay of competing effects will play out in subtle ways, and therefore microtopographic variability will play an important role in their dynamics. For the first time, we develop modeling capabilities to draw out these subtle dynamics by utilizing LiDAR data. However, such modeling is compute-intensive. This study develops a hybrid parallel computing framework to unravel the interaction between ecophysiological responses of vegetation and microtopographic variability using high-resolution modeling. We demonstrate the applicability of our model to study this interaction in an intensively managed landscape in the Midwest, USA. We find that rising CO2 concentration will decrease evapotranspiration, thus increasing soil moisture and surface water and ponding in topographic depressions. However, increased air temperature overcomes this conservative behavior resulting in a net increase of evapotranspiration, leading to reduction in soil moisture storage and persistence of ponding. This work improves our understanding of changes in ecohydrologic systems under climate change.