دانلود رایگان مقاله تجزیه و تحلیل و ارزیابی تبخیر و تعرق چمن زار Tallgrass در مرکز ایالات متحده

Abstract

Understanding the factors controlling evapotranspiration (ET) of spatially distributed tallgrass prairie is crucial to accurately upscale ET and to predict the response of tallgrass prairie ecosystems to current and future climate. The Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) and ground-based climate variables were integrated with eddy covariance tower-based ET (ETEC) at six AmeriFlux tallgrass prairie sites in the central United States to determine major climatic factors that control ET over multiple timescales and to develop a simple and robust statistical model for predicting ET. Variability in ET was nearly identical across sites over a range of timescales, and it was most strongly driven by photosynthetically active radiation (PAR) at hourly-to-weekly timescales, by vapor pressure deficit (VPD) at weekly-to-monthly timescales, and by temperature at seasonal-to-interannual timescales at all sites. Thus, the climatic drivers of ET change over multiple timescales. The EVI tracked the seasonal variation of ETEC well at both individual sites (R2 > 0.70) and across six sites (R2 = 0.76). The inclusion of PAR further improved the ET-EVI relationship (R2 = 0.86). Based on this result, we used ETEC, EVI, and PAR (MJ m−2 d−1) data from four sites (15 site-years) to develop a statistical model (ET = 0.11 PAR + 5.49 EVI − 1.43, adj. R2 = 0.86, P < 0.0001) for predicting daily ET at 8-day intervals. This predictive model was evaluated against additional two years of ETEC data from one of the four model development sites and two independent sites. The predicted ET (ETEVI+PAR) captured the seasonal patterns and magnitudes of ETEC, and correlated well with ETEC, with R2 of 0.87-0.96 and RMSE of 0.35-0.49 mm d−1, and it was significantly improved compared to the standard MODIS ET product. This study demonstrated that tallgrass prairie ET can be accurately predicted using a multiple regression model that uses EVI and PAR which can be readily derived from remote sensing data.

5. Conclusions

For a better understanding ofthe controlling factors of ET in tallgrass prairie ecosystems, we integrated ETEC measurements with ground-based major climatic variables and the MODIS-derived EVI at sixAmeriFlux tallgrass prairie sites.Variability in ETEC was nearly identical across sites over a range of timescales. No single climate variable was the most important driver of ET across a variety of timescales, indicating that climatic forcing of ET changes over multiple timescales. At weekly timescale, EVI and PAR were two most important factors that control ET. Based on this result, we developed and evaluated a simple model that uses EVI and PAR as input data to predict ET at 8-day intervals. Our results demonstrate that the simple ETEVI+PAR model has the potentialto provide estimates of tallgrass prairie ET with reasonable accuracy. This predictive model could be improved as more ET data becomes available from additional tallgrass prairie sites, and ultimately EVI and PAR could be sufficienttomonitor tallgrass prairie ET over large areas. Our results can be further validated using independent tallgrass prairie flux sites. We accept that our specific parameterization for tallgrass prairie cannot be transferred to other ecosystems or regions, but the approach could be applied to develop a statistical model for predicting large-scale ET for other land cover types or regions.