Earth Systems

Spanning Eastern Europe, Scandinavia, the former Soviet Union and Northern China, Northern Eurasia is a bellwether for the future of climate change. Having undergone the fastest rate of climate change in the human-populated world in the past few decades, the region has endured dramatic natural disturbances and significantly altered its land-management practices. And that may be just the beginning.

Understanding climate change’s ground zero

A new study by MIT climate scientists, economists, and agriculture experts finds that certain hotspots in the country will experience severe reductions in crop yields by 2050, due to climate change’s impact on irrigation.

The most adversely affected region, according to the researchers, will be the Southwest. Already a water-stressed part of the country, this region is projected to experience reduced precipitation by midcentury. Less rainfall to the area will mean reduced runoff into water basins that feed irrigated fields.

Climate change to deplete some US water basins, reduce irrigated crop yields
Impact of Canopy Representations on Regional Modeling of Evapotranspiration using the WRF-ACASA Coupled Model

In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. The coupled WRF-ACASA model is compared with the widely used intermediate complexity Noah land surface model in WRF (WRF-Noah) for both potential (ETo) and actual evapotranspiration (ETa). Two LAI datasets (USGS and MODIS) are used to study the model responses to surface conditions. Model evaluations over a diverse surface stations from the CIMIS and AmeriFlux networks show that an increase surface representations increase the model accuracy in ETa more so than ETo. Overall, while the high complexity of WRF-ACASA increases the realism of plant physiological processes, the model sensitivity to surface representation in input data such as LAI also increases.

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