3B.3 Current Progress in Modeling Ecohydrological Processes over Drylands (Invited Presentation)

Monday, 7 January 2019: 2:30 PM
North 126BC (Phoenix Convention Center - West and North Buildings)
Zong-Liang Yang, Univ. of Texas at Austin, Austin, TX; and W. Y. Wu, H. Zheng, P. Lin, J. Liang, and L. Zhao

The United Nations Environment Program (UNEP) defines drylands as a region with an aridity index (the ratio of the average annual precipitation to the potential evapotranspiration) of less than 0.65. With this definition, this study provides a systematic assessment of available observations and simulations of ecohydrological variables over dryland areas. The observed datasets include gauge measurements and satellite-derived estimates (e.g., from MODIS, SMAP, GRACE), while the numerical simulations are from climate model outputs (e.g. CMIP4, 5, and 6) and land surface model outputs (e.g., CLM, Noah-MP). In addition to various reanalysis products (e.g., ERA-Interim/Land, MERRA-2), we will use outputs from our recently developed multisensor multivariate global land data assimilation system. Cold biases in skin temperature (Tskin) are found in almost all climate models over drylands, largely due to overestimation of surface albedo and evaporation cooling. These biases need to be corrected or considered while using these models for future projections. Offline land surface models (LSMs) show similar spatial patterns of biases in Tskin despite the use of observed atmospheric forcing. Both atmosphere and land contribute to biases in Tskin because there are uncertainties in radiation (an aerosol/cloud issue in climate models), surface albedo (input parameter and parameterization in LSMs), land surface processes (ET), and their feedbacks and interactions. While a substantial amount of analysis work is being carried out, our initial results show the offline Noah-MP land surface model performance for evapotranspiration (ET) and runoff is aridity-dependent over Texas in the United States. ET is better modeled in wet than in dry years, whereas streamflow is most poorly simulated in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These early results help identify potential processes for future model improvements. For example, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater observation and modeling. Detailed assessments are important steps towards developing efficient dryland water resources management technologies, methods and strategies.
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