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Development and application of WRF3.3-CLM4crop to study of agriculture - climate interaction


Accurate representation of surface energy partitioning is crucial for studying land surface processes and the climatic influence of land cover and land use change using coupled climate-land surface models. A critical question for these models, especially for newly coupled ones, is whether they can adequately distinguish differences in surface energy partitioning among different vegetation types. In the first chapter, I evaluated three years (2004-2006) of surface energy partitioning and surface climate over four dominant vegetation types (cropland, grassland, needleleaf evergreen forest, broadleaf deciduous forest) across the United States in a recently coupled regional climate model (WRF3-CLM3.5) by comparing model output to observations (AmeriFlux, CERES, and PRISM data) and to standard WRF output. I found that WRF3-CLM3.5 can capture the seasonal pattern in energy partitioning for needleleaf evergreen forest, but needs improvements in cropland, grassland and broadleaf deciduous forest.

To extend the capability of the regional climate model in studying the interaction of climate and agriculture, in the second chapter, I coupled a version of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting model (WRF) and evaluated against multiple observations. The evaluation showed that although the model with dynamic crops overestimated LAI and growing season length, interannual variability in LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and energy fluxes were largely improved at irrigated sites. With this improved model, I further investigated whether the dynamic crop growth influenced the irrigation effects on climate. With prescribed crop LAI and growth, irrigation effects on climate were under-predicted in moderately irrigated regions. Moreover, relative to the dynamic crop growth version, the prescribed crop growth model underestimated irrigation water use and simulated much higher soil evaporation.

The third chapter is an application of the coupled model in studying the irrigation effects on heat waves. A potential decline in irrigation due to groundwater depletion would not only directly affect agriculture, but also could potentially alter surface climate. In this study I investigated how irrigation affects heat wave frequency, duration, and intensity using fifteen heat wave indices and a regional climate model. Across all indices, irrigation reduced heat wave frequency and duration, but increased intensity. Irrigation effects on heat waves are statistically significant over irrigated cropland and but not significant for non-irrigated regions. The magnitude of effect varies by index and is more sensitive to the choice of temperature metric than to the choice of temperature threshold. Regions experiencing strong groundwater depletion, such as the southern high plains, may suffer more and longer heat waves with reduced irrigation.

Overall, my research confirmed the dynamic crop growth model and irrigation are important in studying the agriculture and climate interaction. The research on irrigation effects, as well as on weather and climate prediction, should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

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