Tuesday, 8 January 2013: 4:30 PM
Room 10A (Austin Convention Center)
The application of modern land surface models (LSMs) to agricultural drought monitoring is based on the premise that anomalies in LSM root-zone soil moisture estimates can accurately anticipate the subsequent impact of drought on vegetation productivity and health. In addition, the water and energy balance functions of LSMs are widely assumed to add value to drought predictions. This assumption implies that LSM soil moisture outputs are more valuable for drought monitoring than simpler drought products based solely on the consideration of rainfall anomalies. With these assumptions in mind, this presentation benchmarks the performance of modern LSMs relative to a simple water accounting procedure based solely on observed precipitation (i.e., the well-known Antecedent Precipitation Index (API) model). In particular, the lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely-sensed vegetation indices (VI) is sampled between January 2000 and December 2010 to quantify the skill of modern LSMs (and a baseline API approach) for agricultural drought monitoring. Soil moisture products with the highest correlation versus future VI anomalies are assumed to contribute the most utility to an agricultural drought monitoring system. Results suggest that, when averaged in bulk across the annual cycle, little or no added skill (<5% in relative terms) is associated with applying modern LSMs to off-line agricultural drought monitoring relative to simple accounting procedures based solely on observed precipitation accumulations. However, slightly larger amounts of added skill (5-15% in relative terms) are identified when focusing exclusively on the extra-tropical growing season and/or utilizing root-zone soil moisture values acquired by averaging across a multi-model ensemble. Key presentation results are verified via an independent analysis based on comparisons against satellite-derived surface soil moisture retrievals.
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