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LIS Architecture for FLDAS

Enhancing the Famine Early Warning Systems Network (FEWS NET) over Africa by tracking agricultural drought using NASA product-based FEWSNET Land Data Assimilation System (FLDAS) Soni Yatheendradas Christa Peters-Lidard Amy McNally Chris Funk James Verdin Harikishan Jayanthi

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LIS Architecture for FLDAS

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  1. Enhancing the Famine Early Warning Systems Network (FEWS NET) over Africa by tracking agricultural drought using NASA product-based FEWSNET Land Data Assimilation System (FLDAS) Soni Yatheendradas Christa Peters-Lidard Amy McNally Chris Funk James Verdin Harikishan Jayanthi Acknowledgements: NASA Water Resources Applications Program Clement Alo, Hiroko Kato-Beaudoing

  2. LIS Architecture for FLDAS WRSI RFE2 Crop Map, Rooting Depth Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood and J. Sheffield, 2006. Land Information System - An Interoperable Framework for High Resolution Land Surface Modeling. Environmental Modelling & Software, Vol. 21, 1402-1415.

  3. Which rainfall products and model physics best detect Southern Malawi’s agricultural drought of 2004/05? • Regional rainfall regime • Rainfall • Multiple rainfall products (e.g., CMAP, TRMMv6 and RFE2) • RFE2 (daily, 0.1, Xie and Arkin [1997]) disaggregated here to 6-hrly using GDAS • Climatology fields and unbiasing procedure • Evaluation/comparison of detection capabilities • Hydrological models -geoWRSI (Water Requirement Satisfaction Index) -FLDAS Noah land surface model

  4. Average Malawi Rainfall by Dekad mm/dekad averaged station data 1960-2008

  5. Rainfall Unbiasing procedure CLIM QPE% QPE* = X Funk, C., J. Michaelsen, J. Verdin, et al. 2003, The collaborative historical African rainfall model: description and evaluation, International Journal of Climatology, 23: 47-66.

  6. Annual End of Season WRSI: Southern Malawi % water requirement satisfied

  7. FLDAS/Noah Results for S. Malawi Precipitation Map and JF Anomaly TS Jan-Feb 2005 Jan-Feb mm/month

  8. FLDAS/Noah Results for S. Malawi PET Map and JF Anomaly TS Jan-Feb 2005 Jan-Feb mm/month

  9. FLDAS/Noah Results for S. Malawi AET for ND2004 and JF2005 2004 Nov-Dec 2005 Jan-Feb

  10. FLDAS/Noah Vegetation Parameters for Agricultural Drought Monitoring zo, albedo, GVF Root depth

  11. FLDAS/UMD-25KM Veg Classes and Noah Rooting Depths: South Malawi 0.60m 0.30m 0.60m 0.60m 0.60m 1.0m

  12. Improved vegetation and root zone representation for drought detection Noah simulated soil moisture profile Noah simulated AET Soil moisture (mm) AET (mm) 100 80 60 40 100 80 60 40 20 sm3 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 sm2 sm1

  13. Summary & Future work • High-quality QPE essential for FLDAS-based drought monitoring (e.g., RFE2) • Default vegetation type and associated parameters (e.g., rooting depth) may need updating for agricultural drought monitoring with the LDAS approach • WRSI Being Implemented in LIS-based FLDAS

  14. Backup/Workshop Objectives 1) To review the information requirements for global-scale, drought monitoring products. 2) To evaluate the information value of NASA capabilities and explore ways in which these capabilities can be used more effectively to inform water security/management concerns at global to sub-continent scales. 3) To assess the specific requirements for monitoring agricultural and hydrological droughts and the capabilities of the current suite of NASA data products to provide that information. 4) To develop a set of actions that would enable NASA, either separately or in collaboration with other organizations, to more effectively impact drought information for decision making.

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