Introduction to project objectives Consistent with a vision for seamless climate services, create long time-series gridded rainfall data (CHIRP) based.

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Presentation transcript:

Introduction to project objectives Consistent with a vision for seamless climate services, create long time-series gridded rainfall data (CHIRP) based on the B1 and CPC thermal IR NOAA satellite data (Microwave data deferred pending GPM launch & iMerge development) Niger millet modeling with AMMA surface observations for model output verification Use RFE2 time series with WRSI, Noah, and VIC in LIS FLDAS for confidence that it meets expectations from the FEWS NET monitoring experience since 2000 Use CHIRP rain with FLDAS (WRSI, Noah, VIC) to generate (a) an agricultural drought chronology for African FEWS NET countries, and (b) crop production shortfall time-series with corresponding loss exceedance curves (LECs). Longer term (Year 4) goals discussed include (a) seasonal forecasting of agricultural drought using suitable atmospheric forcings from NOAA CPC and/or IRI, and (b) custom water supply analyses with VIC that address USAID water availability questions.

LIS Integrates Observations, Models and Applications to Maximize Impact

LIS Architecture for NASA product-based FEWSNET Land Data Assimilation System (FLDAS) 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, Land Information System - An Interoperable Framework for High Resolution Land Surface Modeling. Environmental Modelling & Software, Vol. 21, WRSI VIC RFE2 RFE2gdas GeoWRSI-based Crop Parameters

FLDAS East Africa Benchmark: 2009 Oct-Feb End-of-season WRSI USGS FEWS-NET archive GeoWRSI LIS with RFE2 + USGS PET readers LIS with GeoWRSI- processed forcing

FLDAS East Africa Benchmark: 2009 Oct-Feb End-of-season SWI and SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers

FLDAS East Africa Benchmark: 2008 Oct-Feb End-of-season WRSI USGS FEWS-NET archive GeoWRSI LIS with RFE2 + USGS PET readers LIS with GeoWRSI- processed forcing

FLDAS East Africa Benchmark: 2008 Oct-Feb End-of-season SWI and SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers

FLDAS East Africa Benchmark: 2010 May-Nov End-of-season WRSI USGS FEWS-NET archive GeoWRSI LIS with RFE2 + USGS PET readers LIS with GeoWRSI- processed forcing

FLDAS East Africa Benchmark: 2010 May-Nov End-of-season SWI and SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers

FLDAS models East Africa comparison: 2009 Oct-Feb End-of-season WRSI and SWI WRSI Index SWI Index WRSI with RFE2 + USGS PET readers Noah3.2 with RFE2 + GDAS readers VIC with RFE2 + GDAS readers

FLDAS models East Africa comparison: 2010 May-Nov End-of-season WRSI and SWI WRSI with RFE2 + USGS PET readers Noah3.2 with RFE2 + GDAS readers VIC with RFE2 + GDAS readers WRSI Index SWI Index

FLDAS West Sahel Benchmark: 2010 End-of-season WRSI LIS with RFE2 + USGS PET readers GeoWRSI USGS FEWS-NET archive

FLDAS West Sahel Benchmark: 2010 End-of-season SWI & SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers GeoWRSI LIS with RFE2 + USGS PET readers

RFE2 & station forced Noah3.2 vs point measured heat flux: SW Niger P g. 14 Raimer et al Noah modeledPoint observations Min/Max LHFX5/90 Wm-25/115 Wm-2 Min/Max SHFX40/100 Wm-220/95 Wm-2

RFE2 & station forced Noah3.2 vs point estimated water balance: SW Niger P g. 15 Noah modeled rfe/station Point observations October ET350/300mm325mm Aug Soil80/225mm (late peaks)100mm (peak)

Correlation between API anomalies and LIS-Noah modeled soil moisture anomalies for soils with high %sand P g. 16

Correlation between NDVI anomalies and LIS-Noah modeled AET anomalies P g. 17

DRYWETDomain Avg. Cyan shade indicates 2 x standard deviation WRSI SWI Precip (mm) Impact of precipitation uncertainty on FEWS-NET Indicators

Summary The FEWS Land Data Assimilation System (FLDAS) is a tool to: maximize the use of limited observations and streamline application of the different data products that are used routinely for agricultural drought monitoring. improve yield estimates through better representation of WRSI and other drought indicators Noah model outputs (and GDAS inputs) agree reasonably well with AMMA surface observations. Land surface models have the potential to provide better estimates of AET and soil moisture for calculating WRSI.

Summary Future work will use RFE2 time series with WRSI, Noah, and VIC in LIS FLDAS for confidence that it meets expectations from the FEWS NET monitoring experience since 2000 Use FTIP, CHIRP rain with FLDAS (WRSI, Noah, VIC) to generate an agricultural drought chronology for African FEWS NET countries, and crop production shortfall time-series with corresponding loss exceedance curves (LECs). seasonal forecasting of agricultural drought using suitable atmospheric forcings from NOAA CPC and/or IRI, and custom water supply analyses with VIC that address USAID water availability questions.