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.
Slide 2
LIS Integrates Observations, Models and Applications to
Maximize Impact
Slide 3
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, 2006. Land Information System - An Interoperable
Framework for High Resolution Land Surface Modeling. Environmental
Modelling & Software, Vol. 21, 1402-1415. WRSI VIC RFE2
RFE2gdas GeoWRSI-based Crop Parameters
Slide 4
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
Slide 5
FLDAS East Africa Benchmark: 2009 Oct-Feb End-of-season SWI and
SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers
Slide 6
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
Slide 7
FLDAS East Africa Benchmark: 2008 Oct-Feb End-of-season SWI and
SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers
Slide 8
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
Slide 9
FLDAS East Africa Benchmark: 2010 May-Nov End-of-season SWI and
SOS GeoWRSI SWI SOS LIS with RFE2 + USGS PET readers
Slide 10
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
Slide 11
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
Slide 12
FLDAS West Sahel Benchmark: 2010 End-of-season WRSI LIS with
RFE2 + USGS PET readers GeoWRSI USGS FEWS-NET archive
Slide 13
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
Slide 14
RFE2 & station forced Noah3.2 vs point measured heat flux:
SW Niger P g. 14 Raimer et al. 2009 Noah modeledPoint observations
Min/Max LHFX5/90 Wm-25/115 Wm-2 Min/Max SHFX40/100 Wm-220/95
Wm-2
Slide 15
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)
Slide 16
Correlation between API anomalies and LIS-Noah modeled soil
moisture anomalies for soils with high %sand P g. 16
Slide 17
Correlation between NDVI anomalies and LIS-Noah modeled AET
anomalies P g. 17
Slide 18
DRYWETDomain Avg. Cyan shade indicates 2 x standard deviation
WRSI SWI Precip (mm) Impact of precipitation uncertainty on
FEWS-NET Indicators
Slide 19
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.
Slide 20
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.