Pg. 1 Using the NASA Land Information System for Improved Water Management and an Enhanced Famine Early Warning System Christa Peters-Lidard Chief, Hydrological.

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Presentation transcript:

Pg. 1 Using the NASA Land Information System for Improved Water Management and an Enhanced Famine Early Warning System Christa Peters-Lidard Chief, Hydrological Sciences Laboratory, NASA/GSFC World Bank “Ignite” Talk: 29 February 2012 Acknowledgements: The LIS Team Collaborators at AFWA, USACE/CRREL, NOAA/NCEP and USGS/EROS

The Land Information System (LIS; Integrates Observations, Models and Applications to Maximize Impact

LIS Capabilities

Figure 4: Changes in annual-average terrestrial water storage (the sum of groundwater, soil water, surface water, snow, and ice, as an equivalent height of water in cm) between 2009 and 2010, based on GRACE satellite observations. Future observations will be provided by GRACE-II. Figure 5: Current lakes and reservoirs monitored by OSTM/Jason-2. Shown are current height variations relative to 10-year average levels. Future observations will be provided by SWOT. Figure 2: Annual average precipitation from 1998 to 2009 based on TRMM satellite observations. Future observations will be provided by GPM. Figure 1: Snow water equivalent (SWE) based on Terra/MODIS and Aqua/AMSR-E. Future observations will be provided by JPSS/VIIRS and DWSS/MIS. Figure 3: Daily soil moisture based on Aqua/AMSR-E. Future observations will be provided by SMAP. Developing LIS Land Data Assimilation Capabilities

NASA Observations, LIS, and Impacts Pg. 5 AMSR-E Surface Soil Moisture ->  Root Zone Soil Moisture  Evapotranspiration  Streamflow  Downstream Impact: Agricultural, Meteorological Droughts AMSR-E Snow Water Equivalent ->  Snow Depth  Streamflow  Downstream Impact: Floods TRMM-based precipitation ->  Slope Instability  Downstream Impact: Landslides

Backup Pg. 6

Figure 3: Daily soil moisture based on Aqua/AMSR-E. Future observations will be provided by SMAP. Soil Moisture Data Assimilation Impact Assessment: Drought Variables Analyzed: Soil Moisture Steamflow Evapotranspiration Experimental Setup: Domain: CONUS, NLDAS Resolution: deg. Period: to Forcing: NLDASII LSM: Noah 3.2 Data Assimilation: AMSR-E LPRM soil moisture AMSR-E NASA soil moisture

Soil moisture Assimilation -> Soil moisture (Evaluation vs SCAN) Anomaly correlation OLNASA-DALPRM-DA Surface soil moisture (10cm) / / / Root zone soil moisture (1m) / / / ALL available stations (179) (21) Stations listed in Reichle et al. (2007) Anomaly correlation OLNASA-DALPRM-DA Surface soil moisture (10cm) / / / Root zone soil moisture (1m) / / /- 0.05

Soil Moisture Assimilation -> Streamflow Evaluation vs. USGS gauges – by major basins

Soil Moisture Assimilation -> Streamflow (average seasonal cycles of RMSE– using Xia et al. (2011) stations)

Soil Moisture Assimilation -> Latent Heat Flux Pg. 12 Peters-Lidard, Christa D., Sujay V. Kumar, David M. Mocko and Yudong Tian, (2011), Estimating Evapotranspiration with Land Data Assimilation Systems, In press, Hyd. Proc.

Where Does Soil Moisture Assimilation Help Improve Qle (i.e. Reduce RMSE) ? Pg. 13 Peters-Lidard, Christa D., Sujay V. Kumar, David M. Mocko and Yudong Tian, (2011), Estimating Evapotranspiration with Land Data Assimilation Systems, In press, Hyd. Proc. LPRM-DA NASA-DA

Figure 1: Snow water equivalent (SWE) based on Terra/MODIS and Aqua/AMSR-E. Future observations will be provided by JPSS/VIIRS and DWSS/MIS. Snow Data Assimilation Impact Assessment: Floods Variables Analyzed: Snow Depth Steamflow Experimental Setup: Domain: CONUS, NLDAS Resolution: deg. Period: to Forcings: NLDASII LSM: Noah 3.2 Data Assimilation: AMSR-E snow depth AMSR-E bias corrected snow depth

Snow Assimilation -> Snow Depth (Evaluation vs COOP) RMSE(mm)Bias (mm)R OL212 +/ / / CMC197 +/ / / ANSA-DA-UNCORR233 +/ / / ANSA-DA-CORR152 +/ / /- 0.01

Snow Assimilation -> Streamflow (average seasonal cycles of RMSE– using Xia et al. (2011) stations)

Snow DA shows improvements during the melt periods -E.g., New England, Upper Mississippi, Souris Red Rainy, Missouri, Arkansas Snow Assimilation -> Streamflow (average seasonal cycles of RMSE– using Xia et al. (2011) stations)

Figure 2: Annual average precipitation from 1998 to 2009 based on TRMM satellite observations. Future observations will be provided by GPM. TRMM Precipitation -> Landslides

Remotely Sensed Precipitation -> Landslides Rainfall threshold-based landslide prediction model Couples a static susceptibility map with precipitation real-time forcings from Tropical Rainfall Measuring Mission (TRMM) When both susceptibility and rainfall intensity-duration thresholds are exceeded, a forecast is issued indicating landslide potential Test Case: Macon County, NC Poor algorithm performance over this area for Hurricanes Frances and Ivan, September 2004, suggest that the current rainfall thresholds do not accurately resolve landslide-triggering rainfall Algorithm currently running in near real-time at following website:

Improved landslide model performance with optimized rainfall thresholds 1 km algorithm run Probability of detection (POD) False Alarm Ratio (FAR) Default model4.2 %99.1 % Optimized model20.8 %88.7 % 25 km algorithm run Landslide inventory from Hurricanes Frances and Ivan in September, 2004

Summary Pg. 21 Remotely sensed soil moisture, snow and precipitation can be useful for hazard assessment LPRM AMSR-E Soil moisture assimilation can improve soil moisture, streamflow and evapotranpiration -> Drought Bias-corrected AMSR-E Snow depth assimilation improves snow depth and streamflow-> Floods TRMM-based precipitation inputs can be useful for landslide detection, given calibrated thresholds ->Landslides