1. An Integrated Terrestrial Water Analysis System for the NCA (NCA-LDAS) Current PI: Christa Peters-Lidard, Hydrological Sciences Laboratory, NASA GSFC Code 617 Extension PI: Michael Jasinski, Hydrological Sciences Laboratory, NASA GSFC Code 617 Co-Is: John Bolten, James Foster, Dorothy Hall, Matthew Rodell, Kristi Arsenault, Hiroko Kato Beaudoing, Jordan Borak, Sujay Kumar, Bailing Li, Yuqiong Liu, David Mocko, George Riggs

2. NCA-LDAS Science The hypothesis to be tested in this project is that assimilating NASA’s satellite soil moisture, SWE, SCA, TWS and irrigation products into an NCA-LDAS will produce improved characterization of the continental scale water budget, which will directly improve the monitoring and prediction of climate- relevant water availability indicators, including droughts and floods. Specific science questions to be addressed include: How have North American water storages and fluxes evolved in the satellite era? How have the relationships among hydrologic fluxes and states changed? – Snowpack-streamflow-flood anomalies? – Groundwater-soil moisture-evapotranspiration-drought anomalies? – Irrigation impacts? Which global indicators help us understand North American impacts? What are key hydrologic indicators that encapsulate these impacts? pg. 2

3. NCA-LDAS Schematic 1979- present NLDAS2 Forcings and Parameters SCA, SWE, TWS, SM, II LIS Synthesis, Indicators Noah, Catchment LDAS (Land Data Assimilation System), SCA (Snow Covered Area), SWE (Snow Water Equivalent), TWS (Terrestrial Water Storage), SM (Soil Moisture), II (Irrigation Intensity) pg. 3

4. Figure 4: March 2011 GRACE-based Groundwater Percentile from GRACE TWS EDR (2002-present). Figure 2: Irrigation Intensity (% Area) from MODIS circa 2001 (Ozdogan and Gutman 2008) Figure 1: March 2011 Snow Water Equivalent (SWE) Mean Percentile from LPRM v5 – NASA Aqua/AMSR-E EDR (2003-2011). Figure 3: March 2011 Surface Soil Moisture Percentile from LPRM v5 – NASA Aqua/AMSR-E Aqua EDR (2003-2011) NCA-LDAS EDRs Area % Percentiles pg. 4

5. NCA-LDAS Soil Moisture Data Assimilation Data Assimilation: AMSR-E LPRM (Owe et al., 2008; Peters-Lidard et al., 2011) 2002-2011 ESA ECV (Liu et al., 2012; Wagner et al., 2012) 1978-2011 Flags: light and moderate vegetation, precipitation, snow cover, frozen ground, RFI The observations are scaled to the LSM’s climatology using CDF matching 12-member ensemble A spatially distributed observation error standard deviation (between 0.02-0.12 m3/m3) Experimental Setup: Domain: CONUS, NLDAS Resolution: 0.125 deg. Period: 1979-01 to 2012-12 Forcing: NLDASII LSM: Noah 3.3 Percentiles pg. 5 Reference: Kumar, S., C. Peters-Lidard, D. Mocko, R. Reichle, Y. Liu, K. Arsenault, Y. Xia, M. Ek, G. Riggs, B. Livneh, M. Cosh, 2013: Assimilation of passive microwave-based soil moisture and snow depth retrievals for drought estimation, Submitted to J. Hydromet, (Special Collection on Advances in Drought Monitoring and Prediction)

6. Soil moisture DA : Evaluation of soil moisture fields ARS CalVal (surface soil moisture) Open loop (no DA)LPRM DA Anomaly R0.84 +/- 0.020.86 +/- 0.02 Anomaly RMSE (m3/m3)0.021 +/- 0.0010.019 +/- 0.001 ubRMSE (m3/m3)0.024 +/- 0.0020.022 +/- 0.002 SCAN (surface soil moisture) Open loop (no DA)LPRM DA Anomaly R0.67 +/- 0.02 Anomaly RMSE (m3/m3)0.037+/- 0.0020.036 +/- 0.002 ubRMSE (m3/m3)0.043 +/- 0.0030.041 +/- 0.003 SCAN (root zone soil moisture) Open loop (no DA)LPRM DA Anomaly R0.60 +/- 0.020.59 +/- 0.02 Anomaly RMSE (m3/m3)0.032 +/- 0.0020.030 +/- 0.002 ubRMSE (m3/m3)0.041 +/- 0.0030.039 +/- 0.003 Statistically significant improvements in surface soil moisture and root zone soil moisture as a result of soil moisture DA Anomaly R increases, Anomaly RMSE reduces and unbiased RMSE reduces with LPRM assimilation. Statistically significant improvements in surface soil moisture and root zone soil moisture as a result of soil moisture DA Anomaly R increases, Anomaly RMSE reduces and unbiased RMSE reduces with LPRM assimilation. Location of the ARS and SCAN in-situ sites used for the evaluation pg. 6

7. Soil Moisture DA: Evaluation of improvements in streamflow simulation The improvements are expressed using a Normalized Information Contribution (NIC) metric that measures the skill improvement from DA as a fraction of the maximum possible skill improvement NIC_RMSE NIC_R NIC_NSE Overall improvements in all skill metrics (RMSE, R and NSE) are observed in streamflow estimates after data assimilation pg. 7 70% 50% 30% 10% 0% -10% -30% -50% -70%

8. NCA-LDAS Snow Data Assimilation Data Assimilation: SMMR (spans 1978-1987), SSM/I (spans 1987-2002) and AMSR-E (spans 2002-2011); SMMR and SSM/I retrievals are based on the Chang et al. (1987) and AMSR-E retrievals are based on the improved retrieval algorithm from Kelly et al. (2009). The snow depth retrievals are bias corrected using the in-situ measurements from the Global Historical Climate Network (GHCN). Figure 1: March 2011 Snow Water Equivalent (SWE) Mean Percentile from LPRM v5 – NASA Aqua/AMSR-E EDR (2003-2011). Percentiles pg. 8 Reference: Kumar, S., C. Peters-Lidard, D. Mocko, R. Reichle, Y. Liu, K. Arsenault, Y. Xia, M. Ek, G. Riggs, B. Livneh, M. Cosh, 2013: Assimilation of passive microwave-based soil moisture and snow depth retrievals for drought estimation, Submitted to J. Hydromet, (Special Collection on Advances in Drought Monitoring and Prediction) GHCN sites

9. Snow DA: Evaluation of snow depth fields against GHCN Open loop (no DA) SNOW-DACMCSNODAS RMSE (mm) 174.0 +/- 8114.0+/- 8158.0+/-8154.0+/- 8 Bias (mm)-84.1+/- 8-31.6 +/- 8-66.0+/- 833.9 +/- 8 Average seasonal cycle of snow depth RMSE and bias pg. 9

10. Snow DA: Evaluation of the improvements in streamflow simulation The improvements are expressed using a Normalized Information Contribution (NIC) metric that measures the skill improvement from DA as a fraction of the maximum possible skill improvement NIC_RMSE NIC_R NIC_NSE Some improvements in streamflow metrics such as RMSE, R and NSE after snow data assimilation 70% 50% 30% 10% 0% -10% -30% -50% -70% pg. 10

11. Biogeographical Cross-Cuts for NCA-LDAS pg. 11

12. NCA-LDAS Annual Water Budgets pg. 12

13. NCA-LDAS Water Budget Percentiles Flood Year: 1993 (CLSM) pg. 13

14. pg. 14 NCA-LDAS Water Budget Percentiles Drought Year: 2011 (Noah LSM)

15. Comparison of Noah OL and Noah Snow DA on Annual Mean SWE pg. 15 Percentile Below: 1993 & 2011, full domain Right: 1980-2012, NW, NE & UC Regions Northwest Region Northeast Region Upper Colorado Basin Water Year 2011 – Wet NW/UC; Dry South Water Year 1993 – Wet NW & UC 1993 2011

16. SWE Indicators using NCA-LDAS with Snow DA Northeast, Midwest and Northwest Regions pg. 16

17. Snowmelt Runoff Indicator pg. 17

18. pg. 18 NCA-LDAS Extension Plans for 2014: Evaluation and Dissemination PI: M. Jasinski/GSFC Phase 1: Complete NCA-LDAS simulations for the all model runs. - Add GRACE terrestrial water storage (10/2013) and - Multi-Data Assimilation models, including irrigation (as available in 2014) Phase 2: Compute/Eval principal and sub-hierarchical water budget indicators (5/2014) - Evaluate uncertainty of LDAS indicators at NCA regional and basin scales - Evaluate multivariable water indicators Phase 3: Provide all NCA-LDAS components to science community thru GES-DISC (6/2014). Complete as of 9/2013 - Open Loop Noah - Open Loop CLSM - Snow DA Noah - SM DA Noah

19. References Hydrology DISC (HDISC) http://disc.gsfc.nasa.gov/hydrology/ http://disc.gsfc.nasa.gov/hydrology/ NASA/GSFC NLDAS website: http://ldas.gsfc.nasa.gov/nldas/ http://ldas.gsfc.nasa.gov/nldas/ NASA/GSFC LIS website: http://lis.gsfc.nasa.gov/ http://lis.gsfc.nasa.gov/ pg. 19