Drought Monitoring and Soil Moisture Assimilation Using NASA’s Land Information System Christa D. Peters-Lidard, Sujay V. Kumar/SAIC, David M. Mocko/SAIC, and Yudong Tian/ESSIC, Code 617, NASA GSFC Figure 1: Improvement in the root-mean-square error of the latent heat fluxes from the use of soil moisture data assimilation against FLUXNET (Jung et al., 2009). Blue areas indicate improvements; red areas indicate degradations. W/m 2 Sept 27, 2011 Figure 2: Soil moisture percentiles on 27 Sep 2011 from (left) the LIS-Noah-3.3 simulation using NLDAS-2 forcing and from (right) the U.S. Drought Monitor (USDM) weekly map.. Figure 3: Percent area of the Southern U.S. (TX, OK, LA, AR, MS, TN) under D2=severe drought from Jan 2000 to Jan The LIS-Noah- 3.3 results without (No-DA, black) and with (ECV-DA, red) soil moisture data assimilation are compared to the USDM area (blue).

Name: Christa D. Peters-Lidard, NASA/GSFC, Code Phone: Abstract: This study seeks to improve the depiction of historical and emerging droughts from the use of the data assimilation of land surface states retrieved from satellites. This work presents results from land-surface model simulations using NASA’s Land Information System (LIS) software framework. LIS was run using the North American Land Data Assimilation System Phase 2 (NLDAS-2) surface forcing as inputs to produce a 1/8 th –degree dataset over CONUS (25-53N, W) from January 1979 to present. Remotely-sensed soil moisture is assimilated into the Noah land surface model to output states and fluxes, which are evaluated against available observations of in situ soil moisture, gridded fluxes, streamflow (using routed runoff), and snow products. Soil moisture percentiles and anomalies are also examined as part of a real-time Drought Monitor. References: Peters-Lidard, C.D., S.V. Kumar, D.M. Mocko, and Y. Tian, 2011: Estimating evapotranspiration with land data assimilation systems. Hydrological Processes, 25(26), , doi: /hyp Xia, Y., K. Mitchell, M. Ek, J. Sheffield, B. Cosgrove, E. Wood, L. Luo, C. Alonge, H. Wei, J. Meng, B. Livneh, D. Lettenmaier, V. Koren, Q. Duan, K. Mo, Y. Fan, and D.M. Mocko, 2012: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117(D3), doi: /2011JD Data Sources: The NLDAS-2 surface forcing ( the Land Information System software framework ( the LPRM surface soil moisture product from AMSR-E (Owe et al., 2008; the Essential Climate Variable (ECV) soil moisture product (Liu et al., 2012; Wagner et al., 2012); the FLUXNET (Jung et al., 2009) and MOD16 (Mu et al., 2011) gridded evapotranspiration products; the U.S. Drought Monitor ( USGS streamflow; USDA in situ soil moisture. Technical Description of Figures: Figure 1: Improvement in the root-mean-square error of the latent heat fluxes from the use of soil moisture data assimilation against FLUXNET. Figure 2: Soil moisture percentiles on 27 Sep 2011 from the LIS-Noah-3.3 simulation using NLDAS-2 forcing (left) and from the U.S. Drought Monitor.. Five categories of drought severity (From D0 to D4) are noted in the legend. Note the large area of D4-exceptional drought conditions centered over Texas Figure 3: Percent area of the Southern U.S. (TX, OK, LA, AR, MS, TN) under D2=severe drought from Jan 2000 to Jan The LIS-Noah-3.3 results without (No-DA, black) and with (ECV-DA, red) soil moisture data assimilation are compared to the USDM area (blue). Scientific significance: The assimilation of soil moisture has recently been shown to improve the simulation of surface and root zone soil moisture compared to in situ observations. This study extends this type of analysis, to also examine possible improvement from soil moisture data assimilation on surface fluxes, streamflow, and drought monitoring. Data assimilation of snow depth and snow-covered area are also being studied, as well as the use of irrigated area and of terrestrial water storage from the GRACE mission. This study is part of a National Climate Assessment of water availability, trends, and indicators. Relevance for future science and relationship to Decadal Survey: This study used remotely-sensed soil moisture products from a number of different satellite platforms, primarily AMSR-E. The Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November Surface soil moisture products from SMAP will be assimilated into land-surface models (such as through LIS) to improve the depiction of surface and root zone soil moisture, as well as improved drought monitoring, fluxes, etc.

Representation of soil moisture feedbacks during drought in NASA Unified WRF Ben Zaitchik, JHU, Joe Santanello, Sujay Kumar, and Christa D. Peters-Lidard, Code 617, NASA GSFC Figure 1: Difference in (a) AMSR-E LPRM soil moisture, (b) MODIS MCD43C3 white sky albedo, (c) NU- WRF top 10cm soil moisture, and (d) NU-WRF surface albedo between July 2006 and July Box in (b) indicates the NU-WRF modeling domain. Figure 2: Cumulative precipitation for four month simulations with NU- WRF that have no soil moisture memory (NSM), standard soil moisture memory (SMM), and soil moisture memory with active albedo (SMA), compared to observed precipitation from NLDAS for the drought-affected Southern Great Plains. Simulations with soil moisture memory are significantly closer to NLDAS. Observed Modeled Soil MoistureAlbedo

Name: Ben Zaitchik, Johns Hopkins University Phone: Abstract: This work presents the first ever application of NASA Unified WRF (NU-WRF) to seasonal simulations. NU-WRF coupling with the Land Information System (LIS) was used to introduce a new active albedo module to WRF that improved simulation of precipitation in the Southern Great Plains during the severe drought of Simulation results indicate that soil moisture memory improves the realism of NU-WRF simulations of extended drought, and that addition of albedo simulation routines to NU-WRF provides additional improvement in severely drought affected areas. References: Zaitchik BF, JA Santanello, SV Kumar and CD Peters-Lidard (2013) Representation of soil moisture feedbacks during drought in NASA Unified WRF (NU- WRF). Journal of Hydrometeorology. doi: /JHM-D Data Sources: NU-WRF modeling system; MODIS Aqua/Terra BRDF corrected white sky albedo; AMSR-E near-surface soil moisture estimates; North American Regional Reanalysis boundary conditions for NU-WRF simulations; NLDAS-2 meteorological forcing fields for model evaluation. Technical Description of Figures: Figure 1: Difference in (a) AMSR-E LPRM soil moisture, (b) MODIS MCD43C3 white sky albedo, (c) NU-WRF top 10cm soil moisture, and (d) NU-WRF surface albedo between July 2006 and July Box in (b) indicates the NU-WRF modeling domain. Figure 2: : Cumulative precipitation for four month simulations with NU-WRF that have no soil moisture memory (NSM), standard soil moisture memory (SMM), and soil moisture memory with active albedo (SMA), compared to precipitation in NLDAS for the drought-affected Southern Great Plains. Simulations with soil moisture memory are significantly closer to NLDAS. Scientific significance: The simulations performed in this study demonstrate the importance of accounting for soil moisture feedbacks on temperature and precipitation when simulating seasonal-scale drought. In particular, the study shows that surface energy feedbacks involving changes in surface albedo—a process that is not fully included in standard versions of WRF—can have a significant impact on simulation of precipitation, improving the realism of these simulations in regions affected by severe drought. The study further showed that NU-WRF performs stably for extended seasonal simulations, and that NU-WRF coupling tools can be used to implement and evaluate improved surface physics routines. Relevance for future science and relationship to Decadal Survey: Future missions such as SMAP and GRACE-FO will provide measurments that can be assimilated into offline land surface models and coupled NU-WRF simulations in order to improve the accuracy of soil moisture simulations. The power of these data assimilation methods depends in part on the model’s ability to simulate the multiple impacts that soil moisture has on energy and water fluxes. This study has contributed to model development in support of future data assimilation efforts and has shown the importance of NU-WRF as a testbed for model development.

Correction of surface reflectance time series for BRDF effects Eric Vermote, Code 619, NASA GSFC Figure 3a-b: Impact of the coupling atmosphere BRDF on the retrieval of the broad band albedo (a) RMS (b) bias. Figure 1: Time series of surface reflectance derived from Terra/MODIS reflectances at Kaoma (Zambia) for different level of processing. Figure 2: Cumulative histogram of the apparent noise of the reflectance (Black) and corrected reflectance (blue average model, green red and magenta classical and 2009 approach) time series in MODIS Channel 2 (from 2012 paper). Derived from 100 sites over one year.

Name: Eric Vermote, NASA/GSFC, Code Phone: Abstract: Surface reflectance time series measured from space borne instruments, such as the MODIS sensor, show an apparent high-frequency noise that limits their information content. A major contributor to this noise is the directional effect as the target reflectance varies with the observation geometry. We suggested an alternative BRDF inversion method using the assumption that the BRDF model shape (i.e. the BRDF normalized by its overall amplitude) varies little throughout the year so that the two model parameters are linear functions of the NDVI. Consequently, a given target BRDF shape is described by four parameters (slope and intercept for the two NDVI-dependent parameters) which simplify greatly the operational correction of the BRDF effect (2009). This approach was fully evaluated over a range of sites and conditions and proven to be as accurate as a more complex approach with many more degree of freedom that is usually adopted to solve the BRDF correction problem (2012). Finally the problem of the coupling of the atmosphere and the BRDF and its impact on the inversion was fully addressed in References: Franch B., Vermote E., Sobrino J.A. and Fédèle E. (2013). Analysis of directional effects on atmospheric correction, Remote Sensing of Environment, 128, Breon, F.M., & Vermote, E. (2012). Correction of MODIS surface reflectance time series for BRDF effects. Remote Sensing of Environment, 125, 1-9. Vermote, E., Justice, C.O., & Breon, F.M. (2009). Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances. IEEETransactions on Geoscience and Remote Sensing, 47, Data Sources: MODIS data used in the studies processed by the MODIS Adaptive Processing System and archived and distributed by the LAADS system (Code 617) ( Technical Description of Figures: Figure 1: Time series of surface reflectance derived from Terra/MODIS reflectances at Kaoma (Zambia). The four groups of point correspond to different levels of processing. (Black) Bottom group is for the surface reflectance without directional correction. Next toward (red) the top are the corrected value estimated with the classical method. (Green) Next are the values corrected with the simplified method (2 parameters). Finally, the upper time series shows the values obtained when accounting for the variation of those 2 parameters with NDVI. To generate this figure, the time series have been shifted by multiples of 0.15 (From 2009 paper) Figure 2: Cumulative histogram of the apparent noise of the reflectance (Black) and corrected reflectance (blue average model, green red and magenta classical and 2009 approach) time series in MODIS Channel 2 (from 2012 paper). Derived from 100 sites over one year. Figure 3: Impact of the coupling atmosphere BRDF on the retrieval of the broad band albedo (a) RMS (b) bias. The RMS errors around the 1% which is small compared to the required accuracy (10%). Additionally, we get small negative bias values (from − 4 · 10 − 4 until − 10 · 10 − 4 ), which indicate that the Lambertian assumption only generates a slight overestimation of the albedo. For the spectral albedo we obtain RMS from 1.5 to 5.0% both in the red and the green bands and from 0.7 to 3.0% in the near infrared Scientific significance: The approach developed could be applied to normalized a wide range of measurements from different sensors enabling the fusion of different dataset enriching the amount of information given to study a particular earth science problem. It is still necessary to explore how it will hold on at finer spatial scale (here the study was at coarse resolution ~5km). The results obtained for the coupling atmosphere BRDF enable to fully understand the final accuracy of the “actual” surface reflectance under different processing scenario. Relevance for future science and relationship to Decadal Survey: This is extremely relevant to future science that implies use of different data set in combination (e.g. Climate Data Record) that need to be normalized to a single standard and their uncertainties well defined.