NASA Soil Moisture Perspec1ves and Advances John Bolten NASA Applied Sciences Program Water Resources john.bolten@nasa.gov MOISST, Lincoln, NE June 5, 2018
NASA Applied Sciences Program • Explain the basic underlying science and interactions The Program funds projects that enable innova1ve uses of • Discuss outstanding issues and challenges NASA Earth science data in organiza1ons' policy, business, • Illustrate the state of art in earth observing technologies and strategies and management decisions. for environmental monitoring, assessment, and prediction 2
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NASA Earth Science (Pre)Formulation PACE (2022) Implementation Missions: Present through 2023 GeoCARB (~2021) Primary Ops TROPICS (12) (~2021) Extended Ops MAIA (~2021) Landsat 9 (2020) Sentinel-6A/B (2020, 2025) NI-SAR (2021) ISS Instruments SWOT (2021) TEMPO (2018) LIS (2020), SAGE III (2020) InVEST/CubeSats TSIS-1 (2018), OCO-3 (2018), GRACE-FO (2) (2018) ECOSTRESS (2018), GEDI (2018) RAVAN (2016) NISTAR, EPIC CLARREO-PF (2020) IceCube (2017) ICESat-2 (2018) CYGNSS (8) (2019) (DSCOVR / NOAA) MiRaTA (2017) (2019) HARP (2018) JPSS-2 Instruments SMAP Suomi NPP TEMPEST-D (2018) (>2022 ) (NOAA) OMPS-Limb (2019) RainCube (2018) (>2022) CubeRRT (2018) QuikSCAT SORCE, Landsat 7 CIRiS (2018*) (2017) TCTE (NOAA) (USGS) Terra (>2021) (2017) CSIM (2018) (~2022) Landsat 8 (USGS) * Target date, Aqua (>2022) (>2022) not yet manifested CloudSat (~2018) GPM (>2022) CALIPSO (>2022) Aura (>2022) OSTM/Jason-2 (NOAA) OCO-2 (>2022) (>2022) 01.29.18
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Explain the Basic Underlying Science and Interactions
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Memory and Land-Atmosphere Interac+ons Water Cycle Frac,on naturally yields a non-parametric es1mate of soil moisture memory. Not based on fiTng of autocovariance • • Not based on fiTng of seasonal mean (required for autocovariance) Soil moisture memory is co-factor in establishing land-atmosphere feedbacks Strong regional differences D. Entekhabi (MIT)
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Exchanges Between Land and Atmosphere Even Though Soil Moisture is 10 ppm of the Global Water !"#$% ! !"#$% ! !"#$%&'( ! !"#$%&' ! ! ! ! !"#$%& ! ! Budget , it Captures About 20% of the Water Cycle !"#$%!%&'&%() SMAP Water Cycle Fraction Surface Soil Moisture is the ‘ Gate ’ Through Which All Exchanges of Water Between the Atmosphere and Subsurface Must Pass McColl et al. ( Nature-Geoscience , 2016) >4*-E83=&$L(X7P$
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NLDAS soil moisture evalua1ons NLDAS Phase 2 : Four land-surface models (Noah-2.8, Mosaic, SAC, VIC-4.0.3) 1979-present, running in opera1ons at NOAA, with a 3.5-day latency NLDAS-2.5 : NLDAS-2 LSMs with 0-day latency, becoming opera1onal at NOAA late 2018 NLDAS-3.0 : New/upgraded LSMs using LIS with data assimila1on. See white paper on LDAS websites for details. Test data available informally; targe1ng 2019 for opera1ons. Future : Improving forcing, expanding domain, targe1ng 3-4km spa1al resolu1on (LEFT) NLDAS-2 (LEFT) NLDAS-2 and NLDAS-3 soil moisture candidate LSMs against North against SCAN for American Soil 5-cm surface soil Moisture moisture Database (NASMD) Figure from: Mocko et al., Figure from: NLDAS Science Xia et al., JHM, Testbed 2015 D. Mocko (NASA GSFC)
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Discuss outstanding issues and challenges
Data Assimila1on vs. Model Calibra1on The present study focuses on the With model calibra1on: calibra1on of a certain recharge parameter. 1) the land model itself is changed – values of model parameter(s) are The value used in op1mized. the default model gives soil moisture 2) SMAP data contribute to recessions that are too slow. the parameter calibra1on but not to the upda1ng of the Calibra1ng the prognos1c states during parameter allows a simula1on. more realis1c recessions. Koster (NASA GSFC)
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SMAP vs Modeled Soil Moisture Dynamics PUBLICATIONS Geophysical Research Letters RESEARCH LETTER SMAP soil moisture drying more rapid than observed 10.1002/2016GL069946 in situ following rainfall events Key Points: Peter J. Shellito 1 , Eric E. Small 1 , Andreas Colliander 2 , Rajat Bindlish 3 , Michael H. Cosh 3 , Aaron A. Berg 4 , • SMAP and networks of in situ probes David D. Bosch 5 , Todd G. Caldwell 6 , David C. Goodrich 7 , Heather McNairn 8 , John H. Prueger 9 , observe soil drying after rainfall Patrick J. Starks 10 , Rogier van der Velde 11 , and Jeffrey P. Walker 12 • SMAP observes soil drying to occur over a 44% shorter timescale than in 1 Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA, 2 NASA Jet Propulsion situ • SMAP observes soil drying to occur at Laboratory, California Institute of Technology, Pasadena, California, USA, 3 USDA-ARS Hydrology and Remote Sensing twice the rate as in situ Laboratory, Beltsville, Maryland, USA, 4 Department of Geography, University of Guelph, Guelph, Ontario, Canada, 5 USDA-ARS Southeast Watershed Research Laboratory, Tifton, Georgia, USA, 6 Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas, USA, 7 USDA-ARS Southwest Watershed Research Center, Supporting Information: • Supporting Information S1 Tucson, Arizona, USA, 8 Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada, 9 USDA-ARS National Laboratory for • Table S1 Agriculture and the Environment, Ames, Iowa, USA, 10 USDA-ARS Grazinglands Research Laboratory, El Reno, Oklahoma, USA, 11 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands, Correspondence to: 12 Department of Civil Engineering, Monash University, Clayton, Melbourne, Victoria, Australia P. J. Shellito, peter.shellito@colorado.edu Peter Shellito (NASA GSFC)
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Bias, RMSE and Correla+on of Noah_MP at 10 California SCAN sites NLDAS Noah_MP (4 layers, WRF default) WLDAS Noah_MP (20 layers, WRF default) • WLDAS: soil moisture and groundwater es1mates at 1km resolu1on for the western US (PI: Mae Rodell) • Configura1on with 20 layers provides beeer correla1on. Bailing Li (GSFC)
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SMAP Retrievals Used for Precipita+on Es+ma+on Some results! (One of the beXer es+ma+ons): We can characterize the agreement in these /me series with the square of the correla/on coefficient, r 2 . Randy Koster (NASA GSFC)
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