NASA Soil Moisture Perspec1ves and Advances John Bolten NASA - - PowerPoint PPT Presentation

John Bolten NASA Applied Sciences Program Water Resources john.bolten@nasa.gov

MOISST, Lincoln, NE June 5, 2018

NASA Soil Moisture Perspec1ves and Advances

2

NASA Applied Sciences Program

The Program funds projects that enable innova1ve uses of NASA Earth science data in organiza1ons' policy, business, and management decisions.

• Explain the basic underlying science and interactions
• Discuss outstanding issues and challenges
• Illustrate the state of art in earth observing technologies and strategies

for environmental monitoring, assessment, and prediction

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S1ck man with club in his hands – basic needs, followed food, lived near water

(Pre)Formulation Implementation Primary Ops Extended Ops

ISS Instruments

LIS (2020), SAGE III (2020) TSIS-1 (2018), OCO-3 (2018), ECOSTRESS (2018), GEDI (2018) CLARREO-PF (2020)

InVEST/CubeSats

RAVAN (2016) IceCube (2017) MiRaTA (2017) HARP (2018) TEMPEST-D (2018) RainCube (2018) CubeRRT (2018) CIRiS (2018*) CSIM (2018)

* Target date, not yet manifested

NASA Earth Science

Missions: Present through 2023

Landsat 9 (2020) PACE (2022) NI-SAR (2021) SWOT (2021) TEMPO (2018) GRACE-FO (2) (2018) ICESat-2 (2018) CYGNSS (8) (2019) NISTAR, EPIC (DSCOVR / NOAA) (2019) QuikSCAT (2017) Landsat 7 (USGS) (~2022) Terra (>2021) Aqua (>2022) CloudSat (~2018) CALIPSO (>2022) Aura (>2022) SMAP (>2022) Suomi NPP (NOAA) (>2022) Landsat 8 (USGS) (>2022) GPM (>2022) OCO-2 (>2022) Sentinel-6A/B (2020, 2025) MAIA (~2021) GeoCARB (~2021) TROPICS (12) (~2021) SORCE, TCTE (NOAA) (2017) OSTM/Jason-2 (NOAA) (>2022)

JPSS-2 Instruments

OMPS-Limb (2019)

01.29.18

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Explain the Basic Underlying Science and Interactions

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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

Memory and Land-Atmosphere Interac+ons

• D. Entekhabi (MIT)

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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)

SMAP Water Cycle Fraction

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

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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 soil moisture against North American Soil Moisture Database (NASMD) Figure from: Xia et al., JHM, 2015 (LEFT) NLDAS-2 and NLDAS-3 candidate LSMs against SCAN for 5-cm surface soil moisture Figure from: Mocko et al., NLDAS Science Testbed

• D. Mocko (NASA GSFC)

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Discuss outstanding issues and challenges

Data Assimila1on vs. Model Calibra1on With model calibra1on: 1) the land model itself is changed – values of model parameter(s) are

• p1mized.

2) SMAP data contribute to the parameter calibra1on but not to the upda1ng of the prognos1c states during a simula1on.

The present study focuses on the calibra1on of a certain recharge parameter. The value used in the default model gives soil moisture recessions that are too slow. Calibra1ng the parameter allows more realis1c recessions. Koster (NASA GSFC)

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SMAP soil moisture drying more rapid than observed in situ following rainfall events

Peter J. Shellito1, Eric E. Small1, Andreas Colliander2, Rajat Bindlish3, Michael H. Cosh3, Aaron A. Berg4, David D. Bosch5, Todd G. Caldwell6, David C. Goodrich7, Heather McNairn8, John H. Prueger9, Patrick J. Starks10, Rogier van der Velde11, and Jeffrey P. Walker12

Laboratory, California Institute of Technology, Pasadena, California, USA, 3USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA, 4Department of Geography, University of Guelph, Guelph, Ontario, Canada,

• f Geosciences, University of Texas at Austin, Austin, Texas, USA, 7USDA-ARS Southwest Watershed Research Center,

Tucson, Arizona, USA, 8Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada, 9USDA-ARS National Laboratory for Agriculture and the Environment, Ames, Iowa, USA, 10USDA-ARS Grazinglands Research Laboratory, El Reno, Oklahoma, USA, 11Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands,

PUBLICATIONS

Geophysical Research Letters

RESEARCH LETTER

10.1002/2016GL069946

Key Points:

• SMAP and networks of in situ probes
• bserve soil drying after rainfall
• SMAP observes soil drying to occur
• ver a 44% shorter timescale than in

situ

• SMAP observes soil drying to occur at

twice the rate as in situ Supporting Information:

• Supporting Information S1
• Table S1

Correspondence to:

• P. J. Shellito,

peter.shellito@colorado.edu

Peter Shellito (NASA GSFC)

SMAP vs Modeled Soil Moisture Dynamics

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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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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, r2.

SMAP Retrievals Used for Precipita+on Es+ma+on

Randy Koster (NASA GSFC)

Streamflow Es1ma1on

Precipita1on es1mates (from before) Streamflow es1mates capture some of the

• bserved behavior.

Randy Koster (NASA GSFC)

Streamflow Es+ma+on

Technologies and Strategies for Environmental Monitoring, Assessment, and Prediction

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SM-yield rank correlation analysis for corn over central and eastern U.S.

Can we Improve our Es+ma+on of End Of Season Yield Using Satellite Observa+ons?

Mladenova, I. E., J. D. Bolten, et al. 2017. IEEE JSTARS, 10 (4): 1328-1343

Soil moisture and ET

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GRACE Data Assimilation for Drought Monitoring

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Surface Soil Moisture Root Zone Soil Moisture Groundwater

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• 3. SMAP FT identification freeze onset date by subbasin
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SMAP FT will be compared to in-situ observations

• !

SMAP SM values prior to freeze onset date will extracted to adjust state and frozen soil hydraulic parameters

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City of Cape Town: Water Dashboard

30 April 2018

Western Cape Water Supply System (WCWSS) Weekly Dam Drawdown Tracker Water Usage From Large Dams Comprising The Western Cape Water Supply System (WCWSS)

increase since last week AVG DAILY PRODUCTION ALL WATER SOURCES (Ml/d)

20.9! 0.9! 505!

!"#$%&'()*+,-./0( CAPACITY % % % % % % Ml 30 April 2018 Previous week 2017 2016 2015 2014 BERG RIVER 130 010 38.3 37.1 33.3 26.5 55.1 88.9 STEENBRAS LOWER 33 517 38.8 40.6 30.3 40.9 51.9 46.3 STEENBRAS UPPER 31 767 61.1 85.1 54.9 54.8 58.8 82.5 THEEWATERSKLOOF 480 188 11.3 10.3 17.1 32.5 53.8 73.8 VOËLVLEI 164 095 14.4 13.8 18.7 20.4 46.2 59.7 WEMMERSHOEK 58 644 47.6 44.5 36.0 49.9 52.8 60.8 TOTAL STORED 898 221 187 939 179 711 204 695 284 391 472 978 645 479 % STORAGE 20.9 20.0 22.8 31.7 52.7 71.9 NOTES:

Water Stored in Minor Dams Within Cape Town

Ten Year Graph Indicating Volume of Water Stored in Major Dams Comprising Western Cape Water Supply System (WCWSS) Water Stored in Major Dams Comprising Western Cape Water Supply System (WCWSS)

Dam Levels for 30 April 2014-2018

Rainfall

Percentage Water Stored in Major Dams (WCWSS) CCT Daily Average Water Production (7 Day Avg) CCT Water Quality Sample Pass Rate

• 20.00

NASA Satellite Data Volumes

High Performance Computing and The Rise of the Cloud

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John Bolten NASA Applied Sciences Program Water Resources john.bolten@nasa.gov

MOISST, Lincoln, NE June 5, 2018

Thank You