Mapping the Blue Marble: Using Space- Based Observations for Improved Global Water Security and Sustainability
John Bolten Associate Program Manager NASA Applied Sciences Program Water Resources
john.bolten@nasa.gov 2 The Water Landscape
Chagnon, 1989 •How can we reduce our uncertainty in the propagation of hydroclimatic extremes?
•For example, will a meteorological drought lead to a hydrological or agricultural drought? •How? When? Where?
•How do phases in P-E relate to soil moisture, surface drainage, base flow, groundwater storage, river discharge, and vegetation productivity? 3 Inadequacy of Surface Observations
Issues: - Spatial coverage of existing stations - Temporal gaps and delays - Many governments unwilling to share - Measurement inconsistencies - Quality control - Unrepresentativeness of point obsservations
Global Telecommunication System meteorological stations. Air temperature, precipitation, solar radiation, wind speed, and humidity only.
USGS Groundwater Climate Response Network..
River flow observations from the Global Runoff Data Centre. 4 Warmer colors indicate greater latency in the data record. Stick man with club in his hands – basic needs, followed food, lived near water (Pre)Formulation NASA Earth Science PACE (2022) Implementation GeoCARB (~2021) Primary Ops Missions: Present through 2023 TROPICS (12) (~2021) Extended Ops MAIA (~2021) Landsat 9 (2020) Sentinel-6A/B (2020, 2025) NI-SAR (2021) ISS Instruments SWOT (2021) LIS (2020), SAGE III (2020) TEMPO (2018) InVEST/CubeSats TSIS-1 (2018), OCO-3 (2018), GRACE-FO (2) (2018) ECOSTRESS (2018), GEDI (2018) RAVAN (2016) NISTAR, EPIC CLARREO-PF (2020) ICESat-2 (2018) IceCube (2017) (DSCOVR / NOAA) CYGNSS (8) (2019) MiRaTA (2017) (2019) HARP (2018) JPSS-2 Instruments SMAP Suomi NPP TEMPEST-D (2018) (NOAA) (>2022) RainCube (2018) OMPS-Limb (2019) (>2022) Landsat 7 QuikSCAT SORCE, CubeRRT (2018) (USGS) (2017) TCTE (NOAA) CIRiS (2018*) Terra (>2021) (~2022) (2017) CSIM (2018) Landsat 8 (USGS) Aqua (>2022) * Target date, (>2022) not yet manifested CloudSat (~2018) GPM (>2022) CALIPSO (>2022)
Aura (>2022) OSTM/Jason-2 (NOAA) OCO-2 (>2022) (>2022)
01.29.18 International Space Station Earth Science Operating Missions
TSIS-1 (2018) ELC-2 ELC-3 AMS
ESP-3 ELC-4 ELC-1 Columbus EF JEMEF
OCO-3 (2018) GEDI (2018) (2020) SAGE III (2020) LIS ECOSTRESS (2018) CLARREO PF (2020) Formulation External Logistics Carriers: ELC-1, ELC-2, ELC-3 Implementation External Stowage Platforms: ESP-3 Alpha Magnetic Spectrometer Primary Ops Columbus External Payload Facility Extended Ops Kibo External Payload Facility
01.29.18 Precipitation
Tropical Rainfall Measurement Mission Global Precipitation Measurement (TRMM) (GPM) The GPM Core Observatory will provide improved measurements of precipitation from the tropics to higher latitudes
• Global (50S-50N) precipitation measurement – 10 « 85 GHz radiometers – 13.6 GHz precipitation radar • Launched Feb 28, 2014 • Will use inputs from an international – 27 Nov 1997 to present constellation of satellites to increase space and time coverage • Improvements: - Longer record length - High latitude precipitation - including snowfall TRMM 14-year mean rainfall - Better accuracy and coverage 8 Last Week: IMERG Data
9 Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MODIS Data Products: •surface temperature •chlorophyll fluorescence •vegetation/land-surface cover, conditions, and productivity: •- net primary productivity, leaf area index, and intercepted photosynthetically active radiation •- land cover type, with change detection and identification; •- vegetation indices corrected for atmosphere, soil, and directional effects; •cloud mask, cirrus cloud cover, cloud properties characterized by cloud phase, optical thickness, droplet size, cloud-top pressure, and temperature; •aerosol properties •fire occurrence, temperature, and burn scars; •total precipitable water •sea ice cover •snow cover •derived evapotranspiration Soil Moisture Active Passive (SMAP)
31 January 2015
Instruments • Radar(1.26 GHz) ü High resolution, moderate accuracy • Radiometer (1.4 GHz) ü Moderate resolution, high accuracy
Shared antenna • Constant incident angle: 40 degrees • 1000 km wide swath
Orbit • Sun-synchronous • 6 am (Descending) / 6 pm (Ascending) • 685 km altitude • Global coverage every three days 11
Image courtesy: http://www.jpl.nasa.gov/ Ice Cloud and Land Elevation -2
ICESat-2 Technology: Objectives and Science: Multibeam Lidar
Measure polar ice-sheet change and its impact on sea-level change and climate. Estimate sea-ice thickness change and its impact on sea/air exchanges of energy & water. Measure global vegetation canopy height and biomass. Measure global oceans levels to understand sea level change. Measure inland water levels to understand & monitor the global hydrologic cycle.
Details: Main sensor: Advanced Topographic Laser Altimeter System (ATLAS) Six green (532 nm) lidar beams Launch Sept 2018
https://icesat-2.gsfc.nasa.gov/ Surface Water Ocean Topography SWOT Technology: Objectives and Science: Interferometric SAR
Understand the global water cycle on land, including floodplains and wetlands. Provide a global inventory of water resources. Measure ocean currents and eddies at scales as small as 20 kilometers (13 miles). Improve computer models that use ocean circulation data. Better understand coastal processes.
Details: Main sensor: Ka-band SAR interferometric system Two swaths, 60 km each Launch 2021
https://swot.jpl.nasa.gov/home.htm GRACE Derived Terrestrial Water Storage Variations
GRACE Science Goal: High resolution, mean and time variable gravity field mapping for Earth System Science applications Instruments: Two identical satellites flying in tandem orbit, ~200 km apart, 500 km initial altitude Key Measurement: Distance between two satellites tracked by K-band microwave ranging system Key Result: Information on water stored at all depths on and within the land surface
Animation of monthly GRACE terrestrial water storage GRACE measures changes in total terrestrial water anomaly fields. A water storage anomaly is defined here as a storage, including groundwater, soil moisture, snow, deviation from the long-term mean total terrestrial water Matt Rodell 14 and surface water. storage at each location. NASA GSFC Land Surface Model Structure
LSMs solve for the interaction of energy, momentum, and mass between the surface and the atmosphere in each model element (grid cell) at each discrete time-step (~15 min) SURFACE VEGETATION SUBGRID ATMOSPHERE HETEROGENEITY TRANSFER NEEDLELEAF SCHEME GRID TREES: 25% GRASSLAND: 50% SHRUBS: 10%
BARE SOIL: 15%
System of physical equations: Surface energy conservation equation Surface water conservation equation Input - Output = Storage Change Soil water flow: Richards equation P + Gin –(Q + ET + Gout) = ΔS Evaporation: Penman-Monteith equation Rn - G = Le + H etc. Matt Rodell 15 NASA GSFC Data Integration with a Land Data Assimilation System (LDAS) INTERCOMPARISON and OPTIMAL MERGING of global data fields
PRECIPITATION Satellite derived meteorological data used as land surface model SW RADIATION FORCING
ASSIMILATION of satellite based land surface state fields (snow, soil moisture, surface temp, etc.)
MODIS SNOW COVER
Ground-based observations used to VALIDATE model output Examples from NASA’s GLDAS Matt Rodell 16 SNOW WATER EQUIVALENT http://ldas.gsfc.nasa.gov/ NASA GSFC Applications
17 Monitoring Precipitation Memory
Soil Moisture Active Passive Mission
Global Precipitation Measurement Mission, Core Bolten, J., and W. Crow. 2012. GRL, 39 (19) John Bolten, NASA GSFC Observatory How Can We Improve Global Crop Forecasts?
Model Model + satellite observations 04/11/2014 - 04/20/2014 04/11/2014 - 04/20/2014
http://www.pecad.fas.usda.gov/cropexplorer/
Satellite-based soil moisture observations are improving USDA’s ability to globally monitor agricultural drought and predict its short-term impact on vegetation health and agricultural 19 yield. Can we Improve our Estimation of End Of Season Yield Using Satellite Observations?
VI-yield rank correlation analysis for corn over central and eastern U.S.
Mladenova, I. E., J. D. Bolten, et al. 2017. IEEE JSTARS, 10 (4): 1328-1343 Can we Improve our Estimation of End Of Season Yield Using Satellite Observations?
SM-yield rank correlation analysis for corn over central and eastern U.S. ExampleProject Objective: of the EvolutionNorth of American Agricultural Drought MonitoringDrought
Hain, NASA MSFC Global Evaporative Stress Index Methodology ALEXI ESI represents temporal anomalies in the ratio of actual ET to potential ET.
• ESI does not require precipitation data, the current surface moisture state is deduced directly from the remotely sensed LST , therefore it may be more robust in regions with minimal in-situ precipitation monitoring.
• Signatures of vegetation stress are manifested in the LST signal before any deterioration of vegetation cover occurs, for as example as indicated in NDVI, so TIR-based indices such as ESI can provide an effective early warning signal of impending agricultural drought.
• ALEXI ESI inherently includes non-precipitation related moisture signals (such as irrigation; vegetation rooted to groundwater; lateral flows) that need to be modeled a priori in prognostic LSM schemes.
Hain, NASA MSFC Is Cape Town running out of water in 30 days? January 3, 2014 January 9, 2016 January 14, 2018
NASA Earth Observatory images by Joshua Stevens, using Landsat data from the USGS Near Real-Time Flood Detection and Socioeconomic Impact Assessment in the Real Time Flood Impact AssessmentLower Mekong RiverTool Basin John Bolten2, Perry Oddo1,2, Aakash Ahamed3 Figure 1 1Hydrological Sciences Lab, NASA GSFC; 2USRA; 3Stanford University, Geophysics
(A) (B) (C) (D)
Description Inundated Pixels Area (km2) Damages (USD) MODIS-derived surface water extents are used to produce flood Rice - 1 crop/yr 1505187 12192.01 2,317,168 depth estimates in near real-time. Flood depth estimates are Mixed Annual Crops 175947 1425.17 1,435,970 Cleared before 2010 4366 35.36 35,661 then fed into a standardized flood damage framework to Orchard 29958 242.66 73,169 produce damage estimates based on inundated land cover and Flooded Forest 384433 3113.91 28,265,720 Grassland/Sparse Vegetation 218204 1767.45 497,578 affected infrastructure. Deciduous Shrubland 142643 1155.41 319,502 Urban 25421 205.91 12,604 The rapid initial estimates of socioeconomic impacts can provide Barren - Rock Outcrops 8377 67.85 - Industrial Plantation 160 1.30 355 valuable information to governments, international agencies, Deciduous Broadleaved 883 7.15 56,089 and disaster responders in the wake of extreme flood events. Evergreen/ Broadleaved 269 2.18 17,931 Forest Plantation 0 0.00 - Bamboo Scrub/Forest 1369 11.09 101,454 Coniferous Forest 0 0.00 - Mangrove 211 1.71 10,693 Marsh/Swamp 60870 493.05 151,308 Aquaculture 740 5.99 2,496 Aquaculture Rotated with Rice 2168 17.56 3,316
Fayne, J. V., J. D. Bolten, C. S. Doyle, et al. 201,7IJRS, 38 (6): 1737-1757] ASO in the drought (2013-2015) and the near-average (2016)
Source: Tom Painter,jpl.nasa.gov NASA JPL Subsidence in the San Joaquin Valley 2007–2011
Zhen Liu, Vince Realmuto, Tom Farr, JPL jpl.nasa.gov Scanning Imaging lidar spectrometer Jay Famiglietti, NASA JPL Groundwater Depletion in Northern India
30 Total Terrestrial Water Soil Water 20 Groundwater Groundwater Trend 10
0
-10
-20 Water StorageWater Anomaly (cm)
-30 Mar - 11 Mar - 03 Mar - 04 Mar - 05 Mar - 06 Mar - 07 Mar - 08 Mar - 09 Mar - 10 Mar - 12 Date
Rate of change of terrestrial water storage (cm/yr) from 2003-2012 based on GW = TWS – SM – SWE NASA/GRACE satellite observations. Groundwater continues to be depleted in the Indian states of Rajasthan, Punjab, and Haryana by about 16.0 km3/yr, reduced slightly from our previous 29 (2002-08) estimate of 17.7 ±4.5 km3/yr (Rodell et al., Nature, 2009). Global: GLOBAL terrestrial TERRESTRIAL water WATER storage STORAGE
Ø About 2,860km3 of water is stored in rivers and floodplains [previous estimates vary from 2,000km3 (Oki & Kanae, 2006) to 2,120km3 (Shiklomanov, 1993)]; Ø 28% of that water, or 800km3, is concentrated in the Amazon Basin; Ø Uncertainty due to meteorological forcings is about 6% globally.
RESULTS
30 Augusto Getriana, GSFC NLDAS Data and Drought Monitor
Over 33 years of hourly gridded precipitation, surface meteorology, and land-surface model output, including a real-time drought monitor
NLDAS specifications and variables: An example of the NLDAS Drought Monitor 1/8th-degree (~12km) hourly gridded data (below) showing soil moisture percentiles of from Jan 1979 to near real-time the 4 land-surface model ensemble-mean 25-53 North and 125-67 West (Mosaic, Noah, VIC, & SAC) against the long- term soil moisture climatology of NLDAS. Input: Daily gauge precipitation analyses, NARR Figure from 13 June 2012. near-surface meteorology, NEXRAD radar data, bias-correcting GOES shortwave radiation Output: Surface fluxes, snow cover/depths, soil moistures/temperatures, runoff, many others
NLDAS datasets and services are available from the NASA GES DISC: http://disc.sci.gsfc.nasa.gov/hydrology/ Documentation on NLDAS, including a link to the NLDAS Drought Monitor: 31 http://ldas.gsfc.nasa.gov/nldas/ Remote sensing data for landslides
3/12/2018 D. Kirschbaum, NASA GSFC 32 Technologies and Tools
Water Quality Analysis Tool (WQAT) • Provides simplified access to remote sensing imagery of indicators of nutrient pollution (chlorophyll a = algal blooms) and for establishing chlorophyll criteria • EPA’s satellite remote sensing methodology for the Florida nutrient criteria rulemaking could be reproduced with WQAT • GIS and Excel knowledge level (low barrier to entry) • WQAT improves access and use of complex models as well as enhances and supports nutrient management decisions
Chlorophyll-a (mg/l)
NASA Satellite Data Volumes High Performance Computing and The Rise of the Cloud
NASA Applied Sciences Program Water Resources Contact
38 https://appliedsciences.nasa.gov/programs/water-resources-program Thank you!
John Bolten Associate Program Manager NASA Applied Sciences Program Water Resources
Bradley Doorn, Forrest Melton, Christine Lee NASA Applied Sciences Program