Satellite Remote Sensing for Water Resources Monitoring & Mapping

1. University of Semarang – Workshop 4th December 2018Prof. Magaly Koch Satellite Remote Sensing for Water Resources Monitoring & Mapping Magaly Koch, PhD Center for Remote Sensing Boston University mkoch@bu.edu

2. Outline • Hydrological Cycle • Satellite Water Products • Rain • Snow / Ice • Water Vapor • Soil Moisture etc. • What is Remote Sensing? How Does it Work? • Satellite Sensors for Water Resources Mapping

3. Hydrological Cycle

4. Hydrological Cycle • The Sun is the energy that powers the hydrological cycle. Its energy in the form of light and heat causes water to evaporate from oceans, lakes and rivers. Warm air currents rising from the oceans lift water vapor into the atmosphere where it condenses forming clouds. Clouds eventually release their water in the form of precipitation. Part of the precipitation travels back to the ocean as run-off, recharges the groundwater or simply re-evaporates into the atmosphere. • The characterization of the spatial and temporal variability of water and energy cycles is critical to improve our understanding of the land-surface-atmosphere interaction and the impact of land-surface processes on climate extremes. • Land surface processes affecting the climate: changes in water storage in the form of snow / ice, reforestation / deforestation, agriculture and water management, urbanization, pollution etc.

5. Water Resources Management For sustainable water management, it is critical to have accurate estimates of water cycle components

6. Fresh Water Information

7. Global Water Distribution

8. Satellite Water Products • Rain • Snow/Ice • Water Vapor • Clouds • Soil Moisture • Ground Water • Snow/Ice • Rain, Clouds, Water Vapor • Soil Moisture • Evaporation/Transpiration • Run off Products in red - derived from satellite measurements Products in blue - derived from atmospheric/land surface models in which satellite measurements are assimilated

9. Satellite Water Products • Water is critical for food, health, and energy • Water in all forms is controlled by weather and climate and also impacts weather and climate • A major focus of NASA satellite missions and modeling efforts is to • develop improved water cycle observation capabilities • enhance understanding of weather and climate related water cycle variability • make water products available to end-users for water resource management • help train end-users in accessing, interpreting, and using water products • NewMissions: Global Precipitation Measurement (GPM) and Soil Moisture Active Passive (SMAP)

10. ~7000 Stations

11. Why Use Satellites & Models to Monitor/Study Water Resources? • Provides consistent, global information • Complements ground-monitoring networks or provides information where there are no ground-based measurements • Advance warning of impending environmental events and disasters such as flooding • Models provide parameters which are not directly observable, for example, 3-D winds and water vapor movement in the atmosphere • Models help understand processes associated with cycling of water in the climate system and provide prediction capability

12. What is Remote Sensing? How Does it Work?

13. Electromagnetic Radiation Longwave or Terrestrial Radiation Shortwave or Solar Radiation

14. Radiation at the Top of Atmosphere • Parameters influencing the amount of radiation at the top of earth-atmosphere systems: • Solar radiation • surface type – e.g. vegetation, • soil, snow/ice, clouds, aerosol • Infrared radiation • surface temperature, greenhouse • gases (water vapor, carbon dioxide, methane, ozone) and clouds • Microwave radiation • Surface temperature and type (snow/ • ice, soil moisture), rain, water vapor, clouds

15. Satellite Remote Sensing • Satellites carry a variety of instruments to measure electromagnetic radiation reflected or emitted by the earth-atmosphere system • In ‘passive’ remote sensing satellites measure naturally reflected or emitted radiation • In ‘active’ remote sensing satellites ‘throw’ beams of radiation on the earth-atmosphere system and measure ‘back-scattered’ radiation

16. Satellite Remote Sensing • The measured radiant energy by satellites is recorded typically as digital counts on-board and transmitted to ground stations • The counts are processed and converted to appropriate geophysical quantities by using complex procedures (algorithms) • These ‘retrieved’ geophysical quantities are validated for accuracy by comparing them with ground-based and/or aircraft-based measurements

17. Satellite Remote Sensing

18. Modeling of Atmosphere-Land-Ocean Systems Models use laws of physics in terms of mathematical equations to represent atmosphere, ocean, land systems and changes occurring in them in space and time. Models apply these mathematical equations, on horizontal and vertical grids by using numerical methods. Models use observations to represent the atmosphere-ocean-land system at a given time to deduct how the system will evolve over space/time. Models ‘parameterize’ physical processes based on physical/statistical/empirical techniques derived or verified by using observed quantities.

19. NASA Models for Water Products • (Atmosphere-Ocean-Land Models) • GISS GCM ModelE*: The Goddard Institute for Space Studies General Circulation Model II • GEOS-5*:The Goddard Earth Observing System Version 5 • MERRA:Modern Era Retrospective-analysis for Research and Application • GLDAS: Global Land Data Assimilation System • NLDAS:North American Land Data Assimilation System * GISS GCM ModelE and GEOS-5 : Used for IPCC climate model projections

20. Modeling of Atmosphere-Land-Ocean Systems Model Products • MERRAClouds, Rain, Snow Mass, Snow • Cover, Snow Depth, Surface Snowfall Rate, Surface Evaporation, Evapotranspiration, Water Vapor • GLDAS/NLDASEvapotranspiration, Multi-layer Soil Moisture, Snowfall, Snow Melt, Snow-Water Equivalent, Surface and Sub-surface Runoff • GISS GCM ModelE and GEOS-5 Used for IPCC climate model projections, multi-decadal simulations with various climate conditions (greenhouse gases and aerosols) IPCC = Intergovernmental Panel on Climate Change

21. Model Products There are multiple models, with varying spatial/temporal resolutions and accuracies. Modeling of hydrological processes is complex due to presence of water in gaseous, liquid, and solid forms in the earth-atmosphere system. Models use many approximations and assumptions in representing physical processes. Rigorous validation with observations and model-to-model inter-comparisons are conducted to assess accuracy of model products.

22. Example of a NDLAS Model: Noah There are currently four land-surface models with data available over the period of 1979 to present. These are SAC, Mosaic, Noah and VIC. Here we show the Noah model.

23. NDLAS Model Concept The goal of the North American Land Data Assimilation System (NLDAS) is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. http://ldas.gsfc.nasa.gov/index.php

24. Land Surface Models • These models compute spatially averaged energy and water fluxes from the land surface in response to meteorological and radiative forcing. • Typically a model accounts for sub-grid variability in surface characteristics through the following approach: • A grid square area containing different vegetation is divided into homogeneous sub-regions (tiles), each containing a single vegetation or bare soil type. Satellite observed vegetation / soil distribution are used to determine the partition. Energy balance calculations are performed over each tile at every time step, and each tile maintains its own soil moisture reservoir contents and temperatures. This procedure is called physical parameterization. • There are five moisture reservoirs within a land surface tile of the model and each requires a separate water balance equation. The reservoirs are: canopy interception reservoir, top soil layer, middle soil layer, bottom soil layer, and snow pack. To calculate water fluxes, the model uses in addition to point observations on the ground, satellite measurements of land surface temperature, leaf area index (greenness), snow cover, precipitation etc.

25. Water Products from Satellites & Models • Potential Data Usage • Monitoring, Understanding, and Developing Strategies: • Extreme Rain Events and Floods • Droughts • Snow Amount and Snow Melt • Climate Variability/Change and Water Resources: • Seasonal to Inter-annual Changes in Rain, Soil Moisture, Evapotranspiration, Atmospheric Temperature, Humidity, Clouds, Winds

26. Water Resources Application

28. Satellite Data for Water Resources • Not all water cycle components can easily be measured directly, such as: • Evapotranspiration • Runoff • Water vapor transport • Earth Observation (EO) satellites and Earth system models measure and calculate all water cycle components • EO satellites & Earth System Models provide hourly, daily, seasonal, multi-year time scales

29. Satellite Data for Water Resources • To use satellite observations, it is important to understand principles of remote sensing and attributes of satellite data: • What is Remote Sensing? What is Measured? • Types of Satellite Orbits • Types of Satellite Sensors/Instruments, Spectral Bands • Conversion from Sensor Measurements to Geophysical Quantities (i.e. Temperature, Rain, SoilMoisture, CarbonDioxide etc.) • Spatial and Temporal Resolutions and Coverage • Spectral and Radiometric Resolutions • Levels of Satellite Data Products • Strengths and Limitations of Remote Sensing Data

30. Overview of Satellites and Sensors for Water Resources Applications

31. EO Satellites for Water Resources Monitoring

32. EO Satellites for Water Resources Monitoring

33. NASA Satellites for Water Resources Monitoring

34. Landsat Satellites and Sensors

35. Landsat Data for Water Resources Applications

36. Where to get Landsat Data USGS Earth Explorer http://earthexplorer.usgs.gov/ USGS Global Visualization Viewer http://glovis.usgs.gov/ USGS Landsatlook Viewer http://landsatlook.usgs.gov/viewer.html

37. Global Precipitation Measurement (GPM) http://pmm.nasa.gov/GPM

38. GPM Data for Water Resources Applications

39. MODerate Resolution Imaging Spectroradiometer (MODIS) http://modis.gsfc.nasa.gov/

40. MODIS Data for Water Resources Management

41. SoilMoisture Active Passive (SMAP) http://smap.jpl.nasa.gov/

42. SMAP MicrowaveRadiometer & Radar http://smap.jpl.nasa.gov/observatory/instrument/

43. SMAP Data for Water Resources Applications

44. Where to get SMAP Data? http://nsidc.org/data/search/#keywords=soil+moisture/

45. Gravity Recovery and Climate Experiment (GRACE) Satellite http://www.jpl.nasa.gov/missions/details.php?id=5882

46. GRACE: The Gravity Recovery And Climate Experiment A microwave link “connects” the two satellites and makes extremely precise measurements of the changes in distance between the two satellites. These fluctuations are caused by changes in the orbital motion of the twin spacecraft as they respond to changes in the density of the surface they are passing over. Density changes correspond directly to changes in the gravitational field. Due to an uneven distribution of mass inside the earth, the earth's gravity field is not uniform - that is, it has "lumps". This model greatly exaggerates the scale so that many smaller features can be seen.

47. GRACE Data for Water Resources Applications

48. GRACE Data for Water Resources Applications Gravity Recovery and Climate Experiment (GRACE) data can be used to track groundwater depletion rates over regional scales. The map shows the rate of change of groundwater storage in India during 2002–08, with losses shown in deepening shades of red and gains in blue. The estimated rate of depletion of groundwater in northwestern India is 4.0 centimeters of water per year, equivalent to a water table decline of about 33 centimeters per year. Increases in groundwater in southern India are due to greater than normal rainfall in the past few years, whereas northwestern India received close to normal rainfall throughout the study period. Credit: M. Rodell et al. Nature doi:10.1038/nature08238; 2009

49. Where to get GRACEData? • Level-0 to Level-2 • – http://www.csr.utexas.edu/grace/ • – http://isdc.gfz-potsdam.de • Level 3 • – http://grace.jpl.nasa.gov/data/ • – http://geoid.colorado.edu/grace/

50. Thank You