The aim of this study is to showcase and discuss these new technologies for hydrometeorological studies. Six of NASA’s web-repositories that can be used to freely download and visualise such spatial and/or time-series factors are listed and explained with examples for Ireland: ways to access hydrological, meteorological, soil, vegetation and socio-economic data are shown, and estimations of various precipitations statistics, anomalies, and water balance are presented for monthly and seasonal analyses. The advantages, disadvantages and limitations of the satellite datasets are discussed to provide useful recommendations about their proper use, based on purpose, scale, precision, time requirement, and modelling-expansion criteria.

1. Drought monitoring, Precipitation statistics, and water balance with
freely available remote sensing data: examples, advances, and limitations
Alamanos A., Linanne S.
The Water Forum | Centre for Freshwater and Environmental Studies, Dundalk Institute
of Technology, Marshes Upper, Dundalk Co. Louth, A91K584, Ireland.
* angelos.alamanos@dkit.ie
Irish National Hydrology Conference 2021
Athlone Co.Roscommon 16/11/2021

2. Background, Problem Statement & Research Question
• Ireland is increasingly facing dryer conditions
• According to the National Water Resources Plan (NWRP), Met Eirrean and CSO data, 11 cities might have
difficulties meeting their water needs due to the combination of decreased seasonal precipitation,
increasing population, and water consumption per person above the country’s average.
• Cork, Drogheda, Dundalk, Letterkenny, Limerick, Sligo, Waterford, Mullingar, and Wexford have been
identified either as locations with lower rainfall and higher temperature, or as locations of dry periods in
2020.
• Necessity to study drought and precipitation parameters, water balance.
Research Question
• How to overcome complex & data-hungry & time-consuming applications to monitor droughts?
• The use and potential of the new technologies, such as remote sensing and satellite imagery, big data
handling, and geospatial software
• Demonstration,
• examples,
• Pros and cons.
(Alamanos et al., 2021)

3. Aim of the study
• Understand how remote sensing can be used for drought monitoring & similar analyses,
• Acquire remotely-sensed imagery and products for drought, hydro-meteorological and socio-economic
parameters, and analyse them within a geospatial software,
• 6 NASA’s web-repositories  spatial & time-series data / globally applicable  hydrological,
meteorological, soil, vegetation, socio-economic data,
• Estimate various precipitations statistics, anomalies, water balance, socio-economic data
• Use 100% free resources and software
• Discuss the advantages, disadvantages and limitations of the satellite datasets.
• Provide a study – basis for several applications by a wide range of professionals.

4. Droughts
All droughts originate from “below normal” precipitation
• Meteorological Drought
– degree of dryness compared to ‘normal’ precipitation
– region-specific and high spatial variability
• Agricultural Drought
– precipitation shortage, evapotranspiration and agricultural
impact
• Ecological Drought
– prolonged and widespread deficit in naturally available
water supplies & ecosystems
• Hydrological Drought
– related to rain and snow shortfall
– impact on surface and subsurface water supply
– affects agricultural drought
• Social and economic (changes) Drought
– supply and demand rates of goods and economy
– affected by agricultural, ecological, and hydrological
(Wilhite & Glantz, 1985; Mehta, 2017)
Requirements:
Precipitation analysis – time-series & stats
Drought indices / Hydrological Balance
Vegetation indices / Soil Moisture
Meteorological parameters
(ET, Temp, etc.)

5. NASA Satellites for Drought Monitoring
• Landsat: 07/1972 – present
• Tropical Rainfall Measuring
Mission (TRMM): 11/1997 –
04/2015
• Global Precipitation Measurements
(GPM): 02/2014 – present
• Terra: 12/1999 – present
• Aqua: 05/2002 – present
• Soil Moisture Active Passive
(SMAP): 01/2015 – present
• Gravity Recovery and Climate
Experiment (GRACE): 03/2002 –
present
(Huffman et al., 2007; http://pmm.nasa.gov/)

6. NASA Satellites for Precipitation data
Multi-satellite algorithms allow
improved spatial and temporal
coverage of precipitation data and are
widely used for applications:
• TRMM Multi-satellite Precipitation
Analysis (TMPA)
• Integrated Multi-satellitE
Retrievals for GPM (IMERG)
(Huffman et al., 2007; http://pmm.nasa.gov/)
These tools have been shown to be useful for getting regional and global drought indices and precipitation
statistics – several researchers have successfully used them (De Jesus et al., 2016; Sheffield et al.,2012)

7. NASA Satellites for Soil Moisture & Vegetation Indices
MODIS (2000 – present) 
• Spatial Resolution (250 m, 500 m, 1 km)
• Temporal Resolution (Daily, 8 day, 16 day,
monthly, quarterly, yearly)
MOD 13 it the MODIS type that observes Vegetation Indices (spatial
resolution = 250-1000m; frequency = 16 day, monthly)
MOD13 produces:
– NDVI (Normalized Difference Vegetation Index)
• 16-day, 250-meter spatial resolution (gridded)
• Used for characterizing land surface processes
• Anomalies can be used to identify drought
– EVI (Enhanced Vegetation Index)
• Minimizes canopy background
• Improvement in dense vegetation conditions
Soil Moisture Active Passive (SMAP): 01/2015 – present  good for agricultural/ hydrological drought
 Normalized Difference Vegetation Index  based on
how ‘green’ or ‘red’ are the leaves colour to indicate their
dryness.
 EVI is similar to NDVI and can be used to quantify
vegetation greenness. It corrects for some atmospheric
conditions and canopy background noise and is more sensitive
in areas with dense vegetation.

8. Example 1: Monthly precipitation analysis
• GPM satellite – IMERG algorithm
(example for Ireland)
• Period: 2010-01-01 - 2019-12-31
• Month 1,2,3 stands for Jan, Feb, Mar, etc.
• Then in the name of the files, we have the
coordinates (Ireland)
• Rename (shorter names for calcs)
• Various time steps to choose
Downloaded:
• monthly precipitation maps for the averages of the period 2010-2019
• same maps for each month of a year (e.g., 2018)

9. Example 1: Monthly precipitation analysis
Contin.2 Contin.3
Useful for spatial differences,
• identification of dry years, areas, months,
• statistics (St.Dev.) & calcs of SPI,

10. Example 2: Seasonal precipitation
analysis
20 years – seasonal data from GPM IMERG.
Data:
• (Monthly and) Seasonal Averages
• Time series – seasonal
• (Monthly and) Seasonal Averages  here the
example only for JJA
• & all years (2000-2020)
List of statistics that can be calculated (min,
max, average, St. Dev., …)

11. Example 2: Seasonal precipitation analysis
• Seasonal precipitation anomaly can be calculated as shown
• QGIS  free geospatial software
• Raster Calculator  routine for operations among raster files
• All data are extractable to be further edited in spread-sheets
Example – seasonal anomaly  how dry is JJA
of 2018, compared to the averages of 2000-2020
(only negative values)

12. Example 3: Hydrological Balance
Parameters used:
• DEM
https://photojournal.jpl.nasa.gov/catalog/PIA06672
https://www2.jpl.nasa.gov/srtm/ireland.html#PIA06672
• hydrosheds  hydrobasins  downlowed hydrobasins
(e.g. EU level 12 customised with lakes)
https://giovanni.gsfc.nasa.gov/giovanni/
• 0.1deg P (mm/month),
• ET (kg/m²/s monthly),
• I = Subsurface flow (kg/m²/s monthly),
• R = StormRunoff (kg/m²/s)
QGIS  conversion to same Coordinate system
Hydrosheds & Giovanni datasets
QGIS  Raster calculator  conversion to the
same units (multiply)
e.g., here all in kg/m²/s
in order to be able
to apply water
balance Equations:
P – ET – Runoff
(simple estimation)

13. Example 3: Hydrological Balance
or water balance Equation: ΔS = P – ET – I – R
operations 
Raster
Calculator

14. • Earthdata nasa for vegetation index (e.g. search
MOD 13 – A1), and similarly for other parameters
More examples and resources (Vegetation Index & Soil Moisture)
http://www.cpc.ncep.noaa.gov/products/Soilmst_Monito
ring/gl_Soil-Moisture-Monthly.php
• NOAA Climate Prediction Center provides calculated
monthly soil moisture climatology (1971-2000) and
anomalies for present-day and the past 12 months
These maps
visually provide
indications of
soil moisture
deficit and
drought
conditions

15. https://worldview.earthdata.nasa.gov/  e.g., here a video (monthly 2017-2019)
Since early 2015, the SMAP mission provides global soil moisture observations that can be used to monitor soil
moisture variability from day-to-day and month-to-month
More examples and resources (Soil Moisture)
Daily and monthly soil moisture
data can be visualized using
NASA Worldview

16. More examples and resources (ET)
http://eeflux-level1.appspot.com/
• ET for Drought Monitoring (and
various other parameters)
• 30m resolution
• These maps provide information
about changing ET, indicative of
agricultural and hydrological
drought conditions
Dates & Location & select images with percentage of clouds

17. More examples and resources
http://eeflux-
level1.appspot.com/
A) NDVI
B) ET
• DEM
• Land Cover
• T, P, …
http://geoid.colorado.edu
/grace/dataportal.html
GRACE-Based Water
Storage Anomalies for
Drought Monitoring

18. More examples and resources (Socio-economic Data)
https://sedac.ciesin.columbia.edu/data/sets/browse
Free download & Comparison with other parameters
comparing indicatively the population
density with the Standard Deviation of
JJA of 2018 (from the 20-year
Precipitation average)
E.g., data on crops, agriculture, population density, economic
losses from disasters, drought or flood risk mortality, etc.
analyses with SDGs and comparisons with water data (Alamanos &
Linnane, 2021)

19. Conclusions
Limitations
• Not recommended for small areas (e.g. sub-catchment),
• Cannot replace catchment modelling fed with earth-observed data and measurements, in terms of accuracy,
calibration-validation, and testing under various scenarios.
Advantages:
a) education, awareness, and understanding of the natural processes and the magnitudes of the various
parameters, their different spatial and temporal scales;
b) larger areas given the pixel size;
c) when there are no data available;
d) when one seeks a fast, preliminary picture of a situation without needing to develop and solve a model (which
is way more time-consuming);
e) this preliminary estimation assists and provides guidance for a more detailed research (e.g. identifying years,
months or seasons and areas with specific hydrological characteristics).
____________________________________________________________________________________________________________________________________________________________________
Combine with local-data (e.g. EPA maps) to refine any analysis and perform the same basic or more complex
operations, with respect to hydrological, wate quality (colour-based analyses), or other parameters.
Provision of a near real-time monitoring for processes that demand a data-hungry and longer
simulation and modelling effort. Both ways must be used together in a complementary way.

20. Thank you for your attention

21. References
• Wilhite, D.A.; and M.H. Glantz. 1985. Understanding the Drought Phenomenon: The Role of Definitions.
Water International 10(3):111–120
• Mehta, V.M., 2017: Natural Decadal Climate Variability: Societal Impacts. CRC Press, Boca Raton, Florida,
326 pp.
• Alamanos, A., Rolston, A., & Linnane, S. (2021). Irish bathing sites closures and Stormwater Overflows:
Precipitation forecasts, extremes analysis, and comparison with climate change projections. EGU General
Assembly 2021, online, April 19–30, 2021.
• Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, E.F. Stocker, D.B. Wolff, 2007: The
TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation
Estimates at Fine Scale. J. Hydrometeor., 8, 33-55. MERG_ATBD_V4.5.pdf
• http://pmm.nasa.gov/
• Alamanos A, Linnane S. (2021). Estimating SDG Indicators in Data-Scarce Areas: The Transition to the Use of
New Technologies and Multidisciplinary Studies. Earth.; 2(3):635-652. https://doi.org/10.3390/earth2030037
• De Jesús, A., J. Agustín Breña-Naranjo, A. Pedrozo-Acuña, and V. Hugo Alcocer Yamanaka, 2016:The Use of
TRMM 3B42 Product for Drought Monitoring in Mexico, Water, 8, doi:10.3390/w8080325
• Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-yr high-resolution global dataset of
meteorological forcings for land surface modeling, J. Climate, 19 (13), 3088-3111

22. Links
• https://www.usgs.gov/core-science-systems/nli/landsat/landsat-enhanced-vegetation-index?qt-
science_support_page_related_con=0#qt-science_support_page_related_con
• https://giovanni.gsfc.nasa.gov/giovanni/
• https://ldas.gsfc.nasa.gov/gldas/
• https://nsidc.org/
• https://eeflux-level1.appspot.com/
• https://earthdata.nasa.gov/
• https://lpdaac.usgs.gov/products/mod16a2v006/
• https://sedac.ciesin.columbia.edu/data/sets/browse
• http://geoid.colorado.edu/grace/dataportal.html
• https://www.cpc.ncep.noaa.gov/products/Soilmst_Monitoring/gl_Soil-Moisture-Monthly.php
• https://worldview.earthdata.nasa.gov/