copernicus global land operations...figure 14: maximum number of consecutive days wrongly classified...

Copernicus Global Land Operations – Lot 1 Date Issued: 25.03.2020 Issue: I1.00

Copernicus Global Land Operations

“Vegetation and Energy” ”CGLOPS-1”

Framework Service Contract N° 199494 (JRC)

SCIENTIFIC QUALITY EVALUATION 2019

SOIL WATER INDEX

VERSION 3.0

Issue I1.00

Organization name of lead contractor for this deliverable: TU Wien

Book Captain: Bernhard Bauer-Marschallinger

Contributing Authors: Tobias Stachl


Document-No. CGLOPS1_SQE2019-SWIV3 © C-GLOPS Lot1 consortium

Issue: I1.00 Date: 25.03.2020 Page: 2 of 78

Dissemination Level PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)




Document Release Sheet

Book captain: Bernhard Bauer-Marschallinger

Sign Date 25.03.2019

Approval: Roselyne Lacaze Sign Date 03.04.2020

Endorsement: Michael Cherlet Sign Date

Distribution: Public




Change Record

Issue/Rev Date Page(s) Description of Change Release

25.03.2020 All First issue I1.00




TABLE OF CONTENTS

Executive Summary .................................................................................................................. 16

1 Background of the document ............................................................................................. 18

1.1 Scope and Objectives............................................................................................................. 18

1.2 Content of the document....................................................................................................... 18

1.3 Related documents ............................................................................................................... 18

1.3.1 Applicable documents ................................................................................................................................ 18

1.3.2 Input ............................................................................................................................................................ 18

1.3.3 Output ......................................................................................................................................................... 19

2 Review of Users Requirements ........................................................................................... 20

3 Review of the SWI quality .................................................................................................. 22

4 Scientific Quality Evaluation Method ................................................................................. 24

4.1 Global Analysis ...................................................................................................................... 24

4.2 Regional analysis ................................................................................................................... 26

4.3 Model Reference Products ..................................................................................................... 27

4.4 In-situ Reference Products ..................................................................................................... 28

4.5 Other Reference Products ...................................................................................................... 30

4.6 Data Availability .................................................................................................................... 31

4.7 Layer Comparison Matrix ....................................................................................................... 31

5 Results .............................................................................................................................. 32

5.1 Global analysis ...................................................................................................................... 32

5.1.1 Comparison with GLDAS Noah .................................................................................................................... 32

5.1.2 Comparison with ISMN ............................................................................................................................... 48

5.2 Regional Analysis .................................................................................................................. 62

5.2.1 Situation in southern Africa in 2019 ........................................................................................................... 62

5.2.2 Zambia ........................................................................................................................................................ 63

5.2.3 Australia ...................................................................................................................................................... 65

5.2.4 Europe ......................................................................................................................................................... 67

5.2.5 Spain ........................................................................................................................................................... 69

6 Conclusions ....................................................................................................................... 71




6.1 Global Analysis ...................................................................................................................... 71

6.2 Regional analysis ................................................................................................................... 73

7 Recommendations ............................................................................................................. 74

8 References ........................................................................................................................ 75

8.1 Scientific Literature ............................................................................................................... 75

8.2 News Footage ....................................................................................................................... 76




List of Figures

Figure 1: Map showing the regions used to define the regions of special interest for the plots of R

over time. ............................................................................................................................... 25

Figure 2: Map of used ISMN networks, showing all stations with data in the period 2007-2019. ... 29

Figure 3: Pearson’s correlation coefficient between SWI T=1 and GLDAS Noah, as well the

difference between the reference period (2007-01-01 until 2018-12-31) and the current

validation period (2019-01-01 until 2019-12-31). Green colours indicate performance

improvements in the current period. In the difference map only the statistically significant

differences are shown whereas the violin plots show all data points. The violin plots show the

estimated kernel density plot as well as the median (dashed line) and the lower and upper

quartile (dotted lines). ............................................................................................................ 33















Figure 6: Average 3-month Pearson’s correlation coefficient R between SWI T=5 and GLDAS

Noah, for the reference period (2007-01-01 until 2018-12-31, green line) and the current

validation period (2019-01-01 until 2019-12-31, purple line) over various areas. The standard

deviation for the reference is shown as light green surface. ................................................... 36

Figure 7: RMSD between SWI T=1 and GLDAS Noah, as well the difference between the

reference period (2007-01-01 until 2018-12-31) and the current validation period (2019-01-01

until 2019-12-31). Green colours indicate performance improvements in the current period. In

the difference map only the statistically significant differences are shown whereas the violin

plots show all data points. The violin plots show the estimated kernel density plot as well as

the median (dashed line) and the lower and upper quartile (dotted lines). .............................. 38
















Figure 10: Bias between SWI T=1 and GLDAS Noah, as well the difference between the reference

period (2007-01-01 until 2018-12-31) and the current validation period (2019-01-01 until 2019-

12-31). Green colours indicate performance improvements in the current period. In the

difference map only the statistically significant differences are shown whereas the violin plots

show all data points. The violin plots show the estimated kernel density plot as well as the

median (dashed line) and the lower and upper quartile (dotted lines). ................................... 41

Figure 11: Bias between SWI T=20 and GLDAS Noah, as well the difference between the






Figure 12: Bias between SWI T=100 and GLDAS Noah, as well the difference between the






Figure 13: Percent of correctly classified freeze/thaw states compared to GLDAS Noah soil

temperature as well the difference between the reference period (2007-01-01 until 2018-12-

31) and the current validation period (2019-01-01 until 2019-12-31). Green colours indicate

performance improvements in the current period. In the difference map only the statistically

significant differences are shown whereas the violin plots show all data points. The violin plots

show the estimated kernel density plot as well as the median (dashed line) and the lower and

upper quartile (dotted lines). .................................................................................................. 45

Figure 14: Maximum number of consecutive days wrongly classified when comparing the SSF

against GLDAS Noah soil temperature as well the difference between the reference period

(2007-01-01 until 2018-12-31) and the current validation period (2019-01-01 until 2019-12-31).

Green colours indicate performance improvements in the current period. In the difference map




only the statistically significant differences are shown whereas the violin plots show all data

points. The violin plots show the estimated kernel density plot as well as the median (dashed

line) and the lower and upper quartile (dotted lines). .............................................................. 46

Figure 15: Mean number of consecutive days wrongly classified when comparing the SSF against

GLDAS Noah soil temperature as well the difference between the reference period (2007-01-

01 until 2018-12-31) and the current validation period (2019-01-01 until 2019-12-31). Green

colours indicate performance improvements in the current period. In the difference map only

the statistically significant differences are shown whereas the violin plots show all data points.

The violin plots show the estimated kernel density plot as well as the median (dashed line) and

the lower and upper quartile (dotted lines). ............................................................................ 47

Figure 16: Pearson’s correlation coefficient between SWI T=1 and in-situ data, as well the





















Figure 19: RMSD between SWI T=1 and in-situ data, as well the difference between the reference






Figure 20: RMSD between SWI T=20 and in-situ data, as well the difference between the









Figure 21: RMSD between SWI T=100 and in-situ data, as well the difference between the






Figure 22: Bias between SWI T=1 and in-situ data, as well the difference between the reference


















Figure 25: SWI and in-situ data for SNOTEL-station CRAB CREEK ............................................. 59

Figure 26: SWI and in-situ data for USCRN-station Edinburg-17-NNE .......................................... 59

Figure 27: SWI and in-situ data for USCRN-station Mercury 3 SSW ............................................. 60

Figure 28: SWI and in-situ data for SCAN-station Pine Nut ........................................................... 60

Figure 29: SWI and in-situ data for RSMN-station Bacles ............................................................. 60

Figure 30: SWI and in-situ data for TERENO-station Gevenich ..................................................... 61

Figure 31: As Figure 30, but for SWI with T-value=100 ................................................................. 61

Figure 32: Monthly SWI anomalies over southern Africa in 2019. ................................................. 63




Figure 33: Monthly SWI anomalies (top) and Vegetation Health Index (VHI) data (bottom) over

Zambia in 2019. ..................................................................................................................... 64


Australia in 2019. ................................................................................................................... 66

Figure 35: Monthly SWI anomalies over Europe in 2019. .............................................................. 68

Figure 36: Monthly SWI anomalies (top) and ECMWF rainfall anomalies (bottom, provided by FAO

(via the GIEWS) over Spain in 2019. ..................................................................................... 70




List of Tables

Table 1: Target requirements of GCOS for global near-surface soil moisture (up to 5cm soil depth)

as Essential Climate Variable (GCOS-154, 2011) .................................................................. 21

Table 2: SQE 2018: Compliance matrix for the comparison with GLDAS Noah. Percentages of grid

points that fulfill each requirement. Left the referenceperiod (2007-01-01 until 2017-12-31) /

right the validation period (2018-01-01 until 2018-12-31) ....................................................... 22

Table 3: SQE 2018: Compliance matrix for the comparison with ISMN in-situ data. Percentages of

grid points that fulfill each requirement. Left the referenceperiod (2007-01-01 until 2017-12-31)

/ right the validation period (2018-01-01 until 2018-12-31). .................................................... 23

Table 4: Possible values of the SSF and their meaning ................................................................ 26

Table 5: Classification scheme ...................................................................................................... 26

Table 6: Data availability and references for the used in-situ networks. ........................................ 30

Table 7: Available datasets for validation ...................................................................................... 31

Table 8: Which T-values were compared to which layer of the modelled and in-situ datasets ....... 31

Table 9: Mean / median R between SWI and GLDAS Noah as well as percentiles of the absolute

values of the differences. All values in m3/m3. ........................................................................ 35

Table 10: Mean / median RMSD between SWI and GLDAS Noah as well as percentiles of the

absolute values of the differences. All values in m3/m3. .......................................................... 40

Table 11: Mean / median Bias between SWI and GLDAS Noah as well as percentiles of the

absolute values of the differences. All values in m3/m3 ........................................................... 43

Table 12: Mean / median metrics of the SSF validation with GLDAS Noah as well as percentiles of

the absolute values of the differences. ................................................................................... 48

Table 13: Mean / median correlation coefficient between SWI and in-situ data as well as

percentiles of the absolute values of the significant differences ............................................. 51

Table 14: Mean / median RMSD between SWI and in-situ observations as well as percentiles of

the absolute values of the differences. All values in m3/m3. .................................................... 54

Table 15: Mean / median bias between SWI and in-situ observations as well as percentiles of the

absolute values of the differences. All values in m3/m3. .......................................................... 57

Table 16: Station details and climate classifications ...................................................................... 58

Table 17: Pearson’s correlation coefficient (R) and Root mean square deviation (RMSD) for the

selected in-situ stations. SWI T=1 was compared to in-situ soil moisture measured at a depth

of 0.05m. ................................................................................................................................ 59

Table 18: Compliance matrix for the comparison with GLDAS Noah and ISMN stations.

Percentages of grid points that fulfill the GCOS requirement of 0.04 m³/m³ for the reference




period (2007-07-01 until 2018-12-31) (left) / for the current validation period (2019-01-01 until

2019-12-31) (right). ................................................................................................................ 71

Table 19: Compliance matrix for the comparison with GLDAS Noah. Percentages of grid points that

fulfill each requirement. Left the reference period (2007-01-01 until 2018-12-31) / right the

current validation period (2019-01-01 until 2019-12-31). ........................................................ 72

Table 20: Compliance matrix for the comparison with ISMN in-situ data. Percentages of grid points

that fulfill each requirement. Left the reference period (2007-01-01 until 2018-12-31) / right the

current validation period (2019-01-01 until 2019-12-31). ........................................................ 72




List of Acronyms

AD : Applicable document

ATBD : Algorithmic Theoretical Basis Document

BIEBRAZA : In-Situ Network of the Instytut Geodezji i Kartografii, Poland.

CDF : Cumulative Distribution Function

CGLS : Copernicus Global Land service

COSMOS : The COsmic-ray Soil Moisture Observing System

CUR : Current validation period (i.e. 2019)

ECMWF : European Centre for Medium-Range Weather Forecasts

EFFIS : European Forest Fire Information System

ERA-Interim : European Reanalysis – Interim

EUMETSAT : European Organisation for the Exploitation of Meteorological Satellites

FAO : Food and Agriculture Organization of the United Nations

FMI : Finnish Meteorological Institute

GCOS : Global Climate Observing System

GIEWS : Global Information and Early Warning System (of FAO)

GLDAS : Global Land Data Assimilation System

GROW : In-Situ Network of the GROW Observation Network

HOBE : In-Situ Network of the Center for Hydrology – Hydrological Observatory

IFAD : International Fund for Agricultural Development

ISMN : International Soil Moisture Network

JRC : Joint Research Centre of the European Union

LSA-SAF : Land Surface Analysis Satellite Application Facility

MERRA : Modern-Era Retrospective Analysis for Research and Applications

Noah-LSM : Noah Land Surface Model

NASA : National Aeronautics and Space Administration

NF : News Footage

NRT : Near Real Time

OZNET : In-Situ Network of the University of Melbourne, Australia

PUM : Product User Manual

R : Spearman correlation coefficient (R)

REF : Reference validation period (i.e. 2007-2018)

RHEMEDUS : In-Situ Network of the Centro Hispano Luso de Investigaciones Agrarias, Salamanca, Spain

RMSD : Root Mean Square Difference

RSMN : In-Situ Network of the National Meteorological Administration, Romania

SCAN : Soil Climate Analysis Network

SMAP : Soil Moisture Active Passive

SNOTEL : In-Situ Network of the Snow Telemetry

SQE : Science and Quality Evaluation

SSF : Surface State Flag

SVP : Service Validation Plan




SWI : Soil Water Index

SWI10 : Soil Water Index 10-daily average

SWI-TS : Soil Water Index Time Series

TCI : Temperature Condition Index

TERENO : In-Situ Network of the Terrestrial Environmental Observatories

TUG : Technical User Group

UNFCCC : United Nations Framework – Convention on Climate Change

USCRN : U.S. Climate Reference Network

VCI : Vegetation Condition Index

VHI : Vegetation Health Index

WEGENERNET : In-Situ Network of the Wegener Center for Climate and Global Change, Graz, Austria

WFP : World Food Program

WGS : World Geodetic System




EXECUTIVE SUMMARY

The Copernicus Global Land Service (CGLS) is earmarked as a component of the Land service to

operate “a multi-purpose service component” that provides a series of bio-geophysical products on

the status and evolution of land surface at global scale. Production and delivery of the parameters

take place in a timely manner and are complemented by the constitution of long-term time series.

This document describes the Science and Quality Evaluation (SQE) of the Soil Water Index (SWI)

products for the year 2019. A global and a regional analysis were performed. The aim of the global

analysis in this report is to assess the performance of the SWI V3 NRT product in the period from

2019-01-01 until 2019-12-31 relative to the performance of the SWI archive available from the

Copernicus Global Land Service (CGLS) of the previous years, starting in 2007. The validation

presented in this report was performed according to the procedures defined in the Service

Validation Plan (SVP). The regional analysis focuses first on the continental-scale drought

conditions followed by unprecedented bushfire catastrophe in Australia, and severe drought

vegetation stress in central southern Africa, most notably in Zambia. Another focus is over Europe,

where, a second-in-a-row summer heat wave leaded in 2019 to extreme dry condition over large

parts of the continent. Distinguished rainfall patterns over Spain at the end of the year are also

documented.

For the global analysis, the SWI and Surface State Flag (SSF) were compared to the output of a

land surface model as well as in-situ data. The used model was GLDAS-Noah V2.1, whereas the

in-situ data was taken from the International Soil Moisture Network (ISMN). The SWI was

compared to different soil moisture layers of these reference datasets, whereas the SSF was

assessed using the first soil temperature layer of GLDAS. The compliance matrices in Table 19

and

Table 20 show very similar results to those from previous yearly evaluations (Table 2 and Table 3),

documenting a stable performance of the SWI product. Also the spatial patterns of agreement with

GLDAS for SWI and SSF are widely consistent with the previous analysis, with a weak

performance over tropical rainforests, subarctic areas, and deserts, and a good performance over

Europe, subtropical Africa, India, central and eastern Asia, non-arid Australia and the (non-polar)

Americas. Overall, the quality of the SWI and SSF products appears unchanged, with some

regional differences in both positive and negative direction.

In the in-situ analysis, overall scores are slightly reduced compared to last year and few less

stations achieve optimal and target quality when compared to the last SQE exercise, which itself

reported slightly better results than in the preceding year. As the magnitude of these changes is

little and in the range of previous years, a year-to-year fluctuation can be attested, with no

significant long term trend apparent. Also, a repeated analysis of selected in-situ time series

showed that over one station in Germany a distinct time lag may correspond with an inappropriate

attribution of T-value to soil depth.




In the regional analyses over southern Africa, Australia, and Europe, monthly SWI anomalies for

2019 did highly agree with news footage and reference vegetation and rainfall datasets. Drought

conditions over central section of subtropical southern Africa from January to May are captured by

the SWI anomalies, as well as the abundant precipitation sums over Tanzania in late 2019. The

record-hitting drought and bushfire series of 2019 in Australia are well captured by the SWI

anomalies, as well as an intermission with heavy rain falls in Queensland. Finally, the SWI

dynamics recorded Europe dry anomalies over large parts of the continent, which was hit by an

exceptional hot spring and summer in 2019. Hydrological dynamics in Hungary and Spain are well

reflected. However, the overall magnitude of the moisture deficit for the continent is lower in the

SWI datasets as one would expect from reference datasets and media reports. Probably, this is

due to persisting dry conditions in the previous years, and the limited reference timeline for building

the anomalies (starting in 2007).




1 BACKGROUND OF THE DOCUMENT

1.1 SCOPE AND OBJECTIVES

The document presents the results of the annual scientific quality evaluation of the operational SWI

V3.0 product.

The quality evaluation is performed on SWI data for the year from 2019-01-01 to 2019-12-31 with a

reference period from 2007-01-01 to 2018-12-31.

The objective is to check that the operational products keep the same level of quality in the period

under study than the fully validated products.

1.2 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 recalls the users requirements, and the expected performance

Chapter 3 reviews the results of previous validation reports

Chapter 4 describes the methodology for quality assessment, the metrics and the criteria of

evaluation

Chapter 5 presents the results of the analysis

Chapter 6 summarizes the main conclusions of the study

Chapter 7 makes recommendations based upon the results

1.3 RELATED DOCUMENTS

1.3.1 Applicable documents

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S

151-277962 of 7th August 2015

AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical

requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015

AD3: GIO Copernicus Global Land – Technical User Group – Service Specification and Product

Requirements Proposal – SPB-GIO-3017-TUG-SS-004 – Issue I1.0 – 26 May 2015.

1.3.2 Input

Document ID Descriptor

CGLOPS1_SSD Service Specifications of the Global Component of




the Copernicus Land Service.

CGLOPS1_SVP Service Validation Plan of the Global Land Service

CGLOPS1_ATBD_SWIV3 Algorithm Theoretical Basis Document of the SWI

V3.0 product

1.3.3 Output

Document ID Descriptor

CGLOPS1_PUM_ SWIV3-SWI10-

SWI-TS

Product User Manual summarizing all information about the

SWI V3.0, SWI10 and SWI-TS products.

CGLOPS1_SQE2018_SWIV3 Scientific Quality Evaluation report containing the results of

the scientific validation of the SWI V3.0 for 2018.




2 REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2] and [AD3], the user’s requirements relevant for the

Soil Water Index are:

Definition: Amount of water (m3/m3) contained in soil layers identified according to their

depth measured from the top surface.

Geometric properties:

o Pixel size shall be defined on a per-product basis so as to facilitate the multi-

parameter analysis and exploitation

o The target baseline location accuracy shall be 1/3 of the at-nadir instantaneous field

of view.

o Pixel co-ordinates shall be given for the centre of the pixel.

Geographical coverage:

o Geographic projection: regular lat-long

o Geodetical datum: WGS84

o Pixel accuracy: minimum 10 digits

o Coordinate position: centre of pixel

o Window coordinates:

Upper Left:180°W-74°N

Bottom Right: 180°E 56°S

Ancillary information:

o the number of measurements per pixel used to generate the synthesis product

o the per-pixel date of the individual measurements or the start-end dates of the

period actually covered

o quality indicators, with explicit per-pixel identification of the cause of anomalous

parameter result

Ancillary information:

o the number of measurements per pixel used to generate the synthesis product

o the per-pixel date of the individual measurements or the start-end dates of the

period actually covered

o quality indicators, with explicit per-pixel identification of the cause of anomalous

parameter result

Accuracy requirements: wherever applicable the bio-geophysical parameters should meet

the internationally agreed accuracy standards laid down in document “Systematic




Observation Requirements for Satellite-Based Products for Climate”. Supplemental details

to the satellite based component of the “Implementation Plan for the Global Observing

System for Climate in Support of the UNFCCC”. GCOS-#154, 2011” (Table 1).

Table 1: Target requirements of GCOS for global near-surface soil moisture (up to 5cm soil depth) as

Essential Climate Variable (GCOS-154, 2011)

Variable Horizontal

Resolution

Temporal

resolution Accuracy Stability

Volumetric soil moisture 50km Daily 0.04 m3/m3 0.01m3/m3/year

GCOS notes that the targets above “are set as an accuracy of about 10 per cent of saturated

content and stability of about 2 per cent of saturated content. These values are judged adequate

for regional impact and adaptation studies and verification and development of climate models. It is

considered premature to consider global scale changes.” It adds that “stating a general accuracy

requirement is difficult for this type of observation, as this depends not only on soil type but also on

soil moisture content itself. The stated numbers thus should be viewed with some caution”.

We use the target GCOS requirement, set to 0.04m³/m³, as optimal goal in the compliance matrix,

which has to be taken as an upper bound.




3 REVIEW OF THE SWI QUALITY

In the continuous evaluation process of the CGLS, the scientific quality of the SWI V3.0 2018

products was assessed in the last yearly cycle [see CGLOPS1_SQE2018_SWIV3]. A summary of

the results related to the user requirements is presented below.

In the SQE 2018 exercise, the SWI V3.0 product was compared to data from the Global Land Data

Assimilation System (GLDAS) and to in-situ data from the International Soil Moisture Network

(ISMN). Table 2 to Table 3 show the compliance matrices for 2018 products compared to products

for the reference time period between 2007 and 2017. It can be seen that, for e.g. GLDAS, 6 to 60

percent of the points reach for the SWI the optimal quality depending on the validation period and

the T-value.

Table 2: SQE 2018: Compliance matrix for the comparison with GLDAS Noah. Percentages of grid

points that fulfill each requirement. Left the referenceperiod (2007-01-01 until 2017-12-31) / right the

validation period (2018-01-01 until 2018-12-31)

Variable Threshold Target GCOS/optimal

Limit (Upper Bound) SWI 0.20 m

3/m

3

SSF 7 days

SWI 0.10 m3/m

3

SSF 3 days

SWI 0.04 m3/m

3

SSF 1 day

[%] REF / CUR REF / CUR REF / CUR

SWI T=1 100 / 100 80 / 86 10 / 30

SWI T=20 100 / 100 75 / 81 6 / 17

SWI T=100 100 / 100 91 / 96 33 / 60

SSF consecutive days

wrongly classified (max) 13 / 22 6 / 6 1 / 1


wrongly classified (median) 74 / 82 21 / 50 1 / 8




Table 3: SQE 2018: Compliance matrix for the comparison with ISMN in-situ data. Percentages of grid

points that fulfill each requirement. Left the referenceperiod (2007-01-01 until 2017-12-31) / right the

validation period (2018-01-01 until 2018-12-31).


Limit (Upper Bound) SWI 0.20 m3/m

3 SWI 0.10 m

3/m

3 SWI 0.04 m

3/m

3


SWI T=1 96 / 98 58 / 70 6 / 13

SWI T=20 96 / 98 58 / 70 6 / 13

SWI T=100 97 / 97 70 / 81 12 / 34




4 SCIENTIFIC QUALITY EVALUATION METHOD

4.1 GLOBAL ANALYSIS

The aim of the global analysis in this report is to assess the performance of the SWI V3 NRT

product in the period from 2019-01-01 until 2019-12-31, relative to the performance of the SWI

archive available from the Copernicus Land Service of the previous years (starting in 2007). The

validation presented in this report was performed according to the procedures defined in the

Service Validation Plan [CGLOPS1_SVP], but are also shortly summarized here.

For the SWI validation, the following metrics are used:

Pearson’s correlation coefficient (R) with a p value < 0.01

Root mean square difference (RMSD)

Bias (SWI – reference dataset)

The reference datasets (GLDAS and ISMN situ data) were temporally matched to the SWI

observation timestamp using nearest neighbour matching. This was necessary since the SWI is a

daily dataset whereas the reference datasets have multiple observations per day. Since the SWI is

in a different unit than the reference products, data scaling was necessary to ensure consistent

results across reference products. The used approach was that of min-max scaling. With this

method, one dataset is scaled to have the same maximum and minimum values as the other

dataset. This approach was taken since it does not completely remove the bias as does e.g.

scaling to the same mean and standard deviation. At first, also experiments with linear CDF

matching after Liu et al., (2011) were made but it was discovered that the matching failed for high

T-values in very dry regions because the algorithm was unable to calculate the necessary

percentiles.

The SWI data was scaled into the data space of the models and the in-situ data. This approach

was taken to have the same units (m3/m3) for all results. This approach allows a better comparison

of the RMSD and Bias between the modelled data and the in-situ data. Scaling into the in-situ data

space is commonly done in the literature (Brocca et. al 2011), so it was decided that it would be

best to also calculate the error metrics of the modelled data sets in absolute units.

Before the calculation of the metrics, observations where the Surface State Flag showed frozen

were removed. If less than ten observations were available for a grid point, no metrics were

calculated.

For the Pearson correlation coefficient R, the 95% confidence intervals were calculated and used

to determine if there were significant differences between the reference period and the current

validation period (January-December 2019). Confidence intervals were estimated using the Fisher

z-transformation (Fisher, 1915). For each R-value, a confidence band is found with this method.

Two R-values are only significantly different when these confidence bands do not overlap.




In addition to the R-maps, also seasonal/quarterly time series of average R-values over specific

regions are calculated. The regions were chosen to represent well different climate zones and

biomes, comprising subcontinent-scale areas (Continental USA, East Africa, Australia) as well as

state-scale areas (Patagonia, Italy, California).

For this, the temporally and spatially matched values from the SWI time series and the reference

time series of all points in a region per season/quarter are used to calculate an R-value for this

region over the three months periods. The chosen regions are of sub-continental- or country- scale

and are shown in Figure 1. The R-values are calculated for the reference period and the current

validation period. For both periods, only data from pixels were used where each period had valid

observations. However, this does not ensure the same number of values per year, since the

Freeze/Thaw masking is different from year to year. However, for the reference dataset, the

standard deviation of the quarterly R-values are computed per chosen region, and added to the

plots to give insight on the relevance of the difference of current to reference data.

Figure 1: Map showing the regions used to define the regions of special interest for the plots of R

over time.

For the SSF validation, the following metrics are used:

Percentage of correctly classified observations (percent_valid)

Period of consecutive misclassified days (min, max, mean, median) (wrong_days)

Classification of the SSF values (Table 4) into the binary correct-incorrect form was done using the

classification scheme outlined in Table 5. The soil temperature was taken from the first layer of the

used land surface model (GLDAS). The percent_valid metric was then calculated by calculating the

percentage of correctly classified observations as a fraction of all observations. Wrong_days was




calculated by finding consecutive periods of wrong classification and measuring their length in

days. Since each grid point can have multiple such periods the minimum, maximum, mean and

median length of these periods is stored. During the calculation of the Wrong_days-metric, every

year was taken by itself to make the results comparable to the current validation period.

Table 4: Possible values of the SSF and their meaning

SSF value Detected surface state

255 Could not be determined

0 Unknown

1 Unfrozen

2 Frozen

3 Temporary water on the surface

Table 5: Classification scheme

SSF value Soil temperature Classification

0, 1, 3 >= 0°C Correct

2 < 0°C Correct

0, 1, 3 < 0°C Incorrect

2 >= 0°C Incorrect

The SSF analysis bears the caveat that soil temperature is only a rough proxy for the freeze/thaw

state of the soil. However, it is to our knowledge the only possibility for SSF validation at the

moment. Several studies working with freeze/thaw data from active (Naeimi et al., 2012) and

passive (Youngwook et al., 2011) satellites as well as the upcoming SMAP satellite freeze/thaw

product (NASA, 2014) all rely on temperature observations for validation purposes. Since these

validation reports are due 2-3 months after the SWI data was produced, we must rely on datasets

that are updated in a timely manner. This adds to the complexity of finding an alternative dataset to

temperature for SSF validation. Unfortunately, from 2017 ongoing, we found numerous and erratic

data-gaps and -flaws in the ISMN soil temperature dataset (see Section 4.4), so that we evaluated

the SSF only against GLDAS.

4.2 REGIONAL ANALYSIS

For the regional analysis, we focus on five regions with extraordinary hydrological dynamics in

2019, comprising southern Africa (with focus Zambia), Australia, and Europe (with focus Spain). To




examine the hydrology of these regions, we built the monthly anomalies of the SWI10 decadal

product, allowing efficient and robust computation of monthly means. In more detail, we calculated

the anomalies as follows:

1. Building the monthly means for each month of the full SWI data period 2007-2019.

2. The monthly means of the reference period (2007-2018) are averaged again for each

calendar month, yielding per pixel 12 values, building the reference.

3. Again per calendar month, subtracting the reference monthly means from the 2019-

monthly-means.

The SWI10 monthly anomalies are then compared qualitatively against the Vegetation Health

Index (VHI, Kogan et al., 1995, Kogan et al., 2003) as provided on the website1 of the Food and

Agriculture Organization of the United Nations (FAO), to examine how soil moisture anomalies

observed by the SWI relate to crop and vegetation health status. The VHI is an indicator routinely

used by FAO that highlights anomalous vegetation growth and potential drought in arable land

during a given period (Rojas et.al 2014). For the investigation on how Spain’s dry period and the

subsequent transient rainfall events in 2019 are captured by the SWI product, we compare it with

ECMWF-rainfall-estimates (also provided by FAO) at T-value=1, reflecting the topmost soil

surface. Anomalies from ECMWF-rainfall-estimates describe the relative difference between the

monthly 2019 rainfall volume and the average level, which refers to the period 1989-2015. The VHI

is compared with SWI at T-value=5, to account for the importance of the upper soil layer to the

vegetation.

4.3 MODEL REFERENCE PRODUCTS

Modelled reference products to which the SWI product was compared against were taken from the

GLDAS (Global Land Data Assimilation System) Noah-LSM (Noah Land Surface Model), version

2.1. The GLDAS Noah Land Surface Model (Rodell et al., 2004) is produced by NASA. It includes

four soil layers with the bottom depth at 0.1, 0.4, 1.0 and 2.0m respectively, and simulates over 30

land surface parameters in total. Data is available with a temporal sampling of three hours on a

global 0.25° grid. The model soil moisture data was converted from kg/m² to m3/m3 by using the

formula SM[m3/m3] = SM[kg/m2] * 0.001 * 1/d, where d is the thickness of the soil layer in meters.

The factor 0.001 is due to the assumption that 1kg of water represents 1000cm3, which is 0.001m3.

The GLDAS model dataset were chosen for the validation because it provides consistent long

term, global data and is updated regularly. The consistency of the dataset also makes it likely that

future SQE exercises can use them as well. This would have been less likely if we had chosen e.g.

soil moisture products derived from other remote sensing platforms that are vulnerable to satellite

failure etc.

1 www.fao.org/gviews/earthobservation/




The soil moisture component of the model is of course not free of errors, but there are

comprehensive studies on the global quality of the modelled soil moisture. Dorigo et al. (2010)

investigated the quality of the GLDAS and ERA-Interim models with the triple collocation method

using remotely sensed soil moisture datasets as the third datasets. Similar error structures were

found for the two models, indicating that results obtained for the ERA-Interim model can be used

as a proxy for the GLDAS soil moisture output. Albergel et al. (2013) explored the performance of

the ERA-Land model, which is a pre-operational successor to ERA-Interim, and MERRA-Land

model against 196 in-situ stations from the ISMN and found mean correlation coefficients of 0.66

and 0.69 respectively. Chen et al. (2013) compared the GLDAS model output against in-situ

stations on the Tibetan plateau and found that the model underestimates surface soil moisture (0-5

cm) but was in good agreement with a deeper layer (10 – 40cm). Correlations of 0.69 (0-5cm) and

0.76 (10-40cm) were found. Because of these results suggesting a high agreement of the models,

and following review board’s recommendation, the ERA Interim is not used from the present SWI

evaluation. As a note, GLDAS features a higher resolution than ERA (0.25° vs 0.7°), which is

closer to the SWI’s resolution of 0.1°.

For the SQE 2019, the GLDAS-Noah Version 2.1 was used to cover the full evaluation period (as

in the previous SQEs 2017 and 2018).

4.4 IN-SITU REFERENCE PRODUCTS

In-situ data from the International Soil Moisture Network (ISMN) is used as a reference. The ISMN2

provides a harmonized repository of in-situ soil moisture observations. This harmonization makes it

feasible to use in-situ data from a wide array of networks. The data quality of the stations can vary

widely and is not guaranteed to be consistent in time for any station. Wild animals as well as

farmers or other natural phenomena can lead to sensor failure or shifts in the location which

invalidate the sensor calibration. These errors are partly addressed by various quality and

consistency checks that were recently introduced by (Dorigo et al., 2013) which aim to

automatically detect and flag jumps, plateaus and other unrealistic behaviour in the data. Also, it is

checked for realistic maximum and minimum values.

Spatial representativeness is an issue when comparing remotely sensed and in-situ data. This

means how well an in-situ observation, which is essentially a point measurement, represents the

data gathered by the much larger satellite footprint. This is also different for each station and

depends on topography, soil types and microclimates. The geographic distribution of the available

stations in the current validation period is shown in Figure 2, illustrating the inhomogeneous global

distribution of ground data, leaving many parts of the world unobserved. A more detailed view can

be seen on the interactive web portal of the ISMN.

2 https://ismn.geo.tuwien.ac.at/

https://ismn.geo.tuwien.ac.at/




Figure 2: Map of used ISMN networks, showing all stations with data in the period 2007-2019.

Each network has different data availability and references (see Table 6), but most of the networks

and stations used in SQE 2018 are used in SQE 2019, and also newly availably stations are

integrated (totalling 1026 stations).

To get the listed start- and end-date, the maximum and minimum date found in any station of the

network is given. It can be seen that most in-situ networks do not cover the full validation period.

Furthermore, the maximum date might not apply since this could be either for a time series that

was not soil moisture, or not in the right observation depth.

Another issue is that many networks or single stations do not measure soil temperature (in addition

to the soil moisture measurements). Even more critical is that since 2018, many soil temperature

time series are contaminated with inconsistent and erroneous data, especially in the winter season,

so that the ISMN dataset on soil temperature suffers from irregular coverage and quality. From

personal correspondence with ISMN technical staff, we got informed that this is due to unchecked

quality of raw ground temperature data in several networks. As a consequence, the analysis of

SSF against in-situ soil temperature data is skipped in this report.




Table 6: Data availability and references for the used in-situ networks.

Network Name Start End Reference

COSMOS 2008-04-28 2019-12-31 http://cosmos.hwr.arizona.edu/, Zreda et al., 2008

FMI 2007-01-25 2019-12-31 http://fmiarc.fmi.fi/

GROW 2017-02-08 2019-10-08 https://growobservatory.org/index.html

HOBE 2009-09-08 2019-03-13 http://www.hobe.dk/

RHEMEDUS 2007-01-01 2019-12-31 http://campus.usal.es/~hidrus/

RSMN 2014-04-09 2019-12-31 http://assimo.meteoromania.ro/

SCAN 2007-01-01 2019-12-31 http://www.wcc.nrcs.usda.gov/scan/

SNOTEL 2007-01-01 2019-12-31 http://www.wcc.nrcs.usda.gov/, Leavesley et al., 2008

TERENO 2009-12-31 2019-12-31 http://teodoor.icg.kfa-juelich.de/, Zacharias et. al. 2011

USCRN 2007-01-01 2019-12-31 http://www.ncdc.noaa.gov/crn/, Bell et al., 2013, Diamond

et al., 2013

WEGENERNET 2007-01-01 2019-12-31 http://www.wegcenter.at/wegenernet

4.5 OTHER REFERENCE PRODUCTS

For the regional analysis, Vegetation Heath Index (VHI, Kogan et al., 1995, Kogan et al., 2003) and

ECMWF-rainfall-estimates from the FAO website3, as well as several news articles and bulletins

were used. The VHI is a FAO indicator that highlights anomalous vegetation growth and potential

drought in arable land during a given period (Rojas et.al 2014). It is a composite index describing

the health and vigorousness of crops and vegetation in general. It combines both the Vegetation

Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a

similar equation to the VCI, but relates the current temperature to the long-term maximum, as it is

assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A

decrease in the VHI following, for example, a decline in the VCI (relatively poor green vegetation)

and an increasing TCI (warmer temperatures) would signify stressed vegetation conditions, and

over a longer period would be indicative of drought. The VHI components (VCI and TCI) are given

equal weights when computing the index.

3 www.fao.org/gviews/earthobservation/

http://www.wcc.nrcs.usda.gov/




4.6 DATA AVAILABILITY

Table 7 shows the data availability of the SWI and the reference datasets. The GLDAS Noah

model data covers the complete validation period, whereas some of the ISMN networks do not

cover the full validation period.

Table 7: Available datasets for validation

4.7 LAYER COMPARISON MATRIX

The different T-values of the SWI products were compared to different layers of the GLDAS model

as well as in-situ data. This was done since higher T-values represent deeper soil layers. Table 8

shows which T-values were attributed in the comparison to which layer depths of the reference

data. General recommendations and insights on the attribution of T-values to soil depth are given

in the SWI PUM document [CGLOPS1_PUM_ SWIV3-SWI10-SWI-TS].

Table 8: Which T-values were compared to which layer of the modelled and in-situ datasets

SWI T-Value GLDAS layer depth In-situ observation depth

1, 5, 10, 15, 20, 40 0 - 0.1 m 0 - 0.1 m

60, 100 0.4 – 1 m 0.5 m

Dataset Start End Spatial Coverage, Resolution

Observation depth

Unit

SWI V3 NRT 2019-01-01 2019-12-31 Global, 25km T-value Percent

SWI V3 archive

2007-01-01 2018-12-31 Global, 25km T-value Percent

GLDAS 2007-01-01 2019-12-31 Global, 0.25° 0 - 0.1 m 0.4 – 1m

°C and Kg/m²

ISMN 2007-01-01 Different, max 2019-12-31

1026 stations 0 - 0.1 m 0.5 m

m3/m3




5 RESULTS

5.1 GLOBAL ANALYSIS

The comparisons were done for all eight T-Values but we will show in this report only the results for

T=1, 20 and 100 in detail, since nothing out of the ordinary was observed for any other T-Value.

The results are ordered first by reference dataset and second by metric, and are presented as

images well as in tabular form. The tables show the mean and median values of the computed

metrics as well as 50-, 70- and 80- percentiles of the absolute differences between the current

validation period and the reference period. The 70th percentile means for example that 70% of all

differences are smaller than the given value. All images map globally the metric measured during

the reference period as well as during the current validation period. Additionally, the difference

(reference - current) is mapped. Violin-plots show the estimated kernel density function of the

datasets as well as the median and the lower- and upper- quartiles. The violin-plots can be

interpreted similar to boxplots, but show in addition the estimated distribution of the data, which

provides more information than a simple boxplot would.

5.1.1 Comparison with GLDAS Noah

5.1.1.1 Results for SWI

5.1.1.1.1 Pearson’s correlation coefficient

The results for Pearson’s correlation coefficient R (Figure 3 to Figure 5) show that correlations are

different at the 95% confidence interval for some areas. Statistical significance means that the

calculated confidence intervals do not overlap.

Most importantly, the results for the reference period and 2019 show very similar patterns,

indicating a stable performance of the SWI product, not suffering from any substantial or

systematic quality degradation. In the violin-plots, one can see that R-values for the current

validation period are slightly more extreme, as that the distribution in the violin plot is more

stretched. Areas with positive R-values tend to be more positive and areas with negative R-values

tend to be more negative. This behavior was already observed in the previous year and is likely

due to the shorter observation period of only one year. Again in 2019, over desert areas, e.g. over

the Sahara, the Namibian deserts, or the Arabian peninsula, and also over the semi-arid Iranian

area the SWI shows weak or no agreement with the GLDAS model (albeit improved over the

Sahara and Arabia peninsula). This confirms previous findings that the ASCAT data does not

model soil moisture correctly in very arid areas.

However, large parts of the Earth show a slightly improved agreement with the model in 2019 than

in the reference period. The western section of the US, Europe, Turkey and its neighbors, the

Sahel area, and South America (most prominently Argentina) show better agreement in 2019 than

the previous years while the central parts of Australia, scattered parts of Russia, central US and




Alaska, and eastern Europe show worse performance. The negative differences appear stronger

for the T-value=20 (Figure 4) and again similar for T=100 (Figure 5). Overall, the spatial patterns

between the reference period and 2019 are very similar, so a consistent quality deprecation is

unlikely. Continuing the last year’s trend, the difference distributions lean again to the negative,

meaning that the R-values for 2019 are higher than for the reference period 2007-2018.

The mean and median values as well as the differences shown in Table 9 compare well with the

values obtained from the previous exercise performed on 2018 data, with mean and median

correlations being little higher, with biggest improvement for T=20 (mean R raised from 0.29 to

0.34)

Figure 3: Pearson’s correlation coefficient between SWI T=1 and GLDAS Noah, as well the difference

between the reference period (2007-01-01 until 2018-12-31) and the current validation period (2019-01-

01 until 2019-12-31). Green colours indicate performance improvements in the current period. In the

difference map only the statistically significant differences are shown whereas the violin plots show

all data points. The violin plots show the estimated kernel density plot as well as the median (dashed

line) and the lower and upper quartile (dotted lines).





difference between the reference period (2007-01-01 until 2018-12-31) and the current validation

period (2019-01-01 until 2019-12-31). Green colours indicate performance improvements in the

current period. In the difference map only the statistically significant differences are shown whereas

the violin plots show all data points. The violin plots show the estimated kernel density plot as well

as the median (dashed line) and the lower and upper quartile (dotted lines).










Table 9: Mean / median R between SWI and GLDAS Noah as well as percentiles of the absolute

values of the differences. All values in m3/m

3.

mean median 50th percentile 70th percentile 80th percentile

SWI T=1 0.41 0.57 0.20 0.28 0.34

SWI T=20 0.34 0.52 0.23 0.32 0.39

SWI T=100 0.33 0.54 0.35 0.53 0.66




Figure 6 shows the quarterly R-values over selected regions as shown in Figure 1. The yearly

evolution of the performance over Eastern Africa, and Contiguous USA including separately

analysed Californa, is similar for the reference and the current period, showing almost no deviation

in the annual SWI dynamics in these regions.

However, over Mainland Australia there is this year a lower agreement of the SWI product with

GLDAS in Q1 and especially Q4. This is confirming the decreased scores over the Australian dry

areas in the centre of the continent (as in Figure 3 to Figure 5)

On the contrary, for Patagonia, we observe much higher R-values throughout the year 2019,

showing consistency with the higher R-values found over that area (significantly higher in Q1-Q3)

than in the reference period in Figure 3 to Figure 5.

Repeating last year’s analysis, over Italy, featuring Mediterranean climate, one can see for both

periods that the seasonal correlation is higher later in the year, probably due to higher hydrological

dynamics induced by winter rainfalls. The same might be true for eastern Africa, as during the rain

seasons (Q2 and Q4) the correlation is stronger for both evaluation periods.

Figure 6: Average 3-month Pearson’s correlation coefficient R between SWI T=5 and GLDAS Noah,

for the reference period (2007-01-01 until 2018-12-31, green line) and the current validation period

(2019-01-01 until 2019-12-31, purple line) over various areas. The standard deviation for the reference

is shown as light green surface.




5.1.1.1.2 Root mean square difference

Figure 7 to Figure 9 show the RMSD-results for the two periods and their differences on maps and

as violin-plots. Again, there seem to be no systematic differences. When consulting previous

exercises results, most differences can be interpreted as year-to-year fluctuations. Higher RMSDs

are observed this year over southeastern Canada, the southern Sahara area in Africa, and Eastern

Europe, for T=1 and T=20. Lower RMSDs are detected over many areas in high and mid-latitudes,

and in the tropical forests in South America, Africa and Indonesia, but the SWI data should be used

with caution in those regions, as due to the high vegetation density the ASCAT has only little

sensitivity (here Tropical Forest Mask of the SWI-STATIC collection is recommended).

Overall, the RMSD values are smaller in 2019 compared to the reference period, as the distribution

of the difference leans to positive (especially for T=1 and T=100), continuing the trend from SQE

2017 and 2018.

Table 10 shows the mean and median values of the RMSD as well as the absolute values of the

differences between the two periods. The values are identical to the values in last year’s SQE. Also

the shapes of the violin-plots for reference, current and difference are very akin to last years,

suggesting that there are no systematic changes in the SWI data.




Figure 7: RMSD between SWI T=1 and GLDAS Noah, as well the difference between the reference

period (2007-01-01 until 2018-12-31) and the current validation period (2019-01-01 until 2019-12-31).

Green colours indicate performance improvements in the current period. In the difference map only

the statistically significant differences are shown whereas the violin plots show all data points. The

violin plots show the estimated kernel density plot as well as the median (dashed line) and the lower

and upper quartile (dotted lines).










Table 10: Mean / median RMSD between SWI and GLDAS Noah as well as percentiles of the absolute


3.


SWI T=1 0.06 0.05 0.02 0.03 0.03

SWI T=20 0.07 0.06 0.02 0.03 0.04

SWI T=100 0.04 0.03 0.02 0.03 0.04




5.1.1.1.3 Bias

The spatial patterns of the Bias shown in Figure 10 to Figure 12 correspond to the spatial patterns

seen for the RSMD. This is not surprising since the two metrics are closely related. On average,

the (absolute) biases are the same as for the reference period, but show less extreme values (less

stretched violin-plots), which can be expected due to the scaling of only one year of data for the

current validation period. With higher T-values, the bias-values are more and more small, owed to

the much smaller moisture dynamics in the deeper soil layers. Table 11 summarizes the statistics

on bias, with only the 80th-percentile value for T=1 raised by 0.01 against last year, supporting the

insight that there is no systematic difference in the product.
















Table 11: Mean / median Bias between SWI and GLDAS Noah as well as percentiles of the absolute


3


SWI T=1 0.02 0.01 0.02 0.03 0.05

SWI T=20 0.03 0.02 0.02 0.04 0.05

SWI T=100 0.01 0.01 0.02 0.03 0.04




5.1.1.2 Results for Surface State Flag

The SSF was evaluated against the soil temperature data from GLDAS.

Figure 13, Figure 14, and Figure 15 show the percentage of correct SSF classifications, and the

maximum and mean in consecutive wrongly flagged days per grid point, respectively. The spatial

patterns for the percentage of correct SSF are globally almost identical for the current and

reference period, as well as with both results from SQE 2018. However, the difference highlights

reduced accuracy over Scandinavia and scattered parts of North America and central Asia. The

last years’ degradation over Canada was not continuing into 2019, with mostly better scores this

year. However, Scandinavia shows now for the second year worse performance in SSF masking.

Patterns for areas showing on average many consecutive days wrongly classified remain very

stable, with longest periods in Tibetan Plateau and Siberia (but with the latter improved in 2019),

and only short periods in the rest of the world (Figure 15).




Figure 13: Percent of correctly classified freeze/thaw states compared to GLDAS Noah soil

temperature as well the difference between the reference period (2007-01-01 until 2018-12-31) and the

current validation period (2019-01-01 until 2019-12-31). Green colours indicate performance

improvements in the current period. In the difference map only the statistically significant differences

are shown whereas the violin plots show all data points. The violin plots show the estimated kernel

density plot as well as the median (dashed line) and the lower and upper quartile (dotted lines).




Figure 14: Maximum number of consecutive days wrongly classified when comparing the SSF

against GLDAS Noah soil temperature as well the difference between the reference period (2007-01-

01 until 2018-12-31) and the current validation period (2019-01-01 until 2019-12-31). Green colours

indicate performance improvements in the current period. In the difference map only the statistically

significant differences are shown whereas the violin plots show all data points. The violin plots show

the estimated kernel density plot as well as the median (dashed line) and the lower and upper

quartile (dotted lines).




Figure 15: Mean number of consecutive days wrongly classified when comparing the SSF against

GLDAS Noah soil temperature as well the difference between the reference period (2007-01-01 until

2018-12-31) and the current validation period (2019-01-01 until 2019-12-31). Green colours indicate

performance improvements in the current period. In the difference map only the statistically

significant differences are shown whereas the violin plots show all data points. The violin plots show

the estimated kernel density plot as well as the median (dashed line) and the lower and upper

quartile (dotted lines).




Table 12 summarizes the validation results for the SSF. The overall accuracy of the classification

remains very stable, as again 95% of all classifications can be considered correct. The SSF was

on average wrongly set for the same amount of days as in the 2018 SWI product, as the global

metrics for the consecutive days metric are almost identical (e.g. mean of 7.4 consecutively

wrongly set days; compared to 7.5 in 2018). However, this year a higher amount of outliers is in

the data, as the median value improve slightly (4.3 consecutively wrongly set days; compared to

4.8 in 2018), indicating an improvement apart from the outliers.

Table 12: Mean / median metrics of the SSF validation with GLDAS Noah as well as percentiles of the

absolute values of the differences.

mean median 50th

percentile

70th

percentile

80th

percentile

correct classification [%] 95.5 100.0 1.8 3.1 4.1

incorrect classification [%] 4.5 0.0 1.7 2.8 3.7

Consecutive days wrongly

classified (max)

18.8 13.0 5.2 8.1 10.2


classified (min)

4.2 1.0 0.9 1.7 2.5


classified (median)

4.3 3.0 2.2 3.6 4.6


classified (mean)

7.4 5.4 2.4 3.8 4.8

5.1.2 Comparison with ISMN

5.1.2.1 Results for SWI

The following plots show the results for stations with sufficient valid data in both periods (mainly

located in the US), the reference years and the year 2019. The plots do not show any networks in

the southern hemisphere, only one in Africa, none in South America, Oceania, and Asia. Although

there are (a few) ISMN stations holding data for the validation period, not enough in-situ data that

temporally matches the SWI data was available for 2019 in these regions, unfortunately.

5.1.2.1.1 Pearson’ correlation coefficient

Figure 16 to Figure 18 show the R-values and the significant differences between the current

validation period and the reference period. The R-values for the current validation period are on

average lower than in the reference period, with poorer agreement for stations in the Mississippi-




Missouri area in the US, confirming the difference pattern against GLDAS (e.g. in Figure 3). The

degraded performance around the Great Lakes is not seen in the GLDAS analysis.

Apparently, also a number of stations in southern California, Arizona and Nevada show a negative

correlation, especially for 2019, which is also visible in the comparisons with GLDAS over this

area. Again, the ASCAT model appears to not correctly estimate soil moisture in arid conditions.

Overall, the R-values for all ISMN stations are lower in 2019 compared to the reference period,

with biggest differences coming from a significant number of stations with extreme negative

correlation, clearly visible from the violin-plots. All metrics for R show lower values (Table 13) in

this year’s SQE.

Figure 16: Pearson’s correlation coefficient between SWI T=1 and in-situ data, as well the difference









Figure 17: Pearson’s correlation coefficient between SWI T=20 and in-situ data, as well the difference















Table 13: Mean / median correlation coefficient between SWI and in-situ data as well as percentiles of

the absolute values of the significant differences


SWI T=1 0.34 0.51 0.30 0.45 0.58

SWI T=20 0.34 0.51 0.30 0.45 0.58

SWI T=100 0.36 0.56 0.40 0.57 0.66




5.1.2.1.2 Root mean square difference

The RMSD maps in Figure 19 to Figure 21 show that the RMSD are lower in 2019 than in the

multi-year reference period, but higher than in the last SQE’s, revealing here a fluctuating behavior.

Spatial patterns of the differences cannot be deduced, giving the spatially scattered distribution of

negative and positive differences. Also the RMSD statistics for 2019 in Table 14 show lower

agreement compared to last year’s analysis.
















Table 14: Mean / median RMSD between SWI and in-situ observations as well as percentiles of the

absolute values of the differences. All values in m3/m

3.


SWI T=1 0.092 0.083 0.026 0.045 0.060

SWI T=20 0.092 0.083 0.026 0.045 0.060

SWI T=100 0.072 0.060 0.026 0.044 0.060




5.1.2.1.3 Bias

The bias maps and violin-plots in Figure 22 to Figure 24 show similar difference patterns between

the current and the reference period, but an almost symmetric distributions of the differences (as in

SQE 2018). Interestingly, the overall spread of the violin plots is smaller, and the bias statistics in

Table 15 show decreased values (opposed to higher values between 2018, and smaller values in

2017), suggesting year-to-year fluctuations in the differences between SWI and in-situ data.

Figure 22: Bias between SWI T=1 and in-situ data, as well the difference between the reference period

(2007-01-01 until 2018-12-31) and the current validation period (2019-01-01 until 2019-12-31). Green

colours indicate performance improvements in the current period. In the difference map only the

statistically significant differences are shown whereas the violin plots show all data points. The












Table 15: Mean / median bias between SWI and in-situ observations as well as percentiles of the

absolute values of the differences. All values in m3/m

3.


SWI T=1 0.018 0.011 0.033 0.051 0.067

SWI T=20 0.018 0.011 0.033 0.051 0.067

SWI T=100 0.018 0.012 0.025 0.043 0.057




5.1.2.2 Time Series for selected in-situ stations

Six in-situ stations were selected to be discussed in more detail. They were selected at random,

but care was taken that poor and good results are shown, and that different climate zones are

represented. The six selected stations and their climate zones are listed in Table 16. For all in-situ

data printed in Figure 25 to Figure 30, the first layer (measured at a depth of 0.05 m) is shown. For

these plots, SWI with T=5 is plotted, which was found to be in general most corresponding to that

depth.

Table 16: Station details and climate classifications

Station Name Network latitude / longitude [°] Köppen-Geiger Climate class

CRAB CREEK SNOTEL 44.44 N / 111.99 W Dfb - snow - fully humid - warm

summer

Edinburg-17-NNE USCRN 26.53 N / 98.06 W Cfa - warm temperate - fully

humid - hot summer

Mercury 3 SSW USCRN 36.57 N / 116.02 W Bwk - arid - desert - cold arid

Pine-Nut SCAN 36.57 N / 115.20 W Bwk - arid - desert - cold arid

Bacles RSMN 44.48 N / 23.11 E Cfb - warm temperate - fully

humid - warm summer

Gevenich TERENO 50.99 N / 6.32 E Cfb - warm temperate - fully

humid - warm summer

Table 17 shows Pearson R-values and RMSD for the stations. The stations Mercury 3 SSW and

Pine Nut show strong negative correlations, confirming the insight from last reports that there is a

low agreement of SWI and in-situ data over arid areas. The station Edinburgh-17-NNE has

unchanged (good) performance compared to 2018, while the Crab Creek station has a small drop

in scores. Also, the stations in Gevenich and Bacles, representing temperate climate zones that

are important for many users, show satisfying but decreased results. Most notably in 2019,

although showing drier-than-normal values, a substantial overestimation of the SWI against the in-

situ measurements is apparent, suggesting that the reported dry conditions (see Section 5.2.4)

were not fully captured by the SWI.




Table 17: Pearson’s correlation coefficient (R) and Root mean square deviation (RMSD) for the

selected in-situ stations. SWI T=1 was compared to in-situ soil moisture measured at a depth of

0.05m.

Station R RMSD [m³/m³]

CRAB CREEK 0.69 0.05

Edinburg-17-NNE 0.75 0.03

Mercury 3 SSW -0.72 0.08

Pine-Nut -0.79 0.09

Bacles 0.73 0.06

Gevenich 0.55 0.08

Figure 25: SWI and in-situ data for SNOTEL-station CRAB CREEK

Figure 26: SWI and in-situ data for USCRN-station Edinburg-17-NNE




Figure 27: SWI and in-situ data for USCRN-station Mercury 3 SSW

Figure 28: SWI and in-situ data for SCAN-station Pine Nut

Figure 29: SWI and in-situ data for RSMN-station Bacles




Figure 30: SWI and in-situ data for TERENO-station Gevenich

Figure 31: As Figure 30, but for SWI with T-value=100

For the (German) Gevenich station, a time lag to the in-situ data can be seen, may hinting at an

inappropriate attribution of the T-value=5 to the soil depth. When compared to SWI with T=100

(Figure 31), the timing of the peaks and lows of the satellite data agrees much better with the in-

situ observations. This indicates that, at this location, information on local soil properties is

important for the optimal selection of the T-values. It should be noted that there is no general rule

on the attribution of T-values to depth, as it depends strongly on the application and the soil

composition of the area of interest (Paulik et al., 2014).





For the regional analysis, we examine on five regions with extraordinary hydrological dynamics in

2019, comprising southern Africa with a focus on Zambia, Australia, and Europe with a focus on

Spain. The extent and magnitude of the different hydrological situations were assessed in

analogous fashion as in previous SQEs by comparing the monthly SWI anomalies with the

Vegetation Health Index (VHI) provided by the Food and Agriculture Organization of the UN (FAO),

and with rainfall estimates anomalies (built from the long term average of the period 1989-2015)

provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).

It should be noted, that these comparisons are only a qualitative look to track if the SWI product

shows similar patterns to the vegetation health and rainfall estimates maps. While they are

commonly able to illustrate connections between SWI, soil hydrology, and vegetation status, the

comparisons have three main caveats. First, the reference period for the SWI anomaly is only

twelve years, which is relatively short for computing meaningful anomalies. Second, the

comparison datasets are observations of vegetation, which while strongly dependent on soil

moisture, are of course not the same variable and are impacted by other parameters. We see for

instance, that the link between VHI and SWI seems to be stronger and much more direct over hot

climates (as in southern Africa), than over temperate mid-latitude areas (as over Europe). Third,

strong vegetation anomalies will have an effect on the observed soil moisture since the retrieval

model assumes the same vegetation correction for each year. However, we are confident that

monthly SWI anomalies help to understand if the SWI data is capable of capturing hydrological

events, examining not only the temporal evolution, but also their spatial dynamics.

5.2.1 Situation in southern Africa in 2019

The southern part of Africa is still suffering from prolonged dry spells, but also from heavy rainfalls

resulting in displacement and crop loss, which conclude to food insecurity and increasing food

prices. FAO, the International Fund for Agricultural Development (IFAD), and the World Food

Program (WFP) reported that over 45 million people across the Southern African Development

Community were suffering from food insecurity by the end of 2019. Late rains, extended dry

periods and two major cyclones resulted in food insecurity, increasing food prices and driving up

aid needs (NF1, NF2). Dry spells especially in the first half of 2019 can be clearly seen in Figure

32, with a peak of negative SWI anomalies in March. In contrast, October to December 2019 was

one of the wettest seasons since 1981 for northern Tanzania, northern Madagascar and other

regions (NF3), represented as areas with high positive SWI anomalies. For the regional analysis,

we focus in the next subsection on the condition in heavily affected Zambia.




Figure 32: Monthly SWI anomalies over southern Africa in 2019.

5.2.2 Zambia

As reported by the Regional Bureau for Southern Africa of WFP, drought and prolonged dry spells

have left 2.3 million people severely food insecure (NF4). Figure 33 indicates that the dry weather

led to highly negative SWI anomalies, especially in the south-east part of Zambia for the beginning

of the year. Strong agreement is found in the comparison with the vegetation health index (VHI),

with biggest impact in March and April, accounting for some ability of the vegetation to resist

moisture shortage at the onset of the drought in January and February. On the contrary, from June

to October, SWI anomalies do not represent the marked differences as seen in the VHI maps. In

spite of the drought conditions, floods were reported n the north-eastern part for December (NF5),

which can be related to positive SWI anomalies in this section of Zambia.




Figure 33: Monthly SWI anomalies (top) and Vegetation Health Index (VHI) data (bottom) over Zambia

in 2019.




5.2.3 Australia

Severe drought conditions developed in Australia during 2019, resulting a catastrophic bushfire

season, which has left 33 people dead and burned an area of land the size of South Korea (NF6),

colloquially known as the Black Summer. In contradiction to the general hydrological regime,

February and late March brought record-breaking rainfall and disastrous flooding in northern

Queensland (NF7).

2019 was Australia's warmest year on record, with the annual national mean temperature 1.52 °C

above average. Rainfall below average for most of the country as well as significant heat waves in

January and in December resulted in the driest year on record (NF8). The comparison of SWI

anomalies and vegetation health index (VHI), presented in Figure 34, show strong agreement for

drought and flooding events in the eastern half of the continent, and a somewhat lower agreement

of spatial anomaly pattern in the western half. In the northern and eastern part of Australia, high

negative SWI anomalies are detected throughout the whole year.

Clearly distinguishable are the wet anomalies from February to June in Queensland (in north-east

Australia), which are in good accordance with the reported heavy rain falls. In the affected regions,

the soil appeared to remain saturated until winter season.

The overall negative anomalies early and late in 2019, as well as the mixed situation in the middle

of the year, can also be seen in the areal average plot for mainland Australia in Figure 6.





Australia in 2019.




5.2.4 Europe

Also Europe were hit by a dry spell, experienced another extraordinary hot summer in 2019

following already record-breaking hot years. The monthly bulletins produced by the Joint Research

Centre (JRC) give an overview on weather events for crop monitoring in Europe. By the end of the

year, large parts of Turkey, Ukraine and the southern part of the Iberian Peninsula experienced a

lack of rainfall, which affects sowing whereas, in the northern part of the Iberian Peninsula, France,

Italy and regionally in central Europe, a wetter-than-usual weather was recorded (NF9).

As reported by the NASA Earth Observatory (NF10) and EUMETSAT's LSA SAF (NF11), the

summer of 2019 was again extraordinary hot in Europe. In July, at least seven countries broke

temperature records in a torrid heat wave. Data provided by the Copernicus Climate Change

Service showed that it has become the second warmest calendar year on record with a

temperature 0.59 °C above the 1981-2010 average (NF12). Accordingly, SWI anomalies show

large-scale negative patterns over large parts of Europe from January to October, which can be

seen in Figure 35. In this period, countries neighboring the Mediterranean show negative or neutral

values for most months. Central and eastern Europe experienced alternating conditions, but with

largest deviations from average in March, April, and July. As a marked example, Hungary suffered

from severe drought conditions in the beginning of the year resulting in increasing fruit and

vegetable prices and harvest losses for several crops (NF13). The reported drought is supported

by negative SWI anomalies in this area, peaking in March. However, except for Germany, Poland

and France, SWI anomalies only confirm partly (with only small magnitude) with the reported hot

temperatures during the summer months. Probably, this is due to already below long term average

SWI values for the previous years, and the different reference timeline for building the anomalies

(starting in 1981 vs. 2007). From October on, a pressure was relieved for the continent, with

wetter-than-normal conditions starting in Western Europe, and the evolving in southern Europe and

Spain (see also section 5.2.5) from November on.

The exceptional dry conditions can be related to the time series plots of the European in-situ

stations in above chapter. The Gevenich station in Germany featured in 2019 significantly lower

in-situ soil moisture measurements than in the previous years (Figure 30), but not in the SWI data

with T=5, and only little with T=100. This station is located in western Germany, where also the

SWI anomaly maps do not show strong anomalies. Obviously, the dry spring period could not be

observed well there. This might be linked to known wet biases of the ASCAT SSM input data

appearing occasionally during vegetation onset, due to a misinterpretation of vegetation dynamics.

Romania appears overall less affected by the dry summer in the anomaly plots in Figure 35, which

relates also with rather normal soil moisture levels for the station Bacles as plotted in Figure 29,

but also with higher values in the SWI than in the in-situ data.




Figure 35: Monthly SWI anomalies over Europe in 2019.




5.2.5 Spain

In the first half of 2019, Spain suffered from severe drought conditions on wildfires and crop

production deficits (NF14). The European Forest Fire Information System (EFFIS) reported that

four of the five major fire events in Europe of 2019 were located in Spain. For this situation, the

SWI (T=1) and precipitation anomaly maps (collected in Figure 36) show a long period of low

precipitation, as well as corresponding negative SWI anomalies in the beginning of the year. While

the intermediate relief in April is reflected by the SWI data, the magnitude of May and June rainfall

deficits is not visible in the SWI data.

In November and December 2019, severe storms bringing heavy wind and rain caused widespread

damage and floods (NF15), with a gradient from north-west to south-east. These weather

conditions are well represented in positive SWI anomalies with corresponding precipitation patterns

for that period. Additionally, October shows good agreement between precipitation and SWI

anomalies with lower values in the south and higher values in the north of Spain.




Figure 36: Monthly SWI anomalies (top) and ECMWF rainfall anomalies (bottom, provided by FAO (via

the GIEWS) over Spain in 2019.




6 CONCLUSIONS

6.1 GLOBAL ANALYSIS

The SWI and SSF were compared to the output of a land surface model as well as in-situ data.

The model was GLDAS-Noah V2.1, whereas the in-situ data was taken from suitable networks of

the ISMN. The SWI was compared to different soil moisture layers of these reference datasets

(Table 8), whereas the SSF was assessed using the model’s first soil temperature layer. Overall,

no or only little deviations between SWI data from the reference period and the year 2019 were

found, showing overall a weak performance over tropical rainforests, subarctic areas, and deserts,

and a good performance over Europe, subtropical Africa, India, central and eastern Asia, non-arid

Australia and the (non-polar) Americas.

The compliance matrix in Table 18 shows the compliance with the (optimal) GCOS requirements.

For GLDAS, between 6% and 31% of the grid points meet the requirement for the reference

period, whereas between 16% and 58% of grid points meet the requirement for the current

validation period (i.e. 2019), which is slight decrease compared to the analysis for the antecedent

year. The comparisons against ISMN show identical compliance for the lower T-values, but again

slightly poorer results for T=100 than in 2018. As a note, results for the T=100 (as in Table 18) are

relative to the smaller T-values better because there is less variation in deeper soil layers making it

easier to meet the threshold.

Table 18: Compliance matrix for the comparison with GLDAS Noah and ISMN stations. Percentages

of grid points that fulfill the GCOS requirement of 0.04 m³/m³ for the reference period (2007-07-01

until 2018-12-31) (left) / for the current validation period (2019-01-01 until 2019-12-31) (right).

Variable GLDAS ISMN

[%] REF / CUR REF / CUR

SWI T=1 9 / 29 4 / 13

SWI T=20 6 / 16 4 / 13

SWI T=100 31 / 58 12 / 30

Table 19 and Table 20 show the compliance concerning Threshold, Target and GCOS/optimal

levels from analysis against GLDAS and ISMN. For the SWI, the results are again very good and

stable, as results from previous evaluations (Table 2) are at the same level for the Threshold and

Target.

Against GLDAS, also the performance of the SSF was evaluated, where 85% of the pixels meet

the threshold for median of consecutive days wrongly classified in 2019, but almost no pixels meet

the optimum. Compared to 2018 (Table 2) and 2017, these results are slightly improved for

threshold and target, but similar for optimum, and thus are in alignment with evaluations from

previous years.




Also the metrics against ISMN, collected by Table 20, show values at the same level as in the

previous years, although reporting a slight decrease in compliance scores against 2018 (Table 3),

but almost identical as in 2017.

Table 19: Compliance matrix for the comparison with GLDAS Noah. Percentages of grid points that

fulfill each requirement. Left the reference period (2007-01-01 until 2018-12-31) / right the current



SWI 0.20 m3/m3

SSF 7 days

SWI 0.10 m3/m3

SSF 3 days

SWI 0.04 m3/m3

SSF 1 day


SWI T=1 100 / 100 80 / 88 9 / 29

SWI T=20 100 / 100 75 / 81 6 / 16

SWI T=100 100 / 100 90 / 96 31 / 58


wrongly classified (max) 13 / 27 6 / 7 1 / 2


wrongly classified (median) 74 / 85 20 / 54 1 / 8

Table 20: Compliance matrix for the comparison with ISMN in-situ data. Percentages of grid points

that fulfill each requirement. Left the reference period (2007-01-01 until 2018-12-31) / right the current



SWI 0.20 m3/m3 SWI 0.10 m3/m3 SWI 0.04 m3/m3


SWI T=1 96 / 95 56 / 64 4 / 13

SWI T=20 96 / 95 56 / 64 4 / 13

SWI T=100 97 / 97 68 / 76 12 / 30





The regional analyses were carried out over southern Africa, Australia, and Europe, comparing

monthly SWI anomalies with the Vegetation Health Index (VHI) and rainfall anomalies. Drought

conditions over central section of subtropical southern Africa threatening the local population from

January to May are captured by the SWI anomalies. Also the outstanding wet conditions over

Tanzania in the fourth quarter of 2019 are reflected by the SWI. The focus on Zambia showed a

good skill of the SWI to map the drought and its severity in early 2019. From June to October, it is

captured only with little magnitude, though. The record-hitting drought and bushfire series of 2019

in Australia are well captured by the SWI anomalies, as well as the wet intermission in

Queensland. These dynamics are also visible the VHI comparison data, in the quarterly areal

means for mainland Australia (see Figure 6). Europe was hit by an exceptional hot spring and

summer in 2019, and the SWI dynamics over Germany, Poland, and France show dry anomalies

accordingly. However, for the other countries of the continent, the SWI only confirm partly with the

reported hot temperatures during the summer months. Probably, this is due to already below long

term average SWI values for the previous years, and the limited timeline for building the anomalies

(starting in 2007). The very dry spring season for Hungary is well reflected, though. Another focus

on Spain shows a comparison between SWI- and rainfall-anomalies. The dynamics are well

aligned, with drier-than-normal conditions in the first quarter of 2019 and a wet intermission phase

in April, recorded in both datasets. The drier conditions from May to July show less or sparsely

reflection in SWI. However, the increasingly wet conditions from October on (with severe storms

and heavy rain falls in November and December 2019), with a movement from north-east Spain to

the south-west are in good accordance again.

In summary, the SWI anomalies show a good performance in indicating geographic patterns of

outstanding hydrological events, but some dynamics (e.g. the prolonged heat wave in Europe) are

not captured with the same magnitude as reported in other studies. It is not clear whether this due

to biased satellite observations, an insufficient anomaly reference baseline, or due to the different

impacts on temperature, vegetation, rainfall and soil moisture variables, which naturally can

happen with different magnitudes.




7 RECOMMENDATIONS

The overall performance of the SWI and SSF products is consistent with the previous years’

scientific evaluation experiments. No inconsistencies in the SWI were detected in the current SQE

exercise. The detected differences between the reference period and the current validation period

were not consistently in one direction, indicating that they represent year-to-year fluctuations rather

than a systematic difference. The SSF flagging over Siberia, appeared in the previous analysis for

2018 with a turn to a positive trend is supported by results for 2019.

The regional analyses offer interesting insights in medium- and large-scaled hydrological events

and should be continued in coming SQE exercises, as they not only document the skill of the SWI

product to reflect droughts and rainfall anomalies, but also help to improve the understanding of

spatio-temporal dynamics of such processes.




8 REFERENCES

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8.2 NEWS FOOTAGE

NF1 Drought in Africa leaves 45 million in need across 14 countries.

https://www.thenewhumanitarian.org/analysis/2019/06/10/drought-africa-2019-45-million-in-need

Accessed: 2020-03-17.

NF2 As climate shocks intensify, un food agencies urge more support for southern Africa's

hungry people.

https://reliefweb.int/report/angola/climate-shocks-intensify-un-food-agencies-urge-more-support-southern-africa-s-hungry

Accessed: 2020-03-17.

NF3 WFP southern Africa seasonal update - October 2019 - February 2020.

https://reliefweb.int/report/world/wfp-southern-africa-seasonal-update-october-2019-february-2020

https://www.thenewhumanitarian.org/analysis/2019/06/10/drought-africa-2019-45-million-in-need

https://reliefweb.int/report/angola/climate-shocks-intensify-un-food-agencies-urge-more-support-southern-africa-s-hungry

https://reliefweb.int/report/world/wfp-southern-africa-seasonal-update-october-2019-february-2020




Accessed: 2020-03-17.

NF4 Southern Africa: Drought - 2018-2020.

https://reliefweb.int/disaster/dr-2018-000429-zwe

Accessed: 2020-03-17.

NF5 2019: Natural disasters claim more than 1200 lives across east and southern Africa.

https://reliefweb.int/report/world/2019-natural-disasters-claim-more-1200-lives-across-east-and-southern-africa

Accessed: 2020-03-17.

NF6 Australia fires were far worse than any prediction.

https://www.bbc.com/news/science-environment-51590080

Accessed: 2020-03-17.

NF7 Queensland floods: the water journey to Kati Thanda-Lake Eyre.

http://media.bom.gov.au/social/blog/2059/queensland-floods-the-water-journey-to-kati-thanda-lake-eyre/

Accessed: 2020-03-17.

NF8 Annual climate statement 2019.

http://www.bom.gov.au/climate/current/annual/aus/

Accessed: 2020-03-17.

NF9 JRC MARS Bulletin vol 27 no 11.

https://ec.europa.eu/jrc/sites/jrcsh/files/jrc-mars-bulletin-vol27-no11.pdf

Accessed: 2020-03-17.

NF10 A second scorching heatwave in Europe.

https://earthobservatory.nasa.gov/images/145377/a-second-scorching-heatwave-in-europe?src=nha

Accessed: 2020-03-12.

NF11 Heat wave strikes Europe.

https://landsaf.ipma.pt/en/newsmedia/news-show-cases-2/heatwave-strikes-europe-1/

Accessed: 2020-03-17.

NF12 Surface air temperature for December 2019.

https://climate.copernicus.eu/surface-air-temperature-december-2019.

Accessed: 2020-03-17.

NF13 Hungary especially endangered by drought, WWF says.

https://bbj.hu/energy-environment/hungary-especially-endangered-by-drought-wwf-says_169953

Accessed: 2020-03-19.

https://reliefweb.int/disaster/dr-2018-000429-zwe

https://reliefweb.int/report/world/2019-natural-disasters-claim-more-1200-lives-across-east-and-southern-africa

https://www.bbc.com/news/science-environment-51590080

http://media.bom.gov.au/social/blog/2059/queensland-floods-the-water-journey-to-kati-thanda-lake-eyre/

http://www.bom.gov.au/climate/current/annual/aus/

https://ec.europa.eu/jrc/sites/jrcsh/files/jrc-mars-bulletin-vol27-no11.pdf

https://earthobservatory.nasa.gov/images/145377/a-second-scorching-heatwave-in-europe?src=nha

https://landsaf.ipma.pt/en/newsmedia/news-show-cases-2/heatwave-strikes-europe-1/

https://climate.copernicus.eu/surface-air-temperature-december-2019

https://bbj.hu/energy-environment/hungary-especially-endangered-by-drought-wwf-says_169953




NF14 Spain battles biggest wildfires in 20 years as heat wave grips Europe.

https://www.theguardian.com/world/2019/jun/27/hundreds-of-firefighters-tackle-blaze-in-north-east-spain

Accessed: 2020-03-17.

NF15 Nine dead as extreme weather hits Spain, Portugal and France.

https://newseu.cgtn.com/news/2019-12-23/Eight-dead-as-extreme-weather-hits-Spain-Portugal-and-France-

MDOladmGmA/index.html

Accessed: 2020-03-17.

https://www.theguardian.com/world/2019/jun/27/hundreds-of-firefighters-tackle-blaze-in-north-east-spain

https://newseu.cgtn.com/news/2019-12-23/Eight-dead-as-extreme-weather-hits-Spain-Portugal-and-France-MDOladmGmA/index.html

https://newseu.cgtn.com/news/2019-12-23/Eight-dead-as-extreme-weather-hits-Spain-Portugal-and-France-MDOladmGmA/index.html

copernicus global land operations...figure 14: maximum number of consecutive days wrongly classified...

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