5 drought in romania mateescu

4th
Workshop
Integrated Drought Management Programme in
Central an Eastern Europe
Drought in Romania – challenges and opportunities for National
Meteorological Service in the context of Climate Change
Elena MATEESCU, Gheorghe STANCALIE, Andrei DIAMANDI
National Meteorological Administration - Romania
Bucharest, 21 & 22 April 2015

OUTLINE
1. Drought hazard in the context of current and future climate changes
2.The National Meteorological Administration – current status and
perspectives of the observation network infrastructure
3. Project results on drought monitoring/assessment
3.1.“Potential of Remote Sensing for Estimating and Monitoring
Water Footprint of Crops in the Framework of the COST Action ES1106:
Assessment of EUROpean AGRIculture WATer use and trade under
climate change (EURO-AGRIWAT)”
3.2. Assessment of Satellite Derived Soil Moisture Products over
Romania (ASSIMO Project), Program for Research - Development-
Innovation for Space Technology and Advanced Research – STAR
Romanian Space Agency (ROSA)

Source: Atlas of mortality and economic losses from weather, climate and water extremes (1970-
2012), WMO No. 1123, 2014
EUROPE
No. of natural disasters
1971-1980 60
1981-1990 246
1991-2000 379
2001-2010 577
Total Europe / 1352
Franta – 123, Italia – 75,
Romania – 71, Spania – 70
▲Increase

Source: Global Assessment Report on Disaster Risk Reduction, UN - 2015. National Risk Profile - Romania
ROMANIA / 2005-2014
Frequency (%) Economic losses(%)
Floods – 49% Earthquake – 52%
Extreme temperature– 22% Floods – 49%
Storms – 11% Drought – 6%
Flash Floods– 7%
Earthquake – 5%
Drought – 3%

OBSERVED SHIFTS IN THE COURSE OF THE MEAN ANNUAL
AIR TEMPERATURE IN ROMANIA
The warmest 5 years in Romania / 1961-2014
(1961-1990 / 8.8°C)
Annual air temperature Deviation
1. 2007 10.6°C 1.8°C
2. 1994 10.4°C 1.6°C
3. 2009 10.3°C 1.5°C
4. 2000, 2008, 2014 10.2°C 1.4°C
5. 2002, 2013 10.1°C 1.3°C
1961-1990 / 8.8ºC
1981-2014 / 9.3ºC
+0.5ºC

Air temperature trend in Romania / 1961-2013
Air temperature
/ Summer
Heat wave
▲Increase
Summer days
(Tmax>25ºC)

1961-1990 1981-2013
1. Dobrogea 417.0 mm /an 412.6 mm/ an
2. Moldova 576.7 mm/an 575.7 mm/an
3. Muntenia 598.2 mm/an 579.9 mm/an
4. Oltenia 673.4 mm/an 642.0 mm/an
5. Banat-Crisana 711.2 mm/an 702.0 mm/an
6. Transilvania-Maramures, except the areas in
west, central and souht-east part where the
annual values < 600 mm/year
740.0 mm/an 746.2 mm/an
Annual rainfall / agricultural region
- Annual rainfall / decreasing
tendency
< 600 mm/ year = droughty
pluviometric regime for crops

SOL MOISTURE in the critical period for water requirements of maize /
1971-2013
AUGUST / 0-100 cm
JULY / 0-100 cm
Romania / moderate and
strong pedological drought
Romania / moderate, strong
and extreme pedological
drought

Soil moisture / 21 April 2015
http://www.meteoromania.ro/anm/?lang=ro_ro
Winter wheat
MaizeDROUGHT
DROUGHT

Data: EuroCORDEX numerical experiments
Nr.
Centrul de modelare climatică
regională/Regional modeling center
Model regional/
Regional model
Model global/Global model
1 CLMcom (Consorţiul CLMcom) CCLM4-8-17 MPI-ESM-LR
3
IPSL-INERIS (Laboratorul de Stiinţa Climei şi
Mediului, IPSL, CEA/CNRS/UVSQ –
Institutul Naţional al Mediului Industrial şi la
Riscurilor, Halatte, Franţa)
WRF331F IPSL-CM5A-MR
4 KNMI (Institutul Regal Olandez de Meteorologie) RACMO22E ICHEC-EC-EARTH
6 SMHI (Institutul Hidrometeorologic Suedez) RCA4 ICHEC-EC-EARTH
Scenarios
Source: WG 1 AR5 IPCC
Scenarios RCP 2.6, RCP 4.5 and RCP 8.5.
Spatial resolution of EuroCORDEX models is 0.125 deg. in latitude and longitude.

Mean difference of 4-models ensemble for number of days with
T. max greater than 35°C, 2021-2050 vs.1971-2000

Mean difference of 4-models ensemble for number of days with
T. min greater than 20°C, 2021-2050 vs.1971-2000

Scenario RCP 8.5Scenario RCP 2.6
Climate change scenarios / 2021-2050 vs.1971-2000
CMIP5 experiments – summer season

Drought in Romania – challenges and opportunities for National Meteorological Service
in the context of Climate Change
Elena MATEESCU, Gheorghe STANCALIE, Andrei DIAMANDI
National Meteorological Administration - Romania
Bucharest, 21 & 22 April 2015
Climate change scenarios:
- Increasing probability of occurrence for
droughty events due to raising temperature and
decreasing precipitation especially during the
summer season in the Southern, South-Eastern and
Eastern regions;
- Increasing probability of occurrence for
tropical nights, hot days, summer days;
- Local factors modulate the magnitude of the
increasing probability of occurrence for natural
hazards (e.g. topography).

AGROMETEOROLOGICAL
NETWORK
- 7 Regional Meteorological Centres
- 159 weather meteorological stations, 128 being automatic (MAWS)
- 55 weather stations integrating a special program of agrometeorological
measurements – soil moisture and phenological data (winter wheat, maize,
sunflower, rape, fruit trees and vineyards.
National Meteorological Observation Network of Romania
METEOROLOGICAL
NETWORK

- EU Funding Period for 2007-2013 and 2014-2020 periods /
Operational Sectoral Programme for Environment (POS-MEDIU)
- NMA project: The development of the national system of monitoring and
warning of extreme weather phenomena for the protection of life and
property materials (5 million Euro).
- In 2007-2013 period will be implemented the activities related of
modernization of meteo and agrometerological networks:
1. Meteorological network (1 million Euro) – 31 weather meteo stations
(MWAS) in order to complete the automatic meteorologal network and
dedicated software for processing data in automatic flow.
2. Agrometeorological network (200.000 Euro):
- Modernization of agromet network / 25 soil moisture portable systems /
new systems implemented within 5 November 2014
- Windows Server /CISC x86 6-core
- National data base platform / type SQL Server 2008
- Modernization of applications in operational activity – dedicated software
for agrometeorological data and indicators (national/regional level)

1. MAWS – 9 Stations in the mountain area + Băișoara
3
2
5
1
1. Vaisala WMT700
Traductor viteză i direc ie vântș ț
Încălzire traductor+bra e+corpț
1. Vaisala WMT700
Încălzire traductor+bra e+corpț
2. Vaisala HMP155
DTR503A scut antiradia ieț
Umezeală + Temperatură
2. Vaisala HMP155
3. Kipp&Zonen CMP6
Radia ie solară globalăț
3. Kipp&Zonen CMP6
4. OTT Pluvio2
Pluviometru cu cântărire
Încălzit, Paravântuire OTT
PWS Alter
4. OTT Pluvio2
PWS Alter
5. Vaisala AWS310
Logger QML201
Traductor presiune atmosferică
BARO-1 (Clasă A)
Modem GPRS+interfa ă radiomodemț
Baterie 26Ah i încărcătorș
Sursă 220V+Panou fotovoltaic 30W
5. Vaisala AWS310
Logger QML201
BARO-1 (Clasă A)
Stâlp zăbrelit
Cu două rânduri de ancore
Stâlp zăbrelit
4

5. Vaisala QMT110+Scut
Temperatură suprafa ă solț
4. OTT Pluvio2
PWS Alter
4. OTT Pluvio2
PWS Alter
8. Vaisala AWS310
Logger QML201
BARO-1 (Clasă A)
8. Vaisala AWS310
Logger QML201
BARO-1 (Clasă A)
Catarg basculant
Catarg basculant
1
3
4
8
2 6
2. MAWS – 16 Stations for the basis system
5
7
1. Vaisala WMT700
Încălzire traductor
1. Vaisala WMT700
2. Vaisala HMP155
2. Vaisala HMP155
3. Kipp&Zonen CMP6
3. Kipp&Zonen CMP6
6. Vaisala PWD22
Vizibilitate i timp prezentș
6. Vaisala PWD22
Vizibilitate i timp prezentș
7. IRU9400
Înăl ime strat zăpadă x 3ț
7. IRU9400
Înăl ime strat zăpadă x 3ț

4. OTT Pluvio2
PWS Alter
4. OTT Pluvio2
PWS Alter
7. Vaisala AWS310
Logger QML201
BARO-1 (Clasă A)
7. Vaisala AWS310
Logger QML201
BARO-1 (Clasă A)
Catarg basculant
Catarg basculant
1
3
4
7
2
6
3. MAWS – 5 Stations for the agrometeorological network
5
1. Vaisala WMT700
1. Vaisala WMT700
2. Vaisala HMP155
2. Vaisala HMP155
3. Kipp&Zonen CMP6
3. Kipp&Zonen CMP6
6. Vaisala QMT110 x 5
Temperatură sol adâncime
6. Vaisala QMT110 x 5
Temperatură sol adâncime

Rasp
la
pct 2
Update MAWS application data transmision using
a web interface
INTERNET
INTRANET
INTERNET
INTRANET
MAWS -
mountain
MAWS –
basis system
MAWS - agro
Setup
Descriptor
Setup
Descriptor
Setup
Descriptor
HTTP / FTP
SERVER

Agromonitoring system /
conceptual scheme
2 components:
1.Local level / agromet
station - metadata
2.National level – web
application
3.Validation of data at
regional level by 7
responsible with agromet
activity using a web
interface

Index Name Index Name
FD Frost Days R5mm n° of days with RR ≥ 5mm
TD Tropical Days CDD Consecutive Dry Days
CTD Consecutive Tropical Days CWD Consecutive Wet Days
GSL Growing Season Length PRCPTOT Total precipitation
GDD Growing Degree Days SPI Standardized Precipitation Index
WSDI Warm Spell Duration Index SPEI
Standardized Precipitation-Evapotranspiration
Index
CSDI Cold Spell Duration Index AI Aridity Index
PET Potential EvapoTranspiration PDSI Palmer Drought Severity Index
CLIMATIC INDICATORS
SM Soil Moisture CW Cold Wave (ΣTminº≤-10ºC, December- February)
HW Heat Wave (ΣTmax≥32ºC, June-August) DVI Drought Vulnerability Index
AGROMETEOROLOGICAL INDICATORS
OPERATIONAL ACTIVITY

NDVI Normalized Differences Vegetation Index NDDI Normalized Difference Drought Index
NDWI
Normalized Difference Water Index
FAPAR
Fraction of Absorbed Photosynthetically Active
Radiation
LAI Leaf Area Index SMI Soil Moisture Index
SATELLITE DERIVED INDICES
The soil moisture index in the
soil superficial layer issue from
satellite radar data
MetOp - ASCAT

Warnings at national level
and now-casting forecasts
at local level
- Seasonal forecasts
(1-3 months)
- Regional forecasts (2 weeks)
- Agromet forecats /weekly
- Soil moisture maps /daily
- Notes on the drought
evolution
TODAY / Internet – free access of meteorological forecasts and agromet
information
(http://www.meteoromania.ro/anm/?lang=ro_ro)

UE and Romanian Government
Structural Funds 2007-2013
General Inspectorate for Emergency Situation
RO-RISK Project: Risk Assessment on national level for 9 natural
disasters , incl. drought. Cod SMIS 48550 / 2015-2016
Package 2 – DROUGHT
Consortium:
Coordinator – National Meteorological Administration
Partners: National Institute for Soil Science (ICPA Bucuresti),
National Institute for Hydrology and Water Management
(INHGA), Institute of Geography of Romanian Academy (IGAR)
Public tender / Project proposal – under evaluation
Methodology is based on the European Commission’s Risk Assessment and M
(SEC(2010) 1626 final)

3. Project results on drought monitoring/assessment
3.1.“Potential of Remote Sensing for Estimating and Monitoring
Water Footprint of Crops in the Framework of the COST Action ES1106:
Assessment of EUROpean AGRIculture WATer use and trade under
climate change (EURO-AGRIWAT)”
3.2. Assessment of Satellite Derived Soil Moisture Products
over Romania (ASSIMO Project), Program for Research - Development-
Innovation for Space Technology and Advanced Research – STAR

ESSEM COST Action ES1106: Assessment of EUROpean AGRIculture WATer
use and trade under climate change (EURO-AGRIWAT)
The COST Action EURO-AGRIWAT focuses on the assessment of water footprint (WF)
and virtual water trade (VWT) of key food and no-food agricultural products, including
their uncertainties, as well as scenarios concerning WF and VWT under future climatic
conditions. The use of advanced tools and data such as remote sensing, updated
climatic databases, climatic projections/scenarios and agrometeorological models
represents the base of the activity.
An important component of the Action is the preparation and dissemination of
recommendations and guidelines for enabling a more efficient water resource
management in relation with agricultural activities under climate change and
variability.
For most of the crops, the contribution of
green water footprint toward the total
consumptive water footprint (green and blue)
is more than 80%. Wheat and rice have
large blue water footprint.
Globally, 86.5% of the water consumed in
crop production is green water. Even in
irrigated agriculture, green water often has a
very significant contribution to total water
consumption.
The water footprint of national production in France by sector
(Ercin, A.E., Mekonnen, M.M., Hoehstra, A.Y.. The water footprint of France.
Reserch Report Series no. 56, 2012).

Working Group 1 - Water footprint
•Assessment of WF for selected crops at the
different identified spatial scales;
•Evaluation of a specific methodology for the
accounting of grey WF;
•Assessment of the impacts of climate change
and variability on the crop WF;
•Analysis of uncertainties.
Working Group 3 - Sustainability
•Identification of sustainability criteria;
•Identification and evaluation of hotspots and
their causes (i.e. water scarcity, rainfall intensity,
drought periods);
•Evaluation of the eventual unsustainable stage
of the production process;
•Identification of strategic managements and
adaptation options.
Working Group 2 – Water trade
•Evaluation of the VWT of the selected crop
products with the aim to analyze the
relations existing between water distribution
and its transfer between regions and
countries in Europe;
•Accounting of National WF and estimation
of water savings or losses of Nations;
•Assessment of the impacts of climate
change and variability on the crop products
VWT;
•Analysis of uncertainties.
EURO-AGRIWAT - Working Groups

Main goal:
Potential of remote sensing to improve the agriculture Water Footprint assessment
and the Virtual Water trade accounting
Objectives:
•Inventory of the sources of RS data used for estimating variables needed for WF and
VWT assessment based on satellites.
•Identification of the key variables that can be estimated using RS data (with medium
and high spatial resolution) and needed for WF and VWT assessment.
(evapotranspiration, precipitation, water storage, water stress, runoff, land use, etc).
•Assessment of required spatial, spectral and temporal resolution of satellite data for
the Analysis of Water Footprint and the Virtual Water trade.
•Assessment of the most appropriate set of vegetation indices and biophysical variables
in the context of a cost-effective solution to monitor water stress, using satellite data.
•Suggestions and examples concerning the possibility to integrate remote sensing into
WF and VWT accounting.
EURO-AGRIWAT – Remote Sensing Working Group 4
Greece, Portugal, Malta, Poland, The Netherland, Cyprus, Romania

The role of remote sensing in the water footprint assessment
Remote sensing (RS) techniques provides new tools for global Water Footprint (WF)
assessment and represents an innovative approach to regional and global irrigation
mapping, enabling the estimation of green and blue water use.
Satellite data and products, at different temporal and spatial scales, concerning water
cycle estimation of crop characteristics, vegetation state/stress estimation and crops WF
assessment are:
precipitation
soil moisture
vegetation evapotranspiration
vegetation indices
snow cover
land cover type
The identification of the satellite variables needed to be integrated in the WF assessment,
is based on:
Temporal coverage (length of the time series) and temporal resolution (interval between
observations);
Spatial coverage (area covered: river basin, region, nation, continent) and spatial
resolution.
The 4-th Workshop Integrated Drought Management Programme in Central and Eastern Europe, Bucharest
21 – 22 April 2015

The role of remote sensing in the water footprint assessment (cont.)
 Flowchart proposed for obtaining WF of crops from remote sensing data:
P = precipitation
ET = actual evapotranpsiration
Q = surface runoff / streanmflow
S = water storage in a vertical column (snow, canopy
water storage, soil moisture and groundwater)
I = mass water balance in an irrigated area
Etg = green water component of ET
Etb = blue water component of ET
 Additional remote sensing data:
 Vegetation indices for ET estimation: normalized difference vegetation index
(NDVI), normalized difference water index (NDWI), normalized difference drought
index (NDDI).
 Biophysical parameters for ET estimation: leaf area index (LAI).

Soil moisture in root zone (4 layers available: 0-7 cm, 7-28 cm,
28-100 cm and 100-286 cm) by assimilation of ASCAT data to
ECMWF soil Model (31 March 2010).

ET - Land SAF product
Scaling: ET * 10 000
 Spatial distribution of actual evapotranspiration: product generated operationally
by EUMETSAT Land SAF and distributed in near real-time by EUMETCast system.
 Time step:30 min (instantaneous value), 24 hours (cumulated value).
 Spatial resolution – MSG/SEVIRI pixel (1 – 3 km).

NDVI evolution over Romania for the period 01 March – 10 October 2014
(10 days synthesis)
COST ACTION ES1106, Zurich, Switzerland, 12-13.03.2015

The Normalized Difference Water Index (NDWI) is a satellite-derived index from the Near-
Infrared (NIR) and Short Wave Infrared (SWIR) reflectance channels:
NDWI index is a good
indicator of water content
of leaves and is used for
detecting and monitoring
the humidity of the
vegetation cover.
Because it is influenced by
plants dehydration and
wilting, NDWI may be a
better indicator for drought
monitoring than NDVI.
By providing near real-time
data related to plant water
stress, the water
management can be
improve, particularly by
irrigating agricultural areas
affected by drought,
according to water needs.
LANDSAT 8 - NDWI evolution over Caracal area (South of Romania)
(May – September 2013)
Vegetation indices: NDWI

COST ACTION ES1106, Zurich, Switzerland, 12-13.03.2015
The NDDI obtained from MODIS - MOD09A1 products (8-days composite)
NDDI: 26.06-3.07.2007 droughty year NDDI: 26.06-3.07.2014
 The Normalized Difference Drought Index (NDDI):
NDDI = (NDVI - NDWI) / (NDVI + NDWI)
 NDDI had a stronger response to summer drought conditions than a simple difference
between NDVI and NDWI, and is therefore a more sensitive indicator of drought.
 This index can be an optimal complement to in-situ based indicators or for other
indicators based on remote sensing data.
Vegetation indices: NDDI

 Snowmelt is important part of runoff and ground water recharge during
spring;
 Lack on snowmelt can increase possibilities for drought during the spring
and early summer.
Snow cover and snow water equivalent
Standardized SnowPack Index, SSPI – one of the drought indicators
recommended by EU – Expert Group on Water Scarcity and Droughts;
SSPI - developed in EC CryoLand FP7 project (Copernicus Service Snow
and Land Ice);
The SSPI gives information on the relative volume of the snow pack on a 10
days and monthly basis, compared to the reference period 1979 – 2010.
SSPI – spatial resolution 10 – 25 Km, daily.
Snow cover extent is usually based on optical sensors, spatial resolution
250 m – 1000 m, daily global coverage.
The 4-th Workshop Integrated Drought Management Programme in Central and Eastern Europe, Bucharest
21 – 22 April 2015

Daily Snow Water Equivalent 21.01.2014-11.02.2014
Source: CryoLand GeoPortal: http://neso.cryoland.enveo.at/cryoland/cryoclient/

Conclusionss / RS data and product
 Remote sensing data and methods show a potential to be used in the field of Water
Footprint of crops.
 Satellite imagery correlated with in situ measurements of
biophysical/agrometeorological data, offers new opportunities for crops WF
monitoring and assessment and about factors that can influence the vegetation state.
 The use and applicability in specific WF studies may be limited due to different
aspects that need to be analyzed in order to select the proper datasets:
 Spatial coverage of the data depending on the type of desired WF analysis, from local
to regional and global scale;
 Spatial resolution of the data depending on the desired and available pixel sizes. It
can vary from meters to hundreds of kilometers;
 Temporal resolution of the data. It can vary from fifteen minutes to more than fifteen
days;
 Accuracy of the data. The estimation of some variables from remote sensing and their
role in the models is more critical, like precipitation;
 Availability of data. Due to detection issues, data may not be available when there are
cloudy acquisitions.

 Soil moisture plays a key role in the hydrological cycle and in land-atmosphere
interactions.
 For example, evapotranspiration, infiltration and runoff are driven by soil
moisture
 As a consequence, soil moisture is a pivotal variable in land surface –
atmosphere, hydrological and climate models.
 A number of studies have shown the importance of soil moisture in many
applications.
Why Soil Moisture is Important ?
 Earth Observation Satellites can monitor soil moisture on a spatial and temporal
scale which cannot be achieved with in situ sampling
 Indeed, microwave remote sensing is able to provide quantitative information
about the water content of a shallow near surface layer (Schmugge, 1983),
particularly in the low-frequency microwave range, from 1 to 10 GHz.
 Space borne microwave remote sensing has proven to be a valuable tool to fulfill
those needs by quantitatively measuring soil moisture on a global scale under a
variety of conditions
SMOS Satellite Soil Moisture Maps – a potential tool in agro meteorology

• Space borne instruments do not measure directly soil moisture. Microwave
measurements estimate the surface dielectric constant which is converted in soil
moisture.
• As a consequence, in situ soil moisture measurements play an important role in the
calibration and validation of land-surface models and satellite-based soil moisture
retrievals.
• Soil moisture is highly variable in both space and time as a result of heterogeneity in
vegetation, soil properties, topography and climatic drivers.
• In most cases soil moisture networks provide one single observation within a satellite
footprint, which impedes the upscaling of in situ soil moisture to the footprint level.
• Ground based networks are a core component. They provide actual quantitative soil
moisture observations to evaluate algorithm performance.
• Two basic types
– Dense over limited spatial domains
– Sparse over a large geographic region
• There are only a few available that provide the right kind of data for satellite validation
(depth, frequency, latency, access) but none covering the Romanian territory.
INTRODUCING RSMN and the ASSIMO Project !!!
Satellite Moisture Maps Are Great,
Do We Still Need In-Situ Measurements?

• While NMA is operating a
network of 158 weather stations
(mostly automatic), soil moisture
measured every 10 days at 55
stations for agro-meteorological
applications.
• At 247,000 km2 – the areal
extent of the country, the
resulting average spacing of 672
km2
(calculated as the ratio of
areal extent/number of sites)
could be a good starting point
provided that the measurements
are more frequent and the
topography and land cover less
diverse.
RSMN: The Romanian Soil Moisture & Temperature Observation Network
 Adding soil moisture & temperature sensors to the existing weather stations can result in
a fairly adequate soil moisture network for minimal costs.
 RSMN is made up of a “static” component – the SM & T probes at 20 weather station
locations and of a mobile component – 30 autonomous, easy to deploy SM stations.
 RSMN is an essential component and one of the end products of the ASSIMO project

•“The project aims at paving the way for the utilization of satellite derived soil moisture
products in Romania, creating the framework for the validation and evaluation of actual &
future satellite microwave soil moisture derived products, demonstrating its value, and by
developing the necessary expertise for successfully approaching implementations in the
Societal Benefit Areas (as they were defined in GEOSS).
Assessment of Satellite Derived Soil Moisture Products over Romania
(ASSIMO)
Program for Research-Development-Innovation for
Space Technology and Advanced Research – STAR
 launched on November 2, 2009;
 orbit: sun-synchronous;
 accuracy of 4% volumetric soil moisture;
 passive satellite based on microwave retrievals;
 spatial resolution 35-50 km;
 revisit time 1-3 days.
Europe Coverage of SMOS
Satellite

Interpolation from 60
points of measurement
from the agro
meteorological
stations.
In-Situ limitations
relate to the:
-number of
measurements;
-temporal resolution of
the measurements.
The Soil Moisture
Level 3 SMOS Product
spatial resolution: 25
km.
Interpolation from 286
points, extracted from
the Level 3 SMOS
Product.
SMOS limitations
relate to the:
land cover and land
use: forest coverage,
the water surface, the
number and surface of
localities;
soil texture (e.g.
loamy soil is not fit
because it retains
water, like in the center
of Romanian Plain);
higher RFI values
(e.g. radars, airports)
determine lower quality
data.

Example of RSMN configuration for SMOS validation
 The network
density can be
easily increased
using cheap in-
house developed
SM mobile
stations, allowing
for the flexibility
needed to design
validation
campaigns for
different satellite
sensors.
 Validation of SSM
products with in-
situ data depends
on the existence
of adequate
ground based soil
moisture
measurements.

Assessment of Satellite Derived Soil Moisture Products over Romania
(ASSIMO)
Program for Research-Development-Innovation for
Space Technology and Advanced Research – STAR

Proposed follow up(s) activities/projects – based on
lessons learned and exchange experiences on
the IDMP-CEE Project
1. Guidelines for drought monitoring and assessment using RS and GIS methods.
2. Platform for drought risk assessment to share climate information, good practices within
vulnerable sectors, and knowledge on disaster risk reduction policies and adaptation strategies.
3. Innovative financial instruments for drought risk management through ART (Alternative Risk
Transfer) solutions and new insurance mechanisms in agriculture sector – case studies on
adequate implementation instruments based on climatic indicators.
4. Future development of the Early Warning Systems (EWSs) – new datasets, indicators and
maps to support current operational drought monitoring activities and end-user needs.
5.The demonstration projects for development of seasonal forecasting products and water
balance models to projected crop needs and new irrigation technologies.
6. Elaboration of a digital atlas as decision support system on drought management – pilot
areas at national/regional level
7. Elaboration of a e-book on drought risk – concept, indicators, methods and monitoring
networks as future challenges in the context of global warming
8.Training and education programmes to facilitate a science-policy interface for effective
decision-making in disaster drought risk management

Thank you for your attention!
50

5 drought in romania mateescu

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