5 drought in romania mateescu

4th WorkshopIntegrated 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 – STARRomanian 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 / 1352Franta – 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 / 8.8ºC1981-2014 / 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 2015http://www.meteoromania.ro/anm/?lang=ro_ro

Winter wheat

MaizeDROUGHT

DROUGHT

http://www.meteoromania.ro/anm/?lang=ro_ro

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 withT. max greater than 35°C, 2021-2050 vs.1971-2000

Mean difference of 4-models ensemble for number of days withT. 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 softwarefor agrometeorological data and indicators (national/regional level)

1. MAWS – 9 Stations in the mountain area + Băișoara

3

2

5

11. Vaisala WMT700

Traductor viteză i direc ie vântș țÎncălzire traductor+bra e+corpț

1. Vaisala WMT700

Traductor viteză i direc ie vântș țÎncălzire traductor+bra e+corpț

2. Vaisala HMP155

DTR503A scut antiradia iețUmezeală + 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



5. Vaisala AWS310

Logger QML201

Traductor presiune atmosferică BARO-1 (Clasă A)

Modem GPRS+interfa ă radiomodemțBaterie 26Ah i încărcătorșSursă 220V+Panou fotovoltaic 30W

5. Vaisala AWS310

Logger QML201



Stâlp zăbrelit

Cu două rânduri de ancore

Stâlp zăbrelit


4

5. Vaisala QMT110+Scut

Temperatură suprafa ă solț



4. OTT Pluvio2



4. OTT Pluvio2



8. Vaisala AWS310

Logger QML201



8. Vaisala AWS310

Logger QML201



Catarg basculant


Catarg basculant

Cu două rânduri de ancore1

3

4

8

2 6

2. MAWS – 16 Stations for the basis system

5

7

1. Vaisala WMT700

Traductor viteză i direc ie vântș țÎncălzire 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



4. OTT Pluvio2



7. Vaisala AWS310

Logger QML201



7. Vaisala AWS310

Logger QML201



Catarg basculant


Catarg basculant

Cu două rânduri de ancore1

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 / FTPSERVER

Agromonitoring system / conceptual scheme

2 components:1.Local level / agromet

station - metadata2.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 SPEIStandardized 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

NDWINormalized Difference Water Index

FAPARFraction 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)

http://www.meteoromania.ro/anm/?lang=ro_ro

UE and Romanian GovernmentStructural 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 – DROUGHTConsortium:

Coordinator – National Meteorological AdministrationPartners: 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 Mapping Guidelines for Disaster Management (SEC(2010) 1626 final)

http://register.consilium.europa.eu/doc/srv?l=EN&f=ST%2017833%202010%20INIT

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 – STARRomanian Space Agency (ROSA)

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 accountingObjectives:•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 4Greece, 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: precipitationsoil moisture vegetation evapotranspirationvegetation indicessnow 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 = precipitationET = actual evapotranpsirationQ = surface runoff / streanmflowS = water storage in a vertical column (snow, canopy water storage, soil moisture and groundwater)I = mass water balance in an irrigated areaEtg = green water component of ETEtb = 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, 12-13.03.2015The 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

Fractional Snow Cover

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

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

Romanian Space Agency (ROSA)

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

Romanian Space Agency (ROSA)

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 level7. Elaboration of a e-book on drought risk – concept, indicators, methods and monitoring networks as future challenges in the context of global warming8.Training and education programmes to facilitate a science-policy interface for effective decision-making in disaster drought risk management

Thank you for your attention!

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