Global Drought Monitoring Service through the GEOSS Architecture ...

Global Drought Monitoring Service through the GEOSS 

Architecture 

Engineering Report 

GEOSS Architecture Implementation Pilot (AIP) 

Drought and Water Working Group 

Content developed by the GEO Architecture Implementation Pilot 

Licensed under a Creative Commons Attribution 3.0 License 

Version 2.0

Content developed by the GEO Architecture Implementation Pilot 

Licensed under a Creative Commons Attribution 3.0 License

Architectural Implementation Pilot, Phase 3 Version: 2.0 

Global Drought Monitoring and European Drought 

Observatory-Water SBA Engineering Report 

Date: 11/Feb/2011 

Revision History 

Version Date Editor and 

Content 

providers 

1.0 17/Dec/2010 W. Pozzi 

Comments 

1.4 03/Jan/2011 C. Fugazza Revision to semantics-related sections 

1.4 05/Jan/2011 M.J. Brewer Revision to Global Drought Monitor Portal 

1.4 07/Jan/2011 M. Santoro, S. 

Nativi 

1.8 18/Jan/2011 B. Lee 

System Architecture for the Discovery 

Augmentation Component 

1.9 25/Jan/2011 W. Pozzi Incorporation of GEO Ontology Registry 

2.0 4/Feb/2011 M. Enenkel Updating GLOWASIS 

2.0 10/Feb/2011 M.J. Brewer Review of NIDIS and GDMP sections 

2.0 11/Feb/2011 W.Pozzi Release 

Document Contact Information 

If you have questions or comments regarding this document, you can contact: 

Name Organization Contact Information 

W.Pozzi GEO AIP Water and Drought Working Group & 

IGWCO 

Page 3 

Will.pozzi@gmail.com 

M. Santoro Italy National Research Council santoro@imaa.cnr.it 

C. Fugazza Joint Research Center (JRC) cristiano.fugazza@jrc.ec.euro 

pa.eu 

J.Vogt JRC/European Drought Observatory (EDO) jürgen.vogt@jrc.ec.europa. 

eu 

S. Nativi Italy National Research Council nativi@cnr.it 

M. Brewer US National Integrated Drought Information 

System (NIDIS)/NOAA 

R. Heim US National Oceanic and Atmospheric 

Administration (NOAA) 

J. Sheffield Princeton University




Date: 11/Feb/2011 

S. Niemeyer EDO 

D. Cripe GEO Secretariat Scientific Officer for Water dcripe@geosec.org 

K. Korporal Environment Canada 

A. Howard Agriculture and Agri-Food Canada 

B. Lloyd- 

Hughes 

J. Lieberman 

University College London 

W. Wagner Technical University Wien 

Michael 

Piasecki 

Consortium of Universities for the Advancement 

of Hydrologic Science (CUAHSI)/City College of 

New York (CCNY) 

L. Nunez Republic of Argentina Servicio Meteorologic 

Nacional Drought Monitor 

L. Bettio Australia Bureau of Meteorology 

M. Nicholson Australia Bureau of Agricultural and Resource 

Economics and Sciences (ABARES) 

B. Trewin Australia Bureau of Meteorology 

B. Lee CSIRO 

W. Sonntag US Environmental Protection Agency 

V. Guidetti European Space Agency 

R. Lawford IGWCO lawford@umbc.edu 

B. Hofer EDO/JRC 

D. Magni EDO/JRC 

L. Di George Mason University 

Eugene Yu George Mason University 

C. Yang George Mason University 

M. Doubkova Technical University of Vienna 

M. Enenkel Technical University of Vienna 

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Date: 11/Feb/2011 

Table of Contents 

A. Global Drought Monitoring Service and the Global Drought Community of Practice 8 

1.1 Scope of this document 8 

1.2 Importance of Global Drought Monitoring as a Critical Earth Concern and a 

Prime Activity for GEO 9 

1.3 Identification of Starting Conditions Fostering Drought is not Straightforward 9 

1.3.1 Description of the Water Cycle 10 

1.4 What are the User Requirements for an effective Drought Monitoring and 

Forecasting Information System? 11 

2. Drought Monitoring Components and Tools found in Hydrometeorology Drought 

Monitoring Services within the Global Drought Community of Practice 13 

2.1 European Drought Observatory 13 

2.1.1 European Drought Observatory Portal Characteristics: “Drill Down” Capability 13 

2.1.2 Importance of Soil Moisture for Monitoring Agricultural Drought 13 

2.1.3 EDO-deployed Meteorological Drought Indicator: Standardized Precipitation 

Index 15 

2.1.4 Hydrologic Drought Indicator 15 

2.2 USA National Integrated Drought Information System 16 

2.3 Government of Canada Drought Coverage 18 

2.4 Commonwealth of Australia Drought Monitoring 18 

2.4.1 Commonwealth of Australia Water Availability Project 18 

2.5 Africa Continental Drought Monitoring 19 

2.5.1 Princeton Experimental African Drought Monitor 19 

2.6 New Projects Permitting Further Development of the Global Drought 

Monitoring Service 21 

2.6.1 European Framework (EF) Drought Early Warning System for Africa-- 

DEWFORA 21 

2.6.2 GLOWASIS (Global Water Scarcity Information Service) 21 

2.6.3 Satellite Application Facility on Support to Operational Hydrology and Water 

Management (H-SAF) 24 

2.7 South American Continent 26 

2.7.1 Republic of Argentina Servicio Meteorologic Nacional Drought Monitoring 26 

3. Capturing User Requirements for the Global Drought Monitor and its 

Interoperability with the Global Earth Observation System of Systems (GEOSS) 30 

3.1 Assessment of Drought Vulnerability and Susceptibility 30 

3.2 Capturing User Requirements and Implementation of Architecture to Design of 

the Global Drought Monitor 31 

3.2.1 Portal Requirements: Drill-down capability 31 

3.2.2 Top-down versus bottom-up Design 31 

3.2.3 Soil Moisture and Agricultural Drought Monitoring Requirement 32 

3.2.4 Republication of information to help decision makers facilitate drought decision 

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Date: 11/Feb/2011 

making 32 

3.2.5 Hydrologic Drought Monitoring for Semi-Arid Areas and Meeting Hydrologic 

Drought User Requirement through Semantics 32 

3.3 Developing an Architectural Diagram for the GEO Global Drought Monitoring 

Service 33 

3.4 Semantic Development Activities within GEO: the Data Integration and 

Analysis System (DIAS) Contribution from Japan 36 

3.4.1 Adding Advanced Search and Discovery using Semantics 39 

4. Global Implementation of the Drought Monitoring Service through GEOSS 40 

4.1 Components of the System Architecture of the Global Drought Monitoring 

Portal 40 

4.2 Actors 41 

4.3 Capturing User Requirements for the Global Drought Monitor Portal through 

the GDMP Scenario 41 

4.3.1 Display of Selection Bar for Drought Indices, Processing to Derive Dehydration 

and Drought Severity, and Drought Map Republication 42 

4.3.2 Layout and Organization of the GDMP within the NIDIS GIS Server 43 

4.3.3 Implementation of Advanced Search and Discovery in the GDMP 44 

4.4 Support of Increased Global Coverage within the web-based, real-time GDMP 

server 44 

4.5 Integration of GDMP with GEOSS Architecture 44 

4.6 Remote Sensing Soil Moisture Integration 45 

4.7 Adding Water Usage Information Layers, including Agriculture 45 

5. Advanced Search and Discovery Capability within the European Drought 

Observatory 49 

5.1 Components of the European Drought Observatory 49 

5.1.1 European Drought Observatory user access 49 

5.1.2 Organization and layout of the EDO map server page (scenario step 01— 

continued) 50 

5.1.3 Selection of Drought Indices 50 

5.1.4 Processing Step by Running Drought Indicators over a Selected Spatial Domain 51 

5.1.5 Automated Email Alerts and Drought Triggers 51 

5.1.6 Context and pre-conditions 52 

5.2 Implementation of the European Regional Drought Semantic-enhanced 

Monitoring and Information System 52 

5.2.1 Advanced Semantic Search 54 

5.3 EuroGEOSS Deployment of the Foundation Vocabularies 55 

5.4 Fine Tuning the Foundation Vocabularies for SBA Application—Specialized 

Drought Vocabulary 55 

5.4.1 Water Ontology-enablement within the DAC Semantics 56 

5.5 How the EuroGEOSS Discover Augmentation Component supports semantic 

searches 56 

5.6 Operation of the Water Ontology within the EuroGEOSS Discovery 

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Augmentation Component 58 

5.6.1 Searching for Concepts/Terms 58 

5.6.2 Multilingual Concepts/Terms 58 

5.6.3 European Drought Observatory (Client) Query 59 

5.6.4 WPS Request 59 

5.7 Use of EuroGEOSS Semantic Discovery within the European Drought 

Observatory (Returning back to the Scenario) 59 

5.8 Interoperability Arrangements with GEOSS 59 

5.9 Post Deployment Activities 60 

5.9.1 Ontology Engineering 60 

6. Evaluating How the Advanced Semantic EuroGEOSS Search and Discovery System 

Works 61 

7. Drought Metadata for fostering interoperability between EDO and EU national 

drought monitors 62 

8. Range of Issues Covered by the Water Working Group 64 

9. References 65 

10. EuroGEOSS Drought Vocabulary Keywords 70 

11. EuroGEOSS Water Societal Benefit Area Keywords 72 

12. Acknowledgments 72 

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Date: 11/Feb/2011 

Drought Monitoring and Water Activities within the 

Group on Earth Observations (GEO) 

A. Global Drought Monitoring Service and the 

Global Drought Community of Practice 

1.1 Scope of this document 

This is an overview and documentation of the drought monitoring service as implemented 

through the Group on Earth Observations System of Systems (GEOSS) and the European 

Drought Observatory implementation of advanced semantic search capability through the 

EuroGEOSS Discovery Broker tools. A key deliverable is the specification of a set of tools that 

will access information published through a distributed water data infrastructure. The 

development of the specification of these tools includes: 1) capturing user requirements through 

expressing the GEO Water Societal Benefit Area users within a “scenario,” that is, who might 

use the GEO Global Drought Monitoring Service and the types of data and functionality that 

these users require or expect; 2)Design of a system architecture and the enabling framework for 

this at the component level; 3) integration of this system architecture within the Global Earth 

Observation System of System (GEOSS) architecture and its components; and 4) 

implementation. The development efforts of the GEO Global Drought Monitoring Portal have 

involved multiple parties, including the Architectural Implementation Pilot (AIP) Water and 

Drought Working Group, through the GEO Architecture and Data Committee level; the 

Scientific Officer for Water of the GEO Secretariat (through the Global Drought Monitoring 

Initiative); drought task activities of the Integrated Global Water Cycle Observations (IGWCO) 

Community of Practice; Princeton University Land Surface Hydrology Group, USA National 

Integrated Drought Information System (NIDIS), the European Drought Observatory, Italian 

National Research Council, the Joint Research Centre, the University College of London, the 

Technical University of Vienna, Canadian Group on Earth Observations (CGEO), Argentina 

Servicio Meteorologico Nacional, Australia Bureau of Agricultural and Resource Economics and 

Sciences (ABARES), and the Australia Bureau of Meteorology. 

This report is divided into two sections to increase its accessibility. The first section 

explains why certain portal Information Technology (IT) capabilities (“user requirements”) were 

selected for implementation and deployment within the global drought monitoring service. The 

first section deals with development of a web-based, real-time Geographic Information System 

GIS server with a distributed database federation, used for hydrologic alerts in drought 

conditions, a prototype global drought early warning system. The second section explains why 

certain advanced search capabilities (including “semantic” search and discovery)—again, user 

requirements—were developed for implementation within the European Drought Observatory 

and the EuroGEOSS discovery broker. These technologies can also be eventually migrated for 

implementation within the Global Drought Monitoring Portal (GDMP), combining with 

concurrent semantic components being built within the Global Earth Observation System of 

System through the Data Integration and Analysis System (DIAS), the Japanese Aerospace 

Exploration Agency (JAXA) contribution to GEO, and the EuroGEOSS European Union 

contribution. 

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Date: 11/Feb/2011 

1.2 Importance of Global Drought Monitoring as a Critical Earth Concern and a 

Prime Activity for GEO 

Given current concerns with the increasing frequency and magnitude of droughts in many 

regions of the world, especially in the light of expected climate change, drought monitoring and 

dissemination of early warning information in a timely fashion is a critical concern. The 

European Union experienced intense drought and heat waves in 2003, Argentina in 2008/2009, 

southeast Australia in 2009, while, at the same time, the Intergovernmental Panel on Climate 

Change (IPCC) climate projections for the 21 st century suggests an increased frequency of severe 

droughts in continental USA and Mexico, Mediterranean Basin, parts of northern China, 

Southern Africa, Australia, and parts of South America. In addition, current agricultural 

production is being maintained by multiple crop cycles over the course of a single year in India 

and China, for example, and drought is exhausting secondary supplies of groundwater , as the 

drought exhausts surface water supplies, creating a dependency upon the groundwater sources 

needed to maintain these multiple crop cycles. 

Droughts and famine are inseparable from one another: droughts lower agricultural 

production. Current agricultural monitoring efforts, such as the European Union (EU) 

Monitoring of Agricultural Resources with Remote Sensing (MARS Food-Sec), the USA 

Department of Agriculture (USDA) Foreign Agricultural Service, and the Famine Early Warning 

System (FEWSNET) have developed methodologies for estimating the impact of drought upon 

agricultural production, such as the Food and Agricultural Organization (FAO) Water 

Requirements Satisfaction Index (WRSI)(as renamed Global Water Satisfaction Index by MARS 

and GeoWRSI by FEWSNET). The MARS convention implies that a WRSI or GWSI of 50 

represents a famine condition (actual evapotranspiration of half the plant water requirement). 

Advances in Land Surface Modeling, as in more sophisticated representation of soil 

water process, including linkage of groundwater with surface water, is just one way in which 

new technologies are available to upgrade the more schematic soil water balances incorporated 

within WRSI. Additional new technologies are coming online with respect to satellite-based soil 

moisture sensors. Standardization of global meteorological datasets has permitted the running 

Land Surface Models and distributed hydrological models in near-real-time (NRT). IT 

infrastructure and informatics methodologies, combined with all these scientific advances, have 

now created the opportunity to develop a more up-to-date, comprehensive, useful-to-decision 

making drought monitoring capability. Additional advances in web-based, real-time (RT) 

Geographic Information Systems (GIS) with supporting distributed databases (Wangmutitakul, 

et. al., 2003; Wang 2005; Chalainanont, et. al. 2007 or web map services in time-critical 

applications (Zhang and Li 2005; Ozdilik and Seker). 

The role that GEO plays in this process is to provide a rich collaborative environment, 

fostering collaboration among the USA, Canada, European Community, Asia, Australia, and 

South America. 

1.3 Identification of Starting Conditions Fostering Drought is not straightforward 

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Date: 11/Feb/2011 

The American Meteorological Society Glossary defines “drought” as “a period of 

abnormally dry weather sufficiently long enough to cause a serious hydrological imbalance.” 

Agricultural drought is defined as “conditions that result in adverse crop responses, usually 

because plants cannot meet potential transpiration as a result of high atmospheric demand and/or 

limited soil moisture.” Hydrologic drought is defined “prolonged period of below-normal 

precipitation, causing deficiencies in water supply, as measured by below-normal streamflow, 

lake and reservoir levels, groundwater levels, and depleted soil moisture.” The definition of 

agricultural drought stipulates that soil moisture monitoring is the methodology of choice for 

monitoring drought afflicting agriculture. The definition of hydrologic drought stipulates that 

monitoring of streamflow (including baseflow), groundwater levels, and soil moisture may be 

necessary in order to monitor hydrologic droughts. Indeed, the complexities of water cycle 

processes found in semiarid terrain, particularly processes in the vadose zone, may be critical in 

identifying drought’s early stages. Global drought monitoring capability includes the capability 

to monitor drought in many diverse semiarid conditions. The definition of the different types of 

droughts, particularly hydrological droughts stipulate that monitoring capability of groundwater, 

stream flow, soil moisture, snow storage at the start of spring meltwater season, and river water 

level may be prerequisites or user requirements for an effective global drought monitoring 

program. These, in turn, establish user requirements for an information system that support 

global and regional drought monitoring. 

1.3.1 Description of the Water Cycle 

The water cycle begins—after evaporation of water over the oceans—as rain out over 

land through which precipitation—if temperatures are low enough—which falls as frozen water 

which accumulates on top of the surface of land as layers of snow or glacial layers. 

Alternatively, precipitation falls—if temperatures are high enough—in its liquid form and 

infiltrates into soil (unless the soil has a precondition of already being water saturated. This 

infiltration and percolation occurs both as flow through the pores of the soil and flow through 

macropores or fractured rock. Drainage may occur from topsoil through thick vadose zones in 

semi-arid areas, until the water reaches layers of saturation of pores with water, called 

groundwater. The proximity of groundwater to the surface determines whether water is 

exchanged between groundwater, with groundwater discharge occurring into streams or rivers or 

groundwater recharge occurring through river or streamflow. Furthermore, semiarid areas may 

be characterized by ephemeral flashfloods, making the occurrence of such sources of water 

difficult to typify statistically. One key difference is that flow of water through pores in soil is a 

very slow, diffusive process which occurs over much longer time scales—decades or longer— 

than the more rapid, prompt runoff processes occurring at the surface. The point to be made here 

is that drought originates as a deficiency of frozen precipitation stored on the surface or liquid 

precipitation that slowly works its way through the processes of the hydrologic cycle. Some of 

these processes and events, such as decline of soil water, occur rather rapidly and impact the 

growth stage of a crop in agriculture (agricultural drought indicator), while other processes, 

drawdown of groundwater level and lowered discharge of groundwater to river baseflow can 

occur over seasonal time scales or longer (hydrologic drought) (Van Lanen et al., 2004). 

Drought can also exhaust municipal water supplies, both as drops in reservoir levels of 

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stored water or declines of water surface elevations within stream and river networks. 

Environmental flow requirements are not met, causing environmental impacts as well. Cooling 

water for thermal power plants is not available. All of these water cycle processes are often 

lumped under the generic term hydrologic drought, but the actual nature of the drought may be 

caused by a multiplicity of factors. 

Hydrologic droughts can occur through groundwater flow or streamflow. Groundwater 

droughts can be the result of long periods with below average precipitation. Van Lanen & 

Tallaksen (2007) have compared different terrains having a slow and a fast responding 

groundwater system to conclude the effect of the groundwater system on the frequency and 

duration of droughts was larger than the effect of different soil types. The groundwater system 

has large influence on the propagation of droughts through the hydrological cycle and hence on 

drought characterization (Van Lanen & Tallaksen (2008); Wanders, van Lanen, and van Loon 

2010). The Total Storage Deficit Index, developed by Yirdaw et al (2008) used NASA Gravity 

Recovery and climate Experiment observations to attempt to quantify the groundwater role in 

hydrologic drought in the Canadian prairies. Terrestrial water storage changes can also be 

adopted for drought monitoring strategies (Rodell). Drought indicators have also been 

developed for evapotranspiration (Anderson) 

1.3.1.1 Difficulties in Identifying Drought Conditions 

Drought lacks a precise and universally accepted definition. The detection of the 

threshold beyond which a drought episode begins is difficult to determine out of the statistical 

noise that creates random fluctuations (V. Castillo 2009; Moreira et al 2008). Requirements for 

drought detection include methodology that can select drought events from the remainder of the 

meteorological or hydrological time series, a truncation level or threshold which divides the time 

series into “above normal” and “below normal” sections (Dracup et al 1980). The truncation 

level can be set to cut the series at several places, and “run length” is the distance between 

successive crossings across the threshold; the run intensity is the average deviation from the 

threshold (van Lanen et al 2008). Probabilistic prediction tools have also been developed. 

1.4 What are the User Requirements for an effective Drought Monitoring and 

Forecasting Information System? 

The integration of drought information (indices and impact indicators) in a 

comprehensive framework (composite index and maps) is the starting point for developing a 

drought monitoring system. Several integrating methodologies have been explored in AIP-3. 

Drought Monitoring may be summarized as a back-end information system, linked to an 

application that, in turn, is at the back-end of a user accessible portal. 

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For example, lack of soil moisture availability is used to define conditions for 

agricultural drought, and the shortage of ground-based in-situ soil moisture measurement 

stations requires estimation of soil moisture over large land tracts using Land Surface Models 

(such as the NCAR Community Land Model, or the ensemble National Land Data Assimilation 

System (NLDAS within the USA) or distributed hydrological models (such as the Variable 

Infiltration Capacity VIC model or LISFLOOD). Such models are linked systems of partial 

difference equations that ingest multidimensional arrays of near-real-time or real-time 

meteorological and precipitation data as functions of time. However, despite the fact that such 

multi-dimensional data are solved across a lattice of grid cells that emulate spatial locations, the 

output arrays for each respective area have to be geographically registered in order to be 

imported into a Geographic Information System (GIS). In short, the complex land surface model 

and Geographic Information System (GIS) are separate packages (applications). The advantage 

of linking together the Land Surface Models or distributed hydrological models with a GIS is 

that the soil moisture (as well as other water budget component and drought indicators) can then 

be added together or republished as layers within a map, displayed with the drought impact 

information, such as crops dependent upon green water. The soil moisture may then be 

combined with different layers of information within the GIS, published as maps, and exchanged 

using OGC Web Mapping Services (WMS) among individual national hydrometeorological 

service drought monitors and the global drought monitor. This is the information system behind 

the application (and the front end portal user interface), and this integrates the drought 

information (indices and impact indicators) with maps of drought severity rankings and 

vulnerability or impact factors. The observing system is comprised of the ground-based or 

satellite-based observations used to derive the meteorological and precipitation forcing used in 

the Land Surface Models or distributed hydrologic models. 

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2. Drought Monitoring Components and Tools found in Hydrometeorology 

Drought Monitoring Services within the Global Drought Community of Practice 

This section surveys the different types of Drought Monitoring Systems and why certain 

techniques were chosen for a basis of the design of the global drought monitor portal. 

2.1 European Drought Observatory 

2.1.1 European Drought Observatory Portal Characteristics: “Drill Down” 

Capability 

Within the European Community, the European Drought Observatory (EDO)’s map 

server utilizes a common spatial resolution of 20 km, while the national EU drought monitor 

maps have higher spatial resolution. Common registration of datasets through the EuroGEOSS 

discovery broker enables the highest resolution maps to be exchanged with the EDO, since the 

overall system is utilizing a common set of standards. The EDO map server can exchange map 

via web services with the Ministerio de Medio Ambiente (MARM) in Spain, for example, so that 

maps of higher spatial resolution can be republished for the benefit of a user query. The design 

principles for the European Drought Implementation (the combined EuroGEOSS discovery 

broker and EDO and national drought monitors within the EC) were: 1) decentralized data 

holdings but direct linkage and exchange using common format and standards; and 2) a set of 

products agreed in common among all partners to be made available and exchanged, such as 

Standard Precipitation Index and soil moisture anomaly. Common metadata and registration 

through the EuroGEOSS discovery broker make the linking of data among river basin, nation, 

and regional level possible (as well as interoperable). 

2.1.2 Importance of Soil Moisture for Monitoring Agricultural Drought 

The EDO currently measures the presence of agricultural drought by estimating soil 

moisture across the European Union, using the LISFLOOD model. The LISFLOOD model is 

used for forecasting floods, as part of the European Flood Alert System (EFAS), and the soil 

moisture outputs of the model are extracted for use in drought monitoring. Continuous 

simulations with the LISFLOOD model within the European Flood Alert System produce daily 

soil moisture maps of Europe. Having the soil saturated with water is a precondition for flooding, 

since any additional liquid precipitation will run off immediately. LISFLOOD is run using 

near-‐real-‐time meteorological data, including precipitation, derived from measured and 

spatially interpolated meteorological point data provided by the MARS-STAT activity of IPSC- 

JRC (so called JRC-MARS data). Due to the reception via the Global Telecommunication 

System of WMO and further processing the data are typically one to two days behind the current 

date. The LISFLOOD model is run twice daily on a Linux cluster. The spatial resolution of 

LISFLOOD on the pan-European scale is currently at 5 km. 

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Daily soil moisture map on is presented in form of soil suction (pF) values of the top soil layer 

that commonly range between 1.5 for very wet conditions up to 5.0 for very dry soils. 1 The pF 

value describes the forces necessary for plants to apply in order to extract water from the soil for 

their use. 

2.1.2.1 Development of the Soil Moisture Climatology 

The “climatology” for soil moisture has been derived as year-to-year outputs from the 

LISFLOOD model, as having been generated from the Re-Analysis data of the European Centre 

for Medium-Range Weather Forecasts (ERA-40) that comprise the period 1958-2001 (i.e. 44 

years), along with updating made available from measured meteorological data from JRC-MARS 

from the Global Telecommunication System of WMO covering 1990 to 2006, i.e. a period of 17 

years. (Compare this with the Princeton datasets below). Soil moisture anomalies are calculated 

from the climatology. 2 

2.1.2.2 Soil Moisture Anomaly Forecasts prepared from the climatology 

Soil moisture and soil moisture anomaly forecasts are derived using the same modeling 

approach but, with the exception of using the short term meteorological forecasts rather than 

near-real-time meteorology data. In the forecasting mode the European Flood Alert System 

produces information on the development of soil moisture in Europe for up to ten days ahead. 3 

The anomaly forecast is also made. 4 

The trend map of soil moisture describes qualitatively the change in soil moisture, currently 

between today and the seventh day ahead. Orange to red colors indicate drying conditions, while 

yellow to green colors predict wetter conditions during the next week. 

1 http://desert.jrc.ec.europa.eu/action/php/index.php?action=view&id=19 




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Figure 1 LISFLOOD forecasted normalized top soil moisture suction (pF) for Europe. The pF 

values have been normalized by ECMWF ERA-40 statistics. 

2.1.3 EDO-deployed Meteorological Drought Indicator: Standardized 

Precipitation Index 

Precipitation anomalies are expressed the monthly Standardized Precipitation Index (SPI) 

of the last month, a well-known meteorological drought index. Monthly SPI values reflect short 

term changes in precipitation as compared to the long-term average of the respective month. 

Positive SPI values indicate greater than median precipitation, and negative values indicate less 

than median precipitation (McKee et al. 1993). 

2.1.4 Hydrologic Drought Indicator 

A hydrological drought is described usually by the analysis of stream-flow, lake, or 

reservoir level data. Opposite to meteorological information, hydrological data are collected 

throughout Europe, but are generally stored locally at the national or even regional level, often 

with varying formats and qualities less consistent than for meteorological data. Here, after 

careful calibration, the hydrological model LISFLOOD might contribute to the forecasting of 

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low flows by predicting discharge as it is already being doing for the prediction of flood events 

in major pan-European catchment areas. 

2.2 USA National Integrated Drought Information System 5 

The US National Integrated Drought Information System (NIDIS) is the national drought 

early warning system for the US. It employs three key tools: 1) the US Drought Monitor 6 (; 2) 

the Drought Impact Reporter 7 ; and 3) the US Drought Outlook , and hundreds of supplemental 

indicators, services, forecasts, and tools, to provide a snapshot of current drought conditions, 

how those conditions are affecting local populations, and whether the drought will continue. 

2.2.1.1 USA NIDIS Portal Drill-Down Capability 

One interesting feature of the NIDIS map server is that one begins with a national map of 

drought conditions within the USA and then “drills down” to the region level and then to the 

basin level. 

The first drop down tab is “Drought monitor date,” while the second is “Zoom to area.” The 

third drop down tab is “Zoom to basin,” which currently includes the Upper Colorado River 

Basin and the Lower Colorado River Basin. 

Such a “drill down” system—used by both the European Drought Observatory and the 

USA National Integrated Drought Information System portal—can integrate the basin scale 

drought maps, national scale, continental scale, and global scale and, correspondingly, was 

selected for implementation within the Global Drought Monitoring Portal. 

2.2.1.2 Soil Moisture Monitoring for Agricultural Drought 

As in the case of EDO, the USA National Integrated Drought Information System 

(NIDIS) contains soil moisture and soil moisture anomaly maps. 8 

5 http://www.drought.gov/portal/server.pt/community/drought.gov/202 

6 http://www.drought.gov/portal/server.pt/community/drought_indicators/us_drought_monitor 

7 http://www.drought.gov/portal/server.pt/community/impacts/210 

8 http://www.drought.gov/portal/server.pt/community/forecasting/209/soil_moisture/338 

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Ensemble soil moisture that is based upon multiple Land Surface Models and distributed 

hydrologic models are available from the NASA/GSFC National Land Data Assimilation System 

(NLDAS) ensemble Drought Monitor. 9 

The University of Washington experimental US surface water monitor is based on the 

Variable Infiltration Capacity (VIC) distributed hydrologic model. 10 

The Center for Climate Prediction (CPC) produces “Leaky Bucket Model” soil moisture. 11 

2.2.1.3 Agricultural Drought Short Term Forecasting 

The US Drought Outlook provides an integrated drought forecast, relying heavily on the 

NOAA Climate Forecast System (CFS) and is issued for time-scales out to three months 12 . 

The soil moisture anomaly forecasts are based upon the NOAA Global Forecasting 

System (GFS) model; soil moisture anomalies are based upon a 1971-2000 mean climatology. 13 

2.2.1.4 Indicators for Monitoring Meteorological Drought 

A variety of Drought Indicators are made available on the NIDIS site. 14 Examples include such 

items as Standardized Precipitation Indices and Palmer Drought Indices at short time-scales, 

2.2.1.5 Agricultural Impacts Estimation 

Agricultural impacts are currently tracked by a system utilizing color coding for pasture and 

range land in “poor” and “very poor condition” 15 

9 http://www.emc.ncep.noaa.gov/mmb/nldas/drought/ 

10 http://www.hydro.washington.edu/forecast/monitor/ 

11 http://www.cpc.ncep.noaa.gov/products/Soilmst_Monitoring/ 

12 http://www.drought.gov/portal/server.pt/community/forecasting 

13 http://www.cpc.ncep.noaa.gov/soilmst/forecasts.shtml 

14 http://www.drought.gov/portal/server.pt/community/drought_indicators/223 

15 

http://www.drought.gov/portal/server.pt/community/impacts/210/tracking_agricultural_impacts/ 

307 

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2.2.1.6 Hydrologic Monitoring 

Hydrologic drought monitoring measures and forecasts the amount of water in lakes, rivers, and 

aquifers. 16 Drought takes longer to show up in hydrological systems than in agriculture, 

especially when reservoirs and rivers are managed to balance the extremes of wet and dry years. 

Snow is a major component of water supply in the western United States. 

2.3 Government of Canada Drought Coverage 

Canada in 2004 extended drought mapping coverage from agricultural areas to remainder 

of the Canada Provinces. Canada does not carry out drought mapping within the territories 

(Yukon, Northwest territories, and Nunavut north-of-tree line and permafrost underlain areas). 

Near-Real-Time monitoring is carried out for 508 of 761 ground-based stations by Agriculture 

and Agri-Food Canada (AAFC)(Hadwen2008) which runs a national drought model, in which 

Standard Precipitation Index is calculated, soil moisture (as percent of average and difference 

from normal), and Palmer Drought Severity Index. 

2.4 Commonwealth of Australia Drought Monitoring 

Water issues are now considered among the most important drivers and constraints on natural 

resource management in Australia; from environmental hazards like salinity and drought, 

through to security of urban and rural water supplies. At present, Australia has no 

comprehensive, consistent source of information on the water balance of its landscapes; that is, 

on the relationship between rainfall, evaporation, transpiration, soil moisture, runoff and 

drainage to ground and surface water. A better understanding of water availability is needed 

across the entire country and is relevant to the implementation of key Australian Government 

policies such as Exceptional Circumstances, the National Water Initiative, the Prime Minister’s 

National Plan for Water Security and policies in support of improved natural resource 

management. 

2.4.1 Commonwealth of Australia Water Availability Project 

The Australian Water Availability Project is a partnership established in 2004, between the 

Bureau of Rural Sciences, CSIRO, the Bureau of Meteorology and the Australian National 

University. The project aim is to develop an operational system for estimating soil moisture and 

other components of the water balance, at scales ranging from five kilometers (km) to all 

Australia, over time-periods ranging from daily to decades. Data from ground-based climate 

measurements, remote sensing and models (water, plant and climate) are being combined to 

produce maps of historic and current levels of all the main components of the landscape water 

balance, including rainfall, evaporation, transpiration, available soil moisture, runoff, stream 

flow and deep drainage. The future challenge is to deliver a fully web operational system, 

including underpinning procedures for robust real-time product delivery, continuous 

16 http://www.drought.gov/portal/server.pt/community/hydrological_monitoring/224 

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improvement and validation, and links to seasonal forecasting of water balance conditions. The 

fundamental data derived from this project will help underpin future planning and decisionmaking 

on a range of issues including drought management and policy, securing urban and rural 

water supplies, salinity, biodiversity management, ecosystem services and sustainable farming. 

The real-time web operational system to be developed will help agricultural industries 

maintain farm profitability before, during, and after drought events and help water and catchment 

managers quantify the impact of climate cycles or climate change on surface and groundwater 

recharge, vegetation and biodiversity. Additionally, risks to agricultural production may be 

assessed by detailed analysis of moisture availability and moisture utilization trends for all 

Australia. The Bureau of Rural Science presents Water Balances for Recent Months, Water 

Balance Annual Average, Water Maps, Land Use Maps, and Social Data. 17 

2.5 Africa Continental Drought Monitoring 

Regional African drought monitoring networks have been started at AGRHYMET in 

West Africa, the Southern Africa Development Center Drought Monitoring Center. The 

Princeton Experimental African Drought monitor offers pan-Africa coverage. 

2.5.1 Princeton Experimental African Drought Monitor 18 

The Variable Infiltration Capacity (VIC) Model is used to calculate soil moisture. 19 The 

Princeton Land Surface Hydrology Group initially developed a meteorological forcing dataset 

for global land areas for 1950-2000 to force the Variable Infiltration Capacity (VIC) distributed 

hydrologic model. The subsequent task during the first interim period involved updating the 

global macro scale modeling to near-real-time over Africa. A particular challenge over the 

African continent has been to update the forcing dataset (1950-2000) (and thence the VIC 

simulation) to near-real-time (NRT) using available data streams. 

2.5.1.1 Climatology 

The 1950-2000 meteorology—the climatology—is derived from a blending of reanalysis 

(NCEP/NCAR) and gridded observation-based datasets including the Climatic Research Unit's 

TS2.0 monthly precipitation and temperature dataset, the NASA Tropical Rainfall Measurement 

Mission (TRMM) 3-hourly precipitation products and the NASA Surface Radiation Balance 

(SRB) short- and long-wave datasets. In effect, the observation datasets are used to spatially 

17 http://adl.brs.gov.au/water2010/water_cycle/index.phtml 

18 

http://hydrology.princeton.edu/~justin/research/project_global_monitor/index_africa.html 

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downscale the reanalysis, which is available at high temporal resolution, and at the same time 

remove biases in the reanalysis. This work is described in detail in Sheffield et al. (2006). 

2.5.1.2 Bridging the gap between reanalysis data and real time observing system 

data 

To bridge the data gap between the beginning of 2001 and near-real-time, these methods 

were extended to blend reanalysis with available observations. Although reanalysis data are 

available up to real-time, most observation-based datasets are generally only available some 

months of even years behind real-time. Therefore for 2001-realtime we have used a number of 

different datasets depending on their availability. For 2001-2006, we have used the recently 

updated (to 2006) monthly gridded precipitation and temperature dataset of Willmott and 

Matsura. This matches well the CRU dataset (used for 1950-2000) over their overlap period at 

large scales. From the beginning of 2007, we have used the Global Precipitation Climatology 

Project (GPCP) monthly dataset which is available a few months off real-time. Ongoing work is 

looking at the differences between these various datasets during their overlap periods and 

methods to ensure temporal consistency. For the last few months up to real-time, we are relying 

on real-time precipitation products (PERSIANN 20 data from University California Irvine, 

TRMMM data from NASA) and gauge telemetry (Global Telecommunication System (GTS) 

gauge data from NOAA). These products are being downloaded on a daily basis and are 

blended into a forcing dataset for VIC over Africa. 

Having set the initial meteorological forcing into place, the VIC simulations have been 

run, up until near-real-time, in order to establish operational running. Our immediate objectives 

are to finalize the data streams for the real-time running of the VIC model. The rapid timing of 

real-time operational monitoring creates problems, such as the need to assess whether input data 

are available, as well as developing fall-back methods for when data are unavailable or fail 

quality control checks. Furthermore, the real-time meteorological data are likely biased, creating 

the need to periodically re-run the VIC model up to a few months off real-time when the longterm 

gridded observation-based products (which are our best estimates of precipitation and 

temperature) are updated, to avoid a drift in the land surface states. 

The probability distributions of total column soil moisture and runoff for each grid cell 

and each month constitute the climatology, against which current conditions can be compared. 

The screening tools account for drought areal extent and duration using concepts adapted from 

Andreadis et al (2005), which involve a form of spatial cluster analysis to identify drought 

patterns from gridded model output. Based on the historic analysis, we will establish a set of 

severity-area-duration thresholds that can be used to screen evolving droughts. Within the real 

time monitoring framework, we will monitor where drought thresholds are crossed for either soil 

moisture or runoff. Once the prescribed drought thresholds have been crossed, we will continue 

to track drought evolution in time (i.e., in subsequent forecasts), until the nowcasts indicate that 

20 http://chrs.web.uci.edu/research/satellite_precipitation/activities00.html 

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Date: 11/Feb/2011 

the drought has dissipated. Drought dissipation will be evaluated in comparison with severityarea-duration 

thresholds estimated using an approach similar to the one used to establish drought 

screening thresholds. 

2.6 New Projects Permitting Further Development of the Global Drought 

Monitoring Service 

2.6.1 European Framework (EF) Drought Early Warning System for Africa-- 

DEWFORA 

Under the European Framework, the Drought Forecasting and Early Warning System for 

Africa (DEWFORA) project has been funded to set up a regional drought monitor for Africa. 

DEWFORA also includes local and regional pilot projects. This is the reason why DEWFORA 

is listed in parallel with the Princeton African drought monitor. 

Figure 2 DEWFORA study regions (Werner et al 2010) 

2.6.2 GLOWASIS (Global Water Scarcity Information Service) 

GLOWASIS will combine in-situ, satellite derived and statistical data on water supply 

and demand and make them available through a public information portal on water scarcity. 

Funded under the European FP7 framework, the overall objective is to pre-validate a GMES 

(Global Monitoring for Environment and Security) Service for Water Scarcity information, based 

on pilot studies in Europe, Africa and on global level. The main objectives are: 

• Assessment of water demand and supply 

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• Near real-time reporting on disasters (droughts, floods) 

• Medium and long-term forecasting (also with respect to climate change) 

• Promotion of new satellite-capabilities (e.g. Sentinel 1) 

• Matching new satellite-capabilities to specific user requirements 

GLOWASIS will be made interoperable with the Water Information System for Europe 

(WISE-RTD), linking water demand and supply with existing tools, such as the European 

Drought Observatory (EDO) and PCR-GLOBWB, a global hydrological model (the same model 

used in DEWFORA), combining complex water cycle variables in a standardized format with 

respect to water scarcity information. 

Sources of information and data are: 

• Already existing GMES (Global Monitoring for Environment and Security) data, such as 

the LMCS (Land Monitoring Core Service) of GEOLAND2, 

• in-situ data from GEWEX (Global Energy and Water Cycle Experiment) and Global 

Terrestrial Network on Hydrology (GTN-H) initiatives, such as the International Soil 

Moisture Network, 

• statistical databases (e.g. AQUASTAT and SEEAW) 

Results of GLOWASIS can be used in research, for practical implementation and 

management purposes. Therefore, end-users encompass river basin management organizations 

(Rhine, Danube, Elbe, Oder), the European Environment Agency, UN-Water, the Australian 

Bureau of Meteorology, etc. GLOWASIS is coordinated by DELTARES, the Netherlands. The 

Institute of Photogrammetry and remote Sensing (IPF) leads one work package (user 

requirements) and is involved in all others. 

As one of IPF’s most successful projects on soil moisture, SHARE will also contribute to 

GLOWASIS. The following sub-chapters give an overview about soil moisture products from 

ASAR (advanced synthetic aperture radar) and scatterometer sensors. 

SHARE is a DUE Tiger Project of the European Space Agency, which offers an 

operational soil moisture monitoring service. The synergistic use of ENVISAT's ASAR sensor 

and scatterometers (on METOP and ERS) allows for frequent, high resolution monitoring of 

regional soil moisture dynamics. 

An algorithm was developed at IPF to detect surface soil moisture from active microwave 

systems. Active sensors are sensitive to soil moisture mainly due to distinct dielectric properties 

of water stored in soil. Microwaves of the Advanced Synthetic Aperture Radar (ASAR) and the 

advanced scatterometer (ASCAT) cannot penetrate soil deeper than a few centimetres. In case of 

ASCAT an algorithm was developed, which models the soil water content in deeper layers (the 

soil water index, SWI). It is obtained by filtering surface moisture time series with an 

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exponential function (WAGNER et al., 1999). Being able to model the profile soil moisture up to 

one metre facilitates estimations of infiltration capacities and plant available water (defined as 

the difference between field capacity and permanent wilting point). This is the approach used in 

the agricultural monitoring and forecasting models cited in section 1.3 above. Flooded soils are 

more prone to cause flooding, as noted in section 2.1.2 above. 

The two systems to obtain soil moisture data: 

• Medium resolution soil moisture from an imaging Advanced Synthetic Aperture Radar 

(ASAR) onboard ENVISAT can be operated in global monitoring or wide swath mode. It 

was the first system to deliver global backscatter measurements in C-Band (5.3 GHz) at a 

spatial resolution of one kilometre. Spatial resolutions of 150 meters can be achieved by 

SCAN SAR wide-swath mode. In the SHARE project, regions on three continents have 

been monitored once or twice a week. Soil roughness and vegetation effects of each pixel 

are “corrected” by change detection method – the subtraction of a reference image from a 

SAR image. This way the inhomogeneous distribution of soil water in the topmost 

centimetres of the unsaturated zone, where evapotranspiration takes place, can be 

considered. The most recent version of the ASAR data viewer is online at: 

http://www.ipf.tuwien.ac.at/radar/dv/ipfdv/index.php?dataviewer=asar2 

• Scatterometers onboard METOP (ASCAT), ERS-1 and ERS-2 (SCAT) are non-imaging 

sensors and characterised by higher temporal (1-2 days), but lower spatial resolution. 

Change detection works similar to the SAR system. ASCAT is a collaboration of 

EUMETSAT and IPF. It was declared operational in December 2008 and is now 

produced in near real-time by EUMETSAT, using the WARP-NRT software. This 

software had been prototyped by EUMETSAT and developed by IPF. ASCAT soil 

moisture is a Level 2 product delivered in orbit geometry at two different grid spacings: 

25 km and 12.5 km. The two products are derived directly and on the same grid as the 

equivalent ASCAT Level 1b products (normalized backscatter).Consequently, the 

resolution of the soil moisture values is approximately 50km and 35 km. 

Thorough validation of ERS scatterometer and ASAR demonstrated a good 

correspondence of satellite and in-situ data (DORIGO, 2010). The correlation of ASAR results to 

in-situ measurements is slightly weaker than the ones of scatterometers on board ERS, mainly 

due to its lower radiometric resolution. However, the correlation of ASAR and in-situ data 

improves significantly when averaged over larger areas (PATHE et al., 20009, MLADENOVA 

et al., 2010). ASCAT products are spatially variable with high quality over grassland and 

agricultural areas and lower quality in more densely vegetated areas and deserts. The 

investigation of soil moisture at medium scale is a critical assess for IPF’s efforts for 

downscaling of active and passive sensors. Field studies showed that, despite high spatiotemporal 

variability of soil moisture, its correlation to the mean soil moisture over a larger area is 

significant in the temporal domain. 

Recent flood events (January 2011) in Eastern Australia affected more than 200 000 

people and an area as big as the size of France and Germany combined. ASAR observations can 

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now be used to increase the reliability of information that is fed into models for monitoring and 

forecasting. The Australian Commonwealth Scientific and Research Organization (CSIRO) 

currently rely on optical data in combination with passive microwave technologies and digital 

elevation models. Incorporating ASAR in the system would result in several advantages: on one 

hand, reliability and accuracy increases through higher resolution, while, on the other, cloudindependent 

continuous monitoring is possible. Figure 3 illustrates relative soil moisture 

saturation on Australia’s Eastern coast during the flood events of December 2010. 

Figure 3 Relative soil moisture from ASAR onboard ENVISAT during Floods in Australia (25th 

of December). Blue colours represent highly saturated soils, while brown stands for extremely 

dry soil conditions (IPF, 2010) 

2.6.3 Satellite Application Facility on Support to Operational Hydrology and 

Water Management (H-SAF) 

H-SAF was established by the EUMETSAT Council in July 2005. The Development phase 

started in September 2005. Within the H-SAF framework the focus for new satellite products lies 

on: 

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• Precipitation rate and cumulate precipitation, including liquid/solid discrimination, 

• Soil moisture in the surface layer and possibly in the roots region and 

• Snow parameters such as effective cover, wet/dry discrimination and water equivalent. 

H-SAF membership includes 11 EUMETSAT member or cooperating States (Austria, 

Belgium, Finland, France, Germany, Hungary, Italy, Poland, Romania, Slovakia and Turkey) 

and ECMWF. Host of H-SAF is the Italian Met Service. The algorithms for satellite rainfall 

estimation used in H-SAF will be considered and tested with respect to requirements of 

GLOWASIS. 

IPF is again contributing to H-SAF with expertise on soil moisture. The basis for all soil 

moisture products in H-SAF is the radar scatterometer ASCAT on Metop. The three soil 

moisture products that emerged from the development phase were: 

• Large-scale surface soil moisture derived from ASCAT for the H-SAF area (SM-OBS- 1), 

• Small-scale surface soil moisture resulting from disaggregation of the EUMETSAT CAF 

global soil moisture from ASCAT (SM-OBS-2); 

• Volumetric soil moisture (SM-ASS-1) for the H-SAF area (four soil layers up to a depth of 

three metres). SM-ASS-1 is now part of ECMWF’s operational service and not 

considered an H-SAF product anymore. 

The development of two other products is planned in the current phase: 

• A Soil Wetness Index in the root zone resulting from assimilation of CAF global ASCAT- 

Soil Moisture product in a NWP model (SM-ASS-2) and 

• Global large-scale surface soil moisture derived from ASCAT for the H-SAF area (SM- 

OBS-3) as the successor of the EUMETSAT CAF global soil moisture product. 

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2.7 South American Continent 

2.7.1 Republic of Argentina Servicio Meteorologic Nacional Drought Monitoring 

Figure 4 (a) Hydrological Balance and (b) Precipitation is deducted from potential 

evapotranspiration (left) to estimate hydrologic balance difference with the previous decade 

(Nunez 2010). 

The Republic of Argentina SMN assembles maps of the hydrologic balance, which are 

included within daily, monthly, and decadal bulletins which also include maps of number of 

consecutive dry days, and NDVI-based vegetation health. Precipitation exhibits a non-normal 

statistical distribution. Argentina uses 1960-1990 as its base “climatology” in determining 

“average” conditions out of which drought episodes is detected. 

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2.7.1.1 Integration of Republic of Argentina SMN Drought Coverage into the 

Global Drought Monitor 

Users should be able to access the Global Drought Monitor from anywhere on the World 

Wide Web and see the drought coverage for their respective countries in the form that they see it 

and utilize it within their native countries. Yet, at the same time, the methodology that is 

identifying droughts over Brasil or Paraguay should be able to identify droughts, if they are 

present, over Argentina, as well. Hence, some standardization of drought indicators is required, 

since one of the objectives of Global Drought Monitoring is to improve the accuracy with which 

drought is being recorded all over the planet. 

One approach is to find a proportional relationship between the range of the 

drought indicator used in the native country and the drought severity range, either used by the 

US National Drought Mitigation Center or by EDO, as illustrated in Figures 5 and 6. The 

drought severity ranking system employed by the North American Drought Monitor was 

developed by the USA University of Nebraska Lincoln. This system has a colorized code which 

is linked to Standardized Precipitation Index (Figure 5). Figure 5 (a) and (b) compare the 

National Drought Mitigation Center drought severity ranking system with the drought severity 

ranking system employed by the European Drought Observatory. Eventually, the two systems 

will have to be linked (or made interoperable). 

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Figure 5 (a) Drought Severity Ranking System of the USA National Drought Mitigation Center 

(UNL) and (b) Drought Severity Ranking System of EDO (Iglesias and Schlickenrieder 2010) 

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Figure 6 (a) Drought Severity Status (Vargus 2008a) and (b) Drought Severity Indicator in 

practice (Vargus 2008b) 

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Date: 11/Feb/2011 

B Informatics Section 

3. Capturing User Requirements for the Global Drought Monitor and its 

Interoperability with the Global Earth Observation System of Systems (GEOSS) 

A key deliverable is the specification of a set of tools that will access information 

published through a distributed water data infrastructure. The tools in this case are represented 

by the applications which constitute the GEO Global Drought Monitoring Service. The tools are 

specified through completion of three phases: 

1). Capture of User Requirements—who might use the GEO Global Drought Monitoring 

Service and the types of data and the types of functionality these users might require or expect 

2) Design of a System Architecture—and associated enabling framework at the 

component level 

3) Implementation Plan 

The GEOSS Architecture Implementation Pilot (AIP) task develops infrastructure 

components for the GEOSS Common Infrastructure (GCI) and the broader GEOSS architecture 

as a means of coordinating and deploying cross-disciplinary interoperability, such as the display 

on top of drought map layers, combined with layers of different water usage and agricultural 

water needs. The architectural implementation (AIP) task is envisioned as a way of developing 

the GEOSS informatics capability and architecture through pilot projects. The process includes 

user interactions; component deployment and interoperability testing; and SBA-focused 

demonstrations. 

3.1 Assessment of Drought Vulnerability and Susceptibility 

The first section of this report dealt with drought indicators currently utilized by the 

drought community of practice. Indicators do not correlate well with historic drought impacts, 

and they need to be correlated with vulnerability. A direct linear proportionality between the 

severity of the drought, as expressed by a drought indicator and the observed and recorded 

impacts of a drought should not be expected. That is the role of the drought vulnerability factor 

(Iglesias and Schlickenrieder 2010). Values of indicators change with the region and social 

conditions. 

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The same level of drought severity can cause a wide variety of drought impacts due to 

different underlying vulnerability of different regions. The multiple disciplinary information 

sources that assist decision makers in evaluating drought impacts include information on regional 

infrastructures, land use, residential water use, etc, which either are impacted by drought or may 

mitigate drought severity (such as groundwater availability). Land use information (forage for 

pasture animals in agricultural lands), crop type information with crop growing seasons, power 

plant locations (for identifying cooling water requirements), groundwater springs (to identify 

area of groundwater export) are all different types of data that can be combined together as 

“layers” within a Geographical Information System. The display of layers, one type of 

information on top of other layers, is the basis for the integration of multi-disciplinary 

information. Several types of multi-disciplinary data integration exist, and several tools were 

explored through testing for deployment for regional and global drought monitoring. 

3.2 Capturing User Requirements and Implementation of Architecture to Design 

of the Global Drought Monitor 

3.2.1 Portal Requirements: Drill-down capability 

Both the European Drought Observatory and the US NIDIS drought monitoring system 

portals support “drill down” capability from continental to national scale and from national scale 

to river basin scale. The spatial resolution of the drought maps are progressively higher, moving 

from global scale to continental scale to national scale and finally to river basin scale. This is not 

simply a matter of display preference, since a drought early warning system should be developed 

for local scales, particularly in the case of small-scale agricultural plots. Although existing 

national drought monitoring coverage (at its existing resolution) is incorporated into the GDMP, 

the GDMP is not simply the assembly of a collection of web page graphics into one location. 

3.2.2 Top-down versus bottom-up Design 

There are several possible candidates for designing a global drought monitoring service: 

1) a single, top-down system at coarse resolution; or 2) a single, top-down system at fine 

resolution; 3) a bottom-up system, or 4) a bottom-up system complemented with some top-down 

coverage where coverage is lacking. 

One example of a top-down global drought monitor is the University of College London 

Global Drought Monitor. 21 Another is the Beijing Climate Center Drought Monitor. 22 

21 

http://drought.mssl.ucl.ac.uk/drought.html?map=%2Fwww%2Fdrought%2Fweb_pag 

es%2Fdrought.map&program=%2Fcgibin%2Fmapserv&root=%2Fwww%2Fdrought2%2F&map_web_imagepath=%2Ftmp 

%2F&map_web_imageurl=%2Ftmp%2F&map_web_template=%2Fdrought.html 

22 http://bcc.cma.gov.cn/Website/index.php?ChannelID=82&show_product=1 

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However, there are good reasons for embarking upon a bottom-up system. A global top 

down system can imply that drought is present within a local area, although the local area might 

be drought-free due to availability of secondary sources of water such as groundwater. Having 

participation of members who are familiar with local conditions on the ground is invaluable in 

setting up a global drought monitoring system. A global network of national 

hydrometeorological service and ministry-based drought experts can provide the expertise to 

carry out retrospective validations of drought forecasts, along with fine tuning of the drought 

forecasting system, part of the life cycle by which “experimental” (research stage product) 

becomes “operational.” 

Drought monitoring and forecasting is intended for applications. For example soil 

moisture monitoring and forecasting can support farmers’ activities. Since the size of farms may 

vary, a drought early warning system is best applied at local scales. This means that a coarsescale 

system may not be very valuable for providing decision support. A drill down system is 

built upon a combined bottom up-top down system, in which the highest resolution drought maps 

of the system, i.e., those at river basin scale or national scale can be used for drought early 

warning applications or used for agricultural support. 

3.2.3 Soil Moisture and Agricultural Drought Monitoring Requirement 

Given the importance of soil moisture in agricultural drought monitoring, and the 

importance of agriculture in the world food problem, remote sensing-based and modeled-based 

soil moisture should be utilized within the system. 

3.2.4 Republication of information to help decision makers facilitate drought 

decision making 

Integrating together multiple disciplinary and cross-disciplinary information, such as 

drought severity information and agricultural production data, require different informatics 

strategies to carry out such integration. While layers can be added together and removed within 

a Geographic Information System (GIS), more sophisticated tools are required in order to 

assemble all of the information in a form that can be immediately used for decision making. As 

noted by Lemon et. al (2010): “The ‘Discover, Display, and Download’ Use Case has misled us. 

No one simply wants to find, look at, and collect data. To the contrary, they all want to do 

something with the data: subject it to some analysis, make a map, or prepare a basis for making 

rapid decisions.” Search and discovery alone will not make GEOSS a viable system, valuable to 

end users: its information has to be repackaged into a user-friendly form that provides 

application knowledge and accelerates decision making. 

3.2.5 Hydrologic Drought Monitoring for Semi-Arid Areas and Meeting 

Hydrologic Drought User Requirement through Semantics 

The hydrologic drought indicator user requirement creates a need for assembling 

information on water budget components, such as groundwater, streamflow, precipitation, soil 

water, snow cover, etc. The sheer volume of information, particularly if assembled over large 

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segments of the globe, will require some integrative technology in order to accommodate the 

utter complexity of multiple languages, multiple scientific terms within different languages, 

differences in place names to describe geographic entities, and multiple variable names within 

database schema. These are the requirements for a semantic-based information system: datasets 

and records have to be registered at the level of water budget components, i.e., stores of 

groundwater, river water elevation, precipitation, etc, to meet the requirements for hydrologic 

drought monitoring. This also means, conversely, that a semantic ontology has to include these 

concepts, as well, within the water ontology, for the purposes of organizing information. This 

level of detail is a critical requirement. 

Several possible methodologies for achieving multidisciplinary interoperability take 

advantage of the possible integrative power of Semantic Web technologies, developed by Tim 

Berners-Lee (Berners-Lee, Hendler, Lassila 2001; Yu(2007). 

What, simply put, does the semantic web do? It tries to lift the burden off the user of 

having to process huge amounts of information by automating (and making machine readable) 

the collection and processing of information, so that the processing burden may be shifted from 

the user to the machine. Semantic web techniques improve irretrievability of the correct 

document or resource or dataset by providing semantic annotation through Resource Description 

Framework (RDF) or RDFS, perhaps combined with an ontology which provides the structural 

arrangement of the resources in context with one another, along with possibly including some 

simplified artificial intelligence application for sorting or selection. 

Semantics can be directly employed within the decision support services developed by 

GEO, i.e., within the software applications and processing of data. For example, SEAMLESS 

links together application modules (such as used in Delft- Flooding Early Warning System or 

FEWS) and component-based applications that can be orchestrated into a workflow run over a 

framework, in this case, OpenMI (Rizolli, et. al 2007). 

Another use of semantics is the more traditional search and discovery role. This use 

case of semantics is what has been explored within this session of AIP-3, as a test case project 

within the European Union among the architects of the EuroGEOSS discovery broker, the AIP-3 

Semantics Working Group, the European Drought Observatory, and the AIP-3 Water and 

Drought Working Group. 

3.3 Developing an Architectural Diagram for the GEO Global Drought 

Monitoring Service 

Figure 6, derived from the Australia Water Resources Information System, illustrates 

some of the components that are prerequisites for the Global Drought Monitor Portal (GDMP). 

The “system architecture” is a diagram of the applications and the tools, combined with the 

enabling framework at the component level. Figure 6 shows the bottom rung of data entering 

through the observing system, as, respectively, “Numeric data” (as in soil moisture generated by 

the VIC and LISFlood models), or satellite source “Sensor output” originating from space-based 

scatterometer soil moisture data. The upper tier illustrates in schematic boxes some additional 

components, the Open Geospatial Consortium (OGC) geospatial Web Mapping Services (WMS) 

exchange of drought maps. The exchange of drought maps among the European Drought 

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Observatory, the Princeton African Drought Monitor, the NIDIS Server, the University College 

London drought monitor, and the Argentina SMN drought monitor make the functionality of the 

GDMP possible. 

In between the bottom rung (the sensor and model data sources of the observing system) 

and the upper tier (and the OGC-supported web service data exchange over the WWW) are 

additional layers which include controlled vocabularies, dictionaries, ontologies, semantic 

mappings, and transfer format and protocols, along with common data models, for data 

integration and processing this information at multiple levels, republishing the information in a 

format immediately available for decision making. This functionality is depicted as schematic 

boxes in Figure 8(a), but the actual operations are set out below. 

This overall strategy is a scalable system that permits integration multiple data stores 

(information hubs), along with information of multiple disciplines. 

Figure 7 Australia Water Resources Information System (Boston 2010) 

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Figures 8 (a) and (b) Commonwealth of Australia Water Resources Information System 

(Boston 2010) 

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Date: 11/Feb/2011 

Figure 9 EuroGEOSS Broker Discovery Augmentation Component Expansion of 

Drought (diagram kindly provided by Mattia Santoro) 

3.4 Semantic Development Activities within GEO: the Data Integration and 

Analysis System (DIAS) Contribution from Japan 

The DIAS approach is illustrated within Figures 10 and 11. Each GEO Societal Benefit 

Area, i.e., Disasters, Water, Sustainable Agriculture, Biodiversity, Health, Energy is represented 

by a domain within the “application layer.” The overall user requirement is to support crossdisciplinary 

or multidisciplinary sharing of information and data. The DIAS system supports the 

organization of information within each of these areas. 

Figure 9 illustrates a SBA area, in this case drought, using EuroGEOSS (see below). At 

the center of the graph is “drought.” “Drought” is linked to “Species impoverishment” (within 

the Biodiversity cluster) in one node and to “famine” (within the Sustainable Agriculture cluster) 

as another node. The arrangement of concepts for each GEO SBA is the ontology for each GEO 

SBA. DIAS also separates this conceptual scientific terminology from geographic locations and 

place names (Figure 12). The drought lexicon, for example, comprises the lexicographic 

content. Each ontology or collection of ontologies for each of these areas can be loaded into the 

semantic network dictionary. A semantic network illustrates the relationship among concepts. 

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Figure 11 Integration of DIAS into Web-based Information System (Koike 2010) 

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Figures 12 (a) DIAS cross disciplinary areas (Koike 2010) and (b) DIAS Ontology Development 

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Date: 11/Feb/2011 

Architecture (Nagai 2010) 

Figure 13 (Nagai 2010) 

3.4.1 Adding Advanced Search and Discovery using Semantics 

The AIP-3 video 23 , “Drought—European” includes a walkthrough demonstrations, in 

which users select scientific terms (or “concepts”) and, secondly, the geographic region or spatial 

domain which is defined within a bounding box. This reflects the division into lexicographic and 

geographic content which has been cited in Section 3.5. The purpose of the semantic enrichment 

was to supplement keyword searches, such as used on Google, by adding search capability that 

could search through concepts; this is tantamount to adding “Semantics.” In other words, one is 

not searching for “drought” as a keyword; one is searching for “drought” as a concept, combined 

with search functionality that allows the user to select broader or narrower searches within the 

drought field or within allied fields. For example, “water” is a concept which can be broken up 

into underlying processes of “evapotranspiration,” “streamflow,” “precipitation,” “soil 

moisture,” “snow cover,” and “groundwater.” The stores of water are a subset or part of water, 

and this class structure is affected in the arrangement of the terms within the semantic network. 

Datasets can be registered to each of these terms, so that queries of “hydrologic drought 

indicators” reveal “groundwater” data, “streamflow” data, such as baseflow, and other water 

budget information for a selected area. The EuroGEOSS discovery broker has the capability to 

access the GI-Cat registered datasets on groundwater, river discharge, water usage, and other 

data. Search-by-concept is intended to not only improve the “hit-or-miss” success rate of recall 

of datasets through keyword searches alone but also reduce the high amounts of irrelevant 

returned results in keyword searches. 

Figure 8 is a screen capture of the search interface in the AIP-3 “Drought—European” 

23 http://www.ogcnetwork.net/pub/ogcnetwork/GEOSS/AIP3/pages/Demo.html 

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video. Note that the interface and tool has not only has a list of scientific terms but also has 

these terms “arranged,” so that the terms are linked to one another. This tool enables a user to go 

from the general term—“drought”—to an associated term “drought indicator” to specific drought 

indicators, such as “meteorological drought indicator,” then to “precipitation” and then to 

“Standard Precipitation Index.” Alternative branches are “drought” to “drought indicator” to 

“agricultural drought indicator” to “soil moisture” or “drought” to “drought indicator” to 

“hydrologic drought indicator” to “groundwater” to “terrestrial water storage change.” Datasets 

also have tags to the appropriate geographic area, such as “England.” 

4. Global Implementation of the Drought Monitoring Service through GEOSS 

4.1 Components of the System Architecture of the Global Drought Monitoring 

Portal 

The Global Drought Monitor utilizes and is designed to have a “drill-down” capability. 

One begins at the global (and coarsest spatial resolution) and then is directed toward higher 

spatial resolution regional maps. The user can follow the sequence of events by accompanying 

the steps while watching the “Drought—Global” video. 24 

One begins with a Global World Map (a Global Drought Map, as well), accessible on the 

Graphical User Interface (GUI). The Global Drought Monitoring Portal is accessible from the 

World Wide Web. 25 

The layout of the entry web page (index or home page) includes the title bar “”Beyond 

Drought: Global Participation for Better Planning and Response,” underneath of which are four 

underlying header tabs, arranged from left to right: “Current Conditions,” “Interactive Maps and 

Data,” “Regional Drought monitoring,” and “About.” 

The “Current Conditions” tab displays the “Global Drought Monitor” of the University 

College London global drought monitor. 26 As noted in the Disclaimer to the University College 

London Global Drought Monitor, the (UCL) Global Drought Monitor provides the 'overall 

drought picture' on a ~100km spatial scale. The maps are not designed to depict local conditions. 

As a consequence, there could be water shortages or crop failures within an area not designated 

as drought, just as there could be locations with adequate water supplies in an area designated as 

'extreme' or 'exceptional' drought.” 


25 http://www.drought.gov/portal/server.pt/community/global_drought 

26 

http://drought.mssl.ucl.ac.uk/drought.html?map=%2Fwww%2Fdrought%2Fweb_pages%2Fdrou 

ght.map&program=%2Fcgibin%2Fmapserv&root=%2Fwww%2Fdrought2%2F&map_web_imagepath=%2Ftmp%2F&map 

_web_imageurl=%2Ftmp%2F&map_web_template=%2Fdrought.html 

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The differences between the second “Interactive Maps and Data” tab and the third 

“Regional Drought Monitoring” tab are the display of the drought zones on the Interactive Maps 

and Data” tab, while the Regional Drought Monitoring” tab displays highlighted continental 

areas. The drought zones that are displayed on the “Interactive Maps and Data) map viewer are 

not necessarily the same drought zones that are displayed on the “Current Conditions” first tab. 

This is because the “Current Conditions” Global Drought map is largely based upon Standard 

Precipitation Index (SPI), while the “Interactive Maps and Data” second tab displays drought 

coverage for members of the Global Drought Monitor and the Global Drought Monitoring 

Community of Practice. The “Current Conditions” calculates drought globally, based upon API 

and is a top-down system. The “Interactive Maps and Data” drought displays are integrated 

together from national coverage in North America, derived from the regional European 

Community LISFLOOD model application with real time data, or derived from continental scale 

coverage for the African continent. 

The third “Regional Drought Monitoring” tab 27 displays the North American, 

European and western Asian, and African continents highlighted in different colors with the 

indent being to allow users to access regional drought portals for additional, higher resolution 

drought information. The remainder of the terrestrial globe is designated a common color. If 

one points the mouse and clicks anywhere within the Canada, USA, or Mexican spatial domain, 

i.e., anywhere within North America, one is redirected to the North American Drought Monitor. 28 

If one points the mouse and clicks anywhere within the European Community, one is redirected 

to the European Drought Observatory home page. 29 If one points the mouse and clicks 

anywhere within the African continent, one is redirected to the Princeton University African 

Drought Monitor. 30 The remainders of the terrestrial continental areas share a common color, 

because these areas have yet to be integrated and made interoperable within the Global Drought 

Monitor Portal (GDMP). 

4.2 Actors 

While the administrative user has been identified above, the main actors will be officials 

working in the national hydrometeorology drought monitoring services, as well as officials 

working in national and private relief agencies, such as the case for famine relief, countrysponsored 

agricultural agencies, and agricultural commodities insurers. 

4.3 Capturing User Requirements for the Global Drought Monitor Portal through 

27 

http://www.drought.gov/portal/server.pt/community/global_drought/314/regional_drought_moni 

toring/1097 . 

28 http://www.drought.gov/portal/server.pt/community/nadm/303 

29 http://edo.jrc.ec.europa.eu/php/index.php?action=view&id=2 

30 http://hydrology.princeton.edu/~justin/research/project_global_monitor/ 

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the GDMP Scenario 

As noted above, a scenario is the listing, step-by-step, of the user requirements (for 

drought monitoring), showing which GEOSS resources and components are utilized within the 

retrieval of drought maps and information (such as soil moisture) for users. 

Table 4 

Objective: Obtain a Drought Overview of a Given Area on the Terrestrial Earth, along with 

detailed information on affected regions 

Step 01: Obtain Drought Indices from the Global Drought Monitor 

Step 01.1: Obtain Drought Indices through standard services 

Step 01.2: A dedicated WPS processes the drought index and calculates the drought hazard 

Step 02: A dedicated WPS processes the drought index and calculates the drought hazard 

Step 02.1: The WPS retrieves the Drought Index through the WCS 

Step 02.2: The WPS executes the hazard detection model and, where detected, sends an alert to 

the decision support tool 

Step 03: Drought Hazard Related Information Discovery 

Step 03.1: The decision maker uses the decision support tool to submit a query to the augmented 

search component in order to discover drought hazard related information (datasets) 

Step 03.2: The EuroGEOSS Broker mediates the query request, distributing it to its federated 

services 

Step 03.3: The Decision maker uses the Decision Support System to select one or more drought 

hazard related information datasets, among the ones returned by the query 

Presentation of Reachable Services and Alerts 

Step 03.4: The Decision Support Tool submits an access request to the EuroGEOSS Broker in 

order to retrieve the user-selected drought hazard information datasets 

Interact with Services 

Step 04: Visualization and Assessment of Information 

Step 04.1: The Decision Support Tool displays the accessed drought hazard information datasets, 

combining them with the potential hazard layer 

Step 04.2: The decision maker assesses the drought hazard impact 

4.3.1 Display of Selection Bar for Drought Indices, Processing to Derive 

Dehydration and Drought Severity, and Drought Map Republication 

The Global Drought Scenario steps, by themselves, are relatively abstract, and are best 

understood by following the actual presentation given within the videos, “Drought—Global.” 31 


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Step 01 within the European Drought Observatory is equivalent to providing with users 

the ability to select one of multiple drought indicators. EDO makes available to users the choice 

of Standard Precipitation Index (SPI), Soil Moisture, Soil Moisture Anomaly, and several remote 

sensing-based measures of vegetation health, such as fraction of Absorbed Photosynthetic Active 

Radiation (fAPAR) in the case of EDO and VegDRI in the case of NIDIS (over the USA). The 

GDMP does not yet have the extensive development to support independent display of multiple 

drought indicators and indices. As noted above, SPI is already being displayed on the “Current 

Conditions” map. 

Step 02 is the processing loop of taking the selected drought indicator, running the indicator over 

a selected spatial domain or region, and then returning a republished map which displays the 

level of drought intensity or severity within this area (if drought is present at all). The current 

GDMP configuration displays the integrated drought severity ranking system of Figure 5 for 

North America and the integrated drought severity ranking system deployed by EDO for the 

European Community. (A drought severity ranking system is being developed for Africa and is 

not yet deployed. The Figure 5 system can be adapted to soil moisture percentiles, which is a 

drought indicator within the Princeton African Drought Monitor system). Under the current 

system, users would have to drill down to the North American Drought Monitor and from there 

to the USA, Canada, or Mexico, in order to select individual drought indicators, such as those on 

display on the NIDIS portal. Drought Indicators are made available on the NIDIS site. 32 

SPI may not be an adequate drought indicator for semiarid areas, such as areas where 

sources of water may be water crossing a national boundary from a snowmelt runoff mountain 

zone (like Central Asia), or areas where complicated moisture fluxes within the vadose zone may 

reverse direction and return back to the surface. The North American map currently displays 

drought zones using the National Drought Mitigation Center drought severity ranking system 

shown in Figure 5(a), while EDO deploys the “indicator” alert system shown in Figure 5(b). 

4.3.2 Layout and Organization of the GDMP within the NIDIS GIS Server 

The home page (index page) of the NIDIS portal is accessible from the World Wide 

Web. 33 Underneath the NIDIS banner, “US Drought Portal” is a list of subcategories: “Home,” 

“What is NIDIS,” “Current Drought,” “Forecasting,” etc. The URL for “current drought,” 34 the 

URL for “forecasting” 35 show the navigation within the portal: the category item is located in the 

32 http://www.drought.gov/portal/server.pt/community/drought_indicators/223 

33 http://www.drought.gov/portal/server.pt/community/drought.gov/202 

34 http://www.drought.gov/portal/server.pt/community/current_drought/208 

35 http://www.drought.gov/portal/server.pt/community/forecasting/209 

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URL path after community, so that the path to the global drought monitor is analogous. 36 

4.3.3 Implementation of Advanced Search and Discovery in the GDMP 

Advanced semantic-enriched search and discovery is discussed below for the European 

Drought Observatory. Eventual semantic deployment within GDMP would likely be limited to 

national drought monitors that are part of GDMP. In addition, the information resources built 

up by DEWFORA can be incorporated into the system, including water cycle component 

datasets for Africa. The water usage datasets, such as for the Water Information System for 

Europe (WISE), and other areas, from GLOWASIS can also be integrated into the system over 

time, funding permitting. This type of implementation would require registration of the datasets 

within GI-Cat, tantamount to the addition of another drought catalogue (Figure 13). The datasets 

would also be registered with the concepts of the water ontology. More information on 

establishing interoperability of EuroGEOSS with GEOSS is contained in Section 5.8. 

4.4 Support of Increased Global Coverage within the web-based, real-time GDMP 

server 

The GDMP is a web-based, real time (RT) Geographical Information System (GIS) server, 

which is built on top of a distributed database federation and ingests meteorological information 

and hydrologic information in real-time, in order to provide alerts, a prototype Drought Early 

Warning System. An overview of the alert system is provided for the European Drought 

Observatory in Section 5.1.5. 

As mentioned in the “Drought—Global” video, the global drought server is integrated 

and interoperable with continental drought servers, while the national hydrometeorology drought 

monitors within the continental areas are integrated with and made interoperable with the 

continental drought servers. The European Framework project Drought Early Warning System 

for Africa (DEWFORA) would be expected to possibly serve as an African continental (pan 

Africa) continental drought monitor with intercomparisons being prepared between the 

DEWFORA and Princeton African drought monitors. The meteorological forcing data sets are 

being assembled for South America to integrate with real time meteorological observing system 

data to create a continental scale system there. The GEO Community of Practice partner, the 

Asian Water Cycle Initiative Drought Working Group (Ichiro Kaihotsu, Hiroshima University) is 

developing a regional drought network (with ground-based stations) that may be made 

interoperable with the GDMP. An expected, possible configuration for the continental servers is 

depicted in Figures 14 through 16. 

4.5 Integration of GDMP with GEOSS Architecture 

The GDMP is integrated into GEOSS via: 1) metadata creation and addition; 2) catalogue 

addition (via EuroGEOSS and GI-Cat; and 3) utilization of OGC Web Mapping Service, via 

36 http://www.drought.gov/portal/server.pt/community/global_drought/ 

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installed Minnesota MapServer (University College London, Princeton, and EDO) and ESRI GIS 

Server (NIDIS). Additional web services are slated for installation. See section for more details 

in interoperability of EuroGEOSS with GEOSS. 

4.6 Remote Sensing Soil Moisture Integration 

Figure 7’s lowest tier displayed both numeric and sensor sources of data originating from 

the observing system. The numerical models are forced by real-time meteorological data from 

the observing system. Sections 2.6.2 and 2.6.3 showed how space-based scatterometers could 

provide soil profile and root zone soil moisture. These results can be displayed alongside 

modeled soil moisture data; direct data assimilation into a common product is also a possibility 

or even the latter with the display of separate inputs. The US National Aeronautics and Space 

Administration (NASA) Soil Moisture Active and Passive (SMAP) results can also be added 

when they go live and come on line. 

4.7 Adding Water Usage Information Layers, including Agriculture 

GLOWASIS may provide a basis for incorporating water usage information and data into 

the GDMP. This would also be a prerequisite for assessing drought vulnerability. 

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Figures 14 (a) and (b) 

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5. Advanced Search and Discovery Capability within the European Drought 

Observatory 

A set of tools have been developed for deployment within the European Drought 

Observatory, also serving as a contribution to GEO. These technologies provide capability to 

integrate water information for the Water (and drought) Societal Benefit Area, including possible 

deployment within the Global Drought Monitoring Service. This report captures the user 

requirements for who would be using this system (EDO), what data types would be required, and 

the type of functionality users would expect. These user requirements are embodied within a 

“scenario,” with the development of a system architecture providing the associated enabling 

framework. The GEO Architectural Implementation Pilot (AIP) develops components for the 

GEOSS Architecture through component deployment and subsequent testing, interoperability 

testing, followed by Societal Benefit Area (SBA) demonstrations, i.e., demonstrations of a 

decision support service, such as the global drought monitoring service for the Water SBA. GEO 

AIP projects are run by framing a “scenario” which expresses and embodies the user 

requirements, such as GEO tasks. 

5.1 Components of the European Drought Observatory 

The European Drought Observatory is based upon a loosely-coupled system having as 

components: 1) drought indicators; 2) drought climatologies; 3) drought observing systems; 4) 

water usage observing system; 5) internet-based and web-based services which make 

interoperability and exchange of data and maps possible; 6) common formats among the system; 

7) user network to verify nowcasts and forecasts; and 8) hardware infrastructure and technical 

support staff. 

The European Community has identified drought, biodiversity, and forestry as targets for 

GEOSS activity. This AIP effort has included development of the EuroGEOSS search 

capability. As has been mentioned above (Section 2.1.1), common registration of datasets permit 

maps and data to be shared and exchanged among the EDO, the national drought monitors, such 

as MARM and SIA, and the river basin authorities, such as Confidercion Hidrografica del Ebro. 

In short, joint registration supports interoperability. At the same time, some of the data retrieval 

capabilities of EDO were time series of soil moisture, soil moisture anomalies, and Standardized 

Precipitation Index over different time periods. The longer the period of observation for 

precipitation falling upon a landscape, the longer the period of time over which water works its 

way through the soil and drains down into groundwater. 

5.1.1 European Drought Observatory user access 37 

The EDO map server is separated from the index page. 38 



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5.1.2 Organization and layout of the EDO map server page (scenario step 01— 

continued) 

The upper left hand column has “European Drought Products,” underneath of which are 

listed: 

1. Soil Moisture; 

2. Precipitation; 

3. Precipitation from Archive; 

4. Remote Sensing Indicators; 

5. Drought-related products; and 

6. Generate Graphs and Time Series 

Underneath this list are three tabs: 

1. Information from EuroGEOSS Drought Catalogue; 

2. National/International Drought Information; and 

3. Regional/Local Drought Information 

Selecting Soil Moisture, number 1 from the top panel (button), causes a list to fall down, having 

the selection: 

1. Daily Soil Moisture; 

2. Daily Soil Moisture Anomaly; 

3. Forecasted Soil Moisture Anomaly; 

4. Forecasted Soil Moisture Trend. Etc. 

The EuroGEOSS drought catalogue box is accessible. 39 

5.1.3 Selection of Drought Indices 

The Drought Indices of Step 01 are Standardized Precipitation Index and Soil Moisture 

Anomaly (and Soil Moisture). No hydrologic drought indicator is included, although some 

remote sensing drought indicators are given. We have not included remote sensing drought 

indicators here, in order to reduce the length of this report. 

Step 01.1 entails “obtaining drought indices through standard services”: this step is tantamount to 

the process of selecting one of the drought indicators above, such as daily soil moisture anomaly, 

and sending a query to the EDO server from a local machine browser window, in order to request 

a returned map showing daily conditions calculated for western Euro Asia for that day. 

Returning to the EuroGEOSS drought catalog box above, the returned web page contains a given 

drought vocabulary (Section 9), along with a thesaurus button underneath. Pressing the 

thesaurus button prompts a new button to appear “Add term” with a dialog box popping up 

“Select thesaurus from list ‘SBA_EuroGEOSS.” The GEO Societal Benefit Area groupings are 

listed, including water. 

NOTE: This is an important step which is not included within the original scenario. The 

implications of this EuroGEOSS interface—with respect to semantics—are outlined below in 

Section 6.4. 

39 http://eurogeoss.unizar.es/Search/Search.html?opener=EDOMapServer 

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A returned map is loaded and returned. 

5.1.4 Processing Step by Running Drought Indicators over a Selected Spatial 

Domain 

Step 02 is the process of processing current daily conditions using the drought indicator (“daily 

soil moisture anomaly” and the display of the map within the browser on the local machine. 

Step 02 determines whether there is a drought over a given spatial domain, as well as the 

severity (ranking) of the drought. EDO utilizes a drought severity ranking system, 

corresponding to drought of increasing severity: 1) 0-green; 2) 1-yellow; 3) 2-orange; 4) 3-red; 

5) 4-brown. This is the drought severity ranking system that differs from the North American 

Drought Monitor ranking system, as presented within Figure 5. 

The currently displayed drought severity color coding system on the EDO map server 

system does not yet implement this drought severity ranking system. It current system is 

simpler, exhibiting wetter or drier conditions (than average) only: green (indicating wetter 

conditions), yellow (indicating normal or 0), and orange for progressively drier, until red. 40 

5.1.5 Automated Email Alerts and Drought Triggers 

Scenario step 02.2 

If any area within the European Union (including adjacent areas, such as Turkey) is 

designated as having drought of a particular severity, an automated email alert can be sent to 

decision makers, if they have already signed up to be a recipient for such an alert service. This is 

the type of automated email alert system used by the USA National Oceanic and Atmospheric 

Administration (NOAA) Integrated Coral reef Observing Network (ICON), which dispatches 

automated emails to alert the oceanographic community of possible coral reef bleaching, when 

pre-assigned bleaching thresholds are passed. The EuroGEOSS broker offers support for the 

GeoRSS alert mechanism. 

If, after being notified by email alert of a designated drought ranking, a decision maker 

wants to retrieve more information about drought conditions, the semantics-supported advanced 

search and discovery is set up to make it easier to retrieve the information. This is in keeping 

with the philosophy of tailoring a decision support system to make it easier to utilize 

information. 


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5.1.6 Context and pre-conditions 

Some of the existing Global Earth Observation System of System functionality has been 

described above. This section will provide further documentation of EuroGEOSS in providing 

components supporting advanced search and discovery. The following datasets and services are 

assumed to be available before the scenario begins: 

• GEO Portal, through this portal the end user will be able to search, find and access 

the services which are needed for the Scenario execution; 

• EuroGEOSS/GENESIS Client Application is registered on the Components and 

Services Registry (CSR) and accessible through the GEO Portal; 

• EuroGEOSS Discovery Augmentation Component (DAC) Service. This service 

federates both semantics (e.g. SKOS repositories) and ISO-compliant geospatial 

catalog services. The DAC can be queried using common geospatial constraints (i.e. 

what, where, when, etc.). The service exposes a semantics-extended OpenSearch 

interface. 

• EuroGEOSS Discovery Broker Service. This is a distributed catalogue which 

federates several services (exposing them through the CSW-ISO interface). Federated 

services publish the following datasets: 

o Environmental datasets (WCS); 

o Climate Change datasets (WCS); 

• GENESIS Vocabulary Service. This repository publishes a SPARQL interface for 

navigating the aforementioned SKOS-based thesauri. 

• WPS Client. This is web client for configuring and running data retrieval for graph 

construction through the European Drought Observatory 

• GEOSS Ontology Registry 

• GEOSS Geographic Gazetteer 

• Application (WPS): the search interface is designed to be accessible through the 

European Drought Observatory (EDO) portal interface 

• A workflow engine. This component manages all phases of the scenario (browse 

semantic repository, retrieve concepts of interest, search for resources related to such 

concepts, execute WPS) 

5.2 Implementation of the European Regional Drought Semantic-enhanced Monitoring and 

Information System 

The Drought Scenario that has been used for AIP-3 was originally introduced within 

presentations of Barbara Hofer and Stefan Niemeyer (European Drought Observatory), 

“EuroGEOSS for Drought--Linking the EDO to Local and Global Scales,” at the INSPIRE 

conference in June 2010 and “Drought Data, Metadata, and Interoperability,” at the Training 

Workshop on Drought Risk Assessment for the Agricultural Sector in September 2010. These 

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identical drought scenarios are also given within Report D.2.1.1 Report on Requirements for 

Interdisciplinary Interoperability (L. Vacarri, S. Nativi, and M. Santoro), 2 Nov 2010. 

The actual scenario which was used is presented in Table 1 below. This scenario is pretty 

abstract, and the reader may find viewing this scenario more useful by accompanying the reading 

with a viewing of the video “Drought—European.” 41 along with reading this scenario. 

The video is actually a “walkthrough,” showing step-by-step how a user interested in 

drought will use the drought information system, showing the implementation of the scenario. 

The scenario itself in Table 1 is actually the user requirements before construction of the system, 

while the video displays the components that have been assembled and implemented to meet 

these requirements. 

The use cases Semantics Enabled Search and Ontology Engine Search have been 

developed in conjunction with the Semantics WG; further details are contained in the 

EuroGEOSS Broker documentation and the Semantics Working Group Report. 

Table 3 European Drought Observatory Scenario 

European Drought Observatory Scenario 

Step 01: Obtain Drought Indices from European Drought Observatory 

Step 01.1: Obtain Drought Indices through Standard Services 

Step 02: A dedicated WPS processes the drought index and calculates the drought hazard 

Step 02.1: The WPS retrieves the Drought Index through the WCS 

Step 02.2: The WPS executes the hazard detection model and, where detected, sends an alert to 

the decision support tool 

Step 03: Drought Hazard Related Information Discovery 

Step 03.1: The decision maker uses the decision support tool to submit a query to the augmented 

search component in order to discover drought hazard related information (datasets) 

Use Case: Semantics Enabled Search 

Step 03.1.1: The augmented search component submits a query to the ontology query engine and 

extracts 0,…N terms to be inserted into the geospatial query 

Specialized Use Case: Ontology Enabled Search 

Step 03.1.2: The augmented search component generates one or more geospatial queries based 

on the user selection as geospatial constraints and/or as keywords from the previous step and 


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submits the queries to the EuroGEOSS Broker 

Use Case: Discovery: Client Search of Metadata 

Step 03.2: The EuroGEOSS Broker mediates the query request, distributing it to its federated 

services 

Step 03.3: The Decision maker uses the Decision Support System to select one or more drought 

hazard related information datasets, among the ones returned by the query 

Presentation of Reachable Services and Alerts 

Step 03.4: The Decision Support Tool submits an access request to the EuroGEOSS Broker in 

order to retrieve the user-selected drought hazard information datasets 

Interact with Services 

Step 04: Visualization and Assessment of Information 

Step 04.1: The Decision Support Tool displays the accessed drought hazard information datasets, 

combining them with the potential hazard layer 

Step 04.2: The decision maker assesses the drought hazard impact 

5.2.1 Advanced Semantic Search 

Scenario Step 03 embodies the advanced semantics incorporated into the European 

Drought Implementation. 

Upon being notified of a drought alert in an area of interest, the decision maker (drought 

expert) can go online to consult the common EuroGEOSS broker-EDO interface. For example, 

the EuroGEOSS broker incorporates both: 1) Discovery Augmentation Component (DAC) and 

2) the workflow engine, which increases the power of search by integrating together catalogue 

and semantic components. In other words, the workflow engine browses the semantic 

repositories to retrieve concepts. Once the expert drought user has identified a concept of 

interest, the resources (datasets and services) linked to each of these concepts can be retrieved. 

5.2.1.1 Ontology Registration 

An ontology is a technology for organizing information which includes the organization 

of the information together into relationships that are reminiscent of the class structure found in 

programming languages. The ontologies are stored within the Semantic Network within DIAS, 

which preserves this class structure. 

5.2.1.2 Geographic Registration 

As can be seen in Figure 11, two types of ontological information are developed and 

expanded: 1) lexicographic ontologies, i.e., ontologies of scientific disciplines and remote 

sensing; and 2) geographic ontologies, as represented by gazetteers. A gazetteer is defined as a 

reference for information about places and place names used in conjunction with an atlas (hill et 

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al 1999). The gazetteer assembles a correspondence between place names and spatial 

information. Each concept (i.e., place) can be uniquely identified by Resource Description 

framework via a Universal Resource Identifier (URI). Gazetteers can record a triple (place 

names; geographic footprints (locations); and class of described feature or representation of the 

real world geographic entity. The place name is a “handle” to support communication. 

5.3 EuroGEOSS Deployment of the Foundation Vocabularies 

Ontologies are created out of scientific vocabularies, controlled vocabularies, where 

terminology has accepted meaning. So the starting point for the vocabularies in Figure 10 would 

be general vocabularies, such as: 

The GEO SBA ontologies were constructed out of: 

• The General Multilingual Environmental Thesaurus (GEMET): 28 of the 29 languages 

currently provided by the EIONET portal. 

• The INSPIRE Feature Concept Dictionary and Glossary: 21 of the 23 EU official 

languages for INSPIRE Themes, monolingual the other terms. 

• The ISO 19119 categorisation of spatial data services: 21 of the 23 EU official languages. 

• The GEOSS Societal Benefit Areas: 5 languages. 

5.4 Fine Tuning the Foundation Vocabularies for SBA Application—Specialized 

Drought Vocabulary 

At the start of AIP-3, Pozzi parsed the GEMET water thesaurus, in order to identify the 

extent of drought terminology and concepts contained within it. An extensive, developed 

network of drought concepts would be necessary to support the presentation graphs in the user 

interface on the EDO portal (Figure 9) and also provide concepts linked to the drought data and 

information. However, parsing and browsing the GEMET thesaurus for water shows it lacks any 

specialized drought vocabulary. 42 Even the precipitation it lists only includes chemical 

precipitation. 43 Hence, GEMET, by itself, is not adequate to express meteorological drought, 

agricultural drought, and hydrologic drought indicators or their associated water budget 

components (groundwater, streamflow, baseflow, snow cover); the base thesaurus must be 

supplemented by a water ontology. Pozzi (of the Water Working Group), C. Fugazza of the 

AIP-3 Semantics Working Group, and M. Santoro and S. Nativi, the EuroGEOSS architects, 

concurred in developing a water ontology that would include a drought specialization that could 

be used for both EuroGEOSS (and EDO) and the DIAS ontology registration within the semantic 

42 

http://www.eionet.europa.eu/gemet/theme_concepts?letter=0&start=390&th=40&langcode=en& 

ns=4 

43 

http://www.eionet.europa.eu/gemet/theme_concepts?letter=0&start=270&th=40&langcode=en& 

ns=4 

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network registry. Such a specialized drought vocabulary or a water ontology would link to 

drought indicators and drought and water datasets used in common with the European Drought 

Observatory. The Simple Knowledge Organizing System (SKOS) had been used to express this 

GEMET data structure, so the water ontology, developed in AIP-3, would also be translated into 

SKOS data structures and linked to relevant terms in the reference thesauri as an AIP-4 activity. 

5.4.1 Water Ontology-enablement within the DAC Semantics 

One possible candidate as a foundation water ontology was the USA Consortium of 

Universities for the Advancement of Hydrologic Sciences (CUAHSI) water ontology, version 1. 

The CUAHSI water ontology does list water stores of surface and subsurface (soil) water, 

thereby meeting some of the requirements needed in a hydrologic drought indicator. Fugazza 

has converted the CUAHSI Ontology Web Language (OWL) into SKOS, as an AIP-3 

contribution (See AIP-3 Semantics Engineering Report). 

5.5 How the EuroGEOSS Discover Augmentation Component supports semantic 

searches 

To conclude, the Discovery Augmentation Component enables semantics-aware 

discovery by matching the search patterns entered by the end user against a collection of 

multilingual, SDI-related thesauri: these are controlled vocabularies providing multiple textual 

representations for terms and organizing them according to specificity and relatedness. As a 

consequence the user’s query is first related to a set of language-neutral identifiers (URIs) (like 

the URIs used for geographic spatial entities, noted above in section 5.2.1.2). These URIs 

represent entities in a concept graph that the user may navigate for identifying related terms that 

are relevant to her search. These data structures are hosted by the GENESIS Vocabulary Service. 

These thesauri are provided in the Simple Knowledge Organizing System (SKOS) 

format, a lightweight ontology for expressing knowledge organization systems (such as 

taxonomies, classification schemes etc.), and have been harmonized in the context of the 

EuroGEOSS project by relating terms from distinct thesauri, thus allowing the user to move from 

one categorization to the other, i.e., one scientific discipline to another within a GEO SBA or 

from one SBA (water) to another SBA (agriculture). Once the user has identified an exhaustive 

set of terms that are relevant to her query, the broker translates the corresponding URIs back to a 

customizable set of languages and executes multiple queries against the catalogs it is federating 

(recalling the desired datasets). 

The EuroGEOSS Discovery Augmentation Component (DAC) implements a query 

expansion strategy deriving multiple traditional geospatial queries from a single semantic query. 

The DAC is able to accept a semantic query and, accessing a configurable set of external 

semantic services (e.g. controlled vocabularies, gazetteers, etc.), split it into several geospatial 

queries directed to a set of federated traditional/standard services for geospatial resources 

discovery. Results are finally combined in a meaningful way and sent back to the client. 

This framework realizes the Separation-of-Concerns pattern assigning specific tasks to 

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different components, making the architecture flexible and scalable. Moreover, it does not affect 

existing geospatial service interfaces implementing a loosely-coupled solution in compliance 

with the GEOSS architectural principles. 

The system design for the DAC applies the well-known principle of Layered Architecture 

(ISO, 1994), as depicted in Figure 11. Functionalities are grouped and layered according to their 

abstraction level. Figure 11 shows the three layers of the proposed architecture, implementing 

each layer on a different distribution tier: 

• in the Presentation Layer we find components implementing graphic user interfaces 

(GUIs); 

• the Integrated Semantic Layer is composed of components which implement the business 

logic necessary to integrate semantic and geospatial services; 

• The Single Semantic and Geospatial Query Layer provides query functionalities towards 

a set of different services (geospatial, semantic, etc.). 

Figure 17 – System Architecture for the DAC System 

The DAC clearly falls into the integrated semantic layer and makes use of the services in 

the single semantic and query layer in order to implement the query expansion strategy. 

The choice of service interfaces was mainly driven by the need of being as compliant as 

possible with widely adopted catalog service specifications to be interoperable with existing 

systems. Thus, for the interaction between the DAC and the catalog service, the OGC CSW/ISO 

AP (Application Profile) interface is used. Among the present application profiles of the OGC 

CSW core specification; this is presently one of the most widely implemented. Moreover, this is 

the INSPIRE compliant catalog service interface. The access to the semantic service takes place 

through SPARQL (the Query Language for Resource Description Format (RDF) semantic 

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documents, a W3C standard) syntax for queries. However, the DAC was conceived to be flexible 

and federate also semantic services that use different interfaces. 

The DAC shall also provide an interface (towards the presentation layer) for being 

queried with any combination of semantic, geospatial and free text constraints. At the time being 

there is no well-recognized standard interface or syntax allowing such combined queries. Hence, 

the choice was to use the lightweight OpenSearch 44 interface. The OpenSearch is a basic 

interface, allowing querying a catalogue with a simple free text search. There exist several 

extensions of the basic OpenSearch syntax; two widely used extensions to submit geospatial 

queries are: 

• Geo extension: allows to specify a spatial extent/location as constraint in a query; 

• Time extension: allows building queries based on time and time spans constraints. 

In addition to the above extensions, we defined a “Concept-driven” extension to allow the 

discovery of well-defined concepts and relations between concepts form semantic services. 

These three extensions form the DAC query interface. 

A detailed documentation describing the “Concept-driven” extension will soon be 

published on the OpenSearch Web Site. The AIP-3 Engineering Report Best Practices Wiki 45 

will be updated as soon as the detailed documentation will be available. 

5.6 Operation of the Water Ontology within the EuroGEOSS Discovery 

Augmentation Component 

5.6.1 Searching for Concepts/Terms 

EuroGEOSS DAC communicates with the GENESIS Vocabulary Service using SPARQL 

interface. According to user’s request the EuroGEOSS DAC performs different actions: 

1. When the user has searched for concepts/terms related to a keyword of interest (e.g. 

“drought”), the EuroGEOSS DAC performs a “GetConcepts” request; that is, 

EuroGEOSS DAC builds a SPARQL query to retrieve from the GENESIS 

Vocabulary Service all concepts/terms containing the searched keyword in the label 

and/or in the description. The matching concepts/terms are returned to the 

EuroGEOSS/GENESIS Client Application. 

2. When the user is extending a set of concepts/terms, the EuroGEOSS DAC 

transforms the selected relation type (e.g. “more specific concepts”) into formal 

SKOS relations (e.g. skos: narrower and skos: narrowMatch). Using these relations 

a set of SPARQL queries is executed, and matching concepts/terms are returned to 

the EuroGEOSS/GENESIS Client Application. 

5.6.2 Multilingual Concepts/Terms 

Each of the selected concepts/terms is identified by a URI. The EuroGEOSS DAC submits 

a SPARQL query to the GENESIS Vocabulary Service in order to retrieve all available 

44 http://www.opensearch.org/Home 

45 http://wiki.ieee-earth.org/Best_Practices/GEOSS_Transverse_Areas/Data_and_Architecture 

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translations for each of the selected concepts/terms. 

5.6.3 European Drought Observatory (Client) Query 

EuroGEOSS DAC communicates with the EuroGEOSS Discovery Broker through the 

OGC CSW ISO AP 2.0.2 interface. For each of the selected concepts/terms, EuroGEOSS 

DAC creates a query that contains geographic (i.e. the envelope characterizing the specific 

AOI), and multilingual Keywords constraints (i.e. the concepts/terms selected through the 

EuroGEOSS/GENESIS Client Application). This set of queries is submitted to the 

EuroGEOSS Discovery Broker. As shown in the “Drought—Europe” video, the user has 

identified a geographic area on the map interface, while at the same time, highlighted and 

clicked on a concept. Before sending back the results to the client, the EuroGEOSS DAC 

groups them according to the matched concept/term. 

5.6.4 WPS Request 

The European Drought Observatory (EDO) WPS Client sends an Execute request to the 

WPS Server, including references to the input thematic layers selected by the user (WCS 

endpoint and coverage name). 

5.7 Use of EuroGEOSS Semantic Discovery within the European Drought 

Observatory (Returning back to the Scenario) 

The User Interface to DAC includes two tabs: 1) “Search” and 2) “configuration.” The 

process begins with a “Simple Search” text box, in which a user types in the overall query item 

of interest “drought.” The “Advanced Search” panel becomes active, containing a text string box 

in which the user types the keyword, underneath of which are buttons “Get concepts,” “relation,” 

“extend node,” “clean selection,” and “search.” The “Get concepts” button obviously retrieves 

the concepts, which are then displayed in graph form as nodes on a tree or graph. 

The “relation” button, when pressed, offers the selection of “more general terms,” “more 

specific terms,” “corresponding terms,” and “related terms.” The color coding used in the graphs 

are: 1) “orange” for more specific, 2) “yellow” for more general, and 3) “green” for 

corresponding. For example, “drought indicator” is a “more specific” example of “drought.” 

As shown in the demonstration video, a user can draw a box around an area of interest on 

a map, while simultaneously having entered the concept of interest to the user. Then the search 

results will be retrieved. The returning data matches the requested concepts. Then the selected 

datasets can be displayed upon a map server (Scenario Step 04) (see section 6.4) 

5.8 Interoperability Arrangements with GEOSS 

We use the following service interface: 

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Date: 11/Feb/2011 

OGC CSW ISO AP, published by the EuroGEOSS Discovery Broker 

W3C SPARQL, published by the GENESIS SKOS repository 

OpenSearch interface (with geo, temporal and semantic extensions), published by 

the EuroGEOSS DAC 

Use of the GEOSS Common Infrastructure (GCI): 

The EuroGEOSS discovery broker is registered in the GCI. It is accessible through the GEO 

Portal using the following standard interfaces: 

CSW/ISO 2.0.2 

CSW/ebRIM-EO 2.0.2 

OpenSearch with Geo and Time extensions 

5.9 Post Deployment Activities 

5.9.1 Ontology Engineering 

As noted in Section 5.4.1, the CUAHSI version 1 46 water ontology contains the 

subclasses of surface hydrology, subsurface hydrology, atmospheric hydrology, land, water 

quality, aquatic biology, and infrastructure subclasses, but it lacks an extensively developed 

subsurface water subclass and surface subclassification. CUAHSI is preparing to revise and 

update a version 2 release of the water ontology, but not in time for AIP-3. Correspondingly, 

some development work was undertaken, along with more expected for AIP-4, in preparing a 

specialized drought module for the CUAHSI water ontology. 

A concise overview of the documentation for these modules follows. However, a standalone 

drought vocabulary was prepared to use within the EuroGEOSS search tools built into the 

European Drought Observatory. This stand-alone drought vocabulary is already operational and 

provides the tool to test whether concept-oriented drought searches improve retrieval of drought 

information for users—structuring a tool to facilitate the user, as in section 3.2.4. The 

operational tool and its search results are reviewed in section 6. 

The ontology documentation is provided here. Every water domain specialist, drought 

specialist, hydraulic engineer, land surface modeler, hydrologist, and ecologist will recognize 

two basic equations: the surface water equation and the surface energy equation. The 

“atmospheric hydrology” concept contains subclasses “precipitation” and “radiation” and “wind” 

(which creates mechanical turbulence and affects turbulent transfer of water vapor fluxes of 

evaporated water back to the atmosphere. 

A starting point for more comprehensive water ontology is to build upon the ALMA 

convention. 47 The ALMA convention has also been used in the distributed hydrologic model 

intercomparison experiments undertaken by the EU Water and Climate Change (EU-WATCH). 48 

46 http://water.sdsc.edu/hiscentral/startree.html 

47 http://web.lmd.jussieu.fr/~polcher/ALMA/ 

48 http://www.eu-watch.org/templates/dispatcher.asp?page_id=25222765 

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FLUXNet controlled vocabulary, the surface water budget equation, the surface energy budget 

equation, and the land use classification system of Boston University and the University of 

Maryland (as modified by GlobeCover Product Specification of MERIS. These provide a 

framework in which the drought vocabulary module can be included and expanded. 

An example of the expansion of the land module of the water ontology is presented in 

Figure 18. 

Figure 18 Land Surface Module of the Water Ontology 

6. Evaluating How the Advanced Semantic EuroGEOSS Search and Discovery 

System Works 

By the end of AIP-3, the EuroGEOSS search interface had been incorporated into the 

operational European Drought Observatory web site, even though the water ontology was not 

fully functional within the EuroGEOSS Discovery Augmentation Component. Be that as it may, 

a specialized drought vocabulary (section 10) was available and linked to the EuroGEOSS 

broker. How effectively does this system retrieve specialized drought datasets, its stated user 

objective? 

The EuroGEOSS Drought Catalog page pops up, containing a list of drought terms 

(Section 10), followed by a list of terms representing the GEO Societal Benefit Areas (Section 

11). As has been noted, the list of drought terms contains the water budget components, i.e., 

groundwater, discharge, evapotranspiration, low flow, piezometric level, precipitation, snow, soil 

moisture, soil moisture deficit, and snow pack. 

Clicking the groundwater term and hitting the search button brings up two pages of 

references. 

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Date: 11/Feb/2011 

Highlighting the soil moisture term and clicking on the search button retrieves: daily soil 

moisture per region; daily soil moisture anomaly per region (EDO): humedad del suelo en 

Espana, composite drought indicator, forecasted soil moisture trend (EDO); forecasted soil 

moisture anomaly (EDO); daily soil moisture anomaly EDO; and daily soil moisture. 

Although this is not a direct test utilizing the full ontology, the results are encouraging in 

supporting the use of a specialized concept-oriented vocabulary, such as that of drought, in 

supporting more effective searches. 

7. Drought Metadata for fostering interoperability between EDO and EU national 

drought monitors 

European efforts, independent of AIP, have constructed drought metadata and the 

registration of drought datasets (for Spain),part of the metadata creation and registration upper 

tier “Catalog” box of the Australia Water Resources Information System diagram (Figure 6). 

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Date: 11/Feb/2011 

Figure 19 

Drought metadata were defined, as documented in D.5.2, Metadata Catalogue for 

Drought Information (J Nogueras, et. Al. 49 

At the start of the project only two WP5 partners (CNIG and CHE) had metadata 

catalogues describing drought related resources together with other types of resources, while 

some of the other partners had no metadata available for their drought related resources they use. 

In addition to this, CNIG and CHE catalogues have been included in the WP2 broker (GI-Cat), 

together with this WP5 drought catalogue, so all metadata resources available from WP5 partners 

are accessible in a distributed way. 

The technology of this catalogue has been developed by the Universidad de Zaragoza. 

The EuroGEOSS drought catalogue has been registered as a service accessible through the 

EuroGEOSS discovery broker component. 50 Thanks to the connection to the IOC brokering 

framework, EuroGEOSS users can discover drought related resources in a distributed way. 

49 www.eurogeoss.eu 

50 http://217.108.210.73/broker/ 

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Date: 11/Feb/2011 

The EuroGEOSS discovery broker component is based on GI-Cat software 51 . 

8. Range of Issues Covered by the Water Working Group 

The AIP-3 Call for Participation originally included Water Quality and Drought 

(including Agricultural Drought). W. Sonntag and C. Spooner (USA Environmental Protection 

Agency), V. Guidetti of the European Space Agency (ESA), and J. Lieberman joined to raise 

water quality issues with regards to an EO2Heaven project to be based in Africa and beach 

closures in the Gulf of Maine. 

The CSIRO Tasmanian ICT Centre has developed the Hydrological Sensor Web (HSW) 

based on OGC-SWE standards. Near real-time hydrologic observations and flow forecasting are 

published and accessed through the OGC Sensor Observation Service (SOS). 

Fig. 18(a) shows the generation process of flow forecasting. Firstly, rainfall observations 

are collected from different sensor sites, owned by different agencies, and stored in databases. 

The observations are published on the HWS via SOS. A Kepler workflow obtains rainfall 

observations from SOS and generates the gridded rainfall surface. A forecast model then 

consumes the gridded rainfall data and produces flow forecasts. Finally, the forecasting results 

are published onto the HSW through SOS. It can be seen that different agencies are involved in 

producing flow forecasting results. 

For this use case, a provenance information model has been developed which is demonstrated in 

Fig. 18(b). Three sets of ontologies have been adopted, which are the Sensor Ontology, the 

WaterML2 Ontology and the Process Ontology to describe information/ knowledge in the sensor 

domain, the water domain and the data processing domain, respectively. Then, the Proof Markup 

Language (PML) is used to describe the generation processes of information products and link 

multi-domain ontologies together. This allows tracking the lifecycle of hydrologic data products, 

as well as record-related factors that may impact on data qualities, e.g., sensor setting, model 

calibration. 

51 http://zeus.pin.uinfi.it/cgi-bin/twiki/view/GIcat 

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Date: 11/Feb/2011 

Figure 10 (a) and (b) 

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Page 69




Date: 11/Feb/2011 

10. EuroGEOSS Drought Vocabulary Keywords 

DMCSEE - Drought Management Centre 

for Southeast Europe 

Drought 

EDO - European Drought Observatory 

European drought product 

GPCC data 

Hydrolog 

y 

Meteorology 

NDWI - Normalized Difference Water 

Index 

National/multinational drought product 

Natural hazard 

PDSI 

Regional/local drought product 

Remote sensing 

SPI 

Soil 

Statistics 

alert 

anomaly 

arid climate, desert climate, dry climate 

arid zone, dryland, dry zone 

climate 

climate change 

climate variability 

composite drought indicator 

cumulative departure from normal or 

climatologically expected precipitation 

cumulative precipitation deficit 

Page 70 

desertification 

discharge 

drought control 

drought duration 

drought early warning 

drought end 

drought forecast 

drought frequency 

Drought 

hazard 

drought impact 

drought index 

drought indicator 

drought intensity 

drought management 

drought map 

drought mitigation 

drought monitoring 

drought monitoring system 

drought onset 

drought overview 

drought plan 

drought product 

drought region 

drought resilience 

drought risk 

drought severity 

drought spatial extent 

drought status 

drought stress 

drought threshold




Date: 11/Feb/2011 

Dry 

season 

emergency 

evaporation 

evapotranspiration 

fAPAR - Fraction of Absorbed 

Photosynthetically Active Radiation 

groundwater 

heat stress 

hydrological drought 

hydrological drought index 

hydrological status 

low flow 

meteorological drought 

meteorological drought index 

meteorological state 

normality 

piezometric level 

potential evapotranspiration 

pre-alert 

precipitation 

precipitation anomaly 

precipitation deficiency (amount, intensity, 

timing) 

precipitation deficit 

precipitation percentile 

rainfall 

rainfall anomaly 

remote sensing product 

reservoir 

reservoir volume 

semiarid climate 

semiarid zone 

Page 71 

snow 

snow pack 

soil moisture 

soil moisture deficit 

spatial assessment of drought 

susceptibility to drought 

Time series 

trend 

type of drought 

vegetation productivity 

vegetation state index 

vulnerability to drought 

water deficit 

water 

runoff 

water scarcity 

water stored in reservoir 

water stress 

weather extremes




Date: 11/Feb/2011 

11. EuroGEOSS Water Societal Benefit Area Keywords 

Biogeochemistry 

Climate prediction 

Drought prediction 

Ecosystem 

Fisheries and habitat 

Flood prediction 

Human Health 

Impacts of Humans 

Land use planning 

Management 

Production of Food 

Resource management 

Telecommunications-navigation 

Water Cycle 

Weather prediction 

12. Acknowledgments 

The role of the USA National Integrated Drought Information System and the USA 

National Oceanic and Atmospheric Administration (NOAA) is greatly appreciated in 

extending manpower and data and services hub capacity towards hosting the Global 

Drought Monitor Portal. The manpower of the European Drought Observatory staff in 

setting up OGC Web Mapping Service-enabled Map Servers on the Princeton server and 

establishment of interoperability with the NIDIS server is also gratefully acknowledged. 

The role of the Japanese Aerospace Exploration Agency (JAXA) in helping 

support the GEO Water Community of Practice is acknowledged and appreciated. Such 

support was crucial in establishing the initial impetuous for setting up the global drought 

monitor through GEO. 

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Global Drought Monitoring Service through the GEOSS Architecture ...

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