New forms of territorial governance for the promotion of landscape policies in the field of water resources management at water

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Mitchell James
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KEPEMEP-MedRegio Coordinator of NetWet 3 project COORDINATION OF PUBLIC POLICIES FOR FLOOD PROTECTION

Hatzopoulos* Athina Santorinaiou, i Phd Dimitra Gitakou, Ms *Professor, Department of the Environment

1 New forms of territorial governance for the promotion of landscape policies in the field of water resources management at water territories i EU Community Initiative Interreg III B ArchiMed KEPEMEP-MedRegio Coordinator of NetWet 3 project COORDINATION OF PUBLIC POLICIES FOR FLOOD PROTECTION USING REMOTE SENSING AND GIS TECHNOLOGIES FOR COASTAL URBAN LANDSCAPES AT WATER TERRITORIES John N. Hatzopoulos* Athina Santorinaiou, i Phd Dimitra Gitakou, Ms *Professor, Department of the Environment Director of Remote Sensing and GIS Laboratory (RSLUA) University of Aegean Project Partner no11 UNIVERSITY OF THE AEGEAN, GREECE

4 ABSTRACT Management of landscape at water territories and water areas - flood protection a case study in the prefecture of Corinthia, Greece. algorithms and remote sensing / GIS technology to develop: 1. a model of comparative temporal approach to the landscapes of principal urban area which is located at coastal zone to provide information for flood protection. 2. best practices for the coordination of public policies in the field of integrated interventions at modern urban water system compatible to the methods of flood protection of urban landscape of principal urban area located at coastal zone.

5 This study was undertaken in the framework of Netwet 3 Project, Interreg III B Archimed Programme to develop: a model of comparative temporal approach to the landscapes of principal urban area located at coastal zone with regard to the damage caused to the urban landscape by urban floods an integrated methodology -as demonstration example- for the coordination of public policies i regarding the protection of the coastal, urban landscape of the Corinthian case study area. participatory shift of public policies - participative decision-making tools development

6 participatory shift landscape concept: aesthetic or ecological issues - social dimension - a public good. European Landscape Convention (Article 6): each Party undertakes to assess the landscapes taking into account the particular values assigned to them by all interested parties and the population concerns. As the coastal landscape is a meeting ground between a wide range of interests (economic, environmental, social), the participatory processes need to be approached into a conflict management framework (Santorineou et.al. 2008, Davos et. al. 2007).

7 Study area: Prefecture of Korinthia Non homogeneous distribution of rainfalls and water resources Coastal part: agriculture and tourism bounds a well structured and densely populated urban environment.

8 Prefecture of Korinthia

9 4 major water heds Asopos, Zapantis, Rachianis and Xerias

10 Data interpolated from 10 gauging stations by Inverse Distance Weighted

11 Land cover maps the study area is covered by intensive cultivations Voudouris (2006): vineyards, citrus fruits, apricots, olive groves olive tree orchards and cultivated lands cover about 50% of the study area. A temporal series of Landsat TM satellite images for land cover were analyzed June 10, 1987 June 21, 1991 May 20, 2000

12 There is a replacement of shrubs and low vegetation (green colour) by bare soil (brown colour) and infiltration of agricultural areas (yellow colour) in shrubs and low vegetation

13 coastal area: Land cover maps cultivated lands are decreased due to the intense built-up growth. higher altitudes cultivated lands are increased over shrubs and low vegetation. decrease of cultivated lands is rather misleading and does not appoint the continuously increased pressure of human activities and interventions in the natural environment. Land cover information is necessary for hydrological modeling for the determination of soil groups and the specification of curve numbers in the model.

14 Source: Institute of Geological and Mineral Exploitation IGME

15 Temporal Model DEM creation: digitization of 20m contour lines of 1:50000 maps Use Hydro modeling (Arc Hydro) improve drainage pattern AGREE method Fill Sinks method Produce: flow direction flow accumulation Use the watershed boundary, the precipitation grid, the hydrologic soil group grid and the land cover raster files as basic files for the implementation of a temporal approach model, for the years of 1987, 1991 and 2000

16 Hydrologic Soil Group Land Use/Cover Rainfall (P) Combine Framework Curve Runoff Number (CN) Raster Calculator ([P] * (1000 / [CN] - 10)).sqr / ([P] * (1000 / [CN] - 10)) Water available For Runoff Fill Sinks DEM Fill Sinks Flow Accumulation As weight grid Filled Elevation to Remove Sinks Flow Direction Flow Direction Accumulated Water available for Runoff

18 Curve number value per land cover type Land cover Hydrologic Soil Group A B C D Residential Forest Shrub Barren Soil Agriculture Source: National Engineering Handbook (NRCS 2004) and Baloutsos et al. (2000)

19 Prediction of total water available for runoff use the following equation in the Raster Calculator function of the ESRI ArcMap: Q = (sqr(p 0.2 * (1000/CN 10))) / (P * (1000/CN 10)) Where: Q = total water available for runoff P = precipitation grid CN = curve number grid

21 Landscape change models Two landscape change models comparison over their relative ability to decrease the flood risk vulnerability (a) participatory i t decision-making i framework taking into account the perceptions of a multitude of stakeholders in order that a future coordinated landscape change scenario to be spatially formulated. (b) Analysis of spatial and temporal data in order that future land cover allocation patterns to be forecasted in case that the current trend of landscape change will be continued. ( c) existing and future states (scenarios) of landscape according to territorially defined goals and objectives (flood risk assessment index)

22 Framework for the creation of the landscape change models Spatial Impact Assessment Matrix (SIAM)

23 Participatory landscape change model IDRISI Andes software Land cover allocation patterns for the year 2000 in the landscape evaluation units (LEU s) selected LEU is defined each sub-area being examined for future land cover changes Alternative future land cover types per LEU feasible states in which a homogeneous group of cells can be converted within a forecast time horizon of 13 years (i.e ) 2013) Five alternatives per landscape evaluation unit have been identified: (a) Urban (b) Barren Soil (c) Forest (d) Agriculture (e) Makia Olive Trees

24 Evaluation criteria express the factors affecting land cover change dynamics and constitute the basis on which the alternatives are evaluated (3-criteria) Spatial Impact Assessment Matrix (SIAM) is the basic data input of the model, expressing the performance of every e alternative a e to each evaluation criterion

25 Definition of Evaluation Criteria for Corinthian case study EVALUATION CRITERIA Environmental Landscape Protection (ELP) Economic Landscape Development (ELD) Social Landscape Development (SLD) DEFINITION The extent to which the land cover contributes to the prevention of flood events The extent to which the land cover contributes to the economic growth of the landscape. The extent to which the land cover contributes to the social renewal of the landscape, protects the public health and improves the quality of human environment.

26 Participatory landscape change model Inputs: Spatial a Impact Assessment e Matrix (SIAM) Indication of the performance of each alternative per evaluation criterion. Construction of SIAM: usage of especially designed questionnaires distributed to experts, asked to evaluate alternative future directions of land use/land cover change, per landscape evaluation unit. Construction of a theoretical SIAM, for the purposes p of the case study: Land Use Maps RECLASS SIAM MAPS

28 Cluster Analysis Construction of a stakeholder tree (Davos & Lejano 2001). Design questionnaires to extract criteria weights. The participating stakeholders can be asked to rank the criteria in ordinal as well as in cardinal way, and then the direct ratio approach (Davos 1987) may be used in order to extract the individual normalized priority values per participant. Input these values, to identify clusters (potential coalitions) expressing statistically similar priorities for evaluation criteria (coalitional priorities) conduction of a k-means Cluster Analysis in SPSS software There is a logical assumption that these coalitions could cooperate to support the alternatives that best satisfy their values. For this case study, the following theoretical cluster priorities to evaluation criteria have been produced

29 Theoretical cluster priorities to evaluation criteria CLUSTERS ELP weight ELD weight SLD weight Group interest 1 st cluster ELP 2 nd cluster ELD 3 rd cluster SLD

30 Weighted Linear Combination (WLC) Readjust SIAM maps to reflect the relative weights (criteria priorities) of the different stakeholder groups. For every alternative, the Weighted Linear Combination (WLC) aggregation g procedure of the evaluation criteria has been selected. According to this method, the criteria (factors) are standardized to a common numeric range, and then combined by weighted averaging. The result is a continuous mapping of suitability that may then be masked by one or more Boolean constraints to accommodate qualitative criteria and finally thresholded to yield a final decision (Jiang & Eastman 2000). In the framework of the present study, outputs of this sub-model are the aggregated land cover suitability raster images produced per cluster.

31 Landuse Suitability Map per Cluster SIAM MAP: alt.1/crit.1 SIAM MAP: alt.1/crit.2 SIAM MAP: alt.1/crit.3 MCE (WLC) per Cluster 1 st Land Use/Land Cover Suitability Map per Cluster

33 Multi-dimensional choice procedure Constract landscape change scenarios The results of the evaluation of alternatives are translated into different future scenarios, each of which represents the different preferences of the potential stakeholders coalitions. The individual scenarios are spatially formulated by the conduction of a multi-dimensional i l choice procedure MDCHOICE. This algorithm resolves conflicts between competing objectives by means of a multiple ideal-point procedure (Eastman 2006) and it has been used as an inter-cluster conflict management rule. The weight value is multiplied by the image values prior to checking the threshold criteria and choosing the maximum or minimum value. Final output of this sub-model is a landscape scenario per potential coalition.

34 Scenario per Cluster Forest Suitability Map Barren Soil Suitability Map Makia Olives Suitability MDCHOICE SCENARIO per Cluster Urban Suitability Map Agriculture Suitability

35 Definition of threshold suitabilities for 1 st cluster Alternatives Aggregated Landuse Threshold Suitabilities Suitability Maps (Cluster 1) (max cell values) Forest Barren Soil Makia Olive Trees Urban Agriculture Definition of threshold suitabilities for 2 nd cluster Alternatives Aggregated Landuse Suitability Maps (max cell values) Threshold Suitabilities (Cluster 2) Forest Barren Soil Makia Olive Trees Urban Agriculture

36 MOLA (Multi-Objective Land Allocation) Run MOLA procedure as a conflict management rule among clusters. solve multi-objective land allocation problems for cases with conflicting objectives. determine a compromise solution that attempts to maximize the suitability of lands for each objective. During an iterative process, MOLA reclassifies the ranked- suitability images to perform a first-stagestage allocation according to the specific areal needs, checks for conflicts and then allocate them based on the weighted minimum-distance-to-ideal-point- logic (Eastman 2006). Final output of this implementation step is the coordinated landscape change scenario for all clusters

37 MOLA Sub-Model in IDRISI Andes 1 st Landuse Suitability Map: cluster1 1 st Landuse Suitability Map: cluster2 MCE (WLC) 1 st Landuse Suitability Map all clusters 1 st Landuse Suitability Map: cluster3 RANK (cells) 1 st Ranked Landuse Suitability Map all clusters

38 Non Participatory landscape change model What may happen if the current trends of landscape change will be continued, without the adoption of a participatory process of decision-making. A landscape change scenario for 2013 has been projected, taking as inputs the land use/cover data of the years 1987 and Two kinds of IDRISI modules have been utilized: (a) Markov Chain Analysis and (b) the combined cellular automata CA-Markov Analysis. a combined land use/cover prediction procedure that adds an element of a combined land use/cover prediction procedure that adds an element of spatial contiguity, as well as knowledge of the likely spatial distribution of transitions to Markov chain analysis.

39 Cellular automata (CA) The land use at time t+1 depends on a set of rules (transition rules) taking into account the land use at time t and the neighboring land uses. The CA sub-model in IDRISI Andes software uses as input a raster group file, listing the conditional probability images (i.e. outputs of Markov sub-model). These images are reclassified ed via the application of a 5 x 5 contiguity filter. The CA-MARKOV module uses MOLA procedures in order that a projected landscape change scenario for the year 2013 to be produced

40 Coordinated landscape change scenario Non participatory landscape scenario

41 Flood Risk Assessment The landscape change scenarios created by the participatory and the non-participatory models have been compared over their relative ability to decrease the flood risk vulnerability of the landscape. Specifically, a flood risk assessment index was produced indicating the relative vulnerability of different land cover types to flood events. The index was extracted through a qualitative classification of runoff curve numbers attached to the hydrologic soil-cover complexes.

42 Coordinated Scenario RECLASS (Flood Vulnerability Index) Flood Risk Assessment Map Cover Type Hydrologic Soil Group A B C D Residential Forest Shrub Barren Soil

43 Flood risk assessment index LAND COVER TYPE Urban areas Barren soil FLOOD VULNERABILITY 1 st level (Very high vulnerability) 2 nd level (High vulnerability) Agriculture 3 rd level (Medium vulnerability) Makia-Olives Forest 4 th level (Small vulnerability) 5 th level (Very small vulnerability) flood risk assessment images were constructed by reclassifying pixel values stored in landscape change scenarios into new categories indicating the relative flood vulnerability per land cover type

44 Affiliation of future scenarios and pre-existing land use/cover allocation patterns [%] FLOOD VULNERABILITY CATEGORIES Very high vulnerability High vulnerability Medium vulnerability Small vulnerability Very small LANDUSE LANDUSE LANDUSE S 1987 S 1991 S 2000 COORDINATED SCENARIO (SDSM ANALYSIS) LANDUSE 2013 (CA- MARKOV ANALYSIS) vulnerability

45 Flood risk (participatory) high risk vulnerability to flood events is zero very high vulnerability is similar to the year 2000 Flood risk (non participatory) high risk vulnerability to flood events is similar to the year 2000 very high vulnerability tends to be increased

46 Following the same logic, three more flood risk assessment images were also constructed using as inputs the land cover maps of the years 1987, 1991 & Very high vulnerability tends to be increased from 1987 to 2000 high vulnerability tends to be decreased from 1991 to 2000 small vulnerability tends to be decreased from 1987 to 2000.

47 ACKNOWLEDGEMENTS The results presented were part of the project NETWET 3 funded by the European Commission and coordinated by the Center of Euro-Mediterranean Regions for the Environment. The Institute of Geological and Mineral Exploitation IGME of Athens gratefully provided the meteorological data.

48 Thank you

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