Šantl et al_2015_hydropower suitability analysis on a large scale

1 23

Water Resources ManagementAn International Journal - Publishedfor the European Water ResourcesAssociation (EWRA) ISSN 0920-4741 Water Resour ManageDOI 10.1007/s11269-014-0830-9

Hydropower Suitability Analysis on a LargeScale Level: Inclusion of a CalibrationPhase to Support Determination of ModelParameters

Sašo Šantl & Franci Steinman

1 23

Your article is protected by copyright and all

rights are held exclusively by Springer Science

+Business Media Dordrecht. This e-offprint

is for personal use only and shall not be self-

archived in electronic repositories. If you wish

to self-archive your article, please use the

accepted manuscript version for posting on

your own website. You may further deposit

the accepted manuscript version in any

repository, provided it is only made publicly

available 12 months after official publication

or later and provided acknowledgement is

given to the original source of publication

and a link is inserted to the published article

on Springer's website. The link must be

accompanied by the following text: "The final

publication is available at link.springer.com”.

Hydropower Suitability Analysis on a Large Scale Level:Inclusion of a Calibration Phase to Support Determinationof Model Parameters

Sašo Šantl & Franci Steinman

Received: 14 November 2013 /Accepted: 13 October 2014# Springer Science+Business Media Dordrecht 2014

Abstract The paper presents an approach to the modelling of watercourses or their sectionsaccording to and in order to determine their suitability for hydropower water use on a largescale. The method is based on a multi-criteria analysis approach which in addition to existingguidelines defines and describes in detail the main stages for model establishment andhydropower suitability analysis. Since hydropower planning stands in direct conflict withother ecological water-related objectives, evaluation of suitability is based on two maincriteria, which are supported with the belonging criteria. The first main criterion is based onevaluation of watercourses by their attractiveness for hydropower water use; the second one onevaluation of watercourses according to their ecological state or value. To support properdetermination of unknown model parameters (e.g. weights of selected criteria) the paper alsopresents an upgrade of general multi-criteria analysis process with a calibration stage, whichcan efficiently upgrade in cases when calibration data is available. The proposed method wastested and discussed on a real case study with three dislocated Slovenian Alpine watercourses,where weights of preselected criteria and some thresholds of performance functions wereselected as model variables and calibrated.

Keywords Hydropower . Ecology. Suitability .Multiple criteria analysis . Calibration . Geneticalgorithms

1 Introduction

Within the objective of increasing the share of RES (European Commission 2009) thehydropower (HP) electricity production is still considered as very relevant, since it has thehighest electricity production share among RES (66.4 % in 2008 in EU-27; EEA 2008). On theother side, HP implementation causes hydrological alterations and disruption of longitudinaland lateral connectivity of the affected watercourses (Schinegger et al. 2012) and consequentlyhas direct impact on water ecology related objectives (EC 2000; 2003). At the EU level, this

Water Resour ManageDOI 10.1007/s11269-014-0830-9

S. Šantl (*) : F. SteinmanChair of Fluid Mechanics, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Hajdrihova28, 1000 Ljubljana, Sloveniae-mail: [email protected]

Author's personal copy

problem of cross objectives has been recognized, therefore, additional guidelines have beenelaborated to provide an efficient methodological approach to support decision making onregional and strategic levels (Swiss Confederation 2011; Alpine Convention 2011). To inte-grate ecological and HP exploitation objectives within sustainable HP, these guidelinesdetermine two main evaluation criteria (hereafter: “Ecological value” and “HP attractiveness”),classification scheme for determination of potential appropriateness (hereafter: HP suitability)of the watercourses concerned and provide sets of possible criteria to support evaluation.However, a concrete and more detailed method (hereafter: the method) on the evaluation ofwatercourses or their parts is still missing.

Since multiple objectives are involved, it is appropriate that the method for HP suitabilityanalysis is based on multiple criteria analysis (MCA) process, which adds structure, auditabil-ity, transparency and rigour to decisions (Dunning et al. 2000) and has been found as aneffective and widely applied tool in the field of water management (Hajkowicz and Collins2007). Therefore, the method should provide a technique for scoring the watercourses or theirparts (hereafter: the options) as well as guidance to obtain utility functions and for efficientweights assignment of selected criteria (Hajkowicz and Collins 2007).

In the method as the scoring technique, weighted summation technique is proposed anddescribed, which is arguably the simplest and the most widely applied technique of MCA(Howard 1991), also in water resources management (e.g. Tsakiris and Spiliotis 2011; Barlowand Tanyimboh 2014). Determination of utility functions should be sourced from expertjudgement or other available environmental, technical and economic models (Hajkowicz andCollins 2007). The most subjective stage of the MCA is weight assignment, where alsodecision makers usually actively participate (Ribas 2014). In the current practice, the weightassignment is, at first, based on expert and pragmatic judgement (e.g. Supriyasilp et al. 2009).To support decision making in the process of weight assignment, a common practice to testhow changes in model parameters (weights, scoring methods, utility functions etc.) affect finaloutcomes is the application of a sensitivity analysis (Steele et al. 2009). Many researchers testthe sensitivity of a decision to the particular values of criteria weightings selected whether viathe analytic hierarchy process (Gallego-Ayala and Juízo 2014a) or by some other existingmethods (e.g. aggregation methods; Gonzalez-Pachon and Romero 2001). Nevertheless, theweight assignment can still be the greatest source of controversy and uncertainty (Chen et al.2009), even more so when numerous criteria are considered. The weight assignment can alsorequire a tremendous amount of time and effort invested in resolving the conflicts in caseswhere multiple decision makers with different and conflicting objectives are involved (Caiet al. 2004). For this reason, the goal of this research was to examine a possible upgrade ofMCA, which would efficiently support the stage of weight assignment; and also determinationof other unknown model parameters. As a possible improvement, an inclusion of a calibrationphase was examined.

When HP suitability analysis is planned for a larger scale area (e.g. catchment,region) it can be expected that some belonging watercourse sections are jointly agreedto be suitable or not for HP implementation. Hypothesis of this study in such cases isthat an inclusion of a calibration phase, which is based on agreed watercourses sections, in theMCA process can support determination of undefined model parameters (hereafter: modelvariables).

In this paper we propose the method with the focus on the calibration phase. Themethod is applied on a real case study area, i.e. three Alpine watercourses inSlovenia, where unknown parameters are weights and thresholds of some performancefunctions. Calibration and confirmation were performed on the basis of four and two water-course sections respectively.

S. Šantl, F. Steinman


2 The Method

The consecutive stages of the method are:

1. Decision on scoring technique,2. Determination of Decision Options, Pre-selection of Evaluation Criteria and Pre-selection

of Utility functions,3. Selection of Calibration and Confirmation Data, Calibration of selected Model Parame-

ters, Confirmation Stage,4. Scoring of Options,5. Decision.

In comparison to the general MCA process (Howard 1991), instead of the stage of weightassignment, the stage of calibration process is foreseen, which starts with the selection ofcalibration data. To confirm the predictive ability of the calibrated model, a stage of confirmationis included (Oreskes et al. 1994). The confirmation is based on the confirmation data. Also thestage of sensitivity analysis can be skipped if other model parameters (performance functions,scoring technique as well as the length of the options in this case) which are not calibrated aredetermined. If the model results are not confirmed (and also agreed by decision makers) theprocess can return to the previous stages. The stages are described in the following sub chapters.

2.1 Scoring Technique

The guidelines end with the classification scheme with two main criteria and proposal ofsupporting criteria. According to the first main criterion, options are evaluated by theirattractiveness for HP exploitation (“HP attractiveness”) and by their ecological state (“Eco-logical value”). It is proposed that the HP suitability, as overall utility score (S) of the options,is evaluated on the basis of the summation of both main criteria utility scores. The Si of the i-thoption is calculated as a sum of the main utility score of the criterion “HP attractiveness” (Ai)and the reverse value of the main utility score of the criterion “Ecological value” (Bi) (Fig. 1d).

The Si of the i-th option is then expressed as:

S i ¼ Ai þ 1−Bið Þ; ð1ÞThe range of S values is from 0 to 2. Lower S values indicate lower HP suitability and

higher S values higher HP suitability (Fig. 2). The Ai and Bi are valued by expression:

A i ¼X n

j¼1Ai j � wj

�� ; ð2Þ

where Aij is the criterion score and wj is the weight of the j-th criterion and n is the totalnumber of the selected criteria for the main criterion “HP attractiveness” (Fig. 1c). Thecalculation of Bi is expressed similarly. Normalization of weights assures the range of utilityscores from 0 to 1.

Aij is expressed as:

Ai j ¼ f j xi j� �� ; ð3Þ

where normalized fj is the utility function (UF) of the j-th criterion and xij is the rawperformance value (RPV; also performance measure) of the i-th option for the j-th criterion(Fig. 1a). Bik is expressed similarly. Since the MCA method contains RPVs of selected criteria

Hydropower suitability analysis on a large scale


in different units (e.g. energy production, distance, classes etc.) normalization of UFs placesthe utility scores onto the commensurate scale from 0 to 1 (Fig. 1b).

2.2 Decision Options

Each watercourse or its segments in the study area represent one option. To ensure morecomparative scoring among the options, the watercourses of the study area should be divided

Example of continuous increasing utility

function to calculate the score for criterion

HP Potential for i-th option (AiHP)

AiHP = || f j (xiHP)||

Example of a set of selected criteria with

weighting for main criterion HP

Attractiveness

=

Criteria for HP

attractiveness (j = 1 to 6)Weight

Normalized

weigth ||wj||

HP Potential 4.0 0.0615

HPS potential 14.0 0.2154

Road access 14.0 0.2154

Grid access 1.0 0.0154

Barriers 24.0 0.3692

Exisisting HPS 8.0 0.1231

For all selected criteria the criterion scores

for i-th option are calculated. To calculate the

main criterion score (Ai) of the i-th option for

HP Attractiveness they are multipied by

assigned normalized weights and summed

up.

In the same way also main criterion score for

Ecological value (Bi) for the i-th option is

calculated.

1

01

HP

att

racti

ven

ess

Ecological value

i-th optionAi

Bi

Areas with high

and low HP

suitability are

indicated by green

and red colour

respectively.

For example if for the i-th option Ai = 0.63 and Bi = 0.30 then Si=0.63+(1-0.30)=1.33. This option (watercourse segment) is

assessed with higher HP suitability.

(a) (b)

(c) (d)

Fig. 1 Illustrative example of the scoring technique on the basis of evaluation of one option

01

1

01

1

-DA

-DB

+DA

+DB

(a) (b)

Fig. 2 Two examples of the results of the analysed options based on differently selected sets of values of modelvariables with marked ideal (green) and anti-ideal (red) COs and distances between maximums and minimums



into the segments with equal length. The selection of the length of the options is usuallypredetermined by spatial accuracy of the input data as well as by the size of the study areasince the decision to analyse very short segments can lead to numerous options.

2.3 Evaluation Criteria

When comparing different watercourses according to their HP suitability, the spatial scale islarger and the analysis is based on a large amount of input data. To improve the efficiency, thedata for the selected supporting criteria should be available or elaborated by accepted andavailable methods. To reduce the total number of criteria, redundant criteria and criteria whichcan be represented by other criteria can be skipped. For example, if annual energy productionis selected as a criterion, the criteria head, flow, gradient/flow ratio can be skipped since it isdetermined on the basis of the latter criteria.

The guidelines propose the sets of criteria to support the evaluation of both main criteria.Also additional documentation is available where sets of criteria are given (e.g.Lebensministerium 2012).

2.4 Utility Functions

Which type of UF to apply, first depends on the type of the RPV for a certain criterion. If forthe criterion concerned the RPVs are continuous, a continuous UF can be applied. Forexample, HP annual electricity production can be evaluated in this way. On the other hand,if the options are valued by discrete values or classes (for example by finite number of classesof hydro-morphological state) a discrete UF should be applied. When modelling on a largespatial scale, uniform transformation (increase or decrease) of both types of UF can beadequate. If more detailed previous modelling and analyses (environmental, economic etc.)provided more accurate correlations, other shapes of UFs can be applied. When continuousfunctions for UFs are applied, thresholds should be defined too (Fig. 3a and b).

0

0.25

0.5

0.75

1

0 2000 4000 6000 8000 10000HPSPS potential, L = L = 2000 m [MWh[MWh/yeaear]

0

0.25

0.5

0.75

1

1 2 3 4 5Class of of morphpholoologigicacal modificacationon

0

0.25

0.5

0.75

1

NO YESSectction is is within NATUATURA20A2000

Continuous increasing UF Continuous decreasing UF

Discrete decreasing UF with 5

scores

Discrete UF with 2 scores

(0 and 1)

Threshold

Threshold

(a) (b)

(c) (d)

Fig. 3 Examples of applied types of UFs



2.5 Calibration Method

When selecting the calibration data it must be taken into account that the calibration data mustprovide at least two different RPVs for each criterion selected; the calibration data must berepresentative. If all the options used as calibration data have the same RPV for a certaincriterion, this criterion will be recognized as redundant.

Besides selection of the calibration data, the calibration process requires determination of anobjective, how the S of the calibration data should be distributed (scored) in the two-dimensional solution space. Figure 1 presents marked areas of high and low HP suitabilitywhere ideal and anti-ideal options (Hajkowicz and Collins 2007) should be situated respec-tively. It can be concluded that the values of model variables should be selected in a way thatthe S of the calibration options (COs) with recognized high and low HP suitability is closer toideal (S = 2) and anti-ideal value (S = 0) respectively. We proposed that the calibration problemis formulated to maximize the distance between the ideal CO with the minimum A amongideal COs and anti-ideal CO with the maximum A among anti-ideal COs; and the distancebetween the ideal CO with the maximum B among ideal COs and anti-ideal CO with theminimum B among anti-ideal COs. If this approach to the calibration is selected, both ideal(HP suitable) and anti-ideal (HP non-suitable) COs must be selected. The objective functioncan be expressed as:

max E ¼ Δ Aþ Δ B ¼ mink¼1 to NAk−maxl¼1 to MAlð Þþ minl¼1 to MBl− maxk¼1 to NBkð Þ ð4Þ

where Ak and Bk are the main utility scores of the ideal COs in total number N; and Al andBl are the main utility scores of the anti-ideal COs in total number M. The range of score E isfrom −2 to 2. Figure 2 shows two illustrative examples of the results which are based on twodifferent sets of values of model variables. The example (a) where COs distribution results innegative score E and the example (b) where COs distribution results in positive (and closer tothe objective) score E.

3 Case Study

The method was applied to three dislocated Alpine watercourses in Slovenia:

& Watercourse 1: Catchment area 224 km2, Mean annual discharge 5.3 m3/s, Max. elevation2,551 m, Min. elevation 348 m, analysed length 36.1 km, determined number of options679,

& Watercourse 2: Catchment area 85 km2, Mean annual discharge 1.7 m3/s, Max. elevation1,515 m,Min. elevation 277 m, analysed length 29.3 km, determined number of options 567,

& Watercourse 3: Catchment area 43 km2, Mean annual discharge 1.2 m3/s, Max. elevation1,345 m,Min. elevation 267 m, analysed length 13.3 km, determined number of options 265.

The analysed watercourses demonstrate variability in different aspects, for example incatchment size, hydrology, spatial orientation, presence of HP water use and geologicalcomposition (Šantl et al. 2010; Šantl et al. 2012a). Recently, positive and negative decisionson the granting of water rights were issued for some sections of the analysed watercourses, andmore detailed analyses for HP suitability were carried out. This provided calibration andconfirmation data.



Since the data on the terrain elevation model were available with a raster cell size of 12.5×12.5 m, it was decided to split the watercourses into segments with length L=50 m to definethe options. Selecting this length offers 1,511 options in total.

Selection of the criteria is based on the guidelines and references as well as on the dataavailability. A short description of the selected criteria is given below.

3.1 Criteria for the Main Criterion “HPAttractiveness”

The most relevant and representative criterion for evaluation of HP attractiveness is thecriterion “HP potential”. Some studies actually apply only this decisive criterion (e.g.Geisler and Wellacher 2012). This criterion is evaluated by calculating annual electricityproduction. The RPV of annual electricity production for this criterion of the i-th optionis calculated as:

xi HPð Þ ¼ E i; Lð Þ ¼ η0 ˙9; 81 ˙Hni Lð Þ˙ Q ið Þ˙t˙ Cut ð5Þ

where:

& E(i, L) is annual electricity production [MWh/year] of the i-th option with the selectedoptions’ length L=50 m,

& Hni [m] is net head, which is calculated on the basis of the selected L and the requiredhydraulic parameters where the maximum allowed water velocity in pipes is selected asvmax =1.8 m/s, and roughness coefficient of pipe by Strickler is selected as ks =90 s/m3,

& η0 is efficiency considering losses in penstock, turbo generator efficiency, transmissionlosses and is assigned the value η0=80 %,

& Q(i) [m3/s] is available annual average discharge at the location of the i-th option,& t [h] is duration of electricity production in 1 year and is assigned the value t=8,760 h,& Cut [%] defines average percentage of (total) operation over 1 year and is assigned the

value Cut=95 %.

To determine the available annual average discharge, environmental flow (EF) according tothe Slovenian legislation was subtracted from the annual average discharge (Smolar-Žvanutet al. 2008). To provide information on the total HP potential, the existing water uses were notincluded.

Since the selected options’ length (L=50 m) is short, which causes rapid changes in HPpotential between the neighbouring options, and HP schemes are usually longer, additionalcriterion “HPS potential” (HP scheme potential) was included. The RPVs of this criterion arecalculated by Eq. (5) too. The length of HPS (LHPS) was set to 2,000 m. The i-th option can besituated in the location of water release, in the location of water intake and in all other locationsbetween water release and intake. With the selected length of HPS and the selected step=50 m(the same as the options’ length), the total number of HPS schemes checked for each option is40. The RPV of the i-th option is then determined as the maximum value of the calculatedannual electricity production of the checked HP schemes, of which the i-th option can be a part.

To calculate the RPVs for the abovementioned criteria, a GIS-based tool, VapIdroAste,which was developed for large-scale analysis, was applied (Šantl et al. 2012).

Since road and grid access (CEDSC 2003) can significantly influence the investment costsand the data are usually available, this decisive criterion should be taken into account. So thecriteria “Grid access” and “Road access” were included. The RPVs are defined by a lineardistance of the options to the nearest existing power grid and to the nearest public road.



Existing water infrastructure (dams, weirs, sills) shows positive effects on financial feasi-bility (Swiss Confederation 2011). A criterion called “Existing barriers” was included. If thegoal in the decision making process is that HP suitability is higher in watercourses where HPwater use is already present, it is appropriate to include additional criterion “Existing HPS”.For the last two criteria, the RPVs are defined by a river station distance to the nearest existingbarrier and to the nearest existing HPS.

3.2 Criteria for the Main Criterion “Ecological Value”

To select the criteria for the main criterion “Ecological value”, it is proposed that therequirements of the WFD are followed. Its implementation provides data on a set of repre-sentative criteria on hydro-morphology, biological variables, chemical and ecological status,stresses etc. It also considers other national or local water-related objectives in the field ofnature preservation and protection of certain areas. The most representative and relevantcriterion to evaluate this main criterion would be the criterion “Ecological status” (Irvine2004). The main weakness of this criterion is that it is not available for smaller rivers wherebiological monitoring is often missing. This was the reason for this criterion not being used inthis study case is that the Ecological status is determined only for Watercourse 1. Because thiscriterion is not available, hydro-morphology state and length of the watercourse withoutbarriers can be selected as the most representative criteria (Walder and Litschauer 2010).

The criterion “Hydro-morphology” comprises many aspects which support evaluation ofecological status, such as changes in water regime, morphology and longitudinal and lateralconnectivity of watercourses (Tavzes and Urbanič 2009). The RPVs of the options are definedby five classes, from natural stream (1) to heavily modified stream (5).

The next criterion to be applied is the criterion “Barrier free”, by which the RPVs arecalculated according to the distance between the first upstream and the first downstream barrierfrom the certain option.

The criterion “Spawning site” is included since fish is considered as one of the importantbiological criteria. Spawning sites are very sensitive to influences of HP implementation,especially when rapid changes in flow and water depth occur during changes in HPS operation(Tuhtan and Noack 2012). The RPVs are calculated according to their river station distance tothe nearest spawning site.

The criterion “Natura 2000 areas” is included since it follows relevant water-related speciesand ecosystems. The RPVs are determined by their position in (1) or out (0) of the Natura 2000areas determined for water-related species and ecosystems.

Prior to the implementation of the European directives in Slovenia, protection of goodecological status, endangered habitats and endangered species was ensured by the adoption ofthe areas of significant nature value and the areas of ecological importance. This is the reasonthe available criteria “Nature value” and “Ecological importance” are included. The RPVs aredetermined by their position in (1) or out (0) of the areas.

For each selected criterion UF is defined. Figure 3 shows examples of applied UFs in thisstudy case. Also Table 1 (columns 1 and 2) shows the selected criteria and the belonging typeof UFs which are all defined uniformly.

3.3 Calibration

Calibration of the selected model variables is based on the adopted decisions or on moredetailed examination for decision making in the study area. The data were available for sixsuch sections after 2008, four of which were used for calibration and two for confirmation. The



Tab

le1

Selectionof

thetype

ofUF,

predefined

thresholds

andweightsandresults

ofthecalib

ratio

nprocess

12

34

56

7

Criterion

UF

Threshold

(predefined)

Threshold

(calibrated)

highest

Weight[%

](calibrated)

highest

Threshold

(calibrated)

average

Weight[%

](calibrated)

average

HPattractiv

eness

HPpotential

Contin

uous

increasing

330MWh/y

Determined

0.0

Determined

1.2

HPS

potential

Contin

uous

increasing

7300

MWh/y

Determined

23.0

Determined

20.3

Roadaccess

Contin

uous

decreasing

500m

100m

23.0

131m

23.8

Gridaccess

Contin

uous

decreasing

500m

500m

1.6

685m

5.0

Existingbarriers

Contin

uous

decreasing

2000

m1000

m39.3

892m

32.8

ExistingHPS

Contin

uous

decreasing

2000

m500m

13.1

800m

16.8

Ecologicalvalue

Hydro-m

orphology

Discretedecreasing

(5classes)

NA

NA

3.8

NA

9.6

Barrier

free

Contin

uous

increasing

4000

m4000

m45.3

3923

m38.2

Spaw

ning

sites

Contin

uous

decreasing

5000

m11200m

45.3

10708m

39.1

Natura2000

Discrete(1/0)

NA

NA

0.0

NA

3.1

Protectedareas

Discrete(1/0)

NA

NA

5.7

NA

10.0

Ecologicalim

portance

Discrete(1/0)

NA

NA

0.0

NA

0.0

E0.411

0.352



calibration data provide at least two different RPVs for each criterion selected, except for thecriterion “Ecological importance”. Although all three analysed watercourses are entirely withinthe areas of ecological importance and this criterion can be already recognized as redundant, itwas kept to evaluate the performance of the calibration. By selecting the length of the optionsL=50 m, the total number of the ideal COs is 52 and anti-ideal COs is 92.

The model variables are the weights and selected thresholds. The thresholds for the criteria“HP potential” and “HPS potential” are defined with maximum RPV calculated in the studyarea and are not subject to calibration. Also the thresholds for the criteria with discrete UF arenot applicable. Table 1 (column 3) gives predefined values for the thresholds, which areapplied to provide results comparison in the discussion section.

The solution space is large (18 model variables; 12 weights and 6 thresholds), therefore,genetic algorithms were applied for searching of maxE. Genetic algorithms and other evolu-tionary methods have been found to be very effective in searching of the global or near globaloptimum solution and are well introduced and evaluated, also in the field of water management(e.g. Ahmad et al. 2014; Barlow and Tanyimboh 2014).

In the calibration process, the solution with the highest E which was found (maxE =0.411) isgiven in columns 4 and 5.Average values for themodel variables in the calibration process (repeated10 times) are given in columns 6 and 7. Figure 4 presents distribution of utility scores of the optionsin two-dimensional space on the basis of the highest E found in the calibration process, where COsare additionally marked, and an example of calculating the utility score for selected option.

For the main criterion “Ecological value” two criteria are evaluated by continuous UFs.Although the first criterion “Barrier free” is based on continuous UF, the scores are equal forall options situated between two same barriers. Thus, if the options for the second criterionwith continuous UF “Spawning site” are all scored with zero, certain options can be scored

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

HP

HP

attr

acactiv

enenes

s

Ecolological valueue

Watercourse 1 op�onsWatercourse 2 op�onsWatercourse 3 op�onsIdeal COs - Watercourse 1An�-ideal COs - Water course 1Ideal COs - Water course 3An�-ideal COs - Water course 3

E = 0.411

RPV Utility score

Hydro-morph. 3.8 1 0.038

Barrier free 45.3 4600 m 0.453

Spawn. sites 45.3 350 m 0.439

Natura 2000 0.0 NO 0.000

Protect. areas 5.7 NO 0.000

Eco. Importan. 0.0 YES 0.000

HP potential 0.0 190 MWh/y 0.000

HPS potential 23.0 5578 MWh/y 0.175

Road access 23.0 50 m 0.110

Grid access 1.6 24 m 0.020

Exist. barriers 39.3 50 m 0.374

Exist. HPS 13.1 350 m 0.040

A 0.719S = A + (1-B) 0.789

Option on Watercourse 1

River station: 5350 m

B 0.930

CriteriaWeight

[%]

Fig. 4 Presentation of the options’ main criteria utility scores in two-dimensional space on the basis of thehighest E found in the calibration process with additionally marked COs and with example of calculating theutility score for selected option



with equal utility score for the main criterion “Ecological value”. This can be observed in thisstudy case onWatercourse 3 where belonging options situated between two same barriers havethe same score for Ecological value (Fig. 4).

To overview the results and confirm the right predictability of the model which is based onthe calibrated model variables, all utility scores of the options are presented along the analysedwatercourses where locations of COs and confirmation options are marked (Fig. 5).

It can be observed that the confirmation options are scored in accordance to their agreedsuitability and non-suitability for HP implementation.

4 Discussion

On the basis of the results it can be observed that some criteria are irrelevant or of lowimportance. As predicted, the criterion “Ecological importance” was recognized as redundantsince the RPVs for all the COs for this criterion are the same. The criterion “Natura 2000” isfound as less important. This is mainly due to the fact that also a part of ideal COs is situatedwithin Natura 2000. Higher importance of this criterion would be reached if differentiation ofthe areas according to their importance was applied. For example, the options within Natura2000 defined to protect species, which exist only in a water type habitat (fish, frogs etc.),

0.00.20.40.60.81.01.21.41.61.82.0

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000

Sc

ore

Watercourse station from mouth [m]

Water course 3 HP suitability Main criterion Ecological value Main criterion HP a�rac�veness

0.00.20.40.60.81.01.21.41.61.82.0

0 5000 10000 15000 20000 25000 30000

Sco

re



0.00.20.40.60.81.01.21.41.61.82.0

0 4000 8000 12000 16000 20000 24000 28000 32000 36000

Sco

re



Ideal COs

Anti-ideal COs

Ideal confirmation options

Anti-ideal confirmation options

Fig. 5 Comparison of the utility scores along analysed watercourses



should be scored higher than the options within Natura 2000 defined to protect species, thefeeding grounds of which include also riparian areas (bats, terrestrial snails, etc.). When thementioned was tested even higher maxE was found (maxE =0.469; weight for the criterion“Natura 2000” found was w=21.2 %).

For the criterion “HP potential” it is recognized that it varies significantly among COs, in factthe CO recognized with highest RPV is a part of non-ideal COs. Because of that this criterionprevents to reach higher E and its impact is reduced by assignment of lowweight in the calibrationprocess. For the same reason, the criterion “Grid access” is also assigned low weight; two of thenon-ideal COs of Watercourse 1 are closer to the existing electric grid than all the ideal COs.

From this brief comparison analysis of RPVs of COs for these criteria it can be assumedthat a certain criterion is found irrelevant or of low importance if RPVs of the COs varysignificantly among COs or/and if some of anti-ideal COs are scored higher than any of idealCOs or/and if the difference between average RPVs of ideal and non-ideal COs is lower. Theseassumptions should be argued and confirmed in future applicative researches.

If a larger number of COs is used, it can also be expected that lower maxE will be found inthe calibration process. In this case study this is confirmed when the confirmation data isadditionally used as calibration data. The highest E is found with value maxE =0.282. Whencomparing the weight assignments of the selected criteria and utility scores along analysedwatercourses (Fig. 6) of both calibration cases similarity is observed. However, in the second

0.00.20.40.60.81.01.21.41.61.82.0

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000

Sco

re


Water course 3 HP suitability 1 HP suitability 2 HP suitability 3

HP suitability 1 - first calibration process

HP suitability 2 - calibration process based on larger number of COs

HP suitability 3 - on the basis of predefined values of the model variables

0.00.20.40.60.81.01.21.41.61.82.0

0 4000 8000 12000 16000 20000 24000 28000 32000 36000

Sco

re



0.00.20.40.60.81.01.21.41.61.82.0

0 5000 10000 15000 20000 25000 30000

Sco

re



Fig. 6 Comparison of utility scores along analysed watercourses calculated on the basis of discussed determinedvalues of the model variables



calibration case the criterion “Hydro morphology” is much more relevant (31,8 %). Selectionof different calibration data could additionally support final decision on determination ofvalues of the model variables.

The calibration ensures wider distribution of utility scores, which improves distinction inHP suitability. For example, if the options are evaluated on the basis of the predefined valuesfor thresholds (column 3 in Table 2) and predefined equal weighting of the selected criteria, theutility scores show much lower distinction in HP suitability along the analysed watercourses(Fig. 6). Also E is much lower (maxE=−0.07). Nevertheless, similar trends in HP suitabilitycan be observed for all three cases.

In the criteria selection phase, the discussion was to use the criterion “FlowDuration Curve”,but the data were not available. The analysed watercourses show HP suitability decrease whenapproaching water spring sections where periods without water flow can occur (Fig. 5). Thiscriterion, which would require acquiring a lot of data, is well represented by the applied criteria.

The results show some variability on a small scale, however on a large scale, more moderatechanges in HP suitability along the watercourses are observed. In this case variations on a smallscale result mainly from the criterion “Road access”, since the existing road network globallyfollows watercourses with occasional approaching or bridging. Smoothing methods can be appliedto prevent variability on a small scale, but, precisely in the case of road access, this can be questionedsince road accessibility can influence the viability of HP implementation (CEDSC 2003).

According to the guidelines, exclusion areas (e.g. reference network; EC 2000) areconsidered, too. This condition can be simply included by conditional multiplication of S ofthe options by their position in (0) or out (1) of the exclusion areas concerned.

5 Conclusions

The novelty of the paper is a introduction of a detailed objective MCA method to support HPsuitability analysis on a large scale level. Important addition to the method is a calibration phasewhich supports determination of model variables, especially recognition of certain criterionrelevance and consequently the possibility of reducing the total number of applied criteria. Withthe inclusion of the calibration phase the participation and focus of decision makers is actuallyshifted from the process of exact criteria selection and weight assignment to the process of jointdetermination of some suitable and not suitable watercourse sections for HP implementation.

Since modelling is based on a calibration data, it is necessary for the calibration data to becorrect and consistent with the considered objectives and criteria. To properly evaluate theimportance of a certain criterion, calibration data must provide at least two differently scoredoptions for this criterion. The calibration also provides wider distribution of HP suitabilityscores in the solution space. Wider distribution of HP suitability scores is also ensured withless calibration data. The method is applied and tested on the selected study area, i.e. the areaof smaller Alpine watercourses. When, for example, an entire basin or a wider area is analysed,the analysis would probably reveal that modelling of hydropower suitability of the area shouldbe split into areas with similar characteristics to reach higher objective score; for example, intoeco regions (Illies 1978), by river hydro morphology types (Rosgen 1994) or some otherrecognized criterion. But this is part of future applicative research.

Acknowledgments The work presented herein is also based on the data, informatics tool development supportand cooperation provided by various competent authorities within two projects co-financed by EU programmes,SHARE (www.share-alpinerivers.eu/) and SEE Hydropower (www.seehydropower.eu/). We are grateful to allparticipating partners and stakeholders.



http://www.share-alpinerivers.eu/

http://www.seehydropower.eu/

References

Ahmad A, El-Shafie A, Razali SFM, Mohamad ZS (2014) Reservoir optimization in water resources: a review.Water Resour Manag 28(11):3391–3405

Alpine Convention (2011) Common guidelines for the use of small hydropower in the Alpine region. PermanentSecretariat of the Alpine Convention. Innsbruck: Permanent Secretariat of the Alpine Convention

Barlow E, Tanyimboh TT (2014) Multiobjective memetic algorithm applied to the optimisation of waterdistribution systems. Water Resour Manag 28(8):2229–2242

Cai X, Lasdon L, Michelsen AM (2004) Group decision making in water resources planning using multipleobjective analysis. J Water Resour Plan Manag 130(1):4–14

Chen Y, Yu J, Shahbaz K, Xevi E (2009) A GIS-Based Sensitivity Analysis of Multi-Criteria Weights. 18thWorld IMACS / MODSIM Congress, Cairns, Australia 13–17 July 2009: 3137–3143

Clean Energy Decision Support Centre (2003) Retscreen® Engineering & cases textbook – small hydro projectanalysis. minister of natural resources Canada 2001–2004

Dunning DJ, Ross QE, Merkhofer MW (2000) Multiattribute utility analysis for addressing section 316(b) of theclean water Act. Environ Sci and Policy 3:7–14

European Commission (2000) Directive 2000/60/ EC of the European parliament and the council of 23 October2000 establishing a framework for community action in the field of water policy. OJEC L 327:1–73

European Commission (2003) Council directive 92/43/EEC of 21 May 1992 on the conservation of naturalhabitats and of wild fauna and flora. Official Journal of the European Union L 236 33 23.9.2003. Brussels.European Commission 1992/95/2003

European Commission (2009) Directive 2009/28/EC of the European parliament and of the council of 23 April2009 on the promotion of the use of energy from renewable sources and amending and subsequentlyrepealing directives 2001/77/EC and 2003/30/EC. OJ L 140:16–62

European Environment Agency (2008) Energy and environment report 2008. Available from: http://www.eea.europa.eu//publications/eea_report_2008_6 [Accessed 10 August 2014]

Gallego-Ayala J, Juízo D (2014) Integrating Stakeholders’ preferences into water resources managementplanning in the incomati river basin. Water Resour Manag 28(2):527–540

Geisler T, Wellacher J (2012) Potenzialstudie Wasserkraft Steiermark (Hydropower potential study inSteiermark). 12. Symposium Energieinnovation, 15.-17.2.2012, Graz/Austria

Gonzalez-Pachon J, Romero C (2001) Aggregation of partial ordinal rankings: an interval goal programmingapproach. Comput Oper Res 28:827–834

Hajkowicz S, Collins K (2007) A review of multiple criteria analysis for water resource planning andmanagement. Water Resour Manag 21(9):1553–1566

Howard AF (1991) A critical look at multiple criteria decision making techniques with reference to forestryapplications. Can J For Res 21:1649–1659

Illies J (1978) Limnofauna Europaea, 2nd edn. Gustav Fischer Verlag, StuttgartIrvine K (2004) Classifying ecological status under the European water framework directive: the need for

monitoring to account for natural variability. Aquat Conserv Mar Freshwat Ecosyst 14:107–112Lebensministerium (2012) Österreichischer Wasserkatalog Wasser schützen – Wasser nutzen Kriterien zur

Beurteilung einer nachhaltigen Wasserkraftnutzung. Available from: http://www.bmlfuw.gv.at/wasser/wasser-oesterreich/plan_gewaesser_ngp/wasserwirtsch_planung/wasserkatalog.html [Accessed 10 August]

Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation, and confirmation of numerical modelsin the earth sciences. Science. New Ser 263(5147):641–646

Ribas JR, (2014) An assessment of conflicting intentions in the use of multipurpose water reservoirs. waterresources management [Accepted 25 Jun 2014]

Rosgen DL (1994) A classification of natural rivers. Catena 22:169–199Šantl S, Kozelj D, Cimperman A, Bajcar T (2010) WP7.2 – Kokra River Pilot Case Study Monograph. The

Alpine Space Programme & The European Regional Development Fund. Available from: http://share-alpinerivers.eu/tools-and-resources/pilot-case-studies/SHARE_WP4%204_Kokra_MCA%20tree-alternatives_170810_.pdf/view [Accessed 10 August 2014]

Šantl S, Kozelj D, Bajcar T, Kozelj K, Steinman F (2012a) Report on pilot case study area Drava river sub-basinwith tributaries of Oplotnica and Lobnica streams –Deliverable 6. Available from: http://www.seehydropower.eu/download_deliverables/ [Accessed 10 August 2014]

Šantl S, Alterach J, Kozelj D (2012) Development of an informatics tool for efficient hydropower potentialdetermination. Acta hydrotech 25(42):17–28

Schinegger R, Trautwein C, Melcher A, Schmutz S (2012) Multiple human pressures and their spatial patterns inEuropean running waters. Water and Environ J 26(2):261–273

Smolar-Žvanut N, Maddock I, Vrhovšek D (2008) Evaluation and application of environmental flows for runningwaters in Slovenia. Water Resour Dev 24(4):609–619



http://www.eea.europa.eu//publications/eea_report_2008_6

http://www.eea.europa.eu//publications/eea_report_2008_6

http://www.bmlfuw.gv.at/wasser/wasser-oesterreich/plan_gewaesser_ngp/wasserwirtsch_planung/wasserkatalog.html

http://www.bmlfuw.gv.at/wasser/wasser-oesterreich/plan_gewaesser_ngp/wasserwirtsch_planung/wasserkatalog.html

http://share-alpinerivers.eu/tools-and-resources/pilot-case-studies/SHARE_WP4%204_Kokra_MCA%20tree-alternatives_170810_.pdf/view



http://www.seehydropower.eu/download_deliverables/

http://www.seehydropower.eu/download_deliverables/

Steele K, Carmel Y, Cross J, Wilcox C (2009) Uses and misuses of multicriteria decision analysis (MCDA) inenvironmental decision making. Risk Anal 29:26–33

Supriyasilp T, Pongput K, Boonyasirikul T (2009) Hydropower development priority using MCDM method.Energy Policy 37:1866–1875

Swiss Confederation (2011) Recommendations for developing cantonal conservation and exploitation strategiesfor small hydropower plants Available from: http://www.environment-switzerland.ch/ud-1037-e [Accessed10 August 2014]

Tavzes B, Urbanič G (2009) New indices for assessment of hydromorphological alteration of rivers and theirevaluation with benthic invertebrate communities: Alpine case study. Rev of hydrobiol 2:133–161

Tsakiris G, Spiliotis M (2011) Planning against long TermWater scarcity: a fuzzy multicriteria approach. WaterResour Manag 25(4):1103–1129

Tuhtan JA, Noack M, Wieprecht S (2012) Estimating stranding risk due to hydropeaking for juvenile Europeangrayling considering river morphology. KSCE J Civ Eng 16(2):197–206

Walder C, Litschauer C (2010) Ökomasterplan - Stufe II - Schutz für Österreichs Flussjuwele (Eco Master Plan –Phase II – Protection of Austrian River Juweliers). WWF Österreich Available from: http://www.oekomasterplan.at/ [Accessed 10 August 2014]



http://www.environment-switzerland.ch/ud-1037-e

http://www.oekomasterplan.at/

http://www.oekomasterplan.at/

Šantl et al_2015_hydropower suitability analysis on a large scale

Documents