Journal of Hydrology 388 (2010) 41–51

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Journal of Hydrology

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Establishing environmental flow regimes in a Mediterranean watershed basedon a regional classification

Jorge Alcázar *, Antoni PalauDepartment of Environment and Soil Science, University of Lleida, Avda. Alcalde Rovira Roure 191, 25198 Lleida, Spain

a r t i c l e i n f o s u m m a r y

Article history:Received 6 March 2009Received in revised form 15 April 2010Accepted 18 April 2010

This manuscript was handled by L. Charlet,Editor-in-Chief, with the assistance of ShengYue, Associate Editor

Keywords:Environmental flowRegional stream classificationEbro

0022-1694/$ - see front matter � 2010 Elsevier B.V. Adoi:10.1016/j.jhydrol.2010.04.026

* Corresponding author. Tel.: +34 973702675; fax:E-mail addresses: jalcazar@macs.udl.es (J. Alcáz

(A. Palau).

Environmental flow estimation in regulated rivers has become a major issue for watershed managementin Mediterranean countries. The lack of regional environmental flow guidelines to protect ecological val-ues of rivers, especially when the environmental flows must be determined at many locations in largebasins, has led to the establishment of very simplistic criteria to fix instream flow needs in rivers. Thesesimple hydrological methodologies ignore the site-specific characteristics of natural systems, and there-fore should not be used for management practices when maintaining sustainable river ecosystems. Weprovide different methodological approaches to establish an environmental flow regime for regulated riv-ers at regional or watershed levels in both gauged and ungauged rivers, depending on the availability ofsite-specific empirical data and desired accuracy of the results. The types of selected approaches includesimple environmental flow values interpolation from calculated values at a discrete set of points withavailable data, and the development of environmental flow prediction models based on a previousstream-flow classification and regionalization process. A total of 51 hydrological and physical watershedcharacteristic variables were defined at 46 gauging stations. Variables were classified in five groups, and aPrincipal Component Analysis (PCA) was conducted on each group of variables in order to reduce thedimension of the data set. Identified PCA axes were used in a Cluster analysis to find similarities amongthe gauging stations. Results revealed four homogeneous regions in the Ebro watershed, where environ-mental flow values may be predicted from variables such as the flow equalled or exceeded 347 days peryear (Q347), or the average for the period of record of minimum mean monthly flows.

� 2010 Elsevier B.V. All rights reserved.

1. Introduction

The development of regional environmental flow guidelines toprotect ecological values and the integrity of rivers is becoming amajor issue in water resources management (Arthington et al.,2006). Maintaining sustainable river ecosystems is especiallyimportant in Mediterranean regions where rivers are highly regu-lated, water resources are limited, and there is a high variability intime and space of extreme rainfalls, floods and droughts, whichprovides differentiated flow regimes (e.g. Gasith and Resh, 1999;Goodess, 2003).

During the last few decades numerous methodologies havebeen developed to establish environmental flows in regulatedrivers. These methodologies can be grouped in four categories(Tharme, 2003) mainly based on the characteristics of the inputdata: hydrological or historic flow methods (based on the studyof historical flow regime records), hydraulic methods (based onthe study of the hydraulic geometry of stream channels), habitat

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+34 973702613.ar), antonio.palau@endesa.es

simulation methods (based on the simulation of physical habitats)and holistic methodologies (based on a global vision of the riverineecosystem addressing many of its components).

All these methodologies have been the target of numerous crit-icisms in terms of subjectivity, arbitrariness, lack of scientific sig-nificance, or incompleteness, since many just established a fixedminimum flow ignoring the natural patterns of temporal variationof river flows (simple hydrological methodologies), which is now-adays regarded by scientists and managers as a key factor whenprotecting and preserving natural values of rivers (Richter et al.,1996; Poff et al., 1997; Arthington et al., 2006). Most well-estab-lished methods also require a large amount of data, either hydro-logical, hydraulic, or habitat preference data, which is not alwaysavailable and/or requires long-term intensive field work for thecorrect implementation of the methodology. Environmental flowsshould be calculated based on natural flow regimes, but this canonly be done using existing gauging stations and previously stud-ied cross-sections with unimpaired flow regimes, which makesdata availability an important issue. Most of these methods areconsequently applied locally, establishing environmental flowsfor a specific river or river reach with no provision being madefor the watershed or regional level.

42 J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51

The capability to extrapolate environmental flow values to riversections where the necessary computational data are not available,or an impaired flow regime exists, would be extremely useful forwatershed management. However, it is well known that differenttypes of natural flows (including flow variability as a key factor)are related to different ecological ecosystems (e.g. Poff et al.,1997; Biggs et al., 2005). Furthermore, extrapolation or generaliza-tion at regional or large scale basins does not allow managing forspecific characteristics of each river flow regime. So, if differentgroups of streams could be identified based on key attributesdescribing the natural flow regime patterns of the rivers includedin each class, they could be considered adequate regional manage-ment units (Arthington et al., 2006) for the application of environ-mental flow guidelines to protect ecological values and theintegrity of rivers. This procedure is also recommended in TheEuropean Water Framework Directive (2000/60/EC) (WFD) of theEuropean Parliament, which provides a framework for Communityaction in the field of water policy. The WFD promotes the estab-lishment of future environmental protection and conservationobjectives for specific reference communities by defined river-type, based on a previous river classification (OJEC, 2000; Baeza,2002; González del Tánago and García de Jalón, 2004).

There are many studies in the literature pertaining to stream-flow classifications developed worldwide for different purposes,which are based on different types of parameters. Olden and Poff(2003) provide a good review of hydrological indices used forstream-flow regime characterization and classification (e.g. Snel-der et al., 2005; Henriksen et al., 2006). Other non-hydrologicaltypes of variables, such as those related to soil type, vegetationcover, and geomorphology have been found related to low flowcharacteristics (e.g. Nathan and McMahon, 1992; O’Shea, 1995; La-cey and Grayson, 1997) and have also been used for identifyinghomogeneous regions containing streams with similar flow re-gimes (e.g. Huang and Ferng, 1990; Andrade, 1999; OPHCHE,1999; Renschler et al., 2001). Some recent studies specifically sta-ted as their river-classification goal the characterization of lowflows and environmental flows (e.g. Alves et al., 2002; Bérubéet al., 2002; Baeza and García de Jalón, 2005). Nevertheless, theseclassifications could not be generalized to different regions sincethey were focused on specific species conservation objectivesand/or proposed arbitrary minimum flows based on simple hydro-

Fig. 1. Location of selected gauging

logical rules. Since they did not respond to natural flow regime pat-terns, they could not be considered adequate to preserve thestructure and function of riverine ecosystems (Arthington et al.,2006).

The objective of this study was to provide different methodo-logical approaches to establish an environmental flow regime forregulated rivers: the largest scale approach included the wholestudy area, i.e. the Ebro river watershed (watershed level); themedium scale focused on large regions or sub-basins within thestudy area with similar hydrological characteristics (homogeneousregion level); and the most detailed resolution was for specific riv-ers or river reaches with particular or local hydrological character-istics (specific river reach or local level). The availability of site-specific empirical data and desired accuracy of the results willdetermine the type of approach selected. In addition to directapplication of a standard environmental flow methodology whendata is available in gauged rivers (specific river reach or local level,although this approach will not be discussed in the study), othermethodological approaches are provided to establish environmen-tal flows at geographical larger scales (both gauged and ungaugedrivers). In the next sections of the paper two large scale approacheswill be described: (1) the identification of some homogeneous re-gions (watershed classification or regionalization process) basedon hydrological and physical parameters, and the development ofenvironmental flow prediction models (regression models relatingenvironmental flows to different variables) for each region (homo-geneous region level); and (2) a simple environmental flow valuesinterpolation (watershed level). Natural patterns of temporal flowvariation and site-specific characteristics of natural systems wouldbe taken into account at regional or watershed level in both gaugedand ungauged rivers. The Basic Flow Method (Palau, 1994; Palauand Alcázar, 1996; Alcázar, 2007) was the hydrological tool chosento manage rivers following natural patterns of spatial and temporalvariability of flow regimes.

2. Material and methods

2.1. Study area

The Ebro river basin was chosen as the study area. It is locatedin the Northeastern region of Spain, and has a total catchment area

stations at the Ebro river basin.

J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51 43

of 85,362 km2. Multiple demands and uses (irrigation, urban/domestic uses, hydropower generation, etc.) have converted it toa highly regulated watershed over past decades. It is the largestriver in Spain in terms of discharge, with an average flow of430 m3/s at the river mouth. The total length of the river is 910 km.

A total of 46 gauging stations were selected out of the 240 exist-ing within the watershed, all of them presenting natural flow re-gimes with continuous and reliable daily flow records (at least a10-year flow record). The selected gauging stations are randomlydistributed along the high-medium sections of the rivers (Fig. 1)on both banks (27 stations on the left bank and 19 stations onthe right bank), so they were considered representative of thehydrological variability that could exist within that basin area.

2.2. Environmental flow methodology and variables used inregionalization and model development

The Basic Flow Method (Palau, 1994; Palau and Alcázar, 1996)was chosen to determine environmental flow values for two mainreasons. First, it does not provide an arbitrary minimum flow value,but an environmental flow regime that comprises a spatial andtemporal variability similar to that of the natural flow regime, akey factor when organizing and defining the structure and func-tioning of river ecosystems (Poff et al., 1997). Second, it is a meth-odology that has gradually gained recognition in Spain in pastyears and has been applied in numerous Spanish river sections,especially at the interior Catalonian Watersheds in NortheasternSpain (Agència Catalana de l’Aigua, 2005).

The environmental flow regime proposed by the Basic Flowmethodology defines and characterizes the following stream-flowvariability aspects, among others (Palau, 1994; Cachón et al.,2002; Alcázar, 2007):

Basic flow (Qb). It is the absolute minimum flow that should becirculating within the river anytime. Its calculation is based onthe study of irregularities on the hydrological series of dailymean flows (see Palau and Alcázar, 1996; Alcázar et al., 2008,for further details). It includes the application of a moving aver-age forecasting model to increasing intervals of consecutivedata (mean daily flows), identifying the minimum value foreach interval (i.e. minimum average flow for 1, 2, 3, . . . , 365consecutive days). The highest relative increment betweentwo consecutive increasing intervals of minimum average flowsidentifies the end of the longest low flow period and defines Qbvalue for each year. The final operational Qb value is calculatedas the average of the annual Qb when repeating this process foreach year in a period of at least 10 years (in our study case from1985 to 1995).Maintenance flow (Qmt). It is the actual stream-flow in amonthly or seasonal basis proposed in the regulated flowregime. It follows monthly or seasonally natural flow variability(it is calculated applying natural flow variability to Qb), and itsminimum value corresponds to Qb.Bankfull flow (Qg). It is the flow that transports the greatestamount of material over time (Leopold, 1994) and therefore, itmaintains channel shape and structure. It is calculated fromthe 1.5–2.3 year recurrence interval flow in the natural regime(although it can go up to 7 year recurrence interval in Mediter-ranean rivers; Martín-Vide, 1997), reduced accordingly to theenvironmental flow regime conditions.Maximum rate of flow change. It refers to how quickly flowchanges from one magnitude to another, and it is obtained fromflow changes in the natural regime.

Most of the factors described above are based or have to be cal-culated from the Qb value, which is considered the key factor in the

methodology. Thus, the procedure proposed in this paper focuseson the determination of Qb values at the watershed or regional le-vel. The environmental flow regime for each river or river reachcould be easily derived from a Qb which has been either calculatedfrom available hydrological data or predicted from Qb predictionmodels as described in following sections (in gauged and ungaugedrivers respectively).

Since the database of the study area included rivers with variedhydrological characteristics, a small number of basic descriptorswould not be enough to characterize their hydrology (Grownsand Marsh, 2000). A large dataset of 51 variables potentially influ-ential on Qb values was used for the regionalization and predictionmodel development (Table 1). Although some variables were spe-cifically defined for this study (such as variables X28–X30 in Table1 describing the shape of flow-duration curves), most of them werehydrological variables selected from bibliographical references,where they were either cited as parameters that are or can be re-lated to the determination of minimum instream flow in rivers,or cited as variables commonly used to describe hydrological char-acteristics of rivers and river basins. The dataset also includedphysical parameters describing basin characteristics (size, altitude,surface and type of vegetal coverage, etc.) since they have beenfound to be highly related to hydrological characteristics in flowprediction models (e.g. Dawson et al., 2006). Variables weregrouped in five classes describing physical watershed characteris-tics (variables X12–X20, X50 and X51), general hydrological flowregime characteristics (variables X1–X9, and X21–X24), and alsoflow-duration curves (variables X25–X30), low flow events statis-tics from flow-spell analysis (variables X31–X43), and flow vari-ability (variables X44–X49) as important aspects of the naturalflow regime influencing environmental flows.

Qb values as well as the 51 hydrological and physical parametervalues were calculated from alphanumeric and cartographic infor-mation of the Ebro river basin, available from the HydrologicalPlanning Office of the Confederación Hidrográfica del Ebro (CHE)(http://oph.chebro.es). This site contains files with information ofthe Ebro river basin in compatible format with commercial geo-graphic information systems, and this source was used to calculateall variables for the 46 gauged stations in the present study, byusing the latest available 10-year period of records.

2.3. Regionalization of stream-flow regimes

Principal component analysis (PCA) was applied to the 51 data-base indices (using the 51 by 51 correlation matrix as the input tothe PCA) to identify patterns in gauging stations with similarhydrological characteristics. Separate PCA were performed for eachof the five groups of variables, reducing the dimension of the dataset by retaining those characteristics that contribute most to itsvariance (i.e. lower-order principal components, usually containingthe most important aspects of the data). This technique allowsminimizing redundancy and multicollinearity among the indicesused in the analysis (Olden and Poff, 2003).

So, data were transformed to a new orthogonal coordinate sys-tem (each of the lower-order principal components identified forthe five groups of variables), such that the greatest variance byany projection of the data comes to lie on the first coordinate(called the first principal component), the second greatest varianceon the second coordinate, and so on.

A hierarchical cluster analysis using Ward’s procedure (Ward,1963) was then carried out to sort gauging stations and groupthem into clusters of relatively homogeneous stations with similarhydrological regimes. The new variables used in the cluster analy-sis were the principal component axes identified in the PCA foreach group of initial indices, using the loadings of the original dataof each station on each significant principal component as the new

Table 1List of the complete database variables used for the analysis. Variables were calculated for the period of records from 1985 to 1995.

VariablesX1: Mean annual flow (m3/s)X2: Absolute minimum flow (m3/s)X3: Ratio of the mean annual flow to the drainage areaX4: Predictability, as a measure of the relative certainty of knowing a state at a particular time (Colwell, 1974). It is the sum of two components, constancy and

contingencyX5: Constancy, as a measure of the degree to which the state stays the same (Colwell, 1974)X6: Contingency, as a measure describing how closely the different status corresponds to different time periods (Colwell, 1974)X7: Ratio of the constancy to the predictabilityX8: Baseflow index, calculated as the ratio (in percentage) of the lowest mean monthly flow to the mean annual flow (Gordon et al., 1992)X9: Ratio Q90%/Q50%, used as an index of base flow contribution (Gordon et al., 1992)X12: Drainage area in km2.X13: Mean catchment slope (%)X14: Mean catchment altitude (m)X15: Gravelius shape index (Bendjoudi and Hubert, 2002)X16: Drainage densityX17: Maximum soil moisture capacity in mm, based on soil uses map in Spain (MMA, 1998)X18: Maximum soil infiltration capacity in mm, based on soil geology (MMA, 1998)X19: Orographic index, defined as the mean catchment altitude times the ratio of the mean catchment altitude to the orthogonal projection of drainage area (Martonne,

1940, cited by López and Blanco, 1976)X20: Ratio of the difference in elevation between highest and lowest point in the catchment to the drainage areaX21: Month of highest mean flowX22: Average of maximum monthly flows (m3/s)X23: Month of lowest mean flowX24: Average for the period of record of minimum mean monthly flows (m3/s)X25: Flow equalled or exceeded 10 days per year (m3/s)X26: Flow equalled or exceeded 90 days per year (m3/s)X27: Flow equalled or exceeded 347 days per year (Q347) (m3/s)

X28–X30: Variables describing flow-duration curves, defined as: X28 ¼ N1�N2N2�N3

X29 ¼ N1N2

and X30 ¼ N2N3

where: N1 ¼ Q5�Q2520

N2 ¼ Q25�Q7550

N3 ¼ Q75�Q9520

X31–X36: Number of times that the stream-flow is continuously below the 5% (X31), 10% (X32), 20% (X33), 40% (X34), 60% (X35) and 80% (X36) of mean annual flow forthe period of record (1985–1995) (Gordon et al., 1992)

X37–X42: Spell duration (from flow-spell analysis) defined as the length of time (days) that the stream-flow is continuously below the 5% (X37), 10% (X38), 20% (X39),40% (X40), 60% (X41) and 80% (X42) of mean annual flow (Gordon et al., 1992)

X43: Deficiency volume (from flow-spell analysis) defined as the amount of water (m3) which would be required to keep the stream at 5% of mean annual flow (Gordonet al., 1992)

X44: Coefficient of variation of daily flows for the 10-year periodX45: Average of coefficient of variation of daily flows for each yearX46: Average of standard deviation of daily flows for each yearX47: Coefficient of variation of mean annual flow

X48: Variability index as proposed by Confederación Hidrográfica del Ebro, obtained from flow-duration curves as follows:ffiffiffiffiffiffiffiffiffiffiQ25%Q75%

q

X49: Variability index as proposed by Growns & Marsh (2000): Q10%�Q90%median

X50: Land cover index, calculated as the percentage of agricultural surface plus half of the percentage of forest surface in the basin (forest surface reduces waterresources in the basin up to 50%, Hobart et al., 2000)

X51: Percentage of forested land in the drainage area

Variables X10 and X11 had missing data and were excluded.

44 J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51

coordinate system. Distances between the stations were measuredand stations situated closest to each other were grouped togetherinto clusters, which were then generated minimizing the squaredEuclidean distance to the center mean. Those statistical treatmentsclassified stations in groups showing similar stream-flow regimesand watershed characteristics.

Homogeneous regions within the Ebro river basin were thenidentified from the grouped gauging stations. Each station was as-signed to its catchment area or to the drainage basin within whichit is located. This process was possible given that the characteris-tics used for classification were relatively homogeneous within up-stream catchments and no further analyses were needed. So, ahomogeneous region was identified from each cluster of gaugingstations with similar characteristics, even though for some groupsregions were not contiguous when composite of several basins(Andrade, 1999).

2.4. Environmental flow prediction model development

A multiple regression analysis was conducted in each homoge-neous region in order to identify the best explanatory variables of

Qb (response variable). The dataset had two main singularities: thelarge number of variables related to the number of observations(records), and missing values of some variables for a few numberof stations. So, in order to optimize the size of the dataset in eachstep, a step by step correlation analysis was performed (forwardselection) instead of using standard procedures from a statisticalpackage which only use the stations with no missing values forthe process. First, a correlation analysis between Qb and each ofthe 51 variables of the dataset was used to reveal the first variableincluded in the prediction model, as the variable showing the high-est relationship with Qb. After that, successive partial correlationanalysis between Qb and the rest of the variables were performed(removing, then, the effects of the variables already selected) to in-clude parameters in the model, until the maximum Qb is ac-counted for by the variation in the minimum number ofpredictor variables (i.e. applying parsimony principles; Kotz andJohnson, 1985).

Finally, a general approach to Qb determination is provided as asimple and practical, but still a very helpful tool in regional waterresources management. A first order of magnitude of Qb valueswere developed from calculated Qb values at the gauging stations

J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51 45

(linear interpolation) using the inverse distance weighted (IDW)interpolation technique (Shepard, 1968).

3. Results and discussion

3.1. Watershed regionalization

The Ebro river basin presents a great territorial variety in termsof geomorphology, climate, soil uses, etc. (Frutos et al., 2004;

Table 2Results from the principal components analysis on the correlation matrix of the 51 varialoadings on each statistically significant principal component as well as the percentage ostations).

Group of variables Variables with largest loa

Physical watershed characteristics PC I: X19, X20PC II: X17, X14, X13

Xi = 11; n = 46 PC III: X18, X51

General hydrological characteristics PC I: X24, X2, X1PC II: X7, X5, X8, X9

Xi = 13; n = 46 PC III: X4, X6

Flow-duration curves PC I: X27, X26, X25PC II: X29, X30

Xi = 6; n = 46 PC III: X28

Low flow events statistics (spell analysis) PC I: X41, X42, X40, X39,PC II: X33

Xi = 13; n = 46 PC III: X43

Flow variability PC I: X45, X44Xi = 6; n = 46 PC II: X46, X49, X48, X47

-1

-1

PC 2

PC 1

-1

-1

PC 3

PC 2

6 22 1

1

2

2

23

21

3

3

4

4

5

5

6

8

7

7

8 9

9

22

21

23

24

24

Fig. 2. Results of the PCA applied to the group of variables describing the general hydrotriangles) and their loadings for the first three principal components, as well as the gau

Batalla et al., 2004) that makes it necessary to apply a previousclassification of homogeneous regions as a first step to any attemptof analysis and generalization of hydrological parameters, such asthe environmental flows, at the watershed level.

The results of the PCA showed that for the five groups of vari-ables, the first two or three (depending on the group) principalcomponents (PC I, PC II, and PC III) were statistically significantand explained most of the total variance in variables (Table 2).

So, three PC axes have been identified as the groups of variablesdescribing the physical characteristics of the watershed (describing

bles (divided into five groups) showing the variables exhibiting the largest absolutef variation explained. (Xi: number of variables included in each group; n: number of

dings on PC % of variation explained by principal components

322814Total 74352412Total 71392214Total 75

X38, X37 472510Total 824522Total 67

-1

-1

PC 3

PC 1 1 2

3

4

5

6

7

8

9

21

23 22 24

1

2

3

4

GROUPS OF STATIONS

logical characteristics of the flow regime showing the different flow variables (fullging stations classified in homogeneous groups.

46 J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51

32%, 28%, and 14% of the variance, for a total of 74% of variance ex-plained), general hydrological characteristics (describing 35%, 24%,and 12% of the variance, for a total of 71% of variance explained),flow-duration curves (describing 39%, 22%, and 14% of the variance,for a total of 75% of variance explained), and low flow events sta-tistics from flow-spell analysis (describing 47%, 25%, and 10% ofthe variance, for a total of 82% of variance explained). In the groupof variables describing flow variability only two statistically signif-icant PC axes were identified, describing 45% and 22% of the vari-ance, for a total of 67% of variance explained. The variables withthe largest loadings on each PC axis are shown in Table 2.

As an example, Fig. 2 shows the results of the PCA applied to thegroup of variables describing the general hydrological characteris-tics of the flow regime. The different flow variables (full triangles inFig. 2) are plotted as a function of its loadings for the first threeprincipal components, showing the relative strength (distancefrom the axes) and direction (i.e. either positive or negative fromthe quadrants that each variable lie within) of the relationship be-tween the variables and the different PC.

The PC I in Fig. 2 explained 35% of the variance, and classifiedthe gauging stations using the average for the period of record ofminimum mean monthly flows (X24), positively related to abso-lute minimum (X2) and mean annual flow (X1). Stations with highvalues on this PC axis show high mean annual flows, as well as highabsolute minimum and monthly flows. On the other hand, negativevalues on this PC axe are characteristic of stations with low flowregimes, and with absolute minimum and at least one minimummean monthly flow also very low.

The PC II explained 24% of the variance, and graded the stationsas a function of the constancy of the flows (X7 and X5), as well asthe base flow (X8 and X9). Stations showing flow regimes with var-iable flows within and between years, and low base flows, presenthigh positive values on this PC axis. Low and/or negative values onthis PC axe show flow regimes with high base flow values and, ingeneral, low variable flows.

Finally, the PC III explained 12% of the variance and sorted thestations using the predictability of the flows (X4) and, to a lesserdegree, the contingency (X6) (Colwell, 1974). Thus, stations withhigh values on this PC axe present a predictable flow regime, withseasonal flow variability which is the same for all years.

As an application of the analysis of this group of variables, anintermediate classification of the gauging stations can be done

Fig. 3. Results of the cluster analysis of the gauging stations using the principal compon

using the three PC axes identified as the new variables in a clusteranalysis. In this case, four groups of stations were identified usingonly the variables describing the general hydrological characteris-tics of the flow regime (Fig. 2):

– Group 1. Stations with low flow regimes characterized by intraand/or interannual flow variability, and low minimum flows.

– Group 2. Stations with medium flow regimes characterized bylow seasonal variability.

– Group 3. Stations with a flow regime characterized by constantflows, but which also may show small seasonal variations usu-ally appearing all years. Mean flows are relatively high, andthere are not very low flow values.

– Group 4. Only two stations are included in this group, showing aflow regime characterized by the highest mean and minimumflows, and also by a high flow variability within and amongyears.

The application of this procedure to the different groups of vari-ables may provide additional information about the distributionand grouping of stations.

The PC axes identified in the PCA for each group of initial vari-ables were then used in a cluster analysis to classify the gaugingstations in homogeneous groups with similar hydrological regimes.Since principal component axes are orthogonal, the new variablesused in the cluster analysis are relatively independent from oneanother within the single PCAs (Olden and Poff, 2003). Mahalan-obis distance was used in the cluster analysis to avoid multicollin-earity problems on the combined PCA scores. The cluster analysisrevealed four homogeneous groups of gauging stations, which in-clude three main geographically differentiated areas plus a smallwatershed with particular characteristics (Fig. 3):

– Group 1. It includes stations located in basins with high meanaltitude and fractured relief. Maximum soil moisture capacityis usually low, which is related to low vegetation coverage (usu-ally shrubby vegetation) and high runoff coefficient. Thesebasins are located at the upper reaches of the Ebro tributarieson the left bank (headwaters at the southern-facing slopes ofthe Pyrenees) (Fig. 4). Flow regimes are characterized by moreor less constant flows, with small seasonal variations usuallyrepeated every year. Mean flow values are relatively high (i.e.

ent axes identified in the PCA for each group of initial indices as the new variables.

N

EW

S

RegionsRegion 1

Region 2

Region 3

Region 4

Excluded area

Fig. 4. Homogeneous regions identified in the Ebro river watershed.

J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51 47

high values of the principal component associated to variablesexpressing mean flow conditions), and there are not usuallyextreme low flows.

– Group 2. It includes Stations located also up in the mountains,but with lower mean altitude than those in group 1. They arelocated at the head of the Ebro basin (southern-facing slopesof the Cantabrian Range), at the upper reaches of the Ebro trib-utaries on the right bank (headwaters at the northern facingslopes of the Iberian Massif), and right below the stations ofgroup 1 on the left bank of the Ebro river (Fig. 4). These stationsare located in two types of basins: large basins with humidAtlantic mountain climate, showing a flow regime characterizedby relatively high flows, low seasonal flow variability, and awell distributed relationship between the magnitude and fre-quency of daily streamflows (flow-duration curve), i.e. there isnot dominance either of flood flow or low flow events; and alsobasins with Mediterranean mountain climate showing a flowregime characterized by high variability of flows (even thoughthe coefficient of flow variation and the range of variation arerelatively small) and high frequency of low flow periods. Thebasins represented by these stations present high vegetationcoverage (usually forested), and therefore high maximum soilmoisture and infiltration capacities.

– Group 3. It includes stations with lower mean altitude than theother groups, and therefore located in basins below the ones ofgroup 2, showing high forested coverage and high maximumsoil moisture and infiltration capacities. These stations arelocated in Mediterranean mountain climate basins exhibitinga flow regime characterized by high variability of flows, witha high coefficient of flow variation and wide range of variation.It is common to find streamflows below 20% of mean annualflow and for long periods of time, together with a few largeflood flows.

– Group 4. It includes only two stations (located at the Ara river,tributary of the Cinca river at the left bank of the Ebrowatershed) which are different from the rest since they showa flow regime characterized by the highest mean and minimumflows, and also by a high flow variability within and amongyears, because the Ara is a braided river with deep gravel sys-tem and high hyporheic flows that in summer are dominantover surface flows. Streamflows are rarely below 20% of meanannual flow and for short periods of time. These stations are

located in basins with high maximum soil infiltration capacity(predominantly limestones and sandy soils) and a high forestedcoverage.

Before proceeding to identify homogeneous regions from thegrouped stations, the area of application of the results had to be de-fined. Since the gauging stations were not distributed throughoutthe entire Ebro watershed (they were located in the high-mediumsections of the basin, showing a non-altered flow regime), resultscan be generalized only to that distribution area. So, the territorywith an altitude lower than 468 m (this is the altitude of the lowergauging station) was excluded, as well as the rivers or reaches lo-cated downstream of large dams or any other flow regulation oralteration project (e.g. irrigation diversions, etc.). In this excludedarea (Fig. 4), flow regime and/or geometry of the riverbed are usu-ally altered, and therefore the use of only a hydrological method, asthe Basic Flow methodology, may not be adequate for environmen-tal flow estimation and other criteria should be included in thedecision support process (i.e. the establishment of environmentalflow regime should be site specific in a site-by-site study basis).

The regionalization of the Ebro river basin in homogeneous re-gions from the four resulting groups of gauging stations is shownin Fig. 4. Regions were determined by assigning each station tothe drainage basin within which it was located. By doing this wedid include all streams under natural flow regime in the applica-tion area. Thus, four homogeneous regions with similar hydrolog-ical regime and watershed characteristics were identified in thehigh-medium sections of the Ebro river, in order to develop strat-egies to establish environmental flows accordingly to the specificcharacteristics of the river reaches. On the left bank of the river,a first region is located at the headwaters of the southern-facingslopes of the Pyrenees, showing the highest altitude. Basins of re-gion 2 are located at the head of the Ebro basin (southern-facingslopes of the Cantabrian Range), as well as right below the region1 in a humid Atlantic mountainous area, occupying lower latitudeat the center of the watershed. Region 3 includes basins with loweraltitude (western part of the Ebro watershed) and/or Mediterra-nean mountain climate (eastern part of the watershed).

The same pattern of regions distribution can be found in theright bank of the Ebro river. Since the altitude of the Iberian Massifis lower than that of the Pyrenees, there is no representation ofregion 1. Basins of the region 2 are located at the head of the Ebro

48 J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51

basin and at the headwaters at the northern facing slopes of theIberian Massif. Region 3 includes basins situated right below thestations of region 2.

Finally, region 4 includes only one basin (the river Ara basin) lo-cated at the left bank of the Ebro river with particular flow regimecharacteristics, as specified in the group 4 of gauging stations.

3.2. Establishing environmental flow models

Results of the regression analysis conducted in each homoge-neous region between Qb and the 51 independent variables ofthe original dataset are shown in Table 3.

The results of the sequential procedure for entering variables inbuilding the linear regression models for the different identified re-gions are presented as follows.

Region 1Step 1The variable that has the largest correlation with the response

variable (Qb) is the flow equalled or exceeded 347 days per year(X27) (R = 0.99248; Pr = 0.0001; n = 6), which is entered into themodel:

Table 3Models for establishing environmental flows (Qb) in each homogeneous region.

Region 1

Qb ¼ 0:01902þ 0:33771X27 ðR2 ¼ 0:9857Þwhere: X27: flow equalled or exceeded 347 days per year (Q347)Region 2

Qb ¼ �0:42744þ 0:27411X27þ 0:31383X15 ðR2 ¼ 0:8611Þwhere: X27: flow equalled or exceeded 347 days per year (Q347)X15: Gravelius shape indexRegion 3

Qb ¼ 0:000969þ 0:86577X24 ðR2 ¼ 0:9435Þwhere: X24: average for the period of record of minimum mean monthly

flows

There is no model developed for region four since it includes only two gaugingstations.

Region 1

012345

0 0.5 1 1.5 2Qb (m3/s)

Q34

7 (m

3 /s)

Y=0.01902+0.33771X R2=0.9857

Fig. 5. Relationship between dependent and indepen

Region 3

0

0.05

0.1

0.15

0.2

0.25

0 0.05 0.1 0.15 0.2

Qb (m3/s)

Avg

e. o

f m

in.

mon

thly

flow

s (m

3 /s)

y=0.000969+0.86577x R2=0.9435

Fig. 6. Relationship between dependent and indepen

Qb ¼ 0:01902þ 0:33771X27 ðR2 ¼ 0:9857Þ ð1Þ

Step 2Mean catchment slope (X13) is the variable with the highest

partial correlation with the response variable Qb (R = 0.99511;Pr = 0.0049; n = 5); i.e., after Qb and all the variables not in theequation are adjusted for X27, Qb and the mean catchment slopeare the most highly correlated, and this variable, then, is enteredinto the model:

Qb ¼ �0:16841þ 0:336731X27þ 0:00748X13 ðR2 ¼ 0:9863Þð2Þ

When entering the mean catchment slope into the model theincrease of the coefficient of determination is very low, i.e. the in-crease of variability in Qb explained by the new model (Eq. (2)) isvery low respect to model in step1 with just one independent var-iable. Thus, since there is a very strong linear relationship betweenQb and X27 (R2 = 0.9857), in this region Qb can be accurately pre-dicted from the flow equalled or exceeded 347 days per year (Q347)(Eq. (1)), existing a direct relationship between the two variables(minimum environmental flows directly depend on the flow equal-led or exceeded 95% of the time). Fig. 5 shows how Eq. (1) fits therelationship between Qb and X27.

Region 2Step 1The variable that has the largest correlation with the response

variable is the flow equalled or exceeded 347 days per year (X27)(R = 0.89377; Pr = 0.0001; n = 24), which is entered into the model:

Qb ¼ 0:07041þ 0:25755X27 ðR2 ¼ 0:7988Þ ð3Þ

Step 2The variable with the highest partial correlation with the re-

sponse variable Qb is the Gravelius shape index (X15)(R = 0.59703; Pr = 0.0034; n = 23), and it is entered into the model:

Qb ¼ �0:42744þ 0:27411X27þ 0:31383X15 ðR2 ¼ 0:8611Þð4Þ

Region 1

0

2

4

6

Flow

(m3 /

s)

Qb X27

dent variables in model developed for region 1.

Region 3

0

0.05

0.1

0.15

0.2

Flow

(m3 /

s)

Qb X24

dent variables in model developed for region 3.

J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51 49

Step 3The variable with the highest partial correlation (after Qb and

all the variables not in the equation are adjusted for X27 andX15) with the response variable Qb is the contingency (X6)(R = 0.59703; Pr = 0.0034; n = 23). But X6 shows a low correlationwith Qb and a probability >0.05, so it does not enter into themodel.

Thus, in region 2 Qb can be predicted from the flow equalled orexceeded 347 days per year (X27) and the Gravelius shape index(X15) with high precision (R2 = 0.8611) (Eq. (4)). If we apply theprinciple of parsimony, we can go for a simpler model and Qb

0 - 0.016

0.016 - 0.033

0.033 - 0.049

0.049 - 0.099

0.099 - 0.181

0.181 - 0.247

0.247 - 0.33

0.33 - 0.379

0.379 - 0.495

0.495 - 0.742

0.742 - 0.989

0.989 - 1.484

1.484 - 3.1

3.1 - 4.205Excluded area

Qb values (m3/s)

Fig. 7. Interpolated Qb values from the Qb values calculate

can be predicted from just one variable (flow equalled or exceeded347 days per year, as in Eq. (3)) still with adequate precision(R2 = 0.7988). In this case, minimum environmental flows directlydepend on the flow equalled or exceeded 95% of the time as in re-gion 1.

Region 3Step 1The variable that has the largest correlation with the response

variable is the average for the period of record of minimum meanmonthly flows (X24) (R = 0.97132; Pr = 0.0001; n = 14), which isentered into the model:

N

EW

S

d at the gauging stations in the Ebro river watershed.

50 J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51

Qb ¼ 0:000969þ 0:86577X24 ðR2 ¼ 0:9435Þ ð5Þ

Step 2The variable with the highest partial correlation with the re-

sponse variable Qb is the flow equalled or exceeded 90 days peryear (X26) (R = �0.67213; Pr = 0.0333; n = 11), and it is enteredinto the model:

Qb ¼ 0:00247þ 0:97421X24þ 0:00382X26 ðR2 ¼ 0:9453Þ ð6Þ

This case is similar to region 1, and the second variable enteringinto the model (Eq. (6)) does not significantly increase the ex-plained variability in Qb respect to the one-variable model (Eq.(5)). So, Qb can be accurately predicted (R2 = 0.9435) from the aver-age for the period of record of minimum mean monthly flows(X24) through a linear function relating both variables (i.e. Qb ishigher when the minimum average monthly flows are usually highfor the considered period of record, which is a reasonable resultsince in that circumstances river communities are not used to longperiods with very low flows). Fig. 6 shows how Eq. (5) fits the rela-tionship between Qb and X24.

Region 4This homogeneous region includes just two gauging stations lo-

cated in the Ara river basin, and therefore it was not possible to ap-ply regression analysis in order to develop a prediction model withjust such a small number of observations. Since this region is madeup of a small drainage area (Fig. 4), Qb values can be easily pre-dicted by extrapolating the values calculated at the two existinggauging stations.

3.3. Practical approach to environmental flow determination atwatershed level

Some of the variables included in the original database (51 vari-ables) were not directly obtainable from general hydrological dat-abases, but required complex calculations from basic hydrologicalinformation. So, in order to estimate environmental flow values inungauged sites, a simple and practical approach to Qb determina-tion was developed by interpolating from calculated Qb values atthe existing gauging stations.

The interpolating surface is a weighted average of the scatterpoints (Qb values at the gauging stations) where weight assignedto each scatter point diminishes as the distance from the interpo-lation point to the scatter point increases. A maximum distance toany scattered point of influence was set to 75 km, since it wasapproximately the maximum distance between any pair of se-lected gauging stations. This maximum distance did not allowany influence on the Qb estimation at any given point from gaug-ing stations located on the other bank of the Ebro river, i.e. on theother side of the excluded area of application of results (Fig. 7).

Fig. 7 shows the results of the interpolation analysis, where Qbvalues have been estimated in the high-medium Ebro riverwatershed without reference to any calculation process based onhistorical daily flow records.

4. Conclusions and management implications

Implementation of regional environmental flow guidelines forthe protection and restoration of river ecosystems is a major issuethat has to be addressed by water agencies when managing waterresources. Hydrological watershed regionalization and modeldevelopment procedures described above provide an approach toenvironmental flow determination at three different scales:

1. The regional or watershed level. Environmental flow values atany river or river reach can be addressed by interpolating the val-ues calculated from a discrete set of points where ecological dataare available (i.e. inverse distance weighted interpolation). This

simple procedure will provide managers and potential water re-sources stakeholders with a first order of magnitude estimate ofthe amount of water that a river needs to preserve its ecologicalintegrity and, therefore, will provide a helpful tool in regionalwater resources management. Values obtained by interpolation,however, are often less meaningful, and are subject to greateruncertainty than calculated values, which leads to the secondapproach.

2. The homogeneous region level. The development of environ-mental flow prediction models in hydrological homogeneous re-gions would increase the accuracy when establishingenvironmental flow regimes. A previous regionalization shouldbe done based on variables describing the different stream-flowand watershed characteristics related to environmental flow re-gimes. The identified homogeneous regions would reflect the attri-butes describing the natural flow regime patterns of the riversincluded in each class. Models can then be developed within eachregion to predict environmental flows where the necessary compu-tational data are not available, from a reduced number of variableswhich can be easily estimated or synthesized from a calibratedrainfall–runoff model (Bradford et al., 2007).

In this study, four regions were identified in the high-mediumsections of the Ebro river watershed and, in most cases, Qb valuescould be predicted as a function of Q347 and average for the periodof record of minimum mean monthly flows. These results arecoherent with other studies and literature, since those variablesare usually related to instream flow needs and commonly usedin other hydrological methodologies for establishing environmen-tal flows (Alcázar, 2007). Furthermore, Qb values are higher whenthe low flow regimes are usually high for the considered period ofrecord, which is satisfactory for the riverine ecosystem since inthat circumstances river communities are not used to very lowflows for long periods of time. So, measuring or modelling justone or two parameters in order to calculate environmental flowsinstead of following a calculation process based on reliable dailyflow records (sometimes unavailable) can be considered an advan-tage in watershed management, contributing positively to environ-mental flow operational application (Alcázar et al., 2008).

Predicted environmental flows will gather general hydrologicalregime patterns of the rivers included in each group, but not thespecific hydrologic response patterns for each river, which leadsto the third approach.

3. The specific river reach or local level. In rivers or river reacheswith gauging stations or ecological data available, minimum envi-ronmental flows can be established based on specific ecohydrolog-ical characteristics and, therefore, it would allow us to manage foreach river’s natural flow regime individualities.

Once the minimum environmental flow (Qb) is established, anenvironmental flow regime can be defined to maintain the naturalhydrologic variability of streamflows, which is critical for conserv-ing stream ecosystems. The environmental flow regime is based onminimum environmental flow (Qb) and also includes other charac-teristic parameters of the stream-flow regime, such as seasonalvariability (maintenance flow, Qmt), bankfull flow (Qg), and max-imum rate of flow changes, as described earlier in the Materialand Methods section. These other flow metrics can be either calcu-lated or derived from Qb and original data or regional characteris-tics (at local or homogeneous region levels respectively), orinterpolated from values calculated at existing stations with dataavailability (at regional or watershed level).

The procedure described in this paper can be considered a help-ful tool in regional water resources management for water agen-cies and potential water resources stakeholders. It provides anestimation of environmental flow values at the watershed levelovercoming limitations of simplistic hydrological rules proposedby other methodologies. The first step when managing water

J. Alcázar, A. Palau / Journal of Hydrology 388 (2010) 41–51 51

resources at large scales for environmental flows is the establish-ment of indicative environmental flow values at any point in themanaged area. This can be addressed by interpolating the environ-mental flows previously calculated at specific points (usually gaug-ing stations) with available data enough to apply a detailedmethodology (the regional or watershed level approach). Whenhigh accuracy is required on environmental flow values, the envi-ronmental flow methodology should be directly applied to specificrivers or river reaches, so individualities of natural flow regime areconsidered. If no hydrological data are available or the collection ofempirical data is not possible, environmental flow prediction mod-els should be developed in homogeneous regions (hydrological re-gions grouping rivers exhibiting similar hydrological responsepatterns) based on a reduced number of variables easily estimated.

Results provided by this study should be taken into account andapplied focused on the analysis of the proposed methodologyrather than on the models developed and specific parameters in-volved in the models. Updated daily flow data records should beused to properly apply the results of the described procedure.

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