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A critical review of pairedcatchment studies withreference to seasonal flows and climatic variabilityAlice Best, Lu Zhang, Tom McMahon, Andrew Western, Rob Vertessy

A critical review of pairedcatchment studies withreference to seasonal flows and climatic variabilityAlice Best, Lu Zhang, Tom McMahon, Andrew Western, Rob Vertessy

Authors: Alice Best1,2,3, Lu Zhang1,3, Tom McMahon1,2, Andrew Western1,2, Rob Vertessy1,3

1. Cooperative Research Centre for Catchment Hydrology.2. Department of Civil and Environmental Engineering,

University of Melbourne.3. CSIRO Land and Water, Canberra.

CSIRO Land and Water Technical Report 25/03CRC for Catchment Hydrology Technical Report 03/4MDBC Publication 11/03

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Acknowledgements

This work was funded by the Murray-Darling Basin Commission through SI&E Grant NumberD2013: “Integrated Assessment of the Effects of Land use Changes on Water Yield and SaltLoads”, and the Cooperative Research Centre for Catchment Hydrology.

State Forests—NSW, CSIR South Africa and Manaaki Landcare Research kindly provided uswith paired catchment data from Redhill, South Africa and New Zealand respectively. We thankSandra Roberts and Peter Richardson from their helpful comments on this review.

The senior author was supported by a University of Melbourne Research Scholarship, and theCRC for Catchment Hydrology.

A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYi

A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYii

Executive summary

Since European settlement in Australia, large-scale clearing of native vegetation for agriculture has caused alterations in the hydrologic regime of many Australiancatchments. The Forest Plantations 2020Vision states that by 2020 the area of treeplantations within Australia will treble. If implemented, this will impact on water yieldat both local and regional scales. Pairedcatchment studies have been widely used as a means of determining the magnitude ofwater yield changes resulting from changesin vegetation and they provide a possiblemeans of predicting the likely impacts ofbroad-scale vegetation changes on water yield.

This review focuses on the use of pairedcatchment studies as a means for

determining long-term changes in water yieldas a result large scale changes in vegetation.Paired catchment studies can be divided intofour broad categories: afforestationexperiments, deforestation experiments,regrowth experiments and forest conversionexperiments. The methods used to assessthe magnitude of annual and seasonalchanges in water yield have been reviewedand implications for applying pairedcatchments results to large catchments,where the land use is likely to consist of amosaic of vegetation at different stages ofdevelopment, have been identified. Currentknowledge gaps in relation to the impacts ofbroad scale vegetation changes on flowregime and seasonal flows are highlightedand possible methods of addressing thesegaps are suggested.

A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYiii

Acknowledgements i

Executive summary ii

1. Introduction 1

2. Paired catchments 32.1 Methods used to determine annual changes in water yield 32.2 Methods for determining seasonal changes in water yield 32.3 Types of paired catchment experiments 4

3. Hydrological processes in relation to vegetation type 53.1 Precipitation 53.2 Evapotranspiration 5

3.2.1 Transpiration 63.2.2 Interception 6

3.3 Infiltration 63.4 Overland flow 63.5 Deep Drainage, base flow and recharge 73.6 Soil water storage 73.7 Summary of processes 7

4. Changes in water yield due to changes in vegetation type 84.1 Generalisations based on paired catchment data 84.2 Mean annual and annual water yield 114.3 Annual flow regime 15

4.3.1 Flow duration curves 154.3.2 High and low flows 164.3.3 Impact of vegetation changes on annual FDC,

high and low flows 164.4 Seasonal water yield and flow regime 18

5. Summary of limitation of paired catchments studies 225.1 Limitations in reported literature 225.2 Application to large catchments 22

5.2.1 Spatial issues 225.2.2 Climatic variability 23

6. Summary and conclusions 24

Reference list 25

Appendix A 31

Table of contents

A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYiv

A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY

Since European settlement in Australia, large-scale clearing of native vegetation foragriculture has caused alterations in thehydrologic regime of many Australiancatchments. In southern Australia salinity is recognised as one of the most seriousenvironmental degradation issues, affectingboth soil and water quality. The massiveclearing of native vegetation and itsreplacement by shallow-rooted annual cropsand pastures has caused salinity, through thereductions in evapotranspiration andincreases in groundwater recharge. Thedecrease in evapotranspiration is also likelyto lead to an increase in streamflow, whichnot only increases water supply, but alsohelps to dilute salt inflows (Zhang et al.1999). Plantations for Australia: The 2020Vision (DPIE 1997) states that by 2020 thearea of tree plantations within Australia willtreble. If implemented, this will impact onwater yield at both local and regional scales.The response of catchments to such a landuse change is likely to vary in both spaceand time and in order to develop sustainableland management options, it is necessary topredict the effects of such afforestation onwater yield and it seasonal variability.

Paired catchment studies have been widelyused as a means of determining themagnitude of water yield changes as a resultof changes in vegetation. A number of reviewarticles have summarised the results of thesestudies. Bosch and Hewlett (1982) reviewedcatchment experiments to determine theeffect of vegetation change on water yield.They updated an earlier review by Hibbert(1967) in which 39 experimental catchments,predominantly in the USA, were analysedand the following generalisation made:

1. reduction in forest cover increases water yield

2. establishment of forest cover on sparsely vegetated land decreases water yield

3. response to treatment is highly variableand, for the most part unpredictable.

Bosch and Hewlett (1982) added anadditional 55 catchments to those reviewedby Hibbert (1967). Two types of experimentswere reviewed—paired catchment studiesand time-trend studies—that providecircumstantial evidence of the influence ofcatchment management on water yield.While Bosch and Hewlett (1982) supportedthe first two conclusions made by Hibbert,their results indicated that to a certain degreethe influence of afforestation anddeforestation could be predicted. Since 1982a number of additional paired catchmentstudies have been reported in the literature.The results of some of these studies havebeen summarised in the subsequent reviewsof Hornmeck et al. (1993), Stednick (1996)and Sahin and Hall (1996). Vertessy (1999,2000) reviewed the literature available onpaired catchment studies with respect toforestry and streamflow. These two reviewsprovide a comprehensive summary of thepresent understanding of land use changeimpacts on water yield, with particularreference to Australian conditions. While theimpact of afforestation and deforestation onmean annual water yield is well understood,there is little reported in the literature onseasonal water yield and what has beenreported is mainly of a descriptive nature.

While results from the many pairedcatchment studies demonstrates that theycan be used to assess the impact of landuse change on water yield at the local scale,doubts exist about the application of pairedcatchment results to large catchment or aregional scale. On a regional scale, land usechanges are likely to be mosaics withvegetation at different stages ofdevelopment. Therefore uncertainty existsabout the application of small scaleexperimental results to large catchments(Wilk et al. 2001). An alternative method thatcould be used to assess the impacts ofvegetation changes on large catchments is the use of time-trend studies. However this requires the separation of the impact ofvegetation changes from climatic variability.Munday et al. (2001) used a general additivemodel (GAM) to assess to impact of a

1

1. Introduction

A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY2

mosaic of vegetation types (pine plantation,eucalypt forests and pasture) on water yieldin the Adjungbilly catchment in south easternAustralia. This model simulated the annualaverage water yield changes in response to the natural ageing of forests and to userdefined logging regimes. While a time-trendstudy with a good vegetation history wasused in this study, paired catchmentexperiments were used to gain anunderstanding of the impact of pineplantations and the stand age of nativevegetation on water yield.

The purpose of this report is to:

1. review the paired catchments methodsused to assess the magnitude of annualand seasonal changes in water yield thatcan be attributed to alterations invegetation type

2. investigate the possible methods forseparating the impacts of climaticvariability on water yield form the effectsof alterations in vegetation types

3. highlight the knowledge gaps in theliterature in relation to the impacts ofvegetation type on seasonal water yieldand flow regime

4. suggest how generalisation made frompaired catchments can be applied tolarge catchment with a mosaic ofvegetation types.

This review includes an additional 56 pairedcatchments on top on those reviewed byBosch and Hewlett (1982) bringing the totalnumber of paired catchment experimentsreviewed to 150. Details of theseexperimental catchments can be found in Appendix A.


2. Paired catchments

Paired catchment studies have been widelyused to assess the likely impact of land usechange on water yield around the world.Such studies involve the use of twocatchments with similar characteristics interms of slope, aspect, soils, area,precipitation and vegetation located adjacentto each other. Following a calibration period,where both catchments are monitored, oneof the catchments is subjected to treatmentand the other remains as a control. Thisallows the climatic variability to be accountedfor in the analysis. The change in water yieldcan then be attributed to changes invegetation. The paired catchment studiesreported in the literature can be divided into four broad categories:

(i) afforestation experiments;

(ii) regrowth experiments;

(iii) deforestation experiments; and

(iv) forest conversion experiments.

2.1 Methods used todetermine annual changesin water yield

Various methods have been applied in theanalysis of paired catchment data to assessthe impacts of vegetation changes on wateryield a various time scales. The mostcommonly used method is to produce a linearregression between the control and thetreated catchment for annual data collectedduring the calibration period (Hornbeck et al.1993). The regression equation is then used topredict the water yield that would haveoccurred in the treated catchment if thetreatment has not taken place. The differencein the observed and the predicted streamflowis then assumed to be due to land usechange as the method provides a control overclimatic variability (Bari et al. 1996). While themethod of linear regression is most commonlyused on annual data it has also been used onthe components of streamflow, the quick flowresponse and baseflow (Bari et al. 1996).

South Africa has a very comprehensive set ofpaired catchment studies that have been

used to assess the impacts of afforestationon water yield. A significant amount ofliterature is available on these catchments anumber of different methods have been usedto assess the impacts of afforestation onwater yield at an annual scale. The latestSouth African work is summarised in Scott et al. (2000) and provides details of all theafforestation experiments undertaken inSouth Africa. To predict the impacts ofafforestation on annual streamflow and thevariations on between years due todevelopment of plantations, Scott and Smith(1997) developed an empirical model thatpredicts the percentage reduction in wateryield with time. This work is further discussedin Section 4.

2.2 Methods for determiningseasonal changes in water yield

Seasonal or monthly analysis of pairedcatchments data is less common than annualanalysis. As with annual analysis the mostcommonly used method is to use standardlinear regression techniques on monthly data(making no adjustments for the serialcorrelation) to establish pre-treatmentrelationships between the control and thetreated catchments. Lane and Mackay (2001)adopted this method in their analysis of datain the Tantawangalo Creek catchments inNew South Wales as insufficient data wasavailable during the pre-treatment year to useannual data to develop the relationships.Scott and Lesch (1997) also used monthlydata in their analysis of the Mokobulaanexperimental catchments in South Africa. Toadjust for the serial correlation of monthlydata both streamflow and rainfall data wereincluded as independent variables in amonthly multiple regression. The rainfall termwas considered as part of an antecedentwetness index, which considered thewetness index for the previous month andthe rainfall in the present month. The analysisof Scott and Lesch (1997) looked at annualflows as well as wet and dry season flows.Watson et al. (2001) developed an improved


method to assess the water yield changesfrom paired catchment studies and appliedthese to the Maroondah experimentalcatchments in Victoria, Australia. Theyargued that the short pre-treatment periodsin most paired catchment studies, limits thestrength of the regression analysis andrecommended that monthly data with anexplicit seasonal component should be used.In a number of studies in south WesternAustralia, only three years of pre-treatmentdata have been used to generate the linearregression on annual flows (Ruprecht andSchofield 1989), casting doubt on thestrength of the correlation gained. Theadvantage of using monthly data is that thereare 12 times as many data points, than in theanalysis of annual data (Watson et al. 1999).However it is important to note that while the use of monthly data represents moreinformation if the serial correlation is treated,it does not represent 12 times the annual data.

2.3 Types of pairedcatchment experiments

The paired catchment experiments reviewedby Bosch and Hewlett (1982), Whitehead andRobinson (1993), Sahin and Hall (1996) andStednick (1996) focused mainly on regrowthexperiments, where harvesting of forests isundertaken followed by the regrowth of thesame vegetation type. While the activitiesinvolved in regrowth of vegetation may impacton the short-term water yield, permanentvegetation changes such as afforestation anddeforestation are likely to have a muchgreater long-term impact on streamflow andthe associated issues, such as salinity andwater resource security.

The paired catchment experiments reviewedin this report can by divided into four broadcategories.

1. Afforestation experiments—conversion of sparsely vegetated land to forest.Examples of these can be found in SouthAfrica (Scott et al. 2000), New Zealand(McLean 2001), Australia (Hickel 2001)and in the UK (Kirby et al. 1991, Johnson 1995).

2. Regrowth experiments—these look at theeffects of forest harvesting where regrowthis permitted. Experiments in this categoryconstitute the majority of the pairedcatchment studies worldwide. Theyinvolve the removal of vegetation from apercentage of a catchment followed byregrowth of the same vegetation type(Stednick 1996).

3. Deforestation experiments—the clearing ofdensely vegetated land to grass orpasture. Examples include the Colliecatchments in Western Australia(Ruprecht and Schofield 1989, Ruprechtand Schofield 1991a, Ruprecht andSchofield 1991b, Ruprecht et al. 1991,Schofield 1991).

4. Forest conversion experiments—thereplacement of one forest type withanother. This includes the conversion fromsoftwood to hardwood, deciduous toevergreen or pine to eucalypt. StewartsCreek provides an example of theconversion of native vegetation to pine inVictoria, Australia (Mein et al. 1988,Nandakumar 1993).

Vertessy (1999) highlighted some of theproblems with using regrowth experiments forestimating yield increases. Where a forestsare permitted to regenerate only the dataobtained in the first few years followingtreatment are used in building relationshipsbetween the percentage change in cover andthe change in yield. Three problems werehighlighted in relation to the use of such data:

• it takes time for a catchment to adjust itsrun-off behaviour following vegetationchange

• soil compaction and disturbance duringlogging and regeneration burning cantemporarily increase overland flow andchange the pattern of streamflow

• due to the short data set used to buildthe linear relationships that are used topredict water yield change, naturalvariability in the water yield data due toclimatic variability may have a stronginfluence on the results.

The results of various paired catchmentexperiments are discussed in Section 4,following a review of the major hydrologicalprocesses in Section 3.

3. Hydrological processes inrelation to vegetation type


The effects of two main vegetation types on components of the water balance arediscussed in the following section. The twomain vegetation types considered are grassor pasture and forests.

The water balance equation for a givencatchment can be written as:

P = ET + Q + D + ∆S (1)

where P is the precipitation, ET is the actualevapotranspiration, Q in the streamflow, D isthe recharge to the ground water and ∆S isthe change in soil water storage.

The evapotranspiration and streamflow termsin equation (1) can be rewritten as

ET = I + T + E (2)

where I = interception loss, T = transpirationand E = soil evaporation;

Q = OF + BF (3)

where OF = overland flow and BF = baseflow

The hydrological processes and waterbalance components discussed in relation to vegetation types are precipitation,evapotranspiration, interception, transpiration,soil evaporation, infiltration, overland flow,deep drainage, baseflow and recharge.

3.1 Precipitation

Precipitation is the largest term in the waterbalance equation and varies both temporallyand spatially (Zhang et al. 2001). Indiscussing the impact of vegetation type onprecipitation it is important to distinguishbetween gross precipitation and netprecipitation. Gross precipitation is theamount of rainfall (or snow) falling above thevegetation, while net precipitation is theamount of precipitation reaching the ground

surface. In most cases gross precipitationcan be assumed to be independent ofvegetation type (Calder 1998).

Calder (1999) suggests one of the mythsassociated with forests is that forestsincrease precipitation. In most cases it isreasonable to assume that vegetation haslittle or no influence on gross precipitation.However, in some instances there is evidencethat forests increase the amount of grossrainfall. It has been suggested that tall treesincrease the orographic effect, increasing theamount of gross rainfall. However anyincreases in gross rainfall are likely to beoffset by the increased rate ofevapotranspiration of these taller trees,resulting in an overall decrease in waterresources. On a continental scale it isthought that vegetation type may well impacton the amount of gross precipitation throughland-atmosphere feedbacks (Calder 1996).However, there is no data to show that thiseffect operates at the catchment scale.

3.2 Evapotranspiration

As described in equation (2)evapotranspiration can be divided into threecomponents: interception, transpiration andsoil evaporation. Evapotranspiration isdefined as the total process of water transferinto the atmosphere from vegetated landsurfaces. The two major components ofevapotranspiration (transpiration andinterception) are defined and discussed inSections 3.2.1 and 3.2.2. The total amountof evapotranspiration, under differentvegetation types is dependent not only onthe vegetation type, but also on the soil andclimate of the catchment (Calder 1999).

Changes in evapotranspiration due tochanges in vegetation can have a significantimpact on the water balance. For examplewhen comparing the annualevapotranspiration between forest and grassfor catchments with the similar rainfall,

Turner (1991), Holmes and Sinclair (1986)and Zhang et al. (1999) found that forestsconsistently had higher rates ofevapotranspiration than grass.

3.2.1 Transpiration

Transpiration is the process by which waterin plants is transferred to the atmosphere inthe form of vapour (Ward and Elliot 1995).The amount of transpiration differs withdifferent vegetation types and is controlled bythe physiological characteristics of thevegetation, with the majority of transpirationoccurring through the stomates, the smallpores in the leaf epidermis. The combinedeffect of large leaf area and more extensiveroot systems of forests compared to grass orpasture results in much greater transpirationrates (Ward and Elliot 1995). This largertranspiration rates of forests compared topasture is not only due to the increased leafarea, but is also due to the ability of forest toaccess deeper water stores.

3.2.2 Interception

Interception loss is the amount of gross rainfallintercepted by leaves or litter and evaporateddirectly back to the atmosphere. Water that iscaptured on foliage and evaporated does notcontribute to streamflow. Interceptions can bedivided into two types:

1. canopy interception

2. litter interception.

The amount of interceptions is largelydependent on the type of vegetation and onthe intensity, duration, frequency and form ofprecipitation (Dingman 1994). Interception isgenerally proportional to leaf area index (LAI)with forests having a larger LAI then shrubsor grasses. This combined with the greateraerodynamic roughness of forests leads togreater interception loss and reduction in net rainfall under forested conditions(Vertessy 2000).

Using a paired catchment study to determinethe impact of replacing native vegetation withpasture on a small catchment in southWestern Australia, Ruprecht and Schofield(1989) attributed the initial increase instreamflow (~13% of rainfall) to an decreasein the interception loss as a result of changesin vegetation type. Bari et al. (1996) also

observed this response in the March Roadcatchment in south Western Australia. Thedifference in interception between forest andgrass or pasture impacts on the waterbalance equation by affecting theevapotranspiration.

Afforestation, deforestation and forestconversion are all likely to alter the waterbalance through their influence on LAI andinterception loss.

3.3 Infiltration

Infiltration is the process by which waterarriving at the soil surface (after canopy andlitter interception) enters the soil (Dingman1994). The rate of infiltration is affected bythe initial water content and the permeabilityof the soil. Although the antecedent moistureand permeability are largely determined byrainfall and soil type, vegetation also affectssoil moisture levels through transpiration,interception and shading. Soil permeability is also impacted by vegetation through thecontribution of organic matter and number ofmacro and micro-pores that develop aroundthe roots.

The higher transpiration rates of forestsresults in the initial soil moisture contentbeing considerably lower than under crops orpasture (Ruprecht and Schofield 1989) whilethe nature of the root structure associatedwith forests increases the number of macro-pores and the amount of organic mattercontent under forested conditions leads tohigher rates of infiltration under forests thanunder grass or pasture (Mapa 1995).

3.4 Overland flow

Overland flow occurs when the soil issaturated either from above (Hortonian overland flow) or from below (saturation overlandflow) (Dingman 1994). Greater overland flowis generally observed from pastures thenfrom forests. Ruprecht and Schofield (1989)attributed increases in overland flow indeforested catchments to the largerpermanent groundwater discharge areas thatresulted as a consequence of vegetationremoval and decreased evapotranspiration. It is also possible that increased overlandflow occurs for pastures relative to forestsbecause of the changes to the infiltration


A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY

capacity of the soil. Scott and Lesch (1997)noted that the delayed recovery ofstreamflow after the clear felling of aplantation catchment in South Africa. Theyattributed the delayed response to the treestapping deep soil-water reserves reducingthe soil water storage below levels necessaryto generate streamflow, indicating that asaturation excess flow mechanism may alsooperate in this catchment.

3.5 Deep drainage, base flowand recharge

Deep drainage is the water that movesdownward through the soil profile below theroot zone that cannot be used by transpiredby plants (Ward and Elliot 1995). Where thereis no lateral flow of water between the rootzone and the water table, deep drainage isequivalent to the recharge to the groundwater table. Where lateral flow occursbetween the root zone and the water table aportion of deep drainage may contribute tobaseflow. Baseflow can therefore consist oftwo components: the discharge from thegroundwater table; and the lateral flow ofdeep drainage that becomes streamflow.

Deep drainage is generally greater beneathpastures than forests, as the deeper roots offorests and increased evapotranspirationutilises more water, reducing deep drainage.In an experimental catchment in Coweeta,USA, Burt and Swank (1992) observed thatwith the conversion of hardwood to grass theamount of low flow increased, particularlybaseflow. This has been observed almostuniversally in paired catchment studiesinvolving deforestation. The lowerevapotranspiration rates and shallower rootzones of short vegetation compared toforests results in an increase in the deepdrainage and baseflow.

3.6 Soil water storage

The last term in the water balance equationis the change in soil water storage. The soilwater storage represents the amount ofwater stored in the soil profile that can eitherbe transpired or that contributes to baseflow.Over long periods it is reasonable to assumethat changes in soil water storage arenegligible, if no change to vegetation typehas occurs (Zhang et al. 2001) the change in

soil water storage term in the water balanceequation can be ignored. However, on aseasonal basis the changes in soil waterstorage may be significant.

3.7 Summary of processes

The above discussion highlights the majorhydrological processes in relation tovegetation type. The general conclusions thatcan be drawn are:

1. Alterations to vegetation type on thelocal and catchment scale are not likelyto impact on gross precipitation;however, regional or continentalchanges to vegetation may alter grossprecipitation.

2. Interception loss is greater for forestthan for grasses or pasture. Underdeforestation it is likely that the initialincrease in water yield is due to areduction in interception loss.

3. Overland flow or quick flow is less likelyto be affected by changes in vegetationcover than baseflow.

4. Changes in water yield as a result ofchanges in vegetation, particularlypermanent vegetation changes are likelyto be reflected as changes to baseflow.Larger soil moisture stores andgroundwater reserves accumulated inresponse to removal of forest anddecreased evapotranspiration cause theobserved increase in baseflow.

5. Under most climatic conditions,evapotranspiration from forests will begreater than from grasses.

7


4. Changes in water yield dueto changes in vegetation type

Water yield changes have been reported atmean annual, annual and monthly temporalscales for paired catchment studies. Themajority are reported on an annual basis. Thefollowing section summarises the results forprevious reviews and uses specific examplesfrom Australia, South Africa and New Zealandto highlight some of the conclusions that canbe drawn from paired catchment studies.

4.1 Generalisations based onpaired catchment data

A number of reviews have been undertakento draw generalisations from pairedcatchment studies, particularly in reference tochanges in forest cover on water yield. Thefirst of these was by Hibbert (1967). In thisreview 39 experimental catchments werereviewed and the following conclusions weredrawn:

• reduction in forest cover increases wateryield

• establishment of forest cover onsparsely vegetated land decreaseswater yield

• the response to treatment is highlyvariable and, for the most part,unpredictable.

Bosch and Hewlett (1982) undertook at furtherreview of paired catchments and in reviewing94 experimental catchments, they concluded:

1. reducing forest cover causes as increasein water yield

2. increasing forest cover causes a decreasein water yield

3. coniferous and eucalypt cover types cause~40mm change in annual water yield perten per cent change in forest cover;

4. deciduous hardwoods are associatedwith ~25mm change in annual water yieldper ten per cent change in cover;

5. brush and grasslands are associated witha ~10mm change in annual water yieldper ten per cent change in cover;

6. reductions in forest of less than 20%apparently cannot be detected bymeasuring streamflow

7. streamflow response to deforestationdepends on both the mean annualprecipitation of the catchment and on theprecipitation for the year under treatment.

Figure 1: Yield increases following change in vegetation cover (after Bosch and Hewlett 1982). The points representthe maximum annual increase in water yield during the first five years after treatment for cover reduction experimentsand maximum decrease (within the time frame of the experiment) in water yield for afforestation experiments.


Figure 2: The distribution of water yield change after clear cutting of conifer and scrub (scaled to 100% reductionin cover), as a function of mean annual precipitation (after Bosch and Hewlett 1982).

Figure 1 shows the results of the Bosch andHewlett review relating the maximumincrease in water yield during the first fiveyears after treatment to percentage reductionin cover and the cover type. Figure 1illustrates that with an increase in thepercentage reduction in cover an increase inannual streamflow occurs.

To explain some of the within group variabilityevident in Figure 1, the results of pairedcatchment studies involving scrub andconifers were scaled to predict the wateryield increases that would have occurred if100% of catchments had been cleared andthese increase were plotted against the meanannual rainfall for the catchments (Figure 2).

From this work Bosch and Hewlett (1982)concluded that:

• water yield changes are greatest in highrainfall areas

• the effect of clear cutting is shorter livedin high rainfall areas due to the rapidregrowth of vegetation

• the annual change due to treatment inhigh rainfall areas appears to beindependent of the variation in rainfallfrom year to year

• changes in water yield are morepersistent in drier areas because of theslow recovery of vegetation, and arerelated to the precipitation in during theyear following treatment.

In order to include afforestation experiments in their analysis, Bosch and Hewlett (1982)assumed that the maximum decrease in wateryield was analogous to the first year increasein water yield for a deforestation experiment.This allowed general conclusions to be drawn.The use of maximum increase in water yield inthe first five years after treatment may alsointroduce bias into the results as themaximum is likely to be affected by climate.

The reviews of Hibbert (1969) and Bosch andHewlett (1982) mainly focused on catchmentsfrom temperate zone. Bruijnzeel (1988) lookedat the impacts of vegetation changes on wateryield, particularly dry season flows in thetropics. From this work it was concluded that:

• surface infiltration andevapotranspiration associated with therepresentative types of vegetation play akey role in determining what happens tothe flow regime after forest conversion

• if infiltration opportunities after forestremoval decrease to the extent that theamount of water leaving an area asquick flow exceeds the gain in baseflowassociated with decreasedevapotranspiration, then diminished dryseason flows will result

• if surface infiltration characteristics aremaintained the effect of reducedevapotranspiration after clearing willshow up as an increase in baseflow


• the effect of reforesting will not onlyreflect the balance between changes ininfiltration and evapotranspiration, butwill also depend on the available waterstorage capacity of the soil.

The conclusion that under deforestationeither a decrease or an increase in wateryield may occur, seems to conflict with manyof the results of paired catchment studies intemperate zones, in which increases inbaseflow is almost uniformly observed(Hornbeck et al. 1993).

Reviews by Stednick (1996) and Sahin and Hall (1996) expanded on the work byBosch and Hewlett (1982). Stednick (1996)reviewed result of studies from the UnitedStates and looked only at annual water yieldchanges as a result of timber harvesting.Their main focus was on the effect of thepercentage of area treated the ability todetect changes in streamflow. Differenthydrologic areas were defined based ontemperature and precipitation regimes and it was concluded that:

• in general, changes in annual water yieldfrom forest cover reductions of less than20% of the catchment could not bedetermined by streamflow measurement

• the rationalisation of data suggested thisvalue might change depending on thetemperature and precipitation of thearea. With a measurable increase instreamflow being observed fortreatments of 15% of the catchmentareas in the Rocky Mountains,compared with the Central Pains wheretreatment is required over 50% of thearea before changes in water yield canbe detected.

Sahin and Hall (1996) used a similarapproach to Bosch and Hewlett (1982) inthere analysis of 145 experimentalcatchments, dividing the vegetation typesinto broad categories (hardwood, conifer,conifer-hardwood, eucalypts, rainforest,scrub and grassland). However, instead ofusing the maximum increase in water yield inthe first five years after treatment, they usedthe average water yield changes in up to thefirst five years after treatment. Using fuzzylinear regression analysis they concluded thatfor a ten per cent reduction in:

• conifer-type forest, water yield increasedby 20-25 mm

• eucalypt forest, water yield increased by 6 mm

• scrub, water yield increased by 5 mm

• deciduous hardwoods gave a 17-19 mm increase in water yield.

These estimates are lower than those fromBosch and Hewlett’s (1982) review. In theBosch and Hewlett (1982) analysis adoptedthe maximum change in water yield in thefirst five years after treatment, this will leadhigher estimate of the reduction in water yieldas opposed to the same analysis performedwith average increases. However the use ofthe average of up to the first five years aftertreatment may be impacted by regrowth ofvegetation after clearing.

The results of these reviews are limited bythe use of regrowth experiments to buildrelationships about annual increases in wateryield after vegetation change. As discussedin Section 2.3, Vertessy (1999) highlights thelimitations associated with the use ofregrowth experiments for developingrelationships between percentage ofvegetation cover and water yield. The resultsof subsequent studies, that look at change inwater yield as a function of vegetation agehave shown that the maximum change inwater yield may not occur in the first fiveyears after treatment. The results from apaired catchments studies in mountain ashforests of Australia indicate that themaximum water yield changes, when oldgrowth forest is replaced by regrowthvegetation is not see until approximately 20years after treatment as shown by Kuczera(1987). The vigorous regrowth in mountainash forests will in fact cause a decrease inwater yield compared to old growth forests.This concept is further discussed in Section 4.2.

In reviewing paired catchment studies bothStednick (1996) and Sahin and Hall (1996)concluded that in summarising the result ofcatchment experiments, difficulties wereexperienced because of the lack of certainkey statistics from the reported results (Sahinand Hall 1996) or insufficient detail of the sitecharacteristics (Stednick 1996). This mayaccount for the lack of general discussionabout the impacts of land use change on


inter-annual water yield (the change in wateryield with change in vegetation age) andseasonal flows. While the informationcontained in previous reviews may be usefulfor determining the short-term changes inwater yield, it does not allow for the likelylong-term impact of permanent land usechange or the inter and intra annual changesto be investigated.

Taking these factors into account and thelimitations of regrowth experiments (Section2.3) the remainder Section 4 considers theimpact of vegetation changes on water yieldand flow regime at different temporal scales.

4.2 Mean annual and annualwater yield

The main process responsible for changes inwater yield as a result of vegetation changesat the mean annual scale isevapotranspiration (Zhang et al. 2001,Holmes and Sinclair 1986, Turner 1991).Holmes and Sinclair (1986) used therelationship between mean annualevapotranspiration and mean annual rainfallto predict the increase in water yield whenconverting from a forested catchment tograss. Their results were based on a series ofcatchments in Victoria, Australia. Asdiscussed in Section 3, when assessing themean annual changes in water yield therecharge and change in storage terms in thewater balance are small compared to theother terms, hence the change in runoff canbe predicted through the prediction ofchange in evapotranspiration.

The concept that under mean annualconditions it is reasonable to assume the thatrecharge and change in soil water storage arenegligible compared to the rainfall, streamflowand evapotranspiration was further explored byZhang et al. (1999, 2001). They expanded onthe work by Holmes and Sinclair (1986) andincluded results from 250 studies worldwide asopposed to a small number of localcatchments. Using a pair of curves to illustratethe difference in evapotranspiration underdifferent vegetation types along a rainfallgradient, Zhang et al. (2001) developed asimple two parameter model to estimate themean annual evapotranspiration at thecatchment scale for different vegetation types.Figure 3 shows the Holmes Sinclair

relationship (HSR) and the Zhang model. Thedifference between the grass and forest curverepresents the increase in mean annual wateryield for a 100% change in vegetation for agiven mean annual rainfall. It should be notedthat both paired catchments and time-trendstudies were used in the derivation of Zhangcurves.

Vertessy and Bessard (1999) adapted the HSRto predict the impact of afforestation on wateryield in the Middle Murrumbidgee basin.Equations, based on the HSR were defined toestimate the mean annual runoff fromgrassland and eucalypt forest and were alsoadapted to predict the mean annual ET of pineplantations. These were then used in predictthe large scale impacts of afforestation onwater yields.

The nature of most paired catchment studiesdoes not allow for the long term effects (>10 years) of permanent vegetation changesto be investigated. While the mean annualresults based on the HSR or the Zhangmodel provide a means to assess the impactof permanent land use changes on meanannual flows, they do not provide a methodfor the assessment of inter-annual variabilityor the length of time it takes for a catchmentto adjust to changes in vegetation type.Using paired catchment data Hornbeck et al.(1993) looked at the long term effects of

Figure 3: Relationship between land cover, meanannual rainfall and mean annual evapotranspiration,as predicted by Holmes and Sinclair (1986) andZhang et al. (2001). Note the HSR is based on localcatchments mainly in Victoria, Australia, while theZhang Model is based on a worldwide database.


forest treatment on water yield in the USAunder a range of climatic conditions. Theyfound a variety of responses in water yieldincluding:

• initial increases occurring promptly afterforest clearing

• increases could be prolonged bycontrolling the regrowth (analogous withpermanent land use change), whenregeneration of forest cover waspermitted the increase in streamflowdiminished rapidly in about three to ten years

• a small increase or decrease in wateryield may persist for at least a decade.

Figure 4 shows the impact of vegetationchanges for four catchments in the USA. The differing responses are consistent withthe treatments undertaken for example in theHubbard Brook experimental forest (HB2),100% of the catchment was clear-cut andregrowth was then permitted. In this case aninitial increase in water yield is observed (dueto reduced interception and transpiration), as regrowth are permitted the water yieldincrease is reduced. The observed reductionin water yield about 15 years after treatmentis due to the increased evapotranspiration ofthe regrowth compared to the old growth

forest. For the Fernow experimental forest(F7) the increase in water yield is morepersistent than in Hubbard Brook. In the F7catchment clearing was undertaken in twostages, with the clearing of the upper half ofthe catchment in year 0 and the clearing ofthe lower half the catchment in year 4.Herbicides were applied to the catchment toprevent regrowth until year 7. After this pointthe effect of the regrowth on water year canbe seen with water yield returning to pre-treatment levels by year 27 (Hornbeck et al.1993).

While the regrowth experiments of the typesshown in Figure 4 are useful for looking atthe initial increase in water yield and the timetaken for a catchment to return to its pre-disturbance state. It provides very limitedinformation on the long-term impact ofpermanent vegetation changes that mayoccur under deforestation or afforestation,where the water yield will not return to itspre-treatment state.

There are limited examples of pairedcatchment studies looking at the impact ofpermanent land use changes on water yield.A number of paired catchments studied insouth Western Australia have focussed on thedeforestation of native forest for agriculturalland. Figure 5 shows the results of four

Figure 4: Change in annual water yield for four paired catchment studies in the USA. M4–100% Basal area cut.F7, upper half clear-cut (year 0), herbicides on upper half (2-7), lower half cut (year 4), herbicide on entirecatchment (5-7). LR2–Lower 24% clear-cut (year 0), mid slope 27% clear-cut (years 4-5), herbicide on lower andmid slope (Year 7) 40% Upper slope clear-cut (year 8-9), herbicide all catchment (Year 10). HB2–100% clear felled(Year 0), herbicide on entire catchment (Years 2-4). After Hornbeck et al. (1993).


different paired catchments in the Collie Basinin Western Australia. The catchments in theCollie Basin, experience a Mediterraneanclimate with a mean annual precipitationranging from 600 to 1400 mm. Predominatepre-treatment vegetation in these catchmentsare jarrah (Eucalyptus marginata) in the northand karri (E. diversicolor) in the south. March Road, Yarragil 4L, Wights and Lemonscatchments have mean annual rainfalls of 1050 mm, 1120 mm, 1200 mm, 750 mmrespectively.

Looking at the results for the deforestation inthe Wights catchment, it can be seen that aninitial increase in water yield is observed inthe first year after treatment (due todecreased interception andevapotranspiration). This is followed by asteady increase in water yield until a newequilibrium is reached (Ruprecht andSchofield 1989). The results of clearingfollowed by regrowth in the March Roadcatchment show a similar tend to theregrowth in Hubbard Brook Catchment 2USA (Figure 4), with an initial increasefollowed by a return to pre-treatment levels.

This highlights the limitations of regrowthstudies in predicting the long-term effects of deforestation as the initial increase afterclearing are not representative of the long-term increases in water yield. However,regrowth experiments have the potential to be used to investigate the likely changes

in evapotranspiration and streamflow withrelation to forest age. This has been the focusof a number of paired catchment studies insouth eastern Australia, where after clearingand subsequent regeneration, a decrease inwater yield occurs. This decrease is due tothe vigorous nature of the regrowth, whichtranspires more water compared with oldgrowth forests (Cornish and Vertessy 2001,Vertessy et al. 2001, Roberts et al. 2001).Through the use of paired catchment studiesinvolving regrowth, it may be possible topredict the impact of afforestation or tree‘plantations’ on inter-annual water yield.

The Mountain ash forests in southernAustralia provide an excellent example of thisreduction in water yield flowing theregeneration of vegetation after bushfire.Mountain ash forests are confined to thewetter parts of Victoria and Tasmania andgrow at altitudes of between 200 m and1000 m, where mean annual rainfall exceeds1200 mm. Fire is an infrequent but vitalcomponent of the life cycle of these forestswith the seedlings only growing on exposedsoil with direct sunlight (Vertessy et al. 2001).Following fire hundreds of seeds germinateper hectare, the intense competition betweenthe plants for light results in rapid tree growthand natural thinning of weaker trees. There isa significant body of empirical evidence toshow that the amount of water yield fromthese catchments is closely linked with standage (Langford 1976, Kuczera 1987,

Figure 5: Water yield increase for paired catchments, south Western Australia. Wights catchment (Ruprecht andSchofield, 1989), Yarragil (Stoneman, 1993), March Road (Bari et al. 1996), Lemons (Ruprecht and Schofield, 1991)


Watson et al. 1999). The ‘Kuczera curve’ thatdescribes the relationship between stand ageand annual water yield is characterised bythe following features:

• the mean annual water from largecatchments covered with old growthmountain ash forest (>200 year) isapproximately 1195 mm for regionswhere mean annual rainfall is ~1800 mm;

• after burning and full regeneration ofmountain ash forest the water yield reducesto 580 mm at an age of ~ 27 years

• after 27 years of age the mean annualwater yield increases and returns to pre-disturbance levels, taking as long as 150 years to fully recover. (Vertessy et al. 2001)

The work by Cornish and Vertessy (2001)and Roberts et al. (2001) indicates that thismay be a more general behaviour foreucalypt forests in Australia and does notonly apply to mountain ash forests.

Examples of long-term response topermanent vegetation change from grass orpasture to tree plantations can be found inSouth Africa, New Zealand and Australia.Figure 5 showed the response of streamflow

to the clearing of native vegetation foragriculture for deforestation and regrowthexperiments in south Western Australia.South Africa has the longest and mostdetailed record of paired catchmentafforestation experiments, addressingpermanent land use change from grasslandto forest. Using data from South Africanafforestation experiments, Scott and Smith(1997) developed a series of generalisedcurves to predict the impact of afforestationon annual total flows and low flows as afunction of plantation age, species plantedand site suitability as shown in Figure 6.

The curves in Figure 6 are similar to thatobserved in Figure 5 (particularly for Wightscatchment), indicating that the response issimilar for both afforestation anddeforestation, with a period of transience untila new equilibrium is reached. Figure 7 showsthe results of a deforestation and afforestationexperiment in areas of similar rainfall. A similar change in water yield, under eitherdeforestation or afforestation in the long-termis observed. The time taken to reach thisequilibrium is dependent on the treatment,with a new equilibrium being establishedmore rapidly under deforestation thenafforestation.

Figure 6: Generalised curves from estimating the percentage reduction in total and low flow after 100%afforestation with pine and eucalypt afforestation (Scott and Lesch 1997).


The results for the Biesievlei catchment,Jonkershoek, South Africa indicate that it willtake between 15 and 20 years for thecatchment to reach a new equilibrium underafforestation, while the deforestationexperiment from Wights catchments inWestern Australia, indicates that a newequilibrium is reach in eight to ten years. Thiscasts doubt on the use of the assumption ofBosch and Hewlett (1982) that the maximumreduction in water yield under afforestation isequivalent to the maximum increase in wateryield in the first five years after treatments forregrowth and deforestation experiments.

4.3 Annual flow regime

4.3.1 Flow duration curves

The impact of changes in vegetation type onflow regime can be depicted through the useof flow duration curves (FDC). The FDC for acatchment provides a graphical summary ofthe streamflow variability at a given location,with the shape being determined by rainfallpattern, catchment size and the physiographiccharacteristics of the catchment. The shape ofthe flow duration curve is also going to beinfluenced by water resources development(water abstractions, upstream reservoirs etc.)and land-use type (Smakhtin 1999).

The FDC (the cumulative distribution of theriver flows) has been used widely as a measureof the flow regime as it provides an easy way

of displaying the complete range for flows andhow they would be changed under differentland use scenarios in different climatic zones.

FDC can be constructed using multipletemporal scales of streamflow data: monthlyor daily flows and depicted either using allthe flows in a given year (annual flow durationcurve) or flows for subset of yearly flows(seasonal flow duration curve). Smakhtin(1999) adopted the following terminology andthis terminology has been adopted whendiscussion the effect of vegetation changeson the FDC for various vegetation changescenarios:

• One-day annual FDC—Constructed usingdaily data for a complete year

• One-month annual FDC—Constructedusing monthly data for a complete year

• One-day seasonal FDC—Constructedusing daily data for a given season

• One-month seasonal FDC—Constructedusing monthly data for a given season

One of the limitations of using FDC for acomparison of high and low flows underdifferent vegetation types is that the relativedistribution of high and low flows variesdepending on whether a particular year iswet or dry, therefore where possible it isimportant to compare years with similarprecipitation, to minimise the variations dueto climate (Burt and Swank 1992).

Figure 7: Change in water yield as a percentage of rainfall for deforestation (Wights catchment, Western Australia.Mean annual rainfall = 1200 mm after Ruprecht and Schofield 1989), afforestation and clear felling-replanting(Biesievlei catchment, South Africa. Mean Annual Rainfall, 1298mm, Scott et al. 2000).


4.3.2 High and low flows

In discussing the impacts of vegetationchange on flow regime low and high flowneed to be defined. The most widely useddefinition of low flows are the flows within therange of the 70% to 99% time exceeded(Smakhtin 2001), hence this definition hasbeen adopted. High or peak flows have beentaken as the flows that occur one to ten percent of the time.

4.3.3 Impact of vegetation changes onannual FDC, high and low flows

The flow duration curves discussed below areone-day annual FDC, and have been plotted forcatchments in different climatic zones withdiffering vegetation changes. While data existsto plot such curves for a large number ofcatchment only three examples have beenchosen and discussed here. These examplesare the Redhill catchment in south easternAustralia, where a pine plantation wasestablished on pasture, Wights catchment insouth western Australia where pasture replacednative vegetation and the Glendhu catchmentin New Zealand, where a pine plantation wasestablished on tussock grassland.

Figure 8 depicts the change in flow regimefor the Redhill catchment in south eastern

Australia. The catchment is located in aabout 50 km west of Canberra, in theMurrumbidgee Basin and is part of the pairedcatchment study looking at the impact ofpine plantations on water yield. Redhill has acatchment area of 195 hectares while thecontrol catchment Kylies Run is 135hectares. Both catchments range in altitudefrom 590 m to 835 m. The climate of thearea is highly variable with a winter dominantrainfall. The mean annual rainfall of the Redhillcatchment is 876 mm (Hicke 2001). There isno pre-treatment data available for this pairedcatchment study and due to differences insoil properties between the two catchments,there was also marked difference betweenthe flow regimes even before the pines arewell established at the beginning of thetreatment period. It was therefore decided tocompare the FDC for years of similar annualrainfall for the treated catchment only. FDCfor one and eight year old pines (based on awater year from May to April) have been usedto quantify the relative changes in the highand lows flows as a results of vegetationschange. The one-year and eight-year oldpines were chosen as these years havesimilar rainfalls, 887 mm and 879 mmrespectively. The FDC indicated that there isapproximately a 50% reduction in high flowswhile there is 100% reduction in low flows.

Figure 8: Flow Duration curves for the Redhill catchment, near Tumut, NSW 1 year old pines and 8 year old pines.(after Vertessy 2000).


Figure 9 depicts the response to conversionof native forest to pasture in the Wightscatchment in south Western Australia. As discussed in Section 4.2 the Wightscatchment is part of a series on pairedcatchment studies in south WesternAustralia. These catchments have twoimportant local characteristics,

• an increasing soil salinity storage withdistance inland; and

• a local groundwater system.

The interplay between the groundwater andvegetation plays in important role in thehydrological response of these catchments tovegetation change. The hydrological responseto replacement of native forests by pastures isrelated to an increase in groundwaterdischarge area (Schofield 1996).

As with Figure 8, it can be seen that allsections of the flow regime are affected bythe change in vegetation type. Comparingthe FDC for native vegetation (1974-1976)with a period of similar climatic conditions ofpasture (1983-1985). We can see that youwould expect a 50% reduction in high flowswhen going to pasture to forest and a 100%reduction in low flows.

Figure 10 depicts an alternate response tothe establishment of pine plantations in theGlendhu experimental catchments in New

Zealand (169˚45’E, 45˚50’S). The control andtreated catchments has mean annual rainfallsof 1310mm and 1290mm respectively. The treatment involved the planting 67% ofthe catchment with Pinus radiata (McLean2001). Unlike the Redhill and Wightscatchments the control and treated FDC forthe control and treated catchments aresimilar during the calibration period.Therefore the changes in high and low flowshave been assessed through comparison thecontrol to the treated catchment at variousstages after treatment. The reductions in lowand high flows as similar for all sections ofthe flow regime with approximately 30%reduction in both low and high flows. Thisresponse is typical of many catchmentsincluding the mountain ash catchments inVictoria (Watson et al. 1999) and theBiesievlei catchment in South Africa.

Figure 8, Figure 9 and Figure 10 depict twopossible responses in flow regime as a resultof vegetation change. The response seen inthe Redhill and Wights catchments are typicalof areas were annual evapotranspiration offorests approaches annual precipitation, whilethe response seen in Glendhu is typical ofareas where annual precipitation is greaterthan the annual evapotranspiration. In theMountain ash catchments in southernAustralia, Watson et al. (1999) noted that inwetter catchments all flows respond to

Figure 9: Flow Duration curves for the Wights catchment in southwestern Australia. (Based on a water year fromApril to March).


Figure 10: Flow duration curve from the Glendhu experimental catchments New Zealand. 1980—during thecalibration period (both catchments tussock). 1989—6 years after pine plantation established. 1999—16 yearsafter pine plantation established. (McLean 2001)

climatic and vegetation changes in unisonwith the changes in the mean flow, howeverin the drier parts of the study area changes inlow flows are accentuated.

4.4 Seasonal water yield andflow regime

As noted earlier, information on the impact ofland use change on seasonal or monthly flowis seldom reported in quantitative detail in theliterature and generalisations about seasonalityof yield changes under different land use havenot been made. The majority of the previouswork of land use change and water yield hashad an emphasis on annual or mean annualwater yield. The impact of land use change onseasonal yield can be as important as theimpact on annual water yield, particular whereflows during the dry season are of importanceto downstream water users.

Johnson and Kovner (1956) noted thatannual streamflow and evapotranspiration donot tell the complete story because ofseasonal interactions of factors affecting thewater balance, such as soil moisture content.While on an annual basis the changes in soilmoisture between one year and the next canbe assumed to be negligible, this is not thecase on a seasonal basis. This section willaim to provide a summary of the literature onseasonal water yield.

The analysis of paired catchment data in theUSA in the 1970s and early 1980s commonlyused regression by least squares on bothannual and monthly data (Hibbert 1969,Hornbeck et al. 1987, Rich and Gottfried1976, Johnson and Kovner 1956). Thisallowed for the impact of annual water yieldas well as seasonality to be assessed. Theresults of these studies indicate thatvariations can be found in seasonal yield butlack quantitative data on the water yieldchanges. This lack of quantitative datamakes it difficult to generalise the results onseasonal water yield between sites.

Hornbeck et al. (1997) looked at annual andseasonal flows for the first year after clear fellingin the Hubbard Brook experimental forest.Separating annual yields into growing anddormant seasons allowing contrasting oftreatment effects between periods of full leafand maximum evapotranspiration, and periodwhen deciduous forests are dormant andminimum evapotranspiration. They observedthat most of the increases and decrease inannual yield occur during the growing seasonas shown in Figure 11. They concluded thatwater yield increases were a result ofdecreased transpiration and primarily occurredas augmentation to low flows, as illustrated bythe flow duration curves in Figure 11. While thisanalysis is an obvious thing to do for deciduouscatchments the definition of seasons is lessobvious for evergreen vegetation.


Using a similar approach to the analysis ofHornbeck et al. (1997), McLean (2001)produced flow duration curves to assess the hydrological response during Winter(July–September) and Summer(December–February) due to the conversionof tussock to pine plantations in New Zealand.From this study it was concluded that:

• the differences in summer flows weremore variable than the winter differences,due to the high variability in the rainfallover the summer months

• the seasonal effects of land usemodifications are not easily identifiedthrough the use of flow duration curves.

The difference in the results between theUSA catchment, where notable seasonaldifferences were observed, and those in NewZealand, where seasonal changes could notbe detected, can be attributed to thedeciduous nature of the vegetation in theUSA compared with the evergreen vegetationof the pine plantations in New Zealand. Thedistinct dormant season in the USA wherethere are no leaves on the trees results inlower interception and transpiration ratesmaking the evapotranspiration rates offorested areas very similar to those of shortcrops. However where there is no dormantseason, such as in eucalypt or pineplantations, the seasonal changes in wateryield are limited more by climatic conditions

of the seasons than by changingevapotranspiration rates.

Sharda et al. (1998) used a monthly averagedataset to look at the seasonal nature ofwater yield changes at Glenmorgan ResearchFarm, south India. It was observed that themajor reduction in mean annual flow causedby the blue gum plantation occurred duringthe months from July through to October,when 60% of the mean annual rainfalloccurred (Table 1). These results indicatethat the major reductions in flow volumeoccurred during the monsoon (July–October),however the percentage reductions in flowsindicate that significant reductions occur in allmonths of the year. It was also noted thatalthough the reduction in flow in the dryperiod was small on a volume basiscompared to the wet season the percentagereduction in flow is significant in all months.The early and late monsoon periods showdifferent responses in water change yield,which may be related to soil moisturedynamics introducing delays in response time.

Figure 11: Flow duration curves for the first year after the clear-felling treatment—Hubbard Brook experimentalforest (after Hornbeck et al. (1997)).


TABLE 1. Average monthly reduction in total run-off due to bluegum plantation in the secondrotation (after Sharda et al. 1998).

Flow in Catchment B (mm)Deficit

Month Rainfall (Computed—Observed) Percentage(mm) Observed Computed (mm) reduction in flow

Apr 71.3 3.8 4.9 1.1 22

May 111.1 7 9 2 22

Jun 166.4 16.3 21.8 5.5 25

Jul 233 60.9 78.6 17.7 23

Aug 221.2 61.3 78.4 17.1 22

Sep 133.6 27.3 37.7 10.4 28

Oct 165.1 40.4 58.6 18.2 31

Nov 70 24.1 33.8 9.7 29

Dec 64.9 20.5 27.9 7.4 27

Jan 9.9 7.4 9.6 2.2 23

Feb 5.9 3.9 4.6 0.7 15

Mar 17.9 3.2 4 0.8 20

Similar analysis was carried out on the Glendhucatchment in New Zealand and the Cathedral

Peak II catchment in South Africa. The resultsof this analysis are presented in Figure 12.

Figure 12: Average monthly reductions in streamflow from the Glendhu catchment (afforestation with pines 1980)and Cathedral Peak II, South Africa (afforestation 1950-1955).


Other studies reporting monthly or seasonalresults include;

• Lane and McKay (2001) who concludedthat there was no clear or persistentseasonal influence on either totalstreamflow or base flow change followinglogging

• Ruprecht et al. (1991) observed that themajor increase in streamflow occurredfrom July to October, however increaseswere still significant in June, Novemberand December. After thinning there wassignificant flow over the summer and earlyautumn

• Stoneman (1993)—observed that thelargest increases in streamflow occurredbetween June and October (Winter) withsmall increases in November andDecember (Summer).

As stated by Vertessy (1999), the informationon the seasonal variations in water yield islimited and rather confusing. The way inwhich the data on seasonal yield arepresented in the literature is generallydescriptive in nature, making it hard togeneralise between the results of differentstudies. While on an annual basis the resultsof the studies seem to be easily generalisedaccording to vegetation type, this is not thecase on a seasonal basis.

Jones and Grant (2001a, 2001b) noted thatthe nature of the analysis undertaken couldimpact on the results. This was displayed bythe original analysis of peak flow responsesto clear cutting and roads in small and largebasins, western Cascades (Jones and Grant1996) and the subsequent reanalysis of thesame data by Thomas and Megahan (1998)where the use of differing methods on thesame data set yielded different results. Theinterpretation of the results from the twoanalyses has resulted in Jones and Grant(2001a, 2001b) concluding both analysesshowed that forest harvest has increasedpeak discharges in small basins by as muchas 50% and 100% in large basins. Thomasand Megahan (2001) agreed that peak flowincreases (of up to 100%) in small eventsmay occur, but argued that that no evidenceexisted to suggest that this was the case forall event sizes including large floods.


5. Summary of limitation of paired catchments studies

In the following section the limitations ofpaired catchment experiments are discussed.These limitations are divided into twosections. The first section summarises thelimitations in the analysis of paired catchmentdata in terms of what has been reported inthe literature. The second deals with thelimitations associated with the application ofpaired catchment results to time trend studies.

5.1 Limitations in reportedliterature

During the review of literature three majorlimitations were highlighted in relations to theprevious analysis of paired catchment data.These have been disused in Section 4and are:

• generalisations about annual increases inwater yield (Bosch and Hewlett 1982,Stednick 1996, Salin and Hall 1996) aregenerally only based on short term resultsof regrowth experiments (maximumchange in the first five years aftertreatment, or first year increases). Theresults of permanent land use changeexperiments indicate that it may takelonger than five years for the maximumchange to be observed and for a newhydrologic equilibrium to be established

• changes in vegetation type will effect notonly mean annual flow, but also thevariability of annual flow. Peel et al. (2001)noted that the continental differences inthe variability of annual runoff were due totwo factors, the continental differences inthe variability of annual precipitation andthe distribution of evergreen anddeciduous vegetation

• most studies do not evaluate theseasonal changes in water yield. Where seasonal analysis is carried out the results reported are generally of a descriptive nature

• in order to assess the impacts ofvegetation changes on seasonal wateryield, a method needs to be established

that can be applied to a large number ofcatchments, so when comparing resultsbetween sites, the generalisations are notcomplicated by conflicting results fromdifferent analysis methods.

5.2 Application to largecatchments

The major advantage of using pairedcatchment studies in investigating the impactof vegetation changes on water yield is thatthe control catchments provide a means ofseparating out the changes in yield as aresult of climate from those due to land use.However, if the results of small experimentalcatchments cannot be applied to larger non-paired catchments with some degree ofconfidence, then their application is limited.

5.2.1 Spatial issues

Paired catchment studies provide a goodmethod for determining the relationshipsbetween percentage vegetation change andwater yield in relatively small catchments.However, methods are needed for scalingthese results to larger catchments where thearea of subject to land use change is likely tobe patchy and relatively small compared tothe overall catchment size.

The results summarised in Section 4 indicatethat for any impact of land use change to bedetected, at least 20% of the catchmentneeds to be treated (Bosch and Hewlett1982). This result is derived from the researchon small experimental catchments. Munday et al. (2001) developed a model to simulatethe temporal changes in streamflowassociated with reafforestation of existinggrassland and the subsequent managementof the forest for timber harvesting for theAdjungbilly catchment (389 km2) in New SouthWales using results from paired catchmentstudies of Redhill (for pine plantations) andKaruah (for eucalypt forest). The resultsindicated that while the trend in streamflow


changes are statistically insignificant, themodel did satisfactorily simulate themagnitude and nature of the changes in meanannual yield from the catchment given thehistorical changes in vegetation type.

In determining the impact of forest conversionto agriculture in a large river basin in Thailand,Wilk et al. (2001) commented that the resultsof small scale studies have shown that a largereduction in forest cover increases the annualstreamflow and raised the issue of whethersimilar results would emerge from large, partlydeforested catchments with variable vegetativepattern at different growth stages. Their studyconcluded that despite a reduction in forestcover from 80% to 30% in the Nam Pong riverbasin, no change in river discharge could bedetected. This is likely to be due to the factthat land use change in large river basins is notuniform in space or time.

At present the impacts on water yield at thewhole of catchment or regional scale arelimited to mean annual investigations. Scottet al. (1998) used the generalised curves ofScott and Smith (1997) to determine thelikely change in water yield on total run-offand low flows at regional scale as a result ofafforestation in South Africa. This is the bestexample of prediction of water yield changesat a regional scale.

There are limited examples of theextrapolation of the generalisations gainedthrough the used of small experimentalcatchments to the regional scale and howtreatments over less than 20% of thecatchment impact on water yield. In terms ofmaking predictions it is reasonable toassume that 0% land use change will notcause any change in water yield. Thusforcing the linear relationships suggested byBosch and Hewlett (1982) thorough theorigin would allow predictions to be made ifless than 20% of a catchment is subjected tochanges in vegetation. The ability to detectthe change in water yield is of lessimportance than how well the magnitude ofthe water yield change can be predicted.Once the predictions have been made theimportance of regional effects can beassessed. Another scale issue that couldpotentially be significant is the change ingeomorphology as you move down fromuplands to lowlands.

5.2.2 Climatic Variability

One of the advantages of paired catchmentstudies is that they allow the removal ofclimate variability through the comparison oftwo catchments subject to the same climaticconditions, under different land uses. Theseparation of climatic variability effects fromthe water yield changes as a result of landuse alterations is a key problem for timetrend studies.

In cases where paired catchments areavailable, the separation of land use impactsfrom climatic factors can be achievedthrough the comparison of the twocatchments. This can be done not only forannual and mean annual totals, but also forflow regime as depicted by the annual flowduration curves in Figure 8, Figure 9 andFigure 10. There is also the potential to usepaired catchments to determine the seasonalimpacts of vegetation change.

For example, in Figure 10 a change in theflow duration curve for the tussock catchment(control), between 1980 and 1989 can beseen, despite the fact that no land use changehas occurred. The most likely explanation forthis is the climate differences between 1980and 1989, causing a change in the amount ofrunoff. Where both the flow duration curves forthe control catchment and the treatedcatchment are available, separation of theimpact of land use on flow regime is possible.However, for Figure 7, where all the flowduration curves have been plotted for thesame catchment, how does one separatequantitatively the changes due to land usefrom the fluctuations due to climate.

Three possible methods that could be usedfor the removal of climatic variability fromnon-paired catchments are:

1. the use of a generalised additive model toseparate the climate signal from land usefrom the percentiles of the flow durationcurves, either annual or seasonal

2. removing exogenous variable so trendscan be more easily identified in thevariable of interest (land use change inthis case)

3. the use of a rainfall runoff model todetermine flows under different land usesfor the same climatic period.


6. Summary and conclusions

This review highlights the lack of informationavailable in the literature for examining theimpacts of vegetation changes on seasonalyield and flow regime. While the effect ofvegetations change on a mean annual basisis well understood, research on seasonalwater yield reported in the literature is limitedand confusing and is primarily of adescriptive nature.

The processes affected by land use changeare reasonably well understood at a meanannual or annual basis, however changes ona seasonal basis and in flow regime are notas well understood, particularly in relation tosoil water storage. On a mean annual basis,changes in soil water storage can beassumed to be insignificant in relation to theother terms in the water balance equation;however, this is not the case at a seasonaltime scale.

The previous reviews of paired catchmentstudies have focused mainly on regrowthexperiments, where changes in water yield areonly observed in the first couple of yearsfollowing treatment before returning to pre-treatment levels. Given the transient nature ofthe water yield changes in regrowthcatchments the applications of these results topermanent land use changes are questionable.In terms of future land use changes inAustralia, the increase in afforestation due tothe 2020 Vision is likely to be of a morepermanent nature leading to permanentchange in water yield and flow regime.

This review raises a number of issues relatingto land use change and water yield that needfurther investigation. These include:

• Can the results of regrowth studiesprovide relevant information on the effectsof permanent land use change or thelikely changes in evapotranspiration withtime in tree plantations?

• How will the effect of permanent land use change alter over time? Do thegeneralisations made by Scott and Smith(1997) in Figure 6 apply to other areasaround the world?

• How will vegetation changes affect flowregime? Will these effects vary betweenregions or will the major changes in wateryield be reflected in low or high flows?

• Can the generalisations drawn frompaired catchment studies be applied tolarger catchments and at regional scales?

• Can the impacts of climatic variability beseparated from the effects of land usechange?

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Appendix A


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s14

.7/

25.3

450-

940m

116

37/1

4292

456/

3072

Plan

tatio

n es

tabl

ishe

daf

ter t

ract

or c

lear

ing

1976

-198

3

Cor

nish

(199

3);

Cor

nish

and

Ve

rtess

y (2

001)

Lids

dale

, NS

W, A

ustr

alia

L-6

9.4

12SW

Sub

Trop

ical

Euca

lypt F

ores

tPi

nus

Rad

iata

755

L-5

Feb

1978

100

% c

lear

edan

d w

indr

owed

, and

then

burn

t in

April

197

8.D

urin

g w

inte

r 197

8ca

tchm

ent w

as p

lant

edw

ith P

. rad

iata

.

1967

-197

8Pu

tuhe

na a

ndC

orde

ry (2

000)

Tant

awan

galo

Cre

ek, N

SW

, Aus

tral

iaW

illbob

85.6

30%

of a

rea

logg

ed19

86-1

989

Lane

et a

l. (2

001)

Wic

ksen

d68

.280

0-95

01

Reg

iona

l rai

nfal

lis

reas

onab

lyun

iform

thro

ugho

ut th

eye

ar.

Ope

nSc

lero

phyll

fore

stR

egro

wth

1100

Ceb

21.7

1100

38%

of a

rea

logg

ed19

86-1

989

Lane

et a

l. (2

001)

Tum

ut, N

SW,

Red

hill

195

9

Tem

pera

te w

ithhi

ghly

varia

ble

and

win

ter

dom

inan

tra

infa

ll.

Past

ure

Pine

s87

6Ky

lies

Run

135

1287

6

50ha

affo

rest

ed in

198

8an

d th

e re

mai

ning

are

a(1

45ha

) affo

rest

ed in

1989

Non

eH

icke

l (20

01)

Aust

ralia

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

1 El

evat

ion

Ran

ge2 P

re-t

reat

men

t mea

n (1

976/

77 to

198

2/83

)

Yam

bula

Sta

te F

ores

t, N

SW

, Aus

tral

ia

Gee

bung

Cre

ek80

.215

7-33

13E

Inte

grat

ed H

arve

st (J

an -

April

198

7)Po

st L

oggi

ng B

urn

(Jun

e-Ju

ly, 1

987)

1979

-198

6

Pepp

erm

int

Cre

ek12

7.5

230-

4763

Wild

Fire

- Ja

n 19

79In

tegr

ated

Har

vest

(Dec

1986

- Ju

ne 1

987)

1977

-197

9

Gre

ville

aC

reek

92.5

Wild

fire

- Dec

197

2W

ildfir

e - J

an 1

979

1977

-197

9

Strin

gyba

rkC

reek

140

230-

4763

Inte

grat

ed h

arve

stin

g -

May

197

8-Ja

ne 1

979)

Wild

fire

Jan

1979

1977

-197

8

Ger

man

sC

reek

225.

123

0-47

63

Tem

pera

te ra

iny

clim

ate,

moi

st in

all s

easo

ns w

ithw

arm

sum

mer

s

Dry

Scle

roph

yllfo

rest

Reg

row

th90

0Po

mad

erris

Cre

ek75

.990

0

Wild

fire

- Jan

197

9Sa

lvage

Log

ging

: Ju

ne- D

ec 1

979

1977

-197

9

Mac

Kay

and

Cor

nish

(198

2);M

oore

et a

l.(1

986)

; Cra

pper

et

al. (

1989

); R

ober

ts(2

001)

; Rob

erts

et a

l.(2

001)

Wyv

uri e

xper

imen

tal c

atch

men

ts, B

abin

da, Q

ueen

slan

d, A

ustr

alia

Nor

th C

reek

18.3

34W

Mes

ophy

ll vin

efo

rest

4239

2873

Sout

h C

reek

25.7

1971

-197

3 67

% a

rea

logg

ed, c

lear

ed ra

ked

and

plou

ghed

; bar

e 2

year

s

1969

-197

1

Cas

sells

et a

l.(1

982)

; Gilm

ore

etal

. (19

82);

Bon

ell e

tal

. (19

83);

Cas

sells

et a

l. (1

985)

Brig

alow

Res

earc

h S

tatio

n, Q

ueen

slan

d, A

ustr

alia

C2

11.7

Cro

ps68

639

C1

16.8

699

20C

ropp

ing

1985

1965

-198

3

C3

12.7

subh

umid

on

the

coas

t and

exte

ndin

g to

sem

i arid

tow

ards

the

wes

t

Nat

iveB

rigal

owfo

rest

.Pa

stur

e69

532

C1

16.8

699

20Pa

stur

e 19

8319

65-1

983

Law

renc

e an

dSi

ncla

ir (1

986)

;La

wre

nce

and

Thor

burn

(198

9)

Cro

pper

Cre

ek, V

icto

ria, A

ustr

alia

Cle

m C

reek

46.4

ED

ry s

cler

ophy

lleu

calyp

t for

est

Pinu

sR

adia

ta14

00El

laC

reek

/Bet

syC

reek

113.

0/4

4.3

E14

00

Dec

embe

r 197

9ve

geta

tion

rem

oved

leav

ing

30m

buf

fer s

trip

arou

nd s

tream

. Are

apl

ante

d w

ith ra

diat

a pi

ne

1975

-197

9B

ren

and

Papw

orth

(199

1)

3 El

evat

ion

Ran

ge

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Nor

th M

aroo

hdah

exp

erim

enta

l Are

a, V

icto

ria, A

ustr

alia

Bla

ck S

pur 1

177.

1SW

Reg

row

th16

62B

lack

Spu

r 49.

817

.216

6250

% B

asal

are

a re

mov

edby

cle

ar fe

lling

smal

lpa

tche

s

71/7

2-75

/76

(4 Y

ears

)

Bla

ck S

pur 2

9.6

14.6

SER

egro

wth

1662

Bla

ck S

pur 4

9.8

17.2

1662

40%

Bas

al a

rea

rem

oved

by u

nifo

rm th

inni

ng1/

7/19

70-

20/1

2/19

76

Bla

ck S

pur 3

7.7

SER

egro

wth

1662

Bla

ck S

pur 4

9.8

17.2

1662

50%

uni

form

thin

ning

(14-

3-19

77 -

2-5-

1977

)1-

7-19

70 -

14-3

-197

7

O'S

haug

hnes

sy e

tal

. (19

89);

Jaya

suriy

a an

dO

'Sha

ughn

essy

(198

8); N

anda

kum

ar(1

993)

; Wat

son

et a

l.(1

999)

; Wat

son

et a

l.(2

001)

Ette

rcon

111

.67

Reg

row

thEt

terc

on 3

15.0

1Ja

n-19

82 to

Mar

198

239

% s

trip

thin

ning

28/7

/197

1-Ja

n198

2

Ette

rcon

28.

83R

egro

wth

Ette

rcon

315

.01

Und

erst

orey

rem

oved

-19

79In

fest

ed w

ith P

syllid

s -

1998

5/8/

1971

-19

79

Ette

rcon

49.

03R

egro

wth

Ette

rcon

315

.01

35%

stri

p th

inne

d Ja

n19

82-M

ar-1

982

15-7

-197

1 -

Jan-

1982

Wat

son

et a

l. (1

999)

;W

atso

n et

al.

(200

1)

Mon

da 1

6.31

Reg

row

thM

onda

46.

31

79%

cle

arfe

lled

and

rege

nera

ted

with

200

0se

edlin

gs /h

a5/

12/7

7-26

/4/7

8cl

earfe

lled

20/3

/78

- bur

nt17

:5-7

8 - S

eedl

ings

11-6

-197

0 -

5-12

-197

8

Jaya

suriy

a an

dO

'Sha

ughn

essy

(198

8); W

atso

n et

al.

(199

9); W

atso

n et

al.

(200

1)

Mon

da 2

3.98

Reg

row

thM

onda

46.

31

75%

cle

arfe

lled

and

rege

nera

ted

with

500

0se

edlin

gs/h

a5/

12/1

977-

26/4

/197

8cl

earfe

lled

6/3/

1978

Bur

nt17

/5/1

978

Plan

ted

11/6

/197

0-5/

12/1

977

Mon

da 3

7.25

1939

E.

Reg

nans

Reg

row

thM

onda

46.

31

5/12

/197

7-26

/4/1

978

80%

cle

arfe

lled

and

rege

nera

ted

with

500

seed

lings

/ha

21/4

/197

8 B

urnt

9/5/

1978

Pla

nted

11/8

/197

0-5/

12/1

977

Myr

tle 2

30.8

Med

iterra

nean

-co

ol w

et w

inte

rsan

d ho

t dry

sum

mer

s

1759

E.

Reg

nans

Reg

row

thM

yrtle

125

.21

74%

cle

arfe

lled

4/12

/198

4-9/

3/19

85B

urnt

- 20

/3/1

985

Seed

ed. W

inte

r 198

5

13/7

/197

1-4/

12/1

984

Wat

son

et a

l. (1

999)

;W

atso

n et

al.

(200

1)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Cor

ande

rrk

expe

rimen

tal a

rea,

Vic

toria

, Aus

tral

ia

Blu

e Ja

cket

64.8

36.6

SW

Med

iterra

nean

-co

ol w

et w

inte

rsan

d ho

t dry

sum

mer

s

1850

E.

Reg

nans

& E

.O

bliq

uaR

egro

wth

Slip

62.3

40.3

SSe

lect

ive c

ut N

ov 1

972-

Mar

197

311

/8/1

958-

8/11

/197

2

Wat

son

et a

l. (1

999)

;N

anda

kum

ar (1

993)

;N

anda

kum

ar a

ndM

ein

(199

7);

Picc

anin

ny52

.837

.8S

Med

iterra

nean

-co

ol w

et w

inte

rsan

d ho

t dry

sum

mer

s

1850

E.

Reg

nans

& E

.O

bliq

uaR

egro

wth

Slip

62.3

40.3

SC

lear

fellin

g of

85%

of

vege

tatio

n in

Nov

197

1-Ap

r 197

2

7/3/

1956

-16

/11/

1971

Wat

son

et a

l. (1

999)

;N

anda

kum

ar (1

993)

;N

anda

kum

ar a

ndM

ein

(199

7);

Ste

war

ts C

reek

, Vic

toria

, Aus

tral

ia

CA2

4.0

8.3

NE

Bar

e gr

ound

(196

9-19

75)

Past

ure

1976

-

1120

(196

0-19

80)

CA1

4.3

9.0

NE

1400

(196

0-19

67)

Cle

ared

196

9, B

are

Gro

und:

Apr

il 196

9-19

75 P

astu

re 1

976

1960

-196

9

CA5

17.6

7.6

NW

Med

iterra

nean

-co

ol w

et w

inte

rsan

d ho

t dry

sum

mer

s

Mix

ed S

peci

esEu

calyp

t for

est.

Mix

ed S

peci

esEu

calyp

t for

est.

Bar

egr

ound

:Ju

ne 1

969-

April

197

0.Pi

nes:

Apr

il19

70

1120

(196

0-19

80)

CA4

25.3

6.0

NW

1120

(196

0-19

80)

Cle

ared

May

196

9, B

are

grou

nd: A

pril 1

969-

1970

. Gro

win

g Pi

nefo

rest

: Apr

il 197

0-

1960

-196

9

Mei

n et

al.

(198

8);

Nan

daku

mar

(199

3)

Par

wan

Exp

erim

enta

l Are

a, V

icto

ria, A

ustr

alia

Parw

an 1

1.6

NN

ative

Pas

ture

woo

dlan

d53

8Pa

rwan

31.

6N

538

Nat

ive P

astu

re (1

954-

1959

) Reg

ener

atio

n of

woo

dlan

d (1

959-

1968

)G

razin

g (1

968-

74)

1954

-195

9N

anda

kum

ar (1

993)

Parw

an 2

1.6

NN

ative

Pas

ture

impr

oved

past

ure

538

Parw

an 3

1.6

N53

8

Nat

ive P

astu

re (1

954-

1959

) Im

prov

ed P

astu

re(1

959-

1968

) Gra

zing

(196

8-74

)

1954

-195

9N

anda

kum

ar (1

993)

Parw

an 4

1.6

SN

ative

Pas

ture

woo

dlan

d53

8Pa

rwan

51.

6S

538

Nat

ive P

astu

re (1

954-

1959

) Reg

ener

atio

n of

woo

dlan

d (1

959-

1968

)G

razin

g (1

968-

74)

1954

-195

9N

anda

kum

ar (1

993)

Parw

an 5

1.6

S

Med

iterra

nean

type

clim

ate

Nat

ive P

astu

reim

prov

edpa

stur

e53

8Pa

rwan

51.

6S

538

Nat

ive P

astu

re (1

954-

1959

) Im

prov

ed P

astu

re(1

959-

1968

) Gra

zing

(196

8-74

)

1954

-195

9N

anda

kum

ar (1

993)

Ree

fton

Exp

erim

enta

l Are

a, V

icto

ria, A

ustr

alia

Ree

fton

170

.46

559

NM

edite

rrane

anTy

pe c

limat

eN

ative

Euca

lypt f

ores

tR

egro

wth

1233

180

Nat

ive fo

rest

unt

il 198

4,20

% o

f the

fore

st c

lear

edin

Apr

il 198

419

71-1

980

Nan

daku

mar

(199

3);

Ree

fton

276

.112

588

WM

edite

rrane

anTy

pe c

limat

eN

ative

Euca

lypt f

ores

tR

egro

wth

1250

258

Ree

fton

3-6

95.1

107.

215

6.2

521.

2

5 9 5 2

596

594

640

651

NW SW SW S

1265

1249

1308

1440

209

234

228

318

Nat

ive F

ores

t unt

il Apr

il19

84. U

nder

stor

ey b

urnt

in A

pril 1

984

1971

-198

0N

anda

kum

ar (1

993)

;

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Col

lie R

iver

Bas

in, W

este

rn A

ustr

alia

Don

sEu

calyp

t For

est

Agric

ultu

re72

0

Cle

arin

g of

nat

ive fo

rest

by s

trip

clea

ring,

soi

lcl

earin

g an

d pa

rkla

ndcl

earin

g

1974

-197

6R

upre

cht a

ndSc

hofie

ld (1

991b

)

Lem

ons

344

8Eu

calyp

t For

est

Agric

ultu

re75

0

Erni

e27

072

053

.5 %

of c

atch

men

tcl

eare

d be

twee

nN

ovem

ber 1

976

and

Mar

ch 1

977

1974

-197

6R

upre

cht a

ndSc

hofie

ld (1

991a

)

Wig

hts

94Eu

calyp

t For

est

Agric

ultu

re12

00Sa

lmon

100%

cle

ared

in S

umm

er19

76-1

977

1974

-197

6R

upre

cht a

ndSc

hofie

ld (1

989)

Bal

ingu

pB

rook

Trib

utar

y93

.3Ja

rrah

(Euc

alyp

tus.

mar

gina

ta)

Agric

ultu

re88

0Th

omso

nB

rook

/Lud

low

Rive

r

1020

0/10

10

950/

950

Affo

rest

atio

ns

1978

-198

0(fi

rst 3

yea

rssi

nce

refo

rest

atio

n)

Bor

g et

al.

(198

8)

Mar

ch R

oad

261

170-

230m

Nat

ive fo

rest

dom

inat

ed b

yE.

mar

gina

taan

d E.

calo

phyll

a an

dE.

dive

rsic

olor

E.di

vers

icol

or10

5010

50

Cle

ared

Jan

uary

198

2 -

Mar

ch 1

983.

Nur

sery

rais

ed k

arri

seed

lings

wer

e ha

nd-

plan

ted

in th

e sa

me

year

of lo

ggin

g.

1976

-198

2B

ari e

t al.

(199

6)

April

Roa

dN

orth

Euca

lypt F

ores

tre

grow

th10

70

April

Roa

dSo

uth

248

Cle

ar fe

lling

leav

ing

100m

buf

fers

Lew

in S

outh

Euca

lypt F

ores

tre

grow

th12

20Le

win

Nor

thSe

lect

ion

cut a

ndre

gene

ratio

n19

82-1

985

Wel

lbuc

ket

Euca

lypt F

ores

tre

grow

th

Sele

ctio

n cu

t and

rege

nera

tion.

Bas

al a

rea

redu

ced

from

16

m^3

/ha

to 1

1m^3

/ha.

Cro

wn

cove

r red

uced

from

55%

to 2

2%

1977

-198

1

Rup

rech

t and

Scho

field

(198

9)

Yarra

gil 4

L12

62.

0S

Euca

lypt F

ores

tre

grow

th11

204.

3Ya

rragi

l 4X

270

3.1

SE80

% o

f can

opy

cove

rre

mov

edSt

onem

an (1

993)

Yerra

min

nup

SEu

calyp

t For

est

regr

owth

850

Yarra

min

nup

Nor

thLo

ggin

g le

avin

g 50

mbu

ffer a

nd re

gene

ratio

n19

82-1

985

Rup

rech

t and

Scho

field

(198

9)

Han

sen

80

Med

iterra

nean

clim

ate

Euca

lypt F

ores

tLe

wis

80

1985

-86

inte

nsive

unifo

rm th

inni

ngtre

atm

ent w

as a

pplie

dac

ross

the

catc

hmen

tex

clud

ing

the

swam

p an

da

50m

buf

fer s

trip

1978

-198

4R

upre

cht e

t al.

(199

1)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Indi

a

Doo

n Va

lley

1.45

5.1

SED

erive

d sc

rub

with

sal

seed

lings

E. g

rand

isan

d E.

cam

aldu

lens

is

1167

Oye

band

e (1

988)

Gle

nmor

gan

B32

mon

tane

tem

pera

te h

umid

clim

ate

Gra

ssla

nd

Blu

egum

Rot

atio

n 1

then

Blu

egum

rota

tion

2

1380

Gle

nmor

gan

A32

1380

Con

vers

ion

from

nat

ural

gras

slan

d to

Blu

egum

plan

tatio

n (ro

tatio

n 10

year

s). B

lue

gum

harv

este

d in

198

2fo

llow

ed b

y a

seco

ndro

tatio

n of

cop

pice

dbl

uegu

m.

1968

-197

2Sh

arda

et a

l. (1

988)

;Sh

arda

et a

l. (1

998)

Japa

nTa

kara

gaw

a-Sh

ozaw

a11

810

67SW

60%

har

dwoo

d,40

% c

onife

rs21

5317

8319

48-1

954

- 50%

volu

me

sele

cted

cut

ting

Bos

ch a

nd H

ewle

tt(1

982)

Kam

abuc

hiN

o. 2

2.48

36SE

Artif

icia

lfo

rest

, sta

ndin

gas

gro

ups

inha

rdw

ood

stan

d, w

ithde

nse

grou

ndco

ver

Res

trict

edre

grow

th26

1720

75N

o.1

100%

cle

ar fe

lled

Dec

1947

1940

-194

7N

akan

o (1

967)

Ken

yaKe

richo

Sam

bret

702

4.5

2200

NW

Mon

tane

fore

stw

ith b

ambo

ote

a22

3678

919

59-1

963

87%

Cut

for

tea

gard

enO

yeba

ne (1

988)

Kim

akia

35.2

2438

SB

ambo

o fo

rest

Pine

2198

1104

1956

100

% c

lear

ed, p

ine

plan

ted

Oye

bane

(198

8)

Big

Bus

h, N

ew Z

eala

nd

DC

18.

5755

0N

W

83%

of a

rea

skid

der-

logg

ed b

etw

een

April

and

Dec

embe

r 198

0.P.

radi

ata

plan

ted

May

1981

1975

-198

0

DC

420

.19

550

NW

Even

lydi

strib

uted

rain

fall

thro

ugho

ut th

eye

ar, m

ean

annu

alte

mpe

ratu

re o

f10

.5 D

egC

Mix

edev

ergr

een

nativ

e fo

rest

rem

nant

s an

dpl

anta

tions

of

exot

ic s

peci

es

Pinu

sra

diat

a15

30D

C2

4.74

550

NW

94%

cle

arfe

lled

and

harv

este

d by

hau

ler

betw

een

May

198

0 an

dJu

ne 1

981,

Pla

nted

with

Pinu

s ra

diat

a in

Sept

embe

r 198

1

1975

-198

0

Fahe

y an

d Ja

ckso

n(1

997)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Gle

ndhu

Sta

te F

ores

t, N

ew Z

eala

nd

GH

231

046

0-67

0N

Rai

nfal

l occ

urs

as m

any

smal

lev

ents

of l

ong

dura

tion

and

low

inte

nsity

. Dry

spel

ls a

reco

mm

on in

sum

mer

Snow

-Tu

ssoc

kPi

nus

radi

ata

1350

GH

121

846

0-67

0N

1350

67%

Pla

nted

with

Pin

usra

diat

a in

198

2 at

123

0st

ems/

ha19

79-1

982

McL

ean

(200

1);

Fahe

y an

d Ja

ckso

n(1

997)

Mai

mai

, Wes

tland

, New

Zea

land

M13

4.25

3734

0SW

Reg

row

th26

5015

50M

152.

6434

335

SW26

5015

5095

% C

lear

felle

d,ve

geta

tion

left

in ri

paria

nzo

ne19

77

M14

4.62

3634

0SW

Reg

row

th26

5015

50M

152.

6434

335

SW26

5015

5010

0% C

lear

felle

d19

77M

52.

3136

290

SWR

egro

wth

2650

1550

M6

1.63

3628

5SW

2650

1550

100%

Cle

arfe

lled

1977

M8

3.84

3630

5SW

Supe

rhum

id,

mic

roth

erm

al,

with

ade

quat

era

infa

ll in

all

seas

ons

Ever

gree

nm

ixed

bee

ch-

podo

carp

-ha

rdw

ood

fore

st .

Reg

row

th26

5015

50M

61.

6336

285

SW26

5015

5090

% C

lear

felle

d,ve

geta

tion

left

in ri

paria

nzo

ne19

77

Row

e et

al.

(199

4)R

owe

and

Pear

ce(1

994)

Cat

hedr

al P

eak,

Sou

th A

fric

a

Cal

th_i

i19

00.

4518

45-

2454

NC

old

dry

win

ters

and

hot w

etsu

mm

ers

Gra

ssla

nd,

woo

dyco

mm

uniti

esal

ong

the

stre

ams

Pinu

s pa

tula

(with

20m

strip

of

ripar

ian

zone

on

eith

er s

ide

of th

est

ream

)

1400

807

Cat

h_iv

94.7

0.35

1845

-222

6N

1400

673

75%

affo

rest

ed19

49-1

952

Scot

t et a

l. (2

000)

Cal

th_i

ii13

8.9

0.38

1845

-23

17N

Col

d dr

y w

inte

rsan

d ho

t wet

sum

mer

s

Gra

ssla

nd,

woo

dyco

mm

uniti

esal

ong

the

stre

ams

Pinu

s pa

tula

1400

683

Cat

h_iv

94.7

0.35

1845

-222

6N

1400

673

86%

affo

rest

ed19

51-1

960

Dye

(199

6); S

cott

and

Smith

(199

7);

Scot

t et a

l. (2

000)

Jonk

ersh

oek

Fore

st R

esea

rch

Cen

tre,

Sou

th A

fric

a

Bie

siev

lei

27.2

0.35

372-

580

SW

hum

idm

esot

herm

alM

edite

rrane

anty

pe

tall o

pen

tocl

osed

fybo

ssh

rubl

and

Pinu

sra

diat

a12

9859

4La

ngriv

ier

245.

80.

4036

6-14

60SW

1653

98%

affo

rest

ed w

ithPi

nus

radi

ata

1938

-194

8va

n W

yk (1

987)

;D

ye (1

996)

; Sco

tt et

al. (

2000

)

Bos

bouk

oof

200.

90.

2627

4-10

67SW

hum

idm

esot

herm

alM

edite

rrane

anty

pe

tall o

pen

tocl

osed

fybo

ssh

rubl

and

Pinu

sra

diat

a11

2724

6La

ngriv

ier

245.

80.

4036

6-14

60SW

1653

57%

affo

rest

atio

n w

ithPi

nus

radi

ata

1938

-194

0

van

Wyk

(198

7);

Scot

t and

Van

Wyk

(199

0) D

ye (1

996)

;Sc

ott e

t al.

(200

0)

Lam

brec

htsb

os A

31.2

0.45

366-

1067

SW

hum

idm

esot

herm

alM

edite

rrane

an

tall o

pen

tocl

osed

fybo

ssh

rubl

and

Pinu

sra

diat

a11

4556

4La

ngriv

ier

245.

80.

4036

6-14

60SW

1653

89%

affo

rest

ed w

ithPi

nus

radi

ata

1946

-197

2va

n W

yk (1

987)

;Sc

ott e

t al.

(200

0)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Lam

brec

htsb

os B

65.5

0.46

300-

1067

SW

hum

idm

esot

herm

alM

edite

rrane

anty

pe

tall o

pen

tocl

osed

fybo

ssh

rubl

and

Pinu

sra

diat

a11

4551

8La

ngriv

ier

245.

80.

4036

6-14

60SW

1653

825

affo

rest

ed w

ithPi

nus

radi

ata

(20m

buffe

r on

stre

am b

anks

)19

47-1

964

van

Wyk

(198

7);

Scot

t and

Van

Wyk

(199

0); D

ye (1

996)

;Sc

ott a

nd S

mith

(199

7); S

cott

et a

l.(2

000)

Tier

kloo

f15

7.2

0.49

280-

1530

SW

hum

idm

esot

herm

alM

edite

rrane

anty

pe

tall o

pen

tocl

osed

fybo

ssh

rubl

and

Pinu

sra

diat

a13

1910

77La

ngriv

ier

245.

80.

4036

6-14

60SW

1653

36%

affo

rest

ed w

ithPi

nus

radi

ata

1938

-195

6va

n W

yk (1

987)

;Sc

ott e

t al.

(200

0)

Uits

oek

Sta

te F

ores

t, S

outh

Afr

ica

Mok

obul

aan

A26

.20.

2312

92-

1433

EEu

calyp

ts11

6619

7M

okob

ulaa

n C

36.9

0.26

1341

-149

4E

1199

118

100%

affo

rest

ed w

ithEu

calyp

tus

gran

dis

1956

-196

8

Van

Lill e

t al.

(198

0); D

ye (1

996)

;Sc

ott a

nd L

esch

(199

7); S

cott

and

Smith

(199

7); S

cott

et a

l. (2

000)

Mok

obul

aan

B34

.60.

2213

18-

1486

E

Sub

Trop

ical

with

80%

of th

eav

erag

e an

nual

rain

fall o

f11

67m

m fa

lling

with

in th

esu

mm

er m

onth

sof

Oct

ober

toM

arch

. Rai

nfal

lru

noff

ratio

0.1

8

sub-

clim

axgr

assl

and,

Nor

th E

aste

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ount

ain

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aint

aine

d by

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d gr

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1197

196

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2613

41-1

494

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8

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affo

rest

ed w

ithPi

nus

patu

la, J

anua

ry19

71.

1971

- 13

70 s

tem

s/ha

1979

- 65

0 st

ems/

ha

1956

-197

1

Van

Lill e

t al.

(198

0); D

ye (1

996)

;Sc

ott a

nd L

esch

(199

7); S

cott

and

Smith

(199

7); S

cott

et a

l. (2

000)

Wes

tfal

ia, S

outh

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Wes

tfalia

D39

.60.

3310

50-

1320

SESu

b Tr

opic

alw

ith S

umm

erra

infa

ll sea

son

trans

ition

albe

twee

nev

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een

high

fore

st a

ndde

cidu

ous

woo

dlan

d

Euca

lyptu

sgr

andi

s12

5359

0W

estfa

lia B

32.6

0.42

1140

-142

0SE

1253

492

Rip

aria

n zo

ne (1

0%) o

far

ea c

ut in

198

1. 8

3%af

fore

sted

with

Euca

lyptu

s gr

andi

s in

1983

1975

-198

1Sc

ott a

nd S

mith

(199

7); S

cott

et a

l.(2

000)

Taiw

an

LHC

-45.

8640

SE

war

m a

ndhu

mid

, the

aver

age

mon

thly

tem

pera

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5de

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War

m-

tem

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info

rest

Reg

row

th21

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8.39

SW19

78-1

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100%

cle

arcu

t19

70-1

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a an

d Ko

h (1

983)

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TED

CAT

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ENT

CON

TRO

L CA

TCH

MEN

T

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nM

ean

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nM

ean

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nPo

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nual

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alM

ean

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alAn

nual

Area

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evat

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Pre

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nfal

lSt

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flow

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chm

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Area

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evat

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trol

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nfal

lSt

ream

flow

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tion

Cat

chm

ent

(ha)

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ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Adi

rond

acks

, New

Yor

k, U

SA

Saca

ndag

a12

7000

575

Nor

ther

nha

rdw

oods

with

con

ifers

1143

770

1912

-195

0, b

asal

are

ain

crea

sed

from

17

to 2

8m

2 ha-1

Bos

ch a

nd H

ewle

tt(1

982)

Alu

m C

reek

, Ark

., U

SA

WS2

115

412

NE

pine

with

hard

woo

dun

ders

tory

1333

153

WS1

115

NE

1333

1970

, 45%

thin

ned,

unde

rgro

wth

kille

d by

herb

icid

e ap

plic

atio

n

WS3

115

412

NE

pine

with

hard

woo

dun

ders

tory

1333

153

WS1

115

NE

1333

7-19

70, 1

00%

Cle

arcu

tsp

raye

dan

nual

ly fo

r3

year

s

Rog

erso

n (1

971)

Bea

ver

Cre

ek, A

rizon

a, U

SA

WS

314

715

73W

Reg

row

th45

322

Bea

ver C

reek

251

1591

NW

466

2519

68 -

83%

of t

rees

kille

d by

her

bici

deB

aker

(198

4); B

aker

(198

6)

WS1

134

1595

-17

90W

Juni

per-

piny

onfo

rest

Gra

ss45

720

Bea

ver C

reek

251

1591

NW

466

25

1963

- la

rge

piny

on p

ine

and

juni

per t

rees

rem

oved

. Are

a pl

ante

dw

ith g

rass

1958

-196

2B

aker

(198

6)

WS

1218

421

50SW

617

150

WS

1336

921

95SW

609

9310

0% re

mov

al o

fov

erst

ory

Bak

er (1

986)

WS

1454

621

94S

650

117

WS

1336

921

95SW

609

9357

% s

trip

cutti

ng w

ithth

inni

ngB

aker

(198

6)

WS

1610

221

64SE

703

135

WS

1566

2103

S68

599

68%

stri

p-cu

t with

thin

ning

Bak

er (1

986)

WS

1712

121

15SW

726

206

Wat

ersh

ed 1

898

2054

S72

818

077

% re

mov

al o

fov

erst

ory

Bak

er (1

986)

WS

873

022

25W

679

174

WS

1336

921

95SW

609

9333

% re

mov

al o

fov

erst

ory

Bak

er (1

986)

WS

945

421

94W

Une

ven

aged

stan

ds o

fpo

nder

osa

pine

645

155

WS

873

022

25W

658

160

31%

stri

p cu

t with

thin

ning

Bak

er (1

986)

Cas

tle C

reek

, Ariz

ona,

US

A

Wes

t For

k36

4.2

12.6

2165

NW

Pred

omin

atel

ypo

nder

osa

pine

with

an

unde

rsto

ry o

fG

ambl

e oa

k

Reg

row

th68

650

.8Ea

st C

reek

471

13.8

2388

-261

6N

14W

686

76.2

one-

sixt

h ar

ea h

arve

sted

,re

mai

nder

thin

ned

for

optim

um g

row

ing

cond

ition

s

1955

-196

4R

ich

(197

2); B

aker

(199

9)

Wor

kman

Cre

ek, C

entr

al A

rizon

a

Nor

th F

ork

100.

420

10-

2356

SW

Col

d m

oist

win

ters

, dry

war

sprin

gs a

nd h

otm

oist

sum

mer

s.

Pond

eros

a pi

neO

n th

e m

oist

site

s, D

ougl

asfir

and

whi

te fi

rar

eim

porta

nt

gras

slan

d83

587

Mid

dle

fork

210.

920

10-2

356

835

86

1953

- rip

aria

n cu

t of

broa

dlea

f spe

cies

1958

- co

nver

t moi

st s

ite(m

ostly

Dou

glas

fir a

ndw

hite

fir t

o gr

assl

and.

1966

con

vert

dry

site

(mos

tly

1938

-195

2R

ich

and

Got

terfr

ied

(197

6)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Sout

h Fo

rk12

8.7

2010

-23

56SW

Pond

eros

api

ne83

5M

iddl

e fo

rk21

0.9

2010

-235

683

586

1953

- St

art s

ingl

e tre

ese

lect

ion

harv

est

1966

- co

nver

t to

pure

pond

eros

a pi

ne w

ithba

sal a

rea

of 9

.2 m

2/ha

1938

-195

2R

ich

and

Got

tfrie

d(1

976)

Cen

tral

New

Yor

k, U

SA

Col

d Sp

ring

Bro

ok39

156

5S

1030

616

1934

, 35%

refo

rest

edw

ith c

onife

rs

Sage

Bro

ok18

152

5SE

974

535

1932

, 47%

refo

rest

edw

ith c

onife

rsSh

ackl

amB

rook

808

520

S

Con

tinen

tal t

ype

clim

ate

mix

edha

rdw

oods

and

coni

fers

mix

edha

rdw

oods

and

coni

fers

1030

627

Albr

ight

Cre

ek18

30

1931

-193

9, 5

8%re

fore

sted

with

con

ifers

Schn

eide

r and

Aye

r(1

961)

Cos

hoct

on, O

hio,

US

A

172

1822

.830

6-39

3S

30%

har

dwoo

din

193

8

100%

hard

woo

d/w

hite

pin

e97

030

019

612

2.6

1427

6-36

1SE

970

1938

-193

9, 1

0%re

fore

sted

, mos

tly p

ine

Har

rold

et a

l. (1

962)

Cow

eeta

, Nor

th C

arol

ina,

US

A

Cow

eeta

116

.234

705-

988

SO

ak-h

icko

ryfo

rest

Whi

te P

ine

1727

787

1956

- en

tire

catc

hmen

tcl

ear-

cut a

nd w

hite

pin

ese

edlin

gs p

lant

ed19

44-1

953

Swan

k an

d M

iner

(196

8); S

wan

k et

al.

(198

7)

Cow

eeta

, 10

8624

742-

1159

SSE

Oak

-hic

kory

fore

stR

egro

wth

Expl

oitiv

e se

lect

ivelo

ggin

g du

ring

the

perio

d19

42-1

956

with

a 3

0%re

duct

ion

in to

tal

wat

ersh

ed b

asal

are

a

Swan

k an

d C

ross

ley

(198

7)

Cow

eeta

, 13

16.1

1972

5-91

2EN

E

Seco

ndre

grow

th s

tand

with

asc

atte

ring

ofov

erm

atur

etre

es

Reg

row

th18

2987

2W

S18

12.5

726-

993

NW

1939

1034

Sept

193

9-Ja

n 19

40 a

llw

oody

veg

etat

ion

was

cut (

no m

ater

ial

rem

oved

); 19

62R

egro

wth

cut

1936

/37-

1939

/194

0

Swan

k an

d H

elve

y(1

973)

; Sw

ank

et a

l.(1

987)

Cow

eeta

, 17

13.4

5776

0-10

21N

WO

ak-h

icko

ryfo

rest

Whi

te P

ine

1930

868

All s

hrub

and

fore

stve

geta

tion

cut J

an-M

arch

1942

Ann

ual s

prou

tgr

owth

cut

194

3-19

5519

56 w

hite

pin

e pl

ante

d

1938

-194

1Sw

ank

and

Min

er(1

968)

; Sw

ank

et a

l.(1

987)

Cow

eeta

, 19

28.3

3279

6-11

19N

W

mar

ine

with

coo

lsu

mm

ers,

mild

win

ters

, and

adeq

uate

rain

fall

durin

g al

lse

ason

s

Oak

-hic

kory

fore

stR

egro

wth

2032

1219

WS2

123

.982

3-11

74N

2083

1321

Dec

194

8-M

arch

194

9al

l laur

el a

ndrh

odod

endr

on c

ut c

lose

to g

roun

d (2

2% o

f bas

alar

ea)

1941

-194

8Jo

hnso

n an

dKo

vene

r (19

56)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Cow

eeta

, 22

3435

847-

1244

NO

ak-h

icko

ryfo

rest

All w

oody

veg

etat

ion

with

in a

ltern

ate

10m

strip

s de

aden

ed b

ych

emic

als

in 1

955;

redu

ced

tota

l bas

al a

rea

by 5

0%

Swan

k an

d C

ross

ley

(198

7)

Cow

eeta

, 28

144

3196

4-15

51E

Oak

-hic

kory

fore

st

Mul

tiple

use

dem

onst

ratio

nco

mpr

isin

g of

com

mer

cial

har

vest

with

clea

r-cu

tting

on

77ha

,th

inni

ng o

n 39

ha o

f the

cove

fore

st a

nd n

ocu

tting

on

28 h

a;pr

oduc

ts re

mov

ed

Swan

k an

d C

ross

ley

(198

7)

Cow

eeta

, 39.

232

739-

931

EO

ak-h

icko

ryfo

rest

1814

607

All v

eget

atio

n cu

t and

burn

t or r

emov

ed fr

omth

e w

ater

shed

in 1

940.

Unr

egul

ated

agr

icul

ture

on 6

ha fo

r a 1

2 ye

arpe

riod,

follo

wed

by

plan

ting

yello

w p

opla

ran

d w

hite

pin

e

Swan

k an

d C

ross

ley

(198

7)

Cow

eeta

, 37

43.7

3312

80N

E

Seco

ndre

grow

th s

tand

with

asc

atte

ring

ofov

erm

atur

etre

es

2244

1515

Cow

eeta

36

4910

21-1

542

ESE

2222

1675

100%

veg

etat

ion

cut i

n19

63 (n

o pr

oduc

tsre

mov

ed)

1943

/194

4-19

57/1

958

Swan

k an

d H

elve

y(1

973)

Cow

eeta

, 40

2042

1035

SEO

ak-h

icko

ryfo

rest

Com

mer

cial

sel

ectio

ncu

t with

22%

of b

asal

area

rem

oved

in 1

955

Swan

k an

d C

ross

ley

(198

7)

Cow

eeta

, 41

2946

1065

SEO

ak-h

icko

ryfo

rest

Reg

row

th

Com

mer

cial

sel

ectio

ncu

t with

35%

of b

asal

area

rem

oved

in 1

955

Swan

k an

d C

ross

ley

(198

7)

Cow

eeta

, 69

3579

3N

W

mar

ine

with

coo

lsu

mm

ers,

mild

win

ters

, and

adeq

uate

rain

fall

durin

g al

lse

ason

s

oak

- hic

kory

fore

stG

rass

1854

838

WS1

4/W

S18

61/1

370

7-99

2/99

3-72

6N

W/N

W18

76/1

939

988/

1034

1942

- 12

% o

f cat

chm

ent

alon

g st

ream

was

cut

(Reg

row

th) 1

958

-m

erch

anta

ble

timbe

rre

mov

ed 1

959

- rem

inde

rof

cat

chm

ent c

lear

ed a

ndgr

ass

sow

n. G

rass

herb

icid

e in

196

6 an

d19

67 G

rass

her

bici

de in

1966

and

196

7

Hib

bert

(196

9); B

urt

and

Swan

k (1

992)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Coy

ote

Cre

ek, O

rego

n, U

SA

Coy

ote

169

730-

1065

Dou

glas

fir,

mix

ed c

onife

rs12

3062

7C

oyot

e 4

4973

0-10

6550

% s

elec

tion

cut 1

971

1963

-197

0Jo

nes

(200

0)

Coy

ote

268

730-

1065

Dou

glas

fir,

mix

ed c

onife

rsC

oyot

e 4

4973

0-10

6525

-30%

Pat

ch c

ut 1

971

1963

-197

0Jo

nes

(200

0)

Coy

ote

350

730-

1065

Dou

glas

fir,

mix

ed c

onife

rs

Reg

row

th

Coy

ote

449

730-

1065

100%

cle

ar-c

ut 1

971

1963

-197

0Jo

nes

(200

0)

East

ern

Tenn

esse

e, U

SA

Whi

teH

ollo

w69

441

0SE

65%

mix

edha

rdw

oods

and

pine

in 1

934

Reg

row

th11

8446

019

34-1

942,

34%

refo

rest

ed w

ith p

ines

Bos

ch a

nd H

ewle

tt(1

982)

Fox

Cre

ek, O

rego

n, U

SA

FC1

5984

0-92

5R

egro

wth

2730

1750

Fox

225

384

0-19

8819

69-1

7, 2

5% c

lear

-cut

in 3

-4ha

uni

ts19

58-1

964

Sted

nick

(19

96);

Jone

s (2

000)

FC3

7184

0-95

0

The

mar

itim

ecl

imat

e ha

s w

et,

mild

win

ters

and

dry,

coo

lsu

mm

ers

Dou

glas

Fir,

wes

tern

hem

lock

Reg

row

th27

3017

50Fo

x 2

253

840-

1988

1970

-197

2, 2

5% c

lear

cut i

n 8-

10 h

a un

its19

58-1

969

Jone

s (2

000)

Fras

er E

xper

imen

tal F

ores

t, U

SA

Dea

dhor

seC

reek

-N

orth

For

k41

Reg

row

thLe

xen

Cre

ek12

430

02-3

536

Tim

ber r

emov

ed o

n 36

%of

land

are

a (1

977)

1970

-197

7Al

exan

der e

t al.

(198

5); T

roen

dle

and

King

(198

5)

Dea

dhor

seC

reek

-U

pper

Bas

in78

lodg

epol

e pi

neon

all l

ower

and

mid

-sou

thsl

opes

, and

alpi

ne tu

ndra

abov

e th

etim

ber l

ine

Reg

row

thLe

xen

Cre

ek12

430

02-3

536

30%

har

vest

ed in

irreg

ular

sha

ped

clea

r-cu

ts, v

aryin

g in

size

from

1 to

6 h

a (S

umm

ers

of19

83-1

984)

1975

-198

2Al

exan

der e

t al.

(198

5); T

roen

dle

and

King

(198

5)

Fool

Cre

ek28

928

96-

3505

SW

Coo

l and

hum

idw

ith lo

ng, c

old

win

ters

and

shor

t, co

olsu

mm

ers

lodg

epol

e pi

nean

d sp

ruce

-fir

Reg

row

th76

228

3Ea

st S

t Lou

isC

reek

803

2896

-371

919

54-1

956,

40%

com

mer

cial

cut

in s

trips

.19

43-1

953

Alex

ande

r et a

l.(1

985)

Gra

nt M

emor

ial F

ores

t, G

eorg

ia, U

SA

WS1

432

.516

5SW

Fully

fore

sted

pied

mon

t lan

dlo

blol

ly pi

neW

S15

42.5

100%

cat

chm

ent c

lear

edO

ct 1

974-

Jan

1975

. Jan

1976

Lob

lolly

pin

epl

ante

d

1973

-197

4H

ewle

tt et

al.

(198

4);

Hew

lett

and

Dos

s(1

984)

H.J

. And

rew

s Ex

perim

enta

l For

est,

US

A

HJ1

9628

460-

990

WR

egro

wth

2286

HJ

260

530-

1070

NW

2286

100%

cle

ar-c

ut (1

962-

1966

)19

52-1

961

Jone

s (2

000)

;R

otha

cher

(197

0)H

J10

10.1

425-

700

SR

egro

wth

2286

HJ

260

530-

1070

NW

2286

100%

cle

ar-c

ut (1

975)

1968

-197

4Jo

nes

(200

0)

HJ3

101

3249

0-10

70N

W

typi

cally

wet

inw

inte

r and

dry

insu

mm

er

Old

gro

wth

Dou

glas

fir

Reg

row

th22

86H

J 2

6053

0-10

70N

W22

8625

-30%

Pat

ch c

ut 1

963

1952

-195

8Jo

nes

(200

0);

Rot

hach

er (1

970)

HJ6

1388

0-10

10S

Reg

row

th22

86H

J 8

21.4

960-

1130

S22

8610

0% C

lear

-cut

(197

4)19

63-1

973

Jone

s (2

000)

HJ7

15.4

910-

1020

S

typi

cally

wet

inw

inte

r and

dry

insu

mm

er

Old

gro

wth

Dou

glas

fir

Reg

row

th22

86H

J 8

21.4

960-

1130

S22

8650

% C

lear

-cut

in 1

974

and

in 1

984

1963

-197

3Jo

nes

(200

0)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

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nPo

stAn

nual

Annu

alM

ean

Annu

alAn

nual

Area

Slop

eEl

evat

ion

Pre

Trea

tmen

tTr

eatm

ent

Rai

nfal

lSt

ream

flow

Cat

chm

ent

Area

Slop

eEl

evat

ion

Con

trol

Rai

nfal

lSt

ream

flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

Aspe

ctC

limat

eVe

geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Hub

bard

Bro

ok E

xper

imen

tal F

ores

t

WS2

15.8

20-

30%

503-

716m

S31E

Reg

row

th12

19W

S342

.452

7-73

2S2

3W12

19C

lear

fellin

g an

dhe

rbic

idin

g (1

965-

1968

)7

year

s

WS4

3642

2-74

7S4

0ER

egro

wth

1219

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42.4

527-

732

S23W

1219

Strip

cut

in th

ree

phas

esdu

ring

the

autu

mns

of

1970

, 197

2 an

d 19

7419

60-1

969

WS5

2248

8-76

2S

even

age

dde

cidu

ous

hard

woo

ds

Reg

row

th12

19W

S342

.452

7-73

2S2

3W12

19W

hole

tree

har

vest

ing

1983

-198

4.19

63-1

983

Mar

tin a

ndH

ornb

eck

(198

9);

Fede

rer e

t al.

(199

0);

Hor

nbec

k et

al.

(199

7); H

ornb

eck

etal

. (19

87)

Lead

ing

Rid

ge E

xper

imen

tal F

ores

t, U

SA

LR2

4336

0N

E10

0432

1

1967

- cl

earc

ut lo

wes

t9h

a19

71-1

972

Cle

arcu

t mid

-sl

ope

11ha

1974

Her

bici

de lo

wer

and

mid

-slo

pe a

reas

1975

-197

6 C

lear

cut 1

7ha

on u

pper

slo

pe19

77 -

Her

bici

de a

llcl

earc

ut a

reas

LR 3

104

340

Cen

tral

hard

woo

dsR

egro

wth

LR 1

123

800-

1450

SE

1976

-197

7 C

lear

cut

on

45 h

a

Die

tteric

k an

d Ly

nch

(198

9); L

ynch

and

Cor

bett

(199

0);

Hor

nbec

k et

al.

(199

3)

Nat

ural

Dra

niag

es, A

rizon

a, U

SA

A5

1420

SE45

234

1954

, 100

% c

hem

ical

lyco

ntro

lled

C5

1420

SE

Mar

gina

lch

apar

ral

Reg

row

th45

234

1954

100

% c

hem

ical

lyco

ntro

lled

Nor

ther

n M

issi

ssip

pi, U

SA

WSI

I1

E13

54W

SIII

1E

Mix

edha

rdw

ood

Reg

row

th

Als

ea R

iver

Bas

in, O

rego

n, U

SA

Dee

r Cre

ek30

331

260

% d

ougl

as fi

ran

d 40

% a

lder

Reg

row

th24

7419

06Fl

ynn

Cre

ek20

219

66-1

967,

25%

cle

ar-

cut i

n pa

tche

s, ro

ads

cons

truct

ed

1959

-196

5H

arris

(197

7)

Nee

dle

Bra

nch

7031

2

85%

ald

er a

ndm

aple

bef

ore

cutti

ng. 1

3ha

inth

e he

adw

ater

sha

d be

enpr

evio

usly

logg

ed in

ear

ly19

50's

Reg

row

th24

8318

86Fl

ynn

Cre

ek20

224

8319

7482

% c

lear

-cut

, bur

ned

in19

6719

59-1

965

Har

ris (1

973)

; Har

ris(1

977)

TREA

TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

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alM

ean

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alAn

nual

Area

Slop

eEl

evat

ion

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tmen

tTr

eatm

ent

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nfal

lSt

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flow

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chm

ent

Area

Slop

eEl

evat

ion

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trol

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nfal

lSt

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flow

Cal

ibra

tion

Cat

chm

ent

(ha)

(%)

(m)

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limat

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geta

tion

Vege

tatio

n(m

m)

(mm

)C

ontro

l(h

a)(%

)(m

)As

pect

(mm

)(m

m)

Trea

tmen

tpe

riod

Sour

ce o

f inf

o

Pla

cer

Cou

ntry

, US

A

Wat

ersh

ed B

17.2

Oak

woo

dlan

dG

rass

Wat

ersh

ed A

19.1

1966

- C

onve

rsio

n to

gras

slan

d co

mm

ence

d19

56-1

965

Bur

gy a

ndPa

paza

firio

u (1

971)

Wat

ersh

ed C

516

8N

Oak

woo

dlan

dG

rass

635

145

Wat

ersh

ed A

19.1

Vege

tatio

n co

nver

sion

togr

assl

and

com

men

ced

in19

63.

1956

-196

2B

urgy

and

Papa

zafir

iou

(197

1)

San

Dim

as, C

alif.

, U

SA

Mon

roe

Can

yon

354

840

S

Cha

parra

l with

woo

dlan

drip

aria

nve

geta

tion

alon

g st

ream

s

Reg

row

th19

58 -

1.7%

cut

(Rip

aria

n ve

geta

tion)

Bos

ch a

nd H

ewle

tt(1

982)

Thre

e B

ar, U

SA

B1

910

80N

Cha

parra

lG

rass

582

1119

60 -

conv

erte

d to

gras

s19

56-1

959

Hib

bert

(197

1)

C3

911

60N

Cha

parra

lG

rass

638

5819

65 -

conv

erte

d to

gra

ss19

56-1

959

Hib

bert

(197

1)

F27

.741

168

NC

hapa

rral

Gra

ss68

136

D36

.234

1969

- ch

apar

ral c

lear

edgr

ass

plan

ted

1964

-196

9D

avis

(198

4)

Upp

er B

ear

Cre

ek A

la.,

US

A

XF1

5313

97

1971

-197

2, 3

0%cl

earc

ut, a

dditi

onal

22%

sele

ctive

cut

, bur

ned

resi

dual

poi

sone

d pi

nes

plan

ted

XF2

53

Pine

and

hard

woo

dPi

ne

1969

-197

0 85

%co

nver

sion

s to

pin

e af

ter

com

mer

cial

cle

arcu

t

Bos

ch a

nd H

ewle

tt(1

982)

Fern

ow E

xper

imen

tal F

ores

t, U

SA

Fern

ow 1

3075

5N

E15

2458

419

57-1

958

85%

bas

alar

eas

clea

rcut

Fern

ow 2

1578

0S

1500

660

1957

-58

36%

are

clea

rcut

Fern

ow 3

3480

5S

rain

y an

d co

olcl

imat

eM

ixed

hard

woo

dsR

egro

wth

1473

762

Fern

ow 4

39SE

1422

60

1957

-58

13%

bas

al a

rea

rem

oved

by

sele

ctio

n cu

t19

63 -

8% R

emov

ed b

ysa

me

met

hod

1968

- 6%

of b

asal

are

are

mov

ed, s

ame

met

hod

1969

-197

0 - 9

1%cl

earc

ut

Patri

c an

d R

einh

art

(197

1); H

ornb

eck

etal

. (19

93)

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TED

CAT

CHM

ENT

CON

TRO

L CA

TCH

MEN

T

Mea

nM

ean

Mea

nM

ean

Mea

nPo

stAn

nual

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alM

ean

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alAn

nual

Area

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eEl

evat

ion

Pre

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tmen

tTr

eatm

ent

Rai

nfal

lSt

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flow

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chm

ent

Area

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eEl

evat

ion

Con

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nfal

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flow

Cal

ibra

tion

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chm

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(ha)

(%)

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m)

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o

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3678

0N

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0060

7

1957

-195

8 20

% b

asal

area

rem

oved

by

sele

ctio

n cu

t19

68 -

treat

men

tre

peat

ed (1

4%)

Fern

ow 6

2280

5SE

1469

788

1964

- lo

wer

50%

cut

,re

grow

th n

ot p

erm

itted

1968

- up

per 5

0% c

ut19

56-1

963

Fern

ow 7

2480

0N

E14

4049

3

1963

- up

per 5

0%cl

earc

ut.

1967

- lo

wer

50%

clea

rcut

1956

-196

3

Wag

on W

heel

Gap

, US

A

B8

131

10N

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pen

and

Con

ifer

536

158

A90

3110

SE53

415

319

19 -

Cle

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wat

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919

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8)

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SA

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Tre

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160

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ixed

hard

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1230

255

1946

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fore

sted

mos

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Bos

ch a

nd H

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tt(1

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te S

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A

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911

60N

Cha

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549

34

1967

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rub

on 1

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emic

ally

treat

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74-1

976,

shr

ub o

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% c

hem

ical

ly tre

ated

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par

Cre

ek, C

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rnia

, US

A

Sout

h Fo

rk42

437

-320

Med

iterra

nean

,dr

y su

mm

ers.

Nor

th F

ork

483

37-2

30R

oad

cons

truct

ion

1967

Logg

ing

1971

-197

319

63-1

967

Kepp

eler

and

Zie

mer

(199

0); W

right

et a

l.(1

990)

Ply

nlim

on, U

nite

d K

ingd

omSe

vern

1055

Wye

870

Kirb

y et

al.

(199

1)

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exp

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ent,

Uni

ted

Kin

gdom

Kirk

ton

685

540

Mon

oach

yle77

047

0Jo

hnso

n (1

991)

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ENT

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TRO

L CA

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MEN

T

Mea

nM

ean

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nM

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nual

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Cal

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chm

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(ha)

(%)

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Vege

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o

Integrated catchment management in the Murray-Darling BasinA process through which people can develop a vision, agree on shared values and behaviours, make informeddecisions and act together to manage the natural resources of their catchment: their decisions on the use of land,water and other environmental resources are made by considering the effect of that use on all those resources and onall people within the catchment.

Our valuesWe agree to work together, and ensure that our behaviour reflects that following values.

Courage

• We will take a visionary approach, provide leadershipand be prepared to make difficult decisions.

Inclusiveness

• We will build relationships based on trust andsharing, considering the needs of futuregenerations, and working together in a truepartnership.

• We will engage all partners, including Indigenouscommunities, and ensure that partners have thecapacity to be fully engaged.

Commitment

• We will act with passion and decisiveness, takingthe long-term view and aiming for stability indecision-making.

• We will take a Basin perspective and a non-partisan approach to Basin management.

Respect and honesty

• We will respect different views, respect each otherand acknowledge the reality of each other’s situation.

• We will act with integrity, openness and honesty, be fairand credible and share knowledge and information.

• We will use resources equitably and respect theenvironment.

Flexibility

• We will accept reform where it is needed, be willingto change, and continuously improve our actionsthrough a learning approach.

Practicability

• We will choose practicable, long-term outcomesand select viable solutions to achieve theseoutcomes.

Mutual obligation

• We will share responsibility and accountability, andact responsibly, with fairness and justice.

• We will support each other through the necessarychange.

Our principlesWe agree, in a spirit of partnership, to use the followingprinciples to guide our actions.

Integration

• We will manage catchments holistically; that is,decisions on the use of land, water and otherenvironmental resources are made by consideringthe effect of that use on all those resources and onall people within the catchment.

Accountability

• We will assign responsibilities and accountabilities.

• We will manage resources wisely, beingaccountable and reporting to our partners.

Transparency

• We will clarify the outcomes sought.

• We will be open about how to achieve outcomesand what is expected from each partner.

Effectiveness

• We will act to achieve agreed outcomes.

• We will learn from our successes and failures andcontinuously improve our actions.

Efficiency

• We will maximise the benefits and minimise thecost of actions.

Full accounting

• We will take account of the full range of costs andbenefits, including economic, environmental, socialand off-site costs and benefits.

Informed decision-making

• We will make decisions at the most appropriate scale.

• We will make decisions on the best availableinformation, and continuously improve knowledge.

• We will support the involvement of Indigenouspeople in decision-making, understanding the valueof this involvement and respecting the livingknowledge of Indigenous people.

Learning approach

• We will learn from our failures and successes.

• We will learn from each other.

Documents

A critical review of paired catchment studies with ...€¦ · A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY iii Acknowledgements