Journal of Hydrology 446–447 (2012) 24–34

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Journal of Hydrology

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Spatiotemporal analysis of the effects of forest covers on water yield in the WesternGhats of peninsular India

Sunita Singh a,⇑, Arabinda Mishra b,c

a TERI University, Vasant Kunj, New Delhi, Indiab Faculty of Policy and Planning, TERI University, Indiac Earth Sciences and Climate Change Division, TERI, New Delhi, India

a r t i c l e i n f o

Article history:Received 2 February 2012Received in revised form 9 April 2012Accepted 11 April 2012Available online 21 April 2012This manuscript was handled by GeoffSyme, Editor-in-Chief

Keywords:ForestWater yieldRunoff coefficientCanopy densityTropics

0022-1694/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jhydrol.2012.04.021

⇑ Corresponding author. Address: P-4/2 Sector-13,066, India. Tel.: +91 9013626087; fax: +91 11233860

E-mail addresses: sunitaifs87@gmail.com (S.(A. Mishra).

s u m m a r y

Biotic interference has greatly disturbed the forest cover, the forest soils and, therefore, the hydrologicalfunctioning of the forest (Bonell and Bruijnzeel, 2005). Though widely debated, reduction in water yield(Water Yield: Total quantity of surface water that can be expected in a given period from a stream at theoutlet of its catchment (Subramanya, 2008)) appears to be one such consequence. Scientific understand-ing of how this contentious issue affects the benefits of forests for water is critical to avoid unintendedconsequences (IUFRO, 2007). Gaps in research exist for tropical forest areas that are now a general mixof primary forest and secondary vegetation interspersed with patches cleared for agriculture or othernon-forest uses (Bruijnzeel, 2004; Giambelluca, 2002). For this reason, research on spatiotemporal vari-ations in the effects of a mix of primary forest (Primary Forests: Old forests with no or inconsequentialhuman disturbance), mature secondary forests (Secondary Forests: Forests regenerating largely throughnatural processes after significant human and/or natural disturbance of the original forest vegetation at asingle point in time or over an extended period, and displaying a major difference in forest structure and/or canopy species composition with respect to nearby primary forests on similar sites (Chokkalingam andJong, 2001)) and disturbed forests (Disturbed Forests: Forests that have been exploited on moderate tolarge scale for timber, fuel wood, fodder, shifting cultivation and other tangible benefits. Reforestationactivities may or may not have been undertaken in them) on runoff coefficients was conducted in fourwatersheds in the Western Ghats of peninsular India. Forest cover (Forest Cover: All lands with tree coverof canopy density of 10% and above when projected vertically on the horizontal ground with minimumareal extent of one Ha) significantly (0.01 < p < 0.05) and positively influenced the runoff coefficient and,thus, the water yield. Despite wide variations in the forest cover, the Tulsi Watershed having mainly pri-mary forests, the Tansa having over 52% forest cover of primary and mature secondary forests and nearly60–70% of forest cover of Bhatsa and Upper Vaitarna being disturbed forests, the impact of forest cover onrunoff coefficient did not vary significantly from one watershed to the other. However, when the forestcover was segregated into old forests (primary forest, mature secondary forest and undisturbed matureplantations) and mixed forests (disturbed forests and to lesser extent naturally occurring open forest(Open Forests: All lands with tree cover of canopy density between 10% and 40%)), the old forests wereobserved to positively and highly significantly (p < 0.01) influence runoff coefficient. In contrast, themixed forests exhibited negative trend that was not statistically significant. The change in water yieldin relation to the changes in forest cover is quantified for three watersheds that face moderate to heavybiotic pressure.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

Forests are generally perceived to be good for the water envi-ronment under all circumstances. Hence conserving (or extending)

ll rights reserved.

R.K. Puram, New Delhi 11006.Singh), amishra@teri.res.in

forest cover in upstream watersheds was deemed the most effec-tive measure to enhance water availability for agriculture, indus-trial and domestic uses. However, forest hydrology researchesconducted during the last three decades (Bosch and Hewlett,1982; Bruijnzeel, 1990; Hamilton and King, 1983; Nik 1988; Pierceet al., 1970; Robinson, 1998; Scott and Lesch 1997; Sikka et al.,2003; Trimble et al., 1987; van Lill et al., 1980) suggest that thehydrological benefits of forests in respect of increasing down-stream water yield and regulating dry season flow have been

1 Natural stream flow unaffected by works of man such as reservoirs and diversionstructure.2 Though the network of roads act as water pathways during storms acceleratingflows to rivers they have not been taken as explanatory variable as this studyprimarily focuses on spatiotemporal variations in forest cover and its influence onwater yield.

3 Slash-and-burn agriculture refers to the practice of cutting, collection andspreading of biomass like leaf, grasses, twigs and cow dung (slash) on permanentagricultural lands before a dry season. The ‘‘slash’’ is permitted to dry, and thenburned in the following dry season. The resulting ash fertilizes the soil, and theburned field is then planted at the beginning of the next rainy season with crops suchas rice and millets. Most of this work is typically done by hand, using machetes, axes,

S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34 25

exaggerated. Many now agree that in comparison to the shortercrops, forests decrease runoff and dry season flows on account ofhigher interception losses in wet conditions (because of increasedatmospheric transport of water vapour from their aerodynamicallyrough surfaces), and greater transpiration losses in dry (drought)conditions (because of the generally increased rooting depth oftrees, and consequently greater access to soil water), respectively(Bruijnzeel, 2004; Calder et al., 2004; Calder et al., 2007; IUFRO,2007; van Dijk and Keenan, 2007). Calder (2003, 2005) even ques-tions the wisdom of having forest as land cover to increase down-stream water yield in arid or semi-arid ecosystems.

However, Malmer et al. (2009) question such generalizations,particularly for the tropics on account of broad use of the terms‘forests’, ‘afforestation’ and ‘reforestation’, and use of data gener-ated mostly outside the tropics. Too often all forests are ‘bulked’in a single group and distinctions like reforestation and afforesta-tion; different vegetation types/age; climax and non-climax com-munities are rarely maintained. Terms such as ‘secondaryforests’, ‘regrowth forest’, ‘altered forests’, ‘disturbed forest’ areperceived differently by different groups (Chokkalingam and Jong,2001). Further hydrological research has largely concentrated ontwo extremes – undisturbed forest cover versus cleared forest land,whereas most tropical forest areas are now a mix of secondary veg-etation and old forest interspersed with patches cleared for agri-culture or other non-forest use (Bruijnzeel, 2004; Giambelluca,2002).

Water yield and dry flows from catchments are the net result ofcomplex interactions between climate, forest and soil type, andother factors that together make the hydrological effects of forestsin one scenario markedly different from those in another. Forexample, the extent of soil degradation prior to afforestation/refor-estation can influence the hydrological effects of plantations(Bruijnzeel, 2004; Chandler, 2006; Fritzsche et al., 2006; Scottet al., 2005). Similarly, age of the tree stands can influence the pat-terns of increase or decrease in stream flow (Almeida et al., 2007;Farley et al., 2005; Kuczera, 1987; Malmer et al., 2005). Studies onthe catchments with very old forests (Giambelluca, 2002; Jay-asuriya et al., 1993 cited in Vertessy et al., 1996; Kagawa et al.,2009; Langford, 1976) uniformly report higher water yield fromold forest as compared to regrowth forests and plantations. Hencegeneralizations about forest hydrology without accurate classifica-tion and description of the forests involved and their climatic andsoil characteristics could be misleading. Such a classification is pos-sible only after thorough research. Moreover, increasing water de-mand for human use makes it imperative to understand thehydrological processes in forests for predicting trade-offs andopportunities in manipulating the forest–water relationship, andmanagement of forests for efficient water use.

Through spatiotemporal analysis this study analyzes the impactof a varied mix of primary, secondary and disturbed forests onwater yield in four watersheds in the Western Ghats of peninsularIndia. The selected watersheds are those of reservoirs supplyingdrinking water to Greater Mumbai. The wide gap between waterdemand and supply makes appropriate management of forests inthese watersheds critically important. Following a description ofthe study area, this paper summarizes the data acquisition, its pro-cessing and analysis. The results are followed by a discussionexploring the reasons and significance of the results, and thenthe conclusions.

hoes, and other such basic tools.4 Shifting cultivation begins by clearing of forests, typically less than one to several

acres by an individual farmer. The biomass is burned and the site is used to growagricultural crops in manner similar to slash-and-burn agriculture. The area isabandoned after a few years and new area is cleared for cultivation. Earlier theabandoned area was left fallow for period of 15–20 years before reusing it foragriculture but now due to increased population, the abandoned tract is reused after4–5 years.

2. Description of the study area

Greater Mumbai, the second most populated city in the world,is completely dependent on locally stored rainfall for its water sup-ply. Since 1860 the city has impounded water by building of reser-

voirs. These water sources are at greater distance than those ofmany water systems in the world. Domestic water supply to Mum-bai is through six of such watersheds, i.e., Tulsi, Vehar, Tansa, Low-er Vaitarna, Upper Vaitarna and Bhatsa lying in two clustersbetween longitudes 73.23�E and 73.65�E and latitude 19.50�Nand 19.92�N, and longitude 72.895�E and 72.94�E and latitude19.13�N and 19.215�N (Fig. 1). Of these, four watersheds – Tulsi,Tansa, Upper Vaitarna and Bhatsa – having virgin1 flow were se-lected for the study.

2.1. General

The selected watersheds lie along the Western Ghats mountainrange. The main geological formation is the basaltic Deccan trap.This rugged tract is a network of deep cut ravines, numerous crossspurs and isolated hills. Most hills have plateaus with grassy landsand less tree cover as compared to the dense cover on slopes. Thesalient features of the four catchments are listed in Table 1. Tulsi isa much smaller watershed as compared to the other three and hasa warm, humid tropical climate with a mean daily maximum tem-perature of 32.9 �C in the summer and mean daily minimum tem-perature of 16.8 �C in the winter. The other three watersheds arecomparatively less humid with slightly lower average daily mini-mum temperature in the winter and higher average daily maxi-mum temperature in the summer. There are four distinct seasonsin a year: the winter season from December to February; the sum-mer season from March to June; the southwest monsoon seasonfrom June to September and the post-monsoon season from Octo-ber to November, which is hot and humid in the coastal areas. Thesouthwest monsoon season, June–September, provides about 94%of the annual rainfall. July is the wettest month with a rainfall ofabout 40% of the annual total. The average weighted rainfall is2470 ± 575.98 mm per year.

The watersheds have no industry or urban settlements. All ex-cept Tulsi are well served with all weather and fair weather roads.2

Agriculture is the main economic activity and engages most of theinhabitants either as cultivators, share croppers or as agriculturallabourers. Owing to the inadequate irrigation facilities, most of thecrops depend on the monsoon. Paddy (wet rice) is the principal cropwhile some millets and lentils are also grown in this season. Slash-and-burn agriculture3 and shifting cultivation4 are still practicedon a large scale causing rampant fires in forests and grasslands dur-ing the summer season.

2.2. Forest cover

Forests cover most of the Tulsi and Tansa watersheds while theyare largely riparian in Bhatsa and around the rim of the Upper

26 S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34

Vaitarna watershed (Fig. 1). The natural forests in the catchmentsare Southern Tropical Moist deciduous type (according to revisedclassification of forest types of India by Champion and Seth(1968)). The subtype in Tulsi is Moist Mixed deciduous forests,while those in Tansa, Bhatsa and Upper Vaitarna are Moist Teakbearing forests (Table 2). Even in undisturbed conditions forestshave a tree cover canopy density of less than 70%. The tree heightranges between 12.19 m and 21.33 m with few patches in therange of over 21.33–27.43 m. Although most forested areas are leg-ally declared forests under the control of the Government, patchesof private forest do exist.

Local people have depended on these forests for their subsis-tence from time immemorial. Systematic working of forests wasintroduced in 1840. In Bhatsa and Upper Vaitarna best trees inthe forests (mainly teak) were removed particularly from easilyaccessible areas under various silvicultural systems. Plantationsthrough seed dibbling, and stumps of teak and other valuable spe-cies were attempted from 1935 which were only successful inareas with particularly good soils. At the same time, illicit cuttingcontinued unabated along with lopping of trees for fuel woodand slash-and-burn cultivation, clearing of land for shifting cultiva-tion/encroachments, fires for hunting and cultivation, and uncon-trolled grazing. All these activities reduced the forest cover andopened up the canopy considerably. Regeneration in open areaswas largely by coppice growth of inferior timber species whichsuppressed the coppice of superior species like teak. Since theban on green felling in the late eighties, plantations of exotic spe-cies like Eucalyptus, Acaia auriculiformis, Glyricidia along with otherindigenous species have been taken on a larger scale. But oftensuch plantations were destroyed by shifting cultivation. Such deg-radation of forests is much higher in the proximity of human set-tlements, while the distant and inaccessible slopes are stillcovered with primary forests and mature tree cover. Such undis-turbed/less disturbed patches constitute approximately 30% and40% of the forests of Upper Vaitarna and Bhatsa, respectively.

However, the forests in Tulsi have been spared such a fate. Tulsiwhich lies in the upstream of Vehar Lake, source of the first watersupply pipeline to Mumbai, was zealously protected under thecontrol of Municipal Corporation. In 1950, the area was declaredas Krishnagiri National Park5 under the Mumbai National ParkAct, 1950. The area was handed over to the Forest Department in1960 to become a part of the Sanjay Gandhi National Park. The seclu-sion, protection and security provided by the Municipal Corporationand the Forest Department have resulted in thick, lush and undis-turbed forests on the hill slopes. Plateaus and some rugged areasof the catchment support open forests.

The situation in Tansa is a mix of the upper two. The Tansawater supply project was commissioned in 1892. When the TansaDam was constructed, several settlements in the catchment areawere shifted to prevent pollution of the water. No grazing was per-mitted. However, the area was worked for extraction of timber. In1939, felling was restricted to improvement felling i.e., removal ofdead, dying and over mature trees as clear felling was found tocause siltation in the lake. In 1970, the area (along with adjoining

5 Recorded forests (i.e., areas that are legally notified as forest areas) in India arelegally classified as Village forests, Protected forests, Reserved forests, Sanctuaries andNational Parks. The degree of protection and statutory control of Governmentincreases as we move from Village forests to National Parks. Locals exercise theirrights as well as control the village forests; In Protected forests though they canexercise their rights, government is empowered to prohibit certain activities, most ofthe offences are non-cognizable; in Reserved forests all activities of locals areconsidered prohibited unless specifically permitted, sale of forest produce collectedfrom forests is regulated, all offences are cognizable; Sanctuaries impose additionalrestrictions on rights and acts (only acts like grazing and fishing permitted) as well asrestricts the departmental activities; In National Park no rights are allowed, offencestaken more seriously, restriction on departmental activities.

areas) was declared as Tansa Wildlife Sanctuary after whichextraction activities were stopped. No plantations have been takenin the catchment except during 1987–1991 when some 2.94% ofthe watershed was planted with bamboo, teak and mixed species.During 1990–1994 large scale encroachments occurred in thecatchment with the active support of political organizations. How-ever, the difficult terrain, perhaps, prevented agricultural settle-ments spreading across the watershed, except for theinterspersed flat areas. Illicit cutting and grazing have continuedover the years. Consequently about 52% of the forest cover in Tansais a mix of primary forest and mature secondary forests, whileremaining 48% mainly comprises disturbed forests with occasionalpatches of naturally open forests.

3. Methodology

Spatiotemporal analysis of historical data and satellite imagesfrom 1974 to 2009 for the Tulsi and Tansa; 1977–2009 for theUpper Vaitarna and 1990–2009 for the Bhatsa were used to studythe effects of forest cover.

3.1. Data acquisition and processing

3.1.1. PrecipitationRainfall was equated with precipitation being the predominant

form of precipitation causing stream flow in India (Subramanya,2008). Daily or monthly rainfall data from 1974 to 2009 was col-lected for 20 rainfall stations in and around the study area fromvarious agencies viz. the Indian Meteorological Department(IMD), the State Hydrological Project, the Brihan-Mumbai Munici-pal Corporation (BMC) and the State Agriculture Department. Atmost stations of the State Hydrological Project and the AgricultureDepartment, rainfall data was available only for the rainy season(June–October). Monthly rainfall data for missing months was esti-mated by normal ratio method (Subramanya, 2008). Monthlyweighted rain in each of the watershed in million cubic meters(Mcm) was calculated by Thiessen mean method (Subramanya,2008) after plotting Thiessen polygons through ArcView software.Precipitation falling directly over the reservoir (in Mcm) during themonth was worked out by multiplying monthly rainfall recordedby BMC at reservoir site into the surface area6 of the reservoir.Calendar year data by month were converted to water years (June1st–October 31st). Number of rainy days in a water year was workedout from the records of BMC.

3.1.2. EvaporationEvaporation losses from the reservoirs were estimated by mul-

tiplying daily evaporation recorded by BMC at the Tansa reservoirby surface area of the respective reservoirs for that day.

3.1.3. Change in storage of reservoirsRecords of daily or monthly water levels of reservoirs were col-

lected from BMC and the State Water Resources Department. Grosswater storage on 1st of each month was worked out from the tableof lake capacity at different levels (obtained from BMC). Change instorage for the month was estimated by subtracting the volume ofwater stored on 1st of the month from volume of water stored on1st of the preceding month.

3.1.4. Outflow from reservoirsMonthly outflows from the reservoir were collected from the

State Water Resources Department for Bhatsa and Upper Vaitarna

6 Surface Area = Change in water level (in meter)/change in volume of water inreservoir (in cubic meter).

Table 1Salient features of studied watersheds. Source: Hydraulic Engineering Department, BMC; Digital thematic maps – MRSAC, Nagpur; Management plan of Forest department,District Gazetteer.

Features Tulsi Tansa Upper Vaitarna Bhatsa

District Greater Mumbai Thane Nashik ThaneCatchment area (Sq km) 6.7 135.975 160.8 388.5Av. annual rainfall (mm) 2500 2400 2540 3404Gross storage capacity of reservoir (Mcm) 10.41 184.6 350.72 957.1No. of Villages 0 9 22 37No. of Towns 0 0 0 0Industries 0 0 0 0

Landuse (in percentof total area)

Area legallydefined as Forest

85.51 65.12 21.72 36.1

Agriculture 12.66 45.86 45.16Grassland 2.6 0 1.85Wastelands 3.7 9.22 8.83Builtup 0.07 0.58 0.12Waterbodies 14.49 15.85 22.62 7.94

Geomorphology Hills Mix of hills andplateau

Mix of hills and plateau Mix of hills and plateau

Soil texture Gravelly sandyclayey loam

Gravelly sandy clayeyloam (63.27%)

Gravelly clay loam (32%) andGravelly loam (21%)

Gravelly sandy clayey loam (43%) andGravelly sandy loam (33%)

Soil depth (in percentage of total area) Shallow and veryshallow

Shallow (67%) Shallow (44%) and moderatelydeep (31%)

Shallow (53%) and very shallow (23.5%)Very shallow <10 cmShallow �10 to 25 cmModerately deep �25 to 50 cm

Fig. 1. Location, year of operation of reservoirs and land use of catchments supplying drinking water to Greater Mumbai.

S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34 27

Table 2Important vegetation of forest types found in catchments. Source: Working/Management plans of State Forest Department.

Southern moist mixed deciduous forests Southern moist teak bearing forests

Important treespecies

Terminalia tomentosa (Ain), Adina cordifolia (Hed), Anogeissus latifolia(Dhavada), Dalbergia latifolia (Shisav), Bombax ceiba (Savar), Mitragynaparvifolia (Kalam), Madhuca indica (Moha), Schleichera oleosa (Koshimb),Mallotus philippinessis (Shendri), Mangifera indica (Mango), Saracaindica (Ashok), Sterculia urens (Kahandol)

Tectona grandis (Teak), Terminalia tomentosa (Ain), Garuga pinnata(Kakad), Lannes grandis (Shemat), Schleichera oleosa (Koshimb),Mimusops hexandra (Ranjan), Mangifera indica (Amba), Adina cordifolia(Hed), Pterocarpus marsupium (Bija), Bombax malabaricum (Sawar), andSyzygium cumini (Jambul).

ImportantShrubs

Vangueria indica (Aliv), Ziziphus mauritiana (Bor), Blumea lacera(Burando), Woodfordia fruticosa (Dhaiti), Randia dumetrom (Gal),Ziziphus xylopyrus (Ghatbor), Lantena camara (Gulthur), Carvia callosa(Karvi), Carissa carandas (Karwand), Streblus asper (Kharata), Cassia tora(Tarota) .

Carissa carandus (Karvand), Helicteres isora (Murudsheng), Adhatodavasica (Adulsa), and Thespesia lampas (Ranbhendi).

Climbers Caesalpinia decapetala (Chilahar), Tinospora cordifolia (Gulvel), Calastruspaniculata (Kadkangan), Butea superb (Palasvel).

Abrus precatorius (Gunj), Climatis triloba (Ranjai). Zizyphus rugosa(Toria).

Bamboospecies

Dendrocalamus strictus (Manvel), Bambusa arundinacea (Katas). Dendrocalamus strictus (Manvel), Bambusa arundinacea (Katas).

Grass species Chrysopogon fulvus (Kahandol), Heteropogon contortus (Kali –kusal),Dichanthium annulatum (Marvel), Anthistiria igante (Phulora).

Cynodon dactylon (Harali), Dicanthium anulatum (Ranbangdi), Coixigantean (Ranjondhala), Eragrostis spp. (Darbha), and Panicum glabrum(Varai).

28 S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34

dam, and BMC for Tulsi and Tansa. However, BMC did not recordthe overflow from the reservoirs during rainy season. Hence forthese two watersheds, the months in which overflow from damwas recorded (generally July and August during high rainfall years)were treated as missing data. Outflow data for Upper Vaitarna(which became operational in 1972) and Bhatsa (which becameoperational in 1981) were available from water years 1977 and1990, respectively, while for Tulsi and Tansa these records wereavailable from water year 1974 onwards.

7 TF = OF + MF.

3.1.5. Change in forest coverForest cover in the watershed over the years was interpreted

from the orthorectified satellite images for the years 1972, 1973,1989 and 1992 downloaded from Landsat.org; geo-rectified satel-lite images for 1985 acquired from the National Remote SensingCentre (NRSC), Hyderabad; digitized land use/land cover maps(LULC) for the years 1994 and 2004 acquired from NRSC, Hydera-bad; and digitized Forest Cover Maps (FCMs) for the years 2000,2004 and 2007 acquired from the Forest Survey of India (FSI),Dehradun. Watershed boundaries were demarcated with the helpof digitized watershed/micro-watershed maps acquired from theMaharashtra Remote Sensing Applications Centre (MRSAC), Nag-pur and toposheets.

NDVI transformation of the subsets of satellite images was doneusing Digital Image Processing (DIP) software – ERDAS 9.1. FCMclassified land cover of the study area as moderately dense forest(MDF), open forest (OpF), scrub (Sb) and non-forest (NF). Groundtruthing at several locations in all the watersheds confirmed thatthe primary forests, the undisturbed mature plantations and themature secondary forests (henceforth called old forests – OF) weredepicted by MDF in FCM, while the young reforested areas, patchesof forests exploited on moderate to large scale for timber, fuel-wood, fodder, shifting cultivation and other tangible benefits, andthe forest patches that naturally had poor tree density were de-picted by OpF (henceforth called mixed forests – MF). Amongstvarious forest categories under MF, disturbed forests were pre-dominant. Thus, such segregation broadly served the purpose ofdifferentiating the old forest from the disturbed forests in thestudy area. Hence scheme of classification of FSI (Table 3) wasadopted for supervised classification. The training sets were madeusing information gathered in the field, LULC and FCM. Accuracyassessment was done by evaluating the classified image to anunclassified satellite subset (LCC) using accuracy assessment fea-ture of ERDAS. Accuracy ranged from 86% to 92%. Accuracy wasalso confirmed from the forest records with a 23.45% reductionin the forest cover from 1989 to 2004 in Upper Vaitarna broadly

in line with 27.48% reduction reported in B.P. Singh working planfor the period 1988–1989 to 2002–2003 for the West Nashik ForestDivision. Similarly, a 26.47% reduction in forest cover from 1989 to1994 in Tansa matches the large scale encroachments recordedduring this period in the Management Plans of the Tansa WildlifeSanctuary.

The classified images/digitized maps of watershed were inter-preted for OF, MF and total forest – TF7 (Fig. 2 and Table 4). To cal-culate the forest cover in watersheds under each of the category onyear to year basis, the annual rate of change in forest cover betweentwo consecutive time periods were worked out by the following for-mula (Puyravaud, 2003):

r ¼ 1ðt2� t1Þ � ln

A2A1

where r is the annual rate of change in forest cover and A1 and A2are the forest cover at time t1 and t2, respectively. Based on this an-nual rate of change, the forest cover for each year between the twotime periods was worked out.

3.1.6. Slope of micro watershedWeighted average slope of each watershed was calculated from

the digital elevated images downloaded from ASTER GDEM andtheir interpretation using through ERDAS 9.1 and ArcMap 9.3.

3.1.7. Estimation of monthly runoff (surface runoff + basal flow)Monthly runoff to the reservoirs is worked out through the fol-

lowing basic water budget equation for reservoirs of Fetter(1994):

Total inflow� Total outflow ¼ Change in storage

Expanding in line with Subramanya (2008) and Güntner et al.(2004) the water budget equation for reservoir is expressed as

ðRþ PDÞ � ðOþ EDÞ ¼ �DS

) R ¼ �DSþ ðOþ EDÞ � PD

where R is the total runoff to the reservoir during the month, PD thedirect precipitation over the reservoir during the month, O the sur-face outflow of water from the reservoir during the month, ED theevaporation from the reservoir during the month, and DS is thechange in water stored in the reservoir during the month.

Table 3Classification scheme of FSI for forest cover maps.

Very dense forest All lands with tree cover (including mangrove cover) of canopy density of 70% and aboveModerately dense forest All lands with tree cover (including mangrove cover) of canopy density between 40% and 70% aboveOpen forest All lands with tree cover (including mangrove cover) of canopy density between 10% and 40%Scrub All forest lands with poor tree growth mainly of small or stunted trees having canopy density less than 10%Non-forest Any area not included in the above classes

S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34 29

The equation assumes the volume of return flow from irrigationand from domestic water supply to be negligible (in line with Sub-ramanya, 2008). Similarly, as the observations are for sufficientlylong period of time, change in soil water storage/seepage lossesis also assumed to be negligible (in line with Güntner et al.,2004; Leopoldo et al., 1995). By estimating direct precipitationover the reservoir, the need for calculating evapo-transpirationlosses (usually part of water budget equations) has been avoided.

3.2. Analysis for change in water yield

Studies correlating land use with water yield often use the run-off coefficient as a measure of water yield as it removes the impactof precipitation on water yield (Sriwongsitanon and Taesombat,2011; Wang et al., 2011). Moreover, it is useful for representingand comparing runoff generation in catchments (e.g. Castro et al.,1999; Cerdan et al., 2004). Accordingly a time series array ofmonthly runoff and monthly weighted rainfall was constructedindependently for each watershed. Following regression (after con-firming linear regression as best functional form through theMackinnon, White, and Davidson test (MWD test)) was run onSTATA SE

RO ¼ cR ð1Þ

where RO is the monthly runoff, R the monthly weighted rainfall,and c is the regression coefficient (predicted runoff coefficient).

The regression line was constrained to pass through the originto get a regression coefficient that could be equated to runoff coef-ficient. It was also seen that unconstrained regression practicallygave the same line as the regression through origin (Fig. 3). Thepredicted runoff coefficients (c) are listed in Table 5. All regressionlines had high correlation coefficients which were significant at the1%.

Comparisons were drawn between the predicted runoff coeffi-cients of Tansa and Upper Vaitarna where factors like watershedsize, annual rainfall and slope (i.e., factors that tend to influencerunoff coefficient) are similar, but land use pattern are markedlydifferent (in Upper Vaitarna agriculture and grasslands have occu-pied 60–70% area over the years as compared to Tansa where for-ests covered nearly 75% of area in initial years and hassubsequently reduced to 43%).

To analyze the impact of forest cover on water yield over time inthe four watersheds, it was decided to focus on two main variables– the forest cover and the runoff coefficients – in line with Wanget al. (2011). Catchment area (CA), weighted rainfall as depth(wR) and number of rainy days for a water year (Rd) were includedas variables to roughly account for watershed and rainfall charac-teristics8 An array of runoff coefficient (RC)9 TF, OF and MF (all aspercentage of watershed area); CA; wR and Rd for each water yearwas constructed as panel10 data. Following regression was run onSTATA SE

8 Slope was not included as variable at it showed collinearity with OF.9 RC ¼

PRO=

PR For water year.

10 Dataset having both cross-sectional and time series dimension.

RC ¼ aþ b1TFþ b2CAþ b3wR þ b4Rd ð2ÞRC ¼ aþ b5OFþ b6MFþ b2CAþ b3wR þ b4Rd ð3Þ

where a is the constant and b1, b2, b3, b4, b5, and b6 are the regres-sion coefficients for respective variables.

Absence of collinearity between the variables was confirmed.The linear regression model was selected through MWD test. Otherstudies of the relationship between runoff coefficient and land/for-est cover (Arthur, 2001 cited in Carlson, 2002; Sriwongsitanon andTaesombat, 2011; Wang et al., 2011) have similarly used linearregression. Hausman Tests were run to select between the fixed ef-fect and the random effect model.

Based on the regression analysis and current annual rate ofchange in OF and MF in Tansa, Bhatsa and Upper Vaitarna (water-sheds facing biotic pressure), cumulative increase/decrease inwater yield for years 2011–2030 in each of the watersheds waspredicted through simple mathematical calculations.

4. Results

It is generally observed that the watersheds with good forest/vegetal cover give lower water yield as compared to the water-sheds with lesser vegetation and similar soils (Subramanya,2008). Similarly, in comparison to grasses, crops and other shorterrotation vegetation lower water yield is recorded from forests onaccount of higher interception and transpiration losses (Calderet al., 2004; Hamiltion, 1985, Vinnikov and Robock, 1996). In thisstudy, however, the watersheds having fairly high percent of forestcover viz. Tulsi and Tansa (on average 84% and 56%, respectively),had fairly high RC#, i.e., 0.63 and 0.58, respectively (Table 5). Fur-ther, despite having a much larger area under forest cover andmore permeable soil, RC# for Tansa was practically the same asthat of the Upper Vaitarna (Table 5). At the beginning of the studyperiod (i.e., during 1970s) when the forest cover in Tansa was ashigh as 75% while grasslands and agriculture occupied practically60% of the catchment area of Upper Vaitarna (forest cover –29%), a higher RC for Tansa was observed (Fig. 4). During later yearsa higher RC for Upper Vaitarna appear to be recorded in high rain-fall years (Figs. 4 and 5).

Regression between RC, TF and other variables (Eq. (2)) con-firmed significant (0.01 < p < 0.05) and positive relationship be-tween TF and RC, i.e., reduction in TF decreased RC and, thus, thewater yield (Table 5). Hausman Test favored the random effectmodel over the fixed effect model signaling absence of cross-sec-tional variations correlated to the explanatory variables. Using adummy for group Tulsi – Tansa (both having a higher percentageof old forest) gave similar results. Thus, variations in RC were notwatershed specific. This appeared surprising as considerable varia-tions exist amongst the forest cover of the watersheds (Table 5) andsome variations in line with the existing literature were expected.

However, when TF was segregated into OF and MF (Eq. (3)),then OF was observed to positively and highly significantly(p < 0.01) influence RC which is largely in line with the findingsof Kagawa et al. (2009), Kuczera (1987), Langford (1976), andWang et al. (2011) for Northeast China. In contrast MF exhibiteda negative trend that was not statistically significant. The inverserelationship between MF and RC (and, thus, water yield) finds

Fig. 2. Changes in the forest cover during the study period for three watersheds.

Table 4Forest cover (as percentage of the catchment area) interpreted through satellite images/digitized maps.

Year Tulsi Tansa Upper Vaitarna Bhatsa

TF OF MF TF OF MF TF OF MF TF OF MF

1972 75.11 33.83 41.3 28.97 7.6 21.33 –1973 88 69 19 – – –1985 86 68.1 17.9 – – –1989 – 60.68 34.12 26.6 16.04 6.09 9.45 57.06 22.4 34.71992 82.9 68.6 14.3 –1994 82.8 68.7 14.1 44.62 24 23.1 11.33 3.25 8.08 32.066 13.5 18.62000 82.5 68.6 13.9 44 23.9 21.2 10.14 2.95 7.19 31.86 13.36 18.52004 82.7 68.5 14.2 43.91 23.9 20.1 8.71 2.89 5.81 30.86 13.2 17.712007 82.7 68.5 14.2 42.89 22.7 20.2 8.68 2.71 5.96 30.51 12.1 18.4

30 S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34

supports in findings of Bosch and Hewlett (1982), Bruijnzeel(1990), Hamilton (1987), Hamilton and King (1983), Nik (1988),Pierce et al. (1970), Robinson (1998), Scott and Lesch (1997), Trim-ble et al. (1987), Sikka et al. (2003) and van Lill et al. (1980). Theregression coefficients for wR and Rd had expected positive signsand were significant (p < 0.05) in both the regression models. How-ever, the regression coefficients for CA were not significant.

The regression model (Eq. (1)) indicates that unit loss of TF re-duces RC by 0.001. This translates to decrease in water yield by0.42, 0.53 and 1.72 Mcm for Tansa, Upper Vaitarna and Bhatsa,

respectively. At current rate of change in OF and MF cover (Table5), cumulative loss in water yield for years 2011–2030 works outto be 47.66 Mcm, 14.26 Mcm and 243.46 Mcm, respectively (onaverage annual loss in water yield is 2.38, 0.71 and 12.173 Mcm,respectively).

5. Discussions

Comparative studies on hydrological responses of forest coveroften lament at the possibilities of geological and climatic

Fig. 3. Unconstrained and constrained linear regression between monthly runoff and rainfall for the studied watersheds.

11 Drinking water supply to Mumbai is 3.350 Mcm per day.

S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34 31

differences between the catchments that necessitate additionalstudies to draw concrete inferences (Bruijnzeel (2004)). This studyovercomes this drawback by segregating the forest cover withinthe catchments in two contrasting groups i.e., the forests that areundisturbed/less used and less disturbed by people (OF), and thepatches of forests that are largely disturbed with occasional sprin-kling of young/disturbed plantations and naturally occurring openforests (MF).

Through Eq. (2) it is seen that a reduction in TF results in reduc-tion in RC. Through Eq. (3) it can further be inferred that this reduc-tion is on account of the reduction in OF. It is also apparent fromthe forest cover maps that the reduction in OF has occurred byits conversions to MF and to a lesser extent by its conversions tosuch non-forest landscapes as agriculture or grasslands/bare land(due to heavy erosion following tree removal). Such conversionshave occurred because of ‘‘provisioning’’ ecosystem services liketimber, livestock grazing, fuel wood, fodder, leaf-manure extendedby forests to local communities and sustaining livelihoods. Eq. (3)also brings out the diametrically opposite impacts of OF and MF onwater yield. Thus, tradeoffs between the provisioning services andhydrologic functions and services of OF are apparent. Further, themagnitude of the regression coefficients obtained through Eqs.(2) and (3) for TF, OF and MF (Table 5) indicates that in a mixedscenario catchment hydrology is the net result of the individualinfluences of different forest covers.

The difference in response between the forest covers is largelyattributed to the different evaporative characteristics of old/ma-ture tropical forest and regrowth forest/young secondary orplanted vegetation, and to a much lesser extent increases in stormrunoff (Bruijnzeel, 2004). Young/regenerating forests consumemore water as compared to alternative vegetations (refer Section1). In contrast old forests are reported to use two (Kagawa et al.,2009) to three times (Bond et al., 2008) less water than regrowthforest/plantations on account of their structural and functionalcomplexities (Bond et al., 2008; Dunn and Connor, 1993; Mooreet al., 2004; Meinzer et al., 2005; Zimmerman et al., 2000). The in-creased height of trees results in a longer hydraulic path lengthwhich has an inverse relationship with stomatal conductanceand, therefore, transpiration. Similarly, lower sapwood basal area(Macfarlane et al., 2010; Vertessy et al., 1998), complex branching

pattern and other such age-related changes in the hydraulic archi-tecture of older trees (Bond et al., 2008) often decrease stomatalconductance. The understory faces lower radiation and higher rel-ative humidity in comparison to the overstory (Blanken and Black,2004; Scott et al., 2003; Unsworth et al., 2004; Yepez et al., 2003).Therefore, transpiration by the understory is generally less thanthe overstory (Bond et al., 2008; Kagawa et al., 2009; Vertessyet al., 1998). Further, the abundant bryophytes and lichens foundin older forests in most humid regions, the organic layers and largewoody debris serve as water reserves for the roots and mycorrhizalhyphae of shade tolerant trees (Bond et al., 2008).

However, limited studies report reduction in water yield onconversion of OF to non-forest landscapes (Ma, 1987, 1993 citedin Sun et al., 2006; Wang et al., 2011; Wei et al., 2003 cited inSun et al., 2006). For tropical regions no such observations appearto be reported (except for unique case of upper montane cloud for-ests where cloud-water deposits exceeds interception losses). Inthis instance, it could be that the marginal gains through reducedevapotranspiration (on conversion of OF to non-forest areas) areoffset by increased evaporation on account of increased wildfires(due to increased area under slash-and-burn agriculture/shiftingcultivation) and increases in temperatures on loss of forest cover.But more research may be needed to establish it conclusively.

Even at a low annual rate of deforestation (Table 5), average an-nual loss in water yield for the period 2011–2030 is 2.38, 0.71 and12.173 Mcm, respectively for Tansa, Upper Vaitarna and Bhatsa.This means loss of 4.56 days of drinking water supply11 to Mumbaicity every year (assuming that the entire runoff is stored in reservoirand is available for Greater Mumbai).

6. Conclusion

The study supports age old perceptions about forests with re-spect to their responses to water yield (in the sense that the forestsduring the times of our ancestors were climax/old forests andhence their perceptions were with respect to such forestsand not the new kinds that have come up because of biotic

Tabl

e5

Regr

essi

onan

alys

is.

Wat

ersh

edFo

rest

cove

rA

v.fo

rest

cove

r(%

CA

)C

urr

ent

ann

ual

rate

ofch

ange

info

rest

cove

rSl

ope

(Wt.

aver

age)

Av.

rain

fall

(mm

)c

(Eq.

(1))

Reg

ress

ion

anal

ysis

(Eqs

.(2)

and

(3))

OF

MF

TFO

FM

FTF

OF

MF

Tuls

iPr

imar

yfo

rest

Prim

ary

fore

stw

ith

less

tree

cove

r84

.48

68.5

15.9

00

015

.88�

2790

0.63

(N=

385)

RC

=0.

43+

0.00

1TF

**

+0.

0000

4w

R**

+0.

0006

Rd*

*�

0.00

01C

ATa

nsa

Prim

ary

and

mat

ure

seco

nda

ryfo

rest

Prim

ary

fore

stw

ith

less

tree

cove

ran

ddi

stu

rbed

fore

st

5629

27.3

�0.

008�

0.01

70.

0018

7.86

�24

000.

58(N

=36

7)Pr

ob>

chi2

=0.

000

R2w

ith

in=

0.11

6be

twee

n=

0.89

over

all=

0.24

(N=

112)

Upp

erV

aita

rna

Prim

ary

and

mat

ure

seco

nda

ryfo

rest

,m

atu

repl

anta

tion

s

Dis

turb

edfo

rest

refo

rest

edw

ith

exot

ics

and

indi

gen

ous

spec

ies

inpa

tch

es

15.8

94.

910

.7�

0.00

1�

0.02

10.

0085

10.4

3�25

400.

59(N

=39

6)R

C=

0.43

+0.

002

(OF)

***�

0.00

13M

F+

0.00

004w

R**

+0.

0005

Rd*

**

+0.

0000

6CA

Bh

atsa

Prim

ary

and

mat

ure

seco

nda

ryfo

rest

Dis

turb

edfo

rest

wit

hve

ryli

mit

edre

fore

stat

ion

acti

viti

es

34.4

14.2

20.3

�0.

004�

0.02

80.

013

9.81

�34

040.

71(N

=24

0)Pr

ob>

chi2

=0.

00R2

wit

hin

=0.

122

Bet

wee

n=

0.99

Ove

rall

=0.

26(N

=11

2)

RC

=R

un

off

coef

fici

ent,

TF=

%to

tal

fore

sts,

OF

=%

old

fore

stco

ver,

MF

=%

mix

edfo

rest

cove

r,C

A=

catc

hm

ent

Are

ain

sqkm

,wR

=w

eigh

ted

rain

(as

dept

h),

Rd

=n

um

ber

ofra

iny

days

.⁄ 0

.05

<P

<0.

1.**

0.01

<P

<0.

05.

***

P<

0.01

.

.4.5

.6.7

.8.9

runo

ff co

effic

ient

1970 1980 1990 2000 2010year

Tansa U_Vaitarna

Fig. 4. Yearly runoff coefficient for watersheds Tansa and Upper Vaitarna.

1000

2000

3000

4000

wra

infa

ll

1970 1980 1990 2000 2010year

Tansa U_Vaitarna

Fig. 5. Yearly weighted rainfall (as depth) for watersheds Tansa and UpperVaitarna.

32 S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34

interferences). It also brings out contrasting responses of OF vis-à-vis MF on local hydrological processes and a trade-offs betweenthe provisioning services and hydrologic functions and services ofold forests. Further, in a mixed scenario catchment hydrology ap-pears to be the net result of the individual influences of differentforest cover. Extrapolation of the result indicates a marginal lossin water supply to Mumbai. But such losses could be substantial ifthe old forests are lost by conversion to disturbed forests or non-forest areas.

Hence managers targeting at increasing downstream yieldneed to focus on conserving the old/mature forests. Disturbed for-ests as well as plantations in the upstream should similarly beconserved so that with age they are able to extend better hydro-logical services. As suggested by Macfarlane et al. (2010) silvicul-ture to promote old-forest-like attributes could also be tried.Research studies that explore the impact of spatial distributionof forests and model the optimal undisturbed vis-a-vis disturbedforest cover within a catchment for extending the hydrologicaland the provisioning services, respectively may also be in order.

Acknowledgements

Most of the work was carried out with the aid of grant fromthe International Development Research Centre, Ottawa, Canada.Information on the Centre is available on the web at www.idrc.ca.Immense encouragement and support for data collection and fieldwork was extended by Maharashtra State Forest Department andBrihan-Mumbai Municipal Corporation (Hydraulic EngineeringDepartment). Free data/digitized maps/satellite images were

S. Singh, A. Mishra / Journal of Hydrology 446–447 (2012) 24–34 33

made available by Maharashtra State Hydrological Project, Nasik;Maharashtra Water Resource Department, Mumbai; MaharashtraRemote Sensing Applications Centre, Nagpur; and National RemoteSensing Centre, Hyderabad.

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