Estimation of Groundwater Recharge under various land ... - RMZ

182 Purandara B. K., Venkatesh B. & V. K. Choubeypoteka obsežno pogozdovanje z eksotičnimi vrstami dreves (npr.Accacia auriculiformis). Lokalno prebivalstvo na tem območju jezaskrbljeno zaradi vpliva pogozdovanja na zaloge in razpoložljivostpodzemne vode. Zato je osnovni cilj študije ocena napajanja podzemnevode v odvisnosti od različnih pokrovnosti tal. Obsežne terenskeraziskave so bile izvedene za določitev hidravličnih lastnosti tal,ki so osnovni vhodni parametri za modeliranje. Za oceno napajanjaje bil uporabljen model SWIM. Ugotovljeno je bilo, da je napajanjepodzemne vode največje na napajalnih zaledjih, pokritih z gozdom,in na pogozdenih območjih, najmanjše pa na degradiranih območjih.Gledano v celoti, študija osvetljuje določene aspekte strategije upravljanjaz gozdovi na degradiranih območjih, ki lahko prispevajo kohranjanju vodnih virov.Key words: Groundwater, Recharge, infiltration, Hydraulic conductivity,Soil Water Infiltration Movement Model (SWIM), Western GhatsKljučne besede: podzemna voda, napajanje, infiltracija, prepustnost,SWIM-model, zahodni GhatsIntroductionTropical forests in India have undergonedramatic land use changes inthe last few decades. The myriad ofchanges that have, and still are, takingplace are the effect of an equally largenumber of local causes and factors,highlighting a complexity that tends todefy easy generalizations. One of theimportant factor which lead to suchdramatic change is the population explosioncombined with more and moreindustrialization. The ultimate result ofsuch a growth was deforestation in orderto meet the local people needs. Oflate, the degraded and the open land inthe forests have been brought under theforest cover by extensive plantation. Inmany developing countries, extensiveareas are undergoing land use changes.The largest changes in terms of landarea, and arguably also in terms of hydrologicalimpacts, often arise from afforestationand deforestation. Increasingareas are undergoing afforestationwith fast growing monocultures of oftenexotic tree species.There are world wide concern that increasedestablishment of plantation ofexotic forest species for wood, fiberproduction, either as result of conversionof native forests and scrublandsor afforestation of pasture and nativegrassland may have a detrimental effecton the environment. One of themost interesting questions put for-RMZ-M&G 2010, 57

Estimation of Groundwater Recharge under various land covers in parts of ...183ward is that, what happens to wateryield when the headwater catchmentsare planted with monoculture species.There are only limited studies carriedout to evaluate the hydrological impactsof plantation. In India, during thelast few years, considerable effort hasbeen made to understand the impact offorest degradation on the soil hydrologicalregime. However, studies arequite limited as far as the impact of forestdegradation on groundwater regimein western ghat region.The Western Ghat region is the originand primary catchment of many rivers(west and east flowing) in peninsularIndia. The lives of the majority of therural population in the four southernstates (Kerala, Tamil Nadu, AndhraPradesh and Karnataka) plus parts ofMaharashtra are thus critically dependentupon the watershed services providedby the Western Ghats forests.The portion of the Western Ghats thatlies in Karnataka state, contains themajor portion of the forests. It is reportedthat, there has been an increasedanthropogenic activities in the westernghat mountain region, a rapid change invariety of land-use and land cover aretaking place, which could have verysignificant impact on the water regimeof the region, which includes the baseflowand groundwater recharge.In the present study is an attempt tounderstand the impact of the land-us-es on the groundwater recharge underdifferent rainfall regimes with changein land-use conditions. In order to facilitatethe analysis, SWIM (Soil Waterand Infiltration Movement) model ischosen. It is selected in view of its simplicityand use of input parameters (soilmoisture characteristics) that can be directlymeasured in the field/laboratory.Materials and methdsStudy AreaUttara Kannada is a district with an areaof 10,291 km 2 , with its administrativeheadquarters at Karwar (Figure 1). Themain geographic feature of the districtis the Western Ghats (WG), which runsfrom north to south through the district.Between the WG and the sea is narrowcoastal strip, which varies from 8 kmto 24 km in width. Behind the coastalplain are flat topped hills from 60 m to100 m in height, and behind these hillsare the ridges and peaks of the WG.East of the WG is the upland, part ofthe vast Deccan plateau.In the WG region, majority of the rainfalls during June- September., i.e.,south-west monsoon. More than 90 %of the annual rainfall occurs during thefour monsoon months, with an averagenumber 120–140 rainy days per year.During the monsoon, a major portionof the rainfall is contributed by fourto five spells each lasting 8–10 days.RMZ-M&G 2010, 57

Estimation of Groundwater Recharge under various land covers in parts of ...185Geology and SoilsGeologically, the study area consists ofPre-Cambrian formations with gneissand intrusive granites (mostly alongthe coastal tracts and adjoining areastowards east). In the northern part ofthe study area basaltic rocks of UpperCretaceous age are seen. Soil is deepparticularly in coastal areas (few feetto few meters). Laterites are commonlyfound in coastal areas and plateau regionis covered by black soils, where asthe up-ghat region is characterized byboth red and mixed soils. By contrast,large areas along the coastal tracts ofNorth Kanara district, the parts of WesternGhat are severely degraded with laterite(Geologically Recent in age) inducedby natural climatic variability. Inthe plateau areas of the Western Ghats,deep forest soils rich in humus. Blacksoil is found locally, i.e. in areas havingelevation above 500–600 m. Generally,regions with heavy rainfall and dissectedtopography (slope varying between12–15 %) are devoid of black soil indicatingthat, climate, topography andlack of drainage are more importantthan nature of underlying rocks in theformation of black soil.The Karnataka Forest Department(KFD) has taken up various reforestationstrategies depending upon the stateof land degradation. The major speciesused for the afforestation activities areTeak and acacia ariculiformis.MethodologyRainfall AnalysisThe entire Uttara Kannada district hasbeen divided into three distinct regionsbased on the elevation. The three regionsare Coastal, Up-ghats and Plateau.The automatic raingauge stationscovered under coastal region are, Kumta,Aversa and Bhatkal. Sirsi, Yellapur,Joida and Siddapur are included underthe Up-ghat region. Raingauge stationsavailable in the plateau region areMundgod, Dharma, Barchi, Bachanikiand Haliyal. The rainfall intensity andduration data for these rainfall stationswere extracted from the hourly rainfallcharts (The Water Resources DevelopmentOrganisation, Govt. of Karnataka,maintains these stations). Therecords from the raingauge stations ineach region were taken together to giveregional record totals in station years.This amalgamation of annual maximumvalues assumes that they are independentof the stations and they arerepresentative of their regions definedfrom the criteria. These compoundedrecords were then subjected to an analysisusing the Gumbel’s frequency distributionwith the probability weightedmoment method.As the first step of the analysis, the valuesof maximum intensities for 1 h, 2h, 3 h and 5 h duration from all 12 stationswere considered. The frequenciesRMZ-M&G 2010, 57

186 Purandara B. K., Venkatesh B. & V. K. Choubeyof all these maximum intensity valueswere computed using the Weibull plottingposition procedure. A multipleregression model is used to develop arelationship of intensity versus durationand frequency of the form of, I =112,47 D –0.341 T 0.21 , The multiple correlationcoefficient obtained was 0.80Field InvestigationsField experiments were carried out forthe determination of saturated hydraulicconductivity in three typical zonesby using Disc permeameter (Perroux& White, 1988) and Guelph permeameter.In each location a plot of 10 m/10m was selected and carried out 6–8 experimentsin order to get a proper representation.Data has been subjected tostatistical analysis to get log mean values.LSD and F tests were also carriedout for the analysis.Laboratory InvestigationsLaboratory investigations includeddetermination of saturated moisturecontent, and soil moisture retentioncharacteristics using the pressure plateapparatus.charge to groundwater from rainfall)were determined through SWIM.SWIM is an acronym that stands forSoil Water Infiltration and MovementModel. It is a software package developedwithin the CSIRO Division ofsoils for simulating infiltration, evapotranspiration,and redistribution. Themodel is based on a numerical solutionof the Richards’ equation and theadvection-dispersion equation. It canbe used to simulate runoff, infiltration,redistribution, solute transportand redistribution of solutes, plantuptake and transpiration, soil evaporation,deep drainage and leaching. Soilwater and solute transport properties,initial conditions, and time dependentboundary conditions (e.g., precipitation,evaporative demand, solute input)need to be supplied by the user inorder to run the model. The governingpartial differential equation (Richards’equation) applicable for one-dimensionalflow in the unsaturated zone canbe written as:ModelingDaily rainfall and evaporation data of1986 to 2000 were used for the study.Water balance components like runoff,evapotranspiration and drainage (re-where,θ = volumetric water content[cm 3 /cm 3 ]; water/soilt = time [h]x = distance into the soil [cm]K = hydraulic conductivityRMZ-M&G 2010, 57

Estimation of Groundwater Recharge under various land covers in parts of ...187[cm 2 /(cm/h); water/soil]= matric potential [cm]; waterz = gravitational potential[cm] andS = sink strength[cm 3 /(cm 3 /h)];water/soilThe model deals with a one-dimensionalsoil profile. For a vertical soil profile,this means that it may be vertically inhomogeneous,but must be horizontallyuniform. This assumption has two consequencesof importance in many commonsimulations. There is only one hydraulicconductivity function for eachlayer, so that any macropore, or bypassflow can only accounted for in a limitedway. Secondly, the calculated soluteconcentrations apply to the whole soillayer, which means that there is no concentrationgradient from the bulk soilto near the root surface. The presenceof such a concentration gradient mayin reality affect the soil osmotic potentialand hence water and solute uptake(Verburg et al, 1996).Model ConceptualizationIn order to simulate the water balancecomponents of the study area, a soilprofile in each zone, viz., coastal, upghat and plateau areas were consideredwith a thicknes of about 150 cm.Vapour conductivity is not taken intoconsideration nor is the effect of osmoticpotential. There are two hydraulicproperty sets (for upper and lowersoil horizons) that applied to 16 nodesof the 150 cm deep profile. Hysterisesis not taken into account. Initially,there is no water ponded on the surface.Runoff is governed by a simple powerlaw function and a surface conductancefunction. No by pass flow wasincluded. A matric potential gradient of0, i.e., `unit gradient’ has been appliedas bottom boundary condition throughout the simulation. Cumulative rainfalland evaporation records (daily) for theperiod 1986–2000 were given as theinput for determination of water balancecomponents (runoff, evapotranspirationand drainage). The model parameters(soil hydraulic properties andmoisture characteristics) were actuallymeasured in the field and laboratory.Therefore, the model does not requireany calibration as such.Input Data for SWIM Model1. Rainfall: Based upon the availableinformation, two distinct soil layerswere identified. The followinginput data was used for simulationof soil moisture movement throughSWIM. Daily rainfall data of Honnavar,Barchi and Siddapur wereused.2. Evaporation: Daily evaporationdata of Honnavar (coastal), Barchi(plateau) and Siddapur (Up ghat)were considered for the analysis.3. Saturated Hydraulic Conductivity:RMZ-M&G 2010, 57

188 Purandara B. K., Venkatesh B. & V. K. ChoubeySaturated hydraulic conductivitywas measured at 9 locations in thestudy area by using disc permeameter(locations are shown in Figure1).The average saturated hydraulicconductivity values for the surfacewas measured by using disc permeameterand lower layer by usingGuelph permeameter.4. van Genuchten Parameters: Thecollected soil samples from thestudy area were analysed in thelaboratory by pressure plate apparatusfor soil moisture retentioncharacteristics. The averaged van-Genuchten parameters for the twosoil layers were obtained by nonlinearregression analysis (Table 1).5. Vegetation: Forested watershed,minimum xylem potential = –15000cm, exponential root growth withdepth and sigmoid with time wereassumed for the study.Table 1. Measured saturated hydraulic conductivity and van Genuchten Parameters forthe Two Soil LayersZonesCoastalPlateauUp-GhatLand use typeNatural ForestDegradedForestAfforestedNatural forestDegradedForestAfforestedNatural ForestDegradedForestAfforestedSoil LayersDepth (cm)SamplesizeLog-mean Ksat(mm/h)Van-GenuchtenParameterSurface 10 93.92 0.0192 1.6161120–150 10 44.74 0.0105 1.6270Surface 12 6.73 0.0201 1.7008120–150 12 0.20 0.0298 1.5353Surface 10 88.20 0.0792 1.2586120–150 10 88.20 0.8118 1.2446Surface 15 11.88 0.0044 1.9400120 – 150 15 0.21 0.0110 1.8151Surface 12 6.21 0.0119 1.6320120–150 12 0.40 0.0123 1.6296Surface 15 6.01 0.0050 1.8698120–150 15 0.60 0.0051 1.7644Surface 20 179.64 0.0253 1.4570120–150 20 1.66 0.0104 1.4514Surface 20 2.78 0.0214 1.3563120–150 20 2.78 0.0201 1.5861Surface 20 90.00 0.0125 1.6925120 –150 20 43.2 0.003 1.7701anRMZ-M&G 2010, 57

Estimation of Groundwater Recharge under various land covers in parts of ...189Results and DiscussionRegional Analysis of RainfallData of all the station that are groupedunder three regions were pooled togetherto fit Gumbel distribution. Theresults obtained from fitting Gumbeldistribution for individual stations andregion wise were compared with theobserved values. The estimates obtainedfrom fitted distribution matchesthe observed values with an error of(10–15).Table 2, reveals that the rainfall intensityis higher in coastal region for thereturn periods 2 and 5 years. The returnperiod 10, 25 and 50 years show higherintensity in region III, compared to RegionI. However, it is interesting to notethat in Region II, the intensity is comparativelylower for all the return periodsthan the other two regions. This istrue for only for intensity duration of1 h. In the case of 2 h, 3 h and 5 hduration, the intensity is higher in upghat region than in plateau region. Thecoastal region is distinct with the higherintensity than the other two regions.Statistical and Numerical AnalysesStatistical methods provide a satisfactorytool for hydrological analyses.Tukey (1977) indicated that the K datafrequency distributions are closely approximatedby the log-normal function.These observations are in closeagreement with other field investigations(Bonell et al., 1983; Talsma,et. al., 1980) Consequently the use oflog-means for interlayer comparisonof K is more appropriate measure thanthe arithmetic means (Talsma, 1965;Nielsen et. al., 1973). The use of furtherstatistical and numerical analysison the K data is confined to the sur-Table 2. Rainfall intensity –duration estimates for different regions for selected returnperiodRP*Region I Region II Region III1 h 2 h 3 h 5 h 1 h 2 h 3 h 5 h 1 h 2 h 3 h 5 h2 34.06 28.41 29.19 20.27 26.06 21.34 14.27 11.98 29.23 17.17 12.04 9.645 42.40 41.14 39.14 31.3 38.01 29.09 22.00 18.14 41.75 25.73 18.25 14.8210 47.57 49.57 45.73 38.60 45.57 34.23 27.11 22.22 50.04 31.4 22.37 18.2625 54.10 60.22 54.05 47.82 55.12 40.72 33.57 27.38 60.51 38.56 27.56 22.6050 58.94 68.12 60.23 54.66 62.2 45.53 38.37 31.21 68.28 43.87 31.41 25.82Return Period, Region I – Coastal, Region II – Up Ghat, Region III – PlateauValues are in mm/hrRMZ-M&G 2010, 57

190 Purandara B. K., Venkatesh B. & V. K. Choubeyface and layer between 120–150 cm,as these controls the runoff process. Asignificance level of 0.01 was used totest differences between specific pairsof sites. However, results indicatedthat, both F-test (F < 0.001) and LeastSignificant Difference was significantat this level.The measured saturated hydraulic conductivitiesare plotted as box plots toview the variation within the measuredvalues. The length of the box reflectsthe inter-quartile range, and the fence ortails of the box plots are marked by theextremes if there are no outliers, or elseby the largest and smallest observationthat does not qualify for an outlier. Theoutliers are defined as data points morethan 1.5 times the inter-quartile rangeaway from the upper or lower quartile.The middle horizontal bar on the boxplot represents the median of the data.The saturated hydraulic conductivityobserved for soils in the undisturbedforests and also in afforested regionsis comparatively higher in the lateriticsoils, followed by red soil and least isobserved in black cotton soils (Figure2). Another set of data observed acrossan array of land use types, showed thatthe saturated hydraulic conductivityis maximum in forest and plantations(afforested land). Minimum saturatedhydraulic conductivity was observed indegraded forests. However, saturatedhydraulic conductivity in afforested re-gion depends upon both soil type andalso the type of plantation, such as teak,causarina or Acacia. A diagrammaticrepresentation of the variation in saturatedhydraulic conductivity with landuse type is shown in Figure 3. Similarobservation was made by Venkateshet al. (2004) for Barchi watershed ina plateau region. It is reported that inregions afforested with teak plantationhas the lowest K svalue as compared tothe forest and degraded lands.The computed rainfall intensities fordifferent frequencies are superimposedon these box plots to identify the possiblerunoff generation mechanism.From the Figure 2 it is evident that, thered and lateritic soils are more permeablethan the black soils. However, inblack soils, the mean Ks are exceededby the rainfall intensities even at 1 in 1year and above, indicating the dominationof infiltration excess overland flow(Hortonian overland flow) occurrences.The box-plot.3, depicts that, the naturalforest and acacia plantation are comparativelyhas higher permeability than theother land-uses considered for the analysis.From the box-plot, it is observedthat, the K svalues in natural forest aremuch higher than the rainfall intensitiesat 1 in 50 years, indicating that in suchregions the probability of having Hortonianoverland flow is rare. However,Putty et al. (2000) reported the pipeflow phenomenon in such conditions.RMZ-M&G 2010, 57

Estimation of Groundwater Recharge under various land covers in parts of ...191Figure 2. Saturated hydraulic conductivity, K sas a function of soilFigure 3. Saturated hydraulic conductivity, K sas a function of land-useRMZ-M&G 2010, 57

192 Purandara B. K., Venkatesh B. & V. K. ChoubeyTable 3. Estimates of Groundwater Recharge using SWIM ModelSl No1ZonesRainfall recordsConsideredLand use typeForestAverage rainfall(mm)Rechargepercentage52–552 Coastal 1986–2000 Degraded land 3090–4192 28–303 Afforested land 60–654Forest30–355 Plateau 1986–1999 Degraded land 941–152118–226 Afforested land 22–257Forest50–558 Up Ghat 1989–2000 Degraded land 2489–2734 28–329 Affores ted land 48–52Simulations using SWIM ModelThe water balance estimated for thecoastal region (using SWIM model)in three plots, namely, natural forest,degraded forest, afforested land, it isfound that with an average rainfall of3663 mm rainfall, the rainfall got apportionedinto 1648 mm as runoff,1200 mm as evapotranspiration, and811 mm as deep drainage. The estimationof evapo–transpiration by theSWIM model is based on the potentialevaporation. The PE values wereavailable for only one station in eachregion. Therefore, no variation wasobserved in the estimated ET amountunder different land use. The variationobserved in the recharge percentageduring the study period (for thedata of 1986 to 2000) was 51–55 % innatural forest, 28–30 % in degradedforest and 60–65 % in areas where theafforestation was done about 10 yearsback. The runoff coefficient observedfor the catchments in coastal area variedbetween 0.17 and 0.85. The minimumwas noted in the forested plotsand maximum is on degraded forests.This shows that there is a wide variationin runoff characteristics due tocontinuous change in land use and climaticconditions. Other important observationmade was that high rechargeand deep drainages are the characteristicfeatures in coastal region due tohigh permeable lateritic rocks. Similarobservations reported for the basinsoriginating from WG of Karnataka(Shetty, 1999).In the plateau area, especially, in black cottonsoils (vertisol), the estimated rechargein natural forest was 30–35 %, degradedforests, 18–22 % and 22–25 % in affor-RMZ-M&G 2010, 57

Estimation of Groundwater Recharge under various land covers in parts of ...193ested lands. SWIM model analysis carriedout for the Barchi nala catchmentshowed that with an average rainfall of1303 mm, 524 mm is evapo–transpirationand 350 mm was estimated runoff.The deep drainage component wasonly 410 mm.Hydrologically, the up lands of the WGare characterized by steep slopes andhigh rainfall. The major part of the WGis covered by lateritic soil underlain bycrystalline rocks. The runoff percentageestimated is 43 %. The averagerainfall in the area is 2361 mm. SWIMmodel results showed that 878 mm asevapotranspiration and 1015 mm runoff.The drainage component estimatedwas 461 mm. This is quite lower thanthat estimated for the coastal region buthigher than that of plateau region.Baseflow indices were estimated fortwo regions (Venkatesh et al., 2002),one dominated by red soils and the otheris black cotton soils. In the black cottonsoil area it is found that, the baseflowindex showed high value (0.51) ascompared to the red soil region (0.36).This variation is attributed to the factthat, in red soil region major part of therainfall infiltrated into deeper zones andincreased the ground water recharge,where as in the black cotton soil area,the deep drainage component is quitelower due to low infiltration rates asobserved in this part of the study area.ConclusionsThe application of SWIM has beendemonstrated for different land use inparts of WG of North Kanara districtof Karnataka, India. The advantage ofthe model is that it can be used both forlaboratory and field studies to simulatethe soil water and solute transport andcan also be used for understanding theimpact of different land use managementhydrologic regime of the area. Inthis study, simulations using the SWIMhas been substantiated by the field experimentsto understand the runoff generationdynamics under different land–use conditions.Following are some of the importantconclusions drawn from the study1. The impact of afforestationshowed a considerable increasein infiltration and hydraulicconductivity and also generatesinfiltration excess overlandflow at higher rainfall intensities.2. The results obtained throughSWIM model indicated thatthere is marked differences inrecharge percentages as theland cover changes (i.e., convertingthe land from degradedhas been brought under Acaciaplantation in the present case).This higher groundwater rechargemay contribute to theRMZ-M&G 2010, 57

194 Purandara B. K., Venkatesh B. & V. K. Choubeydry season flows in the streams.3. The current study clearly indicatedthat selective reforestation/afforestationwith specificspecies may lead to improvethe surface hydraulic propertiesand encourage greater percolationand conversely, inhibits theoccurrence infiltration excessoverland flow.ReferencesBonell. M., Gilmour, D. A. & Cassells,D. S. (1983): A preliminary surveyof the hydraulic properties of rainforestsoils in tropical north–eastQueens land and their implicationfpr the runoff process. In J. de Ploey(ed.) Rainfall simulation, runoffand soil Erosion, Catena Supplement4, Braunschweig, pp 57–78.Nielsen, D. R., Biggar, J. W. & Erh, K. T.(1973): Spatial variability of fieldmeasured soil water properties.Hilgardia, Vol. 42, pp 215–259.Perroux, K. M. & White, I. (1988): Designsfor disc Permeameters. SoilSci. Soc. Am. J., Vol. 52, pp. 1205–1215.Putty,Y. R. (1994): The Mechanisms ofStreamfflow Generation in the SahyadriRanges (Western Ghats) ofSouth India. Ph. D Thesis, IndianInstitute of Science, Bangalore,India.Puttty, Y. R., Prasad, V. S. R. K. & Ramaswamy,R. (2000): A study onrainfall intensity pattern in theWestern Ghats, Karnataka, Proceedingsof Regional Workshop.Watershed Development Managementand Evaluation in theWesternGhats Region of India, pp. 44–51Shetty, A. V. (1999): Rainfall–Runoffmodelling of Western Ghat Regionof Karnataka. Technical Report,National Institute of Hydrology,Roorkee.Snedecor, G. W. & Cochran, W. G. (1967):Statistical methods, 6 th Edition,Iowa State University Press, Iowa,USA, pp 593.Talsma & Hallam (1980): Hydraulic conductivitymeasurement of ForestCatchments. Aust. J. Soil Res.,Vol. 30, pp 139–148.Talsma, T. (1965): Sample size estimatesin permeability studies. Proceedingsof American Society of EivilEngineers, 91, pp 76–77.Tukey, J. W. (1977): Exploratory DataAnalysis, Addison–Wesley, Reading,Massachusetts, USA, pp. 688.Venkatesh B, Purandara, B. K. & ChandramohanT. (2004): Analysis ofSpatial Variability of HydraulicConductivity of Forest Soils. IntegratedWater Resources Planningand Management; ed. By K. S.Raju, A. K. Sarkar & M. L. Dash.Published by Jain Brothers newDelhi. pp.125–133.Venkatesh B. & Chandrakumar S. (2002):Development of Base Flow IndicesFor Rivers In Hard Rock Region.Technical Report, NationalInstitute of Hydrology, Roorkee.RMZ-M&G 2010, 57