Research Article Assessing Land Suitability for...

Research ArticleAssessing Land Suitability for Rainwater Harvesting UsingGeospatial Techniques A Case Study of Njoro Catchment Kenya

C W Maina1 and J M Raude2

1Department of Agricultural Engineering Egerton University PO Box 536-20115 Egerton Kenya2Soil Water and Environmental Engineering Department Jomo Kenyatta University of Agriculture and TechnologyPO Box 62000-00200 Nairobi Kenya

Correspondence should be addressed to C W Maina mainawcarolinegmailcom

Received 13 April 2016 Revised 5 August 2016 Accepted 19 October 2016

Academic Editor Rafael Clemente

Copyright copy 2016 C W Maina and J M RaudeThis is an open access article distributed under the Creative CommonsAttributionLicense which permits unrestricted use distribution and reproduction in anymedium provided the originalwork is properly cited

Water demand increases as population increases leading to overexploitation of water resource Consequently there is need forimproved water resources management complemented with rain water harvesting within the catchments This study sought toassess land suitability for surface runoff harvesting using geospatial techniques Land useland cover maps of the area were derivedfrom Landsat image Land use and soils data were used in generating curve numbermap of the catchment Lineaments greatly affectthe storage depending on whether runoff is for surface storage or ground water recharge purposes As a result ArcGIS was used indelineating the lineaments from Digital Elevation Model (DEM) of the catchment Further using weighted overlay the catchmentwas grouped into categories of restricted not suitable moderately suitable suitable or highly suitable The study found that forestagriculture and built-up areas occupied about 3942 3632 and 135 of catchment area respectively A large part of catchmentwas found to have curve number range of 82ndash89 About 50 of the catchment was found to fall within suitable and highly suitablecategories This implied that a great potential exists for rain water harvesting within the catchment

1 Introduction

One of the vital requirements for life economic and socialdevelopment is water [1] Water is required by human beingsplants and animals and for ecosystem functions Adequatewater supply is critical in the development of drinking watersupplies agricultural and industrial activitiesThe demand ofwater increases linearly as population increases Accordingto WWO [2] the global water consumption rate doubles inevery 20 years a rate that is twice the population growth rateAs a result optimum efficient use and management of freshwater resources with increase in population are paramountto counter the concerns caused by the observed dwindlingtrends of the water resources Further there are many sectorsfacing serious water shortage For instance approximatelyover 50 of rural household and at least 25 of urbanhouseholds do not have access to adequate clean water [3]Kenya is among the water scarce countries in Africa and has

also seen water storage per capita deteriorate with time tocritical levels By year 2003 the available fresh water supplywas 647m3 per capita and it was estimated that by year 2025per capita water availability would drop to 235m3 as a resultof population growth [4] Water scarcity is a major concernin Kenya and urgent measures are required to arrest thesituation and reverse the trend to an internationally acceptedper capita consumption of 1000m3 As a result there is needfor improved water resources management and one of theviable options is to directmore efforts in rainwater harvestingwithin the catchments

Priority should be given to rainwater harvesting eitherfor surface storage or for artificial recharge since this assistsin sustainable management of water resources [5] Rainwaterharvesting is the process of concentrating runoff from a largearea within the catchment The concentrated runoff can laterbe used in a smaller area [5] for various activities Rain waterharvesting deals with a large number of spatial data that

Hindawi Publishing CorporationApplied and Environmental Soil ScienceVolume 2016 Article ID 4676435 9 pageshttpdxdoiorg10115520164676435

2 Applied and Environmental Soil Science

can be easily handled using geospatial techniques Remotesensing and GIS are widely being applied in the field ofhydrology and water resources development [6] In Kenyalittle has been done in the use of geospatial techniques toidentify sites for rain water harvesting One area that wouldgreatly benefit from such a study is Njoro River catchmentand its environs

Over the years Njoro River catchment has undergonedynamic land use change leading to changes in hydrologicregimes [7] Quantities of runoff have increased over thesurface causing environmental related disasters in the formof flooding and soil erosion However excessive runoff is aresource that can be harnessed for use by households agricul-ture and environmental improvementThe catchmentrsquos mainactivity is rain fed agriculture which lately has been affectedby rainfall variability leading to crops failure Though rainwater harvesting has a great potential a major knowledgegap exists concerning the choice of the most suitable areaswhere harvesting can be practicedTherefore there is need forrobustmethodology that can enable water resourcemanagersidentify and map existing potentials for water harvesting

The Njoro River catchment is approximately 150 km2 Itlies between latitudes 0∘151015840Sndash0∘301015840S and longitudes 35∘201015840Endash36∘051015840E The catchment originates from the Mau Hills atan altitude of about 3060 meters above sea level (masl)to about 1750masl at the mouth where it discharges intoLake Nakuru a Rift valley soda lake This area has a bimodalrainfall pattern starting from March to May sometimesextending to June and September to November thoughrecently climate variability is being experienced Accordingto Kundu [7] the average rainfall recorded within thecatchment is 1020mm per annum The catchment is coveredby loamy soils in the upper forested parts having developedfrom ashes and other pyroclastic rocks of volcanoes anddeep well drained to moderately deep loamy sandy clays(vitric andosol) The lower reaches are composed of erosivelacustrine soils as reported by Chemelil [8]

2 Materials and Methods

21 Data Used Landsat images and Digital Elevation Model(DEM)were downloaded fromhttpearthexplorerusgsgovand both had a resolution of about 30m Landsat imageswere used to generate the land use of the catchment whilethe DEM was used in lineament delineation and slope gen-eration Soil map was first downloaded from KENSORTERwebsite for the region The map was georeferenced andvalidated using soil data from previous studies within thecatchment [7 9] The daily rainfall data for year 1995 to2014 was downloaded from global weather data for SWAT(httpglobalweathertamuedu) This data was correctedwith one collected physically for the same period of timefrom existing weather stations in Egerton University andKenya Agricultural and Livestock Research Organizationin Njoro The correction was carried out by getting theregression equation between the downloaded and existingweather stations data To check the consistency of the rainfalldata a double mass curve was used Homogeneity of the

Table 1 Hydrologic soil groups

Group Soil type Characteristics

A Sand loamy sand sandyloam Low runoff high infiltration

B Silt loam loam Moderate infiltration ratesC Sandy clay loam Low infiltration rates

D Clay silty clay loam sandyclay silt clay

High runoff potential verylow infiltration

data was conducted to test for any possible errors or outliersresulting from the data measurements For instance as aquality control measure any sudden change in the gradientof doublemass curvewas associatedwith inconsistency in thedata and this inconsistency was then corrected

22 Data Processing Techniques The watershed boundarywas delineated in ARCGIS 93 spatial analyst tool Theremotely sensed data and soil maps were georeferenced andgeometrically rectified All data were projected to WGS 1984UTM Zone 36119878 To generate the land use map of thecatchment image preprocessing that is georeferencing andrectification was performed in ERDAS imagine 92 andArcmap 93 Image enhancement was carried out using thecontrast stretch histogram equalization at DN range of 0ndash255 A 3 times 3 filter was used to filter the image so as tosharpen the satellite image and DEM Filtering aided indetecting the linear features like the faults drainages andrivers Supervised classification in false colour compositeusing maximum likelihood classification was conducted Toaid in identifying distinct classes especially built-up area andgrassland false colour combination of bands 4 5 and 3 wasused The combination was informed through use of signa-ture separability which revealed that band 5 was the mosteffective band in classesrsquo separation Image interpretationelements that is tone pattern size association shape andsite were used in land use identification After the supervisedclassification validation was undertaken

23 Curve Number and Runoff The Soil Conservation Ser-vice Curve Number (SCS-CN) method was used in esti-mating volume of direct surface runoff The method wasreported by Fan et al [10] to be enduring in predictingand estimating surface runoff in ungauged rural catchmentlike the Njoro River catchment Further according to theauthors the land useland cover parameters can easily beextracted from remotely sensed images This data aids incatering spatial distribution of runoff generation To calculatecurve number hydrologic soil groups (HSG) land cover typeantecedent soil moisture and hydrologic condition factorswere of paramount importance HSG map of the catchmentwas generated by reclassifying the soils using informationpresented in Table 1 The HSG and land use layer of thecatchment were converted into raster format In ERDASmodel maker was used in matching land cover and HSGAppropriate curve number value was assigned to each cell inthe output layer HSG is classified into 4 groups A B C and

Applied and Environmental Soil Science 3

Table 2 Weightage and ranking for features

Layer Weight Feature class Runoff generation rank Rain water harvestingstorage siteLineaments 15 Lineaments 1 1

Soil 28Loam 1 1

Sandy clay loam 2 2Sandy clay 3 3

Slope 27

gt30 4 18ndash30 3 22ndash8 2 30ndash2 1 4

Land use 30

Waterbody 1 1Forest 2 2

Agriculture 3 3Bare land 4 4Built-up 5 1

D and reflects the infiltration rates of soils [11] Group A hashighest infiltration and low curve number while group D haslowest infiltration and high curve number (Table 1)

In a study conducted by Raude [9] within the Njorocatchment Kenya on surface runoff and soil loss undervarying rainfall intensity and selected land use practices itwas observed that Antecedent Moisture Content (AMC) IIwas useful in determining surface runoff of the catchmentThese findings are based on use of a rainfall simulator tostudy variations from different rainfall intensities Differentmeasurements were taken from runoff plots and the resultswere compared with runoff data collected during actual rain-fall events In the same study soil samples were collected andtheir moisture content was determined using the gravimetricmethod Further runoff coefficients of the area were calcu-lated and a composite runoff coefficient was determinedTheresults were used in understanding runoff values calculatedusing the SCS-CN model As a result this study adopted theAMCII during curve number determination To estimate thedirect runoff from storm the rainfall runoff equation given byBalvanshi and Tiwari [12] was used To apply this equation itwas assumed that initial abstraction of the 5-day antecedentmoisture was well represented by 02119878

119876 =(119875 minus 02119878)

2

119875 + 08119878119875 gt 02119878 (1)

where 119878 is watershed storage mm 119876 is actual direct runoffmm 119875 is total rainfall mm119878 is related to curve number using equation (4) as given

in Fan et al [10]

119878 =25400

CNminus 254 (2)

24 Lineaments Identification Liu et al [13] reported thatDEM is useful in lineaments studies hence in this studyDEM was used for lineament identification Shaded reliefimages from DEM were created In shaded relief imageslineaments were identifiable using variations in sun illumi-nation The illumination is associated with changes in aspect

angle and slope gradient Eight shaded relief images werecreated with light sources from eight different directions thatis 0∘ 45∘ 90∘ 135∘ 180∘ 225∘ 270∘ and 315∘ The first fourshaded relief images were combined into first image whilethe second four sets were combined into the second imageas given by Abdulla et al [14] The lineaments were thendigitized in a GIS environment The lineaments were furtherbuffered at 100m since surface water storage was consideredin selection of a suitable site

25 Suitable Site The different thematic layers were inte-grated in GIS environment where overlay buffering andweighting operations were carried out by first convertingall layers to raster format Since surface water storage wasbeing considered the lineaments were buffered at a distanceof 100m as they showed weak points where infiltrationwas likely to be high Proximity of harvesting site to waterbodies and roads was also buffered to a distance of 300mFor high efficiency runoff generation catchment slope isusually steep Higher slopes were considered to have positiveimpacts on generation of runoff but were undesirable forstorage site identification In assessing the runoff potentialand rain water suitability weighted overlay index in GIS wasused Almost the same methodology was followed by Sarupet al [15] where potential sites for ground water rechargezones were delineated using geospatial techniques In thisstudy individual thematic layers were assignedweightage andalso their classes a rank depending on the influence of theparameter to rain water harvesting or their contribution tothe output (Table 2) The classes with higher values indicatedthe most suitable sites The final score was a product ofrank and weightage where the site suitability was classifiedto be restricted not suitable moderately suitable suitable orhighly suitableThe areas covered by each suitability categorywere calculated using the area tool Further the percentagesuitabilitywas estimated by considering each specific area andthe total catchment area that is

Suitability category areaTotal area

times 100 (3)


Satellite image(i) Georeferencing and

rectification(ii) Enhancement (filtering

histogram equalization PCA)

(iii) Classification

Faults andlineaments

Land useland cover

Soil information(i) Georeferencing

(ii) Digitizing Soil map

Runoff potentialmap

Hydrological soil groups

Topographical map

(i) Georeferencing(ii) Digitizing

contour

DEM(Spatial analyst) Sinks filled

Slope mapFlow direction Flow accumulation and drainage lines

Precipitation data(i) Satellite extraction

Arc GISArc view(i) Curve number

generation(ii) Overlay operation

Distance from settlementsDistance from road

Suitable runoff water harvesting site

Figure 1 Methodology adopted for study

Though rainfall is a key factor when assessing the suit-ability of a region as a harvesting site in this study it was notweighted since it was found that rainfall was almost uniformlydistributed within the study catchment A summary of themethodology adopted is presented in Figure 1

3 Results and Discussion

31 Land UseLand Cover The areas of different land usesland cover as calculated were found to range from 3942and 054 in forest and water body respectively (Table 3)Water body was found to have the smallest area probablybecause river classification was difficult since water was not

visible but the riparian vegetation marked riverrsquos route Theriparian vegetation could have added on the percentage ofthe forest available since the vegetation reflectance was closeto that of forest and this was not separated In additionthe natural forests secondary forest and the agro forestswere not separated in this study which could be associatedwith the fact that forests in general have a higher percentage(Figure 2)

On the other hand agriculture which is the main eco-nomic activity within the study area was found to occupy3652 Baldyga et al [16] had reported small scale farmingand cattle rearing was on the increase within the catchmentAs a result rain water harvesting within the catchment canlead to improved agriculture


Table 3 Land useland cover areas in ha and

Land use Area (ha) Area ()Water body 8046 054Shrub 209295 1414Agriculture 540432 3652Built-up area 19881 135Grass land 67347 455Forest 583362 3942Bare land 51534 348Total 1479897 100

4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

AgricultureBarelandBuilt-up areaForest

GrasslandShrublandWater body

Figure 2 Land use map

32 Soils The soils within the study area were found mainlyto be sandy loam clay loam and sandy clay loam thoughsome sections were found to have loam soils It was foundthat the hydrologic soil groups within the catchment are B Cand D (Figure 3) at 1540 3797 and 4663 respectivelyThe higher percentage of the soil was found to fall in groupD which translated to very low infiltration and high runoffleading to high curve number As a result the soils within thecatchmentwere found to be useful in surfacewater harvestingand generation of much runoff

4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

BDC

Figure 3 Hydrologic soil group map

33 Curve Number It was found that the curve numberswere all above 50 indicating that runoff potential within thecatchment was high The range of curve number was foundto be between 55 and 100 (Figure 4) during normal periodsThe lowest curve number was recorded in natural forest areawhile the highest CNvaluewaswithin thewater body Similarresults were reported by Fan et al [10] where they estimatedcomposite curve numbers for a catchment in GuangzhouChina The current study found that built-up area was foundto have a range of 85 to 94 depending on the soil type andpercentage of impervious area available (Table 4)

The built-up area has more impervious surfaces leadingto low infiltration which led to higher curve number A curvenumber is used to ascertain how much rainfall infiltratesinto soil and how much rainfall becomes surface runoff Thevalues range from 0 to 100 and they represent the ability ofthe land surface to capture water [1] According to Balvanshiand Tiwari [12] low curve number indicates that water easilyinfiltrates into soils giving rise to less runoff while high curvenumber means that water is not captured by the land surfacethus more runoff is generated


N

55ndash616100000001ndash747400000001ndash82

8200000001ndash898900000001ndash100

Curve number

3 15 0 3

(km)

Curve number map

Figure 4 Curve number map

Table 4 Curve numbers

CN range Area area55ndash61 121692 82561ndash74 444486 301474ndash82 359919 244082ndash89 528139 358189ndash100 20578 140Total 1474814 100

34 Runoff It was found that for rainfall amount of 101mmthe runoff generated ranged between 1322 and 8355mmwhere the lowest value was recorded within the forest and8355 was within the built areas This was in agreementwith the curve numbers earlier generated In forested areathe vegetation increases infiltration rates interception lossesand retention which consequently decrease the volume ofrunoffThebuilt-up areas increase the impervious layers thusreducing infiltration rate and this leads to increase in runoffFigures 5 and 6 represent the runoff map and the lineamentsin the study area respectively The low runoff areas werefound to be in forests which are also unsuitable for rain

N

3 15 0 3

(km)


5500000001ndash717100000001ndash101

Runoff (mm)

Runoff map

Figure 5 Runoff map

water harvesting sites It was observed that within the forestsome areas generated higher amount of runoff compared tothe main areas This could have been as a result of forestencroachment or tree cutting for illegal charcoal burningrampant in the study area

35 Lineaments Lineaments were mapped to give moreinformation on potential sites of the rainwater harvestingstructures depending on whether the sites are earmarkedfor surface water storage or ground water recharge In thisstudy the lineaments were buffered since they representedspatial influence that would lead to leakage or seepage forthe surface water storage Lineaments affect surface storageground water recharge and base flow and thus play a vitalrole in the performance efficiency of structural measures [17]They sometimes serve as indicators of rock solubility and canbe associated with fissure conduits The lineaments indicatezones of high permeability and concentrated groundwaterflow [18] Lineaments would be highly suitable when groundwater recharge is to be conducted and least suitable forsurface water harvesting since they would encourage leakageor infiltration


4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary

Figure 6 Lineament map of Njoro catchment

Table 5 Slope classification

Slope Area (ha) area Descriptiongt30 140 001 Steeply dissected to mountainous8ndash30 261925 1776 Rolling to hilly2ndash8 781411 5298 Flat to gently undulating0ndash2 4313377 2925 Generally flatTotal 1474814 100

36 Slope The highest area percentage of the catchment aspresented in Table 5 was found to fall under flat to gentlyundulating category covering 5298 followed by generallyflat class occupying an area of 2925

These two slope classification categories offer favorablerainrunoff potential collection sites Such flat areas allowwater to collect and not to flow away and therefore naturallyhelp in concentrating the rainfall runoff from a given stormOn the other hand steep slopes favor runoff generation andwere ranked high for runoff generation sites while for rainwater harvesting flat slope was ranked higher Further thesites with 8 slope were considered suitable for harvestingrunoff on condition that erosion control is practiced accord-ing to recommendations by Tumbo et al [19]

37 Site Suitability The degree of suitability was assessedthrough sensitivity analysis where weights assigned to the

Runoff potentialLowMediumHigh

Runoff potential map

N

3 15 0 3

(km)

Figure 7 Runoff potential map

factors were varied starting with equal weight assignmentIt was found that soils land cover and slope layers arethe most sensitive layers It was also observed that HSGB areas generated less runoff meaning these areas were oflow runoff potential suitability The runoff potential of thecatchment was found to be 2267 7161 and 572 for lowmedium and high potential areas respectively as presentedin Figure 7 It was also found that low and medium runoffgeneration falls within forested areas This could be as aresult of high infiltration rates within the forests leading toless rainfall being converted into runoff The high runoffpotential areas are characterized by low infiltration ratesin built-up areas and areas with degraded soil conditionsIn addition areas with bare rock or rock outcrops createfavorable conditions for runoff generation

Restricted area includes the forest built-up area waterbody and riparian zones which take about 1017 of the totalcatchment area Classification of the area as restricted wasbased on FAO criteria The criteria indicate that rainwaterharvesting sites should not be within natural forests pro-tected area or areas of ecological importance and built-up


818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)

Site suitabilityRestrictedNot suitableModerately suitable

SuitableHighly suitable

N

Figure 8 Rain water harvesting site suitability

Table 6 Rain water harvesting suitability

Suitability Area (ha) Area ()Restricted 149972 1017Not suitable 425598 2086Moderately suitable 293150 1988Suitable 548210 3717Highly suitable 57884 1192Total 1474814 100

areas To ensure that water harvesting sites were not withinthe water body river buffer was also considered thus addingmore restricted areas as shown in Figure 8 Further areaswithlineaments as presented in Figure 8 were considered sincethey are classified as not being suitable for surface runoffstorage It was observed that only 1192 of the catchmentarea was highly suitable while the highest percentage fell inthe category of suitable at 3717 (Table 6)

Thus it was observed that almost 50 of the catchmentcan be suitable for rain water harvesting If implemented

rainwater harvesting in Njoro catchment can improve avail-ability of water for domestic and nondomestic purposes Alsoagriculture through irrigation can be improved if water har-vesting is practiced other than relying on rain fed agriculture

4 Conclusion

The land useland cover within Njoro catchment was foundto be mainly forest accounting for about 3942 while agri-cultural area was found cover about 3652 of the catchmentarea Built-up area occupied only 135 of the catchmentThe curve numbers within Njoro catchment range from 55 to100 The forested area was found to have low curve numberespecially where hydrologic soil group B dominates resultingin low runoff generation Runoff generation potential withinthe catchment was found to be medium occupying an areaof 72 an indicator of direct runoff that can be harnessedSlopes of the catchment were found to range from 0 to 35since in runoff potential mapping steep slope is favorablewhile for harvesting sites the flat slope is more favorableDEM proved useful in lineament delineation since it was notpossible to delineate the lineaments from Landsat imagerybecause the area has been cultivated Also the catchment hasdifferent other land uses that could not allow identification oflineaments Sensitivity analysis conducted during weightingof layers for suitable rainwater harvesting sites selectionshowed that land use soils and slope are the key parametersthat affect the process Almost 50 catchmentrsquos area wasfound to be suitable for rain water harvesting This is agood indicator that availability of water can be improvedif the proposed technology was adopted Though differentrain water harvesting techniques have specific requirementsin this study a general criterion has been used Furtherstudies can be conducted to narrow down different rainwaterharvesting techniques that can be used to identify the mostsuitable technique for the study area In addition the studycan further be improved by incorporating the social compo-nent where public needs and preference are assessed

Competing Interests

The authors declare that they have no competing interests

References

[1] D RamakrishnanA Bandyopadhyay andKNKusuma ldquoSCS-CN and GIS-based approach for identifying potential waterharvesting sites in the KaliWatershedMahi River Basin IndiardquoJournal of Earth SystemScience vol 118 no 4 pp 355ndash368 2009

[2] WWO (WorldWater Organization) ldquoWater Facts ampWater Sto-ries from Across the Globerdquo 2010 httpwwwtheworldwaterorgwater factsphp

[3] WHO Copying with Water Scarcity A Strategic Issue and Pri-ority for System-Wide Action 2006 httpwwwunwaterorgdownloadswaterscarcitypdf

[4] S R Ariyabandu ldquoVery low cost domestic roof water harvestingin humid tropics Its role in water policyrdquo DFID Kar ContractR7833 Report R4 2003


[5] M Bakir and Z Xingnan ldquoGIS and Remote Sensing applica-tions for rain water harvesting in the Syrian Desert (Al-Badia)rdquoin Proceedings of the 12th International Water Technology Con-ference (IWTC rsquo12) Alexandria Egypt March 2008

[6] M P Sharma and A Kujur ldquoApplications of remote sensing andGIS for ground water recharge zone in and around Gola blockRamgargh district Jharkhand Indiardquo International Journal ofScience and Research Publication vol 2 no 2 pp 1ndash14 2012

[7] P M Kundu An evaluation of the impact of land use andland cover change on stream flows in river Njoro catchmentusing remote sensing and Geographical Information System tech-niques [PhD thesis] Department of Agricultural EngineeringEgerton University Njoro Kenya 2007

[8] M C ChemelilThe effect of human induced watershed changeson stream flow [PhD thesis] LoughboroughUniversity Lough-borough UK 1995

[9] J M Raude Assessing surface runoff and soil loss under varyingrainfall intensity under selected landuse in Njoro catchment [MSthesis] Egerton University 2006

[10] F Fan Y Deng X Hu and Q Weng ldquoEstimating com-posite curve number using an improved SCS-CN methodwith remotely sensed variables in Guangzhou Chinardquo RemoteSensing vol 5 no 3 pp 1425ndash1438 2013

[11] V T ChowD KMaidment and LWMaysAppliedHydrologyMcGrawmdashHill Book Company New York NY USA 2002

[12] A Balvanshi and H L Tiwari ldquoA comprehensive review ofrunoff estimation by the curve number methodrdquo InternationalJournal of Innovative Research in Science Engineering andTechnology vol 3 no 11 pp 17480ndash17485 2014

[13] T Liu H Yan and L Zhai ldquoExtracting relevant features fromDEM for ground water potential mappingrdquo The InternationalArchives of the Photogrammetry Remote Sensing and SpatialInformation Science vol 15 no 7 pp 113ndash119 2015

[14] A Abdulla J M Akhr and I Abdullah ldquoAutomatic mappingof lineaments using shaded relief images derived fromDEMs inthe MaranmdashSungi Lembing Areas Malysiardquo Bund Journal vol15 pp 949ndash957 2010

[15] J Sarup M K Tiwari and V Khatediya ldquoDelineating groundwater prospects zones and identification of artificial rechargesites using Geospatial techniquesrdquo International Journal ofAdvanced Technology and Engineering Research vol 1 no 1 pp6ndash20 2011

[16] T J Baldyga S N Miller W Shivoga and M GichabaldquoAssessing the impact of land cover change in Kenya usingremote sensing and hydrologic modellingrdquo in Proceedings of theASPRS Annual Conference Denver Colo USA May 2004

[17] K M Mayilvaganan P Mohana and K B Naidu ldquoDelineatingground water potential zones in Thurinjapuram watershedusing geospatial techniquesrdquo Indian Journal of Science andTechnology vol 4 no 11 pp 1470ndash1476 2011

[18] J N Bruning A Digital Processing Data Compilation Approachfor Using Remotely Sensed Imagery to Identify Geological Linea-ments in Hard-Rock Terrains An Application for GroundwaterExploration in Nicaragua Department of GeologicalMiningEngineering amp Sciences Michigan Technological UniversityHoughton Mich USA 2008

[19] S D Tumbo B P Mbilinyi H F Mahoo and F O Mkil-amwinyi ldquoIdentification of suitable indices for identification ofpotential sites for rainwater harvestingrdquo Tanzania Journal ofAgricultural Sciences vol 12 no 2 pp 35ndash46 2014

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can be easily handled using geospatial techniques Remotesensing and GIS are widely being applied in the field ofhydrology and water resources development [6] In Kenyalittle has been done in the use of geospatial techniques toidentify sites for rain water harvesting One area that wouldgreatly benefit from such a study is Njoro River catchmentand its environs

Over the years Njoro River catchment has undergonedynamic land use change leading to changes in hydrologicregimes [7] Quantities of runoff have increased over thesurface causing environmental related disasters in the formof flooding and soil erosion However excessive runoff is aresource that can be harnessed for use by households agricul-ture and environmental improvementThe catchmentrsquos mainactivity is rain fed agriculture which lately has been affectedby rainfall variability leading to crops failure Though rainwater harvesting has a great potential a major knowledgegap exists concerning the choice of the most suitable areaswhere harvesting can be practicedTherefore there is need forrobustmethodology that can enable water resourcemanagersidentify and map existing potentials for water harvesting

The Njoro River catchment is approximately 150 km2 Itlies between latitudes 0∘151015840Sndash0∘301015840S and longitudes 35∘201015840Endash36∘051015840E The catchment originates from the Mau Hills atan altitude of about 3060 meters above sea level (masl)to about 1750masl at the mouth where it discharges intoLake Nakuru a Rift valley soda lake This area has a bimodalrainfall pattern starting from March to May sometimesextending to June and September to November thoughrecently climate variability is being experienced Accordingto Kundu [7] the average rainfall recorded within thecatchment is 1020mm per annum The catchment is coveredby loamy soils in the upper forested parts having developedfrom ashes and other pyroclastic rocks of volcanoes anddeep well drained to moderately deep loamy sandy clays(vitric andosol) The lower reaches are composed of erosivelacustrine soils as reported by Chemelil [8]

2 Materials and Methods

21 Data Used Landsat images and Digital Elevation Model(DEM)were downloaded fromhttpearthexplorerusgsgovand both had a resolution of about 30m Landsat imageswere used to generate the land use of the catchment whilethe DEM was used in lineament delineation and slope gen-eration Soil map was first downloaded from KENSORTERwebsite for the region The map was georeferenced andvalidated using soil data from previous studies within thecatchment [7 9] The daily rainfall data for year 1995 to2014 was downloaded from global weather data for SWAT(httpglobalweathertamuedu) This data was correctedwith one collected physically for the same period of timefrom existing weather stations in Egerton University andKenya Agricultural and Livestock Research Organizationin Njoro The correction was carried out by getting theregression equation between the downloaded and existingweather stations data To check the consistency of the rainfalldata a double mass curve was used Homogeneity of the

Table 1 Hydrologic soil groups

Group Soil type Characteristics

A Sand loamy sand sandyloam Low runoff high infiltration

B Silt loam loam Moderate infiltration ratesC Sandy clay loam Low infiltration rates

D Clay silty clay loam sandyclay silt clay

High runoff potential verylow infiltration

data was conducted to test for any possible errors or outliersresulting from the data measurements For instance as aquality control measure any sudden change in the gradientof doublemass curvewas associatedwith inconsistency in thedata and this inconsistency was then corrected

22 Data Processing Techniques The watershed boundarywas delineated in ARCGIS 93 spatial analyst tool Theremotely sensed data and soil maps were georeferenced andgeometrically rectified All data were projected to WGS 1984UTM Zone 36119878 To generate the land use map of thecatchment image preprocessing that is georeferencing andrectification was performed in ERDAS imagine 92 andArcmap 93 Image enhancement was carried out using thecontrast stretch histogram equalization at DN range of 0ndash255 A 3 times 3 filter was used to filter the image so as tosharpen the satellite image and DEM Filtering aided indetecting the linear features like the faults drainages andrivers Supervised classification in false colour compositeusing maximum likelihood classification was conducted Toaid in identifying distinct classes especially built-up area andgrassland false colour combination of bands 4 5 and 3 wasused The combination was informed through use of signa-ture separability which revealed that band 5 was the mosteffective band in classesrsquo separation Image interpretationelements that is tone pattern size association shape andsite were used in land use identification After the supervisedclassification validation was undertaken

23 Curve Number and Runoff The Soil Conservation Ser-vice Curve Number (SCS-CN) method was used in esti-mating volume of direct surface runoff The method wasreported by Fan et al [10] to be enduring in predictingand estimating surface runoff in ungauged rural catchmentlike the Njoro River catchment Further according to theauthors the land useland cover parameters can easily beextracted from remotely sensed images This data aids incatering spatial distribution of runoff generation To calculatecurve number hydrologic soil groups (HSG) land cover typeantecedent soil moisture and hydrologic condition factorswere of paramount importance HSG map of the catchmentwas generated by reclassifying the soils using informationpresented in Table 1 The HSG and land use layer of thecatchment were converted into raster format In ERDASmodel maker was used in matching land cover and HSGAppropriate curve number value was assigned to each cell inthe output layer HSG is classified into 4 groups A B C and




Soil 28Loam 1 1


Slope 27


Land use 30





119876 =(119875 minus 02119878)

2

119875 + 08119878119875 gt 02119878 (1)


in Fan et al [10]

119878 =25400

CNminus 254 (2)





times 100 (3)







Land useland cover



Runoff potentialmap


Topographical map


contour

















4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

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833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N





4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

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833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

BDC





N


8200000001ndash898900000001ndash100

Curve number

3 15 0 3

(km)

Curve number map





N

3 15 0 3

(km)


5500000001ndash717100000001ndash101

Runoff (mm)

Runoff map

Figure 5 Runoff map




4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary









N

3 15 0 3

(km)





818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics

















Soil 28Loam 1 1


Slope 27


Land use 30





119876 =(119875 minus 02119878)

2

119875 + 08119878119875 gt 02119878 (1)


in Fan et al [10]

119878 =25400

CNminus 254 (2)





times 100 (3)







Land useland cover



Runoff potentialmap


Topographical map


contour

















4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N





4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

BDC





N


8200000001ndash898900000001ndash100

Curve number

3 15 0 3

(km)

Curve number map





N

3 15 0 3

(km)


5500000001ndash717100000001ndash101

Runoff (mm)

Runoff map

Figure 5 Runoff map




4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary









N

3 15 0 3

(km)





818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics




















Land useland cover



Runoff potentialmap


Topographical map


contour

















4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N





4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

BDC





N


8200000001ndash898900000001ndash100

Curve number

3 15 0 3

(km)

Curve number map





N

3 15 0 3

(km)


5500000001ndash717100000001ndash101

Runoff (mm)

Runoff map

Figure 5 Runoff map




4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary









N

3 15 0 3

(km)





818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics

















4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N





4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

BDC





N


8200000001ndash898900000001ndash100

Curve number

3 15 0 3

(km)

Curve number map





N

3 15 0 3

(km)


5500000001ndash717100000001ndash101

Runoff (mm)

Runoff map

Figure 5 Runoff map




4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary









N

3 15 0 3

(km)





818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics















N


8200000001ndash898900000001ndash100

Curve number

3 15 0 3

(km)

Curve number map





N

3 15 0 3

(km)


5500000001ndash717100000001ndash101

Runoff (mm)

Runoff map

Figure 5 Runoff map




4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary









N

3 15 0 3

(km)





818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics















4000 2000 0 4000(m)

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

LineamentsBoundary









N

3 15 0 3

(km)





818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics















818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

818000000000

821000000000

824000000000

827000000000

830000000000

833000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

9945000000000

9950000000000

9955000000000

9960000000000

9965000000000

N

4000 2000 0 4000(m)



N







4 Conclusion


Competing Interests


References

























Journal of












Volume 2014

Advances in









Geophysics


































Journal of












Volume 2014

Advances in









Geophysics














Research Article Assessing Land Suitability for...

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