Integrating the NRCS Runoff Curve Number in Delineationof Hydrologic Homogeneous Regions

Binaya Kumar Mishra1; Kaoru Takara, M.ASCE2; and Yasuto Tachikawa3

Abstract: Cluster analysis, the generally used technique in delineation of hydrologic homogeneous regions for regional flood frequencyanalysis, requires standardization of flood governing attributes, and hence makes use of large subjective consideration. A new techniqueis developed for the delineation with no need of standardizing the attributes. The technique uses Natural Resources Conservation Servicesrunoff curve number as a starting point in proposing the hydrologic regions. Five numbers of hydrologic regions were proposed inside theNepalese territory by superimposing monsoon rainfall map over the runoff curve number map. The L-moment based regional hydrologichomogeneity test led finalization of hydrologic regions with minor adjustments. Heterogeneity measure value, which is a regionalhomogeneity test parameter representing dispersion in sample moment coefficients among the basins, was found within or near acceptablelimit in each of the region. The use of the regionalization for estimating extreme flood of different return periods is demonstrated for sixexample sites.

DOI: 10.1061/�ASCE�HE.1943-5584.0000101

CE Database subject headings: Flood frequency; Homogeneity; Runoff; Water resources.

Introduction

Estimation of extreme flood for different return periods is re-quired in design and planning of various water related structures.This extreme flood is popularly known as design flood. Designflood estimation methods can be broadly classified into twogroups: streamflow-based methods and rainfall-based methods.This work is related with streamflow-based methods which basetheir analysis purely on stream-gauging data. The most commonstreamflow-based methods are the flood frequency analysis andregional flood frequency analysis �RFFA�. Flood frequency analy-sis is applicable to gauged locations which possess long recordsof observed flood data. However, the gauged locations rarely co-incide with the locations at which water-related structures aregoing to be constructed. In such situation, regional hydrologiccharacteristics need to be used in estimation of design flood.RFFA is such method which makes the use of regional hydrologiccharacteristics for estimating design flood. RFFA is popularmethod for estimating flood peaks within specified probabilitiesof exceedance at ungauged sites or enhancing estimation atgauged sites where historical records are short.

Among various methods �Cunnane 1998� of RFFA, indexflood method �Dalrymple 1960� is the most popular. In this

1Graduate Student, Dept. of Urban and Environmental Engineering,Kyoto Univ., Uji Campus, Kyoto 611-0011, Japan �correspondingauthor�. E-mail: mishra@flood.dpri.kyoto-u.ac.jp

2Professor, Disaster Prevention Research Institute, Kyoto Univ., UjiCampus, Kyoto 611-0011, Japan.

3Associate Professor, Dept. of Urban and Environmental Engineering,Kyoto Univ., Katsura Campus, Kyoto 615-8540, Japan.

Note. This manuscript was submitted on August 6, 2008; approved onFebruary 11, 2009; published online on February 14, 2009. Discussionperiod open until March 1, 2010; separate discussions must be submittedfor individual papers. This paper is part of the Journal of HydrologicEngineering, Vol. 14, No. 10, October 1, 2009. ©ASCE, ISSN 1084-

0699/2009/10-1091–1097/$25.00.

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method, the estimation of design flood �QT,i� of return period T atbasin i is expressed by Eq. �1� �Solín 2005�

QT,i = qT�i �1�

where qT and �i are called regional frequency factor and indexflood, respectively. The regional frequency factor �qT� is dimen-sionless quantile function of the regional frequency distribution,which remains same to all basins in a hydrologic region for aspecific return period, T. The index flood ��i� corresponds toaverage likely flood at the site of interest. It is usually taken asmean or median of the annual maximum discharge series for thegauged site. The mean annual maximum discharge series corre-sponds to 2.33-years return period flood for Gumbel distribution�Reddy 2004�. The index flood is related with physiographic/climatic factors like catchment area, land cover, soil type, rainfall,etc for ungauged basins.

The index flood-based RFFA method can be said of threemajor steps: hydrologic homogeneous regionalization, selectionof regional frequency distribution function and estimation ofindex flood �scale factor�. This study deals with the first step i.e.,hydrologic homogeneous regionalization. Regionalization refersto grouping of territory/basins having similar flood generatingmechanisms. In other words, all the sites in a hydrologic homo-geneous region possess identical frequency distribution �Hoskingand Wallis 1997�. Regionalization is required to affect the spatialtransfer of hydrologic information.

There are no specific guidelines for identifying homogeneousregions. This is due to the complexity in understanding preciselythe factors that have effect on the generation of floods. Severalattempts have been made by different writers to identify hydro-logic homogeneous regions in different parts of the world. Mostof the works �Mosley 1981; Wiltshire 1985; Burn and Goel 2000�have used cluster analysis to identify objectively hydrologic ho-mogeneous regions. Cluster analysis arranges a set of basins intoclusters such that basins within a cluster are more similar to eachother than they are to basins in other clusters. The similarity

among basins is measured by means of a weighted distance in the

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m-dimensional space defined by a set of m-physiographic/climaticattributes, which are considered to influence the frequency behav-ior of the extreme flows of the basin. Mosley �1981� used clus-tering of flood statistics: specific flood �peak discharge per unitdrainage area� and coefficient of variance �Cv� for delimitation ofNew Zealand river basins. Regionalization using basin character-istics such as catchment area, rainfall, and soil type was suggestedas an attractive approach of regionalization by Wiltshire �1985�.Burn and Goel �2000� used three physiographic characteristics:catchment area, length of the main stream, and slope of the mainstream in central Indian River basins.

Research on regionalization of Nepalese rivers is extremelylimited. McDonald and Partners Ltd. �1990� divided Nepaleseterritory into seven regions for estimation of mean and 80% reli-able flow. WECS �Water and Energy Commission Secretariat�relationships, which have been developed using traditional �re-gression based� RFFA, are frequently used for estimating returnperiod flood �Sharma and Adhikari 2004�. The WECS method hasconsidered the whole Nepalese territory as one hydrologic region.Except the previous study by Mishra et al. �2008�, no other studyis found on hydrologic regionalization of Nepalese rivers for de-sign flood estimation. The previous study is based on hierarchicalagglomerative clustering technique.

A number of difficulties were experienced by the writers in theprevious attempt. One of the major difficulties is different mea-suring units of clustering attributes. For example, one attributelike soil type is expressed in term of infiltration rate �mm/h�whereas another attribute like catchment area is expressed in km2.Another problem is measuring scale �e.g., mm/hr or cm/hr� ofclustering attributes. Because of this different units/scale, a suit-able weight needs to be allocated to each clustering attribute.Allocation of suitable weight to different clustering attributes isdifficult and subjective work. This study has attempted to developa new method of hydrologic regionalization by using Natural Re-sources Conservation Services �NRCS� runoff curve number�CN�. With the introduction of NRCS runoff CN in delineation ofhydrologic regions, the need of allocating weight to the differentattributes gets avoided. In this work, the long-term monsoon rain-fall map of Nepal was superimposed over the NRCS runoff CNmap, which take care the effect of soil type, land cover, moisturecondition etc., in delineation of hydrologic homogeneous regions.

NRCS Runoff Curve Number

The NRCS runoff CN is an index developed by USDA �UnitedStates Department of Agriculture� representing potential for stormwater runoff within a drainage area. Soil type, land cover, andmoisture condition of the land surface mainly govern values ofCN. Further research in different part of the world suggested ad-justments in CN for different range of land slope and others�Ritzema 1994�. A runoff CN is similar to a runoff coefficient �C�which varies from 0 to 1 and thought as the percent of rainfallthat becomes runoff. However, runoff CN is based on a scale of0 �low runoff potential� to 100 �high runoff potential�. The pa-rameters for determining the CN are more defined than the onesgiven in the runoff coefficient and therefore, it is the preferredmethod over the runoff coefficient �Maidment 1992�. The runoffcoefficient is commonly used in urban environments whereas therunoff CN is more commonly used in suburban and agriculturalareas. Runoff is determined primarily by the amount of precipi-tation and infiltration characteristics such as impervious surfaces

and surface retention of water that flows from the land to streams

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or other surface waters. As urban area develops, natural land cov-ers are replaced with impervious surfaces, hence the increasedrunoff coefficients or CN. The NRCS CN based runoff relation-ship �Eq. �2�� is

Q = �P − Ia�2/�P − Ia + S� �2�

where Q=direct runoff �mm�, P=rainfall �mm�, S is potentialmaximum retention �mm�, and Ia=initial abstraction �mm�. Ia

is usually taken 20% of S. The runoff CN is then related to S byEq. �3�

CN = 25400/�254 + S� �3�

The available soil data needs to be expressed into hydrologicsoil groups �HSG�. There are four HSG: A, B, C, and D �Ritzema1994�. These soil groups indicate high, moderate, slow, and veryslow infiltration rates, respectively. The land cover classificationis made similar to the classification in lookup table consistingrunoff CN values for different combinations of HG, land coverand moisture condition.

Soil moisture condition in the drainage basin before runoffoccurs is important factor influencing the CN value. Antecedentmoisture content �AMC� is based on 5-day antecedent rainfall i.e.,the accumulated total rainfall preceding the runoff under consid-eration �Table 1�. AMC I, II, and III indicate dry, average, andsaturated condition of drainage basins, respectively. Initially, therunoff CN is computed for average moisture condition �AMC II�.If necessary, the CN values are modified for other conditions. TheCN values for moisture conditions I and III can be calculated withthe Eqs. �4� and �5� �Neitsch et al. 2002�

CNI = CNII −20�100 − CNII�

�100 − CNII + exp�2.533 − 0.0636�100 − CNII���

�4�

CNIII = CNII . exp�0.00673�100 − CNII�� �5�

Regional Homogeneity Test

Once hydrologic regions are proposed, their regional hydrologichomogeneity check need to be performed. This work employs theL-moment based regional homogeneity test �Hosking and Wallis1997� which needs computation of L-moment ratios: L-coefficientof variance �LCv�, L-skewness �LCs� and L-kurtosis �LCk� at eachstation for the available data series. Regional homogeneity canbe identified visually from a plot of LCv versus LCs or LCs versusLCk on L-moment ratio diagram �Rao and Hamed 1997�. Ifthe plotted points are closer, the region can be expected as hydro-logic homogeneous. Numerically, there are two ways: discor-dancy measure and heterogeneity measure to check regional

Table 1. Seasonal Rainfall Limits for AMC Classes �Ritzema 1994�

Antecedentmoistureconditionclass

5-day antecedent rainfall �mm�

Dormantseason

Growingseason Average

I �13 �36 �23

II 13–28 36–53 23–40

III �28 �53 �40

homogeneity.

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The discordancy measure is intended to identify those sitesthat are unusual with the group as a whole. To illustrate the mean-ing of discordancy measure �Hosking and Wallis 1997�, considerthe sample L-moment ratios �LCv, LCs, and LCk� of a site as apoint in three-dimensional space. A group of sites in the proposedregion will yield a cloud of such points. Distance of individualsites from the center of cloud �group� can be considered as dis-cordancy measure. The site which is largely far away from thecenter of the cloud is called as discordant sites. In other words, itcan be interpreted as the basin with distinct L-moment ratios fromthe group is nonhomogeneous. If ui= �t�i� t3

�i� t4�i��T is L-moment

ratios �coefficient of variance, skewness and kurtosis respec-tively� vector for the site i and superscript T as transpose of thevector, then discordancy �Di� can be expressed as Eq. �6�

Di =1

3�ui − u�TA−1�ui − u� �6�

where u and A for N gauge locations in a region are defined byEqs. �7� and �8�, respectively

u =1

N�i=1

N

ui �7�

A = �i

N

�ui − u��ui − u�T �8�

A site i is declared to be discordant if Di is large. According toHosking and Wallis �1997�, critical value of Di is 3 for the regionhaving 15 or more numbers of gauged sites. For the region withsmaller numbers of gauge sites, critical value of Di varies withnumber of gauge sites in the region.

The second regional homogeneity test parameter is heteroge-neity measure which estimates the degree of heterogeneity in agroup of sites and assesses whether they might be reasonablytreated as homogeneous. This test compares the variability of thethree L-moment ratios �coefficient of variance, skewness, andkurtosis� for the catchments in a region individually. In this test,dispersion in LCv or LCs, or LCk among the sites in the consid-ered regions is checked by employing the terms H1, H2, and H3,respectively. The heterogeneity measure is expressed in generalform by Eq. �9�

H = �V − �v�/�v �9�

where V�sample-size weighted variance in LCv, LCs, or LCk

for the region; and �v and �v are mean and standard deviation ofsimulated V obtained for 500 numbers �in this study� of simula-tions, respectively, by fitting a Kappa distribution to the regionalaverage L-moments �Hosking 2005�. Heterogeneity measure H1

is suggested as the most reliable parameter in testing regionalhomogeneity �Hosking and Wallis 1997; Rao and Hamed 2000�.According to Hosking and Wallis �1997�, a region is consideredhomogeneous if H�1, possibly heterogeneous if 1�H�2, anddefinitely heterogeneous if H�2. In this work, the goal is todefine regions that results H less than 1, although H values withinor near 2 will be considered acceptable.

Study Area

This study is intended to form hydrologic regions inside the Ne-palese territory for carrying RFFA. Nepal, roughly rectangle in

2

shape with an area of 147,181 km , is situated between China

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in the north and India in remaining three sides. It has a length of885 km east-west and width of 145–248 km north–south. Withinthis relatively small latitudinal extent, altitude rises from 60 m insouth to the 8,848 m �Mount Everest, the world’s highest peak� inNorth. The five physiographic classes of the country are �Fig. 1�:Terai �Plain�, Siwalik Hills, Middle Mountains, High Mountains,and High Himalayas. The average annual precipitation is around1,600 mm of which almost 80% or more occurs during the periodof June–September. All the river systems of Nepal are tributariesto the Ganges River draining ultimately to the Bay of Bengal.

Data and Preliminary Analysis

The physiographic/climatic attributes: soil type, land cover, landslope, and monsoon rainfall have been considered in proposinghydrologic regions. Effect of soil type and land cover has beendealt using runoff CN. Consideration on land slope was madein modifying the CN. The GTOPO30-DEM was used in generat-ing sample basins. Annual instantaneous flood data of 49 riverbasins was collected for performing regional hydrologic homoge-neity test. Discussion on these data sets is dealt in the followingsubsections.

Soil Type

Global soil data �FAO 2003� is available in digital format with aresolution of 5 arc-minute �approximately 10 km�. Soil data ofthe study area was extracted from the global Food and Agricul-tural Organization �FAO� soil data set. Textural classification as-sociated with soil data was considered in preparing HSG whichis required in preparation of runoff CN map. The textural classesreflect the relative proportions of clay �fraction less than 0.002mm�, silt �0.002–0.05 mm� and sand �0.05–2 mm� in thesoil. Based on their proportion, HSG map of Nepal �Fig. 2� wasprepared.

Land Cover

The University of Maryland, Department of Geography has gen-erated global land cover classification collection �Hansen et al.1998�. The university made fourteen land cover categories fromthe analysis of AVHRR satellites imagery acquired between 1981and 1994. Land cover data with spatial resolution of 1 km wasdownloaded from the website �http://glcf.umiacs.umd.edu/data/

Fig. 1. Physiographic regions map of Nepal

landcover/� maintained by Global Land Cover Facility �GLCF�.

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Digital land cover map �Fig. 3� required for CN estimationwas prepared after some modification in original classificationnomenclature.

Rainfall Distribution

Distribution of rainfall over time and space can be consideredimportant in hydrologic regionalization. Annual rainfall in Nepaldecreases slightly from east to west and increases with elevationfrom south to north on windward slopes. About 80% or more ofthe annual rainfall occurs during this monsoon season. The rain-fall regime covers the whole country except the northern Hima-layan region. The concentration of rainfall during a few monthsresults large floods and landslides. Monsoon rainfall which occursduring the months of June to September has more importance inregionalization for design flood estimation. This study made threeclasses of long-term average monsoon rainfall map of Nepalavailable at http://www.fao.org/ag/agL/swlwpnr/reports/y_sa/z_np/npmp134.htm. These three classes �Fig. 4� were defined forrainfall value less than 1,000 mm, 1,000 to 1,500 mm and morethan 1,500 mm.

Digital Elevation Model

Digital elevation model �DEM� of the study area is fromGTOPO30-DEM data set available at website �http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html� maintained U.S.

Fig. 2. Hydrologic soil groups �HSG� map of Nepal

Fig. 3. Land cover map of Nepal

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Geological Survey’s center for Earth Resources Observationand Science �EROS�. The GTOPO30-DEM with a resolution of30 arc-sec �approximately 1 km� was used in generating samplebasins and stream network. The stream network derived from thisDEM was found similar to the stream network map available atwebsite of Department of Hydrology and Meteorology �DHM�,Nepal. In addition, the drainage areas of DEM generated riverbasins were found very similar to that of DHM, Nepal.

Flood Data

Regional hydrologic homogeneity test needs to be performed ineach of the proposed hydrologic region. It needs adequate numberof gauge stations with long record length of flood data. Regularlyobserved instantaneous annual maximum stream flow data of 49stations were collected from DHM, Nepal. Selection of thesestream gauging stations were based on basins’ boundary positionand record length of observed data. Most of these stations aresituated in middle mountain region. Out of these 49 river basins,46 have observed length of more than ten years. Because of in-adequate gauging stations in low-elevation region, the availablethree stations were selected although their record length issmaller.

Analysis of flood data was started by visual inspection. Theseflood data have been obtained from the 1998, 2003, and 2004’sDHM publications. In addition, some recent flood data were col-lected in digital format from the authority of DHM, Nepal. Incase of overlapping mismatched data, the latest publication wasconsidered as correct flood data. The flood data series being ho-mogeneous and stationary are the basic assumptions in flood fre-quency analysis. Test of homogeneity and stationary for the flooddata series was performed as discussed by Mann and Whitney�1947�. All the data series were found homogeneous and station-ary at 5% significance level. Presence of outliers in the datacauses difficulties when fitting a distribution to the data. The G-Btest �Grubbs and Beck 1972; Rao and Hamed 2000� was usedto detect outlier. Approximate relationship proposed at 10%significance level by Pilon and Harvey �1993� was used in calcu-lating G-B statistic. The study found one outlier at each 14 sta-tions �station indices: 267, 404.6, 438, 439.8, 445.3, 465, 530,570, 589, 602, 620, 627.5, 650, and 660�, two outliers at stationindex 447.9 and three outliers at station index 241. Except one�station index 241�, all these outlier stations �Fig. 5� are situatedin high rainfall region when their spatial positions were observed

Fig. 4. Monsoon rainfall map of Nepal

minutely.

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Region Forming Process

The region forming started with development of NRCS runoff CNmap for the available different combination of HSG and landcovers. The HSG: A, B, C, and D is based on the textural prop-erties classification of global soil data prepared �FAO 2003�.Nepal has distinct land cover with major influence of climaticvariation, altitude, and relief. The cropland situated in hilly regionwas considered of terraced type for preparation of runoff CNmap. Using the HSG map �Fig. 2�, land covers map �Fig. 3�, andlookup table �Table 2�; the runoff CN map was prepared for av-erage moisture condition �i.e., AMC II�.

The antecedent rainfall is considered important factor in gov-erning soil moisture condition, hence on runoff CN values. Sincethis work is not limited to particular flood event estimation, anaverage value of 5-days rainfall occurring in flood season. In caseof Nepal, 80% or more rainfall takes place during the monsooncausing flood events. Therefore, long-term monsoon rainfallwas considered to account the AMC. The monsoon period isgrowing season consisting of four months �June, July, August,and September� with roughly 120 days. In the present case�Fig. 4�, the area getting low rainfall ��1,000 mm�, mediumrainfall �1,000–1,500� and high rainfall ��1,500� can be consid-ered of AMCI, II and III, respectively, �Table 1� as average 5-daysrainfall values are �36 mm, 36–53 mm and �53 mm, re-spectively. Modification for the AMC I and II can be made using

Table 2. Curve Numbers Association with Different Hydrologic SoilGroups and Land Covers

Land cover

Soil type

A B C D

Meadow 30 58 71 78

Woods-grass �fair� 43 65 76 82

Woods �fair� 36 60 73 79

Deciduous forest 36 60 73 79

Evergreen forest 40 66 77 85

Mixed forest 38 63 75 82

Urban 68 80 88 94

Cropland 49 69 79 84

Cropland �terraced� 65 74 82 86

Shrub/Brush Tundra 48 67 77 83

Glaciers/Stream/Lake 0 0 0 0

Fig. 5. Outlier hydrologic stations of which all the stations except241 are situated in high rainfall region

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Eqs. �4� and �5� although visual technique was applied with themonsoon rainfall map in this study.

Arc CN-runoff tool �Zhan and Huang 2004�, a rainfall-runoffmodel on ArcGIS platform, can generate efficiently runoffCN map. The tool Arc CN-runoff requires with a lookup table�Table 2� consisting specific number between 0 and 100 for dif-ferent soil type and land cover combinations. The runoff CNsassociated with different combination of land cover and HSG arebased from Ritzema �1994� and ArcCN-runoff tool’s lookup table.

Area-weighted average runoff CN was calculated for each ofthe 650 sample basins. This study did not consider sample basinsfrom Himalayan region considering its insignificance in designflood. Hydrologic regions were proposed by superimposing mon-soon rainfall map over sample basins associated with CN values.

The L-moment based regional hydrologic homogeneity testwas applied on each of the proposed regions. To apply the homo-geneity test, L-moments/ratios were computed for collected flooddata at each station. Heterogeneity measure, particularly H1, waschecked for each region. The region for which heterogeneity mea-sure was found smaller/nearer to 2 was accepted as homogeneousregion. For the region having heterogeneity measure far beyond 2,discordancy measure �Di� was taken into consideration to makeadjustment in proposed regions. When heterogeneity measure anddiscordancy measure reduced to limiting value, the region wasdeclared hydrologic homogeneous.

Results and Discussion

Area-weighted average runoff curve values were computed for650 numbers of spatially well-representing sample basins. Thestudy found two distinct ranges of CN. Out of 650 sample basins,450 were found to have CN of 73 to 80. The remaining basinswere found with smaller CN ranging from 50–65. This led twomajor divisions inside the Nepalese territory. The basins withhigher CN were found in midmountain and higher mountainwhereas sample basins with smaller CN values were found inlow-mountain and plain region.

The two classes are for average moisture condition �AMC II�-based runoff CN map. To account the other soil moisture condi-tions �AMC I and AMC III�, monsoon rainfall map wassuperimposed over the AMCII-based regional map for further re-gionalization. Superimposition of monsoon rainfall map over theregional map led three additional hydrologic homogeneous re-gions. In this way, the study proposed a total of five hydrologicregions and proceeded for validation of the regions.

Hydrologic homogeneity test was performed in each regionusing L-moment based regional hydrologic homogeneity test. Thetest parameters were computed from observation flood data seriesof different hydrological stations in the corresponding region.Heterogeneity measures �H1, H2, and H3� before and after remov-ing the discordant stations are given in Table 3.

Heterogeneity parameter H1 is found to have a value of 8.9 forNepal as one hydrologic region. This large value of H1 indicatesthat there is large difference in L-coefficient of variance in floodvalues among different basin of Nepal. In other words, there islarge heterogeneity among the river basins inside the Nepaleseterritory. It justifies the need of regional flood frequency study.The large heterogeneity is expected for the basins with dissimilarphysiographic/climatic attributes. In addition, presence of outliersin the flood data set also causes large heterogeneity values. Insuch situation, discordancy measure value was used for identify-

ing the distinct basins from the group. The station with larger

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discordancy measure values were checked for data errors or un-usual extreme values caused by localized meteorological event. Incase of Nepal as one hydrologic region, the situation did notimprove considerably even after removing all the discordant sta-tions. The heterogeneity measure value remained far beyond theacceptable limit indicating hydrologic nonhomogeneity of Nepal.

Among the proposed five hydrologic regions, the regions 2, 3,and 5 were found with acceptable value of heterogeneity measureand no discordant sites in first attempt. In other words, the basinsof these regions have very similar in physiographic/climatic char-acteristic i.e., similar moment coefficient statistics. The remainingregions 1 and 4 were found to have larger heterogeneity valuethan acceptable value of 2. Therefore, some of the basins of re-gions 1 and 4 can be suspected with dissimilar physiographic/climatic characteristics or presence of outliers. Discordancymeasure values of all the basins in these two regions were calcu-lated for each basin to identify the basins of dissimilar character-istic from the group. The basins with large discordancy measurevalue were moved from one region to another region if the dataset were found with no outliers. Three stations with one extremevalue at each were assumed to have influenced by some localizedmeteorological event. When these discordant sites were removed,the heterogeneity measure values reduced to acceptable limit. Fig.6 shows the finally identified five numbers of hydrologic homo-geneous regions. Himalayan region has been allocated to a par-ticular hydrologic region just based on spatial proximity.

As mentioned earlier, the previous attempt �Mishra et al. 2008�of delineating hydrologic regions using clustering analysis couldnot achieve acceptable value of heterogeneity measure for all theproposed regions despite various trials. Therefore, this approachis justified as better method in delineating hydrologic regions. Inaddition, comparative analysis has been made in one of the regionfor return period flood of this RFFA with that of at-site flood-frequency analysis method and frequently used WECS, Nepal

Table 3. Regional Heterogeneity Measures for Proposed Hydrologic Reg

RegionArea�%�

All

Number of sites�NS� H1

All 100 49 8.90

1 16.24 9 5.72

2 14.67 2 �0.41 �

3 33.20 10 1.96

4 24.63 26 4.77

5 11.26 2 �1.08

Fig. 6. Hydrologic homogeneous regions of Nepal

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method �Sharma and Adhikari 2004�. Selection of regional fre-quency distribution and estimation of index flood are required forestimating return period flood using the RFFA method. The re-gional frequency distribution was obtained using the statisticzDIST �Hosking and Wallis 1997�. The index flood relationshipwas established using multiple regression technique on the at-tributes drainage area and river slope. The following steps wereadopted in assessing the return period flood estimates:1. At-site flood frequency analysis was conducted on flood se-

ries of different sites considering generalized extreme value,generalized normal, generalized logistic, Pearson type III andgeneralized Pareto as possible probability distributions.

2. Floods of 2-, 5-, 10-, 20-, and 50-year return periods wereestimated using best fitting at-site frequency distribution. Theestimated value was considered as true or actual value forcomparative analysis.

3. The relationships of this RFFA were used to predict flood forcorresponding different return periods.

4. The WECS Nepal method was used in comparing any ad-vancement in flood predictability.

5. Finally, flood estimates of RFFA and WECS method werecompared with that of at-site method visually andnumerically.

The region 3 has been considered as example region for thecomparative analysis. At-site flood frequency method can be ex-pected reliable if it is carried with long record data set. Therefore,the gauge stations with record length less than 20 years situated inhydrologic region 3 have not been considered for comparativeanalysis. The prediction made by at-site distribution has been con-sidered as actual flood estimate. The average relative root-mean-square error �RMSE� in 2, 5, 10, 20, and 50 years return periodsflood for the RFFA method and the WECS method is 21.08% and48.65%, respectively �Table 4�. The RFFA method is found tohave better predictability over the WECS method because of rela-tively smaller RMSE.

Discordant sites removed

H3 NS H1 H2

WarningH3

�0.45 44 6.36 1.00 �1.67

�0.44 8 2.06 0.19 �0.97

�0.62 2 �0.41 �0.70 �0.62

0.09 10 1.96 1.10 0.09

�0.14 24 2.09 0.08 �0.96

0.01 2 �1.08 0.69 0.01

Table 4. Flood Predictability Comparison of WECS and RFFA methods

Stationindex

Record length�years�

Relative RMSE

WECSmethod

RFFAmethod

240 34 24.48 31.26

241 28 158.93 36.92

267 22 9.70 29.93

270 32 41.81 13.39

403 29 43.61 6.92

404.6 21 13.37 8.06

ions

H2

2.53

1.41

0.70

1.10

1.22

0.69

009

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Summary and Conclusions

Difficulties associated with mostly used regionalization techniquei.e., clustering technique were discussed. The runoff CN was pro-posed to avoid the use of clustering method which possesses dif-ficulties of units/scale and, hence allocating suitable weight incluster analysis. Land cover map of AVHRR-satellite imagery andFAO-soil data were used in estimating the runoff CN. Hydrologicregions were proposed by superimposing monsoon rainfall mapover runoff CN map. The L-moment based regional hydrologichomogeneity test led minor adjustment in the proposed regions.Finally, five numbers of hydrologic regions got identified insidethe Nepalese territory.

The main objective to delineate hydrologic homogeneous re-gions for RFFA was achieved despite the complexity in under-standing precisely the factors that have effect on generation offloods. Heterogeneity measures for each region were found withinor near critical values. This led to conclude the runoff CN em-ployed regionalization approach as effective approach of hydro-logic regionalization. The proposed regionalization work will beable to predict design flood better as compared to the previouswork which could not achieve the heterogeneity measure value toacceptable limit in all the hydrologic regions. The smaller relativeRMSE in estimated flood in RFFA method as compared to fre-quently used WECS method reflects the advantage of this study.In the next stage, the writers intend to estimate regional distribu-tion function and index flood for all the delineated hydrologicregions. To justify this new approach of hydrologic regionaliza-tion further, the approach will be applied in country/territory otherthan Nepal.

Acknowledgments

The writers thank the Ministry of Education, Culture, Sports, Sci-ence and Technology �MEXT�, Japan for providing financial sup-port in carrying out this research work. The writers also thank allthe members of Innovative Disaster Prevention Technology andPolicy Research Lab, Disaster Prevention Research Institute,Kyoto University, Japan for their comments/help at various in-stances.

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