water

Article Geospatial Modelling of Watershed Peak Flood Discharge in ,

Ryan Cheah 1 , Lawal Billa 2, Andy Chan 1 , Fang Yenn Teo 1 , Biswajeet Pradhan 3,4,* and Abdullah M. Alamri 5 1 Department of Civil Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; [email protected] (R.C.); [email protected] (A.C.); [email protected] (F.Y.T.) 2 School of Environmental and Geographical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; [email protected] 3 Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia 4 Department of Civil Engineering, Indian Institute of Technology Indore (IITI), Khandwa Road, Simrol, Indore 453552, India 5 Department of Geology & Geophysics, College of Science, King Saud Univ., P.O. Box 2455, Riyadh 11451, Saudi Arabia; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +61-2-9514-7937

 Received: 29 October 2019; Accepted: 21 November 2019; Published: 26 November 2019 

Abstract: Conservative peak flood discharge estimation methods such as the rational method do not take into account the soil infiltration of the precipitation, thus leading to inaccurate estimations of peak discharges during storm events. The accuracy of estimated peak flood discharge is crucial in designing a drainage system that has the capacity to channel runoffs during a storm event, especially cloudbursts and in the analysis of flood prevention and mitigation. The aim of this study was to model the peak flood discharges of each sub-watershed in Selangor using a geographic information system (GIS). The geospatial modelling integrated the watershed terrain model, the developed Soil Conservation Service Curve Cumber (SCS-CN) and precipitation to develop an equation for estimation of peak flood discharge. Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) was used again to simulate the rainfall-runoff based on the Clark-unit hydrograph to validate the modelled estimation of peak flood discharge. The estimated peak flood discharge showed a coefficient of determination, r2 of 0.9445, when compared with the runoff simulation of the Clark-unit hydrograph. Both the results of the geospatial modelling and the developed equation suggest that the peak flood discharge of a sub-watershed during a storm event has a positive relationship with the watershed area, precipitation and Curve Number (CN), which takes into account the soil bulk density and land-use of the studied area, Selangor in Malaysia. The findings of the study present a comparable and holistic approach to the estimation of peak flood discharge in a watershed which can be in the absence of a hydrodynamic simulation model.

Keywords: GIS; watershed precipitation; curve number; peak flood discharge; Selangor; Malaysia

1. Introduction Various methods, such as the widely used rational method published by Thomas Mulvaney in 1851, are used to estimate peak discharge. The rational method uses rainfall intensity, drainage area and a runoff coefficient to determine peak discharge [1–3]. This method is often conservative and very dependent on the runoff coefficient that is determined based on land use. Inaccuracies may arise in this

Water 2019, 11, 2490; doi:10.3390/w11122490 www.mdpi.com/journal/water Water 2019, 11, 2490 2 of 12 method if the wrong runoff coefficient is assigned or if the catchment area is incorrectly delineated [4,5]. Additionally, it does not take into account the infiltration rate of the precipitation as well as the soil type. The hydrological modelling methods are becoming the most useful and efficient techniques to address the human error element found in the rational method and they also provide the ability to include and assess the soil type when determining peak discharge. The primary attraction of the rational method has been its simplicity. However, with increasing capacity and capability of computer processing, computational designs and hydrological procedures, the use of appropriate hydrological software have become the preferred option for hydrological model generation [6–8]. Hydrological modelling is a powerful technique for hydrological system investigation, planning and the development of an integrated management approach for hydrological regimes and water resources [9,10]. Growing advances in computational power and the availability of increasingly fine resolution spatial data, has also made it possible to accurately describe watershed characteristics when determining runoff response to rainfall input [11]. Numerous hydrological models such as Simulator for Water Resources in Rural Basins (SWRRB), Environmental Policy Integrated Climate (EPIC), Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) and Technical Release No.20 (TR20) have been developed, but a Geographic Information System (GIS) forms the baseline model for pre-processing and post-processing of data. A GIS as a computer-based tool that provides a suitable environment for efficient management of large and complex databases usually associated with hydrologic modelling and facilitates the processing, managing and interpretation of the hydrological data [12]. Applying geospatial modelling with the combination of digital elevation models (DEM) and the hydrological tools of ArcGIS, allows physical properties such as the catchment area, stream length and slopes of the study area to be accurately defined, and used in different hydrological models [13]. It was commented by [14] that, “With a larger number of parameters, there is a greater possibility that some parameters become to site-specific”. It was concluded by [2] that simple models involving fewer parameters forecasted discharge in the Nile River Basin better than models using more parameters and complex mathematical computations. Hence, the simple and straightforward Soil Conservation Service Curve Number (CN) method was adopted for their study. The soil conservation service Curve Number (SCS-CN) developed by the United States Department of Agriculture [15] is a measure of runoff potential that applies geospatial modelling without the use of hydrological models. The curve number is a dimensionless coefficient that ranges from 0 to 100, taking into account the soil and land-use types of the study area. While the curve number model can be used successfully as a calibration parameter in hydrological models, it was originally developed for use in determining streamflow for single storm events, and not for day-to-day analysis. Thus, it is a simple way to match observed rainfall data to a predicted stream flow. According to [9], the curve number was developed using data from a limited number of regions in the U.S. and may not be applicable for all regions, and this could be a limitation. An infiltration approach should be used to determine the variation of runoff during a storm. The curve number runoff equation is not an infiltration equation. The runoff equation can, however, be used as a surrogate [15]. Data such as soil type and land use can be incorporated into the model hence further increasing the accuracy of the predicted peak discharge [16]. The importance of both soil type and land use in storm-runoff generation studies has also been proven in a research conducted by [17] while investigating the effects of climate change on the storm-runoff generation. The main objectives of this study are to investigate the relationship between the peak flows of a watershed, watershed area, precipitation and land use by geospatial modelling and to quantify the aforementioned factors in a single empirical equation of peak flood discharge estimation. Water 2019, 11, x FOR PEER REVIEW 3 of 12 Water 2019, 11, 2490 3 of 12 northeast monsoon. In general, heavy localised rainfalls associated with thunderstorms of short durations occur throughout the year. The average annual rainfall ranges between 1900 mm to over 26002. Materials mm. and Methods The northern part of Selangor is predominantly underlain by igneous rock, mainly granite with 2.1. Study Area some dacite, rhyolite, and micro granodiorite. The Northwest of Selangor is covered by granitic rocks, vein Thequartz state of ofthe Selangor mesozoic in age Malaysia and schist has anof areaKajang of 8200formation. km2 and The is central located area between of Selangor latitudes is underlain2◦350–3◦60 by0 N limestone and longitudes of the Kuala 100◦45 Lumpur0–102◦00 0formation,E (Figure 1schist). Selangor in the lands is a ff betweenected by Kelang heavy and prolongedGombak Rivers, rainfall while from granite June to underlies September most during of the the Ampang southwest area monsoon [18]. Soils and in DecemberSelangor vary to March from veryduring thin the sandy northeast regolith monsoon. covering In quartize general, heavyridges localisedto the extremely rainfalls deep associated weathering withthunderstorms profile of gentile of graniteshort durations slopes. The occur soils throughout are mostly the derived year. Thefrom average igneous, annual sedimentary rainfall and ranges metamorphic between 1900 rocks mm and to alsoover areas 2600 mm.of marine and river alluvium [18].

Figure 1. Location of Selangor in . Figure 1. Location of Selangor in peninsular Malaysia. The northern part of Selangor is predominantly underlain by igneous rock, mainly granite with someSelangor dacite, rhyolite,consists of and mainly micro lowland granodiorite. dipterocarp The Northwestforests (LDF) of which Selangor is the is coveredmajor forest by granitictype in Malaysiarocks, vein and quartz are taller of the than mesozoic any other age broad-leaved and schist of tr Kajangopical rainforest formation. in Thethe world. central On area the of shorelines Selangor andis underlain riverbanks by of limestone Selangor, of mangrove the Kuala forests Lumpur can formation, be found. The schist state in theof Selangor lands between has six Kelangmain rivers and ofGombak which Rivers,the Selangor while River granite is underliesthe largest, most with of a thecatchment Ampang area area of [ 18about]. Soils 1960 in km² Selangor covering vary about from very25% of thin the sandy state. regolith The Selangor covering River quartize runs from ridges the to upstream the extremely at Kuala deep Kubu weathering Bharu profilein the east, of gentile near thegranite Titiwangsa slopes. Mountains, The soils are to mostly the Straits derived of from igneous, at the downstream sedimentary at and Kuala metamorphic Selangor in rocks the west. and Thealso other areas major of marine river and is the river Klang alluvium River that [18]. is 120 km long, with 11 major tributaries and a catchment area ofSelangor 1288 km². consists It flows of through mainly lowland dipterocarp and ends forests up (LDF)in the Straits which of is Malacca. the major Thus forest Selangor type in comprisesMalaysia and three are major taller thanriver anybasins, other namely broad-leaved the Klan tropicalg, Langat rainforest and, most in the importantly, world. On thethe shorelines Selangor riverand riverbanks basin. of Selangor, mangrove forests can be found. The state of Selangor has six main rivers of which the Selangor River is the largest, with a catchment area of about 1960 km2 covering about 2.2. Data Collection and Geospatial Modelling 25% of the state. The Selangor River runs from the upstream at in the east, near the TitiwangsaGlobal Multi-resolution Mountains, to Terrain the Straits Elevation of Malacca Data at 2010 the downstream(GMTED2010)-type at Kuala DEMs Selangor were in theused west. for Thethe study other [19]. major The river DEM is the with Klang a spatial River resolution that is 120 of km 75 long, m was with processed 11 major in tributaries ArcGIS to and determine a catchment and extractarea of 1288hydrological km2. It flows parameters through such Kuala as Lumpurslope, flow and direction, ends up in flow the Straitsaccumulation, of Malacca. drainage Thus Selangorfeatures andcomprises to delineate three the major watersheds. river basins, Figure namely 2 shows the Klang,the DEM Langat and the and, catchments most importantly, and drainage the Selangor features ofriver Selangor, basin. respectively. Considering the size of Selangor, the DEM with a pixel size (75 × 75m) was sufficient for the coverage of the multiple watersheds of the study area.

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2.2. Data Collection and Geospatial Modelling Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)-type DEMs were used for the study [19Water]. 2019 The, 11 DEM, x FOR withPEER REVIEW a spatial resolution of 75 m was processed in ArcGIS to determine4 of 12 and extract hydrological parameters such as slope, flow direction, flow accumulation, drainage features and to delineate the watersheds. Figure2 shows the DEM and the catchments and drainage features of Selangor, respectively. Considering the size of Selangor, the DEM with a pixel size (75 75m) was Water 2019, 11, x FOR PEER REVIEW × 4 of 12 sufficient for the coverage of the multiple watersheds of the study area.

Figure 2. (a) Digital elevation model (DEM); and (b) catchment and drainage features of Selangor.

2.2.1. Hydrologic Soil Group Classification Soils play an important role in determining CN, with different soil types having different infiltration rates which are a function of surface intake rates and subsurface permeability. In Figuredeveloping 2. (a) the Digital CN, elevationthe United model States (DEM); Department and ( bof) catchmentAgriculture and (USDA) drainage utilized features soil type of Selangor. data to derive the infiltration rate and categorized the soil based on the infiltration rate. In this study, soil Figure 2. (a) Digital elevation model (DEM); and (b) catchment and drainage features of Selangor. 2.2.1. Hydrologicdata for Selangor Soil Group was downloaded Classification from SoilGrids [20] and further processed for soil bulk density. Thus, the bulk density of the soil was used in place of the infiltration rate. Soil bulk density is an 2.2.1.Soils Hydrologicimportant play an factor importantSoil Groupthat affects role Classification in soil determining infiltration capaci CN, withty. As di soilfferent bulk soil density types increases, havingdi soilfferent porosity infiltration ratesSoils whichdecreases, play are aan resulting function important in of a surfacedecrease role in intakein determiningsoil infiltration rates and CN,capacity subsurface with [21]. different permeability.By having soil the bulktypes In developingdensity having data, different the CN, the soil type is no longer significant. Figure 3 shows the processed soil bulk density of Selangor at 1m theinfiltration United Statesrates Departmentwhich are a of function Agriculture of surface (USDA) intake utilized rates soil typeand datasubsurface to derive permeability. the infiltration In rate anddepth categorized classified theinto soil4 hydrologic based on soil the groups infiltration (A, B, C rate.and D). In The this details study, of soil the datahydrological for Selangor soil was developinggroup the classification CN, the Unitare presenteded States in DepartmentTable 1. of Agriculture (USDA) utilized soil type data to downloadedderive the infiltration from SoilGrids rate and [20 ]categorized and further the processed soil bas fored on soil the bulk infiltration density. Thus,rate. the In bulkthis study, density soil of thedata soil for was Selangor used inwas place downloaded of the infiltrationTable from 1. Hydrologic SoilGrids rate. soil Soil group[2 bulk0] and classification density further is. processed an important for factorsoil bulk that density. affects soilThus, infiltration the bulk capacity.densitySoil of As Group the soil soil bulk was Infiltrationdensity used increases,Ratein place (mm/hr) of soil the porosity infiltration Bulk Density decreases, (kg/mrate.3) Soil resulting bulk density in a decrease is an inimportant soil infiltration factor that capacity affectsA [21 ].soil By infiltration having the8–12 capaci bulk densityty. As soil data, bulk the 555–960 soildensity type increases, is no longer soil significant. porosity Figure3 shows the processedB soil bulk density4–8 of Selangor at 1m depth 960–1141 classified into 4 hydrologic soil decreases, resulting in a decreaseC in soil infiltration1–4 capacity [21]. 1141–1264 By having the bulk density data, groupsthe soil (A,type B, is C no and longer D). The significant.D details of Figure the hydrological 30–1 show s the soil processed group classification soil 1264–1488 bulk density are presented of Selangor in Table at 1m1. depth classified into 4 hydrologic soil groups (A, B, C and D). The details of the hydrological soil group classification are presented in Table 1.

Table 1. Hydrologic soil group classification.

Soil Group Infiltration Rate (mm/hr) Bulk Density (kg/m3) A 8–12 555–960 B 4–8 960–1141 C 1–4 1141–1264 D 0–1 1264–1488

Figure 3. Soil bulk density map.

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Table 1. Hydrologic soil group classification.

Soil Group Infiltration Rate (mm/hr) Bulk Density (kg/m3)

Water 2019, 11, x FOR PEERA REVIEW 8–12 555–960 5 of 12 B 4–8 960–1141 CFigure 3. 1–4 Soil bulk density map. 1141–1264 D 0–1 1264–1488 2.2.2. Combination of Hydrologic Soil Group and Land-Use Classes 2.2.2. Combination of Hydrologic Soil Group and Land-Use Classes The latest land use for Selangor was developed from Landsat Operational Land Imager images of theThe year latest 2016 land downloaded use for Selangor from the was United developed State fromGeological Landsat Services Operational (USGS) Land website. Imager The images Landsat of theimages year were 2016 mosaicked downloaded to fromimprove the Unitedcoverage State over Geological the study Servicesarea. A pan-sh (USGS)arpening website. operation The Landsat was imagesconducted were to mosaickedimprove the to spatial improve resolution coverage of over the themultispectral study area. visible A pan-sharpening bands to 15 m operationusing the waspanchromatic conducted band. to improve The Selangor the spatial land resolution use wasof classified the multispectral into six different visible bands classes to 15based musing on a thesupervised panchromatic classification band. using The Selangor the ArcGIS land image use wasanalysis classified tool. The into land-use six different classification classes based(Figure on 4a) a supervisedincludes water classification bodies, residential, using the ArcGIS industrial image and analysis commercial tool. Theareas, land-use plantations, classification green open (Figure spaces4a) includesand forests. water The bodies, land-use residential, classification industrial andwas commercialthen combined areas, plantations,with the hydrologic green open soil spaces group and forests.classification The land-use (Figure classification3) to process wasand thendetermine combined the CN with using the hydrologic a relationship soil between group classification hydrologic (Figuresoil group3) to and process land andcover determine as described the CNin Table using 2. a Figure relationship 4b shows between the outp hydrologicut map of soil the group generated and landsoil conservation cover as described service in curve Table 2number.. Figure 4b shows the output map of the generated soil conservation service curve number.

Figure 4. (a) Land-use classification of Selangor; (b) generated curve number map.

Figure 4. (a) Land-use classificationTable 2. Runo of Selangor;ff Curve Number. (b) generated curve number map.

Table 2. Runoff CurveHydrological Number. Soil Group Description of Land Use ABCDHydrological Soil Group Description of Land Use Cultivated land 72A 81B 88C 91D NaturalCultivated vegetation land 45 72 6681 7788 8391 UrbanNatural commercial vegetation 77 45 8566 9077 9283 UrbanUrban housing commercial 61 77 7585 8390 8792 BareUrban land housing 49 61 6975 7983 8487 Streets andBare roads land 98 49 9869 9879 9884 OpenStreets water and roads 0 98 0 98 0 98 0 98 Open water 0 0 0 0

2.2.3.2.2.3. Spatial Rainfall DataData Processing TheThe dailydaily rainfallrainfall datadata cancan bebe obtainedobtained fromfrom rainfallrainfall gaugegauge stationsstations throughoutthroughout Selangor.Selangor. ForFor thethe study,study, rainfallrainfall data data of of 50 50 years years from from 1965 1965 to to 2015 2015 were were obtained obtained from from the Departmentthe Department of Irrigation of Irrigation and Drainageand Drainage Malaysia Malaysia (DID). (DID). Data from Data each from rainfall each stationrainfall were station checked were and checked filtered and for consistency.filtered for Stationsconsistency. with Stations missing with data missing were not data taken were into not consideration. taken into consideration. The mean maximum The mean dailymaximum rainfall daily of rainfall of each station was then spatially interpolated based on the inverse distance weighting (IDW) method using ArcGIS. Figure 5 shows the results of the maximum daily precipitation of Selangor.

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each station was then spatially interpolated based on the inverse distance weighting (IDW) method Waterusing 2019 ArcGIS., 11, x FOR Figure PEER5 REVIEWshows the results of the maximum daily precipitation of Selangor. 6 of 12

FigureFigure 5. 5. MaximumMaximum daily daily precipitation precipitation of of Selangor. Selangor.

2.3.2.3. Hydrological Hydrological Modelling Modelling 2.3.1. Calculation of Curve Number Peak Discharge 2.3.1. Calculation of Curve Number Peak Discharge The storm has to overcome a threshold determined by depression storage, interception as well as The storm has to overcome a threshold determined by depression storage, interception as well infiltration volume before runoff can occur. Initial abstraction is the amount of rainfall required to as infiltration volume before runoff can occur. Initial abstraction is the amount of rainfall required to satisfy the aforementioned volumes. After runoff begins, additional losses due to infiltration will still satisfy the aforementioned volumes. After runoff begins, additional losses due to infiltration will still occur. In general, accumulated infiltration increases with increasing rainfall up to certain maximum occur. In general, accumulated infiltration increases with increasing rainfall up to certain maximum retention, and runoff also increases as rainfall increases. The ratio of actual retention to maximum retention, and runoff also increases as rainfall increases. The ratio of actual retention to maximum retention is assumed to be equal to the ratio of direct runoff to rainfall minus initial abstraction. This can retention is assumed to be equal to the ratio of direct runoff to rainfall minus initial abstraction. This be expressed mathematically as [15]. can be expressed mathematically as [15]. F Q = (1) FQS P I = − (1) where, SPI− where,F = Actual retention after runoff begins, mm FS == ActualWatershed retention storage, after mm runoff begins, mm SQ == WatershedActual direct storage, runo ffmm, mm QP = ActualTotal rainfall, direct runoff, mm mm P = Total rainfall, mm I = Initial abstraction, mm I = Initial abstraction, mm TheThe initial abstraction should should take take into consideration of of depression storage, interception and infiltrationinfiltration occurring occurring prior prior to to runoff. runoff. The The relationship relationship between between II andand SS was estimated by analysing rainfall-runoffrainfall-runoff datadata for for many many small small watersheds watersheds to eliminate to eliminate the necessity the necessity of estimating of estimating both parameters both parametersI and S in the I and above S in equation. the above The equation. empirical The relationship empirical relationship is given by [is22 given]. by [22]. IS= 0.2 (2) I = 0.2S (2) Substituting Equation (3) into Equation (2) gives: Substituting Equation (3) into Equation (2) gives: (0.2)PS− 2 Q = 2 (3) (P +0.2S) Q = PS− 0.8 (3) P + 0.8S The equation now has only two unknowns, one variable P and one parameter, S. S is related to curve number (CN) by: 25400 S =−254 (4) CN

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The equation now has only two unknowns, one variable P and one parameter, S. S is related to curve number (CN) by: 25400 S = 254 (4) CN − where,

CN = Curve number (From Table2)

Finally, Equation (5) was combined with the spatially distributed parameters of rainfall, soil and land–use to simulate the runoff in the watersheds. The total daily discharge was computed by the following equation: Xn Q = Q A (5) CN i × i i=1 where,

3 QCN = Total daily runoff discharge of the watershed, m Qi = The depth of daily direct runoff of cell No. i, m 2 Ai = The area of corresponding cell No. i, m n = Total cell numbers of the watershed

2.3.2. Simulation of Unit Hydrograph Peak Discharge The Clark-unit hydrograph model in Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) was specifically chosen for peak flow based on the Malaysian Hydrological Procedure (HP) No. 27, published by the Department of Irrigation and Drainage [23]. The HP No. 27 proposes empirical equations for both the storage coefficient R and the time of concentration Tc. For simplicity and consistency, the catchment area, stream slope, and main stream length are used to estimate Tc and R for this procedure.

0.1188 0.9573 0.5074 Tc = 2.32A− L S− (6)

0.1943 0.9995 0.4588 R = 2.976A− L S− (7) where,

A = Catchment area [km2] L = Main stream length [km] S = Weighted slope of main stream [m/km]

The weighted slope of main stream, S is given by the equation:

P 2 li √Si S = ( P ) (8) li

where, li = Incremental stream length Si = Incremental slope

The catchment area, main stream length and weighted slope of main stream is modelled in ArcGIS using the above stated methods. The design baseflow, QB of each stream is then calculated with the equation from HP No. 27: 0.85889 QB = 0.11A (9) where, Water 2019, 11, x FOR PEER REVIEW 8 of 12

Water 2019, 11, 2490 8 of 12 variability of catchment rainfall during a storm causes the time of concentration of a catchment to vary from storm to storm. This makes the assumption of uniform areal distribution of design storm invalid.A = Catchment Some unaccounted area [km2] for storage depression (e.g., wetland, extremely flat catchment slopes) could lead to the overestimation of the peak discharge and the underestimation of the time to peak whenThe using catchment the equations area, A[23].; precipitation, P; storage coefficient, R; time of concentration, Tc; design baseflow, QB were then inputted into HEC-HMS to model the peak discharge, QClark that will serve as 3.a comparisonResults against the ArcGIS-modelled peak discharge. The Clark-unit hydrograph method was used as the control variable of the study. The curve 3.1.number Peak Flood derived Discharge peak discharge Estimation was compared against the Clark-unit hydrograph peak discharge. The Clark-unitIt was observed hydrograph in Figure rainfall-runo 6a that theff relationships two peak discharges are derived agree based with on averageeach other conditions fairly well, and showingshould be a only linear used regression for design of purposes. 0.76. The The peak same discharge applies toderived the Tc andfrom R the values CN derived. method The was areal an underestimationvariability of catchment when rainfallcompared during to the a storm Clark-Unit causes thehydrograph time of concentration peak discharge. of a catchment The Clark-unit to vary hydrographfrom storm tois storm.a lumped This makesmodel thethat assumption includes a oflo uniformt of assumptions areal distribution about hy ofdrological design storm processes. invalid. BecauseSome unaccounted of these assumptions, for storage depression lumped models (e.g., wetland, have a extremely tendencyflat to catchmentover-or underestimate slopes) could runoff lead to valuesthe overestimation [24]. Hence, ofusing the th peake equation discharge of andthe best the underestimationfit line that intercepts of the the time origin, to peak a correction when using factor the wasequations applied [23 to]. the peak discharge modelled after the CN method to calibrate it. = 3. Results QQpeak 1.8118 CN (10)

3.1. PeakThe Floodgraph Discharge of urbanized Estimation area of a watershed was plotted against the total curve number of a watershed.It was observedThis was indone Figure to esta6a thatblish the a twopositive peak relationship discharges agree betw witheen urban each other area fairlyper watershed, well, showing Au 2 (kma linear) and regression the sum of 0.76.the curve The peaknumber discharge per watershed, derivedfrom CN from the CN Figure method 6b, it was was an observed underestimation that the totalwhen curve compared number to the of Clark-Unitthe watershe hydrographd increases peak as the discharge. urbanized The area Clark-unit of a watershed hydrograph increases. is a lumped This strongmodel thatcorrelation includes was a lot further of assumptions proven with about the hydrological high linear processes. regression Because of 0.7107. of these Hence, assumptions, it can be concludedlumped models that an have urbanized a tendency area to increa over-orses underestimate the runoff potential runoff valuesof a watershed. [24]. Hence, using the equation of theHaving best fit proven line that the intercepts hypothesis the of origin, a positive a correction correlation factor between was applied urbanized to the area peak and discharge curve number,modelled the after relationship the CN method between to calibrate the calibrated it. peak discharge (Qpeak) and Curve Number (CN), precipitation (P) and watershed area (Aw) was investigated to formulate Equation (12) based on the three factors. This was successfully doneQpeak by =obta1.8118iningQ CNthe regression coefficients between peak(10) discharge and CN, P, Aw.

Figure 6. (a) Graph of urbanized area per watershed against total curve number (CN); (b) graph of Figureurbanized 6. (a area) Graph per watershedof urbanized against area totalper watershed CN. against total curve number (CN); (b) graph of urbanized area per watershed against total CN.

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The graph of urbanized area of a watershed was plotted against the total curve number of a watershed. This was done to establish a positive relationship between urban area per watershed, Au (km2) and the sum of the curve number per watershed, CN from Figure6b, it was observed that the total curve number of the watershed increases as the urbanized area of a watershed increases. This strong correlation was further proven with the high linear regression of 0.7107. Hence, it can be concluded that an urbanized area increases the runoff potential of a watershed. Having proven the hypothesis of a positive correlation between urbanized area and curve number, the relationship between the calibrated peak discharge (Qpeak) and Curve Number (CN), precipitation (P) and watershed area (Aw) was investigated to formulate Equation (12) based on the three factors. Water 2019, 11, x FOR PEER REVIEW 9 of 12 This was successfully done by obtaining the regression coefficients between peak discharge and CN, P, Aw.By plotting the corrected peak discharge against the curve number, watershed area and precipitation,By plotting it can the be corrected seen that the peak curve discharge number against and watershed the curve area number, are relatively watershed more correlated area and precipitation,to the peak discharge it can be seen than that the the precipitation. curve number Th andis can watershed be explained area are as relatively the peak more discharge correlated being to thedirectly peak related discharge to thanthe surface the precipitation. runoff of the This wate canrshed. be explained Thus, the as the larger peak the discharge watershed, being the directly more relatedsurface toarea the that surface is able runo toff interceptof the watershed. rainfall and Thus, contribute the larger to the the peak watershed, discha therge. moreIn the surface case of area CN thatnumber, is able it towas intercept concluded rainfall that and the contribute urban area to the in peakcreases discharge. the runoff In the potent caseial of CNof the number, watershed. it was concludedPrecipitation, that however, the urban areashowed increases a weaker the runo correlff potentialation because of the watershed.the individual Precipitation, watersheds however, have showeddifferent a characteristics, weaker correlation stream because lengths, the individualand base flows. watersheds Base flow have forms different the characteristics, initial input baseline stream lengths,parameter and for base runoff flows. simulations, Base flow as forms there the will initial usually input be baseline stream discharge parameter regardless for runoff ofsimulations, the existence as thereof rainfall. will usually Runoff be streamoccurs dischargewhen there regardless is rainfall, of the existencethus, precipitation of rainfall. Runowas ffconsideredoccurs when in there the isdevelopment rainfall, thus, of precipitationthe equation. was considered in the development of the equation. ByBy taking the the linear linear regression regression coefficient coefficient as as the the exponent exponent of ofthe therespective respective factors, factors, the themultiplication multiplication product product of the of three the powered three powered factors factorswas plotted was plottedagainst th againste calibrated the calibrated CN modelled CN modelledpeak discharge. peak discharge. From Figure From 7 Figurebelow,7 itbelow, can be it canobserved be observed that the that plotted the plotted best-fit best-fit power power line has line a hasstrong a strong correlation, correlation, with witha regression a regression coefficient coefficient of 0.9445. of 0.9445. Hence, Hence, this this power power best-fit best-fit line line is taken is taken as asthe the product product equation. equation.

0.8019 0.2871 0.64860.7083 0.7083 QQ =⋅⋅=0.3257(A0.3257(A0.8019 P P0.2871 CN CN0.6486) ) (11)(11) peakpeak WW · ·

FigureFigure 7.7. Graph of calibrated CN peak dischargedischarge againstagainst thethe multiplicationmultiplication productproduct ofof adjustedadjusted watershedwatershed area,area, precipitationprecipitation andand curvecurve number.number. 3.2. Classification of Developed Model 3.2. Classification of Developed Model A runoff model is defined by [25] as a set of equations that aid in the estimation of the amount of rainfallA runoff that turns model into is adefined runoff asby a[25] function as a set of of various equations parameters that aid usedin the to estimation describe the of the watershed. amount Accordingof rainfall that to [26 turns], the into runo a ffrunoffmodel, as Equationa function (11), of various would beparameters classified used as an to empirical describe modelthe watershed. or more specifically,According to a [26], regression the runoff equation. model, RegressionEquation (11), equations would be find classified the functional as an empirical relationship model between or more inputsspecifically, and outputs a regression [25]. Itequation. is suggested Regression by both eq [uations2] and [find27] thatthe empiricalfunctional modelsrelationship are black-box between inputs and outputs [25]. It is suggested by both [2] and [27] that empirical models are black-box models, meaning very little is known about the internal processes that control how runoff results are determined. In terms of spatial structuring, according to [26] this developed model would be classified as a lumped model, as it treats the catchment area as a single homogeneous unit. In lumped models, the spatial variability of the catchment is disregarded [28,29]. Both the Clark-unit hydrograph and SNS- CN methods exhibit this property too. By assuming homogeneity over the catchment, the model loses the spectral resolution of the data. For example, rainfall and runoff patterns vary over space and time, but in lumped models, they are considered stationary [26].

3.3. Model Validation

Water 2019, 11, 2490 10 of 12 models, meaning very little is known about the internal processes that control how runoff results are determined. In terms of spatial structuring, according to [26] this developed model would be classified as a lumped model, as it treats the catchment area as a single homogeneous unit. In lumped models, the spatial variability of the catchment is disregarded [28,29]. Both the Clark-unit hydrograph and SNS-CN methods exhibit this property too. By assuming homogeneity over the catchment, the model loses the spectral resolution of the data. For example, rainfall and runoff patterns vary over space and time, but in lumped models, they are considered stationary [26]. Water 2019, 11, x FOR PEER REVIEW 10 of 12 3.3. Model Validation Equation (12) was verified by plotting the estimated peak discharge against the peak discharge Equation (12) was verified by plotting the estimated peak discharge against the peak discharge modelled with the curve number method. From Figure 8, it can be seen that the estimated peak modelled with the curve number method. From Figure8, it can be seen that the estimated peak discharge agrees fairly well with the calibrated CN modelled peak discharge, with a strong linear discharge agrees fairly well with the calibrated CN modelled peak discharge, with a strong linear regression coefficient of 0.7854. Hence, the equation developed to estimate the peak discharge during regression coefficient of 0.7854. Hence, the equation developed to estimate the peak discharge during a a storm event that relates the watershed area, precipitation, and curve number can be considered as storm event that relates the watershed area, precipitation, and curve number can be considered as a a valid equation. valid equation.

Figure 8. Graph of CN modelled peak discharge against estimated peak discharge. Figure 8. Graph of CN modelled peak discharge against estimated peak discharge. The equation to estimate flood peak discharge taking into account the watershed area, precipitation, soil bulkThe density,equation and to landestimate use wasflood successfully peak discha implemented.rge taking into A higher account resolution the watershed DEM and area, soil mapprecipitation, should further soil bulk improve density, the and reliability land use of was the curvesuccessfully number implemented. grid. The use A ofhigher rainfall-runo resolutionff modelsDEM and was soil questioned map should by [30 further] as a better improve option the for reli surfaceability runo of theff modelling. curve number Similar grid. studies The byuse [31 of] andrainfall-runoff [32] suggest models that physically was questioned based models by [30] like as a MIKE-SHE better option (Syst forè mesurface Hydrologique runoff modelling. Européen) Similar and TOPMODELstudies by [31] (TOPography and [32] suggest based that hydrological physically based MODEL) models have like certain MIKE-SHE disadvantages (Système as Hydrologique they require aEuropéen large number) and TOPMODEL of input parameters (TOPography for calibration based hydrological making their MODEL) use highly have data-dependent,certain disadvantages time consumingas they require and expensive.a large number The approach of input presentedparameters in thisfor calibration study provides making a straightforward their use highly method data- withdependent, limited parametertime consuming input to and estimate expensive. peak dischargesThe approach based presented on a GIS. in this study provides a straightforward method with limited parameter input to estimate peak discharges based on a GIS. 4. Conclusions 4. Conclusions The hypothesis suggesting urbanization leads to increased runoff and short lag time of peak dischargesThe hypothesis holds true. suggesting In essence, urbanization the modelled leads peak dischargeto increased based runoff on curve and short number lag was time generally of peak indischarges agreement holds with true. the modelledIn essence, peak the dischargemodelled basedpeak discharge on the hydrologic based on procedurecurve number No. 27.was The generally curve numberin agreement method, with however, the modelled underestimated peak discharge the base peakd on discharge the hydrologic when comparedprocedure toNo. the 27. Clark-unit The curve hydrograph.number method, Hence, however, a correction underestimated factor was the introduced peak discharge to calibrate when the compared curve number to the Clark-unit modelled peakhydrograph. discharges. Hence, All a three correction factors factor (curve was number, introduced precipitation to calibrate and the watershed curve number area) modelled were found peak to bedischarges. correlated All to peakthree discharge.factors (curve This number, result demonstrated precipitation thatand the watershed geospatial area) modelled were found peak floodto be dischargecorrelated was to peak reliable discharge. and provided This anresult alternative demonstrated method that of monitoring the geospatial potential modelled flood inpeak multiple flood discharge was reliable and provided an alternative method of monitoring potential flood in multiple watersheds. Ideally speaking, modeling of spatial variation of runoff generation by the infiltration excess mechanism and the downslope infiltration of runoff onto unsaturated areas require high spatial data input and or field sampling. For data-limited and inaccessible regions of Malaysia and most parts of the world, this has been a limitation in estimating the runoff in various watersheds. This proposed method applied geospatial techniques in the modeling of watershed area, land use, soil bulk density, spatial interpolated rainfall and runoff curve number and the derivation of the equation for estimation of peak discharge. The model serves as an alternative for estimation of peak discharge in watersheds and areas with poor data availability.

Author Contributions: R.C. and L.B. conceived, designed and performed the experiments; they analyzed the data and wrote the paper with contribution from A.C. and F.Y.T., and B.P. and A.M.A. professionally revised

Water 2019, 11, 2490 11 of 12 watersheds. Ideally speaking, modeling of spatial variation of runoff generation by the infiltration excess mechanism and the downslope infiltration of runoff onto unsaturated areas require high spatial data input and or field sampling. For data-limited and inaccessible regions of Malaysia and most parts of the world, this has been a limitation in estimating the runoff in various watersheds. This proposed method applied geospatial techniques in the modeling of watershed area, land use, soil bulk density, spatial interpolated rainfall and runoff curve number and the derivation of the equation for estimation of peak discharge. The model serves as an alternative for estimation of peak discharge in watersheds and areas with poor data availability.

Author Contributions: R.C. and L.B. conceived, designed and performed the experiments; they analyzed the data and wrote the paper with contribution from A.C. and F.Y.T., and B.P. and A.M.A. professionally revised and edited the manuscript. The manuscript was discussed and reviewed by all of authors as they enhanced the context with sufficient references. Funding: The research is supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney under grant numbers: 323930, 321740.2232335; 321740.2232424 and 321740.2232357. This research was also supported by Researchers Supporting Project number RSP-2019 / 14, King Saud University, Riyadh, Saudi Arabia. Acknowledgments: This research was supported by the University of Nottingham Malaysia Campus common project funds and learning resources. The metrological data were made available by the Department of Irrigation and Drainage (DID) Malaysia. Conflicts of Interest: The authors declare no conflict of interest.

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