Ungauged runoff simulation in Upper Manyame

Physics and Chemistry of the Earth xxx (2016) 1e12

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Ungauged runoff simulation in Upper Manyame Catchment, Zimbabwe: Application of the HEC-HMS model Webster Gumindoga a, *, Donald T. Rwasoka b, Innocent Nhapi a, Timothy Dube c a

University of Zimbabwe, Dept of Civil Engineering, Box MP 167, Harare, Zimbabwe Upper Manyame Subcatchment Council, Box 1892 Harare, Zimbabwe c School of Agric. Earth & Environ Sciences, University of KwaZulu Natal, P. Bag X01, Scottsville, 3209 Pietermaritzburg, South Africa b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 April 2015 Received in revised form 22 April 2016 Accepted 12 May 2016 Available online xxx

The Hydrologic Engineering Center Hydrologic Modelling System (HEC-HMS) model was applied to simulate runoff in the ten gauged and ungauged Upper Manyame subcatchments in Zimbabwe. Remote sensing and Geographic Information System techniques were used to determine the geometric and hydrologic parameters required for estimating model parameters. The Snyder Unit Hydrograph method was used for ungauged subcatchment simulations based on parameter transfer from gauged subcatchments. The Marimba and Mukuvisi subcatchments were considered as the gauged subcatchments based on data completeness for the simulation period (2004e2010). Before extrapolating the calibrated model setup to eight ungauged subcatchments, the feasibility of model parameter transferability was tested, using the proxy e catchment approach and evaluated using the Nash Sutcliffe (NSE) and Relative Volume Error (RVE) criterion. Results showed that the model successfully predicted gauged catchment runoff and peakflows for the calibration (Marimba NSE ¼ 68%, RVE ¼ 5.8%; Mukuvisi NSE ¼ 64%, RVE ¼ 8.9%) and validation (Marimba NSE ¼ 61%, RVE ¼ 8.1%; Mukuvisi NSE ¼ 57%, RVE ¼ 9.9%) periods. The study demonstrates the suitability of HEC-HMS for continuous runoff simulation in a complex watershed with numerous subcatchments and channel reaches. The ungauged subcatchments contribute to 51% of Upper Manyame Catchment's runoff. Ruwa and Lake Chivero subcatchments had the highest ungauged subcatchment contribution to Upper Manyame Catchment runoff (19% and 15% respectively). This work will have a significant contribution for the future development of water resources programs in Upper Manyame Catchment in particular and in other data-scarce catchments. © 2016 Elsevier Ltd. All rights reserved.

Keywords: DEM hydro processing Marimba, Mukuvisi, Proxy e catchment Remote sensing Reach and reservoir routing Snyder unit hydrograph

1. Introduction Adequate water availability is important for the attainment of sustainable development. Establishing up to date and timely data and information on the adequacy of available water resources requires a comprehensive water resources assessment (Beven, 2001; Gibbs et al., 2012; Wale et al., 2009). So far, a limited number of catchments have sufficient hydrologic data and information required for comprehensive water resource assessments (Randrianasolo et al., 2011; Wale et al., 2009), whereas others do not have measurements. When available, the quality of the data remains questionable. Catchments with insufficient or

* Corresponding author. E-mail address: [email protected] (W. Gumindoga).

questionable quality data are referred to as ungauged catchments (Hrachowitz et al., 2013; Patil and Stieglitz, 2012; Sivapalan et al., 2003). This definition has been adopted in this paper. The quest to assess water resources and streamflows in such catchments therefore remains at the center of hydrologic sciences; water resources planning and management (Randrianasolo et al., 2011; Schaefli et al., 2011). Runoff response estimation from ungauged catchments is currently a topical issue in water resources management (Adib et al., 2010; Wale et al., 2009). Conventional techniques require inputs such as historical rainfall and runoff datasets. However, these datasets are unavailable for catchments, especially in arid and semi-arid environments of the developing world. Available methods to deal with streamflow estimation in ungauged catchments involves the use of extrapolated response information from gauged catchments based on catchment morphometric similarities,

http://dx.doi.org/10.1016/j.pce.2016.05.002 1474-7065/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Gumindoga, W., et al., Ungauged runoff simulation in Upper Manyame Catchment, Zimbabwe: Application of the HEC-HMS model, Physics and Chemistry of the Earth (2016), http://dx.doi.org/10.1016/j.pce.2016.05.002

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W. Gumindoga et al. / Physics and Chemistry of the Earth xxx (2016) 1e12

coupled meteorological and hydrologic models, unit hydrographs, regionalization of model parameters and hydrologic indices, remote sensing and process understanding obtained from laboratory experiments (Adib et al., 2010; Lakshmi, 2004; Moretti and Montanari, 2008; Sivapalan et al., 2003; Srinivasan et al., 2010; Wale et al., 2009; Yadav et al., 2007). Understanding runoff behavior in any catchment requires a deep appreciation of processes and interactions between flows, releases and storages (Moyo, 1997; Rientjes, 2007; Yadav et al., 2007). The Upper Manyame Catchment, which includes; Harare, the capital city of Zimbabwe and its dormitory towns of Ruwa, Chitungwiza, Epworth and Norton is of strategic importance. However, little is known about water resource yields and runoff behavior in all of its river systems, some of which are ungauged. This makes water balance assessment a challenge. Flows in its ungauged subcatchments have to be determined. Previous studies in the Upper Manyame Catchment have focused only on water quality, pollution and urban drainage issues (Chawira et al., 2013; Gumbo, 2001; Hranova et al., 2001; Kamudyariwa, 2000; Marshall and Falconer, 1973; Marshall, 1981, 1995; Mawere, 2001; Moyo, 1997; Thornton, 1982; Thornton and Nduku, 1982a, b); with a few working on water quantity issues (Gumindoga et al., 2014; Hranova et al., 2001; JICA, 1996; Nhapi et al., 2002) and evaporation (Rwasoka et al., 2011). The few that attempted runoff simulations, only focused on analyzing Lake Chivero's historical trends of inflows and outflows, whereas some concentrated on developing spreadsheetbased models that do not capture the spatial heterogeneity and complexity of the land surface in question. However, the quantification of runoff contributions from ungauged Upper Manyame subcatchments to the overall catchment water balance has not been done. The availability of relatively high resolution satellite based data together with Geographical Information System (GIS) makes it possible for gauged and ungauged catchments rainfall-runoff modelling in space and time. Remote sensing provides large scale timely coverage of hydrologic variables at low cost (Hengl et al., 2007; Lucas et al., 2002; Maathuis, 2007). Exploiting the capabilities of remote sensing technologies is the most suitable alternative given the scarcity of ground-based hydrologic networks in the Upper Manyame Catchment. Remote sensing and GIS techniques can therefore be applied in observing critical catchment characteristics required for estimating model parameters that capture crucial land surface characteristics or dynamics (Dube et al., 2014; Gumindoga et al., 2011; Lucas et al., 2002; USACE, 2008). The objective of this study was therefore to determine runoff contributions from the ungauged subcatchments of the Upper Manyame catchment. This entailed the calibration of the Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS) model on gauged Marimba and Mukuvisi subcatchments, parameter transfer to ungauged subcatchments and then simulating streamflows of the eight ungauged subcatchments through the integration of GIS and remote sensing techniques. 2. Methods and data analysis 2.1. Description of study area The study was based on the subcatchments of the Upper Manyame Catchment, which are ~3600 km2 in area and situated in Zimbabwe (Fig. 1). There are six main reservoirs within catchment: Lake Manyame, Lake Chivero, Cleveland, Gwebi, Seke and Harava. The two hydrological sub-zones in the catchment are called CH4 and CH5. CH4 has a Mean Annual Runoff (MAR) and coefficient of variation of 126 mm and 100% respectively, whilst for CH5 has a MAR and co-efficient of variation of 135 mm and 96%

respectively. CH4 and CH5 have a mean annual rainfall of 799 and 821 mm/yr; and an average evaporation of 1631 and 1696 mm/yr respectively (Mazvimavi, 2005). As of 2010, Marimba and Mukuvisi were the only gauged subcatchments with Ruwa, Musitwe, Manyame Upstream, Umzururu, Gwebi and Nyatsime as the ungauged subcatchments. Lake Chivero and Lake Manyame subcatchments have measured dam releases from the two lakes (Lakes Manyame and Chivero respectively) which are also situated at the outlets of these two subcatchments. However in this study they have been regarded as ungauged subcatchments. 2.2. Rainfall, evapotranspiration and runoff datasets Areal rainfall of Upper Manyame Catchment was interpolated, using the Thiessen Polygons method based on four rain gauges located in and around the catchment (Belvedere, Harare Airport, Kutsaga and Marondera). The rain gauge dataset was obtained from the Meteorological Office of Zimbabwe (Met Office). Daily rainfall for the period 2004e2010 was used in this study. Pan Evaporation for the same time period was used and this was multiplied by a n et al., factor of 0.8 so that it closely reflects plant water use (Lide 2001). Daily runoff data for Marimba (C24), and Mukuvisi (C22) flow gauging stations for October 2004 to October 2010 was provided by the Zimbabwe National Water Authority (ZINWA). This period was selected because it had complete rainfall and runoff records. Rating curve values for the reservoirs were also obtained from ZINWA. The baseflow component was not included in model conceptualization, since it is not critical in most urban watersheds (USACE, 2008). 2.3. Data infilling Since the rainfall and runoff daily time-step dataset had missing values of approximately one to two days per month. The gaps constituted less than six percent of the total data. The mean value infilling method was therefore employed by estimating the symmetric distribution mean and standard deviation (Sivapalan et al., 2003). This method was used because the gaps were not too long and the accuracy is equivalent to the standard error of the mean of the original time series (Ganora et al., 2009). The original time series with gaps was then correlated with the filled time series for all the rainfall and runoff gauging stations. 2.4. Estimating HEC-HMS catchment and land surface parameters The HEC-HMS model simulates rainfall-runoff and routing processes in both natural and controlled systems (Adib et al., 2010; Scharffenberg, 2004). It is designed to be applicable in a wide range of geographic areas for solving the widest possible range of problems. This includes large river basin water supply and flood hydrology, and small urban or natural watershed runoff (Scharffenberg and Fleming, 2008). Hydrographs produced by the model are used directly or in conjunction with other tools for studies of water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain regulation, and systems operation (Feldman, 2000; USACE, 2008). The HEC-HMS ‘continuous’ simulation approach was adopted and simultaneously done for each subcatchment by adopting the same HEC-HMS model components. These components include a model for: computing runoff volume or loss rate (Deficit and Constant), overland flow and interflow transformation in an ungauged catchment (the Snyder Unit Hydrograph), precipitation and evapotranspiration (Gage Weights and Monthly Average respectively) and routing of reach and reservoir (Muskingum



3

Fig. 1. The Upper Manyame Catchment in Zimbabwe showing the 10 subcatchments, drainage network, reservoirs and gauging station locations.

method and Outflow Curve respectively). The datasets used in catchment delineation, transformation method, landuse/cover classification were obtained from remote sensing, whereas routing (reach and reservoir) and loss method parameters were derived from both literature and remote sensing. Only the loss and transformation were calibrated (USACE, 2000, 2008) using streamflow data. The detailed description of the model components is presented in sections 2.4.1e2.4.7. 2.4.1. Catchment delineation Subcatchments were delineated from the 30 m Advanced Spaceborne Thermal and Emission Radiometer (ASTER) Digital Elevation Model (DEM) obtained from the Global ASTER GDEM (http://www.gdem.aster.ersdac.or.jp/), to determine their physical characteristics. DEM hydrologic processing done with the ILWIS software (Maathuis and Wang, 2006) allowed the delineation of the study area into 10 subcatchments representing the main tributaries of the Upper Manyame(see Fig. 1). For each subcatchment, physical watershed geomorphological characteristics such as length of flowpaths, centroid location, average slope, area, slope and length

to the centroid were determined. The above watershed properties were useful in model parameter estimation as shown in Table 1. 2.4.2. Loss method After the occurrence of precipitation, the loss method controls the partitioning between intercepted water, infiltrated and the water that leaves the catchment as direct runoff. Water that “survives” a loss method leaves the catchment as quickflow. The loss method used in this study is the Deficit and Constant, which is a quasi-continuous model of precipitation loss where initial loss can recover after a prolonged period of no rainfall (US SCS, 1986; USACE, 2008) and is most suitable for continuous simulation. The Deficit and Constant loss method uses a single soil layer to account for continuous changes in moisture content (Scharffenberg and Fleming, 2008). The parameters used for this loss method consist of the maximum deficit (mm/day) and the initial deficit (mm/day) which represents the total storage depth and the empty storage depth at the beginning of the simulation respectively. Constant rate (mm/day) is the other parameter under the Deficit and Constant loss method, which defines the infiltration rate when the soil layer

Table 1 Upper Manyame HEC-HMS input and sub-catchment parameters. Parameter

Gauge name Initial Deficit (mm) Perimeter (m) Area (km2) Maximum Deficit (mm) Constant Rate (mm/hr) % impervious Elev divide (m) Elev outlet (m) Longest flow path length (m) Longest drainage length (m) Drainage density (m/km2) Total drainage length (m) Time of concentration (min) tc Time lag (min) tlag Peaking coefficient (cfs)

subatchment Musitwe

Mukuvisi

Ruwa

Marimba

Umzururu

Gwebi

Nyatsime

Manyame upstream

L. Chivero

L. Manyame

85 83858.3 216.2 170 8.75 2 1598 1338 34807.2 26818.40 142.36 30783.7 403.4 242.1

C22 100 96263.0 223.1 200 5.75 75 1598 1338 44659.2 38604.5 190.27 42453.3 538.0 322.8 0.45

C82 100 78332.2 224.5 200 5.75 65 1589 1375 32943.6 26371.90 159.42 35794.7 408.1 244.8

C24 100 85792.0 220.5 200 5.75 80 1537 1381 38595 29887.70 219.73 48449.5 553.3 332.0 0.45

85 129765.8 362.2 170 7.5 5 1501 1409 53568 42283.50 189.58 68668.6 990.2 594.1

85 229735.6 812.4 170 8.75 2 1565 1409 98209 89111.90 168.12 136579.5 1627.4 976.4

C23 85 152281.4 563.2 170 8.75 3 1567 1381 63250 59867.00 208.74 117572.1 914.9 548.9

85 184187.5 359.8 170 7.5 4 1662 1375 74016 66572.30 211.22 75998.3 928.3 557.0

C17 90 158857.0 368.8 190 6.25 50 1422 1338 35097 38322.70 193.43 71327.7 629.3 377.6

C89 90 144181.0 498.3 190 6.25 40 1386 1337 35730.3 44175.10 224.15 111693.5 790.6 474.3


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is saturated. These parameters were estimated using the range of values found in literature (Scharffenberg, 2004; Scharffenberg and Fleming, 2008; USACE, 2000, 2008) and taking into consideration the soil and landcover maps of the Upper Manyame Catchment. In addition, the saturated hydraulic conductivity (Scharffenberg and Fleming, 2008), approximated from the soil texture of the study area was used to improve the estimation of the constant rate. The percentage of each subcatchment was specified based on the 2010 landcover map (see Figure 3) and Google images. 2.4.3. Direct-runoff transformation method (Snyder Unit Hydrograph) The transformation method controls surface runoff concentration time. Water concentration is recorded in a hydrograph, thus transformation methods attempt to build the ‘right hydrograph’ using catchment characteristics. For ungauged subcatchments, the unit hydrograph was derived using Snyder Unit Hydrograph model (Adib et al., 2010; Snyder, 1938). In 1938, Snyder published a description of a parametric unit hydrograph (UH) that was developed for analyzing ungauged catchments in the Appalachian Highlands, US (Snyder, 1938; USACE, 2008). This method estimates UH parameters from catchment characteristics (Adib et al., 2010). The data required for the Snyder method include the Snyder's standard lag (hrs); which in this study was obtained by solving the SCS unit hydrograph (Snyder, 1938) to get the time of concentration (tc) based on the California Culverts Practice (see Eq. (1)), which is the time from the centroid of rainfall excess to the peak flow at the point of analysis.

0:385 11:9L3 tc ¼ 60 H

(1)

where: L ¼ Length of longest watercourse, mi and H ¼ elevation difference between divide and outlet, ft (US SCS, 1986). Then tlag is determined by the formular:

tlag ¼ 0:6*tc

(2)

Also required is the Snyder's peaking coefficient; which represents the peak flow for the unit at the point of analysis. In this study, this coefficient was calculated based on historical rainfall-runoff events dataset. 2.4.4. Catchment similarity assessment A catchment hydrological response similarity was first established (Sreenivasulu and Bhaskar, 2010) before transferring Snyder Unit Hydrograph parameters from the gauged Marimba and Mukuvisi subcatchments to the ungauged. DEM hydro-processing data was used for hydrologic similarity assessment by comparing the physical catchment characteristics. Drainage and rainfall characteristics normally affect runoff and hence hydrograph shape. For this study rainfall intensity, duration, and their spatial and temporal distribution; and storm motion were used as rainfall characteristics (Shaw, 2004). Linking physical and hydrological

catchment characteristics provided an understanding of the hydrological behavior of the different subcatchments (Sreenivasulu and Bhaskar, 2010).

2.4.5. Reach routing (confluence and bifurcation) The Muskingum method which employs the conservation of mass law (Eq.(3)) was used for reach routing. A primary advantage of the method is that it is physically based, hence it is useful in situations when downstream data is unavailable for model calibration (Sauer et al., 1983). The Muskingum routing method uses a simple finite difference approximation of the continuity equation where storage in the reach is modeled as the sum of prism storage and wedge storage (Feldman, 2000). The change in storage with respect to time is calculated following equation [3]

dS=dt ¼ I O

[3]

where: S is the storage [m3], I is the flow input [m3/s] and O is the flow output [m3/s]. Assuming that the storage is proportional to the flow: Volume of Prism A ¼ KO and Volume of wedgeB ¼ KX ðI O Þ, then

S ¼ KO þ KXðI OÞ ¼ KXI þ ðI XÞO

[4]

Where K is a factor proportional to the travel time of the flood wave in the river, X is a weighting factor dealing with the stream shape. 0 < X < 0.5, when X ¼ 0 then the reach is a reservoir. When X ¼ 0.5 (full wedge), the same weight is given to the input and output, thus the wave does not attenuate but just moves through the reach. To get K, the reach length was divided by the product of the wave celerity and the simulation time step (Scharffenberg, 2004).

K¼

L floodwavevelocity

(5)

Where K equals the travel time for a kinematic wave trough the reach, L is the length of the reach and the celerity approximated from Manning equation by assuming a wide rectangular channel (US SCS, 1986).

2.4.6. Reservoir routing In order to define reservoir routing with HEC-HMS, elevation storage (storage capacity curve) and elevation - discharge rating curves were defined. The hydrograph was routed through the reservoir before routing it downstream. The storage indication method was used to route the total inflow to the reservoir according to the storage-discharge curve selected. A similar approach was used if the elevation-area-discharge or elevation-storagedischarge storage curves were selected (USACE, 2000). The Ouflow Curve routing method performed calculations at the same time step as the model simulation (Feldman, 2000). Table 2 shows the basic parameters for each reservoir.

Table 2 Morphometric characteristics of Upper Manyame reservoirs at fully supply, modified after Thornton (1980).

Cleveland Seke (Prince Edward) Harava (Henry Hallam) Chivero (Mcllwaine) Manyame (Robertson) Gwebi

Date of construction

Area (ha)

Volume (106 m3)

Max depth Zmax(m)

Mean depth Z (m)

1913 1929 1973 1952 1976 e

30 109 215 2630 8100 e

1.00 3.64 9.25 250 490 2.00

e 10 17.5 227.4 22.6

3.3 3.3 4.3 9.5 6.0 3.3



2.4.7. Landuse/cover and soils data Landuse and cover changes influence variations in the hydrological parameters of a watershed. The landcover map was derived from 30 m Landsat images acquired 2010 based on the maximum likelihood classification technique. Classification results were validated using the 2010 Google Earth image of the study area and field based ground control points (GCPs). The study area soil map (300 m spatial resolution) was obtained from Food and Agriculture Organization (FAO) world soil map (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009). The HEC-HMS model requires the percentage imperviousness. The landcover map give estimates of impervious percentage associated with each landcover class as derived from lookup tables. For example, highways and parking areas were assigned percentage imperviousness of 95%, commercial, industrial and offices consist of 85e95% impervious areas whilst open spaces are 1e5% impervious. 2.5. Model calibration, validation and model efficiency assessment The freely available HEC-HMS 3.5 model was used (http://www. hec.usace.army.mil/software/hec-hms/). The model was manually calibrated for daily time-step simulation with Marimba and Mukuvisi subcatchments flow dataset for the October 2004 to September 2007 period. The calibration process was done through fitting the low flows, then the peak flows and finally the runoff volumes. The model was then validated using the October 2007-to-September 2010 hydro-meteorological dataset based on the calibrated parameters determined using the 2004e2007 dataset. The simulated runoff was compared with the measured runoff for Marimba and Mukuvisi subcatchments. The Nash-

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Sutcliffe efficiency (NS) (Nash and Sutcliffe, 1970; Winsemius et al., 2009) and Relative Volume Error (RVE) (Castellarin et al., 2004) model performance indicators were used to assess the percentage difference in simulated and observed volume and peak flow and time of peaking of these flows. NS ranges from ∞ to 1 with 1 indicating the best agreement between the modelled and observed time series. RVE can vary between ∞ and þ∞ with an optimum value of 0. 2.6. Parameter transferability A proxy e catchment test was implemented in order to test the transferability of the model parameters to ungauged subcatchments (Klemes, 1986; Refsgaard et al., 1995). In this study, the calibrated parameter values (i.e. time lag (tlag), peaking coefficient, deficit parameters and percentage impervious) from Marimba subcatchment were transferred to Mukuvisi subcatchment, and vice-versa at the same time maintaining the hydro-meteorological series for each subcatchment. This was done manually by allowing model simulation for each subcatchment using the calibrated parameters of the other. Performance indicators used in previous steps were used to test the performance of the parameter transferability. 2.7. Runoff simulation for ungauged subcatchments Runoff simulation in ungauged subcatchments involves using few existing runoff gauges to check and calibrate the rainfall runoff model where both rainfall and stream flow records exist. Once the

Fig. 2. Approximate zones of influence around stations by Thiessen Polygons.


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magnitude and timing of peak flows more closely matched those of the stream gage data, we transferred the parameters of the gauged catchments to the ungauged ones. In this study, the physical parameters from the gauged subcatchments such as areas, lengths, and slopes and Synder Unit's peaking coefficient and standard lag (hrs) were transferred from the gauged to the ungauged subcatchments to allow for runoff simulation. This was done manually on the HEC-HMS interface by identification of the subcatchments with the same physical characteristics Table 1). Reservoir storage, inflows and outflows were also simulated. 3. Results and discussion 3.1. Hydro-meteorological data infilling method and validation of the rainfall interpolation Fig. 2 shows the approximate zones of influence around stations by Thiessen Polygons for each subcatchment. Belvedere station has the greatest influence to the rainfall received in the area as shown by Thiessen weight (0.431). Table 3 shows the Thiessen weights and long term annual rainfall for each subcatchment in the Upper Manyame catchment. The results in Table 4 show the correlation coefficients between rainfall and runoff datasets after mean value infilling. The correlation coefficient values above 0.85 shows the reliability of the mean value infilling approach. 3.2. Landcover assessment results An assessment of catchment vegetation and landcover is critical in estimating model parameters related to infiltration and runoff potential. The result in Fig. 3 shows the land cover of Upper Manyame Catchment as of 2010. The landcover characteristics consists of bare land (50%), settlements (22%), grasslands (20%),

cultivation area (4%) and water and forest (2%). The relatively higher percentage of settlement and low percentage of forests is attributed to a high urbanization rate and high rural-urban migration. It can be noted that the two gauged subcatchments (Marimba and Mukuvisi) have the greatest percentages of settlements (impervious surface) and this has implications on surface runoff generation. However, it should be noted that urban areas typically include a mix of directly and indirectly connected impervious surfaces. An indirectly connected impervious surface is one that drains onto a pervious surface, such as a roof with downspouts that drain onto a lawn. A directly connected impervious surface is one that provides no second chance for infiltration, such as a pavement that drains to a storm-sewer inlet (McEnroe, 2010). Most of the subcatchments in Upper Manyame have indirectly connected impervious surfaces which produce slightly less runoff than directly connected impervious surfaces.

3.3. HEC HMS catchment parameters and catchment comparison results Fig. 4 shows a HEC-HMS Upper Manyame Catchment model schematic, inclusive of the subcatchments, reservoirs, junctions (confluence/outlets) and reaches (rivers). The results in Table 1 show the input and catchment parameters extracted from DEM Hydro-processing and the HEC-HMS loss and transformation parameters. The Deficit and Constant Loss parameters are estimated from literature, in conjunction with landcover of the area and soil parameters. As shown in Table 1, Gwebi subcatchment has relatively the largest catchment area (812 km2) while Musitwe subcatchment has the least (216 km2) area. Because of the size and shape of its catchment, Gwebi subcatchment also has a high concentration time value and lag time (1627 and 976 minutes respectively). Likewise, Musitwe has a relatively smaller time

Table 3 Thiessen weights and long term annual rainfall for each subcatchment in the Upper Manyame catchment. Subcatchment

Thiessen station

Thiessen weight

Mean Annual Rainfall (mm/yr)

Musitwe

Marondera Kutsaga Harare Airport Belvedere Kutsaga Kutsaga Harare Airport Belvedere Belvedere Belvedere Harare Airport Marondera Kutsaga Harare Airport Marondera Kutsaga Harare Airport Belvedere Kutsaga Harare Airport Belvedere

0.37 0.61 0.39 0.30 0.31 1 0.18 0.82 1 1 0.10 0.54 0.36 0.10 0.47 0.43 0.93 0.07 0.00 0.56 0.44

826.98

Mukuvisi

Ruwa Marimba Umzururu Gwebi Nyatsime

Manyame upstream

L. Chivero basin

L. Manyame basin

838.43

830.43 845.61 847.20 847.20 849.08

846.79

838.79

842.17

Table 4 Correlation coefficient values for rainfall and runoff data between the filled and original data. Rainfall Gauging station

Correlation coefficient

Runoff Gauging station

Correlation coefficient

Harare Airport Belvedere Kutsaga Marondera

0.94 0.89 0.91 0.88

Marimba Mukuvisi

0.97 0.96



7

Fig. 3. A classified map of 2010 indicating the different land uses in Upper Manyame Catchment.

Fig. 4. Schematic view of Upper Manyame Catchment in HEC-HMS showing subcatchments, reaches and flow network.

of concentration and lag time (403 min and 242 min respectively). It follows as far as catchment area is concerned that runoff takes more time to flow from the uppermost parts of the subcatchment

to the outlet in the larger subcatchments, when compared to smaller catchments. There is a linear relationship between imperviousness and amount of runoff simulated as also confirmed in


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goodness of fit between simulated and observed values is 68%, which is fairly satisfactory. Furthermore, the Relative Volume Error (RVE) used for quantifying the volume errors is 5.8% and suggests good performance error since it is within the acceptable ranges of 10 to 10% (Castellarin et al., 2004). This satisfactory performance is comparable to a satisfactory (correlation coefficient ¼ 0.75) spreadsheet based model simulation in Hranova et al. (2001)'s work in the same catchment when a monthly simulation was considered for the period 1972e1999. For the three year calibration period, the simulated and observed difference in volume and peak runoff is þ3% and þ6.1% respectively. The time of peak runoff only shows a delay of þ7 days. This indicates that HEC e HMS simulation parameters in Marimba subcatchment can be trusted and are transferrable to ungauged subcatchments since the model performed satisfactorily.

other studies (McEnroe, 2010). However, there is no linearity between the loss methods and the size of the catchment. Based on the observed catchment parameters shown in Table 1 (e.g. range of values of the elevation of the catchments' divide and outlets), the limited variability provided the necessary confidence in transferring gauged catchment parameters to ungauged subcatchments. 3.4. Marimba subcatchment calibration results The calibration simulation results for 2004e2007 in Marimba subcatchment are shown in Fig. 5. The model managed to reproduce the observed patterns in this period. However, the model over e simulated most of the peaks, with several peaks in the simulated hydrograph not present in the observed hydrograph. The model results for the Marimba subcatchment indicate that whenever there is an increase in precipitation there is an increase in direct runoff, whereas at the beginning of the rain there is high initial loss/ abstraction. This is possibly due to high infiltration at the beginning of precipitation. The Nash Sutcliffe (NS) model efficiency that determines the 45

3.5. Marimba subcatchment validation results Fig. 6 shows the results of the model validation for the Marimba subcatchment which is visually satisfactory except for

Observed

Simulated

40

Discharge (m3/s)

35 30 25 20 15 10 5 0

Date (years) Fig. 5. Calibration results for the Marimba subcatchment.

60

Observed

Simulated

Discharge (m3/s)

50 40 30 20 10 0

Date (years) Fig. 6. Validation results for the Marimba subatchment.



AugusteDecember 2009 period where the low flows could not match. On the other hand, the model gave a total simulated runoff of 2310 m3/day against an observed outflow of 2560 m3/day with an acceptable RVE of 8.9%. The model efficiency as given by NS efficiency was 61%; which is slightly lower than the one obtained during the calibration process (68%). The percentage difference in the peak runoff was þ2.6%, indicating that the model was able to simulate better the catchment runoff response even though there is a delay in time of peak in the simulated and observed discharge of 8 days.

3.7. Mukuvisi subcatchment validation results Fig. 8 shows the validation hydrograph for Mukuvisi subcatchment. The NS and RVE for Mukuvisi subcatchment are 57% and 9.9% respectively are satisfactory and within acceptable ranges (Castellarin et al., 2004; Winsemius et al., 2009) although there is over-simulation of the runoff. The total volume of runoff and peak runoff are also successfully simulated by the model. The percentage difference in simulated volume of runoff and peak runoff when compared to the observed runoff was þ0.4% and þ12.6% respectively. The delay time in the simulation of peak runoff is only three days. This suggests a satisfactory performance and together with the Marimba subcatchment, the Mukuvisi subcatchment simulation parameters can be transferred to ungauged subcatchments. The simulation inconsistency in some of the years in both Marimba and Mukuvisi subcatchments suggests that they can be ascribed to: streamflow and raingauge errors, errors from areal rainfall estimation using Thiessen polygons and the general sparse network of rainfall stations. Poor model performance can also be due to an inability of the model to satisfactorily represent the runoff response to rainfall in certain years. It should also be noted that the under and over-simulation by the model in the calibration and

3.6. Mukuvisi subcatchment calibration results Model results for Mukuvisi subcatchment in Fig. 7 show that some years, such as 2005 were consistently over-simulated whilst the 2006 season was simulated satisfactorily. The NS and RVE model efficiency, 64% and 8.9% respectively suggests fair performance of the model in this subcatchment. However, the percentage difference in the peak flow (4.6%) indicates that the model was able to better simulate the peakflows as also supported by the same time of peak in both the simulated and observed discharge. 30

Observed

Simulated

Discharge (m3/s)

25 20 15 10 5 0

Date (years) Fig. 7. Calibration results for the Mukuvisi subcatchment.

50

Observed

Simulated

45 40

Discharge (m3/s)

9

35 30 25 20 15 10 5 0

Date (years) Fig. 8. Validation results for the Mukuvisi subcatchment.


10


validation process could also be due to unavailability of the baseflow data which the model needed as input data. In addition, this could be due to the effluent from the economic activities (such as industries) of Harare which finds its way into the Marimba and Mukuvisi Rivers (Chawira et al., 2013; JICA, 1996; Kibena et al., 2014; Muisa et al., 2011) of which the modelling process could not account for due to the unavailability of such data. 3.8. Proxy-catchment test results For simulation in ungauged basins, parameter transfer from guaged to unguaged basins in important. The proxy-catchment test was approach was used, which involded using parameters of one subcatchment on another and assessing the performance (Klemes, 1986; Refsgaard et al., 1995). The transfer of the Marimba parameters onto the Mukuvisi subcatchment gave a NSE of 0.59 and a RVE of 14.9. Similarly the Mukuvisi parameters on the Marimba subcatchment gave a NSE of 0.52 and a RVE of 19.6. The NS results are quite acceptable though the RVE are out of the acceptable range (þ10 to 10%). The results gave confidence to the HEC-HMS model performance such that the model can be reasonably applied for ungauged runoff simulation. 3.9. Simulation results for ungauged subcatchments The averaged loss parameters computed from Marimba and Mukuvisi subcatchments' historical rainfall-runoff events data were transferred to the following ungauged subcatchments: Ruwa, Musitwe, Manyame Upstream, Umzururu, Gwebi and Nyatsime. Table 5 shows the simulation results for the ungauged subcatchments using the full simulation period (2004e2010). Lake Manyame subcatchment had the highest peak runoff of 138.8 m3/s and Musitwe subcatchment had the least peak runoff of 3.3 m3/s. This might be a reflection of the catchment physical characteristics (e.g. size and shape). Using the HEC-HMS model, we were also able to establish that the size of the watershed area that drains into Lake Manyame and Lake Chivero subcatchments were 3833 km2 and 2153 km2 respectively (Table 3). Our result for Lake Chivero subcatchment is close to the watershed area of 2250 km2 established in the work by Hranova et al. (2001). All the subcatchments achieved their highest peak from 29e30 November 2007 which could be due to the high daily rainfall (118.5 mm/day) on this 29 November received in the catchment. On the cumulative volume of simulated runoff, Ruwa subcatchment simulated runoff is the highest (2439 mm) and the least being from Musitwe subcatchment (73 mm). The difference could be influenced by the percentage imperviousness of the two subcatchments (Ruwa 65% and Musitwe 2%) which influences runoff volumes. In terms of contribution to Upper Manyame Catchments' runoff by the ungauged subcatchments, Ruwa and L. Chivero subcatchments contribute 19.3% and 14.8% respectively whilst the least in terms of contribution to runoff is by Musitwe and Gwebi (all 0.6%) subcatchments (Table 4). The gauged subcatchments (Mukuvisi and

Marimba) and the reservoirs contribute approximately 49 % to the Upper Manyame Catchment's runoff. The six year runoff simulation results for Nyatsime, Musitwe, Manyame upstream, Ruwa and Mukuvisi subcatchments were also compared to the observed runoff at C21 gauging station for validation purposes (see Fig. 2). The total gauged (observed) runoff for the six years was 133,255 m3/s as compared to 130,428 m3/s simulated by the model. This under-simulation difference can be attributed to the fact the area drained by C21 is complex to simulate because of the presence of Seke and Harava dams which are used for water supply. 4. Conclusions and recommendations This study simulated runoff for the gauged and ungauged Upper Manyame subcatchments. Whilst the hydrographs simulated using rainfall-runoff models are not perfect, the measurement of runoff using runoff gauges is a challenge because they are expensive to install and maintain at a level that guarantees good data. In addition, many years of recording data and verifying the gauge rating curves is required before some level of confidence is bestowed on the measurements. In addition, it would be a mammoth task to install runoff gauges in every small areas using runoff gauges for improved water resources management. Thus, to estimate runoff for numerous ungauged places, some techniques using rainfall runoff models, such as HEC-HMS are used. This study aimed at simulating Upper Manyame Catchment runoff, using GIS and remote sensing techniques with cognisant knowledge of reservoir operations and ungauged catchments. Based on our results, three conclusions and two recommendations can be drawn: 4.1. Conclusions (i) A comparison of predicted and observed hydrographs for Marimba and Mukuvisi gauged -subcatchments indicates satisfactory NSE and RVE results with some peaks oversimulated. Nevertheless, the model successful managed to simulate total runoff volume and peak runoff for the six year period (2004e2010). (ii) The simulated parameters for the gauged subcatchments were successfully transferred to six the ungauged subcatchments based on the NSE. Simulation of runoff volumes for the ungauged subcatchments is useful for water resources planning and management. (iii) GIS and remote sensing techniques can provide significant information and analytical capabilities critical for water resources assessments of this catchment. The HEC-HMS model, adequately represents the hydrological response of the catchment and can be used to assess the impact of other future land development scenarios in the Upper Manyame Catchment. (iv) The ungauged subcatchments with the highest contribution to Upper Manyame runoff are Ruwa and Lake Chivero

Table 5 Simulation results for ungauged subcatchments (2004e2010). Subcatchment

Peak runoff (m3/s)

Time of peak

6-year cumulative runoff (mm)

% contribution to Upper Manyame water balance

Nyatsime Ruwa Musitwe Manyame Upstream L. Chivero basin Gwebi L. Manyame basin Umzururu

12.8 114.2 3.3 10.9 133.9 9.5 138.8 12.6

29-Nov-07 28-Nov-07 27-Nov-07 29-Nov-07 29-Nov-07 30-Nov-07 29-Nov-07 28-Nov-07

113.18 2438.97 73.28 150.5 1873.24 74.85 1493.76 187.11

0.9 19.3 0.6 1.2 14.8 0.6 11.8 1.5



(combined 34%). The subcatchments with the highest peak runoff are Lake Chivero and Lake Manyame (134 and 139 m3/s respectively).

4.2. Recommendations (i) Data scarcity and incompleteness made the calibration difficult to fit the simulated and observed values. Future modelling should always be supplemented with available relevant data sources from field based measurements. The versatile GIS and remote sensing tools should also be used to update and supplement the hydrologic description of Upper Manyame Catchment. (ii) This research was conducted using semi-distributed hydrologic modelling. Future research in this catchment should explore fully distributed models in order to accurately account for runoff from ungauged areas of this catchment. Conflicts of interest The authors declare no conflict of interests. Acknowledgements The Upper Manyame Sub-catchment Council (UMSCC) is greatly acknowledged for supporting this research. The U.S. Army Corps of Engineers, Hydrologic Engineering Center, HEC are greatly acknowledged for provision of the HEC-HMS software and manuals. The anonymous reviewers for this work are also greatly acknowledged. Special mention is given to METI and NASA for the ASTER GDEM data. References Adib, A., Salarijazi, M., Najafpour, K., 2010. Evaluation of synthetic outlet runoff assessment models. J. Appl. Sci. Environ. Manag. 14. Beven, K.J., 2001. Rainfall-runoff Modelling: the Primer. John Wiley & Sons, Lancaster, UK. Castellarin, A., Galeati, G., Brandimarte, L., Montanari, A., Brath, A., 2004. Regional flow-duration curves: reliability for ungauged basins. Adv. Water Resour. 27, 953e965. Chawira, M., Dube, T., Gumindoga, W., 2013. Remote sensing based water quality monitoring in Chivero and Manyame lakes of Zimbabwe. Phys. Chem. Earth, Parts A/B/C 66, 38e44. Dube, T., Gumindoga, W., Chawira, M., 2014. Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques. Afr. J. Aquat. Sci. 1e7. FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009. Harmonized World Soil Database (Version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria. Feldman, A.D., 2000. Hydrologic Modeling System HEC-HMS Technical Reference Manual, March 2000. Hydrologic Engineering Center, Davis, CA. Ganora, D., Claps, P., Laio, F., Viglione, A., 2009. An approach to estimate nonparametric flow duration curves in ungauged basins. Water Resour. Res. 45, W10418. Gibbs, M.S., Maier, H.R., Dandy, G.C., 2012. A generic framework for regression regionalization in ungauged catchments. Environ. Model. Softw. 27e28, 1e14. Gumbo, B., 2001. Re-Engineering the urban drainage system for resource recovery and protection of drinking water supplies. In: 2nd WARFSA/WaterNet Symposium: Integrated Water Resources Management: Theory, Practice, Cases; Cape Town, 30e31 Oct. 2001. Gumindoga, W., Rientjes, H.T.M., Shekede, M.D., Rwasoka, D.T., Nhapi, I., Haile, A.T., 2014. Hydrological impacts of urbanisation of two catchments in Harare, Zimbabwe. Remote Sens. 6, 12544e12574 manuscripts; doi:12510.13390/ rs61212544. Gumindoga, W., Rwasoka, D.T., Murwira, A., 2011. Simulation of streamflow using TOPMODEL in the Save River catchment of Zimbabwe. Phys. Chem. Earth, Parts A/B/C 36, 14e15. Hengl, T., Maathuis, B.H.P., Wang, L., 2007. Terrain parameterization in ILWIS. Chapter 3. In: Hengel, T., Reuter, Hannes (Eds.), ‘Geomorphometry’ the Textbook. European Commission, DG Joint Research Centre, Institute for Environment and Sustainability, Land Management and Natural Hazards Unit, Ispra, Italy, pp. 29e48. €schl, G., McDonnell, J.J., Sivapalan, M., Hrachowitz, M., Savenije, H.H.G., Blo

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Ungauged runoff simulation in Upper Manyame

Ungauged runoff simulation in Upper Manyame

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Ungauged runoff simulation in Upper Manyame