332 Runoff Prediction in Ungauged Basins

distribution of discharge from one or several rain storms.Thus, in data-scarce regions, reservoir areas in combination with regional reservoir area—volume relations andpublicly available rain records can provide the catchmentdischarges to validate hydrological models such as theThomthwaite—Mather model.

Especially in semi-arid Africa, irrigated agriculture fromreservoirs is seen as one of the major ways of increasingfood production. This will have a direct impact on streamdischarge (Sachs and McArthur, 2005; UN MillenniumProject). An indirect method, as presented here, can notonly help to assess water resources and to monitor waterstress, but also to understand the runoff generation in thestudy catchments, and allow impact assessment of smallreservoirs on the available water resources.

11.16 MODEL ENHANCEMENTS FORURBAN RUNOFF PREDICTIONSIN THE SOUTH-WEST USA

S. R. KENNEDY, D.c. GOODRICH ANDc. L. UNKRIcH

The issue from societal and hydrological perspectivesPopulation growth and urbanisation have occurred rapidlyin the American south-west over the past several decadesand they are projected to exceed those of other regions ofthe USA in the future. Urbanisation often leads to anincrease in storm runoff. Urban rainfall—runoff modelsand regionalisation approaches typically consider stormrunoff volume due to the increase in impervious surfaces,but less commonly consider the effect of changes in theinfiltration rates of pervious surfaces in the built environment, or the effect of routing runoff from impermeablerooftops across permeable soils. Woltemade (2010 founda decrease in infiltrability in newer developments (post-2000), where the use of heavy machinery for site development was more common (as it is in the south-westUSA). Also, in recent years, the increase in stormwaterrunoff associated with urbanisation has begun to be considered as a potentially renewable water resource. Thisrunoff can be re-used directly, through rainwater harvesting efforts or recharge of aquifers through focused infiltration in detention basins or dry wells, or indirectly, byrouting runoff to natural stream drainages where it canrecharge. In arid environments, where upland surfacerecharge is minimal, increased runoff from urbanisationcan lead to increased recharge, as rainfall that previouslywould have infiltrated and then evaporated or transpired isinstead muted to an area where deep infiltration andrecharge can occur. If PUB is to be successful in basins

with significant and expanding urbanisation, accurate parameters and runoff characteristics for this type of builtenvironment are needed.

Description of the study areaThe study area is a 32-hectare mesquite grassland and aI 3-hectare residential development (referred to as thegrassland and urban catchments, respectively) in the cityof Sierra Vista in south-eastern Arizona at 1300 m elevation (see Figure 11.65). Topographic relief is moderate,with a 31 m elevation difference between the highestpoint in the natural catchment and the outlet of the urbanised catchment. Construction within the urbanised catchment was conducted from 2001 to 2005 and is typical ofmost tract-style housing in the south-western USA. Thesite was completely graded and building pads for houseswere compacted prior to construction. Houses 185 m2 orlarger are built on relatively uniform lots 1670 m2 orlarger and have similar building materials and landscaping(Figure II 65). Streets are asphalt, 7.3 m wide, withrounded curbs. About 90% of roofs are sloped (25% to35%) with corrugated cementitious tiles; the rest are lowslope (2% to 8%) with elastomeric coating. The tile roofsdischarge runoff distributed along eaves, mostly withoutgutters, while flat roofs discharge through focused down-spouts. A I-metre wide pervious right of way existsbetween sidewalks and the street. Storm drainage is viasurface streets except for a I .3-hectare area that drains tothe catchment outlet via a 61 cm corrugated metal pipe.Vegetation is immature, with only small areas of canopycover. All pervious surfaces are covered with 2 to 4 cmdiameter gravel mulch, about 10 cm deep, except for afew small irrigated turf areas. About 10% of yards havepervious weed barrier fabric underlying the gravel mulch.Stormwater runoff from the natural catchment is routedthrough the urbanised catchment. Runoff from bothcatchments is of short duration and baseflow is absent.Vegetation on the natural catchment consists of 3 to 6 mtall mesquite trees (Prosopis velutina), with relativelyabundant inter-canopy grass up to I m tall. Vegetationtransitions from mostly grass in the upper reaches tomostly mesquite in the lower reaches, and is seasonallydormant.

Stream stage was measured at I minute intervals by anautomated bubble gauge upstream of a 90-degree v-notchweir at the channel connecting the two catchments and atthe outlet of the combined catchments (Figure 11.65) fromMay 2005 until September 2008. Rainfall data were collected at I-minute intervals at four weighing recording raingauges in 2005 and 2006 (gauges 401,402,403 and 404),and at two additional rain gauges in 2007 and 2008(gauges 420 and 424). From August 2006 onward, each

PUB in practice: case studies

.. :?.tnt~, ‘~ •‘~•• :~aA.r.rassIn

aters

• :4

• USGS stream gauge

3 0 USDA-ARS rain gauge

(4~I I Watershed boundary

Figure 11.65. Study area photographs and map showing gauge locations, flow paths and catchment boundaries. The area in the upper right of theurban catchment drains directly to the catchment outlet through a buried culvert; runoff from the remaining area is routed along streets.

rain gauge was equipped with a Hydra-Probe soil moisture sensor at 5 cm depth to provide initial soil moisturedata for the rainfall—runoff model. To characterise landsurface slope and catchment boundaries, a real-time kinematic GPS survey was conducted in both catchments.Survey data were used to construct a digital elevationmodel for comparison with the pit-construction elevations,

Approximately 1.73 x lO~ m3 of cut material and 2.54 xlO~ m3 of fill material were moved during the gradingprocess. Therefore, some additional amount of materialbeyond that created from the cut process was likelyimported to the site. Tension infiltrometer measurementswere made at 69 sites throughout both catchments todetermine saturated hydraulic conductivity, on areas ofboth cut and fill in the urbanised catchment, and on boththe upper, grass-dominated areas and the lower, mesquite-dominated areas in the natural catchment. Further details of

!-“ ~‘,~1k

-— _•“_t~t.sIF

£ EXPLANA11ON

i Flowpath direction

‘p IL4

j~’b~ ~ :p

etres

Mention of this or other trade names does not imply endorsement by theU.S. Government.

334 Runoff Prediction in Ungauged Basins

Figure 11.66. AGWA modules, andthe sequence of steps forhydrological modelling and changedetection.

the study area and the overall study are available in Ken-nedy ( ) and Kennedy ci a!. ).

MethodThe KINEROS2 model (Smith ci a!., ; Semmensci aL, 2008) was applied to these two catchments to testthe transferability of model parameters to any catchment(see Chapter 10). The Automated Geospatial CatchmentAssessment (AGWA) tool (Miller ci a!., ) can be usedto set up KINEROS2, parameterise it with initial estimates,execute the model, and display results spatially in a GISframework. In the parlance of PUB regionalisation, initialmodel parameters were estimated by relating readily available basin characteristics (soils, topography, land covenland use) to values reported in field studies and literaturethrough the use of look-up tables in AGWA. KINEROS2is an event-based distributed rainfall—runoff—erosion modelthat represents a catchment as overland flow elements(planes or curvilinear surfaces) draining into channelmodel elements. The urban catchment was modelled withthe KINEROS2 urban element (Semmens ci a!.,Kennedy ci a!., ), a series of overland flow elementsintended to represent a contiguous row of residential lotsalong one side of a street plus one-half of the street itself.The urban element simulates runoff from impervious areasdirectly connected to the street (e.g., roof to driveway tostreet), runoff from impervious areas that flows over apervious area (e.g., roof to yard to street), and runoff frompervious areas directly and indirectly connected to thestreet. Both the geometric parameters (slope, area, flowlength, width) and the hydraulic and infiltration parameters(roughness, porosity, saturated hydraulic conductivity) of

the model elements are estimated within AGWA. AGWAemploys globally available digital geographic coverages ofsoils, topography and land cover/land use.2

The sequence of AGWA operations and initial parameterestimation is depicted in Figure 11.66. After catchmentdelineation and discretisation to define model element polygons and their corresponding geometric parameters, themodel element polygons are intersected with soils and landcover 015 layers to derive area-weighted averages of thoseproperties for each model element. Pedo-transfer functionsbased on soil texture (Rawls eta!., ) are used to defineinitial model element infiltration parameters. Assuming anaverage cover condition for each land cover/land use class,hydraulic roughness is estimated (Chow, amongothers). Trapezoidal channel morphology parameters areestimated using multivariate regressions based on variablesderivable from the GIS coverages (Miller ci a!., ). Ifobserved precipitation is not available, AGWA can accessdesign storms from across the USA.

ResultsIt is instructive to examine the observed cumulative precipitation and runoff from the urban and grassland catchments to see the profound impact of urbanisation

). Total runoff from the urban catchment is 26% oftotal precipitation; runoff from the grassland catchment isjust 1%. If KINEROS2 can effectively model the urban

2 For this study automatic parameterisation of the urban element by

AOwA as a function of zoninglhousing density had not yet beenimplemented and manual parameterisation was conducted.

WatershedDelineation

ParameterEstimation

Generation ofRainfall Input

ModelExecution

DEM processing, watershed delineation, and subdivision ofthe watershed into model computational elements

Deriving relevant hydrologic parameters from land-cover andsoils data using provided (editable) look-up tables

Multiple options for both KINEROS? and SWAT usingprovided and readily available National Weather Service

____________________ (NWS) products

Building model parameter files, running the models, andimporting simulation results

B _______________CD ~~~1

Differencing results from multiple simulations based on

different land-cover or rainfall data to evaluate changeMapping the output of model simulations to visual se spatiavariability and identiFy problem areas

ChangeAnalysis

ResultsVisualisation

PUB in practice: case studies 335

SS.00.a)a)LU

SC)

SS0SD

C

‘000‘00

catchment response using the urban model element, it willbe possible to estimate the contribution to increased runofffrom the pervious and impervious areas independently.Expectations for physical or conceptual modelling of theresponse of the grassland catchment should be tempered byits low ratio of output signal (runoff) to input signal (rainfall). Even for large storms with the highest runoff coefficients, the volume of runoff per unit area is only I to 2 mmfor the grassland catchment. When rainfall measurementerror, up to 0.25 mm per 25 mm for the gauges in this study(Goodrich a a!., ), is taken into consideration, thenoise (rainfall uncertainty) to signal (runoff) ratio is large.This is a common challenge for modelling runoff in aridand semi-arid regions, as runoff ratios are small and runoffper unit area typically decreases with increasing catchmentarea (Goodrich et a!., ).

60~

50

40S030

20

10

KINEROS2 was used to simulate runoff response fromboth catchments with three parameter scenarios: (i)‘regional’ look-up table parameters from AGWA with noadjustment; (ii) a single set of optimised parameters used forall runoff events: and (iii) optimised parameters for eachevent. For scenarios (ii) and (iii) the shuffled evolutioncomplex Metropolis (SCEM — Vrugt a a!., ) schemewas used to optimise four parameters over all catchmentmodelling elements. The parameters varied were saturatedhydraulic conductivity (K3), the coefficient of variation ofK3, the soil suction term (G), and hydraulic roughness. Theresults are illustrated in Figure 11.68. As expected, simulation results for the grassland catchment are poor. Theregional AGWA parameters resulted in relatively goodsimulations for small to medium events. In all cases theoptimised parameter sets improved simulation results.

— Precipitation— Urban Runoff— Grassland Runoff

90

100 Figure 11.67. Cumulativedistribution plots of daily rainfalldepth, showing precipitation andrunoff for the urban and grassland

80 catchments from June 2005 throughAugust 2008. All daily rainfalldepths produced runoff from theurban catchment (mnoff-producingrainfall occu,red on 185 days total).

1200

1000

800

600

400

Ii

25

I 15 20 25 30

Daily rainfall depths, mm

035 40 45

Grassland Watershed

20

15

10

5

0

Urban Watershed

*+

+

p0 5 10 15 20 25

Measured Runoff volume (mm)

SS0SD

C

0‘00

2

1.5

0.5+ +

+

0 ~ -

0 05 1 15Measured Runoff volume (mm)

Figure 11.68. Modelled vs.measured runoff volume/unit area forthe urban and grassland catchmentsusing AGWA look-up tableparameters for all events (redcircles), a calibrated single set ofparameters across all events (bluetriangles), and an optimisedparameter set for each individualevent (black + symbol).

2

336 Runoff Prediction in Ungauged Basins

Table 11.14. Geometric mean and coefficient of variation for the catchment-scate (from SCEM optimisation) andpoint-scale (from tension infiltrometer measurement) estimates of K~

Urban catchment Grassland catchment

Mean K,, mm/hr Coeff. var. Mean K,, mm/hr Coeff. var.

Catchment-scale 9J 0.29 25 0.58Point-scale 2.9 0.55 6.2 0.56AGWA soil texture 26 — 26

The catchment scale values are a sample of values optimised individually for 20 rainfall events. Point-scale values are a spatial sample

0

0..

Sat Hyd. Conductivitymmlhr

o 1.4-2.8

o 2.9-4.3

o 4.4-5.7

o 5.8-72

o 7.3-8.8

o 8.7-101

Figure 11.69. Tension infiltrometermeasured satumted hydmulicconductivity values and theirrespective locations. In the urbancatchment, pink regions denote areasof soil fill for site preparation andblue areas denote sites of soil cuts.

Closer examination of the infilti-ometer measurementsindicated that site preparation for the urban catchmentdid have a substantial impact on the measured K,(Figure 11.69). Analysis of the optimised results resultedin three important findings. First, the effective saturatedhydraulic conductivity determined using SCEM optimisation decreases from about 25 mm/hr in the grassland areato 9.5 mm/hr in the urban area, a change that is confirmedin direction, but not magnitude, by tension infiltrometermeasurements, which average 6.2 and 2.9 mm/hr in thegrassland and urban catchnients, respectively. Second,assuming that prior to development the two catchmentshad identical infiltration properties, it is estimated thatabout 17% of the increase in runoff from the urban areacomes from a change in pervious area properties (Kennedyet at., ). In other words, the expected volume of runoffwould be 17% less than what was measured if rainfallinfiltration occurred at the same rate as it did prior todevelopment. Third, the values for the point-scale andoptimised catchment-scale K, estimates are similar for theurban catchment, but much less than would be expected

from estimates based on soil texture (Table 11.14). Thegreater difference between the model-optimised and measured K, estimates in the grassland catchment is attributed tocompensating parameter adjustments by SCEM for modelerror, parameter error and (or) data error, exacerbated bythe very small runoff coefficient.

DiscussionThere are several important results of this study applicableto PUB. The first is that urbanisation impacts on catchmentrunoff response are not limited solely to the amount ofimpervious area that is constructed. In developments whereheavy earth moving and compaction equipment is used forsite preparation, the developed infiltrating areas have asubstantially lower rate of infiltration than adjacent undeveloped areas. In this study, that decrease in K, resulted in anadditional 17% increase in runoff volume beyond thatcreated by impervious surfaces. Infiltration rates in thesecompacted soils may recover over time but additional measurements will be required. In addition, several strategic

0

0

0 .:

.9

0000

000~0n . a

~O “r~~ -~

o ~ ~ 00

000

at-.

‘.‘1 4fL&,

• 102-118

() 117 13.0

Urban WatershedFill

— Cut

Grassland Watershed~ Upland gassy slopes

Lowland mesquite Rats

PUB in practice: case studies 337

measurements with a tension infihtrometer in compactedsoils would provide valuable infiltration model parameterestimates. Finally, this study forms the basis to modifyAGWA look-up tables (transferrable ‘regional’ parameters)for developed infiltrating areas, which will extend the capability to apply KINEROS2 to this type of land use.

11,17 RUNOFF PREDICTIONS TOHELP MEET MILLENNIUMDEVELOPMENT GOALS INZIMBABWE

P. MAZVIMAVI

The issue from societal and hydrological perspectivesThe sustainable planning and management of waterresources requires knowledge on the availability of theseresources and their spatial and temporal variability. Thisinformation is generally derived from gauging networksmonitoring the various components of the water cycle. Insome river basins, particularly those in Africa, thesenetworks show many gaps, including those basins earmarked for the development of water resources. Thereasons for the inadequate coverage are many. Theyinclude inadequate funding and personnel with relevantexpertise; poor accessibility of some of the basins; anddisfunctionality of institutions due to conflicts. Lack ofadequate hydrological data contributes towards ill-designed infrastructure that does not provide the plannedbenefits. Without information about the spatial and temporal variability of the water resources, the potential andconstraints in developing these resources are not wellunderstood.

The international community, through the MilleniumDevelopment Goals, expressed a commitment to improving human wellbeing through creating and diversi~’inglivelihood options, including increasing access to potablewater. Some of the measures for improving human wellbeing include increasing food production. These effortswill inevitably increase water demand and the competitionfor water among various user groups. Sustainable waterresources management requires balancing the waterdemand with the natural renewal rate of water. Thisrequires information about the availability of water, whichis impossible to estimate in ungauged river basins. Toolsfor improving predictions in river basins with inadequatedata are therefore necessary to provide the informationnecessary for satisfying societal needs for water.

This study carried out in Zimbabwe had the aim ofimproving prediction of river flow statistics used routinelyfor water resources planning in ungauged basins

(Mazvimavi, ). The objectives of the study were (i)to assess the potential of using river basin descriptors forpredicting flow statistics; (ii) to examine the possibility ofdelineating basins into hydrologically homogeneousgroups and to assess if such grouping improves predictionof flow statistics; and (iii) to examine if the parameters ofselected rainfall—runoff models can be regionalised usingbasin descriptors.

Description of the study areaThe study was conducted on 52 river basins in Zimbabwe(Figure 11.70), which is located in Southern Africa andcovers a land area of 390757 km2. Altitude varies from162 to 2592 m a.s.l. The elevation increases from both thesouth and north towards the central part of the country,which defines many of the catchments. The northern partof the country has the Gwayi, Manyame and MazoweRivers draining into the Zambezi River. Rivers on thesouthern part drain into the Limpopo River, which isshared with Botswana, South Africa and Mozambique.The Eastern Highlands, which have an elevation of1800—2592 m, lie along the border with Mozambique.All the rivers in Zimbabwe eventually become part oftrans-boundary rivers and introduce the special challengeof sharing hydrological data between the respectivecountries.

Zimbabwe has a tropical climate with one wet seasonand one dry season. However, areas of high elevationexperience sub-tropical to temperate conditions. The wetseason is from mid-November to mid-March, with the restof the year being thy. Most of the rainfall occurs in theform of isolated thunderstorms with high intensity. Thisposes a problem for accurately measuring anal rainfallusing a sparse network of rainfall stations. The spatialvariation of rainfall is greatly influenced by altitude anddistance from moistures sources such as the Indian Oceanon the east. Rainfall increases from west to east, and fromthe low-lying southern parts to areas at high elevationalong the central part and the Eastern Highlands. Thesouthern low-lying areas receive about 350 to 600 mm/yrof rainfall, while the central catchment receives 700-1200mm/yr. The highest rainfall, 1200—2000 mm/yr, is receivedon the Eastern Highlands. Most parts of the country fallwithin the semi-arid zone. Areas receiving low rainfall,such as the southern and western parts, have high inter-annual variability of rainfall with a coefficient of variationof 30—40%; the coefficient is 20—30% in other parts of thecountry.

There are about 450 river gauging stations, mostly inthe developed central part of the country (Figure 11.71).The earliest stations were developed during the 1920s.A considerable number of river flow measuring stations