1

Aplication of SWAT model to the hidrologic study of rivers in “Costa do Estoril”

Abstract The accuracy of hydrological watershed models depends greatly on how well the existing information

describes the processes occurring in the basins. The Estoril Coast is a location where 14 main streams

rise on an area of about 214 km2 characterized by heterogeneous precipitation regimes in space and

time.

This paper characterizes the existing data and hydrology for 6 basins (10,7km2 to 42,2km2) in Estoril

Coast (84% of the total area), selected because they have daily flow information associated. The

application of this information to SWAT model, a continuous-time watershed model, resulted in 4

models validated in periods of 4 to 7 years for the basins of rivers Vinhas, Caparide, Lage and Barcarena.

The models showed good predictive ability to monthly flow averages. The correlation between daily

data was high, often resulting in predictive ability above average, but not satisfactory. The observation

records reveal inconsistencies that prove its uncertainty, thus enabling a calibration processes well

based on significant observed information. The acquisition of flow data with higher reliability is an

important step to improve predictions, particularly for daily flow.

The rainfall data evaluated showed the need for a dense set of rain gauges, capable of representing the

variability for this area. It follows that if all stations presented were active and submitted with valid

data, study area would be satisfactorily modeled in terms of representation of precipitation variability.

Introduction Objectives

The present study aims to characterize basins in

Estoril Coast, study the quality of the data available to

model and conduct an evaluation of average flow.

We are especially interested in evaluating the

quality on watershed modelling using SWAT [Soil and

Water Assessment Tool] applied to this area. The

model is a daily based model that allows the

prediction of water quantity and quality.

The gold of this work is to build and validate

models for six watersheds, located 10 km West of

Portuguese metropolis-Lisboa. The final objective

being the future integration of daily flow results

obtained in Mohid1 operational model of Tejo River.

Framework Water, its availability, quality and sediments that

are transported are subjects of greater importance,

conditioning food production, public health and

ecosystems. Floods, erosion and contamination of

hydrologic resources are social and economic

problems throughout the world.

Within the framework of the international

conventions, such as OSPARCOM2, HELCOM

3 and in

1 Integrated modeling system maintained and developed by

the MARETEC (Marine and Environmental Technology Research Centre) group of Technical Superior Institute at the Technical University of Lisbon (www.mohid.com) 2 Oslo and Paris Commission (Environmental Regulations for the European Community). The 1992 OSPAR Convention is

the implementation of the EU Water Framework

Directive4 [WFD] it is important to know and properly

characterize our basins, define strategies in controlling

eutrophication (by reducing nitrogen and phosphorus

losses from both point and non-point sources) and

assess the effectiveness of the pollution reduction

strategy.

The WFD establishes that for each river basin

district - some of which will traverse national frontiers

- a "river basin management plan will need to be

established and updated every six years”. This

directive also attributes more responsibilities to the

authorities in informing the public, especially if it’s not

advisable bathing.

There are a number of objectives in respect of

which the quality of water is protected. The key ones

at European level are general protection of the

aquatic ecology, specific protection of unique and

the current instrument guiding international cooperation on the protection of the marine environment of the North-East Atlantic. The work applies the ecosystem approach to the management of human activities. 3 HELCOM is the governing body of the "Convention on the Protection of the Marine Environment of the Baltic Sea Area" - more usually known as the Helsinki Convention. 4 WFD - 23 October 2000, the "Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for the Community action in the field of water policy

2

valuable habitats, protection of drinking water

resources, and protection of bathing water. All these

objectives must be integrated for each river basin.

Watersheds have emerged as environmental units

for assessing, controlling and reducing pollution. The

best model for a single system of water management

is by river basin - the natural geographical and

hydrological unit - instead of according to

administrative or political boundaries.

The land phase of the hydrologic cycle controls the

amount of water, sediment, nutrient and pesticide

loadings to the main channel in each sub basin, and

consecutively, in this case study, to a bathing area.

Computer models have become increasingly

important tools for analyzing complex problems

involving water flow and contaminant transport in the

soils and groundwater.

Justification The rivers of the Estoril Coast (Costa do Estoril) are

characterized by torrential flow regimes, with

relatively low flow for most uses of river water, e.g.

fishing, navigation, energy production and human

consumption. This water must however, be good

enough to ensure the quality of water downstream

and serve to the benefit of coastal populations in

security. With the urban development streams in this

area have negative quality indexes for decades.

The rivers studied are an important contribution to

the quality of bathing and recreation water at Estoril

Coast. The characterization of the flow on these

streams in terms of quantity and quality has been the

subject of some correlation studies without the

wanted success, due to the complex process on flow

generation and the elevated urban area that changed

the natural obstacles.[SANEST,2006; SANEST,2008(B)]

This is also due to the fact that rainfall in this region is

distributed unevenly and the information is scarce,

considering the purpose and characteristics of these

watersheds - The basins studied are generally

associated with processes of short infiltration times,

resulting in torrential flows, highly dependent on soil

moisture and rainfall intensity at every moment.

It is expected that the use of a distributed model -

where for each part of the basin, spatially related with

the others, a different climatic data sets is attributed -

could lead to a better representation of the processes

occurring.

The characterization / prediction of the quantity

and quality of discharge water made in this area are

also boundary conditions, difficult to obtain, in the

context of the Mohid operational model of the Tejo

River. Mohid is a 3D tool for modelling hydrodynamics

developed in MARETEC, being particularly relevant its

application in the Tejo River estuary.

In 2007/2008 the MARETEC monitored the water

quality and flow of some rivers in this area. The

simulation of this conditions in the Mohid model,

generated similar results for the bathing water quality

to those recorded in surveys carried out on the same

days (particularly in the measures for fecal

contamination). [Maretec,2009] These results allowed

validating the application of the model Mohid for the

simulation of contamination along the water,

assuming that the flow rates and levels of

contamination of streams are known variables.

Study area The drainage network of the Estoril Coast leads to

14 major tributaries that flow into the coast between

Tejo River estuary and Atlantic Ocean, affecting a large

bathing and recreation area (with urban beaches

characteristics). The approximately 25 km of Coast are

located between -9.46°W and -9.19°W latitude, with

water lines developing between longitude 38.83°N

and 38.68°N. The maximum elevation found in the

area is 523m and the minimum the sea level along the

coast. The average elevation is 140m.

Among the basins of the Estoril Coast there were 6

streams with significant daily flow data, thus those

basins are the studied area (fig.1). These are also the

largest rivers in the drained area, consequently the

streams with higher flow expected. It is estimated that

the study area represents the drainage of about 179

km2, approximately 84% of the total Estoril Coast area

(evaluated with approximately 214 km2).

Since 1995, Sanest has been campaigning

fortnightly to monitor the water quality of these

streams. The findings of these studies show flow with

indicators of high percentage of contamination,

Vinhas

Caparide

Laje

Barcarena

Jamor

Algés

3

pointing out to flow resulting not only from

precipitation and groundwater recharges but also

from urban wastewater.

Urban areas and the existence of a drainage

system along the route of all these streams contribute

to the occurrence of variations on streams flow, often

unpredictable. The presence of tanks and other

autonomous and individual treatment and disposal of

wastewater and the illegal release of sewage and

wastewater, are characteristic of areas with low

population density, which represent an important part

of the studied area. Problems with the sanitation

system and situations of failures, breakdowns or

accidents along the drainage system may also hinder

the flow of streams.

In the last years, in order to divert polluted flows

observed, particularly in the dry season, the flow of

some of these streams as been deviated to the

collector of sewage to be treated. This deviation

usually happens during 5 months between May and

September.

Model description SWAT is the acronym for Soil and Water

Assessment Tool, a river basin scale model,

continuous, that operates on a daily time step.

Borah e Bera (2003) reviewed the mathematical

bases and simulation of the main processes on 11

basin scale models. In this study they classify SWAT as

a promising model for modelling continuous-time,

particularly in agricultural areas.

In the present work SWAT is preferred for being a

physically based model (opposed to models that use

regressions), using generally available data and being

computationally efficient. It is a worldwide reference

in watershed modelling to address the basic hydrology

of a basin and the cycle of major nutrients (N and P),

with different scenarios of data available.

It was developed by Dr. Jeff Arnold for the USA

Agricultural Research Service to predict the impact of

land management practices on water, sediment and

agricultural chemical yields in large complex

watersheds with varying soils, land use and

management conditions over long periods of time,

producing a graphic presentation of the simulation

results. [Neitsch et al. (2005)].

SWAT is a Windows based computer software that

uses principles and analysis of water flows and solute

transport processes, as well as interactive graphics-

based interfaces for data-preprocessing, generation of

drainage networks and delineation of watersheds

based on topography.

The relative impact of alternative input data,

changes in management practices, climate,

vegetation, etc. on water quality or other variables of

interest can be quantified, but the model was not

designed to simulate detailed, single-event flood

routing.

A watershed is partitioned into a number of

subbasins, this is particularly beneficial when different

areas are dominated by land uses or soils dissimilar

enough in properties to impact the hydrology. This

way we can reference different areas of the

watershed to one another spatially. Subbasins possess

a geographic position in the watershed and are

spatially related to one another.

Each subbasin can be divided in Hydrologic

Response Units [HRU]: lumped areas within the

subbasin that are comprised of unique land cover, soil,

and management combinations. Loadings from each

HRU are calculated separately and then summed

together to determinate the total loadings from the

subbasin.

No matter what type of problem studied with

SWAT, water balance is the driving force behind

everything that happens in the watershed. The

hydrology of the model is based on the water balance

equation that includes runoff, precipitation,

evapotranspiration, infiltration and the lateral

draining in soil profile.

Each HRU has as superior boundary - soil surface -

and as inferior boundary - the aquifer. It receives

precipitation from the superior boundary, of which

part is converted into runoff and another part is

converted in infiltration. The part that is converted

into runoff is directed to the sub-basin channel. The

part that infiltrates is carried along the soil profile,

being able to evapotranspirate, percolated to the

aquifer or carried laterally along the soil profile until it

reaches the channel.

The water that reaches the aquifer is lost for the

channel or the deep aquifer or finally for the

atmosphere (the effect of capillary rise is simulated

like this because SWAT soil hydrodynamics is one

way).

SWAT requires specific information about weather,

soil properties, topography, vegetation, and land

management practices occurring in the watershed.

The physical processes associated with water

movement, sediment movement, crop growth,

4

nutrient cycling, etc. are directly modelled by SWAT

using this input data [Neitsch, et. al. (2005)].

In Arnold et al. (2007) we can find references and

summaries of more than 250 studies conducted with

the SWAT, ranging from the studies applied to

pollution scenarios, calibration and validation of the

model to the studies of uncertainty analysis and

sensitivity analysis to the model inputs.

The less satisfactory results in predicting the

hydrology of a basin are usually obtained for daily

forecasts, although this is not universal.

The worst results are usually associated with

inadequate representation of rainfall data. Both due

to the location of measures available as to its quantity,

data is usually not representative of the variability in

precipitation. Other factors mentioned include the

lack of calibration of the model (Bosch et. al. 2004),

errors in flow measurements (Harmel et al., 2006) and

short periods of calibration and validation (Muleta

Nicklow, 2005).

Data characterization Digital Elevation Model [DEM]

The sensitivity analysis to mesh resolution of the

digital elevation model in forecasting models has been

the subject of numerous recent studies. The idea that

digital terrain models with higher resolution are

required or preferred for a proper modelling is

generalized. Note that greater accuracy leads

generally to higher costs.

The work of Cho et al. (2003), where it is suggested

the use of meshes between 25 and 50 meters, is used

as a reference by most researchers. Chaubey et al.

(2005) shows that the resolution of the DEM affects

the delineation of the basin, the drainage network and

the classification of subbasins, recommending

minimum mesh resolution of 100 to 200m.

Chaplot (2005) studies the effect of mesh size in

predicting the flow, and sediment load levels with the

SWAT model for meshes with resolutions between 20

and 500meters concluding that resolutions between

50m and 90m are the most efficient. He also found

that for less than 50m, although the calculation of the

amount of flow shows no significant differences, the

loads of nitrate and sediment in the flow can decrease

significantly, increasing the errors. The fact that the

erosion process is not linear and the equation that

calculates the erosion process (MUSLE) depend on the

average slope and length of the section is used to

explain these differences.

Three cartographic sources were used, originated

from two DEM from different sources:

- DEM obtained from NASA5 Cartography, with an

original resolution of 70m.

- DEM obtained from NASA Cartography data, with an

original mesh of 70m, interpolated to 25m using Cubic

method [method that uses the value of 16

neighbouring cells to calculate the value a new cell].

- Mapping obtained from the IGEO6 Cartography, with

an original resolution of 25m.

The two mappings based on NASA differ mainly in

detail of route, as expected. But there is also a clear

trend for a deviation of 200 meters (on average) to

south-southeast in relation to military Portuguese

maps (based on measures of water line bifurcations

mapped). Although there were some differences

between streams obtained with IGEO cartography and

the military maps they weren’t systematic. This

caused differences between the maps produced with

the two sources of information in the classification of

soil type and soil use (smaller than 12%).

In this study the differences in results for the

estimation of average flow in these 3 scenarios were

negligible. The points with geographic referral should

however be checked because in any case it was

observed differences that resulted in incorrect

location of information, e.g., gauging stations.

The results presented are the obtained with IGEO

cartography, 25 meters resolution.

Soil Type and Land Use The type of soil was calculated based on the Map

of European Soil Type. This map and the parameters

derived from it can be interpreted considering the

elements in the intermediate classes of soil as coarser

or finer (thinner). The differences between these two

classes consist on properties associated with each

class of soil, for example, permeability and

conductivity. On Vinhas, Caparide and Laje basins area

there were found to be differences in soil type with

interpretations coarser and finer. In those cases both

scenarios were considered for analysis.

For land use it was used Corin Map 2000,

responsibility of the National Center for Geographic

Information.

Clima inputs Given the irregularity of the area we are studding -

due to its proximity to the mountain (Sintra), the Tejo

5 IGEO – Instituto Geográfico do Exército (Portuguese

Geographic Institute) 6 National Aeronautics and Space Administration

5

River estuarine and the Ocean - weather behaves in a

heterogenic way both in space and time, especially in

terms of precipitation

The climate variables required by SWAT consist of

daily precipitation, maximum and minimum air

temperature, solar radiation, wind speed and relative

humidity. This data can be inputted from existing

records or it can be generated from the monthly

average of these values by WXGEN7, incorporated into

SWAT.

The daily rainfall data was completely defined due

to its impact on flow generation. As for other

parameters (considering the lack of information) they

were estimated based on the monthly average of two

meteorological stations located in the basins.

The climate inputs provide the moisture and

energy that drive all the other processes simulated.

The main processes modelled by SWAT were air

temperature, soil temperature and solar radiation.

As the method used to calculate potential

evapotranspiration was Penman-Monteith (Monteith,

1965), wind speed and relative humidity were also

modelled. Bergamaschi et al., 2009, validated the

equation of Penman-Monteith for a high range of

conditions leading a study of uncertainty associated

with this parameter and concluding that this equation

is sensitive enough to detect small differences

between transpiration and percolation. Kay and

Davies (2008), concluded that the uncertainty

associated with potential evaporation is lower than

that generated by climate models, however, may be

relevant in certain applications.

Precipitation Accurate meteorological information is crucial to

get good calibration criteria. Precipitation is

particularly important because one can usually get

good correlation factors between the amount of rain

and the microbiologic contamination in the

watershed.[Sanest,2007]

Typically the study of hydrological regimes is

conducted by continuous period of twelve months

during which there is a complete annual cycle. This

period is chosen to allow a more meaningful

comparison of meteorological data. As defined by

INAG (Portuguese Institute of Water) hydrological

year is "continuous period of twelve months, chosen

so that the overall variation in the water supply is

7 Weather Generator Model – Sharpley and Williams,

1990

minimal, so as to minimize water transfers from one

year to the next. Period from 1st of October to 30th of

September.” (of the following year). The results in this

paper are presented using this definition.

Grouping the data from stations located in the

same isohyet (according with INAG classification), the

watersheds and information were divided in three

major areas. It followed a study to validate the use of

alternative information sources when data from

rainfall in the intended area was not available.

From this study a group of incoherent data that

characterize the south of the basins was detected.

Because this stations were overestimating

precipitation this data could not be used. The problem

seems to be related to the sensitivity of the systems.

Precipitation series are taken at intervals of 5 minutes

and the minimum value of rainfall detected is

0.25mm, higher time gaps are advisable.

Significant differences in the occurrence of

precipitation between close stations (3,5km distant)

were also detected. These results highly suggest the

need of a dense gauge system to characterize

precipitation and predict daily flow with accuracy.

Flow data Among the rivers of the Estoril coast, there are 9

locations with historical flow series data. These series

are the responsibility of INAG8 and were obtained

using measures of level and flow curve method.

Currently there are 4 basins with active stations at

which level measures are taken daily: Caparide, Laje,

Barcarena and Jamor.

The stream of Caparide and Barcarena have

associated with the data level, flow data calculated.

The stream of Jamor and Lage present the level

without flow curve associated.

The IST9 has developed, mainly in 2005, several

campaigns that allowed inferring a flow curve for Laje.

The model results were also compared with data

obtained with this flow-level function. One of the

main problems identified in previous studies carried

out at IST was the fact that the level values measured

in these streams are not always representative of the

actual level at the stream. This, coupled with changes

in the river bed along the areas of measurement

makes it impossible to carry out efficient flow curves,

invalidating the results of flow observed. [Sanest,

2008 (C); Maretec,2008]

8available at http://snirh.pt 9 Instituto Superior Técnico, Universidade de Lisboa

6

1

n

i

i

x

Xn

Equation 1 – Average flow (m3/s)

Equation 4 – bias (m3/s)

2

1

1

n

i i

i

y x

RMSEn

Equation 6 – Root

MeanSquare Error (m3/s)

2

1

2

1

( )

( )

n

i i

i

ny

i

i

y xRMSE

RSRS

y y

Equation 8 - RSR

Equation 7 – Nash-Sutcliffe Efficiency

Equation 2 – Standart Deviation (m

3/s)

Methods and model evaluation Different scenarios were built considering the 3

DEM already described and the 2 soil interpretation

plus different sets of rainfall.

Statistic indicators and hydrograph of mensal and

daily flow were calculated to evaluate the adjustment

of model results to the observed data.

Legates et. al. (1999) recommended using at least

one measure of correlation/goodness of fit, a measure

of relative error and an absolute error measure,

besides graphical tools to support interpretation

Morias et al. (2007) and Krause et al. (2005)

perform a review to the main statistics used today in

fluid modelling. Supported in these documents and

the remaining bibliography a set of indicators and

measures were selected to be used in evaluating the

model. A special attention was given to the physical

interpretation of these equations, when possible.

Moriasi et al. (2007) also compiled the results of

numerous works on hydrologic modelling and listed

classes of results. (General qualitative classification)

These are used here to classify the results obtain.

The indicators used where:

Where xi represents each observed flow record

and xm the respective average. yi represents the value

calculated by the model on day i, and ym the flow

average calculated by the model.

Average (equation 1) and Standart Deviation

(equation 2) are well known statistics. The bias

(equation 4) is an arithmetic mean of the observed

errors - the difference between paired observed and

estimated values. Negative values indicate that the

model is, on average, overestimating values, while

negative numbers indicate that the model is

simulating lower values than those observed.

The percentage of bias (equation 3) measures the

average trend of the simulated data to be larger or

smaller than their observed values, thus represents

the average deviation. It is a dimensionless measure

of the bias with values near zero or low magnitude

indicating an accurate simulation, on average. In

relation to the flow this value will tend to vary more in

dry years than in years with higher rainfall in different

calibration methods (Gupta et al., 1999). For flow

measures PBIAS ≤ ± 10 is considered a very good

indicator, ±10 ≤ PBIAS < ±15 good, ± 15 ≤ PBIAS < ± 25

is satisfactory and PBIAS ≥ ± 25 bad. [Moriasi et al.

(2007)]

The correlation coefficient (equation 5) is

dimensionless, ranging between 0 and 1, and should

be interpreted as the percentage of variability in the

observed values that can be explained by the values

modelled. Values close to 1 indicate more efficient

forecasting. In general, it is admitted as satisfactory

models with a correlation coefficient above 0.5 to 0.6.

The root-mean-squared error (equation 6) is used

to measure the discrepancies between the observed

and calculated value. A greater weight is given to

individual differences as result of the errors being

squared. Thus, we obtain an indicator for assessing

the efficiency of the overall predictive model, the

closer to zero more predictive efficiency. Singh et.

al.,(2004) define that values of RMSE inferior to half

the standard deviation can be considered low.

The RSR (equation 8) has the advantage of being a

statistical index affected by a factor of standardization

- RMSE divided by the standard deviation of simulated

values - thus dimensionless. Singh et al (2004) defined

models with RSR ≤0,70 as satisfactory, RSR ≤0,60

good, and to 0,00 ≤ RSR ≤0,50 very good models.

With the NSE, equation 7, defined by Nash et. al.

(1970) the systematic differences in magnitude of

scale that are not detected with the correlation

1

n

i i

i

y x

biasn

1

1

100%

n

i i

i

n

i

i

y x

Pbias

y

Equation 3 – Percentage of bias

2

2 1

2 2

1

n

i m i m

i

n

i m i m

i

y y x x

R

y y x x

Equation 5 – Correlation

coefficient

2

1

2

1

1

n

i i

i

n

i m

i

y x

NSE

x x

1

1

1

n

i i

i

n

i

i

x y

NSEA

x x

Equation 9 – Modified Nash-Sutcliffe Efficiency

2

1'

n

i

i

x x

Sn

7

coefficient will already be relevant, however, the NSE

is still a statistic sensitive to outliers. NSE ≤ 0,50 are

interpreted as unsatisfactory models, 0,50 < NSE ≤

0,65 satisfactory, 0,65 < NSE ≤0,75 good and 0,75 <

NSE ≤1,00 very good.[Moriasi et al. (2007)]

The NSEA, equation 10, has been selected because

when compared with the NSE can give important

information, as it uses absolute values instead of

squares is not so sensible to outliers. Thus the

observed difference between them is an indicator of

the amount of extreme values or the variance

between the observed and modelled data. If the

absolute difference between these indicators is high

the model is particularly bad at estimating extreme

events.

Given inconsistencies in the flow data and in order

to better understand the processes occurring it was

calculated the total amount of water or equivalent

precipitation that affected each of the basins per

hydrologic year. It is recognized as a reference globally

that approximately 62% of precipitation becomes

evapotranspiration. Dingman (1994) states that in

general, the percentage of precipitation that becomes

flow is lower than evapotranspiration for the rivers in

all continents, except Antarctica.

Vinhas Basin Vinhas basin has an estimated area of 27 km2. It is

born in Sintra, about 478 meters above sea level,

draining from north to south towards the Atlantic

Ocean. Table 1 shows land use distribution,

dominated by pine and agriculture.

Table 1 - Land use, Vinhas Basin

Uso do solo Área

Pine 48,8%

Forest-Deciduous 11,1%

Agricultural Land-Close-grown 16,2%

Industrial 2,1%

Residential-High Density 2,5%

Residential-Medium/Low Density 8,9%

Residential-Low Density 10,4%

This basin has records of flow associated with 2

gauges, located in the centre of the basin. Daily flow

records for 6 hydrologic years (1984 to 1990) are

available at: Quinta (drainage area 21,5km2) and

Ponte (drainage area 9,3 km2) .

Since Quinta is immediately downstream of Ponte

(so the drainage area of Quinta includes Ponte’s

drainage area) the fact that a bigger flow was

observed in Ponte indicated an error in observed data

(there is no significant water consumer in between).

Gauge 1 - Quinta The results in the annual water balance showed

that the observed measures for annual flow were

between 3 to 8% of the precipitation, while the model

averaged the flow around half the precipitation

observed.

A daily flow average of 0,03 m3/s was measured,

against 0,38 m3/s predicted by the model.

Although mensal and daily coefficients obviously

classified the model as bad (due to an pbias of 91%),

the correlation coefficient is 86% for mensal averages

and 63% for daily values. This shows a correlation

between data observed and precipitation values. Thus

it was concluded that there was mistake on flow

records, probably connected to level measurements

and consequent problems on calculating observed

flow.

Gauge 2 - Ponte The results in the annual water balance showed

that the observed measures for flow were between

37% and 138% of precipitation. Last year observed

data that was obviously wrongly calculated causing

the 138% value, and also 2 other months (3/87 and

2/89) caused a high bias on those years.

Daily flow average was 0,14 m3/s accounting the

58 months with no obviously incoherent data. Model

predicted 0,14 m3/s average flow.

For monthly predictions with finer interpretation

pbias was 0% and NSE 0,89, classifying the model as

very good but RSR was 0,85, which still classifies the

model as unsatisfactory.

Daily predictions observed a pbias of 3% and an

NSE of 0,38 with R2 equal to 66%. These results are

not satisfactory but they indicate average predictions

above the ones obtained using average flow values.

Conclusions The model is still not efficient on a daily basis, but

the monthly indicators show that it makes good

predictions for average mensal flow.

The results point to errors in some of the flow

measurements. Due to this uncertainty before

calibration of this basin new data should be acquired.

This information is needed to establish the validation

of the data existent and re-establish a flow curve. New

measures are also needed because changes occurred

in the last 2 decades, e.g., construction of the

drainage system in the 90’s, which may have alter the

parameters for flow in this basin

8

Caparide Basin The Caparide stream is born in Sintra, at about

270m, flowing into the Ocean, in S. Pedro do Estoril,

covering a total distance of about 12.6 Km. It is

estimated with an area of 22.6 km2, divided between

the Municipality of Sintra (North) and the Municipality

of Cascais (South). The highest altitude observed in

the basin is 514m, and the average elevation 139m.

Table 2 shows land use distribution, dominated by

agriculture and low residential urban areas.

Table 2 - Land use, Caparide Basin

Land Use Area

Pine 12,9%

Forest-Deciduous 3,3%

Agricultural Land-Close-grown 45,3%

Industrial 5,8%

Residential Medium-Low Density 27,6%

Residential-Low Density 5,1%

Caparide has 1 gauge (Camilas) measuring the flow

of 10,4 km2 drainage area. This station has 6 years of

historic records (1984 a 1990). Returned to active in

2001 and has 6 additional years of recent records.

Gauge 3 - Camilas

The 3 last years of available data for this station

should not be used because they observed much more

flow than precipitation, showing another time period

of corrupted data. For the 9 years remaining the

observed flow was 0,13 m3/s.

Overall on a monthly basis, model is classified as

satisfactory in terms of is predictive ability and

variability modelling and very good overall balance in

the monthly runoff. Daily results show predictive

ability although not satisfactory. (Table 3) Smaller

residence times of water in the basin are observed.

Table 3 - Results for Camilas station

Conclusions This basin is given as an example on the

advantages of the drainage system built. After the

drainage system the quality of this water clearly

improved. The model estimated less flow than the

observed flow for the first period and overestimated

in the second period. Thus it is recommendable that

calibration is done with more recent data.

The results point to errors in some of the flow

measurements in the last years. Due to this

uncertainty before the calibration of this basin new

data should be acquired, particularly, base flow should

be derived.

Lage Basin The stream of Lage is born North of the

municipality of Sintra, on the eastern slope of the

mountain, corresponding to a basin area of

approximately 42.2 km2. Crosses the municipality of

Cascais and flows in the municipality of Oeiras.

Note on table 4 that this basin has half its area

dominated with urban areas.

Table 4 - Land use, Caparide Basin

Land Use Area

Pine 4,0%

Agricultural Land-Close-grown 40,0%

Range-Grasses 0,7%

Industrial 16,2%

Transportation 2,4%

Residential-High Density 7,0

Residential-Low/Medium Density 32,1%

This basin has records of flow associated with 2

gauges for 4 hydrologic years (1985 to 1989):

Agronomica (drainage area 37,6km2) and Merces

(drainage area 6,3 km2).

Agronomica also has records for level measured in

recent years. A flow curve was derived from records

taken in 2005 by IST, admitted validated to hydrologic

years 2004 and 2005.

Gauge 4 – Agronomica The results in the annual water balance showed

consistent results between the model and the

observed flow, particularly for the 2004-2005 data set

with respectively 16% and 40% of precipitation

transform on flow.

Daily flow global average was 0,53 m3/s versus

0,52 m3/s average flow predicted.

Model showed very good predictive ability for

mensal flow and promising daily results. High

correlations was found, especially with recent data.

Montly Daily

Average (m3/s) 0,149 0,141

Standart deviation (m3/s) 0,181 0,253

Bias (m3/s) 0,015 0,008

Pbias 10,0% 5%

R2 81% 64%

RMSE (m3/s) 0,120 0,224

RSR 0,66 0,89

NSE 0,645 0,332

NSEA 0,037 0,329

9

Table 5 – Results for Agronomica data

Gauge 5 – Merces The historic results for 1985 to 1988 showed

inconsistent results for 1988. Considering the first 3

years the mensal predictions of flow were satisfactory.

The daily results were unsatisfactory.

Its important to note that the drainage area of this

basin is dominated by 2 important urban centres, so

the uncertainty associated with this records was hi.

Conclusions Although the model built is still not daily efficient

to predict daily flow results are better than average.

The adjustment of the flow curves suggests that with

calibration the results could be good on a daily basis.

The 2 years of “good” records are considered small

considering the unknown variables. More research

can conduct to the definition of basin parameters.

Barcarena Basin Barcarena stream is born at about 310 meters

above sea level, draining from north to south towards

the Atlantic Ocean. This basin has an area of

approximately 35.5 km2 and the longest stream rivers,

covering a total of about 19.2 kilometers. Table 6

shows a highly urban area (40%) and an important

agriculture percentage.

Table 6 – Land Use distribution Barcarena Basin

Land Use Area

Pine 15,9%

Forest-Deciduous 5,4

Agricultural Land-Close-grown 32,8%

Range-Grasses 5,4%

Industrial 5,2%

Transportation 0,2%

Residential-High Density 7,2

Residential-Low Density 0,1%

Residential-Low Density 27,9%

This basin has records of flow associated with 2

periods in 1 gauge (Laveiras) - from 1987 to 1989 and

after 2001. Drainage area is aproxiamtly 33,2 km2.

In the case of Barcarena it was not possible to

calculate a flow curve with 2005 data. The locations

where flow rates and levels were measured, with the

hydrometric station of INAG, have a very large section.

The fact that the section is very large causes low

levels, therefore more sensitive to wind and

turbulence, which led to the existence of different

flow values with the same level. [Maretec,2008]

Gauge 6 – Laveiras After evaluation of the annual and monthly water

balances, considering precipitation data, the initial

109 months were reduced to 75 months due to

incoherent information observed.

The results for the model validates with 6,25 years

are showed on table 7. These indicators classify the

model as very good predicting monthly flow averages.

The NSEA and the observations of the monthly and

daily hydrographs are indicators of a big difference in

extreme values observed. This however seemed more

linked to the observed data errors and failure in

precipitation characterization than to model results.

Tabela 7 – Results for Laveiras data

Conclusions

The results for the application of the model to this

basin showed that the model has predictive ability and

correlation factors with daily flow observed. Montly

results are good.

The progress of the basin hydrographs presents

some general characteristics similar Laje: the model

shows decay constants and baseflow larger than the

observed values.

Jamor Basin Jamor basin as an estimated area of 42,6 km2 with

about 50% of urban area.

This basin presents a collection of daily flow from

1988 and 1989 hydrologic years. Considering that

there were considerable changes in basins area last 2

decades this is not enough to validate the model.

Montly Daily

Average (m3/s) 0,52 0,52

Standart deviation (m3/s) 0,64 1,42

Bias (m3/s) -0,01 -0,02

Pbias -2,8% -2,6%

R2 89,2% 67,9%

RMSE (m3/s) 0,37 1,21

RSR 0,581 0,852

NSE 0,787 0,104

NSEA 0,123 0,205

Montly Daily

Average (m3/s) 0,424 0,412

Standart deviation (m3/s) 0,558 1,181

Bias (m3/s) -0,040 -0,038

Pbias -9,4% -9%

R2 92,3% 74%

RMSE (m3/s) 0,219 0,795

RSR 0,392 0,67

NSE 0,830 0,08

NSEA -0,276 0,204

10

Considering that level values from recent years are

available at the station and with further investigation

and surveys will be possible to derive, flow data

observations and model results were reviewed.

Conclusions Model showed more flow than the observed flow

and predictive indicators were bad for both gauges

existing. However values and hydrographs showed a

general correlation with absence of significant

variations on observed flow with precipitation (leading

to less flow observed).

Because data was scarce and uncertainties already

discussed hi it was not possible to conclude the cause

for the observed flow behaviour.

Algés Basin The basin of Algés has a total area of 10.7 km

2, of

which only 11.5% can be admitted not urban. The

main line comes in the area of Alfragide. The most

important tributary crosses the area Carnaxide and

intersects the main line just before arriving at

Miraflores. These are 3 important urban centres

located near Lisboa. It is known for the destructive

floods that causes in the Algés town centre and

consequent material damages.

Conclusions The 2 years records of flow record were incoherent

with precipitation observed data, showing values of

182% and 3%. It could not be concluded that this was

related to urban areas because of the uncertainty

found associated with flow records.

Overview Present work reviewed all the available climatic

data for this area and built models based on best

scenario of available data.

Vinhas, Caparide, Lage and Barcarena basins model

where validated with 4 to 7 years of observed flow

records. Overall monthly predictions are good.

Daily flow shows good correlation indexes in

general and good averages balances but bad

prediction ability. Flow records observed were found

to be insufficient for calibration of these models. It

was interesting to observe the good results obtained

to Lage on recent years, where urban areas represent

40% of the area.

It was found that if all rain gauges were working

precipitation would be satisfactory modulated.

The summary for the results obtained can be

observed in image 1, where: arrows show the

predicted year flow average based on 27 years of

simulations for the 4 basins validated with best rain

gauges scenario where: Pushpin represents used rain

gauges and its observed average; interrogation marks

represent rain gauges believed to be corrupted;

Numbered paddle represent the hydrometric stations

with flow data associated. These show predicted flow

and observed flow between parenthesis for periods

studied. The isohyets, built with historic date, are also

represented.

Image 1 – Summary of results in mm per year

500mm-600mm

600mm-700mm

700mm-800mm 800mm-1000mm

1000mm-1200mm

700mm-800mm

600mm-700mm

11

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