In Cooperation with the World Bank, the Romanian National Agency for 

Mineral Resources, and Futures Group 

Initial Sediment Transport Model of the MiningAffected Aries River Basin, Romania 

By Michael J. Friedel and Joshua I. Linard 

Open-File Report 2008–1171 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2008 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Friedel, M.J., and Linard, J., 2008, Initial Sediment Transport Model of the mining-affected Aries 

River basin, Romania: U.S. Geological Survey, Open-File Report 2008-1171, 23 p.

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 

ii
Abstract.........................................................................................................................................1

Introduction ...................................................................................................................................1

Description of the Study Area........................................................................................................2

Methods ........................................................................................................................................3

Model Construction ....................................................................................................................3

Topography.............................................................................................................................4

Land Use and Soils .................................................................................................................4

Climate and Streamflow ........................................................................................................10

Model Calibration and Validation..............................................................................................12

Results ........................................................................................................................................15

Suggestions for Model Improvement...........................................................................................18

Model Input ..............................................................................................................................18

Spatial Precipitation and Temperature ..................................................................................18

Spatial Soil Parameters.........................................................................................................19

Point Sources and Sinks .......................................................................................................19

Model Calibration .....................................................................................................................20

Streamflow ............................................................................................................................20

Validation ..............................................................................................................................20

Sensitivity Analysis and Nonlinear Regression .....................................................................20

Predictive Analysis ...................................................................................................................21

Summary and Conclusions .........................................................................................................21

References Cited ........................................................................................................................21

Figures 

1. Aries River basin with streamflow discharge station locations and the approximate location of 

Valea Sesei tailings impoundment, Rosia Poieni mine.......................................................2 

2. Digital elevation model data with outline of Aries River basin ...................................................5 

3. Aries River sub-basins used in model construction...................................................................5

4. Land use within the Aries River sub-basins...............................................................................6

5. Surficial soil types within the Aries River sub-basins.................................................................8 

6. Temperature range at Baisaora monitoring station .................................................................11

7. Daily precipitation recorded at Baia de Aries...........................................................................11 

8. Observed versus simulated mean monthly streamflow discharge at Turda ............................15

9. Comparison of precipitation at Basisaora and Baia de Aries monitoring stations....................17 

10. Stations (triangles) for which NOAA has climate data available for estimating sub-basin time 

series using a downscaled approach................................................................................19

iii
Tables 

1. Aries River sub-basin spatial characterization used in model construction ...............................7

2. Aries River sub-basin land use characterization used in model construction............................8

3. Aries River sub-basin soil type characterization used in model construction. ...........................9

4. Summary of available climate and streamflow data for Aries River basin ...............................13

5. Summary of selected model files used in Aries River basin simulation...................................14

6. Correlation matrix for streamflow discharge between gage stations over total period of record 

in Aries River basin...........................................................................................................17

7. Correlation matrix for median streamflow discharge between gage stations in Aries River 

basin, 2004.......................................................................................................................18

iv
Conversion Factors 

Inch/Pound to SI 

Multiply By To obtain 

Length 

foot (ft) 0.3048 meter (m) 

mile (mi) 1.609 kilometer (km) 

yard (yd) 0.9144 meter (m) 

Volume 

ounce, fluid (fl. oz) 0.02957 liter (L) 

gallon (gal) 0.003785 cubic meter (m3) 

cubic inch (in3

) 0.01639 liter (L) 

cubic foot (ft3

) 0.02832 cubic meter (m3) 

Flow rate 

foot per second (ft/s) 0.3048 meter per second (m/s) 

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s) 

Mass 

ounce, avoirdupois (oz) 28.35 gram (g) 

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8 

v
SI to Inch/Pound 

Multiply By To obtain 

Length 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

meter (m) 1.094 yard (yd) 

Volume 

liter (L) 0.2642 gallon (gal) 

cubic meter (m3

) 264.2 gallon (gal) 

61.02 cubic inch (in3 liter (L) ) 

cubic meter (m3

) 35.31 cubic foot (ft3) 

Flow rate 

meter per second (m/s) 3.281 foot per second (ft/s) 

cubic meter per second (m3

/s) 35.31 cubic foot per second (ft3/s) 

Mass 

gram (g) 0.03527 ounce, avoirdupois (oz) 

kilogram (kg) 2.205 pound avoirdupois (lb) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

vi
Initial Sediment Transport Model of the 

Mining-Affected Aries River Basin, Romania 

By Michael J. Friedel and Joshua I. Linard 

Abstract 

The Romanian government is interested in understanding the effects of existing and future 

mining activities on long-term dispersal, storage, and remobilization of sediment-associated metals. 

An initial Soil and Water Assessment Tool (SWAT) model was prepared using available data to 

evaluate hypothetical failure of the Valea Sesei tailings dam at the Rosia Poieni mine in the Aries 

River basin. Using the available data, the initial Aries River Basin SWAT model could not be 

manually calibrated to accurately reproduce monthly streamflow values observed at the Turda gage 

station. The poor simulation of the monthly streamflow is attributed to spatially limited soil and 

precipitation data, limited constraint information due to spatially and temporally limited streamflow 

measurements, and in ability to obtain optimal parameter values when using a manual calibration 

process. Suggestions to improve the Aries River basin sediment transport model include accounting 

for heterogeneity in model input, a two-tier nonlinear calibration strategy, and analysis of 

uncertainty in predictions. 

Introduction 

The basin degradation of fluvial systems by heavy metals can occur through natural 

weathering processes (Macklin, 1992), but more commonly degradation occurs from anthropogenic 

processes in response to base- and precious-metal mining operations. Some commonly associated 

mining-related processes that lead to elevated metal concentrations in basin-fluvial systems include 

acid-mine drainage (Gray, 1998), release of waste slurries containing solute and particulate metals 

(Salomons, 1995), erosion of contaminated impoundments and floodplains (Dennis and others, 

2003), and failure of mine tailings dams (Grimalt and others, 1999; Macklin and others, 2003). 

Often the timing and interrelation among these processes lead to a catastrophic degradation of 

basin-fluvial systems. One recent example was the tailings dam failure at the Baia Borsa mine, 

2000, which resulted in releasing acid water and about 20,000 tons of polluted sediment into the 

1
Danube River of the Tisa basin, Romania. For this and other reasons, the Romanian government is 

interested in understanding the effects of existing and future mining activities on long-term dispersal, 

storage, and remobilization of sediment-associated metals. To achieve an outcome that could be 

applied in other mining-affected basins of Romania, the objective in this study is to develop and 

calibrate an initial model for quantifying the effects of sediment transport following a hypothetical 

tailings dam failure at the Valea Sesei tailings dam, Rosia Poieni mine (fig. 1). 

N 

Valea Sesei 

tailings impoundment

0 15 km

Figure 1. Aries River basin (about 2,500 km2) with streamflow station locations and 

approximate location of the Valea Sesei tailings impoundment, Rosia Poieni mine. 

Description of the Study Area 

The Aries River is a major tributary to the River Mures that drains eastward along the northern 

edge of the Metaliferi Mountains, in northwestern Romania (fig. 1). The Metaliferi Mountains form the 

southern part of the larger Apuseni Mountain region, situated in the northwestern Carpathians. The 

River Aries (drainage area of 2,540 km2

) is a gravel-bed river with mountain headwaters rising to 1,160 

m above sea level. The channel gradient is steepest upstream of Campeni, where the channel has also 

2
been dammed to create a reservoir. Near Baia de Aries, the streamflow discharge ranges from 5 to 63 

m3s

-1, with an average of 24 m3

s

-1 (Forray and Hallbauer, 2000). With the exception of the Abrud and 

Iara Rivers, tributaries are relatively small, high-gradient streams with discharges of 0.01–2 m3

s

-1. The 

River Abrud (drainage area 274 km2), the largest tributary to the Aries River, joins the main stem 

downstream of Campeni (fig. 1). The Abrud is a gravel-bed river that drains the southeastern and 

eastern flank of the Bucium, Rosia Poieni, and Rosia Montana ore deposits. 

Methods 

The physically based numerical model known as the Soil and Water Assessment Tool 

(SWAT); (Neitsch, Arnold, Kindrey, and others, 2002; Neitsch, Arnold, and Srinivasan 2002; 

Arnold and others, 2002) was selected to simulate long-term dispersal, storage, and transport of 

sediment-associated metals following hypothetical failure of the Valea Sessi tailings dam. SWAT is 

a free computer program developed to predict the impact of land management practices on water, 

sediment, and chemical yields in large, complex basins having varying soils, land use, and 

management conditions over long time periods. The benefits of using the physically-based SWAT 

model are that the relative long-term effects of alternate input data (i.e., alternate model scenarios), 

such as management practices, climate, vegetation, and others, on water and sediment quality can 

be quantified. Before SWAT can be used to perform Aries River basin simulations, the following 

three modeling steps must be completed in order: construction, calibration, and validation. While 

not mandatory, many investigators are now performing the additional step of predictive analysis to 

evaluate the uncertainty in key surface-water predictions (Friedel, 2006). 

Model Construction 

An initial SWAT model was constructed during the first project year to represent 

hydrological processes in the Aries River basin. As is true with all models, the quality and degree 

of usefulness is based on the quality of information available. Because SWAT is a physically-based 

model, the types of information included topography, gage (precipitation and streamflow) station 

locations, climate (precipitation and temperature), land use, soil properties, and land management 

practices. Preprocessing of the information was done using programs such as Excel, Access, and 

Basins 3.1 (USEPA, 2001). The SWAT modeling software is included as part of the U.S. 

Environmental Protection Agency’s preprocessing software called BASINS (USEPA, 2001). The 

BASINS acronym is for Better Assessment Science Integrating point and Non-point Sources software. 

3
In addition, two Environmental Science Research Institute (ESRI) geographical information systems 

(GIS) programs, ArcView 3.x and Spatial Analyst (ESRI, 2006), were used. 

Topography 

SWAT requires three primary spatial data sets: a digital elevation model (DEM), a land-use 

grid, and a grid of soil map units. Each of these data sets required reprojection into an appropriate 

coordinate system. A 90-m-resolution DEM (Land_cover2000_Albers.dbf) was purchased from 

Romanian counterparts for use in subdividing the Aries River basin (fig. 2). This DEM compared 

favorably to one downloaded from a USGS site (seamlessusgs.gov) hosting global data obtained 

during U.S. shuttle flights. Each tributary basin was delineated from a point in the Aries basin that 

is co-located with seven stream gage sites (fig. 3). The Aries River basin then was subdivided into 

38 sub-basins (fig. 3) of similar sizes (table 1), consistent with recommendations by Jha and others 

(2004). Following the determination of sub-basins, the BASINS program created a flow 

accumulation grid and a flow direction grid based on the physical characteristics of each sub-basin. 

Land Use and Soils 

The spatial representation of 15 land-use categories (fig. 4) was based on a 30-m grid 

(LanduseSoilRepSwat.txt), of which the largest lumped categories included forest (evergreen and 

mixed), 45.7%; pasture, 19.9%; range (grasses), 15.8%; and agricultural (generic and row crops) 

13.8% (table 2). The map-unit grid (Soils_ST70_Project.dbf) corresponding to the Romanian Soil 

Geographic database characterized 30 soil types (table 3) at a 30-m resolution (fig. 5). At the time 

of model construction, however, there was no soil property information available from the 

government or other sources. For this reason, the Aries River basin was assumed to be 

characterized by a single soil layer with homogeneous soil properties. That is, the same set of initial 

soil properties were used for each soil type (characterized by percentages of 7.5% clay, 6.6% silt, 

and 86% sand, and a soil water capacity of 0.13, a permeability of 21, and dimensionless universal 

soil loss coefficient of, 17) (Usersoil.dbf). 

4
N

0 15 km

Figure 2. Digital elevation model data (90 m) used in SWAT model construction. Aries River basin 

outline on digital elevation model data. Darker color represents greater elevation. 

N

0 15 km

Figure 3. Aries River sub-basins used in SWAT model construction. Sub-basins were 

derived so that gage stations coincided with outlets. Sub-basins are outlined in black and 

numbered; other features include streamflow gage stations (dots), rain gage stations 

(squares), and temperature gage station (triangles). 

Because land use and soils vary in the Aries River basin, multiple hydrologic response units 

(HRUs) were created in each sub-basin (for a total of 227). Because lumping of soil and land-use 

5
features may result in losing important hydrologic features, multiple HRUs within a sub-basin were 

defined if a land use category occupied more than 20% of the sub-basin area and a soil class 

consisted of at least 10% of the sub-basin area. No more than seven HRUs were created in any subbasin, and each HRU had a unique parameter set (described in ruLanduseSoilRepSwat.txt). 

N 

0 15 km 

Figure 4. Land use within the Aries River sub-basins. Colors represent different land use 

regions. 

6
Table 1. Aries River sub-basin spatial characteristics used in SWAT model construction. 

[ha, hectares; m, meters; %, percent] 

SUBBASIN AREA LENGTH SLOPE ELEVATION 

 ha m % m 

1 19875 24031 27.9 945 

2 3319 11450 28.4 926 

3 9879 20705 29.0 818 

4 5380 15031 26.3 1044 

5 3455 13689 31.8 740 

6 16024 36463 29.7 880 

7 4450 13491 20.9 690 

8 2345 9955 25.9 695 

9 4329 20277 32.4 983 

10 8891 22310 26.3 1200 

11 17392 32856 26.9 723 

12 426 4717 33.9 593 

13 3457 9055 30.5 737 

14 2602 9806 34.1 999 

15 13182 27072 31.4 889 

16 5154 19482 23.6 829 

17 2708 13029 34.8 725 

18 6774 21442 31.6 1138 

19 3336 11942 32.2 422 

20 5193 27627 27.2 632 

21 7907 17428 17.1 457 

22 3547 14866 36.9 766 

23 3419 12800 28.2 515 

24 2739 11278 21.1 557 

25 2855 14379 14.4 612 

26 11685 22148 10.1 487 

27 7656 17243 9.7 378 

28 7360 18368 10.5 602 

29 5356 11398 12.6 605 

30 9774 24566 11.8 567 

31 3135 17830 12.5 702 

32 2631 17740 28.9 1060 

33 11760 30508 30.1 1134 

34 3391 10497 32.7 835 

35 4895 16380 31.1 1142 

36 18010 25834 24.6 705 

37 3228 17804 27.8 1487 

38 6505 17428 27.0 1265 

7
Table 2. Aries River sub-basin land use characteristics used in the SWAT model 

construction. 

[ha, hectares; m, meters; %, percent] 

Land Use SWAT Area Area Basin 

Aries River Basin Parameter Ha Acres % 

Agricultural Land-Generic AGRL 6117 15115 2.41 

Agricultural Land-Row Crops AGRR 28996 71651 11.4 

Forest-Evergreen FRSE 36545 90304 14.4 

Forest-Mixed FRST 79516 196489 31.3 

Industrial UIDU 269 665 0.11 

Institutional UINS 1006 2486 0.40 

Orchard ORCD 504 1246 0.20 

Pasture PAST 50577 124979 19.9 

Range-Brush RNGB 17505 43256 6.89 

Range-Grasses RNGE 22728 56163 8.95 

Residential-Low Density URLD 90.8 224.8 0.04 

Residential-Med/Low Density URML 7992 19750 3.15 

Water WATR 582 1439 0.23 

Wetlands-Mixed WETL 1508 3727 0.59 

Wetlands-Non-Forested WETN 88.9 217.2 0.03 

N 

0 15 km 

Figure 5. Surficial soil types within the Aries River sub-basins. Colors represent different 

soil types. 

8
Table 3. Aries River sub-basin soil type characteristics used in model construction. 

[ha, hectares; m, meters; %, percent] 

Soil 

Type 

 

Area 

ha 

 

Area 

acres 

 

Basin 

% 

 # 

Soil 

Type 

 

Area 

ha 

 

Area 

acres 

 

Basin 

% 

# 

1 ra025 47256 116772 18.6 31 ra058 1660 4102 0.65 

2 ra030 45099 111441 17.8 32 ra003 1557 3848 0.61 

3 ra054 21255 52523 8.37 33 ra017 1324 3271 0.52 

4 ra031 14610 36102 5.75 34 ra067 1264 3124 0.5 

5 ra033 8567 21169 3.37 35 ra044 1156 2857 0.46 

6 ra023 8181 20217 3.22 36 ra051 1172 2895 0.46 

7 ra036 7259 17937 2.86 37 ra039 985.2 2434 0.39 

8 ra019 6076 15014 2.39 38 ra011 771.2 1906 0.3 

9 ra037 5739 14183 2.26 39 ra063 701.4 1733 0.28 

10 ra043 5155 12737 2.03 40 ra021 668.2 1651 0.26 

11 ra034 4719 11660 1.86 41 ra066 659.8 1631 0.26 

12 ra008 4599 11366 1.81 42 ra018 587.7 1452 0.23 

13 ra046 4512 11150 1.78 43 ra047 596.1 1473 0.23 

14 ra035 4441 10974 1.75 44 ra059 554.4 1369 0.22 

15 ra055 4407 10889 1.73 45 ra006 540.1 1335 0.21 

16 ra002 4298 10620 1.69 46 ra049 492.6 1217 0.19 

17 ra040 4097 10124 1.61 47 ra041 422.5 1044 0.17 

18 ra048 3995 9872 1.57 48 ra050 443.0 1095 0.17 

19 ra045 3913 9668 1.54 49 ra005 385.8 953.3 0.15 

20 ra042 3642 8999 1.43 50 ra007 329.8 814.9 0.13 

21 ra024 3353 8286 1.32 51 ra001 310.9 768.3 0.12 

22 ra061 3004 7423 1.18 52 ra012 282.1 696.7 0.11 

23 ra065 2659 6571 1.05 53 ra057 268.7 664.1 0.11 

24 ra028 2636 6512 1.04 54 ra064 206.2 509.4 0.08 

25 ra038 2590 6400 1.02 55 ra060 183.4 453.3 0.07 

26 ra020 2455 6065 0.97 56 ra026 99.76 246.2 0.04 

27 ra010 2168 5356 0.85 57 ra027 63.99 158.1 0.03 

28 ra029 1978 4888 0.78 58 ra032 21.06 52.06 0.01 

29 ra052 1841 4549 0.72 59 ra062 21.87 54.04 0.01 

30 ra056 1792 4427 0.71 60 ra022 2.298 5.679 0 

9
Climate and Streamflow 

Climate and streamflow measurements taken in the Aries River basin were obtained from six 

national hydrometeorological stations—Abrud, Albac, Baia de Aries, Buru, Campeni, and Turda—and 

a seventh at Valea Sesei operated by the Rosia Poieni mine. All of these data were presented as paper 

records with no indication of quality control or quality assurance. To be able to use and evaluate these 

data, all paper records were converted to digital format. A cursory review was performed on all of the 

data to identify time gaps in the record, as well as measurement outliers. Because SWAT requires a 

continuous record of both daily precipitation (rain and snow) and temperatures (minimum and 

maximum) over the period of model calibration and simulation, the most obvious and immediate 

concerns were associated with differing periods of record and differing types of measurements 

(precipitation, temperature, radiation, and streamflow discharge) recorded (table 4). 

Whereas all of the hydrometeorological stations have continuous precipitation and streamflow 

measurements, the temperature measurements cover only 6 months from October to March. Because 

the recorded temperatures provided by the Romanians represented a daily average, they could not be 

used. To remedy this situation, the hourly temperature data recorded at the Baisaora station located 

northeast of Baia de Aries between sub-basins 20 and 32 (latitude: 46.53300 N, longitude: 23.31700 E, 

elevation 1,356 m) were retrieved using the National Oceanic and Atmospheric Administration 

(NOAA) web server at the National Climatic Data Center (NCDC) (go to discovery map at 

http://gis.ncdc.noaa.gov/aimstools/gis.jsp). The Baisaora data provided a continuous record of daily 

minimum and maximum temperatures (fig. 6) for input to the SWAT model (baisaora_C.dbf). Daily 

precipitation information (fig. 7) used in the initial SWAT model (baia_P.dbf) were taken from the Baia 

de Aries station located on the main stem of the Aries River (latitude: 46.38180

 N, longitude: 23.28340

E, elevation: 483 m). A summary of selected SWAT files used in the model construction process is 

provided in table 5. 

10
-40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

40.00

10/1/199010/1/199110/1/199210/1/199310/1/199410/1/199510/1/199610/1/199710/1/199810/1/199910/1/200010/1/2001

DATE

TEMPERATURE, degree C

Maximum

Minimum

Figure 6. Temperature range at Baisora monitoring station from 10/1/1990 to 10/30/2001 

(latitude: 46.53300 N, longitude: 23.31700 E, elevation: 1,356 m) 

0

10

20

30

40

50

10/1/19914/18/199211/4/19925/23/199312/9/19936/27/19941/13/1995

8/1/1995

2/17/1996

9/4/1996

DATE

Precipitation, mm

Figure 7. Daily precipitation recorded at the Baia de Aries monitoring station (latitude: 

46.38180

 N, longitude: 23.28340 E, elevation: 483 m). Period of record shown (10/1/1991 

to 9/30/1996) is used in the SWAT calibration. 

11
Model Calibration and Validation 

To effectively use the SWAT model for the evaluation of long-term storage, mobilization, 

and transport of contaminated sediment, both the flow and transport model components require 

model calibration (optimal estimation of their respective parameter values) and validation 

(comparative SWAT simulation to streamflow observed over an alternate time period for 

measurements not used in calibration). The period from 10/1/1991 to 9/30/1996 was used for 

calibration, and the period from 10/1/1996 to 9/30/2001 was available for model validation. The 

first year of each simulation was not included in assessing the calibration because that year was 

needed as an initialization period to reduce the effect of the assumed initial condition specified for 

soil moisture. 

The Aries River basin SWAT model calibration consisted of manually changing selected 

parameter values. For example, the magnitude of direct runoff in the model was controlled using 

the curve number parameter (CN) specific for each hydrologic response unit. In addition to 

adjusting the CN values, the hydraulic conductivities were increased, generally by an order of 

magnitude. After calibrating to the runoff peaks in each model, attempts were made to match 

simulated base flow to observed base flow by modifying values of parameter groups such as 

ground-water delay, infiltration, and soil capacity. Following these adjustments, the magnitude and 

temporal distribution of some streamflow peaks were similar to those of the observed discharge 

(fig. 8). The calibration progress following these changes to model parameter values was monitored 

by evaluating several statistical criteria such as a least-squares objective function that was updated 

following each simulation (flow_sep.xls).

12
Table 4. Summary of available climate and streamflow data for Aries River basin. 

Station Location Measurement Frequency Period of record Number Filename 

 type Begin End years 

Abrud Aries 

River Precipitation daily 1993 2001 9 Abrud.xls 

Mainstem 2002 1,2 mos Abrud.xls 

Streamflow daily 2003 2004 2 Abrud.xls 

Temperature daily NONE NONE NONE NONE 

 Radiation daily NONE NONE NONE NONE 

Albac Aries Precipitation daily 1989 2004 15 Albac.xls 

River Streamflow daily 1989 2004 15 Albac.xls 

Mainstem Radiation daily 1989,90,92,93,94 5 Albac.xls 

Temperature daily 2000 2004 5 Albac.xls 

 Solid flows daily 1991,94,95-2004 11 Albac.xls 

Buru Aries Precipitation daily 1978 2004 26 Aries_buru.xls 

River Streamflow daily 1978 2004 26 Aries_buru.xls 

Mainstem Termperature daily 2000 2004 5 Aries_buru.xls 

Radiation daily 1986 2004 19 Aries_buru.xls 

Solid flows daily NONE NONE NONE NONE 

Aries Precipitation daily 1950 1955 6 

Baia de 

Baia de Aries Aries.xls 

River Streamflow daily 1950 1955 6 

Baia de 

Aries.xls 

Mainstem Precipitation daily 1978 2004 26 

Baia de 

Aries.xls 

Streamflow daily 1978 2004 26 

Baia de 

Aries.xls 

Temperature daily 2001 2004 3 

Baia de 

Aries.xls 

Radiation daily NONE NONE NONE NONE 

 Solid flows daily NONE NONE NONE NONE 

Aries Precipitation daily 2001 2004 4 

Valea 

Valea Sesei Sesei.xls 

River Streamflow daily 2001 2004 4 

Valea 

Sesei.xls 

Tributary Temperature daily 2002 2004 3 

Valea 

Sesei.xls 

Radiation daily NONE NONE NONE NONE 

 Solid flows daily NONE NONE NONE NONE 

Turda Aries Precipitation daily 2003 2004 2 Aries_turda.xls 

River Streamflow daily 2003 2004 2 Aries_turda.xls 

Basin Temperature daily NONE NONE NONE NONE 

Outlet Radiation daily NONE NONE NONE NONE 

 Solid flows daily NONE NONE NONE NONE 

 Streamflow monthly 1951 2004 54 turda.xls 

13
Table 5. Summary of selected SWAT model files used in Aries River Basin 

simulations. These data are available on the accompanying CD. 

Model Aries River Basin 

Folder SWAT_model_files 

File location Path\filename.grd,shp,mxd,xls 

Spatial Data C:\BASINS\data\ 

DEM romania\DEM\srtm_41_03\project_extr1.grd 

Mask romania\Watershed\aries\shapes\Waters8.shp 

Streams romania\Watershed\aries\shapes\riv9.shp 

Sub-basins romania\Watershed\aries\shapes\watsub12.shp 

Gage stations romania\streamflow\aries_gages_Project.shp 

Land use 

location romania\Features\Land_cover2000_Albers.shp 

Soil type 

location romania\Features\feature_soil3.grd 

Arc .mxd file romania\swat_check_090606.mxd 

Soil parameters Usersoil.dbf 

Soil location Soils_ST70_Project.dbf 

Temporal Data 

Precipitation romania\ baia_P.dbf 

Solar radiation romania\ romania_monthly_PCP_nSolrad.xls 

Temperature romania\ baisaora_C.dbf 

Streamflow romania\ *.dbf 

romania\ *.dbf 

Coordinates 

and filenames 

romania\Climate.dbf 

Filenames used 

for weather 

data 

romania\Precip.dbf 

Summary Data 

Land use and 

soils \text\LanduseSoilRepSwat.txt 

Hydrologic 

response units \text\HruLanduseSoilRepSwat.txt 

Calibration 

Data 

Period of record 1991-1996 

flow_sep.xls 

macro to update flow_sep.xls data/figs. following each simulation Cntl-S 

14
0

0.5

1

1.5

2

2.5

3

Oct-91 Apr-92 Oct-92 Apr-93 Oct-93 Apr-94 Oct-94 Apr-95 Oct-95 Apr-96

water (mm)

0

20

40

60

80

100

120

140

160

Precipitation (mm)

Observed

Simulated (SWAT)

Precipitation

Figure 8. Observed versus simulated (SWAT) mean monthly streamflow discharge at 

Turda. 

Results 

The initial Aries River basin SWAT model could not be manually calibrated to accurately 

reproduce monthly streamflow discharge values observed at Turda (fig. 8). Whereas the least-squares 

objective function values (defined as the sum of squared differences between observed and simulated 

streamflow discharge) dropped with consecutive parameter value modifications, indicating a 

progressively better profile match, the final statistical criteria showed that the estimated parameter 

values were of poor quality. For example, one of the traditional statistical criteria used to determine the 

success of the model calibration process is a Nash-Sutcliffe-score (Nash and Sutcliffe, 1970) equal to or 

greater than 0.5. Following this model calibration process, the Nash-Sutcliffe score improved from a 

negative value to about 0.1. For this reason, no attempt was made to validate the streamflow discharge, 

to calibrate soil erosion and transport parameters, or to conduct predictive analysis (Friedel, 2005; 

2006). 

15
The inability to estimate reasonable model parameter values during the manual calibration 

process is attributed to the limited and poor data quality. Specifically, the climate and soils data used to 

represent basin processes were treated as spatially homogeneous, despite the fact that there were 67 soil 

types mapped. In addition, the correlation coefficient between precipitation recorded at two stations 

with different elevations (Baisaora, 1,134 m, and Baia de Aries, 543 m) was weak (0.2), also indicating 

heterogeneous spatial processes (fig. 9) not accounted for in the model. 

Another likely factor contributing to the poor match between observed and simulated 

streamflow discharge is the comparatively limited streamflow information available to calibrate the 

model when using only the Turda discharge record. Note how the correlation coefficients decrease 

when comparing the complete period of record (table 6) to the 2004 water year (table 7). This 

decrease shows temporal heterogeneity and the importance of calibration using multiple stations 

and longer periods of record. In addition, the intermediate correlations between gage stations not 

including Valea Sesei and Turda probably reflect similar precipitation but differences in local 

surface-water withdrawals for public consumption. The poor correlation of Valea Sesei discharge 

with other gage sites can be attributed to the controlled source outlet of mine discharge from the 

tailings pond. The decreased correlation between gage records upstream with Turda is uncertain. 

The poor correlation between streamflow records at Turda with the other gage stations underscores 

the importance of using multiple gage sites for calibration of our model parameters. To reach the 

ultimate goal of understanding long-term storage, mobilization, and transport of contaminated 

sediment following failure of a mine tailings dam, additional model refinements and calibration 

should be undertaken. The following section provides suggested improvements in order of their 

importance. 

16
0

10

20

30

40

10/1/1990

10/15/1990

10/29/1990

11/12/1990

11/26/1990

12/10/1990

12/24/1990

1/7/1991

1/21/1991

2/4/1991

2/18/1991

3/4/1991

3/18/1991

4/1/1991

4/15/1991

4/29/1991

5/13/1991

5/27/1991

6/10/1991

6/24/1991

7/8/1991

7/22/1991

8/5/1991

8/19/1991

9/2/1991

9/16/1991

9/30/1991

Precip (mm)

Baisora

Baia de Aries

Figure 9. Comparison of precipitation at monitoring stations at Baisaora (latitude: 

46.53300 N, longitude: 23.31700

 E, elevation: 1356 m) and Baia de Aries (latitude: 

46.38180 N, longitude: 23.28340 E, elevation: 483 m) from 10/1/1990 to 10/30/1991. The 

correlation coefficient over complete record was poor (0.2), showing spatial heterogeneity. 

Table 6. Correlation matrix for streamflow between gage stations (in order downstream 

from headwaters) over their total period record in the Aries River basin. High correlation 

coefficients are bolded, showing a strong relation between stations. 

 Albac Abrud Baia de Aries Buru Turda 

Albac 1.00 

Abrud 0.79 1.00 

Baia de Aries 0.92 0.89 1.00 

Buru 0.88 0.89 0.96 1.00 

Turda 0.30 0.25 0.31 0.32 1.00 

17
Table 7. Correlation matrix for median streamflow discharge between gage stations in 

Aries River basin, 2004. Intermediate correlation coefficients are bolded (there were no 

high correlation coefficients), showing a relation between stations. 

 Location Mainstem Mainstem Tributary Mainstem Mainstem Tributary 

Location Station Albac Abrud V. Sesei 

Baia de 

Aries Buru Turda 

Mainstem Albac 1 

Mainstem Abrud 0.43 1 

Mainstem V. Sesei 0.55 0.42 1 

Baia de 

Mainstem Aries 0.55 0.23 0.29 1 

Tributary Buru 0.22 0.44 0.28 0.62 1 

Tributary Turda 0.00 -0.26 0.09 0.25 -0.23 1 

Suggestions for Model Improvement 

Model input 

Spatial precipitation and temperature 

Presently, the climate information over the calibration period of record is represented by a 

temperature/time series and precipitation/time series from two different locations and elevations. 

Because spatial climate information has a first-order influence on model uncertainty, introducing 

spatial precipitation and temperature time series would improve the parameter estimation process, 

resulting in a better match between observed and simulated streamflow. One suggestion is to derive 

time series using the downscaled climate approach described by Hay and others (2003), and Clark 

and Hay (2005). In this approach, climate data from the NOAA website for about 35 Romanian 

meteorological stations (fig. 10), representing various elevations across the Aries River Basin, 

would be used to statistically derive spatial and temporal precipitation and temperature data sets for 

each model sub-basin. Because these data sets would represent climate information at the centroid 

of each model sub-basin, these time series could be used in the Aries River basin SWAT model for 

improved model calibration, simulation, and predictive analysis. 

18
Spatial soil parameters 

Because there was no soil information available during the initial model construction phase, 

the initial model calibration uses 67 basin soil types (fig. 5), but all have the same parameter values 

(homogeneous). After this initial model was prepared, new soil parameter information was 

provided to the team. Consequently, an obvious suggestion is to update the soil model parameters 

using values from this published soil information to account for spatial variation (heterogeneity) 

across the Aries River basin. Whereas introducing soil heterogeneity into the model will 

undoubtedly improve the estimated parameter values, the manual model calibration process will 

take longer. 

Point sources and sinks 

Another suggestion to improve the model is to find and introduce information on the 

location and magnitude of streamflow withdrawal and return flows within the basin. 

N

0 15 km 

Figure 10. Stations (triangles) for which NOAA has climate data available for estimating 

sub-basin time series using a downscaled approach. Blue lines represent streams in 

surrounding basins. 

19
Model calibration 

Streamflow 

Given the level of implied heterogeneity discussed in the model input and the lack of 

correlation between streamflow gage stations, a two-tiered calibration strategy is suggested. First, 

the calibration of parameter values should be based on the long-period (1951-2003) monthly 

median streamflow at Turda. Second, these estimated parameter values should be used as the initial 

parameter values for calibration using daily streamflow discharge values over various periods at the 

seven primary gage stations (Albac, Abrud, Campeni, Valea Sesei, Baia de Aries, Buru, and 

Turda). 

Validation 

For the models to be accepted as reasonable representations of hydrologic processes in the 

Aries River basin, the simulated hydrographs should be similar to the observed hydrographs and 

the estimated mass balances should be consistent with the long-term means noted previously. The 

Nash-Sutcliffe scores for the calibration and validation periods should be greater than 0.5. 

Sensitivity Analysis and Nonlinear Regression 

The introduction of heterogeneity into the SWAT model will increase the number of soil 

parameters by more than an order of magnitude. For example, instead of one set of 10 primary soil 

parameters there will be 67 sets, giving 670 soil parameter values that may require calibration. This 

number of parameters is an intractable problem for manual calibration. One suggestion is to apply a 

nonlinear regression algorithm, such as described by Doherty (2004), to calibrate the model 

parameter values. One advantage of using nonlinear regression to calibrate the model is the ability 

for a direct evaluation of parameter sensitivities and for eigenvector analysis (Friedel, 2005). In 

using this approach, the insensitive parameters can be identified and removed from the calibration 

process, thereby reducing model uncertainty. Another advantage is the possibility of 

simultaneously calibrating all parameter values in a one-tiered approach, using all of the 

streamflow measurements. 

20
Predictive analysis 

When model calibration is conducted mathematically, the process leads to the estimation of 

an optimal set of parameter values that will be non-unique (Friedel, 2006). For this reason, the 

simulation for water or sediment transport will represent one of an infinite number of possible 

outcomes; therefore, computing the minimum and maximum limits of nonlinear uncertainty for a 

prediction is suggested. The ability to place limits on the prediction uncertainty is inherently 

stronger than simply testing the model validity using a traditional split-sample approach (Friedel, 

2005). 

Summary and Conclusions 

The initial Aries River Basin SWAT model could not be manually calibrated to accurately 

reproduce monthly streamflow discharge values observed at the Turda gage station. The inability to 

estimate reasonable model parameter values during the manual calibration process is attributed to 

three issues. First, the available soils and precipitation data at the time of this study was spatially 

limited and of poor quality. Second, the streamflow information content used to calibrate the model 

was limited to the Turda discharge record. Third, the manual calibration process is time consuming 

and can not arrive at an optimal set of parameter values. Given the level of implied heterogeneity 

discussed in model input and the lack of correlation between streamflow gage stations, a two-tiered 

calibration strategy is suggested for model improvement. 

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