GeoJournal (2007) 70:281–288 DOI 10.1007/s10708-008-9134-z
A methodological approach to water quality assessment in an ungauged basin, Buenos Aires, Argentina Silvana Arreghini Æ Laura de Cabo Æ Rafael Seoane Æ Nicola´s Tomazin Æ Roberto Serafini Æ Alicia Fabrizio de Iorio
Published online: 15 April 2008 Ó Springer Science+Business Media B.V. 2008
Abstract The Reconquista River is one of the most polluted rivers in Latin America. This paper aims at identifying the dynamics of water quality in an area with low or ‘‘background’’ concentrations of pollutants within the Reconquista River system in order to better define levels of pollution in the main system. In S. Arreghini (&) R. Serafini A. F. de Iorio Departamento de Recursos Naturales y Ambiente, Facultad de Agronomı´a, Universidad de Buenos Aires, Av. San Martı´n, 4453, 1417 Buenos Aires, Argentina e-mail:
[email protected] A. F. de Iorio e-mail:
[email protected] S. Arreghini L. de Cabo R. Serafini Museo Argentino de Ciencias Naturales ‘‘Bernardino Rivadavia’’, Av. Angel Gallardo 470, 1405 Buenos Aires, Argentina L. de Cabo e-mail:
[email protected] R. Seoane N. Tomazin Instituto Nacional del Agua, AU Ezeiza-Can˜uelas, Tramo J. Newbery km 1.620, 1804 Buenos Aires, Argentina R. Seoane Departamento de Hidra´ulica, Facultad de Ingenierı´a, Universidad de Buenos Aires, Av. Las Heras 2214, 1127 Buenos Aires, Argentina R. Seoane National Research Council (CONICET), Buenos Aires, Argentina
order to describe the dynamics of water quality in the background area, we propose a methodology based on flow estimation with the instantaneous unit hydrograph model and on measurements of physical and chemical water variables under different hydrological conditions. Because of high dissolved oxygen and low ammonium and o-phosphate concentrations, the Arroyo Durazno, a tributary stream of the Reconquista River, is defined as a background area. When a storm event begins, the concentration of nitrates and the electrical conductivity diminish. An increase in dissolved organic carbon suggests an important input of carbon from hillslope runoff. The proportion of fulvic and humic acids also increases. On the receding limb of the hydrograph, nitrate concentration was lower than during maximum flow and organic carbon concentration remained high. This behavior, known as the ‘‘flushing effect’’, suggests that the soluble material accumulated in the drainage area during dry periods is transported to the stream by leaching or ‘‘lixiviation’’ and surface runoff, thus raising solute concentrations during the first few hours of the storm. Water quality changes rapidly, even in background areas, due to its dependence on the flow. The methodology followed in this paper can also be applied to other basins with similar characteristics. Due to the difficulty in defining baseline areas for surface waters, a knowledge of background water quality and its dynamics is essential for understanding pollution trends and anthropogenic impacts on rivers.
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282
Keywords Background area Background water quality Unit hydrograph Temperate river Hydrological model Ecohydrology Pollution Introduction Hydrologic cycle components, land use, soil properties, topography, and the activity of organisms in the drainage basin have an impact on the chemical composition of a water body. In recent years, attempts have been made to integrate knowledge of hydrological and biological processes at basin scale (Zalewski 2002). Identification and quantification of natural and anthropogenic impacts on the chemical composition of river water should be an important part of land and water management in a basin (Petts and Calow 1996). Water quality is a major issue in sustainable development, especially in humid areas of Latin America, where water problems relate more to quality preservation than to shortages. In Argentina, direct estimation of rainfall/flow data is difficult due to a lack of sufficient data from the basins. Therefore, the hydrologic model parameters needed to determine the flows that will be used in chemical data interpretation must be estimated indirectly with, for example, the instantaneous unit Fig. 1 Map of the Reconquista river basin
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hydrograph theory (IUH) (Clark 1945). A basin’s IUH makes it possible to obtain direct flow estimates with a certain level of associated uncertainty, such as the use of a constant value of the Curve Number obtained from the tables as a function of the basin’s soil type and vegetation cover (Soil Conservation Service 1972). The Reconquista River is a lowland river of the Pampa Ondulada in the great Plata Basin. Its main streambed is about 55 km long and drains an area of some 1670 km2 (Fig. 1). Forty-three percent of the land is used as natural pastureland for cattle grazing and for agriculture. The other 57% is urbanized (http://www.indec.mecon.ar). Almost 8% of the country’s population, more than 3 million people, lives in the basin. It comprises a critical area with respect to the conservation status of fresh water ecoregions in Latin America (Olson et al. 1998). Though many studies have assessed the water quality of this river (Castan˜e´ et al. 1998, de Cabo et al. 2000), there is no detailed information on the water quality of a background area, considering as background the natural condition of a system that may include some anthropic components and that is used as the starting point for monitoring activities (Edmunds et al. 2003). This paper seeks to identify a background area for a temperate lowland basin with no regular
GeoJournal (2007) 70:281–288
hydrometeorological records, the Reconquista River, and to describe the dynamics of water quality in that background area using a method of hydrograph estimation.
283
Statistical analysis The Kruskal–Wallis test was used to compare all the data sets, and two-sample comparisons were made using Kolmogorov–Smirnov test when deemed appropriate. A 5% level of significance was used.
Materials and methods Flow estimation Ten water samplings were undertaken in the Reconquista River basin between April 1996 and March 1998 at five sites along the main streambed—CAS, REY, GOR, SMT, BAN (Fig. 1)––and at two sites in the tributaries––DUR and CHO. Downstream from the Roggero Reservoir, where the tributaries meet, industrial and urbanization activities have inexorably led to an inflow of pollutants from point and non-point sources. In addition, 19 water samples were taken at DUR under different hydrological conditions, including a heavy storm (March 2001). Physical–chemical analysis The following measurements were taken in situ: dissolved oxygen (DO) using a YSI 51B oxymeter; pH using an Orion pH meter 250 A; electrical conductivity (EC) with a Luftman conductimeter; temperature; transparency (with a Secchi disk); and depth of the streambed. Triplicate surface water samples were filtered through Whatman GF/C filters, and sent to the laboratory at 4°C for analysis. Concentrations were determined using the following methods: soluble reactive phosphorus (SRP), nitrates (N–NO3 ), and nitrites (N–NO2 ) according to Strickland and Parsons (1972); ammonium (N–NH+4 ) and bicarbonates (HCO3 ) according to Mackereth et al. (1989); calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), sulphates (SO24 ) and chlorides (Cl-) according to the method of APHA (1992); suspended solids (SS), particulate organic carbon (POC) and total organic carbon (TOC) according to Golterman et al. (1978). The content of dissolved inorganic nitrogen (DIN) was calculated as the sum of ammonium, nitrates and nitrites. Dissolved organic carbon was determined as the difference between TOC and POC. Absorbance values at 250 and 365 nm were recorded for the calculation of E2/E3 and for estimating the humic and fulvic acid ratio in water (Peuravuori and Pihlaja 1997).
Due to a lack of flow data on the Arroyo Durazno, the direct flow corresponding to each rainfall event was estimated with the Instantaneous Unit Hydrograph (IUH) model. The method for estimating the hydrograph consists in calculating effective rainfall for each precipitation event by means of an infiltration method that uses the Curve Number and rainfall value (Soil Conservation Service 1972), and then using the IUH model (Clark 1945) for estimating the flow hydrograph for the stream. The associated uncertainty of the model is due to factors such as intrinsic parameter estimation, lack of information on spatial rainfall distribution, and lack of flow measurements to calibrate it. However, when only rainfall data are available, the IUH model is one of the most commonly used to estimate direct runoff from small basins. The software used was HEC1-HMS 2.0 (Hoggan 1989). The parameters needed to estimate rainfall-flow values are: basin concentration time tc (hours) and the storage constant K (hours), both estimated from the basin’s topographic characteristics. The parameters were calculated according to Wilson and Brown (1992) for use in basins with minimal hydrological data. Basin concentration time was estimated following Kirpich’s (1940) formula. In each sampling, flow (Q) was estimated in situ from channel cross-sectional area and stream velocity. To this end, the time it takes a floating object to travel a given distance was measured (Gordon et al. 1994). These results were compared with those estimated by the IUH model.
Results The highest DO median concentrations (p \ 0.01) were found at DUR and CAS; values lower than 4 mg/ l––the permissible limit for aquatic life protection (USEPA 1986)––were never recorded. Concentrations of SS, nitrates, nitrites, POC and TOC, and EC at both sites did not differ significantly (Table 1). Lower
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Table 1 Chemical and physical variables measured at different sites on the Reconquista river Site
DO (mg l-1)
DUR Median
6.9
EC (lS cm-1) 1891
SS (mg l-1) 33
N–NH+4 (lg l-1)
N–NO3 (lg l-1)
403
365
N–NO2 (lg l-1) 70
SRP (lg l-1)
POC (mg l-1)
366
1.3
TOC (mg l-1) 23.2
Min
4.8
352
8
43
117
46
67
0.4
3.8
Max
12.5
2600
111
1219
1038
108
684
3.1
51.8
n
10
CHO Median Min Max n CAS Median
10
10
10
10
10
10
1190
20
4716 *
320
114 *
917 *
0.4
362
8
1170
52
11
324
4.4
1533
70
10262
867
680
3606
9
10
10
10
10
10
1.8 *
10
10 2.10 * 0.8 5.4 10
10 24.1 8.8 36.9 10
7.4
962
54
311 *
378
30
600 *
1.5
Min
4.6
371
17
181
82
7
195
0.9
2.2
Max
9.8
2000
86
1192
1556
112
1161
2.5
29.0
n REY Median
10 4.5 *
9
10
10
10
10
10
1073
35
6323 *
775 *
322 *
1168 *
10
22.0
10
2.08 *
27.0
Min
2.4
973
19
2813
342
180
659
1.2
21.4
Max n
6.8 6
1357 5
610 6
14424 6
2218 6
443 6
1367 6
3.6 6
43.7 6
1.0 *
2.29 *
17.0
GOR Median
1217
31
8264 *
1287 *
390 *
2091 *
Min
0.4
526
17
2565
59
3
854
0.7
2.2
Max
7.8
2350
74
11047
3588
726
4164
4.6
33.7
n SMT Median Min Max n BAN Median
10
9
9
10
10
10
10
1550
72 *
10235 *
150
27
1905 *
0.0
890
44
7250
23
\4
732
0.4
2125
156
22459
530
1164
3477
9
10
10
10
10
10
0.2 *
10
10
6.56 *
52.68 *
4.3
24.4
22.3
80.8
10
10
1545
53 *
11067 *
47 *
20
1826 *
7.63 *
34.36 *
Min
0.0
889
29
6798
12
\4
858
4.3
23.8
Max
0.4
2118
119
14820
220
31
3735
12.4
50.8
9
10
10
10
10
10
10
10
n
0.3 *
10
10
* indicates significant differences between DUR and the other sites
median ammonium concentrations were detected at CAS, followed by those at DUR (Table 1). The highest concentrations were found at SMT and BAN. The low nitrate levels detected at CHO, SMT and BAN coincide with the lower DO levels that promote denitrification. The lowest concentration of o-phosphate was found at DUR (p \ 0.01). Suspended solids were significantly higher at SMT and BAN (p \ 0.01). According to these results, it seems that the best water quality of all sampled sites was that of DUR and CAS, but, since CAS is downstream from the Roggero Reservoir and land use there is mainly urban, we consider that DUR is the site that best represents background conditions for the Reconquista River.
123
The 360 km2-sub-basin of the Arroyo Durazno is 32 km long, and has the lowest population density in the basin. Sixty percent of the land is used as natural pastureland for cattle grazing, and 20% for agriculture (INDEC 1988). The main topographic and geo-morphological characteristics are: area ratio (RA), 3.4; bifurcation ratio (RB), 3.2; length ratio (RL), 1.7; length of the highestorder stream (LX), 26 km; and area of the watershed (AX), 257 km2. Relief consists of very flat plains and low hills (about 20 m a.s.l.) with a 0.05% slope and low-permeability, poorly drained soils. The section under study belongs to secondorder streams, according to the Strahler classification (Gordon et al. 1994).
GeoJournal (2007) 70:281–288
285
16 CN=50-81 14
CN=50-75 CN=60-81 CN=60-75 CN=55- 75
10
3
Direct flow (m /s)
12
8
The storm event between March 1st and 5th, 2001, produced 124 mm of rain. According to the basin’s characteristics, surface flow prevailed during the first hours of the event. Nitrate concentration and EC diminished, SRP and POC concentrations did not change, and DOC and the E2/E3 rate increased before peak flow (Fig. 3). On the receding limb of the hydrograph, nitrate concentration was lower than during peak flow. Concentrations of POC and DOC remained high. However, the E2/E3 rate did not change.
6
4
Discussion
2
Because baseline areas along many surface watercourses are difficult to define, knowledge of background water quality is essential for understanding pollution trends and human impact on rivers (Edmunds et al. 2003). In Argentina, runoff estimates cannot be made due to lack of sufficient rainfall and discharge data from the basins, some of which are slightly to seriously polluted. This is why we propose a methodological approach to water quality assessment in basins that satisfy the hypotheses of the IUH theory (Clark 1945). This methodology involves an analysis of land use and population density, physical and chemical characterization of river water, identification of a background area for the basin, rainfall/ flow modeling with the IUH, relationships between water quality and flow at the background site (water quality dynamics), and water quality monitoring. We compared different sets of data with the median instead of the mean. Several authors (such as Edmunds et al. 2003) recommend using the median because it is more robust and less affected by outliers. When compared to other sites in the Reconquista River basin, water quality at the Arroyo Durazno is most similar to that of natural environments. Rendina et al. (2001) determined that heavy metal mobility in the Arroyo Durazno is the lowest in the basin. As land is used mainly for agriculture and cattle-raising and population density is low, we identified DUR as the background site for the Reconquista River. With an indirect estimation technique, a good approximation was found between flow values determined by the IUH and values estimated at site for the rainfall/flow event. However, direct flow estimation was influenced by the CN variation. McCuen (2002)
53
45
49
41
37
33
29
25
21
17
13
9
5
1
0 Time (h)
Fig. 2 Hydrographs for the rainfall-runoff event observed in March 2001 using different CN values
Parameters used for the IUH of the Arroyo Durazno sub-basin were: tc= 27.8 h and K = 28.9 h. The impact of the Curve Number (CN) variation on direct flow estimation for the Arroyo Durazno was analyzed by means of five tests of the IUH model for the rainfall/flow event of March 2001, using different CN values, each associated with different previous humidity conditions (Fig. 2). The CN values used fall within the confidence intervals suggested by McCuen (2002) for basins of similar characteristics. The analysis of Fig. 2 indicates sensitivity of the form of the hydrographs estimated for the CN range under consideration. The CN values selected for the March 2001 event were 55 and 75, 76 h after the storm event began (Fig. 2). At the background site (DUR), the pH was slightly alkaline (about 8). Sodium was the prevailing cation (Na [[ Ca = Mg [ K) and bicarbonates the prevailing anion in the water (HCO3 [[ Cl [ SO4) (Table 2). Dissolved organic carbon was the main TOC fraction (75–95%). Under high flow conditions, concentrations of TOC and DOC increased, Secchi/ depth values decreased (Table 2), and the water was darker. Concentrations of nitrates, ammonium and major ions, and EC were significantly lower under high flow conditions.
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GeoJournal (2007) 70:281–288
Table 2 Chemical and physical variables measured under different hydrological conditions at DUR Low flow Median DO (mg l-1) Temperature (°C) EC (lS cm-1) Secchi:Depth (%) SS (mg l-1) pH
High flow Min
Max
n
Median
Min
Max
n
6.8
4.5
12.5
17
5.9
3.9
8.1
12
18.0
6.0
29.1
17
20.2
7.4
24.4
12
1650
850
2600
17
349
229
785
12
100
60
100
17
27
15
53
12
26
6
111
17
32
19
86
12
8.0
7.5
8.4
17
7.5
7.1
8.2
12
N–NH+4 (lg l-1)
88
19
1219
17
49
15
1205
12
-1 N–NO3 (lg l )
324
35
3121
17
116
11
402
12
41
3
108
17
25
0
108
12
235
70
684
17
211
118
561
N–NO2
-1
(lg l )
SRP (lg l-1) POC (mg l-1)
1.0
TOC (mg l-1)
17.2
Ca+2 (mg l-1)
33
0.1 3.8 12
2.7
17
1.2
37.9
17
34.6
45
17
6
0.4
3.1
12 12
6.9
67.4
12
3
13
12
Mg+2 (mg l-1)
21
11
33
17
4
2
6
12
Na+ (mg l-1) K+ (mg l-1)
259 15
198 12
476 18
17 17
68 10
22 8
193 14
12 12
-1 HCO3 (mg l )
539
411
745
17
178
118
528
12
81
46
183
17
14
1
70
12
174
95
317
17
22
12
44
-1 SO24 (mg l )
Cl- (mg l-1) Q (m3 s-1)
0.07
0.04
0.70
pointed out that although the CN is treated as a constant, it varies for each observed rainfall event, which means that the confidence intervals are inadequate to assess such variation. At the background site and under high- and lowflow conditions, the ionic composition of the riverwater is consistent with soil characteristics in the sub-basin, where almost 50% have a natric horizon. Data on chlorides are considered as good tracers for surface water source identification (Ribolzi et al. 2000). During dry periods, chloride concentrations and EC were similar to those determined by EASNE (1973) for groundwater, thus showing that under these hydrologic conditions the water table is the predominant source of stream water. The increase in TOC and DOC concentrations during high-flow conditions suggests a significant input of carbon from runoff. Meybeck (1982) reported high concentrations of TOC in other rivers with poorly drained areas. The main DOC fraction in water from the soil is represented by humic substances, as evidenced by the dark color of water during high-flow
123
17
3.85
2.67
8.60
12 12
conditions and the low Secchi/depth values. Also, the increase in the E2/E3 ratio shows an increase in the fulvic and humic acid proportion during the high-flow period. In the presence of fulvic acids, heavy metal solubility, mobility, environmental prevalence, and toxicity increase (Varshal et al. 2000), thus increasing pollution risk during high-flow conditions. Besides, nitrate, ammonium and major ion concentrations and EC decreased due to the dilution effect of the massive inflow of water into the stream. On the receding limb of the hydrograph, nitrate concentration was lower than during maximum flow and organic carbon concentration remained high. This behavior, known as the ‘‘flushing effect’’ (Mitchell et al. 1997), suggests that the soluble material accumulated in the drainage area during dry periods is transported to the stream by lixiviation and surface runoff, thus raising solute concentrations during the first storm hours. Water quality in rivers of low to moderate flow changes rapidly, even in background areas due to its dependence on the flow.
GeoJournal (2007) 70:281–288 0
Rain (mm)
Fig. 3 Hydrographs and physico-chemical variables corresponding to the rainfall-runoff event of March 2001
287
20
40
60 1,4
10
9
9
8
8
7
7
1
6
0,8
5 4 3
5 4
0,6
3
0,4
2
2
1
1
0
0 50
100
150
200
0,2
0
250
Discharge in situ
POC
10
40
1600
9
35
8
1400
8
1200
6
1000
5 800
4
600
3
d Discharge (m3 s-1)
7
6
25
5
20
4
15
3
2
2
1
200
1
0
0
0
50
100
150
200
30
7
400
250
10 5 0
50
EC
8
600
7 6
500
5
400
4
300
3
200
2
f
2,5
5
2
4
1,5
3
0
At the same time, a lack of rainfall/flow records in many parts of the world complicates the integration of hydrologic and geochemical observations. In view of this situation, our proposal here is a contribution towards an improved description of processes for proper surface water quality management based on eco-hydrological processes.
1
2 1
Rainfall event (hours) Discharge (IUH model) N-NO3
3
6
0 250
3,5
7
100 200
4
9
0 150
DOC
10
1 100
0 250
200
8 Discharge (m3 s-1)
700 N-NO3- and SRP (µg l -1)
800
9
50
150
Discharge (IUH model)
10
0
100
Rainfall event (hours)
Discharge (IUH model)
3 -1
0 250
1800
Rainfall event (hours)
Discharge (m s )
200
9
0
e
150
10
EC (µS cm -1)
Discharge (m3 s-1)
c
100
Rainfall event (hours) Discharge (IUH model)
Rainfall event (hours) Discharge (IUH model)
50
E2/E3
0
POC (mg l -1)
6
1,2
DOC (mg l-1)
b
10
Discharge (m3 s-1)
Discharge (m3 s-1)
a
0,5 0
50
100
150
200
0 250
Rainfall event (hours) SRP
Discharge (IUH model)
E2/E3
Acknowledgments The authors wish to acknowledge the logistic support of the Prefectura Naval Argentina during sampling operations.
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