Ocean Dynamics

, Volume 61, Issue 6, pp 753–765

Temporal and spatial variability in the Guadalquivir estuary: a challenge for real-time telemetry

Authors

    • Department of Ecology and Coastal ManagementInstituto de Ciencias Marinas de Andalucía ICMAN-CSIC
    • Grupo de Dinámica de Flujos AmbientalesUniversidad de Granada
  • Francisco Javier Gutiérrez
    • Department of Ecology and Coastal ManagementInstituto de Ciencias Marinas de Andalucía ICMAN-CSIC
    • Grupo de Dinámica de Flujos AmbientalesUniversidad de Granada
  • Manuel Díez-Minguito
    • Department of Ecology and Coastal ManagementInstituto de Ciencias Marinas de Andalucía ICMAN-CSIC
    • Grupo de Dinámica de Flujos AmbientalesUniversidad de Granada
  • Miguel Angel Losada
    • Department of Ecology and Coastal ManagementInstituto de Ciencias Marinas de Andalucía ICMAN-CSIC
    • Grupo de Dinámica de Flujos AmbientalesUniversidad de Granada
  • Javier Ruiz
    • Department of Ecology and Coastal ManagementInstituto de Ciencias Marinas de Andalucía ICMAN-CSIC
    • Grupo de Dinámica de Flujos AmbientalesUniversidad de Granada
Article

DOI: 10.1007/s10236-011-0379-6

Cite this article as:
Navarro, G., Gutiérrez, F.J., Díez-Minguito, M. et al. Ocean Dynamics (2011) 61: 753. doi:10.1007/s10236-011-0379-6
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  1. Multiparametric observation and analysis of the Sea

Abstract

Meteorological, hydrological, and hydrodynamic data for 3 years (2008–2010) have been used to document and explain the temporal and spatial variability of the physical–biogeochemical interactions in the Guadalquivir River Estuary. A real-time, remote monitoring network has been deployed along the course of the river between its mouth and Seville to study a broad range of temporal scales (semidiurnal, diurnal, fortnightly, and seasonal). This network consists of eight hydrological monitoring stations capable of measuring temperature, conductivity, dissolved oxygen, turbidity, and chlorophyll fluorescence at four depths. In addition, six stations have been deployed to study hydrodynamics, obtaining 20-cell water column current profiles, and there is a meteorological station at the river mouth providing data for understanding atmospheric interactions. Completing this data-gathering network, there are several moorings (tide gauges, current/wave sensors, and a thermistor chain) deployed in the estuary and river mouth. Various sources of physical forcing, such as wind, tide-associated currents, and river discharge, are responsible for the particular temporal and spatial patterns of turbidity and salinity found in the estuary. These variables force the distribution of biogeochemical variables, such as dissolved oxygen and chlorophyll fluorescence. In particular, episodes of elevated turbidity (when suspended particle matter concentration >3,000 mg/l) have been detected by the network, together with episodes of declining values of salinity and dissolved oxygen. All these patterns are related to river discharge and tidal dynamics (spring/neap and high/low tide).

Keywords

Guadalquivir estuaryReal-time remote monitoringSensor technologyTelemetryWater quality

1 Introduction

The Guadalquivir River estuary is located on the SW coast of the Iberian Peninsula (Fig. 1). The source of the river is in the Cazorla mountains at about 1,400 m above sea level, and it flows into the Gulf of Cadiz; its total length is 680 km and it drains a basin of 63,822 km2 in area (Granado-Lorencio 1991). The present Guadalquivir estuary is enclosed by the spits of Doñana and La Algaida, in the inland of which there is a large area of freshwater marshland of 140,000 ha. (Rodriguez-Ramirez and Yáñez-Camacho 2008). The estuary extends as far as the Alcalá del Río dam, 110 km upstream from the river mouth at Sanlucar de Barrameda (Fig. 1). This estuary presents several notable characteristics, including the largely protected estuary marshes forming part of the Doñana Natural and National Parks, which are a UNESCO-MAB Biosphere Reserve. The river between Sanlucar de Barrameda and Seville is unique, being the only navigable river in Spain; its navigation channel carries a relatively heavy traffic and has a minimum depth of 6.5 m, requiring annual dredging. Moreover, the estuary has undergone rapid agricultural, fisheries, and anthropogenic development, particularly in recent decades (Ruiz et al. 2009). The catchment area of Guadalquivir has recently increased the use of watering for olive trees, probably resulting in acute erosion and sediment loading to the river. On the other hand, the original salt marshes adjacent to the estuary have been transformed to grow rice that contributes significantly to nutrient loadings, and the amount of nutrients reaching the coastal fringe is very large (Navarrro et al. 2006; Prieto et al. 2009).
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Fig. 1

aInset, the area of interest located in the SW Iberian Peninsula; binset and main illustration, locations of the various stations of the RTRM, moorings, and water sampling points of the Agencia Andaluza del Agua in the Guadalquivir estuary and along the river. Other insets are photographs/drawings of various buoys: c the Don Isaias water quality station, no. 52; d the water dynamics station, no. 47; e the Salmedina buoy; f water quality station no. 9; and g water dynamics station no. 7

In spite of its importance, until very recently, only a few studies have been carried out in the estuary; these have focused mainly on fisheries, crustacean decapods (Baldó and Drake 2002; Fernández-Delgado et al. 2007; González-Ortegón et al. 2010), and chemical contamination following the Aznalcollar mining waste spill (Grimalt et al. 1999; Blasco et al. 1999). Three years ago, to obtain a better understanding of the physical and biological processes governing the estuary ecosystem, an interdisciplinary research program was established, with the following main goal: to “develop an integral method to diagnose and forecast the consequences of human actions on the Guadalquivir estuary.” To achieve this goal, a comprehensive monitoring program has been established in the estuary and river mouth. This program has comprised three main parts: the real-time remote monitoring (RTRM) network, the deployment of several moorings in the estuary and river mouth, and monthly cruises for water sampling. The stations of the network and moorings are strategically positioned at locations that are important for hydrological dynamics. The locations span the whole estuary but with increased spatial resolution and shorter distance between stations close to the mouth, where environmental gradients are more acute and dynamic (Fig. 1).

This monitoring program was set up in the summer of 2007 in the estuary and near the continental shelf and is providing detailed data for research, policy-makers, government agencies, and education/outreach applications on coastal and transition waters management. In this paper, we report on the technical layout of the various stations and on the experience gained of their performance during the last 3 years of operation. The paper is focused mainly on the RTRM network that has been operated in the study area by the Instituto de Ciencias Marinas de Andalucía (ICMAN). This network provides data with a high degree of temporal resolution within a Eulerian framework and has the advantage that constant surveillance can be carried out to rapidly detect any changes and trends in critical indicators. Remotely acquired data from continuous in situ monitoring provides important early warning information to decision-makers so that they can respond appropriately. RTRM technologies have emerged as an economically viable means of monitoring key hydrological parameters (Glasgow et al. 2004). The calibration/validation of the instruments is described in the “Results and discussion” section together with the approaches adopted to overcome the limitations that were encountered during the operational use of these stations. Also, in the “Results and discussion” section, we present a preliminary analysis of the data sets obtained with the network with the aim of understanding the temporal and spatial variability of the physical and biological processes that dominate in the estuary. Finally, in the “Conclusions” section, some conclusions about the RTRM are presented.

2 Materials and methods

2.1 Location

The Guadalquivir estuary (SW Spain: 36 45′–37 15′ N, 6 00′–6 22′ W) is a well-mixed system with a longitudinal salinity gradient (Vannéy Vanney 1970; Álvarez et al. 2001). The tidal influence extends up to the Alcalá del Río dam and the maximum tidal range for the Guadalquivir mouth is 3.86 m (Rodriguez-Ramirez and Yáñez-Camacho 2008). Between the river mouth and the Port of Seville, several navigation buoys (Fig. 1) are deployed by the Port Authority of Seville, and these have been transformed into environmental laboratories.

2.2 Monitoring program

The three parts that comprised the monitoring program were set up on different dates: monthly oceanographic cruises started in June 2007 and the installation of the moorings and RTRM stations started in March 2008 and at the beginning of 2008, respectively. The real-time telemetry system was aimed at providing online continuous meteorological, hydrographic, and water quality information. The water samples were used both to supplement the telemetered water quality information and to provide a cross-check on some of the other important parameters. To complete the monitoring program, daily data on discharges from the Alcala del Rio dam were obtained from the regional water management agency (Agencia Andaluza del Agua, Junta de Andalucía, http://www.juntadeandalucia.es/agenciadelagua/saih/). This agency also carried out several cruises in the estuary to measure hydrological parameters (conductivity, dissolved oxygen, and turbidity), and its water quality stations are situated along the estuary (Fig. 1). Moreover, there are some meteorological stations located in Sanlucar de Barrameda and Chipiona at the estuary mouth that we have used to validate data obtained by our meteorological station. In addition, RGB MODIS images have been downloaded from the NASA server (http://rapidfire.sci.gsfc.nasa.gov/). These images are processed and projected using MATLAB(c) software. Satellite imagery can resolve patterns of turbidity on a large spatial scale but is confined to surface data.

Water samples were collected each month at the field stations from June 2007. Parameters measured in the routine water sampling included: nutrient concentration (nitrite, nitrate, phosphate, and silicate) following JGOFS standards (UNESCO 1994), chlorophyll concentration using fluorometric methods following JGOFS protocols (UNESCO 1994), dissolved organic carbon and total nitrogen (TOC-VCPH, Shimadzu, Japan), dissolved oxygen (Winkler method), total alkalinity (Pérez and Fraga 1987), suspended particle matter (SPM) using the gravimetric method, phytoplankton, and zooplankton. Vertical profiles of temperature, salinity, dissolved oxygen, turbidity, and chlorophyll fluorescence were measured with a CTD-Seabird 19 probe with external sensor. Water samples were taken at the same depths as the pumping levels of the RTRM station to validate quality data from the RTRM.

Several moorings were deployed in the estuary to study the tidal regime and hydrodynamics. Seven tide gauges (Aqualogger 520PT and NKE-SP2T logger) were deployed on the bank of the river between Sanlucar de Barrameda and Seville (Fig. 1). Currents at 1-m intervals and surface waves were measured with an upward-looking Nortek model AWAC-AST acoustic Doppler profiler, which was put into operation in May 2008 at a depth of 16 m in the river mouth (Fig. 1). A thermistor chain was moored on the continental shelf nearby to study the seasonal stratification/mixing and to establish the boundary conditions for models. More details on the sensor equipment deployed in the moorings are given in Table 1.
Table 1

Sensor equipment installed in moorings

Tide gauges

Aqualogger 520 PT, between March and June 2008. sampling rate, 10 min

NKE SP2T logger between June 2008 and March 2009. Sampling rate, 10 min

Thermistor chain

RBR-Thermometrics, between November 2007 and August 2008. Sampling rate, 1 min

NKE-S2T. From June 2009. Sampling rate, 10 min

Currents/waves

AWAC-AST Nortek 1,000 kHz. Currents: sampling rate, 10 min; cell size, 1 m. Waves: sampling rate, 1 h

The RTRM network comprises three types of station: for water dynamics, six stations; for water quality, eight stations; and one meteorological station. These stations are installed on the navigation buoys positioned between the river mouth and Seville harbor for water dynamics and water quality and on the Salmedina buoy for meteorological monitoring (Fig. 1). The stations were designed for year-round operation, with planned maintenance quarterly. All stations were strategically positioned. The technologies employed offer several advantages over historical monitoring techniques: they streamline the data collection process, they potentially minimize human errors and time delays, they reduce the overall cost of data collection, and they significantly increase the quantity and potentially increase the quality of data obtained on temporal and spatial scales (Glasgow et al. 2004). Details of the technology applied in the construction of the prototype stations for water dynamics and water quality can be found in Gutierrez et al. (2009).

2.2.1 Meteorological station

The meteorological station was installed on the Salmedina buoy (Fig. 1e) located over the continental shelf off Chipiona (Fig. 1, map) in May 2008. The system comprises an array of sensors for meteorological measurements (air temperature, relative humidity, incident solar radiation, barometric pressure, and wind speed and direction). Meteorological data is collected as follows. Barometric pressure is obtained with a Young 61202 L barometer. Air temperature and relative humidity data are acquired via a Geonica STH-5031 instrument (Geonica, Spain). Wind speed and direction are obtained using a marine wind sensor (R.M. Young Wind Monitor). Incident solar radiation is collected by a pyranometer (model Licor Li200 LiCor Biosciences, Lincoln, NE, USA). Different measuring intervals can be selected but this station was designed to sample every second, and the average, maximum, and minimum values for a 10-min interval are sent to the laboratory by telemetry. A Geonica Hydrodata 2008CP datalogger serves as the central processing unit of the system and the energy supply is from a gel battery charged by a bank of three solar panels connected in parallel. Bi-directional communication between the meteorological station and the laboratory enables the instruments and sensors installed to be controlled and serviced remotely.

2.2.2 Water dynamics stations

Six stations have been deployed along the estuary (Fig. 1, map) since February 2008. Figure 1d, g also shows photographs of the transformed navigation buoy at a current telemetry station. These stations are equipped with a Nortek AS Aquadopp acoustic Doppler current profiler (ADP) operating at a frequency of 1,000 kHz. The deployment configuration has been set to a cell size of 1 m, with an integration period of 2 min and four profiles per hour. The ADP has been programmed independently from the telemetry module as the two modules work asynchronously. The ADP also measures temperature, head pressure, and pitch and roll. These parameters are used in real time to assess the quality of data. The ADP is controlled by a Geonica Hydrodata 2008CP datalogger, and the data are sent to the laboratory every 15 min by GPRS system. The energy supply to the ADP and datalogger is from a gel battery charged by a bank of three 45-W solar panels connected in parallel. The compass was calibrated following the manufacturer’s specifications. Information on the amount of suspended particles can be deduced from the intensity of the backscatter signal measured by the ADP (Hill et al. 2003).

2.2.3 Water quality stations

Eight stations have been deployed along the estuary (Fig. 1, map) since February 2008. Figure 1c, f also shows photographs of the transformed navigation buoys operating as water quality laboratories. This type of station comprises four modules: the power module (a bank of three 120-W solar panels connected in parallel, charging a gel battery), the hydraulic module (a SHURflo suction pump, flow meter, batch filter, and silicon piping), the measurement module, and the control module (a Geonica Hydrodata 2008CP datalogger). The functioning of this station is described in more detail in Gutierrez et al. (2009). Routine maintenance of the water quality stations was carried out quarterly and involved replacing the pumping module and cleaning the CTD and external sensors. When necessary, the anti-fouling cylinders (SBE A24173) inside the CTD were replaced.

The measurement module is comprised of a Seabird Electronics SBE16plus conductivity and temperature recorder with external sensors for dissolved oxygen (SBE43), a chlorophyll fluorometer (Turner Designs, model Cyclops-7), and a turbidimeter (Turner Designs, model Cyclops-7). The salinity (S) and density (ρ) are calculated from temperature, conductivity, and hydrostatic pressure (p) with the equations of state of seawater (UNESCO 1985). Measuring intervals can be selected, but the standard measuring rate is two cycles per hour.

During the first 9 months, the water quality stations took measurements at four depths (1-, 2-, 3-, and 4-m depth from the sea surface). After November 2008, measurements were only taken at a depth of 1 m because the estuary was well mixed and the maintenance cost could thus be reduced. Continuous monitoring generates more than 7,000 water quality records per day, accessible in near real-time.

Logged data were uploaded to a data acquisition computer located in the laboratory via GPRS communications and posted in graphical form on the website (http://www.guadalquivir.csic.es); this webpage is currently password-protected and therefore not accessible to the general public. Data acquisition programs were modified for the specific instrument packages.

3 Results and discussion

3.1 Calibration/validation program

The Guadalquivir estuary is characterized by high current velocities, high loadings of suspended matter (González-Ortegón et al. 2010), and high biological productivity (Ruiz and Navarro 2008). All of these characteristics affect the stability of the instruments, which have been deployed for extensive periods of time. Therefore, a calibration/validation program is required to monitor and maintain the quality of the data collected by the RMRT network during its operation.

3.1.1 Calibration program

ADP instruments (an AWAC-AST located at the river mouth and the several Aquapro’s deployed at water dynamics stations) and meteorological sensors were calibrated by their manufacturers. For the water dynamics stations, every compass was calibrated at the buoys to compensate for the spurious or residual magnetic effect. The tide gauges (Aqualogger 520 PT and NKE SP2T) and the thermistor sensor of the thermistor chain were also calibrated by their manufacturers.

Sensors for temperature (SBE16plus), conductivity (SBE16plus), and dissolved oxygen (SBE43) installed at the water quality stations were calibrated by the manufacturer, Seabird Electronics. Turbidity and chlorophyll fluorescence sensors (Turner Design's Cyclops) were calibrated in our laboratory as detailed in the following subsections.

High turbidity levels are very common in the Guadalquivir estuary (González-Ortegón et al. 2010). When turbidity levels are above 1,000 Formazin Nephelometric Unit (FNU), the response of the turbidity sensor becomes nonlinear and a detailed calibration of every sensor becomes necessary. For this purpose, a protocol was designed to characterize the response of the turbidity sensors up to about 8,000 FNU, using formazin dilutions from 400 to 4,000 FNU (Anderson, 2005) and milk dilutions up to 8,000 FNU. Figure 2a represents the response of each turbidity meter versus the fraction of milk dilution. Differences between sensors make it necessary to calibrate each sensor individually before being used. The relationship between the sensor response (volts) and the turbidity dilution (FNU) for sensor number 634 is represented in Fig. 2b, c and it has been fitted by a fourth-degree polynomial. We carried out this analysis for each of the nine turbidity sensors to establish the polynomial relationship between volts and turbidity standard units (FNU). The results of calibration experiments are presented in Table 2.
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Fig. 2

a Comparison between sensor response in volts and fraction of milk dilution for each turbidimeter. b Relationship between turbidity sensor response (in volts) for sensor 634 and fraction of milk dilution. The horizontal green line corresponds to 1.6094 V, which is 90% of the maximum saturation signal (∼1.7882 V, at ∼0.2 % dilution). The vertical green line corresponds to the fraction of dilution for that voltage (0.1325%). The black line represents the sensor response in volts (∼1.1512 V) for a calibrated formazin dilution of 4000 FNU. Assuming a linear relationship between fraction of milk dilution and turbidity level of this dilution, it is possible to apply a linear correspondence between turbidity (in FNU) and fraction of milk dilution in the range between 0 and 0.1325%. c Turbidity in FNU (calculated from fraction of milk dilution) versus maximum dynamic range of sensor signal. The fourth-degree polynomial fitting has been calculated from this dataset

Table 2

Fourth-degree polynomial calibration coefficients for turbidity sensor. Turbidity (FNU) = p1 * V3 + p2 * V2 + p3 * V + p4, where V is the turbidity sensor response in volts (Gain 1x). R2 is regression coefficient, Voffset is minimum sensor response in millivolts, Vmax is 90% of the sensor saturation signal, and Tmax represents the turbidity in FNU corresponding to Vmax

Sensor

p1

p2

p3

p4

R2

Voffset (mV)

Vmax (mV)

Tmax (FNU)

2100632

118.83

−54.73

1,107.82

−83.97

0.999

76

3.5008

8,600

2100633

593.16

−335.53

1,840.72

−124.99

0.999

68.7

2.1183

8,200

2100634

1,243.68

−420.99

2,460.51

−125.24

0.999

51.3

1.6094

8,200

2100635

229.49

−160.58

1,285.21

−96.15

0.999

75.4

2.9043

8,200

2100636

477.79

−262.97

1,702.19

−118.47

0.999

70.3

2.253

8,100

2100637

495.97

−163.21

1,600.33

−107.39

0.999

67.5

2.1783

8,100

2100638

416.76

−81.42

1,547.69

−99.30

0.999

64.3

2.312

8,500

2100639

1,716.89

−676.78

2,569.83

−141.33

0.999

55.7

1.4972

8,200

2100397

188.68

−209.86

1,460.40

−113.23

0.999

78.4

3.18

8,800

The dynamics of suspended particulate matter are important for the functioning of the ecosystems of the Guadalquivir estuary and coastal zone. These dynamics govern processes such as sediment transport and primary production, the latter being limited by the availability of light in the water column. To measure the SPM with the RTRM network, we have established a relationship between SPM (measured by gravimetric method) and turbidity (FNU units). This relationship for all water quality stations is depicted in Fig. 3.
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Fig. 3

Relationship between suspended particle matter (SPM, in mg/L) and turbidity levels (in FNU) in the Guadalquivir estuary (SPM = 1.6015 * turbidityCTD , R2 = 0.93, n = 25 samples from five stations)

With respect to chlorophyll fluorescence and due to the high turbidity levels found in the Guadalquivir estuary, turbidity has two primary effects on chlorophyll fluorescence readings: first, it may increase blank readings due to increased light scatter and, second, it may reduce the fluorescence reading due to light absorption. To calibrate the chlorophyll fluorometer in turbid water, we used the method proposed by the manufacturer (Turner Designs, Application Note S-0035), whereby we have simultaneously measured turbidity and fluorescence in estuary waters. We have demonstrated that fluorometers run correctly when the turbidity is lower than 1,500 FNU.

3.1.2 Validation program

To validate the quality of the data collected through the RTRM network, we have obtained external data as measured by other institutions (see “Materials and methods” section) and in our own monthly cruises (ICMAN) for comparison purposes. For example, Fig. 4 shows the time series of air temperature measured by our own meteorological station and by another station belonging to IFAPA, part of the Junta de Andalucía (http://www.juntadeandalucia.es/agriculturaypesca/ifapa/ria/). The correlation is very good for this and the rest of the parameters (data not shown).
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Fig. 4

Time series of air temperature. Blue dots correspond to data from our own meteorological station located on the Salmedina buoy (10-min intervals). Red and green dots correspond to daily mean data from the meteorological stations belonging to the Junta de Andalucía, located at Chipiona and Sanlucar de Barrameda, respectively

Data for currents from the water dynamics stations were validated with a different ADP (ADP-1,000 kHz SonTek) integrating GPS and bottom tracking. During a tidal cycle, we measured water velocity profiles near the buoy using the same equipment configuration (cell size, integration time, etc). The results show that the water velocity profiles recorded by the RTRM stations are measured correctly (data not shown).

Figure 5 shows an example of the validation of the parameters measured by several water quality stations. For this validation, we have used data from cruises carried out by the Agencia Andaluza del Agua (GQ) and our own monthly cruises (ICMAN). A comparison between the RTRM network and in situ measurement shows a good validation for all parameters. This figure shows clearly the advantage of the RTRM over cruises; information captured only by cruises is not enough to resolve all frequencies involved in the forcing of this estuary dynamics.
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Fig. 5

Real-time remote monitoring data (n > 40,000 observations at 30-min intervals) for temperature (a), conductivity (b), dissolved oxygen (c), turbidity (d), and chlorophyll fluorescence (e) measured by several water quality stations from March 2008 to August 2010. Yellow and red dots correspond to data from our own monthly cruises (ICMAN) and from the water sampling station of the Agencia Andaluza del Agua (GQ), respectively

3.2 General field operation of the RTRM

The RTRM network has been operating successfully, providing good quality data since its installation (Figs. 4 and 5). One criterion for time series quality is the availability of data. Figure 6 presents the availability of data for each station (water quality, a to h; water dynamics, i; and meteorological data, j). In June of 2008, all the instruments of all the water quality stations, except number 52, were transported to the laboratory for general maintenance. This work caused the 1-week gap observed in the time series at that time. To prevent the time series from being interrupted for such a long period by the general maintenance required in the following summers, the instruments removed from the stations and brought to the lab for maintenance were substituted by similar ones in the field, thus avoiding such big data gaps. However, some other data gaps occurred between maintenance dates: the main reason for data gaps at the water quality stations was suction loss at the pump module (filter saturation, premature wear of pump membrane). Another reason was that the stainless steel cases of the Cyclops fluorometers systematically became corroded after 20 months of operation and they had to be returned to the factory. A third problem was that the data reception pin at the male end connector of the Aquadopp cables started to fail: data continued to be received by the datalogger, but the operator was prevented from re-programming the equipment in the field and retrieving the full data files.
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Fig. 6

Bar chart of daily data availability (more than 97%). Panels ah represent each water quality station. Color indicates each parameter: Blue is temperature, red is conductivity, green is dissolved oxygen, yellow is turbidity, and cyan is chlorophyll fluorescence. Panel i is data availability for water dynamics stations. Color indicates each station: blue, red, green, yellow, cyan, and magenta are buoys 7, 11, 16, 20, 30, and 47, respectively. Panel j is data availability for the meteorological station. Color indicates each parameter: blue, red, green, yellow, and cyan are wind, temperature, radiation, barometric pressure, and relative humidity, respectively

Figure 6 shows that data availability can be considered very good since more than 97% of the total possible data was acquired. In addition, the data quality is sufficient in terms of accuracy, as is evident from Figs. 4 and 5. System reliability is guaranteed by the fact that measurement and analysis were still operational under severe conditions, including storms, high winds, spring tides, and huge river discharges. Results obtained during some of these extreme events will be analyzed, and they will be used to validate hydrodynamic and ecological models.

3.3 Multi-year time series data

This paper presents high-resolution, long-term hydrological and hydrodynamics measurements obtained at the Guadalquivir estuary. The data reveal the wide fluctuations of water quality parameters in highly dynamic tidal regimes. In addition, special events, such as a high river discharge, are captured (April–May 2008, March–April 2009, and December 2009; Fig. 5). These data are valuable for estimating the impact of such events on the biogeochemical dynamics in the Guadalquivir estuary. For instance, they can be used to validate model calculations. These topics will be discussed in more detail in a separate paper.

Most of the time, the tidal regime controls the morpho-hydrodynamics of the estuary (Fig. 7). Only high river discharges (Q > 1,000 m3/s) during the wet season are capable of reversing this situation, influencing free surface elevation, tidal currents (see Fig. 8), and sediment transport. The RTRM also diagnoses the transition between a tidally and a discharge-dominated regime in the estuary (Díez-Minguito et al. 2009). These results are particularly relevant for the estuary managers and public decision-makers.
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Fig. 7

Time series of the predicted tidal amplitude (a), daily water river discharge from the Alcala del Río dam (b), barometric pressure from the Salmedina buoy (c), air temperature from the Salmedina buoy (d), wind velocity (e) and direction (f) from the Salmedina buoy, and g water current velocity from water dynamics station no. 47 (negative, downstream current in the estuary) at 1-m depth

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Fig. 8

Time series of the predicted tidal amplitude (a), current at 1-m depth measured by the water dynamics station no. 47 (b), and daily river discharge from the Alcala del Río dam (c)

Over the 3-year period, the biogeochemical parameters clearly show several sources of variability. Figure 9 shows the time series of buoy 34 during the 2 years that the RTRM system was in operation. From the data, the importance of the tidal dynamics and periodic inputs of fresh water from the Alcala del Río dam can be clearly seen. We can observe the seasonal cycle of temperature with maxima in summer and minima in winter. Temperature monitoring is basic to understanding the current on the continental shelf (García-Lafuente et al. 2006; García-Lafuente and Ruiz 2007). Salinity shows higher short-term fluctuations than the water temperature, and salinity is much less dominated by seasonal factors, although there are high correlations with heavy discharges into the river from the Alcala del Río dam. The maxima of river discharges (April 2008, February 2009, January and March 2010) coincide with increases in turbidity and decreases in salinity, especially during the winter of 2009–2010 (Fig. 10). The salinity pattern in the estuary is very important since it plays a dominant role in determining the composition and spatial distribution of its aquatic communities (Drake et al. 2002; Cuesta et al. 2006; González-Ortegón et al. 2006). Therefore, time series measurements offer valuable possibilities for identifying trends in environmental parameters, e.g., seawater temperature and salinity, which have been presented above.
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Fig. 9

Time series of the predicted tidal amplitude (a); daily water river discharge from the Alcala del Río dam (b). Hydrological parameters measured by water quality station no. 34 at 1-m depth for temperature (c), salinity (d), dissolved oxygen (e), chlorophyll fluorescence (f) and turbidity (g)

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Fig. 10

Time series of the predicted tidal amplitude (a), daily water river discharge from the Alcala del Río dam (b), and salinity patterns from water quality stations (ci)

Discharges into the river, together with tidal dynamics, also affect the levels of dissolved oxygen and turbidity. During the 2 years of study, there were several dry and wet seasons, with minima and maxima, respectively, in the turbidity pattern. The importance of the turbidity pattern is that it affects many diverse variables, from primary production (Gonzalez-Ortegón et al. 2010) to tourism activity. During the dry season, normally the summer months, the turbidity pattern seems to follow the spring tides, as has been reported for other areas like the Tagus (Valente and Da Silva 2009) and the German Wadden sea (Bartholomä et al. 2009). In fact, the RTRM results enable all the mechanisms involved in the salinity and turbidity patterns to be differentiated. Figure 11 shows the time series of salinity and turbidity data from buoy 34 during the dry season in summer 2008. The salinity distribution is governed by tidal dynamics (the main harmonic is M2), whereas in the turbidity distribution harmonic components like M4, which are related to hydrodynamics, appear. Moreover, the turbidity pattern shows a fortnightly regime associated with spring tides, also related to estuary hydrodynamics. The dynamics of chlorophyll fluorescence is highly complex and does not appear to follow any clear seasonal pattern.
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Fig 11

Tidal amplitude (a, e), time series of salinity (b, f), turbidity (c, g) and power spectrum for salinity (d) and turbidity (h). Significant constituents are included

4 Conclusions

After more than 2 years of operation, the RTRM system described provides an effective means of monitoring estuarine Guadalquivir water, with high-temporal resolution. This platform is very important because estuarine ecosystems are characterized by high inherent spatio-temporal variability (Day et al. 1989). Reference has been made both to the mechanical modification of navigation buoys to convert them into valuable environmental laboratories and to the sensors and loggers shown to be adequate for reliably measuring the key meteorological, hydrodynamic, and hydrological parameters. These parameters are used by specialists to make decisions on maintenance and the operation of each station for monitoring water dynamics and water quality. The stations can be mounted without any alteration of the buoy, and this can be performed in situ in a few hours (two or three stations can be mounted per day). The modularity of the stations allows them to be adapted easily and deployed in other estuaries, ports, dikes, piers, etc., with minimal maintenance.

The high data availability and the good quality of the data obtained make RTRM a powerful tool for monitoring and analyzing the diverse processes that govern this kind of environment. Moreover, in this case, RTRM has been complemented with moorings and sampling cruises that provide additional information for understanding the estuary ecosystem. The limited information that can be captured by cruises alone is not enough for a full understanding of the estuary ecosystem. Another advantage of the RTRM network is that it has been shown to be capable of continuing to operate reliably during exceptionally hydrographic conditions, like a huge river discharge, high current velocities, and high water turbidity. Figure 12 shows some RGB MODIS images of the river plume before, during, and after a large river discharge. This plume has an enormous amplitude and accounts for the large quantity of suspended matter that the estuary contains. The RTRM network has proved to be an excellent platform for obtaining long-term hydrologic data irrespective of weather conditions.
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Fig. 12

Time series of the daily water river discharge from Alcala del Río dam (a) and turbidity patterns from water quality station number 34 (b). Shaded areas indicate the data corresponding to the dates of the RGB MODIS images presented in the bottom panel

RTRM enables a variety of types of study and application to be carried out, as demonstrated in the previous section. The sampling rates were 10, 15, and 30 min for meteorological, water dynamics, and water quality stations, respectively. These rates allow processes such as overtides (harmonics like M6 and M4) and inter-annual variability to be resolved. Numerical models have been applied to this large volume of high-frequency information to make forecasts of how ecosystems in the estuary are likely to develop. In addition, real-time remote monitoring could be used to generate early warnings for navigation, algal bloom, turbidity increases, etc. The data can also be sent automatically to other cooperating agencies.

This type of system would aid in the earlier forward planning of measures to mitigate undesirable events or changes. In the near future and given the modularity of the network, we hope to install a new type of biosensor (Altamirano et al. 2004) to detect and give immediate warnings of phytoplankton blooms, a crude oil sensor to warn about oil spills, and a nutrient analyser to study the eutrophication of the estuary. Despite the present economic context, the long-term continuity of the system is under consideration by regional administration given its diagnosing power, autonomous simplicity, and low-cost robustness.

Acknowledgements

This project was supported by the Autoridad Portuaria de Sevilla (APS) and by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía). MODIS images have been processed in the context of P09-RNM-4583 Project. Thanks are due to all the APS technicians and divers of AGUAYO S.L. who participated. The authors also thank, in particular, David Roque, Raúl García, Joaquín Pampin, and Antonio Moreno (ICMAN-CSIC) for their assistance.

Copyright information

© Springer-Verlag 2011