Using equilibrium temperature to assess thermal ...

USING EQUILIBRIUM TEMPERATURE TO ASSESS THERMAL DISTURBANCES IN RIVERS Buendía, C.1,2*, Sabater, S.1,3, Palau, A.2,4, Batalla, R.J.1,2,5, Marcé, R.1

1

Catalan Institute for Water Research -ICRA, Scientific and Technological Park of the University of Girona,

Emili Grahit 101, H2O Building, 17003 Girona, Catalonia, Spain. 2

Fluvial Dynamics Research Group-RIUS, University of Lleida, Catalonia, Spain

3

Institute of Aquatic Ecology, University of Girona, Campus Montilivi s/n, 17003 Girona, Catalonia, Spain

4

Department of Environment and Climate Change of Spain and Portugal, ENDESA SA, Crta. Tarragona km 89,

25191 Lleida, Catalonia, Spain 5

Department of Environment and Soil Sciences, University of Lleida, Alcalde Rovira Roure 191, 25198 Lleida,

Catalonia, Spain

(*)

Corresponding author: [email protected]

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/hyp.10489

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Abstract

Flow regulation is widely known to modify the thermal regime of rivers. Here we examine the sensitivity of an empirical approach, the Equilibrium Temperature Concept (ETC), to detect both the effects of hydraulic infrastructures on the annual thermal cycle and the recovery of the thermal equilibrium with the atmosphere. Analysis was undertaken in a Pyrenean river (the Noguera Pallaresa, Ebro basin) affected by a series of reservoirs and hydropower plants. Equilibrium temperature (Te) is defined as the water temperature (Tw) at which the sum of all heat fluxes is zero. Based on the assumption of a linear relationship between Te and Tw, we identified changes in the Te-Tw regression slope, used as an indicator of a thermal alteration in river flow. We also assessed the magnitude of the alteration by examining the regression slope and its statistical significance. Variations in the regression parameters were used as indicators of the influence of factors other than atmospheric conditions on water temperature. Observed Tw showed a linear relationship with Te at all river stations. However, the slopes of the Te-Tw relationship appeared to be lower in the reaches downstream from hydraulic infrastructures, particularly below large dams. A seasonal analysis indicated that Te-Tw relationships had higher slopes and lower p-values during autumn, while no significant differences were found at other seasons. Although thermal characteristics did not strongly depend on atmospheric conditions downstream of hydraulic infrastructures, the river recovered to pre-alteration conditions with distance downstream, indicating the natural tendency of water to attain thermal equilibrium with the atmosphere. Accepting associated uncertainties, mostly due to the quality of the data and the lack of consideration of other factors influencing the thermal regime (e.g. discharge), ETC appears to be a simple and effective method to identify thermal alterations in regulated rivers.

Keywords: Water temperature, equilibrium temperature, thermal regime, dams, hydropower, Noguera Pallaresa, Ebro basin


1. INTRODUCTION The alteration of thermal regimes has important consequences for the quality and functioning of fluvial ecosystems (e.g. Webb, 1996; Langan et al., 2001). Water temperatures fluctuate both spatially and temporally due to natural factors such as weather conditions and basin hydrogeological inputs (e.g. Imholt et al., 2013). Since water temperature is to a large extent weather-driven, there is a good relationship between air temperature and water temperature in running waters (e.g. Stefan and Preud’Homme, 1993; Pilgrim et al., 1998; Caissie et al., 1998, Caissie, 2006; Johnson et al., 2014). Nevertheless, anthropogenic factors (e.g. reservoirs, water diversions, land use) influence the natural pattern of temperature of a stream reach. Some of these alterations impose a discontinuity in the natural longitudinal (downstream) dimension of fluvial ecosystems, including water temperature (e.g. Standford and Ward, 2001; Prats et al., 2012). Of particular interest are the effects of dams and other hydropower infrastructures on water temperature. Water bodies created by such infrastructures have a very high thermal inertia, which can lead to substantial changes in the thermal regime in the downstream reaches (Prats et al., 2010). For example, in Northern, temperate rivers, water released from a dam during summer months typically shows a lower temperature than that in the river upstream, while the opposite tendency may be observed during winter (e.g. Gu et al., 1998). However, the effects on water temperature are spatially transitory, since the river tends to regain the equilibrium with the atmosphere downstream from the disturbance (e.g. Dolz et al., 1994; Bogan et al., 2003). Studies assessing thermal alterations in rivers and streams normally base their conclusions on the comparison of sections upstream and downstream from the alteration (e.g. Raddum et al., 2008; Prats et al., 2012). However, it is also possible to compare altered water temperatures with a reference value that is specific to the place and time of measurement. Using a reference value offers a new assessment tool when (as in many cases) the water temperature before-after the thermal disturbance is not monitored or only low-frequency data are available. In this context, the equilibrium temperature (Te) can be used as a reference to assess alterations in the natural thermal regime of rivers. The Equilibrium Temperature Concept (hereafter ETC) was first introduced by Edinger et al. (1968) and it is defined as the temperature that a water body reaches when the sum of the heat fluxes across the airwater interface equals zero. ETC has been demonstrated to be a simple yet useful and effective modelling tool to predict river water temperatures and assess alterations (e.g. Mohseni and Stefan, 1999; Bogan et al., 2003; Caissie et al., 2005; Marcé and Armengol, 2008; Prats et al., 2012). Although air temperature has often been related to water temperature (Webb, 1987; Erickson and Stefan, 2000), and despite it is an easily available and comprehensible weather parameter, according to Bogan et al., (2003): “it is insufficient to quantify all heat transfer processes through a water surface (e.g. solar radiation and evaporative cooling)”. Alternatively, Te represents in a more accurate


way the mechanisms affecting water temperature since it accounts for all heat transfers across the airwater interface (Caissie et al., 2005). The aim of this paper is to assess the ability of the ETC to detect thermal alterations downstream from dams and other hydraulic infrastructures (i.e. hydropower plants). The study was conducted in the Noguera Pallaresa (Ebro basin, NE Spain); a river draining an important part of the Central Pyrenees. For this purpose, we use available low-frequency and high frequency water temperature data from a series of water quality stations in the basin, and assess how the relationship between measured water temperature (Tw) and equilibrium temperature (Te) is affected by hydropower facilities. Changes in the Te-Tw relationship, represented by changes in the slope of the linear relationship, can be used as an indicator of alteration of the natural thermal regime of a river (Bogan et al., 2003). To further confirm the sensitivity of the ETC, we then apply the same approach using continuous Tw data from a study reach containing a reservoir and a hydropower plant. We hypothesize that the Te-Tw relationship (as expressed by the regression slope) will vary in relation to: (i) the distance from the hydraulic infrastructure; for this, we assume that the river will recover its natural thermal regime with distance downstream de alteration (and hence show increasing Te-Tw regression slopes); (ii) the size of the infrastructure i.e. Considering that the larger the infrastructure, the larger the residence time of the water, hence the thermal alteration of the water body; and (iii) the season of the year i.e. We assume that Te-Tw relationship will also vary throughout the year due to the marked differences found between the temperature of the water stored in the reservoirs and the river reaches downstream; such difference is likely to be larger during the summer, particularly if water is released from the cold bottom layers.

2. MATERIALS AND METHODS

2.1. Study Area The Noguera Pallaresa basin is located in the north east of the Iberian Peninsula (Figure 1). It belongs to the Ebro basin and occupies an area of 2,800 km2. Elevation ranges from 2,906 m a.s.l. at the headwaters to 300 m at the catchment outlet (where it flows into the River Segre, in turn the main tributary of the Ebro). Mean annual water yield is 1,330 hm3 (equating to a mean runoff of 475 mm/yr); the river’s hydrology follows a snow-rain fed regime with most runoff occurring between May and July. However, the flow regime has been highly modified during the 20th century, mostly for hydropower (hereafter HP) production. A reservoir chain formed by three large reservoirs in the mainstem river impounds ca. 1/3 of the basin’s annual runoff. These reservoirs are Talarn, built in 1916 and with an original capacity of 258 hm3; followed by Terradets (1935, 33 hm3) and Camarasa (1912, 163 hm3), the latest located in the downstream end of the catchment close to the confluence with the Segre (Figure 1). A total of 29 HP plants are located in the catchment, with a total capacity of 1,040 MW. A number of small reservoirs and weirs, mainly used for HP production, are located in the headwaters and in the main tributaries of the river. Altogether, this makes the basin one of the most


important HP networks in the Ebro basin. The catchment is relatively undisturbed in terms of land use. For example, 83% of the basin is occupied by woodland and grassland, while just 9% is dedicated to agriculture; the remaining 8% encompasses other uses, such as urban and industrial areas as well as various water bodies and rock outcrops.

2.2. Data acquisition Meteorological data were provided by the Catalan Meteorological Service and included hourly data (mostly since 1996) for air temperature (ºC), solar radiation (W m-2), relative humidity (%) and wind speed (km h-1) from 12 meteorological stations (Figure 1). These data were used for the computation of Te. In turn, Tw data was provided by the Ebro Water Authorities and the Catalan Water Agency (CHE and ACA respectively). Our study included 16 points located along the mainstem of the Noguera Pallaresa (from the headwaters to the outlet of the catchment, Figure 1) and in the main tributaries (e.g. Noguera de Cardós and Flamisell). These points belong to the official monitoring network of the water authorities (CHE and ACA), where Tw is measured manually by means of a multi-parameter probe (WTW Multi 340i; precision of 0.1ºC). Data collected in the field goes through a process of verification. However, we acknowledge that some bias in the data may arise due to the inherent characteristics of the dataset: temperature is measured at uneven time intervals (one measure per month approximately) and only discrete (i.e. punctual) values of Tw are obtained. Available water temperature data varied from 20 to 107 values per station, with ca. 2/3 of the stations having a dataset with more than 50 values (Table 1). Additionally, Tw was continuously measured in a 7 km reach of the Noguera Pallaresa close to the HP plant of Esterri (Figure 1c). Five sites along this reach were instrumented strategically to further assess the ability of ETC to detect the combined effects of the Borén Reservoir (with an impoundment capacity of ~ 1hm3) and an associated HP plant located further downstream. Water in this HP plant comes mainly from the Borén Reservoir, although there is also a water uptake in the Unarre (Figure 1c for location details). The uppermost monitoring site was located 500 m upstream of Borén and was considered a reference location (i.e. no HP infrastructures are present upstream); the lowermost site was located 1 km downstream from the HP plant (Figure 1c) just after the confluence with one of the main tributaries (the River Unarre). Tw measures were taken in the five sites at 5-min intervals for a period of 3 months (mid-August to mid-November 2012) by means of water stage sensor/loggers (TruTrack® WT-HR and Levelogger®, precision of 0. 3ºC).

2.3. Equilibrium temperature The ETC assumes that of all the processes that affect Tw, heat exchange across the air-water interface is the most important (Edinger et al., 1968; Sinokrot and Stefan, 1993). Te was originally described as the Tw at which net heat exchange across the water surface is zero (Edinger et al., 1968; see Equation 1). When Te is used in deterministic stream temperature models instead of air temperature (Ta), the


relationship with Tw is assumed to be linear over the entire temperature range (above 0ºC as per Bogan et al., 2003). Thus, this concept is based on a linear relationship between Te and Ta and neglects other heat exchange processes (e.g. conductive heat flux into or out of the riverbed). Although ETC is an empirical approach, it has been proved to be a valid method for predicting Tw (Dingman, 1972; Mohseni et al., 1999; Bogan et al., 2003; Caissie et al., 2005). For the estimation of Te we used the approach of Caissie et al., (2005), as described below. Te can be calculated as a function of meteorological data when the net heat flux (Ht; MJ m-2 day-1) is set at zero (i.e. summing to zero all non-advective heat exchanges): (1) where Hs is the absorbed solar radiation or net short-wave radiation, Hl is the net long-wave radiation, He is the evaporative heat flux, and Hc is the convective heat transfer of the water body all terms in MJ m-2 day-1. Equation (1) can be further reduced to: (2) Equation 2 shows that Te can ultimately be reduced to a function of Hs (solar radiation), Ta (air temperature) and Td (dew point temperature). All other coefficients largely depend on V (wind speed): (3) (4) SF is the riparian or shading factor (from 0 to 1). V is the wind speed (km h-1), ŋ is the slope of the linear vapour pressure approximation (which refers to the relationship between the differences in saturated vapour pressure at the water temperature and the air water temperature (in mm Hg), and the differences in water and dew point temperatures (ºC); as per Edinger et al., 1968), (5) is the atmospheric emissivity calculated from cloudiness (i.e. 0=clear sky and 1= total cloud cover) and the air water vapour pressure (ea, in mm Hg, as per Morin and Couillard, 1990) (6) (7) For our calculations, hourly data on air temperature, solar radiation, relative humidity and wind speed were obtained from the nearest weather station to respective sample points. Cloudiness (Ca) was calculated following Martin and McCutcheon (1999) using the ratio of the measured solar radiation at the weather station and the theoretical incident solar radiation (Cl, cloud cover): Ca=1-0.65Cl2.


Shading was quantified visually as the percentage of the channel width covered by riparian vegetation at the reach where the water temperature measurements were taken. For this, we used orthorectified aerial photographs at a 0.5 m resolution available at the Cartographic Institute of Catalonia web page (www.icc.cat). Due to the lack of orthophotographs for all the years and seasons, a constant SF was used for each location, calculated from the photographs taken in 2012. This may introduce a source of error in the computation of SF due to the potential temporal trends and seasonal changes in vegetation cover (i.e. the latter linked to the presence of deciduous vegetation). Mean hourly meteorological data were used as inputs for the Te calculations for each river station and for each time for which Tw data were available. However, meteorological stations are sparse and some are located in high altitudes. For this reason, prior to the computation of Te, air temperatures were corrected for elevation differences between each monitoring location and the closest weather station. We used a gradient of -6.5ºC per 1,000 m of elevation, considered the average rate at which temperature decreases with increasing elevation (Maurer et al., 2002; Stahl et al., 2006). Several studies have reported that, in order to overcome inertial effects, mean daily values are the most appropriate timescale to assess the relationship between Te and Tw (Mohseni and Stefan., 1999; Bogan et al., 2003). Despite the fact that averaging temperatures over longer timescales (daily, weekly or monthly) reduces fluctuations, Bogan et al. (2003) found that Te and Tw relationship was linear at the three timescales. The nature of our Tw data across the basin did not permit the computation of mean daily, weekly or monthly values, and hence we used hourly time scales for this study. To test the validity of the method at the hourly time scale (considering punctual values collected at random time frequencies), we used the continuous dataset available in the study reach located in Esterri (Figure 1c). In order to use non-correlated (i.e. punctual) hourly Tw values in this test, we performed a resampling exercise by randomly selecting 100 values of Tw and the corresponding Te from each of the five continuous datasets available (one for each site). Results showed a significant linear relationship (p-value