SPATIO-TEMPORAL ANALYSIS OF SURFACE WATER TEMPERATURE IN A RESERVOIR AND ITS RELATION WITH WATER QUALITY IN A CLIMATE CHANGE CONTEXT Ferral A1,2., German A1,2., Beltramone1,2, Bonansea M2, Burgos Paci M2, Saunders de Carvalho L.3, Shimoni M.4, Roque M.5, Scavuzzo M1. 1- Instituto Gulich, UNC-CONAE, Argentina; 2-CONICET, Argentina; 3- Universidade Federal do Rio de Janeiro, Brazil; 4- Royal Militar Academy, Belgium; 5-Administración Provincial de Recursos Hídricos, Córdoba, Argentina Introduction ● ● ● Surface Water temperature (SWT) influences most of the physical, chemical and biological properties of water bodies (Bonansea et al, 2019). Water temperature is included as a variable to calculate Water Quality Indexes (WQI) (Pesce & Wundeling, 2000; Uddin et al, 2021) To ensure availability and sustainable management of water and sanitation for all is one of the Sustainable Development Goals (SDGs) 2030. By modifying environmental temperature, molecules can achieve Activation Energy to react and also to find new equilibrium states https://www.bbc.co.uk/bitesize/ guides/zwfr2nb/revision/3 Uddin et al, 2021 https://www.un.org/ sustainabledevelopment/ 2 Introduction ● ● ● Lakes are sentinels of climate change and SWT is a key variable because it impacts in thermal stratification, algal bloom ocurrence, bottom hipoxia, dissolved gases.(Adrian et al 2009, Williamson et al 2014) SWT can be used as an input in water quality risk models EOXposure The aim of this work is to propose a valid method to perform accurate water surface temperature measurements from space (SWT), by LANDSAT 8-TIRS and insitu data, and relate them with psychical and biological perturbations which occur in San Roque reservoir, Córdoba, Argentina. 3 Study Zone San Roque Córdoba, Argentina 31o 22o 56" S 64o 27o 56" W -Built in 1888 and expanded in 1944. -It was the largest artificial lake in the world and the first Argentine reservoir. It is 156 years old. -Supply water to 70 % of Córdoba city -Drainage area: 1750 km2 -Surface: 15.01 km² -Volume: 201 hm3 -Average depth: 13 m -Residence time: 0.1 to 0.7 years -Main tributaries: San Antonio, Cosquín, Los Chorrillos rivers and Las Mojarras stream -Emissary: Suquía River 4 Study Zone Artificial airation from October 2008 LANDSAT 5-TM. RGB(321). March 2004 Antenucci et al, 2003; Ferral et al, 2017; Ferral et al 2018, German et al, 2021 5 Material and Methods ● Field data ● ● ● In situ SWT meassurements were carried out with a Horiba U-22 multiparametric probe at 0.2 m depth (APHA, 2005). Measurements were carried out seassonally at eight stations, shown in black circles, during 2013-2020 period. Atmospheric precipitable Water content was obtained from a National Meteorological Service station located 30 km far from the reservoir. Its data are available as station SACO number 87344 at(http://weather.uwyo.edu/upperair/sounding.ht ml). 6 Material and Methods ● ● ● ● ● Satellite data All available LANDSAT-8, Level 2 Collection 2 (L2C2), images for the period 2013-2020 were download from USGS server (https://earthexplorer.usgs.gov/). L2C2 images has been recently released and includes scene-based global Level2 surface reflectance and surface temperature science products which have 30m and 100 m spatial resolution respectively (Engebretson, 2020). A total of 65 images for the period 2013-2020 free of clouds were used to carry out spatio-temporal study. In order to obtain Surface Temperature (ST) in 0C, Band 10 (B10) is re scaled according to equation (1), in which the constant term includes the re scale parameter and the difference from Kelvin to Centigrade grade (149 – 273.15). ST = B10 ∗ 0.00341802 − 124.15 (1) 7 Material and Methods ● ● ● Satellite data A LANDSAT 8-OLI image from 22/02/2017 in which an algal bloom was registered were use to calculate a chlorophyll-a map. A semiempirical model validated for San Roque Reservoir were used to calculated chlorophyll-a concentration, equation (2), (Cosano et al, 2020) [Chl − a] = 236.31 ∗ NDVI + 124.45 (2) Where NDVI is the Normalized Vegetation Differential Index, equation 3, Chl-a is clhorophyll-a concentration in mg/m3 . NDVI=(B4-B5)/(B5+B4) and (3) Where B4 and B5 are bands 4 and 5 respectively from OLI sensor 8 Material and Methods ● ● ● ● ● ● Satellite data Processing consisted in re-scaling, cropping, masking, extracting geolocalized data, band algebra calculations and map generation. R studio open platform was used to process images with “raster” and “sp” packages (R Core, 2014). Land was masked using the date of 15-12-2020 which presented the lowest reservoir level. MNDWI was calculated to build land mask used in the temporal series generation (Xu et al, 2005), according with a threshold equal or greater than 0.5 for open water (Ferral et al, 2019). QGIS open software was used to performed customized maps. 9 Material and Methods ● ● ● ● SWT analysis and modeling For validation purposes of ST product (L2C2), we have used temperature field data measured simultaneously with LANDSAT 8 pass during days with no clouds: 09/10/2013, 13/01/2014, 05/03/2015, 08/05/2015, 17/08/2017, 21/11/2017 and 15/12/2020. A set of 48 pairs field-satellite data were used since Garganta sampling point was removed from analysis. Linear regression analysis was used to develop a semiempirical model of Water Surface Temperature from L2C2 ST products and field data, (31 pairs to build the model and 17 pairs to validate, control group). 10 Material and Methods ● ● ● SWT analysis and modeling Root Mean Square Error (RMSE) and Mean Absolute Percentual Error (MAPE) analysis were calculated to analyze ST (L2C2) product and SWT performance in the study zone. Model was validated by analyzing slope and intercept values of the lineal regression built from modelled vs field data values (y=x), which should be significant equal to 1 and 0 respectively 0 where n is the number of observations, et is the absolute error of a modeled data and yt is the field data value for that date and sampling point 11 Results ● Evaluation of Surface Temperature L2 C2 products Precipitable water content, RMSE and MAPE calculated between ST L2 C2 and temperature measured in situ. A linear regression statistic analysis showed that ST (L2C2) product values resulted not significant equal to in situ temperature data with 95 % confidence. 12 Results ● Surface Water Temperature Modeling SWT = ST ∗ 0.85 + 2.38 R2=0.93 RMSE= 1.1 oC (65%_data) Validation (35%_data) 2.5 % 97.5 % (Intercept) -2.9936086 3.511564 test$predict 0.8196201 1.112078 The linear regression analysis shows that SWT model values resulted significant equal to in situ temperature data with 95 % confidence (slope and intercept from second graph equal to 1 and 0 respectively) 13 Results ● Temporal behavior Temporal series of SWT derived over Centro station. Black points corresponds to retrieved satellite data and red points to in situ measurements. Solid line is a lineal interpolation over black points. Periodic behavior with a period of twelve months, showing peaks in summer and valleys in winter. High thermal amplitude, 20 o C average, in accordance to a semiarid template region. 2017 Summer temperature is greater than others years and it coincides with a low level of the reservoir and a very dry year. 14 Results ● Summer and spatial behavior Mean SWT values for Summer maps shows artificial aeration patterns, Max values match algal bloom occurrence (German et al, 2021) and Min values are according with depth patterns. Bathymetric map (ILEC, 2021) 15 Results ● Winter spatial behavior Mean SWT values for Winter maps shows depth patterns, Max values match warmer water from river entrances and Min values are found near deep zones and wastewater pipe discharge Bathymetric map (ILEC, 2021) 16 Results ● Algal Bloom & SWT behavior RGB Landast 8 OLI (left), Surface Water Temperature (middle) and Chlorophyll-a (right) images for a Bloom event occurred in San Roque reservoir on 22/02/2017. A threshold of 150 mg/m3 in Chlorophyll-a concentration was considered to define bloom (German et al, 2021). Average values of (30.2 ±0.5)oC and (27.1 ±0.1)oC for bloom and no bloom regions respectively were found. Significant different (p<0.001) 17 Results Linear regression for 22-02-2017 date (Chl-a vs SWT) r2=0.28 ; p<0.001 in accordance with previous results obtained through MODIS data analysis for this reservoir (German et al, 2018) Changes in modeled temperature could be associated with changes in emissivity of water due to high amount of algae presence. For ST retrieval, a water emissivity value of 0.99 is used (Cook et al, 2014). Emissivity measurements for different surfaces in the region of 8-12 mm found the following average values regarding water systems (Buettner and C. D. Kern, 1965): -0.993 -0.972 -0.966 -0.961 (pure water) (water plus thin film of petroleum oil) (water plus thin film of corn oil) (water plus a thin film of polyethylene) 18 Conclusions ● ● ● ● ● ● ● ● Water Surface Temperature (SWT)derived from satellite measurements remains being a challenge which deserved attention since it can be used as input in water quality risk models, as EOXposure, and climate change studies. This study presents novel performance results of recently released Surface Temperature LANDSAT 8 L2 C2 products for Central Argentine region, which present a RMSE of 1.7 oC when comparing against in situ data. A seven year field data set was used to build a semiempirical model to retrieve SWT from ST L2C2 products with 94% accuracy, a RMSE of 1.1 and a MAPE of 5%. A temporal series of SWT could be built for the period 2013-2020 and a clear seasonal pattern could be observed, as well as the incidence of a dry year. SWT spatial patterns for Summer and Winter seasons could be associated to physical and chemical factors as depth, artificial aeration, river entrances and algae presence. Zones with high level of algal biomass, detected as high amount of chlorophyll-a concentration, present temperatures significantly higher from those with no blooms (3 oC). More studies should be done in this direction in order to evaluate water emissivity behavior when high amount of algae are present Chlorophyll-a and SWT temperature relationship should be studied deeper by long term spatio-temporal series analysis by means of MODIS-TERRA and LANDSAT-TM data, among others. 19 References ● ● ● ● ● ● ● ● Adrian, R., O'Reilly, C. M., Zagarese, H., Baines, S. B., Hessen, D. O., Keller, W., ... & Winder, M. (2009). Lakes as sentinels of climate change. Limnology and oceanography, 54(6part2), 2283-2297. Antenucci, J. P., Alexander, R., Romero, J. R., & Imberger, J. (2003). 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Use of water quality indices to verify the impact of Córdoba City (Argentina) on Suquı ́a River. Water research, 34(11), 2915-2926. Uddin, M. G., Nash, S., & Olbert, A. I. (2021). A review of water quality index models and their use for assessing surface water quality. Ecological Indicators, 122, 107218. Williamson, C. E., Brentrup, J. A., Zhang, J., Renwick, W. H., Hargreaves, B. R., Knoll, L. B., ... & Rose, K. C. (2014). Lakes as sensors in the landscape: Optical metrics as scalable sentinel responses to climate change. Limnology and Oceanography, 59(3), 840-850. 21 Thank you very much for your attention! 22 Gulich Institute, Códoba, Argentina
