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Trophic State Index Estimation from Remote Sensing of Lake Chapala, México

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REVISTA MEXICANA DE CIENCIAS GEOLÓGICAS Trophic State Index estimation from remote sensingv. of 33, lake núm. Chapala, 2, 2016, p. México 183-191 Trophic State Index estimation from remote sensing of lake Chapala, México Alejandra-Selene Membrillo-Abad1,*, Marco-Antonio Torres-Vera2, Javier Alcocer3, Rosa Ma. Prol-Ledesma4, Luis A. Oseguera3, and Juan Ramón Ruiz-Armenta4 1 Posgrado en Ciencias de la Tierra, Instituto de Geofísica, Universidad Nacional Autónoma de México, Ciudad Universitaria, C.P. 04510, Ciudad de México, Mexico. 2 Asociación para Evitar la Ceguera en México, Vicente García Torres 46, San Lucas Coyoacán, C.P. 04030, Ciudad de México, Mexico. 3 Facultad de Estudios Superiores-Iztacala, Universidad Nacional Autónoma de México, Av. de los Barrios No. 1, Los Reyes Iztacala, 54090 Tlalnepantla, Estado de México, Mexico. 4 Instituto de Geofísica, Universidad Nacional Autónoma de México, Ciudad Universitaria, C.P. 04510, Ciudad de México, Mexico. * [email protected] ABSTRACT áreas urbanas, agricultura y ganadera. Mediante técnicas de percepción remota se estimó el Índice de Estado Trófico (IET) del lago, como un The Trophic State Index (TSI) was estimated for Lake Chapala índice de calidad de agua. Los parámetros de clorofila_a y Profundidad by remote sensing techniques as an indicator of water quality. Our de disco de Secchi (que están relacionados con el estado trófico), fueron results show that multispectral satellite images can be successfully monitoreados mediante el uso de imágenes satelitales multiespectrales, used to monitor surface water parameters related to trophic state con base en el aumento en los valores de turbidez y concentración de (i.e., chlorophyll-a and Secchi disc depth). Lake Chapala is the largest clorofila_a, los cuales están relacionados con las propiedades ópticas del water body in Mexico and supplies Guadalajara City with over 60% agua. Se estimó la concentración de clorofila_a y turbidez (parámetro of its freshwater requirements. The Lerma River is the main tributary inverso a la profundidad de Disco de Secchi) para el Lago de Chapala a of Lake Chapala and exhibits significant water quality deterioration partir de relaciones empíricas entre reflectancia en la banda SPOT B2 associated with nutrient enrichment coming from drainage from urban, y la profundidad de disco de Secchi así como la relación de las bandas agriculture and livestock areas. This study is based on the modification SPOT S3/S2 y la concentración de clorofila_a. Los resultados de la of the optical properties of the water caused by increased values of determinación del Índice de Estado Trófico basada en la reflectancia turbidity and Chl-a. We estimated turbidity on the basis of empirical de las imágenes multiespectrales muestran un lago eutrófico. El uso relationships between reflectance data in band SPOT S2 and Secchi de datos multiespectrales permite el monitoreo del Índice de Estado disc depth, and Chl-a concentration values from the relationship Trófico de forma continua, de bajo costo y eficiente para el apoyo en la between band ratio SPOT S3/S2 and Chl-a concentration in the water implementación de regulaciones que permitan el mejoramiento de la samples. The results show that application of defoliants to eradicate calidad del agua en el Lago de Chapala. aquatic weeds did not ameliorate the trophic state of the lake. The Trophic State Index determinations are based on multispectral image Palabras clave: Índice de Estado Trófico; SPOT; clorofila_a; calidad de agua. reflectance values, which revealed Chapala to be in the range from oligotrophic to mesotrophic. The use of remote sensing data allows a more efficient, continuous and low-cost Trophic State Index monitoring INTRODUCTION of Lake Chapala to assist in the implementation of regulations toward water quality improvement. Remote sensing has been successfully used over the last 40 years to estimate water quality worldwide (Dekker and Peters, 1993; Baban, Key words: Water quality; SPOT; Trophic State Index; Chlorophyll 1993; Cheng and Lei, 2001;Tyler et al., 2006; Chang et al., 2014;). reflectance. Multispectral image processing applications are based on the modifi- cation of the water optical properties associated with turbidity related to the presence of suspended particles as well as Chl-a enrichment RESUMEN (Jensen, 2000; Ritchie et al., 2003). Monitoring water quality in a large lake by direct sampling is costly and does not cover the entire area at El Lago de Chapala (México), muestra un deterioro en su calidad de agua, the same time. In contrast, remote sensing provides continuous spa- asociado con el enriquecimiento de nutrientes derivados de drenajes de tial coverage of multispectral data over the complete geographic area Membrillo-Abad, A.S., Torres-Vera, M.A., Alcocer, J., Prol-Ledesma, R.M., Oseguera, L.A., Ruiz-Armenta, J.R., 2016, Trophic State Index estimation from remote sensing of lake Chapala, México: Revista Mexicana de Ciencias Geológicas, v. 33, núm. 2, p. 183-191. RMCG | v. 33 | núm. 2 | www.rmcg.unam.mx 183 Membrillo-Abad et al. of the water body. This areal determination of relevant parameters 1997). Since 2005, diverse eutrophication control programs have been generates synoptic and significant information on water quality to implemented with unclear results. There is an urgent need to develop facilitate decision-making for basin management and proper regula- low-cost evaluation strategies that can be easily executed to monitor tion of the water entering the lakes from the tributaries (Brezonik et the trophic state of Lake Chapala. al., 2005; Fuller and Minnerick, 2007; Pavelsky and Smith, 2009; Usali In this study, we propose to prove the effectiveness of applying and Ismail, 2010). remote sensing techniques as a fast, low-cost, and accurate method to The main pollutants from agricultural activities including nitrogen estimate Lake Chapala’s trophic state. Image processing includes mosaic and phosphorous are nutrients that promote primary productivity and construction, spatial enhancement, reflectance calculation and band cause eutrophication in the long term. Eutrophic lakes exhibit poor ratio. Calculation of turbidity and Chl-a concentrations for all pixels water quality unsuitable for freshwater supply, human health, fisheries within the lake was done by linear regression of satellite multi-spectral and recreation (Hammer and Mackichan, 1981); therefore, it is neces- image data with field truth measurements. sary to perform a continuous evaluation of the trophic state of a lake that is used as a water supplier. The scale used to define eutrophication has been adapted to diverse STUDY AREA local conditions. Table 1 shows the trophic state denominations based on values of chlorophyl-a concentration (Chl-a) and Secchi disk depth Lake Chapala is located in the western part of central Mexico (SDD) as suggested by Harper (1992). Carlson (1977) proposed the (20°06’36’’–20°18’00’’ North, 102°42’00’’–103°25’30’’ West, 1520 m calculation of an index to give a numerical scale to evaluate the trophic above sea level) in the Lerma-Chapala Basin (Figure 1), which drains state of a water body (Carlson's Trophic State Index – CTSI) by meas- an area of 53,591 km2. Lake Chapala has an area of about 1100 km2. Its uring tubidity and Chl-a concentration. CTSI is calculated based on larger axis has an East-West orientation with a maximun length of 77 empirical relationships between SDD, Chl-a and TP. These empirical km, and its smaller axis has a length of 22 km. The average depth varies relationships are local properties defined for environments similar to between 4–7 m. (Trujillo-Cardenas et al., 2010). The annual inflow of Carlson's study area; therefore, CTSI should be modified according Lake Chapala is 1162 Mm3yr-1 (precipitation: Mm3yr-1; inflow of water- to the specific local environmental parameters (Cheng and Lei, 2001; shed: 178 Mm3yr-1 and river’s input 273 Mm3yr-1). The annual output Sheela et al., 2011a). In this study, we performed a model callibration is 1666 Mm3yr-1 (evaporation: 1394 Mm3yr-1; pumping and Santiago to calculate the local Chapala parameters to be used in the CTSI. River: 272 Mm3yr-1). Therefore, the Lake Chapala is overexploited Lake Chapala supplies freshwater to Guadalajara, which is the sec- (Aparicio, 2001). Its main tributary is the Lerma River with a longitude ond most populated city in Mexico. However, diverse human activities of 705 km. Two large cities, Guadalajara and Mexico City, have a strong in the drainage basin (urban development, agriculture and industry) influence on the basin hydrology due to their intense economic activi- have deteriorated the water quality. The increasing freshwater demand ties and large freshwater requirements (Cotler et al., 2006). from Guadalajara has raised awareness of freshwater protection; there- Lake Chapala lost part of its original surface area in 1905 when fore, the Mexican government has implemented a monitoring program the shallow littoral area of the eastern section of the lake (“Ciénega to preserve, restore, protect, and sustainably use the water resources de Chapala”) was converted into agricultural land. The lake shrank in the Lerma−Chapala Basin (SEMARNAT, 2005). As a result, the 500 km2 and lost 856 × 106 m3 of water (Sandoval, 1994). The lake has government launched a program in 2005 to regulate the water use and experienced additional water level reductions (up to 42% of the total the disposal of potentially polluting products (mainly from agricultural volume) due to intense water extraction and long dry periods. The activities). However, regulations have to be enforced to be successful volume reduction has worsened the effect of the wastewater and nutri- (Cotler et al., 2006). In addition to the new regulations of waste water ent input by the tributaries (Lind and Dávalos-Lind, 2002). Previous disposal, the authorities tried to control the growth of the dense popula- reports on nutrient content, Chl-a concentration turbidity of the lake tion of aquatic weeds that covered large areas of the lake through the and total phosphorus indicate diverse CTSI values: (CTSI(TP)) = 94 intense use of defoliants—mainly glyphosate (SEMARNAT, 2006).
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