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sa para perfiles de velocidad promediados en la dirección
vertical. La altura h(x,t)y el campo de velocidad obtenidos
fueron validados por las mediciones de campo.
El objetivo de este trabajo es cuantificar el caudal en dife-
rentes sitios a lo largo del ERQG en función de la distancia
xa la desembocadura según la magnitud de la descarga flu-
vial y la evolución del nivel mareal. Para ello se desarrolló
un modelo analítico, basado en la resolución de las ecua-
ciones unidimensionales de Saint Venant, que complemen-
ta al propuesto por Thomas y Marino [1] (modelo TM de
aquí en adelante), para calcular el caudal en diferentes sitios
del estuario. Los resultados se comparan satisfactoriamen-
te con aquellos obtenidos aplicando el modelo SisBaHiA y
con mediciones in-situ.
II. MATERIALES Y MÉTODOS
El estuario micro-mareal y de planicie costera del río
Quequén Grande es un sistema primario con un ancho de
entre 150 y 200 m, que se extiende hasta el primero de una
serie de saltos naturales, conocido como paraje Las Cas-
cadas, ubicado a 13.9 km desde la posición E0 en la Fig.
1que muestra el perfil del lecho. El estuario presenta una
profundidad de 3-5 m en sus tramos medio y superior con
una topografía irregular que exhibe meandros naturales y
canales angostos con profundidades de entre 5 y 7 m. Un
puerto de aguas profundas se extiende en los primeros 2 km
en los que la profundidad se mantiene en 12-14 m por dra-
gado continuo y la forma del cauce es casi rectangular. La
marea tiene una amplitud media de 1.03 m, con un máximo
de 1.85 m durante las mareas vivas.
FIG. 1: Perfil del fondo a lo largo de la vaguada del estuario del
río Quequén Grande. Se indican las posiciones de las estaciones
de medición.
Las distribuciones transversales de velocidad u(u,v)para
estimar el caudal QADCP se obtuvieron en 4 secciones ubi-
cadas a 1.4 (E1), 2.9 (E2), 7.5 (E3) y 11.3 (E4) km de E0
e indicadas en la Fig. 1, entre el 23 y 24 de agosto de 2016
con dos ADCP Workhorse (Teledyne RD Instruments) que
operan a 600 y 1200 kHz. La evolución del nivel mareal
en la boca del estuario (E0) y la información meteorológi-
ca fueron proporcionadas por el Consorcio de Gestión de
Puerto Quequén. Durante la campaña, las condiciones me-
teorológicas fueron buenas y sin viento apreciable.
La simulación del comportamiento hidrodinámico del
ERQG con el programa SisBahia emplea una malla de 3287
rectángulos con nueve nodos en los que se calculan las dife-
rentes variables: cuatro nodos en las esquinas, cuatro nodos
en el centro de los lados y uno central, que representa el
contorno y la batimetría estuarial con buena resolución. Se
definió una zona marítima cercana (donde el aporte del río
es insignificante) y el contorno superior en el paraje Las
Cascadas. Se presta especial cuidado a las partes del con-
torno que tienen extremos agudos como las escolleras y el
muelle, y en obtener una distribución regular donde el ta-
maño de los nodos cambia progresivamente. La salinidad
y la temperatura del agua se consideraron constantes en el
tiempo y uniformes en profundidad.
Las principales forzantes que actúan sobre el sistema se
introdujeron a través de las condiciones de borde. La in-
fluencia marítima es dada fundamentalmente por el cam-
bio del nivel del mar debido a la protección de las corrien-
tes marinas que brindan las dos escolleras construidas en
la desembocadura. Por consiguiente, en la frontera abierta
del contorno marítimo se impone el nivel del agua que varía
con el tiempo de acuerdo a la marea astronómica calculada
a partir de las principales constantes armónicas. El análisis
armónico de la marea indica que la componente semidiurna
lunar M2 es la más importante, siguiendo las componentes
diurnas O1 y K1, y la semidiurna solar S2. Las simulaciones
se efectuaron incluyendo estas cuatro primeras componen-
tes y para un tiempo total de al menos 30 días. En el otro
extremo del contorno se impone una descarga constante del
río dejando que el código calcule el nivel correspondien-
te. El caudal medido en una estación ubicada aguas arriba
del paraje Las Cascadas es de entre 5 y 10 m3/s, con cre-
cidas esporádicas en las que suele alcanzar 170 m3/s. Aquí
se empleó un caudal de 10 m3/s para caracterizar una situa-
ción normal. El principal parámetro de ajuste del modelo es
la rugosidad del lecho cuyo valor se eligió de modo que la
diferencia de nivel del agua entre dos estaciones correspon-
da a la información de campo para diferentes caudales del
río. Con esta rugosidad se verificó que, cuando se considera
la marea, el tiempo de retraso entre la onda de nivel y la del
flujo en los resultados numéricos sea el medido en las esta-
ciones E0-E4 (≈20-30 min), dependiendo de la amplitud y
fase mareales.
III. RESULTADOS
La Fig. 2ilustra la razonable concordancia entre las me-
diciones de caudal realizadas con ambos ADCP, QADCP, y
los valores QT M(t) proporcionados por el modelo TM. Un
análisis detallado de los resultados muestra que las medi-
ciones realizadas en E1 (símbolos rojos) son las que mejor
concuerdan con el caudal teórico, y que QADCP depende del
momento del ciclo mareal en E0 y de la distancia x entre el
lugar de medición y E0. Estas dependencias se consideran
en el análisis a través de las relaciones x/L, con L= distan-
cia entre E0 y el paraje Las Cascadas, y QADCP(x,t)/QT M (t).
La Fig. 3ilustra como QADCP/QT M disminuye aguas-arriba
con la distancia desde E0, implicando que el modelo TM
explica el comportamiento de Qen la desembocadura, pero
no en el interior del estuario. Como se muestra más adelan-
te, esta dependencia es confirmada por los resultados de la
simulación numérica.
La evolución de las variaciones del nivel de la columna
de agua en la desembocadura proporcionada por la simula-
ción durante 3 meses se presenta en la Fig. 4 (a) para q= 10
Thomas et al. / Anales AFA - XVI Meeting on Recent Advances of Physics of Fluids and its Applications 16-20 17