MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01D91C3D.5631B020" Este documento es una página web de un solo archivo, también conocido como "archivo de almacenamiento web". Si está viendo este mensaje, su explorador o editor no admite archivos de almacenamiento web. Descargue un explorador que admita este tipo de archivos. ------=_NextPart_01D91C3D.5631B020 Content-Location: file:///C:/8CEA55D4/974-RTE-34-4.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="windows-1252" 974

 

 

 

 

https://doi.org/10.37815/rte.v34n4.974

Artículos originales=

 

Regionalización de las Curvas de Intensidad-Duración-Frecuencia (IDF) en las Provincias de Santa Elena y Manabí

Regionalization of the Intensity-Duration-Frequency (IDF) Curves in = the Provinces of Santa Elena and Manabí

 

Pietro Corapi1 h= ttps://orcid.org/0000-0001-8626-0134,

Nedin Nuñez Neira1  <= /sup>https://orcid.org/0000-0003-1759-3116

 

1Universidad de Guayaquil, Guayaquil, Ecuador

pietro.corapi@ug.edu.ec, nedinsteven@gmail.com

 

Enviado:         2022/09/10

Aceptado:       2022/12/13

Publicado:      2022/12/30                         

Resumen

Una baja densidad de estaciones pluviométricas en la costa del Ecuad= or, junto con registros discontinuos e incompletos, ocasionan que la estimación= de las ecuaciones de las curvas de intensidad-duración-frecuencia (IDF) sea compleja, dando lugar a sobrestimaciones o subestimaciones de las precipitaciones en sectores sin datos. El propósito de la investigación es crear un mapa regionalizado de los parámetros para la ecuación de las curvas IDF en las provincias de Santa Elena y Manabí. Los datos de precipitación, = de las estaciones meteorológicas disponibles, fueron empleados en un análisis estadístico con el objetivo de verificar si se ajustaban a una distribución probabilística. Posterior a esto, mediante regresión lineal múltiple, se determinó cada parámetro de las ecuaciones correspondientes a cada estación. Los parámetros obtenidos fueron distribuidos en el área de estudio por medi= o de métodos de análisis espacial como la Distancia Inversa Ponderada y Kriging Ordinario en el software QGIS. Se demostró la= validez de cada método con estaciones que han sido excluidas en el análisis espacia= l, donde se ha comprobado un error menor al 10%. El método de Kriging Ordinario ha demostrado una distribución más adecuada, a partir de la cual = se ha generado un mapa publicado en ArcGIS Online. Este mapa permitió la lectu= ra de los parámetros de la ecuación de curvas IDF en cualquier coordenada dent= ro de las provincias de estudio.

 

= Palabras clave: Análisis espacial, interpolación, parámetros hidrológicos, precipitación, zonificación.

Sumario: Introducc= ión, Materiales y Métodos, Resultados y Discusión y Conclusiones.

 

Como citar: Corapi, P., & Nuñez, N. (2022). Regionalización de las Curvas de Intensidad-Duración-Frecuencia (IDF) = en las Provincias de Santa Elena y Manabí. Revista Tecnológica - Espol= , 34(4), 44-68. http://www.rte.espol.edu.ec/index.php/tecnologica/article/view/9= 74


Abstract

A low density of pluviometric stations on the coast of Ecuador, together with discontinuous and incomplete records, induce the estimation of the equations of the intensity-duration-frequency (IDF) curves to be complex. It leads to overestimates or underestimates of rainfall in sectors without enough data.= The purpose of this research is to create a regionalized map of the parameters = for the equation of the IDF curves in the provinces of Santa Elena and Manabí. = The precipitation data from the available meteorological stations were used in statistical analysis to verify if they adjust a probabilistic distribution.= In addition, each parameter of the equations corresponding to each station has been determined by using multiple linear regression. The resulting paramete= rs were distributed in the study area through spatial analysis methods such as Inverse Distance Weighting and Ordinary Kriging in the QGIS software. The validity of each method has been demonstrated with stations that have been excluded in the spatial analysis. Therefore, an error of less than 10% has = been verified. The Ordinary Kriging method has shown a more adequate distributio= n, from which the map published in ArcGIS Online has been generated. This map allows the reading of the parameters for the equation of the IDF curves at = any coordinate inside the study provinces.

 

Keywords: Spatial analysis, interpolation, hydrolog= ical parameters, rainfall, zoning.

 

Introducción

En proyectos hidráulicos que requieren de un diseño hidrológico es primordial estudiar l= as precipitaciones mediante un proceso que se conoce como determinación de los eventos de lluvia. La determinación de dichos eventos se realiza mediante un análisis que establezca una relación entre la intensidad de lluvia, duració= n y frecuencia (IDF). En esta relación, los datos generalmente se presentan con= un gráfico donde el eje horizontal corresponde a la duración y el eje vertical= a la intensidad, cada período de retorno considerado (frecuencia) se muestra a través de una serie de curvas (Chow et al., 1994).

 =

Para la ela= boración de las curvas IDF se puede recurrir a registros de precipitación de estacio= nes pluviográficas o estaciones pluviométricas, instrumen= tos cuya presencia en algunos sitios puede ser escasa o nula. Estos instrumentos pueden volverse obsoletos de manera temprana cuando no hay un correcto mantenimiento, provocando ausencia de datos en los registros; además, la información generada por las estaciones no es de libre acceso en su totalid= ad. Este escenario es muy común en el Ecuador y se puede corroborar con los anuarios meteorológicos del Instituto Nacional de Meteorología e Hidrología (INAMHI), considerando el período de 1990-2013; los registros incompletos s= on evidentes según varios trabajos de investigación (Carr= era V. et al., 2016; INAMHI, 2008).  

 =

El objetivo= de esta investigación es crear un modelo de regionalización de las constantes de la ecuación de las curvas de IDF en las provincias de Santa Elena y Manabí, mediante un análisis estadístico de datos de precipitación para obtener parámetros hidrológicos en sitios con información escasa o inexistente. <= /o:p>

 

Materiales y Métodos

Curvas de Intensidad-Duración-Frecuencia (IDF)

Las curvas de intensidad-duración-frecuencia son gráficas que representan las característ= icas de las tormentas que tienen ocurrencia en una zona o región. La lluvia es un evento definido por tres variables: magnitud (lámina de precipitación), duración y frecuencia (período de retorno); sin embargo, para un análisis m= ás completo se toma en consideración la intensidad en lugar de la lámina de precipitación (Campo= s A., 1998).

Período de Retorno

El período de retorno hace referencia a un intervalo promedio de tiempo dentro del cual un evento puede suceder en igual o mayor magnitud (Varas= C. & Bois, 1998).

 <= /o:p>

El estudio d= e las precipitaciones a través de un análisis de curvas IDF es fundamental en las primeras etapas de proyectos que demanden diseños hidrológicos e hidráulico= s. Para trazar estas curvas, la intensidad se expresa en mm/hr, la duración en minutos y la frecuencia o período de retorno en años. La Figura 1 muestra un esquema general de presentar las curvas IDF en es= cala aritmética (Chow et al., 1994).

 

Figura 1=

Esquema de las Curvas IDF en Escala Aritmética.

 <= /o:p>

Una de las expresiones para representar las curvas IDF fue presentada por Bernar= d (1932):

 <= /o:p>

[1]<= /o:p>

 <= /o:p>

Donde:

 <= /o:p>

 intensidad, en mm/hr;

 Duración de lluvia, en minutos;

 Período de retorno en años;

 Parámetros de ajuste.

 <= /o:p>

Para el desa= rrollo de un mapa de zonificación de las curvas de intensidad, duración y frecuenc= ia (IDF), primero se deben obtener los parámetros de la ecuación para tales curvas. En la Figura 2 se presenta un esquema de las fases involucradas en el proce= so de regionalización de las curvas IDF.

 <= /o:p>

 <= /o:p>

 <= /o:p>

Figura 2=

Diagrama de Flujo de la Metodología

&nbs= p;

Recolección de Información Pluviométrica

Las medicion= es de lluvia pueden variar de un lugar a otro, y en un mismo lugar según el tiemp= o. Antes de emplear métodos estadísticos para modelar este comportamiento espacio-temporal, es necesario constatar que los registros sean completos, = consistentes y con una extensión suficiente (Chere= que M., 1989).

 <= /o:p>

En esta investigación se recopilaron datos de 42 estaciones meteorológicas, con información que acumula entre 10 y 24 añ= os de registros. Parte de esta información meteorológica fue proporcionada directamente por el Instituto Nacional de Meteorología e Hidrología y, otra parte, se obtuvo a partir de los anuarios disponibles en la biblioteca virt= ual de tal institución. Como producto de completar los registros, empleando el método de regresión lineal, el 47,62% tienen 20 o más años con registros completos, un 69,05% cuentan con 15 o más años y un 30,95% tienen entre 10 = y 14 años de información. En la Tabla = 1 se pueden visualizar los datos generales de cada estación: código, nombre, provincia, ubicación en sistema de coordenadas geográficas (grados, minutos y segundos), entre otros.

 <= /o:p>

                      Tabla 1=

Información General de las Estaciones Meteorológicas Estudiadas

CÓDIGO

ESTACIÓN<= o:p>

PROVINCIA=

ZONA

LATITUD

LONGITUD<= /span>

AÑOS CON REGISTROS

M0005=

Portoviejo-UTM

Manabí

17S

1°2'3'' S

80°27'34'' W=

24

M0006=

Pichilingue<= /span>

Los Ríos

17S

1°6'0'' S

79°27'42'' W

24

M0025=

La Concordia=

Esmeraldas=

17S

0°1'36'' S

79°22'17'' W

24

M0026=

Puerto Ila

Santo Domi= ngo

17S

0°28'34'' S<= /span>

79°20'20'' W

24

M0037=

Milagro (Ingenio Valdez= )

Guayas

17S

2°6'56'' S

79°35'57'' W

24

M0051=

Babahoyo-UTB=

Los Ríos

17S

1°49'0'' S

79°32'0'' W<= /span>

21

M0124=

San Juan La Maná

Los Ríos

17S

0°57'15'' S<= /span>

79°19'8'' W<= /span>

20

M0160=

El Carmen

Santo Domi= ngo

17S

0°19'4'' S

79°16'47'' W=

12

M0162=

Chone=

Manabí

17S

0°41'0'' S

80°5'0'' W

24

M0165=

Rocafuerte

Manabí

17S

0°55'20'' S<= /span>

80°27'9'' W<= /span>

24

M0166=

Olmedo-Manabí

Manabí

17S

1°23'41'' S<= /span>

80°12'25'' W=

24

M0167=

Jama<= /span>

Manabí

17S

0°11'58'' S<= /span>

80°15'47'' W=

15

M0169=

Julcuy

Manabí

17S

1°28'1'' S

80°37'14'' W=

24

M0171=

Camposano #2=

Manabí

17S

1°35'16'' S<= /span>

80°24'0'' W<= /span>

24

M0172=

Pueblo Viejo=

Los Ríos

17S

1°32'12'' S<= /span>

79°31'45'' W=

24

M0174=

Ancón=

Santa Elen= a

17S

2°19'28'' S<= /span>

80°50'54'' W=

10

M0175=

El Progreso-Guayas=

Santa Elen= a

17S

2°24'0'' S

80°22'0'' W<= /span>

11

M0176=

Naranjal

Guayas

17S

2°39'44'' S<= /span>

79°39'23'' W=

22

M0218=

Ingenio San Carlos=

Guayas

17S

2°12'0'' S

79°26'35'' W=

24

M0245=

El Suspiro

Santa Elen= a

17S

1°54'46'' S<= /span>

80°39'11'' W=

21

M0348=

Santa Anita<= /span>

Santo Domi= ngo

17S

0°14'20'' S<= /span>

79°14'43'' W=

13

M0447=

24 de Mayo (Jaboncillo)=

Manabí

17S

1°19'26'' S<= /span>

80°19'10'' W=

17

M0449=

Sancán

Manabí

17S

1°15'29'' S<= /span>

80°35'5'' W<= /span>

14

M0451=

El Anegado

Manabí

17S

1°28'5'' S

80°32'55'' W=

11

M0452=

Zapote

Manabí

17S

0°54'23'' S<= /span>

80°3'23'' W<= /span>

10

M0453=

Chorrillos

Manabí

17S

1°2'25'' S

80°40'36'' W=

14

M0455=

Joa - Jipijapa

Manabí

17S

1°22'15'' S<= /span>

80°37'49'' W=

13

M0457=

Puerto Cayo<= /span>

Manabí

17S

1°20'58'' S<= /span>

80°44'11'' W=

15

M0458=

Colimes De Paján

Manabí

17S

1°34'42'' S<= /span>

80°30'21'' W=

18

M0459=

San Pablo - Manabí=

Manabí

17S

1°34'0'' S

80°35'13'' W=

13

M0462=

Junín=

Manabí

17S

0°55'21'' S<= /span>

80°12'8'' W<= /span>

13

M0465=

Ventanas INAMHI

Los Ríos

17S

1°27'25'' S<= /span>

79°28'26'' W=

16

M0466=

Vinces

Los Ríos

17S

1°32'33'' S<= /span>

79°45'44'' W=

21

M0470=

Mocache

Los Ríos

17S

1°10'37'' S<= /span>

79°29'39'' W=

16

M0475=

Colimes de Balzar INAMH= I

Guayas

17S

1°32'34'' S<= /span>

80°0'29'' W<= /span>

12

M0476=

La Capilla INAMHI<= /o:p>

Guayas

17S

1°41'46'' S<= /span>

79°59'45'' W=

12

M0619=

Manglaralto

Santa Elen= a

17S

1°51'10'' S<= /span>

80°44'55'' W=

22

M0780=

Colonche

Santa Elen= a

17S

2°1'34'' S

80°40'19'' W=

16

M0782=

Barcelona

Santa Elen= a

17S

1°54'0'' S

80°41'0'' W<= /span>

22

M0783=

Salanguillo

Santa Elen= a

17S

1°58'10'' S<= /span>

80°34'08'' W=

18

MA2U<= /o:p>

Ingenio Aztra (La Troncal)

Cañar=

17S

2°22'27'' S<= /span>

79°22'27'' W=

21

MA2V<= /o:p>

Guayaquil-Radio Sonda

Guayas

17S

2°12'0'' S

79°53'0'' W<= /span>

18

Fuente: Instituto Nacional de Meteorología e Hidrología

 <= /o:p>

En la Figura 3 se aprecia la distribución espacial de las estaciones meteorológicas.

 <= /o:p>

Figura 3=

Distribución de las Estaciones en el Área de Estudio

 <= /o:p>

Curvas IDF a partir de Datos de Precipitación Máxima en 24 Horas

El procesami= ento estadístico de las precipitaciones máximas en 24 horas (eventos extremos de precipitación) tiene como propósito obtener una estimación de las lluvias máximas diarias para determinados períodos de retorno, acorde a las necesid= ades de diseño de obras hidráulicas. Los datos estimados sirven de base para la construcción de curvas IDF (Campo= s A., 1998).

 <= /o:p>

Para el anál= isis estadístico, de los datos pluviométricos de una estación meteorológica, se seleccionan los valores máximos diarios por cada año (dentro del intervalo = de tiempo considerado, en el cual se posean datos pluviométricos). Esta colecc= ión es la que se emplea para la determinación de la precipitación máxima probab= le, aplicando métodos como la distribución de probabilidad Tipo I o de Gumbel.<= /span>

 <= /o:p>

Distribuc= ión de Probabilidad para Valores Extremos Tipo I o de Gumbel

Gumbel (1958)= ha demostrado que al suponer un número  de muestras, que a la vez contienen  eventos cada una, y seleccionar el máxim= o  de los  eventos, se puede exponer que, a medida = que  incrementa, la función de distribución de probabilidad de  tiende a:

 <= /o:p>

  Fx<= /span>=3De-eα= x-β <= /p>

[2]<= /o:p>

 <= /o:p>

 es la función de la probabilidad de no ocurrencia (o de no excedencia) de acuerdo con la distribución de Gumbel.

 <= /o:p>

Donde:

 <= /o:p>

 y  son los parámetros de la función y se es= timan como (Apa= ricio M., 1992):

 <= /o:p>

 

[3]

 

[4]

 <= /o:p>

 y  son la desviación estándar y media de or= den estadístico, , de una mue= stra de tamaño n  ordenada de tal modo que <= span style=3D'mso-bookmark:_Toc38966068'><= ![if !msEquation]>  para el mayor de los eventos extremos y =  para el evento extremo con menor magnitu= d. Estos parámetros son función del tamaño de la muestra.

 <= /o:p>

 es la desviación estándar de la muestra = y se obtiene como:

 <= /o:p>

<= /p>

[5]<= /o:p>

 <= /o:p>

 es la media de los  eventos máximos.

 <= /o:p>

En la Tabla = 2 se muestran los diferentes valores que adoptan  y  para distintos tamaños de muestra:

 <= /o:p>

=          Tabla 2=

Valores de  y  en Función del Número= de Datos para Distribución de Gumbel

n<= span lang=3DES-US style=3D'font-size:8.0pt;mso-ansi-language:ES-US'>

= u= y= =

&#= 963;y<= /span> =

10

0,4952

0,9496

15

0,5128

1,0206

20

0,5236

1,0628

25

0,5309

1,0914

30

0,5362

1,1124

35

0,5403

1,1285

40

0,5436

1,1413

45

0,5463

1,1518

50

0,5485

1,1607

55

0,5504

1,1682

60

0,5521

1,1747

65

0,5535

1,1803

70

0,5548

1,1854

75

0,5559

1,1898

80

0,5569

1,1938

85

0,5578

1,1974

90

0,5586

1,2007

95

0,5593

1,2037

100

0,5600

1,2065

Fuente: Aparicio M. (1992)

 <= /o:p>

Para obtener= un evento máximo , dado su pe= ríodo de retorno , se utiliza la fórmula correspondiente a la distribu= ción de Gumbel:

 

<= /p>

[6]<= /o:p>

 

Mediante la = ecuación [6], se determinan las precipitaciones máximas probables para distintos períodos de retorno, con respecto a cada una de las estaciones sujetas a estudio. Segú el U.S. Weather Bureau,= se recomienda aplicar, a estas precipitaciones máximas probables, un factor de corrección de 1,13 para convertir datos medidos en intervalos fijos (pluviómetros) a mediciones en intervalos continuos (pluviógrafos). El fact= or 1,13 es un valor empírico, producto de un análisis de series anuales de val= ores máximos de precipitación; concretamente, fue obtenido como la media de la r= azón entre los valores de precipitación acumulada (diaria) de intervalos continu= os y los de intervalos fijos de una medición cada 24 horas (Hersh= field, 1961).

 <= /o:p>

Los valores obtenidos de precipitación máxima probable son validados mediante lo que se denominan pruebas de bondad de ajuste: Test Kolmogorov= Smirnov y Coeficiente de determinación.

 <= /o:p>

Test KolmogorovSmirnov

Esta prueba de bondad de ajuste parte de la comparac= ión entre las funciones de distribución acumulativa (frecuencia observada), que= se aprecia al ordenar la muestra, y la distribución propuesta o frecuencia teó= rica (Canav= os, 1994).

 <= /o:p>

La prueba se basa en la diferencia absoluta máxima <= /a> , entre los valores de la distribución acumulativa observada de una muestra cualquiera de tamaño , y una distribución acumulativa teóri= ca (Johnson et al., 2012):

 <= /o:p>

[7]<= /o:p>

 <= /o:p>

Donde:

 <= /o:p>

 frecuencia observada;<= /span>

 frecuencia teórica (Distribución normal, log-normal, Gumbel, entre otras).

 

El valor  es comparado a un valor crítico  que varía según el número  de datos y de acuerdo con el nivel de significancia.

 <= /o:p>

La frecuenci= a observada se obtiene como:

 <= /o:p>

[8]<= /o:p>

 <= /o:p>

Donde:

 <= /o:p>

m: nú= mero de orden del dato ;

: número total de datos.

 <= /o:p>

El nivel de significancia  es una probabilidad de error, un nivel de significancia de 0,05 representa una confianza del 95%, por ejemplo.

 <= /o:p>

Finalmente, = si , la distribución utilizada es aceptada. Los valores que puede adoptar , se muestran en la Tabla 3.

 <= /o:p>

 Tabla 3=

Valores para  según la significanci= a y el número de datos

 

NIVEL DE SIGNIFICANCIA

n

0,2

0,1

0,05

0,02

0,01

1

0,90000

0,95000

0,97500

0,99000

0,99500

2

0,68337

0,77639

0,84189

0,90000

0,92929

3

0,56481

0,63604

0,70760

0,78456

0,82900

4

0,49265

0,56522

0,62394

0,68887

0,73424

5

0,44698

0,50945

0,56328

0,62718

0,66853

6

0,41037

0,46799

0,51926

0,57741

0,61661

7

0,38148

0,43607

0,48342

0,53844

0,57581

8

0,35831

0,40962

0,45427

0,50654

0,54179

9

0,33910

0,38746

0,43001

0,47960

0,51332

10

0,32260

0,36866

0,40925

0,45562

0,48893

11

0,30829

0,35242

0,39122

0,43670

0,46770

12

0,29577

0,33815

0,37543

0,41918

0,44905

13

0,28470

0,32549

0,36143

0,40362

0,43247

14

0,27481

0,31417

0,34890

0,38970

0,41762

15

0,26589

0,30397

0,33750

0,37713

0,40420

16

0,25778

0,29472

0,32733

0,36571

0,39201

17

0,25039

0,28627

0,31796

0,35528

0,38086

18

0,24360

0,27851

0,30936

0,34569

0,37062

19

0,23735

0,27136

0,30143

0,33685

0,36117

20

0,23156

0,26473

0,29408

0,32866

0,35241

21

0,22517

0,25858

0,28724

0,32104

0,34426

22

0,22115

0,25283

0,28087

0,31394

0,33666

23

0,21646

0,24746

0,27490

0,30728

0,32954

24

0,21205

0,24242

0,26931

0,30104

0,32286

25

0,20790

0,23768

0,26404

0,29518

0,31657

26

0,20399

0,23320

0,25908

0,28962

0,30963

27

0,20030

0,22898

0,25438

0,28438

0,30502

28

0,19680

0,22497

0,24993

0,27942

0,29971

29

0,19348

0,22117

0,24571

0,27471

0,29466

30

0,19032

0,21756

0,24170

0,27023

0,28986

31

0,18732

0,21412

0,23788

0,26596

0,28529

32

0,18445

0,21085

0,23424

0,26189

0,28094

33

0,18171

0,20771

0,23076

0,25801

0,27577

34

0,17909

0,21472

0,22743

0,25429

0,27271

35

0,17659

0,20185

0,22425

0,25073

0,26897

36

0,17418

0,19910

0,22119

0,24732

0,26532

37

0,17188

0,19646

0,21826

0,24404

0,26180

38

0,16966

0,19392

0,21544

0,24089

0,25843

39

0,16753

0,19148

0,21273

0,23785

0,25518

40

0,16547

0,18913

0,21012

0,23494

0,25205

Fuente: Canavos (1994)<= !--[if supportFields]>.

 <= /o:p>

Coeficien= te de Determinación

El coeficien= te de determinación se expresa como (Pizar= ro & Novoa, 1986):

 <= /o:p>

<= span lang=3DES style=3D'font-family:"Cambria Math",serif;mso-fareast-font-fami= ly:"Times New Roman"; mso-fareast-theme-font:minor-fareast;mso-ansi-language:ES'>

[9]<= /o:p>

 <= /o:p>

Donde:

 <= /o:p>

: <= /i>coeficiente de determinación 0≤R2= ≤1 ;

 frecuencia observada;<= /span>

 frecuencia teórica;

: <= /i>media de las frecuencias acumuladas observadas.

 <= /o:p>

Mientras el coeficiente de determinación sea más cercano a 1, mejor es el grado de asoc= iación o correlación de la distribución empleada.

 <= /o:p>

Las precipit= aciones máximas probables, para cada período de retorno establecido (2, 5, 10, 25, = 50, 100, 150, 200, 300 y 500 años), son convertidas de duraciones diarias a duraciones en horas, esto se logra por medio de los coeficientes propuestos= por Campos= A. (1998) que se muestran en la Tabla = 4:

 <= /o:p>

Tabla 4=

Coeficientes para Conversión de Lluvias Máximas Diarias a Lluvias Máximas Horarias

DURACIONES EN HORAS

1

2

3

4

5

6

8

12

18

24

0,3

0,39

0,46

0,52

0,57

0,61

0,68

0,8

0,91

1

Fuente= : Campos A. (1998).<= /o:p>

 <= /o:p>

Las precipit= aciones obtenidas son divididas por sus duraciones para obtener las respectivas intensidades de precipitación.

 <= /o:p>

Regresión= Lineal Múltiple

La ecuación = [1] en su forma logarítmica se convierte en la ecuación <= !--[if supportFields]> REF _Ref108631734 \h = [10]:

 <= /o:p>

[10]<= /o:p>

 <= /o:p>

Al realizar = un cambio de variables a la ecuación [10], se obtiene la ecuación [11]:

 <= /o:p>

<= span lang=3DES style=3D'font-family:"Cambria Math",serif;mso-fareast-font-fami= ly:"Times New Roman"; mso-fareast-theme-font:minor-fareast;mso-ansi-language:ES'>

[11]<= /o:p>

 <= /o:p>

Donde:

<= o:p>

 <= /o:p>

Resolviendo = el sistema de ecuaciones [12], se obtienen los parámetros  y .

 <= /o:p>

<= span lang=3DES style=3D'mso-bidi-font-size:10.0pt;line-height:102%;font-family= :"Cambria Math",serif; mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fa= reast; mso-ansi-language:ES'>

<= span lang=3DES style=3D'mso-bidi-font-size:10.0pt;line-height:102%;font-family= :"Cambria Math",serif; mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fa= reast; mso-ansi-language:ES'>

<= span lang=3DES style=3D'font-family:"Cambria Math",serif;mso-fareast-font-fami= ly:"Times New Roman"; mso-fareast-theme-font:minor-fareast;mso-ansi-language:ES'>

[12]<= /o:p>

 

 <= /o:p>

Regionalización

Conforme a la Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) = del gobierno de México, la regionalización es una metodología que involucra la modificación del orden territorial de un Estado para obtener componentes territoriales más pequeños (CONA= BIO, 2017).

 <= /o:p>

En la hidrol= ogía, la regionalización se sustenta en un concepto de homogeneidad hidrológica en u= na región. Dicho de otro modo, consiste en la división de una zona de estudio = en regiones menores con características similares en los aspectos climáticos, orográficos, entre otros (Acost= a C. & Caro C., 2016).

 <= /o:p>

Esta metodol= ogía permite trasladar los datos hidrológicos observados en un lugar a otro sitio donde la información es nula.

 <= /o:p>

Métodos de Análisis Espacial para Regionalización de Parámetros

La espacialización incluye un conjunto de métodos que describen como un conjunto de datos en un sistema de coordenadas, generalme= nte cartesianas, dependen de los datos de estaciones vecinas. Los principales objetivos de la espacialización son la condensa= ción y la visualización de datos, permitiendo la obtención de información nueva o ampliada, partiendo de información existente (Schön= er & Tveito, 2002).

 <= /o:p>

Método de Ponderación Inverso a la Distan= cia

El método de ponderación inverso a la distancia o Inverse Distance<= /span> Weighting (IDW) es un tipo de interpolación que= hace la suposición de que los puntos medidos tienen una influencia local, que va disminuyendo conforme aumenta la distancia, permitiendo que un número de estaciones vecinas sean incluidas en la estimación del valor interpolado. C= uanto más cerca están las estaciones, el método pondera con un mayor peso, dado q= ue es el inverso de la distancia.

 <= /o:p>

El peso de la distancia es elevado a una potencia de ponderación  que tiene, en general, valores que van d= e 0 a 2.

 <= /o:p>

Le correspon= de la siguiente expresión (Shepa= rd, 1968):

 <= /o:p>

  <= /p>

[13]

 <= /o:p>

: valor interpolado en la localización  (ubicación sin datos)

: valor medido en la localización =

: dist= ancia entre la i-ésima ubicación medida (estación vecina) y la ubicación <= span style=3D'mso-bookmark:_Toc38966068'>

<= ![if !msEquation]> : función indicadora con valor = de 1 si  y con valor 0 si .

 <= /o:p>

Donde r deno= ta el radio de las estaciones vecinas; es decir, la región alrededor de la ubicac= ión predicha, desde la cual se toman en cuenta los valores medidos para la interpolación espacial.

 <= /o:p>

Kriging= Ordinario

La predicción obtenida por Kriging Ordinario es una combinaci= ón lineal de valores medidos debido a que es un promedio pesado de tales datos= :

 <= /o:p>

<= /p>

[14]

 <= /o:p>

Donde:

 <= /o:p>

  : repr= esenta los pesos o ponderaciones de los valores medidos, calculados en función de = la distancia entre los puntos muestreados y el punto a estimar;

: valor interpolado en la localización  (ubicación sin datos);

: valor medido en la localización = .

 <= /o:p>

Los coeficie= ntes lineales ( ) se calculan bajo la condición de insesgadez (que l= os valores teóricos sean idénticos a los reales; es decir, con error =3D 0) de= tal manera que la suma de estos pesos sea igual a 1:

<= ![if !msEquation]> <= /p>

[15]<= /o:p>

 <= /o:p>

De este modo= , la esperanza del predictor se supone idéntica a la esperanza real de la variab= le, respectivamente:

 <= /o:p>

<= /p>

[16]

 <= /o:p>

Otra restric= ción es de la varianza mínima del error de predicción (varianza kriging):

 <= /o:p>

<= /p>

[17]

 <= /o:p>

Los detalles= de esta teoría pueden ser observados en Cressi= e (1991) y Wacker= nagel (2003).

 <= /o:p>

Los métodos = de interpolación o análisis espacial descritos son empleados por medio de softwares SIG (Sistema de Información Geográfica) como QGIS.

 <= /o:p>

Validació= n de los Métodos de Análisis Espacial

La muestra h= a sido dividida en 2 partes: una muestra para realizar el modelo de interpolación, correspondiente al 95% de los datos y otra muestra de prueba, con el 5% restante para la validación. La distribución de las estaciones asignadas en cada parte se ilustra en la Figura 4.

 

=     Figura 4             

                  Identificación de Muest= ras

 <= /o:p>

 <= /o:p>

Las estacion= es M0005 y M0780 son las consideradas para la muestra de prueba. Los valores de  y  (calculados por medio de regresión lineal múltiple), correspondientes a estas estaciones, son comparados a los obteni= dos mediante la lectura de los mapas generados por Distancia Inversa Ponderada = y Kriging Ordinario para el cálculo del error relativo.= El error se calcula con la ecuación [18].

 <= /o:p>

<= ![if !msEquation]> <= /p>

[18]<= /o:p>

 <= /o:p>

Estándares del Open Geospatial Consor= tium (OGC)

El Open Geospatial Consortium (OG= C) es una organización dedicada a la creación de estándares geoespaciales abierto= s, esto hace referencia a determinado conjunto de normas cuyo propósito es impulsar que la información y servicios geoespaciales sean accesibles, interoperables, reutilizables y fáciles de encontrar. La interoperabilidad = hace referencia a la capacidad de intercambiar información y utilizarla <= !--[if supportFields]>ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"URL":"https://www.ogc.org/about","autho= r":[{"dropping-particle":"","family":&qu= ot;Open Geospatial Consortium (OGC)","given":"","non-= dropping-particle":"","parse-names":false,"su= ffix":""}],"container-title":"Vision and Mission","id":"ITEM-1","issued":{"d= ate-parts":[["2022"]]},"title":"About OGC","type":"webpage"},"uris":["htt= p://www.mendeley.com/documents/?uuid=3Dd28e38df-71ca-4943-bc6e-a9cf488fa2fd= "]}],"mendeley":{"formattedCitation":"(Open Geospatial Consortium (OGC), 2022a)","manualFormatting":"(OGC, 2022a)","plainTextFormattedCitation":"(Open Geospatial Consortium (OGC), 2022a)","previouslyFormattedCitation":"(Open Geospatial Consortium (OGC), 2022a)"},"properties":{"noteIndex":0},"schema= ":"https://github.com/citation-style-language/schema/raw/master/c= sl-citation.json"}(OGC, = 2022a).

 <= /o:p>

Los servicio= s que técnicamente obedecen a las normas establecidas por la = OGC, son reconocidos como estándares OGC y hacen posible que organizaciones de carácter público puedan intercambiar la información geoespacial de manera interna o de manera abierta a la ciudadanía.

 <= /o:p>

Servicio = de Mapas Web (WMS)

El Servicio = de Mapa Web o Web Map Service Interface Standard (WMS) es un estándar OGC que permite servir y solicitar un conjunto de imágenes georreferenciadas, por medio de internet. JPEG y PNG son los formatos de imagen típicos que devuelve tal solicitud, esto hace posible (indirectamente) que los mapas puedan ser mostrados en un navegador web (OGC, = 2022b).

 <= /o:p>

Servicio = de Cobertura Web (WCS)

El Servicio = de Cobertura Web o Web Coverage Service (WCS) es un estándar OGC que posibilita servir y solicitar por medio de internet un conjunto de archivos en diferentes formatos de ráster (píxeles)= (OGC, = 2022c).

 <= /o:p>

Los servicio= s WMS y WCS pueden ser generados a partir de QGIS con ayuda del complemento de Gisquick. Para compartir mapas desde los servidores d= e Gisquick, es necesario registrarse en https://projects.gisquick.org/user/.

 <= /o:p>

Resultados y Discusión

Los datos de precipitación máxima diaria, de cada estación meteorológica, fueron evaluad= os para comprobar si se ajustan a la distribución de Gumbel, esto a través de = las pruebas de bondad de ajuste: KolmogorovSmirnov y Coeficiente de Determinación.

 <= /o:p>

Para la prue= ba de KolmogorovSmirnov se ha considerado un nivel de significancia de 0,05 (confianza del 95%), este es = uno de los dos parámetros requeridos para obtener el valor crítico  a partir de la Tabla = 3, el otro parámetro es el número de datos  de cada estación y que corresponde a los= años con registros completos. Los resultados de esta prueba se contemplan en la = Tabla = 5.

 <= /o:p>

 <= /o:p>

 <= /o:p>

Tabla 5=

Test de KolmogorovSmirnov para la Distribución de Gumbel por Estación

CÓDIGO

ESTACIÓN

AÑOS CON REGISTROS

=

=

OBSERVACIÓN

M0005

Portoviejo-UTM

24

0,1135

0,2693

Se ajusta

M0006

Pichil= ingue

24

0,1071

0,2693

Se ajusta<= /span>

M0025

La Con= cordia

24

0,0790

0,2693

Se ajusta

M0026

Puerto= Ila

24

0,1200

0,2693

Se ajusta

M0037

Milagro (Ingenio Valdez)

24

0,1053

0,2693

Se ajusta

M0051

Babaho= yo-UTB

21

0,0969

0,2872

Se ajusta

M0124

San Ju= an La Mana

20

0,0576

0,2941

Se ajusta

M0160

El Car= men

12

0,0901

0,3754

Se ajusta

M0162

Chone<= o:p>

24

0,1435

0,2693

Se ajusta

M0165

Rocafu= erte

24

0,1000

0,2693

Se ajusta

M0166

Olmedo= -Manabí

24

0,0824

0,2693

Se ajusta

M0167

Jama

15

0,1234

0,3375

Se ajusta

M0169

Julcuy

24

0,3628

0,2693

No se ajusta

M0171

Campos= ano #2

24

0,0919

0,2693

Se ajusta

M0172

Pueblo= Viejo

24

0,1079

0,2693

Se ajusta

M0174

Ancón<= o:p>

10

0,1077

0,4093

Se ajusta

M0175

El Progreso-Guayas

11

0,0868

0,3912

Se ajusta

M0176

Naranj= al

22

0,1555

0,2809

Se ajusta

M0218

Ingeni= o San Carlos

24

0,0683

0,2693

Se ajusta

M0245

El Sus= piro

21

0,1612

0,2872

Se ajusta

M0348

Santa Anita-Km.10 Vía Chone

13

0,1197

0,3614

Se ajusta

M0447

24 De = Mayo (Jaboncillo)

17

0,0779

0,3180

Se ajusta

M0449

Sancán

14

0,1075

0,3489

Se ajusta

M0451

El Ane= gado

11

0,1074

0,3912

Se ajusta

M0452

Zapote=

10

0,3387

0,4093

Se ajusta

M0453

Chorri= llos

14

0,0950

0,3489

Se ajusta

M0455

Joa-Jipijapa

13

0,1452

0,3614

Se ajusta

M0457

Puerto= Cayo

15

0,0797

0,3375

Se ajusta

M0458

Colime= s De Paján

18

0,0706

0,3094

Se ajusta

M0459

San Pablo-Manabí

13

0,1654

0,3614

Se ajusta

M0462

Junín<= o:p>

13

0,1785

0,3614

Se ajusta

M0465

Ventan= as Inamhi

16

0,1364

0,3273

Se ajusta

M0466

Vinces=

21

0,0874

0,2872

Se ajusta

M0470

Mocach= e

16

0,1614

0,3273

Se ajusta

M0475

Colime= s De Balzar Inamhi

12

0,1307

0,3754

Se ajusta

M0476

La Cap= illa Inamhi

12

0,1058

0,3754

Se ajusta

M0619

Manglaralto

22

0,0858

0,2809

Se ajusta

M0780

Colonc= he

16

0,0822

0,3273

Se ajusta

M0782

Barcel= ona

22

0,1236

0,2809

Se ajusta

M0783

Salanguillo

18

0,1127

0,3094

Se ajusta

MA2U

Ingeni= o Aztra (La Troncal)

21

0,0840

0,2872

Se ajusta

MA2V

Guayaq= uil-Radio Sonda

18

0,0971

0,3094

Se ajusta

 <= /o:p>

Como se obse= rva en la Tabla 5, la prueba de KolmogorovSmirnov no admite la distribución de Gumbel para la estación M0169, puesto que para tal estación
<= ![if !msEquation]> . En el resto de las estaciones sí se cumple con <= ![if !msEquation]> .

 <= /o:p>

Los resultad= os de la prueba del Coeficiente de Determinación ( ) se muestran en la Tabla 6.

 <= /o:p>

Tabla 6=

Coeficiente de Determinación (<= /span> ) de la Distribución de Gumbel por Estación

CÓDIGO

ESTACIÓN

=

OBSERVACIÓN

M0005

Portoviejo-UTM

0,9609

Se ajusta

M0006

Pichil= ingue

0,9695

Se ajusta<= /span>

M0025

La Con= cordia

0,9848

Se ajusta

M0026

Puerto= Ila

0,9713

Se ajusta

M0037

Milagro (Ingenio Valdez)

0,9600

Se ajusta

M0051

Babaho= yo-UTB

0,9749

Se ajusta

M0124

San Ju= an La Mana

0,9912

Se ajusta

M0160

El Car= men

0,9666

Se ajusta

M0162

Chone<= o:p>

0,9338

Se ajusta

M0165

Rocafu= erte

0,9715

Se ajusta

M0166

Olmedo= -Manabí

0,9785

Se ajusta

M0167

Jama

0,9454

Se ajusta

M0169

Julcuy

0,4564

No se ajusta

M0171

Campos= ano #2

0,9722

Se ajusta

M0172

Pueblo= Viejo

0,9605

Se ajusta

M0174

Ancón<= o:p>

0,9656

Se ajusta

M0175

El Progreso-Guayas

0,9714

Se ajusta

M0176

Naranj= al

0,9523

Se ajusta

M0218

Ingeni= o San Carlos

0,9838

Se ajusta

M0245

El Sus= piro

0,9074

Se ajusta

M0348

Santa Anita-Km.10 Vía Chone

0,9597

Se ajusta

M0447

24 De = Mayo (Jaboncillo)

0,9741

Se ajusta

M0449

Sancán

0,9611

Se ajusta

M0451

El Ane= gado

0,9181

Se ajusta

M0452

Zapote=

0,4599

No se ajusta

M0453

Chorri= llos

0,9731

Se ajusta

M0455

Joa-Jipijapa

0,9271

Se ajusta

M0457

Puerto= Cayo

0,9769

Se ajusta

M0458

Colime= s De Paján

0,9828

Se ajusta

M0459

San Pablo-Manabí

0,8851

Se ajusta

M0462

Junín<= o:p>

0,8806

Se ajusta

M0465

Ventan= as Inamhi

0,9174

Se ajusta

M0466

Vinces=

0,9812

Se ajusta

M0470

Mocach= e

0,8825

Se ajusta

M0475

Colime= s De Balzar Inamhi

0,8831

Se ajusta

M0476

La Cap= illa Inamhi

0,9619

Se ajusta

M0619

Manglaralto

0,9716

Se ajusta

M0780

Colonc= he

0,9681

Se ajusta

M0782

Barcel= ona

0,9500

Se ajusta

M0783

Salanguillo

0,9677

Se ajusta

MA2U

Ingeni= o Aztra (La Troncal)

0,9771

Se ajusta

MA2V

Guayaq= uil-Radio Sonda

0,9608

Se ajusta

 <= /o:p>

De acuerdo c= on la Tabla = 6, la prueba del Coeficiente de Determinación no valida la distribución de Gumbel Tipo I para las estaciones M0169 y M0452 (no son recomendables coeficientes por debajo de 0.70). En el resto de las estacion= es, el R2 adopta valores superiores a 0,88 y esto indica que la distribución de frecuencias se ajusta bastante bien.

 <= /o:p>

Debido a los resultados de las pruebas de bondad de ajuste, se ha decidido excluir las estaciones M0169 y M0452 en el proceso de zonificación de las curvas de intensidad, duración y frecuencia.

 <= /o:p>

La  REF _Ref122092325 \h = Tabla = 7 evidencia los parámetros  y  obtenidos para las 40 estaciones que se ajustan a la distribución de Gumbel.

 <= /o:p>

Tabla 7=

Parámetros  y  de cada estación

CÓDIGO

ESTACIÓN

=

=

=

M0005

Portoviejo-UTM

250,41432

0,23388

0,61639

M0006

Pichil= ingue

526,24709

0,14694

0,61639

M0025

La Con= cordia

597,87694

0,12491

0,61639

M0026

Puerto= Ila

521,54441

0,16444

0,61639

M0037

Milagro (Ingenio Valdez)

477,65757

0,15019

0,61639

M0051

Babaho= yo-UTB

500,72551

0,12267

0,61639

M0124

San Ju= an La Maná

527,05666

0,17164

0,61639

M0160

El Car= men

575,38210

0,12512

0,61639

M0162

Chone<= o:p>

348,88120

0,19441

0,61639

M0165

Rocafu= erte

230,75436

0,23136

0,61639

M0166

Olmedo= -Manabí

418,79630

0,15533

0,61639

M0167

Jama

306,07010

0,23745

0,61639

M0171

Campos= ano #2

353,85239

0,15270

0,61639

M0172

Pueblo= Viejo

490,09762

0,16927

0,61639

M0174

Ancón<= o:p>

323,46180

0,30109

0,61639

M0175

El Progreso-Guayas

318,97427

0,24590

0,61639

M0176

Naranj= al

82,04625

0,18295

0,61639

M0218

Ingeni= o San Carlos

511,48366

0,22009

0,61639

M0245

El Sus= piro

233,35813

0,23820

0,61639

M0348

Santa = Anita

363,76729

0,24908

0,61639

M0447

24 de = Mayo (Jaboncillo)

311,65384

0,15800

0,61639

M0449

Sancán

217,92065

0,21445

0,61639

M0451

El Ane= gado

167,98535

0,23676

0,61639

M0453

Chorri= llos

266,72981

0,22832

0,61639

M0455

Joa - Jipijapa

204,73164

0,27159

0,61639

M0457

Puerto= Cayo

248,86706

0,25505

0,61639

M0458

Colime= s De Paján

282,26509

0,15051

0,61639

M0459

San Pa= blo - Manabí

306,66816

0,15927

0,61639

M0462

Junín<= o:p>

375,09095

0,18388

0,61639

M0465

Ventan= as INAMHI

498,41080

0,12316

0,61639

M0466

Vinces=

466,83273

0,15816

0,61639

M0470

Mocach= e

527,12280

0,20797

0,61639

M0475

Colime= s de Balzar INAMHI

475,01339

0,19372

0,61639

M0476

La Cap= illa INAMHI

411,34195

0,16973

0,61639

M0619

Manglaralto

238,75830

0,28517

0,61639

M0780

Colonc= he

224,84364

0,25476

0,61639

M0782

Barcel= ona

193,56532

0,27035

0,61639

M0783

Salanguillo

166,81343

0,25930

0,61639

MA2U

Ingeni= o Aztra (La Troncal)

446,00366

0,18957

0,61639

MA2V

Guayaq= uil-Radio Sonda

472,74519

0,20514

0,61639

 

Los resultados de la zonificación de los parámetros de las ecuaciones para las curvas de intensidad, duración y frecuencia (IDF) en las provincias de Santa Elena y Manabí, mediante los métodos de interpolación de Distancia Inversa Ponderada (IDW) y Kriging Ordinario (KO), se muestran en la Figura 5 y Figura 6, respectivamente.


 

Figura 5=

Regionalización de  y . Método IDW

<= /p>

<= /p>

<= /p>

 <= /o:p>

Figura 6=

Regionalización de  y . Método KO

<= /p>

<= /p>

<= /p>

 <= /o:p>

En la Figura 5 y Figura 6 se puede apreciar que el valor de  tiene una tendencia creciente en direcci= ón Suroeste – Noreste, mientras que para  es todo lo contrario; es decir, tiene una tendencia creciente en dirección Noreste – Suroeste. De los dos términos mencionados, el parámetro =  es el que mayor influencia tiene en la determinación de las intensidades de lluvia; entonces, es evidente que, en = el Norte de la zona estudiada, se presentan las mayores intensidades.

 <= /o:p>

Comparando l= os métodos IDW (Figura 5) y KO (Figura 6) se deduce que, el primer método de análisis espacial empleado, tiene mayor presencia de picos locales, lo q= ue significa que la interpolación por Kriging Ordi= nario distribuye de una mejor manera los datos analizados. En términos de homogeneidad y regularidad, la manera en que el método de Distancia Inversa Ponderada concentra algunos valores, lo convierte en el menos adecuado para= la regionalización de las curvas IDF en las provincias de Santa Elena y Manabí= .

 <= /o:p>

Según lo evi= denciado en la Tabla 7, el valor de  es constante (0,61639), esto indica que = sin importar el método que se aplique para su regionalización, el resultado ser= á un mapa cuya magnitud no difiere en toda su extensión, como se aprecia en la <= /span>Figura 5 y Figura 6. Por este motivo, en la determinació= n de errores para los métodos IDW y KO, solo se hace referencia a los parámetros=  y .  Estos errores son en relación con los v= alores de la Tabla 8 y se muestran en la  REF _Ref122092891 \h Tabla 9 y Tabla 10.

 

Tabla 8=

Parámetros  y  Obtenidos por Regresi= ón Lineal Múltiple para Estaciones de Prueba

CÓDIGO

ESTACIÓN

=

=

M0005

Portoviejo-UTM

250,41432

0,23388

M0780

Colonc= he

224,84364

0,25476

 

Tabla 9=

Parámetros  y  Obtenidos por IDW para Estaciones de Prueba con Respectivos Errores.

CÓDIGO

ESTACIÓN

=

ERROR (%)

=

ERROR (%)

M0005

Portoviejo-UTM

257,997

3,03 %

0,2181

6,76 %

M0780

Colonc= he

214,082

4,79 %

0,2542

0,24 %

 

Tabla 10

Parámetros  y  Obtenidos por KO para Estaciones de Prueba con Respectivos Errores.

CÓDIGO

ESTACIÓN

=

ERROR (%)

=

ERROR (%)

M0005

Portoviejo-UTM

251,981

0,63 %

0,2132

8,85 %

M0780

Colonc= he

211,512

5,93 %

0,2616

2,67 %

 

Ambos métodos de interpolación presentan errores menores al 10%, por lo que, son completamente válidos. Como se ha indicado en la comparación gráfica, debid= o a criterios de regularidad y homogeneidad, el método de = Kriging Ordinario ha sido considerado como el más apropiado para representar una zonificación de las curvas IDF en las provincias de Santa Elena y Manabí.

 <= /o:p>

Los mapas ge= nerados, aplicando Kriging Ordinario para la regionaliza= ción de los parámetros de las ecuaciones de las curvas de intensidad-duración-fr= ecuencia en las provincias de Santa Elena y Manabí, han sido publicados como una aplicación web creada en ArcGIS Online. Esta aplicación permite visualizar = las capas del mapa a través de un navegador web, accediendo desde el siguiente enlace: https://arcg.is/1CCKeO0. La aplicación, dentro de sus opciones, pone a disposición otro enlace que permite el acceso remoto desde QGIS a cada una de las capas que pertenecen a los mapas mencionados. Las capas se pueden agregar como servicio de imágenes (WMS) o como capas ráster (WCS) añadiendo tal enlace.

Conclusiones

Los métodos de análisis espacial de Distancia Inversa Ponderada y Kriging Ordinario son válidos para generar un modelo de regionalización que represente la variación de los parámetros de las curvas de intensidad, duración y frecuencia (IDF). Ambos presentan un error inferior al 10% en la determinación de tales parámetros,= si se los compara a los obtenidos mediante regresión lineal múltiple en estaci= ones excluidas en las respectivas interpolaciones. Se evidencia que el método de= Kriging Ordinario se adapta mejor a la distribución d= e los datos, dado que no genera picos locales como la Distancia Inversa Ponderada, por e= nde, el mapa publicado en ArcGIS online se desarrolló a partir de Kriging Ordinario. Este mapa permite leer los parámet= ros =  y   de las ecuaciones para las curvas IDF en cualquier sitio dentro de las provincias de Santa Elena y Manabí. Para acce= der al mapa web se debe visitar el siguiente enlace: https://arcg.is/1CCKeO0, entre sus opc= iones ofrece un vínculo para integrar el servicio WMS/WCS en softwares SIG de escritorio como QGIS.

 

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6

Pietro Corapi, Nedin Nuñez Neira

5

Regionalización de las Cu= rvas de Intensidad-Duración-Frecuencia (IDF) en las Provincias de Santa Elen= a y Manabí

 

Escuela Superior Politécn= ica del Litoral, ESPOL

 

Revista Tecnológica Espol= – RTE Vol. 34, N° 4 (Diciembre, 2022) / e-ISSN 1390-3659

 

Escuela Superior Politécn= ica del Litoral, ESPOL

 

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