Observatorio de Cambio Climático y Resiliencia

Santo Domingo
23°

algo de nubes
Humedad: 100%
Viento: 1m/s N
Máx: 21 • Mín: 21

24°
Jue

27°
Vie

26°
Sab

25°
Dom

Weather from OpenWeatherMap

Santiago de los Caballeros
23°

algo de nubes
Humedad: 100%
Viento: 1m/s SE
Máx: 22 • Mín: 19

24°
Jue

25°
Vie

24°
Sab

26°
Dom

Weather from OpenWeatherMap

Mantente Informado

OBSERVATORIO DE CAMBIO CLIMÁTICO Y RESILIENCIA

Soluciones concretas para enfrentar el cambio climático y mejorar la resiliencia.

Es una plataforma de conocimiento para contribuir al análisis, evaluación y adaptación frente al cambio climático y su interrelación con la economía, ambiente y sociedad, con el fin de generar y compartir información de utilidad para aportar al conocimiento, la planificación y la toma de decisiones para la prevención de los impactos negativos del climático y la variabilidad. El Observatorio proporcionará soporte científico a las instituciones de República Dominicana y las comunidades para reducir el riesgo climático, suministrando información de alta calidad, útil, así como desarrollar las capacidades necesarias para obtener y utilizar este tipo de información climática.

Weather Station Data

Dominican Republic is a small island located within the Caribbean Basin that has recently been subjected to many environmental changes. According to Germanwatch 2013, this country has been ranked as the tenth most vulnerable country in the world for long term risk. In addition, detected regional warming will enhance extreme events over the Caribbean region.

The City College of New York (CCNY) has been collecting the accessible climate data, applying quality data criterion, analyzing climatology and extreme events, and mapping the watersheds that contain the following four municipalities: Santo Domingo National District, Santiago, San Pedro de Marcorís, and Samaná.

Weather station data were collected from ONAMET and the National Climate Data Center (NCDC) from NOAA. In Samaná province, one station provides useful information from ONAMET (see Figure 1). Data for Samaná can be download here.

Figure 1. Weather station location in Samana.
Figure 1. Weather station location in Samana.

Samana Climatology

The Caribbean region is characterized by a rainfall season with a bimodal nature, where the initial peak of this season, called early rainfall season (ERS), begins in May and it extends until July, with a brief dry period in July. The second half of the overall rainy season or late rainfall season (LRS) spans from August to November (see Figure 2). From November to March, the low rainfall season is called dry season (DS).

Figure 2. Caribbean rainfall bimodal nature.

At local scale, the rainfall shape and intensity could be affected by local urbanization, deforestation, and terrain elevation as well as aerosol concentration in the atmosphere. Weather station data capture the local spatial variability and provide a good representation of human activity impact on local climate. In the province of Samaná, Dominican Republic, ground station data were collected from ONAMET to calculate climatology and time series for rainfall, and air temperature. Heat index is not calculated because this station does not provide relative humidity or dew point temperature data. The rainfall climatology calculated from ONAMET data depicts a bimodal pattern in similar way than the Caribbean region, with a mid-summer drought shifted toward June, and with a very intense rainfall activity in May (see Figure 3).

Figure 3. Rainfall climatology at the weather station in Samaná, ONAMET.
Figure 3. Rainfall climatology at the weather station in Samaná, ONAMET.

A near term rainfall time series was divided into early rainfall season and late rainfall season. During the early rainfall season, the rainfall show a tendency to increase with time at a rate of 6.36 mm per year, while in the late rainfall season a rainfall deficit tendency is detected with a rate of decrease of -3.3 mm per year (see Figure 4).

Figure 4. Early and late rainfall season annual rainfall in Samaná, ONAMET.
Figure 4. Early and late rainfall season annual rainfall in Samaná, ONAMET.
Figure 4. Early and late rainfall season annual rainfall in Samaná, ONAMET.
Figure 4. Early and late rainfall season annual rainfall in Samaná, ONAMET.

The air temperature climatology describe a unimodal pattern with a hotter atmosphere in July and August, reaching a peak temperature of 27.6oC (see Figure 5). Furthermore, a time series analysis indicates a clear decreasing tendency with a rate of change of -0.3oC per year (see Figure 6). In this weather station there is cooler environment tendency.

Figure 5. Air temperature climatology at the weather station in Samaná, ONAMET.
Figure 5. Air temperature climatology at the weather station in Samaná, ONAMET.
Figure 6. Near term time series for air temperature in Samaná, ONAMET.
Figure 6. Near term time series for air temperature in Samaná, ONAMET.

Drought index

Lack of precipitation causes a complex reverse process of evaporation and slow depletion of soil moisture. In a brief period without rainfall, plants with deeper roots, such as bushes, trees and most field crops, will not suffer negative. When lack of precipitation is combined with exceptionally high temperatures, evaporation increase from the soil and plant transpiration, which launch a severe drought. Drought events could have negatively effects on the economy and population health. Farmers could lose their crops or spend more money on irrigation, Ranchers will spend more money on feed and water their livestock and the food will become more expensive. Low water flows and poor water quality could generate health problems as well as loss of human life. Economic loss and reduced incomes could incentive the anxiety or depression in the population.

The meteorological drought is defined as episodes with a very low rainfall activity. Precipitation is concentrated within a short period, followed by no precipitation for months. This lack of precipitation could lead to a deficit in soil moisture.

The standardized Precipitation Index (SPI) is a meteorological drought index used to monitor periods of anomalous dry events. To calculate SPI, a sliding windows technique is used, where the sliding window accumulate the rainfall according the selected time scale. A gamma distribution is fitted to the accumulated monthly rainfall and the cumulative distribution function is transformed to the standardized normal distribution. The z-scores is the SPI. This index characterize a severe drought when get values between -1.5 to -1.99, and extreme droughts when SPI is less than -2.0 (see Table 1).

Table 1. SPI classification.
Table 1. SPI classification.

Drought events in Dominican Republic

The CPC Merged Analysis of Precipitation data with a resolution of 2.5 degree was selected to calculate the SPI over the Dominican Republic.  The closest coordinate to Dominican Republic corresponds to 18.25oN, 71.25oW, which is placed at Southeast of Dominican Republic and represent the mean value for the whole Dominican Republic Island.

Long term monthly SPI time series with a three month sliding window indicate a periodicity decrease of 60 months during the last years (2003 – 2014) but with intense drought events. In addition, in this monthly time series an extreme drought event were identified in Dominican Republic, in the month of June 1994 (see Figure 7).

Figure 7. Monthly drought index time series for the whole Dominican Republic.

At local scale, weather station rainfall data from Samaná – ONAMET was used to analyze drought events in order to detect climate variability at local scale. Samaná gage stations were taking into account to identify the intensification or weakening of extreme rainfall.

Long term trend of monthly drought index in Samaná shows periods of extreme drought events. Samaná has an extreme drought in October 2000, March 2009, among other drought events (see Figure 8). Furthermore, Samaná shows a small period with drought intensification from 2011 to 2014.

Figure 8. Drought index in Samaná, ONAMET.

References

[1]. Stull, Roland. 2000. Meteorology for scientists and engineers. Second. Belmont: Brooks/Cole Cengage Learning.

[2]. NWS. 2014. The Heat Index Equation. May 28. Accessed July 1, 2015. http://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml.

Water Cycle

The hydrological cycle drives the water availability in the watershed as well as groundwater recharge. Although rainfall data is loss by infiltration, evaporation and retention, large amount of water is turned into surface and sub-surface runoff to recharge the river network in the watershed. Furthermore, infiltration and percolation could return to the cycle providing the river base flow. In the water cycle, evaporation from the surface ocean is one of the main factors. Some water in the oceans evaporates into the air, and the rising air takes the vapor toward the upper atmosphere. The water vapor content in the atmosphere is then advected towards the land mass, where cooler temperatures cause it to condense into clouds (see Figure 1). Later, cloud particles collide, grow, and fall out of the sky as precipitation.

Regional warming with higher sea surface temperature (SST) could intensify the ocean surface evaporation, increasing the water content in the atmosphere. Too high SST along with changes in the wind patterns in the upper and lower atmosphere, changes in the high pressure system frequency and location could lead to drought events instead of heavy rainfall in the Caribbean region. In this way, the entire hydrological cycle could be affected and the water availability in the watersheds.

Figure 1. Hydrological water cycle.
Figure 1. Hydrological water cycle.

Drought Definition

Heavy and prolonged rain saturate the soil, creates runoff that accumulates in lower areas, causing flash floods, economic loss and in some cases loss of life. On the opposite side, lack of precipitation causes a complex reverse process of evaporation and slow depletion of soil moisture. The time without rainfall plays a crucial role in the drought evolution. In a brief period without rainfall, plants with deeper roots, such as bushes, trees and most field crops, will not suffer negative effects because they can draw moisture from deeper levels of the soil. When lack of precipitation is combined with exceptionally high temperatures, evaporation increase from the soil and plant transpiration, which in combination with a prolonged dry period can launch a severe drought. Drought events could have negatively effects on the economy and population health. Farmers could lose their crops or spend more money on irrigation, Ranchers will spend more money on feed and water their livestock and the food will become more expensive. Low water flows and poor water quality could generate health problems as well as loss of human life. Economic loss and reduced incomes could incentive the anxiety or depression in the population.

There is not a unique definition for the drought because this phenomenon is identified by its impact over different systems (agriculture, water resource, ecosystems, etc.). According with the environments, a drought could be defined as:

Meteorological drought: very low rainfall episodes. Precipitation is concentrated within a short period, followed by no precipitation for months. This lack of precipitation could lead to a deficit in soil moisture.

Hydrological drought: shortfalls in surface water and groundwater on whole watersheds. Water resources become depleted and rivers and reservoirs drop to lower than normal levels. There is a lag between the rainfall decrease and the streamflow reduction.

 

Drought Index

The standardized Precipitation Index (SPI) is meteorological drought index defined to monitor drought at a given time scale and rainfall station. This index also can be used to monitor periods of anomalous wet events. To calculate SPI, a sliding windows technique is used, where the sliding window accumulate the rainfall according the selected time scale. A gamma distribution is fitted to the accumulated monthly rainfall and the cumulative distribution function is transformed to the standardized normal distribution. The z-scores is the SPI.

Drought index can be used to identify the beginning and the end of a drought event. A drought event start when the index take values below a given threshold, and end when again is about this threshold. The SPI characterize a severe drought when get values between -1.5 to -1.99, and extreme droughts when SPI is less than -2.0 (see Table 1).

Table 1. SPI classification.
Table 1. SPI classification.

There are relevant parameters to assess droughts (see Figure 2), such as:

Duration: time period where the index is blow the selected threshold.

Intensity: mean value of indexes below the threshold.

Magnitude: multiplication of duration and intensity, which means shortfalls accumulation below the selected threshold.

Drought area extension: drought event extend beyond one weather station.

Figure 2. Drought definition
Figure 2. Drought definition

Drought Time Scale

1-month  SPI: it is considered to be a more accurate representation of monthly precipitation for a given location because the long-term precipitation is fitted to a probability distribution.

3-month SPI: reflects short/medium moisture condition and provides a seasonal estimation of rainfall. In agriculture gives an indication of Soil Moisture condition at the growing season.

6-month SPI: indicates medium-term trends in precipitation. A 6-month SPI may also begin to be associated with anomalous streamflows and reservoir levels.

12-month SPI: associated with streamflows, reservoir levels, and even groundwater levels.

 

IPCC Scenarios

Accelerated increase of greenhouse gases (GHGs) and dramatic climate changes are influenced by anthropogenic activity, and according with this tendency, the IPCC conducted a study of technical, scientific and socioeconomic information to determine the risk of climate changes generated by human activity [1]. The IPCC issued new scenarios in 2011, called Representative Concentration Pathways (RCPs). These new scenarios not only provides projections of GHG concentration in the, but also the pathway required over time to reach specific radiative forcing outcome.

Four main scenarios were developed, RCP2.6 which considers GHGs emissions start decreasing after the first decade since 2013 and in 60 years to reduce almost to zero emission. This scenarios is considered unlikely to exceed in 2oC the earth temperature since pre-industrial time at the end of 21st century. In consequence, RCP2.6 is assuming an aggressive mitigation strategy [2]. RCP4.5 scenario is considered as a medium-low scenario. Because this scenario take into account some actions to control emissions, it is considered as a stabilization scenario. The target in this scenario is to stabilize the radiative forcing at 4.5 W/m2 in the year 2100. To reach this target, this scenario consider lower emissions technologies, intense use of carbon capture technology, geological store technology, and apply emission price to land use emissions to extend forest land areas from the present day [2,3].  RCP6.0 is a medium-high scenario and also considered as a stabilization scenario. In this scenario, CO2 emissions will continue increasing until 2080 and at the end of the 21st century concentration will be around 25% higher than RCP4.5 [2]. RCP8.5 is considered as a ‘business-as-usual’ approach, which combines high population and relative slow income growth. In addition, this scenario specifies a low rate of technology changes and energy improvements. Consequently, RCP8.5 is the pathway with highest GHG emissions in absence of climate change policies. In this scenario, CO2 concentration at the end of the 21st century will be three to four times higher than pre-industrial concentration in the atmosphere [2, 4]. In these scenarios, the RCP4.5 is more likely not exceed 2oC, while RCP8.5 is likely not exceed 4oC at the end of the 21st century. According with the RCP8.5 and RCP6.0 scenarios a more acidic oceans and global sea level rise between half to one meter are expected. In addition, more heat waves and changes in rainfall patterns should be expected (see Figure 3).

Figure 3. Four potential radiative pathways depending of policies governments adopted to cut emissions [10].
Figure 3. Four potential radiative pathways depending of policies governments adopted to cut emissions [10].

General Circulation Models

The fifth phase of the Coupled Model Intercomparison Project (CMPI5) provides large set of climate simulation using general circulation models to produce trustworthy multi model dataset along the 21st century. These GCMs provides near-term (10-30 years), and long term simulations (century scale), where long-term simulation projects the climate response to a changing atmospheric composition and land cover [5, 6]. Four GCMs were selected for this study; these models are the Community Climate System Model version 4 (CCSM4), the Community Earth System Model, version 1 (CESM1)–Biogeochemistry (CESM1-BGC), the CESM1 – Community Atmospheric Model version 5 (CESM1-CAM5), and the CESM1 – Whole Atmosphere Community Climate Model (CESM1-WACCM). These models have a uniform resolution of 0.94 degree in latitude and 1.25 degree in longitude and 26 vertical layers [7, 8, 9, 10]. Furthermore, CCSM4 has the capability to simulate a more realistic El Niño–Southern Oscillation, as well as the Madden–Julian oscillation [7, 11]. The GCMs are composed by four components, an atmospheric model, a land model, a sea ice model, and an ocean model. A flux coupler allow to exchange mass and energy fluxes between these components (see Figure 4). Data for General Circulation Models can be download here.

A mean multi-model ensemble will be used to account modeling uncertainties, taking the average of the climate variation simulated by every GCMs. RCP4.5 and RCP8.5 scenarios were selected to analyze long-term time series.

Figure 4. General Circulation Model components.
Figure 4. General Circulation Model components.

Caribbean Drought Projection

SPI was calculated for every GCMs over the Caribbean region, and from 2007 to 2099 under the RCP4.5 and RCP8.5 scenarios. A mean multi-model ensemble was applied to account the uncertainty in the climate modeling. Furthermore, this drought index was averaged across the Caribbean to identify the region tendency of rainfall shortfall. This average tends to provide low SPI values, but the tendency remains and it will indicate a wetter or drier atmosphere in the future.

The Caribbean region under the RCP4.5 scenario tends to intensify the drought in the future with a trend of -0.0032 per year (see Figure 5). According with the scenario RCP8.5, a more intense regional warming leads to a more intense drought in the region, where the SPI index decrease at a rate of change of -0.0096 per year (see Figure 6).

Figure 5. Mean multi-model ensemble for SPI long term trend from 2007 to 2100, RCP4.5.
Figure 5. Mean multi-model ensemble for SPI long term trend from 2007 to 2100, RCP4.5.
Figure 6. Mean multi-model ensemble for SPI long term trend from 2007 to 2100, RCP8.5.
Figure 6. Mean multi-model ensemble for SPI long term trend from 2007 to 2100, RCP8.5.

The 21st century was divided in four climate periods: 2020-2039, 2040-2059, 2060-2079, and 2080-2099. In the RCP4.5 scenario, the maximum number of drought events in the region is centered in the period 2060-2079, obtaining 15 droughts in 20 years (see Figure 7). From 2040 to 2099, the number of drought events remains almost constant. In the RCP8.5 scenario, the drought events increase fast, getting the maximum number of events in the last two decades (2080-2099). In this scenario and in the last two decades, there are more than 20 events per two decades (see Figure 8).

Figure 7. Number of drought events along the 21st century under the RCP4.5 scenario.
Figure 7. Number of drought events along the 21st century under the RCP4.5 scenario.
Figure 8. Number of drought events along the 21st century under the RCP8.5 scenario.
Figure 8. Number of drought events along the 21st century under the RCP8.5 scenario.

A SPI frequency analysis indicate that the RCP4.5 scenario depicts a probability of 60% to develop drought events in the Caribbean region (see Figure 9), while the scenario RCP8.5 describes  a probability of 55% to get drought events in this region (see Figure 10).

Figure 9. Relative and cumulative distribution for SPI under the RCP4.5 scenario.
Figure 9. Relative and cumulative distribution for SPI under the RCP4.5 scenario.
Figure 10. Relative and cumulative distribution for SPI under the RCP8.5  scenario.
Figure 10. Relative and cumulative distribution for SPI under the RCP8.5 scenario.

Conclusions

The Caribbean region shows a drought intensification in the future. The RCP4.5 scenario tends to intensify the droughts, but the scenario RCP8.5 accelerate the process with a SPI rate of change of -0.0096 per year. Although the scenario RCP8.5 increase very fast the number of drought events in the future, the scenario RCP4.5 possess the higher probability to develop drought events.

 

References

[1]. IPCC, 2000, Emissions scenarios: summary for policymakers, Intergovernmental Panel on Climate Change.

[2]. Symon, C., 2013, Climate change: Action, trends and implications for business, European Climate Foundation.

[3]. Thomson, A., Calvin, K., Smith, S., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M., Clarke, L., and Edmonds, J., 2011, “RCP4.5: a pathway for stabilization of radiative forcing by 2100,” Clim. Change, 109, pp. 77–94.

[4]. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P., 2011, “RCP 8.5—A scenario of comparatively high greenhouse gas emissions,” Clim. Change, 109, pp. 33–57.

[5]. Meehl G, Goddard L, Murphy J, et al (2009) Decadal prediction, can it be skillful? BAMS 1467–1485.

[6]. Taylor K, Stouffer R, Meehl G (2012) An overview of CMIP5 and the experiment design. BAMS 485–498.

[7]. Gent P, Danabasoglu G, Donner L, et al (2011) The community climate system model version 4. J Clim 24:4973–4991.

[8]. Gettelman A, Kay J (2011) The evolution of climate sensitivity and climate feedbacks in the community atmosphere model. J Clim 25:1453–1469.

[9]. Danabasoglu G, Bates S, Briegleb B (2012) The CCSM4 Ocean component. J Clim 25:1361–1389.

[10]. Holland M, Bailey D, Briegleb B, et al (2012) Improved sea ice shortwave radiation physics in CCSM4: The impact of melt ponds and aerosols on Arctic Sea ice. J Clim 25:1413–1430.

[11]. Subramanian C, Jochum M, Miller A, et al (2011) The Madden–Julian oscillation in CCSM4. J Clim 24:6261–6282.

Fuente: www.panoramio.com

Ambar Mesa

ESES Undergraduate Student, CCNY

Jorge E. Gonzalez, PhD

Mechanical Engineering Dept., CCNY

Moises Angeles, PhD

Mechanical Engineering Dept., CCNY

Introducción

El patrón atmosférico de la región del Caribe es controlado fundamentalmente por la temperatura de la superficie del mar, ondas del Este, divergencia de los vientos alisios, así como eventos de tele-conexión tales como El Niño-Oscilación del Sur. La convergencia de todos estos factores así como el desarrollo de anomalías debido a un calentamiento regional generan una región altamente vulnerable a eventos extremos tales como actividades ciclónicas, sequías, ondas de calor, entre otros eventos. República Dominicana es parte de las Antillas Mayores y la región del Caribe, siendo por ende afectada por el calentamiento regional detectado recientemente (Glenn et al., 2015). Una de las ciudades más vulnerables a inundaciones es la capital, Santo Domingo.

La región sureste del país frecuentemente es más afectada por huracanes y tormentas tropicales, lo que coloca a Santo Domingo como una de las áreas más afectadas por estos fenómenos. Uno de los eventos ciclónicos más dramáticos que ha impactado a esta ciudad y la parte sur del país fue el ciclón David en 1979. Este fenómeno fue de categoría 5, y destruyó 70% del alumbrado eléctrico y la red telefónica, también el acueducto de Santo Domingo fue gravemente afectado, según Ruddy German Pérez (2009). En este reporte, también se hace referencia a daños en la infraestructura de puentes, carreteras, canales de riego, entre otras estructuras.

Según estudios del Banco Interamericano de Desarrollo (2001), más de un 46% de los eventos de inundación ocurridos entre los años 1966 y 2000 se encuentran en zonas urbanas importantes tales como el Distrito Nacional (Santo Domingo) y varias ciudades de la sub-región del Cibao Central. Santo Domingo, posee uno de los ríos más caudalosos del país, el Río Ozama, donde la cuenca del río abarca 2,686 kilómetros cuadrados y recorre 148 kilómetros. Este estudio se enfoca en 4.38 kilómetros del río antes de desembocar en el mar Caribe, tal como se muestra en la figura.1. En las zonas aledañas al río se desarrollan tres tipos de actividades económicas: agropecuarias, industriales, y comercio marítimo.
El objetivo de la presente investigación es desarrollar una herramienta que permita identificar las zonas de riesgo de inundación debido a eventos de tormenta extremos. Por la importancia de la zona y su historial de inundaciones, la ciudad de Santo Domingo fue seleccionada para desarrollar esta investigación.

Figure 1 Área de estudio del Río Ozama en Santo Domingo

Metodología

El proyecto se ha dividido en dos secciones. La primera parte del proyecto consiste en el desarrollo de un análisis de frecuencia para determinar caudales extremos en el Río Ozama. Este análisis requiere información de caudales diarios del río Ozama y sus efluentes, los cuales fueron proporcionados por el Instituto Nacional de Recursos Hidráulicos (INDRHI). La figura.2, obtenida del Plan Hidrológico Nacional por INDRHI, muestra la localización de las estaciones de caudal y sus respectivos periodos de datos. En este análisis, fue necesario seleccionar un periodo común (1967-1982) entre las estaciones para obtener el caudal total del río en el área de análisis. Este periodo fue seleccionado debido a que todas las estaciones compartían el mismo periodo de tiempo, siendo este periodo uno de los más completos. Posteriormente, los caudales de los afluentes fueron acumulados para obtener el caudal total del río en la zona de interés, durante el periodo de análisis.
Asimismo, fueron estimados los caudales correspondientes a diferentes periodos de retorno (25, 50, 75, y 100 años), utilizando los caudales máximos anuales y aplicándoles la distribución de Log Pearson III. Esta distribución estadística es ampliamente utilizada en el análisis de frecuencia de caudales máximos y recomendada por el Servicio Geológico de USA (USGS). (Oregon State University, 2002)

Figure 2 Diagrama Topológico de la región Hidrográfica Ozama-Nizao

La segunda parte del proyecto se desarrolló utilizando Sistema de Información Geográfica (GIS) y HEC-RAS (Sistema de Análisis de Ríos). HEC-RAS es un software desarrollado por el Hydrologic Engineering Center. Este software permite al usuario desarrollar cálculos para flujos constantes de una dimensión, flujos no permanentes de una y dos dimensiones, transporte de sedimentos, entre otros análisis (US Army Corps of Engineers Website). Los datos requeridos en esta parte del proyecto fueron: una imagen aérea de la zona y un modelo digital de elevación (DEM, por sus siglas en inglés) obtenidos del USGS. Utilizando la herramienta ArcMap de ArcGIS, fue posible diseñar la geometría del río y obtener las secciones transversales del Río Ozama. El perfil de elevación de cada sección transversal fue comparado con los perfiles proporcionados por Google Earth, para validar los valores obtenidos del DEM. La figura.3 muestra las secciones transversales del río.

Figure 3 Muestra las secciones transversales perpendiculares al Río Ozama.

Esta información fue importada en HEC-RAS. Este sistema de modelación de ríos está dividido en varios componentes. Dos componentes fueron utilizados en este trabajo, en el primer componente se define la geometría de las secciones del río y de los puentes, mientras que en el segundo componente se establece los caudales a analizar. En el tramo del río seleccionado se encuentran dos principales puentes, Francisco Sánchez y Juan Bosch, cuyas dimensiones y áreas de obstrucción de flujo se introdujeron en HEC-RAS, teniendo en cuenta las pilastras, la longitud y ancho del puente, así como su altura máxima y mínima, tal como se muestra en la figura.4. La tabla.1 muestra los datos considerados para cada puente. Estos datos fueron proporcionados por el Ministerio de Obras Publicas y Comunicaciones (MOPC). La tabla.2 presenta los valores asumidos en nuestro análisis. La pendiente del río Ozama en el tramo seleccionado es 0.000414,  la cual caracteriza un flujo sub crítico, es por ello que se seleccionó como condición de frontera una profundidad normal.  Además, el número de Manning fue estimado usando imágenes aéreas para determinar la aspereza del suelo.

Para la sección central del rio se asumió un cauce natural siendo el número de Manning, 0.040. Las secciones adyacentes se caracterizaban con cierta concentración de árboles, cemento, y casas. Los valores de Manning asignados para estos se muestra en la Tabla.2.

Figure 4 muestra los puentes Francisco Sánchez y Juan Bosch y sus respectivas localizaciones en el modelo.
Tabla.1 Características generales de los puentes introducidos al modelo de HEC-RAS
Tabla.1 Características generales de los puentes introducidos al modelo de HEC-RAS
Tabla.2 Valores asumidos en el modelo de HEC-RAS

En el segundo componente utilizado en HEC-RAS se introdujo una serie de caudales máximos anuales. Estos caudales máximos corresponden a periodos de retorno de 25, 50, 75 y 100 años, de este modo se genera en HEC-RAS un perfil para cada periodo de retorno. El perfil número uno corresponde al caudal máximo correspondiente a 25 años, el perfil numero dos corresponde a 50 años, el perfil número 3 a 75 años y el perfil número 4 a 100 años.

Resultados del Análisis de Frecuencia de Flujo

En el análisis de frecuencia se utilizó caudales máximos anuales para obtener una serie de caudales a diferentes periodos de retorno. La serie de tiempo de caudales máximos anuales es contrastada con los diferentes periodos de retorno para identificar la frecuencia con que el Río Ozama alcanzó o superó estos eventos extremos.

En la figura.5 se observa la frecuencia con que el río Ozama ha alcanzado o superado los caudales correspondiente a los 4 periodos de retorno calculados en este trabajo y que corresponde al periodo 1967-1982. Como se puede observar, la línea roja (caudal estimado para 25 años) se intercepta con el año 1980. En este año el Huracán Allen, categoría 5 afectó principalmente la parte sur del país. También, se puede observar que la línea azul oscura (caudal estimado para 100 años) se intercepta con el año 1981. Durante este año, la tormenta tropical Gert afectó drásticamente al noroeste del país. Finalmente, se observa un caudal mayor en el año 1979. En este año ocurrieron dos eventos extremos. El primero fue el Ciclón David, categoría 5, considerado uno de los huracanes más fuertes que impactó el país. Este fenómeno causó más de 2,000 muertes, inundaciones, y daños en la infraestructura principalmente en el sur del país. Más adelante, la tormenta tropical Frederick causó fuertes precipitaciones e inundaciones también en la región sur del país.

Figure 5 Caudal Máximo Anual versus Los caudales Picos estimados para cada periodo de Retorno

Niveles del Rio Calculados en HEC-RAS

La elevación de la superficie del agua en las diferentes secciones transversales del río Ozama fue calculada utilizando HEC-RAS. La figura.6 muestra la elevación de la superficie del agua a la sección transversal #73 y para cada perfil. Los puntos rojos representan la orilla del rio, y se puede observar que los niveles del agua están por encima de estos, lo que significa que parte de las zonas aledañas al río están bajo una inundación. La figura.7 muestra la evolución de la elevación de la superficie del agua a lo largo del tramo del Río Ozama para un periodo de retorno de 25 años.

Figure.6 Niveles del agua para cada perfil a la misma sección transversal

Figure 6b
Figure 6c
Figure 6d
Figure 7 Perfil del río y los niveles del agua de todas las secciones transversales para el perfil 1

Mapas de Inundaciones y Zonas Vulnerables

La elevación de la superficie del agua para los diferentes perfiles definidos en HEC-RAS es importado a ArcGIS para identificar las áreas de riesgo de inundación. Para cada perfil, el río fue dividido en cuatro tramos tomando una elevación representativa en cada tramo, como se muestra en la Tabla.3. Luego usando la herramienta “Calculadora Raster” en ArcGIS, se cuantificó las áreas de riesgo de inundación, tal como se muestra en la figura.8. Según estas áreas de inundación, ha sido posible identificar los barrios que serían afectados por estos caudales. La Tabla.4 muestra las zonas afectadas para cada perfil.

Tabla 3 Los niveles del agua desde rio arriba hasta rio abajo y las secciones transversales donde termina cada división.
perfil1
perfil3
perfil2
perfil4

Figure 8 Áreas de Inundaciones a Diferentes Periodos de Retorno

Tabla.4 Zonas vulnerables para cada perfil o periodo de retorno.

La comparación de las áreas de inundación correspondiente a diferentes periodos de retorno indica que el Río Ozama es altamente sensitivo a elevaciones repentinas de caudales debido a eventos de tormenta. Con un periodo de retorno de 25 años, gran parte del área inundable es cubierta por el agua, mientras que para periodos de retorno mayores el incremento en el área inundado es relativamente pequeño. La particular geometría de las secciones transversales del Río Ozama, debido a que atraviesa la ciudad, ocasiona que un caudal de flujo no muy grande inunde rápidamente el lecho del rio y su llanura de inundación cercana a la orilla del rio. Luego esta llanura de inundación se expande rápidamente ocasionado que caudales mayores generen elevaciones pequeñas de la superficie del agua. Es por esto que la cantidad de agua requerida para inundar áreas relativamente pequeñas es cada vez mayor. La figura.9 ejemplifica este suceso, comparando las áreas de inundación del perfil 1 y el perfil 4.

Figure 9 Comparación del Perfil 1 y 4
Figure 9 Comparación del Perfil 1 y 4

Referencias

República Dominicana Live. (n.d.). Retrieved September 22, 2016, from http://www.republica-dominicana-live.com/republica-dominicana/tiempo/historia-ciclones-republica-dominicana.html

Secretarion Tecnico de La Presidencia, Banco Interamericano de Desarrollo, Cardona, O., La Red, & ICF consulting. (2001). Retrieved from: Http://www.desenredando.org/public/varios/2002/pdrd/7-1DRD_F-may_28_2002.pdf.

Manning’s n Values. (n.d.). Retrieved September 22, 2016, from http://www.fsl.orst.edu/geowater/FX3/help/8_Hydraulic_Reference/Mannings_n_Tables.htm

Río Ozama. (n.d.). Retrieved September 22, 2016, from https://es.wikipedia.org/wiki/Río_Ozama

Analysis Techniques: Flood Frequency Analysis. (n.d.). Retrieved September 22, 2016, from http://streamflow.engr.oregonstate.edu/analysis/floodfreq/

Center, H. E. (n.d.). Hydrologic Engineering Center. Retrieved September 22, 2016, from http://www.hec.usace.army.mil/software/hec-ras/

Instituto Nacional de Recursos Hidráulicos. Plan Hidrológico Nacional, 2012. Chapters 1 – 9. Print.

Glenn, E. (2015, August 28). Detection of recent regional sea surface temperature warming in the Caribbean and surrounding region. Geophysical Research Letter, 42(16), 6785-6792. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/2015GL065002/abstract

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