**5.2. A flood early warning system for Vargas, Venezuela**

542 Risk Management – Current Issues and Challenges

presented in Table 2.

decision-making efforts.

Maize Plantations in coastal areas likely to be affected by above-normal preci-pitation

Beans Plantations in coastal area likely to be affected by above-normal precipitation

for December 2011-February 2012.

This same method is used at the national level to produce specific recommendations for diverse sectors in each individual country. Those are then distributed among national authorities and stakeholders in private sector with the seasonal outlook map and lists of suggested prevention measures. An example of the matrix of climate risk for agriculture is

As part of the same, routine-specific measures are identified at a national level by similar

A process is now underway to evaluate the use of outlooks and climate risk information directly with the beneficiaries, both for the private and the public sectors, to strengthen

Belice Costa Rica El Salvador Guatemala Honduras Nicaragua Panama

harvest damages due to higher humidity associated with abovenormal rainfall. Increased cost of postcrop product management

No risk No risk Risk in the

**Table 2.** Central American Climate Outlook Forum risk assessment for two crops (maize and beans),

Risk in the Cari-bbean planting area due to abovenormal rainfall. Second planting season likely to be affected

Cari-bbean planting area due to abovenormal rainfall. Second planting season likely to be affected

Second crop in the Atlantic Autonomo us Regions likely to be affected by abovenormal rainfall. Exportation s likely to be impacted

Second crop in the Atlantic Autonomo us Regions likely to be affected by abovenormal rainfall. Yield reduction may reach 15% .

Crop loss risk in Western Cari-bbean area. Posible impacts in food security of indi-genous population.

Loss of crop risk in Western Caribbean area. Posible impacts on food security of indigenous population.

working groups of climatologists and national experts, and suggested to authorities.

Climate Risk /potential damages for Main Crops. Quarter December 2011 -March 2012

No risk No risk Risk of post-

Crop Country

Risk of crop damage for plantations in Northern Plains, Cari-bbean area and South Pacific

In Venezuela, heavy rains represent a significant problem for human populations, not only in rural areas where farms and crops are affected, but also in some urban settings where many inhabitants reside in poorly-constructed houses that are highly vulnerable to floods and landslides, or that are sometimes located on steep terrains and floodplains.

**Figure 4.** Aerial view of Caraballeda, Vargas state (Venezuela), after mudslides and debris flows event on December 15 1999.

On December 15, 1999, on the northern coast of Venezuela, torrential rains led to flash floods and debris flows that killed tens of thousands of people, destroyed thousands of households, and meant the complete collapse of the area's infrastructure (see Figure 4). The "Vargas disaster", as it has been known ever since,, is considered the worst natural disaster in Venezuela's last half century (Table 1). Even though flood prediction is an essential piece of Climate Risk Management, Venezuela did not have, at the time, a consolidated early warning system that could alert decision-makers and stakeholders about this extreme event. In this section, the implementation of an early warning flood system for Vargas state is described in some detail. It is a completely general methodology, so other regions in LAC can benefit from the experience described here.

The Vargas disaster is also an important case study on flood prediction, not only because of its unusual rainfall amounts, but also for the nature of the terrain where it took place. The basin is located in a mountainous region of metamorphic rock, coarse soils and steep slopes, making the area highly vulnerable to floods and debris flows [10].

The early warning system involves the estimation of vulnerabilities and probabilities related to heavy precipitations and mudslides. Guenni *et al.* [11] has computed the vulnerabilities using the total affected and exposed people, considering both the spatial and temporal variability, as discussed in section 4. As an example, Figure 5 presents a zoom-in for most parts of Venezuela showing months of rainfall exposure in colors and the population density as black pixels (for details see [11]). Regions near the Venezuelan coast are in general

more vulnerable (due to more population density and exposure). Using map algebra in a Geographical Information System (GIS), the map is then intersected with other maps containing information about the terrain (e.g. water holding capacity or mudslide probabilities). Following equation (1), the final map, in sketching homogeneous vulnerability zones, must be written in terms of probabilities for different hazard intensities.

Risk Management at the Latin American Observatory 545

**Figure 6.** Caraballeda's simulated runoff time series for the Vargas disaster event using CWRF, VIC and the Lohman routing model (see main text). The runoff has been normalized using the standard deviation. The critical day (December 19, 1999) corresponds to simulation day 62 in this plot. The yellow and red lines at 2 and 2.3, respectively, were defined as the "yellow" and "red" alerts in the

As a final result, series of maps are provided with the hazard's probability of occurrence in terms of the hazard intensity. They are produced computing the convolution between the corresponding hazard and vulnerability maps, following equation (1). For additional details

This probabilistic approach has an important advantage: it takes into consideration the possible uncertainties related to each one of the processes involved in the final flood risk

Climatic factors play a significant role in the transmission dynamics of several infectious diseases [14]. Therefore it should be a critical priority to incorporate climate information into disease risk assessments and early warning and response systems. (See figure 7.) This, in fact, has been one of the goals of the Colombian and Ecuadorian meteorological and health services in recent years. In Colombia, in particular, the National Institute of Health (INS) recently teamed up with the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) and several universities and research centers to design and implement a proactive, collaborative, multidisciplinary, Integrated Surveillance and Control System –

**5.3. Bridging the gap between climate information and health services in** 

designed early warning system. (After Torres and Muñoz [12,13])

see [12,13].

probability map.

**Colombia and Ecuador** 

**Figure 5.** Map of exposition (in colors) and population density (pixels in black) for Venezuela. After Guenni *et al.* [11]

On the other hand, in order to compute flood risk probabilities, hazard probability maps must be produced (also in terms of intensities). In this case, Torres and Muñoz [12,13] have suggested a methodology based on hindcasts involving an off-line coupling of a regional climate model, a process-based hydrological model and a routing model.

The Climate Weather Research and Forecasting model (CWRF) was used to simulate 13 years (1996-2008) of data, paying special attention to the Vargas disaster atmospheric conditions. Rainfall and maximum and minimum temperatures were bias corrected using observed values. Then they were used as input data along with soil properties and vegetation parameters for the Variable Infiltration Capacity model (VIC). This energy and mass balance model was finally coupled to the Lohmann routing model to produce realistic stream flows that adequately considered the local topography (for details about the different models and procedures see [2,12,13] and references therein). Simulation outputs were consistent with the flooding event of December 1999. The study was able to reproduce how long rains, along with low evapo-transpiration due to high cloudiness, contributed to saturate soil layers. Moreover, the study was able to identify critical values (see Figure 6) in order to establish "yellow" and "red" alerts in the Vargas flood early warning system, using the statistics obtained for the 13 years simulation and local stream flow data provided by the National Weather Service. Model outputs were then used to create hazard probability maps in terms of hazard intensity.

Guenni *et al.* [11]

in terms of hazard intensity.

more vulnerable (due to more population density and exposure). Using map algebra in a Geographical Information System (GIS), the map is then intersected with other maps containing information about the terrain (e.g. water holding capacity or mudslide probabilities). Following equation (1), the final map, in sketching homogeneous vulnerability zones, must be written in terms of probabilities for different hazard intensities.

**Figure 5.** Map of exposition (in colors) and population density (pixels in black) for Venezuela. After

climate model, a process-based hydrological model and a routing model.

On the other hand, in order to compute flood risk probabilities, hazard probability maps must be produced (also in terms of intensities). In this case, Torres and Muñoz [12,13] have suggested a methodology based on hindcasts involving an off-line coupling of a regional

The Climate Weather Research and Forecasting model (CWRF) was used to simulate 13 years (1996-2008) of data, paying special attention to the Vargas disaster atmospheric conditions. Rainfall and maximum and minimum temperatures were bias corrected using observed values. Then they were used as input data along with soil properties and vegetation parameters for the Variable Infiltration Capacity model (VIC). This energy and mass balance model was finally coupled to the Lohmann routing model to produce realistic stream flows that adequately considered the local topography (for details about the different models and procedures see [2,12,13] and references therein). Simulation outputs were consistent with the flooding event of December 1999. The study was able to reproduce how long rains, along with low evapo-transpiration due to high cloudiness, contributed to saturate soil layers. Moreover, the study was able to identify critical values (see Figure 6) in order to establish "yellow" and "red" alerts in the Vargas flood early warning system, using the statistics obtained for the 13 years simulation and local stream flow data provided by the National Weather Service. Model outputs were then used to create hazard probability maps

**Figure 6.** Caraballeda's simulated runoff time series for the Vargas disaster event using CWRF, VIC and the Lohman routing model (see main text). The runoff has been normalized using the standard deviation. The critical day (December 19, 1999) corresponds to simulation day 62 in this plot. The yellow and red lines at 2 and 2.3, respectively, were defined as the "yellow" and "red" alerts in the designed early warning system. (After Torres and Muñoz [12,13])

As a final result, series of maps are provided with the hazard's probability of occurrence in terms of the hazard intensity. They are produced computing the convolution between the corresponding hazard and vulnerability maps, following equation (1). For additional details see [12,13].

This probabilistic approach has an important advantage: it takes into consideration the possible uncertainties related to each one of the processes involved in the final flood risk probability map.
