September 2014 

NASA/CR–2014-218526 

Colombia Mi Pronóstico Flood Application: 

Updating and Improving the Mi Pronóstico Flood 

Web Application to Include an Assessment of 

Flood Risk 

Stephanie Rushley, Matthew Carter, Charles Chiou, Richard Farmer, Kevin Haywood, Anthony 

Pototzky Jr., Adam White, and Daniel Winker 

Langley Research Center, DEVELOP National Program, Hampton, Virginia 
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September 2014 

NASA/CR–2014-218526 

Colombia Mi Pronóstico Flood Application: 

Updating and Improving the Mi Pronóstico Flood 

Web Application to Include an Assessment of 

Flood Risk 

Stephanie Rushley, Matthew Carter, Charles Chiou, Richard Farmer, Kevin Haywood, Anthony 

Pototzky Jr., Adam White, and Daniel Winker 

Langley Research Center, DEVELOP National Program, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
2 

I. Abstract 

Colombia is a country with highly variable terrain, from the Andes Mountains to plains 

and coastal areas, many of these areas are prone to flooding disasters. To identify 

these risk areas NASA’s Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER) was used to construct a digital elevation model (DEM) for the study 

region. The preliminary risk assessment was applied to a pilot study area, the La Mosca 

River basin. Precipitation data from the National Aeronautics and Space Administration 

(NASA) Tropical Rainfall Measuring Mission (TRMM)’s near-real-time rainfall products as 

well as precipitation data from the Instituto de Hidrologia, Meteorologia y Estudios 

Ambientales (the Institute of Hydrology, Meteorology and Environmental Studies, 

IDEAM) and stations in the La Mosca River Basin were used to create rainfall distribution 

maps for the region. Using the precipitation data and the ASTER DEM, the web 

application, Mi Pronóstico, run by IDEAM, was updated to include an interactive map 

which currently allows users to search for a location and view the vulnerability and 

current weather and flooding conditions. The geospatial information was linked to an 

early warning system in Mi Pronóstico that can alert the public of flood warnings and 

identify locations of nearby shelters. 

II. Introduction 

Colombia is prone to frequent flooding due to heavy precipitation that is influenced by 

the Intertropical Convergence Zone (ITCZ) and the Andes Mountain range [ColombiaSA, 2014] which causes precipitation patterns to be dominated by orographic lift 

[Poveda, Álvarez, and Rueda, 2011; Poveda et al., 2005]. Due to the high amount of 

precipitation that falls over Colombia and the highly variable slopes, Colombia’s 

mountainous regions are highly prone to floods and landslides, which has caused 

hundreds of deaths in the area over the past ten years [Künzler, Huggel, and Ramíez, 

2012]. Currently, Colombia’s Instituto de Hidrologia, Meteorologia y Estudios 

Ambientales (Institute of Hydrology, Meteorology and Environmental Studies, IDEAM) 

has the Mi Pronóstico web and mobile application which notifies people where there is 

a flood warning. 

The DEVELOP Tech Team is addressing the DEVELOP national application area crosscutting to create a flood risk assessment map for the La Mosca river basin. The La 

Mosca River watershed, which is located in the southern portion of the Antioquia 

Department in Northwest Colombia served as a pilot study area for an updated flood 

risk assessment and warning system. The flood risk map will provide information for 

planning future regional development and land use [Spachinger et al., 2008] as well as 

provide current watches and warnings. The DEVELOP Tech Team has partnered with 

IDEAM, which has provided rain gauge and streamflow data for the purpose of 

expanding the Mi Pronóstico web application. Using TRMM and DRIVE will allow for 

near-real-time monitoring of precipitation for the purpose of flood warning and 

preparation. 
3 

Topography is an important factor in causing precipitation as well as determining the 

flow of water once it reaches the ground. Several digital elevation models (DEMs) exist, 

including two NASA open source global DEMs (GDEMs), from the Shuttle Radar 

Topography Mission (SRTM) and from the Advanced Spaceborne Thermal Emission and 

Reflection Radiometer (ASTER) aboard the Terra satellite. The SRTM GDEM is publicly 

available at a 3 arc-second resolution for Colombia [Nikolakopoulos, Kamaratakis, and 

Chrysoulakis, 2006]. SRTM has voids and gaps within the GDEM [Tighe and 

Chamberlain, 2009]. Various interpolation methods exist for filling these voids, however, 

the accuracy of the interpolated elevations has been shown to be poor quality [Zhao 

and Ling, 2010]. 

ASTER has been collecting along-track stereo images since 2000 using a pair of infrared 

cameras [Tachikawa et al, 2011]. TheASTER GDEM has a 1 arc second horizontal 

resolution and was created by processing the 1.26 million scenes of Level-1A data 

[Czubski, Kozak, and Kolecka, 2013; Tachikawa et al, 2011]. The ASTER GDEM also has 

voids, mostly due to cloud cover, but they are limited because of the size of the 

dataset. In processing cloud pixels are removed from the scenes, then scenes of the 

same area are stacked [Zhao and Ling, 2010]. A higher number of scenes used to 

produce a DEM correlates with a smaller error, especially for DEMs produced from 

between one and ten scenes [Tachikawa et al, 2011]. The ASTER GDEM has a generally 

higher spatial resolution and fewer voids [Zhao and Ling, 2010], therefore, the ASTER 

DEM was used for the study. 

South America has some of the most intense Deep moist convection (DMC) in the 

world and accumulates a high amount of precipitation [Rasmussen et al., 2013]. DMC 

is caused by moisture from the Pacific Ocean to the west and the Amazon River Basin 

to the east that is lifted by the winds interacting with the mountains [Poveda, Álvarez, 

and Rueda, 2011]. As a result the variable topography plays a large role in the daily 

variation of local precipitation [Poveda et al., 2005]. The location and timing of 

precipitation is critical to any kind of hydrological model, especially for flood risk 

analysis. 

In addition to orographic lift due to local topography, precipitation is highly affected by 

large scale tropical dynamics. Colombia gets a double passage of the (ITCZ), first in 

March, April and May and again in September, October and November, causing a 

significant amount of precipitation during these two seasons [Poveda, Álvarez, and 

Rueda, 2011]. Phase changes in the El-Niño – Southern Oscillation (ENSO) cause wide 

spread teleconnections, which influence precipitation patterns in Colombia. Trends 

have shown that during the El Niño (warm) phase, precipitation accumulation over 

most of Colombia is relatively low, while during the La Niña (cold) phase, precipitation 

amounts are typically high [Poveda, Álvarez, and Rueda, 2011; Poveda et al., 

2005]. Although precipitation in Colombia shows a high amount of spatial and 

seasonal variability, strong diurnal patterns are present. Local minimums in precipitation 

accumulation occur in the morning, regardless of season and location, while local 

maximums vary by location. Northeastern and Western Colombia experience a peak 

in precipitation during the afternoon, while west of the Andes Mountains peaks occurs 

around local midnight [Poveda et al., 2005]. 
4 

Any change in precipitation due to climate and land use around a river can cause 

changes to flooding patterns [Spachinger et al., 2008]. An increased necessity for a 

more accurate and detailed flood risk analysis in Colombia has arisen because of 

increased frequency of significant flooding events whose magnitude and impact have 

been further exacerbated by development along Colombia’s rivers [MosqueraMachado and Ahman, 2007]. Floods result from a chain of events that starts with 

meteorological and hydrological factors that can be amplified by human factors 

[Mrekva and Engi, 2012; Spachinger et al., 2008]. Meteorological factors include 

accumulated rainfall, storm type and size, and temperature. The chronological events 

preceding the precipitation event are large because the amount of runoff from these 

storms is dictated by the current water level, moisture content of soil, infiltration of 

surfaces, and where the runoff is coming from. The ability of water to infiltrate surfaces, 

which helps to mitigate flooding, is lessening due to the development of cities in flood 

plains which causes large areas of land to be covered with impermeable surfaces 

[Mrekva and Engi, 2012; Spachinger et al., 2008]. 

Due to the importance of precipitation to hydrological modeling, the National 

Aeronautics and Space Administration’s (NASA) Tropical Rainfall Measuring Mission 

(TRMM) Multi-Satellite Precipitation Analysis (TMPA) near-real-time dataset, 3B42RT, was 

used to get consistent precipitation information [Wu et al., 2014]. TRMM was launched 

in 1997 and provides measurements for the tropics (50S to 50N). TRMM carries a 

spaceborne precipitation radar which examines the vertical structure of precipitation in 

a swath 215km long and 4.3km wide [Kawanishi et al., 2000]. Precipitation must be at a 

minimum of 0.7mm/hr for the spaceborne precipitation radar to detect it. The 

precipitation radar is calibrated to a less than 1 dBZ error [Kawanishi et al., 2000]. TRMM 

passes over the same area two to four times a day; however, due to the cycle for small 

locations, such as La Mosca River, data is received less frequently, which requires that it 

be corrected with in-situ data. In a study of the Himalayas Bookhagen and Burbank 

(2006) found this correction to be a constant scaling factor. 

The TRMM near-real time data set does not use gauge adjustment for the purpose of 

having a shorter lag time [Dinku et al., 2009]. This dataset is known to have a high level 

of error [Dinku et al., 2009]; however, with a limited amount of in situ measurements in 

Colombia [Mosquera-Machado and Ahmad, 2007], satellite observations from TRMM 

are the best available source for modeling severe flood events in tropical countries. In 

addition, it has been shown to be relatively accurate in the Dominant river- tracing 

routing integrated with VIC (Variable Infiltration Capacity) Environment model (DRIVE) 

[Wu et al., 2014]. Therefore, TRMM will be used as a substitute for the real time 

precipitation data for the flood application, in addition to the Global Flood Monitor 

System (GFMS) created by Wu et al (2014) to look at severe flooding events using TMPA. 

Previous studies have pointed out several errors specific to TRMM precipitation in 

Colombia. In addition to the high level of error found by Dinku et al. (2009), TRMM has a 

low correlation coefficient of 0.48mm/day and has a magnitude of underestimation of 

0.7mm/day [Dinku et al., 2009]. Rasmussen et al. (2013) found that the precipitation 

radar on TRMM is the cause for significant underestimation of rainfall during DMC 

events. TRMM’s 2A25 algorithm gives inconsistent estimates of precipitation, in 

particular with DMC where the horizontal 40dBZ core extends above the 0°C level. This 
5 

is due to presence of mixed hydrometeors in tandem with the assumption by TRMM’s 

algorithm to correct freezing precipitation to snow particles, which does not accurately 

depict the hail or graupel present in severe convection [Rasmussen et al., 2013]. Taking 

this information into account we have used the in-situ precipitation gauge data from 

IDEAM to set the risk indices and check the accuracy of TRMM in La Mosca river basin. 
6 

III. Methodology 

Data Acquisition

Topographical data for the study area was acquired from the ASTER GDEM2. The data 

was distributed in GeoTIFF and was ordered through NASA Reverb, Global Data 

Explorer. The DEM tiles were produced by Japan’s Sensor Information Laboratory 

Corporation (SILC) from the ASTER Level-1A archive [Land Processes, 2014]. 

Streamflow rate measurements from 43 river monitoring stations and precipitation 

measurements from 102 weather stations within Colombia’s national network of 

observation stations were provided by IDEAM from January 1st, 1900 to December 31st, 

2013. A number of the stations data only included monthly measurements, only the 

station’s with both daily and monthly data were used in the study. Data was therefore 

used from 43 stations. 

Additionally, near real-time precipitation data from TRMM was used to complement 

Colombia’s national network of observation stations. The TRMM 3B42RT data was 

obtained from NASA Giovanni data access portal operated out of Goddard Space 

Flight Center. TRMM outputs precipitation data with a 0.25° grid spacing over the 

tropics. In addition to Giovanni, the TRMM open source public directory was used to 

access the gridded binary data files and input those images into ArcGIS. Flood intensity 

data was gathered from the GFMS DRIVE model, which outputs flooding intensity in 

millimeters over a flooding threshold [Wu et al., 2014]. 

Data Processing

In order to obtain a continuous DEM for the study area, four ASTER images were 

mosaicked together. The ArcGIS Hydro Toolbox was used to create a watershed from 

the ASTER DEM and calculate its area, flow direction, and accumulation. With this a 

stream mask for the watershed was created. Using the Stream Order tool in ArcGIS, 

major intersections of flow in the watershed were identified and pour points were set to 

separate the watershed into seven sub basins. 

For calculating the correlation between streamflow and precipitation, flow stations 

were used as the limiting factor. The daily and monthly streamflow stations were 

connected to the closest daily and monthly precipitation stations, respectively, by using 

a function in ArcGIS to find the closest station. The data from these pairs was combined 

to match flow and precipitation measurements for each day and for each month, 

missing values were eliminated, and the correlation for each pair was calculated. 

Additionally correlograms were created for the precipitation and streamflow stations 

[Friendly, 2002]. After data processing, several stations were eliminated due to missing 

values, leaving us with 19 station data points for the correlation calculations. 

Data Analysis

Once the closest precipitation station to each streamflow station was determined, the 

precipitation-streamflow station pairs were used to calculate the correlation coefficient 

between both precipitation and streamflow and between the streamflow and slope in 

order to assess any linear correlation (Appendix 1). Additionally, autocorrelations were 

calculated for the streamflow and precipitation data to look for recurring time 
7 

dependent trends. Cross-correlations were used to examine the precipitationstreamflow relationship. A cross-correlation as opposed to a simple correlation 

coefficient calculation can account for a lag in the relationship, for example if 

streamflow is more affected by rainfall from days earlier [Friendly, 2002]. Künzler, 

Huggel, and Ramíez (2012) found that when data covering long periods of time (100 

years or greater) were used, the discharge patterns were highly dependent on the 

statistical methods used. Therefore, only twenty years of data, from 1993 to 2013, were 

used in this analysis. 

Morphometric 

Index Scale

Area of 

Drainage 

Basin

(km2)

Categories

1 2 3 4 5

Drainage 

Density 

(km/km2)

1:10,000 <15 <1.50 1.51-2.00 2.01-2.50 2.51-

3.00 >3

1:25,000 16 - 50 <1.20 1.21-1.80 1.81-2.00 2.01-

2.50 >2.5

1:100,000 >50 <1.00 1.01-1.50 1.51-2.00 2.01-

2.50 >2.5

Low Moderate Moderate 

High High Very High

Mean Basin 

Slope (%)

1:10,000 <15 <20 21-35 36-50 51-75 >75

1:100,000 >50 <15 16-30 30-45 46-65 >65

Hilly Strong Very 

strong Steep Very Steep

Coefficient of 

Shape

>1.625 1.376-

1.500

1.251-

1.375

1.126-

1.250

1.00-

1.125

Ovaloblong to 

oblong 

rectangular

Oval-round to ovaloblongAlmost round 

to oval-round

The Morphometric Classification Torrential Index (MCTI) set by IDEAM incorporates the 

drainage density, slope of the basin, and the shape of the basin [IDEAM, 2013]. By 

separating the watershed into sub basins, it was possible to examine the different areas 

for varying levels of flood risk. Each sub basin was calculated to find the individual 

categories and the risk associated with MCTI. Table 1 shows the different risk value 

criteria for MCTI. The corresponding risk color associated with the combination of all 

three indices is shown in Figure 1. The Index of Variability was calculated from the flow 

duration curves which show the percentage of time that the flow is above a certain 

value. Table 2 shows the vulnerability that is determined based on the index of 

variability (untranslated figures can be found in Appendix 2). 
8 

Variability Index Vulnerability

<10° Very low

10.1° - 37° Low

37.1° - 47° Medium

47.1° - 55° High

>55° Very high

Figure 1: Once all of the indices have 

been put together they are combined 

to determine the risk assessment. This 

determines the MCTI [IDEAM, 2013]

Table 2: Vulnerability values based on the variability index.
9 

Once the Index of Variability and the MCTI has been determined, the final Vulnerability 

Index of Torrential Events (VITE) can be determined. The VITE shows flood risk based on 

the hydrological features of the basin and the historical flow percentage. These criteria 

show the risk of the watershed and sub basins to flooding in the event of a significant 

rain event. Table 3 shows the final criteria for the final Index that will give the VITE. 

Table 3: Vulnerability Index of Torrential Events. 

Variability Index

Morphometric Classification Torrential Index

Very 

Low Low Medium High Very High

Very low Low Low Medium High High

Low Low Medium Medium High Very High

Medium Low Medium High High Very High

High Medium Medium High Very High Very High

Very high Medium High High Very High Very High
10

IV. Results & Discussion 

Analysis of Results

The streamflow stations were used as the limiting factor for calculating the correlation 

between streamflow and precipitation, precipitation and slope, and stream flow and 

slope (Appendix 1). Correlation between streamflow and precipitation is relatively low 

for many of the daily stations, with few higher than 0.5, one of which is a daily station 

(23087130) located nearly 2 km from the closest precipitation station. Correlations for 

the monthly data were much higher with more stations above 0.5 than below. These 

correlations are good for hydrological purposes and it appears that when the time 

scale is increased from daily to monthly, trends arise and the distance between stations 

seems to have less of an influence. Correlation between slope and precipitation and 

slope and streamflow had a correlation of magnitude of 10-15, which is to be expected 

because the precipitation and streamflow data changes over time where, for the 

purposes of this study, the time scale was small enough that it can be assumed that 

slope is constant over the study period. Potentially the correlation between streamflow 

and precipitation could have been negatively affected by the topography, the 

interaction of rain that falls over areas not covered by precipitation gauges or missing 

data from several gauges. 

7

6 5

4

3

2

1

Figure 2: La Mosca Watershed and sub basins that were calculated from ArcGIS. Flow stations, shown as blue 

stars on this map, were used to compute the flow duration curve and Index of Variability. Station 23087860 is 

in sub basin 2, Station 23087170 is in sub basin 4, Station 23087030 is the station in sub basin 5, and 

Station23087670 is to the south of the watershed.
11

The correlograms illustrate how the stations are correlated (Appendix 3). The time series 

correlation plots have shown that there is very little correlation but there is a clear 

positive and negative pattern that arises in the different seasons. This may be indicative 

of smaller rainfall amounts in different seasons and therefore a smaller volume of 

streamflow in those seasons (Figure 3). Not only do these show oscillation in different 

seasons, but minimums in streamflow correspond well with minimums in precipitation. 

The time lag shows that the highest correlation is between the precipitation on a day 

and streamflow a day later; this indicates that there is a lag in the time that it takes for 

the streamflow to increase with rainfall (Figure 4). Therefore, the potential for flooding 

does not end when the precipitation ends. 

Figure 3: The seasonal time series correlations for daily precipitation and streamflow and monthly 

precipitation and streamflow. 
12

Figure 5: MCTI, colors correspond to the colors in Figure 1. 

Figure 5 shows the watershed values based on the MCTI described in Figure 1. This 

indicates an overall moderate risk throughout the watershed, with a lower risk in sub 

basin 2. The La Mosca River Basin is more susceptible to flooding in areas where the risk 

is moderate and should be watched closely when heavy rainfall occurs. 

Figure 4: Time lag correlation 

between Daily Streamflow 

Station 23087670 and Daily 

Precipitation Station 23080640.

7

6 5

4

3

2

1
13

The flow duration curves show the percentage of time that the flow is over a certain 

value at each of the four stations that are within the watershed (Figure 6). The stations 

correspond with the stars on the map in Figure 2. The flow stations were connected 

with the watersheds that flowed most directly into them. The limited number of stations 

and the distance between the flow stations and pour points create added uncertainty. 

However, the index of variability is similar to the process completed by IDEAM [IDEAM, 

2013]. The Index of Variability (Table 4) is low for most of the stations, with Station 

23087860 and Station 23087030, being in the medium range for the smaller percentage 

ranges. Overall, this creates a low Index of Variability for the entire watershed; 

additionally the average Index of Variability for all stations is in the low range. Once the 

MCTI has been determined and the Index of Variability calculated, these two values 

are put together to get the VITE (Table 3). Combined the moderate MCTI and the low 

Index of Variability, most sub basins have a medium VITE. Sub basin 2 has a low MCTI 

and a low Index of Variability, which also gives a medium VITE. 

Figure 6: Flow duration curves for the stations within the watershed. 

Table 4: The Index of variability, green colors indicate a low vulnerability and yellow colors indicate a 

medium vulnerability. 

Variability Index Station 23087670 Station 23087860 Station 23087170 Station 23087030

40%-60% 30.2022 38.9326 30.4553 38.2458

30%-70% 31.7183 35.2720 29.2970 38.0296

20%-80% 32.0733 32.8636 27.3563 36.2351

10%-90% 31.5347 31.0470 26.5651 34.7007
14

Once the watershed areas and their vulnerability were calculated precipitation data 

from TRMM and flood intensity data from the DRIVE model was overlaid to provide 

information on current weather to assist is assessing risk within the study area. The data 

is coarse and therefore just an option for the user of the Mi Pronóstico application to 

toggle on and off. Shown in Figure 7 are the DRIVE model and TRMM precipitation 

data over Colombia on July 5th, 2014 at 0900Z. The La Mosca Watershed (outlined by 

the red boxes in Figure 7) is relatively small which makes the pixel size for these products 

a bad representation for the area. It can be seen that the DRIVE model gives a greater 

value for the flooding intensity in area with higher precipitation, due to the coarse yet 

visual appeal of TRMM and DRIVE they will be an option for users of the Mi Pronóstico 

web application to view, but not used for analysis. This information can be 

redownloaded and updated on the Mi Pronóstico web application. 

Figure 7: On July 05, 2014 at 0900Z. a) The flooding intensity (mm over threshold) as determined by the 

DRIVE model from the University of Maryland and b) the TRMM near-real-time precipitation data in mm/hr. 

Errors & Uncertainty

Large errors exist because of the large amount of missing data in the streamflow and 

precipitation data as well as the sparse amount of in situ observations. The location of 

the stations is also limited as some of the closest usable precipitation stations are a 

significant distance away from streamflow stations (Appendix 1). The TRMM data has a 

very large grid spacing, which in the small study area tended to be about four pixels, 

which was very limiting for the analysis of the mesoscale convection that occurs daily in 

the region. Flow stations within the watershed are few and, due to possible errors in the 

DEM analysis, do not appear on streams as output by ArcGIS. 

In addition, the stations used are clustered in lower elevation areas with only five on a 

slope greater than 20°. In these shallower slope areas, it is possible that the same storm 

a) b) 
15

will have an effect on several areas. This may cause an addition problem for the 

comparison of streamflow and precipitation because low area streams may be strongly 

affected by precipitation high in the mountains. Additionally with the sparse data 

available over the steeper terrain it is difficult to evaluate at how much the mountain 

ranges influence the streamflow, especially as some of the precipitation stations are on 

the opposite side of the mountain to the streamflow stations. 

Future Work

The goal of the slope-precipitation-stream flow analysis is to improve flood prediction 

and warning capabilities. The flood risk indices created from this project are to be 

applied to an interactive map on IDEAM’s publicly accessible website. Additionally, this 

map can have TRMM precipitation data and DRIVE flooding information overlaid, and 

provide flood warnings using these two pieces of information. The same information is 

intended to be used in the Mi Pronóstico mobile phone app. The mobile app would 

have the additional capability of directing the user to safety in the case of a flood. 

These safe areas could be determined using the calculated flood risk indices. The 

vulnerability is also dependent on the development and land use of the floodplain. 

Therefore, in the future, it would be beneficial to develop a more sophisticated risk 

analysis using the population and land usage in flood basins [Künzler, Huggel, and 

Ramíez, 2012]. 

V. Conclusions 

As there is a low index of variability for the stations within the La Mosca watershed and 

a moderate MCTI, the overall risk of La Mosca Watershed is moderate. Therefore, in 

events where there is heavy precipitation over the area La Mosca river basin will be at 

a medium risk for flooding. The process has been automated in order for it to be run for 

different watersheds and any number of sub basins. The pour points have to be placed 

by the user manually before the program can be run, and the TRMM and DRIVE model 

data must be pulled off line and input into ArcMap, but allow for a more dynamic view 

of flood risks. 

The web application can be updated to include this information on a map and will 

allow for users to view their area for flood risk as well as what the current conditions are. 

Once this product is linked to the Mi Pronóstico web application it will be extremely 

helpful to individuals, businesses, city planners, and emergency management for 

Colombia. The methodology can be expanded to other watersheds throughout 

Colombia. It can then be updated into the format of the mobile Mi Pronóstico 

application to give users flood warnings and identify locations where safe areas and 

shelters are located. 

VI. Acknowledgments 

The DEVELOP Tech Team would like to thank NASA DEVELOP as well as our science 

advisors Lauren Childs-Gleason and Dr. Kenton Ross for working with us this term at 

DEVELOP. We would also like to thank Dr. Angelica Gutierrez for being our point of 

contact and translator to the team in IDEAM as well as for her support throughout the 
16

project. We would like to thank our partners in Colombia at IDEAM, Pilar Galindo and 

Ricardo Quiroga for their data and support throughout the term. 

VII. References 

Bookhagen, B. and D. W. Burbank (2006), Topography, relief, and TRMM-derived rainfall 

variations along the Himalaya, Geophysical Research Letters, 33, 1-5, 

doi:10.1029/2006GL026037 

Colombia-SA, (2014), Antioquia. Accessed on June 23, 2014 from: http://www.colombiasa.com/departamentos/antioquia/antioquia-in.html 

Czubski, K., J. Kozak, and, N. Kolecka (2013), Accuracy of SRTM-X and ASTER Elevation Data and 

its Influence on Topographical and Hydrological Modeling: Case Study of the Pieniny Mts. 

in Poland, International Journal of Geoinformatics, 9, 7-14

Dinku, T., F. Ruiz, S.J. Connor, and P. Ceccato (2009), Validation and intercomparison of satellite 

rainfall estimates over Colombia, Journal of Applied Meteorology and Climatology, 49,

1004 - 1014, doi:10.1175/2009JAMC2260.1. 

Friendly, M. (2002), Corrgrams: Exploratory displays for correlation matrices, The American 

Statistician, 1.5, 1-16.

Hijmans, R. (2014), DIVA-GIS, Colombia Administrative Areas and Inland Water. Accessed on 

June 23, 2014 from: http://www.diva-gis.org/gdata 

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18

VIII. Appendices 

Appendix 1: 

Daily Correlation Monthly Correlation

Streamflow Station 

Identifier Streamflow and Precipitation Streamflow and Precipitation

23087010 0.43 0.68

23087020 0.35 0.74

23087030 0.42 0.67

23087040 0.37 0.80

23087060 0.51 0.78

23087080 0.29 0.68

23087090 0.34 0.68

23087100 0.18 0.70

23087130 0.54 0.67

23087150 0.25 0.58

23087170 0.45 0.68

23087180 0.18 0.45

23087190 0.16 0.49

23087200 0.16 0.56

23087210 0.12 0.44

23087690 0.26 0.69

23087700 0.19 0.38

23087740 0.32 0.73

23087830 0.19 0.53

Stream 

Flow 

Station

Precipitation 

Station

Distance 

Between 

(KM)

Stream 

Flow 

Station

Precipitation 

Station

Distance 

Between 

(KM)

Stream 

Flow 

Station

Precipitation 

Station

Distance 

Between 

(KM)

23087010 23080210 1.82433687 23087180 23080680 1.5091696 23087700 23080640 1.48176667

23087020 23080340 1.8491406 23087190 23080160 8.72947549 23087710 23080350 1.8491406

23087030 23080220 6.68010951 23087200 23085140 2.12371292 23087720 23080350 1.8491406

23087040 23080270 2.60631301 23087210 23080720 0.10968285 23087730 23080290 2.60639067

23087050 23080950 4.11991656 23087260 23080270 2.1788916 23087740 23085200 1.03625426

23087060 23085050 1.84902966 23087300 23080270 1.39257768 23087770 23080150 2.58163304

23087080 23080210 0 23087460 23085010 0 23087780 23080210 1.82433687

23087090 23080340 2.61908978 23087470 23080470 1.08051902 23087790 23080300 1.8491406

23087100 23085190 0 23087630 23080190 4.09513769 23087800 23080340 1.85424748

23087110 23080940 0.38285586 23087640 23080570 4.13749914 23087810 23080290 0

23087120 23080690 0 23087650 23080430 4.14310778 23087820 23080350 0

23087130 23080430 1.85435874 23087660 23080170 0.83303199 23087830 23080640 4.02337916

23087150 23080270 1.64044937 23087670 23080270 3.51743822 23087860 23080220 1.84902966

23087160 23080030 3.56893002 23087680 23080060 2.61937321

23087170 23080220 0 23087690 23085200 1.03625426

Distance between the closest precipitation and streamflow station in kilometers. 

Correlations 

between streamflow 

and precipitation 

slope for 19 

combination 

precipitation and 

streamflow stations.
19

Appendix 2: 

Below are the untranslated charts for Figure 1 and Tables 1-3. 

Untranslated Table 1. Morphometric Indices. 
20

Untranslated Table 2: Index of variability classification. 

Untranslated Figure 1: 

Morphometric index of 

torrential events.
21

Untranslated Table 3: Vulnerability classification that will be seen by users in Colombia. 
22

Appendix 3: 

Correlogram between 

daily streamflow stations 

and daily precipitation 

stations, DP indicates a 

precipitation station, 

DSF indicates a monthly 

streamflow stations. 

Stations without data for 

the past ten years were 

eliminated from the 

analysis.
23

Correlogram between 

monthly streamflow 

stations and monthly 

precipitation stations, 

MP indicates a 

precipitation station, 

MSF indicates a monthly 

streamflow stations. 

Stations without data for 

the past ten years were 

eliminated from the 

analysis.
REPORT DOCUMENTATION PAGE Form Approved

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Colombia Mi Pronóstico Flood Application: Updating and Improving the 

Mi Pronóstico Flood Web Application to Include an Assessment of Flood 

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Rushley, Stephanie; Carter, Matthew; Chiou, Charles; Farmer, Richard; 

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14. ABSTRACT

Colombia is a country with highly variable terrain, from the Andes Mountains to plains and coastal areas, many of these areas are prone to flooding disasters. 

To identify these risk areas NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to construct a digital elevation 

model (DEM) for the study region. The preliminary risk assessment was applied to a pilot study area, the La Mosca River basin. Precipitation data from the 

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precipitation data from the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (the Institute of Hydrology, Meteorology and Environmental Studies, 

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DEM, the web application, Mi Pronóstico, run by IDEAM, was updated to include an interactive map which currently allows users to search for a location and 

view the vulnerability and current weather and flooding conditions. The geospatial information was linked to an early warning system in Mi Pronóstico that 

can alert the public of flood warnings and identify locations of nearby shelters. 

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Correlation; Flooding risk; Precipitation; Risk assessment; Streamflow; TRMM

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