Universidad de los Andes
Facultad de Ciencias
Departamento de Geociencias
SURFACE GEOMORPHOLOGY ANALYSIS OF
AN AVULSION EVENT IN A TROPICAL CLIMATE
Manuel Eduardo Ariza Fuentes
Director:
Camilo Montes
Co-Director:
Jillian Pearse
Bogotá, Colombia
May, 2015
Abstract
An avulsion event took place in Brazo de Loba, in the Magdalena river in 2007. This event was captured in successive LandSat images, revealing that: 1) sinuosity values decreased 0.06 from 2007 to 2015, 2) maximum change of number of active channels decreased from 12 in 2011 to 4 in 2015, 3) same relationship with the average channels width, from 217 meters to 63 meters and 4) channels had a length extent of 3 km in 2007 to 20 km in 2015. It was revealed that the avulsion
triggers were the high discharge values (5846 !!
! ) and river level values (1019 cm) in 2007, associated with la Niña event. Contrasts between seasons were undertaken, in order to find that in 2014-2015, dry season had 16% more floodplain area, while wet season had 12% more lake and 4% more channel area. Extreme channels had a maximum separation at 6.4 kilometers southwest from the avulsion zone, were the number and width of channels were the highest.
Resumen
Un evento de avulsión ocurrió en el Brazo la Loba, en el río Magdalena en el 2007. El evento fue capturado por imágenes satelitales, que arrojaron los siguientes resultados: 1) Del 2007 al 2015, la sinuosidad decreció 0.06, 2) el máximo número de canales activos bajó de 12 en el 2011, a 4 en el 2015, 3) misma relación para el ancho de los canales, que pasó de un promedio de 217 metros a 63 metros y 4) los canales tenian una extensión de 3 kilometros en el 2007, y llegaron a tener 20
kilometros en el 2015. Se descubrió que los altos valores de caudal (5846 !!!) y de nivel de río (1019 cm), antes del 2007 fueron los causantes del evento, que están relacionados con el fenómeno de la Niña. Se hicieron comparaciones entre temporadas (húmeda y seca), para averiguar que en el 2014-2015, la temporada seca tenía 16% más área de llanura de inundación, mientras que la temporada húmeda tenía 12% más área de lagos y 4% más área de canales. Los canales extremos tenían una separación a los 6.4 kilómetros al suroeste de la zona de avulsión, lugar donde el número y ancho de canales era máximo.
TABLE OF CONTENT
INTRODUCTION………..………4
CONCEPTUAL FRAMEWORK...……….….…5
GEOGRAPHIC AND GEOLOGIC SETTING……….……11
METHODOLOGY..………14
RESULTS………26
DISCUSSION…….………40
CONCLUSIONS.……….………43
BIBLIOGRAPHY………45
ACKNOWLEDGMENTS………...………48
APPENDIX……...………..……49
ELECTRONIC APPENDIX……...57
INTRODUCTION
Anastomosed rivers are characterized by interconnected multiple channels separated by stable islands (Knighton & Nanson, 1992). Previous investigations proposed that avulsion events were the mechanism that leads to the creation of this type of rivers, which consists of abandonment and creation of channels in the floodplain (Makaske, 2001). However, there have been few avulsion events seen in real time, therefore, the understanding of formation and development of this type of rivers is low compared to meandering or braided rivers. Despite that, their preservation in the geological record is high, because they are usually present in high aggradation-rates basins (Smith D., 1986)
Documentation of an avulsion event (Figure 1) in 2007 in Brazo de Loba stream, in the Rio Magdalena 40 km southwest of El Banco, Magdalena, Colombia contributes to understanding of the formation and development of anastomosed rivers in tropical climates and high aggradation rate basins (Moron, et al., 2015). These types of rivers create good hydrocarbon reservoirs; therefore, their study improves the exploration and exploitation of hydrocarbons. (Smith D., 1986; Moron, et al., 2015).
I analyzed six satellite images that captured the event, between 1984 and 2015. The images were used to calculate sinuosity, area of water bodies, and number and width of channels. Seasonal values for these variables were calculated to determine season impact. These data are then compared with river level, discharge and pluviosity data to quantitatively analyze this avulsion event.
Figure 1: Satellite images showing the avulsion event in El Brazo de Loba. In A), Brazo de Loba’s main channel, with marshes and lakes (2001) and B) where it became a complex network channel system (2014). Black arrows indicate river flow. Length of scale bar is 1.5km. Red arrow indicates avulsion zone.
Conceptual Framework
Geomorphology is the scientific study of landscapes and the processes that shape them (Bloom, 1998). A sedimentary environment is known as a particular geomorphologic setting where physical and chemical processes interact in order to generate a certain kind of sedimentary deposit (Nichols, 2009; Boggs, 2012).
The flow of rivers and streams is confined in channels that are surface depressions and are generated by gravity-flow processes. The floodplain is a land area that contains the channels, that gets flooded when a flood event takes place. These two are part of the fluvial system, dominated by water processes, while alluvial system is more related with land processes that includes water movement. In this system there are alluvial plains, in which sediment is accumulated in low relief zones, because of river or floodplain aggradation action (Boggs, 2012; Nichols, 2009).
In the fluvial system, most of the flux is concentrated in the channels. When the discharge is high and overpasses the banks (barriers that confines the water flux), a flood occurs on the floodplain. The channel and bank friction and the air control
the discharge. These effects are lowered when they get away from the thalweg (the line that determines the deepest zone of the channel), which determines the deposition zones inside the channels (Nichols, 2009; Smith N., 1989).
The sediment load sometimes is deposited inside the river, in places known as bars. These are made up of sand or gravel and cause a river flux division and tend to be not cohesive, depending on vegetation cover, making them migrate with the river flow (Nichols, 2009; Boggs, 2012).
There are many known river types, but they are the combination of four end-members of rivers that are categorized by several parameters (Makaske, 2001). Four main parameters are: sinuosity (channel length divided by length of the valley), presence of depositional bars inside the channel, number of separated channels and braided ratio (total channels length divided by the widest channel length) (Makaske, 2001; Nichols, 2009). According to these parameters, the four end-members of the rivers are: straight, meandric, braided and anastomosed (Figure 2).
Straight rivers have sinuosity values near one; they have no bars, only one channel and are the simplest form of rivers (Makaske, 2001; Boggs, 2012; Nichols, 2009). Meandric rivers are known for having a sinuosity greater than 1.3 (Makaske, 2001; Knighton & Nanson, 1992). They also are known for eroding sediment in the bank part nearest the thalweg, while depositing sediment in the opposite side (meanders), where the flow is slowest (Boggs, 2012; Nichols, 2009). Contrary to braided rivers, meandric river bars are cohesive, making them difficult to erode. Meandric rivers have high energy level, having coarse sediment in its lower part, and fining upward. The channel migrates laterally with time (therefore increasing its sinuosity), until it returns to its original location, as it represents the shortest flow distance (Knighton & Nanson, 1992; Makaske, 2001). Meandric rivers are associated with low gradients and finer sediment load (Bloom, 1998; Makaske,
2001). The above types of rivers are single-channel rivers, which makes them have a braided ratio of 1. (Makaske, 2001).
Braided rivers are known for having bars inside, which are the result of sediment deposition. (Makaske, 2001). They have a braided ratio greater than 1.5 and a low sinuosity ratio (Makaske, 2001). The discharge is normally high, until it encounters the bars, which causes erosion of the bar and a subsequent deposition, making the bars migrate with the river flow (Makaske, 2001). When the river migrates laterally, it abandons the bars, increasing the chances of preservation in the geological record (Bridge & Tye, 2000). It seems that braiding takes place because of rapid, large fluctuations in river discharge, abundance of coarse sediment, high sediment supply and non-cohesive easily erodible banks (Nichols, 2009; Boggs, 2012; Nanson & Knighton, 1998).
Anastomosing rivers are characterized by interconnected multiple channels separated by stable islands (Knighton & Nanson, 1992). They can be confused with braided rivers, but they have only one channel belt, but multiple thalwegs (Makaske, 2001). They are considered laterally stable and develop in low energy conditions (Smith N. , 1989). Each channel can have the form or combination form of the other three rivers, being straight, meandric or braided (Smith N., 1989; Makaske, 2001). The stable islands are the floodplains, which are extensive, as opposed to the bars, and are made up of fine-grained sediment and are cohesive (Makaske, 2001; Smith N., 1989). In cold to medium temperatures the cohesion of the bars is because the presence of vegetation (Smith N., 1989; Makaske, 2001). They are also known for having high vertical aggradation rates, because they have natural vertical levees, causing a high preservation potential in the geological record (Makaske, 2001). Even though they are more often preserved, their understanding is low compared to the other types of rivers, because there are fewer modern analogues (Smith N. , 1989). The few studies of them are focus on temperate climates, making them climatically slanted (Smith N. , 1989).
Figure 2: Classification of rivers (Makaske, 2001)
These four types of rivers also have in common that they can accumulate sediment in different sub environments and, if optimal processes occur, they can be preserved in the geological record (Bridge & Tye, 2000). Some of these sub environments are channels, bars, natural levees, floodbasin and crevasse splays (Boggs, 2012). The last three are deposited when the stream floods and overtops its banks (Boggs, 2012). Fine sediment is deposited near the banks, making natural levees (Boggs, 2012). It results in an upward building of the sediment surface, called vertical accretion, while lateral accretion refers to sediment accumulation in bars (Boggs, 2012). Floodplain deposits are fine-grained sediments that settle out of suspension from floodwaters, in a low relief plain (Boggs, 2012). In addition, crevasse-splays are created when rising floodwaters breach natural levees (Makaske, 2001; Knighton & Nanson, 1992).
Avulsion is the process of abandonment and creation of new channels, which is the main process of formation of anastomosed rivers (Smith N., 1989; Makaste, 2001). It is considered as an inevitable consequence of floodplain aggradation, causing diversion of the river channel to a new course at a lower elevation on its floodplain (Makaske & Smith, 2002; Smith N, 1989).
To trigger avulsion, some conditions have to be met, such as high aggradation rates and a low gradient floodplain (Slingerland & Smith, 2004). The main trigger is that high discharge causes a breach in the natural levee or bank collapse, creating a crevasse splay that eventually will become a complex network of channels (Slingerland & Smith, 2004). Other triggers are known such as obstructions in the discharge that force the river to seek a new course (Slingerland & Smith, 2004). After avulsion starts, new channels will be created depending on the crevasse- splay evolution. They start as single, small (less than 1 𝑘𝑚!) lobate bodies and
evolve to be larger (up to 20 𝑘𝑚!) with an elliptical elongate form. If the evolution succeeds, a complex network of channels develops eventually (Makaske, 2001)
Figure 3: Anatomy of an avulsion (Makaske, 2001).
After an avulsion event, the floodplain evolves in order to adapt to it. What follow is the initiation, development and abandonment of splay complexes together with
diversion of flow and sediment to previously not invaded regions. The process is dependent on the number of active channels, as seen in Figure 4. The initial avulsion stage starts with a single splay and rapidly evolves to the anastomosed stage, in which a complex network of multiple channels are created. Discharge is able to sustain a limited number of channels; therefore creation of a new channel is balanced by the abandonment of another, keeping constant the number of channels for a time. Then, the reversion stage occurs, causing a decrease in the number of active channels, because the remaining areas of the floodplain become aggraded. After that, flow is concentrated into few but larger channels, until it returns to the single channel stage, where gradient keeps constant. The process is highly related with aggradation rates, therefore if aggradation is rapid, avulsion forces abandonment of channels before they develop high sinuosity, which is connected with long-lived rivers (Smith N, 1989).
Geographical and Geological Setting
The headwaters of the Magdalena river are located in a region known as the Macizo Colombiano, at more than 3600 m.a.s.l. The drainage basin has 266.541 𝑘𝑚! that corresponds to 23% of the country’s continental surface, and is home to 85% of the Colombian population. The Magdalena river has generally south-north flow in its 1,615 kilometers path, until forming a delta when arrives to the Caribbean Sea. It is generally divided in three sections, known as the Alto Magdalena, Medio Magdalena and Bajo Magdalena (Castro, et al., 2008).
The Alto Magdalena runs from La Magdalena lagoon to Salto de Honda, in the Tolima Department at an elevation of 229 m.a.s.l. The section has a length of 638 kilometers and has a drainage basin of 55.441 𝑘𝑚!, with an average discharge of
1385 𝑚! 𝑠. The Medio Magdalena runs from Honda to Regidor municipality in Bolivar Department, with an average elevation of 35 m.a.s.l. The section has a
length of 501 kilometers and the average discharge is 4200 𝑚! 𝑠. The Bajo Magdalena runs between Regidor and the river´s mouth in the Caribbean Sea, with
a length of 476 kilometers. It has an average discharge of 7100 𝑚! 𝑠. A defining feature of the Magdalena River is a tectonic depression (Mompox) that develops between kilometer 400 and kilometer 223 from its mouth (Smith D., 1986; Castro, et al., 2008).
The river exhibits an anastomosed behavior from Barrancabermeja town to Magangue (Figure 5) at kilometer 252 (Being kilometer 0 when the river enters in the Caribbean sea). From Magangue (northwest corner of the Mompox depression) to Calamar at kilometer 93, all channels converge to form a meandering channel reach. Downstream from Calamar, the Magdalena River bifurcates forming a 100-kilometer long delta with the main channel until entering the Caribbean Sea. In el Banco, at kilometer 400, the Magdalena River bifurcates into two big streams, named Brazo Mompox and Brazo de Loba, with numerous small, interconnected streams, floodplains and swamps between them, acting as a
complex anastomosed river system. The avulsion event described in this study is located 40 km downstream from el Banco, on the Brazo de Loba. (Smith D., 1986; Castro, et al., 2008).
Figure 5: A) Magdalena River in Colombia, denoted by the black lines. WC= Western Cordillera, CC= Central Cordillera and EC= Eastern Cordillera. Dashed lines represent major faults. In B) Mompox depression, which is a part of the anastomosed reach of the river. Names of main cities and channels are indicated. Study area in the right rectangle, in the Brazo de Loba stream (Moron, et al., 2015).
The Mompox depression is considered an incomplete floodplain, with lakes and marshes but lacks scroll bars and oxbow lakes. These extensive wetlands are the result of an aggrading depositional environment, related to tectonic subsidence, which is higher than the overbank sedimentation. This basin has an approximate area of 75x130 kilometers. Between El Banco and Magangue there are 140 kilometers of the river, causing a gentle gradient, about 9.5 𝑐𝑚 𝑘𝑚 (Smith D,
1986). The depression is the result of the separation of the Santa Marta Massif and the cordillera central de los Andes in the Tertiary (Montes, et al., 2010; Duque-Caro, 1979).
In this zone, crevasse splays tend to be 8 to 100 meters wide and 2 to 10 meters deep. Large splays can be up to 10 kilometers long by 5 kilometers wide. They are made up of sheets of medium-grained sand with interbedded mud. Meanwhile, natural levees across the channels are narrow, forested and made up of silty mud with thin layers of very fine sand. They tend to be wide (2 to 4 kilometers) in the Mompox depression and narrow in the newly created channels. Channels are unstable due to rapid infilling, resulting in major avulsions, starting with a crevasse splay. In the northern half of the basin, open shallow lakes dominate. In the other half, shallow marshes and swamps with organic-rich mud dominate (Smith D, 1986).
River hydrology depends on seasonal rainfall and location. The annual river discharge has its highest peak in November and December, and its lowest point in February-March. Therefore, it can be assumed that November-December corresponds to the most humid season of the Magdalena river basin, and February- March to the driest one or at least in the catchment area. The zone between the Brazo de Loba and Brazo Mompox is the most susceptible area, due to long flooding season (50 days in between October, November, December). In the dry season, the river tends to be 2 meters below its bankful stage (Smith D. , 1986) (Figure 6).
Figure 6: Hydrograph of monthly extreme, average, maximum and minimum flow discharges (1941-2010) of the Magdalena River at Calamar gauging station. Monthly discharge from 2007 and 2010 also shown (Restrepo, et al., 2014).
Methodology
LandSat Images
LandSat images were used in this project, which are part of the LandSat program. “The LandSat program offers the longest continuous global record of the Earth’s surface; it continues to deliver visually stunning and scientifically valuable images of our planet” (USGS Geological Survey, 2014). Images have been acquired by 8 different satellites, each one covering a different time span. The satellite images used in this study were acquired by LandSat 4-5 (images from 1982 to 2013), LandSat 7 (images from 1999 to 2015) and LandSat 8 (images from 2013 and continues in operation) (USGS Geological Survey, 2014). From now on they will be referred to L4-5, L7 and L8 respectively.
LandSat images have a temporal resolution of approximately 16 days. The satellite imagery captures a portion of the world by specifying the nominal scene center designated by a path and row number. It uses the Worldwide Reference System (WRS), which is a global notational system for LandSat data. The combination of a path and a row number uniquely identifies a nominal scene center. The path number is always given first, followed by the row number (Bolde, 2015).
The satellite images were downloaded from the website
http://earthexplorer.usgs.gov, identified by path 8 and row 54. Data were downloaded as Level 1 Product. The data include a multispectral image, which captures image data at specific frequencies across the electromagnetic spectrum. Each band has a range of specific frequencies, in TIFF format (Tables 1,2 and 3). All the features in the bands are georeferenced and orthorectificated. Each band has a specific wavelength range and spatial resolution (size of a pixel), which are in the following tables (Table 1, 2 and 3). (USGS Geological Survey, 2014). All images were downloaded with the default coordinate system WGS 1984, UTM zone 18. This same system was kept constant through the digitizing and analytical steps.
Table 1: LandSat 4-5 bands wavelengths and spatial resolution. All bands have 30 meters of spatial resolution, except band 6 which can vary from 30 to 120 meters. (USGS Geological Survey, 2014)
Table 2: LandSat 7 bands wavelengths and spatial resolution. All bands have 30 meters of spatial resolution, except band 6 which can vary from 30 to 60 meters and band 8 which has 15 meters (USGS Geological Survey, 2014).
Table 3: LandSat 8 bands wavelengths and spatial resolution. All bands have 30 meters of spatial resolution, except band 10 and 11, which can vary from 30 to 100 meters (USGS Geological Survey, 2014).
Each band is useful for mapping a spatial feature, for example with band 3 forests are differentiated from the rest of the features. Up to combination of 3 bands can be made, making a spatial combination that allows better differentiation of certain features in the images.
According to the anastomosed river evolution (Figure 4), three images per season are enough to document the event: the pre-avulsion event (pre-2006), the initial avulsion stage and anastomosed stage (2007-2012) and the reversion stage (2012 to today). The criteria to choose images are 1) that the image was taken in either
wet or dry season, and 2) that the cloud coverage is minimal. In the following table there are the six images required for the documentation of the avulsion event of 2007. Unfortunately, LandSat 7 was damaged in 2003, causing noise in the subsequent captured images, a damage that caused a loss of information of 22% (USGS Geological Survey, 2014). Therefore, there were few images from 2003 to 2012, to choose from. In order to pick good images, restrictions have to be lowered, so the selected image for the anastomosed stage in the humid season is LT50080542007254CHM00 (date 11/09/2007), which is almost two months before the humid season. Image LC80080542014049LGN00 (date 18/02/2014) was chosen, even if the cloud coverage is large, because it was during these days that there was ground observations of the channel, including sonar, making it a good complement to the LandSat data.
Date
(DD/MM/YYYY) Name LandSat Season 29/01/2001 LT50080542001029XXX01 L4-5 Dry 10/12/2002 LE70080542002344AGS00 L7 (1999-2003) Humid 11/09/2007 LT50080542007254CHM00 L4-5 Humid 14/03/2011 LT50080542011073CHM00 L4-5 Dry 18/02/2014 LC80080542014049LGN00 L8 Dry 4/01/2015 LC80080542015004LGN00 L8 Humid
Table 4: LandSat images used in this study with their respective dates, names, and season.
Digitizing
To digitize the satellite images, ArcGIS 10.3 was used, with the following extensions: 3D Analyst, ArcScan, Geostadistical Analyst, Spatial Analyst and
Tracking Analyst, together with the applications ArcCatalog, ArcMap and
ArcToolbox.
Polygons representing lakes, river channels and floodplains were digitized and saved as shapefiles (Table 5).
Date
(DD/MM/YYYY) Name Shapefile Name 29/01/2001 LT50080542001029XXX01 Digitalization2001 10/12/2002 LE70080542002344AGS00 Digitalization2002 11/09/2007 LT50080542007254CHM00 Digitalization2007 14/03/2011 LT50080542011073CHM00 Digitalization2011 18/02/2014 LC80080542014049LGN00 Digitalization2014 04/01/2015 LC80080542015004LGN00 Digitalization2015
Table 5: Dates and names of the satellite images with their corresponding shapefile name.
Figure 7: Images from the humid seasons used for digitizing channels, lakes and floodplain. From left to right: 2002, 2007 and 2015. White arrows indicate river flow direction.
Figure 8: Images from the dry season used for digitizing channels, lakes and floodplain. From left to right: 2001, 2011 and 2014. White arrows indicate river flow.
False RGB composites (Red, Green and Blue), were created using bands 4, 5 and 3, from LandSat 4-5 and 7respectively. With LandSat 8 bands 5, 6 and 4 were used. With this combination, water bodies have blue or black color, while the floodplains have green to dark yellow colors. Clouds have white color, while shadows are black. This combination was used because it creates high contrast between water bodies with the other bodies, which is what is needed. This was done with the Composite Bands Tool, (ArcToolbox: Data Management Tools: Raster: Raster processing: Composite Bands), with the input raster parameters as the three mentioned bands.
Figure 9: Example of false color composition image. White arrows indicate river flow.
Images were clipped to an are of interest defined by the following decimal coordinates: (573116.05N, 957623.6W), (573093.087N, 976910.627W), (593150.45N, 976934.611W), (593173.507N, 957647.439W).
Two main types of digitalization were undertaken: manual and semiautomatic.
As for manual digitalization, using the Cut Polygons Tool in the Editor Toolbar, polygons were cut from the clipped image. The three bodies that were digitized were the river channels, lakes and floodplains. The river channels were digitized as bodies that have a high length-to-width ratio and also that usually interconnect water bodies. Lakes were digitalized as bodies that have a length-to-width ratio near to 1, and normally are isolated (surrounded by floodplain). Floodplains were the leftover area, as they are bodies made up of land. Sometimes, floodplains have to be cut, as they were inside channels or lakes (in this case they are polygons inside other polygons).
As for the semiautomatic digitalization, the process was quite different. A new raster was made using a manual color pixel classification. The division was in the pixel value 8 (in for LandSat 4-5 and 7) or 10 (in for LandSat 8); meaning that only two features would be shown (pixels with 0 value, below the division value, and pixels with 255 value, above the division value). With this method, water bodies and shadows look black, and clouds and floodplains look white. The polygons were created with the Vectorization tool in ArcScan. Sometimes, clouds and shadows eclipsed the water bodies; therefore a manual interpolation for both type of digitalizations had to be used in order to ignore these bodies that are not part of the surface geomorphology. Images from the years 2002 and 2015 were digitized automatically.
After that, a new field was added in the Attributes Table, called “Cuerpo”. For the channels, the “Cuerpo” field was “Channel”, for the lakes, “Lake” and for the floodplain, “Floodplain”. Floodplain attributes include features that were not floodplains, but were land bodies, such as places with relief, levees or stable islands inside the channels. The resolution of the satellite image (30 m) does not allow a differentiation of some of these features, therefore a generalization had to be made.
Discharge, river level and rainfall data
Discharge, river level and rainfall data were acquired with the help of the Departamento de Ingeniera Civil de la Universidad de los Andes. The data is from the IDEAM Instituto de hidrología, meteorología y estudios ambientales. Also, discharge data from Calamar station was taken from Restrepo, et al., 2014.
As for the discharge information (electronic appendix: IDEAM: Discharge: Discharge.xlsx), is called “Valores medios diarios de Caudales (M3/Seg)”, which includes the discharge data from the Station 25027410 located in 630196m N, 956360m W and elevation of 35 m.a.s.l. Is in the municipal Regidor, in the Department Bolivar, which collects data from the Magdalena river downstream. The station is about 40 kilometers southeast from the avulsion zone. The data
includes mostly daily caudal information in !"#"$!"#$%&! units, from 1973 to 2012. Also,
includes monthly and yearly average data, maximum absolute and minimum medium data.
In the same archive, there is also data of the river level. The data includes mostly daily river level information in centimeters, from March 1973 to November 2012. Also, includes monthly and yearly average data, maximum absolute and minimum medium data.
As for rainfall data (electronic appendix: IDEAM: Pluviosity: Pluviosity.xlsx), it was acquired from station 25025100 in the municipality of Magangue in the Bolivar Department. It is located in 518305m N 1024339m W and elevation of 18 m.a.s.l. And is about 20 kilometers west from the avulsion zone. It has data from 1973 to 2013. It has daily total values of precipitation in millimeters, and includes the rainy days in every month and the total precipitation in the year.
Spatial data
ArcGIS spatial and temporal statistics were run on the digitized polygons, in order to characterize the event. The following parameters were studied: sinuosity, area, centroid position, number of channels, channels width and length of transect.
Sinuosity
Sinuosity is the ratio between the total length of the channel and the distance between starting and ending points. Several values of sinuosities near the avulsion zone were measured, until the point where all the channels converge and diverge again (587367.171m N, 967379.22m W) (Figure 11, black arrow). The total length of the channel was obtained through the automatic digitalization, but instead of using polygons, lines were made. The sinuosity values were measured for the images 2007, 2011, 2014 and 2015, because channels near the avulsion zone appear from 2007 to today (2015).
After that, total length of the channel was added as an attribute in “Shape_Leng” in meters. The distance from the end and start points of the channel was measured with the ArcGIS ruler. The following archives are in the folder sinuosity (electronic appendix: Sinuosity):
Folder
(Year) Shapefile
2007 Sinuosity2007.shp 2011 Sinuosity2011.shp 2014 Sinuosity2014.shp 2015 Sinuosity2015.shp Table 6: Files with sinuosities.
The following image shows an example of the measurements (Figure 10):
Figure 10: Image of the process to acquire sinuosity values. Lines represent the 2011 channel configuration.
Area of bodies
Manipulations in Excel were undertaken in order to determine the total area of each body (Channel, Lake and Floodplain), to understand how they evolve temporarily. In table 7 the files names with the manipulations (electronic appendix: Areas&Centroids).
Year Archive 2001 Area2001.xlsx 2002 Area2002.xlsx 2007 Area2007.xlsx 2011 Area2011.xlsx 2014 Area2014.xlsx 2015 Area2015.xlsx
Table 7: Files with area and polygons centroids values.
Centroid analysis
In iPython I wrote a code for manipulations to the latitude and longitude centroid coordinate values. The images have horizontal and vertical distance near 20,000 meters, therefore, a grid of 20x20 unit was made, and in each cell counted how many polygons centroids were inside. This analysis is useful to understand the migration of the polygons. Also, graphs were done for Channel-counting and
Lake-counting, to understand how these features migrate independently. The Python archive is called Centroids.ipynb (electronic appendix: Areas&Centroids). The code is explained in appendix 2.
Channel Analysis
Manipulations in ArcGIS were done in order to count the number of channels, channel width and length of transect. A shapefile of lines was created, called Transecta.shp (electronic appendix: Channels), which includes a transect that goes with the flow direction of the avulsion process. It starts from the coordinates (Figure 11, yellow arrow) 589394.22 m N, 975360.724 m W and ends in 587367.171 m N, 967379.22 m W (Figure 11, black arrow). Has a distance of about 8000 meters. The end point is the same endpoint of the sinuosity values, because is where channels converge and diverge again afterwards. At 588950.803 m N, 973678.29 m W (Figure 11, orange arrow), which is where channels start to diverge, perpendicular transects were made until the end point. Nine transects were made every 800 meters approximately (Figure 11). In each of the nine transects, the number of channels were counted (manually). The mean channel width was made by calculating the width perpendicular to the flow direction of the channel, it necessarily was not in the transect route. The length of transect was measured as the distance from the extreme channels that the transects crosses. This process was done only to channels that started or were the result of the avulsion process, and that part of its flow converge in the convergence point (end point) (Figure 11, black arrow). Table 8 shows the transect distance from the avulsion zone:
Transect Distance(m)
T1 1669
T2 2469
T3 3269
T4 4069
T5 4869
T6 5669
T7 6469
T8 7269
T9 8069
Table 8: Transect distance from the avulsion zone.
Figure 11: Image of the transects (red lines) with their respective names. Yellow arrow denotes the avulsion zone. Black arrow denotes the convergence zone of the flow. Orange arrow denotes the divergence zone of the flow.
Results
Avulsion Analysis
Figures 12-18 show images used for this analysis. Channels are shown in purple, Lakes are shown in dark blue and Floodplains are shown in green. There is also shown percentage of area of the bodies.
Figure 12: Digitalization of the pre-avulsion image from the dry season (date 29/01/2001). The white arrows represent the flow direction. Legend is on the right, with the corresponding percentage of area of each body.
Figure 13: Digitalization of the pre-avulsion image from the wet season (date 10/12/2002). The white arrows represent the flow direction. Legend is on the right, with the corresponding percentage of area of each body.
Figure 14: Digitalization of the initial avulsion-anastomosed stage image from the wet season (date 11/09/2007). The white arrows represent the flow direction. Legend is on the right, with the corresponding percentage of area of each body.
Figure 15: Digitalization of the anastomosed stage image from the dry season (date 14/03/2011). The white arrows represent the flow direction. Legend is on the right, with the corresponding percentage of area of each body.
Figure 16: Digitalization of the reverse stage image from the dry season (date 18/02/2014). The white arrows represent the flow direction. Legend is on the right, with the corresponding percentage of area of each body.
Figure 17: Digitalization of the reverse stage image from the wet season (date 04/01/2015). The white arrows represent the flow direction. Legend is on the right, with the corresponding percentage of area of each body.
Figure 18: Digitalization images in chronological order to have a better view of the event. The images are in the order 2001, 2002, 2007, 2011, 2014 and 2015. The white arrows represent the flow direction.
Discharge, Pluviosity and River level data
Figure 19: Yearly Mean Discharge from 1974 to 2012. Data from IDEAM. Red line depicts the avulsion event. Trend line (black) is increasing with time. Avulsion event occurs at a high discharge value.
Figure 20: Yearly Mean River Level from 1974 to 2012. Data from IDEAM. Red line depicts the avulsion event. Tendency line (black) is increasing with time. Avulsion event is in a high river level value.
Figure 21: Yearly Mean Pluviosity from 1974 to 2012. Data from IDEAM. Red line depicts the avulsion event. Tendency line (black) is increasing with time. Avulsion event is in a pluviosity increase.
Sinuosity
Year Average Sinuosity Number of samples
2007 1,2309 4
2011 1,2066 7
2014 1,1576 6
2015 1,1737 7
Table 9: Sinuosity values for each period of time analyzed. Number of samples also shown.
Figure 22: Graphic that shows the temporal evolution of sinuosity, since the avulsion event. It can be seen that sinuosity values decrease about 0.06 with time. They are higher in the avulsion stage and lower in the reverse stage. Red line depicts the avulsion event. Yellow lines denote the stage change.
Area
Figure 23: Graphic that shows the changes in channel, lake and floodplain areas between 2001 and 2015. Red line depicts the avulsion event.
Number of Channels
Figure 24: Graphic that shows the Spatial Evolution of number of Channels. The six years are represented. Most of the channels started near the avulsion zone, and tend to migrate farther away within the flow direction. As described by Smith, 1989, the anastomosed stage is represented for having the most channels. In transect 7, the initial avulsion event has 0 channels, the anastomosed stage has 12 channels, and the reversion stage has 4 channels. As for the reversion stage (2013 to today), it can be seen that the number of channels in all the transects is almost stable (about four channels), meaning that with time the number of channels have a low decreasing rate in most of the zone. Black arrow denotes the avulsion zone. More details, in the Temporal Evolution of number of Channels, dry and wet season (Appendix 4, Figure 34 and Figure 35), there is shown that number of channels after the avulsion event are mostly in transects 7 and 6, which is quite far from the avulsion zone (6400 m and 5600 m away respectively). Data from the wet season is not too relevant, mainly because 2007 image is in the initial avulsion-anastomosed stage, which eclipses the data.
Figure 25: Graphic that shows number of channels in selected transects during the dry season. (T1 and T9 are the initial and final transects, and T7 is the second largest one). Number of channels in T1 increase from 0 in 2001 to 3 in 2011. Number of channels in T7 increase from 0 in 2001 to 12 in 2011 and decrease to 4 in 2014. Number of channels in T9 increase from 0 in 2001 to 6 in 2011 and decrease 100% in 2014.
Channel width
The figures 26-27 have a non-linear interpolation between points. In some parts, it goes below zero, even if it is not possible, although it shows good interpolation patterns.
Figure 26: Graphic that shows the Spatial Evolution of width of Channels. The six years are represented. Channels get wider the farther they get from the avulsion zone. More details, in the Temporal Evolution of width of Channels, Dry and Humid Season (Appendix 4, Figure 36 and Figure 37), it could be said that the channels get wider in the anastomosed stage, and get narrower (decrease of 110% in T7) in the reversion stage. Black arrow denotes the avulsion zone.
Figure 27: Graphic that shows average width of channels in selected transects in the dry season. (T1 and T9 are the extreme transects, and T6 is the largest one). Width of channels in T1 increase from 0 in 2001 to 142 m in 2011 and decrease 60% in 2014. Width of channels in T6 increase from 0 in 2001 to 177 m in 2011 and decrease 110% in 2014. Width of channels in T9 increase from 0 in 2001 to 261 m in 2011 and decrease 171% in 2014.
Centroids values
GeneralFigure 28: Number of centroids (channels, lakes and floodplains) according to their geographic position in chronological order. (2001-2002-2007-2011-2014-2015). It gives us the idea of how bodies are being created and migrating. Observations
show how bodies start being created in the northeast of the image, and they migrate towards southwest. This pattern is similar to the one shown by channel centroids (Figure 29), meaning that channel creation and migration are the features that are more interacting in the zone. In the number of centroids of Lakes (Appendix 3, Figure 41, lakes do not show a representative pattern, only that they end up zoning at the southwest of the zone.
Figure 29: Number of Channels centroids in order of its position in chronological order. (2001-2002-2007-2011-2014-2015)
Figure 30: Graphic that shows the percentage change of the lake and channel centroids in the dry season. It can be seen that lakes centroids decrease 33% from 2001 to 2011, and increase 9% to 2014. Channel centroids, almost gains what lakes centroids loose: increase 19% from 2001 to 2011, and increase 1% from 2011 to 2014. Yellow lines denote stage change.
Figure 31: Graphic that shows the percentage change of the lake and channel centroids in humid season. It can be seen that lakes centroids decrease 19% from 2001 to 2011, and increase 18% to 2014. Channel centroids, almost gains what lakes centroids loose: increases 9% from 2001 to 2011, and decreases 5% to 2014. Yellow lines denote stage change.
Discussion
In the pre-avulsion stage (pre-2006), the floodplain covers most of the study area (81% and 77% of area of the site in the wet and dry season respectively). Oddly, dry season has more area of water bodies than wet season (4% more), due to local seasonal changes (Figure 23). Discharge data (Figure 19) and river level data (Figure 20) has an increasing pattern that is not highly related with pluviosity (Figure 21), which has various patterns.
In the initial avulsion-anastomosed stage (2007-2012), it can be seen that the avulsion event created a complex network of channels that enclosed floodplains that allowed channelization of the lakes located in the southwest section of the study area (Figure 18). Discharge, river level and pluviosity data show high values, which were the trigger for this avulsion event. Discharge had an increase of 37% and river level 21% from 2004 to 2007 and pluviosity 43% from 2006 to 2007. There are other years with high values for these parameters that did not result in
avulsion events, such as in 1997 to 1998, when discharge had an increase of 42%, river level 17% and pluviosity 17%. In 1998 discharge values were 5% and river level 8% lower than 2007. Nevertheless, there was no avulsion in 1998, even though it has discharge and river level values close to 2007, meaning that or it was really close to have an avulsion process, or there were another factors that were not present such as high aggradation rates or gentle slopes to enact the avulsion.
Figure 32: Magdalena river level (m) in el Banco, Magdalena, with several la Niña events. Wide red horizontal bar denotes the height of the river level where inundation occurs. In narrow red line the river level influenced by la Niña event of 2007-2008, which has three major peaks over the level where flooding occurs. (Martinez, 2011).
This increase in discharge, river level and pluviosity is associated with la Niña event (Figure 32), (Moron, et al., 2015). This event, had devastating consequences in the north of the country, where many zones were constantly flooded, mainly in Atlántico and Bolivar departments (Martinez, 2011). During the initial avulsion stage, where channels are created, they start being straight (sinuosity values of 1.2) (Figure 22). Channels gain 5% of area (2001 to 2011), while lakes gain about 6% (Figure 23). The stage is characterized for having multiple channels (Figure 24) (up to 12 channels in T7 in 2011) that can have an average width of 106 m (Figure 25), six kilometers southwest downstream from the avulsion zone. Channels get wider the farther they get from the avulsion zone (increase of 45% channels
average width from T1 to T9 in 2011). The main reason for this is that they have not completely channelized the main lake; consequently there is still lake area to be conquered from the channels when they are in this stage. The channels had about 3 km of length in 2007. Channel centroids increase by 19% from 2001 to 2011, while lake centroids decrease 33%.
In the reverse stage (2013-2015), most of the southwest lakes have been channelized (Figure 18). Floodplains have regained area (13% in the dry season) from the lakes (lost 10%), (Figure 23). There is a stable number of channels (about four), (Figure 24) and channels width (about 80 m) (Figure 24) along the transects, mainly because there is no more lake area to be gained. Therefore, after the lakes channelization, these two parameters stabilized. In T8, from 2011 to 2014 channel average width decreased 240%, and in all the transects the width decreases between these two years. As for humid season, in two of the three transects there is decrease in channel width. In T3 from 2007 to 2015 decreases 80%. Sinuosity values decrease 0.06, meaning that rivers get straighter (Figure 22). The transect length (Appendix 3, Figure 38) shortens more than 1 km compared to the anastomosed stage (in T7 shortens 1.9 km and in T8 shortens 1.6 km). Consequently, accommodation space between channels decreases. This is highly related with the sinuosity of the channels, because they get straighter with time (sinuosity decrease 0.06 from anastomosed to reverse stage in both seasons), they cover less transect length. The channels increase their length extent up to 20 km in 2015. Despite, discharge, river level and pluviosity data has almost the same values than from the anastomosed stage, area of water bodies have decreased in the reverse stage. Probably because most of the avulsion channels water is getting infiltrated in the southwest part of the zone and most of the floodplain has already been invaded. General number of channels centroids increases 1% from 2011 to 2014, while lake centroids increase 10%.
Generally, the transect length has a “lens geometry”, having its wider part about 6 kilometers from the avulsion zone (in T6) (Appendix 3, Figure 38). In its wider part,
there is also the highest number and width of channels, for the anastomosed and reverse stages (Figures 24 and 26).
Conclusions
High discharge values (5846 !!!) and river level (1019 cm) were the trigger of the
avulsion event at Brazo de Loba stream, in Magdalena River in 2007. Discharge
values of 5922!!! and river level values of 920 cm were not enough to trigger an
avulsion event in 1999. Probably other factors were not favored such as high aggradation rates or the absence of a non-cohesive levee, which would be easier to breach. La Niña had a huge impact on Colombia, which is the main responsible for the high discharge and river level values of 2007 (Figure 19, Figure 20 and Figure 21).
Sinuosity values decreased 0.06 from anastomosed (2011) to reverse stage (2015), making the rivers straighter and decreasing the distance between each channel (Figure 22).
Dry season had a maximum impact on floodplain area of 16% (in reverse stage), on lakes area of 12% (in reverse stage) and channels area of 6% (in the initial avulsion-anastomosed stage) (Figure 23).
Smith, N., 1989 noted that channels got fewer and longer with time. In this case they also get narrower: number of channels decrease from 12 to 4 in T7 from 2011 to 2014, channels width decrease from 217 m to 63 m in T8 from 2011 to 2014 and channels increase their length of 3 km in 2007 to 20 km in 2015. (Figure 24 and Figure 26).
Transect length has a “lens geometry”, having its wider part about 6 kilometers from the avulsion zone. In its wider part, there is also the highest number and width of channels, for the anastomosed and reverse stages.
Channels area increases about 6% from pre-avulsion to anastomosed stage (dry season), lakes about 8% (dry season). From anastomosed stage to reverse stage, channels area decrease about 3% and lakes decrease 10% (in dry season, wet season was temporally slanted) (Figure 23).
The number of channels increased until a certain distance it reached in the anastomosed stage (about 6.4 km in this case, in T7), then decreases (Figure 24).
Anastomosed stage continues for as long as new floodplain areas are invaded, which ends when most of the lake area has been conquered by the avulsion channels (Figure 18).
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Acknowledgments
Agradecimiento a la Beca para tesis de pregrado financiada por el Fondo Corrigan- ACGGP-ARES.
Agradecimiento a mis padres, hermanos y amigos quienes me han apoyado en todo momento.
Agradecimiento especial a Sara Moron, Camilo Montes y Jillian Pearse, quienes me orientaron y ayudaron con el proyecto.
Agradecimiento al IDEAM y al Departamento de Ingeniería Civil por aportar datos que contribuyeron al desarrollo de la tesis.
Appendix
Appendix 1: Area Methodology
To measure Percentage of Area and Polygons centroids position, the attribute table was used. Three new integer fields were added, the first one being “Area”, the second one being “X” and the third one “Y”. Then, clicking in the heading of Area, option “Calculate Geometry” was selected, next “Area” option was selected. For “X” and “Y” the same process was done, but instead of selecting “Area” option, it was selected “X coordinate” and “Y coordinate” respectively.
Appendix 2: IPython Code to categorize centroids.
import numpy as np
import matplotlib.pyplot as plt from __future__ import division import xlrd
#Reads the Excel file that contains the columns of FID (Number of polygon), ID (0 to all features), Cuerpo (Channel, Lake or Floodplain, depending on the body geometry),
#The Archives names are: Area2001,Area2002,Area2007,Area2011,Area2014,Area2015
doc = xlrd.open_workbook('Area2015.xlsx')
#The Sheets names are: Area2001,Area2002,Area2007,Area2011final,Area2014,Area2015 respectively area = doc.sheet_by_name(u'Area2015')
#The total values are 293, 770, 401, 591, 475, 1293 respectively total=1293
cuerpo=[] x=[] y=[]
# Makes lists to add the Excel columns for n in range (0,total):
x.append(area.cell_value(n+1, 4)) y.append(area.cell_value(n+1, 5)) cuerpo.append(area.cell_value(n+1, 2))
limiteinfx=min(x) limitesupx=max(x) limiteinfy=min(y) limitesupy=max(y) rangox=limitesupx-limiteinfx rangoy=limitesupy-limiteinfy
#Makes empty matrixes in order to fill them later contorno=np.zeros((20,20))
channel=np.zeros((20,20)) lake=np.zeros((20,20)) floodplain=np.zeros((20,20))
#This algorithm will fill the matrixes values, by counting how many polygons are in each cell
for i in range(0,20): for j in range (0,20): for k in range (0,total):
if x[k] < (limiteinfx+rangox*(i+1)/20) and x[k]> (limiteinfx+rangox*(i)/20) and y[k] < (limiteinfy+rangoy*(j+1)/20) and y[k]>(limiteinfy+rangoy*(j)/20):
contorno[i][j]=contorno[i][j]+1 else:
a=0
#The graph plotting of the Number of Centroids in 2015 figure()
contourf(contorno) xlabel("x")
ylabel("y")
cbar = plt.colorbar()
plt.title('Number of Centroids in 2015')
#Fills the matrixes with the values of the independent attributes. for i in range(0,20):
for j in range (0,20): for k in range (0,total):
if x[k] < (limiteinfx+rangox*(i+1)/20) and x[k]> (limiteinfx+rangox*(i)/20) and y[k] < (limiteinfy+rangoy*(j+1)/20) and y[k]>(limiteinfy+rangoy*(j)/20):
contorno[i][j]=contorno[i][j]+1 if cuerpo[k]=="Channel": channel[i][j]=channel[i][j]+1 if cuerpo[k]=="Lake": lake[i][j]=lake[i][j]+1 if cuerpo[k]=="Floodplain": floodplain[i][j]=floodplain[i][j]+1 else: a=0
#Plotting of the Lakes Centroids figure()
contourf(lake) cbar = plt.colorbar()
plt.title('Number of Lakes Centroids in 2015') xlabel("x")
ylabel("y")
#Plotting of the Channels Centroids figure()
contourf(channel) cbar = plt.colorbar()
plt.title('Number of Channel Centroids in 2015') xlabel("x")
ylabel("y")
Appendix 3: Spatial Analysis Graphics
Area per Season
Figure 32: Graphic that shows the temporal Evolution of Area of Bodies in the Dry Season. Red line represents the avulsion event.
Figure 33: Graphic that shows the temporal Evolution of Area of Bodies in the Humid Season. Red line represents the avulsion event.
Number of Channels per Season
Figure 34: Graphic that shows the Temporal Evolution of number of Channels of the Dry Season. The nine transects are represented. Red line represents the avulsion event.
Figure 35: Graphic that shows the Temporal Evolution of number of Channels of the Humid Season. The nine transects are represented. Red line represents the avulsion event.
Width of Channels per Season
Figure 36: Graphic that shows the Temporal Evolution of width of Channels of the Dry Season. The nine transects are represented. Red line represents the avulsion event.
Figure 37: Graphic that shows the Temporal Evolution of width of Channels of the Humid Season. The nine transects are represented. Red line represents the avulsion event.
Transect length
The graphs have a non-linear interpolation between points. In some parts, it goes below zero, even if it is not possible, although it shows good interpolation patterns.
Figure 38: Graphic that shows the Spatial Evolution of the Transect Length. The six years are represented. Black arrow denotes the avulsion zone.
Transect length per Season
Figure 39: Graphic that shows the Temporal Evolution of the Transect Length of the Dry Season. The nine transects are represented. Red line represents the avulsion event.
Figure 40: Graphic that shows the Temporal Evolution of the Transect Length of the Humid Season. The nine transects are represented. Red line represents the avulsion event.
Lakes centroids images
Figure 41: Function of number of Lakes centroids in order of its position in chronological order. (2001-2002-2007-2011-2014-2015)
Electronic Appendix (Folders)
Digitalizations
2001 2002 2007 2011 2014 2015
Areas&Centroids
Channels
IDEAM
Discharge Pluviosity
Sinuosities
2007 2011 2014 2015
