An explorative Research into the An explorative Research into the An explorative Research into the An explorative Research into the An explorative Research into the An explorative Research into the An explorative Resear

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An explorative Research into the

orographic lift in the Grands Causses

Park, France.

The relation between orographic lift and the flight patterns of griffon vultures in the Grands Causses Park, in cooperation with Jim Groot and Dorien van

Kranenburg Bachelor Thesis Marnix Wittebrood 10449035

University of Amsterdam

Supervisor: Prof. Dr. Ir. W. Bouten Amsterdam 28t h_{of June 2015}

Abstract:

In 1981, a group of Griffon Vultures have been reintroduced in The Grands Causses Park in France. Some of the vultures were equipped with solar-powered GPS trackers to record their feeding and flight patterns.

Research has been done in linking orographic lift and migratory patterns of. However, not much research has been done in linking orographic lift to more local flight patterns of birds.

The research was done in cooperation with two fellow students, Jim Groot who modelled the convective lift, and Dorien van Kranenburg who linked the orographic lift and convective lift to the flight patterns of the Griffon Vultures.

The research focusses on modelling the orographic lift in the research area, the Grands Causses Park near Millau in France. A model was created in MATlab to simulate the orographic lift in the research area, using meteorological data (wind speed and wind direction) from both a

meteorological station near Millau and data from the ECMWF model.

The first set of results from the model include 2 dimensional maps of the area showing orographic lift, based on a static wind speed and wind direction. The second and third set of results are maps of orographic lift at 1-hour intervals over 2 months in the winter and 2 months of the summer of 2011 based on the data from the station at Millau and ECMWF data.

Based on the results, some general conclusions can be drawn on the orographic regime. The orographic lift is highest in the mountainous regions in the north and east, and in the canyons in the middle of the research area. The areas in between these are relatively flat and do not produce a high amount of orographic lift. The ECMWF data and data from the meteorological station in Millau seem to contradict each other in terms of wind direction, possibly due to interpretation errors, but the orographic lift calculated from both data sets is in the same order of magnitude. The model itself provides a decent approximation of the orographic lift in the research area. When comparing orographic lift and convective lift, the type of lift that dominates depends on the slope angle and weather conditions. Generally speaking, higher slope means more

orographic lift, and convective lift dominates on the flat areas between the canyons and the mountains, while orographic lift dominates in the canyons and mountains.

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Introduction

On the 5th of June 2015, an uncommon sight occurred in the Netherlands. Griffon vultures have arrived in Friesland, The Netherlands. On this very hot day, a group of Griffon Vultures arrived in the

Netherlands, looking for food. These birds were able to make use of the thermal lift created by the warm weather to travel all the way over here. It is unknown where they came from. The closest colonies of Griffon vultures are in France, Spain and Croatia where the birds could have originated from. The colony in France was only recently reintroduced in 1981, in the Grands Causses Park in southern France. More recently in 2010, 22 of the Griffon Vultures were

equipped with solar-powered GPS devices to track their behavior and flight patterns. These large birds require uplift, or upwards moving air, to fly long distances since flapping costs too much energy compared to soaring.

One part of the uplift generated in this area is orographic lift, this occurs when

horizontal moving air meets an obstacle and gets deflected upwards (as shown in figure 1). Orographic lift occurs commonly along mountain ranges.

The research area that will be examined in this research lies to the northeast of Millau and is 80 km by 75 km in size.

On a migratory scale, research has been done linking orographic lift to migration patterns. Bohrer et al. (2012) found that Golden Eagles preferred using orographic lift during their migrations. In a different research by Bishop et al. (2015) it was concluded that, based on the heart rate and ascension rates from bar-headed geese during their migration over the Himalaya, that they used orographic lift as a way to conserve energy.

Other research on a migratory scale was done by Dennhart et al. (2015), simulating migration pathways for golden eagles.

Dennhart et al. (2015) concluded that golden eagles use more orographic lift than thermal lift during migration.

However, very little research has been done on a finer scale, linking uplift in an area to daily foraging patterns.

This research was done in cooperation with two students, Jim Groot and Dorien van Kranenburg. Jim Groot modelled the convective lift in the research area, and Dorien van Kranenburg used the data generated by Jim Groot and Marnix Wittebrood to link the uplift regime in the area to the flight patterns of the Griffon Vultures.

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Aim and Research Question

This research aims to create a model to simulate the orographic lift in the Grands Causses Park and create a better

understanding of how the orographic lift behaves in the research area.

Secondly, this research aims to determine what factors influence orographic lift in the park, and how the orographic lift that is generated changes over time.

The results produced by this research will be used by Dorien van Kranenburg to

determine the reliance of Griffon Vultures on orographic lift to fly around the area when searching for food.

The main research question that will be attempted to be answered in this paper will be: “What factors influence the Orographic Lift in the Grands Causses and what does the Orographic Lift look like in this when it is modelled?”

Orographic Lift Model

There are several ways to model orographic lift, for instance Mohamed et al. (2015) used a computational fluid dynamics model to calculate the orographic lift produced by buildings. This is a very complex way to model orographic lift, using differential equations to calculate turbulence between buildings caused by the wind. However, this method is far too complex and complicated for the scope of this research, as well as too time consuming.

Brandes & Ombalski (2004), as well as Bohrer et al. (2012) take a different

approach to modelling orographic lift. Their models are two-dimensional and take into account the slope of the terrain, the direction the slope is facing, as well as the wind direction. This model is less

complicated than the one used by Mohamed et al. (2015), and is therefore better suited for the scope of this research.

Brandes et al. (2012) describe the model used in great detail, and this model will be used as the starting point in this research. The orographic lift calculated by this model is dependent on the following factors:

1. Wind Speed 2. Wind direction 3. Terrain Slope 4. Terrain Aspect

Brandes & Ombalski (2004) describe how these are used to calculate the orographic lift. Updrafts will be strong where the wind is perpendicular to the terrain and the terrain is steeply sloped and weak where the wind is parallel to the terrain, or the terrain is relatively flat. For a particular wind direction, we use the product of two

variables to determine the relative orgraphic uplift strength at each location: the cosine of the angle between the terrain aspect and the wind direction (ranging from 0 for parallel winds to 1 for perpendicular winds), and the terrain slope.

Based on Brandes & Ombalski (2004) the following equations are given by Bohrer et al (2012) to calculate the orographic lift.

𝑤

_𝑜

= 𝑣 ∙ 𝐶

_𝛼 (1)

𝐶

_𝛼

= sin(𝜃) ∙ cos(𝛼 − 𝛽)

(2) Where 𝑤_𝑜 is the orographic lift, 𝑣 is the wind speed in m/s, 𝐶_𝛼is the updraft coefficient which is dependent on the constant slope angle, 𝜃 (in degrees, a level slope = 0°) and terrain aspect, 𝛽 (facing of the slope in degrees, north = 0°) and the wind direction, where the wind is coming from, 𝛼 (in degrees, north = 0°).

A Digital Elevation Model (DEM) of the area near Millau was used to create a map of the slope and aspect of the terrain so they could be used in the model. The DEM, with a cell-size of 50 by 50 meters, was imported in ArcGIS and clipped to the appropriate size

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5 for the research area. Next, the DEM

was rescaled to a cell-size of 100 by 100 meters, creating a matrix of 800 by 750 cells. A slope map and aspect map were then constructed from this new DEM.

Besides the DEM, slope and aspect maps of the area, meteorological data is also required as an input for the model. Data from the meteorological station in Millau, Cornus and Saint Pierre des Tripiers was available, which included dates, wind speeds and wind direction. Data from the ECMWF (European Centre for Medium-Range Weather Forecasts) model was also available for the area. However some calculations had to be performed first, since the ECMWF data only includes wind velocity components in North and East direction, and not wind direction. To calculate the wind speed and wind direction the following equations were used:

𝑣 = √𝑈

2

_{+ 𝑉}

2 ₍₃₎

Equation 3 calculates the total wind speed 𝑣 based on the north facing velocity

component 𝑉 and the east facing velocity component 𝑈.

𝛼 = (tan

−1

₍

𝑈 𝑉

)) (

180

𝜋

) − 180

(4) Equation 4 calculates the wind direction 𝛼 based on the north facing velocity

component

𝑉

and the east facing velocity component 𝑈.

Methods

For the orographic lift model it is necessary to define the research area, the

specifications of the model and the input data.

Research Area

The research area (figure 2) lies Northeast of Millau, and is 80 kilometers wide and 75 kilometers tall. The research area is

centered roughly on the following coordinates:

North: 44.25° East: 3.33°

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Model Specifications

The Orographic Lift model shows the total orographic lift per cell in the area, at any given time. The model ran for two distinct time periods, namely January and February 2011 and July and August 2011. These two time periods were chosen to investigate the difference between the lift in the summer and winter. 2011 specifically was chosen since the Griffon vultures in the area were equipped with solar-powered GPS tracking devices in 2010, and not many would have disposed yet, meaning 2011 would most likely have the most data available. There are two separate models, one that deals with the data from the meteorological data from the station in Millau, and another that uses the ECMWF data instead.

For this model the wind speed and wind direction are assumed to be uniform over the entire research area.

Input Data

The following paragraphs will discuss the data required as input for the Orographic Lift model.

The meteorological data from the

meteorological station in Millau first of all is converted from a excel spreadsheet to a TXT file so it can be easily imported into matlab.

Wind Speed (v)

The wind speed is imported from the data available from the meteorological station in Millau. This data spans a much greater time period than is required for this research and is therefore first trimmed down to fit the months that are being investigated. The wind speed acquired from this data does not need to be processed further.

The data available from the ECMWF model needs to be further processed. The specific time periods for the importing of this data can be selected so the data does not need to be trimmed, but since the model provides two components of the wind speed,

equation (3) will need to be applied before the data can be used in the model.

For the ECMWF model the wind speed data is also interpolated linearly to go from the 3 hour intervals to the required 1 hour

intervals for the model.

Wind Direction (α)

The wind direction is imported from the data available from the meteorological station in Millau. Just like the wind speed data, the data needs to be trimmed down to the months investigated in this research. The wind direction is then in the correct format for the model.

There is no wind direction data available directly from the ECMWF model, but the wind direction can be calculated based on the two velocity components. By using equation (4), the wind direction is calculated for the 2 months in the winter and 2 months in the summer of 2011. For the ECMWF model the calculated wind direction is also interpolated using nearest neighbor in matlab to go from the 3 hour interval to the required 1 hour interval for the model.

Slope Map (θ) and Aspect Map (β)

The DEM is imported into ArcGIS, where it is first converted to the proper cell-size of 100 by 100 meters. After that, the DEM is used to create a slope map of the area, and an aspect map. These maps are also at 100 by 100 meters cell-size so they all overlap. The slope and aspect map are then exported as TIFF files so they can easily be imported into matlab.

Matlab

Matlab 2015a is used to create and run the Orographic Lift model.

The DEM, Slope Map and Aspect map are imported into matlab, using the imread command. ArcGIS added an extra row and column to the TIFF file, so these are removed.

For the model based on the data from the meteorological station in Millau, the TXT file with the data is also imported. The data

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7 is then trimmed down to the required

months. The orographic lift is then

calculated for both the winter and summer period, using equations (1) and (2) and the imported data.

For the model based on the data from the ECMWF model, a script made by Dorien van Kranenburg is used to connect to the UvA-BiTS (UvA-BiTS, 2015) server to import the data. The data is imported separately first for the summer period, and after the calculations for the summer period, the data for the winter period is imported and used. The data is then interpolated.

Data for both models is saved to a hard drive using the following format:

yyyy_mm_dd_HH.mat. The data is then compressed into a .zip archive and sent to Dorien van Kranenburg for her analysis.

Results

The results are presented in 3 different parts. First, an assessment of the area with a static wind speed and different wind

directions. Second, the data from the dynamic part of the model is presented. Last, a comparison between the orographic lift from this research and the convective velocity from the research by Jim Groot.

Static Conditions

The first runs of the model were done with static conditions. A wind speed of 5 m/s was applied to the whole area. The orographic lift condition were then checked for wind originating from 0° (North) to 315°

(Northwest) with 45° interspacing, creating 8 maps of the area with orographic lift. This was done to gain a general

understanding of which specific areas in the park generate high amount of orographic lift.

Figure 3; a map of the orographic lift with wind coming from the North with a wind speed of 5 m/s

Figure 4; a map of the orographic lift with the wind coming from the east with a wind speed of 5 m/s

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8 Included are the maps of the orographic lift

for the wind direction of 0° (figure 3) and 90° (figure 4) for comparison of the orographic lift between two different wind directions.

Generally speaking, regardless of wind direction, the areas that produce the most orographic lift are the canyons in the middle of the area, and the mountainous areas around the northern and eastern edges. Maximum values for the orographic lift range from 3.5 to 3.8 m/s over all wind directions. The higher values all occur in the areas with steep slopes. Along the flats, the values generally are between -1 and 1 m/s. The lowest values can be found on the lee-sides of the mountains and valleys, in the same order of magnitude as the maximum values, only negative (around -3.5 m/s).

Dynamic Model

Wind speed from the meteorological station and the interpolated ECMWF data are both in the same order of magnitude (between -4 and 4 m/s wind speed under normal

conditions).

The ECMWF model and meteorological data fom the station in Millau both give different values for the highest recorded (or

simulated) wind speed, as well as differing wind direction. In figures 5 and 6 the different conditions for a point in the

research area with a slope of 6.9° are shown, centered a week around the highest

recorded wind speed on the 23rd_{of July in} the summer by the meteorological station. This is to show the difference between the two datasets.

The difference between the two datasets will be further discussed in the discussion.

Figure 5; Uplift in the summer from the 22nd_{of July to the 29th, with sparse vegetation and a slope angle of 6.9°, based on the data from Millau. The blue}

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9 As can be seen when comparing

the two figures, the wind direction is different for both, and the two peaks from the meteorological data do not show up in the ECMWF data.

Having a look at the orographic lift map of the 23rd_{of July (figure 7),} at 15:00, with a wind direction of 300°, when the wind speed is the highest according to the

meteorological data from Millau, it shows a similar pattern as the static maps (as shown in figure 7). The areas with the highest lift are located in the mountainous areas and the canyons. Zooming in on the canyon (figure 8) we find that the side of the canyons closest to the direction the wind is coming from has a negative lift, whereas

Figure 6; Uplift in the summer from the 22nd_{of July to the 29th , with sparse vegetation and a slope angle of 6.9°, based on the data from the ECMWF}

model. The blue arrows indicate the direction the wind is coming from.

Figure 7; Orographic lift on the 23rd of July 2011, at 15:00. Highest recorded wind speed in the summer by the meteorological station in Millau

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10 the side furthest from this direction has a

positive lift.

Plotting the lift in the canyon along the black line in figure 8 yields the following graph (figure 9). The cell number in figure 9 corresponds with the cell number in horizontal direction in figure 8. The graph shows the orographic lift from the west to the east side of the canyon in the top graph, as well as the elevation in the canyon in the bottom graph.

The graph of the canyon along the red line is included in the appendix (figure A.1). The graph of the canyon shows that halfway into the canyon, the lift suddenly changes from negative to positive over a very short distance.

Figure 8; Zoom in on the canyons on the 23rd_{of July in the middle of the research area}

Figure 9; Lift in the canyon along the black line in figure 8. The cells numbers along the bottom correspond with the cell numbers in figure 8 along the bottom. The bottom graph shows the corresponding elevation in the canyon.

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Comparison

Another important part of the uplift regime in the Grands Causses Park is the convective lift. Using data provided by Jim Groot, a comparison was made between orographic lift and convective lift.

Figure 10 is a figure of the DEM of the research area, and includes the locations of the points of comparison. These specific points were chosen as representative points for the rest of the area.

The time period shown in the graphs was chosen at random, to give a good

representation of an average uplift period.

Looking at the uplift at the slope of 11° (figure 11), in the summer the lift is mostly dominated by Convective lift. On some days where the convective lift is lower, perhaps due to a rainfall event or a large amount of cloud cover, orographic lift becomes dominant in determining the lift on this point.

In the winter however (figure 12), the orographic lift and convective lift have similar magnitudes. This means that depending on the wind direction and wind speed, the total lift experienced at this point by a Griffon Vulture is either positive or negative.

Graphs of the other points are included in the appendix (figure A.2 to A.7).

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12 Figure 11; Orographic, Convective lift and the sum of both lifts at the point with an 11° slope in figure 10 in the summer months.

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Discussion

There are several improvements that can be made on the model.

Scale

The model is adapted from the model used by Bohrer et al. (2012). Bohrer et al. specifically use the model over a much larger scale than the one in this paper. For their research, the model operated on a 32 by 32 km scale cell size, which is much larger than the one presented in this paper. However, to compensate for the larger scale, Bohrer et al. used a method to select terrain features on a 1x1 km scale within the large cells with the best updraft coefficient

𝐶

_𝛼

for

any given point in time the model

calculates. This means that for their model, Bohrer et al. only looked at the most orographically significant features in their research area.

The DEM in this research was however scaled up from 50 by 50 meters cell size to 100 by 100 meters cell size without taking the more significant slopes into account. This means that the slope map and aspect map are both based on averaged values within the upscaled cells. Perhaps it would have been better to employ a method similar to Bohrer et al.’s, using the higher slope values within the 100 by 100 cells to better represent the actual terrain.

As can be seen in figure 12, the canyons can have very steep upper parts, which are smoothed out in the cells of the DEM. Because of this, the slope angle never exceeds 55°, whereas the image suggests it should approach 90°.

Also of importance is that due to the change in scale, some values might not be

representative of the actual orographic lift generated. Especially along the flatter areas, it is unlikely that a low angled slope that is only a hundred meters in size generated a lift that can be used by the Griffon Vultures when they soar, since the air flow higher up would not be disturbed by the low obstacle.

Turbulence

Oke (2002) goes into the workings of canyons, and how air flows through them. “If upwind or downwind slope of the ground exceeds 17° flow separation occurs.”, since the canyons have a higher slope than 17°, flow separation would occur. This means that the flow going over the canyons is separated from the flow in the canyons. This would imply that the canyons do not in actuality produce any orographic lift.

Several figures (A.?-A.?) have been included in the appendix where the canyons have been removed from the DEM.

However, it was decided to keep the canyons in the model as is, because the Griffon Vultures use them in the winter. The current implementation of canyons in the model is far from perfect. Air does not move in a perfectly straight line, and a canyons especially will divert the flow of air if the air flow is more parallel to the canyon. However, when the air flow is more

perpendicular to the canyon, a situation similar to figure 13 occurs. While the figure describes a situation for urban canyons, the space in between the two buildings

describes a similar situation to the canyons Figure 12; Millau Canyon taken and edited from:

https://static.panoramio.com.storage.googleapis.com/photos/original/613476 70.jpg

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14 in Millau. What can be seen in part (a) of

figure 13 is that in a wide canyon two eddies (patches of turbulence) are present, one on the leeward side of the canyon, and one on the opposite side. These generate a different lift regime than would be implied by the current model.

Part (c) of figure 13 shows flow

separation when the width of the canyon is even smaller. This then generates an orographic lift the complete opposite of what the model predicts.

Part (b) shows the intermediate variant, where the flow is not completely

separated yet.

Vardoulakis et al. (2003) explain how wind flows over urban canyons. The wind regime is dependent on the height/width (H/W) ratio of the canyon.

Figure 13 (a) occurs at a H/W <0.3, 13 (b) happens when H/W≈0.5, 13 (c) happens when H/W≈1.

The canyon figure 9 is roughly 2 km wide, and 500 m deep. This gives an H/W ratio of ~0.2. This indicates that the flow in the canyon, when the wind is perpendicular to the canyon, is isolated roughness flow. This means that there are patches of turbulences in the canyon.

Similarly, the canyon along the red line in figure 8 (as seen in figure A.?) also has an H/W ratio of ~0.2.

Since the Griffon Vultures primarily use canyons in the winter, it stands to reason that the orographic lift in the canyons needs to be more closely examined. Perhaps a computational fluid dynamics model can be used to examine just the canyons and how it interacts with the rest of the landscape.

Negative values

The model by Bohrer et al. (2012) assumes any negative values are not used by the emigrating birds and sets these to zero. However, in this research the decision was

made to keep these values at their original negative values. This was done because not only orographic lift was looked at, but convective lift was also taken into account in the overall research.

This way, whenever an area generates convective lift, there could potentially be negative orographic lift, bringing the total lift generated in that area to zero.

However, it is unsure whether the negative lift calculated by the model is in the correct order of magnitude. It can be reasonably assumed that the negative lift on a slope with a certain slope angle is equal to the positive lift it can create when the wind direction shifts 180°, since the wind follows the terrain features.

Differences meteorological station

Millau and ECMWF

The difference in data between the meteorological station in Millau and the data from the ECMWF model can be explained by a few different theories. First of all, there is a possibility of an error in the interpretation of the data. The wind direction from a meteorological station is usually given as the direction from which

Figure 13; flow separation in urban canyons, Oke (1988) Street design and urban canopy layer climate.

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15 the wind originates. However it is possible

that the wind direction from the Millau station is the direction the wind is blowing towards. This means there can be a 180° difference between the actual wind direction, and the direction given in the meteorological data from the station in Millau.

Secondly, the calculation of the wind direction from the data from the ECMWF model. Since the wind direction is not given by the ECMWF model, it needs to be

calculated by using the two velocity

components. This was done by the code that Dorien van Kranenburg provided. However, after looking into it, the calculation might have been off. The appendix has both versions, however, at this time it is unsure which one is correct.

Thirdly, data from several other

meteorological stations was available. One station in Cornus, France, and one in Saint Pierre des Tripiers, France. Calculating the mean wind direction for these station using directional statistics gives the results presented in table 1.

Table 1; mean wind direction for all three meteorological stations

It should be noted that to calculate the means, all directions were converted to a point on the unit circle, of which the x- and y-coordinate were averaged, before being converted back into a direction. Therefore the wind direction obtained is between –π and π (or -180° and 180°.

There are some differences between the stations. The winter value for Cornus is different from the winter value in Saint Pierre and the winter value in Millau. The summer value in Saint Pierre is different from the summer value in Cornus and the summer value in Millau.

This could be explained by the location the meteorological stations are located, where the geography of the area influences the prevalent wind directions.

The wind direction for the original and adjusted ECMWF are presented in table 2.

Table 2; mean wind direction for the ECMWF data, both with the original and adjusted calculation

Comparing the ECMWF data with the data from the meteorological station, the ECMWF data is around 180° off from the meteorological station data. This leads me to believe there has been a mistake in the calculation of the wind direction of the ECMWF model.

Potentially the function atan2 has been used incorrectly. Adjusting the atan2 function by switching around the U velocity component and V velocity component gives a wind direction of -43.95°, using the adjusted method of calculation, in the summer. The mean wind direction in the winter is -21.33°. These directions coincide with the

directions from the meteorological stations. Sadly, these mistakes were not caught in time, and were therefore not taken into account in this research.

The difference in peaks, as shown in figure 5 and 6 can be easily explained. The ECMWF model operates at a 3 hour interval, whereas the data from the meteorological stations is

at 1 hour intervals. Since the ECMWF data is interpolated for the model, many of the peaks in between the 3 hour intervals will not show.

Saint Pierre Winter -42.99° Saint Pierre Summer -2.59° Cornus Winter -29.80° Cornus Summer -46.99° Millau Winter -7.45° Millau Summer -32.74°

ECMWF Original Winter 158.67° ECMWF Original Summer 136.05° ECMWF Adjusted Winter 111.33° ECMWF Adjusted Summer 133.95°

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Reach of the Orographic lift

The reach of orographic lift specifies the maximum height at which the orographic lift is still usable by the Griffon Vultures. Orographic lift sticks relatively close to terrain features. Wind that does not directly interact with these geographic features does not experience much interference from the terrain. This effect tapers off quite quickly, and the height at which the orographic lift is still usable is most likely related to the amount of elevation difference and the slope

angle of the terrain. A steeper slope and higher elevation difference will most likely lead to a higher reach of the orographic lift. The low angle slopes on the flat areas in the research area probably generate no

noticeable orographic lift, whereas the mountainous areas and the canyons most likely generate a lift that is only usable at lower flight altitudes. This needs to be examined to gain a better understanding of the area. It could potentially be investigated by examining the flight height of the Griffon Vultures

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Conclusion

The Orographic Lift in the Grands Causses Park is dependent on several factors, the slope aspect, slope angle, wind direction, and wind speed. The lift on a specific slope can be calculated using the direction the wind is coming from, the direction the slope is facing, the slope angle as well as the speed the wind is blowing at.

There are several areas in the Grands Causses Park with high orographic lift, in the research area. Those include the

mountainous areas around the edges of the research area to the North and East, and the

canyons running through the middle of the research area. The flatter areas that lie in between these geographic locations do not produce a high amount of lift.

When comparing the orographic lift and convective lift, it is apparent that convective lift is dominant on the flat areas, and the orographic lift is more dominant in areas with steeper slopes. The convective lift is reduced when there is cloud cover or a large rainfall event, and therefore depends on the season. Orographic lift is not dependent on seasonality, and stays within the same order of magnitude both in the summer and winter.

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References

Bishop, C. M., Spivey, R. J., Hawkes, L. A., Batbayar, N., Chua, B., Frappell, P. B., ... & Butler, P. J. (2015). The roller coaster flight strategy of bar-headed geese conserves energy during

Himalayan migrations. Science, 347(6219), 250-254.

Bishop, C. M., Spivey, R. J., Hawkes, L. A., Batbayar, N., Chua, B., Frappell, P. B., ... & Butler, P. J. (2015). The roller coaster flight strategy of bar-headed geese conserves energy during

Himalayan migrations. Science, 347(6219), 250-254.

Bohrer, G., Brandes, D., Mandel, J. T., Bildstein, K. L., Miller, T. A., Lanzone, M., ... & Tremblay, J. A. (2012). Estimating updraft velocity components over large spatial scales: contrasting migration strategies of golden eagles and turkey vultures. Ecology Letters, 15(2), 96-103. Brandes, David, and D. W. Ombalski. "Modeling raptor migration pathways using a fluid-flow analogy." (2004).

Dennhardt, A. J., Duerr, A. E., Brandes, D., & Katzner, T. E. (2015). Modeling autumn migration of a rare soaring raptor identifies new movement corridors in central Appalachia. Ecological

Modelling, 303, 19-29.

Katzner, T. E., Brandes, D., Miller, T., Lanzone, M., Maisonneuve, C., Tremblay, J. A., ... & Merovich, G. T. (2012). Topography drives migratory flight altitude of golden eagles:

implications for on‐shore wind energy development. Journal of Applied Ecology, 49(5), 1178-1186.

Mohamed, A., Carrese, R., Fletcher, D. F., & Watkins, S. (2015). Scale-resolving simulation to predict the updraught regions over buildings for MAV orographic lift soaring. Journal of Wind Engineering and Industrial Aerodynamics, 140, 34-48.

Oke, T. R. (1988). Street design and urban canopy layer climate. Energy and buildings, 11(1), 103-113.

Oke, T. R. (2002). Boundary layer climates. Routledge.

UvA-BiTS, retrieved 25-06-2015 from http://www.uva-bits.nl/virtual-lab

Vardoulakis, S., Fisher, B. E., Pericleous, K., & Gonzalez-Flesca, N. (2003). Modelling air quality in street canyons: a review. Atmospheric environment, 37(2), 155-182.

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19

Appendix

Extra figures

Figure A.1; Plot of the orographic lift and DEM of the canyon along the red line in figure 8. The cell number at the bottom follows the cell number from figure 8 from North to South. The orographic lift in the canyon shows the same pattern as the lift in figure 9. However, there is an area of high lift before the canyon. This is because the terrain is 3-dimensional, with a slight hill at this location, which is not visible in this plot of the DEM. The aspect of this hill is also almost perpendicular to the wind direction, which results in the high amount of lift.

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20 Figure A.2; The point of comparison with a low slope in the summer, as specified in figure 10. This figure illustrates the dominance of convective lift at low angle slopes.

Figure A.3; The point of comparison with a low slope in the winter, as specified in figure 10. This figure illustrates the dominance of convective lift at low angle slopes, even in the winter when the convective lift is particularly low.

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21 Figure A.4; The point of comparison with the highest slope in the research area, as specified in figure 10. This figure illustrates the dominance of orographic lift at high angle slopes in the summer. The orographic lift determines if the total lift experienced is positive or negative.

Figure A.5; The point of comparison with the highest slope in the research area, as specified in figure 10. This figure illustrates the dominance of orographic lift at high angle slopes in the winter. The orographic lift determines if the total lift experienced is positive or negative.

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22 Figure A.6; The point of comparison with a low slope and spare vegetation, as specified in figure 10. This figure illustrates the dominance of convective lift in the summer at a point where vegetation is sparse, which means convective lift is high.

Figure A.7; The point of comparison with a low slope and spare vegetation, as specified in figure 10. This figure illustrates the equal dominance of convective lift and orographic lift in the winter at a point where vegetation is sparse.

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23 Figure A.8; this figures shows what happens when the canyons are removed from the DEM, based on the Millau data at 23rd_{of July.}

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24

Matlab Code for Static Conditions

%%% Modelling Orographic Lift in the Grands Causses %%% by Marnix Wittebrood - 10449035

%%% Started on: 01-05-2015, last edit: 15-05-2015

clear clc

close all

%%Initilization

%Import DEM,ASPECT,SLOPE maps

DEM = imread('pr_DEM_100M.tif');

%Arcgis added stupid rows, these have to be removed

DEM(751,:)=[]; DEM(:,801)=[]; DEM=double(DEM);

ASPECT = imread('a03st_Aspect.tif');

ASPECT(751,:)=[]; ASPECT(:,801)=[]; ASPECT=double(ASPECT);

SLOPE = imread('a03st_Slope.tif');

SLOPE(751,:)=[]; SLOPE(:,801)=[]; SLOPE=double(SLOPE);

% %Removing the canyons to simulate flow separation

% CANYON=((DEM<900)&(SLOPE>17)); %Canyons

% SLOPE(CANYON)=0; %Set slope to 0 to emulate %%Calculations

% Create a cell structure to store results

WindMaps=cell(1,8); i=1;

% Loop for calculating orographic lift

for winddir=0:45:315; %Winddirection(°) at 45°

intervals

wSpd = 5; %Windspeed (m/s), static

alpha = ones(size(DEM)).*winddir; %DEM-sized wind direction

vwind = ones(size(DEM)).*wSpd; %DEM-sized wind speed

% calculate Coefficient

CA = sind(SLOPE).*cosd(alpha-ASPECT);

% calculate orographic lift per cell

wo = vwind.*CA;

% Store data

WindMaps{i}=wo; i=i+1;

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25

Matlab Code for the Millau meteorological station

%%% Modelling Orographic Lift in the Grands Causses %%% by Marnix Wittebrood - 10449035

clear clc

close all

%%Initilization

% SLOPE(CANYON)=0; %Set slope to 0 to emulate %Important weather station data. needs to be adjusted slightly for

%different input station data

[DATE,PRECIP,AIRTEMP,TEMP,SOLT,PRESS,PRESSHPA,WINDSPEED,...

WINDDIRECTION,WINDMAX,WINDDIRMAX,HUMID,ENTHAL,INSOL,RAYON]=...

textread('millau.txt','%s%f%f%f%f%f%f%f%f%f%f%f%f%f%f','headerlines',24,'deli

miter','\t');

DATE=datetime(DATE,'InputFormat','dd/MM/yyyy HH:mm'); WINTER=DATE>='01-Jan-2011'&DATE<='01-Mar-2011';

SUMMER=DATE>='01-Jul-2011'&DATE<='01-Sep-2011';

%Store only data used in seperate vectors for ease of use

windSpWinter=WINDSPEED(WINTER); windDiWinter=WINDDIRECTION(WINTER); dateWinter=DATE(WINTER); windSpSummer=WINDSPEED(SUMMER); windDiSummer=WINDDIRECTION(SUMMER); dateSummer=DATE(SUMMER);

FOut='yyyy_mm_dd_HH'; %For datestring, used when saving data

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26

%% Calculations

%Loop for summer calculations

for h=1:length(windSpSummer)

vWindS = ones(size(DEM)).*windSpSummer(h); %DEM-sized wind speed

alphaS = ones(size(DEM)).*windDiSummer(h); %DEM-sized wind direction

CA = sind(SLOPE).*cosd(alphaS-ASPECT);

OroLift = single(vWindS.*CA);

% store data so it can be imported

save(strcat('H:\Documents\BachelorProject\Matlab\Map

Outputs\',datestr(dateSummer(h),FOut),'.mat'),'OroLift');

end

%Loop for winter calculations

for h=1:size(windSpWinter)

vWindW=ones(size(DEM)).*windSpWinter(h); %DEM-sized wind speed

alphaW=ones(size(DEM)).*windDiWinter(h); %DEM-sized wind direction

CA = sind(SLOPE).*cosd(alphaW-ASPECT);

OroLift = single(vWindW.*CA);

% store data so it can be imported

save(strcat('H:\Documents\BachelorProject\Matlab\Map

Outputs\',datestr(dateWinter(h),FOut),'.mat'),'OroLift');

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27

Matlab Code for ECMWF model data

%%% Modelling Orographic Lift in the Grands Causses based on ECMWF MODEL %%% by Marnix Wittebrood - 10449035

clear clc

close all

%%Initilization

% SLOPE(CANYON)=0; %Set slope to 0 to emulate %Create date matrices for storing data and running loops

startDate=datetime('01-Jan-2011 00','InputFormat','dd-MM-yyyy HH'); stopDate=datetime('01-Mar-2011 00','InputFormat','dd-MM-yyyy HH');

dateWinter=linspace(startDate,stopDate,datenum(stopDate-startDate)*24+1); startDate=datetime('01-Jul-2011 00','InputFormat','dd-MM-yyyy HH');

stopDate=datetime('01-Sep-2011 00','InputFormat','dd-MM-yyyy HH');

dateSummer=linspace(startDate,stopDate,datenum(stopDate-startDate)*24+1);

%Important ECMWF data

javaaddpath(

'H:\Documents\BachelorProject\Matlab\postgresql-9.4-1201.jdbc4.jar')

% Getting the data from UvA-BiTS

username = 'dorien_kranenburg'; % put your user name and password here.

password = 'DorieN123';

% Set start time and stop time for summer months

starttime = '2011-07-01 00:00:00'; stoptime = '2011-09-01 00:00:00';

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28 [Wind]=GetMeteoData2Original(username, password, starttime, stoptime);

% GetMeteoData2 has been edited, GetMeteoData2Original is the originally % used function

%Store wind speed and direction in corect variables

windSpSummer=Wind.windsp; windDiSummer=Wind.Wdir;

% Interpolation of windspeed and Direction from 3-hour to 1-hour interval

windSpSummer=interp1(1:length(windSpSummer),windSpSummer,1:1/3:length(windSpS ummer))';

windDiSummer=interp1(1:length(windDiSummer),windDiSummer,1:1/3:length(windDiS ummer),'nearest')';

% Linear interpolation for wind speed, nearest neighbor for wind direction

%% Calculations %Summer loop

for h=1:length(windSpSummer)

vWindS = ones(size(DEM)).*windSpSummer(h); %DEM-sized wind speed

alphaS = ones(size(DEM)).*windDiSummer(h); %DEM-sized wind direction

CA = sind(SLOPE).*cosd(alphaS-ASPECT);

OroLift = single(vWindS.*CA);

% store Orographic Lift data

save(strcat('H:\Documents\BachelorProject\Matlab\Map Outputs

ECMWF\',datestr(dateSummer(h),FOut),'.mat'),'OroLift');

end

% Set start time and stop time for winter months

starttime = '2011-01-01 00:00:00'; stoptime = '2011-03-01 00:00:00';

% Marnix: voor jou heb ik de functie GetMeteoData2:

[Wind]=GetMeteoData2Original(username, password, starttime, stoptime);

% GetMeteoData2 has been edited, GetMeteoData2Original is the originally % used function

%Store wind speed and direction in corect variables

windSpWinter=Wind.windsp; windDiWinter=Wind.Wdir;

% Interpolation of windspeed and Direction from 3-hour to 1-hour interval

windSpWinter=interp1(1:length(windSpWinter),windSpWinter,1:1/3:length(windSpW inter))';

windDiWinter=interp1(1:length(windDiWinter),windDiWinter,1:1/3:length(windDiW inter),'nearest')';

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29

%Winter loop

for h=1:size(windSpWinter)

vWindW=ones(size(DEM)).*windSpWinter(h); %DEM-sized windspeed

alphaW=ones(size(DEM)).*windDiWinter(h); %DEM-sized winddirc

CA = sind(SLOPE).*cosd(alphaW-ASPECT);

OroLift = single(vWindW.*CA);

% store Orographic Lift data

save(strcat('H:\Documents\BachelorProject\Matlab\Map Outputs

ECMWF\',datestr(dateWinter(h),FOut),'.mat'),'OroLift');

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30

Code for comparing Convective Lift and Orographic Lift

%%% Comparing Convective Lift and Orographic Lift %%% by Marnix Wittebrood - 10449035

clear clc

close all

%%Initilization

%Import DEM,ASPECT,SLOPE

% Grab points with different characteristics to use later for making % graphs. MaxSl=find(SLOPE==max(max(SLOPE))); [MaxSlRow,MaxSlCol]=ind2sub(size(SLOPE),MaxSl); MinSl=find((and(SLOPE<1,SLOPE>0.5))); MinSl=MinSl(9000); [MinSlRow,MinSlCol]=ind2sub(size(SLOPE),MinSl); MeanSl=find((and(SLOPE>11,SLOPE<12))); MeanSl=MeanSl(10000); [MeanSlRow,MeanSlCol]=ind2sub(size(SLOPE),MeanSl);

% Sparsely Vegetated Area

SparseSlRow=497; SparseSlCol=178;

% Create date matrices for storing data and running loops

startDate=datetime('01-Jan-2011 00','InputFormat','dd-MM-yyyy HH'); stopDate=datetime('01-Mar-2011 00','InputFormat','dd-MM-yyyy HH');

dateWinter=linspace(startDate,stopDate,datenum(stopDate-startDate)*24+1); startDate=datetime('01-Jul-2011 00','InputFormat','dd-MM-yyyy HH');

stopDate=datetime('01-Sep-2011 00','InputFormat','dd-MM-yyyy HH');

dateSummer=linspace(startDate,stopDate,datenum(stopDate-startDate)*24+1);

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31 OroSparseWinter=[]; ConSparseWinter=[]; TotSparseWinter=[]; OroSparseSummer=[]; ConSparseSummer=[]; TotSparseSummer=[]; OroFlWinter=[]; ConFlWinter=[]; TotFlWinter=[]; OroFlSummer=[]; ConFlSummer=[]; TotFlSummer=[]; OroMeanWinter=[]; ConMeanWinter=[]; TotMeanWinter=[]; OroMeanSummer=[]; ConMeanSummer=[]; TotMeanSummer=[]; OroMaxWinter=[]; ConMaxWinter=[]; TotMaxWinter=[]; OroMaxSummer=[]; ConMaxSummer=[]; TotMaxSummer=[];

for h=1:length(dateSummer)

% Change Path to load different data, such as ECMWF data or data from

% Millau

load(strcat('H:\Documents\BachelorProject\Matlab\DataJim_Final\',datestr(date Summer(h),FOut),'.mat'));

load(strcat('H:\Documents\BachelorProject\Matlab\Map Outputs Canyon

ECMWF\',datestr(dateSummer(h),FOut),'.mat'));

% Store data from point with maximum slope

OroMaxSummer(h)=OroLift(MaxSlRow,MaxSlCol); ConMaxSummer(h)=W(MaxSlRow,MaxSlCol);

TotMaxSummer(h)=OroMaxSummer(h)+ConMaxSummer(h);

% Store data from point with sparse vegetation slope

OroSparseSummer(h)=OroLift(SparseSlRow,SparseSlCol); ConSparseSummer(h)=W(SparseSlRow,SparseSlCol);

TotSparseSummer(h)=OroSparseSummer(h)+ConSparseSummer(h);

% Store data from point with flat slope

OroFlSummer(h)=OroLift(MinSl); ConFlSummer(h)=W(MinSl);

TotFlSummer(h)=OroFlSummer(h)+ConFlSummer(h);

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32

% Store data from point with "mean" slope

OroMeanSummer(h)=OroLift(MeanSl); ConMeanSummer(h)=W(MeanSl);

TotMeanSummer(h)=OroMeanSummer(h)+ConMeanSummer(h); end

for h=1:length(dateWinter)

% Change Path to load different data, such as ECMWF data or data from

% Millau

load(strcat('H:\Documents\BachelorProject\Matlab\DataJim_Final\',datestr(date Winter(h),FOut),'.mat'));

load(strcat('H:\Documents\BachelorProject\Matlab\Map Outputs Canyon

ECMWF\',datestr(dateWinter(h),FOut),'.mat'));

% Store data from point with maximum slope

OroMaxWinter(h)=OroLift(MaxSlRow,MaxSlCol); ConMaxWinter(h)=W(MaxSlRow,MaxSlCol);

TotMaxWinter(h)=OroMaxWinter(h)+ConMaxWinter(h);

% Store data from point with max slope

OroSparseWinter(h)=OroLift(SparseSlRow,SparseSlCol); ConSparseWinter(h)=W(SparseSlRow,SparseSlCol);

TotSparseWinter(h)=OroSparseWinter(h)+ConSparseWinter(h);

% Store data from point with flat slope

OroFlWinter(h)=OroLift(MinSl); ConFlWinter(h)=W(MinSl);

TotFlWinter(h)=OroFlWinter(h)+ConFlWinter(h);

% Store data from point with "mean" slope

OroMeanWinter(h)=OroLift(MeanSl); ConMeanWinter(h)=W(MeanSl);

TotMeanWinter(h)=OroMeanWinter(h)+ConMeanWinter(h); end

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33

Calculating wind direction from ECMWF data

Originally used, by Dorien van Kranenburg:

Wind.Wdir = 180/pi*atan2(-tracks.u10,-tracks.v10)+180; Uses equation (4)

Adjusted calculation

wind_dir_trig_to = atan2(tracks.u10./Wind.windsp, tracks.v10./Wind.windsp); wind_dir_trig_to_degrees = wind_dir_trig_to .* 180/pi;

wind_dir_trig_from_degrees = wind_dir_trig_to_degrees + 180; Wind.Wdir = 90 - wind_dir_trig_from_degrees;

Based on:

http://stackoverflow.com/questions/21484558/how-to-calculate-wind-direction-from-u-and-v-wind-components-in-r

Corrected for U and V

Wind.Wdir = 180/pi*atan2(-tracks.v10,-tracks.u10)+180;

Adjusted calculation corrected for U and V

wind_dir_trig_to = atan2(tracks.v10./Wind.windsp, tracks.u10./Wind.windsp); wind_dir_trig_to_degrees = wind_dir_trig_to .* 180/pi;

wind_dir_trig_from_degrees = wind_dir_trig_to_degrees + 180; Wind.Wdir = 90 - wind_dir_trig_from_degrees;

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