Hydromorphology - Korbinian Enzinger, Prof. C. Pasquero, M. Kuptsova

Hydromorphology

by Korbinian Enzinger

Master Thesis submitted in fulfillment of the requirements for the degree Diplom-Ingenieur

to the University of Innsbruck Faculty of Architecture

Supervising Tutors: Univ.-Prof. Claudia Pasquero Maria Kuptsova, MA

ioud

synthetic landscape lab

Innsbruck, August 2020

prologue

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water

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morphology

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kathmandu

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coding as gardening

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tesselation 04.1

interaction 04.2

emergence 04.3

translation

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proposition

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6

image 01: morphology of moving water I

Hydromorphology is a project that aims to respond to the trend that humans shape landscapes through anthropocentric environmentalism, meaning that the primary goal of conserving the environment lies in the exploitation by and from human purposes (Kopnina et al.,2018).

With a quickly growing global population, especially since the turn of the 20th century, the impact of anthropocentric environmentalism on the biosphere became increasingly significant.

The philosophic idea of “deep ecology” is that

our ecosystem is formed by inter-relationships between many organisms that depend on each other (Smith, 2014).

[..] “it recognizes diverse communities of life on Earth that are composed not only through biotic factors but also, where applicable, through ethical relations, that is, the valuing of other beings as more than just resources.”

This project proposes a new workflow for data based design that that incorporates these ideas in order to shape a new urban environment, allowing both human and non-human networks to interact in the sense of a “true democracy”.

To clarify this workflow i chose the Kathmandu Valley as a case study because of its demographic, geologic and topographic properties, but it is applicable to a variety of other sites with similar features.

Smith, Mick (2014). “Deep Ecology: What is Said and (to be) Done?”, p.148

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01

WATER

increased food demand

growing Population

increased demand for building space

On the following pages I want to elaborate on the causes and consequences of soil erosion and water pollution. This knowledge shall serve as a framework to further discussions in this study and will also set the basis for my research interest.

Chart Nr. 1 illustrates 10

the effects of a growing human population on water and soil quality. The most fundamental causes for shifts in water quality are pollution from agriculture as well as from growing urban environments. Both increase as the population is growing. In 2019

the global population reached 7.7 Billion People and is expected to reach 9.7 Billion by 2050 (World Population Prospects, 2019), with more than half of the global population already living in cities today.

chart 01: correlation between growing population and degradation of hydrology

intensifying Agriculture

Water pollution

As Chart Nr. 1 shows, a growing demand for food, housing and electricity lead to intensified landuse which entails soil erosion and increased surface water runoff as well as increased pollution caused by the disposal of untreated wastewater. In Asia and

increased Land-use for Agriculture

increased surface water runoff

loss of biodiversity

the pacific region, 8090% of waste-water are returning to the environment without proper treatment, contaminating surface water bodies and facilitating an onset of water-related diseases and decreasing Biodiversity (WWAP, 2019). Such issues seem to be espe-

cially relevant in cities in which the waste-water treatment cannot keep up with the pace of Urbanization.

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chart 02: global land distribution

Soil erosion, on the other hand, represents a different but equally alarming issue, predominantly caused by agriculture. Latest estimations assume that soil erosion of croplands carries away 25 – 40 tons of topsoil every year, significantly reducing crop yields and weakening the soil’s ability to regulate its

nutrient balance.

Increased surface water run-off results in a degradation of water quality and decreased groundwater recharge capabilities.

The Food and Agriculture Organization of the United Nations estimates that a total reduction of over 253 million tonnes of cereals, equivalent to all the arable land in In-

dia, could be projected by 2050 if no action to reduce erosion is undertaken. (Fao.org, 2015) Additionally, wetlands as well as grasslands have been replaced by croplands, leading to higher evaporation rates, lower soil water storage and increased surface runoff.

image 02: morphology of moving water II

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chart 03: average annual impact of natural disasters

The impact of soil erosion and water pollution must not be underestimated. Soil erosion and increased water runoff can lead to natural disasters like extreme drought and/or major flooding, depending on precipitation levels.

Inadequate waste-water reprocessing and/or sanitation, on the other

hand, can lead to the outburst of severe diseases.

Chart Nr. 3 puts these effects in a numerical perspective and compares them with other catastrophic events. It shows that “water-related” disasters account for a much higher body count as well as economic damage compared to “non-water-related” events

like earthquakes or human-caused conflicts.

This emphasizes that water, beside being crucial to any human and non-human society, involves certain risks that have to be pointed out in order to develop a strategy for a safe and sustainable urban environment.

image 03: morphology of moving water III

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fig. 01: reinterpretation of topographical map

perception vs. awareness We have a certain way of perceiving landscapes and topographies. From the ground topographies are mostly hard to understand in its larger ensemble. We are just able to conceive our environment at a certain scale but the micro and macro scale stays hidden without additional help. Despite being invisible to the human eye these networks play a vital role to earths ecosystem.

Through technologies like satellite imagery in the macro scale and micros-

copy on the micro scale we are able to conceive earth in a completely different way. The systemic interconnections of how the whole biosphere behaves and changes become recognizable. the maps we are confronted with in our regular life are designed to make us more productive in the society we live in.

They show us the fastest way to work, how we avoid the upcoming traffic jam or how to get the cheapest bus connection to the city center. But they don’t allow us to

understand how we interact with our environment.

Especially in city planning it is an important role for us to treat our environment like our garden which provides us with food and water. Especially water builds the source of all living organisms on the planet, therefore we have the responsibility to protect it from our own destruction in order to sustain a healthy equilibrium.

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02

MORPHOLOGY

10cm

Water is the main driving force when looking at the morphology of a landscape. Rainfall removes soil and rocks and transports it downstream to another location. Image 04 and image 05 show 20

image 04: small scale erosion

that this process of erosion is happening at all scales. The scale of the erosion varies by the amount of precipitation and the factor of time.

Especially in areas with

rainy seasons these morphological processes are happening much faster at a specific time of the year and can cause massive landslides sometimes affecting thousands of people.

10km

image 05: large scale erosion

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Houdini Hydro Erosion Generated Topography Resample 3 times 4.res-4.h.Ero-5.res-5.h.ero-6.res-6.h.ero 2 20fr 2 20Fr 2 20fr

Fig. 02: catalog: fractal zoom into erosion

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Fig. 03: catalog: thermal- and hydro-erosion

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Fig. 04: catalog: hydro-erosion

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03

KATHMANDU

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Fig. 05: river networks around kathmandu

The hydrology of Nepal is characterized by an abundance of water which leads to an increased risk of flooding and landslides in this area.

It consists of about 6000 rivers which accumulate to 2.7% of earths fresh water (Poudyal, 2019).

At the same time 73% of households in Nepal do

not have access to safe sources of drinking water (Wang et al., 2019). This is caused by the lack of infrastructure as well as a high level of pollution due to the disposal of untreated sewage in dense urban areas and the use of pesticides and fertilizers in the agricultural sector. The degradation

of the water quality not only affects the ecology of Nepal but also has a negative effect on the entire biosphere downstream, including large areas of India and Pakistan, affecting over one billion people.

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Image 06: Morphology of moving water IV

500 000

1950

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1 939 000

1 424 000

965 000

642 000

225 000

1 000 000

398 000

1 500 000

147 000

population

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U.N. prediction

Since only 40.5% of the population in Nepal had access to improved sanitation in 2016 (Wang et al., 2019), the existing surface water is exposed to pollution through industry, agriculture as well as the discharge of untreated sewage from dense urban areas, making it unsuitable as a safe source of drinking water. As a result ground water is becoming the major source of drinking water putting an immense stress on the ground water levels and the ecology depending on it. Statistics are showing that

the annual extraction of groundwater is exceeding the recharge ability leading to a tremendous depletion in groundwater levels (Pandey et al., 2012). Looking at population growth (chart 04) and rate of urban sprawl in the metropolitan region of Kathmandu it becomes obvious that this trend is likely to worsen if no countervailing measures are undertaken.

The United Nations are predicting that the population of Kathmandu will rise from currently 1.42 million people to almost 1.94 million by 2030.

Therefore the strategies of urban sprawl and the necessary infrastructure have to be re-planned in order to deal with these future challenges.

The following pages illustrate various mappings referring to soil erosion, the hydrology as well as human impact on the ecology of the Kathmandu Valley.

chart 04: population growth kathmandu

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Fig. 06: Morphology Kathmandu Valley

This map of the Kathmandu valley was modified with different erosion techniques mentioned in the prior chapter. This enhances the awareness how the process of water dynamics and the accompanying morphology of the landscape behaves over time. Additionally the

shading of the map conveys information about the slope of the topography reduced to a black and white image. The lightest areas represent the steepest slopes and therefore the highest rates of surface water runoff. On the other hand the black areas de-

pict the rather flat areas with slower surface water runoff allowing higher rates of groundwater recharge. this map lets us conceive valuable information about hydromophological processes independently from human interventions.

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Fig. 07: Anthropocene Morphology Kathmandu Valley

Over the course of the past century the human impact on the environment became increasingly significant, making it impossible to analyze hydrological processes without involving human networks. This map adds the Anthropocene as an

extra layer of information involving houses and street networks. unfortunately the areas with the highest potential for groundwater recharge (dark areas) are also the areas best suited for urban development, leading to a negative effect on

the hydrology. The sealing of the soil surface not only leads to an increased surface water run-off and lowering of the groundwater level but also to a higher risk of flooding.

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Fig. 08, 09, 10: Satellite Image of relevant locations

27.7611, 85.4219

27.7453, 85.4183

27.7374, 85.3883

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Fig. 11: hydro-morphology Kathmandu Valley

Since water is the main impulse of morphological processes of the topography and the source for all living beings (human as well as non-human) it is vital to set it in the focus of attention when trying to develop a new strategy for a sustainable urban sprawl. it is important to understand

where the water comes from and what the major influences on the water quality are. Chart 05 (next page) illustrates how the water quality decreases depending on the local land-use of the environment. Clarifying these hydromophological processes and using them as a base map is used as

a foundation for the development of a new network that allows human and non-human networks to interact with each other to form a “true democracy� that incorporates nature as an indispensable part of the world we live in.

Fig. 12: hydro-morphology bagmati river 41

mp1

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land-use proportions and river quality class (RQC) for the bagmati river. Land-use proportions shown for six land-use classes with reference to the primary (left) vertical axis. RQC shown for 2016 monsoon (black line with triangles) and 2017 pre-monsoon (white line with circles) periods with reference to the secondary (right) vertical axes. The location of the monitoring points (mp1-mp7) are shown on the right.

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chart 05: land-use proportions and river quality class for the bagmati river

monitoring point 1

monitoring point 2

monitoring point 3

monitoring point 4

monitoring point 5

monitoring point 6

monitoring point 7

Fig. 13: location of measuring points

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The Goal of this Analysis was to improve the understanding of the Linkages between the Dynamics of Water and the impact it has on the environment.

Through a visual representation, how the dynamics of water and the resulting erosion of the landscape behaves, it is easier to understand what areas are vulnerable to 44

pollution, floods, landslides and other water related disasters. Erosion is a representation of different Geo-morphological processes in a landscape with the difference in time and scale. Conceiving the morphology of the Kathmandu Valley in this way makes it possible to rethink the strategy of urban sprawl and

the accompanying effects on the Biosphere.

By redistributing the land-use and infrastructure according to the morphology, the metropolitan area of Kathmandu can fulfill the human needs while becoming more sustainable.

“We have to learn to think more long-therm about the consequences of what we are doing, while we are doing it.�

Edward Bourtinsky

image 07: hydro-erosion in sand

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04

CODING AS GARDENING

The formation of an urban landscape like the Kathmandu valley consists of two contrary processes. The first one, described in prior, is the morphology of the landscape through erosion processes mostly driven by water. The second one is the emergence of urban structures that grow according to a systemic interconnection between various social, economic and ecologic parameters on multiple scales. these two contrary processes (Growth <> Erosion) occur at different scales and also in a different time frame but like any other network 48

chart 06: basic algorithm

they have an important influence on the systemic interconnections of the overall system.

I used computational Design as an approach to develop growth patterns.

Using algorithms in the design process allowed me to use simple principles that can create complex results that correspond to growth patterns found in nature.

“The successful survival of the “real-time world city” requires participation and exchange at the various social levels and material scales; a code

that incorporates participation must be able to grow as the network grows, it cannot be defined a priori in a controlled or predetermined environment. “Urban algorithms” co-evolve within their milieu, the articulation of their structure increases in relation to the complexity and diversity of the urban network they serve. “Urban algorithms” are the necessary coding logics for the self-organizing city.”

Poletto/Pasquero, Systemic Architecture, p. 20

Image 08: Lemna minor: non-human network influenced by hydrodynamics

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04.1

TESSELATION

My first approach for a growth algorithm is based on the idea of 3d-tesselation. Repeating branching systems, similar to the growth of a tree or a coral, can be generated using simple building 50

chart 07: looping algorithm

elements. The figure on the right illustrates how this â&#x20AC;&#x153;looping algorithmâ&#x20AC;? can create a complex geometry by adding a V-shaped branch on top of itself and iterating this process multiple times. This

process can be implemented to an infinite variety of building elements and thus create endless outcome possibilities.

Fig. 14: tessellation with single surface as base

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Fig. 15: tessellation with distorted cube as base

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Fig. 16: tessellation with trident as branch

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material 1

material 2

These 3D-tessellations describe a growing process that does not react to any external parameters. We have to conceive the growing urban environment as an adaptive system with constant 56

feedback loops, rather then a deterministic system because of the constantly changing external parameters. As the image on the right shows, most patterns found in nature are created through the

interaction between two or more materials with external forces (water, wind, gravity etc.) driving the process.

Image 09: Hydro-morphology of two different materials

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Diff A Diff B Scale F K

04.2

0,200 0,090 0,70 0,0550 0,0620

interaction

The process of reaction/ Diffusion is the next step from a deterministic System like the Tissue-tessellation to a system that is self-organizing and can change and evolve by adjusting parameters and conditions. The so called Turing Patterns, manifested

in mathematical terms by Alan Turing in 1952, are believed to be the reason for the emergence of a wide variety of patterns found in nature (staff, 2018). This theory might explain patterns like the stripes of a zebra, the ripples in sand or even the arrangement of fin-

58 chart 07: looping algorithm with multiple inputs

gers. It can represent the interconnection between two contrary networks. In terms of an urban environment this could be interpreted as the interconnection between human and non-human networks.

Image 10: reaction diffusion in homogeneous material I

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Fig. 17: catalog: reaction-diffusion I

Image 11: reaction diffusion in homogeneous material II

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Fig. 18: catalog: reaction-diffusion II

Image 12: reaction diffusion in homogeneous material III

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BASE

10.000x10.000px blur 5px 1 iter.

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10.000x10.000px blur 5px 10 iter.

10.000x10.000px blur 5px 15 iter.

10.000x10.000px blur 5px 20 iter.

Fig. 19: catalog: reaction-diffusion on topography of kathmandu I

Image 13: reaction diffusion in homogeneous material IV

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10.000x10.000px blur 5px 1 iter.

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Fig. 20: catalog: reaction-diffusion on topography of kathmandu II

Fig. 24: reaction diffusion on topography of kathmandu

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Fig. 21: reaction diffusion as an urban network

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reaction diffusion in homogenous material

resistor

Reaction-Diffusion is a process that incorporates two systems to the equation. When analyzing landscapes like the Kathmandu Valley in a systemic way it becomes obvious that the entity of the biosphere is a lot more complex and many more external forces play a role in the final outcome of 70

the system.

The image on the right shows that in a homogeneous material like sand, Turing-patterns emerge, but the moment when more forces and materials interact with the system, completely different patterns of self organization occur.

An urban algorithm that represents a dynamic system should be described as a â&#x20AC;&#x153;particle or ensemble of particles whose state varies over time and thus obeys differential equations involving time derivativesâ&#x20AC;? (Nature.com, 2020).

Image 14: reaction diffusion with resistor

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04.3

emergence

An urban network has properties of emergence meaning that the entity of the system shows characteristics that an individual part of it does not (Joss Colchester, 2016).

Examples for emergence in nature would be weather 72

phenomenons like storms, the swarm behavior of fish or the fractal patterns of snow flakes. Important for an emerging system is that the interacting parts must retain their independence while at the same time affect

each other. Urban sprawl operates in the same manner. Each person behaves in their individual way but the entity of how urban networks are growing on a larger scale relies on the interconnection between all of them.

chart 08: looping algorithm with multiple inputs and external forces

Fig. 22: Emergence described as a particle system

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F1: vertical force

F2: vertex force

To create a system that takes these interconnections into account i used particles. This allows me to emit a large number of elements in a â&#x20AC;&#x153;digital Labâ&#x20AC;? that behave individually, can be controlled by a variety of forces and also interact with each other. They ei-

ther merge to an ensemble of a self organizing geometry (Fig. 28, 29) or they repel each other to symbolize counteracting properties of networks and generate new patterns (Fig. 30, 31). The geometry on the right emerges by adding two forces to a set of particles which

randomly spawn on a flat surface. The first force (F1) influences the particles to move upward while a vertex force (F2) makes them move in a spiral shape. As a result a seemingly complex geometry emerges.

74 Fig. 23: Emergence: self-organizing geometry influenced by two forces

Fig. 24: Emergence: self-organizing geometry

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external forces affecting particles

particles influencing each other

In this digital lab particle systems are placed which represent different Networks. These networks can range from microscopic organic networks to large scale river net-

works. For the development of an urban environment that interacts sustainable within its milieu all aspects have to be taken into account. that means the forces that

76 Fig. 25: self-organizing network

influence the outcome of the algorithm have to be constantly adapted as the knowledge about the environment grows.

Fig. 26: Emergence: redistribution of existing networks

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Fig. 27: Digital Lab Kathmandu Valley

Fig. 28: Digital Lab zoom

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Human network

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non-human network

transition

Fig. 29 (right): redistribution of human networks on digital lab

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Human network

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non-human network

transition

Fig. 30 (right): redistribution of non-human networks on digital lab

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Human network

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non-human network

transition

Fig. 31 (right): transition between human and non-human networks

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Fig. 32: transition between human and non-human networks

Applied on the topography each particle network is affected by different forces and the topography itself. In this way they re-organize and by connecting them they create optimized, self organizing networks. When overlapping human and non-human networks there are different areas where

many particles accumulate to dense spots. In these areas it becomes clear that both networks strive to areas with high surface water deposit since it is vital to their existence. As illustrated in prior the accumulation of dense urban areas as well as agriculture have a significant

negative impact on the water quality and therefore the entire biosphere downstream. To ease the tensions between human and non-human networks and their impact on the surface waters we have to rethink the strategies how water is being distributed and used.

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05

TRANSLATION

INPUT_A

REAL_B

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INPUT_B

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chart 10: cycleGAN image translation process

REC_A

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In order to create a consistent output that incorporates all the computed data described in the last chapters (Erosion, Slope, Particle Networks, Water-flow, etc.) i used “unpaired image to image translation using conditional GAN’s”, known as CycleGAN. With this machine learning algorithm, models are trained using a collection of images from a source (A) and a target domain (B). During this training process so called “discriminators”

f

B

(Da, Db) constantly check the accuracy of the output by translating it back to the input and comparing it with the initial input. Once trained, the algorithm can “translate from one domain to another (A>B (G) or B>A (F)) without a one-to-one mapping between the source and the target domain” (CycleGAN, 2020).

For this project I used the Erosion drawings, describing the hydromorphology of the landscape, and trained it to be translated into a sat-

ellite image.

I repeated the training process individually for the different types of networks (Non-Human Networks, Urban Networks).

This workflow allows me to use the Particle-Maps from the last chapter which represent the density and distribution of the Human and Non-Human Networks to re-describe the landscape based on this data input.

chart 11: cycleGAN training process 91

REAL_B

FAKE_A

FAKE_B

EPOCH 200

EPOCH 169

EPOCH 125

EPOCH 100

EPOCH 080

EPOCH 041

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REAL_A

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Fig. 33: catalog: training rural network

REC_A

REC_B

IDT_A

IDT_B

fig. 34: result rural network

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INPUT_A

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fig. 35: training input rural network

INPUT_B

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EPOCH 200

EPOCH 169

EPOCH 124

EPOCH 104

EPOCH 086

EPOCH 040

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REAL_A

96 fig. 36: catalog: training urban network

REC_A

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IDT_A

IDT_B

fig. 37: result urban network

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INPUT_A

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fig. 38: training input urban network

INPUT_B

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fig. 39: result urban network I

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simulation

[1]

HYDROMORPHOLOGY

growth

analysis

[2]

erosion

flow

human

non-human

translation

transmission

interaction

application

[4]

components

[3]

slope

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distribution

chart 09: interaction of algorithmic approaches

The proposed workflow starts with simulations of the two main processes (growth and erosion) determinative for the emergence of an urban environment [1]. These simulations build the base for a variety of analytical mappings, clarifying both opportunities as well as side effects of hydromorphological processes [2]. The flow be-

havior and the steepness of the slopes elaborate how the urban environment should be distributed to minimize the risk of natural disasters caused by erosion. The data gained from the (human and non-human) network analysis is used to specify the distribution of the urban environment. Algorithms, like reaction diffusion, are further

used as a strategy for an interaction between human and non-human networks [3]. In sum, a new urban network emerges which entirely adapts to the hydro-morphology of Kathmandu, by introducing new typologies for transmitting and distributing water, and offers a safe and sustainable water supply for the entire valley [4].

image 15: hydro-erosion in sand with resistor 105

06

PROPOSITION

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fig. 41: 3D topography of site

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dense urban area

My proposal aims to offer an alternative city distribution and water infrastructure that is designed based on various data inputs from my analysis. The goal is to reduce the stress on the surface water quality, caused by pollution, of110

fig. 42: extract of site

bagmati river

suburburban area

fering a safe source of drinking water for the inhabitants of Kathmandu, reducing surface water run-off, and decreasing the risk of flooding and landslides. The main focus of the proposal is on the suburban areas allowing urban sprawl

to further progress in a sustainable way while considering non-human networks as an integral part of the urban environment.

7.5km x 7.5km

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water elevating towers

I was inspired by the â&#x20AC;&#x153;Water Elevating Towersâ&#x20AC;?, a low tech approach that has been used in the late 19th century in Tamesloht, Morocco. It is an alternative technique to an Aqueduct, that uses hydro-static pressure, 112

connecting underground pipelines

to transport water from one side of a valley to another. An underground pipeline connects the towers with each other in a directional way. At each tower, the water is transported to the top and subsequently trans-

fig. 43: water elevating towers of tamesloht

ferred to a second pipe redirecting it to the next tower via hydro-static pressure (Margarete van Ess, 2017). This system only works in one direction and is dependent on the gradual decline of the tower heights.

image 16: hydro-erosion with single resistor

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connecting underground pipelines

I adapted this linear approach of transporting water and applied it to the topography of the Kathmandu Valley. This allows me to freely distribute water around the entire topography in a 114

distribution towers

bidirectional way.

In this sense, areas with low surface water deposit can be supplied by areas with an abundance of water.

This will eliminate local dependencies on sur-

fig. 44: 3D water distribution network

face waters and therefore allow a reorganization of the structure of urban development.

image 17: hydro-erosion with multiple resistors

115

a script is being used to calculate local high and low points on the topography through a flow line analysis. the “Low 116

points” are the locations where the water is being absorbed, and the “high points” are the spots where the water will be

fig. 45: flow analysis diagram

distributed from, to cover the entire valley.

fig. 46: flow analysis on topography

117

water is absorbed at the “low points” and transported to the next “high points” at a lower elevation through hydro-static 118

pressure. from these locations water can be redistributed again to its surrounding environment to provide a safe source

fig. 47: calculation of “High/Low-Points”

of water to households as well as agriculture.

fig. 48: “High/Low-Points” on topography

119

as a result we get a network showing the locations for the two proposed typologies (water absorption and water distribution) and how they can connect inbetween each other to form a wa120

ter distribution system that can cover the entire valley without any additional input of energy. furthermore these calculated connections are adapted to the topography, using machine learn-

fig. 49: calculation of connection network

ing, to translate this diagrammatic data into an artificial landscape that follows the hydrology of the Kathmandu Valley.

fig. 50: connection network on topography

121

122

fig. 51: reorganization of urban environment through machine learning I

fig. 52: reorganization of urban environment through machine learning II

123

non-human network

flow analysis

human network

124

fig. 53: input parameters for reorganization

fig. 54: reorganization of urban environment through machine learning III

125

126

fig. 55: reorganization of urban environment zoom

127

128

fig. 56: reorganization of urban environment zoom II

129

typology 1

(“low-points”)

typology 2

(“high-points”)

‘water elevating towers’ transferring water to the next “high-point”

‘water distribution system’ redistributing water to surrounding environment

two main typologies are introduced which are located at each high-/ and low-point.

At the lowpoints water is absorbed from the surrounding environment through a pipe network and transferred to the next high-Point via hydro-static pressure. The high-points are

generated based on the process of reaction-diffusion, allowing an interaction between human and non-human networks, and hosting the necessary functions to provide a safe water supply for irrigation and drinking water. Besides distributing water, the struc-

130 fig. 57: two typologies for hydraulic infrastructure

tures at the high points are able to store water, allowing groundwater recharge, and providing habitable space for a variety of floral and faunal species, increasing the local biodiversity.

fig. 58: cycleGAN 3D displacement

131

132 fig. 59: cycleGAN 3D displacement II

this data based transformation of the landscape represents an optimized distribution of the existing landscape.

As you can see all the steep parts of the topography which are the most vulnerable to landslides are overgrown by trees to prevent them to further

erode and cause economic damage to the inhabitants. At the same time the river course is optimized to the flow behaviour offering retentions areas where water can accumulate, allowing groundwater recharge, to improve the overall hydrology of the Valley.

The distribution of the urban environment also follows the course of the flow lines making the whole city act as a drainage system. This allows a water distribution without any external input of energy makes it safer against water related disasters.

133

134 fig. 60: perspective view of proposed urban distribution [7,5 x 7,5km]

135

water intake at “low-Points” transfer of waters’ potential energy

136 fig. 61: section explaining the water distribution process

redistributed urban environment following the flow analysis

use of potential engergy to transport water to “high points” water distribution from “high-points“

137

138 fig. 62: interaction between urban distribution and water infrastructure

watertower

used as infrastructure to absorb surrounding waste waterand transport it to surrounding distribution Points

water distribution to distribute water to surrounding households and agriculture

water purification water storage

to compensate dry periods

139

140 fig. 64: Visualisation of transformed urban environment

fig. 63: visualization of water distribution structure

141

water storage

to compensate dry periods

water retention

retention areas allowing groundwater recharge and water purification

structure

framework providing habitable space for non-human networks

distribution

adaptable water distribution depending on local demands

environment

human as well as non-human networks following flow analysis and particle distribution

142

fig. 64: water distribution structure

143

distribution network

human network

non-human network

water network

144

fig. 65: layers of different networks

145

146 fig. 66: Visualization of transformed urban environment 3x3km

147

148 fig. 67: Visualization of transformed urban environment

149

150

151

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image 18: hydro-erosion in sand II

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staff, S.X. (2018). New theory deepens understanding of Turing patterns in biology. [online] Phys.org. Available at: https://phys.org/news/2018-06-theory-deepens-turing-patterns-biology.html [Accessed 2 Aug. 2020].

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image 19: hydro-erosion in sand III

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charts chart 01: correlation between growing population and degradation of hydrology chart 02: global land distribution Adapted from:

Ritchie, H. (2019). Half of the world’s habitable land is used for ag

riculture. [online] World Economic Forum. Available at: https://www.

weforum.org/agenda/2019/12/agriculture-habitable-land [Accessed 07.

June 2020]. chart 03: average annual impact of natural disasters (adapted from PBL

Netherlands Environmental Assessment Agency (2018, p.14) chart 04: Population Growth Kathmandu Adapted from:

Macrotrends.net. (2020). Kathmandu, Nepal Metro Area Population 1950-

2020. [online] Available at: https://www.macrotrends.net/cities/21928/

kathmandu/population [Accessed 2 Aug. 2020]. ‌hart 05: land-use proportions and river quality class for the bagmati river c Adapted from:

Davids, J.C., Rutten, M.M., Shah, R.D.T., Shah, D.N., Devkota, N.,

Izeboud, P., Pandey, A. and van de Giesen, N. (2018). Quantifying the

connections—linkages between land-use and water in the Kathmandu Val

ley, Nepal. Environmental Monitoring and Assessment, p.9 ‌ chart 06: basic algorithm (Korbinian Enzinger, 2020) chart 07: looping algorithm with multiple inputs (Korbinian Enzinger, 2020) chart 08: looping algorithm with multiple inputs and external forces (Korbinian Enzinger, 2020) chart 09: interaction of algorithmic approaches (Korbinian Enzinger, 2020) chart 10: cycleGAN image translation process (Korbinian Enzinger, 2020) chart 11: cycleGAN training process Adapted from:

CycleGAN (2020). CycleGAN | TensorFlow Core. [online] TensorFlow.

Available at: https://www.tensorflow.org/tutorials/generative/cyclegan

[Accessed 1 Aug. 2020] 156

image 20: hydro-erosion in sand IV

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FIGURES fig.

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

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*all

01: 02: 03: 04: 05: 06: 07: 08: 09: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28: 29: 30:

reinterpretation of topographical map catalog: fractal zoom into erosion catalog: thermal- and hydro-erosion catalog: hydro-erosion river networks around kathmandu Morphology Kathmandu Valley Anthropocene Morphology Kathmandu Valley Satellite Image of relevant locations I Satellite Image of relevant locations II Satellite Image of relevant locations III hydro-morphology Kathmandu Valley hydro-morphology bagmati river location of measuring points tessellation with single surface as base tessellation with distorted cube as base tessellation with trident as branch catalog: reaction-diffusion I catalog: reaction-diffusion II catalog: reaction-diffusion on topography of kathmandu I catalog: reaction-diffusion on topography of kathmandu II reaction diffusion as an urban network Emergence described as a particle system Emergence: self-organizing geometry influenced by two forces Emergence: self-organizing geometry self-organizing network Emergence: redistribution of existing networks Digital Lab Kathmandu Valley Digital Lab zoom redistribution of human networks on digital lab redistribution of non-human networks on digital lab

figures have been created by Korbinian Enzinger in 2019/20

image 21: thermal-erosion in sand IV

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Fig. Fig. Fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig. fig.

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31: 32: 33: 34: 35: 36: 37: 38: 39: 40: 41: 42: 43: 44: 45: 46: 47: 48: 49: 50: 51: 52: 53: 54: 55: 56: 57: 58: 59: 60: 61: 62: 63: 64: 65: 66: 67:

transition between human and non-human networks transition between human and non-human networks catalog: training rural network result rural network training input rural network catalog: training urban network result urban network training input urban network result urban network I result urban network II 3D topography of site extract of site water elevating towers of tamesloht 3D water distribution network flow analysis diagram flow analysis on topography calculation of “High/Low-Points” “High/Low-Points” on topography calculation of connection network connection network on topography reorganization of urban environment through machine learning I reorganization of urban environment through machine learning II input parameters for reorganization reorganization of urban environment through machine learning III reorganization of urban environment zoom reorganization of urban environment zoom II two typologies for hydraulic infrastructure cycleGAN 3D displacement cycleGAN 3D displacement zoom perspective view of proposed urban distribution [7,5 x 7,5km] section explaining the water distribution process interaction between urban distribution and water infrastructure visualization of water distribution structure water distribution structure layers of different networks Visualization of transformed urban environment 3x3km Visualization of transformed urban environment

figures have been created by Korbinian Enzinger in 2019/20

image 22: thermal-erosion in sand V

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IMAGES

image image image image image Image image Image Image Image Image Image Image Image image image image image image image image image image image

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01: 02: 03: 04: 05: 06: 07: 08: 09: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24:

morphology of moving water I morphology of moving water II morphology of moving water III small scale erosion large scale erosion (google earth, 2020) Morphology of moving water IV hydro-erosion in sand Lemna minor: non-human network influenced by hydrodynamics Hydro-morphology of two different materials reaction diffusion in homogeneous material I reaction diffusion in homogeneous material II reaction diffusion in homogeneous material III reaction diffusion in homogeneous material IV reaction diffusion with resistor hydro-erosion in sand with resistor hydro-erosion with single resistor hydro-erosion with multiple resistors hydro-erosion in sand II hydro-erosion in sand III hydro-erosion in sand IV thermal-erosion in sand IV thermal-erosion in sand V thermal- & hydro-erosion in sand hydro-erosion in sand V

images, unless otherwise specified, have been created by Korbinian Enzinger in 2019/20

image 23: thermal- & hydro-erosion in sand

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Eidesstattliche Erklärung

Ich erkläre hiermit an Eides statt durch meine eigenhändige Unterschrift, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Alle Stellen, die wörtlich oder inhaltlich den angegebenen Quellen entnommen wurden, sind als solche kenntlich gemacht.

Die vorliegende Arbeit wurde bisher in gleicher oder ähnlicher Form noch nicht als Magister-/Master-/Diplomarbeit/Dissertation eingereicht.

Innsbruck, August 2020

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image 24: hydro-erosion in sand V

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acknowledgement

first off i want to thank my familly for always supporting me, in every thinkable way, and therefore making this work possible.

Special thanks to Professor Claudia Pasquero as well as Professor Marco Poletto. Your way of thinking really inspired me and made me see the world from a different perspective.

I also want to thank Maria Kuptsova. Your passion for this kind of work was a huge motivation for me throughout the past semesters. you always found the right balance to keep me progressing despite the different circumstances over the last months.

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ME

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