workbook

ACA*GIScience Austria-Central Asia Centre for GIScience

Graz University of Technology Institute of Geoinformation

Tajik Agrarian University named after Shirinsho Shotemur

Austrian Academy of Sciences Institute for GIScience

enerGIS’10 Staff Development Workshop Geographic Information Systems (GIS) for Energy Issues in Central Asia

WORKBOOK September 20th – 24th, 2010 Dushanbe, Tajikistan

Editors: Scientific Workshop Organization: Gilbert Ahamer, Rainer Prüller, Johannes Scholz, Clemens Strauß, Carolina Lehner Workshop Committee: Josef Strobl, Izzatullo Sattori, Brigitte Winklehner Tajik Workshop Partner: Zamira Qodirova Verlag der Technischen Universität Graz www.ub.tugraz.at/Verlag ISBN: 978-3-85125-124-1 Date of publication: 20.09.2010 Bibliografische Information der Deutschen Bibliothek: Die Deutsche Bibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.ddb.de abrufbar. Link of the entire enerGIS‘10 Seminar: http://energis.tugraz.at (contains also the link to this workbook) Direct link for this publication: http://energis.tugraz.at/download/energis_workbook.pdf

Contents

Announcement Folder

5

Participants

7

Lecturers and Organization Team

11

The Rationale of this Workshop

13

Workshop presentations (in alphabetical order) Ahamer, G.: Climate Change Requirements are “Energetic”

15

Ahamer, G.: Renewable Energy Strategies in Central Asia

27

Boltayev, T.: GIS Training at Tashkent Institute of Irrigation and Melioration

39

Boronbaev, E.: GIS Spatial Tools: Design and Maintenance Optimization for Energy Efficiency, Passive and Healthy Buildings and Settlements, Adjusted to Daily and Seasonal Effects of the Sun, Wind and Environment

47

Boronbaev, E., Nazarkulova, A. & Strobl J.: Geoinformatics: Managing Energy, Resources, Environment

55

Griesebner, G.: Practical Session GPS Task

63

Imomov, S.: Application of Alternative Energy Sources

69

Navruzov, S.: Methodological Approach to Application of GIS & DSS for the Management of Water Resources of the Transboundary Rivers

71

Nazarkulov, K.: Feature Manipulation Engine Introduction Course – What is FME?

87

Paulus, G.: Theory on GIS & GPS – Selected Aspects

101

Prüller, R. & Strauß, C.: Practical Session GIS Tasks

163

Smith, A.: Hydrological Run-off Modelling for Determination of Hydroelectric Potential in ArcGIS, SAGA and GRASS

173

Smith, A.: Hydrological Run-off Modelling for Determination for Hydroelectric Potential; Practical using ArcGIS Spatial Analyst and Model Builder

187

Weidmann, Y.: Preparing Small-Scale Hydropower Projects for Private Sector Participation

199

Weidmann, Y.: Flächendeckende GIS-gestützte Identifikation potentieller Standorte von Kleinkraftwerken (Comprehensive GIS-Supported Identification of Potential Locations for Small Hydropower Plants)

217

motivation

data acquisition

data organization

data analysis

data presentation

Workshop Schedule

Sept 24th 2010

Sept 23rd 2010

Friday

Thursday

Wednesday

Tuesday

Monday

Sept 22rd 2010

Sept 21st 2010

Sept 20th 2010

afternoon 2:00 pm – 5:00 pm

morning 9:00 am – 12:30 pm

lunch



lunch



lunch


lunch Welcome, opening ceremony, introduction of all participants, morning administrative work, keynote presentations 9:00 am – 12:30 pm

Lecture: Motivation: GIS, energy and climate

Practical session: Preparation of fieldwork

Practical session: GPS Measurement

Practical session: Organization of spatial data

Conference session: Local application studies; presentations and poster session

Practical session: Spatial data analysis

Lecture


lunch


Practical session: Presentation of spatial data (map making)

Presentation of user-created Maps, closing ceremony, handover certificate of attendance, administrative work

Austrian Academy of Sciences Institute for GISciences

Staff Development Workshop


September 20th – September 24th 2010, Dushanbe, Tajikistan

Geographic Information Systems (GIS) for Energy Issues in Central Asia

ener GIS


Austrian-Central Asia Centre for GIScience

ACA*GIScience

Workshop Announcement

Topics presented at the Workshop The aim of the Staff Development Workshop enerGIS is to provide participants with an introduction to Geographic Information Systems (GIS) based on energy issues relevant for Central Asia. GIS is able to support decision-making processes by assisting the management of renewable energy resources like biomass, hydro, solar or wind energy. Workshop language: English, no translation provided.

Workshop Targets • • •

Theoretical and practical introduction to GIS and GPS. Understand the motivation for renewable energy sources. Ability to evaluate concrete energy potentials using GIS (ESRI ArcGIS).

Workshop Committee Academician Prof. Dr. Josef Strobl, Director of ÖAW/GIScience, Austria.

Academician Prof. DVSc Izzatullo Sattori, Rector of Tajik Agrarian University named after Shirinsho Shotemur, Tajikistan.

Prof. Dr. Brigitte Winklehner, President of Eurasia-Pacific Uninet.

Workshop Lecturers (preliminary) Dr. Gilbert Ahamer, ÖAW, Uni Graz / Salzburg Global change, Climate change and energy strategies DI Mag. Rainer Prüller, TU Graz WebGIS, Geodatabases, Land use modelling DI Clemens Strauß, TU Graz GIS technologies, Location Based Services Mag. Gerald Griesebner, Uni Salzburg GPS and GIS lecturer Mag. Manfred Mittlböck, Uni Salzburg GIS modelling, GIS metadata Dr. Gernot Paulus, FH Kärnten Geoinformation, Spatial Decision Support Systems

Guidelines for Participation • • • •

Registration on http://www.energis.tugraz.at Sufficient knowledge of the English language Interest in basic training on GIS Completion of the mandatory ArcGIS tutorial on http://tinyurl.com/energis2010 and submitting of the received certificate.

End of registration period Notification of acceptance

Important Deadlines: Participants August 15th August 30th

Important Deadlines: Local application studies (15 min.) and lectures (45 min.) June 30th Deadline for extended abstracts (1000 words) July 17th Notification of acceptance and review of submitted extended abstract August 15th Deadline for final version of extended abstract (to be published on the website) August 30th Review of presentation slides / completed poster September 10th Handover of final version of presentation slides / poster

Website

http://energis.tugraz.at

Tajik Workshop Partner

Dr. Zamira Qodirova Tajik Agrarian University named after Shirinsho Shotemur email: [email protected]

Austrian Workshop Organization Team

Dr. Gilbert Ahamer Austrian Academy of Sciences - GIScience email: [email protected]

Participants

ABDULLOBEKOV Bekhzod Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur AKBAROV Odil Uzbekistan [email protected] Tashkent Institute of Irrigation and Melioration AKHMEDOV Khamid Tajikistan [email protected] AKRAMOV Abdugaffor Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur ALIEV Nozim Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur ALIMBEKOVA Nagima Kazakhstan [email protected] Kyrgyz State University for Construction, Transportation and Architecture BAIBAGYSHOV Ermek Kyrgyzstan [email protected] Naryn State University BOLTAYEV Tolmasbek Uzbekistan [email protected] Tashkent Institute of Irrigation and Melioration BUTABEKOV Dilovar Tajikistan [email protected] University of Central Asia DJURABOEV Djamshid Uzbekistan jamshi[email protected] Tashkent Institute of Irrigation and Melioration

ERGESHOVA Gulshaan Kyrgyzstan [email protected] Kyrgyz State University for Construction, Transportation and Architecture ERSHOVA Nataliya Kyrgyzstan [email protected] Kyrgyz Russian Slavic University FROLOVA Galina Kyrgyzstan [email protected] Kyrgyz Russian Slavic University HASANOV Anvar Tajikistan Tajik Agrarian University named after Shirinsho Shotemur IMOMOV Shavkat Uzbekistan [email protected] Tashkent Institute of Irrigation and Melioration ISLOMOV Obid Tajikistan [email protected] Tajik Ararian University named after Shirinsho Shotemur JANBOEV Ernist Kyrgyzstan [email protected] Kyrgyz National Agrarian University JEENTAEV Erik Kyrgyzstan [email protected] Kyrgyz State University for Construction, Transportation and Architecture KARIMOVA Gulmira Kyrgyzstan [email protected] Kyrgyz National Agrarian University KEREMBAY Nurzhan Kazakhstan [email protected] Al-Farabi Kazakh National University

KUKANOV Firdavs Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur MIRZOEV Mirasil Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur NASYROV Adylbek Kyrgyzstan [email protected] Naryn State University named after S. Naamatov RAHMATILLOEV Foteh Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur SABOIEV Rizo [email protected] University of Central Asia SAFAROV Hasan Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur UMIRBEKOV Atabek Uzbekistan [email protected] Regional Environmental Center for Central Asia ZEVARSHOEV Askarsho Tajikistan Askarsho.Zeva[email protected] Mountain Societies Development Support Programme

Lecturers and Organization Team

AHAMER Gilbert Austria [email protected] Austrian Academy of Science / GIScience & University of Graz BORONBAEV Erkin Kyrgyzstan [email protected] Kyrgyz State University for Construction, Transportation and Architecture GRIESEBNER Gerald Austria [email protected] University of Salzburg KADIROVA Zamirakhon Tajikistan [email protected] Tajik Agrarian University named after Shorinsho Shotemur LEHNER Carolina Austria [email protected] Eurasia Pacific Uninet NAVRUZOV Sobir Tajikistan [email protected] Technology University of Tajikistan NAZARKULOV Kydyr Kyrgyzstan [email protected] Kyrgyz State University for Construction, Transportation and Architecture PAULUS Gernot Austria [email protected] Carinthia University of Applied Sciences PRÜLLER Rainer Austria [email protected] Graz University of Technology SATTORRI Izzatullo Tajikistan [email protected] Tajik Agrarian University named after Shirinsho Shotemur

SMITH Andrew New Zealand / Kyrgyzstan [email protected] Kyrgyz State University for Construction, Transportation and Agriculture STRAUSS Clemens Austria [email protected] Graz University of Technology WEIDMANN Yvo Switzerland [email protected] Ernst Basler + Partner AG

The Rationale of this Workshop Dear Participants! We welcome you to our common workshop “enerGIS’10” in Dushanbe. Thanks to everybody from several institutions who has contributed to this event by their continuous inputs! After “openSolarCA’09”1 the same organizing team presents you the enerGIS’10 workshop using Geographic Information Systems (GIS) for energy issues, targeted to Central Asia, again under the ACA*GIScience umbrella. The motivation stems from climate change that affects all regions, and in a characteristic pattern also Central Asia. Fossil energy has to be replaced by renewable sources such as solar, wind and small hydro plants that abate CO2 emissions. Both the causes and effects of climate change are highly spatially related and call for professional instruments to understand and manage georeferenced facts, threats and opportunities. This is the theoretical deliberation and rationale behind enerGIS’10. At right you find the overall guiding idea of the practical workshop procedure. The lectures and the hands-on sessions will lead you through 1. motivation 2. data acquisition 3. data organization 4. data analysis 5. data presentation. Please find more information on our website http://energis.tugraz.at/ where you can also download this workbook. Our target is to foster skills and knowledge and to contribute to future networking. We wish you an enjoyable and a successful workshop and look forward to seeing you and collaborating with you again! The Austrian organization team Rainer Prüller Clemens Strauß Johannes Scholz Carolina Lehner Gilbert Ahamer 1

See link at http://www.aca-giscience.org/opensolar/.

Climate change requirements are “energetic” Gilbert Ahamer “Global climate change” is increasingly understood by scientists and increasingly communicated to the global citizenship. Politicians increasingly take into account climate protection in their policies. Additionally, energy supply security determines national and supranational energy planning. Based on above two globally understood motivations, this staff development workshop “enerGIS’10” suggests to: 1. first to reduce energy demand while guaranteeing suitable energy services 2. second to use renewable energy sources to cover remaining energy demand. Especially the climate change motivation is explained on a global scale using simple models, long-term projections and using a logical chain of cause and effect symbolised by the puzzle below. The mechanisms of the greenhouse effect are explained and lead to the conclusion that only abatement of global CO2 concentration will lead to lowering CO2 concentrations – whereas deforestation is of comparatively lesser importance. Only considerable decrease of energy consumption as such can lead to lower CO2 emissions – fuel switch to biomass or other has lower potential. However, the remaining energy demand must be covered as much as possible by other fuels than fossil fuels because their remaining reserves would boost the global CO2 concentration to several times the pre-industrial value. Within a countries options to reduce (a) energy demand and (b) to switch towards renewable and carbon neutral energy sources, the following result of analyses is stated: (a) the highest technical, economic and practical potential lies in the sector of household, namely heating (b) a high potential is biomass energy which, however, cannot be implemented in Kyrgyzstan for climatic reasons. (c) Hence solar, hydro and wind potentials take the lead of practice-oriented climate protection. (d) Strategies of solar energy for heating or wind energy for electricity generation appear as best adapted to the Central Asian situations because of (i) their practicality, (ii) relative low capital input, (iii) adaptability to local circumstances and (iv) openness to personal craftwork of local citizens.

climate change

energy supply

energy demand

renewable potential

Welcome to enerGIS’10!

> www.oeaw.ac.at/giscience

Gilbert Ahamer

Thursday

Sept 23rd 2010

data analysis

Wednesday

data organization

Tuesday

Sept 21st 2010

Monday

Sept 20th 2010

Friday

Sept 24th 2010

data presentation

Sept 22rd 2010

Workshop Schedule

Workshop Announcement

afternoon Presentation of user-created Maps, closing ceremony, 2:00 pm – 5:00 pm handover certificate of attendance, administrative work lunch Practical session: Presentation of spatial data (map morning making) 9:00 am – 12:30 pm

Lecture

afternoon 2:00 pm – 5:00 pm lunch

Practical session: Spatial data analysis

Conference session: Local application studies; presentations and poster session



Practical session: Organization of spatial data

data acquisition

Practical session: GPS Measurement

Lecture: Motivation: GIS, energy and climate

motivation



Practical session: Preparation of fieldwork

ACA*GIScience Austrian-Central Asia Centre for GIScience



lunch Welcome, opening ceremony, introduction of all morning participants, administrative work, keynote presentations 9:00 am – 12:30 pm


ener GIS


Austrian Academy of Sciences Institute for GISciences

Staff Development Workshop

Geographic Information Systems (GIS) for Energy Issues in Central Asia September 20th – September 24th 2010, Dushanbe, Tajikistan

Topics presented at the Workshop The aim of the Staff Development Workshop enerGIS is to provide participants with an introduction to Geographic Information Systems (GIS) based on energy issues relevant for Central Asia. GIS is able to support decisionmaking processes by assisting the management of renewable energy resources like biomass, hydro, solar or wind energy. Workshop language: English, no translation provided.

Workshop Lecturers (preliminary) Dr. Gilbert Ahamer, ÖAW, Uni Graz / Salzburg Global change, Climate change and energy strategies DI Mag. Rainer Prüller, TU Graz WebGIS, Geodatabases, Land use modelling DI Clemens Strauß, TU Graz GIS technologies, Location Based Services Mag. Gerald Griesebner, Uni Salzburg GPS and GIS lecturer Dr. Gernot Paulus, FH Kärnten Geoinformation, Spatial Decision Support Systems

Workshop Targets • • •

Theoretical and practical introduction to GIS and GPS. Understand the motivation for renewable energy sources. Ability to evaluate concrete energy potentials using GIS (ESRI ArcGIS).

Guidelines for Participation • • • •

Registration on http://www.energis.tugraz.at Sufficient knowledge of English language Interest in basic training on GIS Completion of the mandatory ArcGIS tutorial on http://tinyurl.com/energis2010 and submitting of the received certificate.

Important Deadlines: Participants August 15th August 30th

End of registration period Notification of acceptance

Website

Important Deadlines: Local application studies (15 min.) and lectures (45 min.) Workshop Committee Academician Prof. Dr. Josef Strobl, Director of ÖAW/GIScience, Austria.

Academician Prof. DVSc Izzatullo Sattori, Rector of Tajik Agrarian University named after Shirinsho Shotemur, Tajikistan.

Prof. Dr. Brigitte Winklehner, President of Eurasia-Pacific Uninet.

June

30th

Deadline for extended abstracts (1000 words) Notification of acceptance and review of submitted extended abstract August 15th Deadline for final version of extended abstract (to be published on the website) August 30th Review of presentation slides / completed poster September 10th Handover of final version of presentation slides / poster July 17th

http://energis.tugraz.at Tajik Workshop Partner Dr. Zamira Qodirova Tajik Agrarian University named after Shirinsho Shotemur email: [email protected]

Austrian Workshop Organization Team Dr. Gilbert Ahamer Austrian Academy of Sciences - GIScience email: [email protected]

Welcome...! ... from our organisation team: Clemens, Gilbert, Carolina, Rainer ... to our series of enerGIS‘10 presentations Covering the entire range: Î motivation & energy Î GPS measurement Î GPS methods & data analysis Î Map production

Today‘s presentations give the broad context Gilbert Ahamer


Climate change requirements are “energetic” and motivate for renewable energy and for GIS Gilbert Ahamer, Dushanbe, 20.9.2010 > www.oeaw.ac.at/giscience

The global logical chain glob al cl imat e ch ange

climate change

energy supply

energy demand renewable potential

„global change“ is mainly „climate change“

Gilbert Ahamer

CO2 stems from fossil fuels

what we need is „energy services“

CO2 free, simple technology

housing sector > www.oeaw.ac.at/giscience

My motto: real science for real people Global Change

science

people

Gilbert Ahamer

Source: Executive Summary, Summary for Policymakers, 4th IPPC Report, www.ipcc.ch


Global science and local effects Global warming

The greatest concern in Tajikistan has been an increase in air temperature.

science

Gilbert Ahamer

Source: Executive Summary, Summary for Policymakers, 4th IPPC Report, www.ipcc.ch

people


Real people feel real climate change Droughts

Gilbert Ahamer


Local people feel global climate change

Gilbert Ahamer


Stability of climate and society


Gilbert Ahamer

Glaciers accelerate melting

Gilbert Ahamer


Extreme weather events

Gilbert Ahamer


Results of climate change

Gilbert Ahamer


Effects on agriculture


Gilbert Ahamer

Electricity generation

Gilbert Ahamer


Too little and too much of water


Gilbert Ahamer

Resulting action in Tajikistan

Gilbert Ahamer


Resulting local action


Gilbert Ahamer

What does this mean for us? This is exactly what we do here! See how this fits into our enerGIS‘10 program (and last year‘s openSolarCA‘09) Insulation of houses & energy efficiency Renewable energy sources: hydro, solar Agriculture & land use

All these issues are spatial Î GIS-related Gilbert Ahamer


Renewable energy strategies in Central Asia Gilbert Ahamer Planning documents for international and national energy policies are analysed regarding their relevance to protect the global climate. Among other institutions and supra-national conglomerates, the documents of the European Union (EU), the European Union Neighbourhood Policy (ENP) and EuropeAid are doublechecked for their consistency both with national energy programs and practical feasibility. Data from the International Energy Agency are used to draw a picture of energy supply and demand in all five Central Asian States Kyrgyzstan, Tajikistan, Uzbekistan, Kazakhstan and Turkmenistan (see center of map below), focusing on the very diverse “fuel mix”, i.e. the share of coal, oil, gas, nuclear, hydro and other renewables. Intergovernmental organisms such as the Shanghai Cooperation Organisation, CIS, INOGATE, TRACECA, the Baku Initiative, develop energy relevant guidelines. Geographic Information Systems are useful and needed to quantitatively assess regional distributions of (i) energy supply, (ii) energy transport, e.g. by high-voltage lines and (iii) energy consumption. These patterns are not likely to match, hence require a consensus-based network of international policy-making.

Renewable energy strategies in Central Asia

Gilbert Ahamer, Dushanbe, 20.9.2010 > www.oeaw.ac.at/giscience

t expor ricity rt Elect po l im ue il f ss Fo

ca pi ta l kV l i ne s

Lo w

Gilbert Ahamer

es s s Lo

Theft

Ta riff s

ts ke ar m ew N y ienc Effic

Th e en er Sum gy me en rw at e te r fo rp rw inte rise r ga s Interna tional s coope ration Water for irrigation rity ... several Central u se c y Asian states ... g er ... on energy... En

The rmal in sulation Ce nt r a l As ian citiz ens

Let‘s take the perspectives of ...


The argument: global climate change

energy supply

Gilbert Ahamer


ressource limitation

large investment

decentralisation

local economy

Gilbert Ahamer


The local causes for climate change Kazakhstan is among the world’ world’s three dozen largest GHG emitters, and emissions per dollar of GDP produced in Turkmenistan and Uzbekistan are among the world’ world’s highest. Still, none of the Central Asian countries can rely solely on national mitigation efforts to reduce the threats posed by climate change. Source: Tajikistan 2002: State of the Environment Report (http://www.caresd.net/site.html? en=0*id=13), Gilbert Ahamer


Perelet, R. (2008), Climate Change in Central Asia, http://www.developmentandtransition.net/index.cfm?module=ActiveWeb&page=WebPage&DocumentID=683.

Energy economics in Central Asia - 1

Gilbert Ahamer


Energy economics in Central Asia - 2

Gilbert Ahamer


Energy economics in Europe and Asia - 1

Gilbert Ahamer


Energy economics in Europe and Asia - 2

Gilbert Ahamer


Energy economics in Tajikistan

Gilbert Ahamer


Energy economics in Kyrgyzstan

Gilbert Ahamer


Energy economics in Kazakhstan

Gilbert Ahamer


Energy economics in Uzbekistan

Gilbert Ahamer


Energy economics in Turkmenistan

Gilbert Ahamer


Correct priorities in energy use! 1.

First reduce energy demand

2.

Then take care of energy efficiency

3.

Last care for sustainable energy


Gilbert Ahamer

What means „potential“? What nature offers

What can be built with “reasonable effort” …with “reasonable cost” What is “practically, politically feasible” & “demanded” Îalways, “potentials” are no constant figure but a function of costs: Gilbert Ahamer

Potential = f(cost)


Hydro projects map

Gilbert Ahamer


Hydro electricity: need for export lines

Gilbert Ahamer


Thank you for your attention!

Gilbert Ahamer


EnerGIS’10 Staff Development Workshop “Geographic Information Systems (GIS) for Energy Issues

GIS training at Tashkent Institute of Irrigation & Melioration (TIIM)

Tolmas Boltayev Land Tenure Development Center (LTDC), TIIM, Uzbekistan

Dushanbe 2010

BRIEF HISTORY OF TIIM

Established in 1920 and in 1934 by name of Tashkent Institute of Engineers for Irrigation & Agricultural Mechanization

In 2004 the Institute was re-organized into Tashkent Institute of Irrigation & Melioration (TIIM) www.tiim.uz


FACULTIES

TIIM

Irrigation & Drainage

Hydraulic Engineering

Automation & Mechanization

Land Management & Cadastre

Economics & Management


FACULTY MEMBERS

Figures Doctor of sciences, Professors

33

PhD, Associate Professors

155

Senior lectures

69

Teaching assistants

73

TOTAL

330

WOMEN

115


COOPERATION WITH INTERNATIONAL ORGANIZATONS


GIS Subjects taught at BSc level Introduction to GIS GIS and Technologies GIS application in Land Management Use of ArcView 3.2


GIS Subjects taught at MSc level GIS (Continuation) GIS in Environmental Sciences GIS in Land Management Remote Sensing Use of ArcGIS 9.3


RESEARCH CENTERS EcoGIS Center Land Tenure Development Center Information Analytical Center “Water Resources” BioEnergy Laboratory etc.


About Land Tenure Development Center

Established Sep.2007 in frame of Tempus funded Project LAREMA Our motto is

“Improving land resources management for socio-economic development and environmental sustainability”

www.ltdc.uz Licensed ESRI ArcGIS 9.3 Software EnerGIS’10 Staff Development Workshop “Geographic Information Systems (GIS) for Energy Issues

Activities of LTDC

Training of highly qualified master students; Training courses in ArcGIS 9.3 for specialists as well as teachers; Support of projects implementation at relevant organizations and governmental agencies; Consultancy; Research;


Projects of LTDC

Land Resource Management in Uzbekistan (Tempus) Support of Sustainable Livestock Sector in Uzbekistan (UNDP) Issues of Agricultural land use efficiency (USAID) Impact of land reform on salinity (IWMI) Sustainable water resources management in Central Asia (Tempus) GIS and Data Base Management (GTZ) Digital Mapping of WUAs of Ferghana Valley (IWMI)


Partners of LTDC

EU-Tempus; Eurasia Pacific Network – UNINET (new) International Water Management Institute (IWMI) German Technical Cooperation (GTZ) Royal Institute of Technology (KTH) University of Stuttgart University of Helsinki University of Ljubljana EnerGIS’10 Staff Development Workshop “Geographic Information Systems (GIS) for Energy Issues

Further goals of LTDC

Creation of GIS Center Creation of Data Base by ArcGIS in Uzbekistan GIS education in Engineering fields Development of Teaching materials in practical use of GIS hardware and software Organizing different GIS courses


Classes of LTDC


Thank you for attention!

You are welcome to TIIM!! EnerGIS’10 Staff Development Workshop “Geographic Information Systems (GIS) for Energy Issues

Kyrgyz Republic

Kyrgyz State University of Contraction, Transportation and Architecture GIS spatial Tools: Design and Maintenance Optimization for Energy Efficiency, Passive and Healthy Buildings and Settlements, Adjusted to Daily and Seasonal Effects of the Sun, Wind and Environment

Professor Erkin Boronbaev [email protected]

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment Central Asia region characteristics: Former Soviet Central Asia countries have transition economy, cold winter and hot summer with the big daily amplitude of the ambient temperature The buildings have low contraction quality, not healthy indoor comfort and 40…60% energy saving and CO2 emission reduction potential Now the poor people expend 30…60% of the family budget for energy use For example in Kyrgyzstan: The buildings consume 41% of a national energy production, the houses take 65…75 % of it It is needed the new knowledge and tools for energy investigation, design and maintenance of buildings and settlements 2

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment Our Department

“Heat-Gas Supply and Ventilation” •

since 1995 has and had the building energy efficiency projects of EC, UN, WB, ADB, EBRD, Denmark, Germany, Norway, Sweden, Switzerland, etc.

•

EC TEMPUS projects completed at 2008

“Development of Master Program in Environmental Protection and Rational Use of Natural Resources at KSUCTA” with KTH (Sweden);

started at 2010 :

1. “Creation of third cycle studies – Doctoral Programme in Renewable Energy and Environmental Technology” with KTH (Sweden);

2. “Geoinformatics: Managing Energy, Resources and Environment” with Salzburg University (Austria) 3

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment Climate of Kyrgyzstan:

Lines of Degree-days (Dd) Heating period: duration – from 133 to 365 days; average temperature – from 1,6 to – 9,4 ºC; Degree-days – from 2100 to 9500; The lowest temperature – minus 56,6 ºC.

4

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment General situation of building sector The energy consumption and CO2 emission structure of the houses in Bishkek city Heating/Cooling

Ventilation Hot water

Cooking

Domestic electricity Transport

Constructed – 2003 Building volume – 314 м3

Constructed – 1990 Building volume – 27000 м3

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment General situation of building sector

157

548

95 189

До

После

До

Project EC TACIS «Improving the Energy Efficiency of Buildings in Kyrgyzstan» (1995-96)

Energy saving – 40%

После

Project CAMP (Switzerland) «Thermal insulation of rural buildings in Kyrgyzstan, Tajikistan, Kazakhstan» Energy saving – 65%6 (2002-04)

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment General situation of building sector t, 0C

Q, кВт*ч

24

b

320

21

280

18

240

15

200

12

160

9

120

6

80

3

40

0

0

-3

-40

-6

-80

-9

-120

-12

-160

-15

-200 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

February,02

Monitoring shows that there is needed: • Dissemination of knowledge • Improving of building energy efficiency • Managing of energy and natural resources • Use of renewable energy • Decrease of GHG emissions

7

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment General situation of building sector A new demonstration building

Project Basel city (Switzerland) «Straw-Bale passive-solar energy efficiency buildings in Kyrgyzstan» (2004-05) Energy saving – 95% 8

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment PC optimization of the Seasonal Thermal Effects of the Sun: • Summer shadowing • Winter max solar gain

Project Basel city (Switzerland) «Straw-Bale passive-solar energy efficiency buildings in Kyrgyzstan» (2004-05) Energy saving– 95% 9

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment

GIS spatial Tools: The district buildings location optimization on Daily and Seasonal Thermal and Lighting Effects of the Sun

(Video film)

10

GIS spatial Tools: Design/Maintenance Optimization of Energy Efficiency/Passive/Healthy Buildings/Settlements, Adjusted to Daily/Seasonal Effects of the Sun/Wind/Environment GIS spatial Tools:

The Settlements location optimization On Daily and Seasonal the Sun shining effects

The shortest in KG the Sun shining duration 1698 h/y measured in Kysyl-Suu canyon (the next open site has 2655 h/y)

In Jety-Oguz resort the daily Sun shining starts very late because of East high hill


Suggestion for a new national level demonstration project:

«Kyrgyzstan: Renewable energy use actions for reduction of buildings energy consumption and GHG emission: Strategy, Trainings, Measures and Monitoring» (2011-2015)

12


Thank you!

13

Geoinformatics: Managing Energy, Resources, Environment

GEM

Tempus 43 510978-TEMPUS-1-2010-1 Erkin Boronbaev Ainura Nazarkulova Josef Strobl

Topics Содержание Objectives Цели y Partners (18) Партнеры y Work Plan and Outcomes Рабочий план и Результаты y Success Factors Параметры успеха

GEM

y

Key Facts Основные факты y y

GEM

y

y

Starting in October 2010 (likely), 3 years Начало октябрь 2010, 3 года 18 Partners from EU and CA 18 партнеров из ЕС и ЦА Budget approx. € 900.000 (10% cofinancing) Бюджет ≈ 900000 евро (10% ‐ софинансирование) Development of int‘l MSc programs Разработка М/ународной магистрской программы

Coordinating Institution Координирующая организация y

GEM

y y y y

Centre for Geoinformatics, U of Salzburg Центр Геоинформатики, Университет Зальцбурга Experience with Tempus, Erasmus etc. Работал с Tempus, Erasmus и др. Central Asia activities since 2002 Работает в ЦА с 2002 г. 40 Researchers, expertise in GI+app‘s 40 Научных работников CA facilitator partner: KSUCTA Основной партнер в ЦА: КГУСТА

GEM

Objective Цели y

The main objective of the project is to develop and implement an international, interdisciplinary postgraduate curriculum in Geoinformatics, with a clear focus on the management of critical resources. In particular, important pillars of the success of emerging economies in the Central Asia region will be addressed. The program will focus on the contents, teaching methods, academic integrity and a collaborative form of delivery as well as laying the foundation for lifelong learning in accordance with the Bologna Declaration.

y

Основная цель проекта – разработать и реализовать международную, междисциплинарнуюй учебную программу на базе в.о.по Геоинформатике, сфокусированную на управление необходимыми и важными ресурсами. Программа будет нацелена на содержании, методике преподавания, академической целостности и совместной форме преподавания, а также на создании основы для длительного обучения согласно Болонской декларации.

Project Objectives Цели проекта y

GEM

y

y

y

Focus / Action: Curricular Reform Фокус / Деятельность: Реформа учебного плана Geoinformatics MSc at all Acad. Partners Магистратура по геоинформатике для всех академических партнеров Sustaining Program beyond Project (!!) Устойчивость программы после завершения поекты Contribute to Knowledge-Based Societies Вклад в общество основанное на знании

Why (Applied) Geoinformatics? Почему Геоинформатика? y

GEM

y

y

y

Sustainability always is ‚spatial‘ Устойчивость всегда связана с простанством Integrates information across domains Интегрирует информацию с разных областей Key for managing economiсs and societies Ключевое для управления экономикой и обществом GI is key competence, needed locally!!! ГИ – это основные знания необходимые на местах

CA Partners – Academic Академические партнеры в ЦА 1. 2.

GEM

3. 4. 5. 6. 7.

Kyrgyz State University for Construction, Architecture and Transportation (КГУСТА) Osh Technological University (ОшТУ) University of Central Asia (УЦА) Kazakh National University - Al-Faraby Korkyt Ata Kyzylorda State University Tajik Agrarian University (ТАУ) Tajik State Pedagogical University (ТГПУ)

CA Partners – Admin and Industry ЦА Партнеры ‐ Неакадемические Ministry of Education and Science – KG y Ministry of Education and Science – KZ y Ministry of Education – TJ y Osh Enterprise of High Voltage Station y Osh Engine Generating Station y Ministry of Industry, Power and Fuel Resources – KG y State enterprise Kyrgyzgilkommunsoyus

GEM

y

EU Partners Партнеры из ЕС University of Salzburg Centre for Geoinformatics y Vrije Universiteit Amsterdam Spatial Information Laboratory y University of West Hungary Faculty of Geoinformatics y European Geography Association for Students and Young Geographers

GEM

y

GEM

Work Packages Рабочие блоки 1.

Review of programs, demand, potentials Обзор програм, спроса, понтенциалов

2.

Establish curriculum, learning media Определение учебного плана, обучающего медиа

3.

Faculty development Повышение квалификации преподавателей

4.

Program implementation (Course start) Реализация програмы (Начало курса)

5.

Dissemination and outreach Распространение и информирование

6.

Sustainability: business model and market Устойчивость: бизнес модель и рынок

7.

QC: monitoring and academic integrity Контроль качества: мониторинг и академическая целостность

8.

Project Management Управление проектом

Main Activites Основная деятельность y y

GEM

y

y y

Curriculum and media development Разработка учебного плана и медиа Teacher training, starting courses Обучение преаподавателей, начала курса Integrated int‘l program, parts as eLearning Интегрированные международные программы, часть как eLearning Workshops and summer schools Семинары и летние школы Quality Assurance Гарантия качества

Innovative Learning + Pedagogy Инновационное обучение + педагогика Integrated courses across institutions (sharing teaching resources!) Совместные курсы y eLearning components eLearning компоненты y Active learning, collaborative learning Активное обучение, совместное обучение y Competence-based assessment

GEM

y

Success Factors Факторы успеха Full committment by partners! Выполнение обязательств партнерами y Realistic expectations: students, Реалистические ожидания: студенты y Faculty – teachers Штат ‐ преподаватели y Quality – academic integrity Качество – академическая целостность y Development of job market Развитие рынка труда

GEM

y

A Few Key Points … Summary Несколько основных пунктов ... y y

GEM

y

y

Identify ‚right‘ students Определение нужных студентов E-Learning to share teaching resources E-Learning ‐ обмен материалами обучения Building communities of stakeholders Создание сообщества заинтересованных лиц Outreach to broader public Информирование широкой общественности

Links and References http://eacea.ec.europa.eu/tempus y www.zgis.at y www.ksucta.kg y www.zgis.net/GEM (soon online)

GEM

y

Contacts: y [email protected] y [email protected]

GPS Measurement Problem definition: Practical session with GPS tools (Trimble Juno ST). Requested results: • • • •

Getting familiar with the usage of a GPS system Collecting point features in the surrounding of the university Process the collected data in the computer lab Short technical report with a workflow description

Assistance:

Getting familiar with a GPS system • •

• • • •

Switch on the device (Trimble Juno ST) Start the GPS software: Start – Programs - TerraSync

Check the different possibilities of the status of the GPS Receiver (Rover) 1. Pull-down menu: Status 2. Pull-down menu: Sky Plot Check other possibilities (like: Sat Info….) + different other parameters like PDOP, position, amount of satellites, accuracy……

Collecting point features in the surrounding of the university •

1. Pull-down menu: - Data - Create - height of antenna - Point, line or area (depending on which type of feature you are interested in)

• • • • • • •

Create = Start to measurement feature 1 Ok = Stop measurement feature 1 (but leave the file open) Create = Start measurement feature 2 Ok = Stop measurement feature 2 Close Close file and save it Close “Terrasync” Check files in the “File Explorer” Start – Programs – File Explorer Path: My Device / My Documents / TerraSync

Process the collected data in the computer lab (Data Transfer, Export…) • •

•

Connect the GPS device w via USB with the computer. The software “ActiveSync” should start automatically

Start the software “Pathfinder Office Pro”

•

Generate new project in “Pathfinder Office Pro” by using the button New

•

Type a name for this project and confirm it with OK

•

A new project was generated (! Keep the path for the project in mind !)

•

Transfer the collected data from the GPS device to the computer Utility – Data Transfer

•

Check if the GPS device is connected (upper right corner) Add “Data File” to the “Data Transfer” window Add – Data File

•

•

Select the file you have collected in the field Open

•

Transfer the selected file(s) Transfer All

•

Export the file(s) to a different file format (for example ESRI – shape format Utilities - Export

•

Check the properties for exporting the file(s) - Projection (like WGS84) - Attribudes (like height, GPS time and date… - Filters (like Uncorrect….) (! Keep the projection you will use in mind !)

• •

There will be a message that no projection has been found. Confirm with Yes You will get a short report about the export of the file(s) Confirm with Close

•

Define the projection with the software ESRI ArcCatalog

•

Start the software ArcCatalog

•

Navigate to the “Export” folder in your GPS project

•

Right click your exported files and click Properties

•

Select the same projection you used in the “Export” window in Pathfinder Office Pro (like WGS 84)

•

Confirm the the right projection with OK

•

Check the result with the Preview window

Technical report: Description of workflow to measure some features with GPS and to bring the results in a different data format (with some screenshots of the main tasks).

ИСПОЛЬЗОВАНИЕ НЕТРАДИЦИОННЫХ ИСТОЧНИКОВ ЭНЕРГИИ

Многофункционалная лабораторная биогазовая установка работающем на разрежение (Установка на 500 литров биомасс температурный режим которой 520с)

Биопруды для очистки канализационных отходов

Стратегические направления развитие энергетики в мире предусматривают широкое использование нетрадиционных источников – ветера, ветера, солнечные, солнечные, незкотемпературных источников энергии , в том числе и энергии органической биомассы (навоз, навоз, ботва, ботва, выжимки, выжимки, отходы полеводства и др.). др.).

Биогазовая завод на 100 м3 работающим птичьем помете в г. Чирчике Ташкентском области

Получение биогаза из высших растение из очистительных биопрудов

Биогазовые установки работающих (чистым свином навозе) навозе) неправильном режиме

Верхняя растение для очистки загрязнение

Биореактор на 50 м3 работающим в режиме разрежение чистого свиного навоза

Проблема обработки свиного навоза (басен

Wind Energy Creation of Wind Maps Modeling the location Data from Remote Sensing Existing Analogue Data Spatial Analysis of the Wind Maps for determination of correct location of the Wind Power plants

Спасибо за внимание!!!

Metodological approach to applicatcation of GIS & DSS for the management of water resources of the transboundary rivers Sobir Navruzov The questions of creation of GIS system for decision-making related to management of water resources in transboundary river basins are considered in this paper. Process of application of GIStechnology is described in the field of management of water-economic systems in general, and water resources managements of the Central Asia transboundary rivers, in particular. Various aspects of GIS application are analyzed: GIS database management, basic software, establishing the relationship of GIS and database; specific features of GIS mapping. The description of GIS software which contains functions and the tools necessary for storage, analysis and visualization of the geographical (spatial) information are resulted. Developed classifications of technologies by the formation of thematic layers are described based on the existing geographical basis. The technique of choice for GIS map projection is offered and also the structure of the GIS database as an example of Amudarya and Syrdarya transboundary river basins is developed. The method of applying coatings using the software Arc / View [1] developed a variety of maps, which are using for spatial data analysis. Maps of the level of soil salinity and irrigation area in selected areas of planning zones prepared on the basis of this method are examples of such constructions. The conceptual scheme the interaction of the main components of decision support system (DSS) is offered (see picture 1). Database management system (DBMS) is based on the methodology of databases (DB) construction. Effective use of accumulated data files of various departments (and the amount is typically a few thousand units of storage) is possible only with active involvement in the processing technology computers and the creation of special software. This interface system provides the user the opportunity to work in interactive mode with the database to search for acceptable solutions to the problems with the use of mathematical models [2,3]. The mathematical models of water management of transboundary basin as a natural object, surface runoff mainly governed by accumulating reservoirs are considered. Three levels of mathematical models are offered: analytical, optimization and imitational. Within the framework of analytical models [4], the theoretical game models for the distribution of water resources between the states of the Amudarya basin are considered. A number of characteristic examples of games with non-opposite interests are discussed.

DSS GIS

Interface

XII

1992

XI

1400

1200 X 1200

IX 1000 VIII 1000

1991

NST NST

1991 1992 1991 1993 1993

PFA PFA

200

Блок ввода информации из модели зоны планирования

DBMS

III II

I

II II

200 III III

IV

IV

400 V

600 V

VI

VII VI

800 VIII VII

IXVIII

1000 X IX XI

1200 XXII

1991

0 I

Field

WCL WCL

XI

WDR WDR

1400

База данных

XII

Instrumental System

Блок расчета ущербов Аралу и Арнасаю

WTD WTD

Industr Industryy

Блок водносолевого баланса

Блок формирования целевой функции

WGZ WGZ

Graund Graund W Water ater

Graund W ater

Блок гидроэнергетики

Блок расчетной информации для пользователя Drain

WDL WDL

Lake

WIR WIR

WTR WTR

WDT WDT

Drain Drain

Расчетные блоки Lake Lake

WGR WGR

Блок ввода информации из базы данных

I 0

WCR WCR

Поль з ова тель

Структурный блок Suppl Supplyy

WRI WRI

Supply

IV

400

WFA WFA

Field Field

Блок расчетной информации для модели зоны планирования

Canal

Информация

600

200

PAF PAF

Результаты

VI

400

WAF WAF

Canal Canal

WRS WRS

600 V

0

WCA WCA

WTS WTS

VRS VRS

Модель Бассейна Реки

VII 800 800

WCT WCT

NTS NTS

Модель Зоны Планирования

Управлени е Блоки выбора целевой функции, ограничений и начальных условий Industr y

WGT WGT

WDA WDA

WIL WIL

Filtration Filtration Field Field

Filtration Field

Mathematical Models

Pic. 1.

Optimization models [3] of the reservoir management of the upstream water resources of the Amudarya are offered, which use as a base the “block-hierarchical” principle [5], which at the initial stage provides for the development of different national models and their further coordination within the framework of regional models. According to this, we can divide the territory of the Amudarya basin into two zones: the zone of water demand and the zone of water production. The technique of finding a compromise solution among the needs of the states, in terms of the volumes of water consumed at the level of the coordination of management between zones of consumption and formation is offered. Construction of imitational models is carried out on example the Vakhsh–Amudarya cascade of reservoirs [2,5]. The cascade includes three large reservoirs, such as Rogun, Nurek and Tuymuyn. Mean while, these reservoirs are located in the territory of different Central-Asian republics, namely: Rogun and Nurek belong to Tajikistan and Tuymuyn to Uzbekistan. Using mathematical models and computer technologies provide improved reservoir management rules with a uniform approach to their preparation of specific water bodies located in both national and transboundary basins [6,7]. The developed mathematical models the general principles and approaches to water resources management of the transboundary rivers also have been approved for the Syrdarya basin. Relations of countries between zones of consumption and formation proposed procedure for finding a negotiated solution on the distribution of water resources: Kazakhstan and Uzbekistan (zone of consumption), receiving necessary extra water for the vegetative period at the same time accept also generated electricity by Kyrgyzstan and Tajikistan (zone of formation). During the winter (deficit

period) Kazakhstan and Uzbekistan return to the zone of formation generate electricity or an equivalent amount of other energy sources. The structure of the main menu of user (see pic. 2) is based on a block-hierarchical principle, where the user can choose one or another menu item is arbitrary, or based on strictly ordered logic view information. For example, selecting a block of background information, the user is satisfied with the information on the simulated object - Syrdarya transboundary river.

COMPUTER MODELING

Ka zak hst an

Tu rkm eni sta n

Central Asia River Basin

Uz bek ista n

01 Version, 2009

HELP HELP INFORMATION INFORMATION

Kyrgyzstan

PARAMETERS PARAMETERS

Ta jik ista n

SCHEMES SCHEMES (linear and survey) (linear and survey)

MODEL MODEL DESCRIPTION DESCRIPTION

WATER WATER BALANCE BALANCE

SCRIPTS

QUALITY QUALITY BALANCE BALANCE

REPORTS

GIS GIS (SPATIAL (SPATIALANALYSIS ANALYSIS) )

GRAPHIC GRAPHIC ANALYSIS ANALYSIS MENU

Pic. 2

With regard to the direct participation of the user in the process of scripting, it is part of the block "Inputs". This unit was granted by the opportunity to view a demo, or the most directly modify the input data for various objects. A similar structural arrangement is implemented for all blocks of the system (see pic. 3-7). Develop a scenario of using of water is an important component for making decisions on assessment of water balance in the basin of Syrdarya transboundary river. The interests of the forming zone countries centered on the energy use of water resources, while countries of the zone of consumption of using water basin meets the demands of irrigated agriculture. In these circumstances, there is a conflict of interest. In this regard, the main task is to find suitable options cooperation on water resources under review the basin, which contribute to reducing tensions in the region. To do this, in principle, it is proposed computer simulation of possible scenarios of water use in which users of the system will be able to simulate different situations of water distribution among water users and water users, as well as to determine the balance of water in selected areas of the transboundry river basin.

Gathering and processing of the monthly data

INPUT THE KEY PARAMETERS Parameters of water basins: • Volume, one million in m3 (full and useful) • Height, m ; • The area км3 • Length, km

Inflow: • to Toktogul (V1); • to Andijan; (V2); • to Kayrakum (V3);

• between Toktogul & st. Uchteppa • between st. Uchteppa & Kayrakum -

( ω , ω ); T 2

ω5K ,..., ω9K

• up & lower Kayrakum (

V5K

Other parameters: • Evaporation; • Minimal outflow; • etc.

A A • Below Toktogul ( ω3 , ω4 );

• Below Andijan

V2T V4A

• between Andijan & st. Uchteppa -

Irrigational requirements :

Input of parameters are carried out :

);

- Users

The aggregated irrigational requirements the countries of the bottom current (Uzbekistan + Kazahstan)

Akdjar

Hydropost:

Year

I

II

III

IV

V

1987

280,00

343,00

329,00

470,00

397,00

494,00

Chilmahram Акджар Akdjar Cal VII VIII IX X Uchkurgan 707,00 327,00 293,00 502,00 Uchteppa

712,00

657,00

1988

586,00

599,00

499,00

665,00

1027,2

936,60

954,62

678,32

620,33

545,30

597,77

579,91

1989

596,44

590,33

479,03

338,43

579,37

801,67

938,41

754,13

360,77

408,72

558,47

556,46

1990

492,57

524,31

423,23

401,53

446,81

336,87

604,42

634,13

340,03

483,87

646,63

599,76

1991

593,50

555,73

467,40

425,43

624,77

708,90

688,67

437,42

284,43

428,28

563,93

673,07

1992

583,13

574,81

530,93

461,60

771,50

560,97

472,23

459,55

309,10

511,19

638,27

711,80

1993

650,88

690,23

637,71

477,57

822,33

751,73

402,58

348,18

299,23

521,78

779,63

900,40

1994

655,90

898,13

907,73

813,07

892,42

503,93

511,81

350,62

543,88

502,09

752,13

952,02

1995

927,38

896,61

755,06

449,37

303,75

247,23

501,14

305,48

214,40

371,02

580,70

867,89

1996

859,28

846,41

722,57

614,35

429,96

645,47

398,77

327,58

260,33

482,77

785,13

960,33

1997

874,08

788,85

640,52

502,67

310,15

405,70

406,79

339,68

196,90

252,71

607,67

815,53

1998

826,33

857,84

714,54

464,74

611,06

930,70

474,66

342,27

433,25

469,61

617,90

953,00

Lateral Inflow:

HYDROELECTRIC POWER STATION: • The established capacities; • Factors energy generation; • Other restrictions;

T 1

The monthly data on objects gather in tables:

VI

XI

XII

or

The similar table of the monthly data is entered by the user on all to characteristic objects of the Syrdarya basin

- Selected

Pic. 3

Pic. 4 INTAKES Graphic illustrations (in a cut of month)

The survey scheme of an operative range of territorial managements in Syrdarya basin

Main intake of Golodnostepsko canal, April 1998.

Q, м3/сек Syrdarya

Toktogul

Uzbekistan

Kyrgyzstan

Kalles

200

Parke nt

ca na l

Uzbekistan Чардаринское

Ch rd Sy

BFK

150

Karadarya

100

K UG

Ta ji

kis

ta n

ik stl Du

Arnasay reservoir

Andijan Kayrakkum

a ary

Uzbekistan

LN K

hik irc ry n

Kis ilk um

250

Charvak

Kazakhstan

Na

Aral sea

50 Symbols:

Golodnostep territorial management Boundary of states Naryn-Karadarya territorial management

Canals

Verhnochirchik territorial management

Hydrounit

Management of Toktogul reservoir

Pamp station

Management of Charvak reservoir

Hydrostation

Pic. 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

t, сут

Pic. 6

Construction of such scenarios is the most important link to the creation of effective water balance model of the Syrdarya. Some formulated scenarios are presented below and its discussion should take place between the stakeholders involved in the process of water allocation of transboundry rivers. To assess the level of supply and demand of country of the consumption zone of water for irrigation purposes please explain or cite the algorithm who this was computed, provided that the Toktogul reservoir will be working in the energy mode discharges water; To assess the role of Andijan and Kayrakum reservoirs while meeting the requirements of country of the zone of consumption to cover the deficit of water in the growing season; Determine the water balance for selected reservoirs and watershed areas of transboundry rivers; Evaluate the volume of return water to the selected sites.

THE GRAPHIC ANALYSIS Having chosen object or some objects and the time period it is possible to overlook and analyze dynamics of parameters on time (schedules and the tabulated data): Kayrakkum

Dynamics of filling (realize) water reservoir:

Year

Show

Update Update

1990 1991 1992

1993 1994 1995 1996 1997

млн.м3 2500

2000

Data Data

1500

1993 1994 1995 1996 1997 1998 Year

Andijan Kayrakkum Toktogul Charvak

1000

500

0 I

II

III

IV

1993

I 1443 1389 1342 1132 1145

II 1447 1067 1315 1128 1072

III 1270 723 1079 1142 1008

IV 1251 716 1117 1598 1330

V 1613 1217 1597 1887 1838

VI 1857 1915 1811 1979 1997

V

VI

1994

VII 1969 1941 1569 2002 1822

VII

VIII

1995

VIII 1714 1737 1659 1609 1434

IX

X

1996

IX 1553 1661 1247 1619 1297

X 1708 1657 1219 1414 995

XI

XII

Месяцы

1997

XI 1574 1531 1176 1248 941

XII 2429 1993 2042 2335 1503

Pic. 7 In what sense can such figures help the decision makers to really! Understand the situation much better. Is this more than showing date? What is the essence of the model? What does it compute?

Block of “Instrumentation System” used to search for errors of input variables, as well as the restoration of missing data using analog series of observations or graphic information. Block "GIS" is designed to effectively address the problems associated with the spatial nature of information, and provides delivery information to the user in the most convenient form (maps, charts, tables, etc.). As an example we consider the basin area, which include of Kayrakum reservoir (Tajikistan). Built block of dynamics of water availability of Kayrakum reservoir maybe it is interesting to know how much power is produced and how many % of the total Tajik electricity production this is in which the user is offered a demo version of the regulation of the reservoir or choose options for scenarios. The user can modify the original data. Then based on inputting parameters and the selected scenario is calculated water balance with the actual water releases from reservoirs for irrigation purposes and for purposes of hydropower. In the structure of methodology of decision problem of multi-purpose management of water resources the original approach proposed to solution complex problem of planning and using water resources of the transboundary basin. The first aspect approach related to spatial division territory of considering basin on levels of management. The second determined by multi-purpose problems related with identifying appropriate compromise solutions between states on using water resources

by taking into consideration of economic interests. Combination of multilevel and multidisciplinary goals represents the key moment of methodology offered approach. The specialized applied program of DSS uses of program software MS Office operation system Windows XP & GIS Arc Info/Arc View. Information database is constructed based on MS Access and mathematical models are used for finding of optimal solution. Analysis and representation of spatial data is carried out based on GIS technology. References 1. Arc View GIS. Manual. - M.: Izdatel'stvo Date +. - 368 pp. 2. NAVRUZOV S.T. (1986) Calculation of management rules for a reservoirs cascade of the irrigation-power purpose. Moscow ,Computer Center of the USSR Academy of Sciences. . 3. NAVRUZOV S.T. (1990) On a method of constructing a zone guaranteed return for a linear cascade of reservoirs.Dushanbe, Report of Tajik Academy of Sciences, vol.33, no. 3.. 4. NAVRUZOV S.T. (1991) Qualitative research of a problem of optimum control to a cascade of reservoirs.Dushanbe, Izvestiya of Tajik Academy of Sciences, no. 3 (117).. 5. NAVRUZOV S.T. (2007) Optimization model of management of water and energy resources of transboundary river basin. Dushanbe, Bulletin of the Institute of economy of Tajikistan № 1.,, pp. 82-85. 6. USMANOV Z.D., NAVRUZOV S.T. (2008) Scenario water allocation in the model of transboundary river basin.Dushanbe, Reports of the Academy of Sciences of the Republic of Tajikistan, Vol.51, № 7. , pp.496-500. 7. NAVRUZOV S.T., SHOMURODOV Z.B. (2009) Creating a decision support system in transboundary river basins based on GIS technologies / / Third International Scientific Conference of young scientists and talented students "Water, Ecology and Hydrology security", Proceedings. Moscow, Organized by the Institute of Water Problem of the Russian Academy of Sciences, 16-18 December, pp. 13-15.

FME introduction course What is spatial ETL? How does FME work?

What is ETL? To overcome challenges and capitalize on the value of spatial data, organizations require the ability to: • Efficiently extract spatial data from a desired datastore • Transform it into the requested format and data model • Load it into a target system and present it to end users

ETL, FME concept

Source data

Extract, Transform and Load

Destination data

FME, Workflow

Transformers Source

Destination

Primary FME Components y FME Workbench (FME Workbench offers powerful

transformation and translation capabilities traditionally reserved for custom software solutions) y FME Universal Viewer (Inspect both attributes and geometry, and even check multiple sources in multiple formats) y FME Universal Translator (This is the fastest way to perform translations, simply by dragging and dropping files, and using the supplied defaults.)

FME Universal Viewer 1 2 4

3 5

6

7

8

FME Universal Viewer 1. 2. 3. 4. 5. 6. 7. 8.

Menu bar Toolbar Display control window Tab window View window Information window Log window Status bar

FME Universal Translator 1. Format of input data 2. Dataset of input data 3. Format of output data 4. Dataset of output data 5. Coordinate system of

output data

1 2

3 4 5

FME Workbench

FME Universal Viewer Format of input data Dataset of input data Coordinate system

FME Universal Viewer

FME Universal Viewer Filtering features

FME Universal Viewer Adding another format

FME Universal Viewer

We can see two different formats in one window and we can save this in another format

FME Universal Translator From Geography Markup Language (GML) format we get ESRI Shape format,and if you want change the projection than we need to change coordinate system.

FME Workbench Creating a workspace

FME Workbench

FME Workbench Adding 2DPointReplacer Transformer allow us to create geometrical objects from text file.

FME Workbench

Now we have points and we can see them with the help of Universal viewer

FME Workbench To connect points we use PointConnector Transformer.

FME Workbench Now we see that this is roads, but we don`t know the types of roads but we have attribute features in database and we adding them with Joiner transformer

FME Workbench

FME Workbench

FME Workbench

FME Workbench With the help of AttributeFilter transformer we can classify roads by attribute features

FME Workbench

If we connect Visualizer to MAJOR we will get such result

Carinthia University of Applied Sciences Austria

Theory on GIS & GPS Selected Aspects Dr. Gernot Paulus School of Geoinformation, Carinthia University of Applied Sciences, Villach, Austria.

Content Introduction Definitions

Representations – Data Models Raster Vector

Georeferencing Overview about methods for Georeferencing

Geodata Modelling Lab Exercise ArcGIS Introduction to GPS 2

1. Introduction

Geographical Information Systems and Science Longley P A, Goodchild M F, Maguire D J, Rhind D W (2001) John Wiley and Sons Ltd © John Wiley & Sons Ltd

3

Geoinformation is everywhere ! More than 80 % of all business data used world wide can be seen in a spatial context, e.g. customer adresses, post codes, or locations of industry. 4

Geospatial technology is one of the three most important emerging and evolving job fields, along with nanotechnology and biotechnology! Reference: US Department of Labour; Nature, Volume 427, January 22, 2004. 5

Motivation Application Examples: Road Planning River Basin Management Traffic Flow Optimization International Terrorism – 9/11 Flooding in Europe during summer 2002 Tsunami in Asia in December 2004 Forest fires in Portugal during Summer 2005: more than 170.000 ha forest destroyed, Greece 2007 Hurricanes: „Katrina“, „Rita“, “Ike”,….. Bird Flu

Fast, up to date and accurate analysis and visualization of the spatial situation is crucial for experts and decision makers. 6

Why GIS Matters – Technology for problem solving Almost everything happens somewhere Knowing where some things happen is critically important UN: Position of country boundaries Health Care Management: Location of hospitals Delivery Conmpanies: Routing delivery vehicles Forest company: Management of forest stands Government: Allocation of funds for sea defenses Tourists: Find sights, best route Farmers: bring out fertilizers National park: path maintainance ……. 7

Geographic Information System Organized collection of Hardware Software Network Data People Procedures

Software People Data Network

Procedures Hardware 11

Geographic Information System Technical Definition: Systems for input, storage, manipulation, analysis and visualization of geographic information a combination of software, hardware, data, a user, etc., to solve a problem, support a decision, help to plan 12

GIScience is Multidisciplinary (1) Geographic information technologies cartography, geodesy, surveying, remote sensing, photogrammetry, image processing

Digital technology and information in general computer science (databases, computational geometry, image processing, pattern recognition), information science

that have studied the Earth, particularly its surface and near-surface, in either physical or human aspect geology, geophysics, oceanography, agriculture, biology, environmental science, geography, sociology, political science, anthropology, ... 18

GIScience is Multidisciplinary (2) disciplines that have traditionally studied the nature of human understanding, and its interactions with machines psychology, particularly cognitive psychology, environmental psychology cognitive science, semantics artificial intelligence

19

2. Representing Geography

© John Wiley & Sons Ltd

21

Ancient and historic Representations Hand- drawn maps and speech Hunting and gathering information

Papyrus & paper as first communication media Printing Press - 15th century Knowledge distribution

Age of discovery in early 15th century as important period of geographic representation – maps media for sharing information about discoveries and administrating new colonies. 22

GIS Data Model The aim of a GIS data model is to provide a practical template for implementing GIS projects Constructing data models for GIS applications is the crucial first step of GIS projects. Already some Data Model evailable, e.g. developed by ESRI: ArcHydro, ArcMarine, Atmospheric

Role of Data Modeling Feature Feature

GIS Data Model Description and Representation

Line Line

Polygon Polygon

Building Building

Pump PumpHouse House

Street Street

Water Water Line Line

Operational GIS Analysis and Presentation

House House

People Interpretation and Explanation

Real World

Data Model Levels ANSI/SPARC Scheme (American National Standards Institute/Standards Planning and Requirement Comittee)

Humanoriented

External External Model/Reality Model/Reality Conceptual Conceptual Model Model Logical Logical Model Model

Computeroriented

Physical Physical Model Model

Increasing Abstraction

Modeling Process

Conceptual Model Lists, flow diagrams, etc

External Model Real World Objects and relationships

Logical Model Tables & Diagram in CASE Tool

Physical Model Database Schema (Object state)

CASE: Computer Aided Software Engineering

ANSI/SPARC – External Model 5 different views on the same part of the real world...

Remote Sensing specialist analyses spectral reflexion patterns of a quadratic part pof the earth surface..

Geologist is interested in subsurface rock formations and processes, tectonic structures. Suryeor defines exact boundaries of parcels.

Farmer has a lot of local experience and knowledge about his property.

Ecologist is interested in ecosystems, species distribution patterns, interaction 28 .... Different views can not directly be observed!

ANSI/SPARC – External Model Strategies for capture of focused, user-centered description of real world Expert interviews, literature research

Who will be the main GIS- user of your project, application – categorization - stakeholders? - User - Expert user with high analytical needs - Decision Maker Define project focus, scope – objectives – expected results? - selected user groups - client - involved departments Detailed requirement analysis (Object Catalog): - spatial objects, data types (geometry, attributes) - special analysis functions? - Evaluation of data sources, data formats, corrdinate systems 29 - spatial reference systems of the project?

ANSI/SPARC – Conceptual model E-R Diagram Forest Management (Spatial Data types)

Specific ESRI Data Model examples http://support.esri.com/index.cfm?fa=downloads.dataModels.gateway

Agriculture , Atmospheric , Basemap, Biodiversity, Building Interior Space , Carbon Footprint , CensusAdministrative Boundaries, Defense-Intel , Energy Utilities , Energy Utilities - MultiSpeak TM, Environmental Regulated Facilities , Fire Service, Forestry , Geology , GIS for the Nation, Groundwater , Health , Historic Preservation and Archaeology , Homeland Security , Hydro, International Hydrographic Organization (IHO) S57 for ENC , Land Parcels , Local Government , Marine, National Cadastre , Petroleum , Pipeline , Raster , Telecommunications , Transportation , Water Utilities

Example ArcHydro http://support.esri.com/index.cfm?fa=downloads.dataModels.gateway

The Fundamental Problem Geographic information links a place (geometry), and often a time, with some property (attributes) of that place (and time) “The temperature at 34 N, 120 W at noon local time on 12/2/99 was 18 Celsius”

The potential number of properties is vast In GIS we term them attributes Attributes can be physical, social, economic, demographic, environmental, etc.

GIS: GEOMETRY + ATTRIBUTES + TIME 36

5 Types of Attributes Nominal, e.g. land cover class, colour -

Classify

Ordinal, e.g. a ranking of soil quality - Order Interval, e.g. Celsius temperature - Measure Differences make sense

Ratio, e.g. Weight, distance, Kelvin temperature - Measure Ratios make sense

Cyclic, e.g. wind direction in – Measure 360° scale

37

Cyclic Attributes Do not behave as other attributes What is the average of two compass bearings, e.g. 350 and 10?

Occur commonly in GIS Wind direction Slope aspect Flow direction

Special methods are needed to handle and analyze

39

Discrete Objects and Fields – 2 conceptualized views of the real world Two ways of categorizing geographic variation Discrete objects Objects with well-defined boundaries in empty space Points (fire hydrants), lines (roads), areas (zones) Use vector representations in GIS

Fields Properties that vary continuously over space Elevation, Temperature, Air pressure Use raster representations in GIS 40

Field vs Discrete Object Representation FIELD: Objects of real world as continuous gray scale variation – Satellite Image

DISCRETE OBJECT: Objects of real world as geometrically distinct polygon objects 41

Discrete Object View World is empty except where it is occupied by objects. Points, lines, and areas – dimensionality Points (0-dim); lines (1-dim.), polygons (2-dim)

Countable Persistent through time, perhaps mobile Biological organisms Animals, trees, persons, cars

Human-made objects Vehicles, houses, fire hydrants,….

42

Continuous Field View World described by variables measurable at any point on the earth‘s surface. Variables (properties, attributes) that vary continuously over space Value is a function of location Property can be of any attribute type, including direction

Elevation as the archetype A single value at every point on the Earth’s surface The source of metaphor and language • Any field can have slope, gradient, peaks, pits

43

Examples of Fields Soil properties, e.g. pH, soil moisture Population density But at fine enough scale the concept breaks down

Identity of land owner A single value of a nominal property at any point

Name of county or state or nation Atmospheric temperature, pressure

44

Phenomena conceptualized as fields. The illustration shows elevation data from the Shuttle Radar Topography Mission draped with an image from the Landsat satellite, looking SE along the San Andreas Fault in Southern California, plus a simulated sky

Difficult Cases – Which to choose? Lakes and other natural phenomena Often conceived as objects, but difficult to define or count precisely Boundary Problem! Task: Count the number of lakes in Louisiana?

Weather forecasting Forecasts originate in models of fields, but are presented in terms of discrete objects • Highs, lows, fronts 46

Rasters and Vectors – 2 methods of representing spatial data in digital computers spatial entity location

attributes

basic geographical data primitives

Vector

Raster

47

Raster Data – “Top Down approach” Divide the world into square cells Register the corner (Center of upper left pixel) to the Earth Represent discrete objects as collections of one or more cells Represent fields by assigning attribute values to cells More commonly used to represent fields than discrete objects

(x,y)

Each color represents a different value of a nominal-scale field denoting land cover class.

48

Electromagnetic spectrum

49

50

Raster and Vector Models

Raster Structure and Metadata Array of cells (pixels) Stored as compressed file

1 attribute value per pixel (categories, integer, floating point numbers), assignment scheme Metadata (header file) Coordinate values of center of upper left pixel (location on earth surface) Pixel size (resolution) Number of rows and columns (dimension) Projection

(x,y)

N columns

N rows

pixel size (e.g. 5m)

Characteristics of Rasters Pixel size The size of the cell or picture element, defining the level of spatial detail All variation within pixels is lost

Assignment scheme Real world objects, measurements and observations are mapped on a pixel – „Averaging effect“ „“Mixed pixel problem“ • The value of a cell may be an average over the cell, or a total within the cell, or the commonest value in the cell • It may also be the value found at the cell’s central point • Important issue in remote sensing – e.g. urban environment with a lot of variation and heterogeneous spectral values 53

The mixed pixel problem

W ater dominate s

W inner takes a ll

E dges s epa rat e

W W

G

W G

G

W

E

G

W W

G

W W

G

W

E

G

W W

G

W G

G

E

E

G

(Zaslovsky, 2004)

The mixed pixel problem

Largest Share Rule

Center Point Rule

Vector Data – “Bottom up approach” Used to represent points, lines, and areas All are represented using coordinates defined by a coordinate system. set (xi,yi) (A1,A2,…An)

set (xi,yi) (A1,A2,…An) y

A1, A2,…An

y

y

(xi, yi)

x

x

x

56

Vector – Simple Geometry types

Point

Line

Polygon

Volume

Each vector geometry is a unique Object-ID assigned Used as „Primary Key“ and „Index“ in a relational database Link to corresponding attribute properties.

57

58

Raster vs Vector Volume of data Raster becomes more voluminous as cell size decreases

Source of data Remote sensing elevation data come in raster form Vector favored for administrative data

Software Some GIS better suited to raster, some to vector E.g. IDRISI vs. ArcView

59

Vector vs. Raster Representation

Real World

Discrete Object View

Vector Representation

Raster Representation (Heywood et al. 2006)

60


(Heywood et al. 2006)

61


(1) River in real world (2) Raster – Pixel (3) Vector: Simple Line (4) Vector: Complex line (5) Vector: Network (6) Vector: Polygon

62

Raster vs Vector Representation Raster

Vektor

Volume of data

Depends on cell size

Depends on density of vertices

Sources of data

Remote sensing, scanning, imagery, elevation

Survey, administrative, social, infrastructure

Applications

Resources, Environmental

Social, economic, administrative

Resolution

Fixed

Variable

Software

Raster GIS, e.g. IDRISI, ArcGIS Spatial Analyst, Geomedia Grid

Vector GIS, e.g. GeoMedia, ArcView 63

Summary Vector - Raster

Real World Reality

Objects or Entities

Smooth, continuous spatial variation

Set of discrete objects, their attributes and relations

Continuous Conceptual view smooth fields

Sets of simpler objects (atomic entities), their attributes and relations (vector data models)

tessellation (raster data models, TINs) continuous math. functions

GIS Data Models for digital representation

64

Geo-representation problems Defining what needs to be represented Accuracy of representation Volume of data required Fundamental problem Linking an “event” to a place and time Event properties are termed attributes World is vast & infinitely complex How do you represent it properly? 67

Conclusions GIS represents real world phenomena Uses (digital) models Many different types (raster, vector etc.) Simplifies the real world Breaks it into objects, fields etc. Used to answer questions / solve problems Representations vary depending on goals 68

3. Georeferencing

© John Wiley & Sons Ltd

Outline Introduction Placenames Postal addresses and postal codes Linear referencing systems Cadasters Coordinate System: Geographic Coordinate Systems (latitude, longitude) Projected Coordinate systems (x,y) 70 Converting georeferences

Commonly used georeferencing systems

71

Placenames The earliest form of georeferencing And the most commonly used in everyday activities

Many names of geographic features are universally recognized Others may be understood only by locals

Names work at many different scales From continents to small villages and neighborhoods Non-metric, can be very coarse e.g “Europe”

Names may pass out of use in time Where was Camelot, (…the ancient castle of King Arthur in Cornwall)? 72

Postal Addresses and Postcodes Every dwelling and office is a potential destination for mail Dwellings and offices are arrayed along streets, and numbered accordingly Streets have names that are unique within local areas Local areas have names that are unique within larger regions If these assumptions are true, then a postal address is a useful georeference Global, non-metric, spatial resolution may correspond to the size of one mailbox Many ways to write an address – Uniqueness? Address Geocoding: transforming the address (nonmetric) in a point location defined by coordinates 73 (metric).

Where Do Postal Addresses Fail as Georeferences? In rural areas Urban-style addresses have been extended recently to many rural areas

For natural features Lakes, mountains, and rivers cannot be located using postal addresses

When numbering on streets is not sequential E.g. in Japan

74

Positional uncertainty concerning postal addresses representing “mailbox coordinates”

75

Postcodes as Georeferences Defined in many countries E.g. ZIP codes in the US • five-digit ZIP code ZIP (Zoning Improvement Plan) code instituted by the U.S. Postal Service to facilitate mail handling and delivery. • The first digit represents one of ten areas of the country (0 = New England, 9 = West Coast). • The first three digits together represent a sectional center facility or main post office. The last two digits further define the destination point in terms of a post office or delivery center area within a large city or in terms of a small city or town whose residents share the same ZIP code.

Hierarchically structured The first few characters define large areas Subsequent characters designate smaller areas Coarser spatial resolution than postal address

Useful for mapping 77

ZIP code boundaries are a convenient way to summarize data in the US. The dots on the left have been summarized as a density per square mile on the right

Spatial Distribution of Middle/High School Students School Catchement Area BG/BRG St. Martin, Villach, 2005

79

Coordinate Systems: Geographic Coordinate System: Latitude/ Longitude

84

Coordinate Systems: Geographic Coordinate System: Latitude/ Longitude The most comprehensive and powerful method of georeferencing Metric, standard, stable, unique

Uses a well-defined and fixed reference frame Based on the Earth’s rotation and center of mass, and the Greenwich Meridian

85

Latitude and Longitude

Source: http://www.gs-enduro.de/html/navigation/karte.htm 86

Definition of Longitude Prime Meridian at Royal Observatory at Greenwich, London North Pole

Equator Greenwich

Definition of longitude. The Earth is seen here from above the North Pole, looking along the Axis, with the Equator forming the outer circle. The location of Greenwich defines the Prime Meridian. The longitude of the point at the center of the red cross is determined by drawing a plane through it and the axis, and measuring the angle between this87plane and the Prime Meridian.

Definition of Latitude Requires a model of the Earth’s shape The Earth is somewhat elliptical The N-S diameter is roughly 1/300 less than the E-W diameter More accurately modeled as an ellipsoid than a sphere An ellipsoid is formed by rotating an ellipse about its shorter axis (the Earth’s axis in this case)

88

Latitude and the Ellipsoid N

E

W

S

Latitude (of the blue point) is the angle between a perpendicular to the surface and the plane of the Equator WGS 84 Radius of the Earth at the Equator 6378.137 km Flattening 1 part in 298.257 89

Geoid – Ellipsoid- Geodetic Datum

North pole

South pole Source: http://www.gs-enduro.de/html/navigation/karte.htm 90

Geodetic Datums: What are they? Define the size and shape of the earth Used as basis for coordinate systems Variety of models: Flat earth Spherical Ellipsoidal

WGS 84 defines geoid heights for the entire earth

The History of Ellipsoids Because the Earth is not shaped precisely as an ellipsoid, initially each country felt free to adopt its own as the most accurate approximation to its own part of the Earth We must distinguish between national ellipsoids &

global ellipsoids

Today an international standard has been adopted known as WGS 84 Its US implementation is the North American Datum of 1983 (NAD 83) Many US maps and data sets still use the North American Datum of 1927 (NAD 27) Differences can be as much as 200 m 92

Longitude & Latitude & Distance Lat –Lon are equally far apart at the Equator; towards the poles lines of longitude converge Longitude (-180 ≤ λ ≤ + 180) Shortening towards the north pole (≈ cosine of latitude) 1° long (Equator) = 111km 1° long (60° N (North Boundary Alberta province, CA) = 55 km

Latitude (-90 (S) ≤ θ ≤ + 90 (N)) 2 points on same degree longitude separated by: • 1° lat: 111km • 1‘ lat: 1.86 km (1 nautical mile) 93 • 1‘‘ lat: 30m

Coordinate Systems: Projected Coordinate Systems: Projections and Coordinates There are many reasons for wanting to project the Earth’s surface onto a plane, rather than deal with the curved surface Cartesian Coordinate system The paper used to output GIS maps is flat Flat maps are scanned and digitized to create GIS databases Rasters are flat, it’s impossible to create a raster on a curved surface The Earth has to be projected to see all of it at once It’s much easier to measure distance on a plane

94

Distortions Any projection must distort the Earth in some way Two types of projections are important in GIS Conformal property: Shapes of small features are

preserved: anywhere on the projection the distortion is the same in all directions Equal area property: Shapes are distorted, but features have the correct area Both types of projections will generally distort distances 95

Cylindrical Projections Conceptualized as the result of wrapping a cylinder of paper around the Earth The Mercator projection is conformal

96

Conic Projections Conceptualized as the result of wrapping a cone of paper around the Earth Standard Parallels occur where the cone intersects the Earth

97

The Universal Transverse Mercator (UTM) Projection (Global) Developed 1940‘s by US Army Corps of Engineers A type of cylindrical projection Implemented as an internationally standard coordinate system Initially devised as a military standard

Uses a system of 60 zones Maximum distortion is 0.04%

Transverse Mercator because the cylinder is

wrapped around the Poles, not the Equator 98

N

S

Zones are each six degrees of longitude, numbered as shown at the top, from W to E

Comparison of 3 different map projections

103

Coordinate Systems - Summary Coordinate System Types: Geographic (lat, lon) • Ellipsoid (geodetic datum): Model of the earth – Global: WGS 84

Projected (x

(easting),y (northing))

• Projection algorthim, e.g. Gauss Krüger Projection • Ellipsoid (geodetic datum) – Global: e.g. WGS 84 – Local/national: e.g. Bessel 1841 (Austria)

104

Georeferencing Raster Datasets

Overview Why georeferencing raster datasets? Workflow Overview Ground Control Points Raster Transformation Quality assessment of transformation

Mean Square Error)

Resampling process

World file

(Root

Why georeferencing raster datasets? Raster data are obtained by Scanning maps Aerial photographs Satellite images

In order to align raster with other data in your project, spatial reference information (position of raster in relation to real world) is needed Georeferencing the raster to a map coordinate system

Spatial reference information Scanned maps No real world spatial reference information!

„Scanner geometry“

Aerial photographs & satellite images

N columns

May have spatial reference information in a separate header file Proprietary formats N rows Sometimes incomplete pixel size (e.g. 5m)

General steps for georeferencing a raster Use of existing spatial data with known map coordinate system (real world coordinates) Identify ground control points • Can be accurately definied on raster and in real world coordinates

Perform raster registration • If alignment quality is sufficient (RMS Error check) • Select transformation & resampling method

Permanent transformation of raster

Ground Control Points Accurately definied position on raster and in real world coordinates Often vector data, but also already georeferenced raster (e.g. orthoimage) can be used

Look for significant recognizable locations on raster • Road or stream intersections • Rock outcrops • Mouth of a stream • „Corner of a field“ (– really?), street or building

Ground Control Points Position of control point on raster (source)

Position of control point in real world (target)

Ground Control Points Use enough ground control points Spread over the entire raster

(“tent poles“)

• At least one at corner regions, and several in interior • Number depends on raster size

Accuracy of raster georeferencing is only as accurate as the data (e.g. vector data, orthoimage) which are used for the transformation (Motto: „Poor input, poor output“).

Raster Transformation Process of matching the raster to map coordinates of the target data („Rubber sheeting“) Different transformation algorithms/equations Polynominal (1st i.e. affine; 2nd; 3rd order) • Affine 1st order commonly used for georeferencing rasters • Global optimization

Spline • local optimization

Adjust

Raster Transformation (Example affine 1st order polynominal transformation)

x´,y´: New calculated coordinates of center of upper left pixel of raster

General formula (Least Squares fitting algorithm ) in order fit all control points (global accuracy, not a local one!)

Quality assessment of Transformation Interpretation of Root Mean Square Error (RMS)

Global adjustment optimization – not all control points may be matched to 100% Residual positional difference (in map units, e.g. m) between true position of a control point and calculated position by transformation algorithm. Calculated for each control point Total Error is Root Mean Square sum of all the individual residuals , ie. RMS Value Assessment for quality of transformation

RMS Error The RMS error measures the errors between the destination control points and the transformed, new locations of the source control points Minimum of 3 control points is needed to calculate a RMS Error

Resampling Process of changing the geometry of a raster data set and adjusting cell values Georeferencing („Rubber Sheeting“) Change in Projection Translation, Rotation Change in cell size

Resampling Cell values must be also adjusted! During georeferencing, a matrix of empty cells is computed using map coordinates Each empty cell is given a value based on original values in the ungeoreferenced data set and the resampling

process

Resampling techniques: • Nearest neighbor – Doesn‘t change input values – Nominal and ordinal data, e.g. land use, forest type

• Bilinear interpolation – Uses values of the 4 nearest input cells to determine the new value in the output raster – Continuous data, e.g. elevation, temperature

• Cubic convolution – 16 nearest input cells; aerial photography, satellite images

World file Georeferencing information for raster data sets (ESRI Format)

The y-scale (E) is negative because the origins of an image and a geographic coordinate system are different. The origin of an image is located in the upper left corner, whereas the origin of the map coordinate system is located in the lower left corner.

World file Georeferencing information for raster data sets (ESRI Format) After rectification and resampling, world file is assigned Simple text file accompanying an image file with the following naming convention • Same name as image file, but „w“ at end of file name

Georeferencing a raster data set in ArcGIS View video in ArcGIS Help Open ArcMap > ArcGIS Desktop Help > Search „georeferencing raster“

Intro Lab „Earthquake Risk Zones Tadjikistan“ • Task: Visualize Earthquake Riskzones in Tadjikistan using simples GIS techniques -

Natural Hazards Data Base on EARTHQUAKES: world_earthquakes.shp Administrative boundaries of Tadjikistan: TJK_admin0-3.shp

WWW.FH-KAERNTEN.AT

Intro Lab „Earthquake Risk Zones Tadjikistan“

WWW.FH-KAERNTEN.AT

Introduction to GPS

What is GPS? The Global Positioning System (GPS) A Constellation of Earth-Orbiting Satellites Maintained by the United States Government for the Purpose of Defining Geographic Positions On and Above the Surface of the Earth. It consists of Three Segments: User Segment Control Segment Space Segment

GPS – System Overview

GPS – Global Positioning System • Space Segment -

30 Satellites 20200 km 12 h Orbital 6 Inclinations (with 60° offset) Every time at every point 4 Satellites visible

• Control Segment -

5 Control Stations Observation and Synchronization der Sat-Clocks Transmitting of orbit information to the satellites

• User Segment -

Receiver WWW.FH-KAERNTEN.AT

Common Uses for GPS • Land, Sea and Air Navigation and Tracking • Surveying/ Mapping • Military Applications • Recreational Uses 179

How the system works Space Segment 24+ Satellites

The Current Ephemeris is Transmitted to Users

Monitor Stations

Diego Garcia Ascension Island Kwajalein Hawaii Colorado Springs

GPS Control

End User

Colorado Springs

GPS – Global Positioning System • Determination of Position -

Trilateration Distance measurement between satellites & receiver is based on signal runtime At least 3 Distances for x and y (Satellites) At least 4 Distances for x, y and z (Satellites) Position accuracy adjustment with every additional satellite (distance).

• Signals( 2 carrier frequencies L1 and L2) -

Navigation and system information is modulated on the carrier P-Code C/A Code WWW.FH-KAERNTEN.AT

Triangulation Satellite 1

Satellite 3

Distance Measuring The whole system revolves around time!!! Distance = Ratemiles x Time Rate = 186,000 per second (Speed of Light) Time = time it takes signal to travel from the SV to GPS receiver

Satellite 2

Satellite 4

Each satellite carries around four atomic clocks Uses the oscillation of cesium and rubidium atoms to measure time Accuracy? plus/minus a second over more than 30,000 years!!

SV and Receiver Clocks • SV Clocks 2 Cesium & 2 Rubidium in each SV $100,000-$500,000 each

• Receiver Clocks Clocks similar to quartz watch Always an error between satellite and receiver clocks ( Δ t)

• 4 satellites required to solve for x, y, z, and Δ t 184 ESSC 541 542

4 SOLUTION

• PROBLEM

Can’t use atomic clocks in receiver Cesium Clock = $$$$$$$!!! Size of PC

– Receiver clocks accurate over short periods of time – Reset often – 4th SV used to recalibrate receiver clock

Breaking the Code The Carrier Signal...

Transmission Time

combined with… The PRN code...

Satellite

produces the Modulated carrier signal which is transmitted... demodulated...

Receiver

And detected by receiver, Locked-on, but With a time delay...

Time delay 186

Sources of Error • Selective Availability o Intentional degradation of GPS accuracy o 100m in horizontal and 160m in vertical o Accounted for most error in standard GPS o Turned off May 2, 2000

187

Sources of Error • Geometric Dilution of Precision (GDOP) Describes sensitivity of receiver to changes in the geometric positioning of the SVs

• The higher the DOP value, the poorer the measurement QUALITY Very Good Good Fair Suspect

DOP 1-3 4-5 6 >6 ESSC 541 542

188

Sources of Error • Clock Error Differences between satellite clock and receiver clock

• Ionosphere Delays

nal g i S l

d a gn ct e e Si l f t c Re re Di GPS Antenna

Caused by local reflections of the GPS signal that mix with the desired signal

Re fle cte d

Sig

• Multipath Error

Satellite

na l

Delay of GPS signals as they pass through the layer of charged ions and free electrons known as the ionosphere.

Hard Surface 189 ESSC 541 542

GPS – Global Positioning System • SPS (Standard Positioning Service) -

Public available Originally 100 m accuracy Since May 2000 15 m accuracy (US military switched off artificial degradation (i.e. Selective Availability)

• PPS (Precise Positioning Service) -

Available for the US military Originally 22 m accuracy, but current accuracy is unknown. Signals are encrypted

WWW.FH-KAERNTEN.AT

GPS – Global Positioning System • Accuracy factors -

Runtime errors of satellite signals Reflections on objects and earth surface (multi-path effects) To few satellites because of hidden horizon (in valleys or “street canyons”) Signal attenuation by dense vegetation (especially in the Forrest) Clock errors a) specially of the GPS-receivers! b) Satellites have atomic clocks!

WWW.FH-KAERNTEN.AT

Differential GPS • Method of removing errors that affect GPS measurements • A base station receiver is set up on a location where the coordinates are known • Signal time at reference location is compared to time at remote location • Time difference represents error in satellite’s signal • Real-time corrections transmitted to remote receiver Single frequency (1-5 m) Dual frequency (sub-meter)

Reference location

Remote location

= Error

• Post-Processing DGPS involves correcting at a later time

GPS – Global Positioning System

GPS-Satellite

db1 + eb1

• DGPS Approach

d + e? db2 + eb2

eb1 Base station

eb2 Error Base station Correction D = F(d, eb1, eb2, …) + optional WAAS, EGNOS or MSAS

Error Correction

GPS – Global Positioning System • NAVSTAR GPS -

USA – Global Positioning System – 31 Satellites

• GLONASS -

Russia – 24 Satellites (since September 2010, planned 30 Satellites)

• GALILEO -

EU – 2 Satellites for Tests (planned 30 Satellites)

• KOMPASS -

China – planned 35 Satellites

WWW.FH-KAERNTEN.AT

GPS – Global Positioning System • Applications -

Car navigation (z.B.TomTom) Theft protection (in combination with GSM) Outdoor Sport – Trekking, Biking, Running, … Seafaring (Standard) Aviation (Standard) Mobile Mapping (GIS) Precision Farming Fleet management (transport logistic) Land survey … WWW.FH-KAERNTEN.AT

Geocaching • A great way to introduce students to GPS - Teach latitude/longitude - Take advantage of the wonderful features and capability of your GPS unit

• An entertaining adventure for GPS users - Individuals and organizations all over the world have set up caches and shared the locations of these caches on the Internet - Participate in a cache hunt to find an existing cache or create your own

• www.geocaching.com WWW.FH-KAERNTEN.AT

“Mobile Mapping” • Integrates GPS technology and GIS software • Makes GIS data directly accessible in the field • Can be augmented with wireless technology

WWW.FH-KAERNTEN.AT

Practical session To learn about the capability of GIS analysis some practical tasks will explain how to work with tools out of ArcGIS. The tasks begin with a familiarization of the software environment and lead over data capturing to data analysis and visualization skills. Some practical GPS measurements will be the base for the georeferencing of an aerial image of a part of Dushanbe. The following six tasks should give an introduction to the use of spatial analysis tools of ArcGIS 9.3. After completing Task 1- 6 a map layout will be created as one publication option of spatial data. Following assignments have to be done: Task 1: Familiarization with the given spatial data Task 2: Georeferencing of given aerial images and measuring of image related attributes Task 3: Proving that more than one half of Tajikistan is situated above 3000m Task 4: Calculation of the mean height above sea level of neighboring countries Task 5: Investigation of connectivity of Tajikistan to its capital Dushanbe Task 6: Detection of potential solar power areas plants within Tajikistan After these GIS analysis a map out of one of these 6 tasks will be produced. For every task the requested results and assistance how to come to these results is added to the problem definition. The required spatial data will be provided by the lecturer.

Practical Session: Task 1 Problem definition: Familiarize with the given spatial data. Requested results: •

List all spatial data sets and give information about data type – raster, vector – and about the spatial reference system.

Assistance: Data investigation within ArcMap • • • •

:

File > Add Data… or Right mouse click on data set > Open Attribute Table Right mouse click on data set > Zoom To Layer Right mouse click on data set > Properties…

Data investigation within ArcCatalog : • Navigate to the data set. • Select Contents/Preview/Metadata tab. • Right mouse click on data set > Properties…

. . .

.

Practical Session: Task 2 Problem definition: Do a georeferencing of the given aerial images and measure image related attributes. • • • •

Use the self-measured GPS reference points to georeference the potanical_garden.tif. Use corresponding matching points between potanical_garden.tif and university.tif to georeference the university.tif image. Measure the spatial size, length and width in meters, of the aerial images. Measure the geometrical resolution, length and width in meters of a pixel, of the aerial images.

Requested results: •

Specify the spatial size and the geometrical resolution of the given aerial images.

Assistance: Show Georeferencing Toolbar in ArcMap for georeferencing images: • View > Toolbars > Georeferencing. • •

New Toolbar: Select aerial image to georeference in drop-down menu Layer.

In case you have control point coordinates: • Add a control point in the aerial image; single left mouse click! • Single right mouse click to control point context menu:

•

.

.

Enter self-measured GPS coordinates: (X = Longitude in DD, Y = Latitude in DD) .

In case you use corresponding matching points: • Add a control point in the ungeoreferenced aerial image; single left mouse click; you have set a green cross. • Add a matching point in the georeferenced aerial image; single left mouse click; you have set a red cross. Show Tools Toolbar in ArcMap for distance measurement: • View > Toolbars > Tools.

• •

New Toolbar: Start measuring:

. .

Practical Session: Task 3 Problem definition: Prove that more than one half of Tajikistan is situated above 3000m – as it is said in travel guides. Requested results: •

Contrast the size of area below 3000m with the size of area above 3000m. Quote the absolute size in square kilometers and the relative size referred to the total size of Tajikistan in percent.

Assistance: Show Toolbox window in ArcMap for a variety of functions: . • Window > Toolbox Create a raster data subset masked by a polygon: • Toolbox > Spatial Analyst Tools > Extraction > Extract by Mask. • Input raster: initial raster data set. • Input raster or feature mask data: masking polygon data set. Find raster cells above a special value: • View > Toolbars > Spatial Analyst. • • •

New Toolbar: Spatial Analyst > Raster Calculator… Express a pseudo SQL statement and execute it. A new raster with true (1) and false (0) values results.

Get statistic information: • Toolbox > Spatial Analyst Tools > Zonal > Zonal Statistics as Table. • Input raster or feature zone data: Result of Raster Calculator. • Zone field: VALUE (1 for true and 0 for false). • Input value raster: Result of Raster Calculator. • Open resulting table in ArcCatalog.

Practical Session: Task 4 Problem definition: Calculate the mean height above sea level of all countries which name ends on ‘-stan’ and which is neighbor to Tajikistan including Tajikistan. Requested results: •

List all detected countries including their mean height above sea level.

Assistance: Select features by attribute values: • Selection > Select By Attributes. • Layer: Country data set. • Method: Create a new selection. • Express the Where clause of the SQL statement and apply. Export selected features as new shape file (It will be necessary to export Tajikistan form the world country data set!): • Right mouse click on data set > Data > Export Data… • Export: Selected features. • Use the same coordinate system as: this layers’ source data Select features by location: • Selection > Select By Location. • I want to: select from the currently selected feature in (If the result of the previous selection is still active!). • the following layer(s): Country data set. • that: touch the boundary of. • the feature in this layer: Tajikistan data set Get statistic information: • Toolbox > Spatial Analyst Tools > Zonal > Zonal Statistics as Table. • Input raster or feature zone data: Features of the requested countries (data export). • Zone field: NAME. • Input value raster: Digital elevation model of the region. • Open resulting table in ArcCatalog.

Practical Session: Task 5 Problem definition: Investigate the connectivity of Tajikistan to its capital Dushanbe based on the slope values of the terrain as cost indicator. • • • • •

Create a new point shape file and digitize the city of Dushanbe. Calculate a slope model based on the given digital elevation model. Do a general area based distance investigation to Dushanbe using the slope values as cost indicator. Create a new point shape file and digitize four arbitrary cities of Tajikistan. Calculate the shortest routes from the four cities to Dushanbe based on the slope model.

Requested results: • •

Create a map which shows the connectivity of Tajikistan to its capital. Show four shortest routes from the cities to Dushanbe.

Assistance: Create a new point shape file within the ArcCatalog: • Right mouse click on the folder where you want to create a new shape file > New > • • • • •

Shape file… . Name: arbitrary, but wise. Feature Type: Point. Edit… for Spatial Reference System > Select… > Geographic Coordinate System > World > WGS 1984.prj Several times OK Right mouse click on new created shape file > Properties… > Fields: Type in a new Field Name for city names from Data Type Text.

Show editing toolbar for digitizing new features: • View > Toolbars > Editor •

New Toolbar:

Creating new features in point shape file: • Editor > Start Editing • Check if Source is highlighting the folder which contains the new created shape file. Otherwise click on the correct folder. • Editor toolbar activates more menu buttons. • Task: Create New Feature. • Target: new created shape file. • •

Click on the sketch tool to start digitizing Click on the places you want to digitize in the map. One click, one new feature!

• •

After digitizing click on Attributes button to add attribute information, e.g. name. Editor > Save Edits and Editor > Stop Editing quits edit mode.

Calculate a slope model: • Activate the Spatial Analyst Toolbar (cp. Task 3). • Spatial Analyst > Surface Analysis > Slope… • Input surface: Digital elevation model of Tajikistan. Calculate distances: • Activate the Spatial Analyst Toolbar (cp. Task 3). • Spatial Analyst > Distance > Cost Weighted… • Distance to: City of Dushanbe point shape. • Cost raster: slope model. • Activate Create direction and Create allocation for following route computation. Calculate routes: • Activate the Spatial Analyst Toolbar (cp. Task 3). • Spatial Analyst > Distance > Shortest Path… • Path to: Point shape of four digitized cities. • Cost distance raster: Results from distance calculation. • Cost direction raster: Results from distance calculation. • Path type: For Each Cell.

Practical Session: Task 6 Problem definition: Detect all areas within Tajikistan which can be seen as potential locations for solar power plants. • • • • • •

Calculate a slope model based on the given digital elevation model. Calculate an aspect model based on the given digital elevation model. Areas which hold slope values between 10 and 30 degrees and aspect values between south east and south west can be seen as optimal. Potential locations should be placed near areas of high population – maximum distance of 50km. Do detect highly populated areas use the nightlight data set. Create a data subset of nightlights raster for Tajikistan. Potential location should be placed near road infrastructure – maximum distance of 15km. Create a data subset of roads for Tajikistan. The size of a single potential location should not be smaller than 10km²

Requested results: • •

Create a map which shows the connectivity to the major centers of Tajikistan. Show four shortest routes from the cities to Dushanbe based on the minimum slope values of the terrain.

Assistance: Calculate an aspect model: • Activate the Spatial Analyst Toolbar (cp. Task 3). • Spatial Analyst > Surface Analysis > Aspect… • Input surface: Digital elevation model of Tajikistan. Create a raster data subset masked by a grid value: • Toolbox > Spatial Analyst Tools > Extraction > Extract by Attributes. • Input raster: initial raster data set. • Where clause: Express a SQL-Where clause e.g. “VALUE” > 0. Convert raster to polygon features: • Toolbox > Conversion Tools > From Raster > Raster to Polygon. • Input raster: Raster to convert. • Field: Value for polygon attribute field. Create a vector data subset masked by a polygon (For extraction of the Tajik road infrastructure): • Combine Select By Location and export selected feature as new shape file. or • Toolbox > Analysis Tools > Overlay > Intersect. • Input Features: Polygon of the Tajik country and Line of the Asian road infrastructure.

Create a buffer around a vector data set: • Toolbox > Analysis Tools > Proximity > Buffer. • Input Features: Vector data set. • Linear unit: 50 / 15 Kilometers. Convert a vector data set to a raster data set: • Toolbox > Conversion Tools > To Raster > Polygon to Raster. • Input Features: Buffer of the centers of Tajikistan / Buffer of Tajik roads. • Value field: GRIDCODE • Cellsize: should be same size as digital elevation model (e.g. 882). Upgrade vector data set – Area size attribute: • Right mouse click on vector data set > Open Attribute Table. • Options > Add Field… • Name: area • Type: Float • OK • Right mouse click on headline of the new area attribute > Calculate Geometry… • Property: Area • Units: Square Kilometers.

Map Layout Problem definition: Create a map out of one of the results from Task 1 – 6. Requested results: • • •

Complete map layout with map features like title, north arrow, scalebar, legend, overview map, copyright information and a brief description of the map content. Technical report with a workflow description Prepare a short presentation of the generated map for the closing ceremony on Friday, 24th afternoon.

Assistance: Preparation of Overview map: • Insert a second Data Frame for the overview map with Insert > Data Frame (see Fig. 1) • •

File > Add Data (e.g. country boundaries) … or Right mouse click on data set > Properties…

• •

Layer Properties > Label … Layer Properties > Symbology > Show: Categories > o Unique values, many fields

•

.

> ‘Tajikistan’ o Add Values o Change color of Tajik polygon Right mouse click on New Data Frame > Properties > Extent Rectangles

•

Add Layer with frames’ list

•

Click Frame Button

to ‘Show extent rectangle for these data to format the rectangle

Fig. 1: Insert Menu

Layout of the map • Change to Layout View with View > Layout View … • Insert Title of the map with Insert > Title (see Fig. 1) • Insert the same way Legend, North Arrow, Scale Bar, Scale Text • Insert Text with a copyright information and a brief description of the map content • Arrange a attractive design of the map Export of the map • File > Export Map • Choose PDF as file format and edit the export options Technical report and presentation • TR: Description of workflow to generate the map with screenshots of the main tasks • Presentation of max. 5 slides for the presentation of the map

Dushanbe GIS Training Hydrological Run-off Modelling for Determination of Hydroelectric Potential in ArcGIS, SAGA and GRASS

Andrew D. Smith Department of Geodesy and Geoinformatics Kyrgyz State University of Construction, Transport and Architecture

A couple of words on raster (or cell based) processing z

Raster vs vector (include why raster)

z

Types of raster analysis −

Cell arithmetic (one or more rasters)

−

Neighbourhood processing

−

Zonal processing

−

Modelling of movement

−

Interpolation

Vector/Raster? GIS data can be represented in either raster or vector format. Vector format represents data as objects and has three different types:- Point - Line and - Polygon (or Area)

This diagram shows how the same objects are represented in both vector and raster format

Raster format is like a digital photo and uses cells (or pixels) to represent data.

Raster/Vector Raster

Vector

type of data

discreet and continuous

discreet

boundary representation

fuzzy

exact

file size

large

small

3200 m pixel 15 KB

Raster Resolution

1600 m pixel 60 KB 800 m pixel 239 KB

400 m pixel 960 KB 200 m pixel 3.75 MB

Sm Mo all Mo re er c re det ell m ail /pix em el or siz y e

Two Types of Raster Data z

Continuous Elevation

z

Discrete Landcover

Regions

Population

Cell Arithmetic Raster 1

+

Raster 2

= Raster 3

Source of Diagrams – ESRI: Using ArcGIS Spatial Analyst 9

Neighbourhood, or Focal, Processing Neighbourhood functions can return the sum, mean, maximum, minimum, standard deviation for the cells in the immediate or extended neighbourhood

e.g. 24 is the sum of all the surrounding cells

So for the whole raster the result would look like this diagram

Neighbourhood functions are often used for filtering data Source of diagrams ArcGIS Desktop Help http://webhelp.esri.com/

Neighbourhood, or Focal, Processing example Focal Mean

Focal mean is often used to filter a dataset

Zonal Processing Zonal functions uses two input layers and is useful for example: z

z

z

to calculate the populations per oblast Or highest point in each oblast Or the total rainfall in each oblast

Source of diagrams ArcGIS Desktop Help http://webhelp.esri.com/

Value Raster

Zonal Processing example Zonal Sum Result Raster

Zones Raster

Modelling of movement To model the flow of water we don't need to look at the entire area first off – we consider only how water will flow from one cell to the next for example:If the diagram left represents a DTM (height) then water will flow from one cell to the lowest of the neighbouring cells (e.g. 55 to 35) and from there it will continue to flow to the lowest cell. and so on as in the diagram on the right

and process is simply repeated for all for all cells in the study area

Interpolation Interpolation is the process that predicts values for cells in a raster from a limited number of sample data points.

For example predicting rainfall from weather gauge stations

Our data & goal Study Area

Digital Terrain Model

Our Our goal goal is is to to determine determine how how much much of of this this rainfall rainfall will will be be available available to to generate generate electric electric power power Rainfall

Runoff Curve Number (a measure of drainage)

Preprocessing 1 Rainfall to volume/cell z

Rainfall is in mm per month

z

Each cell is 50x50 metres (i.e. 2500m2)

z

Therefore to obtain a volume per cell in m3 per month the following formula can be used:-

;;

Preprocessing 2 Rain volume to runoff volume The SCS Runoff Equation

Where Q = run-off P = precipitation, and S is a simplified parameter representing losses (that is the water that doesn't become run-off) S is a function of the Run-off Curve Number (CN) which are a function of land-use and soil type, S is defined as:

NRCS. Module 205 – SCS Runoff Equation. July 1999

Hydrological Modelling

Preprocessing 1 - Sinks

Sinks are depressions in the DTM which prevent water from flowing flowing out. Sometimes sinks are valid e.g. lakes but for hydrological modelling modelling we usually want to remove them


Preprocessing 2 - Filling Sinks Sinks Unfilled

To fill sinks we need to replace them with the lowest value of the surrounding cells


Preprocessing 3 - Filling Sinks Sinks Filled

To fill sinks we need to replace them with the lowest value of the surrounding cells

Hydrological Modelling – 1 Flow Direction

From the DTM it is possible to calculate Flow Direction

Hydrological Modelling 2 Flow Direction

Flow Direction is usually represented by numeric values – this example is for SAGA GIS

0N

1 NE

2E

3 SE

4S

5 SW

6W

7 NW

Hydrological Modelling 3 Flow Accumulation

Flow Direction can be used to calculate Flow Accumulation which will be used to provide runoff volume

Hydrological Modelling 4 Stream Network

Flow Direction can also be used to generate a vector stream network

References z

z

z

z

z

z

z

z

NRCS. Module 205 – SCS Runoff Equation. July 1999 Texas Dept. of Transportation, “Hydraulic Design Manual” Chapter 5, Section 7 — NRCS Runoff Curve Number Methods. 2009 Vandal N. Using GIS to Model Runoff Time, Runoff quantity, and Stream Flow. 2005 Middlebury College Wikipedia. Runoff model (reservoir). http://en.wikipedia.org/wiki/Runoff_model_(reservoir).htm - access date 23February-2010 Wikipedia. Surface runoff. http://en.wikipedia.org/wiki/Surface_runoff.htm access date 23-February-2010 Wikipedia. Runoff curve number. http://en.wikipedia.org/wiki/Runoff_curve_number.htm- access date 1-March2010 ESRI: Using ArcGIS Online help. http://webhelp.esri.com/arcgisdesktop – access date 13-September-2010 ESRI: Using ArcGIS Spatial Analyst - 2001-2002

Dushanbe GIS Training

Hydrological Run-off Modelling for Determination of Hydroelectric Potential Practical using ArcGIS Spatial Analyst and Model Builder Andrew D. Smith Department of Geodesy and Geoinformatics Kyrgyz State University of Construction, Transport and Architecture

Open ArcGIS project

ArcGIS/RunoffModel_Start92.mxd And examine each data layer...

Activate the Spatial Analyst and 3D Analyst extensions From the “Tools” menu select “Extensions” Ensure that there is a check in the bod by Spatial Analyst and 3D Analyst and select “Close”

Open ArcToolbox and create a new model Step 1 – Open Arc Toolbox

Step 3 – Create a new Model

Step 2 – Create a new Toolbox

NB you can rename this toolbox

Add the base data to the model 1, Drag the Layer “Rainfall” onto The Model

2, Repeat for all three Raster layers

3, You should save your model Now, and after every step

Set up basic model properties 2

1 3

1) Select “Model Properties” Properties” from the “Model” Model” menu 2) Check “Current Workspace” Workspace” and “Extent” Extent” in the “General Settings” Settings” and “Cell Size” Size” in the “Raster Analysis Settings” Settings” 3) Click the “Values” Values” button and update as shown

Processing rainfall for model Before we start hydrological modelling proper we need to convert rain fall to runoff, this is a two stage process: −

First we need to convert rainfall in mm to rain volume in cubic metres (m3) as we are working with a 50 metre grid square we can use the following conversion: This is Step 1

−

Secondly we need to work out how much of this volume will actually become run off. This is Step 2

Adding processing for Step 1 - “Rainfall to Volume” (part a) 1, Drag the Tool “Single Output Map Algebra” onto The Model

2. Double click on the tool to open it

Step 1 - “Rainfall to Volume” (part b) 2

3 1) Select the dropdown box to select “Rainfall” as the input raster 2) Enter the formula in the Map Algebra field as shown* 3) Change the raster to Rainvolume 4) Select OK when finished

1

4

*Note the formula:

int ( 2500 * ( float ( [rainfall] ) / 1000 ) )

Is the same as that shown right but we need to use the functions int() and float() to cntrol the format of the answer and the input

Step 1 - “Rainfall to Volume” (part c)

You can now right click on the tool and rename it to something meaningful

☺ ☺ ☺ It would be a good idea to save your model now

Test the model so far

1

1) Right click on the tool and select “Run” 2) Right click on the rainvolume raster and select “Add to Display”

2

☺ ☺ ☺ It would also be a good idea to save your ArcMap Project now

Adding processing for Step 2 - “Volume to Run off” The process for this step is very similar to the previous, except that there are two inputs CN and RAINVOLUME. The combined “Run-off” equation:

Corresponds to the following in ArcGIS: Pow( [rainvolume] - 0.2 * ( (1000 / [CN] ) - 10 ) , 2 ) / ( [rainvolume] + 0.8 * ( (1000 / [CN] ) - 10 ) )

Your task is to complete this step on your own Don't forget to save your work

Adding processing for Step 3 – Filling Sinks 1, Drag the “Fill” Tool, from the “Hydrology” section of the “Spatial Analyst Tools”, onto the Model

This Fill process will take some time to run so be patient

2, Edit the input and output to match the diagram

Adding processing for Step 4 – Flow Direction

In the same way add the “Flow Direction” tool and add update the inputs and outputs as shown NB the drop raster is an optional output that can use later

Step 4 – Flow Direction Run the model and add the result to the display to view the output

East Southeast South Southwest West Northwest North Northeast

NB the numbers refer to the direction of flow as indicated

Adding processing for Step 5 – Flow Accumulation

Flow Accumulation has two inputs it is important that you set “dtm_flowdir” as the “Input flow raster” and “runoff” as the “Input weight raster”

Step 5 – Flow Accumulation (cont.) Now run the model Note that Flow Accumulation will take some time to run so be patient

Add the result to the display. Note you will need to change the colour-ramp to the one shown.

Now zoom in and view the result – also use the identify tool to view pixel values in a number of locations Note this raster “Riverflow” represents the cumulative runoff in m3 at every point in the study area

Step 6 – Extracting riverflow volumes to pourpoints The shapefile “pourpoints” has points just downstream of all significant river intersections. We will nor use our “riverflow” raster to calculate flow volumes at each of these intersections The steps to follow are:−

Add the “pourpoints” layer to the model

−

Add the tool “Extract values to Points” from the “Extraction” section of the “Spatial Analyst”

Step 6 – Extracting riverflow volumes to pourpoints The resultant point shape file,once added to the map, can be symbolised to represent the flow volumes at these pour points

The completed model

Additional – Making the Model Generic and Repeating Processing for Tajikistan Using Parameters Tajik Data Different rainfall coverages

Preparing Small-Scale Hydropower Projects for Private Sector Participation Consulting Services to Government of Tajikistan Presentation of the current findings enerGIS’10 Dushanbe / Tajikistan

Authors: • Ernst Basler & Partner • ITECO • GeoIdee.ch

enerGIS‘10, 23 September 2010

2

Project scope and timeline Phase I: Strategic Review December 2009 - July 2010

Baseline assessment of SSHP-Development Elaboration of SSHP-Development Strategic Plan (Initial Site Screening) Site selection for Phase II/III Review of existing regulatory/commercial framework regarding SSHP-Integration Elaboration of action plan to improve regulatory/commercial framework

Commitment of GOT to immediate implementation of action plan Letter of invitation to potential investors on EBRD website with results of Phase I Government should receive several Expressions of Interests to above invitation for EoI

Phase II: (Pilot) Project Preparation 5 Months

Elaboration of Preliminary Site Documention – feasibility assessment (33 sites) Envrionmental and Social Impact Assessment (2 sites) Refinement of financing framework

Phase III: Concession Tender Assistance 6 Months 19.10.2010

Concession tender assistance


3

Development Strategic Plan PHASE I - Development Strategic Plan

Data collection for 46 sites (sites proposed by MEI)

Transparent assessment and ranking

Selection of 33 sites for further specification (Phase II) and subsequent tendering (min. 20 sites)

Potential SSHP sites identified in previous SSHP-programmes (more than 160 sites) Proposed 54 potential sites as per UNDP SSHP Strategy (2007) - medium/ longterm development programme) Selected 46 potential sites by the MEI / (February 2010)

Site specific data

Initial Site Screening

Technical criteria Social and environmental criteria Commercial criteria Economical criteria

GIS Data Ranking 1 2 3 4

... 46

Site Name ... ... ... ...

...

19.10.2010


Selection Criteria I/II

Economical criteria Power = Economy of Scale Gradient (Head) = Economy of Density of Resource Geology, Natural Hazard, etc. Length of Feed-in Line (Distance to high tension grid) As far as available for selection

Distance to next transport road Synergies and conflicts with other (water-) infrastructures

10/19/2010

4


5

Selection Criteria II/II

Qualitative criteria Risks (Natural hazards, hydrology, data accuracy, …) Climate change risks Social impact Considered positive and negative impact Ecological impact Fish migration Reserved flow (riparian flow) Landscape … 10/19/2010


6

Screening Sites

19.10.2010

Category

Nr. of sites

< 1MW

29

1 -10 MW

15

10 – 30 MW

2

Reconstruction

1 (< 1 MW) 3 (1-10 MW)


7

Small Hydropower – Site Database I/III Collection of site specific data (Access-Database)

Technical scheme data Site conditions

Site hydrology Site access (grid, road) Environmental and social impact

Issues/ Remarks Desk work No onsite-data collection in this phase Limited data availability

19.10.2010


Small Hydropower – Site Database II/III

19.10.2010

8


9

Small Hydropower – Site Database III/III

19.10.2010


10

Small Hydropower – GIS I/VI Collection of available GIS-data Digitalization of analog data Simple Modeling

Topographic basemaps Digital elevation model (ASTER)

Electrical Grid-Data Various geographical mapsets Sociological data ... -> Open system for further data Issues No central data host Restricted access to official data Limited digital data availability – e.g. El. Grid only on paper!

10/19/2010


11

Small Hydropower – GIS II/VI Scale

Base data (Maps, Population, DEM, Glacier, …) Specific Information (Geology, Hydrology, Precipitation, Observation Stations, Protected Areas, …) Project data (Site location, Scheme, Catchment …)

1:800’000

Level Of Detail 1:50’000

10/19/2010


Small Hydropower – GIS III/VI

19.10.2010

12


13

Small Hydropower – GIS IV/VI

19.10.2010


14

Small Hydropower – GIS V/VI ite:31

Schematics of all Sites which have to be ranked Scale 1:50 000 Main features of the Site (Power House, Diversion # Structure, Reserved Flow Segment, …) !

41

Legend # !

Transmission Line

PowerStation DischargePoint AbstractionPoint

")

HeadPond Headrace Pipe

Uplink to Main Grid

Headrace Channel Headrace Tunnel Penstock Tailrace Dam Reservoir

0.1 Kilometers

19.10.2010

± 0

Reserved flow segm

d

d

GridConnection Transmission line


15

Small Hydropower – GIS VI/VI ite:45

Legend #

PowerStatio

!

DischargePo AbstractionP

")

HeadPond Headrace P Headrace C Headrace Tu Penstock Tailrace

45

Dam Reservoir

1.25

0

±

Kilometers

Reserved flo

44

d

GridConnec Transmissio

19.10.2010


Hydrology I/V Observation Stations Mean Run-Off Catchment Catchment Characteristics Flow Duration Curve Estimation Accuracy Cross-Check

19.10.2010

16


17

Hydrology II/V Hydrological observation stations Location of observation stations

Base Information Localization of Observation Stations 4 5

91

86

51 49 52

85

94 72

No data available

79

81

84

Data collected (see annex)

X

3

Name of Station and River 92

X

Catchement of hydrological observation stations

53

93

Run-Off Data

59

65 64

75

38

Catchment

32

40

23

29

36

22

35

43

45

26 27 44

12

13 19 18 20

6

19.10.2010


18

Hydrology III/V Characteristics of catchment 3

Size

4

91

5

79

Mean Altitude / Slope 81

92

84

85

51 49 52

86

Altitude Classes (Rain, Rain / Snow, Snow / Glacier) 94 72

75

53

93

65

59

64

Glacier Coverage

40 43

45

38

32 29

36

22

26 27 44

12

13

19 18 20

6

19.10.2010

23

35


19

Hydrology IV/V Summary Report for each Hydro Observation Station Catchment Characteristics Run-Off Graphs

19.10.2010


20

Hydrology V/V Classified Discharge

35

Monthly mean

30 25 Monthly mean

Monthly maximum

25

Monthly max

20

Monthly min

Monhthly minimum

m3/s

20 Max. /Minimum value recorded [m3/s]:

m3/s

15

15 10

10

5 5

0

0

Jan

1

Feb

2

March 3

April 4

May 5

Jun 6

Month Month

19.10.2010

Jul 7

Aug 8

Sept 9

Oct

10

Nov

11

Dec

12


21

Precipitation I/I Observation Stations Mean precipitation per Observation Station Accuracy estimation Cross-Check

19.10.2010


22

Electrical Grid I/II Digitized from schematic layouts

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Oblast Boundary

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National Boundary

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Transmission Line

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19.10.2010

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Partly uplink of SSHP

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Existing Power Stations Substation, Transformers

Legend

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0

35

70

140 Kilometers

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Electrical Grid II/II

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! 19.10.2010


24

Protected Areas I/I Aktosh Aktosh

7

Source of Information

8

6

Digitized from different Sources

9

12 10 11

Kusavlin

Zaravshon

4

5

Difficult to obtain 2

13 Sayvatin 14

1

41 40 39

37

3

38

Pamir

Kamarob

Sites and Infrastructure within Protected Areas rated with care Iskanderkul

35

43

36

45 Romit 44

Shirkentskyi

Almosi

19

Nurek

Sarihissor

42

15

18

Childukhtaron

46

Sangvor Tajik National Park

34

Muzkol

28

33 29

27

20 25

17

26 24 23

21

22 31 Dashtijum

16 Dashtijum Karatau

Zorkul

30

Tigrovaya Balka

32

19.10.2010

!


25

Geoprocessing and –analysis I/III Geoprocessing

Geoanalysis

Compilation of Base Maps

Catchment Characteristics (Altitude, Slope, Aspect, Glacier Coverage)

Derivation of Catchments of Hydropower Station and Hydro Observation Station

Local Benefit Run Off and Precipitation

¾ Python scripting

19.10.2010


Geoprocessing and –analysis II/III Example Characteristics of Catchments Geometry and Size of Catchment Mean Altitude Glacier Coverage Classification of Altitude

19.10.2010

26


27

Geoprocessing and –analysis III/III Example Local Benefit Amount of beneficiaries around Grid uplink 10 kV

Beneficiary Radius: < 1 MW -> 15km 1 – 10 MW -> 20km 10 – 30 MW -> 40km

10 kV

19.10.2010


Limitations of GIS I/III Difficult available Data (analog data, …) Uncertain ownership of Data (Government, Ministry, Committee, Agency, Private, …) Legislation and State Secrecy of the Republic of Tajikistan Limitation in Spatial Resolution / Scale Not updated Base Information (Maps, …) / Temporal Resolution Location of SSHP sites not suitable for GIS analysis (Irrigation channels, …)

19.10.2010

28


29

Limitations of GIS II/III Site:26

Example Location of SSHP sites on Irrigation channels Difficult to locate Catchment is not driven by topography Run-Off is not driven by hydrological regime 26

Available DEM are not detailed enough to represent the topography

Legend # !

PowerStation DischargePoint AbstractionPoint

")

HeadPond Headrace Pipe Headrace Channel Headrace Tunnel Penstock Tailrace Dam Reservoir

0.250.125 0

±

Kilometers

Reserved flow segmen

d

GridConnection Transmission line

19.10.2010


Limitations of GIS III/III Example State Secrecy Top maps with scales 1:25 000 – 1:100 000 are secret information Top maps with scale 1:200 000 only for internal use Satellite images only for areas < 20 km2

19.10.2010

30


31

Evaluation Matrix I/III Rating of all Sites based on the Information in the DB and GIS Hydrology and Resulting Power Construction Geology Environmental and Social Impact Main Grid Risk Other Investor Risks ¾Transparent assessment and ranking

10/19/2010


32

Evaluation Matrix I/III

n ⎛∑ ⎜ ∑ j =1 ⎝ i =1 m

f p i j 10/19/2010

f

⎞ ⎟ i⎠ j

p

j

Factor derived from DB and / or GIS Weight per Group of factors Number of Factors Number of Groups of Factors


33

Evaluation Matrix III/III Final Result Total amount of points per site Ranking of all sites

10/19/2010


GIS approach – What for / Benefits Goals Optimization of resource harnessing Watershed management optimization Synergies and conflicts Quality and efficiency Tools Exchange of information between experts Common database between different projects Planning tool Public accessible information 10/19/2010

34


19.10.2010

35

Agenda

Flächendeckende GIS-gestützte Identifikation potentieller Standorte von Kleinwasserkraftwerken

1. 2. 3. 4. 5. 6. 7. 8. 9.

Entnahme Rückgabe

Ausgangslage Ziele Datengrundlage Methodik Potentialstudie Kanton Bern Standortanalyse sol-E suisse Probleme und Restriktionen Weitere Arbeiten Fazit

Dipl. Ing. Yvo Weidmann, WaterGisWeb AG, Bern WaterGisWeb AG Donnerbühlweg 41 CH-3012 Bern

Tel. 031 / 305 18 11 Fax 031 305 18 14

www.watergisweb.ch [email protected]

Ausgangslage Verschiedenste Anforderungen an die Energieversorgung der Schweiz • Klimaproblematik / CO2 Ausstoss • Energieversorgung • Sozialer und politischer Druck für die Förderung von erneuerbaren, dezentralen Energie • Revision Stromversorgungsgesetz (StromVG) und Energiegesetz (EnG) -> 5400 GWh bis 2030 aus erneuerbaren Energien • Einführung der Kostendeckenden Einspeisevergütung (KEV) durch den Bund -> Anfrage von Energieversorger (sol-E suisse) und Kanton (Bern) für die Erstellung einer flächendeckenden Analyse an die WaterGisWeb AG

Ziele Unterschiedliche Zielsetzungen bei der Durchführung einer Potentialstudie bei Energieversorger und Behörden Energieversorger

Behörden Wissen über hydroelektrisches Potential

Vorevaluation möglicher Kraftwerksstandorte

Grundlagen für politische Entscheide

Ausweisen spezifischer Kraftwerksleistungen

Hilfsmittel für Bewilligungsverfahren

Erkennen attraktiver Regionen für Neubauten

Gesamtübersicht über hydroelektrisches Potential

Begriffe

Datengrundlage

Einige Begriffsdefinitionen

Erforderliche Geodaten sind beim Bund und bei den Kantonen in ausreichender Güte vorhanden

Kleinwasserkraftwerk

Bis 10 MW Leistung (mittlerer Haushalt ca. 1 kW)

Theoretisches Potential

Basierend auf Gelände und Abfluss berechnetes Energiepotential

Genutztes Potential

Bereits durch Konzessionen aus einem Gewässerabschnitt bezogene Energiemenge

Ausgeschlossenes Potential

Energiemenge, welche durch kantonale und eidgenössische Verordnungen nicht genutzt werden darf

•

Limitiertes Potential

Energiemenge, welche durch kantonale und eidgenössische Verordnungen nur in Grenzen genutzt werden darf

•

Unbeeinflusstes Potential

Aus vollzugstechnischer Sicht nutzbare Energie

Geodatenbank

• • •

Mittlere monatliche Abflüsse (MQ-CH) Digitale Gewässernetze (GWN25, GN5) Digitale Höhenmodelle (DHM25, DHM25_10) Beeinflussende Faktoren (Auen, Hochmoore, Grundwasserschutz, Amphibien, …) Bestehende Kraftnutzung (Konzessionierte Restwasserstrecken)

Abfluss Zeit

Grundwasser Tourismus Naturschutz

MQ-CH Rasterdatensatz der mittleren jährlichen und mittleren monatlichen Abflüsse über die ganze Schweiz

MQ-CH Abschätzung der Genauigkeit der mittleren monatlichen Abflüssen basierend auf einzelnen Regimetypen

Periode 1981 – 2000 Fusion zweier Datensätze unterschiedlicher Auflösung Validierung mit realen Abflüssen Quelle: BAFU und WSL, 2006: Rasterdatensatz mittlere Abflüsse der Schweiz für die Periode 1981-2000

MQ-CH Natürliche Variabilität des Abflusses ist z.T. grösser als der Schätzfehler der modellierten mittleren Abflüssen

Quelle: BAFU und WSL, 2006: Rasterdatensatz mittlere Abflüsse der Schweiz für die Periode 1981-2000

Einflussfaktoren Schutzgebiete können die Kraftnutzung in einem Gewässer einschränken, resp. verhindern. Verhinderung Einschränkung (Killerfaktoren) (Einflussfaktoren)

Quelle: BAFU und WSL, 2006: Rasterdatensatz mittlere Abflüsse der Schweiz für die Periode 1981-2000

Bestehende Kraftnutzung Gewässerabschnitte mit bestehenden Wasserkraftkonzessionen können nicht mehrfach genutzt werden. Bestehende Konzessionsmengen geben Aufschluss über den Ausnutzungsgrad des theoretischen Potentials.

Unterschiedliche DatenWasserkraftrechtlichRestwasserkarte (schweizweit) relevanten Objekte lage zwischen Bund (Kanton Bern) Und Kantone

Methodik Berechnen geographischer und hydrologischer Kennwerte für diskrete Gewässerpunkte • Diskrete Gewässerpunkte festlegen • Räumliche Entsprechung der Abflusslinie im DHM25 suchen • Einzugsgebiet und Abfluss ermitteln • Kennwerte speichern

Abfluss Zeit

Abfluss 876.8

Zeit

882.4

869.3

Abfluss

870.7

863.1

Zeit

863

854.1 859.7

848.8

841.8

m 50 m 50 m 50

844.6

836.1

836.5 833.6 832

834.5 825.5

838.8 842.2 847.5

829.4 864.3

825.2

818.9 819

876.4 839.7

Methodik

Methodik

Das digitalisierte Gewässernetz und die Abflusslinie eines Höhenmodells stimmen nicht überein.

Zwischen Abflusslinie und digitalisiertem Gewässernetz muss eine Zuweisung der Geometrien vorgenommen werden. Quelle

Berechnung von Einzugsgebieten auf Gewässernetz führt zu Fehler

Festlegen von Quelle und Mündung Mündung

Herleitung von Abflusslinie aus Höhenmodell

NetzwerkRouting

Berechnung von Einzugsgebieten auf Abflusslinie

Speichern der Zuordnungen

Methodik

Methodik

Das Gewässernetz wird in diskrete Punkte aufgeteilt. Zu jedem Punkt auf dem Gewässernetz wird der korrespondierende Punkt auf der Abflusslinie ermittelt.

Korrespondierende Punkte auf Abflusslinie

Bestimmung der monatlichen und des jährlichen Abflusses für jedes Einzugsgebiet

Speicherung der beiden Koordinaten (2D und 2.5D)

Methodik

Software Verwendung von ArcGIS als Framework.

Ermittlung des hydroelektrischen Potentials über die Abflussmenge, Erdbeschleunigung und Fallhöhe. Abfluss Zeit

P = ρ · g · Q · Δh · η Leistung [kg · m2 / s3], [W] Dichte [kg / m3] Erdbeschleunigung [m / s2] Abfluss [m3 / s] Fallhöhe [m] Wirkungsgrad

Für jeden Gewässerpunkt wird das Einzugsgebiet und die modellierten Abflüsse (monatlich, jährlich) bestimmt. Bestimmung der Einzugsgebiete für alle Punkte

Punkte auf Gewässernetz

P ρ g Q Δh η

?

Programmierung von spezifischen Tools für die Berechnungen: ¾ ca. 15 Tools ¾ ca. 30‘000 Zeilen Code

Δh

ΔL

Speicherung

Standortanalyse vs. Potentialstudie

Sämtliche berechneten Daten werden in einer relationalen Datenbank gespeichert und stehen für die weiteren Auswertungen zu Verfügung. Gewässer

Energieversorger (sol-E suisse)

Gewässerpunkt

Rangierung von möglichen Anlagen

Inventar in unterschiedlichen Massstabsbereichen

Berücksichtigung von Restwasser, Wirkungsgrad und techn. Limitierungen -> wirtschaftliches Potential

Fluss- und Gebietsbasierte Auswertung mit potentieller Leistung pro 1000m Gewässerlänge

Potentialstudie Kanton Bern

Potentialstudie Kanton Bern Basierend auf der Länge der Restwasserstrecke: 200 m Konzessionierte Leistung: 350 kW Information von Spezifische Leistung: 350 kW / 200 m = 1.75 kW/m Restwasserstrecken wird die konzessionierte Nutzung pro GewässerPunkt ermittelt.

Ermittlung von StandortFaktoren an jedem Gewässerpunkt mittels geografischem Verschnitt Aufteilung in Einflussklassen • Einflussfaktoren • Killerfaktoren • Bestehende Kraftnutzung

m

Einflussfaktoren

Abfluss

50

Abschnittsleistung

Einzugsgebiet

Information zu theoretischem, genutztem, möglichem und ausgeschlossenem Potential

m

Gewässerpunkt

Hinweiskarte möglicher Standorte von Kleinwasserkraftwerken

0

Gewässer

Potentiale

m

Potential pro Gewässerpunkt

20

Potential pro Gewässer

Behörden (Kanton Bern)

75

Potential pro Gewässersystem

Unterschiedliche Auswertung der Resultate je nach Zielsetzung der Studie

75

m

Leistung: 1.75 kW/m * 50 m = 87.5 kW Leistung: 1.75 kW/m * 75 m = 131.25 kW

Leistung: 1.75 kW/m * 75 m = 131.25 kW

Grundwasser Berechnete Punkte

DB

Nicht berechnete Punkte

BLN

Gewässer Restwasserstrecke

Naturschutz

Relevanzlänge

Potentialstudie Kanton Bern Das theoretische Potential wird in 4 Klassen aufgeteilt:

1

Potentialstudie Kanton Bern 13

Darstellung der Resultate in zwei Kartenmassstäbe:

12

Gewässerpunkt Gewässer

11

Restwasserstrecke (Kraftnutzung)

¾ Detailkarte 1:25‘000 ¾ Übersichtkarte 1:100‘000

10

Killerfaktor 9

Einflussfaktor 8

¾ ¾ ¾ ¾

7

Genutzt Ausgeschlossen Limitiert Unbeeinflusst

5

1187

6

4 3 2 1

1 Potential [kW] Theoretisch Genutzt Verfügbar Killerfaktor Nutzbar Einflussfaktor Unbeeinflusst

3

4

5

140 125

2

95

70

90

140 125

95

70 70 0

90 90 0

140 125 125 140 0

95 95 0

0

0

Gewässerpunkt 6 7 8 100 120 40 100 80 100 100

80 80

75 40 35 35 35

Blatt Süd

Münsingen 9

10

11

12

13

80 40 40

90 40 50 50 0 0 0

105

75

60

105

75

60

40 40 0

105 105 0

75 75

60 60

Summe 1225 160 1065 210 855 365 490

Wa s s e r k r a f t Po t e n zi a l s t u d i e K ant on Bern

Wa s s e r k ra ft Po t e n zi a l s t u d i e K a n t o n B e rn

1087 1105 1106

1107 1108

1124 1125 1126

1127 1128

1144 1145 1146

1147 1148

1:25 000

Ausgabe 2009

Ausgabe 2009

1165 1166

1167 1168 1169

1185 1186

1187 1188 1189

1206

1207 1208 1209

1210 1211

1226

1227 1228 1229

1230 1231

1245 1246

1247 1248 1249

1250

1265 1266

1267 1268

1285 1286

1:100 000

Blatt Nord

Blatt Süd

A WA Amt für Wasser und Abfall Bau-, Verkehrs- und Energiedirektion des Kantons Bern

A WA Amt für Wasser und Abfall Bau-, Verkehrs- und Energiedirektion des Kantons Bern



Detailkarte 1:25‘000

Übersichtkarte 1:100‘000

Standortanalyse sol-E suisse


Berechnung von möglichen Kraftwerksstandorten in 3 Leistungsklassen: ¾ 0.25 MW, 0.5 MW und 1.0 MW

Für die Berechnung von möglichen Standorten wurde die Abflusswerte Q120 verwendet. Bestimmung der Jahresganglinie

Berücksichtigung technischen, ökologischen und ökonomischen Limitierungen: ¾ Wirkungsgrad (η = 0.7) ¾ Restwassermenge (Q120) ¾ Maximale Länge Ausleitstrecke (Leistungsabhängig) ¾ Berücksichtigung der Abflussvariabilität (Q120 ± 20%)

160

140 140

60 60 40 40 20

21 J u 0 li: Au 24 gus S e 0 t: p Q tem 27 be 0 r: O Q kto 30 be 0 r: N ov Q em 33 be De 0 r: z Q em 36 be 0 r: Q

1 8 J un 0 i:

Q

Mit der Berücksichtigung der Abflussvariabilität (Q120 ± 20%) kann die Robustheit der Standorte eruiert werden.

Q120 AEZG = 26 km2 Q120 = 1200 l/s

Δh

Leistung [kg · m2 / s3], [W] Dichte [kg / m3] Erdbeschleunigung [m / s2] Mind. Abfluss an 120 Tagen im Jahr [m3 / s] Fallhöhe [m] Wirkungsgrad

15 M a 0 i:


Berechnung der für die gewünschte Leistung benötigten Fallhöhe.

P ρ g Q120 Δh η

Q

Jahresgang Dauerkurve


P = ρ · g · Q120 · Δh · η Δh = P / (ρ · g · Q120 · η)

Q

0

12 Apr 0 il:

20 0

J Q anu 30 a r : Fe Q br u 60 ar :

Darstellung der Resultate: ¾ Hinweiskarten 1:25‘000 ¾ GoogleEarth

80 80

Q Mä 90 rz :

Wahl des Wertes Q120

100 100

Q

Sortierung der Abflüsse

Abfluss Abfluss [l/s] [l/s]

120 120

ΔL

P = 500 kW

Standort detektiert bei: • Q120 + 20% • Q120 • Q120 - 20%

Standortanalyse sol-E suisse Darstellung der berechneten möglichen Standorte in Hinweiskarten.

Standortanalyse sol-E suisse Hinweiskarte 1:25‘000

Entnahmestelle Konzessionsstrecken mit Schlüsselwerte

Restwasserkarte Schutzgebiete Karst

Probleme und Restriktionen

Probleme und Restriktionen

Falsche Übereinstimmung der Tallinie und digitalisiertem Gewässernetz

Anthropogen überprägte Regionen erschweren die korrekte Berechnung von Einzugsgebieten

Regionen mit schwachem Relief

Kanäle und Druckleitungen

Karstregionen

Drainagen

Veränderte Gewässerläufe

Umleitungen

Nicht analysierte Gewässerpunkte

Probleme und Restriktionen Regionen mit (Pump-) Speicherkraftwerke besitzen veränderte hydrologische Regimes Berücksichtigung Von Zu- und Ableitungen Speicherbetrieb verändert AbflussCharakteristik eines Gebietes

Abflusslinie des generischen Gewässers

Digitalisiertes Gewässer

Weitere Arbeiten Aktuell werden weitere Projekte bearbeitet. Dabei werden verschiedenste Software- und Methodikverbeserungen vorgenommen. BFE – Forschungsprojekt (Erhebung des Kleinwasserkraftpotentials der Schweiz): ¾ Zusammenarbeit mit Geografischem Institut der Universität Bern (PhD-Thesis) ¾ Gesamtheitliche Beurteilung der Nutzung von Gewässer durch Kleinwasserkraftwerke

Weitere Arbeiten Swiss Mountain Water Award: ¾ Berechnung möglicher Standorte schweizweit ¾ Web – Portal für die Publikation der Standorte GEWISS – Web: ¾ Berechnete Einzugsgebiete werden für Web-basiertes Gewässerinformationssystem weiterverwendet ¾ Berücksichtigung der Bodenbedeckung pro Einzugsgebiet ¾ Weitere hydrologische Parameter Ausland: ¾ Abklärung für die mögliche Portierung der Methodik ins nahe und weitere Ausland

Schluss

Vielen Dank für Ihre Aufmerksamkeit! Fragen?

Fazit Potentialstudie Kanton Bern: ¾ Flächendeckende Studie mit grosser Aussagekraft ¾ Datensatz für weitere Analysen ¾ Kartografische Darstellung verschiedenster Parameter ¾ Werkzeug für Planer und Behörde Standortanalyse sol-E suisse: ¾ Erarbeitung der Methodik mit Industriepartner ¾ Praxiserprobte Methodik ¾ Problemorientierte Darstellung ¾ Prüfung der Resultate im Feld

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