Based on heterogeneous and often topographically complex terrain, high altitude ecosystems are characterised by distinct vertical climatic gradients and respective altitudinal vegetation zonations.
Compared to other high mountains of the world, such as the Alps or the Rocky Mountains, the Himalayas are often underrepresented in scientific modelling literature.
Modelling studies in remote mountainous regions such as the Himalayas face numerous challenges:
- limited data availability due to difficult accessibility of the terrain
- poor data basis with unknown magnitude of uncertainties
- limited number of reference studies for comparison
Download the poster.
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Target group and previous experience
For the second time the section Physical Geography of the Institute of Geography, Universität Hamburg invites international and national PhD students interested in challenges of modelling methods to answer current questions of mountain research. Experience in R is required at beginner to intermediate level. More experienced PhD students are also warmly welcomed.
We are looking forward to lively exchanges of knowledge about the challenges of exploring mountain areas, as well as international networking of junior scientists with a climatological and/or ecological background. The winter school will be hosted climate neutral and all carbon emissions will be compensated (incl. travel costs).
Organising committee & instructors
Dr. Maria Bobrowski, University of Hamburg, Germany
Dr. Niels Schwab, University of Hamburg, Germany
Johannes Weidinger, University of Hamburg, Germany
The Winter School will start on Monday (February 24th) at 9 am, in room 838 on the 8th floor, Bundesstraße 55, 20146 Hamburg. Please plan to be really on site for the entire event. Thereby you contribute to a pleasant working atmosphere and an efficient learning for all participants.
All hands-off and hands-on sessions as well as the discussions rounds will take place in the computer lab (room 1241) of the Institute of Geography, Bundesstraße 55, 20146 Hamburg (“Geomatikum”, the tall brown building).
The Winter School finishes on Friday (Feb 28th) at 3 pm for your return home at the same day, if you prefer.
Please find the detailed course programme below (subject to changes):
Please click here to download the timetable as .pdf
The Winter School comprises keynote lectures by Prof. Dr. Jürgen Böhner (chair physical geography, University of Hamburg) and Prof. Dr. Udo Schickhoff (University of Hamburg), leading experts of mountain geography and researchers in the Himalayan mountains for over 30 years.
To welcome all participants we will meet on Sunday (February, 23th) at 5pm at the Institute of Geography, Bundesstraße 55, 20146 Hamburg, Germany (“Geomatikum”, the tall brown building) in room 838, 8th floor.
Cold beverages and snacks will be ready for you.
Winter school dinner
We plan to have dinner together on Wednesday at a local restaurant.
The programme is structured in short, introductionary lectures (“hands-off”) and current research-orientated practicals (“hands-on”). In the Winter School we will cover the essential methods for modelling climate and ecological data. In addition to the R-scripts the participants will receive extensive material summarizing the contents (PowerPoint presentations, example data, R-scripts, literature references).
The Winter School is divided into two main topics:
- statistical modelling of climate parameters
- modelling the ecological niche of a treeline species.
We will address potential model pitfalls, discuss solutions and provide example data from the Himalayas. We will create all models with freely available remote sensing data (MODIS) and climate data (Chelsa), using the open-source software R.
During the course, all students are required to make a short presentation (1 minute) of their research projects, participate in lectures, paper discussions and practicals. Full attendance will give 6 credit points.
For more information please refer to the “Pre-winter school preparation” section.
Besides the provided example data, all participants are encouraged to prepare and work with their own data. During the MCME-2019, data and problems brought in by participants will be discussed and analysed. The last day of the winter school is designated as a “kick-off”-day for the participants to work on their own data.
Pt. 1: Modelling climate data
Based on the generated time series, spatio-temporal statistical climate modelling will be presented. The aim is to analyse and predict surface parameters such as land surface temperature and snow cover in the Himalayas.
First of all, basic concepts of time series analyses will be shown on the basis of several climate parameters (global radiation [Wm -2 ], air temperature [° C] and precipitation [mm]). Subsequently we will identify problems and develop potential solution strategies. Based on the generated time series, spatio-temporal statistical climate modelling will be presented. The aim will be to analyse and predict surface parameters such as snow cover in the Himalayas. We will use remote sensing and topography data from a geo-information system to develop modelling approaches for the spatiotemporal prediction of snow cover in the Himalayas.
Special focus is on automatization of data preparation and processing as well as machine learning algorithms (Random Forests and Artificial Neural Networks).
Pt. 2: Ecological niche models
First the basic principles of modelling ecological niche of species will be introduced. The underlying concept of most modelling studies is the prediction of species distribution ranges using climatic variables. The choice of environmental variables used to model species distributions may result in different distribution maps for the same species. To date, there are only very few studies aiming at comparing and evaluating modelling results obtained by different climate data sets. We selected the treeline-forming species Betula utilis as a target species since an improved accuracy in modelling the current distribution is a precondition for a more precise modelling of potential range expansions of treeline trees under climate change conditions. In the hands-on sessions, the modelled results of Pt. 1 will be used as predictor variables to model the ecological niche.
Additionally we will use different climate predictor variable sets (Chelsa and Worldclim) to model the distribution under current climatic conditions. In order to investigate the impact of each climate data set we will compare the predicted current distribution of B. utilis in the Himalayan region. We will apply Generalized Linear Models. Each climate data set will be used respectively, to
model the species distribution range, to compare and to evaluate projected distribution range maps. We hypothesize that there will be discrepancies in the predictions of the two climate data sets. We assume a higher prediction accuracy of Chelsa because of its capability to reflect mountain-specific climatic conditions, in particular in terms of precipitation-related variables.
Special focus is on model comparison, evaluation and prediction.
Chelsa: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].
Worldclim: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology , 25, 1965-1978.
Application for scholarship
We offer scholarships for (ongoing) PhD students from developing countries. The funding includes support for travel expenses, accommodation and registration fees.
To apply, you can upload your CV and short (250 – 500 words) motivation letter during registration process.
If you are interested in commencing your doctoral research at one of our working groups we can explore options for funding of an insight visit. Please address your interest in insight visit funding in the motivation letter (potential topic of PhD project, previous work, contacts).
Please upload a short motivation letter (max. 200 words, including your current and / or future research topic / interest, status e.g., "PhD, beginner" and experience in R) during the registration process.
Please register and upload your application via https://www.conferences.uni-hamburg.de/e/mcme2020 until October 31th 2019.
After registration, confirmation emails we will send including payment details for the fees.
The number of participants will be limited to 20.
Registration fees* and deadlines
- Deadline for registration: October 31th, 2019
- Early bird: 60€ until November 18th 2019
- Regular: 80€ until December 31th 2019 (later payments: 100€)
Fees include the icebreaker (for details see above) and the coffee breaks during both morning and afternoon sessions.
Inexpensive lunch (4-5€ on own expenses) is available in the Geomatikum canteen or at numerous places nearby.
*Not included in the Winter School fees: travel cost, accommodation and any other personal expenses.
As a participant, you will receive a pen drive with all necessary software, data and readings on site. We will do all computation on Windows Desktop PCs and these pen drives. No technical equipment is mandatory from your side.
This has three major advantages in comparison to your own laptop:
- everybody has the same folder organization structure (makes it easier to follow the reading and writing of data paths)
- we make use of more the higher computing power of the Desktop PCs
- all content we create can be used on any Windows based Computer or Laptop
Additionally, the programme includes discussion rounds offering the opportunity for the participants to discuss recent publications. As a participant you will need to do some mandatory reading (approx. 3-4 h). We will send detailed instructions upon your confirmed registration.
The Winter School will take place at the University of Hamburg, Hamburg, Germany.
Please book your accomodation on your own. Hamburg offers a wide variety from low budget to high-class. We provide assistance by suggesting following hostels:
- MEININGER Hotel
We recommand to usee booking.com.
Please feel free to contact us via firstname.lastname@example.org( mcme2020"AT"uni-hamburg.de)
Offsetting carbon emissions
The greenhouse gas emissions resulting from the winter school activities including your travel expenses will be compensated by a Gold Standard carbon offset project (“afforestation in India”, GS 4240). For further Information please visit the ARKTIK website.