---
# System prepended metadata

title: Extremes proposal
tags: [proposals]

---

###### tags: `proposals`
# Extremes proposal

### Agenda 23 March

- define /clarify aims:
    - models: ICAR/TopoSCALE/ Boundary data: ERA5/Global forecast product/CORDEX
    - low cost stations - debias/ evaluation (WWCS/ CARITAS)
    - geo focus? Piggy back on existing? Options: CA/ India/ Nepal 
    - use case events (e.g. Chemoli / GLOF CA etc)
    
- scope (time, budget etc)
- postdoc hire?
- SDC as co-pi (possible matching)
- GEOtest > Stefan Tobler?
- Jeannette/Michi cant be co-pi on 2 projects 

### Todos
* contact manfred/ Stefan
* use chemoli to frame the issue
* joel prepares 2 pager this week
* michi -> Jeromes project

### Resources
Main page
https://intra.wsl.ch/de/forschung/wsl-program-extremes#c5219

Q&A
https://docs.google.com/presentation/d/1jUO6IY-f525El9IGB_Nmx4OMCTuMzcu-61nsyauKAek/edit#slide=id.gbe55427de6_0_10

Due: 23 April
Amount: 4-700k CHF
Time: 3-4yr

Posted on Marketplace:
> ### Project goal
> Understanding the impact of climate change on extreme events in the mountain cryosphere requires fundamentally improved ability to analyse and predict hydrometeorological drivers at a range of scales. This project would support the Swiss development agenda by providing tools to improve natural hazard assessments and therefore community resilience in vulnerable mountain regions in the global south.
> 
> ### Why is it extreme
> Natural hazards are by definition extreme - here we want to understand the climate/hydrometeorological preconditioning that are related to such events and develop a range of tools that would contribute to post event analysis (e.g. Chamoli rock/ice avalanche, February 2021) or in developing community preparedness through hazard mapping, early warning and mitigation measures.
> 
> ### Description
> Extreme events occuring in mountain regions are often associated with a lack of information related to meteo conditions at both event and climate scales. The interpretation of these events or prediction of future events, requires better understanding of meteo drivers in remote places, at appropriate scales. While satellite remote sensing gives us eyes on the aftermath and to some extent preconditioning - the experience of the recent Chamoli event has shown that a great deal of uncertainty exists around the hydrometeorological drivers. Understanding these is crucial if we are to better understand and simulate how these extreme events will evolve in terms of both future magnitudes and frequencies. Thawing of the mountain cryosphere under a warming climate can represent a step change in natural systems as they transition this phase change - often with extreme consequences and cascading downstream impacts.
> This project addresses this gap by combining in situ data with downscaling methods of varying complexity and new low-cost weather stations currently being developed together with CARITAS CH and MeteoSwiss in Tajikistan. This combination of technology would enable provision of slope scale meteo time-series for historical, near and far future events. 
> 
> ### Products
> Development of new climate downscaling toolbox for remote mountain regions that enables post event analysis, and addresses climate change impacts on extreme events in mountain regions at slope scale. This toolbox will be suitable for use in development projects related to water and natural hazards at both event and climate scales. 
>
>### Potential Partners
> Dr. Simon Allen (GAPHAZ, UZH), Dr. Boris Orlowsky (CARITAS CH), Manfred Kaufmann (SDC), Dr. Jakob Steiner (ICIMOD), Dr. Yves-Alain Roulet (MeteoSwiss), Dr. Rodica Nitu (WMO), Daniel Kull  (Senior Disaster Risk Management Specialist at The World Bank Group)


```mermaid
gantt
    title Timeframe 
    dateFormat  YYYY-MM-DD
    axisFormat  %b %y
    todayMarker off

    section JF, RM, pdoc
    WP1 Tscale/HICAR          :a1, 2021-09-01, 52w
    section JF, JN, RM, pdoc
    WP2 Surface model    :a2, 2022-01-01  , 52w
    section JF, pdoc
    WP3 Data assimilation : a3,after a2, 25w
    section JF, pdoc
    WP4 climate scenarios: a4,after a3, 25w
    section JF, JN, pdoc
    WP5 Chamoli     :a5,2022-09-01  , 109w
    section JF, Developer
    WP6  Dashboard   : a6,2023-09-01 ,52w
    
    
```

```mermaid
gantt
    title Timeframe for Project Description (PD)
    dateFormat YYYY-MM-DD
    axisFormat  %b %
    
    section IFM carbon analysis
    New Files Analysis: a1, 2021-03-01, 30d
    update   :a2, after a1  , 20d
    
    section APDD Analysis
    Complete APDD Analysis: b1, after a1, 50d
    update   :after b1  , 20d
    
    section Social Baseline
    data entry: c1, 2021-03-01, 20d
    Write up current: after c1, 35d

    section Writing
    Outline: d1, 2021-03-08, 15d 
    Writing IFM Analysis      :after d1 , 30d
    Writing APDD Analysis      : 60d
    APDD Analysis    :after a1  , 40d
```

Gantt Chart for PD, initially based on very rough estimates


```mermaid
gantt
    title Timeframe for Monitoring Report
    axisFormat  %B

    section Plot Remeasuring
    Mend Roads           :a1, 2021-03-08, 60d
    Measure close plots :a2, after a1  , 60d
    Measure Heli plots : a3, after a2, 60d
    
    section Social Monitoring
    Revisit villages           :c1, 2021-06-08, 60d
    Farming help :c2, after c1  , 60d
    What about Hunting : c3, after c2, 60d
    
    
    section Writing
    Carbon  Writing      :after a3, 60d
    Social Writing      : after c2, 70d
```
Gantt Chart for Monitoring Report,  based on even rougher estimates
    
    1.	TopoSCALE / HICAR integration (Fiddes/ Mott/ PostDoc)
2.	ESM model integrations (snow / permafrost) (Fiddes / PostDoc1)
3.	Data assimilation (station, S1, S2, fodar, GEE integration) (Fiddes / PostDoc1)
4.	Data integrations (NWP, ERA5, CORDEX) (Fiddes / PostDoc1)
5.	Software dev (py package / user interface/ etc) (Fiddes / Dev )
6.	Case study Chemoli (HIST, CURRENT) (Fiddes / Noetzli/ PostDoc2 )


