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Climate Change Data Analysis,

Risks Assessments On agric/Water

Resources and Adaptation Strategies

In Some AAP-CountriesSeyni Salack

(UNOPS-IRTSC, Consultant)

Contributors: Intsiful J., Obuabie E., Moufouma W.

Email: seyni.salack@ucad.edu.sn

AAP Countries Meeting, Dakar, Senegal, 12-16 November 2012

Overall objective of our team

Help AAP countries build upon their local knowledge and capabilities.

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“Strengthen the strengths and make weaknesses irrelevant in CC info

use and applications”

Focus on few to help many !19/04/23 3

How ?

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The challenges (1): Understanding the Complex climate system…

The atmosphere and the chemical components are linked with other components

of the Earth system: oceans; land; terrestrial; plants and animals

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…..GCM outputs…

Hundreds of km

tens of km

km

point

Impacts needs…

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The challenges (2): End users handling the methods in dynamical and/or statistical

downscaling technics

Source: S. Salack (2007)

RMC are used to downscale GCM outputs: Capture the sub-grid processes (orgaphic effects, local convection …)

GCM scale

RCM

RCM

Stat

Station data

Statistical link

Zoom 1

Zoom 2

b) Dynamical-statistical methods

GCM scale

Gamma Distribution,

EOF, Transform. mul.

Gauss. etc., Mark. Ch.

Station dataa) Classical Methods: Baron et al, (2005), Hansen et al, (2006),

Schmidli et al. (2006) , Ines & Hansen (2006)

Statistical

Dowscalling

???

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The challenges (3): Climate & CC data archiving, formatting (NetCDF), QC

technics

1 ni

nj

→ lon(ni), lat(nj) and time(nj,ni) for different levels (nk)Note: in Netcdf files, lon and lat are often both dimensions (ni and nj) and name of longitude and latitude vectors → lon(lon), lat(lat), level (nk) and time (lat, lon)

Methods and tools provided (1): open source tools

19/04/23 8New_locClim (FAO, 2006): to solve problem of data scarcity, data interpolation/spatialisation

Methods and tools provided (2): open source tools

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NCO: NetCDF Command Operators for managing

NetCDF data format

CDO: Same as NCO + extraction of climate extremes

R packages and scripts: browse_NCDF.r (for handling

NetCDF files by Salack et al., 2012), Rclimdex.r (for

climate extremes extraction by ETCCM/WMO, 2006)

Stochastic weather generator for downscaling: LARS-

WG, EOFs and their limitations in CC info.

AMMA-ENSEMBLES & CORDEX data: RCM outputs

IRI data library: Observations, re-analysis

NOAA (GHCN), CRU, GPCP, TRMM

Climate information portal of the CSAG-UCT

FAO database, including CLIMWAT

Other data sources such estimated, interpolated, self-owned data etc.

>>> Because Good and true information is power !

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Methods and tools provided (3): open source data

Workshops successfully organized (feedbacks & reports) Public conference in Congo (special) National average CC and extremes scenarios reports National average and local CC risks on agric & water resources and

adaptation measures

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The achievements (1): Strengthened & sustained capacity

MZ CG NE BF GH Mauritius

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AAP-Mozambique (23 participants)

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AAP-Niger (22 participants)

AAP-Congo (2x25 participants)

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Results (1): Example of Natl report on CC in Congo

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…and output oriented… useful to any other decision making project

Impacts on agric & water resources

Major challenges:

Managing uncertainties in CC info.

Local information to parametrize & validation of crop

models (DSSAT, CROPWAT, SARRAH)

Information on local water levels and runoff

Water basin metadata and evaporation data

Etc…

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Implementations in AAP countries

Deploy crop models: DSSAT, CROPWAT, SARRAH

Deploy hydrological models: SWAT, WEAP

Deploy GIS tools: ARCGIS, IDV

The parameterizations and validations are done

using mostly the FAO parameters and data in

most cases but also local data.

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Impacts on agric & water resources

Results 2: Example of Natl report on agric in Congo

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…and output oriented… useful to any other decision making project

Adaptation Measures in agric sector

The “Where” to adapt Adaptation is local. Case to case approach.The “how” to adapt Technical Adaptation measures have been

suggested. Easy to use, to implement and sustained Low cost (financially and in manpower) Do not oppose indigenous knowledge and

practices

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Results 3: Example of Natl report on agric in Congo

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…and output oriented… useful to any other decision making project

Lessons learnt (Recommendations)

Open source data sets are very useful (support it) Open source tools provide precise and good quality

results (Build on the acquired skills). AAP experiences can increase knowledge of

climate science and can provide breakthrough ideas for follow up projects (per-review papers)

Strong relationship between AAP and the national Met. Off. or Agency helps reduce the problem of local data availability (build on it).

Strong links between AAP and the local Universities is a long term solution to research-end-users relationship (sustain this process).

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Thank you

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