WMO Tools derived from drought monitoring and agricultural
meteorology projects
Jose Camacho SO/AgM WMO
2
Summary
• List of agricultural meteorology projects
• Drought indexes
• Crop models
• Crop calendars
• LDAS Météo -France
� ACREI-East Africa – ICPAC
� IKI Project - SE ASIA - ASEANCOF
� SADIS - South American Drought Information System
(RCC-SSA)
� EUROCLIMA+ - RCC-WSA (CIIFEN)
� CREWS Burkina Faso, Niger, Mali
� CREWS Western Africa - AGRHYMET
� FAO-WMO Senegal-Rwanda
Project list
• Project title: Seamless operational forecast systems and
technical assistance for capacity building in west Africa
• Cost: USD 1.835m
• Duration: 2018-2020 (three years)
• Main implementing partners: WMO and KNMI, DWD,
Hydrologic Research Centre (HRC), University of Reading,
International Research Institute for Climate and Society
(IRI), with ACMAD, AGRHYMET, RSMC Dakar and
participating country NMHSs
CREWS West Africa work plan
• Objective: An operational severe weather, flood and
climate forecast system, underpinned by on-going
observations and continuously updated historical data,
that provides monitoring and forecast outputs and
products, as well as related knowledge, in support of
CREWS-related activities in Burkina Faso, Mali, Niger, and
other countries in the region, through enhanced capacity
by regional centers to support national level provision of
risk information and end-to-end early warning services.
ComponentsIn context of CREWS and other relevant country programmes
� Site areas visited four times each
� User´s requirements learnt
� Radio broadcasts in local languages
� Weather alerts and seasonal forecasts
translated in farmers advisories
� Collaboration with Météo France (LDAS),
AGRHYMET (training crop models) and
AEMET (SDS-WAS)
Local action – Burkina Faso
Pilot regions
Visits to the communities of Titao, Tenado et Niangoloko (North, Centre and Southwest)
STANDARDIZED PRECIPITATION INDEX (SPI)
Widely used index to characterize meteorological drought on a range of timescales. On
short timescales, the SPI is closely related to soil moisture, while at longer timescales,
the SPI can be related to groundwater and reservoir storage. Able to be used under
markedly different climates. Raw precipitation data are typically fitted to a gamma or a
Pearson Type III distribution, and then transformed to a normal distribution. The SPI
values can be interpreted as the number of standard deviations by which the observed
anomaly deviates from the long-term mean.
Drought Indexes - SPI
Drought Indexes – SPI vs percentiles
STANDARDIZED PRECIPITATION – EVAPORATION INDEX (SPI)
A relatively new drought index, SPEI uses the basis of SPI but includes a temperature
component, allowing the index to account for the effect of temperature on drought
development through a basic water balance calculation. SPEI has an intensity scale in
which both positive and negative values are calculated, identifying wet and dry events.
It can be calculated for time steps of as little as 1 month up to 48 months or more.
Monthly updates allow it to be used operationally, and the longer the time series of
data available, the more robust the results will be.
Drought Indexes - SPEI
Resources: SPEI code is freely available at the SPEI website by the Consejo Superior de
Investigaciones Científicas (CSIC) and the calculations are also described in the
literature. Additional resources are available at the Flood and Drought
portal developed by GEF, UN Environment, IWA and DHI.
Reference: Vicente-Serrano, S.M., S. Begueria and J.I. Lopez-Moreno, 2010: A multi-
scalar drought index sensitive to global warming: the Standardized Precipitation
Evapotranspiration Index. Journal of Climate, 23: 1696–1718. DOI:
10.1175/2009JCLI2909.1.
Drought Indexes - SPEI
http://www.droughtmanagement.info/indices/
http://www.droughtmanagement.info/normalized-difference-vegetation-index-ndvi/
http://www.droughtmanagement.info/find/glossary/
http://www.droughtmanagement.info/find/library/
IMPD Publications
TETIS : Territoires, Environnement, Télédétection et
Information Spatiale
Two scientific dimensions: thématic and méthodologic
Three research institutions (in Paris and Montpellier , France):
SARRA Models
AGRHYMET Centre in Niamey, Niger
RCC for Western Africa
1995 the SARRA trilogy : From
weather/climate, through soil
conditions to agroecological zones
• Three models developed by F. Forest, F. Maraux,
C. Baron and A. Clopes:
– SARRAMET : climate data analysis and graphic
representation.
– SARRABIL : Two-layer daily water balance, risk
analysis…
– SARRAZON : Zone analysis over big number of
weather stations.
Users in Africa, Indonesia and, big succes in Brazil
PRÉCIPITATION
Indicateurs
(dates, stress)
-
-
- - -
- -
-
-
-
-
50º18' 44º57' 39º37'
21º55'
18º02'
14º10'
40 km
CICLO: PRECOCE SOLO: TIPO 3 SEMEADURA: 01/12 a 10/12
MAA/FINATEC/EMBRAPA-CNPSo-CPAC/DNAEE/INMET
F A V O R Á V E L
D E S F A V O R Á V E L
ZONEAMENTO AGROCLIMÁTICO DA CULTURA DA SOJANO ESTADO DE MINAS GERAIS
SARRA-H « crops but also varieties"
• 2000 à 2003 : reprises des travaux évolution
de SARRA en SARRA-H (M. Dingkhun, C. Baron
& V. Bonnal)
– Water balance (SARRA),
– Phenology
– Carbon balance.
Fullfil Kyoto protocol agrement (2000)
Front racinaire
Front d ’humectation
Pluies
Semis
Réserve
ETo
2 compartimentssimulés
De
SA
RR
A à
HPhénologie
(PPisme, temps thermique...
Dynamique du Kc(fonction de LAI
utilisant la loi de Beer,séparation E et Tr)
Assimilation de C(fonction de ℇa et ℇ b,
frein hydrique FAOou Eagleman…)
Répartition de la BM
utilisant des lois d ’allométrie
Boîte à outils : Base de données, traitement de données, graphiques…
TrPot = Kcp * EToEPot = Kce* ETo
19
• Deux variétés de mil
• Deux dates de semis,
• Deux niveaux de fertilisation azotée (N1, N0)
0500
100015002000250030003500400045005000550060006500700075008000
Dry
wei
ght (
kg h
a-1 )
Observation date
HKP x N1
0500
100015002000250030003500400045005000550060006500700075008000
Observation date
Total abovegroundLeavesGrains
MTDO x N1
0500
100015002000250030003500400045005000550060006500700075008000
Dry
wei
ght (
kgha
-1)
Observation date
HKP x N0
0500
100015002000250030003500400045005000550060006500700075008000
Observation date
Total abovegroundLeavesGrains
MTDO x N0
Calage et Validation du modèle SARRA-H
Site AGRHYMET
Thèse Agali
Calage et Validation du modèle SARRA-H
SENEGAL
Diourbel (450 mm)
Tambacounda (800 mm)
MALI
Cinzana (550 mm)
Koutiala (700 mm)
BURKINA FASO
Tougou (600 mm)
Dano (900 mm)
NIGERNiamey (500 mm)Bengou (700 mm)
On-farm surveys & Experimental trials
Millet Varieties
sorghum Sowing dates
maize Planting densities
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
0 5000 10000 15000 20000
ratio
leav
es /
(leav
es+s
tem
s)
leaves + stems biomass (kg ha-1)
Souna
Thialack
Sanio
HKP
MTDO
Zatib
Choho
Toroniou
SNTC
Relations allométriquesde différentes variétés locales de mil au Sénégal, Mali and Niger
Durées des stades semis-feuille drapeau de différentes variétés locales de sorgho au Mali
Calage et Validation du modèle SARRA-H
Traore et al. 2010.
SARRA_H & site Web (Français, Anglais, Portugais)
Year Download Visite
2014 255 870
2015 172 830
2016 254 1100
2017 245 1100
Total 926 4200
Le 07/26/2018 : 1093 téléchargements & 4,8k visites
De 2014 to 2017: Evolution Constante…
• Amérique du Sud 9% (8% Brésil),
• Amérique du Nord 7% (6% Etats Unis),
• Europe 9% (plus 42% France),
• Afrique 26% (4% Sénégal, 3% Algérie,
3% Côte d’ivoire, 2.5% Niger…)
• Asie 3% ( Iran, Inde)
22Presentation EMBRAPA,
Bresil 2018
Site: http://sarra-h.teledetection.fr/
But ! SARRA-H is only point
representative and do not run if any
single data is missing in 30 year series
Changes towards an spatialized version
• 2012-2014 : first propotypes (C.Baron, H.
Songoti, S. Traore, A. Agali)
• 2016-2018 : SARRA-O , spatialized version of
SARRA-H under Ocelet modelling platform (C.
Baron, M. Castets)
First crop monitoring performed by
Agrhymet in 2016
Christian Baron, Agnès Bégué, Mathieu Castets, Camille Jahel, Danny Lo Seen
Seydou B.Traoré,
Alhasanne Agali, Henri Songoti
Rendement :Flash info November 2016
AGRHYMET monthly bulletins during
2016 cropping season
(pearl millet, sorghum & maize)
SARRA-O interface 2018
Friendly interface – Training performed for Met. Services Burkina Faso, Niger and
Mali in November 2018
FAO – WMO project in Senegal and Rwanda
Crop calendar from FAO. Agroecological zones
Collaboration with University of Utrech
Crop calendar based on literature and AEZ
(developed for Rwanda)
Manual on a climate derived crop calendar
(developed for Senegal)
Crop calendar
Crop calendar – Rwanda - AEZ
Crop calendar – Rwanda – Crop calendar
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Imbo Rainy season
Impara& Lake Kivu Borders Long dry season
Birunga Short dry season
Congo-Nile Watershed Divide & Buberuka Highlands Varying rainy/ dry season
Central Plateau
Eastern Plateau & Eastern Savanna
First season corresponds to rainy season of September to mid- December, Second season corresponds to rainy season February to May.
Short dry season is not an official dry season: rainfall still occurs, however, it is less prominent as during the rainy seasons.
Crop calendar – Rwanda – Crop calendar
General sowing periods
(from 2015/2016/2017) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMaize
Paddy rice season A
Sorghum
season B
Wheat
season C
Bush bean
Climbing bean
Pea
Irish potato
Sweet potato
Soybean
Groundnut
Taro
Yam
Cassava
Cooking banana*
Dessert banana*
Banana for beer*
Fruits
vegetables
Shannon de Roos (University of Utrech)
Crop calendar - Senegal
The methodology can be described in three general steps.
In the first step a definition is given to the onset and cessation of the rainy season, to
define the Length of Growing Season (LGS).
The second step is to derive an early, mean and late onset and cessation period.
The final step is to asses for each crop the Crop Cycle Length (CCL) and determine the
planting and harvest periods for a mean, early and late onset.
For each onset (early, late, mean) the LGS and CCL have to be compared and the
calendar should be adapted to reduce any risks in crop failure or yields.
This method has to be applied to each agrometeorological zone independently.
Crop calendar - Methodology
The most commonly applied definition for an Agrometeorological rainy season onset
describes a P amount of rainfall within an X period of days, followed by an Y amount of
days within which no dry spells occur of more than Z days (Stern et al., 1981;
Omotosho et al.,2000; Dodd & Jolliffe, 2001).
Stern et al.(1981) and Sivakoumar et al. (1993) defined the potential rainy season
onset as an event of 20 mm rainfall in 2 to 3 days. They marked the actual onset as the
first time after the dry season that the potential onset was not followed by a dry spell
of at least 7 out of 30 days.
Crop calendar - Methodology
To define the length of the dry spell for the actual onset, the drought sensitivity and
value of the different crops should be taken into account. Drought resistance crops,
such as millet, can endure a dry spell of up to 20 days, while for other crops the dry
spell cannot exceed 15 or even 10 days.
Additionally, for cash crops (such as maize, groundnut), farmers will be more careful
regarding the planting date. They sometimes wait for the second rainfall event of 20
mm before planting their first seeds. For these reasons, it is advised to classify the
crops in groups in terms of maximum dry spell length, and provide a definition
of the actual onset for each group.
The end of the wet season can be simply defined as the last period in which significant
rainfall occurs.
Sivakumar et al. (1993) defined the end of the rains for Niger as the first dry spell of 20
days after September 1.
Crop calendar - Methodology
Crop calendar - Methodology
LGS can be derived by subtracting the onset day from the cessation day for each year.
The LGS provides important information about the average length of the season for a
specific region and also helps to give a better insight about which crops are suitable
to plant around which time, regarding the variation in Crop Cycle Lengths.
Applied to Onset day, and
to Cessation day. The
difference provide the
Lenght of Growing Season
(LGS)
Crop calendar - Methodology
Each AEZ should be represented by at least one meteorological station providing
daily rainfall data. The computations require data from a reliable record (preferably
40-50 years or more). The program RINSTAT 0.4.22 was used to calculate onsets and
cessations of the rainy season for each year in the record and for each
meteorological station. INSTAT is a software program developed by Stern (2006),
specifically designed to study climatological events.
The climatological analyses have been completed at this stage. It is now time to look
at the crop characteristics. The Crop Cycle Lengths (CCL) of the different crops are
compared to length of the Growing Season (LGS). The early, mean and late onsets are
used as a starting point, the planting periods. Depending on the LGS, it is decided if
these crops can complete the cycle during each onset. That is, if they can be
harvested before or around the end of the LGS. In each AEZ, farmers use different
varieties for one crop, resulting in a variation of CCL.
Advise should be given as to which crop variety (short growing or long growing) can
be planted around which time.
Crop calendar - Methodology
Crop calendar - Senegal
Crop calendar - Senegal
Projects METAGRI – Western Africa
Project GFCS – Eastern Africa
Projects Irish Aid – Ethiopia
FAO Manuals – Farmer Field Schools
WMO – Climate Field Schools and Roving Seminars
Training materials
Validation of different satellite rainfall estimates against gauge data and publically
available gridded gauge datasets for West Africa, with case studies for each of the
CREWS countries (Burkina Faso, Niger and Mali). For each of the case study countries,
validation will ideally be against independent gauge records not included in the
TAMSAT calibration Daily rainfall data from 1983 until present, with some dense areas
(South Ghana, AMMA, etc.) and overall coverage of 1 station per 10,000 km2
Development and validation of a historical probabilistic rainfall product, based on
optimal rainfall estimation using both gauges and satellite imagery. A key feature of
this product is calibrated estimates of observational error, which enable us to identify
meteorological regimes in which satellite-based rainfall estimates are likely to be
reliable, or conversely subject to large error. The conventional validations carried out
Task 1 will be complemented by additional comparisons with the new historical
product and in depth analysis of error.
Relevant because TAMSAT works closely with the ENACTS team at the IRI. Subsequent
versions of ENACTS will incorporate the new gauge-satellite merging methods, initially
alongside existing ENACTS products.
TAMSAT improvements
IRI – Under CREWS Western Africa
Météo-France – Under CREWS Burkina Faso
Development of an objective seasonal forectast methology for RCOFs
Improvements and operational use of Sub-seasonal forecasts. Those are more relevant
for agricultural meteorology than seasonal forecasts. Need to deliver a S2S products
for decision making
Seasonal forecasts S2S
Land Data Assimilation System
LDAS
LDAS
Thank you
Merci
Asante
Gracias