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WMO Tools derived from drought monitoring and agricultural

meteorology projects

Jose Camacho SO/AgM WMO

[email protected]

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


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


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.


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.



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)


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 - 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

Thank you

Merci

Asante

Gracias

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