d422lot1.smhi.5.1.1b: detailed workflows of each case

C3S_422_Lot1_SMHI – D5.1.1B |

Copernicus Climate Change Service

D422Lot1.SMHI.5.1.1B:

Detailed workflows of each case-study

on how to use the CDS for CII

production and climate adaptation

Full Technical Report:

Using seasonal forecast information to

strengthen resilience and improve food

security in West Africa

Bernard Minoungou, Dr Abdou Ali, Hamatan Mohamed

AGRHYMET Regional Centre

REF.: C3S_422_Lot1_SMHI D5.1.1B

C3S_422_Lot1_SMHI – D5.1.1B |


Summary The case study focuses on improving the quality of seasonal forecast information to strengthen the resilience and improve the food security in West Africa,

especially on the Niger River Basin. We used seasonal climate forecasts and the HYPE hydrological model to predict some characteristics of the rainy season in

West Africa. The ECMWF seasonal forecast ensemble (system 5) from 1993 to 2015 (hindcast) and 2018 (forecast), available in the Climate Data Store (CDS) catalogue were used. The climatic variables considered are daily precipitation,

mean and extreme temperatures (minimum and maximum) at the seasonal scale. The main objective was to assess the ability of the HYPE hydrological

model to predict runoff over the historical period and to produce hydrological seasonal forecasts for 2018. The main season’s characteristics produced are: (i) cumulative rainfall map for the rainy season (May to November), (ii) map

showing the rainfall situation of the season (above, near or below normal considering 1993-2015 as reference period), (iii) graph of the mean seasonal

precipitation over the basin compared to the reference period (1993-2015), (iv) map showing the hydrological situation of the season (above, near or below normal considering 1993-2015 as reference period), (v) graph of the mean

seasonal streamflow over the Niger Basin compared to the reference period (1993-2015), (vi) graphs comparing monthly streamflow of 2018 to those of the

reference period (1993-2015). We organized a workshop to share and discuss produced indicators with clients.

These clients represented the national hydrology services of four countries in the Niger River Basin (Burkina Faso, Mali, Niger and Nigeria). This study shows that bias correction of climate data, which are the main inputs

to hydrological rainfall-runoff models, is a very important step and requires appropriate methods. Initialization of hydrological models was an important

phase in the study.


C3S_422_Lot1_SMHI – D5.1.1B |

Contents

1- Case study description ........................................................................................................ 4

1.1 Issue to be addressed ......................................................................................................... 4

1.2 Decision support to client .................................................................................................. 4

1.3 Temporal and spatial scale ............................................................................................... 4

1.4 Knowledge brokering ........................................................................................................... 4

2- Potential adaptation measures ........................................................................................ 4

2.1 Lessons learnt ........................................................................................................................ 4

2.2 Importance and relevance of adaptation .................................................................... 5

2.3 Pros and cons or cost-benefit analysis of climate adaptation ............................. 5

2.4 Policy aspects ......................................................................................................................... 5

3- Contact ...................................................................................................................................... 6

3.1 Purveyors ................................................................................................................................. 6

3.2 Clients/users ........................................................................................................................... 6

4- Data production and results ............................................................................................. 6

4.1 Step 1: Data collection from CDS, quality assessment and bias adjustment8

4.2 Step 2: Hydrological forecasting................................................................................... 14

4.3 Step 3: Evaluate predictive capacity ........................................................................... 20

4.4 Step 4: Revised ensemble ............................................................................................... 20

4.5 Step 5: Stakeholder communication ........................................................................... 21

5- Conclusion of full technical report ................................................................................ 22

References ......................................................................................................................................... 24


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1- Case study description

1.1 Issue to be addressed

The rainy season occupies a central place in socio-economic activities in the

Sahelian regions, as more than 80% of the population lives on agriculture and livestock. However, extreme hydroclimatic events such as droughts and floods affect these activities. Efforts made in recent years in the production of

hydroclimatic information to enhance the resilience of populations have become insufficient, given the variability and climate change.

1.2 Decision support to client

The clients, that are the national hydrology departments, received the

information produced, mainly the daily, monthly and seasonal cumulative rainfall and the daily, monthly and seasonal streamflow within River Niger basin. These information are usually used to support the decision of dam managers (to better

plan dam operations), farmers (to better plan their activities during the rainy season), and society (on the potential risks of floods).

1.3 Temporal and spatial scale

This work is focused on seasonal hydrological forecasts. These forecasts covered

the Niger River basin, which has an area of 2 170 000 km2. The Niger Basin was subdivided into 803 sub-basins and the information was produced at the scale of these sub-basins.

1.4 Knowledge brokering

The AGRHYMET Regional Centre, in collaboration its partners, organizes the

PRESAO (Seasonal Forecast over West Africa) forum for the elaboration of seasonal hydrological forecasts in the beginning of May every year. This forum brings together all the national hydrology and meteorological services, basin

organizations and the departments in charge of disaster and risk management. The information purveyors therefore have a knowledge of the needs of the clients

in terms of information to be provided to them. The interactions with customers are done each year through a forum. As part of the C3S_422_Lot1_SMHI contract, we held a physical meeting with customers to share with them the

products from the case study.

2- Potential adaptation measures

2.1 Lessons learnt

A similar study previously conducted forcing the HYPE hydrological model with

the ECMWF System 4 seasonal forecast indicates that the quality of seasonal hydrological forecasts may be improved. Accessibility to data and methodology

was made possible thanks to the collaboration between the AGRHYMET Regional Center and its partner institutions including SMHI. Interactions between institutions producing climate data and large-scale tools and local institutions

allow their practical use to solve recurring societal problems in our regions such


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as adaptation to extreme events (floods and droughts), improvement of

agricultural production. It is also important to point out that some planned activities are not always carried out due to lack of technical resources. This is the

case, for example, of the Distribution Based Scaling (DBS) bias correction method that was originally planned for this study.

2.2 Importance and relevance of adaptation

In West Africa, climate variability and change were assessed on the basis of indigenous knowledge. For example, the behavior of some birds is used to

determine the start of the rainy season. The efforts made in recent years and the current outlook for seasonal forecasting are helping to better qualify and quantify

the physical processes responsible for the different characteristics of the rainy season in West Africa. For instance, in agriculture, an evaluation showed that agrometeorological assistance through the provision of climate services

contributed to an increase in agricultural production in Mali of about 30% (Faustin Gnoumou, personal communication). Until now, agro-hydro-

meteorological assistance to end-users through seasonal forecasting has been based on purely statistical methods. With the new approach (using deterministic predictions) that is expected to be developed, resilience to climate variability and

change is likely to be enhanced. We will be able to provide local characteristics such as the periods of occurrence of extreme hydrological events, their amplitude

and the availability of water resources and associated margins of error.

2.3 Pros and cons or cost-benefit analysis of climate adaptation

Studies have shown that end users can benefit from climate change adaptation in terms of increased income and reduced risk through the use of seasonal forecasting despite the uncertainties associated with current forecasts. Generally,

investments that prevent climate-related disasters are much lower cost than the economic and human losses caused by such disasters. One of the major

shortcomings of previous hydrological forecasts (statistical approach) is that they did not meet users' expectations for assessing water resources for decision support. As part of this study, it was possible to produce quantitative

hydrological forecasts on the availability of water resources at the Niger Basin scale.

2.4 Policy aspects

In the past, adaptation strategies have focused on managing crises rather than preventing them. With the increase in extreme weather events and the high cost

of crisis management, public opinion has become aware of the importance of climate change adaptation measures in anticipating such events. The activity of

seasonal hydrological forecasts in West Africa is already included in policies to strengthen resilience to climate change and variability. However, the previous

forecasts no longer sufficiently met the expectations of increasingly demanding users and decision-makers. Thus improved forecasts better met existing policies.


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3- Contact

3.1 Purveyors

Abdou Ali, Minoungou Bernard and Hamatan Mohamed AGRHYMET Regional Centre, Niamey, Niger

3.2 Clients/users

Nakohoun Lokou Pascal, Direction des Etudes et de l’Information sur l’Eau,

Ouagadougou, Burkina Faso Sidibé Ibrahima, Direction Nationale de l'Hydraulique du Mali, Bamako, Mali

Housseini Mohamed, Direction Nationale de l'Hydraulique, Niamey, Niger Olorunoje Addi Shuaib, Nigeria Hydrological Services Agency (NIHSA), Abuja,

Nigeria

4- Data production and results The aim of this study is to produce hydrological seasonal forecasts using the

Hydrological Prediction for Environment (HYPE) model, when forced with the System 5 seasonal forecast from ECMWF. The main steps of the study included:

Data collection: The data collected include ECMWF seasonal forecast ensemble (system 5) available in the CDS catalogue, WFDEI-reanalysis data, river discharge station data;

Quality assessment and bias adjustment by considering WFDEI-Reanalysis data as reference data. The quantile-quantile method was used for bias

correction; Forecasting river discharge with HYPE for Niger River: streamflow were

forecasted by forcing the Niger-HYPE model with ECMWF seasonal climate

forecasts at the scale of the rainy season in the Sahel (May-November). To properly characterize the initial conditions of the hydrological model, the

model was run over the historical period with the WFDEI re-analysis data. The hydrological forecast were done by considering first the ECMWF system 5 raw seasonal forecast and the ECMWF system 5 bias adjusted

seasonal forecast; The assessment of the predictive capacity;

The ensembles are revisited by putting less weight on low-performing ensemble members;

Interactions with clients at the regional level.

Area of study, observational data and hydrological model

The basic requirement to produce operationally hydrological seasonal forecast is

that all data sources are operationally available, which means that the data is

http://agrhymet.cilss.int/

http://www.dnhmali.org/

http://nihsa.gov.ng/


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continuously updated near real-time. The input data selected could be grouped

as hydrological measurements, climate and reanalysis data as well as seasonal

climate forecasts.

We used a semi-distributed, process-oriented, rainfall-runoff model called HYPE

(Lindström et al., 2010). The HYPE model was setup for the Niger River Basin

using available datasets on topography (Lehner et al., 2006), land use (JRC,

2003), soil (Batjes, 2012; FAO et al., 2009), lakes (Lehner and Döll, 2004), and

reservoirs (Lehner et al., 2011). The model uses the WFDEI reanalysis dataset

as climate forcing for daily precipitation, mean temperature, minimum,

temperature and maximum temperature (Weedon et al., 2012). The potential

evapotranspiration was estimated from daily minimum and maximum

temperature using the equation by (Hargreaves and Samani, 1982). The 2.1

million km2 river basin (figure 1) was divided into 803 linked sub-basins based on

the topography and derived drainage direction. The sub-basin size was chosen to

fit the coarseness of the available forcing data (0.5°x0.5°). Each sub-basin was

further divided into areas of homogeneous soil and land cover (in total 57 unique

hydrologic response units). The 48 largest reservoirs and lakes in the basin were

included in the model. A tailor-made extension to the HYPE model was developed

in order to simulate the Inland Niger Delta wetland. The principle was to allow

flooding of incoming discharge onto a dynamic floodplain area (growing and

shrinking in size with water stage) on which precipitation and evaporation occurs.

Part of the water on the floodplain can also return to the river system as the

flood is receding.

Figure 1: Selected key input data for the Niger-HYPE 2.0 model in the Niger River Basin.

Note that although the basin stretches into Sahara, the hydrologically active part of the basin is limited by the desert in the North

The model was calibrated for the period 1994-01-01 to 2009-12-31 against 56

discharge stations spread across the hydrologically active part of the basin,

(ABN, 2008; GRDC, 2012); and against potential evapotranspiration from the


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MODIS satellite (Mu et al., 2007). A conjunctive multi-objective calibration

approach was employed in order to obtain a balanced model for both river flow

and evaporation. The parameters in each sub-basin were not calibrated to each

gauging station. Instead they were linked to soil type and land cover type, and

applied throughout the basin. As such the model was calibrated against all

stations simultaneously (to assist in making predictions in ungauged basins). The

calibration objective was hence to obtain a balanced model performance at all

stations simultaneously with this single parameter set, rather than to obtain

optimal performance at any given station. The period 1979-01-01 to 1993-12-31

was used for independent validation.

4.1 Step 1: Data collection from CDS, quality assessment and bias

adjustment

Description

Data collection: Seasonal climate forecasts available from the C3S and the CDS

catalogue were used to force the Niger-HYPE model. In particular, ECMWF

System 5 seasonal forecasts (S5) was used. A seasonal forecast is produced

each month. The forecasts have an initial date of the 1st of each month, and run

for 7 months. Forecast data and products are released at 12Z UTC on a specific

day of the month. For SEAS5, this is the 5th. While SEAS5 is expected to be

operational from 1 Nov 2017, forecasts for 1 Jan 2017 through to 1 Oct 2017

have also been completed and archived for reference.

Quality control: To force the HYPE model, climate data are estimated at the

scale of the 803 sub-watersheds in the Niger basin. The data are available on

regular grids and it was first necessary to analyze their consistency once

estimated on the subwatersheds. We compared the seasonal climate forecast

data with the reference data used to calibrate the HYPE model.

Bias adjustment: Bias correction uses historical WFDEI re-analysis data to

readjust the simulations from the seasonal forecasts (ECMWF system 5). This

correction uses a statistical transformation whose goal is to find a function h

which considers the data of the model as being equal to the distribution of the

data observed according to the following formula:

𝑃𝑜 = ℎ(𝑃𝑚) (1)

𝑃𝑜 = 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑑𝑎𝑡𝑎

𝑃𝑚 = 𝑆𝑖𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑑𝑎𝑡𝑎

In practice, one must first find a common period for both data sources. The

WFDEI historical data, used to calibrate the HYPE model, being available over the

period 1979-2013 and those of the seasonal forecasts over the historical period

1993-2015, the period 1993-2013 was considered for the correction of bias.


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The statistical transformation is an application of the probabilistic integral

transformation (Angus 1994) and if the distribution of the variable of interest is

known, the transformation is defined as:

𝑃𝑜 = 𝐹0−1(𝐹𝑚(𝑃𝑚)) (2)

𝑤ℎ𝑒𝑟𝑒 𝐹𝑚 𝑖𝑠 𝑡ℎ𝑒 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 (𝐶𝐷𝐹) 𝑜𝑓 𝑃𝑚

𝐹0−1 𝑖𝑠 𝑡ℎ𝑒 𝑖𝑛𝑣𝑒𝑟𝑠𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐶𝐷𝐹 𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑡𝑜 𝑃𝑜.

The practical goal is to find the best approximation of h and different approaches

have been proposed in the literature. The method chosen to solve equation (2)

was to use the CDFs of observed and simulated values. Following the procedures

of Boé et al (2007), CDFs are estimated using empirical percentile tables. Values

between percentiles are obtained by linear interpolation. The "qmap" package

developed under the R language contains this method of bias correction. This is

what we used in this study.

Results Data collection: ECMWF SEAS5 hindcast and forecast were collected. Historical

data range from 1993 to 2015 while the 2018 forecast was considered. Each

year, data cover West Africa over the period from May to November. The 25

ensembles member were used.

Quality control: The figures below represent the parameters estimated at the

level of the sub-basins of the Niger River. Considering the outputs of the model

initialized on April of each year, the season covers the period from May to

November of each year. Over this period, the seasonal average rainfall over the

period 1993-2013 varies from 5 mm in the North to over 3280 mm in the South,

considering the average of the 25 ensemble member used (see figures 2 and 3).

WFDEI mean inter-annual rainfall varies from 28 mm in the North to 2383 mm in

the South. Figure 4 below shows the absolute difference between the average

inter-annual rainfall over the period 1993-2013 and that of system 5. This

corresponds to a relative error of -80 to 80% over the entire Niger basin between

re-analysis data and system 5 data (Figure 4). The mean temperature varies

from 20.62 ° C to 33.62 ° C, over the period 1993-2013 for the re-analysis data

and between 17.92 ° C and 31.70 ° C for the average of 25 member sets at the

same time. The relative error between these two data sources ranges from -4 to

0.5 ° C (Figure 7). The inter-annual comparison of the two data sources is

represented by the figures 8 and 9. Figures 10 and 11 indicate the PBIAS for

comparison between the two datasets.


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Figure 2: Seasonal average (1993-2013) precipitation

(mm) from WFDEI reanalysis data

Figure 3: Seasonal average (1993-2013) precipitation

(mm) from ECMWF system 5 data (mm)

Figure 4: Relative error

between WFDEI and ECMWF System 5 Precipitation

Figure 5: Seasonal average

(1993-2013) temperature (°C) from WFDEI reanalysis data

Figure 6: Seasonal average

(1993-2013) temperature (°C) from ECMWF system 5 data

Figure 7: Relative error

between WFDEI and ECMWF System 5 temperature

Figure 8: Temporal evolution of WFDEI precipitation compared to precipitation of ECMWF system 5 (before bias correction)


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Figure 9: Temporal evolution of WFDEI temperature compared to temperature of ECMWF system 5 (before bias correction). Y axis is Temperature in degree Celsius.

Figure 10: PBias (in %) between WFDEI

precipitation and ECMWF System 5 Precipitation


temperature and ECMWF System 5 temperature

Bias correction: The figures (12-15) represent the quantile-quantile plots of the

data from the ECMWF system 5 (before and after bias correction) according to

those of the WFDEI data. The first ensemble of seasonal forecast data from

models initialized on May 1 were used to illustrate the results obtained. After bias

correction, the inter-annual comparison of the two data sources is represented

by the figures 16 and 17. Figures 18 and 19 indicate the PBIAS for comparison

between the two datasets. It is realized that the bias correction has improved the

quality of the temperature data. This is not the case for precipitation.


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Figure 12: Q-Q Plot of WFDEI Precipitation vs ECMWF system 5 Precipitation corrected and

uncorrected (corrected in green and uncorrected in red)

Figure 13: Figure 12: Q-Q Plot of WFDEI

maximum temperature vs ECMWF system 5 maximum temperature (corrected in green and

uncorrected in red)

Figure 14: Figure 12: Q-Q Plot of WFDEI

minimum temperature vs ECMWF system 5 minimum temperature (corrected in green and

uncorrected in red)

Figure 15: Figure 12: Q-Q Plot of WFDEI mean

temperature vs ECMWF system 5 mean temperature (corrected in green and

uncorrected in red)


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Figure 16: Temporal evolution of WFDEI precipitation compared to precipitation of ECMWF system 5 (after bias correction)

Figure 17: Temporal evolution of WFDEI temperature compared to temperature of ECMWF system 5 (before bias correction)


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precipitation and ECMWF System 5 Precipitation (after bias correction)


temperature and ECMWF System 5 temperature (after bias correction)

4.2 Step 2: Hydrological forecasting

Description:

Once climate data, i.e., precipitation and daily seasonal temperatures, were

available, it was used to force the HYPE model with these data. To properly

characterize the initial conditions of the model, the model was run over the

historical period with the WFDEI re-analysis data.

Results

Figures 20 to 24 show the results for simulated streamflow at 5 stations for daily

values during the period from 1993-2013. As a preliminary evaluation, we

computed the percent bias (PBias) and the (KGE) for the complete observed-

simulated record (1993-2013). We consider as “observed” data here, the

discharges obtained by forcing HYPE with the WFDEI re-analysis data.

Figure 20: Comparison of the simulated

(orange) and simulated with WFDEI (blue) long-term (1993-2013) annual mean

streamflow at Koulikoro station



streamflow at Lokoja station


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streamflow at Makurdi station



streamflow at Niamey station

Figure 24: Comparison of the simulated (orange) and simulated with WFDEI (blue) long-term (1993-2013) annual mean streamflow at Sirba station

The figures below give KGE and PBIAS on the main hydrological stations

considered. For example for Koulikoro station some years are clearly different

whereas for Niamey the performance is not really related to any given year. To

improve the quality of the model, investigations must be conducted to better

understand the initial conditions considered by the model.

Figure 25: KGE between simulated with WFDEI

and simulated discharge at Koulikoro station

Figure 26: PBIAS (in %) between simulated with

WFDEI and simulated discharge at Koulikoro station


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and simulated discharge at Lokoja station

Figure 28: PBIAS (in %) between simulated with WFDEI and simulated discharge at Lokoja station


and simulated discharge at Makurdi station


WFDEI and simulated discharge at Makurdi

station


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and simulated discharge at Niamey station


WFDEI and simulated discharge at Niamey station


and simulated discharge at Garbey Kourou a station


WFDEI and simulated discharge at Garbey Kourou station


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Forecast for 2018: After evaluating the quality of seasonal forecasts over the historical period, the

forcing of the model also produced the 2018 seasonal hydrological forecasts. Some derived products are represented by the figures 37, 39 and 41 and graphs 38, 40 and 42 below.

In figures 37 and 39, for “Compare to normal conditions”, green (yellow) [orange] colors indicate the probability of forecasts being above (near) [below]

normal conditions for the forecast period (May to November). The reference data is based on system5. The thresholds to define the normal conditions are the 66th and 33rd percentiles for the seasonal averages as these

are derived from the HYPE model simulation for the period 1993-2015.

The graphs of figure 40 showed one seasonal forecast at monthly time step from May to November.

Figure 35: Seasonal (May to November) total rainfall in the Niger River Basin from ECMWF climate seasonal forecasts in

2018


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Figure 36: Graph of the mean seasonal precipitation over the river Niger basin compared to the reference period

Figure 37: Seasonal precipitation forecast of 2018

compared to reference

Figure 38: Graph of the mean seasonal streamflow over the river Niger basin compared to the reference period

Figure 39: seasonal streamflow forecast of 2018 compared

to reference


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Figure 40: 2018 monthly streamflow (May to November) in some stations

4.3 Step 3: Evaluate predictive capacity

Description

It was a question of checking whether the forecasting of the characteristics of the

rainy season corroborated with the observations.

Results

The flows were above normal on all the hydrometric stations situated on the

main course of the Niger River or on the outlet of the large sub-basins. These observations match well with those of the seasonal hydrological forecasts of

2018. However, given the problems of model initialization and bias correction, forecasts should mainly be improved on small sub-watersheds with a fairly rapid

response.

4.4 Step 4: Revised ensemble

Description

It is to construct a revised ensemble, by putting less weight on low-performing

ensemble members. As a reminder, 25 ECMWF ensemble members were used to

produce seasonal hydrological forecasts.

Results

Figures 25 to 34 show the performance of each ensemble member to reproduce

the flow over the historical period considered (1993-2015). These performance

criteria show that all ensemble member have almost the same predictability

capabilities. Given this fact, one way of representing the products would be to

consider the characteristic quantiles of flows, obtained from the 25 ensemble

member (minimum, first quantile, median, third quantile and maximum). The

graphs of figure 41 below show the performance of the model obtained taking

into account the mentioned quantiles.


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Figure 41: Evaluation of the model's performance by considering the quantiles of streamflow

4.5 Step 5: Stakeholder communication

Description

The communication took place through a workshop organized from 20 to 21 2018 December in Niamey (Niger). It saw the participation of the representatives of the national hydrology departments of four countries of the Niger River basin

(Burkina Faso, Mali, Niger and Nigeria). The main objectives of this workshop were to share and get feedback from clients on the new hydrological products of

seasonal forecasts developed as part of the C3S_422_Lot1_SMHI contract implementation.

Results


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Some points discussed during this workshop include:

Presentation of the C3S_422_Lot1_SMHI contract; Presentation of the seasonal forecast approach using hydrological rainfall-

runoff modeling; Presentation of new seasonal hydrological forecast products followed by

exchanges;

Presentation of the interactive atlas.

Figure 42: Group photo of workshop participants

Figure 43: Presentation session of the interactive atlas

Clients said that using climate seasonal data for hydrological rainfall-runoff

model meets their needs more than using old statistical methods of seasonal forecasting. The main suggestions made would be to consider the season's

analyzes according to the flood return periods specific to each hydrometric station. They also propose to define warning thresholds from which potentially floodable areas can be identified from the results of forecasts.

5- Conclusion of full technical report

Seasonal hydrological forecasts are part of the annual activities of the AGRHYMET Regional Center for more than 20 years. They allow to have a global vision of the

rainy season, necessary for a better planning of the agro-hydro-pastoral activities. The use of Copernicus Climate Change Service data (seasonal climate forecasts) as input for the HYPE model has provided more information to end-

users to support decision-making. The methodology developed in this case study will be improved and will be used for annual hydrological seasonal

forecasting in the West African region. That allowed us to produce the following information: i) cumulative rainfall map for the rainy season (May to November), (ii) map showing the rainfall situation of the season (above, near or

below normal considering 1993-2015 as reference period), (iii) graph of the mean seasonal precipitation over the basin compared to the reference period

(1993-2015), (iv) map showing the hydrological situation of the season (above, near or below normal considering 1993-2015 as reference period), (v) graph of the mean seasonal streamflow over the Niger Basin compared to the reference

period (1993-2015), (vi) graphs comparing monthly streamflow of 2018 to those of the reference period (1993-2015).

The meeting with the clients showed that using climate seasonal data for hydrological rainfall-runoff model meets their needs more than the methods previously used (empirical methods of seasonal forecasting). The


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predictability of 2018 hydrological seasonal products were assessed. The main

challenges we faced were the initialisation of the model, the bias correction (the reference data to be considered and the appropriate method). Investigations in

these areas should continue to improve the quality of results.


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