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Copernicus Climate Change Service – Global Impacts

Quality Assurance Checklist (QUACK) - reporting

What is QUACK:

This tool is developed for the showcases of in C3S_422_Lot1_SMHI to promote high quality in each showcase

For a set of quality indicators it offers you structured questions in combination with guidance material

How to apply QUACK*:

For each quality indicator you will find assessment questions. Please state if they apply for your showcase.

For each assessment question; you will find supporting material under: http://climateservice-global.eu/quality-assurance/

Give a short description about how you treated the quality indicators

Wherever possible – provide any documents, links, scripts, etc. that support your performance

Note: Please save the file name as: Organization_Country_QUACK_reporting (e.g SMHI_Sweden_QUACK_reporting.docx)

*in case you have any questions contact: maida.zahid@hzg.de

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Showcase name: Improve the quality of seasonal forecast information to strengthen the resilience and improve the food security in West Africa

Short description of a showcase: The objective of this case study in the C3S global service is to contribute to improve the seasonal forecast for food security in the region and also to contribute to flood risk assessment at seasonal level. In fact, despite the efforts made over the last 20 years by the AGRHYMET Regional Centre and its partners, the seasonal forecasts developed are less useful because they are purely probabilistic and not deterministic. They no longer sufficiently meet the needs of users. At the end of the project, this case study will provide decision-support products for strengthening the resilience of Sahelian populations.

Organization: AGRHYMET Regional Centre, Niamey-Niger

Reporter name & email: MINOUNGOU Bernard, minoungoubernard@yahoo.fr Last updated and Place: 20/01/2019

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Dimension: Input data

Criterion: Scientific & methodological quality

Quality indicator

Applied

Short description

Supporting documents

Comments

Yes

No

Transparency

Are all data sources referenced? Please provide which category (observation, simulation, etc) of data you used and the source.

Example: Forcing data: Precipitation , Station data, 1 h, 24 stations, national weather service

Simulation: GCM data, 50 km resolution; ensemble of five models; RCP 4.5 and 8.5, provided by C3S_422_Lot1_SMHI

x The WFDEI meteorological forcing

data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA‐Interim reanalysis data. The data used in the framework of this study cover the period 1979–2013. The methodology of data production is described in the article entitled « The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data » (Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo (2014), The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim

ECMWF SF System5: http://www.uio.no/studier/emner/matnat/geofag/GEF4220/v09/undervisningsmateriale/Persson_user_guide.pdf. WFDEI Reanalysis data: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014WR015638

The HYPE model was used in this study. This model was developed using the following static data: Topography from USGS HydroSHEDS (15’) Soil from Harmonized World Soil Database (HWSD) + World Inventory of Soil Emission Potentials (WISE) and Food and Agriculture Organisation (FAO) Land Use Land Cover from ESA GlobCOVER

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reanalysis data, Water Resour. Res., 50, 7505–7514,

doi: 10.1002/2014WR015638.). The data necessary for the hydrological modeling are the daily average flows recorded on the different hydrological stations of the basin. The data used for this summary analysis come from several sources and may exhibit differences in the lengths of the series, and especially in their amplitudes. The main sources of data available are:

The database of the Niger Basin Authority (NBA) : These data are largely measured ;

The database of AGRHYMET Regional Centre (ARC). These data are divided into two groups: Measured data collected from the hydrological services of the different countries involved in the Niger Basin ; and Reconstructed data from hydrological models.

The database of Nigeria Hydrological Services Agency (NIHSA). These data come

Lakes and dams from Global Lakes and Wetlands Database (GLWD) and GRanD

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directly from hydrological services in Nigeria.

56 discharge stations spread across the hydrologically active part of the basin, (ABN, 2008; GRDC, 2012) are used. The Copernicus Climate Change Service (C3S) has developed seasonal forecast products. These products are based on data from several state-of-the-art seasonal prediction systems. The ECMWF forecasts are created by using computational models to calculate the evolution of the atmosphere, ocean and land surface starting from an initial state based on observations of the Earth system. The ECMWF forecasts are created using numerical models. These models solve a complex set of hydrodynamic equations that describe the evolution of the atmosphere and ocean and include a set of parameterizations, which approximate how some processes such as convective precipitation affect this evolution ( available in the CDS catalogue). These data are described is the document downloadable with this link. The

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ECMWF seasonal forecast ensemble (system 5) are available in the CDS catalogue Below are the characteristics of ECMWF data: Spatial Resolution: 30km Spatial coverage: West Africa Temporal coverage: 7 months Parameters: Precip, Tmean, Tmin, Tmax Number of ensembles: 25 Initialization: each month The full metadata is given in Annexure 1.

Do the metadata follow international standards?

x Yes, the metadata follow international standards

Appropriateness

Are the used data appropriate for the case study in terms of spatial and temporal scale?

x The hydrological model used within this study uses the WFDEI reanalysis dataset as climate forcing for daily precipitation, mean temperature, minimum, temperature and maximum temperature (Weedon et al., 2014). It therefore seemed appropriate to consider these data as baseline data in the rest of the process of producing

Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo (2014), The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data, Water Resour. Res., 50,

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and assessing seasonal hydrological forecasts for the Niger Basin. The hydrometric stations considered cover the whole basin as well as possible. They are regularly monitored and their data is updated. The ECMWF system 5 climate seasonal forecast data are appropriate for the case study both spatially and temporally. They are on a daily scale and cover the entire study area.

7505–7514, doi: 10.1002/2014WR015638 https://www.ecmwf.int/en/newsletter/154/meteorology/ecmwfs-new-long-range-forecasting-system-seas5

Completeness

Is the technical consistency checked (no outliers, no gaps in time series, etc.)?

x

Like most of hydrological observed data, the data that we have used contains gaps but can be used. The system 5 climate seasonal forecast data doesn’t have gaps in time series.

Do you have all available data sets considered?

The WFDEI data are available for the period 1979-2013 for the four parameters required by the HYPE model. All of these data were used in this study.

WFDEI Reanalysis data: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014WR015638

The hydrological data were provided by the national hydrological services of West Africa countries.

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Like most of hydrological observed data, the data that we have used contains gaps but can be used. The ECMWF seasonal forecast data are complete. These data are complete and cover the period considered: the rainy season in the Sahel region.

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Dimension: Processing

Criterion: Scientific & methodological quality

Quality indicator

Applied

Short description

Supporting documents

Comments

Yes

No

Transparency

Are the processing steps (i.e. scripts) well documented and reproducible?

x 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. Bias correction uses historical WFDEI re-analysis data to readjust the simulations from the seasonal forecast models (ECMWF system 5). The documented scripts used for quality control and bias adjustment are available through this link. The source codes of the HYPE model are also available on the SMHI website.

Scripts: https://gist.github.com/Benminoungou HYPE source codes: https://sourceforge.net/projects/hype/files/ HYPE documentation : http://www.smhi.net/hype/wiki/doku.php

We have uploaded all the scripts that we have written in github.

x To develop hydrological forecasts, most studies force a rain-flow model with

https://www.hydrol-earth-syst-

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Are the applied methods well documented and follow scientific standards (i.e. peer reviewed)?

climate data and this is what we are talking about. The bias correction method used to correct the climate data is the quantile-quantile method and is well documented. This is one of the most used methods for correcting climate data.

sci.net/16/3383/2012/hess-16-3383-2012.pdf

Appropriateness

Are the used methods, for instance bias-adjustment, appropriate for the case study?

x The quantile-quantile method is one of the most used methods for bias correction of climate data. This method is used within this study case. The method is well documented in this paper. However, given the unsatisfactory results obtained with precipitation, we are in contact with SMHI to test other correction methods (DBS method)

https://www.hydrol-earth-syst-sci.net/16/3383/2012/hess-16-3383-2012.pdf

Accuracy/Robustness

Are the uncertainties assessed systematically in a standard manner?

x The case study aims to produce seasonal forecasts by forcing the HYPE model with seasonal climate data from ECMWF. The 25 available member ensembles were therefore used.

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Is the case study based on different global or regional climate model simulations? In case that not all simulations of an ensemble are used, is the selection well explained and justified?

x Not applicable

Is the showcase using climate emission scenarios i.e. a low, medium and high scenario? (Please specify the name of scenarios used in the showcase).

x The showcase considers all the 25 available ensemble members from ECMWF system 5

Validation

Is validation possible for the showcase? Is there a procedure in place for a validation against independent data?

x The performance of the model to

simulate flows was evaluated over the historical period (1993-2013). The usual performance criteria have been calculated. If there are other sources of climatic data that can be inputs to the HYPE model, it is possible to validate this study with them. For example, we conducted the same study with seasonal climate

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data from ECMWF, system 4. This data can therefore be used for validation.

Reflectivity

Is the scientific consistency among multiple data sets and their findings well documented? (i.e. comparing your results to the existing peer reviewed studies in the region)

We had already conducted this study with other data sources.

https://www.weadapt.org/placemarks/maps/view/19476

Criterion: Practical Relevance

Suitability for target group

Is the relevance for the client assured?

All indicators to be produced are relevant to the client. As highlighted in the study description, clients are interested in quantitative seasonal indicators of water resources.

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Dimension: Output

Criterion: Scientific & methodological quality

Quality indicator

Applied

Short description

Supporting documents

Comments

Yes

No

Transparency

Are the produced results provided with metadata?

x Results have been provided to clients. The metadata are also available for any purpose

Are the limits of provided information disclosed?

x Yes, the performances of the HYPE hydrological model that we used in this study are shared. The performance criteria considered are, among others, the correlation coefficient, the Nash coefficient, the bias, etc.

Consistency

About the visualization of your output: Do the graphics and tables of the presentation match the captions and explanations?

x Yes, the graphics and tables of the presentation match the captions and explanations.

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Reliability

Is the confidence of the data presented (e.g. the spread of the whole ensemble)?

x Yes, the spread of the data is presented using the appropriate graphs (boxplot of the output from the 25 ensemble members used for instance)

Criterion: Practical Relevance

Lucidity/Clarity

Is the presentation of your output clear and does It follow visualization standards?

x

Yes the presentation of the output follow visualization standards

These are maps, figures, graphs with explanations

Do the pictures, and maps used match the explanation i.e. story?

x Yes, the products are accompanied by explanations that allow them to be understood and exploited.

Rights of Use

Are the rights of dissemination clarified (i.e. open access)?

x The information produced are published in the interactive atlas. Here is a link to access to the interactive

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atlas: http://climateservice-global.eu/agrhymet/. For more information, contact the AGRHYMET Regional Center using the following address: administration.agrhymet@cilss.int

Usability

Is the output intuitively presented and freely accessible?

X The output are freely accessible in the interactive atlas (http://climateservice-global.eu/agrhymet/). The main output 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 stream flow over the Niger Basin compared to the reference period (1993-2015), (vi) graphs comparing monthly streamflows of

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2018 to those of the reference period (1993-2015).

Criterion: Review Process Review by third party

Are the results of the showcases reviewed externally or internally? To what level the review process was taken.

The study was validated internally by colleagues working in the field of hydrology. SMHI experts also gave their opinion on the study

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Dimension: Outcome

Criterion: Satisfaction

Quality indicator

Applied

Short description

Supporting documents

Comments

Yes

No

Usefulness

Does the product help for problem solving of your client?

We had a meeting with clients in December 2018. This 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).

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Annexure 1: Metadata of ECMWF system 5

Summary

The purpose of this paper was to study the predictability of hydrological forecasts in West Africa over the historical period (1993-2015) and to

produce seasonal forecasts for the year 2018. The aim was to force the hydrological model HYPE with ECMWF Seasonal Climate Predictions,

System 5. The analysis focused on the model input data (precipitation and temperature) and discharge, main outputs considered.

Units

Precipitation: mm

Temperature: °C

Discharge: m3/s

Geographic area

Minimum Longitude : -27 Maximum Longitude : 25 Minimum Latitude : 4 Maximum Latitude : 28

Spatial resolution (Forcing data) Historical data Longitude resolution: 0.28° Latitude resolution: 0.28°

Seasonal forecast for 2018

Longitude resolution: 0.5°

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Latitude resolution: 0.5°

Temporal resolution

May-November

Time periods

Reference period: 1993 - 2015 Current: 2018