CL2.17

Between 1970 and 2019, 79% of disasters worldwide involved weather, water, and climate-related hazards. These disasters accounted for 56% of deaths and 75% of economic losses from disasters associated with natural hazards reported during that period. As climate change continues to threaten human lives, ecosystems and economies, risk information and early warning systems (EWS) are increasingly seen as key for reducing these impacts. The majority of countries, including 88% of least developed countries and small island states, that submitted their Nationally Determined Contributions (NDCs) to UNFCCC have identified EWS as a “top priority”.

This latest multi-agency report, coordinated by WMO, highlights progress made in EWS capacity – and identifies where and how governments can invest in effective EWS to strengthen countries’ resilience to multiple weather, water and climate-related hazards. Being prepared and able to react at the right time, in the right place, can save many lives and protect the livelihoods of communities everywhere.

How to cite: Grasso, V.: 2020 State of Climate Services report: Risk Information and Early Warning Systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-74, https://doi.org/10.5194/egusphere-egu21-74, 2021.

Despite recent and mostly global efforts to promote climate services in developing countries, Africa still faces significant limitations in its institutional infrastructure and capacity to develop, access, and use decision-relevant climate data and information products at multiple levels of governance. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society, strives to overcome these challenges by targeting the way climate-sensitive decisions are made at the local, regional, and national levels. The ENACTS approach is executed by working directly with the National Meteorological and Hydrological Services (NMHS) to build capacity for improving the availability, access, and use of quality climate data and information products at relevant spatial and temporal scales. The ENACTS approach has shown to be an effective means to transform decision-making surrounding vulnerabilities and risks at both national and local scales in over a dozen countries at the national level as well as at regional level East and West Africa. In the ENACTS approach, challenges to the availability of climate data are alleviated by combining quality-controlled station observations with global proxies to generate spatially and temporally complete climate datasets. Access to climate information is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products. Use of the generated climate data and the derived information products are promoted through raising awareness in relevant communities, training users, and co-production processes.

How to cite: Dinku, T.: ENACTS: Transforming Climate Services Across Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-39, https://doi.org/10.5194/egusphere-egu21-39, 2021.

In the former Cordex program, regional climate models were run over Africa and only covered the western part of the South Indian Ocean at a coarse 50-km resolution while a 12-km resolution was used for Europe. A 50-km resolution is insufficient for island territories as small and steep as those in the Indian Ocean. Yet this area is especially vulnerable to natural catastrophes related to the effects of climate change: it is the third region in the world most affected by extreme climatic events. The need for climate services over that populated area has now become a critical issue.

Both dynamical and statistical downscaling from a few ongoing CMIP6 simulations were therefore used to obtain regional climate information on a large area of the southwest Indian Ocean that includes most of the inhabited countries from the coasts of Mozambique (33°E) to 74°E as well as the main area of tropical cyclone genesis [2-28°S]. The limited area model ALADIN was implemented in its climate version at 12-km resolution and the first runs were coupled by outputs from one of the CMIP6 Earth Simulation Coupled Models named CNRM-ESM2-1.

We will present the numerical and statistical tools used for this regional climate study as well as the first projections obtained for ssp126 (RCP2.6), ssp245 (RCP4,5) and ssp585 (RCP8.5) scenarios over the 2015-2100 period. Results will be illustrated for the southwest Indian ocean basin as well as for the main islands of the IOC member countries: Madagascar, Reunion, Mauritius, Seychelles, and Comoros where observations over the 1981-2010 period were used for model bias correction using the quantile-quantile matching method. For climate uncertainty representativeness, the 2015-2100 evolution of both ALADIN and CNRM-ESM2-1 temperature and precipitation averages over our region will be compared to that of the other available CMIP6 simulations.

This work is part of the BRIO (Building Resilience in the Indian Ocean) project which aims at supporting the Indian Ocean Commission (IOC) member countries in the implementation of their adaptation policies with respect to climate change (regarding water resources, health and other issues). This project will provide a set of high quality climate-related data on a free-access online regional portal as well as climate services. It is funded by the Agence Française de Développement (AFD), through the Adapt’Action Facility, in cooperation with the IOC.

How to cite: Leroux, M. D., Bonnardot, F., Kotomangazafy, S., Veerabadren, P., Ridhoine, A. O., Somot, S., Alias, A., and Chauvin, F.: Regional climate projections and associated climate services in the southwest Indian ocean basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7029, https://doi.org/10.5194/egusphere-egu21-7029, 2021.

The aim of the Flagship Pilot Study (FPS) “Modelling the Southeast African regional Climate” is to study processes and phenomena relevant to regional climate change in south-eastern Africa. The region is vulnerable to climate change due to socio-economic factors as well as its exposure to weather and climate extremes such as floods, droughts and heat waves. The FPS will foster regional collaboration on modelling and the analysis of precipitation and temperature that will be beneficial for the society in general. The FPS South-eastern Africa includes various scientists from the National Meteorological and Hydrological Services (NMHSs) and academia of South Africa, Mozambique, Zimbabwe, Malawi, Tanzania, Kenya, Rwanda, Burundi and Norway. The research will involve analysis of local observations, reanalysis, simulations from regional climate models (RCMs) and empirical-statistical downscaling (ESD) to study dependencies between large-scale conditions and local variability in the rain and temperature statistics. The expected impacts of the FPS are skills development in data analysis and modelling, and a better understanding of regional climate that is fundamental to climate services and provides guidance to decision-makers and planners. The involvement of NMHS in the project provides access to their observational networks, whose use will assist with verification of model simulations, and also increase the value of NMHSs’ work with observations and data management. Actionable information will be extracted for decision-makers, based on a synthesis of multiple sources of information which take into account the local climate, past and future trends, models’ skill, known weather/climatological phenomena, and other geographical information. Biases between the model climate and observations will be adjusted through appropriate adjustment methods such as the Quantile Mapping approach. The work will also involve capacity building on R programming language as well as other tools (e.g. CDO, python) and use R-based shiny web applications in distillation efforts and to provide a gateway to the information embedded in complex data structures.

How to cite: Benestad, R., Nhantumbo, B., Prosper, A., Ndakize Sebaziga, J., Tuyisenge, A., Vanya, C., Chaimite, B., and Bopape, M.-J.: Modelling the Southeast African regional Climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5319, https://doi.org/10.5194/egusphere-egu21-5319, 2021.

Flooding is a rapidly growing concern in West Africa. In 2020 alone, several hundred people died and 100 000 were displaced by the floods that occurred across the region. The floods damaged houses and crops and washed away livestock, threatening the livelihoods of millions. Niamey, the capital of Niger, experienced a record flood with the highest ever recorded water levels in nearly 100 years. Flooding is also projected to increase with climate change. One component in addressing this challenge – and a concrete way to adapt to the changing climate – is to provide operational forecasting and warning services to enable pre-emptive stakeholder action and thereby minimize damages.

Since 2018, a pre-operational flood forecasting and warning service for West Africa has been co-designed, co-developed, co-adapted, and co-operated within the FANFAR project (https://fanfar.eu/, https://doi.org/10.5194/egusphere-egu2020-7660). This study presents results from two approaches employed to assess the accuracy and utility of the service.

Firstly, representatives from hydrological services, emergency management agencies, river basin organisations, and regional expert centres in 17 countries have contributed to develop and evaluate the service. Specifically, each participating organisation was asked to test the service during the 2019 and 2020 rainy seasons, to record the most critical flood events and the extent to which FANFAR captured the location, timing, magnitude and severity of the floods. The results indicate that both the use and accuracy of the service varies substantially (e.g. from 90% correct in some countries to not even used in others). This people-centred assessment approach also provided an important opportunity to learn about the many events that occur outside of hydrometric monitoring networks, and the way in which agencies communicate flood risk information to multiple audiences for appropriate decision-making.

Secondly, we evaluated FANFAR forecasts against conventional gauge observations at key locations (e.g. Niamey). The effect of different system configurations on forecast performance was assessed (e.g. the benefit of model calibration and assimilation of gauge observations). The results likewise indicate a performance spread, and sometimes ability to capture certain features of a flood but not all. For example, for the record flood in Niamey in 2020, FANFAR managed to forecast the timing and severity level at the onset of the flood, but not the extent or long duration of the flood.

We finish off by reflecting on some challenges and opportunities for operational, scalable and reliable 24/7 weather and climate services in West Africa, with potential applicability in the global South.

How to cite: Andersson, J., Hamatan, M., Kuller, M., and Shuaib, A.: Toward operational flood forecasting and warning services across West Africa – recent experiences at national and regional scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3027, https://doi.org/10.5194/egusphere-egu21-3027, 2021.

Variations in water levels of the Negro River, that flows through the Port of Manaus, can cause considerable regional environmental and socio-economic losses. It is therefore critical to advance predictions for water levels, especially flood levels, to provide more effective and earlier warnings to safeguard lives and livelihoods. Variations in water levels in free-flowing river systems, like the Negro follow large-scale precipitation anomalies, which offers an opportunity to predict maximum water levels using observed antecedent rainfall. This study aims to improve the performance and extend the lead time of statistical forecasts for annual maximum water level of the Negro River at Manaus, relative to operational forecasts. Multiple linear regression methods are applied to develop forecast models, that can be issued in March, February and January, with the best possible combinations potential predictors: observed antecedent catchment rainfall and water levels, large-scale modes of climate variability and the linear trend in water levels. Our statistical models gain one month of lead time against existing models, but are only moderately better than existing models at similar lead time. Using European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal reforecast data with our statistical models, further gains an additional month of lead time of skilful performance. Our models lose performance at longer lead times, as expected. Our forecast models can issue skilful operational forecasts in March or earlier and have been successfully tested for operational forecast of 2020. This method can be applied to develop statistical models for annual maximum water level over other free-flowing rivers in the Amazon basin with intact catchments and historical water level record.

How to cite: Chevuturi, A., Klingaman, N. P., Woolnough, S. J., Rudorff, C. M., Coelho, C. A. S., and Schongart, J.: Forecasting annual maximum water level for Negro River at Manaus, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4594, https://doi.org/10.5194/egusphere-egu21-4594, 2021.

The 4onse project (Four times Open Non-conventional system for Sensing the Environment) was born by the collaboration between SUPSI and two universities in developing countries namely the University of Moratuwa in Sri Lanka and the Institue of Space Technologies in Pakistan. The activities led to the installation of more than 30 stations in the Deduru Oya basin in Sri Lanka, following the development and testing of a prototype. The rise of technologies for Smart City and the Internet of Things (IoT) makes this project of interest to both the scientific and the private world, also considering the growing concern for environmental and climate issues.

The environmental monitoring system has been designed and developed on the wave of openness, which increasingly pervades not only scientific activity, but also the commercial sectors at different levels. Based on this philosophy, the selected hardware and software technologies have been evaluated in terms of quality, durability and sustainability and are showing very promising results. Unlike conventional systems, where the adoption of closed solutions strongly limits interoperability and data sharing, the designed solution is characterized by a high reproducibility and interoperability, guaranteed by the adoption of open software and standards for the collection and distribution of data. Such a technology can be applied and further developed for monitoring natural and non-natural environments that require low-cost sensor components with a level of quality comparable to conventional systems commonly used. This cost-effective solution is a possible alternative for the implementation of sensor networks in particular in low-income or developing countries in order to manage natural risks or water resources.

The solution consists of three different layers: hardware, server and communication layer. The hardware layer consists of a weather station based on Arduino and sensors measuring environmental variables. This kind of prototype has been validated thanks to the comparison of the time series with the data of an official weather station of the hydro-meteorological network of the Canton Ticino. The second layer is characterized by the server infrastructure that stores the measured data using the istSOS database management system (DBMS), which makes them accessible thanks to the implementation of open standards such as the Sensor Observation Service (SOS) of the Open Geospatial Consortium (OGC). Finally, the communication layer concerns the use of GPRS for the transmission of data from the node to the server that has been optimized in terms of energy and bandwidth consumption in order to guarantee stable and fast communication.

The research project has reached the end of the activities and during this presentation the main results and outputs will be presented.

How to cite: Strigaro, D., Cannata, M., Ratnayake, R., Sudantha, B., and Sahid, I.: 4onse project results: quality and costs evaluation of an open and low-cost monitoring network of 30 stations in Sri Lanka, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12468, https://doi.org/10.5194/egusphere-egu21-12468, 2021.

Seasonal hydrometeorological forecasts have the potential to significantly improve the regional water management, disaster preparedness and climate proofing, particularly in water-scarce regions. They also allow for the development of forecast-based action plans for extreme climatic events like droughts and anomalous wet conditions. However, raw global products from data providers like the European Centre for Medium Range Weather Forecasts (ECMWF) cannot be directly used for regional applications due to model biases and drifts as well as a coarse spatial resolution of 35km and more. Furthermore, for transferring the information from ensemble-based forecasts into practice, we have to provide derived and tailormade forecast quantities for the water management in a user-friendly way. In this study, we hence present an operational post-processing and online decision support system with which we a) regionalize ECMWF’s latest seasonal forecast system SEAS5 through a Bias-Correction and Spatial Disaggregation (BCSD) technique, b) compute tailored forecast measures like categorical forecast and drought indicators and c) visualize this information through an online platform. As reference, we are using the offline re-run of ERA5’s land surface component, namely ERA5-Land. Our final forecast product comprises daily ensemble forecasts for precipitation, temperature, and radiation, has a spatial resolution of 0.1°, covers the whole period from 1981 to the present and is provided for several climate-sensitive river-basins including the Rio São Francisco (Brazil), the Blue Nile (Sudan / Ethiopia) and the Karun (Iran). Derived forecast quantities are operationally computed and visualized through an online decision support system, that was jointly developed with water experts from the different study regions. As both the forecast repository and the online decision support system are publicly available, they provide a comprehensive framework for demonstrating how seasonal forecasts can be post-processed and tailored for the day-to-day water management. They further allow for the training and education of local stakeholders and water experts how to deal with seasonal forecasts. Our forecasting system is already used by several authorities and weather services in Iran, Sudan and Brazil. It thereby constitutes a large step towards an improved disaster preparedness and, hence, the climate proofing of the water sector particularly in these semi-arid regions.

How to cite: Lorenz, C., Portele, T., Kukuk, T., and Kunstmann, H.: From global forecasts towards regional decision support: development of a full-fledged seasonal forecasting framework for semi-arid regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15715, https://doi.org/10.5194/egusphere-egu21-15715, 2021.