Displays

In this study we assess the seasonal hydrological forecast skill of river flow persistence across a sample of 46 catchments representative of Ireland’s diverse range of hydrogeological conditions. This statistical approach is straightforward to implement as it uses a river’s most recently observed flow anomaly (calculated over a predictor period of a given duration) as its forecasted flow anomaly (for a given horizon). In our hindcast experiment, persistence skill is evaluated against a streamflow climatology benchmark and by assessing the correlations between predicted and observed anomalies. Using the most skilful predictor period of 1-week, we find that the majority of persistence forecasts outperform the benchmark between April and September at the 1-month forecast horizon. However, this narrows to solely the summer months when using 2- and 3-month horizons.  Skill declines with increasing durations of the predictor and/ or forecast horizon period as a catchment is given more time to “forget” initial anomalous streamflow conditions and/or to be impacted by “new” anomalies. High rainfall events, for example, tend to disrupt the persistence of flows and greater forecast skill is thus found in the relatively drier months.

The degree of persistence skill is also strongly conditional on the “memory” inherent to each catchment (i.e. their storage capacity), as indicated by physical catchment descriptors such as the Base Flow Index (correlation ρ with skill = 0.86). Persistence skill is greatest in lowland regions characterised by permeable lithologies, well drained soils and lower annual average rainfall totals. Physical descriptors can thus be used to anticipate the likely performance of river flow persistence as a forecasting tool in rivers outside the catchment sample. Through multiple linear regression analysis, we identified the combination of predictors that produced the best-performing model (adjusted R²= 0.89) and used it to predict the persistence forecast skill level expected in 215 catchments across the country at different horizons and seasons. Highlighting exactly when and where persistence provides higher predictive skill than the reference climatology forecast, we show the value of statistical flow persistence methods as a tougher-to-beat benchmark in the development of more sophisticated seasonal river flow forecasting methods at the catchment-scale. This research also underscores the scope for development of dynamical hydrological forecasting approaches in the wetter, poorly drained catchments underlain by impermeable lithologies, found mainly in the north-western and south-western regions of Ireland.

How to cite: Quinn, D., Murphy, C., Wilby, R. L., Matthews, T., Broderick, C., Golian, S., Donegan, S., and Harrigan, S.: Spatial and temporal patterns in seasonal forecast skill based on river flow persistence in Irish catchments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17646, https://doi.org/10.5194/egusphere-egu2020-17646, 2020.

Streamflow information for the months ahead is of great value to existing decision-making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate-related risks. Despite the large effort, there is still limited knowledge of the key drivers controlling the quality of the seasonal streamflow forecasts. In this investigation, we show that the seasonal streamflow predictability can be clustered, and hence regionalised, based on a priori knowledge of local hydro-climatic conditions. To reach these conclusions we analyse the seasonal forecasts of streamflow volumes across about 35400 basins in Europe, which vary in terms of climatology, scale and hydrological regime. We then link the forecast quality to various descriptors including physiography, hydro-climatic characteristics and meteorological biases. This allows the identification of the key drivers along a strong hydro-climatic gradient. Results show that, as expected, the seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. In addition, the predictability deteriorates with increasing lead months particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of drivers, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin’s hydrological regime, with quickly reacting basins (of low river memory) showing limited predictability. On the contrary, snow and/or baseflow dominated regions with long recessions (and hence high river memory) show high streamflow predictability. Finally, climatology and precipitation biases are also strongly related to streamflow predictability, highlighting the importance of developing robust bias-adjustment methods.

How to cite: Pechlivanidis, I., Crochemore, L., and Bosshard, T.: Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9149, https://doi.org/10.5194/egusphere-egu2020-9149, 2020.

Seasonal hydrological forecasts are a powerful tool for water-related decision making associated to hydropower production, water supply and irrigation. The skill of these forecasts depends mainly on knowledge of the initial hydrologic conditions (ICs) on the start date of the forecast and knowledge of climate forcing (CF) during the forecast period. Identification of the sensitivity of the forecast skill to these two main predictability sources is crucial to funnel the efforts into improving the appropriate predictive tools, by either improving the ICs estimates or by enhancing the quality of the CF. This work aims at investigating the impact of catchment properties in terms of soil permeability on the contribution of the dominant predictability sources (ICs, CF) to the seasonal forecast skill. To this end, we apply the End Point Blending (EPB) framework to create forecasts with intermediate levels of uncertainty concerning ICs and CF. The methodology is applied in two catchments in the upper Adige River Basin that are representative of the two extremes of hydrological response: the Gadera catchment closed at Mantana (area: 390 km², elevation range: 810–3050 m a.s.l.) that is highly permeable, hence slow-responding and the Passirio catchment closed at Merano (area: 402 km², elevation range: 360–3500 m a.s.l.) that is characterized by low permeability, hence by a fast-responding regime. Our analysis highlights the contribution of each predictability source to the forecast skill over catchments of contradicting hydrological response, as well as the added value of the elasticity framework introduced by the EPB in comparison to the traditional ESP/revESP approach for identifying the sources of seasonal hydrological predictability in alpine areas.

How to cite: Stergiadi, M., Di Marco, N., Avesani, D., Borga, M., and Righetti, M.: Sensitivity of seasonal hydrological predictability sources to catchment properties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18796, https://doi.org/10.5194/egusphere-egu2020-18796, 2020.

Headwater catchments with wetlands represent important buffer areas by decreasing peak discharges and providing water in meteorological droughts. Wetlands act also as key feature of the riverine carbon cycle and are able to store significant amounts of carbon. Therefore, understanding and predicting discharge generating processes in the context of climate change is essential for such catchments. We use a Regional Climate Model (RCM) Ensemble to study possible changes in discharge patterns due to climate change at the Lehstenbach catchment, located in the Fichtelgebirge Mountains. Our aim is to quantitatively estimate periods of hydrological droughts and floods, their temporal length and intensity, their recurrence intervals as well as possible connections to snow melt. In order to achieve this goal, we use the process-based model HydroGeoSphere to simulate discharge until 2100 based on the RCM Ensemble. Statistical Analysis, including Trend and Wavelet Analysis aids us in detecting changing discharge conditions. Discharge seems to follow an increasingly variable pattern making droughts and floods more likely in the future. Since the overall length of drought conditions increases although precipitation amounts remain fairly stable, we identified evapotranspiration and altered precipitation patterns as main driving forces of droughts in this headwater. Snow conditions and subsequent spring floods seem to decrease in likelihood until 2100.

How to cite: Hennig, L. and Frei, S.: Analysis and prediction of hydrological extreme conditions for a small headwater catchment in a German lower mountain range, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1533, https://doi.org/10.5194/egusphere-egu2020-1533, 2020.

In the coming decades, climate change will likely become a complex issue affecting hydrological regimes and flood hazard conditions. According to the IPCC reports, significant changes in atmospheric temperature, precipitation, humidity, and circulation are expected which may lead to extreme events including flood, droughts, heatwaves, heavy precipitation, and more intense cyclones. Although the effects of climate change on flood hazard indices is subject to large uncertainty, the evaluation of high-flows plays a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Intercomparison Project Phase 6 (CMIP6), flood managers are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. Here we examine catchment based projected changes in flood quantiles and extreme high flow events for Irish catchments, selected to be representative of the range of hydrological conditions on the island. Conceptual hydrological models, together with different downscaling techniques are used to examine changes in flood risk projected from the CMIP6 archive for mid and end of century. Results will inform the range of plausible changes expected for policy relevant flood indices, the sensitivity of findings to use of different climate model ensembles and inform the tailoring of adaptation plans to account for the new generation of climate model outputs.

How to cite: Meresa, H., Murphy, C., and Fealy, R.: Flood hazard estimation and climate change: impacts and uncertainties for Irish catchments using CMIP6, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20032, https://doi.org/10.5194/egusphere-egu2020-20032, 2020.

Nowadays flood warning systems are extremely important since they can provide critical information that can protect property and save lives. These systems should alert about whether a flood should be expected, when it will occur and how severe it will be.

A warning system can be based on the analysis of historical events and a good monitoring system or it can be based on the capacity of predict the channel flow in key locations. In the second case, these type of systems, known as forecast systems, consider the meteorological predictions as driving forces for a hydrological model which estimates the channel flow for the next few hours and days, considering the processes that take place in a watershed. A hydrological forecast can only be reliable when a good calibration and validation of watershed processes is performed.

This study aims to calibrate and validate the channel flow in Ulla river watershed (Galicia, Spain) using MOHID-Land model considering a sensitivity analysis of some parameters and user’s options that can affect model results. MOHID-Land model is a physically based, fully distributed model that considers four compartments or mediums: atmosphere, porous media, soil surface and river network. Water dynamics is computed through the different mediums using mass and momentum conservations equations.

The model was firstly implemented in the studied domain with a resolution of 500 m. Data inputs included the digital Global Digital Elevation Model from NASA with a resolution of 30 m; the Corine Land Cover map from 2012 with a resolution of 100m; the soil hydraulic properties from the multilayered European Soil Hydraulic Database with a resolution of 250 m; hourly meteorological data (precipitation, solar radiation, wind velocity, air temperature, surface pressure and dew point temperature) from ERA5-Reanalysis with a resolution of 31 km; and daily total outflow for three reservoirs present in this watershed.

The sensitivity analysis was performed to test the impact of grid and elevation data source resolution, cross-sections geometry, soil parameters, vertical soil discretization, surface and channel Manning coefficients, the infiltration process and deactivation of different modules such as porous media and vegetation on streamflow. The results of these tests were compared with a reference simulation by the analysis of flow duration curves.

The hydrological model was calibrated and validated in 4 hydrometric stations not influenced by reservoirs and the river flows considering the reservoirs operation were compared with measured values in 2 hydrometric stations. Four statistical parameters (R2, RMSE, PBIAS and NSE) were used to evaluate model performance at a daily scale which was considered good.

How to cite: Oliveira, A., Ramos, T. B., Simionesei, L., Pinto, L., and Neves, R.: Sensitivity analysis of MOHID-Land model. Calibration and validation of Ulla river watershed., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9321, https://doi.org/10.5194/egusphere-egu2020-9321, 2020.

Water resources studies often rely on simulated streamflow from hydrologic models. Model-based streamflow estimates are often not directly usable in water resources studies because all models, no matter how well-calibrated, contain systematic errors. Water resources studies rely on simulated streamflow as inputs to compute reservoir releases and diversions and do not function well if those inputs are significantly biased in time and/or space. Post-processing is therefore used to reduce these systematic errors in model outputs. This post-processing step to remove model errors is typically referred to as bias-correction, and often impacts the entire distribution of flows rather than just the mean.

Existing post-processing techniques typically have three short-comings. First, simulated streamflow at unique locations are often bias-corrected independently, disregarding the connection between locations that is imposed by the river network. This destroys the spatial consistency of the streamflow across a river network. Second, bias-correction methods often rely on simple, time-invariant mappings between observed and simulated streamflow, without regard for the different hydrological processes that drive streamflow. For example, a hydrological model may have different systematic errors in representing snowmelt than in representing soil drainage, necessitating different corrections. Third, the application of a bias-correction method is often restricted to locations where observed and simulated streamflow exist, even though these locations represent only a small subset of streamflow input locations to a water resources model.

We present a post-processing method for streamflow that addresses all three of these shortcomings of existing streamflow bias-correction methods. The method accounts for the spatial relations imposed by the river network, allows for the incorporation of process-information, and applies the bias-correction for all reaches in a stream network. We develop a mapping from the modeled output at the gages with flow observations, which we use as the basis for training a machine learning (ML) model to perform the site-specific bias-correction. We then apply the ML model to local streamflow contributions for each river segment, including river segments without flow observations. Finally, we combine the local bias-corrections across the stream network, to create accumulated bias-corrected streamflow time series that are spatially-consistent across the stream network. We demonstrate our method for daily streamflow in a river basin in the western United States.

How to cite: Bennett, A., Nijssen, B., Cheng, Y., Stein, A., and McGuire, M.: Post-processing Hydrologic Model Output for Water Resources Studies: A Spatially-consistent, Process-based Correction Method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6036, https://doi.org/10.5194/egusphere-egu2020-6036, 2020.

Due to climate change, meteorological extremes affect the environment and our society in the past decades. But not only the extremes are piling up, the average temperatures and the precipitation regimes have changed in recent decades. The change in meteorological conditions also affects the water balance and thus also the generation processes of runoff. The aim of this work is to estimate this future change for a small low-mountain catchment in central Germany using climate projections and hydrological modelling.

As input to the hydrological model HBV Light, climate data from seven different combinations of global and regional climate models are used. However, due to their substantial bias it is necessary to apply bias correction. For each of the three climate input time series used by HBV Light, different bias correction methods are tested: Precipitation (Linear Scaling Multiplication, Quantile Mapping, Power Transformation, Distribution Mapping Gamma), Temperature (Linear Scaling Addition, Quantile Mapping, Variance Scaling, Distribution Mapping Normal) and Potential Evapotranspiration (Linear Scaling Multiplication, Linear Scaling Addition, Quantile Mapping). The corrected climate model outputs are compared to the observed timeseries and rated based on three different efficiency criteria. Overall, the combination of different climate models and bias correction methods generates 63 future hydrological projections. Based on this ensemble, the future water balance of the catchment is assessed. The results show that (1) the biggest uncertainties in the hydrological simulation were generated by uncorrected climate model outputs; (2) the uncertainties in hydrological simulations increase till the end of the century; (3) Power Transformation and Quantile Mapping perform best for precipitation, Linear Scaling Addition and Quantile Mapping for temperature, Linear Scaling Addition and Quantile Mapping for potential evapotranspiration; (4) the total annual outflow increases till 2070 because of an increase of the outflow in winter and spring; (5) in the future, interflow will increase in spring and winter and reduce in summer and autumn; (6) till the end of the century the baseflow will rise in spring and in the rest of year the baseflow will decrease. This study shows that even if changes in the annual total discharge for small catchments have no significant trend, the generation processes and the seasonal values may change in the future.

How to cite: Sonntag, K. and Gassmann, M.: Modelling runoff generation of a small catchment in the context of climate change by using an ensemble of different climate model outputs and bias correction methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2167, https://doi.org/10.5194/egusphere-egu2020-2167, 2020.

Real-time hydrological forecasting provides valuable information to mitigate the impact of extreme hydrological events such as flood and drought. An ensemble hydrological forecasting system is developed to investigate the hydrological predictability at sub-seasonal to seasonal (S2S) time scale over Germany. The ensemble hydrological simulations are performed with the mesoscale hydrologic model (mHM) which benefits from a multiscale parameter regionalization module (MPR). The model is forced by the operational ensemble prediction System from the European Center for Medium-range Weather Forecast (ECMWF). 51 hydrological ensemble forecasts are generated in real-time (twice a week) for up to 45 days in advance. We used the initial condition records from the German Drought Monitor (GDM, www.ufz.de/duerremonitor) which provides daily up-to-date high resolution drought information at a spatial resolution of 4 km. The performance of the system is evaluated for three consecutive years started from 2016 for Soil Moisture Index (SMI) and real-time streamflow records (222 based in Zink et al 2017). Comparison between forecasted Soil Moisture Index (SMI) and the one derived by the GDM suggested promising results for certain areas over the study area at S2S time scale. The predictability of the ensemble forecasting system is evaluated against that generated with the Ensemble Streamflow Prediction (ESP) method. This research is one of the first attempts to investigate the hydrological forecasting skill at S2S time scale in Europe. The study is supported as a part of the Modular Observation Solutions for Earth System (MOSES) project.

How to cite: Najafi, H., Thober, S., Boeing, F., Rakovec, O., Kelbling, M., Müller, S., Marx, A., and Samaniego, L.: A Real-time Ensemble Hydrological Forecasting System over Germany at Sub-seasonal to Seasonal Time Range, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9773, https://doi.org/10.5194/egusphere-egu2020-9773, 2020.

Seasonal hydrometeorological forecasts have the potential to significantly improve the regional water management, particularly in water-scarce regions. This includes a better disaster preparedness by developing e.g. 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 resolutions of 35km and more. In this study, we present a comprehensive dataset of operationally available seasonal hydrometeorological forecasts based on ECMWF’s newest seasonal forecast system SEAS5 that is a) corrected for biases against ECMWF ERA5 reanalysis data and b) spatially disaggregated to a higher spatial resolution of 0.1° (approx. 10km). We adopt a modified version of the Bias-Correction and Spatial Disaggregation (BCSD) technique, which is a highly robust method for regionalizing e.g. global climate data. The final repository contains daily ensemble forecasts for precipitation, temperature and radiation from 1981 to the present for 7 different semi-arid river basins in Iran (Karun), Sudan and Ethiopia (Tekeze-Atbara and Blue Nile), West-Africa (Niger and Volta), Brazil (Rio São Francisco) and Ecuador (Catamayo-Chira). In total, forecasts for more than 2.5 Million days for each variable and study region are corrected and disaggregated. An evaluation against reference data from ERA5 shows significantly reduced biases for the monthly averages as well as consistent and lead-independent forecast characteristics like wet/dry-day frequencies. As the entire repository is freely available, it provides an optimal test-bed for evaluating the forecast skill in different study regions; it allows to develop and implement e.g. hydrological forecasting systems and to train and educate local stakeholders and water experts. Our operational output of the forecasting system is already used by several authorities and weather services in Iran and Sudan; 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., Laux, P., and Kunstmann, H.: Towards improved disaster preparedness and climate proofing in semi-arid regions: development of an operational seasonal forecasting system , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20290, https://doi.org/10.5194/egusphere-egu2020-20290, 2020.

In small hydropower plants management, the operation feasibility is subjected to the Run-of-River (RoR) flow which is also depending on a high variability in water availability. The management has to accomplish with some particular operation conditions of the plant but also some environmental flow requirements. Normally hydropower plants managers use historical information of inflows in order to predict the production of energy. Although some forecast models have been already proposed and applied in the small hydropower production field, there are still an existing gap to link the results of the forecast with the decision support process. 

In the framework of the H2020 project CLARA (Climate forecast enabled knowledge services) a climate service was developed in a co-generation process, bridging the gap between data providers who provides climate-impact data on one side, and managers and policy makers on the other side. The result is SHYMAT (Small Hydropower Management and Assessment Tool), a technological solution for the integrated management of RoR plants which offers a scalable and automatically updated database accessible through an administration panel and a web end user interface. 

The pilot area is a three RoR system in the Poqueira River (southern Spain) where inflow is highly variable due to the irregularity in precipitation and snow cover duration in the contributing basin. The service combines past hydro-meteorological and forecast climate data stored with operation data for the particular plant in order to give the user a) a global view of the hydrological state of the basin, from measurements and a physically based hydrological model; b) a comparison of current information with past data; c) the expected operability of the RoR plant; d) information about the accomplishment of environmental flow requirements and water flow spill; e) the expected energy production. 

SHYMAT is easy and fully scalable to new systems thanks to the administration panel and the topology panel. The service is addressed to technicians in charge of the control operation center of this kind of plants and managers at the regional administrative headquarters of hydropower companies. Energy market operators, river basin authorities and consultants can be also potential users.

This research is supported by CLARA Project, which has received funding from the European Union's Horizon 2020 research and innovation programme under the Gran Agreement No 730482.

How to cite: Contreras Arribas, E., Herrero Lantarón, J., Aguilar Porro, C., and Polo Gómez, M. J.: Using seasonal forecast for energy production: SHYMAT climate service, a small hydropower management and assessment tool , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5494, https://doi.org/10.5194/egusphere-egu2020-5494, 2020.

The management of multipurpose reservoirs has to deal with the competitive needs of water for human consumption, for irrigation, for hydroelectric exploitation, for flood regulation, and for environmental flow requirements. This question has special importance in a Mediterranean environment where water is a limiting resource, and decisions have a large media and social impact. In this context, water-systems managers have to take decisions that will condition the operation and availability of water for the following months. Currently they have to rely on spreadsheets where different past data-based scenarios (last year, driest year, wettest year) are compared to the current situation on a monthly basis, as a simple forecast approach.

In the framework of the H2020 project CLARA (Climate forecast enabled knowledge services), the climate service ROAT (Reservoir Operation Assessment Tool) was conceived to support reservoir management through seasonal forecast information to foresee the water availability for the supply of the water demands. The climate service was developed in a co-generation process in which data purveyors, services providers and end-users are involved. The chosen study area was Béznar-Rules reservoirs system in the Guadalfeo River Basin (southern Spain). This system is a good example of a multi-purpose reservoir in a region where water is a limiting resource and the management decisions have to be very accurate. Besides, the presence of snow makes seasonal forecast of precipitation and temperature critical for the evolution of the water reserves throughout the year.

ROAT is conceived as an on-line application aimed at the use of real-time meteorological data and hydrological modelling of the river basin and the seasonal forecast of precipitation, temperature and reservoir inflow for the operational assessment of multi-objective reservoirs. The climate service supports the decision-making process of water managers by anticipating the actual risk of drought based on forecast, optimizing the timing of water allocation taking into account the future availability of water and gaining a global view of the current hydrological state of the watershed.

The service is addressed to water authorities and reservoir managers. Users of the reservoir itself, such as agricultural cooperatives, farmers and hydropower companies can be also potential users. It will allow managers to make operation decisions knowing that they will have at their disposal the most up-to-date hydrological knowledge combining measurements and modeling, together with the most forward-looking seasonal forecast that already exist at European level, but also all this adapted to their real operating needs.

This research is supported by CLARA Project, which has received funding from the European Union's Horizon 2020 research and innovation programme under the Gran Agreement No 730482.

How to cite: Herrero Lantarón, J., Contreras Arribas, E., Aguilar Porro, C., and Polo Gómez, M. J.: How seasonal forecast can improve the water planning in multipurpose reservoirs: ROAT climate service, a reservoir operation assessment tool, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5550, https://doi.org/10.5194/egusphere-egu2020-5550, 2020.

Water management is strongly dependent both on the short-term and seasonal variability of weather patterns. The increase in evapotranspiration and temporal shift of snow melt due to temperature rise is expected to have strong impact on water resources in Sweden with risk of severe deficit in summer and surplus in winter. For drinking water producers and freshwater managers a good understanding of the current hydro-meteorological situation is essential to ensure both urban water supply and compliance of water regulations.

This study is the result of collaboration between SMHI and Nodra, the municipal water company in Norrköping, Sweden. In 2016, warmer temperatures and reduced precipitation rates led to very low water levels in a ground water treatment plant used to supply drinking water to Kolmården, a region highly influenced by tourism in the summer season. This raised the need of monitoring freshwater availability and hydrological seasonal forecasts to be implemented for ensuring optimal water usage. To this end, a hydrological model is setup to simulate the water balance in freshwater reservoirs for evaluating groundwater recharge in the soil. Short to medium range (1-10 days) weather forecasts and seasonal climatological forecasts (6 months ahead) of water levels are produced at the local scale. Aiming at supporting long-term water planning, different management strategies of water withdrawal are used to feed the operational forecasting systems to assess groundwater availability in the following months.

Within the framework of the Horizon 2020 CLARA project; SMHI co-developed Aqua, a water supply assessment service tailored to the needs of public authorities and private companies involved in the water supply sector. Aqua includes a web-based platform that incorporates real-time station observations of precipitation, temperature, water levels, water discharge and raw water withdrawal.  Forecasts of relevant hydro-meteorological modelled parameters are also included and presented in an intuitive way through maps, graphs and tables. To overcome the challenges of communicating results of the probabilistic component of hydrological seasonal forecasts to the users, the visualization of forecasted groundwater levels is kept simple, whilst the provision of historical values allows an easy comparison against normal conditions.

The availability of tools displaying observations, modelled results and forecasts facilitates the understanding of the current hydro-meteorological situations as well as future wet/dry periods also to non-expert users, increasing preparedness of public and private organizations to extreme conditions while ensuring water security. Operational since March 2019, the Aqua service has provided Nodra with valuable insights for planning of groundwater withdrawal and decision support for coping with water scarcity, showing the potential of the co-generated hydro-climate service to bridge the gap between operational management and scientific innovation.

How to cite: Cantone, C., Ivars Grape, H., Dahné, J., Andreasson, J., Kindahl, M., and Blixt, M.: SMHI Aqua: a new co-generated hydro-climate service to enable sustainable freshwater management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15853, https://doi.org/10.5194/egusphere-egu2020-15853, 2020.

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.

In this context, we have conducted a study 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, developed by Swedish Meteorological and Hydrological Institue, to predict runoff over the historical period and to produce hydrological seasonal forecasts for next years.

The main season’s characteristics produced are: (i) cumulative rainfall map for the rainy season (May to November), (ii) the rainfall situation of the season (above, near or below normal considering 1993-2015 as reference period), (iii) 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).

The predictability of 2018 hydrological seasonal products were assessed and the results are promising. The main challenges we faced were the initialisation of the model, the bias correction (the reference data to be considered and the appropriate method). Further research on these topics should continue to improve the quality of results.

How to cite: Minoungou, B., Andersson, J., Ali, A., and Hamatan, M.: Using seasonal forecast information to strengthen resilience and improve food security in Niger River Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9006, https://doi.org/10.5194/egusphere-egu2020-9006, 2020.