S10

Radar rainfall nowcasting promises to result in more accurate and timely rainfall forecasts up to several hours in advance, compared to numerical weather prediction model forecasts. As flood early warning systems can benefit from this, we assessed the potential of radar rainfall nowcasting for (flash) flood forecasting with a large sample of 659 individual rainfall events for 12 catchments in the Netherlands. In this assessment, we tested four open-source nowcasting algorithms: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D) and Pysteps probabilistic (PS-P) with 20 ensemble members. Eulerian Persistence (EP) forecasts and zero precipitation input (ZP) were the benchmark. The discharge forecasts had a 12-h forecast horizon and were issued for every 5-min step in the available nowcasts for the set of events. We regarded the simulations using the observed radar rainfall as reference.

We found that rainfall and discharge forecast errors increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship also depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity, when the initial conditions are wet and groundwater tables are shallow. Overall, discharge forecasts using RM-DR, PS-D and PS-P outperform the other nowcasting methods.

Furthermore, we tested the potential for forecasting threshold exceedances by setting the highest discharge, per event, as threshold. Compared to benchmark ZP, a threshold exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D) and 143 min (PS-P) earlier. The relatively high time profits with EP are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P show both lower FAR and inconsistency index values, which can lead to more trust in the simulations.

Based on this large-sample analysis, we conclude that all nowcasting methods have shown a benefit for short-term discharge forecasting compared to issuing no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in (flash) flood forecasting, we recommend a future focus on improving this aspect in nowcasting.

How to cite: Imhoff, R., Brauer, C., van Heeringen, K.-J., Uijlenhoet, R., and Weerts, A.: Radar rainfall nowcasting for flash flood forecasting, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-148, https://doi.org/10.5194/iahs2022-148, 2022.

Earth Observations (EO) have become popular in hydrology because they provide valuable information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EO’s into hydrological models to improve the estimation of initial states and fluxes which can further lead to improved forecasting of different hydrometeorological variables. When assimilated, the data exert additional controls on the quality of the forecasts; it is hence important to apportion the effects according to model forcings and the assimilated data. Here, we investigate the hydrological response and seasonal predictions over the snow-melt driven Umeälven catchment in northern Sweden. The HYPE hydrological model is driven by two meteorological forcings: (i) a down-scaled GCM product based on the bias-adjusted ECMWF SEAS5 seasonal forecasts, and (ii) historical meteorological data based on the Ensemble Streamflow Prediction (ESP) technique. Six datasets are assimilated comprising of four EO products (fractional snow cover, snow water equivalent, and the actual and potential evapotranspiration) and two in-situ measurements (discharge and reservoir inflow). We finally assess the impacts of the meteorological forcing data and the assimilated data on the quality of streamflow and reservoir inflow seasonal forecasting skill for the period 2001-2015. The results show that all assimilations generally improve the skill but the improvements vary depending on the season and assimilated variable. The lead times until when the data assimilations influence the forecast quality are also different for different datasets and seasons; as an example, the impact from assimilating snow water equivalent persists for more than 20 weeks during the spring. We finally show that the assimilated datasets exert more control on the forecasting skill than the meteorological forcing data, highlighting the importance of initial hydrological conditions for this snow-dominated river system.

How to cite: Musuuza, J. L., Crochemore, L., and Pechlivanidis, I. G.: The impact of assimilating Earth Observation and in-situ data on seasonal hydrological predictions, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-225, https://doi.org/10.5194/iahs2022-225, 2022.

Uncertainty and change in geophysical processes can be robustly quantified by analyzing the observed variability. A challenging task in engineering studies is to introduce a framework that can simulate this observed variability while preserving only important stochastic attributes. An innovative methodology for genuine simulation of stochastic processes is presented based on the recent work by Koutsoyiannis and Dimitriadis (2021). The proposed algorithm includes the demanding task of simulating any second-order dependence structure of a process (with a focus on long-range dependence behaviour) and any marginal distribution (with focus on heavy tails) through the explicit preservation of its autocovariance function and its cumulants. The long-range dependence behaviour (i.e., power-law drop of variance vs. scale) and heavy-tails are known to be highly associated with the variability magnitude of a process, through which the range of its predictability-window can be also quantified. To estimate this range, an extensive global-scale network of stations of key hydrological-cycle processes (i.e., near-surface hourly temperature, dew point, relative humidity, sea level pressure, atmospheric wind speed, streamflow, and precipitation; for details see Dimitriadis et al., 2021) is analyzed using ensemble techniques and the proposed stochastic simulation algorithm. The limitations of existing methodologies for the stochastic simulation and estimation of the predictability-window, and how can they be tackled through the proposed approach, are discussed over applications in flood risk management.

Koutsoyiannis, D., and P. Dimitriadis, Towards generic simulation for demanding stochastic processes, Sci, 3, 34, doi:10.3390/sci3030034, 2021.

Dimitriadis, P., D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.

Acknowledgment: This research is in the context of the project “Development of Stochastic Methods for Extremes (ASMA): identification and simulation of dependence structures of extreme hydrological events” (MIS 5049175), which is co-financed by Greece and the European Union (European Social Fund; ESF).

How to cite: Dimitriadis, P., Iliopoulou, T., and Koutsoyiannis, D.: Theoretical framework for the stochastic synthesis of the variability of global-scale key hydrological-cycle processes and estimation of their predictability limits under long-range dependence., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-610, https://doi.org/10.5194/iahs2022-610, 2022.

Flood risk warning and pertinent decision-making before an upcoming flood event can greatly benefit from real-time probabilistic flood inundation mapping. Deterministic flood inundation maps can be erroneous and misleading for reliable and timely decision-making given the epistemic and aleatory uncertainties involved in the modeling of a nonlinear and complex flood event. Therefore, a real-time probabilistic modeling framework to forecast the inundation areas before the onset of a flood event is of paramount importance. Ensemble data assimilation methods are known as effective procedure for real-time operation of dynamic models while accounting for all sources of uncertainties. In this study, we present a multivariate data assimilation modeling framework that accounts for correlation structure among point source observations and then multiple gauge observations are integrated into a hydrodynamic model to improve its forecasting performance. Through a synthetic experiment, we first evaluate the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in the state of Texas in USA in 2017. We show how the accuracy and reliability of inundation mapping is improved using this robust probabilistic approach that can provide uncertainty in modeling and forecasting.

How to cite: Moradkhani, H., Jafarzadegan, K., and Abbaszadeh, P.: How can we use Data Assimilation for Real-Time Probabilistic Flood Inundation Mapping?, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-297, https://doi.org/10.5194/iahs2022-297, 2022.

Data Assimilation can improve forecast accuracy of flood inundation models by an optimal combination of uncertain model simulations and observations. Particle Filter (PF) has gained interest in the research community for its ability of dealing with non-linear systems and with any kind of observation and model error distributions. Using PF, one of the most difficult issues to deal with are degeneracy and sample impoverishment. In this study, we have investigated a novel approach, based on a Tempered Particle Filter (TPF), aiming to circumvent these issues, and increase the persistence in time of the assimilation benefits. Flood probabilistic maps derived from Synthetic Aperture Radar (SAR)
data are assimilated into a flood forecasting model. The process is iterative and includes a particle mutation in order to keep diversity within the ensemble. Results show an improvement of the model forecast accuracy as a result of the assimilation with respect to the ensemble without any assimilation (Open Loop, OL): on average the Root Mean Square Error decreases by 80% at the assimilation time and by 60% 2 days after the assimilation.
A comparison with a standard PF, the Sequential Importance Sampling (SIS), where degeneracy occurred, is carried out. Results are similar at the assimilation time but the increase of performance using the TPF are lasting longer. For instance, TPF-based RMSE are still lower than the OL RMSE 3 days after the assimilation, while the SIS-based RMSE becomes larger than the RMSE-OL 2 days after the assimilation.

How to cite: Di Mauro, C., Hostache, R., Matgen, P., Pelich, R., Chini, M., van Leewen, P. J., Nichols, N., and Blöschl, G.: A novel approach for assimilating SAR derived floodextent map into flood forecasting model: the temperedparticle filter, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-734, https://doi.org/10.5194/iahs2022-734, 2022.

Suppression of large precipitation amounts in the forecasts of  heavy-to-extreme events has been a critical limitation of contemporary postprocessing schemes. To tackle this limitation, we investigate a statistical postprocessing scheme that explicitly accounts for the Type-II conditional bias in establishing the predictand-predictor relationship.  This scheme is a variant of Mixed-type Meta-Gaussian Distribution (MMGD) that relies on Conditional Bias Penalizing Regression (CBPR), rather than simple linear regression to relate precipitation forecast and observations.  To assess the effectiveness of this scheme, we perform a set of hindcast experiments wherein both CBPR and MMGD are applied to the Global Ensemble Forecast System (GEFS) to create probabilistic quantitative precipitation forecasts (PQPFs) over subbasins of three major river basins in California, namely the American, Russian, and Elk River Basins. These experiments broadly confirm that CBPR scheme is capable of producing PQPFs that are both better calibrated and less biased (in Type-II sense) than the baseline from MMGD.  Potential regionalization approach for determining the weight parameter a priori is discussed.

How to cite: Zhang, Y., Ghazvinian, M., and Seo, D.-J.: An enhanced meteorological ensemble postprocessing scheme for improving the prediction of extreme rainfall events, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-308, https://doi.org/10.5194/iahs2022-308, 2022.

Study aim: To test the performance of 7 global hydrological models against local South African datasets from the Earth2Observe platform in terms of the monthly water balance estimation.

Methodology: The remotely-sensed global forcing datasets tested are actual evapotranspiration (ET) and runoff provided by Earth2Observe models. The local datasets were retrieved from the Water Resources of South Africa (WR2012) database, which quantifies the water resources of South Africa, Lesotho and Swaziland per quaternary catchment. The Quaternary Catchments consist of the lowest and most detailed level of operational catchment in the Department of Water and Sanitation (DWS), South Africa. An assessment of outputs between WR2012 and the ensemble outputs available to download from the seven Earth2Observe models (CEH, CNRS, UNIVK, UNIVU, METFR, JRC, and ECMWF) was conducted in South Africa. The mean annual runoff (MAR) time series analysis for the WR2012 and the Earth2Observe models were calculated. The Percentage of Bias (PoB) of each of the seven models with respect to the WR2012 data were computed as: %bias = (MAR_i - MAR_WR) x 100/ MAR_WR.

Results: Models JRC, UNIVU, and CNRS showed excessive bias values in large areas while the remaining four models generally exhibited large negative bias values, but with high positive values in the extreme Northeast and the dry areas of the country. The wetter regions of the country (Eastern Cape and KwaZulu Natal) displayed consistent results across all models. Apart from CNRS, all the results have an approximate water balance (precipitation = evapotranspiration + runoff). This is largely due to compensating over or under estimation of both precipitation and evapotranspiration.

Conclusion and Recommendations: The seven models are therefore rendered unreliable for water balance information in South Africa. Additionally, this study shows that ET data from the Earth2Observe platform is reliable to use, but the global runoff datasets should not be used in South Africa. It is recommended that organisations responsible for the generation of these global datasets issue warnings that these outputs have not been validated thus cannot be relied on.

How to cite: Okal, H., Tanner, J., and Hughes, D.: Comparison and validation of outputs from global hydrological models against local hydrological data in South Africa, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-695, https://doi.org/10.5194/iahs2022-695, 2022.

Switzerland and the nearby alpine countries are not commonly associated with the occurrence of droughts, but in recent years, Switzerland has experienced several unprecedented drought events. As many sectors in the European Alps depend heavily on the water resources, e.g. for hydropower production, navigation and transportation, agriculture, and tourism, it is important for decision makers to have early warnings of drought. Machine learning (ML) approaches have shown potential to compete with traditional hydro-meteorological models. By combining a traditional hydrological model and a ML model in a hybrid setup, the forecasting system is able to benefit from the statistical power of ML while maintaining the understanding of physical processes from the traditional model.

The objective of this study is to investigate the predictability of a hybrid forecasting system composed of a traditional hydrological model PREVAH and an ensemble of ML algorithms to provide sub-seasonal streamflow and lake level predictions for major rivers and lakes in Switzerland. Uncertainty of the hydro-meteorological prediction chain is accounted for by using 51 hydrological ensemble members, and the ML uncertainty is accounted for by performing different rounds of initial randomization. We also investigate different drivers of the drought predictability by considering input features such as initial conditions, European weather regime forecasts and a hydropower proxy.

We are able to demonstrate that the proposed hybrid forecasting system is able to perform runoff routing scheme and provide sub-seasonal forecasts of streamflow and lake level for Swiss basins. Informed ML models with additional input features achieve better performance than those obtained using hydrological model outputs only. In the first half of the forecast period (weeks 1 and 2), model performance is improved by the initial conditions and in the second half (weeks 3 and 4) by the hydropower proxy. Lake level prediction shows promising skill for different basin sizes, whereas the streamflow prediction skill is linked to basin size. This study shines light on the use of hybrid forecasting for sub-seasonal drought prediction to provide useful information for medium- to long-term planning from an integrated risk management perspective.

How to cite: Chang, A. Y.-Y., Jola, S., Bogner, K., Domeisen, D. I. V., and Zappa, M.: Sub-seasonal Hydrological Drought Forecast – the use of a Hybrid Forecasting System for Streamflow and Lake Level Predictions in Switzerland, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-113, https://doi.org/10.5194/iahs2022-113, 2022.

In recent years, the number of large-scale hydrological forecasting systems has been steadily growing. This may lead to regions having numerous models spatially overlapping each other. Some of these regions have what we will refer to as a regional, more specialized, model for the area that performs generally better than their large-scale counterpart, considering the coarser spatial resolution and sometimes lack of calibration of the latter. Our work explored the possibility of using simple methods to retrieve hydrological information from a large-scale model, namely the National Surface and River Prediction System (NSRPS) that will eventually cover the Canadian territory, in order to improve the forecasts from a local system, namely the Système de Prévision Hydrologique (SPH) that covers most of the province of Quebec. Outputs from the two forecasting systems were thus combined using methods including the simple mean, a weighted average in which the weights are optimized either using the Kling-Gupta Efficiency (KGE) or the Continuous Ranked Probability Score (CRPS) as cost functions, or weights calculated from the residual errors of the forecasts. Bayesian Model Averaging (BMA) was also explored to combine the ensemble forecasts from both systems. The results show that it is possible to improve the local hydrological forecasts by using simple weighted combinations with forecasts from the large-scale system. Performance was assessed using many well-known criteria such as the Nash-Sutcliffe Efficiency (NSE), KGE and CRPS. Results were averaged over the 61 available gauging stations and analyzed at lead times ranging from 3 to 120 hours. We observed improvements in all criteria for lead times over 60 hours as well as no loss in performance at any lead times. Finally, the methods were also used in a leave-one-out setup to simulate performance on ungauged basins. The performance gain for ungauged basins is similar to that of the gauged basins, hinting at the fact that these simple methods could also improve forecasts in more remote territories where no measurements are available.

How to cite: Fontaine, N., Boucher, M.-A., Odry, J., Lachance-Cloutier, S., Fortin, V., Anctil, F., and Turcotte, R.: Combination of Global and Regional Hydrological Forecasts, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-440, https://doi.org/10.5194/iahs2022-440, 2022.

A new methodological framework for the event-based evaluation of high resolution short-range hydro-meteorological ensemble forecasts is presented. It is specifically designed to address the questions of spatial and temporal scales at which ensemble forecasts should be evaluated, according to the characteristics of a flash-flood event. We adopt the point of view of end-users in charge of organizing evacuation and rescue operations. For this purpose, the potentially local exceedance of discharge thresholds need to be anticipated in time and accurately localized in space. A step-by-step approach is proposed, involving, first, an evaluation of the rainfall forecasts to define the spatial and temporal scales for the event-based evaluation. Second, a spatial analysis of the anticipation lead-times of hydrological responses is performed, focusing on the flood rising limbs, with the evaluation carried out against a reference forecast based on simulated flows. Based on this second step, several gauged sub-catchments are selected, at which a detailed evaluation of the hydrological forecasts is finally conducted.

This methodology has been tested and illustrated on the October 2018 flash-flood which affected part of the Aude River basin (south-eastern France). Three ensemble rainfall nowcasting research products recently developed by the French meteorological service (Météo-France) coupled with rainfall-runoff models (GRSDi and CINECAR) are evaluated and compared. The originality of the method is in the evaluation of the whole hydro-meteorological forecast chain by defining criteria corresponding to the users. The evaluation may seem limited (a single event and a limited number of outlets) but flash floods justify the implementation of such an evaluation framework.

Even if evaluating ensemble hydro-meteorological forecasts based on a limited number of documented flood events remains dependent on the statistical representativeness of the available data, the evaluation framework proposed herein helps drawing rapid and robust conclusions about the usefulness of newly developed rainfall ensemble forecast approaches and about their limits and improvement possibilities. It also contributes to pull together the community towards better framing post-event evaluations, so that we can put together events and evaluations from different parts of the world to collectively enhance our capacity to forecast, take decisions and increase preparedness for floods.

How to cite: Charpentier-Noyer, M., Peredo, D., Ramos, M.-H., Payrastre, O., Gaume, E., Nicolle, P., Bouttier, F., Marchal, H., and Fleury, A.: A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-375, https://doi.org/10.5194/iahs2022-375, 2022.

Drought currently threatens the food security of 26 million people in the Horn of Africa, and without urgent action this could have a devastating impact during the first half of 2022. Local agro-pastoralists living in the Horn of Africa drylands are among the most vulnerable people to these droughts. Drought forecasts are crucial for them to prepare, as these allow for life-saving forecast-based actions. However, there is a mismatch between forecasts and the impacts felt on the ground leading to local agro-pastoralists not using these forecasts.

Therefore, we will develop and evaluate impact-based forecasting techniques for agro-pastoralists in the Horn of Africa using the TAMSAT-ALERT early-warning system. Over Kenya, these ensemble forecasts indicate a good relationship to pasture availability (NDVI/VCI) and maize yield, and can reliably anticipate drought conditions (<20^th percentile soil moisture) up to 2 months before the end of the season. We build further on these results by testing its capabilities to forecast more specific drought variables relevant for agro-pastoralists. This includes a transition to drought (i.e. the drought onset), drought duration, the occurrence of dry spells in the rainy season, and possibly wet periods during the dry season. Moreover, we will assess how these variables relate to drought impacts, using impact data on livestock losses and IPC Acute Food Insecurity Classifications from FEWS-NET. This will pave the way for an estimation of the capabilities of TAMSAT-ALERT to serve as an impact-based forecasting system in African drylands.

Furthermore, we will improve the model by integrating ensemble sub-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation forecasts, and will evaluate how much these forecasts increase the forecast accuracy of TAMSAT-ALERT. Using the potential economic value theory to trigger anticipatory action, we will reveal on when effective anticipatory actions can best be triggered, such as distribution of emergency fodder or migration of pastoralists. These research steps will provide decision-makers with tailored drought forecasts and decision-support, which will be crucial to increase drought resilience in African drylands.

How to cite: Busker, T., de Moel, H., van den Hurk, B., Asfaw, D., Boult, V., and Aerts, J.: Impact-based drought forecasting for agro-pastoralists in the Horn of Africa drylands, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-255, https://doi.org/10.5194/iahs2022-255, 2022.

In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower…) and can be strongly affected by water shortages. Therefore, there is a need of early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low-flows in the context of climate change. Several French institutes (BRGM, EDF, INRAE, Lorraine University and Météo-France) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE (Tilmant et al., 2020). This platform produces forecasts in real time on more than 900 catchments in metropolitan France since 2017, in cooperation with French operational services of water management. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts based on ensemble streamflow prediction (ESP) can be issued up to either 15 days ahead, based on scenarios from the European Centre for Medium-Range Weather Forecasts (ECMWF) or 90 days ahead, using historical climatic data as ensembles of future input scenarios. These climatic data (precipitation and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis on the 1958-2020 period, which includes a wide range of conditions. Outputs from the different hydrological models are combined into a simple multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. In 2020, a website has been developed allowing easier access to the forecasts for users. The presentation will show the main characteristics of this operational tool and results on the recent low-flow periods.

Keywords: low-flow forecasting, hydrological model, ensemble streamflow prediction, water resource management

Reference
Tilmant, F., Nicolle, P., Bourgin, F., Besson, F., Delaigue, O., Etchevers, P., François, D., Le Lay, M., Perrin, C., Rousset, F., Thiéry, D., Magand, C., Leurent, T., Jacob, E., 2020: PREMHYCE: an operational tool for low-flow forecasting, La Houille Blanche (5) 37-44, DOI: 10.1051/lhb/2020043

How to cite: Tilmant, F., Bourgin, F., Véron, A.-L., Rousset, F., Willemet, J.-M., François, D., Le Lay, M., Vergnes, J.-P., Perrin, C., Magand, C., and Morel, M.: Low-flow forecasting in France using the PREMHYCE operational platform: recent advances and perspectives, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-66, https://doi.org/10.5194/iahs2022-66, 2022.

Changes in hydrology and society are growing concerns in West Africa and eastern Siberia. Floods cause loss of lives, damaged infrastructure, and reduced food security. A warming climate impacts river ice conditions, affecting ice roads and summertime river transportation. Operational hydrological forecasting systems are needed to boost climate adaptation and resilience in these regions.

Within the projects FANFAR (www.fanfar.eu) and HYPE-ERAS (www.hype-eras.org), we work with key stakeholders (e.g. hydrological services and disaster managers) to jointly design, develop, operate and evaluate hydrological forecasting systems in each domain.

In West Africa, we used Multi-Criteria Decision Analysis (MCDA) to engage users in the definition and development of the forecasting system. MCDA was employed to clarify and prioritize objectives and system configurations. We found that the most highly prioritized objectives were: high accuracy, clear flood risk information, reliable access, and timely production and distribution of the information. The stakeholders also contributed to forecast evaluation, e.g. by comparing forecasts with field experiences and local observations. Skills vary, with higher probability of detection (POD) in unregulated areas, and lower POD downstream of wetlands and reservoirs. Overall, 75% of the West African agencies considered the forecast accuracy to be “good” or “very good”. This also translated into changed operational warning practices, e.g. in Nigeria where FANFAR information was used to save thousands of lives and property on at least two occasions in 2020.

In eastern Siberia, a key target variable to forecast is river ice breakup date. To this end we combined meteorological data (HydroGFD and ECMWF 1-10day forecasts), hydrological modelling (Artic-HYPE), and local observations of river discharge, water level, ice thickness and river ice breakup dates. In both 2020 and 2021, the model successfully predicted the river ice breakup in the Lena River near Yakutsk up to 10 days in advance. We also found a systematic trend of too early breakup in upstream locations, and too late breakup in downstream locations, indicating problems in ice melt and/or growth calibration.

Here we summarize the research design, results and experiences gained in FANFAR and HYPE-ERAS; indicating that operational forecasting can be useful to boost climate adaptation and resilience.

How to cite: Andersson, J., Gustafsson, D., Lienert, J., Kuller, M., Ali, A., Ibrahim, A., Lebedeva, L., Boyakova, S., and Arheimer, B.: Experiences from co-developing hydrological forecasting systems across West Africa and eastern Siberia, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-233, https://doi.org/10.5194/iahs2022-233, 2022.

Efficient water resources management requires robust platforms to manage the information related to water resources and support decision-making processes. These platforms must incorporate information on water uses, real-time hydrological and water quality data, and weather forecasts. In Uruguay, the Ministry of the Environment and the National Emergency System (SINAE), in collaboration with the University of the Republic and Deltares from the Netherlands, are developing a hydrological platform based on the Delft-FEWS system: FEWS-Uruguay. This work describes the development and implementation of hydrological flood early warning models for the cities of Durazno and Artigas. Flood forecasts are generated by assimilating real-time data from different telemetric networks, the Global Ensemble Forecast System (GEFS) model results, and running different hydrological models. The platform generates hydrographs and maps of flood zones that are used by the National Emergency System to coordinate actions to safeguard the physical integrity of the people who inhabit the affected areas. Currently, there is an ongoing project which adds to FEWS-Uruguay the Santa Lucía Basin, the most important freshwater body of Uruguay, which supplies drinking water for 60% of the population. This basin has had quality issues related to the intense agricultural activity carried out in it. In this work, we also present the main advances made related to the water quantity, quality, and management models implemented in FEWS-Uruguay.

How to cite: Gamazo, P., Failache, N., Saracho, A., Bessone, L., Navas, R., Ramos, J., Alvareda, E., Talsma, J., and Valles, J.: FEWS - Uruguay: A platform for Flood Early Warning System and water management, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-756, https://doi.org/10.5194/iahs2022-756, 2022.