Floods are the most common and disastrous natural hazards, but early warning systems can help mitigate the damage by increasing preparedness. However, the products from these early warning systems must be skilful and actionable to be useful in the event of a flood. The European Flood Awareness System (EFAS), part of the European Commission's Copernicus Emergency Management Service, provides complementary flood forecasts to EFAS partners across the whole of Europe. One forecast product provided by EFAS is the ‘post-processed forecast product’ which is generated for the location of approximately 1600 river gauge stations where sufficient historic and near-real time river discharge observations are available. The aim of this product is to provide an error-adjusted forecast up to a maximum lead-time of 15 days. However, the post-processing methodology and the product design of the post-processed forecast product has not evolved over the past few years and therefore may not satisfy user’s changing requirements nor benefit from recent scientific advances. Following a skill assessment of the EFAS post-processed forecasts and a consultation with the EFAS partners a roadmap for future developments of the EFAS post-processed forecast product was designed. Here, we present this roadmap, and the results of the first stages, which include increasing the temporal resolution to 6-hourly timesteps, improvements to the post-processing methodology to better account for the different hydroclimatic regimes across Europe, and changing the post-processed forecast product to make it more locally relevant and useful to the EFAS Partners.

How to cite: Matthews, G., Cloke, H., Dance, S., Mazzetti, C., and Prudhomme, C.: Developing a user-focused flood forecast product for a continental-scale system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-282, https://doi.org/10.5194/egusphere-egu23-282, 2023.

Water-related applications and decisionmaking for flood forecasting and seasonal water management commonly rely on hydrologic modeling and forecasting that must provide accurate information over large domains as well as at local watershed scales.  We present progress on an experimentally operational hydrologic forecasting system being developed in a project between NCAR and the US Army Corps of Engineers to increase situational awareness in the US Columbia River basin of the Pacific Northwest, where myriad management concerns include flood risk mitigation, hydropower generation, navigation, water supply, recreation, fisheries and environmental management. The components of the system arise from process-oriented hydrologic modeling, analysis and prediction research that has been developed over the last decade in a collaboration between NCAR, federal US water agencies, and several academic institutions. In particular, a calibrated, watershed-based SUMMA hydrologic model and MizuRoute channel routing model are run in both retrospective and real time modes to provide 3-hourly timestep ensemble flood forecasts for short to medium range lead times, as well as ensemble seasonal streamflow and water supply forecasts up to a 1-year lead time. A 36-member meteorological forcing analysis is used to initialize the model states, while ensemble meteorological forecasts from GEFS, sub-seasonal-to-seasonal (S2S) climate forecasts and ESP are used to drive future flow predictions. We present the current status of the system, which runs in real time at NCAR, and discuss different elements of the forecast approach, including model calibration, ensemble initialization, data assimilation, downscaling of NWP, S2S climate forecast use, post-processing, and hindcasting. We also discuss project links to a related streamflow forecasting testbed initiative through the new NOAA Cooperative Institute for Research to Operations in Hydrology (CIROH)

How to cite: Wood, A., Sturtevant, J., Mizukami, N., and Tang, G.: A new process-oriented ensemble hydrological prediction system for flood prediction and water management in the US Pacific Northwest, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8578, https://doi.org/10.5194/egusphere-egu23-8578, 2023.

The reliability diagram is often used to assess the reliability of a set of probabilistic forecasts. It plots the observed relative frequency of an event against its predicted probability. Reliability is evaluated by assessing the distance of the plotting positions from the diagonal which designates 'perfect reliability'. The reliability diagram is easy to construct and to understand. However, there is a caveat: it is not immediately clear which distance from the diagonal would still be considered 'reliable'. Due to finite sample size, even a perfectly reliable forecasting system could result in a reliability diagram where not all points are on the diagonal. Various authors have proposed visual guidance that allows a forecaster to assess whether the observed relative frequencies fall within the variations that can be expected even when a forecasting system is perfectly reliable. These include 'consistency bars', a modified version of the reliability diagram which is plotted on probability paper and  a 'standardized reliability diagram' based on the Normal transform of the Poisson binomial distribution. The present contribution provides another method to visualize the expected deviation from the diagonal: confidence intervals based on the Poisson binomial distribution. The application is demonstrated in various case studies.

How to cite: Verkade, J.: Confidence intervals for the reliability diagram, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4349, https://doi.org/10.5194/egusphere-egu23-4349, 2023.

Whether they refer to it as validation, verification, or evaluation, hydrological practitioners regularly need to compute performance metrics to measure the differences between observed and simulated/predicted streamflow time series. While the metrics used are often the same (MAE, NSE, KGE, Brier, CRPS, etc.), the tools used to compute them are seldom the same. In some cases, specific tools are not used and the computation of the metrics are directly hand written in the scripts used to analyse model outputs. In addition, the computation of performance metrics is often accompanied with a variety of pre- and post-processing steps that are rarely documented (e.g. handling of missing data, data transformation, selection of events, uncertainty estimation). This can be error prone and hinder the reproducibility of published results. The sharing of tools computing these performance metrics is likely limited by the variety of programming environments in the hydrological community, and by well-established practices in operational environments that are difficult to modify. In order to enable the sharing between researchers and practitioners and move towards more reproducible hydrological science, we argue that an evaluation tool for streamflow predictions must be polyglot (i.e. that it must be usable in several programming languages) and that it must not only compute the performance metrics themselves, but also the pre- and post-processing steps required to compute them. To this end, we present a new open source, polyglot, and compiled tool for the evaluation of deterministic and probabilistic streamflow predictions. The tool, named “evalhyd”, can be used in Python, in R, and as a command line tool. We will present the concept behind its development and illustrate how it works in practice through examples from operational streamflow predictions in France. We will also discuss further steps and remaining challenges in the evaluation of hydrological model predictions.

How to cite: Hallouin, T., Bourgin, F., Perrin, C., Ramos, M.-H., and Andréassian, V.: A polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5735, https://doi.org/10.5194/egusphere-egu23-5735, 2023.

The European Flood Awareness System (EFAS) of the Copernicus Emergency Management Service is an operational forecasting system whose aim is to raise awareness about floods in European transnational rivers. It produces probabilistic, medium-range discharge forecasts twice a day by running the open-source hydrological model LISFLOOD with four different meteorological forcings, two deterministic forecasts from the DWD (German Weather Service) and the ECMWF (European Centre for Medium Range Weather Forecasts), respectively, and two probabilistic forecasts from ECMWF and the Cosmo Consortium (COSMO-LEPS). Based on these forecasts, flood notifications are issued to the EFAS partners if a set of criteria is met: contributing area larger than 2000 km², lead time from 48 to 240 h, at least one deterministic model exceeds the discharge threshold (5-year return period), and at least one probabilistic model predicts 30% exceedance probability of that discharge threshold for three or more consecutive forecasts.

The operational EFAS is being regularly updated, so the configuration of EFAS has changed since the time these notification criteria were defined. For instance, the temporal resolution has increased from daily to 6-hourly, and the spatial resolution is planned to improve from 5km to approximately 1.5 km (1 arcminute).

This study aims at assessing the skill of the notification criteria above presented with the current system setup, and to derive a new set of criteria that optimizes the notification skill. We will focus on three research questions: (i) how can we combine the different models (deterministic and probabilistic) into a grand ensemble and what probability threshold optimizes skill? (ii) Is the persistence criterion (i.e. 3 consecutive forecasts need to provide persistent predictions of high flood risk) adding to the skill both at shorter and larger lead times? (iii) Can we reduce the contributing area threshold without compromising skill?

The study will make use of reanalysis, driven by meteorological observations, and forecast data at over 2300 stations across Europe for a time span from October 2020, which was the release time of the last major change in the EFAS setup, until present. By comparing the reanalysis data with the simulated discharge threshold, a total of 1327 “observed” flood events have been identified in the 2 years from October 2020 to October 2022. The “notified” events will be computed by comparing the forecast data against the notification criteria; we will compute skill metrics (f1, Hanssen-Kuipers) at each daily lead time for different combinations of meteorological forcing and notification criteria in order to find the procedure that maximizes the skill of EFAS notifications and to assess the above research questions.

The outcome of this study will be applied to the EFAS operational system, directly impacting the preparedness of the relevant authorities in future flood events.

How to cite: Casado Rodríguez, J., Carton De Wiart, C., Grimaldi, S., and Salamon, P.: A skill assessment of the European Flood Awareness System notifications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5511, https://doi.org/10.5194/egusphere-egu23-5511, 2023.

The Ganga and Godavari are major rivers of India and are known for satisfying the agricultural needs of most part of the country. In the past few decades, these basins have seen increased geohazard scenarios such as floods, flash floods, landslides, etc. The availability of fine-scale precipitation data is a necessity for accurate monitoring and routine issuance of flood warnings. Downscaling of precipitation is a challenging task due to the complex topography of the basin, seasonality of the Indian rainfall, and large-scale influence of meteorological variables. In this study, we set up a Multiple-Point Statistics (MPS) based statistical downscaling approach using available precipitation data of the previous years to generate precipitation data for future at a finer resolution. The MPS approach uses the Training Image (TI) as input, hence we investigate into the adequate length of the past data record required for setting up the statistical model. We also investigate whether the length of data used as a TI in one River basin has any similarity in the other River basin. Further, what is the minimum year of data required to set up the statistical model. This is done by diving the datasets into five sets of TIs with each succeeding set larger than the previous one. This study uses datasets from High Asia Refined Analysis (HAR) (30×30 km) and the Integrated Multi-satellitE Retrievals for GPM (IMERG) (10×10 km) as the reanalysis and observation data respectively for a time period of 2001 to 2014. The idea is to explore if MPS is able to reproduce proper spatial features even with smaller TI data. The work is significant as it will benefit the hydrologists and water resource managers. The work is in progress and the results of the study will be presented at the conference.

How to cite: Samal, N., Singhal, A., Singh, A., and Jha, S. K.: Statistical generation of fine-resolution precipitation data in Ganga and Godavari River basins of India using limited training datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1536, https://doi.org/10.5194/egusphere-egu23-1536, 2023.

The operation of hydropower systems is carried out based on operation rules and environmental flows requirements. The effects of the highly temporal variability of hydrological regime in Mediterranean areas are more pronounced in Run-of-River hydropower systems, located in mountainous areas as they must often cease operation due to flow rates either below the turbine minimum discharge and the environmental flow requirements, or over the turbine maximum discharge. Conversely, regulated basins with storage systems are more resilient to changes in the short-medium term. In any case, having a forecast operational tool with delimited uncertainty and sufficient reliability would mean an improvement in the hydropower production planning, as well as a decrease in opportunity costs.

A stochastic flow forecast tool is applied in two selected Mediterranean hydropower systems (Southern Spain). In particular, the mini hydropower plants in Poqueira river, as representative of Run-of-River systems; and Los Hurones plant, with a reservoir, are presented. The forcing agents of the increase in humidity were first identified, being the snow and rainfall regimes in Poqueira, and the atmospheric pressure and NAO index in Los Hurones respectively. Secondly, the statistical modelling of dependent variables was carried out with parametric and non-parametric approaches to, finally, generate the probability distribution functions of occurrence of the flow regime. This structure of Bayesian dynamics forecast of water inputs to the plants on a week-month and month-season scale allows the forecast based on observable and verifiable antecedent conditions in quasi-real time.

The operationality of the hydropower plants refers to the probability of producing energy, so that its complementary value, probability of failure, is defined as the number of days in which the plant is not operating. Failure frequency and the associated operationality were calculated from the 250 stochastic replications of the 20-year period of the forcing agent in the selected case study. Including the 250 replications of the inputs allows considering the effect of different combinations of wet and dry years on the variables analysed, and provides the uncertainty associated with both, a certain value of operationality, and a fixed value of the hydrological variable at the desired time scale.

Results reveal that the higher operationality in Poqueira is given between April and May when the snowmelt produces greater flows, with a 25% probability of having less than 4 days of failure, lower than in the winter months (December to February), with a 25% probability of having 8-18 days of failure. In Los Hurones, with a 25% probability, the lowest failure will be 8-12 days between April and June, being significantly higher the rest of the year. Operational graphs obtained from the uncertainty analysis allow estimating how to plan the operation of hydroelectric plants to maximize its production based on the data observed in previous weeks and months.

Acknowledgments: This work has been funded by project TED2021-130937A-I00, ENFLOW-MED "Incorporating climate variability and water quality aspects in the implementation of environmental flows in Mediterranean catchments" with the economic collaboration of MCIN/AEI/10.13039/501100011033 and European Union "NextGenerationEU"/Plan de Recuperación, Transformación y Resiliencia.

How to cite: Gómez-Beas, R., Polo, M. J., Moreno, M. F., del Jesus, M., and Aguilar, C.: Stochastic flow forecast tool in Mediterranean watersheds for hydropower plants management at operational time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16465, https://doi.org/10.5194/egusphere-egu23-16465, 2023.

The exploitation of rivers and hydropower reservoirs involves daily monitoring of  the water resources, the meteorological conditions, the status of  the river banks, the flood areas, etc. As maximum river discharge often results in flooding, it is of importance to provide with timely and reliable forecasts of discharge and water levels. Predicting river discharge and water levels has been a subject of hydrological modelling and a topic of serious research. However, only in recent years scholars and practitioners have turned to consider earth observation data for their studies, mainly to compare evidence of flood mapping. We present an approach of using earth observation data to feed AI architectures – EO4AI – and produce forecasts for discharge and water level with significant degrees of accuracy.  Our starting point is that river discharge and water levels depend on a variety of meteorological and environmental factors like precipitations, snow cover, soil moisture, vegetation index and satellite data offer rich variety of datasets, supplying this information. We adopt a pipeline of deep learning architectures consisting of GAN, CNN, LSTM and EA to actually generate forecasts for river discharge and water level by using historic satellite data of the meteorological features listed above, and in-situ measurements for water level and discharge. The satellite data are provided by ADAM  via the NoR service of ESA. ADAM provides data access to satellite datasets from different satellites with semantic relevance for the construction of sediment transport and deposition forecast model as discussed above.  We explain the purpose of the pipeline components. Our forecast models are calibrated for 3, 5, 7, 30 days ahead, and our experiments provide predictions for one year ahead with each of the calibrated models. We discuss experiment results carried out with data from the Danube and Arda rivers, including three dams from cascade Arda and compare them with predictions derived with other methods. We demonstrate the viability of the approach and the reliability of the forecasting results. We further show how the forecasts can be used in hydrodynamic modelling context, for early warning applications and for routine water resources management and monitoring tasks.

How to cite: Damova, M. and Stankov, S.: Forecasting Discharge and Water Levels of Rivers and Dams using Earth Observation and AI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17141, https://doi.org/10.5194/egusphere-egu23-17141, 2023.

A key challenge in continental and global hydro-climate services deals with the incomplete (or even lack of) incorporation of local knowledge and data from the users. Here, we demonstrate the regional skill of seasonal forecasts from large-scale hydro-climate services, while we present a framework that accounts for local data and with the use of machine-learning enhances the seasonal forecasts by better capturing the local information. Five European case studies subject to different hydro-climate conditions and user needs are selected. We test our framework using the E-HYPE hydrological model forced by bias-adjusted ECMWF SEAS5 seasonal meteorological forecasts. We firstly assess the skill of seasonal hydrological forecasts using pseudo-reality and “real” local observations as reference. The skill assessment is driven by the local needs and hence it is conducted for different target hydro-climatic variables and conditions (i.e. floods and droughts). This first evaluation sets the benchmark for quantifying the added value from a machine-learning enhanced hydro-climate service. We next introduce a post-processing workflow to take advantage of the available local observations and potentially improve the forecasting skill. Here, quantile mapping and machine-learning post-processors are tested in the case study areas to further tune the output from the European hydro-climate service towards the local observations. Results from these hybrid seasonal forecasts show potentials to meet the local conditions and consequently address the user expectations from the service. The current work is highlighting the way forward for machine-learning enhanced services that allow tailoring large-scale hydro-climate services using local knowledge and data.

How to cite: Du, Y., Clemenzi, I., and Pechlivanidis, I.: Enhancing the seasonal forecasts from large-scale hydro-climate services to better meet the local conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6796, https://doi.org/10.5194/egusphere-egu23-6796, 2023.

Hydrological forecasts often contain biases or uncertainty that make them less useful to water resources system managers. They can, however, be further improved using post-processing methods. Post-processing has the capability to reduce overall bias and improve the uncertainty quantification (spread), in order to enhance the usefulness of the forecasts in decision-making. In this study, a Quantile Mapping (QM) post-processing method was implemented on meteorological forecasts assessed in three different configurations: A monthly, a seasonal, and an annual quantile mapping schemes. The evaluation was carried out over 22 watersheds with different basin areas in the south of Canada. Post-processing methods were trained on ECMWF operational forecasts from 2015-2019 inclusively, then applied on forecasts from 2020 and fed to 8 assimilated hydrological models on each catchment. The hydrological forecasts for the year 2020 were generated at a lead time of 8 days and a timestep of 6 hours. The methodology and results were evaluated using the Continuous Ranked Probability Score (CRPS) metric. Results show that all three QM combinations improve the performance of the forecasts at the most distant lead times, showing significant improvements from day 4. The annual QM implementation was shown to perform the best, followed by seasonal or monthly, depending on the watershed.

How to cite: Aguilar Andrade, F. S., Arsenault, R., and Poulin, A.: Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10361, https://doi.org/10.5194/egusphere-egu23-10361, 2023.

Flood risk has been increasing in many basins of the world, due to the global water cycle change driving by the global climate warming. To deal with the nonstationary properties of hydrological extremes, some new concepts, methods and models on flood frequency analysis and risk assessment are developed and applied. However, the robustness of nonstationary frequency analysis models, e.g. those based on the Generalized Additive Models for Location, Scale and Shape, is yet a big concern because the uncertainty of the parameters introduced by the methods and its impact on design flood values are difficult to quantify. This study aims to develop sensitivity degree indexes to assess the robustness of the nonstationary estimation of flood risk rates and their attributions, based on classical and Bayesian statistics, respectively. The results of the case study showed that the proposed method was efficient in identifying significant driving factors of nonstationary flood frequency; the results of the sensitivity index based on the Bayesian statistics showed that the uncertain degree of the nonstationary flood risk estimation increases with uncertain degree of the nonstationary model parameters as expected, but the sensitivity degree is decreased. It is indicated that the degree of influence of model parameters uncertainty on the risk estimation results is model dependent. This study will benefit the application of nonstationary frequency analysis methods in the flood risk assessment and flood design inference fields.

Keywords: Flood frequency analysis; Flood risk; Non-stationarity; Attribution

*This work was supported by the Research Council of Norway (FRINATEK Project 274310).

How to cite: Xiong, B., Zheng, S., Xiong, L., and Xu, C.-Y.: Sensitivity Analysis of Flood Risk Estimation under Nonstationary Conditions: A Case Study of the Weihe River, China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7873, https://doi.org/10.5194/egusphere-egu23-7873, 2023.

Streamflow simulated from hydrological models is associated with uncertainty from a variety of sources. The chief sources of uncertainty are: i) errors in the measurement of the observed inputs to the hydrologic models, say precipitation, discharge, and temperature observations, ii) calibration uncertainty associated with algorithms used to estimate the parameters of the model iii) structural uncertainty, associated with incomplete or approximate representation of the catchment with hydrologic models. Operational forecasts generally ignore these uncertainties for important management decisions in water resources, for example, in issuing flood warnings. However, several works have shown that these uncertainties can substantially impact large streamflow forecasts made through hydrologic models. In this work, we explore different error models for estimating the relative contribution of individual error sources to overall uncertainty in the streamflow simulations. Four hydrologic models are used to estimate error distributions at various flow quantiles due to individual sources. The strategy can be adopted to improve the sources contributing to these uncertainties for future predictions from these systems. The approach may be used to reduce the major sources of uncertainty, which will help in reducing the computational efforts in estimating the uncertainties in streamflow simulations.

How to cite: Akhter, M., Nayak, M. A., and Ahanger, M. A.: Estimation of different uncertainties in simulated streamflow from hydrological models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14944, https://doi.org/10.5194/egusphere-egu23-14944, 2023.