HS4.4

Hydrological forecasting systems represent an important decision-making tool for water and risk management. In this context, there is increasing development and implementation of such systems worldwide, which are commonly tailor-made: designed and configured according to the information and hydrological models available for a specific location and/or extent to answer to precise needs. Therefore, the concepts of setup automation and replicability of configuration of such systems are often overlooked, especially when they follow a model-centric approach.

However, in a global forecasting context such as the one adopted by Deltares’ GLOFFIS (den Toom et al. 2020), the automation of hydrological forecasting systems’ set up becomes an essential part for the development, as it enables the fast forward and constant addition of local specialized models where available in the system on a global extent, as well as by using local regional weather forecasts, reanalysis models or satellite data as forcing to produce estimates of various hydrological parameters, instead of focusing on a single model or NWP source.

In that sense, a prototype of a configuration production system for GLOFFIS was developed, which comprises two main components: (1) an external relational database holding the information regarding the set of hydrological models to be incorporated and weather data products used and, (2) a set of python scripts, that query the database and generate the configuration XML files needed for the system (as GLOFFIS is based on Delft-FEWS) to accomplish an automated deployment.

This new approach for system’s configuration boosts the potential related to system maintenance, expansion, and replicability, which could be beneficial not only when developing large hydrological forecasting systems, but also for local systems developed using Delft-FEWS, as well as to encourage the distribution of forecasting systems worldwide.

References:

den Toom, M., Verkade, J., Weerts, A., and Schotmeijer, G.-J.: Development of the Deltares global fluvial flood forecast system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22344, https://doi.org/10.5194/egusphere-egu2020-22344, 2020.

How to cite: Onate-Paladines, A., van Osnabrugge, B., Verkade, J., and Weerts, A.: Deployment automation of hydrological forecasting systems on a global scale., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3011, https://doi.org/10.5194/egusphere-egu21-3011, 2021.

Operational near real-time flood forecasting relies heavily on adequate spatial interpolation of precipitation forcing which bears a huge impact on the accuracy of hydrologic forecasts. In this study, the generalized REGNIE (genRE) interpolation technique is examined. The genRE approach was shown to enhance the traditional Inverse Distance Weighting (IDW) method with information from existing observed climatological precipitation data sets (Van Osnabrugge, 2017). The successful application of the genRE method with a re-analysis precipitation data set, expands the applicability of the method as detailed re-analysis data sets become more prevalent while high density observation networks remain scarce.

Here, the approach is extended to use climatological precipitation data from the Met Éireann’s Re-Analysis (MÉRA). Investigations are carried out using hourly precipitation accumulations for two major flood events induced by Atlantic storms in the Suir River Basin, Ireland. Alongside genRE, the following techniques are comparatively explored: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Regression Kriging (RK). Cross-validation is applied in order to compare the different interpolation methods, while spatial maps and correlation coefficients are utilized for assessing the skill of the interpolators to emulate the climatology of MÉRA. In the process, a preliminary intercomparison between the observed precipitation and MÉRA precipitation for the two events is also made.

In a statistical sense, cross-validation results verify that genRE performs slightly better than all three interpolation techniques for both events studied. Overall, OK is found to be the most inadequate approach, specifically in terms of preserving the original variance in observed precipitation. MÉRA manages to reproduce the temporal variations of observations in a good manner for both events, whereas it displays less skill when considering spatial variations especially where topography has a major influence. Finally, genRE outperforms all other interpolators in mimicking the climatological conditions of MÉRA for both events.

Van Osnabrugge, B., Weerts, A.H. and Uijlenhoet, R., 2017. genRE: A method to extend gridded precipitation climatology data sets in near real-time for hydrological forecasting purposes. Water Resources Research, 53(11), pp.9284-9303.

How to cite: Boumis, G., van Osnabrugge, B., and Verkade, J.: Application of the "genRE" approach to spatial interpolation of precipitation gauge data for the Suir River Basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-520, https://doi.org/10.5194/egusphere-egu21-520, 2021.

Flood has become the most frequent and deadliest type of disaster by far, responsible for the 43.5% of deaths in 2019. What is more, the number of flood events has extremely increased during the last decade (2000-2019), compared to the previous one (1980-1999) (CRED 2020). Therefore, policy and decision makers, more than ever, need efficient flood monitoring tools in order to facilitate their work towards increasing disaster resilience, especially in the urban and peri-urban areas, where most of the population and critical infrastructure are located. For this purpose, the FloodHub system has been developed by the Center of Earth Observation and Satellite Remote Sensing BEYOND, at the National Observatory of Athens, in the framework of the EuroGEO Disaster Resilience Action Group, supported by on-going actions (SMURBS / ERA-PLANET and Excelsior H2020 projects and the sponsor Hellenic Petroleum S.A.). The innovation of the system lies in the integration of different data sources, so as to deliver a reliable flood early warning system, and an operational awareness picture of the crisis every 5’ to the relevant authorities, namely on three levels: municipality, region, and national civil protection. FloodHub allows the near-real-time ingestion and assimilation of hydrometeorological measurements from in-situ telemetric stations, Sentinels data, and crowdsourced data, in a multi-source data fusion concept, using sophisticated hydrologic and hydraulic modelling and statistical regression techniques. It offers increased reliability through a continuous validation and optimization of results, automation in assimilating flood modeling in real time, computational efficiency, openness, flexibility, scalability, transferability, and the speed to meet rapid awareness during the crisis. Therefore, FloodHub is a useful tool in the hands of the relevant authorities and key stakeholders, contributing to an effective flood risk and crisis management. This is in line with the requirements for the implementation of the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, as well as the GEO’s Societal Benefit Areas.

How to cite: Tsouni, A., Kontoes, H., Herekakis, T., Sigourou, S., and Perrou, T.: The innovation of the FloodHub system for a reliable flood early warning and crisis management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8894, https://doi.org/10.5194/egusphere-egu21-8894, 2021.

The Berkel catchment in the east of the Netherlands and western Germany is an area with a long history of river flooding. Flooding in the area is caused by a combination of fast responding hydrological characteristics in the upper catchment and impermeable glacial till in the shallow sub-surface. In the past, flood mitigation in the Berkel catchment involved straightening river channels, minimising vegetation growth in the watercourse and an extensive weir network to control water flow. The changing climate and an integral approach to water management demand a modern, robust approach to mitigating flood damage in the Berkel catchment. As a result, an operational flood forecasting system (Delft-FEWS) which utilises recently developed hydrologic rainfall-runoff models and state of the art data assimilation (DA) methods has been developed. This system generates 7-day discharge forecasts at hourly intervals using meteorologic forecast and local discharge observations.

The lowland rainfall-runoff model, WALRUS (Brauer et al., 2014) is implemented to generate discharge forecasts. The WALRUS model set-up has been designed and calibrated in a semi-distributed layout to ensure the spatial and temporal elements of discharge peaks are captured. Importantly, this flood forecasting system has adopted recent advancements in DA to strengthen the accuracy of flood forecasts. Specifically, the DA method used in this system follows the work by Sun et al. (2020). The DA allows the model to be updated using field observations at 5 locations in the catchment, available in near real-time. Reforecasting illustrates the advantages of using rainfall-runoff models that capture the specific hydrologic characteristics of a catchment as well as the benefit of using advanced DA methods in flood forecasting.

Reference

Brauer, C. C., Teuling, A. J., Torfs, P. J. J. F., & Uijlenhoet, R. (2014). The Wageningen Lowland Runoff Simulator (WALRUS): a lumped rainfall–runoff model for catchments with shallow groundwater. Geoscientific model development, 7(5).

Sun, Y., Bao, W., Valk, K., Brauer, C. C., Sumihar, J., & Weerts, A. H. (2020). Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter. Water Resources Research, 56(8), e2020WR027468.

How to cite: Burke, E.: Applying ensemble Kalman Filtering to improve operational flood forecasting for the Berkel catchment (Eastern Netherlands), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10549, https://doi.org/10.5194/egusphere-egu21-10549, 2021.

An extreme weather event hit the coastal city of Chennai, India, in November-December 2015 causing severe damage to infrastructure worth billions of dollars, people’s lives and their livelihood. Nearby districts to Chennai, such as Cuddalore, Kancheepuram and Tiruvallur were also affected by rainfall over 300mm during the first week of December. This was caused by the unusual wind surges in the troposphere providing favorable environmental conditions for the extensive rainfall and the formation of a deep depression in the Bay of Bengal on 30 November 2015, which was blocked by Eastern Ghats that inhibited the movement of the synoptic system. Electricity and telecommunication lines were suspended and some hospitals were shut down for a few days. It brought the whole city into a state of emergency and National Disaster Rescue Force were deployed in an effort to take care of the evacuation of people.

In this work, we present the estimation of the hydrological stress caused by the extreme rainfall event in Chennai and the nearby river basins during the course of this northeastern monsoon event in India. The hydrological stress is given through the application of Best Discharge based Drainage (BDD)  Index, calculated by the CETEMPS Hydrological Model (CHyM). Hydrological simulation is carried out by forcing the model with the 3-hourly NASA IMERG 0.1x0.1 grid precipitation dataset. Preliminary results show a spatial coherence between the hydrological stress detected by the index and the most impacted river segments, due to heavy precipitation. The application of hydrological stress indices is helpful for forecasting fluvial floods in the river network with minimum calibration requirements, providing a useful tool for warning the respective authorities for minimal losses due to natural calamities.

How to cite: Neduncheran, A., Lombardi, A., Tomassetti, B., Verdecchia, M., and Colaiuda, V.: The Chennai flood (India): preliminary results using CETEMPS Hydrological Model (CHyM) stress index, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9786, https://doi.org/10.5194/egusphere-egu21-9786, 2021.

Traditional flood risk studies often focus on direct economic impact, such as property damage or agricultural loss. However, the impact of floods is not limited to these direct damages. In fact societal costs and/or cascading effects are often much higher than the direct impact of floods. Cascading effects, such as access to healthcare and infrastructure accessibility are vital components for efficient emergency response management. This requires methodologies to quickly analyze the impact of large-scale floods on infrastructure networks.

In this case study, the use of satellite-based flood maps are examined in combination with network criticality in the Mandalay region in central Myanmar. This region was severely affected by flooding after heavy monsoon rains in 2019. Many regions in the world are affected by this type of floods every year, resulting in large scale evacuations and limited access to health care. During these type of events, the transportation network is a crucial part for emergency response, as it is used for the delivery of goods, evacuation and deployment of emergency hospitals.

The core of this study is a methodology to assess near real-time flood extents based on Sentinel-1 satellite imagery and the impact on network criticality. These tools were used to analyze the redundancy of the infrastructure network and quantify cascading impacts of flood hazards such as road accessibility and access to medical services. The methodology shows potential for operational use by linking with flood early warning systems (e.g. Delft-FEWS) enabling impact-based forecasting.

How to cite: Kwant, M., de Groen, F., van Marle, M., Haag, A., and Haaksma, H.: Near Real-Time Flood Impact Analysis on Road Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16545, https://doi.org/10.5194/egusphere-egu21-16545, 2021.

Helmholtz Centres are developing a research infrastructure in Germany to investigate the interactions of short-term events and long-term trends across Earth compartments under the Modular Observation Solutions for Earth System initiate (MOSES- https://www.ufz.de/moses/). A near-real time hydroclimate forecasting system at sub-seasonal to seasonal time range (HS2S) is developed for MOSES to provide tailored information for early warning of extreme events. 

Here, we introduce two components of the HS2S which benefits from operational forecasts provided by the European Center for Medium-range Weather Forecast (ECMWF). The first component is weekly averaged forecasts of two atmospheric variables (total precipitation and maximum air temperature) which are bias corrected using a trend-preserving approach. The second component is German hydrological forecasting system. We use the mesoscale Hydrological Model (mHM- https://www.ufz.de/mhm) for generating hydrological initial conditions and ensemble forecasting. The same approach by German Drought Monitor (www.ufz.de/duerremonitor) is applied to interpolate near-real time in-situ observations from the German Meteorological Service (DWD) into 1-km grids. Then 51 real-time atmospheric daily ensemble forecasts from ECMWF ensemble extended product are bias corrected to generate of soil moisture and streamflow forecasts up to 30-day in advance. By post-processing mHM ensemble forecasts, an overview of drought conditions for the next 30-days horizon is disseminated online over Germany (https://www.ufz.de/moses/index.php?en=47304). Hydroclimate forecast are updated operationally twice a week to support MOSES event-driven campaigns for flood, drought and heat waves and to understand the predictability and skill of near-real time hydroclimate forecasts in Central Europe based on the state-of-the-art models and tools.  

How to cite: Najafi, H., Thober, S., Rakovec, O., Shrestha, P. K., and Samaniego-Eguiguren, L.: German Near-Real Time Ensemble Hydroclimate Forecasting System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16397, https://doi.org/10.5194/egusphere-egu21-16397, 2021.

During the summer of 2020, five bathing sites in Ireland were closed for the full season because of bad water quality, and 12 more received warnings and closed temporarily. Wastewater and sewage discharges, and Stormwater Overflows (SWOs) were the main causes. Although SWOs are not regarded as a management priority, they contributed to almost every bathing site’s closure, sharpening other existing issues. In this study, the precipitation in Ireland was analysed to inform a national stakeholder forum (An Fóram Uisce/The Water Forum), which provides guidance on water management to the national government, and the national water utility on the rainfall-driven SWOs issues. A correlation analysis of the observations of the closest meteorological stations of each bathing site is presented, showing that there are significant variances across the country, and each area (bathing site) must be examined separately. The Greater Dublin Area (GDA)’s precipitation was then further analysed because eight bathing sites in the GDA are facing SWO problems. Daily, monthly, and annual timeseries (10 years) were studied for peaks, trends, and seasonality. A daily forecast was performed for 1-year, using five techniques, starting from the simplest to the more complex: Seasonal naïve, Seasonal ARIMA, Holt-Winters Seasonal Exponential Smoothing, Non-seasonal ARIMA using seasonality as an exogenous covariate, and Christiano-Fitzgerald filtering. The peculiarities of the observed GDA’s precipitation timeseries are further highlighted through monthly, seasonal, and annual analyses. The trends showed that more extreme events (higher peaks) occurred over the last 30-20 years, thus, a brief extreme analysis was carried out using 120-year daily precipitation data. The Generalised Extreme Value (GEV) distribution was fitted to the historic precipitation using the L-moments method, and was compared to other theoretical distributions, commenting on their goodness of fit. Additionally, by comparing the historic data of temperature and rainfall from all the stations, with the respective reported projections of the future climate change scenarios, all stations we found to have already faced greater ranges than the predicted (e.g. the GDA has already experienced 45% higher temperature than forecast by the worst-case climate change predictions). Overall, the analysis indicates that water quality deterioration from SWOs caused by heavy rainfall events is forecast to become more frequent in the future. Consequently, managing authorities need to pay more attention to SWOs, instead of continuing to consider them as an occasional problem impacting water quality. This is the first study in the country approaching the issue of bathing water quality from the perspective of precipitation analysis. Very few similar rainfall analyses have been carried out in Ireland, thus this work has also a significant added value to the Irish climate literature.

How to cite: Alamanos, A., Rolston, A., and Linnane, S.: Irish bathing sites closures and Stormwater Overflows: Precipitation forecasts, extremes analysis, and comparison with climate change projections, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5350, https://doi.org/10.5194/egusphere-egu21-5350, 2021.

Across the globe, there has been an increasing interest in improving the predictability of weekly to monthly (sub-seasonal) hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydro-power production, and hazard warnings. A Precipitation-Runoff-Evapotranspiration HRU model (PREVAH) has been previously set up with raw metrological forcing of 51 ensemble members and 32 days lead time taken from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) extended-range forecast. The PREVAH model is used to generate hydrological forecasts for the study area, which consists of 300 catchments covering approximately the entire area of Switzerland. The primary goal of this study is to improve the quality of the categorical forecast of weekly mean total discharge in a catchment laying in the lower, normal, or upper tercile of the climatological distribution at a monthly horizon. Therefore, we explore the approach to post-process PREVAH outputs using machine learning algorithm Gaussian process. Weather regime (WR) data, based on 500 hPa geopotential height in the Atlantic-European region are used as an added feature to further enhance the post-processing performance.

By comparing the overall accuracy and the ranked probability skill score of the post-processed forecasts with the ones of raw forecasts we show that the proposed post-processing techniques are able to improve the forecast skill. The degree of improvement varies by catchment, lead time and variable. The benefit of the added WR data is not consistent across the study area but most promising in high altitude catchments with steep slopes. Among the seven types of WRs, the majority of the corrections are observed when either a European blocking or a Scandinavian blocking is forecasted as the dominant weather regime. By applying a “best practice” to each individual catchment, that is the processing technique with the highest accuracy among the different proposed techniques, a median accuracy of 0.65 (improved from a value of 0.53 with no processing technique) can be achieved at 4-week lead time. Due to the small data size, the conclusions should be considered preliminary, but this study highlights the potential of improving the skill of sub-seasonal hydro-meteorological forecasts utilizing weather regime data and machine learning in a real-time deployable setup.

How to cite: Chang, Y.-Y. (., Bogner, K., Zappa, M., Domeisen, D. I. V., and Grams, C. M.: Improving Sub-Seasonal Hydrological Forecasts in Switzerland: An Exploratory Study of Post-Processing Techniques by Using Machine Learning and Weather Regime Diagnostics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2244, https://doi.org/10.5194/egusphere-egu21-2244, 2021.

Low-flow forecasting can help to improve water management at places where a number of uses can be affected by diminishing water supply from rivers. Several French institutes (INRAE, BRGM, EDF, Lorraine University and Météo-France) have been collaborating to set up an operational platform, called PREMHYCE, for low-flow forecasting at the national scale, in cooperation with operational services. PREMHYCE includes five hydrological models and low-flow forecasts can be issued up to 90 days ahead for more than 800 basins. Several input scenarios are considered: ECMWF 14-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data, and a no precipitation scenario. Outputs from the different hydrological models are combined into a multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, as well as key low-flow indicators, such as the probabilities of being under low-flow thresholds provided by operational services. The presentation will show the main characteristics of this operational forecast platform, its latest developments and the results on the recent low-flow periods.

How to cite: Bourgin, F., Tilmant, F., Véron, A.-L., Besson, F., François, D., Le Lay, M., Nicolle, P., Perrin, C., Rousset, F., Thiéry, D., Willemet, J.-M., Magand, C., and Morel, M.: Low-flow forecasting in France: update on the latest developments of the PREMHYCE operational forecast platform, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2856, https://doi.org/10.5194/egusphere-egu21-2856, 2021.

How to cite: Zappa, M., Bernhard, L., Brunner, M. I., Bogner, K., Liechti, K., Lustenberger, F., Spirig, C., Seidl, I., and Staehli, M.: www.drought.ch – A 10-year span from technology readiness level 1 to 8, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5471, https://doi.org/10.5194/egusphere-egu21-5471, 2021.

In the last years, several Etna eruption events are documented, forming lava flows and explosive activity. The Pilot EO4D_ash – Earth observation data for detection, discrimination & distribution (4D) of volcanic ash of the e-shape project provides the PANhellenic GEophysical observatory of Antikythera (PANGEA) of the National Observatory of Athens (NOA), in Greece with near-real-time alerts from Etna volcano eruptions. These alerts are used in the PANGEA station to monitor and reveal the presence of volcanic particles above the area the days following an eruption, also the station is supported by a volcanic particle monitoring and forecasting warning system. In this work, we investigate the volcano eruption between 30 May and 6 June 2019 which affected the southern parts of Greece and reaching the Antikythera station. Due to the prevailing meteorological conditions, volcanic particles and gases followed an easterly direction and were dispersed towards Greece. FLEXPART dispersion model simulations confirm the volcanic plume transport from Etna towards PANGEA, mixing also with co-existing desert dust particles. Model simulations are evaluated with Polly^XT lidar measurements performed at PANGEA and satellite-based SO₂ observations from the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor (TROPOMI/S5P). This is the first time that Etna volcanic products are monitored at the Antikythera station, in Greece with implications for the investigation of their role in the Mediterranean weather and climate.

Acknowledgments: We acknowledge the support by EU H2020 E-shape project (Grant Agreement n. 820852). Also, this research was supported by data and services obtained from the PANhellenic Geophysical Observatory of Antikythera (PANGEA) of the National Observatory of Athens (NOA), Greece, and by the project “PANhellenic infrastructure for Atmospheric Composition and climatE change” (MIS 5021516) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). NOA team acknowledges the support of the Stavros Niarchos Foundation (SNF).

How to cite: Kampouri, A., Amiridis, V., Solomos, S., Gialitaki, A., Marinou, E., Spyrou, C., Georgoulias, A. K., Akritidis, D., Papagiannopoulos, N., Mona, L., Scollo, S., Pytharoulis, I., Karacostas, T., and Zanis, P.: Investigation of volcanic emissions in Antikythera PANGEA station using near-real-time alerts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13213, https://doi.org/10.5194/egusphere-egu21-13213, 2021.