Runoff in small catchments tend to response quickly to heavy precipitation input. The potential disastrous consequences demand a reliable precipitation forecast and flood early warning for an appropriate flood defense management. Numerical weather models provide relatively long lead times that allow early warnings of heavy precipitation. Further, meteorological ensembles consider the uncertainty of the forecast. Feeding this data to a hydrological model propagates the meteorological information and its uncertainty to catchment discharge time series.

Within the scope of the project HoWa-PRO (funded by the Federal Ministry of Education and Research, Germany) we propose a flood early warning system for the example of three catchments in the Free State of Saxony. The so-called sentinel watches meteorological ensemble forecasts of the German Weather Service (DWD). If a specific precipitation criterion is surpassed, the sentinel starts collecting and concatenating various precipitation products. For operational use, a combination of radar, nowcast, and (ensemble) forecast data is created (Radolan-RW, Radolan-RV, Icon-D2-EPS, Icon-EU). Besides this renowned precipitation products, we set up a second hydrologic ensemble forecasting system using prototypic data of upcoming products for precipitation observation and forecasting. Here we combine (1) observed radar data assimilated to precipitation gauges and commercial microwave links (pyRADMAN), and (2) the seamless prediction data SINFONY-INTENSE. The latter is a combination of nowcasting and numerical forecast ensembles. Both data products are delivered some minutes earlier than the classic data. The sentinel evaluates the concatenated precipitation data in the catchments according to further criteria for heavy precipitation events. If a criterion is met, the hydrological model is started with the formerly concatenated full ensemble precipitation data. The results are used in a prototypic web demonstrator to depict the current flood situation in the covered catchments. An easy to grasp traffic light scheme and – if needed or wanted – additional information including the uncertainty range facilitate quick decisions and actions of the flood defense management in the appropriate region.

The sentinel scales well with additional catchments which can be simulated in parallel. Currently, the sentinels for both data versions (operational and upcoming precipitation products) are invoked each 30 min, shortly after new observed data is delivered. The used WeatherDataHarmonizer library (Wagner and Grundmann, 2023) ensures a temporally, spatially, and formally homogeneous precipitation data set with a lead time of maximum 180 h, a time resolution of 15 min, and a spatial resolution of about 1 km. Each component of the sentinel is robust in a sense of handling missing operational data or machine faults.

Additionally to the technical aspects, we present results of operational hydrologic ensemble forecasts for selected events and catchments and compare the performance of both systems.

Wagner, M. and Grundmann, J.: Precipitation Data Harmonizer: Harmonizing radar, nowcast, and forecast precipitation data for hydrological applications, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8978, https://doi.org/10.5194/egusphere-egu23-8978, 2023.

How to cite: Wagner, M. and Grundmann, J.: Operational Hydrological Ensemble Forecasts in Small Catchments – Implementing Seamless Precipitation Predictions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7372, https://doi.org/10.5194/egusphere-egu24-7372, 2024.

High-resolution ensemble weather forecasts are essential for forecasting floods in complex topography with fast-responding catchments. However, they are computationally expensive. This study explores a cost-effective alternative via the use of time-lagged ensembles, defined as ensemble forecasts from the same model initialised at different times but verifiying at the same time.

We evaluate lagged ensemble products, with varying configurations and proportions of lagged members, constructed from convective-scale numerical weather predictions (NWP) ensembles to forecast extreme flood events for fast-responding catchments. We compare four lagged products for two extreme event case studies in France and New Zealand. Lagged NWP ensemble products are used to drive hydrological models and evaluate flood forecasts across a range of performance evaluations based on traditional event-based metrics and user-focused strategies. We construct forecast diagrams and associated metrics based on the Brier Score for varying flood threshold severities to evaluate anticipatory and overly alarmist predictions.  

Comparisons with a control burst ensemble (without any lagging) reveal that flood forecasts derived from lagged convective-scale NWP ensembles have the potential to better capture extreme flood events, albeit with some limitations. Benefits vary with time and lagged product configuration, but generally, lagged ensemble products match or surpass their control counterparts, particularly in spread-skill, anticipatory prediction of a severe flood threshold, and consistency of forecasts -- critical to retaining trust from the emergency management sector. However, lagged products tend to increase overly alarmist predictions at early forecast ranges but become a more effective strategy at longer ranges over a larger region.

Utilising forecast diagram metrics based on the Brier Score allows the evaluation of multiple basins and ensemble products, incorporating an end-user-focused perspective for decision-making, such as anticipation of flood exceedance thresholds. The results provide valuable insights into lagged convective-scale weather ensembles' potential benefits and limitations in enhancing flood forecasting accuracy and reliability.

How to cite: Cattoën, C., Ramos, M.-H., Peredo, D., Moore, S., and Carey-Smith, T.: Assessing lagged convective-scale weather ensembles for improved flood forecasting in fast-responding catchments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1543, https://doi.org/10.5194/egusphere-egu24-1543, 2024.

We introduce Neighbourhood Ensemble Copula Coupling, a technique for post-processing ensemble precipitation forecasts to produce physically realistic, well-calibrated scenarios.

Modern precipitation forecasting typically uses ensemble forecasts produced by numerical weather prediction (NWP) models. These ensembles aim to represent the range of probable weather outcomes, and enable us to derive probabilistic predictions (for example, we may predict a 20% chance of at least 10 mm rainfall at a particular location). Because NWP models employ a simplified representation of the atmospheric dynamics, and model processes at a coarse scale, the probabilities derived from the ensemble must be calibrated to accurately describe the probability distribution at the specific forecast locations.

Moreover, for many applications, such as flood prediction, as well as a probabilistic prediction of rainfall at each location, it is also useful to know the correlations between different locations. A river is most likely to flood when there is high rainfall at several nearby locations, so the probability of a flood depends on the joint probability distribution of rainfall at these locations. This is not easy to calculate, since the individual distributions are not independent. For example, if there is a rain band and we are unsure how fast it will move, we may know that at a particular forecast time it will rain either in location A or location B, but not both simultaneously. Thus for hydrology applications, scenario-based forecasts are often more useful than probability forecasts, but we still require the ensemble of scenarios to be well-calibrated; that is, the distribution of scenarios at a location should approximate the true expected probability distribution.

One popular approach to this problem is Ensemble Copula Coupling (ECC). Given an NWP ensemble forecast, and a probabilistic forecast derived from it, ECC is a method to derive a calibrated ensemble forecast by arranging quantiles of the probabilistic forecast in the order specified by the original ensemble. This process improves the statistical accuracy of the ensemble; in other words, the distribution of the calibrated ensemble members at each grid point more closely approximates the true expected distribution. However, the trade-off is that the individual members are not as physically realistic as the original ensemble, with noisy variation among neighbouring grid points. Also, depending on the calibration method, extremes in the original ensemble are sometimes muted: in particular, reliability calibration, a simple and widely used non-parametric probability calibration method, suffers from this problem. Neighbourhood Ensemble Copula Coupling (N-ECC) is a simple modification of ECC designed to address these drawbacks. Testing N-ECC with the calibrated probability forecasts produced by reliability calibration shows that, compared to standard ECC, our method produces forecasts which are less noisy and more visually plausible, and which also have improved statistical properties. Specifically, the forecast is sharper, so that extremes are better predicted, and the continuous rank probability score (CRPS) is also slightly improved.

How to cite: Trotta, B.: Producing calibrated ensemble precipitation forecasts using Neighbourhood Ensemble Copula Coupling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14291, https://doi.org/10.5194/egusphere-egu24-14291, 2024.

Sub-seasonal to seasonal (S2S) weather forecasting is widely regarded as a big challenge because of less predictability than short-term and seasonal forecasts, which significantly impedes the streamflow forecasts at the same time scale. In this study, we propose an integrated numerical and statistical approach to improve the accuracy of S2S streamflow forecasting based on a physically based hydrological model, i.e., the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model with a Bayesian joint probability (BJP) model. The DRIVE model provides S2S streamflow simulations by leveraging physical processes, while the BJP model could mitigate the issue of over-fitted flood peaks and partially correct under-fitted flows. The main strategy to reduce the streamflow prediction uncertainty is through optimizing the integration of hydrological model simulations and statistical predictions. We applied the integrated DRIVE-BJP model to the Pear River Basin during the 2020-2022 time period and performed the validation according to observations at 24 hydrological stations located within the river basin. The results show the proposed ensemble approach yields significant improvements compared to the single BJP or DRIVE model. This study of fusion of the DRIVE and BJP models to enhance the sub-seasonal flood prediction, showing promising practical values in flood early warning and water resource management.

Key words: S2S, ensemble streamflow forecast, DRIVE model, BJP model, Pearl River basin

How to cite: Li, L., Wu, H., Lu, L., and Chen, W.: Ensemble streamflow probability prediction at the sub-seasonal to seasonal (S2S) timescale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9909, https://doi.org/10.5194/egusphere-egu24-9909, 2024.

Creating forecast systems that add value across spatial scales and time horizons is crucial for a variety of fields, from meteorology and climate science to business and public policy. The priorities for developing such systems may vary depending on the specific domain and objectives. The international community of practice of the Hydrological Ensemble Prediction Experiment (HEPEX) has been seeking to advance the science and practice of hydrological ensemble prediction and its use in impact- and risk-based decision-making. Over the years, HEPEX has been promoting knowledge utilising cutting-edge techniques and data to innovate hydrological forecasting methods, products and systems, and improve services for users in the water-related sectors. During the 2023 HEPEX workshop in Norrköping, Sweden, (https://hepex.org.au/hepex-workshop-2023-forecasting-across-spatial-scales-and-time-horizons/), the community proposed and elaborated on the key priorities for (co-)creating hydrological forecast systems that are broadly applicable and can add value for local/regional decision-making. The notes from five breakout groups (about 10 participants in each group) were collected and analysed, while the proposed efforts and ways forward were classified and prioritised. Here, we present the outcomes from these breakout discussions, while we support the identified priorities providing backgrounds on the science needed, the HEPEX contribution towards these priorities, and the path forward for contributing to the United Nations Early Warnings for All (EW4All) initiative.

How to cite: Bennett, J., Pechlivanidis, I., Du, Y., Boucher, M.-A., and Wetterhall, F.: What are the top priorities for (co-) creating hydrological forecast systems that add value across spatial scales and time horizons? Outcomes from the 2023 HEPEX workshop, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20930, https://doi.org/10.5194/egusphere-egu24-20930, 2024.

Operational hydrological forecasting systems play a vital role for effective decision-making in disaster management and resource planning. This study addresses the critical need to conduct a fair and realistic assessment of the skill in one such system: the European Flood Awareness System (EFAS). As an integral component of the Copernicus Emergency Management Service (CEMS), EFAS undergoes monthly forecast verification, spanning lead times from 6 hours to 10 days. This verification process provides users with a valuable insight into the system’s performance in predicting streamflow for the preceding month.  

Motivated by the importance of providing stakeholders with trustworthy information, our research focuses on a thorough examination of benchmark forecasts to evaluate EFAS performance. The choice of benchmark forecasts significantly influences the perceived accuracy of the system, and using benchmarks that are too easy to beat can lead to artificially inflated skill. Therefore, the primary objective of this work is to pinpoint the most suitable benchmark, serving as a robust reference for assessing the true capabilities of EFAS. This will then feed into the development of a ‘headline score’ which is a unique value of a key metric representative of a geographical domain that enables to track performance evolution.

The study employs various benchmark forecasts, including persistence forecast, climatology, and the previous day’s forecast, using the Continuous Ranked Probability Skill Score (CRPSS) for skill assessment. Expanding on previous findings that identified persistence forecast as the most suitable for short lead times and climatology for longer lead times, this work refines and extends these results. We specifically examine the influence of catchment characteristics on the selection of the optimal benchmark at different lead times for operational forecasting evaluation. By uncovering the most robust benchmark, our study contributes to a more accurate understanding of EFAS capabilities, ultimately enhancing the overall performance assessment of EFAS. The nuanced insights gained from this focused examination serve as a step toward refining the methodology and criteria employed to develop new ‘headline scores’, instrumental in evaluating the evolution of the system’s forecasting skill.

How to cite: Tanguy, M., Harrigan, S., Carton De Wiart, C., Wortmann, M., Haiden, T., and Prudhomme, C.: Forecast Verification in Operational Hydrological Forecasting: A Detailed Benchmark Analysis for EFAS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9172, https://doi.org/10.5194/egusphere-egu24-9172, 2024.

The potential use of European Centre for Medium-Range Weather Forecast (ECMWF) ensemble prediction system SEAS5 over Mainland Southeast Asia was evaluated. The evaluation spans 30 years (1985–2014), examining SEAS5's skill in predicting temperature and precipitation. Subsequently, SEAS5 data was used to force the Variable Infiltration Capacity (VIC) hydrological model for runoff and streamflow forecasts, as well as the WOrld FOod Studies (WOFOST) crop model for rice production forecasts. These hydrological and agricultural results were compared against the WFDE5-driven reanalysis using verification skill metrics at grid cells for each month. Furthermore, the hydrological results were compared against observed station data. The reanalysis of rice yield was also compared against FAO observations, but proved inconclusive. The findings reveal promising predictive capabilities for temperature beyond a 2-month forecast, while the skill of precipitation and streamflow forecasts extend to a 1-month. Noteworthy, strong seasonal and regional dependence occurs, with high forecast skills during the pre-monsoon (April–May) and post-monsoon (October–November). Year–to–year precipitation tercile plots highlight skill in predicting the anomalous seasonal conditions associated with ENSO. The significant streamflow skill at each initiation month and lead time corresponds to the forecasting skill of meteorological variables. Nevertheless, it is important to note that the skill level of discharge and runoff forecasts is generally lower compared to the skill in temperature and precipitation. For the rice prediction, SEAS5 exhibits high performance at the beginning of the rainy season, where strong seasonal climate predictions are observed. The model shows the ability to capture anomalous rice yields and consistent accuracy throughout a 1-month to 3-month forecast. However, limitations in skill are evident when rice planting times are delayed by one or two months during the rainy season, as well as when planting in the dry season. SEAS5 shows useful skills that can potentially be used for hydrological and agricultural anticipatory management. The results could already support an initial step to come to potential anticipatory (agro-)hydrological management and could be utilised as an input for an early warning system in various sectors.

How to cite: Wanthanaporn, U., Hutjes, R., and Supit, I.: Skill of the ECMWF SEAS5 ensemble prediction system in streamflow and rice yield forecasting for Mainland Southeast Asia , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1371, https://doi.org/10.5194/egusphere-egu24-1371, 2024.

The potential of user-centric climate services to facilitate proactive drought management approaches is leading to increased efforts to develop and test climate services that include hydro-meteorological forecast products. In Spain, there is an interest to incorporate seasonal forecasts into drought management, including by integrating the information these provide into the system of drought indicators that are used in current operations. However, despite seasonal forecast information being available to users, their actual use to support operational decisions is limited. One aspect that fosters uptake is how credible users consider seasonal forecasts, including the quality of the forecasts. Additionally, the salience of the information provided is important, with information being credible at the spatial and temporal scales commensurate to those of the indicators used to support decisions. Bias-correction of seasonal forecasts has a crucial role in enhancing forecast quality, though there are several aspects intrinsic to bias-correction processes that may influence the degree of quality enhancement from the perspective of the users’ interests. An aspect that has so far received little attention is the spatial resolution of the forecast and the reference precipitation datasets that are applied.

Here, we examine the influence of spatial resolution of both the forecast and the reference precipitation datasets used in the bias-correction process on the skill of seasonal forecasting, from the perspective of the indicators used in operational drought management decisions. We apply bias-corrected daily rainfall forecasts (ECMWF System 5) for a region within the Spanish Douro River Basin. We use a Bayesian Joint Probability (BJP) modelling approach for bias correction and the Schaake Shuffle method to conserve the temporal and spatial correlations across lead times and sub-catchments, respectively (Schepen et al., 2018). We consider two resolutions of the forecast product (1° and 0.4°), and apply the bias correction at three nested spatial scales, namely independent sub-catchments, aggregated sub-catchments and the entire catchment. These spatial aggregations have been selected to match those of the indicators used in the drought management plan implemented by the Douro Basin Authority. Forecast quality is evaluated through a set of skill scores and metrics at the daily scale, as well as at the aggregated temporal scales of the indicators used. Our results show that the bias correction applied to the seasonal forecasts does improve the skill of the forecast information at the spatial and temporal scales that are relevant to the indicators used operationally. We also show the influence on the improvement of skill of choices made in selecting the spatial resolution of the forecasts themselves and at which bias correction is applied, and discuss how this can help inform the design of climate services to support operational drought management decisions.

How to cite: Ramos Sánchez, C., Werner, M., De Stefano, L., and Schepen, A.: Influence of spatial resolution on forecast quality of bias-corrected seasonal forecasts from a drought management perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17468, https://doi.org/10.5194/egusphere-egu24-17468, 2024.