How to cite: Schepen, A., Bennett, J., Pokhrel, P., and Robinson, K.: S2S rainfall forecast calibration in real-time for a dense network of hydropower catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5859, https://doi.org/10.5194/egusphere-egu23-5859, 2023.

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491-0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the National Agency for Electric Energy (ANEEL) under its research and development programme, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). The SIN is composed of more than 150 hydropower plants and reservoirs located over a wide range of climate and hydrological conditions. It is responsible for more than 50% of the total electricity produced in the country. In this work we describe the overall characteristics of this project, comprising its structure, main research results and its usefulness for assisting decision makers in the field of energy, as will be demonstrated through some application sceneries. We used the precipitation short-medium-range, sub-seasonal, and seasonal ECMWF forecasts as input to a continental-scale, hydrologic hydrodynamic model (MGB-SA) to produce streamflow forecasts for the SIN hydropower reservoirs. On the short-medium-range horizon we used persistency and the control member as benchmarks, while in the sub-seasonal and seasonal we used the ESP. On the short-medium-range and sub-seasonal, the ensemble average performance was superior to the control deterministic predictions for ECMWF (MGB-SA), both for the prediction quality metrics and for the event discrimination metrics. On the seasonal forecast, the ECMWF results were consistently superior to the benchmark on the first lead time month, decreasing performance with the horizon. The results of the project are expected to benefit energy generation planning, routine and emergency hydraulic operation (e.g., flood and droughts), as well as energy commercialization procedures in the country.

How to cite: Freitas, C., Silveira, R., Petry, I., Paranhos, C., Fan, F., Collishonn, W., and Tucci, C.: Short- to seasonal-range streamflow forecasting in reservoirs of the Brazilian National Interconnect Electric Power System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16559, https://doi.org/10.5194/egusphere-egu23-16559, 2023.

Historically, Switzerland and the nearby alpine countries have not been associated with major droughts. However, in recent years, the European Alpine space has experienced several unprecedented low-flow conditions and drought events. As many economic sectors in the region depend heavily on sufficient water availability, such as hydropower production, navigation and transportation, agriculture, and tourism, it is important for decision-makers to have early warnings of drought tailored to their needs and geographical conditions.

The European Flood Awareness System (EFAS) has been in operation since 2012 providing flood risk overviews for Europe up to 15 days in advance. More recently, it has also run long-range hydrological outlooks for sub-seasonal to seasonal horizons. While EFAS early flood warnings have been extensively evaluated in the past years, less attention has been paid to evaluating the system’s ability to detect upcoming drought conditions. In this study, we turn our focus to this other extreme of the spectrum and on EFAS’ predictability of drought events in large Alpine catchments. Our goal is to investigate how hydrological patterns of skill at a large spatial scale can be combined with local model outputs to more accurately inform decision makers on droughts and their spatio-temporal evolution.

For this, we evaluate the performance of EFAS comparatively to that of a local model in terms of the ability to simulate drought conditions. The Precipitation-Runoff-Evapotranspiration HRU (PREVAH) local model was set up for 59 stations in Switzerland. The PREVAH model is a distributed conceptual hydrological model that accounts for processes such as evapotranspiration, interception, snow- and ice-melt, soil moisture storage, groundwater storage, and runoff generation. We analyse 25 overlapping stations between the local model and the EFAS reporting stations (river network points where EFAS outputs are available to users), and compare the drought simulation performances of the two models. We focus on evaluating the duration, deficit, and magnitude of the drought events, as well as metrics including Nash–Sutcliffe model efficiency coefficient (NSE) and Kling-Gupta efficiency (KGE).

The outcome of this study will lay a foundation for how a large-scale hydrological model like EFAS can complement a local model like PREVAH to improve the predictability of sub-seasonal drought forecasting and provide more reliable early warnings for better water resources management.

How to cite: Chang, A. Y.-Y., Bogner, K., Ramos, M.-H., Harrigan, S., Domeisen, D. I. V., and Zappa, M.: Comparing drought simulation performance from large-scale and locally set up hydrological models for large mountainous rivers in Switzerland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12694, https://doi.org/10.5194/egusphere-egu23-12694, 2023.

Hydrological forecasts ranging from two weeks to months in advance are critical for decision making in water resources management and economic sectors. In the subseasonal timescale, there is an opportunity to anticipate events of hydrological interest, such as periods of floods and droughts. The development of subseasonal forecasts with good quality for decision support systems is still a great challenge for the technical and scientific community, as it fits into a predictability gap between medium-range weather (3 to 15 days) and seasonal climate prediction (2 to 7 months). In South America, the climate and weather variability can represent risk to activities such as agriculture and hydropower energy production. For instance, Brazil, the larger country in terms of area and economy in the continent, has an electrical power generation matrix with 63% of hydropower and rely on weather forecasts spanning multiple timescales for its integrated system operation. This work evaluated the potential skill of subseasonal streamflow forecasts over South America based on ECMWF ensemble forecasts with lead times up to 46 days obtained from the Subseasonal-to-Seasonal (S2S) project database. A continental-scale hydrologic-hydrodynamic model was used to carry out the simulation runs for obtaining subseasonal ensemble streamflow forecasts. Forecast bias was evaluated against a reference model run (i.e., pseudo-observations) for both raw and bias corrected precipitation, and the forecast skill was evaluated against the Ensemble Streamflow Prediction (ESP) method. Forecasts and pseudo-observations were aggregated into weekly averages, ranging 6 weeks for verification, and were divided into subsets for each season of the year (DJF, MAM, JJA, SON) to access seasonal patterns over South American regions. The results highlight that the forecast skill is dependent on initialization month, season, basin, and forecast lead time, with greater skill on shorter lead times. Bias correction was able to reduce the mean forecast error over most regions of the continent. In addition, the bias correction improved skill and maintained positive skill after the third week of forecast, especially in northeastern regions and on wet seasons (DJF, MAM), meanwhile in central regions the improvements were not clear. However, the ESP method outperformed the ECMWF-based ensemble in many regions. Finally, the results presented here provide insights for investigations and applications of S2S forecasts in the operational scope on a continental scale, which can bring benefits, for example, in the optimization of the operation of electricity generation reservoirs.

Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian Agency of Electrical Energy (ANEEL) under its Research and Development program Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).

How to cite: Quedi, E., Fan, F., Siqueira, V., Collischonn, W., Petry, I., Gama, C., Paiva, R., Silveira, R., Paranhos, C., and Freitas, C.: Continental-scale evaluation of subseasonal–to–seasonal (S2S) streamflow forecasts over South America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4540, https://doi.org/10.5194/egusphere-egu23-4540, 2023.

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491-0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the Brazilian Electricity Regulatory Agency (ANEEL) under its research and development programme, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days, at hydro power enterprises, which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). We present in this work the framework built up as interface to the results of this project, which integrate shapefiles of main river basins in Brazil, hydro meteorological information, forecasts of precipitation from seasonal models (e.g., ECMWF’s SEAS5) and derived streamflow from hydrological model used in the project (MGB-SA mainly) for the entire electric energy network of the country. The platform encompasses layers of maps and graphics synchronized by date, respectively to locations of hydro power plants in Brazil, which allows users to perform multiple analysis for either energy planning or routine hydraulic operations. We shall demonstrate examples of applications, such analysis of a flood event happened during the 2014/2015 El Niño episode, which caused heavy precipitation, increased river level and flow into reservoirs in the Iguaçu River basin, disruption of services and economic losses in the South of Brazil. Given the limitations of seasonal precipitation forecasting, the model was successful in predicting the heavy accumulated rainfall in the analyzed period. In parallel, the hydrological model was able to simulate flow peaks well in advance. In addition, the platform allows an overview of the SIN subsystems and respective stored energy, which allows intercomparison and pragmatic analysis of the country's electric energy capacity.

How to cite: Bomfim da Silveira, R., Freitas, C., Silmara Aver Paranhos, C., Cristina Pinheiro, L., Santos, R., de Campos, A. L., Ávila Rangel, L., Rogiski da Silva, N., Mainardi Fan, F., de Araújo Gama, C. H., Quedi, E., and Petry, I.: Hydrometeorological System for Seasonal Streamflow Forecasting at Brazilian Hydro Power Enterprises, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9689, https://doi.org/10.5194/egusphere-egu23-9689, 2023.

The wealth of available global datasets at high spatial and temporal resolutions opens many opportunities for hydrological modelling and forecasting. It is now possible to provide high-resolution hydrological simulations for a river basin anywhere in the world, even in basins without in situ observations. Combined with the strength of global parameter estimations of the wflow_sbm concept (Imhoff et al, 2021), this allows us to build and run hydrological models without calibration. These models can ultimately be used to provide discharge forecasts for the seasonal time scale. Here we present a workflow that tests the interoperability, scalability, and performance of combining cloud and high-throughput compute and data resources. The workflow combines open source technologies, including containerization, to provide automated monthly river discharge forecasts for practically every basin on the globe on cloud, HTC and in the future HPC platforms. We leverage the global ERA5 and SEAS5 products from the Copernicus Climate Data store as input for the wflow hydrological model. The workflow automatically downloads the required input data for the model domain, resamples the data to the required model grids, and runs the simulations. The workflow is automatically triggered every month when new SEA5 forecasts become available. Prior to running the forecasts, the ERA5 files are used to update the hindcast model states in preparation for the forecast. Next, 50 wflow ensemble members, forced using the SEAS5 forecasts, are run in parallel to provide estimates on the probability of discharge events. The workflow is currently set up and running for the Rhine basin on SURF’s high-throughput computing platform, but can easily be deployed on different infrastructures and for different river basins. 

How to cite: Sperna Weiland, F., Buitink, J., Langemeijer, J., Oonk, R., and Backeberg, B.: Towards automated seasonal river discharge ensemble forecasts on a federated compute and data infrastructure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15287, https://doi.org/10.5194/egusphere-egu23-15287, 2023.

The flow forecast is used in several sectors of society, bringing benefits in relation to the mitigation of possible impacts in flood events and it is information of great value for the economic sectors associated with agriculture and energy generation. In South America, climate and meteorological variability directly impact these economic sectors. In Brazil, for example, the production of electricity is predominantly hydroelectric generation, which currently represents about 63% of the installed power in the country, in addition to the complementarity between different hydrographic basins and the other sources that make up the Brazilian energy matrix.

The Brazilian electricity sector relies on flow forecasts for different time scales, which are used to optimize the available water resources and for the energy commercialization. The National Electric System Operator (ONS) is responsible for coordinating the operation of 153 Hydroelectric Power Plants (HPPs) and uses different hydrological models for flow forecasting. For the 14-day horizon (short term) it’s used the deterministic rain-flow model called SMAP. For the horizon of 15 to 45 days (sub seasonal) it’s used the PREVIVAZ, a univariate stochastic model.

This work presents the evaluation of the performance of the SMAP model for forecasting in a sub seasonal horizon for 6 reservoirs in the Iguaçu River basin, associated with HPPs with a total installed capacity of 7,024 MW, located in the southern region of Brazil. Streamflow forecasts were evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) sub seasonal forecast, with lead time up to 46 days, from the Subseasonal-to-Seasonal (S2S) project database, and using the Global Ensemble Forecast System (GEFS) sub seasonal forecast, with lead time up to 35 days, from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA).

The results showed that the flow forecasts for the sub seasonal horizon present good performance for the initial forecast horizon, with degradation in the quality of the results after this horizon. There was also evidence of gain associated with forecasts for the ensemble over the entire horizon. The use of the SMAP model combined with precipitation forecasts in the sub seasonal horizon proved to be superior to the PREVIVAZ model, currently in use at the National Electric System Operator (ONS), with a significant improvement being observed, evidencing the usefulness of flow forecasts based on numerical models of precipitation prediction for the sub seasonal horizon.

Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian National Electricity Regulatory Agency (ANEEL) under its Research and Development Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).

How to cite: Aver, C., Freitas, C., Quedi, E., Fan, F., Siqueira, V., Collischonn, W., Araujo, C., Petry, I., and Silveira, R.: ANALYSIS OF SUB SEASONAL STREAMFLOW FORECASTS FOR HPPs RESERVOIRS AT SOUTH AMERICA BASED ON ECMWF AND GEFS MODELS DATA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16515, https://doi.org/10.5194/egusphere-egu23-16515, 2023.

Streamflow prediction by using time series models is one of the practical methods that can play an important role in water resource management.  But choosing the correct model based on the linear or non-linear streamflow behavior is so crucial. Additionally, streamflow non-linearity can be affected by physiographic characteristics of watershed.  In this study, streamflow non-linearity of daily, monthly and yearly time series and its relationship with the hydrometric station area have been examined. To fulfill this purpose, ten hydrometric stations with various catchment area were selected in Hesse state in Germany. The Brock-Dechert-Scheinkman (BDS) test for testing non-linearity patterns in time series, was used to test non-linearity. The results showed that daily streamflow time series exhibit strong non-linearity. In addition, as the timescale increases, the intensity of nonlinearity decreases. Monthly and yearly streamflow time series showed less evidence of non-linearity. Furthermore, regarding the investigation of the relationship between streamflow non-linearity and watershed area, pearson and spearman tests were used. The results revealed that in none of the time scales, daily, monthly, and yearly there is no significant correlation between these two parameters. It should be noted that the novelty of this article is examining the relationship between intensity of the non-linearity and watershed area as a factor in choosing the best fitted model for time series.

How to cite: Khazaeiathar, M. and Schmalz, B.: Investigation of Non-linearity of Daily, Monthly, and Yearly Streamflow and the Correlation Between Non-linearity and Catchment Area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-371, https://doi.org/10.5194/egusphere-egu23-371, 2023.

Accurate prediction of drought is essential for assessing agricultural production, water resources management, and early risk warning. Various machine learning models have been developed to enhance the accuracy of drought prediction. However, most drought models do not account for data uncertainty. Novel approaches such as the stacking model consider the predictor uncertainty and include multi-source satellite-based products. Here, we develop and test a fusion-based ensemble stacking model that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought modeling and prediction. Multi-source data, including meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics are incorporated in the proposed stacking model. The modeling framework forecast the one-month lead standardized precipitation evapotranspiration index (SPEI) on 12 month scale. In particular, data uncertainty is taken into account allowing for a rigorous model performance evaluation. The proposed method is applied and tested in the German federal states of Brandenburg, and Berlin. The results show that the ST model outperforms XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R²) value of 0.845 in each month of the year 2018. The spatial-temporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the relationship between predictors and the meteorological drought index and outperforms the other three models. Counterfactual sensitivity analysis indicated that extreme precipitation, soil moisture, runoff, and precedent SPEI explain more than 80 % of the total variance of the prediction. Based on the accuracy and flexibility of the method, it seems to be a promising approach for predicting other environmental phenomena.

How to cite: Zhang, H., Sauter, T., and Loaiciga, H.: A transparency fusion-based methodology for meteorological drought prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1012, https://doi.org/10.5194/egusphere-egu23-1012, 2023.

As contractor and sub-contractor for C3S, SMHI and WU-DES have set up a multi-model operational service for hydrological seasonal forecasts. The service currently produces forecasts of monthly mean river discharge for a pan-European domain using several hydrological models, namely E-HYPEcatch, on irregular catchment delineations, and E-HYPEgrid and VIC-WUR on regular 5km gridded drainage network of EFAS. Also, the EFAS-Lisflood forecasts from the separate ECMWF production line are included in the final data set. All forecasts use the SEAS5 meteorological forecasts with 51 ensemble members. The data from all models are available from the CDS data portal for a re-forecast (https://doi.org/10.24381/cds.13c18212) and a forecast period (https://doi.org/10.24381/cds.52f45864). Further, the most probable forecast is displayed in an online application (https://cds.climate.copernicus.eu/cdsapp#!/software/app-hydrology-seasonal-forecast-explorer?tab=app).

Ongoing work is aimed at introducing a second meteorological forecasting system, and adding more output variables at a higher daily temporal resolution. The quality of the forecasts is at this point mainly addressed by working on the input data and statistical downscaling and bias adjustment. The new system is expected to be operational in mid-2024.

How to cite: Yang, W., Berg, P., Hutjes, R., McKnight, U., Nauta, L., and Paparrizos, S.: A pan-European service for hydrological seasonal forecasts at C3S, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13170, https://doi.org/10.5194/egusphere-egu23-13170, 2023.

Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. Flash droughts have raised a wide concern, but whether they can be predicted at sub-seasonal time scale remains unclear. We investigate the forecast skill of flash droughts over China with lead times up to three weeks by using model hindcasts from the sub-seasonal-to-seasonal prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence. The ensemble of the two models increases equitable threat score from ECMWF and NCEP models for lead 1 week. In terms of probabilistic forecast, ECMWF also has higher brier skill score than NCEP especially over Eastern China, which is consistent with higher temperature and precipitation forecast skill and flash drought predictability of ECMWF model. The multi-model ensemble shows a higher skill than ECMWF model. This study suggests the importance of multi-model ensemble flash drought forecasting.

How to cite: Ma, R. and Yuan, X.: Evaluation of Predictive Skill of Flash Droughts over China based on S2S Forecast Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6481, https://doi.org/10.5194/egusphere-egu23-6481, 2023.

Seasonal climate predictions with global climate models which are developed based on the ocean-atmosphere interactions, contribute to the water resources management and hazard mitigation. Nowadays, multi-model ensemble seasonal climate prediction system, such as North American Multi-Model Ensemble (NMME), has become an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean-atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, it’s still unclear that whether the NMME real-time forecasts perform as well as the hindcasts and how the changes in ocean-atmospheric teleconnections affect the prediction skill. Here we show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14%-51% (38%-75%) and error increased by 30%-31% (51%-55%) for seasonal precipitation (temperature) predictions up to the sixth lead-seasons, and the skill decrease larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that improved simulations of the changes in the ocean-atmospheric teleconnections are necessary for skillful seasonal climate predictions in the real-time.

How to cite: Shao, C., Yuan, X., and Ma, F.: Decadal variation of predictive skill of seasonal climate over the Yangtze River and its possible causes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7396, https://doi.org/10.5194/egusphere-egu23-7396, 2023.