The understanding of hydrology in rivers is based not only on continuous water level measurements at gauging stations but also on discharge measurements and the derivation of rating curves. When validating this data, the Federal Waterways and Shipping Administration carries out water balance studies to validate and if applicable correct the consistency of the discharge time series at different gauges. In this paper, we describe the application of a new concept based on balance models in combination with a data assimilation approach and an AI-based error correction in application to reaches the Rhine and Main rivers.

Das Verständnis der Hydrologie von Fließgewässern basiert neben der kontinuierlichen Wasserstandsmessung an Pegeln auch auf der Abflussmessung und der Ableitung von Abflusskurven. Bei der Validierung dieser Daten werden in der Wasserstraßen- und Schifffahrtsverwaltung des Bundes Bilanzierungen durchgeführt, um die Konsistenz der Abflusszeitreihen verschiedener Pegel untereinander zu validieren und ggf. zu korrigieren. In diesem Beitrag beschreiben wir die Anwendung eines neuen Konzepts auf der Basis von Bilanzmodellen in der Kombination mit einem Assimilationsverfahrens und einer KI-basierten Fehlerkorrektur in Anwendung auf Abschnitte des Rheins und des Mains.

The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management process can be structured as a prescriptive analytics setup that considers both current and predicted system states to recommend actions and outline potential implications. In application to a reservoir and river system, it combines hydrological modelling components for the system schematization, observations and data assimilation for the identification of the current system state, meteorological and hydrological predictions as well as optimization-based techniques to support decision-making regarding reservoir operations. In this paper, we present the application of such a framework to the short-term management of the Eder and Diemel storage reservoirs. These reservoirs are operated by the German Federal Waterways and Shipping Administration (WSV) with the primary goal to support navigation in the River Weser during low flow periods. In addition, partially conflicting objectives such as flood protection, energy generation and recreation are considered. The implementation includes an explicit consideration of forecast uncertainty and its impact on the decision-making by using probabilistic forecasts in combination with a multi-stage stochastic optimization approach. We demonstrate the applicability of the approach based on low and high water use cases. Special attention is paid on the benefits of the probabilistic forecast in combination with the multi-stage stochastic optimization versus a deterministic setup. It provides an explicit translation of the forecast uncertainty in the decision variables, in this case the reservoir releases helping the operators to better anticipate the range of future release decisions. Furthermore, the stochastic approach is expected to provide more stable decisions in an operational setting, based on more stable forecasts by considering various possible realizations of the future instead of picking a single one, which gets random after 4-5 days. Die Echtzeitbewirtschaftung von Mehrzweckspeichern zielt auf einen effizienten Betrieb vorhandener hydraulischer Infrastruktur ab. Dieser Prozess kann als pr & auml;skriptives Optimierungsmodell strukturiert werden, das sowohl aktuelle als auch prognostizierte Systemzust & auml;nde ber & uuml;cksichtigt, um Handlungsempfehlungen zu geben und potenzielle Auswirkungen zu skizzieren. In Anwendung auf ein Talsperren- und Flusssystem kombiniert es hydrologische Modellierungskomponenten f & uuml;r die System-Schematisierung, Beobachtungen und Datenassimilation zur Identifizierung des aktuellen Systemzustands, meteorologische und hydrologische Vorhersagen sowie optimierungsbasierte Techniken zur Unterst & uuml;tzung der Entscheidungsfindung bez & uuml;glich des Talsperrenbetriebs. In diesem Paper pr & auml;sentieren wir die Anwendung eines solchen Rahmens auf das Kurzzeitmanagement der Eder- und Diemeltalsperre. Diese Speicher werden von der deutschen Wasserstra ss en- und Schifffahrtsverwaltung des Bundes (WSV) betrieben mit dem Hauptziel, die Schifffahrt auf der Weser w & auml;hrend Niedrigwasserperioden zu unterst & uuml;tzen. Dar & uuml;ber hinaus sind teilweise konkurrierende Ziele wie Hochwasserschutz, Energieerzeugung und Erholung zu ber & uuml;cksichtigen. Die Umsetzung beinhaltet eine explizite Ber & uuml;cksichtigung der Prognoseunsicherheit und ihrer Auswirkungen auf die Entscheidungsfindung durch die Verwendung probabilistischer Prognosen in Kombination mit einem mehrstufigen stochastischen Optimierungsansatz. Wir zeigen die Anwendbarkeit des Ansatzes anhand von Niedrig- und Hochwassersituationen auf. Besonderes Augenmerk wird auf die Vorteile der probabilistischen Prognose in Kombination mit der mehrstufigen stochastischen Optimierung im Vergleich zu einem deterministischen Setup gelegt. Es bietet eine explizite & Uuml;bersetzung der Prognoseunsicherheit in die Entscheidungsvariablen, in diesem Fall die Talsperrenabgabe, um den Betreibern zu helfen, den Bereich zuk & uuml;nftiger Abgabeentscheidungen besser abzusch & auml;tzen. Dar & uuml;ber hinaus wird erwartet, dass der stochastische Ansatz in einem operationellen Umfeld robustere Entscheidungen bietet, basierend auf stabileren Vorhersagen durch die Ber & uuml;cksichtigung verschiedener m & ouml;glicher Realisierungen der Zukunft anstelle der Auswahl einer einzigen, die nach 4-5 Tagen zuf & auml;llig wird.

While decision makers in climate-dependent sectors are increasingly considering climate change (CC) in their risk portfolios, there is a structural lack of information on how to assess specific CC-related risks and what to do in practice. The ERA4CS (European Research Area for Climate Services) supported the CO-MICC research project (2017-2021) that aimed to co-develop in a participatory manner with potential end-users how the output of global hydrological models can be optimally used to support climate change risk assessment of freshwater-related hazards at different scales.In particular, it was investigated how the output of multiple global hydrological models (e.g., groundwater recharge or streamflow), each driven by the output of multiple global climate models, can be best provided in an interactive map-based web service to show the range of plausible future impacts of climate change on freshwater. Data sources are state-of-the-art global future projections following the ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) protocol simulated by the modelling groups of the CO-MICC consortium from PIK, IIASA and Goethe University Frankfurt. In addition, methods for using the relatively coarse information (0.5° by 0.5° grid cells) in regional and local climate change risk assessments were investigated. Through an iterative dialogue process in three rounds of workshops, scientists and end-users learned from each other which particular hydrologic information is valuable for end-user risk assessments - and how to best communicate that information so that it can be practically used by end-users around the world in local, transboundary, and global climate change adaptation and mitigation planning.The climate service was developed by the CO-MICC consortium and is freely available as a pilot application to all users worldwide at www.co-micc.eu. The web portal of the climate service consists of two components, the knowledge portal and the data portal, respectively. The interactive data portal provides free and easy access to multi-model-based data on future freshwater availability on a global scale. It is a web-based information system that provides access to freshwater-related indicators of climate change hazards for all land areas of the globe except Greenland and Antarctica. The data are visualized and provided for individual 0.5° grid cells or aggregated at the basin or country level. The data viewer contains map display, showing spatio-temporal developments, and a data analysis tool can be used to create statistical and graphical representations of the data. In the knowledge portal, in addition to the introduction to the methodology, online trainings as well as the PUNI (Providing and Utilizing eNsemble Information) handbook are included.In December 2021, the CO-MICC knowledge and data portal was launched supported by WMO and UNESCO. The pilot climate service is hosted by the UNESCO Center ICWRGC in conjunction with the German Federal Institute of Hydrology. We will demonstrate the capabilities of the interactive web platform and will provide details on the development process.

Abstract. Recently, projects such as the S2S (Sub-seasonal to Seasonal) have surfaced with the goal of investigating the potential benefits of operational applications of medium- to long-term weather forecasts from two weeks to three months. Key challenges are to quantify forecast uncertainty and verify these predictions considering the downstream users. This work evaluates the meteorological lead-time performance and 5-years skill evolution of nine models of the S2S project alongside discharge predictions from a coupled hydrological model. Moreover, an analysis of the predictors of Numerical Weather Prediction (NWP) quality and an evaluation of the correlation between meteorological and hydrological quality improvement over time is carried out. Results show that the S2S models have skill at the catchment-scale, particularly for lower threshold levels, and that ensemble size is the main predictor of NWP performance. Discharge simulations forced with S2S predictions remain skilful up to one month. The quality of the S2S has increased over time, and there is a strong correlation between meteorological and hydrological improvements. We conclude that S2S products may provide added value to end-users of water resources applications.

Successful adaptation to climate change worldwide will require many local climate change risk assessments. However, appropriate and tailored climate services and information tools are lacking, particularly in developing countries. Co-produced, user-driven climate services are a recognized means for effective generation and provisioning of relevant climate information and support the utilization by decision-makers, enabling them to account for climate change in their risk portfolios. In the CO-MICC project (ERA4CS), a data and knowledge portal is co-developed with stakeholders based on global-scale multi-model simulations of hydrological variables. In a participatory manner, we focussed on (1) eliciting the relevant hydrological hazard indicators, (2) representing their uncertainty quantitatively in a way that is both scientifically correct and utilizable to the diverse users of the hazard information, and (3) creating guidance on how to integrate the uncertain global information into regional-scale assessments of water-related climate change risk and adaptation assessments. Adapting the tandem framework of the Swedish Environmental Institute (SEI), participatory stakeholder dialogues including seven workshops with stakeholders from focus regions in Europe and Northern Africa, and finally with globally-acting companies serve to integrate the various experiences, needs and expectations of various regions and users. Participants included local researchers, experts from meteorological services and decision-makers from regional and national hydrological agencies. Together, we co-produced relevant model output variables and appropriate end-user products encompassing static and dynamically generated information in a web portal. The global-scale information products include interactive maps, diagrams, time series graphs, and suitably co-developed statistics, with appropriate visualization of uncertainty. In complement, the knowledge tool provides transparent meta-information, tutorials and handbook guidelines to utilize the provided information in models of local participatory risk assessments. While CO-MICC enables access to this information to a broad range of stakeholders from around the world (policy makers, NGOs, the private sector, the research community, the public in general) for their region of interest, it additionally sheds light on the optimal design and methods of co-development processes.

Streamflow forecasts include uncertainties related with initial conditions, model forcings, hydrological model structure and parameters. Ensemble streamflow forecasts can capture forecast uncertainties by having spread forecast members. Integration of these forecast members into real-time operational decision models which deals with different objectives such as flood control, water supply or energy production are still rare. This study aims to use ensemble streamflows as input of the recurrent reservoir operation problem which can incorporate (i) forecast uncertainty, (ii) forecasts with a higher lead-time and (iii) a higher stability. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). This approach reduces the number of ensemble members by its tree generation algorithms using all trajectories and then proper problem formulation is set by Multi-Stage Stochastic Programming. The method is relatively new in reservoir operation, especially closed-loop hindcasting experiments and its assessment is quite rare in the literature. The aim of this study is to set a TB-MPC based real-time reservoir operation with hindcasting experiments. To that end, first hourly deterministic streamflows having one single member are produced using an observed flood hydrograph. Deterministic forecasts are tested with conventional deterministic optimization setup. Secondly, hourly ensemble streamflow forecasts having a lead-time up to 48 hours are produced by a novel approach which explicitly presents dynamic uncertainty evolution. Produced ensemble members are directly provided to input to related technique. Uncertainty becomes much larger when managing small basins and small rivers. Thus, the methodology is applied to the Yuvacik dam reservoir, fed by a catchment area of 258 km2 and located in Turkey, owing to its challenging flood control and water supply operation due to downstream flow constraints. According to the results, stochastic optimization outperforms conventional counterpart by considering uncertainty in terms of flood metrics without discarding water supply purposes. The closed-loop hindcasting experiment scenarios demonstrate the robustness of the system developed against biased information. In conclusion, ensemble streamflows produced from single member can be employed to TB-MPC for better real-time management of a reservoir control system.

Abstract Because of climate change, the frequency, intensity and/or duration of extreme weather events such as floods, droughts, storms and extreme temperatures is increasing. These events are often related to loss of property, money and life, especially in poor and developing countries where there is no or poor disaster management due to social and financial difficulties or due to a lack of synergy between the mitigation actions taken. Negative impacts can be reduced and losses can be better handled with proper water management techniques. However, these should not be handled solely with traditional management. The actual problem cannot be over simplified as merely a question of coping with resources availability and demand. Therefore, the present paper aims to summarize advances in weather forecasting and reservoir operation in the Upper São Francisco River, strategic to Brazil because it provides water to the semi-arid region and energy for economically thriving Brazilian regions. Moreover, it discusses challenges, opportunities and improvements needed to implement these advances in the current national integrated water resources management. This is mainly focused on water-related disaster mitigation.

Increasing data volumes and decreasing human resources limit the continuation of established, often manual quality control processes. In response to this challenge, we suggest to automate data validation processes and provide the user with tools to supervise and intervene. This paper deals with technical, conceptual and organizational aspects of such an implementation in a modern software solution. This includes the verification of the data flow from the sensor into the database, a conceptual data validation and data editing tools as well as software support for business processes.

The short-term, optimal management of storage reservoirs is challenging due to multiple objectives, i.e. hydropower, water supply or flood mitigation, and inherent uncertainties of forecasts for inflow and water demand. Model Predictive Control (MPC) provides an online solution for this management problem by combining a process model, forecasts and the formulation of objectives in an objective function and its solution by an optimization algorithm. This anticipatory management has many advantages, but may suffer from forecast uncertainty. In practice, there are several sources of forecast uncertainty, which can jeopardize control decisions. In this study, hindcast experiments integrating deterministic and probabilistic streamflows in a closed-loop mode of MPC are tested to mimic a real-time flood mitigation case. Probabilistic inflow forecasts in combination with multi-stage stochastic optimization model are used with tree-based reduction techniques. According to the results, tree-based MPC proposes less spillway discharges during a real-time control of a major flood case by incorporating longer the forecast horizon and consideration of forecast uncertainty in the decision process. On the other hand, energy generation is compared with deterministic method, and the results are promising to be used without compromising the energy production.

The use of surrogate and data-driven models has the potential to decrease the computational effort of streamflow predictions in time-critical model applications such as Data Assimilation (DA) or Model Predictive Control (MPC). In the present work, it was evaluated the use of Artificial Neural Network (ANN) as replacement of a physical model in a MPC implementation for the multi-objective optimization of a reservoir system. The presented application covers the flow routing of a reservoir release from Tres Marias dam in Brazil and downstream tributaries to Pirapora gauge for lead times between 1 and 360 h (15 days). The ANNs were trained using Levenberg–Marquadt algorithm, and three different transfer functions were evaluated. It was also tested two output correction techniques, namely an ARX model for error prediction and bias correction for lead time until 15 days ahead. The ANNs model shows good capability of handling with minor disturbances. Best performance was found using the purelin transfer function. For the error correction, the ARX models showed better error reduction when compared with the bias correction technique, which could not reduce the error at the end of lead time.

Optimal control of reservoirs is a challenging task due to conflicting objectives, complex system structure, and uncertainties in the system. Real time control decisions suffer from streamflow forecast uncertainty. This study aims to use Probabilistic Streamflow Forecasts (PSFs) having a lead-time up to 48 h as input for the recurrent reservoir operation problem. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). Deterministic Streamflow Forecasts (DSFs) are provided by applying random perturbations on perfect data. PSFs are synthetically generated from DSFs by a new approach which explicitly presents dynamic uncertainty evolution. We assessed different variables in the generation of stochasticity and compared the results using different scenarios. The developed real-time hourly flood control was applied to a test case which had limited reservoir storage and restricted downstream condition. According to hindcasting closed-loop experiment results, TB-MPC outperforms the deterministic counterpart in terms of decreased downstream flood risk according to different independent forecast scenarios. TB-MPC was also tested considering different number of tree branches, forecast horizons, and different inflow conditions. We conclude that using synthetic PSFs in TB-MPC can provide more robust solutions against forecast uncertainty by resolution of uncertainty in trees.

FieldVisits is an application for the digital acquisition of data during measurement tours and monitoring tours on mobile devices such as notebooks, tablets or smartphones. Field protocols can be entered in the field and, if connected via GPS, GPRS, Bluetooth or WLAN, can be directly processed, stored and made available to all users in a building monitoring system such as WISKI. The energy company Vorarlberger Illwerke AG opted for FieldVisits as the solution for data acquisition of on-site measurement. FieldVisits is currently used in both dam monitoring and hydrology.

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

The global dissemination of operational systems for flood forecasting, early warning and risk management is much more heterogeneous than comparable forecasting services found in the meteorological community. This results primarily from the need for local 'on-the-ground' knowledge, such as addressing regional hydrological phenomena or the impact of local water resources management, and the challenge for small or weak institutions to integrate and operate such sophisticated systems. In this paper, we summarize and discuss the state-of-the-art of this kind of systems from a scientific, technical and institutional perspective and provide some recent applications. This leads to the development of an improved integration approach of existing building blocks and cross-organizational collaboration aimed at defining a next-generation flood risk management approach. The approach is implemented through the combination of a general platform with a community-driven effort, which substantially relies on local expertise.

State of the art • sequential techniques such as the Ensemble Kalman Filter (EnKF) require no additional features within the modeling process, • variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function can be formulated as a trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables, • sequential techniques have been commonly applied to hydrological processes, variational techniques have been seldom used, • lack of thoroughly comparisons in hydrological applications.

Reservoir operations require enhanced operating procedures for water systems under stress attributed to growing water demand and consequences of changing hydro-climatic conditions. This study focuses on the management of the Yuvacik Dam Reservoir for water supply and flood mitigation in the Marmara Region of Turkey. We present an improved operating technique for fulfilling the conflicting water supply and flood mitigation objectives. This is accomplished by incorporating the long term water supply objectives into a Guide Curve (GC) whereas the extreme floods are attenuated by means of short-term optimization based on Model Predictive Control (MPC). The reference case implements operating rules with a constant GC at maximum forebay elevation targeting the fulfillment of the water supply objective. We compare the reference with a new time-dependent GC, derived using an Implicit Stochastic Optimization (ISO) approach. This new curve shows nearly the same performance regarding the water supply objectives, but significantly reduces the flooding risk downstream of the dam. Possible flood events observed at the end of the wet season, when the reservoir is at the maximum level to enable water supply for the dry season, can be eliminated by the application of an additional short-term optimization by MPC. The robustness of the approach is demonstrated via hindcasting experiments.