Rivers are an important source of water for the economy. However, in dry periods continuous abstraction could function as a catalyst for low-flow events and, hence, run counter to hydrological-ecological requirements and lead to imbalances in competing use demands. In the absence of hydro-economic incentive systems, there is no economic motivation for water users to refrain from this procedure. Using a hydro-economic model approach, this article examines the hydrological and economic implications of dynamic water abstraction charges in response to low-flow conditions. The developed approach combines hydrodynamic river modeling with an abstraction cost model. This requires hydrological input data, such as flow rates, but also economic inputs, such as withdrawal quantities and charge rates. The cost rates are linked to discharge conditions, which allows a dynamic simulation of the cost structure at different flow rates. As a result, the simulated abstraction costs per flow range and a failure day risk are obtained. By simulating different scenarios, the study indicates that dynamic pricing mechanisms can provide flow-dependent economic signals relevant for sustainable water resource management under scarcity conditions. The approach was tested using the discharges of the middle Elbe between 1990 and 2022. The simulated application examples showed that dynamic abstraction charges lead to cost differences of up to 14 to 17 percent compared to fixed charges. For an exemplary extractor with an approved extraction rate of 3,500 m3/h, additional costs from cost dynamization amount to approximately EUR 200,000 per year in years with consistently low, but not extremely low, discharge volumes. Furthermore, it was found that the specified minimum flow rate was not met on up to 162 days in 2018, a year characterized by extremely low water levels. The findings highlight the potential of adaptive abstraction charges as a policy instrument to reflect hydrological scarcity in economic terms. However, as the model does not explicitly represent behavioral responses, the results should be interpreted as changes in cost signals rather than realized changes in water use. Furthermore, as part of a comprehensive low-flow risk approach, it can help decision-makers to understand hydro-economic interactions and develop strategies for sustainable water management.

Study focus: In a changing climate, the propagation of drought through coupled groundwater-surface water systems remains difficult to investigate, particularly in catchments strongly influenced by human activities. This study presents a joint analysis of groundwater and streamflow droughts in the anthropogenically influenced Rur river catchment. Standardized indices were computed for 209 groundwater wells (standardized groundwater level index, SGI) and 16 streamflow gauges (standardized streamflow index, SSI). Hierarchical clustering yielded five groundwater clusters (SGI_C1-SGI_C5) and three streamflow clusters (SSI_C1-SSI_C3). Clustermean hydrographs were analyzed using run theory and cross-correlation to characterize regional drought attributes and the propagation process. New hydrologic insights: Regional groundwater droughts are strongly modulated by anthropogenic activities and local hydrogeology. Notably, SGI_C2 shows a persistent declining trend, associated with nearby lignite open-pit mining. SGI_C5, comprising wells near the Rur, exhibits shorter mean drought durations. In contrast, streamflow droughts respond rapidly to precipitation deficits and vary with topographic setting. The mountainous cluster SSI_C2 shows the strongest correlation with the standardized precipitation index (SPI; r = 0.60). In lowland regions, strong groundwater-surface water coupling is evident, with moderate-to-strong correlations between SGI_C5 and SSI_C1 (r = 0.55) and between SGI_C1 and SSI_C3 (r = 0.45), implying that concurrent groundwater drought can intensify streamflow drought. Reservoir operations mitigate streamflow drought severity in SSI_C1 relative to SSI_C3, which more closely reflects natural flow and exhibits more pronounced droughts. The study provides a transferable workflow for investigating hydrological drought propagation under anthropogenic influence. It highlights the roles of human water management, precipitation variability, and groundwater-river interaction in shaping drought dynamics across the groundwater and stream flow system.

Low-flow events significantly impact water users and ecosystems due to reduced flow rates and deteriorating water quality. Elevated water temperatures during these periods have led to economic and ecological consequences. Therefore, water temperature is a key aspect in the context of low-flow risk analysis, and it is essential to model it accurately. This study introduces a one-dimensional water temperature model optimized for integration into low-flow risk analysis frameworks. Results demonstrate good performance in simulating water temperatures for both rivers, with Nash–Sutcliffe efficiency values of 0.85–0.98 and root mean square errors of 0.96–1.96 K. The model was evaluated on two contrasting river systems: the small Selke River and the large Elbe River. The model effectively captures anthropogenic influences and altered environmental conditions. Key factors influencing water temperature varied by river size, with tributaries and shading having more impact on smaller rivers, while air temperature was the primary driver for larger rivers. The model’s computational efficiency enables the practical implementation of long-term risk assessments. This temperature model fulfills the requirements for integration into low-flow risk management frameworks, providing a valuable tool for assessing temperature-related impacts and evaluating mitigation strategies across diverse river systems.

Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R2) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management.

Low-flow events cause socio-economic impacts in various sectors. However, there are few hydro-economic models to quantify these. This article presents a hydro-economic modeling approach to determine the direct economic damage of low flows to freight and tourist navigation on free-flowing rivers. This includes the description of the required hydrological and economic input variables, a presentation of the calculation approach and the development of the damage functions. Damage and replacement costs were calculated for the study area of the middle Elbe in order to test the model and show how low-flow events affect freight and tourist navigation. Our results indicate that considerable economic losses occur not only during low flows, but also when the reference water levels for navigation are only slightly exceeded over a long period of time. It was also shown that certain river sections are more vulnerable to low-flow events and therefore cause higher costs. The model represents an analytical tool that considers both economic and hydrological aspects. As a part of a holistic risk assessment, it can help decision-makers to understand the interactions between water and the economy and to develop strategies that promote sustainability in water resource management.

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

Critical infrastructure (CI) networks face diverse natural hazards, such as flooding. CI network modeling methods are used to evaluate these hazards, enabling the analysis of cascading effects, flood risk, and potential flood risk-reducing measures. However, there is a lack of linkage between analytical methods and potential multisectoral, structural, and nonstructural measures. This deficiency impedes the development of CI network (CIN) models as robust tools for active flood risk management. CI operators have significant expertise in managing and implementing flooding-related measures within their sectors. The objective of this study is to bridge the gap between the application of CIN modeling and the consideration of flood measures in three steps. The first step is conducting a literature review and CI stakeholder interviews in Central Europe on flood measures. The second step is the culmination of the findings in a comprehensive catalog detailing flood measures tailored to five CI sectors, with a generalized category spanning each phase of the disaster risk management cycle. The third step is the validation of the catalog’s utility in a proof-of-concept study along the Vicht River in Western Germany with a model-based flood risk analysis of five flood measures. The application of the flood measure catalog improves the options available for active and residual flood risk management. Additionally, the CI flood risk modeling approach presented here allows for consideration of disruption duration and recovery capability, thus linking the concept of risk and resilience.

Low-flow events, characterized by a significant water deficiency in river systems, have profound impacts on various water users and river ecology. Recent low-flow events in Europe have had severe economic and ecological consequences such as disruptions to hydropower production, irrigation bans, constraints on navigation and complete river drying. These events highlight the urgent need for effective low-flow risk management and demand a holistic risk analysis as a basis. The existing approaches to low-flow analysis often focus on hydrological aspects, utilizing indices such as the Standardized Runoff Index (SRI) or Low-flow Index. However, these indices lack information regarding consequences and impacts. Other approaches consider parts of a risk approach but often focus on special aspects, such as the economy; in general, no holistic assessment is made. This study introduces a conceptual approach to a holistic low-flow risk analysis. The approach provides a continuous long-term simulation to capture the special long-term behaviour of low-flow events and therefore avoids the complex definition of scenarios. In this conceptual approach, the low-flow risk is analysed using a combination of various analyses that cover all aspects from occurrence to consequences. Meteorological analysis is used to generate synthetic long-term weather data time series, which are transformed into runoff time series in hydrological analysis. Based on these results, hydrodynamic analysis quantifies the water levels, water temperatures, and flow velocities along the river. The consequences are analysed in terms of socio-economic and ecological consequences. The results represent a long-term series of damage values. Finally, the damage values are summed in the risk analysis and divided by the number of years considered in the analysis. For testing and demonstration purposes, the presented conceptual risk approach is partly applied to a proof-of-concept at the Selke catchment, a small river catchment in Germany. Finally, the results are presented, evaluated, and discussed. This paper describes and tests a holistic low-flow risk approach consisting of various analyses on a proof-of-concept. The proof-of-concept shows that the approach is suitable for determining the low-flow risk and can therefore serve as a basis for low-flow risk management. image

<p><em>Precipitation scenario analysis is a crucial step in flood risk assessment, in which storm events with different probabilities are defined and used as input for the hydrological/hydrodynamic calculations. Rainfall generators may serve as a basis for the precipitation analysis. With the increase in the use of high resolution spatially-explicit hydrological/hydrodynamic models in flood risk calculations, demand for synthetic gridded precipitation input is increasing. In this work, we present a dynamic spatiotemporal rainfall generator. The model is capable of generating catchment-scale rainfields containing moving storms, which enable physically-plausible and spatiotemporally coherent precipitation events. This is achieved by the tools event-based approach, where dynamic storms are identified as clusters of related data that occur at different locations in space and time, and are then used as basis for event regeneration. The implemented methodology, mainly inspired by Dierden et al. (2019), provides an improvement in the spatial coherence of precipitation extremes, which can in turn be beneficial in flood risk calculations.</em></p> <p><em>The model has been validated under different databases such as the radar-based RADALON dataset or spatially-interpolated historical raingauge timeseries of different catchments in Germany, which is also presented in this work. The validation indicates the models ability to adequately preserve observed storm statistics in the generated timeseries. </em><em>The generator is developed as an extension to the state-of-the-science flood risk modelling tool ProMaIDes (Promaides 2023). The model also puts great focus on user accessibility with offering features such as an easy installation process, support for most operating systems, a user interface and an online user manual.</em></p> <p><em> </em></p> <p>Diederen, D., Liu, Y., 2020. Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes. Stoch Environ Res Risk Assess 34, 1369–1383. https://doi.org/10.1007/s00477-019-01724-9</p> <p>ProMaIDes (2023): Protection Measures against Inundation Decision support. https://promaides.h2.de</p>

Das Interesse an Virtual Reality (VR) für die Hochschullehre steigt aktuell vermehrt durch die Möglichkeit, logistisch schwierige Aufgaben abzubilden sowie aufgrund positiver Ergebnisse aus Wirksamkeitsstudien. Gleichzeitig fehlt es jedoch an Studien, die immersive VR-Umgebungen, nicht-immersive Desktop-Umgebungen und konventionelle Lernmaterialien gegenüberstellen und lehr-lernmethodische Aspekte evaluieren. Aus diesem Grund beschäftigt sich dieser Beitrag mit der Konzeption und Realisierung einer Lernumgebung für die Hochschullehre, die sowohl mit einem Head Mounted Display (HMD) als auch mittels Desktops genutzt werden kann, sowie deren Evaluation anhand eines experimentellen Gruppendesigns. Die Lernumgebung wurde auf Basis einer eigens entwickelten Softwareplattform erstellt und die Wirksamkeit mithilfe von zwei Experimentalgruppen – VR vs. Desktop-Umgebung – und einer Kontrollgruppe evaluiert und verglichen. In einer Pilotstudie konnten sowohl qualitativ als auch quantitativ positive Einschätzungen der Usability der Lernumgebung in beiden Experimentalgruppen herausgestellt werden. Darüber hinaus zeigten sich positive Effekte auf die kognitive und affektive Wirkung der Lernumgebung im Vergleich zu konventionellen Lernmaterialien. Unterschiede zwischen der Nutzung als VR- oder Desktop-Umgebung zeigen sich auf kognitiver und affektiver Ebene jedoch kaum. Die Analyse von Log-Daten deutet allerdings auf Unterschiede im Lern- und Explorationsverhalten hin.

Rainfall is a spatiotemporally variated process and one of the key elements to accurately capture both catchment runoff response and floodplain extents. Flash floods are the result of intense rainfall, typically associated to highly variable rain in both space and time, such as convective storms. In this work, the extent within-storm variability affects runoff and flooding is explored. The Kan catchment (Tehran, Iran) is used as base topography for the simulations. The allochthonous nature of floods in the catchment and how they interact with the effects of storm variability are further investigated. For this, 300 synthetic rainfall signals with different hyetograph variabilities are generated and imposed on a 1D/2D hydrodynamic model. Additionally, a set of simulations with different levels of spatial variability are performed. The results suggest that temporal and spatial variability affect the runoff response in different degrees. Peak discharge and hydrograph shapes, as well as flooded areas, are affected. The effect of storm temporal variability is shown to be significantly higher than storm spatial variability and storm properties such as return period, duration, and volume. Further on the influence of storm spatiotemporal variability on stream discharge and flood response is seen to be strongly dependent on the location within the drainage network at which it is assessed.

Recent drought events in Europe have highlighted the impact of hydrological drought and low-flow events on society, ecosystems, and the economy. While there are numerous publications about flood risk management and the socio-economic consequences of floods, these have hardly been systematically dealt with in the scientific literature regarding low flows. This paper fills this gap by summarizing the current state of research in the form of a systematic literature review combining the criteria of the drivers–pressures–state–impacts–responses (DPSIR) framework with the propositional inventory method. In particular, the driving forces of low-flow events, their pressures, and the impacts on different economic sectors such as navigation, fisheries, industry, agriculture, forestry, energy, and tourism and recreation as well as resulting competitive usage claims for water and responses are presented and validated through expert interviews. In doing so, the study examines the causal chain of low-flow events and serves as a fundamental base for the future development of a damage cost database for low-flow events by preparing literature data on the socio-economic consequences of low-flow events for parameterization.

The recent drought events (e.g. 2022) highlighted the impacts caused by hydrological drought and low-flow events to society and ecosystems. The consequences of low-flow events in recent years emphasizes the urgent need for a structured low-flow risk management. The DryRivers project aims to develop a software-based tool for an effective support of low-flow risk management. The low-flow risk analysis is the core of the supporting tool and will be described in detail in this work.In the field of flood risk applications, scenario-based calculations are often performed. Due to a relative short duration of flood events between a few days to a few weeks and in general negligible hydrological interaction between temporal distant flood events, a clear distinction of such events is quite simple. However, for low-flow risk modelling, the definition of scenarios is considerably more complex due to their long-term development and occurrence. Thus, hydrological conditions from previous years can be essential for the development of a low-flow event. Due to this, the use of long-term continuous time series seems to be more suitable for low-flow risk modelling than a scenario-based approach. This continuous approach has been used in flood risk analysis by Sairam et. al. (2021). In this work it is adapted and extended for low-flow risk analysis.The approach to low-flow risk analysis consists of four basic analyses. These include the meteorological-hydrological analysis, which generates synthetic long-term weather data - using, e.g., a stochastic weather generator - and transforms these weather data into long-term runoff time series. Therefore, a rainfall-runoff model is applied, considering the catchment-specific characteristics. The hydrodynamic analysis quantifies water levels, water temperatures and flow velocities along the river. Core of the analysis is a numerical 1D-river model, which calculates the hydraulic values using runoff time series and river characteristics (e.g., cross sections). The influence of the near-surface groundwater on the river by in-/exfiltration is considered via a bidirectionally coupled 2D-groundwater model. Water temperature is determined in a unidirectionally coupled temperature model. Weather data and hydraulic values are transformed into water temperature within the river. Based on the time series of the hydraulic values the consequences of low-flow are quantified as sum over the considered period within the analysis of consequences. Different categories of low-flow consequences are considered: socioeconomic consequences, e.g., for shipping or industrial water use, as well as ecological consequences for fish and macrozoobenthos. Threshold approaches for quantifying impacts are generally applied in both categories. Finally, in the risk analysis, the low-flow risk is calculated by dividing the damage sums per consequence category with the number of simulated years. This results in an annual low-flow risk, e.g., in €/a. The calculated low-flow risk is an essential basis for a transparent and objective decision support in low-flow risk management.This research is funded within the research framework of WaX (Wasser-Extremereignisse) by the Federal Ministry of Education and Research of Germany. Sairam, N., Brill, F., Sieg, T., Farrag, M., Kellermann, P. and Nguyen, V. D. (2021), Process‐Based Flood Risk Assessment for Germany, Earth's Future 9 (10), DOI: 10.1029/2021EF002259.

Critical infrastructure (CI) networks are essential for the survival and functionality of society and the economy. Disruptions to CI services and the cascading effects of these disruptions are not currently included in flood risk management (FRM). The work presented in this study integrates CI into every step of FRM, including flood risk analysis, risk mitigation and risk communication. A CI network modelling technique enables the flood consequences for CI to be quantified as part of the flood risk analysis. The CI consequences derived from this analysis include spatial overviews and the temporal succession of CI disruptions. The number of affected CI end-users and the duration of the disruption are arranged in a risk matrix and in a decision-making matrix. Thus, the total flood risk is extended with CI consequences. By integrating CI and CI network characteristics into the flood risk assessment and the mitigation steps, a wider range of measures for action can be considered. Additionally, the continuous participation of CI operators is introduced as beneficial for every step of the FRM. A case study in Accra, Ghana proves the benefits of CI integration for all FRM steps. During participatory CI stakeholder engagements for this study six CI sectors were identified for the assembly of the CI network. The backbone of the analysis is a multisectoral, layered CI network model with 433 point elements, 1216 connector elements and 486 polygon elements.

In flood risk analysis, it is state-of-the-art to determine the direct consequences of flooding for assets and people. Flooding also disrupts critical infrastructure (CI) networks, which are vital in modern society. Cascading effects in a CI network can exceed the hydrological catchment boundaries. The effects of directly impacted CI cascade to other infrastructures, which are thus indirectly affected by a flood. A robust modelling approach of CI networks is a basis for including these effects in flood risk analysis. One challenge is to balance the simplicity of the modelling approach, the reproduction of a CI network's complexity and the decisions made based on potential model outputs. In this article, a topology-based modelling approach of CI networks for catchment-wide flood risk analyses is proposed. The basic model elements are points, connectors and polygons, which are utilised to represent a multisectoral and layered CI network. The newly defined approach is implemented as CI network module to the state-of-the-art flood risk analysis framework ProMaIDes. It analyses the CI's direct and cascading impacts as well as the indirect disruption of CI services triggered by flooding scenarios. It quantifies the consequences by determining the number of disrupted CI users or the disruption time. A proof of concept in Accra, Ghana demonstrates the method's capabilities.

<p>Water related extreme events are causing most of the disasters worldwide. Between 2001 and 2018 around 74% disasters were water-related and during the past 20 years, the total number of deaths caused only by floods and droughts exceeded 166,000, while floods and droughts affected over three billion people, and caused total economic damage of almost US$700 billion (EM-DAT, 2019). The number of events with water related extremes are expected to increase due to climatic and land use changes.</p><p>Thus, it is of utmost importance to identify ways to reduce water related risks. Potential hazards, exposure and vulnerability have to be identified, published and communicated. This is done for example in the European Union in a coordinated way, based on the European Flood Risk Management Directive. However, identifying risk only is not leading to actual actions and is not  sufficient to reduce risks. This became again obvious in 2021 when extreme floods caused more than 180 deaths in Germany and caused around 46 billion Euro damage. This and other events are revealing the need for closing the gap between theory and practise to establish proactive and preventive strategies and implementation of measures.</p><p>Ghana is one of the countries most prone to floods in West Africa. Its annual occurrence often leads to disasters that are mostly felt by the urban poor. Despite the existence of salient activities conducted in order to reduce the flood risk in Ghana, there are still persisting challenges (Almoradie et al. 2020).</p><p>In order to overcome the implementation gap from science to practice a participatory and transdisciplinary mixed-method approach for Ghana is pursuied. Transdisciplinary research integrates knowledge from various scientific disciplines and non-academic actors.In this way, results can be developed for challenges that are of use to society and science.In the long term, transdisciplinary research strengthens knowledge about the human-water/flood system, the desired state of this system and the way to achieve this state by transformative adaptation.</p><p>A participatory mixed-method approach comprising hydrological and hydrodynamic modelling, participatory mapping, questionnaires, workshops, focus group discussion, system dynamic modelling and the analysis of vulnerability including failure of critical infrastrcuture were employed (Evers et al. 2021) for three case study areas in Ghana. The dynamics of human-flood-interaction are identified together with practioneers and adaptation measures were identified in a participatory way. By this approach we are aiming to make our research actionable and to design and implement knowledge translation mechanisms.</p><p> </p><p>Almoradie, A.*, de Brito, M.M.*Evers, M., Bossa, A., Lumor, M., Norman, C., Yacouba, Y., Hounkpe, J. (2020) Current flood risk management practices in Ghana: gaps and opportunities for improving resilience. International Journal of Flood Risk Management, doi:10.1111/jfr3.12664.</p><p>Evers, M., Almoradie, A., de Brito, M. M., Höllermann, B., Ntajal, J., Lumor, M., Bossa, A., Norman, C., Yira Yacouba, Y. Y.,J. H. Jean Hounkpe (2021): Flood risk management in Ghana: gaps, opportunities, and socio-technical tools for improving resilience, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12683, doi:/10.5194/egusphere-egu21-12683</p><p> </p>

Rainfall is a spatiotemporally varied process and key to accurately capturing catchment runoff and determining flood response. Flash flood response of a catchment can be strongly governed by a rainfall’s spatiotemporal variability and is influenced by storm movement which drives a continuous spatiotemporal change throughout a rainfall event. In this work, the sensitivity of runoff and flooded areas to rainfall movement are assessed in the Kan catchment (Iran). The allochthonous nature of floods in the catchment and how they interact with the effects of rainfall movement are investigated. Fifty synthetic rain hyetographs are generated and traversed over the catchment under different velocities and directions and used to force a 1D/2D hydrodynamic model. The results suggest rainfall movement affects the runoff response in different degrees. Peak discharge, hydrograph shapes and flooded areas are affected. Storms with higher velocities result in higher peaks and faster onsets of runoff and consequently higher flooded areas in comparison to slower storms. The direction of the movement also plays a role. Storms moving along the average direction of the stream result in higher peaks and flooded areas. The relevance of storm direction is greater for slow moving storms. Additionally, the influence of rainfall movement is modulated by hyetograph structure, and the allochthonous behavior is greatly dependent on the location within the drainage network at which it is assessed.

In recent years, the issue of high groundwater levels has caught attention. Unfavorable consequences of high groundwater levels are especially damage to buildings, infrastructure, and the environment. Processes that lead to high groundwater levels are hydrological (heavy or extended rainfall and flood events), or anthropogenic (reduced groundwater extractions, interaction with sewer networks, hydraulic engineering measures, structural interventions in the water balance, and mining activities). Several different map products have been prepared for the information of inhabitants and for planning purposes, and also methods for damage and risk analysis related to high groundwater levels have been developed. Groundwater management measures and structural measures are available to reduce the risk related to high groundwater levels. An operational management system could be combined from existing components, but operational forecasting systems for high groundwater levels are-different to flood forecasting systems-not yet common practice. A better understanding of the processes and the development of integrated approaches for modeling, design, planning, forecasting, and warning, as well as improvement of interdisciplinary collaboration between different organizations, are recommendations for the future. This article is categorized under: Engineering Water > Engineering Water Water and Life > Conservation, Management, and Awareness Science of Water > Hydrological Processes Science of Water > Water Extremes