Peak River Flows Research Articles

Rainfall, peak river flow and flow variability drive spatio‐temporal change in the extent of riparian woodland in an African protected area savanna

AbstractVerbal accounts, supported by limited ground‐based and satellite images, reveal decreasing riparian woodland and a loss of large trees along the rivers of the Kruger National Park (KNP, South Africa) over the last century. A multi‐decadal analysis was conducted to identify trends in extent and possible drivers of riparian woodland change. Aerial and satellite imagery (1936–2018) were used to quantify changes in the extent of riparian woodland at 18 sites on 14 rivers in KNP. These changes were compared in a multivariate time‐series with river flow and local rainfall. Particular attention was paid to cumulative flow effects, as well as the frequency and magnitude of large infrequent disturbances such as droughts and floods. Riparian tree cover fluctuated over the time period, and the trajectory of change varied between sites. Most (11) sites experienced a decrease in overall riparian tree cover over ~80 years, with these declines being significant at six sites. Peak flow and maximum rainfall events were strongly associated with these decreases, indicating that flood events are potentially the biggest driver of tree loss from the system. Indeed, the mega‐flood event of 2000 and subsequent large floods have resulted in substantial declines in riparian woodland extent in recent decades. Alternatively, flow variability and cumulative rainfall significantly influenced woodland expansion in isolated cases. With global change models predicting more erratic rainfall and an increased likelihood of large infrequent disturbances, together with increasing demands to abstract more water, the long‐term future of these dynamic habitats and their associated biota here is uncertain.

Multi-decadal floodplain classification and trend analysis in the Upper Columbia River valley, British Columbia

Abstract. Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in land cover and hydroclimatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A land cover classification time series from 1984 to 2022 was generated from the Landsat image archive using a random forest algorithm. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed land cover trends. The land cover classifier used to segment changes in wetland area and open water performed well (kappa of 0.82). Over the last 4 decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and the flow duration has been reduced. The frequency of both high-discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snowmelt, from April to mid-May, and with seasonal changes in the vegetative communities over the 39-year period. Thus, woody shrubs (+6 % to +12 %) have expanded as they gradually replaced marsh and wet-meadow land covers with a reduction in open-water area. This suggests that increasing temperatures have already impacted the regional hydrology, wetland hydroperiod, and floodplain land cover in the Upper Columbia River valley. Overall, there is substantial variation in seasonal and annual land cover, reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing areas of woody/shrub habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open-water habitats, are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future.

Leeds FAS2 FSR: environmental challenges for the design of an in-river control structure

As flood-risk authorities look to counter increasing peak river flows due to a changing climate, traditional linear flood defences are in many cases proving to be an unsustainable method of managing flood risk. Full containment of flows in the river channel will require ever-increasing defence heights, cutting off communities from their rivers and natural heritage. Upstream flood storage provides a potential alternative means of flood management, allowing the peak of the flood to attenuate and downstream river levels to be kept within a tolerable range. The Leeds Flood Alleviation Scheme Phase 2 uses a combined approach: linear defences are constructed in urban reaches and complemented by an online flood storage reservoir (FSR) upstream of the city. The FSR halves the annual probability of defences overtopping within the city centre from 1 to 0.5%, achieved through active variable flow control at the FSR. This paper explores some of the challenges faced in the design and construction of the in-river flow control structure which is integral to the operation of the FSR. In particular this paper will consider some of the key environmental objectives for the scheme and offer considerations for working in a dynamic and environmentally sensitive fluvial environment.

Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations

AbstractThe UK Met Office state‐of‐the‐art, deterministic, convection‐permitting, coupled land‐atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in‐situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw‐Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field‐scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS‐UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS‐UK soil moisture observations is generally good, and that changes made to the land data assimilation approach during a recent operational upgrade had a generally beneficial impact on UKV soil moisture analyses under very dry conditions.

Parameterization of a National Groundwater Model for New Zealand

Groundwater is a vital source of water for humanity, with up to 50% of global drinking water and 43% of irrigation water being derived from such sources. Quantitative assessment and accounting of groundwater is essential to ensure its sustainable management and use. TopNet-GW is a parsimonious groundwater model that was developed to provide groundwater simulation at national, regional, and local scales across New Zealand. At a national scale, the model can help local government authorities estimate groundwater resource reliability within and between regions. However, as many catchments are ungauged, the model cannot be calibrated to local conditions against observed data. This paper, therefore, describes a method to derive an a priori, reach-scale groundwater model parameter set from national-scale hydrogeological datasets. The parameter set includes coefficients of lateral (kl) and vertical (kr) conductivity and effective aquifer storage (S). When the parameter set was used with the TopNet-GW model in the Wairau catchment in the Marlborough region (South Island, New Zealand), it produced a poor representation of peak river flows but a more accurate representation of low flows (overall NSE 0.64). The model performance decreased in the smaller Opawa catchment (NSE 0.39). It is concluded that the developed a priori parameter set can be used to provide national groundwater modeling capability in ungauged catchments but should be used with caution, and model performance would benefit greatly from local scale calibration. The parameter derivation method is repeatable globally if analogous hydrological and geological information is available and thus provides a basis for the parameterization of groundwater models in ungauged catchments. Future research will assess the spatial variability of parameter performance at a national scale in New Zealand.

Monitoring and forecasting the condition of structures in the small river basins in southern Russia

Introduction. The southern regions of Russia need to adapt to higher peak river flows and flash floods. Since 1960, the number of recorded flood events has increased. The intensity and severity of flood events tend to decrease, but their annual frequency is increasing. This illustrates the social component of flooding. Trends in the number of flood events are closely related to the increasing population in river valleys. “Integrated management” of construction sites in river basins is based on three elements: monitoring, forecasting and early warning, vulnerability assessment, mitigation and response. The purpose of this study is to establish means of prediction, risk assessment of operational construction projects in river basins taking into account the current natural and climatic factors.
 
 Materials and methods. The research is based on the factual material collected in the field studies in the river basins of recreational regions of southern Russia. Design, construction and operational documentation of complex structures was analyzed and their structural elements were evaluated.
 
 Results. On the basis of the carried out theoretical, field and numerical researches, the problem of increasing ecological safety of the urban environment was solved by monitoring, assessing, ranging of the technical condition of building structures and forecasting risks in the urbanized areas of river basins. The results of the research should be considered as a basis of practical methods for the complex estimation of ecological safety of natural and technical system of river basins. Further development of the topic can be connected with the scaling studies of urbanized territories, monitoring crisis sites and forecasting of the further condition of construction projects by subjects.
 
 Conclusions. The developed software package has practical importance, the product is implemented in the Republic of Crimea, in the operation of the long-term operated HES complexes of the Partizanskoe reservoir supplying water to the city of federal importance Simferopol and the Alminsky reservoir irrigating Bakhchisaray region of the peninsula and it is also being tested in the Krasnodar Territory, the Republic of Dagestan, the Stavropol Territory and Caucasian Mineral Waters.

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

AbstractIn low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

Abstract Rainfall–runoff modeling is one of the most well-known applications of hydrology. The goal of rainfall–runoff modeling is to simulate the peak river flow caused by an actual or hypothetical rainfall force. In existing methods, the rainfall–runoff relationships are quantified to predict the daily streamflow of each catchment from its landscape attributes to measure the daily rainfall. However, the structural model error, infiltration rate, and the steep slopes of the hill affect the prediction process. To tackle these issues, this paper proposed a novel rainfall prognostic model-based artificial framework, which predicts day-to-day rainfall to prevent environmental disasters. The day-to-day predictions minimize the risks to life and property and also manage the agricultural farms in a better way because the possibility of rainfall has been estimated earlier. Furthermore, the posterior fire-breathing network is utilized to estimate model errors in the computational runoff by using time-dependent and random noise to the model's internal storage to solve the uncertainty problem. Since the model errors are estimated, there are limits to the infiltration rate and thus a prophetic multilayer network is utilized which relies on the soil runoff levels. Moreover, the network measures the dynamics of soil moisture to regulate the infiltration rate according to the rural or urban section. Moreover, to measure the surface water from the steep slopes, the system offered a well-ordered selective genetic algorithm to calculate the velocity of runoff in different bend areas to overcome the numerical problem. Thus, the model results showed that the work effectively predicts the rainfall from the investigation of model errors, infiltration rates, and velocity to achieve a better prediction range in the rainfall.

Approximate Bayesian inference for analysis of spatiotemporal flood frequency data

Extreme floods cause casualties and widespread damage to property and vital civil infrastructure. Predictions of extreme floods, within gauged and ungauged catchments, is crucial to mitigate these disasters. In this paper a Bayesian framework is proposed for predicting extreme floods, using the generalized extreme-value (GEV) distribution. A major methodological challenge is to find a suitable parametrization for the GEV distribution when multiple covariates and/or latent spatial effects are involved and a time trend is present. Other challenges involve balancing model complexity and parsimony, using an appropriate model selection procedure and making inference based on a reliable and computationally efficient approach. We here propose a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models, which include catchment descriptors as covariates and spatially correlated model components, are proposed for the four parameters at the latent level. To achieve computational efficiency with large datasets and richly parametrized models, we exploit a highly accurate and fast approximate Bayesian inference approach which can also be used to efficiently select models separately for each of the four regression models at the latent level. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom. The framework performed well in terms of flood predictions for both ungauged catchments and future observations at gauged catchments. The results show that the spatial model components for the transformed location and scale parameters as well as the time trend are all important, and none of these should be ignored. Posterior estimates of the time trend parameters correspond to an average increase of about 1.5% per decade with range 0.1% to 2.8% and reveal a spatial structure across the United Kingdom. When the interest lies in estimating return levels for spatial aggregates, we further develop a novel copula-based postprocessing approach of posterior predictive samples in order to mitigate the effect of the conditional independence assumption at the data level, and we demonstrate that our approach indeed provides accurate results.

The Effects of Hurricanes and Storms on the Composition of Dissolved Organic Matter in a Southeastern U.S. Estuary

Extreme events such as hurricanes and tropical storms often result in large fluxes of dissolved organic carbon (DOC) to estuaries. Precipitation associated with tropical storms may be increasing in the southeastern U.S., which can potentially impact dissolved organic matter (DOM) dynamics and cycling in coastal systems. Here, DOM composition at the Altamaha River and Estuary (Georgia, U.S.A.) was investigated over multiple years capturing seasonal variations in river discharge, high precipitation events, and the passage of two hurricanes which resulted in substantial storm surges. Optical measurements of DOM indicate that the terrigenous signature in the estuary is linearly related to freshwater content and is similar after extreme events with or without a storm surge and during peak river flow. Molecular level analysis revealed significant differences, however, with a large increase of highly aromatic compounds after extreme events exceeding what would be expected by freshwater content alone. Although extreme events are often followed by increased DOC biodegradation, the terrigenous material added during those events does not appear to be more labile than the remainder of the DOM pool that was captured by ultrahigh-resolution mass spectrometry analysis. This suggests that the added terrigenous organic matter may be exported to the coastal ocean, while a fraction of the organic matter that co-varied with the terrigenous DOM may contribute to the increased biomineralization in the estuary, with implications to carbon processing in coastal areas.

Large-scale hydrological modeling in a multi-objective uncertainty framework – Assessing the potential for managed aquifer recharge in the North China Plain

The region studied is the 137.000 km2 North China Plain (NCP). A region with high population density and a major agricultural production leading to unsustainable exploitation of groundwater resources.Previous modeling studies in the region have utilized simplified representations of model boundary fluxes, both regarding lateral inflows from surface and groundwater and related to water demands and consumptions. The current study focusses on developing a hydrological modeling framework, with better spatial descriptions of major water balance components regarding water demands, model boundary conditions of surface and groundwater inflows and evaporative losses due to irrigation. Compared to previous efforts, the modeling framework utilizes a novel multi-objective parameter optimization strategy combined with an ensemble modeling approach to illuminate optimization trade-offs and impacts of parameter uncertainty.Groundwater storage declines are estimated to be in the order of 25–55 mm/y for the period 2000–2013. The impacts of water management strategies are explored using the model ensemble and show that this decline can be counterbalanced by approximately 15–20 mm/y by substantial reductions in irrigation (20%) or implementation of planned inter-basin water transfers. Managed aquifer recharge in the form of infiltrating excess river peak flows, can only reduce groundwater storage declines to a limited degree. However, at the local to regional scale storage decline reductions from MAR are in the same order of magnitude as other extensive water management strategies.

A novel illustration of real options analysis to address the problem of probabilities under deep uncertainty and changing climate risk

Changing risk profiles over time that are exacerbated by climate change, challenge assessments of the relative value of investing in different adaptation options. There is a growing discourse around methods that are better suited to addressing changing risk and uncertainties for large infrastructure projects — robust decision making (RDM), real options analysis (ROA) and those based on dynamic adaptive pathways planning (DAPP). While such approaches are receiving greater attention in the academic literature, there is a gap in critiques of them in real-life decision practice. Here we present two novel applications from New Zealand of real options analysis that formed part of actual decision processes — a flood risk management plan and a coastal hazards strategy, both based on DAPP over 100 years. In both cases, we examined the value of pursuing adaptation options that are flexible under peak river flows, coastal erosion, imminent inundation, storm surge and long-term sea-level rise. From lessons learned we examined three questions relating to ROA: 1) is ROA ‘fit for purpose’ for investment evaluations in situations of both imminent threat and deep uncertainty and its scalability? 2) can ROA address path dependency and move beyond static applications? 3) can ROA be used alongside DAPP to address managed retreat as an adaptation option. We conclude the affirmative for each question and provide a method for using ROA within a DAPP and RDM framework.

Flood Frequency Analysis using L-Moment For Segamat River

Flood frequency analysis is critical in water system design and estimating flood recurrence. This study aims to conduct the flood frequency analysis on Segamat River streamflow site to find the optimum distribution that fits the flood frequency data. In terms of estimating parameters, the L-moment method is more robust and more efficient than the maximum likelihood method. Besides, the L-moment method is not affected by sampling variability. Therefore, in this study, we applied the technique of L-moment for parameter estimation on five candidate distributions, the generalised Pareto (GPA) distribution, generalised extreme value (GEV) distribution, generalised logistic (GLO) distribution, log-Pearson 3 (LP3) distribution, and log-normal (LN3) distribution. The rank score approach is implemented to determine the optimum distribution for the annual Segamat River peak flow. Probability distribution identification is essential and it is a fundamental step in statistical analysis. The goodness of fit test and efficiency assessment are employed to evaluate the distributions' performance. The results show that the LN3 distribution is selected as the optimum function for the yearly peak flow for the Segamat River streamflow site. The outcome of this study can be used to understand the flood frequency analysis for the Segamat River.

The 2019/2020 floods in the UK: a hydrological appraisal

The 2019/2020 floods in the <scp>UK</scp>: a hydrological appraisal

Climate Change and Changes in Compound Coastal‐Riverine Flooding Hazard Along the U.S. Coasts

AbstractThe cooccurrence of coastal and riverine flooding leads to compound events with substantial impacts on people and property in low‐lying coastal areas. Climate change could increase the level of compound flood hazard through higher extreme sea levels and river flows. Here, a bivariate flood hazard assessment method is proposed to estimate compound coastal‐riverine frequency under current and future climate conditions. A copula‐based approach is used to estimate the joint return period (JRP) of compound floods by incorporating sea‐level rise (SLR) and changes in peak river flows into the marginal distributions of flood drivers. Specifically, the changes in JRP of compound major coastal‐riverine flooding defined based on simultaneous exceedances above major coastal and riverine thresholds, are explored by midcentury. Subsequently, the increase in the probability of occurrence of at least one compound major coastal‐riverine flooding for a given period of time is quantified. The proposed compound flood hazard assessment is conducted at 26 paired tidal‐riverine stations along the Contiguous United States coast with long‐term data and defined flood thresholds. We show that the northeast Atlantic and the western part of the Gulf coasts are experiencing the highest compound major coastal‐riverine flood probability under current conditions. However, future SLR scenarios show the highest frequency amplification along the southeast Atlantic coast. The impact of changes in peak river flows is found to be considerably less than that of SLR. Climate change impacts, especially SLR, may lead to more frequent compound events, which cannot be ignored for future adaptation responses in estuary regions.

An updated national-scale assessment of trends in UK peak river flow data: how robust are observed increases in flooding?

Abstract A cluster of recent floods in the UK has prompted significant interest in the question of whether floods are becoming more frequent or severe over time. Many trend assessments have addressed this in recent decades, typically concluding that there is evidence for positive trends in flood magnitude at the national scale. However, trend testing is a contentious area, and the resilience of such conclusions must be tested rigorously. Here, we provide a comprehensive assessment of flood magnitude trends using the UK national flood dataset (NRFA Peak Flows). Importantly, we assess trends using this full dataset as well as a subset of near-natural catchments with high-quality flood data. While headline conclusions are useful for advancing national flood-risk policy, for on-the-ground flood-risk estimation it is important to unpack these local changes to determine how climate-driven trends compare with those from the wider dataset that are subject to a wide range of human disturbances and data limitations. We also examine the sensitivity of reported trends to changes in study time window using a ‘multitemporal’ analysis. We find that the headline claim of increased flooding generally holds up regionally to nationally, although we show a much more complicated picture of spatio-temporal variability. While some reported trends, such as increased flooding in northern and western Britain, appear to be robust, trends in other regions are more mixed spatially and temporally – for example, trends in recent decades are not necessarily representative of longer-term change, and within regions (e.g. in southeast England) increasing and decreasing trends can be found in close proximity. While headline conclusions are useful for advancing national flood-risk policy, for flood-risk estimation it is important to unpack these local changes, and the results and methodological toolkit provided here could provide such supporting information to practitioners.

Exploring the Fingerprints of Past Rain-on-Snow Events in a Central Andean Mountain Range Basin Using Satellite Imagery

Rain-on-snow (ROS) events can alter nival regimes and increase snowmelt, peak river flow, and reduce water storage. However, detection of ROS events is challenging and only the most intense and obvious cases are identified. Rain is known to reduce snow cover and decrease near-infrared reflectance due to increased grain size. This study explored the fingerprints of ROS events on mountain snowpack with a simple typology that classifies changes in snow reflectance using fifteen years of MODIS imagery, reanalysis, and surface hydrometeorological data. The Maipo River Basin, with strong nival regime and a steep topography, in the western Andean mountain range was selected as a case study. Statistical analysis showed two distinct and opposite responses in the near infrared reflectance distribution of snow-covered pixels after precipitation, consistent with the typology for rain or snow events. For the probable ROS events, the daily maximum and minimum temperature increased in the days preceding the event and subsequently decreased, in some cases followed by a less consistent response in river flow. Although much remains to be studied, this approach can be used to expand historical records and improve modelling and detection schemes.

Parametrisation of change-permitting extreme value models and its impact on the description of change

The potential for changes in environmental extremes is routinely investigated by fitting change-permitting extreme value models to long-term observations, allowing one or more distribution parameters to change as a function of time or some other covariate. In most extreme value analyses, the main quantity of interest is typically the upper quantiles of the distribution, which are often needed for practical applications such as engineering design. This study focuses on the changes in quantile estimates under different change-permitting models. First, metrics which measure the impact of changes in parameters on changes in quantiles are introduced. The mathematical structure of these change metrics is investigated for several change-permitting models based on the Generalised Extreme Value (GEV) distribution. It is shown that for the most commonly used models, the predicted changes in the quantiles are a non-intuitive function of the distribution parameters, leading to results which are difficult to interpret. Next, it is posited that commonly used change-permitting GEV models do not preserve a constant coefficient of variation, a property that is typically assumed to hold for environmental extremes. To address these shortcomings a new (parsimonious) model is proposed: the model assumes a constant coefficient of variation, allowing the location and scale parameters to change simultaneously. The proposed model results in changes in the quantile function that are easier to interpret. Finally, the consequences of the different modelling choices on quantile estimates are exemplified using a dataset of extreme peak river flow measurements in Massachusetts, USA. It is argued that the decision on which model structure to adopt to describe change in extremes should also take into consideration any requirements on the behaviour of the quantiles of interest.

Climate change impacts on peak river flows: Combining national-scale hydrological modelling and probabilistic projections

Potential future increases in flooding due to climate change need to be taken into consideration when designing flood defences or planning new infrastructure or housing developments. Existing guidance on climate change allowances in Great Britain was based on research that developed a sensitivity-based approach to estimating the impacts of climate change on flood peaks, which was applied with catchment-based hydrological models. Here, the sensitivity-based approach is applied with a national-scale grid-based hydrological model, producing modelled flood response surfaces for every river cell on a 1 km grid. This provides a nationally consistent assessment of the sensitivity of flood peaks across Britain to climatic changes. The flood response surfaces are then combined with the most recent climate change projections, UK Climate Projections 2018 (UKCP18), to provide location-specific information on the potential range of impacts on floods across the country, for three flood return periods, three future time-slices and four emissions scenarios. An accompanying web-tool provides a convenient way to explore the large amount of data produced. Consideration is now being given to how to use the latest work to update guidance on climate change and flood peaks, including a workshop held to gather stakeholder views.

Frequency Analysis of Hydraulic Modeling As A Way to Forecast Flooding on the Allala River

The article presents research on modeling the recurrence of emergency situations caused by flooding on the Allala river. It is proposed to predict the frequency of floods using the Saint-Venant equation for hydrodynamic modeling. The HEC RAS model was used to compare flood intensity with different water level positions in the river. The parameters of the water source were measured on a section of 8 km long. As a result of research, it was found that the existing coastal protection structures on the banks of the Allala river are insufficient to prevent an emergency flood situation at peak river flows.