HS2.4.3

Flood estimation in ungauged basins is important for flood design, and for improving our understanding of the sensitivity of flood magnitude to changes in climate and land cover. Flood estimates by current methods (e.g. statistical regression, unit hydrograph) have high uncertainty, even in places with dense observing networks (e.g. +/- 50-100% in the UK). Reductions in this uncertainty are being sought by using alternative methods, such as continuous simulation using hydrological models (spatially-distributed or lumped), and event-scale derived distribution approaches. The very significant challenges for reliable application of continuous simulation models in ungauged catchments are well described in the literature.

The research reported here is part of a larger project to estimate the probability distribution of flood peak magnitude in ungauged catchments, using an event-based derived distribution method. The derived distribution approach at the event scale typically combines the following elements: a stochastic rainfall model, an event-scale rainfall-runoff model (usually with “losses” and a “baseflow” component), and a runoff routing model. In principle, every element of this approach may be considered as a (seasonally varying) random variable, though previous research has typically considered only the rainfall as stochastic. The flood peak distribution is obtained by integrating over joint distributions of the model elements.

One of the novel aspects of the proposed approach is that, in place of an explicit routing method, we estimate the flood peak magnitude as the ratio of the event runoff depth (mm) to a characteristic timescale of the hydrograph (hours). The event runoff depth is the product of rainfall depth and event runoff coefficient, which in turn depends on both antecedent conditions and event rainfall. The characteristic timescale of the hydrograph is a second temporal moment (temporal “width” of the hydrograph). Although a comprehensive theory exists for space-time influences on this hydrograph time scale, research to date suggests that it depends, to first order, on time scales associated with rainfall and catchment response.

Here we report on extensive (many events, many catchments) testing in the UK of (i) whether the temporal standard deviation of the flow hydrograph is a good choice for the characteristic time scale of the hydrograph in the context of predicting the flood peak (Viglione et al 2010, Journal of Hydrology) (ii) whether the temporal standard deviation of the hydrograph can be predicted from time scales associated with rainfall and catchment response, as proposed by Woods and Sivapalan (1999, Water Resources Research) and Gaál et al (2012, Water Resources Research).

How to cite: Woods, R., Zheng, Y., Quaglia, R., Giani, G., Han, D., Rico-Ramirez, M., and Coxon, G.: Estimating Flood Peaks from Event Runoff Depth and Hydrograph Time Scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3001, https://doi.org/10.5194/egusphere-egu22-3001, 2022.

Reliable assessment of the flooding hazard of river basins is crucial for many social and economic activities. We present here the Physically-based Extreme Value (PHEV) distribution of river discharges. PHEV is a process-based alternative to empirical estimates and statistical methods hitherto used to characterize extremes of hydrometeorological variables. It arises from a description of key hydro-meteorological processes driving runoff production (e.g., precipitation inputs, evapotranspiration rates, soil moisture states and catchment responses) through solution of the master equation for the probability distribution of streamflow in a catchment. PHEV pairs physical understanding of the mechanisms producing extreme events and defining their chance of occurrence with an easily tractable mathematical descriptions of them, thus providing a theoretical underpinning to the study of manifold flood-related issues, such as the emergence of heavy tails in streamflow and flood distributions, flood rich and poor periods, and the reasons leading to the occurrence of extreme flood events.

In this work we benchmark capabilities of PHEV for predicting odds and magnitudes of floods against a standard distribution and the latest statistical approach for extreme estimation. The methods are first applied to an extensive dataset to compare their skills for predicting observed flood quantiles in a wide range of case studies. Synthetic time series of streamflow, generated for select river basins from contrasting hydro-climatic regions, are later used to assess performances for rare events. The analyses outline the domain of applicability of PHEV and reveal less biased capabilities to estimate flood magnitudes with return periods much longer than the sample size used for calibration. Results also show reduced prediction uncertainty of PHEV for rare floods, notably if the flood magnitude-frequency curve displays an inflection point.

Such discontinuities typically hinder estimation of high streamflow quantiles. PHEV reveals itself as a reliable tool to foresee their occurrence in a large set of case studies from the US and Germany, also when using shortened data series where the highest observations were removed. Case studies for which PHEV predicts the occurrence of an inflection point which is not visible in the empirical flood magnitude-frequency curve mostly belong to river flow regimes characterized by values weakly oscillating around their mean, which rarely exhibit extreme flow values by their nature. The limited length of the available data series might be thus constraining the possibility to observe extreme floods that shall be expected. These results indicate the possibility to reliably appraise the propensity of rivers to generate extreme floods by means of a process-based description of watershed dynamics, thus laying the foundation for a better comprehension of their physio-climatic controls.

How to cite: Basso, S., Botter, G., Merz, R., and Miniussi, A.: The Physically-Based Extreme Value (PHEV) distribution of river discharges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3060, https://doi.org/10.5194/egusphere-egu22-3060, 2022.

Heavy-tailed probability distributions of streamflow are frequently observed in river basins. In case of right skewed distributions, the larger probability assigned to relatively high flows translates into an unneglectable chance of occurrence of extreme floods in a long-term hydrological response. Although the spatial variability of rainfall has been identified as an impactful driver of flood events, delineating its effects for the emergence of heavy tails of streamflow distributions is still challenging.

In this study we apply a simple stochastic approach to generate spatially various rainfall as the input of a well-established continuous hydrological model. The model embeds the soil water balance in hillslopes, the probability distributions of transit times in the hillslopes of subcatchments, and the response time distribution in channels derived from a geomorphological analysis of the river network. We investigate the role of spatially variable rainfall for the emergence of heavy tails in streamflow distributions by simulating a wide range of spatial rainfall variability in five catchments in Germany, and then put the modelling results into real world context by analyzing historical data in 175 catchments across the whole Germany.

We find that increasing spatial variability of rainfall determines heavier streamflow tails only beyond a certain increase threshold which depends on physiographic features of catchments. Small and elongated catchments are less resilient to increasing spatial rainfall variability, i.e., their streamflow distributions begin to exhibit heavier tails for smaller increments of the spatial variability of rainfall. The distribution of runoff-routing pathway is suggested to be an effective attribute of catchments in this process.

How to cite: Wang, H.-J., Yang, S., Merz, R., and Basso, S.: The role of spatial rainfall variability for the emergence of heavy tails in streamflow distributions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2556, https://doi.org/10.5194/egusphere-egu22-2556, 2022.

The frequency and magnitude of flood events in Kenya have increased over the past decade. Observations show a shift in timing and variability in flood occurrences in most parts of the country. Trend analysis is useful in detecting and supporting the evidence of change in flow series, as well as variability in flood timing. In this study, the frequency and magnitude of floods observed in the annual maximum flood (AMAX) and peak over threshold (POT) flood series from 1981 to 2016 are compared in 19 Kenyan catchments. Flood peaks are identified using a threshold technique from Kenyan daily discharge data, and notable patterns in the AMAX series are compared to those in the POT series, which is created for three distinct exceedance criteria. The timing and variability of the annual floods is determined from the AMAX flow. Our findings show that, the AMAX series detects more trends in flood magnitude than the POT series, while the POT series detects more significant trends in flood frequency than flood magnitude. Sensitivity of trends to different exceedance thresholds selection reveal variable trend patterns across the stations. The timing of inter-annual floods occurs in peak rainfall months of April, May and November and shows a higher variability index in most of the coastal and western stations, and a low variability in stations whose annual floods occur in dry months of June, July, and August. This information is useful to hydrological applications such as flood protection facility design, risk assessment, and risk management for improved livelihoods in Kenya

How to cite: Wanzala, M., Cloke, H., Stephens, E., Ficchi, A., and Harrigan, S.: Detecting trends in flood series and shifts in flood timing across Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9872, https://doi.org/10.5194/egusphere-egu22-9872, 2022.

Floods are a constant threat to communities within and around estuaries worldwide. This is because several drivers may occur there at the same time which leads to the compound flooding (e.g. high river discharge + storm surge). In this study LISFLOOD-FP hydrodynamic model is applied to examine the efficiency of different management strategies on flooding in the Dyfi Estuary, in Mid Wales. Five different bathymetries, including the current one, were developed in ArcGIS and included in the analysis: a) “hold the line”, b) “remove the line”, c) “advance the line”, d) “retreat the line”, e) “breach in the line”. Modified November 2020 compound flood event boundary conditions were forced from the coast and from the Dyfi bridge. Model results shown, among other things, that nature-based solutions in the lower estuary, represented by salt marshes and floodplain restoration measures, have a great potential in reducing water elevations across the estuary, unlike the advance the line scenario or the current bathymetry.

Additionally, results obtained from different management scenarios are analyzed and compared against results from the selected design storm events. These events were based on modifying parameters such as 1) relative timing of flood peaks, 2) storm duration and 3) climate change sensitivity (SLR and increase in discharge) which provided set of different compound flood events that were forced by LISFLOOD, in combination with the current estuary shape, unlike when modelling different management scenarios. This approach enabled us to perform an effective comparative analysis which addressed the key hypothesis of the research stating that changes in estuary shape will have bigger effect on flooding than changes in flood event itself. Indeed, it is shown that variability in water elevations caused by different management scenarios is bigger compared to variability caused by changing the boundary conditions only, although not always leading to higher water elevations along the estuary.

How to cite: Barada, M., Robins, P., Lewis, M., and Skov, M.: The importance of estuary shape in evaluating the flood risk in estuaries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4086, https://doi.org/10.5194/egusphere-egu22-4086, 2022.