Characteristics Of Flood Events Research Articles

Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Abstract. Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM, the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.

Analysis model of flood spatiotemporal change characteristics based on k-means clustering algorithm

<p>In order to analyze the spatiotemporal change characteristics of regional flood disaster, analyze the main change characteristics and hydrological response of precipitation distribution in Guangxi, and improve the comprehensive utilization efficiency of water resources, this paper introduces the k-means clustering algorithm to design an analysis model of spatiotemporal change characteristics of flood disaster. First of all, obtain the flood data in Guangxi and the observation data of the national meteorological station within 5 km, complete the collection of the basic data of mountain flood disasters. Based on the collected data, an analysis was conducted on the spatial and temporal distribution of flash floods in the Guangxi region. Secondly, the Kriging spatial interpolation method was used to analyze the spatial distribution of precipitation data in Guangxi. The Mann-Kendall trend test was then employed to examine the trend of precipitation-related statistical parameters over time. Additionally, wavelet theory was applied to analyze the time series of annual precipitation and precipitation with different durations in Guangxi. Subsequently, the k-means clustering algorithm was introduced to construct a model for analyzing the spatiotemporal characteristics of flood changes, determining the concentration and duration of precipitation in different years in the region. Finally, analyze the spatiotemporal change characteristics of flood events in different seasons under various indicators, and realize the analysis of flood spatiotemporal change characteristics. The research results indicate that the Frank Copula function fits the best correlation between annual precipitation and temperature, and can better characterize the correlation between the two. The Frank Copula function has the best fitting effect on the correlation between precipitation and temperature in autumn in Guilin, summer in Nanning, and summer and winter in Beihai. In Guangxi Zhuang Autonomous Region, the annual precipitation shows a gradually decreasing trend, especially at R and P stations. In summary, the Frank Copula function can effectively characterize the correlation and trend of precipitation and temperature in different seasons and regions of Guangxi.</p>

Copula-Based Joint Flood Frequency Analysis: The Case of Guder River, Upper Blue Nile Basin, Ethiopia

The univariate analysis of hydrological extremes is a well-established practice in developing countries such as Ethiopia. However, for the design of hydrological and hydraulic systems, it is essential to have a thorough understanding of flood event characteristics, including volumes, peaks, time of occurrence, and duration. This study utilizes copula functions for bivariate modeling of flood peak and volume characteristics, examining the performance of four Archimedean copulas in the Guder basin located in Ethiopia from 1987 to 2017. Flood peak and volume were extracted using the theory of runs for analysis of their joint characteristics with the truncation level chosen as equal to the lowest annual maximum event. Univariate distributions with the best fitness on both variables were determined, and results showed that gamma and GEV-fitted flood peaks and lognormal-fitted flood volumes are the most suitable. Four Archimedean copulas were evaluated, and the Gumbel-Hougaard copula was found to be the best fit for the data based on graphical and measurable tests. Bivariate probability and return period were computed in “AND” and “OR” states. The joint return period for flood peak (97.49 m3/s) and volume (77.35 m3/s) was found to be 15 years in the “AND” state and approximately 4 years in the “OR” state. The study also evaluates univariate and conditional return periods, comparing them with the primary one. The copula method was an effective method for distributing marginal variables, highlighting its potential as a valuable tool in flood management.

When good signatures go bad: Applying hydrologic signatures in large sample studies

AbstractHydrologic signatures are quantitative metrics that describe streamflow statistics and dynamics. Signatures have many applications, including assessing habitat suitability and hydrologic alteration, calibrating and evaluating hydrologic models, defining similarity between watersheds and investigating watershed processes. Increasingly, signatures are being used in large sample studies to guide flow management and modelling at continental scales. Using signatures in studies involving 1000s of watersheds brings new challenges as it becomes impractical to examine signature parameters and behaviour in each watershed. For example, we might wish to check that signatures describing flood event characteristics have correctly identified event periods, that signature values have not been biassed by data errors, or that human and natural influences on signature values have been correctly interpreted. In this commentary, we draw from our collective experience to present case studies where naïve application of signatures fails to correctly identify streamflow dynamics. These include unusual precipitation or flow regimes, data quality issues, and signature use in human‐influenced watersheds. We conclude by providing guidance and recommendations on applying signatures in large sample studies.

Changes in Mediterranean flood processes and seasonality

Abstract. Floods are a major natural hazard in the Mediterranean region, causing deaths and extensive damages. Recent studies have shown that intense rainfall events are becoming more extreme in this region but, paradoxically, without leading to an increase in the severity of floods. Consequently, it is important to understand how flood events are changing to explain this absence of trends in flood magnitude despite increased rainfall extremes. A database of 98 stations in southern France with an average record of 50 years of daily river discharge data between 1959 and 2021 was considered, together with a high-resolution reanalysis product providing precipitation and simulated soil moisture and a classification of weather patterns associated with rainfall events over France. Flood events, corresponding to an average occurrence of 1 event per year (5317 events in total), were extracted and classified into excess-rainfall, short-rainfall, and long-rainfall event types. Several flood event characteristics have been also analyzed: flood event durations, base flow contribution to floods, runoff coefficient, total and maximum event rainfall, and antecedent soil moisture. The evolution through time of these flood event characteristics and seasonality was analyzed. Results indicated that, in most basins, floods tend to occur earlier during the year, the mean flood date being, on average, advanced by 1 month between 1959–1990 and 1991–2021. This seasonal shift could be attributed to the increased frequency of southern-circulation weather types during spring and summer. An increase in total and extreme-event precipitation has been observed, associated with a decrease of antecedent soil moisture before rainfall events. The majority of flood events are associated with excess rainfall on saturated soils, but their relative proportion is decreasing over time, notably in spring, with a concurrent increased frequency of short rain floods. For most basins there is a positive correlation between antecedent soil moisture and flood event runoff coefficients that is remaining stable over time, with dryer soils producing less runoff and a lower contribution of base flow to floods. In a context of increasing aridity, this relationship is the likely cause of the absence of trends in flood magnitudes observed in this region and the change of event types. These changes in flood characteristics are quite homogeneous over the domain studied, suggesting that they are rather linked to the evolution of the regional climate than to catchment characteristics. Consequently, this study shows that even in the absence of trends, flood properties may change over time, and these changes need to be accounted for when analyzing the long-term evolution of flood hazards.

SAR Change Detection to Evaluate Flood Event Characteristics, Pre- and Post-Restoration of a Floodplain

SAR Change Detection to Evaluate Flood Event Characteristics, Pre- and Post-Restoration of a Floodplain

Warming of 0.5 °C may cause double the economic loss and increase the population affected by floods in China

Abstract. Based on future scenario data and an improved quantitative natural-disaster risk assessment model, in this study, we analysed the response of the characteristics of flood events in China to 1.5 and 2 ∘C of global warming, quantitatively assessed the population affected and the economic risks of floods, and determined the integrated risk levels. The results indicate that, for the RCP4.5 (Representative Concentration Pathway) and RCP8.5 scenarios, the probability and distribution area of the floods increase with increasing temperature and the influence range of the floods of different levels expands more rapidly under the RCP4.5 scenario. The floods mainly affect the social economy in the regions with lower altitudes and smaller slopes in eastern China. As the increase in temperature intensifies, the population affected and the direct economic losses are aggravated. For 2 ∘C of global warming, under the RCP8.5 scenario, the population affected by floods increases by 2 million, and the economic risk nearly doubles compared with 1.5 ∘C of global warming. The economic risk under the RCP4.5 scenario even reaches 3 times that for 1.5 ∘C of global warming, but its proportion to the gross domestic product (GDP) is lower than that under the RCP8.5 scenario. Under both scenarios, the ranges of the zones of medium–high flood risk gradually expand westward and northward.

A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar

In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding flood event preparedness plans and mitigation measures. Introducing an initial framework of spatially probabilistic analysis of flood research, this study highlights an integrated statistical copula and satellite data-based approach to modelling the complex dependence structures between flood event characteristics, i.e., duration (D), volume (V) and peak (Q). The study uses Global daily satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (spatial resolution of ∼5 km) during 1981–2019 to derive a Standardized Antecedence Precipitation Index (SAPI) and its characteristics through a time-dependent reduction function for Myanmar. An advanced vine copula model was applied to model joint distributions between flood characteristics for each grid cell. The southwest (Rakhine, Bago, Yangon, and Ayeyarwady) and south (Kayin, Mon, and Tanintharyi) regions are found to be at high risk, with a probability of up to 40% of flood occurrence in August and September in the south (Kayin, Mon, and Tanintharyi) and southwest regions (Rakhine, Bago, Yangon, and Ayeyarwady). The results indicate a strong correlation among flood characteristics; however, their mean and standard deviation are spatially different. The findings reveal significant differences in the spatial patterns of the joint exceedance probability of flood event characteristics in different combined scenarios. The probability that duration, volume, and peak concurrently exceed 50th-quantile (median) values are about 60–70% in the regions along the administrative borders of Chin, Sagaing, Mandalay, Shan, Nay Pyi Taw, and Keyan. In the worst case and highest risk areas, the probability that duration, volume, and peak exceed the extreme values, i.e., the 90th-quantile, about 10–15% in the southwest of Sagaing, southeast of Chin, Nay Pyi Taw, Mon and areas around these states and up to 30% in the southeast of Dekkhinathiri township (Nay Pyi Taw). The proposed approach could improve the evaluation of exceedance probabilities used for flood early warning and risk assessment and management. The proposed framework is also applicable at larger scales (e.g., regions, continents and globally) and in different hydrological design events and for risk assessments (e.g., insurance).

Probabilistic Flood Hazard Mapping Considering Multiple Levee Breaches

AbstractProbabilistic methods are widely adopted for residual flood hazard assessment in flood‐prone areas protected by levees. Such methods can consider various sources of uncertainty, including breach location and flood event characteristics, and allow for the quantification of the result confidence. However, the possible occurrence of multiple levee breaches during the same flood event is usually disregarded. This paper presents a probabilistic method based on levee fragility functions and basic probability rules to estimate the probability of selected breach scenarios, including multiple breaching events. The flood hazard classification is based on inundation variables calculated for flood events of different return periods. A combined 1D‐2D hydrodynamic model is used for flood simulations. Probabilistic inundation extent maps and probabilistic flood hazard level maps are then created. Finally, probabilistic flood hazard estimates are summarized in a map of a suitable central tendency of the hazard level, coupled with a map of the Shannon entropy as an uncertainty indicator. This pair of statistical maps provides concise and effective information on a reference flood hazard along with the associated uncertainty. The method was applied to a region located along the middle reach of the Po River (northern Italy). Comparable central flood hazard estimates are obtained for the two sets of breaching events including or not including multiple breaches, with higher uncertainty if multiple breaches are considered. This result highlights the importance of considering multiple breaching events in flood risk management.

An accuracy-improved flood risk and ecological risk assessment in an interconnected river–lake system based on a copula-coupled hydrodynamic risk assessment model

Understanding flood event-related risk in an interconnected river–lake system (IRLS) is a prerequisite for developing water resource management strategies and enhancing disaster resilience. Previous flood event-related risk assessments either hardly consider the context in which water diversion encounters flood events or hardly consider the physical process, stochastic analysis and multivariate interactions of flood event characteristics simultaneously, thereby resulting in a less reliable assessment of flood event-related risk. To resolve this limitation, a copula-coupled hydrodynamic risk assessment model (CHRAM) is proposed to characterize the physical, multivariate, and stochastic nature of risk, improving the accuracy of risk assessment. In this study, CHRAM is capable of accurately quantifying flood event-related risks (i.e., flood risk and ecological risk) of the Nansi Lake Basin in the flood season under the operation of the eastern route of the South-to-North Water Diversion Project in China. Our findings revealed that the total flood volume (Rsum) and duration (Td) of flood events were significantly sensitive characteristic indexes for flood risk and ecological risk. In addition, the security domains of flood risk (Rsum < 35 mm and Td < 23 d) and ecological risk (Rsum < 50 mm and Td < 27 d) were determined based on the analysis of the relationship between sensitive characteristic indexes and risk indicators. The results also showed that the impact of Td on flood risk, ecological risk and flood-ecological combined risk was 3–14 times greater than the impact of Rsum. The occurrence probability of flood-ecological combined risk was smaller than that of flood risk but greater than that of ecological risk, with the corresponding maximum risk probabilities of 11.2%, 36.5%, and 4.7%, respectively. This study extends the current knowledge about risk assessment in IRLS and is beneficial to instruct the management of projects for similar systems.

Modeling Daily Floods in the Lancang‐Mekong River Basin Using an Improved Hydrological‐Hydrodynamic Model

AbstractDaily floods including event, characteristic, extreme and inundation in the Lancang‐Mekong River Basin (LMRB), crucial for flood projection and forecasting, have not been adequately modeled. An improved hydrological‐hydrodynamic model (VIC and CaMa‐Flood) considering regional parameterization was developed to simulate the flood dynamics over the basin from 1967 to 2015. The flood elements were extracted from daily time series and evaluated at both local and regional scales using the data collected from in‐situ observations and remote sensing. The results show that the daily discharge and water level are both well simulated at selected stations with relative error (RE) less than 10% and Nash‐Sutcliffe efficiency coefficient (NSE) over 0.90. Half of the flood events have NSE exceeding 0.76. The peak time and flood volume are well reproduced while both peak discharge and water level are slightly underestimated. The results tend to worsen when the characteristics of flood events are extended to annual extremes. These extremes are generally underestimated with NSE less than 0.5 but RE is within 20%. The simulated rainy season inundation area generally agrees with observations from remote sensing, with about 86.8% inundation occurrence frequency captured within the model capacity. Ignoring the regional parameterization and reservoir regulation can both deteriorate flood simulation performance at the local scale, resulting in lower NSE. Specifically, systematically higher water levels and up to 27% overestimation of peak discharge are found when ignoring regional parameterization, while ignoring reservoir regulation would cause up to 23% overestimation for flood extremes. It is expected that these findings would contribute to the regional flood forecasting and flood management.

A statistics-based automated flood event separation

The classification of characteristics of flood events, like peak, volume, duration and baseflow components is essential for many hydrological applications such as multivariate flood statistics, the validation of rainfall-runoff models and comparative hydrology in general. The basis for estimations of these characteristics is formed by flood event separation. It requires an indicator for the time when a flood peak occurs as well as the definition of the beginning and end of a flood event and a subdivision of the total volume into direct and baseflow components. However, the variable nature of runoff and the multiple processes and impacts that determine rainfall-runoff relationships make a separation difficult, especially an automation of it. We propose a new statistics-based flood event separation that was developed to analyse long series of daily discharges automatically to obtain flood events for flood statistics. Moreover, the related flood-inducing precipitation is identified, allowing the estimation of the flood-inducing rainfall and the runoff coefficient. With an additional tool to manually check the separation results easily and quickly, expert knowledge can be included without much effort. The algorithm was applied to seven basins in Germany, covering alpine, mountainous and flatland catchments with different runoff processes. In a sensitivity analysis, the impact of chosen parameters was evaluated. The results show that the algorithm delivers reasonable results for all catchments and only needs manual adjustment for long timeslots with increasing or high baseflow. It reliably separates flood events only instead of all runoff events and the estimated beginning and end of an event was shifted in mean by less than one day compared to manual separation.

Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale

The dynamics of flood event characteristics, such as the runoff coefficient and the recession time constant, differ in time and space, due to differences in climate, geology, and runoff generation mechanisms. This study examines the variability of event runoff characteristics and relates them to climatic and hydro-geological characteristics available at the regional scale. The main focus is to examine the role of rainfall patterns (i.e., event precipitation volume, precipitation intensity, and antecedent precipitation) and runoff regime (i.e., initial flow before runoff event and event duration) characteristics on the seasonal dynamics of runoff response. The analysis is performed in four small Austrian catchments representing different hydro-geological settings obtained by field mapping. The results are based on an analysis of 982 runoff events identified from hourly measurements of streamflow and precipitation in the period 2002 to 2013. The results show that larger event runoff coefficients and flow peaks are estimated in catchments with high mean annual precipitation than in drier catchments. In contrast to some previous studies, the results show only poor relation between antecedent precipitation (as an index of catchment wetness) and event runoff response. The initial flow is found to be the main factor influencing the magnitude of runoff coefficient and event peaks in all analyzed catchments and geological settings. The recession time constant tends to be inversely related to the maximum event precipitation intensity, with an exception for one catchment (Wimitzbach), which is characterized by the largest proportion of deep interflow contribution to runoff. The analysis of the runoff response by different event types indicates that runoff coefficients and recession time constants are the largest for snowmelt runoff events.

Investigation on flood event variations at space and time scales in the Huaihe River Basin of China using flood behavior classification

Flood is one of the severest natural disasters in the world and has caused enormous causalities and property losses. Previous studies usually focus on flood magnitude and occurrence time at event scale, which are insufficient to contain entire behavior characteristics of flood events. In our study, nine behavior metrics in five categories (e.g., magnitude, duration, timing, rates of changes and variability) are adopted to fully describe a flood event. Regional and interannual variations of representative flood classes are investigated based on behavior similarity classification of numerous events. Contributions of geography, land use, hydrometeorology and human regulation on these variations are explored by rank analysis method. Results show that: five representative classes are identified, namely, conventional events (Class 1, 61.7% of the total), low discharge events with multiple peaks (Class 2, 5.3%), low discharge events with low rates of changes (Class 3, 18.1%), low discharge events with high rates of changes (Class 4, 10.8%) and high discharge events with long durations (Class 5, 4.1%). Classes 1 and 3 are the major flood events and distributed across the whole region. Class 4 is mainly distributed in river sources, while Classes 2 and 5 are in the middle and down streams. Moreover, the flood class is most diverse in normal precipitation years (2006, 2008–2010 and 2015), followed by wet years (2007, 2013–2014), and dry years (2011 and 2012). All the impact factor categories explain 34.0%–84.1% of individual flood class variations. The hydrometeorological category (7.2%–56.9%) is the most important, followed by geographical (1.0%–6.3%), regulation (1.7%–5.1%) and land use (0.9%–2.2%) categories. This study could provide new insights into flood event variations in a comprehensive manner, and provide decision-making basis for flood control and resource utilization at basin scale.

Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes

AbstractFloods often affect large regions and cause adverse societal impacts. Regional flood hazard and risk assessments therefore require a realistic representation of spatial flood dependencies to avoid the overestimation or underestimation of risk. However, it is not yet well understood how spatial flood dependence, that is, the degree of co‐occurrence of floods at different locations, varies in space and time and which processes influence the strength of this dependence. We identify regions in the United States with seasonally similar flood behavior and analyze processes governing spatial dependence. We find that spatial flood dependence varies regionally and seasonally and is generally strongest in winter and spring and weakest in summer and fall. Moreover, we find that land‐surface processes are crucial in shaping the spatiotemporal characteristics of flood events. We conclude that the regional and seasonal variations in spatial flood dependencies must be considered when conducting current and future flood risk assessments.

The role of flood wave superposition in the severity of large floods

Abstract. The severity of floods is shaped not only by event- and catchment-specific characteristics but also depends on the river network configuration. At the confluence of relevant tributaries with the main river, flood event characteristics may change depending on the magnitude and temporal match of flood waves. This superposition of flood waves may potentially increase the flood severity downstream in the main river. However, this aspect has not been analysed for a large set of river confluences to date. To fill this gap, the role of flood wave superposition in the flood severity at downstream gauges is investigated in four large river basins in Germany and Austria (the Elbe, the Danube, the Rhine and the Weser). A novel methodological approach to analyse flood wave superposition is presented and applied to mean daily discharge data from 37 triple points. A triple point consists of three gauges: one in the tributary as well as one upstream and downstream of the confluence with the main river respectively. At the triple points, differences and similarities in flood wave characteristics between the main river and the tributary are analysed in terms of the temporal match and the magnitudes of flood peaks. At many of the confluences analysed, the tributary peaks consistently arrive earlier than the main river peaks, although high variability in the time lag is generally detected. No large differences in temporal matching are detected for floods of different magnitudes. In the majority of cases, the largest floods at the downstream gauge do not occur due to perfect temporal match between the tributary and the main river. In terms of spatial variability, the impact of flood wave superposition is site-specific. Characteristic patterns of flood wave superposition are detected for flood peaks in the Danube River, where peak discharges largely increase due to inflow from alpine tributaries. Overall, we conclude that the superposition of flood waves is not the driving factor behind flood peak severity at the major confluences in Germany; however, a few confluences show the potential for strong flood magnifications if a temporal shift in flood waves was to occur.

Comparison of nonstationary models in analyzing bivariate flood frequency at the Three Gorges Dam

Traditional flood frequency analysis is usually performed under the stationary assumption that flood elements, such as flood peak and flood volume, are independent and identically distributed (i.i.d). However, with global climate change and human activities across the landscape, stationary approaches cannot capture the nonstationary characteristics of flood events. This study aims to explore the key elements in establishing an effective bivariate nonstationary flood frequency model for flood peak and volume series at the Three Gorges Dam in China. A stationary reference model and six nonstationary models were established and rigorously compared to evaluate their benefits and limitations, including two time-informed models and four climate-informed models that consider time and climate indices as explanatory variables. The results suggest that nonstationary models are clearly superior to stationary models with respect to model performance based on the deviance information criterion (DIC). The time-informed nonstationary model provides a potential tool to project future trends in flood return periods with minimal data availability, yet the uncertainty in the estimation of design values of such models is limited. On the other hand, explicitly incorporating climate indices as explanatory variables of flood frequency distribution can significantly improve model performance and reduce uncertainty but at the cost of increased model complexity.

The significance of soil moisture in forecasting characteristics of flood events. A statistical analysis in two nested catchments

Abstract We examine the feasibility and added value of upscaling point data of soil moisture from a small- to a mesoscale catchment for the purpose of single-event flood prediction. We test the hypothesis that in a given catchment, the present soil moisture status is a key factor governing peak discharge, flow volume and flood duration. Multiple regression analyses of rainfall, pre-event discharge, single point soil moisture profiles from representative locations and peak discharge, discharge duration, discharge volume are discussed. The soil moisture profiles are selected along a convergent slope connected to the groundwater in flood plain within the small-scale catchment Husten (2.6 km²), which is a headwater catchment of the larger Hüppcherhammer catchment (47.2 km², Germany). Results show that the number of explanatory variables in the regression models is higher in summer (up to 8 variables) than in winter (up to 3 variables) and higher in the meso-scale catchment than in the small-scale catchment (up to 2 variables). Soil moisture data from selected key locations in the small catchment improves the quality of regression models established for the meso-scale catchment. For the different target variables peak discharge, discharge duration and discharge volume the adding of the soil moisture from the flood plain and the lower slope as explanatory variable improves the quality of the regression model by 15%, 20% and 10%, respectively, especially during the summer season. In the winter season the improvement is smaller (up to 6%) and the regression models mainly include rainfall characteristics as explanatory variables. The appearance of the soil moisture variables in the stepwise regression indicates their varying importance, depending on which characteristics of the discharge are focused on. Thus, we conclude that point data for soil moisture in functional landscape elements describe the catchments’ initial conditions very well and may yield valuable information for flood prediction and warning systems.

A synthesis of space–time variability in multicomponent flood response

Abstract. Catchment flood response consists of multiple components of flow originating from different surface and subsurface layers. This study proposes an extension of Viglione et al. (2010a) analytical framework to represent the dependence of catchment flood response to the different runoff generation processes. The analytical framework is compared to simulations from a distributed hydrologic model. A large number of rainfall–runoff events from three catchments of Tar River basin in North Carolina are used to illustrate the analytical framework. Specifically, the framework is used to estimate three flood event characteristics (cumulative runoff volume, centroid, and spreadness of hydrograph) through three corresponding framework parameters: the rainfall excess and the mean and variance of catchment response time. Results show that, under the smooth topographic setups of the study area, the spatial and/or temporal correlation between rainfall and runoff generation are insignificant to flood response; delay in flood response due to runoff generation and routing are of equal importance; the shape of the flood is mainly controlled by the variability in runoff generation stage but with non-negligible contribution from the runoff routing stage. Sensitivity tests show that the framework's main error source is the systematic underestimation of the flood event's centroid and spreadness, while the random error is relatively low.

Direct estimation of catchment response time parameters in medium to large catchments using observed streamflow data

AbstractIn single‐event deterministic design flood estimation methods, estimates of the peak discharge are based on a single and representative catchment response time parameter. In small catchments, a simplified convolution process between a single‐observed hyetograph and hydrograph is generally used to estimate time parameters such as the time to peak (TP), time of concentration (TC), and lag time (TL) to reflect the “observed” catchment response time. However, such simplification is neither practical nor applicable in medium to large heterogeneous catchments, where antecedent moisture from previous rainfall events and spatially non‐uniform rainfall hyetographs can result in multi‐peaked hydrographs. In addition, the paucity of rainfall data at sub‐daily timescales further limits the reliable estimation of catchment responses using observed hyetographs and hydrographs at these catchment scales. This paper presents the development of a new and consistent approach to estimate catchment response times, expressed as the time to peak (TPx) obtained directly from observed streamflow data. The relationships between catchment response time parameters and conceptualised triangular‐shaped hydrograph approximations and linear catchment response functions are investigated in four climatologically regions of South Africa. Flood event characteristics using primary streamflow data from 74 flow‐gauging stations were extracted and analysed to derive unique relationships between peak discharge, baseflow, direct runoff, and catchment response time in terms of TPx. The TPx parameters are estimated from observed streamflow data using three different methods: (a) duration of total net rise of a multipeaked hydrograph, (b) triangular‐shaped direct runoff hydrograph approximations, and (c) linear catchment response functions. The results show that for design hydrology and for the derivation of empirical equations to estimate catchment response times in ungauged catchments, the catchment TPx should be estimated from both the use of an average catchment TPx value computed using either Methods (a) or (b) and a linear catchment response function as used in Method (c). The use of the different methods in combination is not only practical but is also objective and has consistent results.