Flood Statistics Research Articles

Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics

A major source of uncertainty in flood statistics are the different flood generation processes. These make the assumption of homogeneous samples questionable. To overcome this issue, a framework for assessing the influence of catchment and climate attributes on flood-generating processes and their effect on flood statistics has been developed and applied to 252 catchments in New Zealand. Mean daily discharge data time series with a length ranging from 20 to 81 years were used. Flood events were classified according to their hydrograph shape. Three types were considered based on the different forcing: heavy rainfall of short duration (termed R1), moderate rainfall of medium intensity and duration (R2), and long-duration rainfall sequences of usually larger spatial extent (R3). The dominant flood type in each catchment was then linked to catchment and climate attributes. This allowed to identify the impact of each flood type on flood statistics and how the flood types have changed over time. The main drivers determining the flood type were rainfall variability and antecedent conditions. Small and steep catchments were dominated by heavy-rainfall floods of shorter duration, while flat and wet catchments were dominated by long-duration floods with larger volumes. Such information can support selection of effective flood protection and management measures.

Frequency of Italian Record-Breaking Floods over the Last Century (1911–2020)

This study provides an in-depth analysis of the frequency of extreme streamflow in Italy, adopting the innovative perspective of the theory of records, and focusing on record-breaking floods. (i.e., annual maximum series, AMS) observed in Italy between 1911 and 2020. Our research employs an extensive dataset of 522 annual maximum series (AMS) of streamflow observed across Italy between 1911 and 2020. We consider three time intervals (1911–2020, 1911–1970, and 1971–2020), and we define pooling-groups of AMSs based on (a) hydrological (e.g., catchment size, mean annual precipitation, etc.) and (b) spatial proximities of the gauged sites. First, within each group and for each time period, we compute the regional average number of record-breaking events (NRbins). Second, with a series of resampling experiments that preserve the spatial correlation among the AMSs, we test the hypothesis that NRbins result from a group of stationary sequences. Our results show spatially coherent patterns of an increasing number of record-breaking floods in central and in northeastern Italy over the last 50 years. In the same time interval, significant deviations in the regional number of record-breaking events from what would be expected for stationary flood sequences seem to be more common in drier climates or at higher altitudes, while the catchment size does not seem to be a meaningful descriptor.

Incorporating historical flood events in type-based statistics

Flood statistics form the basis of many hydrologic designs. However, short observation periods can lead to a high degree of uncertainty in the quantile estimates, especially for extreme floods. One way to expand the information used in the statistics and, in particular, to include other exceptional floods in the sample is to take historical floods into account. The use of these floods, which are usually reconstructed from chronicles or flood marks, has long been established for classic flood frequency analysis. However, the fact that historical floods can also have different origins and are therefore not statistically identically distributed has so far been ignored. To avoid this violation of the statistical assumptions, we show how historical floods, subdivided according to their genesis, can be taken into account in type-based flood statistics. For this purpose, the classical partial probability weighted moment (PPWM) method is extended for the type-based mixture model of partial duration series (TMPS). The result is compared to a Markov chain Monte Carlo method in order to compare the differences between the two methods. Type-based statistics can also be used to attribute the different influences of historical floods on the quantile estimate. An example shows that floods associated with long-lasting precipitation in particular are overestimated in short samples, while heavy rainfall floods are comparatively well represented. The results show that the consideration of historical events in type-based statistics not only allows a more balanced view of extreme floods, but also enables the influences of these to be attributed.

Temporal changes in the frequency of flood types and their impact on flood statistics

Standard flood frequency analysis assumes stationarity of flood conditions, i.e., no change of the distribution over time. However, long-term variability in climate and anthropogenic impacts question this assumption. Consequently, more and more non-stationary models are considered in flood frequency analyses. Yet, most of them only consider a change-point or trend in the magnitude of flood peaks while ignoring changes in the underlying flood geneses. Recent climate reports suggest such a change in frequency of certain flood-generating factors, e.g., the increase of frequency of heavy-rainfall events. In this study, flood types are applied to detect changes in the meteorological drivers of flood regimes. By application of a robust change-point test for the variance based on Gini’s Mean Difference, significant changes in the frequency of occurrence of certain flood types are detected. A clear tendency to more frequent heavy-rainfall floods and less snowmelt-induced floods is observed for many catchments in Central Europe. A special focus is laid on the shifts in winter floods, which occur less often and are replaced by rainfall-driven floods. The impacts of such changes on flood statistics are demonstrated by several approaches. Though the magnitude of flood peaks does not (necessarily) change, the changing frequency of floods leads to changing flood quantiles. Quantile estimations from traditional statistical analyses of annual series are compared to results of type-based flood statistics. It is shown how standard models are more affected by these changes because they are not able to compensate for changes in the frequency of individual flood types.

Using a scenario-neutral approach to assess the impacts of climate change on flooding in the Ba River Basin, Viet Nam

Abstract Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.

Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile

Flood frequency analysis lies at the core of hydrology and water engineering as one of the most required estimates for water planning and design of hydraulic structures. For ungauged basins, where no information is available, various flood regionalisation techniques have varying degrees of complexity and resulting performance, depending on the study's goal, the region analysed, and the information available. This study evaluates the use of hydrological models for flood regionalisation in Chile, using 1) A large sample dataset of 101 catchments; 2) the continuous simulation approach with the GR4J model; 3) the leave-one-out strategy for performance testing; and, 4) two regionalisation methods: Nearest Neighbour (NN) and Physical Similarity (PS), together with several alternative objective functions for calibration purposes and regionalisation strategy (in all cases adopting a single criterion, single variable and determinist approach for the parameter’s selection). Our results showed that performance (both in calibration–validation and regionalisation) is highly variable (in terms of reproducing the runoff hydrograph and flood statistics), depending on the catchment’s aridity (e.g., around 66–82% of catchments with NSE above 0 in humid regions but it severely drops to 12–44% of catchments with NSE above 0 when evaluating arid catchments). We also found that flood-specific calibration strategies produce better results for floods but poorer performance in runoff hydrograph reproduction. Finally, we highlight that our regionalisation results were in close agreement with those from one of the currently recommended methods by Chilean engineering for flood regionalisation. This is particularly promising, considering that the continuous simulation approach gives access to the complete time series and not only flood statistics. We end this manuscript by discussing several sources of uncertainty, hoping that these can be accounted for in future studies.

Comparative study on appropriate drought and flood index selection in a tropical farming island in China

The traditional drought and flood analysis method had not fully considered the proportion analysis of different drought and flood grades in the historical years of each rainfall station. This made results unconvincing and made it difficult to deeply understand the characteristics and applicability of various methods. Based on the daily rainfall data of 88 stations in Hainan Island from 1970 to 2019, the China-Z index and the Standardized Precipitation Index (SPI) were used to compare and analyze the spatial and temporal distribution characteristics of droughts and floods from three different time scales (flood season, non-flood season and the whole year). The results showed that both SPI and China-Z index can well reflect the actual drought and flood situations in Hainan Island. The analysis of the proportions of different drought and flood grades in the historical years of each rainfall station and regional historical drought and flood statistics suggested that the China-Z index had a better indication effect than SPI on the extreme drought and flood grades. The alternation of drought and flood between different eras were obvious. Hainan Island generally presented an east-west reverse drought-flood variation trend, as well as a north-south reverse drought-flood variation trend. The drought and flood in the central mountainous area of Hainan Island had been relatively stable. The distribution pattern of drought and flood had a good spatial consistency in the three periods. On the whole, Hainan Island had shown a trend of flood in the east and drought in the west in the past 50 years.

Regionalization of catchments for flood frequency analysis for data scarce Rift Valley Lakes Basin, Ethiopia

Study regionRift Valley Lakes Basin, Ethiopia Study focusWe performed regionalization of catchments using K-means method based on variety of catchment characteristics and tested hydrological homogeneity of the regions using flood statistics. Following that, flood frequency analysis (FFA) for the identified regions was computed using regional flow data. New hydrological insights for the regionFour hydrologically homogeneous regions were identified. Generalized extreme value (GEV), Lognormal (LN2), Wakeby, and Generalized Pareto (GP) were the best fitted distribution models for regions; one up to four respectively. Maximum likelihood was chosen as the most efficient parameter estimation method for regions two, three, and four, whereas the method of moment was chosen for region one. Region one contained one gauged catchment, therefore regression equation was not developed for this region. The linear regression between mean annual flood (MAF) and catchment characteristics performed well (R2 = 0.827, 0.899 and 0.994) for regions two, three and four respectively. The relative errors between observed and estimated MAF in the pseudo ungauged catchments resulted 0.511, 0.039 and 0.166 for regions two, three and four respectively. Hence, the developed regional frequency curves and regression equations can be used for flood estimation at the required return period (T) in the homogeneous regions of the basin.

Handling the stochastic uncertainty of flood statistics in regionalization approaches

Regional flood frequency analyses are highly affected by the number of gauged catchments and lengths of observation periods at the individual gauges. Therefore, information is unevenly distributed in the region of interest. Especially the occurrence of single extreme floods which were observed or not observed at some gauges, depending on the observation period, may have a large impact on the regionalization. We evaluated the impact of the sample length on the regionalization of a type-specific statistical mixture-model. In a case study it is shown how the regionalization error can be reduced by more than 50% if the sample sizes increase. We compare three different approaches to handle this stochastic uncertainty in regionalization. The alignment of the statistical distribution parameters to consider the impact of extreme floods proved to be most beneficial when aiming to obtain homogeneous regionalised flood quantiles for hydrologically similar regions in a region with heterogeneous observation periods.

Decreasing flood hazard evaluated in Turkey using nonstationary models

AbstractAn assessment of changing flood hazard in the river basins of Turkey is currently lacking. This study evaluates the drivers of flood flows using distributional regression models. The generalized additive models for location, scale, and shape (GAMLSS), a flexible framework well‐suited to probabilistic nonstationary modeling, is applied to annual instantaneous maximum flows (AIMF) from 12 gauging stations located in the upper, middle and lower parts of the Euphrates basin, the most important river basin in the Middle East. First, the Pettitt test was used to detect abrupt changes in the AIMF data series, and the Mann–Kendall test to evaluate temporal trends. Significant change points for two stations and significant decreasing temporal changes for seven stations were observed. Three strictly positive, continuous distributions (namely, Gamma, Lognormal, and Weibull) were then fitted to the AIMF data series and the distribution parameters were specified as functions of both time and physically based covariates (precipitation, temperature, and climate oscillation indices). Among the candidate distributions, the Lognormal was found to be the most appropriate for describing flood flows across the basin. Annual precipitation, seasonal temperature and annual oscillation indices were the most relevant covariates. Among the climate indices, the East Atlantic Oscillation was most relevant in the lower part of the basin, the Southern and North Atlantic Oscillations in the middle, and the East Atlantic‐West Russia pattern in the upper part. The models were then employed to estimate and compare historical stationary flood quantiles with nonstationary quantiles (identified with physically based covariates) under various return periods (10, 20, 50, 100, and 200 years). Results indicate that flood processes can be better described by employing distributional regression models with physical covariates and this study can serve as a reference for regional water resources management in the basin and possibly for other river basins.

Climate change allowances, non‐stationarity and flood frequency analyses

AbstractWhen considering future adaptation to climate change in UK fluvial flood alleviation schemes, the current recommendation by the Environment Agency (England) is to increase peak design flood flows by a preselected percentage. This allowance varies depending on the period for which the estimate is being made, the vulnerability of the development being considered and its location. Recently, questions have been raised as to whether these percentage uplifts should be kept the same, or whether change has already happened within the baseline period and so uplifts should be reduced. A complicating factor is that changes in flood frequency can occur for reasons in addition to climate change, such as land‐use change or natural variability. This article describes current approaches taken by different stakeholders for catchments in England and Wales to account for climate change, and discusses these allowances where there is already an observed presence of trend in flood regimes. Theil–Sen estimators of trend were used in comparing non‐stationary and stationary flood frequency curves with allowances applied, leading to a recommendation of evaluating non‐stationary models at 1990, the end of the reference period. Examples were explored such as the Eden catchment, which was heavily affected by Storm Desmond in December 2015.

Causes and consequences of floods: flash floods, urban floods, river floods and coastal floods

Undoubtedly, the flood is known as a natural disaster. But in practice, the flood is considered the most terrible natural disaster in terms of mortality and financial losses. In this regard, a worrying trend is the increasing trend of mortality and flood damage in the world in recent decades. The increase in population and assets in the floodplain the changes in hydro systems and the destructive effects of human activities have been a major cause of this trend. In this chapter, due to the importance of this natural phenomenon in the ZayandehRud basin, the general study of flood and its effective factors in creating it, based on library studies and reports, and the collection of flood statistics in the basin during a 40-year period and the damage caused by this flood, has been attempted. With the causes and factors influencing the flooding and also the use of EXCEL software for various damages caused by these floods in high risk cities of this basin, has been identified. In general, the cause of many floods in the central parts of Iran, including ZayandehRud basin, is high rainfall. The causes of these rainfall are also related to the Elenino and Lenina phenomenon, as well as the passage of low pressure systems, which after affecting a large amount of steam from the Mediterranean, affect the western parts of the province that overlooks the Zagros mountains.

Regionalisation of flood frequencies based on flood type-specific mixture distributions

The regionalisation of flood frequencies is a precondition for the estimation of flood statistics for ungauged basins. It is often based on either the concept of hydrological similarity of catchments or spatial proximity. Similarity is usually defined by comparing catchment attributes or distances. Here, we apply flood types in regionalisation directly to consider the type-specific aspects of similarity. The different flood types are classified according to their meteorological causes and hydrographs. Their probability distributions are modelled by type-specific distribution functions which are combined into one statistical annual mixture model afterwards. For regionalisation, we specified the parameters of each type-specific probability distribution separately with hierarchical clustering and regressions from catchment attributes. By selection of most relevant features, depending on the flood type, the specifics of flood-generating processes and meteorological causes were considered. The results demonstrate how this consideration of deterministic aspects can improve the transferability of distribution parameters to ungauged catchments. The type-specific regionalisation approach offers a higher degree of freedom for regionalisation as it describes the relationships between catchment characteristics, meteorological causes of floods and response of watersheds.

Impact of Flood Types on Superposition of Flood Waves and Flood Statistics Downstream

AbstractFlood events may be caused by different runoff generating processes and can be differentiated in their genesis by the application of flood types. Additionally, the spatial interaction of ca...

Ubiquitous increases in flood magnitude in the Columbia River basin under climate change

Abstract. The USA and Canada have entered negotiations to modernize the Columbia River Treaty, signed in 1961. Key priorities are balancing flood risk and hydropower production, and improving aquatic ecosystem function while incorporating projected effects of climate change. In support of the US effort, Chegwidden et al. (2017) developed a large-ensemble dataset of past and future daily streamflows at 396 sites throughout the Columbia River basin (CRB) and selected other watersheds in western Washington and Oregon, using state-of-the art climate and hydrologic models. In this study, we use that dataset to present new analyses of the effects of future climate change on flooding using water year maximum daily streamflows. For each simulation, flood statistics are estimated from generalized extreme value distributions fit to simulated water year maximum daily streamflows for 50-year windows of the past (1950–1999) and future (2050–2099) periods. Our results contrast with previous findings: we find that the vast majority of locations in the CRB are estimated to experience an increase in future streamflow magnitudes. The near ubiquity of increases is all the more remarkable in that our approach explores a larger set of methodological variation than previous studies; however, like previous studies, our modeling system was not calibrated to minimize error in maximum daily streamflow and may be affected by unquantifiable errors. We show that on the Columbia and Willamette rivers increases in streamflow magnitudes are smallest downstream and grow larger moving upstream. For the Snake River, however, the pattern is reversed, with increases in streamflow magnitudes growing larger moving downstream to the confluence with the Salmon River tributary and then abruptly dropping. We decompose the variation in results attributable to variability in climate and hydrologic factors across the ensemble, finding that climate contributes more variation in larger basins, while hydrology contributes more in smaller basins. Equally important for practical applications like flood control rule curves, the seasonal timing of flooding shifts dramatically on some rivers (e.g., on the Snake, 20th-century floods occur exclusively in late spring, but by the end of the 21st century some floods occur as early as December) and not at all on others (e.g., the Willamette River).

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.

Flood Frequency Analysis of River Niger, Shintaku Gauging Station, Kogi State, Nigeria

The study outlines a frequency distribution study on the highest annual flood statistics in Niger River located at Shintaku hydrologic Station for period of 58years. In order to determine the optimal model for highest annual flood analysis Generalised extreme value, Log normal, Gumbel maximum, Generalised Pareto and Log Pearson III, were tested. Based on error produced by criteria of goodness of Fit tests, the optimal model was determined. Results obtained indicated that Log Pearson type III was best to model maximum flood magnitude of Niger River at Shintaku station. The flood frequency curve was therefore derived using Log Pearson type III frequency distribution. Flood frequency curve showed that return periods of 50 and 100 years with the probabilities of 2% and1% respectively, yielded discharges of 15300m3/s and 15600m3/s respectively. These results were strongly influenced by their topographical, geographical and climatic factors. The findings of this work will be useful for design engineers in deciding the dimension of hydraulic structures such as spillway, dams, canals, bridges and levees among others. Future studies are required to include flood forecasting in the development of inundation maps for Niger River.Keywords—Return period, Frequency Distribution, Flood, Niger River, Flood Modeling

Exploring multidecadal changes in climate and reservoir storage for assessing nonstationarity in flood peaks and risks worldwide by an integrated frequency analysis approach

The changing climate and reservoir storage have a far-reaching influence on the nonstationarity in flood peaks worldwide, but the quantification of the relative contribution of each covariate (i.e., climate and reservoir storage) is fundamentally challenging especially under the time-varying mechanisms in statistical properties. This study proposed an integrated flood frequency analysis for assessing the impacts of changing climate and reservoir storage on the nonstationarity in flood peaks and flood risks worldwide. The 32 major river catchments covering more than 60% of hydro-meteorological observation stations and 70% of reservoir storage worldwide constituted the case study. The proposed three-faceted approach was explored systematically through: modeling the nonstationarity in global flood peaks, identifying the contribution of changing climate and reservoir storage to the nonstationarity of flood peaks, and quantifying the change in flood risks under the nonstationary condition. The findings pointed out that global flood trends varied from increasing +19.3%/decade to decreasing −31.6%/decade. Taking the stationary flood frequency analysis as the benchmark, the comparative results revealed that the flood risk in 5 rivers under the nonstationary condition in response to warming climate significantly increased (1% → 5%) over the historical period whereas the flood risk in 7 rivers in response to increasing reservoir storage largely reduced (1% → 0.5%). Despite the spatiotemporal heterogeneity of observations, the changes in flood peaks evaluated here were explicitly in lined with the changing climate and reservoir storage, supporting the demand for considering the nonstationarity of flood peaks and risks in social infrastructure planning and designing as well as water management.

Simultaneous Regionalization of Gauged and Ungauged Watersheds Using a Missing Data Clustering Method

AbstractUse of flood statistics in the regionalization of watersheds may improve the homogeneity of regions. However, it is not possible to use the feature vectors, including the flood statistics, ...

21st Century flood risk projections at select sites for the U.S. National Park Service

Assessing flood risk using stationary flood frequency analysis techniques is commonplace. However, it is increasingly evident that the stationarity assumption of these analyses does not hold as anthropogenic climate change could shift a site’s hydroclimate beyond the range of historical behaviors. We employ nonstationary flood frequency models using the generalized extreme value (GEV) distribution to model changing flood risk for select seasons at twelve National Parks across the U.S. In this GEV model, the location and/or scale parameters of the distribution are allowed to change as a function of time-variable covariates. We use historical precipitation and modeled flows from the Variable Infiltration Capacity model (VIC), a land-surface model that simulates land–atmosphere fluxes using water and energy balance equations, as covariates to fit a best nonstationary GEV model to each site. We apply climate model projections of precipitation and VIC flows to these models to obtain future flood probability estimates. Our model results project a decrease in flood risk for sites in the southwestern U.S. region and an increase in flood risk for sites in northern and eastern regions of the U.S. for the selected seasons. The methods and results presented will enable the NPS to develop strategies to ensure public safety and efficient infrastructure management and planning in a nonstationary climate.