scholarly journals Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1896 ◽  
Author(s):  
Gabriel-Martin ◽  
Sordo-Ward ◽  
Garrote ◽  
García
Keyword(s):  
Peak Flow ◽  
Total Duration ◽  
Weather Generator ◽  
Return Periods ◽  
Storm Events ◽  
Stochastic Weather Generator ◽  
Rainfall Events ◽  
Annual Peak ◽  
Extreme Flood ◽  
Physically Based

This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.

scholarly journals Stochastic Hybrid Event Based and Continuous Approach to Derive Flood Frequency Curve

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1931
Author(s):  
Alvaro Sordo-Ward ◽  
Ivan Gabriel-Martín ◽  
Paola Bianucci ◽  
Giuseppe Mascaro ◽  
Enrique R. Vivoni ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hybrid Method ◽  
Peak Flow ◽  
Weather Generator ◽  
Flood Frequency ◽  
Storm Events ◽  
Frequency Curve ◽  
Continuous Simulation ◽  
Event Based ◽  
Frequency Curves

This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.

scholarly journals Simulation of Extreme Heatwaves with Empirical Importance Sampling

2019 ◽  
Author(s):  
Pascal Yiou ◽  
Aglaé Jézéquel
Keyword(s):  
Probability Distribution ◽  
Importance Sampling ◽  
Extreme Events ◽  
Climate Models ◽  
Weather Generator ◽  
Return Periods ◽  
Stochastic Weather Generator ◽  
Temporal Properties ◽  
Extreme Behavior ◽  
Circulation Analogues

Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyse their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heatwaves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heatwaves in a realistic way. This algorithm is a modification of a stochastic weather generator, which gives more weight to trajectories with higher temperatures. This presentation outlines the methodology on European heatwaves and illustrates the spatial and temporal properties of simulations.

scholarly journals Observed Spatiotemporal Trends in Intense Precipitation Events across United States: Applications for Stochastic Weather Generation

Climate ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 36 ◽  
Author(s):  
Sanjeev Joshi ◽  
Jurgen Garbrecht ◽  
David Brown
Keyword(s):  
Climate Change ◽  
Weather Generator ◽  
Precipitation Intensity ◽  
Storm Events ◽  
Stochastic Weather Generator ◽  
Spatiotemporal Trends ◽  
Extreme Precipitation Events ◽  
Intense Precipitation ◽  
Stochastic Weather Generation ◽  
Precipitation Events

An increasing focus of climate change studies is the projection of storm events characterized by heavy, very heavy, extreme, and/or intense precipitation. Projected changes in the spatiotemporal distributions of such intense precipitation events remain uncertain due to large measures of variability in both the definition and evidence of increased intensity in the upper percentile range of observed daily precipitation distributions, particularly on a regional basis. As a result, projecting changes in future precipitation at the upper tail of the distribution (i.e., the heavy to heaviest events), such as through the use of stochastic weather generator programs, remains challenging. One approach to address this challenge is to better define what constitutes intense precipitation events and the degree of location-specific adjustment needed for the weather generator programs to appropriately account for potential increases in precipitation intensity due to climate change. In this study, we synthesized information on categories of intense precipitation events and assessed reported trends in the categories at national and regional scales within the context of applying this information to stochastic weather generation. Investigations of adjusting weather generation models to include long-term regional trends in intense precipitation events are limited, and modeling trends in site-specific future precipitation distributions forecasted by weather generator programs remains challenging. Probability exceedance curves and variations between simulated and observed distributions can help in modeling and assessment of trends in future extreme precipitation events that reflect changes in precipitation intensity due to climate change.

scholarly journals An Application of a Stochastic Semi-Continuous Simulation Method for Flood Frequency Analysis: A Case Study in Slovakia

2017 ◽  
Vol 25 (3) ◽  
pp. 30-44
Author(s):  
Peter Valent ◽  
Emmanuel Paquet
Keyword(s):  
Simulation Method ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Reliable Estimate ◽  
Return Periods ◽  
Rainfall Events ◽  
Continuous Simulation ◽  
Wide Range ◽  
Extreme Flood ◽  
Rainfall Runoff Model

Abstract A reliable estimate of extreme flood characteristics has always been an active topic in hydrological research. Over the decades a large number of approaches and their modifications have been proposed and used, with various methods utilizing continuous simulation of catchment runoff, being the subject of the most intensive research in the last decade. In this paper a new and promising stochastic semi-continuous method is used to estimate extreme discharges in two mountainous Slovak catchments of the rivers Váh and Hron, in which snow-melt processes need to be taken into account. The SCHADEX method used, couples a precipitation probabilistic model with a rainfall-runoff model used to both continuously simulate catchment hydrological conditions and to transform generated synthetic rainfall events into corresponding discharges. The stochastic nature of the method means that a wide range of synthetic rainfall events were simulated on various historical catchment conditions, taking into account not only the saturation of soil, but also the amount of snow accumulated in the catchment. The results showed that the SCHADEX extreme discharge estimates with return periods of up to 100 years were comparable to those estimated by statistical approaches. In addition, two reconstructed historical floods with corresponding return periods of 100 and 1000 years were compared to the SCHADEX estimates. The results confirmed the usability of the method for estimating design discharges with a recurrence interval of more than 100 years and its applicability in Slovak conditions.

Navigating through extreme flood simulations with intelligently chosen parameter sets

2020 ◽  
Author(s):  
Anna E. Sikorska-Senoner ◽  
Bettina Schaefli ◽  
Jan Seibert
Keyword(s):  
Climate Variability ◽  
Hydrological Model ◽  
Weather Generator ◽  
Model Parameters ◽  
Large Set ◽  
Return Periods ◽  
Simulation Framework ◽  
Model Parameter ◽  
Extreme Floods ◽  
Extreme Flood

<p>The quantification of extreme floods and associated return periods remains to be a challenge for flood hazard management and is particularly important for applications where the full hydrograph shape is required (e.g., for reservoir management). One way of deriving such estimates is by employing a comprehensive hydrological simulation framework, including a weather generator, to simulate a large set of flood hydrographs. In such a setting, the estimation uncertainties originate from the hydrological model, but also from the climate variability. While the uncertainty from the hydrological model can be described with common methods of uncertainty estimation in hydrology (in particular related to model parameters), the uncertainties from climate variability can only be represented with repeated realizations of meteorological scenarios. These scenarios can be generated with the help of the selected weather generator(s), which are capable of providing numerous and continuous long time series. Such generated meteorological scenarios are then used as input for a hydrological model to simulate a large sample of extreme floods, from which return periods can be computed based on ranking.</p><p>In such a simulation framework, many thousands of possible combinations of meteorological scenarios and of hydrological model parameter sets may be generated. However, these simulations are required at a high temporal resolution (hourly), needed for the simulation of extreme floods and for determining infrequent floods of a return period equal to or lower than 1000 years. Accordingly, due to computational constraints related to any hydrological model, one often needs to preselect meteorological scenarios and representative model parameter sets to be used within the simulation framework. Thus, some kind of an intelligent parameter selection for deriving the uncertainty ranges of extreme model simulations for such rare events would be very beneficial but is currently missing.</p><p>Here we present results from an experimental study where we tested three different methods of selecting a small number of representative parameter sets for a Swiss catchment. We used 100 realizations of 100 years of synthetic precipitation-streamflow data. We particularly explored the reliability of the extreme flood uncertainty intervals derived from the reduced parameter set ensemble (consisting of only three representative parameter sets) compared to the full range of 100 parameter sets available. Our results demonstrated that the proposed methods are efficient for deriving uncertainty intervals for extreme floods. These findings are promising for the simulation of extreme floods in comparable simulation frameworks for hydrological risk assessment.</p>

scholarly journals A process-based model for the definition of hydrological alert systems in landslide risk mitigation

2012 ◽  
Vol 12 (11) ◽  
pp. 3343-3357 ◽  
Author(s):  
M. Floris ◽  
A. D'Alpaos ◽  
A. De Agostini ◽  
G. Stevan ◽  
G. Tessari ◽  
...  
Keyword(s):  
Rainfall Threshold ◽  
Return Periods ◽  
Alert System ◽  
Rainfall Events ◽  
Ne Italy ◽  
Rainfall Depth ◽  
Physically Based ◽  
Physically Based Modelling ◽  
Set Up ◽  
Definition Of

Abstract. The definition of hydrological alert systems for rainfall-induced landslides is strongly related to a deep knowledge of the geological and geomorphological features of the territory. Climatic conditions, spatial and temporal evolution of the phenomena and characterization of landslide triggering, together with propagation mechanisms, are the key elements to be considered. Critical steps for the development of the systems consist of the identification of the hydrological variable related to landslide triggering and of the minimum rainfall threshold for landslide occurrence. In this paper we report the results from a process-based model to define a hydrological alert system for the Val di Maso Landslide, located in the northeastern Italian Alps and included in the Vicenza Province (Veneto region, NE Italy). The instability occurred in November 2010, due to an exceptional rainfall event that hit the Vicenza Province and the entire NE Italy. Up to 500 mm in 3-day cumulated rainfall generated large flood conditions and triggered hundreds of landslides. During the flood, the Soil Protection Division of the Vicenza Province received more than 500 warnings of instability phenomena. The complexity of the event and the high level of risk to infrastructure and private buildings are the main reasons for deepening the specific phenomenon occurred at Val di Maso. Empirical and physically-based models have been used to identify the minimum rainfall threshold for the occurrence of instability phenomena in the crown area of Val di Maso landslide, where a retrogressive evolution by multiple rotational slides is expected. Empirical models helped in the identification and in the evaluation of recurrence of critical rainfall events, while physically-based modelling was essential to verify the effects on the slope stability of determined rainfall depths. Empirical relationships between rainfall and landslide consist of the calculation of rainfall Depth-Duration-Frequency (DDF) curves, which allow one to determine rainfall depth (or intensity) as a function of duration for given return periods or probabilities of exceedance (frequencies). Physically-based modelling was performed through coupled seepage and slope stability analyses. Combining results from empirical and physically-based modelling, the minimum alert threshold for a reactivation of the phenomenon was found in rainfall cumulated up to 60 days with a return period of 2 yr. These results were used to set up a hydrological alert system based on the calibration of DDF curves which can be used as a sort of abacus to plot in real time rainfall depths and to set increasing levels of alert on the basis of the degree of exceptionality of rainfall. The alert system for Val di Maso was successfully tested by the rainfall events that produced displacements which have been recorded by extensometers placed in the crown area after the November 2010 landslide. However, further tests are recommendable to improve the process-based model that led to the implementation of the alert system. To this end, a monitoring system is currently being realized. In the near future, monitoring data will help in testing and improving landslide evolution and alert models. The proposed hydrological alert system proves to be effective mainly because it can be applied to different scales of investigation and geological and geomorphological contexts. In fact, it might also be applicable to territorial scale analyses, as showed by the brief example provided in this paper on how the alert system could be used for landslide early warning in the area surrounding Val di Maso. Furthermore, it is easy to set up. The needed components are a rain gauge station, a software that compares rainfall data to rainfall events with different return periods and degree of alert, and a transmission system of the warning levels to authorities.

scholarly journals Simulation of extreme heat waves with empirical importance sampling

2020 ◽  
Vol 13 (2) ◽  
pp. 763-781
Author(s):  
Pascal Yiou ◽  
Aglaé Jézéquel
Keyword(s):  
Importance Sampling ◽  
Extreme Events ◽  
Climate Models ◽  
Heat Waves ◽  
Weather Generator ◽  
Return Periods ◽  
Stochastic Weather Generator ◽  
Temporal Properties ◽  
Extreme Behavior ◽  
Circulation Analogues

Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyze their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heat waves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heat waves in a realistic way. This algorithm is a modification of a stochastic weather generator, which gives more weight to trajectories with higher temperatures. This presentation outlines the methodology using European heat waves and illustrates the spatial and temporal properties of simulations.

Rainfall Events as Paths of a Stochastic Process: Problems of Design Storm Analysis

1984 ◽  
Vol 16 (8-9) ◽  
pp. 131-138 ◽  
Author(s):  
Johannes Brummer
Keyword(s):  
Stochastic Process ◽  
Peak Flow ◽  
Rainfall Events ◽  
Peak Flows ◽  
Design Storm ◽  
Adequate Evaluation ◽  
Rainfall Patterns

Problems in the construction of design storms are expressed in mathematical terms. Introduced here is a concept for approximating natural peak flow values by means of the distribution of typical rainfall patterns. A comparison demonstrates the quality of this concept and the competency of some well-known design storms for the adequate evaluation of peak flows.

scholarly journals Hillslope Contribution to the Clark Instantaneous Unit Hydrograph: Application to the Seolmacheon Basin, Korea

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1707
Author(s):  
Chulsang Yoo ◽  
Huy Phuong Doan ◽  
Changhyun Jun ◽  
Wooyoung Na
Keyword(s):  
Peak Flow ◽  
Peak Time ◽  
Rainfall Runoff ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Storage Coefficient ◽  
Channel Velocity ◽  
Linear Reservoir ◽  
Mountainous Basin

In this study, the time–area curve of an ellipse is analytically derived by considering flow velocities within both channel and hillslope. The Clark IUH is also derived analytically by solving the continuity equation with the input of the derived time–area curve to the linear reservoir. The derived Clark IUH is then evaluated by application to the Seolmacheon basin, a small mountainous basin in Korea. The findings in this study are summarized as follows. (1) The time–area curve of a basin can more realistically be derived by considering both the channel and hillslope velocities. The role of the hillslope velocity can also be easily confirmed by analyzing the derived time–area curve. (2) The analytically derived Clark IUH shows the relative roles of the hillslope velocity and the storage coefficient. Under the condition that the channel velocity remains unchanged, the hillslope velocity controls the runoff peak flow and the concentration time. On the other hand, the effect of the storage coefficient can be found in the runoff peak flow and peak time, as well as in the falling limb of the runoff hydrograph. These findings are also confirmed in the analysis of rainfall–runoff events of the Seolmacheon basin. (3) The effect of the hillslope velocity varies considerably depending on the rainfall events, which is also found to be mostly dependent upon the maximum rainfall intensity.

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