scholarly journals New Approach to Estimate Extreme Flooding Using Continuous Synthetic Simulation Supported by Regional Precipitation and Non-Systematic Flood Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3174
Author(s):  
Carles Beneyto ◽  
José Ángel Aranda ◽  
Gerardo Benito ◽  
Félix Francés
Keyword(s):  
Hydrological Model ◽  
Extreme Rainfall ◽  
Weather Generator ◽  
Data Series ◽  
Distributed Hydrological Model ◽  
Return Periods ◽  
Regional Study ◽  
New Approach ◽  
Extreme Floods ◽  
Synthetic Simulation

Stochastic weather generators combined with hydrological models have been proposed for continuous synthetic simulation to estimate return periods of extreme floods. Yet, this approach relies upon the length and spatial distribution of the precipitation input data series, which often are scarce, especially in arid and semiarid regions. In this work, we present a new approach for the estimation of extreme floods based on the continuous synthetic simulation method supported with inputs of (a) a regional study of extreme precipitation to improve the calibration of the weather generator (GWEX), and (b) non-systematic flood information (i.e., historical information and/or palaeoflood records) for the validation of the generated discharges with a fully distributed hydrological model (TETIS). The results showed that this complementary information of extremes allowed for a more accurate implementation of both the weather generator and the hydrological model. This, in turn, improved the flood quantile estimates, especially for those associated with return periods higher than 50 years but also for higher quantiles (up to approximately 500 years). Therefore, it has been proved that continuous synthetic simulation studies focused on the estimation of extreme floods should incorporate a generalized representation of regional extreme rainfall and/or non-systematic flood data, particularly in regions with scarce hydrometeorological records.

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 Rainfall Intensity Analysis for Synoptic Stations in Northern Nigeria

2020 ◽  
Vol 17 (3) ◽  
pp. 223-228
Author(s):  
S.O. Oyegoke ◽  
A.S. Adebanjo ◽  
H.J. Ododo
Keyword(s):  
Rainfall Intensity ◽  
Linear Regression Analysis ◽  
Extreme Rainfall ◽  
Data Series ◽  
Northern Nigeria ◽  
Return Periods ◽  
Accurate Forecast ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Frequency Relationship

With the large inter-annual variability of rainfall in Northern Nigeria, a zone subject to frequent dry spells which often result in severe and widespread droughts, the need for intense study of rainfall and accurate forecast of rainfall intensity duration frequency (IDF) curves cannot be over emphasized. The Intensity Duration Frequency relationship is a mathematical relationship between the rainfall intensity and rainfall duration for given return periods. Using a subset of the network of fifteen continuous auto recording rain gauges available in Northern Nigeria, a total of seven different time durations ranging from 12 minutes to 24 hours were developed for return periods of 2, 5, 10, 25, 50 and 100 years. The maximum data series so obtained was fitted to Gumbel’s Extreme Value Type 1 distribution. Linear Regression Analysis was then used to obtain the intensity-duration relationships for the various locations from which Intensity-Duration Frequency (IDF) curves were generated using Microsoft Excel for various return periods. Keywords:  Extreme rainfall, intensity, duration, frequency, Northern Nigeria

scholarly journals A new approach to accurate validation of remote sensing retrieval of evapotranspiration based on data fusion

2010 ◽  
Vol 7 (2) ◽  
pp. 1745-1784 ◽  
Author(s):  
C. Sun ◽  
D. Jiang ◽  
J. Wang ◽  
Y. Zhu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Assessment Tool ◽  
High Spatial Resolution ◽  
Meteorological Data ◽  
New Method ◽  
Distributed Hydrological Model ◽  
New Approach

Abstract. The study presented a new method of validating the remote-sensing (RS) retrieval of evapotranspiration (ET) under the support of a distributed hydrological model: Soil and Water Assessment Tool (SWAT). In this method, the output runoff data based on a fusion of ET data, meteorological data and rainfall data, etc. were compared with the observed runoff data, so as to carry out validation analysis. A new pattern of validating the ET data obtained from RS retrieval, which was more appropriate than the conventional means of observing the ET at several limited stations based on eddy covariance, was proposed. It has integrated the advantage of high requirement of ET with high spatial resolution in the distributed hydrological model and that of the capacity of providing ET with high spatial resolution in RS methods. First, the ET data in five years (2000–2004) were retrieved with RS according to the principle of energy balance. The temporal/spatial ditribution of monthly ET data and related causes were analyzed in the year of 2000, and the monthly ET in the five years was calculated according to the PM model. Subsequently, the results of the RS retrieval of ET and the PM-based ET calculation were compared and validated. Finnaly, the ET data obtained from RS retrieval was evaluated with the new method, under the support of SWAT, meteorologic data, Digital Elevation Model (DEM), landuse data and soil data, etc. as the input, being compared with the PM-based ET. According to the ET data analysis, it can be inferred that the ET obtained from RS retrieval was more continuous and stable with less saltation, while the PM-based ET presented saltation, especially in the year of 2000 and 2001. The correlation coefficient between the monthly ET in two methods reaches 0.8914, which could be explained by the influence from clouds and the inadequate representativeness of the meteorologic stations. Moreover, the PM-based ET was smaller than the ET obtained from RS retrieval, which was in accordance with previous studies (Jamieson, 1982; Dugas and Ainsworth, 1985; Benson et al., 1992; Pereira and Nova, 1992). After the data fusion, the correlation (R2=0.8516) between the monthly runoff obtained from the simulation based on ET retrieval and the observed data was higher than that (R2=0.8411) between the data obtained from the PM-based ET simulation and the observed data. As for the RMSE, the result (RMSE=26.0860) between the simulated runoff based on ET retrieval and the observed data was also superior to the result (RMSE=35.71904) between the simulated runoff obtained with PM-based ET and the observed data. As for the MBE parameter, the result (MBE=−8.6578) for the RS retrieval method was obviously better than that (MBE=−22.7313) for the PM-based method. The comparison of them showed that the RS retrieval had better adaptivity and higher accuracy than the PM-based method, and the new approach based on data fusion and the distributed hydrological model was feasible, reliable and worth being studied further.

scholarly journals Coupling a distributed grid based hydrological model and MM5 meteorological model for flooding alert mapping

2005 ◽  
Vol 2 ◽  
pp. 59-63 ◽  
Author(s):  
B. Tomassetti ◽  
E. Coppola ◽  
M. Verdecchia ◽  
G. Visconti
Keyword(s):  
Hydrological Model ◽  
Mesoscale Model ◽  
Extreme Rainfall ◽  
Mediterranean Area ◽  
Climate Change Signal ◽  
Distributed Hydrological Model ◽  
Meteorological Model ◽  
Extreme Rainfall Events ◽  
Applied Meteorology ◽  
Major Floods

Abstract. The increased number of extreme rainfall events seems to be one of the common feature of climate change signal all over the world (Easterlin et al., 2000; Meehl et al., 2000). In the last few years a large number of floods caused by extreme meteorological events has been observed over the river basins of Mediterranean area and they mainly affected small basins (few hundreds until few thousands of square kilometres of drainage area) . A strategic goal of applied meteorology is now to try to predict with high spatial resolution the segments of drainage network where floods may occur. A possible way to reach this aim is the coupling of meteorological mesoscale model with high resolution hydrological model. In this work few case studies of observed floods in the Italian Mediterranean area will be presented. It is shown how a distributed hydrological model, using the precipitation fields predicted by MM5 meteorological model, is able to highlight the area where the major floods may occur.

scholarly journals Distributed Hydrological Model Application for Estimating the Groundwater Resource at Cu De River Catchment, Viet Nam

10.29007/qxxf ◽  
2018 ◽  
Author(s):  
Ngoc Duong Vo ◽  
Quang Binh Nguyen ◽  
Philippe Gourbesville
Keyword(s):  
Ground Water ◽  
Hydrological Model ◽  
Base Flow ◽  
Viet Nam ◽  
Distributed Hydrological Model ◽  
Basic Information ◽  
Flow Process ◽  
Model Application ◽  
New Approach ◽  
River Catchment

Groundwater is a fundamental component in the water balance of any watershed. It affects considerably on flow regime, especially on base flow. However, it is not easy to survey this component, notably towards the lack of data catchment and developping countries. This study is to present a new approach to overcome the limitation in simulating the ground water. By using the deterministic distributed hydrological model, the study is hope to provide basic information about ground water for a catchment in Vietnam coastal central region, Cu De river catchment. The modelling is realized for an area of 425.2 km2 in period of 2006 – 2010. The results are analyzed in many aspects such as: groundwater spatial distribution, groundwater flow process, groundwater storage, and groundwater recharged volume.

scholarly journals Estimation of extreme flooding based on stochastic weather generators supported by the use of non-systematic flood data

2020 ◽  
Author(s):  
Carles Beneyto ◽  
José Ángel Aranda ◽  
Gerardo Benito ◽  
Félix Francés
Keyword(s):  
Distribution Function ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Weather Generator ◽  
Data Series ◽  
Stochastic Weather Generator ◽  
Systematic Information ◽  
Continuous Simulation ◽  
Extra Information ◽  
Weather Generators

<p>An adequate characterization of extreme floods is key for the correct design of the infrastructures and for the flood risk estimation. Traditionally, these studies have been carried out based on the design storm. However, we now know that this approach is uncertain since peak discharges and hydrographs are strongly dependant on the initial conditions of the basin and on the spatio-temporal distribution of the precipitation.<br>One of the possible solutions that has recently been better welcomed between the scientific community is the continuous simulation. This combination of statistical and deterministic methods consist of the generation of extended synthetic data series of discharges by combining the use of a stochastic weather generator and a hydrological model. Nevertheless, weather generators still need robust data series of observed precipitation in order to perform adequately, especially when trying to capture extremes. To date, however, the length of both available precipitation and discharge records are still not sufficient to guarantee an adequate estimation of extreme discharges, presenting these high uncertainty.<br>In the present study, the same approach is taken (i.e. continuous simulation). However, in order to deal with the short length of the data records and to improve the estimations of extreme discharges, non-systematic information (i.e. historical and Palaeoflood) is integrated in the methodology, extending the length of the flow records and giving extra information of the higher tail of the distribution function, thus reducing the uncertainty of these estimations.<br>This methodology was implemented in a Spanish Mediterranean ephemeral catchment, Rambla de la Viuda (Castello, Valencia). The study area comprises an approximate area of 1,500 km2 and presents a mean rainfall of 615 mm, most of them falling within the autumn months (SON) as a consequence of medicanes. The weather generator used was GWEX, which was designed to focus on extremes, and the hydrological model implemented was TETIS, which is a conceptual model and spatially distributed. Both of them were implemented at a daily scale. Non-systematic information was obtained from previous studies, having information at two locations and, therefore, being able to validate the results in more than one point.<br>The results, in terms of precipitation, showed that weather generators using heavy-tailed marginal distribution functions outperform those using light-tailed distributions (e.g. Exponential or Gamma), especially when extra information is incorporated, as in this study, where regional maxima precipitation studies were integrated for the parametrisation of the weather generator.<br>With regards to discharges, the incorporation of non-systematic information clearly gave extra information of the higher tail of the distribution function (up to approx. T=600 years in this study), allowing to validate the generated discharges up to larger return periods and, therefore, reducing the uncertainty of the extreme discharge estimations</p>

scholarly journals Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm

2009 ◽  
Vol 6 (1) ◽  
pp. 243-271 ◽  
Author(s):  
M. Shafii ◽  
F. De Smedt
Keyword(s):  
Genetic Algorithm ◽  
Model Calibration ◽  
Hydrological Model ◽  
Low Flow ◽  
Data Series ◽  
Pareto Optimal ◽  
Distributed Hydrological Model ◽  
Nsga Ii ◽  
Multi Objective ◽  
Model Efficiency

Abstract. A multi-objective genetic algorithm, NSGA-II, is applied to calibrate a distributed hydrological model (WetSpa) for predicting river discharge. The evaluation criteria considered are the model bias (mass balance), the model efficiency (Nash-Sutcliffe efficiency), and a logarithmic transformed model efficiency (to emphasize low-flow values). The concept of Pareto dominance is used to solve the multi-objective optimization problem and derive Pareto-optimal parameter sets. In order to analyze the applicability of the approach, a comparison is made with another calibration routine using the parameter estimator PEST to minimize the model efficiency. The two approaches are evaluated by applying the WetSpa model to the Hornad River (Slovakia) for which observations of daily precipitation, temperature, potential evapotranspiration, and discharge are available for a 10 year period (1991–2000). The first 5 years of the data series are used for model calibration, while the second 5 years for model validation. The results revealed that the quality of the solutions obtained with NSGA-II is comparable or even better to what can be obtained with PEST, considering the same assumptions. Hence, NSGA-II is capable of locating Pareto optimal solutions in the parameter search space and the results obtained prove the excellent performance of the multi-objective model calibration methodology.

Sign in / Sign up

Export Citation Format

Share Document