Probabilistic Rainfall/Flood Estimation

2019 ◽  
pp. 44-63
Author(s):  
Monzur A. Imteaz
2016 ◽  
Vol 49 (8) ◽  
pp. 719-729
Author(s):  
Hyunseung Lee ◽  
Taesam Lee ◽  
Taewoong Park ◽  
Chanyoung Son

2021 ◽  
Vol 165 ◽  
pp. 112106
Author(s):  
L. Piazzi ◽  
E. Cecchi ◽  
M.F. Cinti ◽  
G. Ceccherelli

1975 ◽  
Vol 59 (3) ◽  
pp. 549-559
Author(s):  
BN GUNTHER ◽  
MR HASAN ◽  
TM PRUS CHACINSKI ◽  
ARB EDGECOMBE ◽  
MJ HALL ◽  
...  

2016 ◽  
Vol 48 (3) ◽  
pp. 726-740 ◽  
Author(s):  
Daniele Masseroni ◽  
Alessio Cislaghi ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Brocca

Many rainfall–runoff (RR) models are available in the scientific literature. Selecting the best structure and parameterization for a model is not straightforward and depends on a broad number of factors, including climatic conditions, catchment characteristics, temporal/spatial resolution and model objectives. In this study, the RR model ‘Modello Idrologico Semi-Distribuito in continuo’ (MISDc), mainly developed for flood simulation in Mediterranean basins, was tested on the Seveso basin, which is stressed several times a year by flooding events mainly caused by excessive urbanization. The work summarizes a compendium of the MISDc applications over a wide range of catchments in European countries and then it analyses the performances over the Seveso basin. The results show a good fit behaviour during both the calibration and the validation periods with a Nash–Sutcliffe coefficient index larger than 0.9. Moreover, the median volume and peak discharge errors calculated on several flood events were less than 25%. In conclusion, we can be assured that the reliability and computational speed could make the MISDc model suitable for flood estimation in many catchments of different geographical contexts and land use characteristics. Moreover, MISDc will also be useful for future support of real-time decision-making for flood risk management in the Seveso basin.


2021 ◽  
Author(s):  
Trevor Hoey ◽  
Pamela Tolentino ◽  
Esmael Guardian ◽  
Richard Williams ◽  
Richard Boothroyd ◽  
...  

<p>Assessment of flood and drought risks, and changes to these risks under climate change, is a critical issue worldwide. Statistical methods are commonly used in data-rich regions to estimate the magnitudes of river floods of specified return period at ungauged sites. However, data availability can be a major constraint on reliable estimation of flood and drought magnitudes, particularly in the Global South. Statistical flood and drought magnitude estimation methods rely on the availability of sufficiently long data records from sites that are representative of the hydrological region of interest. In the Philippines, although over 1000 locations have been identified where flow records have been collected at some time, very few records exist of over 20 years duration and only a limited number of sites are currently being gauged. We collated data from three archival sources: (1) Division of Irrigation, Surface Water Supply (SWS) (1908-22; 257 sites in total); (2) Japan International Cooperation Agency (JICA) (1955-91; 90 sites); and, (3) Bureau of Research and Standards (BRS) (1957-2018; 181 sites). From these data sets, 176 contained sufficiently long and high quality records to be analysed. Series of annual maximum floods were fit using L-moments with Weibull, Log-Pearson Type III and Generalised Logistic Distributions, the best-fit of these being used to estimate 2-, 10- and 100-year flood events, Q<sub>2</sub>, Q<sub>10</sub> and Q<sub>100</sub>. Predictive equations were developed using catchment area, several measures of annual and extreme precipitation, catchment geometry and land-use. Analysis took place nationally, and also for groups of hydrologically similar regions, based on similar flood growth curve shapes, across the Philippines. Overall, the best fit equations use a combination of two predictor variables, catchment area and the median annual maximum daily rainfall. The national equations have R<sup>2</sup> of 0.55-0.65, being higher for shorter return periods, and regional groupings R<sup>2</sup> are 0.60-0.77 for Q<sub>10</sub>. These coefficients of determination, R<sup>2</sup>, are lower than in some comprehensive studies worldwide reflecting in part the short individual flow records. Standard errors of residuals for the equations are between 0.19 and 0.51 (log<sub>10</sub> units), which lead to significant uncertainty in flood estimation for water resource and flood risk management purposes. Improving the predictions requires further analysis of hydrograph shape across the different climate types, defined by seasonal rainfall distributions, in the Philippines and between catchments of different size. The results here represent the most comprehensive study to date of flood magnitudes in the Philippines and are being incorporated into guidance for river managers alongside new assessments of river channel change across the country. The analysis illustrates the potential, and the limitations, for combining information from multiple data sources and short individual records to generate reliable estimates of flow extremes.</p>


2014 ◽  
Vol 14 (5) ◽  
pp. 1283-1298 ◽  
Author(s):  
D. Lawrence ◽  
E. Paquet ◽  
J. Gailhard ◽  
A. K. Fleig

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom–up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.


Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA183-WA193 ◽  
Author(s):  
W. Steven Holbrook ◽  
Scott N. Miller ◽  
Matthew A. Provart

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.


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