scholarly journals Flood simulation errors show an unexpected seasonal trend: results obtained on a set of 229 catchments and 11,054 flood events

2021 ◽  
Author(s):  
Paul C. Astagneau ◽  
François Bourgin ◽  
Vazken Andréassian ◽  
Charles Perrin
Keyword(s):  
Seasonal Trend ◽  
Large Set ◽  
Strongly Correlated ◽  
Flood Events ◽  
Antecedent Soil Moisture ◽  
Flood Simulation ◽  
Rainfall Runoff Model ◽  
Moisture Conditions ◽  
Simulation Errors ◽  
Runoff Model

<p>To improve the predictive capability of a model, one must identify situations where it fails to provide satisfactory results. We tried to identify the deficiencies of a lumped rainfall-runoff model used for flood simulation (the hourly GR5H-I model) by evaluating it over a large set of 229 French catchments and 11,054 flood events. Evaluating model simulations separately for individual flood events allowed us identifying a seasonal trend: while the model yielded good performance in terms of aggregated statistics, grouping results by season showed clear underestimations of most of the floods occurring in summer. The largest underestimations of flood volumes were identified when high-intensity precipitation events occurred and when the precipitation field was highly spatially variable. Low antecedent soil moisture conditions were also found to be strongly correlated with model bias. Overall, this study pinpoints the need to better account for short-duration processes to improve the GR5H-I model for flood simulation.</p>

A grid-based rainfall-runoff model for flood simulation including paddy fields

2010 ◽  
Vol 9 (3) ◽  
pp. 275-290 ◽  
Author(s):  
In-Kyun Jung ◽  
Jong-Yoon Park ◽  
Geun-Ae Park ◽  
Mi-Seon Lee ◽  
Seong-Joon Kim
Keyword(s):  
Paddy Fields ◽  
Rainfall Runoff ◽  
Flood Simulation ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Grid Based

Modelling design flood hydrographs in catchments with mixed urban and rural land cover

2013 ◽  
Vol 44 (6) ◽  
pp. 1040-1057 ◽  
Author(s):  
T. R. Kjeldsen ◽  
J. D. Miller ◽  
J. C. Packman
Keyword(s):  
Land Cover ◽  
Model Performance ◽  
Predictive Ability ◽  
Extended Model ◽  
Flood Events ◽  
Rural Land ◽  
Design Flood ◽  
Antecedent Soil Moisture ◽  
Rainfall Runoff Model ◽  
Urban Effects

The effect of urban land cover on catchment flood response is evaluated using a lumped rainfall–runoff model to analyse flood events from selected UK catchments with mixed urban and rural land use. The present study proposes and evaluates a series of three extensions to an existing model to enable a better representation of urban effects, namely: an increase in runoff volume, reduced response time and a decrease in baseflow (resulting from decreased infiltration). Based on observed flood events from seven catchments, cross-validation methods are used to compare the predictive ability of the model variants with that of the original unmodified model. The results show that inclusion of urban effects increases the predictive ability of the model across catchments, despite large between-event variability of model performance. More detailed investigations into the relationship between model performance and individual event characteristics (antecedent soil moisture, rainfall duration, depth and intensity) did not reveal systematic inabilities of the model to reproduce certain types of events. Finally, it is demonstrated that the new extended model has the ability to simulate urban effects in accordance with the expected changes in storm runoff patterns.

scholarly journals Use of the KINFIL rainfall-runoff model on the Hukava catchment

2009 ◽  
Vol 4 (No. 1) ◽  
pp. 1-9
Author(s):  
P. Kovář ◽  
V. Kadlec
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Practical Implementation ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Event Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Runoff Events ◽  
Small Catchments

The paper reports on the flood events on the forested Hukava catchment. It describes practical implementation of the KINFIL rainfall-runoff model. This model has been used for the reconstruction of the rainfall-runoff events and thus for the calibration of its parameters. The model was subsequently used to simulate the design discharges with an event duration of t<sub>d</sub> = 30, 60, and 300 min in the period of recurrence of 100 years, and during the scenario simulations of the land use change when 40% and 80% of the forest in the catchment had been cleared out and then replaced by permanent grasslands. The implementation of the KINFIL model supported by GIS proved to be a proper method for the flood runoff assessment on small catchments, during which different scenarios of the land use changes were tested.

scholarly journals Estimating Flash Flood Peak Discharge in Gidra and Parná Basin: Case Study for the 7-8 June 2011 Flood

2012 ◽  
Vol 60 (3) ◽  
pp. 206-216 ◽  
Author(s):  
Pavla Pekárová ◽  
Aleš Svoboda ◽  
Pavol Miklánek ◽  
Peter Škoda ◽  
Dana Halmová ◽  
...  
Keyword(s):  
Flash Flood ◽  
Temporal Distribution ◽  
Peak Discharge ◽  
Flood Events ◽  
Flood Peak ◽  
Direct Measurements ◽  
Extreme Flood ◽  
Rainfall Runoff Model ◽  
Runoff Model

Estimating Flash Flood Peak Discharge in Gidra and Parná Basin: Case Study for the 7-8 June 2011 FloodWe analyzed the runoff and its temporal distribution during the catastrophic flood events on river Gidra (32.9 km2) and Parná (37.86 km2) of the 7th June 2011. The catchments are located in the Small Carpathian Mountains, western Slovakia. Direct measurements and evaluation of the peak discharge values after such extreme events are emphasized in the paper including exceedance probabilities of peak flows and of their causal flash rainfall events. In the second part of the paper, plausible modeling mode is presented, using the NLC (Non Linear Cascade) rainfall-runoff model. Several hypothetical extreme flood events were simulated by the NLC model for both rivers. Also the flood runoff volumes are evaluated as basic information on the natural or artificial catchment storage.

scholarly journals Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls. Part 2: Flood forecasting using 1600 member 4D-EnVAR predicted rainfalls

2019 ◽  
Author(s):  
Kenichiro Kobayashi ◽  
Le Duc ◽  
Tsutao Oizumi ◽  
Kazuo Saito ◽  
Keyword(s):  
Flood Control ◽  
Optimization Technique ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Flood Simulation ◽  
Nonhydrostatic Model ◽  
Rainfall Runoff Model ◽  
Inflow Discharge ◽  
Runoff Model ◽  
Assimilation System

Abstract. This paper elaborated the feasibility of flood forecasting using a distributed rainfall-runoff model and huge number of ensemble rainfalls with an advanced data assimilation system. Specifically, 1600 ensemble rainfalls simulated by a four-dimensional ensemble variational assimilation system with the JMA nonhydrostatic model (4D-EnVAR-NHM) were given to the rainfall-runoff model to simulate the inflow discharge to a small dam catchment (Kasahori dam; approx. 70 km2) in Niigata, Japan. The results exhibited that the ensemble flood forecasting can indicate the necessity of flood control operation and emergency flood operation with the occurrence probability and a lead time (e.g. 12 hours). Thus, the ensemble flood forecasting may be able to inform us the necessity of the early evacuation of the inhabitant living downstream of the dam e.g. half day before the occurrence. On the other hand, the results also showed that the exact forecasting to reproduce the discharge hydrograph several hours before the occurrence is yet difficult, and some optimization technique is necessary such as the selection of the good ensemble members.

scholarly journals Origin and variability of statistical dependencies between peak, volume, and duration of rainfall-driven flood events

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
L. Rahimi ◽  
C. Deidda ◽  
C. De Michele
Keyword(s):  
Catchment Area ◽  
Hydrological Model ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Daily Discharge ◽  
Pairwise Dependence ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Flood Characteristics ◽  
Conceptual Hydrological Model

AbstractFloods are among the most common and impactful natural events. The hazard of a flood event depends on its peak (Q), volume (V) and duration (D), which are interconnected to each other. Here, we used a worldwide dataset of daily discharge, two statistics (Kendall’s tau and Spearman’s rho) and a conceptual hydrological rainfall-runoff model as model-dependent realism, to investigate the factors controlling and the origin of the dependence between each couple of flood characteristics, with the focus to rainfall-driven events. From the statistical analysis of worldwide dataset, we found that the catchment area is ineffective in controlling the dependence between Q and V, while the dependencies between Q and D, and V and D show an increasing behavior with the catchment area. From the modeling activity, on the U.S. subdataset, we obtained that the conceptual hydrological model is able to represent the observed dependencies between each couple of variables for rainfall-driven flood events, and for such events, the pairwise dependence of each couple is not causal, is of spurious kind, coming from the “Principle of Common Cause”.

Application of IHACRES rainfall-runoff model to the Wadi Dhuliel arid catchment, Jordan

2011 ◽  
Vol 2 (1) ◽  
pp. 56-71 ◽  
Author(s):  
Eyad H. Abushandi ◽  
Broder J. Merkel
Keyword(s):  
Daily Basis ◽  
Calibration Data ◽  
Storm Events ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
North East ◽  
Flood Estimation ◽  
Rainfall Runoff Model ◽  
Moisture Conditions ◽  
Parameter Values

With increasing stress on water resources in Jordan, application of rainfall-runoff models can be part of the solution to manage and sustain the water sector. In this paper, the metric conceptual IHACRES model is applied to the Wadi Dhuliel arid catchment, north-east Jordan. Rainfall-runoff data from 19 storm events during 1986 to 1992 have been used in this study. Flood estimation was performed on the basis of daily scales and storm events scales. The model was extended for snowfall in order to cope with such extreme events. Although the best performance of the IHACRES model on a daily basis is poor, the performance on storm events scale showed a good agreement between observed and simulated streamflow. Apart from model parameter values, the principal reasons for IHACRES model success in this region are thought to be based on antecedent soil moisture conditions, rainfall duration and rainfall intensity before and during each storm. The model outputs were likely to be sensitive when the monitored flood was relatively small. The optimum parameter values were influenced by the length of calibration data and event specific changes.

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