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Effects of Storm Movement on Flash Flood Modelling

Authors:
Shahin Khosh Bin Ghomash at GFZ Helmholtz Centre for Geosciences
Shahin Khosh Bin Ghomash
Daniel Bachmann at Magdeburg-Stendal University of Applied Sciences
Daniel Bachmann
Daniel Caviedes-Voullième at Forschungszentrum Jülich
Daniel Caviedes-Voullième
Christoph Hinz at Brandenburg University of Technology Cottbus - Senftenberg
Christoph Hinz
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Abstract

Rainfall is a complex, spatial and temporally variated process and one of the core inputs for hydrological and hydrodynamic modelling. Most rainfalls are known to be moving storms with varying directions and velocities. Storm movement is known to be an important influence on runoff generation, both affecting peak discharge and the shape of hydrographs. Therefore, exploring the extent rainfall dynamics affect runoff generation and consequently flooded areas, can be an asset in effective flood risk management. In this work, we study how storm movement (e.g. characterized by velocity and direction) can affect surface flow generation, water levels and flooded areas within a catchment. Moreover, the influence of rainfall temporal variability in correlation with storm movement is taken into account. This is achieved by means of numerical-based, spatially explicit surface flow simulations using the tool ProMaIDes (2021), a free software for risk-based evaluation of flood risk mitigation measures. The storm events are generated using a microcanonical random cascade model and further on trajected across the catchment area. The study area is the Kan river catchment located in the province of Tehran (Iran) with a total area of 836 km², which has experienced multiple flooding events in recent years. Due to its semi-arid climate, steep topography with narrow valleys, this area has high potential for flash flood occurrence as a result of high intensity precipitation. The results of this study show a range of possible magnitudes of influence of rainfall movement on the catchment´s runoff response. The resulting flood maps highlight the importance of rainfall velocity and most importantly the direction of the movement in the estimation of flood events as well as their likelihood in catchment area. Moreover, its shown that the magnitude of influence of storm velocity and direction on discharge strongly depends on the location within the river network which it is measured. ProMaIDes (2021): Protection Measures against Inundation Decision support. https://promaides.h2.de
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Rainfall
•50 synthetic rains generated using a random
cascade model (Pohle et al., 2018) with same
volume and durations
•Upper domain discretized in dx = 0.5km resolution
•Velocities of 5, 10, 25 km/h with 4 directions
(West-East, East-West, North-South, South-North)
Effects of Storm Movement on Flash Flood Modelling
Shahin Khosh Bin Ghomash, Daniel Bachmann, Daniel Caviedes-Voullième, Christoph Hinz
Introduction
•Rainfall is a complex, spatial and temporally
variated process (Marani, 2005)
•Rainfall movement can cause a continuous
change in spatiotemporal variability
•Most storms are known to be moving storms
with varying speeds and directions (Upton,
2002)
Study Area
The study area is the Kan catchment, Tehran
(Iran), which has seen multiple instances of flash
flooding in recent years. Rain is mostly
concentrated in the northern part, resulting in
allochthonous flash floods further downstream.
Catchment Properties
•Total area of 836 km²
•Semi-arid climate, steep topography
•High potential for flash floods
Model Setup
•1d-2d coupled hydrodynamic model
•3 x 2d floodplains (~ 1400 Km2 ~ 1.4m cells) & 14
x 1d rivers (1075 profiles)
•Elevation based on the TanDEM-x 12m DEM
Results
The results suggest that rainfall movement can affect
the runoff response in different degrees. Peak
discharge, hydrograph shapes and flooded areas are
affected
Peaks & Discharges
Conclusions
•Storms with higher velocities are shown to produce
higher peaks and faster onsets of runoff and
consequently higher flooded areas in comparison to
slower storms.
•Storms moving along the average direction of the
stream are shown to cause the highest peaks and
flooded areas.
•Magnitude of influence of rainfall movement is strongly
influenced by hyetograph variability and
measurement location within the drainage network
References
1. Marani, M., 2005. Non-power-law scale properties of rainfall in space
and time, Water Resour. Res.,41, W08413, doi:10.1029/2004WR003822.
2. Pohle, I., Niebisch, M., Müller, H., Schümberg, S., Zha, T., Maurer, T., &
Hinz, C., 2018. Coupling Poisson rectangular pulse and multiplicative
microcanonical random cascade models to generate sub-daily precipitation
timeseries. Journal of Hydrology, 562, 50-70
3. Upton, G. J. G., 2002. A correlation-regression method for tracking rain-
storms using rain-gauge data, J. Hydrol., 261(1–4), 60–73,
doi:10.1016/S0022-1694(01)00618-7
shahin.khoshbinghomash@h2.de daniel.Bachmann@h2.de d.caviedes.voullieme@fz-juelich.de christoph.hinz@b-tu.de
Our Software: PROMAIDES
•Powerful tool for supporting flood risk
management with a holistic flood risk approach
•Free and open-source
•Modular and integrative design
promaides.h2.de
Rainfall
Flooding
Measuring point A
Measuring point B
Measuring point C
tn= time of rain in cell n v = velocity
n = cell number dx = domain resolution
  
Flooded Areas
Effect of Velocity Effect of Direction
Peaks in Measuring Point A Peaks in Measuring Point B
Peaks in Measuring Point C Example Hydrographs in
Measuring Point A
Research Group Flood Risk Management
Magdeburg-Stendal university of applied sciences
Breitscheidstraße 2
39114 Magdeburg
Germany
High variated rain
Low variated rain
ResearchGate has not been able to resolve any citations for this publication.
Non-power-law-scale properties of rainfall in space and time
Article
  • Aug 2005
1] The identification of general relationships linking statistical properties of rainfall aggregated at different temporal and spatial scales possesses clear theoretical and practical relevance. Among other properties it is important to characterize the scale dependence of rainfall variability, which may, for many purposes, be summarized by its variance. This paper presents theoretical results and observational analyses connecting the variance of temporal and spatial rainfall to the scale of observation under an assumption of second-order stationarity. Previous results regarding the theoretical form of the temporal variance as a function of aggregation are refined and extended to the spatial case. It is shown that the variance of aggregated rainfall exhibits an overall nonscaling form within the range of scales of usual hydrological interest. It is further shown that the theoretically derived form of the variance as a function of scale is incompatible with power law relations usually assumed for the second-order statistical moment within multiscaling approaches to rainfall modeling. Predictions of the theoretical derivations are validated by use of observations representing a wide variety of resolutions (from 2 s to 1 hour in time and 4 km in space), climates, and measurement instruments. The validation shows a good agreement between observations and the theoretically predicted forms of the variance as a function of aggregation. The proposed theoretical framework thus provides a useful context for rainfall analysis, modeling, and downscaling in space and time and suggests careful reexaminations of usual multiscaling assumptions.
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A correlation–regression method for tracking rainstorms using rain-gauge data
Article
  • Apr 2002
  • J HYDROL
This paper focuses on the short-term tracking of rainstorms, using data from a network of 20 tipping-bucket rain-gauges located in a 600 km2 region near Bolton in Northwest England. The tip-time data are first converted into rainrate profiles (the moving averages of hourly rainrates). The cross-correlation between pairs of profiles is examined to obtain estimated inter-gauge lags. Estimates from gauges that correlate poorly are discarded, since these gauges may be malfunctioning. The remaining estimated lags are modelled using linear dependencies on the spatial displacements of the gauge-pairs and the velocity of the rainstorm is deduced using standard formulae. Sums of squared deletion residuals give a second opportunity for identifying gauges associated with misleading estimates. Empirical confidence intervals are obtained by multiple analyses using different combinations of tip sequences. All rainstorms between February and December 2000 were analysed. Of these, 171 had a mean rainrate of at least 0.4 mm/h. Apparently accurate estimates of storm velocity were obtained for 107 storms. The mean speed was 34.5 km/h (9.6 m/s) with mean direction 80°.
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