Investigation of the hydrodynamics of flash floods in ephemeral channels: Scaling analysis and simulation using a shock-capturing flow model incorporating the effects of transmission losses

Abstract. Channel transmission losses in drylands take place normally in extensive alluvial channels or streambeds underlain by fractured rocks. They can play an important role in streamflow rates, groundwater recharge, freshwater supply and channel-associated ecosystems. We aim to develop a process-oriented, semi-distributed channel transmission losses model, using process formulations which are suitable for data-scarce dryland environments and applicable to both hydraulically disconnected losing streams and hydraulically connected losing(/gaining) streams. This approach should be able to cover a large variation in climate and hydro-geologic controls, which are typically found in dryland regions of the Earth. Our model was first evaluated for a losing/gaining, hydraulically connected 30 km reach of the Middle Jaguaribe River (MJR), Ceará, Brazil, which drains a catchment area of 20 000 km 2 . Secondly, we applied it to a small losing, hydraulically disconnected 1.5 km channel reach in the Walnut Gulch Experimental Watershed (WGEW), Arizona, USA. The model was able to predict reliably the streamflow volume and peak for both case studies without using any parameter calibration procedure. We have shown that the evaluation of the hypotheses on the dominant hydrological processes was fundamental for reducing structural model uncertainties and improving the streamflow prediction. For instance, in the case of the large river reach (MJR), it was shown that both lateral stream-aquifer water fluxes and groundwater flow in the underlying alluvium parallel to the river course are necessary to predict streamflow volume and channel transmission losses, the former process being more relevant than the latter. Regarding model uncertainty, it was shown that the approaches, which were applied for the unsaturated zone processes (highly nonlinear with elaborate numerical solutions), are much more sensitive to parameter variability than those approaches which were used for the saturated zone (mathematically simple water budgeting in aquifer columns, including backwater effects). In case of the MJR-application, we have seen that structural uncertainties due to the limited knowledge of the subsurface saturated system interactions (i.e. groundwater coupling with channel water; possible groundwater flow parallel to the river) were more relevant than those related to the subsurface parameter variability. In case of the WEGW application we have seen that the non-linearity involved in the unsaturated flow processes in disconnected dryland river systems (controlled by the unsaturated zone) generally contain far more model uncertainties than do connected systems controlled by the saturated flow. Therefore, the degree of aridity of a dryland river may be an indicator of potential model uncertainty and subsequent attainable predictability of the system.

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“…This kind of solution of streamflow and water stage is a two-step procedure, which was discussed e.g. by Mudd (2006) and Bronstert et al (2005).…”

Section: Modelling Of Channel Transmission Lossesmentioning

confidence: 99%

A channel transmission losses model for different dryland rivers

Costa

Bronstert

Araújo

2012

Hydrol. Earth Syst. Sci.

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“…Meirovich et al, 1998) and high rates of transmission loss (e.g. Mudd, 2006). Limited sediment supply may also infl uence the fi ne sediment concentrations at the seasonal and annual time scale (Walling and Webb, 1982).…”

Section: Grain-size Dependent Sediment Transport In Low-ordered Ephementioning

confidence: 99%

Patterns of grain‐size dependent sediment transport in low‐ordered, ephemeral channels

Yuill

Nichols

2011

Earth Surf Processes Landf

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ABTRACT: Sediment data were analyzed to determine grain-size dependant factors affecting sediment transport in a low-ordered, ephemeral watershed. Sediment and fl ow samples were collected during 22 fl ow events at the outlet of a 4·53 ha sub-watershed within the Walnut Gulch Experimental Watershed in south-eastern Arizona. Measured concentrations ranged from 4191 to 115 045 mg l −1 and included grain sizes up to 8·0 mm in diameter. Two grain-size dependent transport patterns were observed, that of the fi ner grain-size fraction (approximately < 0·25 mm) and that of a coarser grain-size fraction (approximately ≥ 0·25 mm). The concentration of the fi ne fraction decreased with fl ow duration, peaking near the beginning of a fl ow event and declining thereafter. The concentration of the fi ne fraction showed slight trends with season and recovery period. The concentration of the coarse fraction displayed a slight negative trend with instantaneous discharge and was not correlated with event duration. These patterns typically produced a condition where the majority of the fi ne fraction of the sediment yield was evacuated out of the watershed before the hydrograph peak while the majority of the coarser sediment was evacuated during the falling limb of the hydrograph. Each grain-size dependent transport pattern was likely infl uenced by the source of the associated sediment. At the fl ow event time scale, the fi nes were primarily wash load, supplied from the hillslopes and the coarser grains were entrained from the channel bed. Because transport patterns differ based on grain size, attempts to defi ne the total sediment concentration and sediment yield by the behavior of a single grain-size fraction may lead to erroneous results, especially when a large range of sediment grain sizes are present.

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“…The benefit of coupling the two models is to solve the data scarcity problem when the finer input data required by the HEC-RAS were not available. In addition, there have been some studies using the KINEROS2 model alone for the aim of FF prediction [16,33,35,36] that recommended the use of it; however, the KINEROS2 does not accurately estimate the river water level and flow force (two important criteria for identifying the FF), but the use of HEC-RAS afterwards could be a good solution. As the FFs often occur within six hours of causative rains [37] or a shorter event of a few hours [38], the total model operation time (including preparing new model inputs, model runs and the result display and analyses) should meet this time criterion.…”

Section: Introductionmentioning

confidence: 99%

Flash Flood Prediction by Coupling KINEROS2 and HEC-RAS Models for Tropical Regions of Northern Vietnam

2015

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Northern Vietnam is a region prone to heavy flash flooding events. These often have devastating effects on the environment, cause economic damage and, in the worst case scenario, cost human lives. As their frequency and severity are likely to increase in the future, procedures have to be established to cope with this threat. As the prediction of potential flash floods represents one crucial element in this circumstance, we will present an approach that combines the two models KINEROS2 and HEC-RAS in order to accurately predict their occurrence. We used a documented event on 23 June 2011 in the Nam Khat and the larger adjacent Nam Kim watershed to calibrate the coupled model approach. Afterward, we evaluated the performance of the coupled models in predicting flow velocity (FV), water levels (WL), discharge (Q) and streamflow power (P) during the 3-5 days following the event, using two different precipitation datasets from the global spectral model (GSM) and the high resolution model (HRM). Our results show that the estimated Q and WL closely matched observed data with a Nash-Sutcliffe simulation efficiency coefficient (NSE) of around 0.93 and a coefficient of determination (R 2 ) at above 0.96. The resulting analyses reveal strong relationships between river geometry and FV, WL and P. Although there were some minor errors in forecast results, the model-predicted Q and WL corresponded well to the gauged data. OPEN ACCESSHydrology 2015, 2 243

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Investigation of the hydrodynamics of flash floods in ephemeral channels: Scaling analysis and simulation using a shock-capturing flow model incorporating the effects of transmission losses

Cited by 52 publications

References 35 publications

A channel transmission losses model for different dryland rivers

A channel transmission losses model for different dryland rivers

Patterns of grain‐size dependent sediment transport in low‐ordered, ephemeral channels

Flash Flood Prediction by Coupling KINEROS2 and HEC-RAS Models for Tropical Regions of Northern Vietnam

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